docs: 编写客户版交付文档

Simulation mode reads scheme data for both burst and normal observations. Monitoring mode reuses the burst window when no normal window is provided.

.dockerignore

@@ -0,0 +1,19 @@
+.git
+.github
+.gitea
+__pycache__/
+.pytest_cache/
+.mypy_cache/
+.venv/
+venv/
+build/
+dist/
+package/
+temp/
+data/
+# db_inp/
+inp/
+.env
+*.pyc
+*.dump
+app/algorithms/health/model/my_survival_forest_model_quxi.joblib

.env.example

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+# TJWater Server 环境变量配置模板
+# 复制此文件为 .env 并填写实际值
+# CI/CD: 将生产 .env 的完整内容保存为 Gitea 仓库密钥 TJWATER_SERVER_ENV。
+ENVIRONMENT="production"
+NETWORK_NAME="tjwater"
+# ============================================
+# 敏感配置加密 (必填)
+# ============================================
+
+# Fernet 格式，生产环境必须替换为独立密钥
+# 生成方式: python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())"
+# 用于项目数据库 DSN、GeoServer 管理密码等敏感配置
+DATABASE_ENCRYPTION_KEY="replace-with-generated-fernet-key"
+
+# ============================================
+# 数据库配置 (PostgreSQL)
+# ============================================
+DB_NAME="tjwater"
+DB_HOST="localhost"
+DB_PORT="5432"
+DB_USER="tjwater"
+DB_PASSWORD="password"
+
+# ============================================
+# 数据库配置 (TimescaleDB)
+# ============================================
+TIMESCALEDB_DB_NAME="tjwater"
+TIMESCALEDB_DB_HOST="localhost"
+TIMESCALEDB_DB_PORT="5433"
+TIMESCALEDB_DB_USER="tjwater"
+TIMESCALEDB_DB_PASSWORD="password"
+
+# ============================================
+# 元数据数据库配置 (Metadata DB)
+# ============================================
+METADATA_DB_NAME="system_hub"
+METADATA_DB_HOST="localhost"
+METADATA_DB_PORT="5432"
+METADATA_DB_USER="tjwater"
+METADATA_DB_PASSWORD="password"
+
+# ============================================
+# Keycloak JWT (可选)
+# ============================================
+KEYCLOAK_PUBLIC_KEY="-----BEGIN PUBLIC KEY-----\n...\n-----END PUBLIC KEY-----"
+KEYCLOAK_ALGORITHM=RS256
+KEYCLOAK_AUDIENCE="account"
+
+
+# ============================================
+# Bocha Web Search API
+# ============================================
+BOCHA_API_KEY="sk-your-bocha-api-key"
+BOCHA_WEB_SEARCH_URL="https://api.bochaai.com/v1/web-search"
+BOCHA_WEB_SEARCH_TIMEOUT_SECONDS=30
+
+# ============================================
+# Tianditu Geocoding API
+# ============================================
+TIANDITU_GEOCODER_TOKEN="your-tianditu-geocoder-token"
+TIANDITU_GEOCODER_URL="https://api.tianditu.gov.cn/geocoder"
+TIANDITU_GEOCODER_TIMEOUT_SECONDS=30

.gitignore

@@ -1,7 +1,9 @@
-*.pyc
-.env
 db_inp/
 temp/
 data/
+build/
+*.pyc
+.env
 *.dump
-api_ex/model/my_survival_forest_model_quxi.joblib
+.vscode/
+app/algorithms/health/model/my_survival_forest_model_quxi.joblib

AGENTS.md

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+# Repository Guidelines
+
+## Project Purpose
+
+This repository is the customer-delivery edition of the TJWater backend. Treat it as a deployable delivery package, not as the primary internal development repository. Changes should be limited to customer-facing fixes, deployment compatibility, configuration templates, packaging, and delivery documentation.
+
+Do not introduce internal-only experiments, debug utilities, local data, or source material that is not required for customer operation.
+
+## Source Encapsulation Requirement
+
+Core backend source code must be encapsulated before delivery. The sensitive implementation areas include business services, native integrations, hydraulic/network algorithms, and EPANET-related logic, especially:
+
+- `app/services`
+- `app/native`
+- `app/algorithms`
+- `app/infra/epanet`
+
+Use the existing Cython packaging flow in `scripts/compile.py` for these areas. Do not ship uncompiled core `.py` files in the final customer package when compiled extension modules are expected. Keep public entry points, configuration loading, route wiring, and minimal package files readable only where required for runtime and operations.
+
+Before removing source files with `--delete-source`, verify the build from a clean working tree or disposable copy. The delete mode is destructive by design.
+
+## Workspace Structure
+
+- `app/main.py` is the FastAPI entry point.
+- `app/api` contains HTTP route handlers.
+- `app/services` contains core business orchestration and must be protected in delivery builds.
+- `app/native`, `app/algorithms`, and `app/infra/epanet` contain specialized computation and integration code that must be protected in delivery builds.
+- `infra/` and `Dockerfile` contain deployment assets.
+- `scripts/` contains operational and packaging helpers.
+- `tests/` contains backend tests.
+
+## Common Commands
+
+Run commands from this repository root:
+
+```bash
+conda run -n server python -m pytest tests -q
+conda run -n server python scripts/run_server.py
+conda run -n server python scripts/compile.py
+```
+
+Clean compiled extensions when needed:
+
+```bash
+conda run -n server python scripts/compile.py --clean
+```
+
+Only use source deletion in a prepared delivery copy:
+
+```bash
+conda run -n server python scripts/compile.py --delete-source
+```
+
+## Change Policy
+
+Keep changes scoped and conservative. Prefer compatibility patches, configuration adjustments, packaging fixes, and customer deployment hardening. If a change requires substantial business logic updates, make it first in the internal backend repository and then port the reviewed result here.
+
+Do not weaken the encapsulation process to make debugging easier. If debugging requires readable source, do it in the internal repository or a non-delivery branch/copy.
+
+## Testing Guidelines
+
+Run the narrowest useful test command for the affected area. For delivery packaging changes, verify both:
+
+- tests still pass before packaging;
+- the packaged/compiled runtime can import and start the FastAPI app.
+
+Do not add tests that depend on untracked customer data, local database dumps, or machine-specific files.
+
+## Security & Delivery Rules
+
+Never commit `.env`, production credentials, customer data, logs, generated caches, `node_modules/`, database dumps, or temporary delivery archives. Use `.env.example` or deployment documentation for required configuration.
+
+Review Docker and deployment files carefully before delivery. Customer packages should contain only the files needed to run, operate, and diagnose the deployed service.

AUTHENTICATION_AND_USER_MANAGEMENT.md

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+# TJWater Authentication and Metadata Management
+
+## Ownership
+
+Keycloak owns login identity, credentials, token issuance, and token expiry.
+TJWater metadata stores only business snapshots and authorization data:
+
+- `users.keycloak_id` is the stable identity binding.
+- `users.username`, `users.email`, and `users.last_login_at` are Keycloak claim caches.
+- `users.role`, `users.is_active`, and `users.is_superuser` control TJWater system access.
+- `user_project_membership.project_role` controls project access.
+
+The backend does not accept passwords, does not issue local JWTs, and does not
+trust frontend-supplied user IDs.
+
+## Login Snapshot Refresh
+
+Every authenticated metadata-user resolution validates the Keycloak access token
+and reads `sub`, `preferred_username` or `username`, and `email` claims. The
+backend finds `users` by `keycloak_id = sub`, rejects inactive or missing users,
+then refreshes `username`, `email`, and `last_login_at`.
+
+This keeps local display data current without changing the identity binding.
+There is no Keycloak webhook requirement; second-level user or permission sync is
+out of scope unless explicitly requested later.
+
+## Admin APIs
+
+All admin APIs require metadata admin access: `users.is_superuser = true` or
+`users.role = 'admin'`.
+
+User and membership management:
+
+- `GET /api/v1/admin/me`
+- `POST /api/v1/admin/users/sync`
+- `POST /api/v1/admin/users/sync/batch`
+- `GET /api/v1/admin/users`
+- `GET /api/v1/admin/users/{user_id}`
+- `PATCH /api/v1/admin/users/{user_id}`
+- `GET /api/v1/admin/projects/{project_id}/members`
+- `POST /api/v1/admin/projects/{project_id}/members`
+- `PATCH /api/v1/admin/projects/{project_id}/members/{user_id}`
+- `DELETE /api/v1/admin/projects/{project_id}/members/{user_id}`
+
+Project configuration:
+
+- `GET /api/v1/admin/projects`
+- `POST /api/v1/admin/projects`
+- `PATCH /api/v1/admin/projects/{project_id}`
+- `GET /api/v1/admin/projects/{project_id}/databases`
+- `PUT /api/v1/admin/projects/{project_id}/databases`
+- `DELETE /api/v1/admin/projects/{project_id}/databases/{db_role}`
+- `POST /api/v1/admin/projects/{project_id}/databases/{db_role}/health`
+
+## Secret Handling
+
+Admins submit plaintext DSNs only through HTTPS admin APIs. Operators should not
+write encrypted columns manually.
+
+- `project_databases.dsn_encrypted` is encrypted with `DATABASE_ENCRYPTION_KEY`.
+- Admin responses return only `has_dsn`.
+- Audit logs record whether a secret was updated, but never store plaintext DSNs
+  or other secrets.
+
+Generate the database encryption key with:
+
+```bash
+python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())"
+```
+
+Keep keys stable for the lifetime of encrypted metadata. Rotating a key requires
+decrypting with the old key and re-encrypting with the new key.
+
+## Metadata Schema Patches
+
+Apply metadata patches in order:
+
+1. `resources/sql/004_metadata_auth_management.sql`
+2. `resources/sql/005_metadata_project_configuration.sql`
+
+`004` creates Keycloak-backed metadata users and project memberships. `005`
+creates project and project database routing tables with uniqueness, role/type,
+and pool-size constraints.
+
+## Frontend System Management
+
+`/system-admin` is shown only after `GET /api/v1/admin/me` confirms metadata
+admin access. The page lets admins maintain metadata users, project members,
+projects, project database routing for `biz_data` and `iot_data`, connection
+health checks. This replaces direct SQL editing for normal project onboarding.

BACKEND_NAMING_AUDIT.md

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+# Backend Naming Audit
+
+DOC-003 audit for the customer-delivery `TJWaterServerCustomer` backend.
+
+## Scope
+
+Reviewed FastAPI route decorators under `app/api/v1/endpoints`, router prefixes in `app/api/v1/router.py`, and public request/response schema fields in `app/api` and `app/domain`.
+
+The backend is mounted only under `/api/v1` from `app/main.py`; the old no-prefix router include remains commented out.
+
+## Current Good Surface
+
+These newer routes already follow the naming rule for public HTTP paths:
+
+- Audit: `/api/v1/audit/logs`, `/api/v1/audit/logs/count`
+- Metadata: `/api/v1/meta/project`, `/api/v1/meta/projects`, `/api/v1/meta/db/health`
+- Business APIs: `/api/v1/burst-detection/detect`, `/api/v1/burst-location/locate`, `/api/v1/leakage/identify`
+- Time-series APIs: `/api/v1/scada/by-ids-time-range`, `/api/v1/scada/by-ids-field-time-range`, `/api/v1/composite/clean-scada`
+- Project data APIs: `/api/v1/scada-info`, `/api/v1/scheme-list`, `/api/v1/burst-locate-result`
+- Web integrations: `/api/v1/web-search`, `/api/v1/geocode`
+
+Path template parameters such as `{user_id}`, `{device_id}`, `{scheme_name}`, and `{link_id}` intentionally remain `snake_case`.
+
+## Legacy URL Categories
+
+### Keep With Compatibility
+
+These now have `kebab-case` aliases. The frontend has been migrated to the replacement paths; keep the old paths as deprecated compatibility aliases for Agent planning, tests, customer scripts, or external callers:
+
+| Current URL | Suggested replacement |
+| --- | --- |
+| `/api/v1/openproject/` | `/api/v1/projects/open` |
+| `/api/v1/project_info/` | `/api/v1/project-info` |
+| `/api/v1/getallschemes/` | `/api/v1/schemes` |
+| `/api/v1/getallsensorplacements/` | `/api/v1/sensor-placement-schemes` |
+| `/api/v1/sensorplacementscheme/create` | `/api/v1/sensor-placement-schemes` |
+| `/api/v1/burst_analysis/` | `/api/v1/burst-analysis` |
+| `/api/v1/valve_isolation_analysis/` | `/api/v1/valve-isolation-analysis` |
+| `/api/v1/flushing_analysis/` | `/api/v1/flushing-analysis` |
+| `/api/v1/contaminant_simulation/` | `/api/v1/contaminant-simulation` |
+| `/api/v1/runsimulationmanuallybydate/` | `/api/v1/simulations/run-by-date` |
+
+### Broad Legacy Surface
+
+These route groups expose many command-style concatenated paths. They should not be copied into new work; replace only when a caller migration is planned:
+
+- Project lifecycle: `listprojects`, `createproject`, `deleteproject`, `isprojectopen`, `closeproject`, `copyproject`, `importinp`, `exportinp`, `readinp`, `dumpinp`, `lockproject`, `unlockproject`
+- Network object CRUD: `addjunction`, `getjunctionelevation`, `setpipediameter`, `getvalvesetting`, and similar junction/pipe/pump/tank/reservoir/valve routes
+- Region/DMA/VD commands: `calculatedistrictmeteringareaforregion`, `getdistrictmeteringarea`, `generatevirtualdistrict`, and related routes
+- SCADA native CRUD: `getscadadevice`, `setscadadevicedata`, `cleanscadaelement`, and related routes
+- Snapshot/cache utilities: `takesnapshotforoperation`, `syncwithserver`, `clearrediskey`, `queryredis`
+- Advanced simulation endpoints with underscore paths: `pressure_regulation`, `daily_scheduling_analysis`, `network_update`, `pressure_sensor_placement_kmeans`
+
+### Direct Cleanup Candidates
+
+These are likely safe only after confirming no caller uses them:
+
+- `/api/v1/test_dict/`: development/test utility in `misc.py`.
+- `/api/v1/takenapshotforcurrentoperation`: typo compatibility path; keep deprecated if any client may still call it.
+- `/api/v1/getpumpenergyproperties//` and `/api/v1/setpumpenergyproperties//`: double-slash paths in options endpoints.
+
+## Field Naming
+
+Most public JSON, query, and SSE fields are already `snake_case`, including `user_id`, `scheme_name`, `scheme_type`, `start_time`, `end_time`, `device_ids`, `session_id`, and `request_id`.
+
+Known legacy exception:
+
+- `BurstAnalysis.burst_ID` in `app/api/v1/endpoints/simulation.py` should become `burst_id` on a new API contract. Preserve `burst_ID` only for the legacy body shape.
+
+Headers keep standard HTTP casing:
+
+- `X-Project-Id`
+
+## Internal vs Customer Difference
+
+The customer backend retains local auth/user-management routes under `/api/v1/auth` and `/api/v1/users`; the internal backend has migrated to Keycloak/metadata admin routes. Treat those Customer-only auth routes as delivery compatibility surface, not a source for new internal API naming.
+
+## Recommendation
+
+Do not rename existing legacy routes in place. For each active legacy route, keep the new `kebab-case` alias as the documented path, keep the old route marked deprecated, migrate remaining Agent/customer/script callers, then remove only after a documented compatibility window.

DELIVERY_PACKAGING_NOTES.md

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+# tjwater-server Customer Image Packaging Notes
+
+This repository is the customer-delivery backend package. Build delivery images from this repository root.
+
+## Image Build
+
+Build the customer backend image:
+
+```bash
+docker build -t tjwater-server:latest .
+```
+
+The `Dockerfile` already performs source encapsulation in the builder stage:
+
+```bash
+python scripts/compile.py
+python scripts/compile.py --delete-source
+```
+
+The compiled and source-deleted `app/` directory is then copied into the final runner stage. The source deletion happens inside the Docker builder layer only; it does not delete local workspace files.
+
+## Encapsulation Scope
+
+`scripts/compile.py` defaults to compiling these sensitive areas:
+
+- `app/services`
+- `app/native/wndb`
+- `app/algorithms`
+- `app/infra/epanet/epanet.py`
+
+These areas should not contain uncompiled `.py` source files in the delivered image. Public entry points, API route wiring, schemas, configuration, and operational files may remain readable when required for runtime.
+
+## Verification
+
+After building, verify the image tag:
+
+```bash
+docker image ls tjwater-server
+```
+
+Verify that core source files were removed and compiled extensions exist:
+
+```bash
+docker run --rm --entrypoint sh tjwater-server:latest -c "\
+printf 'core_py_count='; \
+find /app/app/services /app/app/native/wndb /app/app/algorithms /app/app/infra/epanet/epanet.py -name '*.py' 2>/dev/null | wc -l; \
+printf 'core_so_count='; \
+find /app/app/services /app/app/native/wndb /app/app/algorithms /app/app/infra/epanet -name '*.so' 2>/dev/null | wc -l; \
+python -c 'import app.main; print(\"import_app_main=ok\")'"
+```
+
+Expected delivery result:
+
+```text
+core_py_count=0
+core_so_count=<non-zero>
+import_app_main=ok
+```
+
+Warnings from third-party packages during import are not necessarily build failures. Treat non-zero exit codes, failed imports, or leftover core `.py` files as blockers.
+
+## Export For Windows Delivery
+
+Export the image tarball to the Windows desktop from WSL:
+
+```bash
+docker save -o /mnt/c/Users/admin/Desktop/tjwater-server-latest.tar tjwater-server:latest
+```
+
+Adjust the Windows username if needed. To find available desktop paths:
+
+```bash
+find /mnt/c/Users -maxdepth 2 -type d \( -name Desktop -o -name 桌面 \) 2>/dev/null
+```
+
+On the target machine, import the image with:
+
+```bash
+docker load -i tjwater-server-latest.tar
+```
+
+## Deployment Caution
+
+The development `infra/docker/docker-compose.yml` bind-mounts local source:
+
+```yaml
+volumes:
+  - ../../app:/app/app
+  - ../../resources:/app/resources
+```
+
+Do not use that source mount for customer delivery of the encapsulated image. Mounting `../../app` over `/app/app` replaces the compiled code inside the image with local source files and defeats the encapsulation. For delivery compose files, use the built image directly and mount only required runtime data/config paths.
+
+## Local Safety
+
+Do not run this destructive command in the normal working tree:
+
+```bash
+python scripts/compile.py --delete-source
+```
+
+Only use `--delete-source` in Docker builder stages or in a disposable delivery copy. The normal delivery image build already handles this safely.

Dockerfile

@@ -0,0 +1,43 @@
+FROM condaforge/miniforge3:latest AS runtime-base
+
+WORKDIR /app
+
+ENV PIP_INDEX_URL=https://pypi.tuna.tsinghua.edu.cn/simple \
+    PIP_TRUSTED_HOST=pypi.tuna.tsinghua.edu.cn \
+    UV_INDEX_URL=https://pypi.tuna.tsinghua.edu.cn/simple
+
+# 安装 Python 3.12 和 pymetis (通过 conda-forge 避免编译问题)
+RUN mamba install -y python=3.12 pymetis && \
+    mamba clean -afy
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir uv
+RUN uv pip install --system --no-cache-dir -r requirements.txt
+
+FROM runtime-base AS builder
+
+RUN mamba install -y c-compiler cxx-compiler && \
+    mamba clean -afy
+
+COPY app ./app
+COPY scripts ./scripts
+
+RUN python scripts/compile.py && \
+    python scripts/compile.py --delete-source
+
+FROM runtime-base AS runner
+
+# 将代码放入子目录 'app'，将数据放入子目录 'db_inp'
+# 这样临时文件默认会生成在 /app 下，而代码在 /app/app 下，实现了分离
+COPY --from=builder /app/app ./app
+RUN python -c "from pathlib import Path; from zipfile import ZipFile; model_dir = Path('app/algorithms/health/model'); zip_path = model_dir / 'my_survival_forest_model_quxi.zip'; joblib_name = 'my_survival_forest_model_quxi.joblib'; joblib_path = model_dir / joblib_name; assert zip_path.exists(), f'Model archive not found: {zip_path}'; archive = ZipFile(zip_path); archive.extract(joblib_name, model_dir); archive.close(); assert joblib_path.exists(), f'Model file not extracted: {joblib_path}'" && \
+    rm -f app/algorithms/health/model/my_survival_forest_model_quxi.zip
+# COPY db_inp ./db_inp
+RUN mkdir -p db_inp temp data inp
+
+# 设置 PYTHONPATH 以便 uvicorn 找到 app 模块
+ENV PYTHONPATH=/app
+
+EXPOSE 8000
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "4"]

README.md

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+# TJWaterServerCustomer 客户版后端
+
+`TJWaterServerCustomer` 是 TJWater 客户交付版 Python 后端。该仓库应被视为可部署交付包，只保留客户运行、配置、部署、诊断和交付说明所需内容。
+
+## 技术栈
+
+- Python 3.12
+- FastAPI / Uvicorn
+- Pydantic / SQLAlchemy / psycopg
+- Redis、PostgreSQL、PostGIS、TimescaleDB
+- WNTR、EPANET、Cython、科学计算与空间分析依赖
+- pytest
+
+## 目录结构
+
+```text
+app/main.py           FastAPI 入口
+app/api/              HTTP API 路由
+app/auth/             认证和权限上下文
+app/core/             配置、日志和基础设施初始化
+app/domain/           领域模型和 Pydantic schema
+app/infra/            数据库、缓存、EPANET 和外部集成
+app/services/         核心业务服务，交付镜像中必须封装
+app/algorithms/       核心算法，交付镜像中必须封装
+app/native/           本地管网数据读写与转换，交付镜像中必须封装
+scripts/compile.py    Cython 封装脚本
+infra/docker/         Docker Compose 编排
+tests/                后端测试
+```
+
+## 本地开发
+
+推荐使用已有 conda 环境：
+
+```bash
+conda run -n server python -m pytest tests -q
+conda run -n server python scripts/run_server.py
+```
+
+本地调试不要在正常工作树执行源码删除命令。
+
+## 客户版镜像打包
+
+交付镜像标签通常为：
+
+```bash
+docker build -t tjwater-server:latest .
+```
+
+`Dockerfile` 会在 builder 阶段执行：
+
+```bash
+python scripts/compile.py
+python scripts/compile.py --delete-source
+```
+
+源码删除只发生在 Docker 构建层内，不会删除本地工作树源码。详细封装、验证和 Windows 导出说明见：
+
+```text
+DELIVERY_PACKAGING_NOTES.md
+```
+
+## 封装范围
+
+`scripts/compile.py` 默认封装：
+
+- `app/services`
+- `app/native/wndb`
+- `app/algorithms`
+- `app/infra/epanet/epanet.py`
+
+交付镜像中这些核心目录不应残留未编译的 `.py` 源码，应以 `.so` 扩展模块运行。
+
+## 验证命令
+
+构建后检查镜像：
+
+```bash
+docker image ls tjwater-server
+```
+
+检查核心源码是否已删除、FastAPI 入口是否可导入：
+
+```bash
+docker run --rm --entrypoint sh tjwater-server:latest -c "\
+printf 'core_py_count='; \
+find /app/app/services /app/app/native/wndb /app/app/algorithms /app/app/infra/epanet/epanet.py -name '*.py' 2>/dev/null | wc -l; \
+printf 'core_so_count='; \
+find /app/app/services /app/app/native/wndb /app/app/algorithms /app/app/infra/epanet -name '*.so' 2>/dev/null | wc -l; \
+python -c 'import app.main; print(\"import_app_main=ok\")'"
+```
+
+期望结果：
+
+```text
+core_py_count=0
+core_so_count=<非零>
+import_app_main=ok
+```
+
+## 部署注意
+
+客户交付时不要把本地 `../../app` 挂载到容器 `/app/app`，否则会覆盖镜像内已封装代码。交付 compose 文件应使用构建好的镜像，只挂载必要的运行数据和配置。
+
+## 安全规则
+
+不要提交 `.env`、生产凭据、客户数据、数据库 dump、日志、生成缓存、临时交付压缩包或本地运行目录。客户版仓库不应加入内部实验、调试工具或非交付源码材料。

ReadMe.md

@@ -1,4 +0,0 @@
-当前 适配 szh 项目的分支 是  dingsu/szh
-
-Binary 适配的是 代码 中dingsu/szh 的部分
-当前只是把 API目录（也就是TJNetwork的部分）加密了

app/algorithms/__init__.py

@@ -0,0 +1,45 @@
+"""Algorithm package with side-effect-free, lazy compatibility exports."""
+
+from importlib import import_module
+from typing import Any
+
+
+_EXPORT_MODULES = {
+    "flow_data_clean": "app.algorithms.cleaning",
+    "pressure_data_clean": "app.algorithms.cleaning",
+    "pressure_sensor_placement_sensitivity": "app.algorithms.sensor",
+    "pressure_sensor_placement_kmeans": "app.algorithms.sensor",
+    "valve_isolation_analysis": "app.algorithms.isolation.valve",
+    "LeakageIdentifier": "app.algorithms.leakage",
+    "PipelineHealthAnalyzer": "app.algorithms.health",
+    "run_burst_location": "app.algorithms.burst_location",
+    **{
+        name: "app.algorithms.simulation.scenarios"
+        for name in (
+            "convert_to_local_unit",
+            "burst_analysis",
+            "valve_close_analysis",
+            "flushing_analysis",
+            "contaminant_simulation",
+            "age_analysis",
+            "pressure_regulation",
+        )
+    },
+}
+
+__all__ = list(_EXPORT_MODULES)
+
+
+def __getattr__(name: str) -> Any:
+    try:
+        module_name = _EXPORT_MODULES[name]
+    except KeyError as exc:
+        raise AttributeError(f"module {__name__!r} has no attribute {name!r}") from exc
+
+    value = getattr(import_module(module_name), name)
+    globals()[name] = value
+    return value
+
+
+def __dir__() -> list[str]:
+    return sorted({*globals(), *__all__})

app/algorithms/_utils.py

@@ -0,0 +1,88 @@
+import os
+
+import pandas as pd
+
+
+def fill_time_gaps(
+    data: pd.DataFrame,
+    time_col: str = "time",
+    freq: str = "1min",
+    short_gap_threshold: int = 10,
+) -> pd.DataFrame:
+    """
+    补齐缺失时间戳并填补数据缺口。
+
+    Args:
+        data: 包含时间列的 DataFrame
+        time_col: 时间列名（默认 'time'）
+        freq: 重采样频率（默认 '1min'）
+        short_gap_threshold: 短缺口阈值（分钟），<=此值用线性插值，>此值用前向填充
+
+    Returns:
+        补齐时间后的 DataFrame（保留原时间列格式）
+    """
+    if time_col not in data.columns:
+        raise ValueError(f"时间列 '{time_col}' 不存在于数据中")
+
+    # 解析时间列并设为索引
+    data = data.copy()
+    data[time_col] = pd.to_datetime(data[time_col], utc=True)
+    data_indexed = data.set_index(time_col)
+
+    # 生成完整时间范围
+    full_range = pd.date_range(
+        start=data_indexed.index.min(), end=data_indexed.index.max(), freq=freq
+    )
+
+    # 重索引以补齐缺失时间点，同时保留原始时间戳
+    combined_index = data_indexed.index.union(full_range).sort_values().unique()
+    data_reindexed = data_indexed.reindex(combined_index)
+
+    # 按列处理缺口
+    for col in data_reindexed.columns:
+        # 识别缺失值位置
+        is_missing = data_reindexed[col].isna()
+
+        # 计算连续缺失的长度
+        missing_groups = (is_missing != is_missing.shift()).cumsum()
+        gap_lengths = is_missing.groupby(missing_groups).transform("sum")
+
+        # 短缺口：时间插值
+        short_gap_mask = is_missing & (gap_lengths <= short_gap_threshold)
+        if short_gap_mask.any():
+            data_reindexed.loc[short_gap_mask, col] = (
+                data_reindexed[col]
+                .interpolate(method="time", limit_area="inside")
+                .loc[short_gap_mask]
+            )
+
+        # 长缺口：前向填充
+        long_gap_mask = is_missing & (gap_lengths > short_gap_threshold)
+        if long_gap_mask.any():
+            data_reindexed.loc[long_gap_mask, col] = (
+                data_reindexed[col].ffill().loc[long_gap_mask]
+            )
+
+    # 重置索引并恢复时间列（保留原格式）
+    data_result = data_reindexed.reset_index()
+    data_result.rename(columns={"index": time_col}, inplace=True)
+
+    # 保留时区信息
+    data_result[time_col] = data_result[time_col].dt.strftime("%Y-%m-%dT%H:%M:%S%z")
+    # 修正时区格式（Python的%z输出为+0000，需转为+00:00）
+    data_result[time_col] = data_result[time_col].str.replace(
+        r"(\+\d{2})(\d{2})$", r"\1:\2", regex=True
+    )
+
+    return data_result
+
+
+def _cleanup_temp_files(prefix: str) -> None:
+    """清理 EPANET 仿真产生的临时文件。"""
+    for ext in [".inp", ".rpt", ".bin", ".out"]:
+        temp_file = prefix + ext
+        if os.path.exists(temp_file):
+            try:
+                os.remove(temp_file)
+            except OSError:
+                pass

app/algorithms/api_ex/Pdataclean.py

@@ -1,238 +0,0 @@
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-from sklearn.cluster import KMeans
-from sklearn.impute import SimpleImputer
-import os
-
-
-def clean_pressure_data_km(input_csv_path: str, show_plot: bool = False) -> str:
-    """
-    读取输入 CSV，基于 KMeans 检测异常并用滚动平均修复。输出为 <input_basename>_cleaned.xlsx（同目录）。
-    原始数据在 sheet 'raw_pressure_data'，处理后数据在 sheet 'cleaned_pressusre_data'。
-    返回输出文件的绝对路径。
-    """
-    # 读取 CSV
-    input_csv_path = os.path.abspath(input_csv_path)
-    data = pd.read_csv(input_csv_path, header=0, index_col=None, encoding="utf-8")
-    # 标准化
-    data_norm = (data - data.mean()) / data.std()
-
-    # 聚类与异常检测
-    k = 3
-    kmeans = KMeans(n_clusters=k, init="k-means++", n_init=50, random_state=42)
-    clusters = kmeans.fit_predict(data_norm)
-    centers = kmeans.cluster_centers_
-
-    distances = np.linalg.norm(data_norm.values - centers[clusters], axis=1)
-    threshold = distances.mean() + 3 * distances.std()
-
-    anomaly_pos = np.where(distances > threshold)[0]
-    anomaly_indices = data.index[anomaly_pos]
-
-    anomaly_details = {}
-    for pos in anomaly_pos:
-        row_norm = data_norm.iloc[pos]
-        cluster_idx = clusters[pos]
-        center = centers[cluster_idx]
-        diff = abs(row_norm - center)
-        main_sensor = diff.idxmax()
-        anomaly_details[data.index[pos]] = main_sensor
-
-    # 修复：滚动平均（窗口可调）
-    data_rolled = data.rolling(window=13, center=True, min_periods=1).mean()
-    data_repaired = data.copy()
-    for pos in anomaly_pos:
-        label = data.index[pos]
-        sensor = anomaly_details[label]
-        data_repaired.loc[label, sensor] = data_rolled.loc[label, sensor]
-
-    # 可选可视化（使用位置作为 x 轴）
-    plt.rcParams["font.sans-serif"] = ["SimHei"]
-    plt.rcParams["axes.unicode_minus"] = False
-
-    if show_plot and len(data.columns) > 0:
-        n = len(data)
-        time = np.arange(n)
-        plt.figure(figsize=(12, 8))
-        for col in data.columns:
-            plt.plot(time, data[col].values, marker="o", markersize=3, label=col)
-        for pos in anomaly_pos:
-            sensor = anomaly_details[data.index[pos]]
-            plt.plot(pos, data.iloc[pos][sensor], "ro", markersize=8)
-        plt.xlabel("时间点（序号）")
-        plt.ylabel("压力监测值")
-        plt.title("各传感器折线图（红色标记主要异常点）")
-        plt.legend()
-        plt.show()
-
-        plt.figure(figsize=(12, 8))
-        for col in data_repaired.columns:
-            plt.plot(
-                time, data_repaired[col].values, marker="o", markersize=3, label=col
-            )
-        for pos in anomaly_pos:
-            sensor = anomaly_details[data.index[pos]]
-            plt.plot(pos, data_repaired.iloc[pos][sensor], "go", markersize=8)
-        plt.xlabel("时间点（序号）")
-        plt.ylabel("修复后压力监测值")
-        plt.title("修复后各传感器折线图（绿色标记修复值）")
-        plt.legend()
-        plt.show()
-
-    # 保存到 Excel：两个 sheet
-    input_dir = os.path.dirname(os.path.abspath(input_csv_path))
-    input_base = os.path.splitext(os.path.basename(input_csv_path))[0]
-    output_filename = f"{input_base}_cleaned.xlsx"
-    output_path = os.path.join(input_dir, output_filename)
-
-    if os.path.exists(output_path):
-        os.remove(output_path)  # 覆盖同名文件
-    with pd.ExcelWriter(output_path, engine="openpyxl") as writer:
-        data.to_excel(writer, sheet_name="raw_pressure_data", index=False)
-        data_repaired.to_excel(writer, sheet_name="cleaned_pressusre_data", index=False)
-
-    # 返回输出文件的绝对路径
-    return os.path.abspath(output_path)
-
-
-def clean_pressure_data_df_km(data: pd.DataFrame, show_plot: bool = False) -> dict:
-    """
-    接收一个 DataFrame 数据结构，使用KMeans聚类检测异常并用滚动平均修复。
-    返回清洗后的字典数据结构。
-    """
-    # 使用传入的 DataFrame
-    data = data.copy()
-    # 填充NaN值
-    data = data.ffill().bfill()
-    # 异常值预处理
-    # 将0值替换为NaN，然后用线性插值填充
-    data_filled = data.replace(0, np.nan)
-    data_filled = data_filled.interpolate(method="linear", limit_direction="both")
-    # 如果仍有NaN（全为0的列），用前后值填充
-    data_filled = data_filled.ffill().bfill()
-
-    # 标准化（使用填充后的数据）
-    data_norm = (data_filled - data_filled.mean()) / data_filled.std()
-
-    # 添加：处理标准化后的 NaN（例如，标准差为0的列），防止异常数据，时间段内所有数据都相同导致计算结果为 NaN
-    imputer = SimpleImputer(
-        strategy="constant", fill_value=0, keep_empty_features=True
-    )  # 用 0 填充 NaN，包括全 NaN，并保留空特征
-    data_norm = pd.DataFrame(
-        imputer.fit_transform(data_norm),
-        columns=data_norm.columns,
-        index=data_norm.index,
-    )
-
-    # 聚类与异常检测
-    k = 3
-    kmeans = KMeans(n_clusters=k, init="k-means++", n_init=50, random_state=42)
-    clusters = kmeans.fit_predict(data_norm)
-    centers = kmeans.cluster_centers_
-
-    distances = np.linalg.norm(data_norm.values - centers[clusters], axis=1)
-    threshold = distances.mean() + 3 * distances.std()
-
-    anomaly_pos = np.where(distances > threshold)[0]
-    anomaly_indices = data.index[anomaly_pos]
-
-    anomaly_details = {}
-    for pos in anomaly_pos:
-        row_norm = data_norm.iloc[pos]
-        cluster_idx = clusters[pos]
-        center = centers[cluster_idx]
-        diff = abs(row_norm - center)
-        main_sensor = diff.idxmax()
-        anomaly_details[data.index[pos]] = main_sensor
-
-    # 修复：滚动平均（窗口可调）
-    data_rolled = data_filled.rolling(window=13, center=True, min_periods=1).mean()
-    data_repaired = data_filled.copy()
-    for pos in anomaly_pos:
-        label = data.index[pos]
-        sensor = anomaly_details[label]
-        data_repaired.loc[label, sensor] = data_rolled.loc[label, sensor]
-
-    # 可选可视化（使用位置作为 x 轴）
-    plt.rcParams["font.sans-serif"] = ["SimHei"]
-    plt.rcParams["axes.unicode_minus"] = False
-
-    if show_plot and len(data.columns) > 0:
-        n = len(data)
-        time = np.arange(n)
-        plt.figure(figsize=(12, 8))
-        for col in data.columns:
-            plt.plot(
-                time, data[col].values, marker="o", markersize=3, label=col, alpha=0.5
-            )
-        for col in data_filled.columns:
-            plt.plot(
-                time,
-                data_filled[col].values,
-                marker="x",
-                markersize=3,
-                label=f"{col}_filled",
-                linestyle="--",
-            )
-        for pos in anomaly_pos:
-            sensor = anomaly_details[data.index[pos]]
-            plt.plot(pos, data_filled.iloc[pos][sensor], "ro", markersize=8)
-        plt.xlabel("时间点（序号）")
-        plt.ylabel("压力监测值")
-        plt.title("各传感器折线图（红色标记主要异常点，虚线为0值填充后）")
-        plt.legend()
-        plt.show()
-
-        plt.figure(figsize=(12, 8))
-        for col in data_repaired.columns:
-            plt.plot(
-                time, data_repaired[col].values, marker="o", markersize=3, label=col
-            )
-        for pos in anomaly_pos:
-            sensor = anomaly_details[data.index[pos]]
-            plt.plot(pos, data_repaired.iloc[pos][sensor], "go", markersize=8)
-            plt.xlabel("时间点（序号）")
-            plt.ylabel("修复后压力监测值")
-            plt.title("修复后各传感器折线图（绿色标记修复值）")
-            plt.legend()
-            plt.show()
-
-    # 返回清洗后的字典
-    return data_repaired
-
-
-# 测试
-# if __name__ == "__main__":
-#     # 默认使用脚本目录下的 pressure_raw_data.csv
-#     script_dir = os.path.dirname(os.path.abspath(__file__))
-#     default_csv = os.path.join(script_dir, "pressure_raw_data.csv")
-#     out_path = clean_pressure_data_km(default_csv, show_plot=False)
-#     print("保存路径：", out_path)
-
-# 测试 clean_pressure_data_dict_km 函数
-if __name__ == "__main__":
-    import random
-
-    # 读取 szh_pressure_scada.csv 文件
-    script_dir = os.path.dirname(os.path.abspath(__file__))
-    csv_path = os.path.join(script_dir, "szh_pressure_scada.csv")
-    data = pd.read_csv(csv_path, header=0, index_col=None, encoding="utf-8")
-
-    # 排除 Time 列，随机选择 5 列
-    columns_to_exclude = ["Time"]
-    available_columns = [col for col in data.columns if col not in columns_to_exclude]
-    selected_columns = random.sample(available_columns, 5)
-
-    # 将选中的列转换为字典
-    data_dict = {col: data[col].tolist() for col in selected_columns}
-
-    print("选中的列:", selected_columns)
-    print("原始数据长度:", len(data_dict[selected_columns[0]]))
-
-    # 调用函数进行清洗
-    cleaned_dict = clean_pressure_data_df_km(data_dict, show_plot=True)
-
-    print("清洗后的字典键:", list(cleaned_dict.keys()))
-    print("清洗后的数据长度:", len(cleaned_dict[selected_columns[0]]))
-    print("测试完成：函数运行正常")

app/algorithms/api_ex/__init__.py

@@ -1,3 +0,0 @@
-from .Fdataclean import *
-from .Pdataclean import *
-from .pipeline_health_analyzer import *

app/algorithms/burst_detection/__init__.py

@@ -0,0 +1,3 @@
+from app.algorithms.burst_detection.burst_detector import BurstDetector
+
+__all__ = ["BurstDetector"]

app/algorithms/burst_detection/burst_detector.py

@@ -0,0 +1,259 @@
+from __future__ import annotations
+
+from typing import Any
+
+import numpy as np
+import pandas as pd
+from scipy.fft import fft, ifft
+from sklearn.ensemble import IsolationForest
+
+PressureDataInput = (
+    pd.DataFrame
+    | dict[str, list[Any]]
+    | list[dict[str, Any]]
+    | list[list[Any]]
+    | np.ndarray
+)
+IGNORED_OBSERVATION_COLUMNS = {"time", "timestamp", "datetime", "date"}
+
+
+class BurstDetector:
+    """FFT + IsolationForest based burst detection for daily aligned pressure data."""
+
+    def __init__(
+        self,
+        *,
+        mu: int = 100,
+        points_per_day: int = 1440,
+        iforest_params: dict[str, Any] | None = None,
+    ) -> None:
+        if points_per_day <= 0:
+            raise ValueError("points_per_day 必须大于 0。")
+        if mu <= 0:
+            raise ValueError("mu 必须大于 0。")
+
+        self.mu = int(mu)
+        self.points_per_day = int(points_per_day)
+        self.iforest_params = {
+            "n_estimators": 50,
+            "random_state": 42,
+            "contamination": "auto",
+        }
+        if iforest_params:
+            self.iforest_params.update(iforest_params)
+
+        self.data: np.ndarray | None = None
+        self.sensor_names: list[str] = []
+        self.high_freq_features: np.ndarray | None = None
+
+    def load_data(
+        self,
+        data_source: PressureDataInput,
+        *,
+        sensor_nodes: list[str] | None = None,
+    ) -> pd.DataFrame:
+        """
+        标准化输入观测数据为 DataFrame。
+
+        支持的 `data_source` 格式：
+        - `pd.DataFrame`
+          每一列代表一个传感器，每一行代表一个时间点。
+        - `dict[str, list[Any]]`
+          键为传感器 ID，值为该传感器按时间顺序排列的压力序列。
+          例如：`{"J1": [101.2, 101.0], "J2": [99.8, 99.7]}`。
+        - `list[dict[str, Any]]`
+          每个字典代表一个时间点，键为传感器 ID，值为该时刻压力。
+          例如：`[{"J1": 101.2, "J2": 99.8}, {"J1": 101.0, "J2": 99.7}]`。
+        - `list[list[Any]]`
+          二维列表，格式为 `(时间点数, 传感器数)`。
+          例如：`[[101.2, 99.8], [101.0, 99.7]]`。
+        - `np.ndarray`
+          二维数组，形状必须为 `(时间点数, 传感器数)`。
+
+        参数：
+        - `sensor_nodes`:
+          可选的传感器列筛选列表。传入后，数据中必须包含这些列名。
+
+        返回：
+        - 标准化后的 `pd.DataFrame`，列为传感器，行为时间点。
+        """
+        if isinstance(data_source, np.ndarray):
+            observation_df = pd.DataFrame(data_source)
+        elif isinstance(data_source, pd.DataFrame):
+            observation_df = data_source.copy()
+        else:
+            observation_df = pd.DataFrame(data_source)
+
+        return self._normalize_observation_frame(
+            observation_df=observation_df, sensor_nodes=sensor_nodes
+        )
+
+    def process(
+        self,
+        observed_pressure_data: PressureDataInput,
+        *,
+        sensor_nodes: list[str] | None = None,
+    ) -> np.ndarray:
+        """
+        对输入压力序列按天切片，并提取每天末时刻的高频特征。
+
+        `observed_pressure_data` 的格式与 `load_data()` 一致，统一要求：
+        - 数据必须表示为“行=时间点、列=传感器”。
+        - 总行数必须是 `points_per_day` 的整数倍。
+        - 至少需要 2 天数据，即总行数 `>= 2 * points_per_day`。
+
+        例如：
+        - 当 `points_per_day=1440` 时，15 天数据的形状通常为 `(21600, 传感器数)`。
+        - 若传入 `sensor_nodes=["J1", "J2"]`，则输入中必须存在 `J1/J2` 两列。
+
+        返回：
+        - `np.ndarray`，形状为 `(天数, 传感器数)`，
+          每个值表示对应传感器在当天末时刻提取出的高频分量。
+        """
+        observation_df = self.load_data(
+            observed_pressure_data,
+            sensor_nodes=sensor_nodes,
+        )
+        matrix = observation_df.to_numpy(dtype=float)
+        total_points, sensor_count = matrix.shape
+        if sensor_count == 0:
+            raise ValueError("压力观测数据中未找到可用传感器列。")
+        if total_points < self.points_per_day * 2:
+            raise ValueError("至少需要 2 天的观测数据才能执行爆管侦测。")
+        if total_points % self.points_per_day != 0:
+            raise ValueError("观测数据长度必须能被每日采样点数整除，以便按天切分。")
+
+        day_count = total_points // self.points_per_day
+        high_freq_features = np.zeros((day_count, sensor_count), dtype=float)
+
+        for sensor_idx in range(sensor_count):
+            sensor_series = matrix[:, sensor_idx]
+            for day_idx in range(day_count):
+                start = day_idx * self.points_per_day
+                end = (day_idx + 1) * self.points_per_day
+                day_data = sensor_series[start:end]
+                mirrored_data = np.concatenate([day_data, day_data[::-1]])
+                transformed = fft(mirrored_data)
+                transformed[self.mu : len(mirrored_data) - self.mu + 1] = 0
+                low_freq = ifft(transformed).real
+                high_freq = day_data - low_freq[: self.points_per_day]
+                high_freq_features[day_idx, sensor_idx] = float(high_freq[-1])
+
+        self.data = matrix
+        self.sensor_names = [str(column) for column in observation_df.columns]
+        self.high_freq_features = high_freq_features
+        return high_freq_features
+
+    def detect(self) -> pd.DataFrame:
+        if self.high_freq_features is None:
+            raise ValueError("特征未提取。请先调用 process()。")
+
+        day_count = self.high_freq_features.shape[0]
+        if day_count < 2:
+            raise ValueError("孤立森林至少需要 2 天特征数据。")
+
+        clf = IsolationForest(
+            n_estimators=self.iforest_params.get("n_estimators", 50),
+            max_samples=day_count,
+            random_state=self.iforest_params.get("random_state", 42),
+            contamination=self.iforest_params.get("contamination", "auto"),
+            **{
+                key: value
+                for key, value in self.iforest_params.items()
+                if key not in {"n_estimators", "random_state", "contamination"}
+            },
+        )
+        clf.fit(self.high_freq_features)
+
+        scores = clf.decision_function(self.high_freq_features)
+        predictions = clf.predict(self.high_freq_features)
+        result_df = pd.DataFrame(
+            {
+                "Day": range(1, day_count + 1),
+                "Score": scores.astype(float),
+                "Prediction": predictions.astype(int),
+            }
+        )
+        result_df["IsBurst"] = result_df["Prediction"].eq(-1)
+        result_df.attrs["sensor_nodes"] = self.sensor_names.copy()
+        result_df.attrs["high_freq_features"] = self.high_freq_features.copy()
+        result_df.attrs["day_count"] = day_count
+        result_df.attrs["points_per_day"] = self.points_per_day
+        result_df.attrs["sample_count"] = (
+            int(self.data.shape[0]) if self.data is not None else 0
+        )
+        return result_df
+
+    def run_detection(
+        self,
+        observed_pressure_data: PressureDataInput,
+        *,
+        sensor_nodes: list[str] | None = None,
+    ) -> pd.DataFrame:
+        """
+        执行完整爆管侦测流程。
+
+        输入格式与 `process()` 相同：
+        - `DataFrame` / `dict[str, list[Any]]` / `list[dict[str, Any]]` / `list[list[Any]]` / `np.ndarray`
+        - 行表示时间点，列表示传感器
+        - 总行数必须能被 `points_per_day` 整除
+
+        返回结果包含列：
+        - `Day`: 第几天（从 1 开始）
+        - `Score`: IsolationForest 异常分数，越小越异常
+        - `Prediction`: `-1` 表示异常，`1` 表示正常
+        - `IsBurst`: 是否判定为异常日
+        """
+        self.process(observed_pressure_data, sensor_nodes=sensor_nodes)
+        return self.detect()
+
+    @staticmethod
+    def _normalize_observation_frame(
+        *,
+        observation_df: pd.DataFrame,
+        sensor_nodes: list[str] | None,
+    ) -> pd.DataFrame:
+        if observation_df.empty:
+            raise ValueError("压力观测数据为空。")
+
+        normalized_df = observation_df.copy()
+        normalized_df.columns = [str(column) for column in normalized_df.columns]
+        normalized_df = normalized_df.drop(
+            columns=[
+                column
+                for column in normalized_df.columns
+                if column.lower() in IGNORED_OBSERVATION_COLUMNS
+                or column.lower().startswith("unnamed:")
+            ],
+            errors="ignore",
+        )
+
+        if sensor_nodes:
+            selected_columns = [str(node) for node in sensor_nodes]
+            missing_columns = [
+                column
+                for column in selected_columns
+                if column not in normalized_df.columns
+            ]
+            if missing_columns:
+                preview = ", ".join(missing_columns[:10])
+                raise ValueError(f"观测数据缺少传感器列: {preview}")
+            normalized_df = normalized_df.loc[:, selected_columns]
+        else:
+            candidate_df = normalized_df.apply(pd.to_numeric, errors="coerce")
+            normalized_df = candidate_df.loc[:, candidate_df.notna().any(axis=0)]
+
+        if normalized_df.empty:
+            raise ValueError("未识别到可用的数值型压力观测列。")
+
+        normalized_df = normalized_df.apply(pd.to_numeric, errors="coerce")
+        invalid_columns = [
+            column
+            for column in normalized_df.columns
+            if normalized_df[column].isna().any()
+        ]
+        if invalid_columns:
+            preview = ", ".join(invalid_columns[:10])
+            raise ValueError(f"压力观测数据包含非数值或缺失值: {preview}")
+
+        return normalized_df.reset_index(drop=True)

app/algorithms/burst_location/__init__.py

@@ -0,0 +1,3 @@
+from .burst_location import run_burst_location
+
+__all__ = ["run_burst_location"]

app/algorithms/burst_location/burst_location.py

@@ -0,0 +1,342 @@
+import argparse
+import json
+import logging
+from multiprocessing import cpu_count
+from pathlib import Path
+from typing import Any, Iterable
+
+import pandas as pd
+
+from app.algorithms.burst_location import leak_simulator
+
+from .burst_locator import (
+    DN_search_multi_simple_add_flow_count_new,
+)
+from .network_model import (
+    _build_node_pipe_maps,
+    cal_node_coordinate,
+    construct_graph,
+    load_inp,
+    read_inf_inp,
+    read_inf_inp_other,
+)
+
+DEFAULT_N_WORKERS = max(1, min(cpu_count() - 1, 4))
+# DEFAULT_N_WORKERS = max(1, cpu_count() - 1)
+logger = logging.getLogger(__name__)
+
+
+def _read_id_list_json(path):
+    if path is None:
+        return None
+    data = json.loads(Path(path).read_text(encoding="utf-8"))
+    if isinstance(data, list):
+        return [str(item) for item in data]
+    if isinstance(data, dict):
+        if "ids" in data and isinstance(data["ids"], list):
+            return [str(item) for item in data["ids"]]
+        raise ValueError(f"ID JSON must be list or dict with key 'ids': {path}")
+    raise ValueError(f"Unsupported ID JSON format: {path}")
+
+
+def _read_series_csv(path):
+    if path is None:
+        return None
+    df = pd.read_csv(path)
+    if df.shape[1] < 2:
+        raise ValueError(f"CSV must contain at least two columns (id,value): {path}")
+    if {"id", "value"}.issubset(df.columns):
+        id_col, value_col = "id", "value"
+    else:
+        id_col, value_col = df.columns[0], df.columns[1]
+    series = pd.Series(
+        df[value_col].values, index=df[id_col].astype(str).values, dtype=float
+    )
+    return series
+
+
+def _align_scada_series(
+    series: pd.Series, ids: Iterable[str], series_name: str
+) -> pd.Series:
+    ids = [str(item) for item in ids]
+    aligned = series.copy()
+    aligned.index = aligned.index.map(str)
+    missing_ids = [item for item in ids if item not in aligned.index]
+    if missing_ids:
+        preview = ", ".join(missing_ids[:10])
+        raise ValueError(f"{series_name} missing IDs: {preview}")
+    aligned = pd.to_numeric(aligned.loc[ids], errors="coerce")
+    invalid_ids = aligned[aligned.isna()].index.tolist()
+    if invalid_ids:
+        preview = ", ".join(invalid_ids[:10])
+        raise ValueError(
+            f"{series_name} contains non-numeric values for IDs: {preview}"
+        )
+    return aligned
+
+
+def _validate_flow_inputs(
+    flow_scada_ids: list[str] | None,
+    burst_flow: pd.Series | None,
+    normal_flow: pd.Series | None,
+) -> tuple[bool, list[str]]:
+    has_any_flow = any(
+        value is not None for value in [flow_scada_ids, burst_flow, normal_flow]
+    )
+    has_all_flow = all(
+        value is not None for value in [flow_scada_ids, burst_flow, normal_flow]
+    )
+    if has_any_flow and not has_all_flow:
+        raise ValueError(
+            "flow_scada_ids, burst_flow, and normal_flow must be provided together."
+        )
+
+    if not has_all_flow:
+        return False, []
+
+    flow_ids = [str(item) for item in (flow_scada_ids or [])]
+    if len(flow_ids) == 0:
+        raise ValueError("flow_scada_ids cannot be empty when flow data is provided.")
+    return True, flow_ids
+
+
+def _build_top_candidates(similarity_series: pd.Series) -> list[dict[str, Any]]:
+    top_series = similarity_series.iloc[:10]
+    return [
+        {"pipe_id": str(pipe_id), "similarity": float(score)}
+        for pipe_id, score in top_series.items()
+    ]
+
+
+def run_burst_location(
+    wn_inp_path: str,
+    pressure_scada_ids: list[str],
+    burst_pressure: pd.Series,
+    normal_pressure: pd.Series,
+    burst_leakage: float,
+    flow_scada_ids: list[str] | None = None,
+    burst_flow: pd.Series | None = None,
+    normal_flow: pd.Series | None = None,
+    min_dpressure: float = 2.0,
+    basic_pressure: float = 10.0,
+    n_workers: int = DEFAULT_N_WORKERS,
+    partition_on_full_graph: bool = True,
+    visualize_partition: bool = False,
+    visualize_pause_seconds: float = 0.3,
+    final_candidates_csv_path: (
+        str | None
+    ) = "temp/burst_location/final_round_candidates.csv",
+) -> dict[str, Any]:
+    if pressure_scada_ids is None or len(pressure_scada_ids) == 0:
+        raise ValueError("pressure_scada_ids cannot be empty.")
+    if burst_pressure is None or normal_pressure is None:
+        raise ValueError("burst_pressure and normal_pressure are required.")
+
+    has_all_flow, flow_ids = _validate_flow_inputs(
+        flow_scada_ids=flow_scada_ids,
+        burst_flow=burst_flow,
+        normal_flow=normal_flow,
+    )
+
+    inp_path = Path(wn_inp_path)
+    wn = load_inp(
+        inp_name=inp_path.name,
+        inp_location=str(inp_path.parent) + "/",
+        inp_time=0,
+        driven_mode="PDD",
+        require_p=float(basic_pressure),
+        minimum_p=0.0,
+    )
+
+    (
+        all_node,
+        _,
+        node_coordinates,
+        all_pipe,
+        _,
+        _,
+        pipe_length,
+        pipe_diameter,
+    ) = read_inf_inp(wn)
+
+    candidate_pipe, _ = leak_simulator.cal_possible_pipe(
+        burst_leakage, all_pipe, pipe_diameter
+    )
+
+    _, pipe_start_node_all, pipe_end_node_all = read_inf_inp_other(wn)
+    node_x, node_y = cal_node_coordinate(all_node, node_coordinates)
+    G0 = construct_graph(wn)
+    node_pipe_dic, couple_node_length = _build_node_pipe_maps(
+        all_node,
+        all_pipe,
+        pipe_start_node_all,
+        pipe_end_node_all,
+        pipe_length,
+    )
+    all_node_series = pd.Series(range(len(all_node)), index=all_node)
+
+    pressure_ids = [str(item) for item in pressure_scada_ids]
+    normal_pressure_aligned = _align_scada_series(
+        normal_pressure, pressure_ids, "normal_pressure"
+    )
+    burst_pressure_aligned = _align_scada_series(
+        burst_pressure, pressure_ids, "burst_pressure"
+    )
+    pressure_normal = normal_pressure_aligned.to_frame().T
+    pressure_monitor = burst_pressure_aligned.to_frame().T
+    pressure_predict = pressure_normal.copy()
+    timestep_list = list(pressure_normal.index)
+
+    if has_all_flow:
+        normal_flow_aligned = _align_scada_series(normal_flow, flow_ids, "normal_flow")
+        burst_flow_aligned = _align_scada_series(burst_flow, flow_ids, "burst_flow")
+        flow_normal = normal_flow_aligned.to_frame().T
+        flow_monitor = burst_flow_aligned.to_frame().T
+        flow_predict = flow_normal.copy()
+        similarity_mode = "CDF"
+        max_flow = flow_normal.iloc[0, :].abs()
+    else:
+        flow_normal = pd.DataFrame(index=timestep_list)
+        flow_monitor = pd.DataFrame(index=timestep_list)
+        flow_predict = pd.DataFrame(index=timestep_list)
+        similarity_mode = "CAD_new_gy"
+        max_flow = pd.Series(dtype=float)
+
+    stage_timing: dict[str, Any] = {}
+    try:
+        (
+            located_pipe,
+            elapsed_seconds,
+            simulation_times,
+            _,
+            similarity_series,
+            exit_condition,
+            final_candidates_csv,
+        ) = DN_search_multi_simple_add_flow_count_new(
+            wn=wn,
+            wn_inp_path=str(inp_path),
+            G0=G0,
+            all_node=all_node,
+            node_x=node_x,
+            node_y=node_y,
+            pipe_start_node_all=pipe_start_node_all,
+            pipe_end_node_all=pipe_end_node_all,
+            pipe_diameter=pipe_diameter,
+            couple_node_length=couple_node_length,
+            node_pipe_dic=node_pipe_dic,
+            all_node_series=all_node_series,
+            top_group_ratio=0.3,
+            top_pipe_num_max=80,
+            top_pipe_num_min=10,
+            candidate_pipe_input_initial=candidate_pipe,
+            similarity_mode=similarity_mode,
+            pressure_monitor=pressure_monitor,
+            pressure_predict=pressure_predict,
+            pressure_normal=pressure_normal,
+            pressure_leak_all=None,
+            flow_monitor=flow_monitor,
+            flow_predict=flow_predict,
+            flow_normal=flow_normal,
+            flow_leak_all=None,
+            timestep_list=timestep_list,
+            max_flow=max_flow,
+            group_basic_num=30,
+            Top_sensor_num=min(5, len(pressure_ids)),
+            if_gy=0,
+            pressure_threshold=float(min_dpressure),
+            leak_mag=float(burst_leakage),
+            n_workers=max(1, int(n_workers)),
+            stage_timing=stage_timing,
+            partition_on_full_graph=partition_on_full_graph,
+            visualize_partition=visualize_partition,
+            visualize_pause_seconds=visualize_pause_seconds,
+            final_candidates_csv_path=final_candidates_csv_path,
+        )
+    except Exception as exc:
+        logger.exception("Burst location algorithm execution failed.")
+        raise RuntimeError(f"Failed to run burst location algorithm: {exc}") from exc
+
+    return {
+        "located_pipe": located_pipe,
+        "burst_leakage": float(burst_leakage),
+        "elapsed_seconds": elapsed_seconds,
+        "simulation_times": int(simulation_times),
+        "top_candidates": _build_top_candidates(similarity_series),
+        "similarity_mode": similarity_mode,
+        "exit_condition": exit_condition,
+        "final_candidates_csv": final_candidates_csv,
+        "stage_timing_seconds": stage_timing,
+    }
+
+
+def _parse_args():
+    parser = argparse.ArgumentParser(description="爆管定位主函数入口")
+    parser.add_argument("--wn-inp", required=True, help="EPANET inp 文件路径")
+    parser.add_argument(
+        "--pressure-ids-json", required=True, help="压力SCADA ID列表 JSON 文件"
+    )
+    parser.add_argument(
+        "--flow-ids-json", default=None, help="（可选）流量SCADA ID列表 JSON 文件"
+    )
+    parser.add_argument(
+        "--burst-pressure-csv", required=True, help="爆管时压力 CSV（id,value）"
+    )
+    parser.add_argument(
+        "--normal-pressure-csv", required=True, help="正常时压力 CSV（id,value）"
+    )
+    parser.add_argument(
+        "--burst-flow-csv", default=None, help="（可选）爆管时流量 CSV（id,value）"
+    )
+    parser.add_argument(
+        "--normal-flow-csv", default=None, help="（可选）正常时流量 CSV（id,value）"
+    )
+    parser.add_argument(
+        "--burst-leakage", type=float, required=True, help="爆管漏损流量"
+    )
+    parser.add_argument(
+        "--min-dpressure",
+        type=float,
+        default=2.0,
+        help="（可选）最小压降阈值，默认 2.0",
+    )
+    parser.add_argument(
+        "--basic-pressure",
+        type=float,
+        default=10.0,
+        help="（可选）基础服务压力，默认 10.0",
+    )
+    parser.add_argument(
+        "--n-workers",
+        type=int,
+        default=DEFAULT_N_WORKERS,
+        help="（可选）特征中心模拟进程数，默认 max(1, min(cpu_count()-1, 4))",
+    )
+    parser.add_argument(
+        "--final-candidates-csv-path",
+        default="temp/burst_location/final_round_candidates.csv",
+        help="（可选）最后一轮候选管道明细 CSV 输出路径",
+    )
+    return parser.parse_args()
+
+
+def main():
+    args = _parse_args()
+    result = run_burst_location(
+        wn_inp_path=args.wn_inp,
+        pressure_scada_ids=_read_id_list_json(args.pressure_ids_json),
+        burst_pressure=_read_series_csv(args.burst_pressure_csv),
+        normal_pressure=_read_series_csv(args.normal_pressure_csv),
+        burst_leakage=args.burst_leakage,
+        flow_scada_ids=_read_id_list_json(args.flow_ids_json),
+        burst_flow=_read_series_csv(args.burst_flow_csv),
+        normal_flow=_read_series_csv(args.normal_flow_csv),
+        min_dpressure=args.min_dpressure,
+        basic_pressure=args.basic_pressure,
+        n_workers=args.n_workers,
+        final_candidates_csv_path=args.final_candidates_csv_path,
+    )
+    print(json.dumps(result, ensure_ascii=False))
+
+
+if __name__ == "__main__":
+    main()

app/algorithms/burst_location/burst_locator.py

@@ -0,0 +1,772 @@
+"""爆管定位主模块。"""
+
+import copy
+import math
+import os
+import sys
+from datetime import datetime
+from time import perf_counter
+
+import networkx as nx
+import numpy as np
+import pandas as pd
+
+from .leak_simulator import cal_signature_pipe_multi_pf
+from .network_partitioner import (
+    cal_group_num,
+    metis_grouping_pipe_weight,
+    visualize_metis_partition,
+)
+from .similarity_calculator import (
+    adjust_ratio,
+    cal_similarity_all_multi_new_sq_improve_double_lzr,
+    decode_mode,
+    extra_judge,
+    update_similarity,
+)
+
+
+def _ensure_signatures_for_centers(
+    wn,
+    wn_inp_path,
+    center_list,  # 本轮要用到的中心（list[str]）
+    pressure_leak_all,
+    flow_leak_all,  # 全量缓存（可为空 DF）
+    timestep_list,  # 你现有的时序列表
+    pressure_monitor,
+    flow_monitor,  # 用来推断传感器列名
+    leak_mag,
+    n_workers=1,
+):
+    """
+    只为缺失的中心补算 SLF（调用你现有的 cal_signature_pipe_multi_pf），
+    并把补算结果并回缓存。返回：
+      pressure_leak_subset, flow_leak_subset, pressure_leak_all_new, flow_leak_all_new
+    其中 subset 只包含 center_list 的行（顺序与 center_list 保持一致）。
+    """
+
+    center_list = _dedupe_preserve_order(center_list)
+
+    # 1) 推断传感器列名（与现有数据保持一致）
+    sensor_name_all = list(pressure_monitor.columns)
+    sensor_f_name_all = (
+        list(flow_monitor.columns)
+        if (flow_monitor is not None and hasattr(flow_monitor, "columns"))
+        else []
+    )
+
+    # 2) 取出缓存里已经有的中心（考虑 MultiIndex 的第 0 层为 pipe）
+    def _existing_pipes(df):
+        if df is None or len(df) == 0:
+            return set()
+        idx = df.index
+        if isinstance(idx, pd.MultiIndex):
+            return set(idx.get_level_values(0))
+        else:
+            return set(idx)
+
+    exist_p = _existing_pipes(pressure_leak_all)
+    need = [p for p in center_list if p not in exist_p]
+
+    # 3) 若有缺失中心，仅为这些中心补算一次
+    if len(need) > 0:
+        p_new, _ = cal_signature_pipe_multi_pf(
+            wn,
+            leak_mag,
+            need,
+            timestep_list,
+            sensor_name_all,
+            n_workers=n_workers,
+            wn_inp_path=wn_inp_path,
+        )
+        # 初始化空缓存时，做一次“同构化”
+        if pressure_leak_all is None or len(pressure_leak_all) == 0:
+            pressure_leak_all = p_new
+        else:
+            pressure_leak_all = pd.concat([pressure_leak_all, p_new], axis=0)
+        # if (flow_leak_all is None or len(flow_leak_all) == 0) and f_new is not None:
+        #     flow_leak_all = f_new
+        # elif f_new is not None:
+        #     flow_leak_all = pd.concat([flow_leak_all, f_new], axis=0)
+
+        # 去重（如果既有缓存里不小心有重复中心）
+        if isinstance(pressure_leak_all.index, pd.MultiIndex):
+            pressure_leak_all = pressure_leak_all[
+                ~pressure_leak_all.index.duplicated(keep="last")
+            ]
+            if flow_leak_all is not None and len(flow_leak_all) > 0:
+                flow_leak_all = flow_leak_all[
+                    ~flow_leak_all.index.duplicated(keep="last")
+                ]
+        else:
+            pressure_leak_all = pressure_leak_all[
+                ~pressure_leak_all.index.duplicated(keep="last")
+            ]
+            if flow_leak_all is not None and len(flow_leak_all) > 0:
+                flow_leak_all = flow_leak_all[
+                    ~flow_leak_all.index.duplicated(keep="last")
+                ]
+
+    # 4) 从更新后的缓存里，取出这轮需要的中心子集（顺序与 center_list 一致）
+    if isinstance(pressure_leak_all.index, pd.MultiIndex):
+        pressure_subset = pressure_leak_all.loc[center_list]
+        flow_subset = (
+            flow_leak_all.loc[center_list]
+            if (flow_leak_all is not None and len(flow_leak_all) > 0)
+            else None
+        )
+    else:
+        pressure_subset = pressure_leak_all.loc[center_list, :]
+        flow_subset = (
+            flow_leak_all.loc[center_list, :]
+            if (flow_leak_all is not None and len(flow_leak_all) > 0)
+            else None
+        )
+
+    return pressure_subset, flow_subset, pressure_leak_all, flow_leak_all
+
+
+def area_output_num_ki_improve(
+    candidate_center,
+    candidate_group,
+    similarity,
+    new_all_node,
+    top_group_ratio,
+    top_pipe_num_max,
+    top_pipe_num_min,
+    cut_ratio,
+):
+    final_area = []
+    final_center = []
+    all_node_iter = []
+    if similarity.index.is_unique == False:
+        total_center_num = len(set(similarity.index))
+    else:
+        total_center_num = len(similarity.index)
+
+    next_group_num = min(
+        total_center_num, math.ceil(total_center_num / cut_ratio * top_group_ratio)
+    )
+
+    for i in range(next_group_num):
+        top_center = similarity.index[i]
+        top_center_index = find_list_repeat(candidate_center, top_center)
+        for j in range(len(top_center_index)):
+            final_area = final_area + candidate_group[top_center_index[j]]
+            all_node_iter = all_node_iter + list(new_all_node[top_center_index[j]])
+        final_center.append(top_center)
+        final_area = sorted(set(final_area))
+
+    if len(final_area) > top_pipe_num_max:
+        if_end = 0
+    elif len(final_area) > top_pipe_num_min:
+        if_end = 1
+    elif total_center_num == next_group_num:
+        if_end = 1
+    else:
+        if_end = 1
+        for i in np.arange(next_group_num, total_center_num, 1):
+
+            before_list = copy.deepcopy(final_area)
+            top_center = similarity.index[i]
+            top_center_index = candidate_center.index(top_center)
+            temp_group = final_area + candidate_group[top_center_index]
+            temp_area = sorted(set(temp_group))
+
+            if len(temp_area) < top_pipe_num_min:
+
+                final_center.append(top_center)
+                all_node_iter = all_node_iter + list(new_all_node[top_center_index])
+                final_area = temp_area
+
+            elif len(temp_area) < top_pipe_num_max:
+                final_center.append(top_center)
+                all_node_iter = all_node_iter + list(new_all_node[top_center_index])
+                final_area = temp_area
+                break
+            else:
+                a = len(temp_area) - top_pipe_num_max
+                b = top_pipe_num_min - len(before_list)
+
+                if a >= b:
+                    final_area = before_list
+                else:
+                    final_center.append(top_center)
+                    all_node_iter = all_node_iter + list(new_all_node[top_center_index])
+                    final_area = temp_area
+                break
+
+    final_center = sorted(set(final_center))
+    all_node_iter = sorted(set(all_node_iter))
+    return final_area, final_center, all_node_iter, if_end
+
+
+def find_list_repeat(candidate_center, target):
+    repeated_list = []
+    for index, nums in enumerate(candidate_center):
+        if nums == target:
+            repeated_list.append(index)
+    return repeated_list
+
+
+def _dedupe_preserve_order(items):
+    seen = set()
+    output = []
+    for item in items:
+        if item in seen:
+            continue
+        seen.add(item)
+        output.append(item)
+    return output
+
+
+def _accumulate_stage(stage_timing, stage_name, started_at):
+    stage_timing[stage_name] = stage_timing.get(stage_name, 0.0) + (
+        perf_counter() - started_at
+    )
+
+
+def _write_last_round_candidates_csv(
+    csv_path,
+    exit_condition,
+    iteration_count,
+    similarity_mode,
+    candidate_details,
+    fallback_similarity,
+):
+    if not csv_path:
+        return None
+    timestamp_suffix = datetime.now().strftime("%Y%m%d_%H%M%S")
+    base_path, ext = os.path.splitext(csv_path)
+    ext = ext or ".csv"
+    output_path = f"{base_path}_{timestamp_suffix}{ext}"
+    if candidate_details is not None and len(candidate_details) > 0:
+        export_df = candidate_details.copy()
+        if export_df.index.name == "pipe_id":
+            export_df = export_df.reset_index()
+    else:
+        export_df = pd.DataFrame(
+            {
+                "pipe_id": [str(pipe_id) for pipe_id in fallback_similarity.index],
+                "final_similarity": [float(value) for value in fallback_similarity.values],
+            }
+        )
+    export_df["exit_condition"] = exit_condition
+    export_df["iterations"] = int(iteration_count)
+    export_df["similarity_mode"] = similarity_mode
+    parent_dir = os.path.dirname(output_path)
+    if parent_dir:
+        os.makedirs(parent_dir, exist_ok=True)
+    export_df.to_csv(output_path, index=False, encoding="utf-8-sig")
+    return output_path
+
+
+def cal_DtoTop1(
+    G0, pipe_leak, located_pipe, pipe_start_node_all, pipe_end_node_all, pipe_length
+):
+    if pipe_leak == located_pipe:
+        result_DtoTop1 = 0
+        result_DtoTop1_num = 0
+    else:
+        pipe_leak_start_node = pipe_start_node_all[pipe_leak]
+        pipe_leak_end_node = pipe_end_node_all[pipe_leak]
+        located_pipe_start_node = pipe_start_node_all[located_pipe]
+        located_pipe_end_node = pipe_end_node_all[located_pipe]
+        DtoTop1_series = pd.Series(dtype=object)
+        DtoTop1_num_series = pd.Series(dtype=object)
+        DtoTop1_series["ss"] = nx.shortest_path_length(
+            G0, pipe_leak_start_node, located_pipe_start_node, weight="weight"
+        )
+        DtoTop1_series["se"] = nx.shortest_path_length(
+            G0, pipe_leak_start_node, located_pipe_end_node, weight="weight"
+        )
+        DtoTop1_series["es"] = nx.shortest_path_length(
+            G0, pipe_leak_end_node, located_pipe_start_node, weight="weight"
+        )
+        DtoTop1_series["ee"] = nx.shortest_path_length(
+            G0, pipe_leak_end_node, located_pipe_end_node, weight="weight"
+        )
+        DtoTop1_num_series["ss"] = nx.shortest_path_length(
+            G0, pipe_leak_start_node, located_pipe_start_node
+        )
+        DtoTop1_num_series["se"] = nx.shortest_path_length(
+            G0, pipe_leak_start_node, located_pipe_end_node
+        )
+        DtoTop1_num_series["es"] = nx.shortest_path_length(
+            G0, pipe_leak_end_node, located_pipe_start_node
+        )
+        DtoTop1_num_series["ee"] = nx.shortest_path_length(
+            G0, pipe_leak_end_node, located_pipe_end_node
+        )
+
+        if DtoTop1_num_series.min() == 0:
+            result_DtoTop1_num = 1
+            result_DtoTop1 = DtoTop1_series.max() / 2
+        else:
+            result_DtoTop1_num = DtoTop1_num_series.min() + 1
+            DtoTop1_type = DtoTop1_series.argmin()
+            result_DtoTop1 = (
+                DtoTop1_series[DtoTop1_type]
+                + (pipe_length[pipe_leak] + pipe_length[located_pipe]) / 2
+            )
+    return result_DtoTop1, result_DtoTop1_num
+
+
+def cal_RR(located_pipe, similarity_sp):
+    if located_pipe in similarity_sp.index:
+        rank = similarity_sp.index.get_loc(located_pipe)
+        RR = rank / len(similarity_sp.index)
+    else:
+        RR = 1.1
+    return RR
+
+
+def cal_cover(similarity, leak_pipe):
+    if leak_pipe in list(similarity.index):
+        cover = 1
+    else:
+        cover = 0
+    return cover
+
+
+def cal_SD(located_pipe, real_pipe, pipe_x, pipe_y):
+    dx = pipe_x[located_pipe] - pipe_x[real_pipe]
+    dy = pipe_y[located_pipe] - pipe_y[real_pipe]
+    SD = math.sqrt(dx * dx + dy * dy)
+    return SD
+
+
+def DN_search_multi_simple_add_flow_count_new(
+    wn,
+    wn_inp_path,
+    G0,
+    all_node,
+    node_x,
+    node_y,
+    pipe_start_node_all,
+    pipe_end_node_all,
+    pipe_diameter,
+    couple_node_length,
+    node_pipe_dic,
+    all_node_series,
+    top_group_ratio,
+    top_pipe_num_max,
+    top_pipe_num_min,
+    candidate_pipe_input_initial,
+    similarity_mode,
+    pressure_monitor,
+    pressure_predict,
+    pressure_normal,
+    pressure_leak_all,
+    flow_monitor,
+    flow_predict,
+    flow_normal,
+    flow_leak_all,
+    timestep_list,
+    max_flow,
+    group_basic_num,
+    Top_sensor_num,
+    if_gy,
+    pressure_threshold,
+    leak_mag,
+    n_workers=1,
+    stage_timing=None,
+    partition_on_full_graph=True,
+    visualize_partition=False,
+    visualize_pause_seconds=0.3,
+    final_candidates_csv_path=None,
+):
+    if stage_timing is None:
+        stage_timing = {}
+    exit_condition = "unknown"
+    final_candidates_csv = None
+    iter_count = 0
+    all_node_iter = copy.deepcopy(all_node)
+    candidate_pipe_input = copy.deepcopy(candidate_pipe_input_initial)  # 可能漏损管段
+    t1 = datetime.now()
+    if_flow, if_only_cos, if_only_flow = decode_mode(similarity_mode)  # 定位方法
+    # threshold
+    if if_only_flow == 1:
+        dpressure = (flow_predict - flow_monitor).mean()
+        dpressure = dpressure.abs()
+        effective_sensor = list(dpressure.index)
+
+    else:
+        dpressure = (pressure_predict - pressure_monitor).mean()
+        dpressure = dpressure.abs()
+        dpressure = dpressure[dpressure > pressure_threshold]
+        effective_sensor = list(dpressure.index)
+    simulation_times = 0  # 模拟次数
+    if len(dpressure) > 0:
+        break_flag = 0
+        last_round_candidate_details = None
+
+        cos_h = 0
+        dis_h = 0
+        dis_f_h = 0
+        if_compalsive = 0
+        record_center_dataset = []
+        record_center_set = set()
+        # iter
+        while 1:
+            final_area = []
+            final_center = []
+            group_num = cal_group_num(candidate_pipe_input, group_basic_num)
+            partition_nodes = all_node if partition_on_full_graph else all_node_iter
+
+            # group 分组，得出候选漏损中心
+            stage_start = perf_counter()
+            (
+                candidate_center_list,
+                candidate_group_list,
+                new_all_node,
+                candidate_center_candidates,
+            ) = (
+                metis_grouping_pipe_weight(
+                    G0,
+                    wn,
+                    partition_nodes,
+                    candidate_pipe_input,
+                    group_num,
+                    node_x,
+                    node_y,
+                    pipe_start_node_all,
+                    pipe_end_node_all,
+                    node_pipe_dic,
+                    all_node_series,
+                    couple_node_length,
+                    pipe_diameter,
+                )
+            )
+            _accumulate_stage(stage_timing, "group_partitioning", stage_start)
+            if visualize_partition:
+                visualize_metis_partition(
+                    G0,
+                    candidate_center_list,
+                    candidate_group_list,
+                    node_x,
+                    node_y,
+                    pipe_start_node_all,
+                    pipe_end_node_all,
+                    title=(
+                        f"METIS Partition Iteration {iter_count + 1} | "
+                        f"candidate pipes={len(candidate_pipe_input)} "
+                        f"groups={len(candidate_group_list)}"
+                    ),
+                    block=False,
+                    pause_seconds=visualize_pause_seconds,
+                )
+            simulation_times = simulation_times + len(candidate_center_list)
+            # pick_pressure_leak
+            # pressure_leak = pressure_leak_all.loc[candidate_center_list].loc[:, :]
+            # flow_leak = flow_leak_all.loc[candidate_center_list].loc[:, :]
+            # —— 新增泄漏量（保持你现在的一致，或从外部传入）——
+            # —— 只为缺失中心补算，然后取本轮需要的中心子集 ——
+            stage_start = perf_counter()
+            pressure_leak, flow_leak, pressure_leak_all, flow_leak_all = (
+                _ensure_signatures_for_centers(
+                    wn=wn,
+                    wn_inp_path=wn_inp_path,
+                    center_list=candidate_center_list,
+                    pressure_leak_all=pressure_leak_all,
+                    flow_leak_all=flow_leak_all,
+                    timestep_list=timestep_list,
+                    pressure_monitor=pressure_monitor,
+                    flow_monitor=flow_monitor,
+                    leak_mag=leak_mag,
+                    n_workers=n_workers,
+                )
+            )
+            _accumulate_stage(stage_timing, "signature_for_candidates", stage_start)
+
+            # pressure_leak_f= pressure_leak.swaplevel()
+
+            # --------------------------------------------------------
+
+            add_center = []
+            leak_center_dict = dict()
+            for i in range(len(candidate_center_list)):
+                primary_center = candidate_center_list[i]
+                houxuan_center = [
+                    center
+                    for center in candidate_center_candidates[i]
+                    if center != primary_center
+                ]
+                candidate_group_set = set(candidate_group_list[i])
+                for each_center in record_center_dataset:
+                    if (
+                        each_center in candidate_group_set
+                        and each_center != primary_center
+                    ):
+                        houxuan_center.append(each_center)
+                add_center = add_center + houxuan_center
+                leak_center_dict[primary_center] = _dedupe_preserve_order(
+                    houxuan_center + [primary_center]
+                )
+            add_center = _dedupe_preserve_order(add_center)
+            for each_group_centers in candidate_center_candidates:
+                for each_center in each_group_centers:
+                    if each_center not in record_center_set:
+                        record_center_dataset.append(each_center)
+                        record_center_set.add(each_center)
+            for each_center in add_center:
+                if each_center not in record_center_set:
+                    record_center_dataset.append(each_center)
+                    record_center_set.add(each_center)
+
+            # --------------------------------------------------------
+            # --------------------------------------------------------
+            # if len(add_center) > 0:
+            #     s3 = pressure_leak_all.loc[add_center]
+            #     pressure_leak = pd.concat([pressure_leak, s3])
+            #     s4 = flow_leak_all.loc[add_center]
+            #     flow_leak = pd.concat([flow_leak, s4])
+            # --------------------------------------------------------
+            # 只为 add_center 里还没算过的中心补算，并与本轮中心合并
+            if len(add_center) > 0:
+                stage_start = perf_counter()
+                pressure_add, flow_add, pressure_leak_all, flow_leak_all = (
+                    _ensure_signatures_for_centers(
+                        wn=wn,
+                        wn_inp_path=wn_inp_path,
+                        center_list=add_center,
+                        pressure_leak_all=pressure_leak_all,
+                        flow_leak_all=flow_leak_all,
+                        timestep_list=timestep_list,
+                        pressure_monitor=pressure_monitor,
+                        flow_monitor=flow_monitor,
+                        leak_mag=leak_mag,  # 与上面一致
+                        n_workers=n_workers,
+                    )
+                )
+                _accumulate_stage(
+                    stage_timing, "signature_for_extra_centers", stage_start
+                )
+                pressure_leak = pd.concat([pressure_leak, pressure_add], axis=0)
+                if (flow_leak is not None) and (flow_add is not None):
+                    flow_leak = pd.concat([flow_leak, flow_add], axis=0)
+            # --------------------------------------------------------
+            #
+            if len(candidate_pipe_input) < 1.2 * top_pipe_num_max / top_group_ratio:
+                if_compalsive = 1
+            cos_h, dis_h, dis_f_h = adjust_ratio(similarity_mode, cos_h, dis_h, dis_f_h)
+            candidate_center_list_sup = _dedupe_preserve_order(
+                candidate_center_list + add_center
+            )
+            stage_start = perf_counter()
+            similarity, cos_h, dis_h, dis_f_h, break_flag, similarity_details = (
+                cal_similarity_all_multi_new_sq_improve_double_lzr(
+                    candidate_center_list_sup,
+                    similarity_mode,
+                    pressure_leak,
+                    pressure_monitor,
+                    pressure_predict,
+                    pressure_normal,
+                    if_flow,
+                    if_only_cos,
+                    if_only_flow,
+                    flow_leak,
+                    flow_monitor,
+                    flow_predict,
+                    flow_normal,
+                    timestep_list,
+                    Top_sensor_num,
+                    if_gy,
+                    effective_sensor,
+                    cos_h,
+                    dis_h,
+                    dis_f_h,
+                    if_compalsive,
+                    max_flow,
+                )
+            )
+            last_round_candidate_details = similarity_details
+            _accumulate_stage(stage_timing, "similarity_ranking", stage_start)
+            if break_flag == 1:
+                exit_condition = "similarity_break_flag"
+                break
+
+            new_similarity = update_similarity(
+                candidate_center_list, similarity, leak_center_dict
+            )
+            if len(candidate_pipe_input) > top_pipe_num_max / top_group_ratio:
+                cut_ratio, new_similarity = extra_judge(new_similarity)
+            else:
+                cut_ratio = 1
+            stage_start = perf_counter()
+            final_area_t, final_center_t, all_node_new_1, if_end = (
+                area_output_num_ki_improve(
+                    candidate_center_list,
+                    candidate_group_list,
+                    new_similarity,
+                    new_all_node,
+                    top_group_ratio,
+                    top_pipe_num_max,
+                    top_pipe_num_min,
+                    cut_ratio,
+                )
+            )
+            _accumulate_stage(stage_timing, "candidate_area_selection", stage_start)
+            final_area = final_area + final_area_t
+            final_center = final_center + final_center_t
+
+            final_area = sorted(set(final_area))
+            final_center = sorted(set(final_center))
+            if if_end == 1:
+                exit_condition = "candidate_area_if_end"
+                break
+            elif len(candidate_pipe_input) == len(final_area):
+                exit_condition = "candidate_size_no_change"
+                break
+            else:
+                candidate_pipe_input = final_area
+                if not partition_on_full_graph:
+                    all_node_iter = all_node_new_1
+                iter_count += 1
+                sys.stdout.write(
+                    "\r"
+                    + "已经完成"
+                    + str(iter_count)
+                    + "次迭代计算"
+                    + "候选节点"
+                    + str(len(final_area))
+                    + "个"
+                )
+        # if break_flag == 0:
+        #     final_area_pipe = copy.deepcopy(final_area)
+        #     simulation_times = simulation_times + len(final_area)
+        #     pressure_leak_sp = pressure_leak_all.loc[final_area_pipe].loc[:, :]
+        #     flow_leak_sp = flow_leak_all.loc[final_area_pipe].loc[:, :]
+        #     similarity_sp, cos_h, dis_h, dis_f_h, break_flag = cal_similarity_all_multi_new_sq_improve_double_lzr(
+        #         final_area_pipe, similarity_mode, pressure_leak_sp,
+        #         pressure_monitor, pressure_predict, pressure_normal, if_flow,
+        #         if_only_cos, if_only_flow,
+        #         flow_leak_sp, flow_monitor, flow_predict, flow_normal,
+        #         timestep_list, Top_sensor_num, if_gy, effective_sensor, cos_h, dis_h, dis_f_h, if_compalsive, max_flow)
+        if break_flag == 0:
+            final_area_pipe = list(final_area)  # 确保是 list
+
+            # 只为还没算过的管段补齐 SLF（按需计算）
+            stage_start = perf_counter()
+            pressure_leak_sp, flow_leak_sp, pressure_leak_all, flow_leak_all = (
+                _ensure_signatures_for_centers(
+                    wn=wn,
+                    wn_inp_path=wn_inp_path,
+                    center_list=final_area_pipe,  # 这次要用的“最终区域里的所有管段”
+                    pressure_leak_all=pressure_leak_all,  # 累积缓存（会被更新）
+                    flow_leak_all=flow_leak_all,
+                    timestep_list=timestep_list,
+                    pressure_monitor=pressure_monitor,
+                    flow_monitor=flow_monitor,
+                    leak_mag=leak_mag,
+                    n_workers=n_workers,
+                )
+            )
+            _accumulate_stage(stage_timing, "signature_for_final_area", stage_start)
+
+            # 如果你要精确统计模拟次数，这里可以加上“本次新补的数量”，
+            # 做法：让 _ensure_signatures_for_centers 额外返回 need_cnt，再 simulation_times += need_cnt
+
+            stage_start = perf_counter()
+            (
+                similarity_sp,
+                cos_h,
+                dis_h,
+                dis_f_h,
+                break_flag,
+                similarity_details,
+            ) = (
+                cal_similarity_all_multi_new_sq_improve_double_lzr(
+                    final_area_pipe,
+                    similarity_mode,
+                    pressure_leak_sp,
+                    pressure_monitor,
+                    pressure_predict,
+                    pressure_normal,
+                    if_flow,
+                    if_only_cos,
+                    if_only_flow,
+                    flow_leak_sp,
+                    flow_monitor,
+                    flow_predict,
+                    flow_normal,
+                    timestep_list,
+                    Top_sensor_num,
+                    if_gy,
+                    effective_sensor,
+                    cos_h,
+                    dis_h,
+                    dis_f_h,
+                    if_compalsive,
+                    max_flow,
+                )
+            )
+            last_round_candidate_details = similarity_details
+            _accumulate_stage(stage_timing, "similarity_final", stage_start)
+
+        else:
+            dpressure = (pressure_predict - pressure_monitor).mean()
+            dpressure = dpressure.abs()
+            simulation_times = simulation_times + len(dpressure.index)
+            similarity_sp = pd.Series(dtype=float)
+            for each_node in dpressure.index:
+                pipe = node_pipe_dic[each_node][0]
+                similarity_sp.loc[pipe] = dpressure.loc[each_node]
+            similarity_sp = similarity_sp.sort_values(ascending=False, kind="mergesort")
+            t2 = datetime.now()
+            final_area_pipe = []
+
+        sys.stdout.write(
+            "\r"
+            + "已经完成"
+            + str(iter_count + 1)
+            + "次迭代计算"
+            + "候选节点"
+            + str(len(final_area_pipe))
+            + "个"
+        )
+        t2 = datetime.now()
+        dt = (t2 - t1).seconds
+        final_candidates_csv = _write_last_round_candidates_csv(
+            csv_path=final_candidates_csv_path,
+            exit_condition=exit_condition,
+            iteration_count=iter_count + 1,
+            similarity_mode=similarity_mode,
+            candidate_details=last_round_candidate_details,
+            fallback_similarity=similarity_sp,
+        )
+    else:
+        exit_condition = "no_effective_sensor_after_threshold"
+        dpressure = (pressure_predict - pressure_monitor).mean()
+        dpressure = dpressure.abs()
+
+        similarity_sp = pd.Series(dtype=float)
+        for each_node in dpressure.index:
+            pipe = node_pipe_dic[each_node][0]
+            similarity_sp.loc[pipe] = dpressure.loc[each_node]
+        similarity_sp = similarity_sp.sort_values(ascending=False, kind="mergesort")
+        t2 = datetime.now()
+        dt = (t2 - t1).seconds
+        final_candidates_csv = _write_last_round_candidates_csv(
+            csv_path=final_candidates_csv_path,
+            exit_condition=exit_condition,
+            iteration_count=0,
+            similarity_mode=similarity_mode,
+            candidate_details=None,
+            fallback_similarity=similarity_sp,
+        )
+    stage_timing["iterations"] = iter_count + 1 if len(dpressure) > 0 else 0
+    stage_timing["total_elapsed_seconds"] = float(dt)
+    stage_timing["exit_condition"] = exit_condition
+    stage_timing["final_candidates_csv"] = final_candidates_csv
+    return (
+        similarity_sp.index[0],
+        dt,
+        simulation_times,
+        wn,
+        similarity_sp,
+        exit_condition,
+        final_candidates_csv,
+    )
+
+

app/algorithms/burst_location/leak_simulator.py

@@ -0,0 +1,563 @@
+"""漏损模拟模块。"""
+
+import math
+import multiprocessing as mp
+import os
+import sys
+
+import pandas as pd
+import wntr
+
+from app.algorithms._utils import _cleanup_temp_files
+
+_PIPE2LEAKNODE = None
+_SIGNATURE_WORKER_DATA = {}
+
+
+def _make_temp_prefix(tag):
+    temp_dir = os.path.abspath(os.path.join("temp", "burst_location"))
+    os.makedirs(temp_dir, exist_ok=True)
+    safe_tag = str(tag).replace(os.sep, "_").replace(" ", "_")
+    return os.path.join(temp_dir, f"{safe_tag}_{os.getpid()}")
+
+
+def _snapshot_hydraulic_options(wn):
+    options = wn.options
+    return {
+        "demand_model": options.hydraulic.demand_model,
+        "duration": float(options.time.duration),
+        "hydraulic_timestep": float(options.time.hydraulic_timestep),
+        "pattern_timestep": float(options.time.pattern_timestep),
+        "report_timestep": float(options.time.report_timestep),
+        "required_pressure": float(options.hydraulic.required_pressure),
+        "minimum_pressure": float(options.hydraulic.minimum_pressure),
+    }
+
+
+def _apply_hydraulic_options(wn, option_values):
+    options = wn.options
+    options.hydraulic.demand_model = option_values["demand_model"]
+    options.time.duration = option_values["duration"]
+    options.time.hydraulic_timestep = option_values["hydraulic_timestep"]
+    options.time.pattern_timestep = option_values["pattern_timestep"]
+    options.time.report_timestep = option_values["report_timestep"]
+    options.hydraulic.required_pressure = option_values["required_pressure"]
+    options.hydraulic.minimum_pressure = option_values["minimum_pressure"]
+
+
+def simple_add_leak(wn, leak_mag, leak_pipe):
+    whole_inf = dict()
+    leak_pipe_self = wn.get_link(leak_pipe)
+    pipe_diameter = leak_pipe_self.diameter
+    pipe_length = leak_pipe_self.length
+    pipe_roughness = leak_pipe_self.roughness
+    pipe_minor_loss = leak_pipe_self.minor_loss
+    # pipe_status = leak_pipe_self.status
+    # pipe_check_valve = leak_pipe_self.check_valve
+    pipe_start_node = leak_pipe_self.start_node_name
+    pipe_end_node = leak_pipe_self.end_node_name
+
+    # close the pipe
+    # leak_pipe_self.status = 'Closed'
+    wn.remove_link(leak_pipe)
+    # add the pipe
+    add_pipe1 = leak_pipe + "A"
+    add_pipe2 = leak_pipe + "B"
+    add_node = leak_pipe + "_"
+
+    start_n = wn.get_node(pipe_start_node)
+    end_n = wn.get_node(pipe_end_node)
+    if start_n.node_type == "Reservoir":
+        end_n_elevation = end_n.elevation
+        start_n_elevation = end_n_elevation
+    elif end_n.node_type == "Reservoir":
+        start_n_elevation = start_n.elevation
+        end_n_elevation = start_n_elevation
+    else:
+        end_n_elevation = end_n.elevation
+        start_n_elevation = start_n.elevation
+    elevation_self = (start_n_elevation + end_n_elevation) / 2
+    coordinates_self = (
+        (start_n.coordinates[0] + end_n.coordinates[0]) / 2,
+        (start_n.coordinates[1] + end_n.coordinates[1]),
+    )
+
+    wn.add_junction(
+        add_node, base_demand=0, elevation=elevation_self, coordinates=coordinates_self
+    )
+    leak_node = wn.get_node(add_node)
+    wn.add_pipe(
+        add_pipe1,
+        start_node_name=pipe_start_node,
+        end_node_name=add_node,
+        length=pipe_length / 2,
+        diameter=pipe_diameter,
+        roughness=pipe_roughness,
+        minor_loss=pipe_minor_loss,
+    )
+    wn.add_pipe(
+        add_pipe2,
+        start_node_name=pipe_end_node,
+        end_node_name=add_node,
+        length=pipe_length / 2,
+        diameter=pipe_diameter,
+        roughness=pipe_roughness,
+        minor_loss=pipe_minor_loss,
+    )
+
+    # simulation
+    leak_node.add_demand(base=leak_mag, pattern_name="add_leak")
+
+    whole_inf["leak_node_name"] = add_node
+    whole_inf["add_pipe1"] = add_pipe1
+    whole_inf["add_pipe2"] = add_pipe2
+    whole_inf["leak_pipe"] = leak_pipe
+    whole_inf["pipe_start_node"] = pipe_start_node
+    whole_inf["pipe_end_node"] = pipe_end_node
+    whole_inf["pipe_length"] = pipe_length
+    whole_inf["pipe_diameter"] = pipe_diameter
+    whole_inf["pipe_roughness"] = pipe_roughness
+    whole_inf["pipe_minor_loss"] = pipe_diameter
+    return wn, whole_inf, add_pipe1
+
+
+def simple_recover_wn(wn, whole_inf):
+    leak_node = wn.get_node(whole_inf["leak_node_name"])
+
+    del leak_node.demand_timeseries_list[-1]
+    # update
+    wn.remove_link(whole_inf["add_pipe1"])
+    wn.remove_link(whole_inf["add_pipe2"])
+    wn.remove_node(whole_inf["leak_node_name"])
+    # open the pipe
+    # leak_pipe_self.status = 'Open'
+    wn.add_pipe(
+        whole_inf["leak_pipe"],
+        start_node_name=whole_inf["pipe_start_node"],
+        end_node_name=whole_inf["pipe_end_node"],
+        length=whole_inf["pipe_length"],
+        diameter=whole_inf["pipe_diameter"],
+        roughness=whole_inf["pipe_roughness"],
+        minor_loss=whole_inf["pipe_minor_loss"],
+    )
+
+    return wn
+
+
+def disable_all_controls_temporarily(wn):
+    """返回(控制名, 控制对象)的列表，之后可用 restore_controls 还原。"""
+    removed = []
+    # WNTR 的控制都在 wn.control_name_list / wn.get_control / wn.remove_control
+    for cname in list(wn.control_name_list):
+        ctrl = wn.get_control(cname)
+        removed.append((cname, ctrl))
+        wn.remove_control(cname)
+    return removed
+
+
+def restore_controls(wn, removed):
+    """把先前禁用的控制全部加回去。"""
+    for cname, ctrl in removed:
+        wn.add_control(cname, ctrl)
+
+
+def set_pipe2leaknode_mapping(mapping):
+    global _PIPE2LEAKNODE
+    _PIPE2LEAKNODE = mapping
+
+
+def _get_or_create_leak_demand_ts(leak_node):
+    """
+    返回：泄漏专用 demand 的下标 idx。
+    若不存在，以 category='leak' 新建一条 base=0.0 的 demand。
+    """
+    # 先尝试找到已有的 'leak' 分类
+    for i, ts in enumerate(leak_node.demand_timeseries_list):
+        # WNTR 的 Demand object 存在 category 属性
+        if getattr(ts, "category", None) == "leak":
+            return i
+    # 没有则新建（base=0.0，后续临时改 base_value）
+    leak_node.add_demand(base=0.0, pattern_name=None, category="leak")
+    return len(leak_node.demand_timeseries_list) - 1
+
+
+def ensure_mid_node(wn, leak_pipe):
+    add_pipe1 = f"{leak_pipe}A"
+    add_pipe2 = f"{leak_pipe}B"
+    add_node = f"{leak_pipe}__mid"
+
+    if add_node in wn.node_name_list:
+        return add_node
+
+    if leak_pipe in wn.link_name_list:
+        leak_pipe_self = wn.get_link(leak_pipe)
+        pipe_diameter = leak_pipe_self.diameter
+        pipe_length = leak_pipe_self.length
+        pipe_roughness = leak_pipe_self.roughness
+        pipe_minor_loss = leak_pipe_self.minor_loss
+        pipe_start_node = leak_pipe_self.start_node_name
+        pipe_end_node = leak_pipe_self.end_node_name
+
+        start_n = wn.get_node(pipe_start_node)
+        end_n = wn.get_node(pipe_end_node)
+        if start_n.node_type == "Reservoir":
+            end_elev = end_n.elevation
+            start_elev = end_elev
+        elif end_n.node_type == "Reservoir":
+            start_elev = start_n.elevation
+            end_elev = start_elev
+        else:
+            end_elev = end_n.elevation
+            start_elev = start_n.elevation
+        elev_mid = (start_elev + end_elev) / 2.0
+        x_mid = (start_n.coordinates[0] + end_n.coordinates[0]) / 2.0
+        y_mid = (start_n.coordinates[1] + end_n.coordinates[1]) / 2.0
+
+        wn.remove_link(leak_pipe)
+        wn.add_junction(
+            add_node, base_demand=0.0, elevation=elev_mid, coordinates=(x_mid, y_mid)
+        )
+        wn.add_pipe(
+            add_pipe1,
+            start_node_name=pipe_start_node,
+            end_node_name=add_node,
+            length=pipe_length / 2.0,
+            diameter=pipe_diameter,
+            roughness=pipe_roughness,
+            minor_loss=pipe_minor_loss,
+        )
+        wn.add_pipe(
+            add_pipe2,
+            start_node_name=add_node,
+            end_node_name=pipe_end_node,
+            length=pipe_length / 2.0,
+            diameter=pipe_diameter,
+            roughness=pipe_roughness,
+            minor_loss=pipe_minor_loss,
+        )
+        return add_node
+
+    # 若 A/B 已存在但中点不在，建议确认网络一致性
+    raise KeyError(f"Cannot ensure mid node for pipe '{leak_pipe}'.")
+
+
+def leak_simulation_pipe_dd_multi_pf(
+    wn, leak_mag, leak_pipe, sensor_name, file_prefix=None
+):
+    """
+    优化版：
+    - 不再 remove/add link/node
+    - 直接在预插入的中点泄漏节点上设置 base_demand = leak_mag；仿真后设回 0
+    """
+    wn.options.hydraulic.demand_model = "DD"
+
+    # 确保中点节点存在
+    leak_node_name = ensure_mid_node(wn, leak_pipe)
+    leak_node = wn.get_node(leak_node_name)
+
+    # 拿到泄漏专用的 demand time-series 下标
+    leak_idx = _get_or_create_leak_demand_ts(leak_node)
+    ts_obj = leak_node.demand_timeseries_list[leak_idx]
+
+    # 记录原值（通常是 0.0）
+    orig_base = ts_obj.base_value
+    try:
+        # 打开泄漏：只改 base_value，不碰 base_demand（只读）
+        ts_obj.base_value = float(leak_mag)
+
+        # 仿真
+        sim = wntr.sim.EpanetSimulator(wn)
+        if file_prefix is None:
+            results = sim.run_sim()
+        else:
+            results = sim.run_sim(file_prefix=file_prefix)
+
+        # 输出（保持列顺序）
+        pressure_output = results.node["pressure"].loc[:, sensor_name]
+        # flow_output = results.link['flowrate'].loc[:, sensor_f_name]
+        return wn, pressure_output
+    finally:
+        # 关闭泄漏：还原 base_value
+        ts_obj.base_value = orig_base
+        if file_prefix is not None:
+            _cleanup_temp_files(file_prefix)
+
+
+def prepare_leak_infrastructure(wn, candidate_pipes):
+    """
+    把 candidate_pipes 每条管段切成两段，并在中点插入一个泄漏节点（base_demand=0）。
+    返回一个映射：pipe_id -> leak_node_name
+    注意：只做一次；后续仿真通过在该节点设置 base_demand 实现“打开泄漏”，结束后恢复为 0。
+    """
+    pipe2leaknode = {}
+    for leak_pipe in candidate_pipes:
+        if leak_pipe in pipe2leaknode:
+            continue
+
+        leak_pipe_self = wn.get_link(leak_pipe)
+        pipe_diameter = leak_pipe_self.diameter
+        pipe_length = leak_pipe_self.length
+        pipe_roughness = leak_pipe_self.roughness
+        pipe_minor_loss = leak_pipe_self.minor_loss
+        pipe_start_node = leak_pipe_self.start_node_name
+        pipe_end_node = leak_pipe_self.end_node_name
+
+        # 计算中点高程/坐标（与原逻辑一致）
+        start_n = wn.get_node(pipe_start_node)
+        end_n = wn.get_node(pipe_end_node)
+        if start_n.node_type == "Reservoir":
+            end_elev = end_n.elevation
+            start_elev = end_elev
+        elif end_n.node_type == "Reservoir":
+            start_elev = start_n.elevation
+            end_elev = start_elev
+        else:
+            end_elev = end_n.elevation
+            start_elev = start_n.elevation
+        elev_mid = (start_elev + end_elev) / 2.0
+        x_mid = (start_n.coordinates[0] + end_n.coordinates[0]) / 2.0
+        y_mid = (start_n.coordinates[1] + end_n.coordinates[1]) / 2.0
+
+        # 先删原管，再加中点与两段半长管（只做一次）
+        wn.remove_link(leak_pipe)
+
+        add_pipe1 = f"{leak_pipe}A"
+        add_pipe2 = f"{leak_pipe}B"
+        add_node = f"{leak_pipe}__mid"  # 唯一命名，后面直接用它当泄漏节点
+
+        wn.add_junction(
+            add_node, base_demand=0.0, elevation=elev_mid, coordinates=(x_mid, y_mid)
+        )
+        wn.add_pipe(
+            add_pipe1,
+            start_node_name=pipe_start_node,
+            end_node_name=add_node,
+            length=pipe_length / 2.0,
+            diameter=pipe_diameter,
+            roughness=pipe_roughness,
+            minor_loss=pipe_minor_loss,
+        )
+        wn.add_pipe(
+            add_pipe2,
+            start_node_name=add_node,
+            end_node_name=pipe_end_node,
+            length=pipe_length / 2.0,
+            diameter=pipe_diameter,
+            roughness=pipe_roughness,
+            minor_loss=pipe_minor_loss,
+        )
+
+        pipe2leaknode[leak_pipe] = add_node
+
+    return pipe2leaknode
+
+
+def normal_simulation_pf(
+    wn, drive_mode, sensor_name, sensor_f_name, inp_time, require_p, minimum_p
+):
+    # inp_time = 0
+    if drive_mode == "PDD":  # 需水量根据节点压力动态调整
+        wn.options.hydraulic.demand_model = "PDD"
+        wn.options.hydraulic.required_pressure = require_p
+        wn.options.hydraulic.minimum_pressure = minimum_p
+    elif drive_mode == "DD":  # 需水量固定，与压力无关
+        wn.options.hydraulic.demand_model = "DD"
+    sim = wntr.sim.EpanetSimulator(wn)
+    results = sim.run_sim()
+    pressure_all = results.node["pressure"][sensor_name]
+    pressure = pressure_all.iloc[inp_time]
+    demand_all = results.node["demand"]
+    demand = demand_all.iloc[inp_time]
+    sum_demand = cal_sum_demand(demand)
+    flow_all = results.link["flowrate"][sensor_f_name]
+    flow = flow_all.iloc[inp_time]
+
+    top_sensor = pressure.idxmin()
+    basic_p = results.node["pressure"]
+    basic_p = basic_p.iloc[inp_time]
+    return pressure, flow, basic_p, top_sensor, sum_demand
+
+
+def normal_simulation_multi_pf(
+    wn, drive_mode, sensor_name, sensor_f_name, require_p, minimum_p
+):
+    # inp_time = 0
+    if drive_mode == "PDD":
+        wn.options.hydraulic.demand_model = "PDD"
+        wn.options.hydraulic.required_pressure = require_p
+        wn.options.hydraulic.minimum_pressure = minimum_p
+    elif drive_mode == "DD":
+        wn.options.hydraulic.demand_model = "DD"
+    sim = wntr.sim.EpanetSimulator(wn)
+    results = sim.run_sim()
+    pressure_all = results.node["pressure"][sensor_name]
+    pressure = pressure_all
+    demand_all = results.node["demand"]
+    demand = demand_all
+    flow = results.link["flowrate"][sensor_f_name]
+    sum_demand = pd.Series(dtype=object)
+    for i in range(len(demand.index)):
+        sum_demand[str(demand.index[i])] = cal_sum_demand(demand.iloc[i])
+    if type(pressure) == pd.core.series.Series:
+        top_sensor = pressure.idxmin()
+    else:
+        mean_pressure = pressure.mean()
+        top_sensor = mean_pressure.idxmin()
+    basic_p = results.node["pressure"]
+    return pressure, flow, basic_p, top_sensor, sum_demand
+
+
+def simple_simulation_pf(wn, sensor_name, sensor_f_name, leak_pipe, add_pipe1):
+    sim = wntr.sim.EpanetSimulator(wn)
+    results = sim.run_sim()
+    pressure_all = results.node["pressure"][sensor_name]
+    if len(leak_pipe) > 0 and leak_pipe in sensor_f_name:
+
+        f_sensor_name = [add_pipe1 if i == leak_pipe else i for i in sensor_f_name]
+        flow_all = results.link["flowrate"][f_sensor_name]
+        flow_all.columns = sensor_f_name
+    else:
+        flow_all = results.link["flowrate"][sensor_f_name]
+    return pressure_all, flow_all
+
+
+def cal_sum_demand(demand):
+    sum_demand = 0
+    for i in range(len(demand)):
+        if demand.iloc[i] > 0:
+            sum_demand += demand.iloc[i]
+    return sum_demand
+
+
+def cal_signature_pipe_multi_pf(
+    wn,
+    leak_mag,
+    candidate_center,
+    timestep_list,
+    sensor_name,
+    n_workers=1,
+    wn_inp_path=None,
+):
+    candidate_center_num = len(candidate_center)
+    pressure_leak = pd.DataFrame(
+        index=pd.MultiIndex.from_product([candidate_center, timestep_list]),
+        columns=sensor_name,
+    )
+    # flow_leak = pd.DataFrame(index=pd.MultiIndex.from_product([candidate_center, timestep_list]),
+    #                          columns=sensor_f_name)
+    pressure_leak = pressure_leak.sort_index()
+    # flow_leak = flow_leak.sort_index()
+    can_parallel = (
+        n_workers > 1
+        and candidate_center_num > 1
+        and wn_inp_path is not None
+        and len(str(wn_inp_path)) > 0
+    )
+    if can_parallel:
+        option_values = _snapshot_hydraulic_options(wn)
+        worker_count = min(n_workers, candidate_center_num)
+        start_methods = mp.get_all_start_methods()
+        context_name = "spawn" if "spawn" in start_methods else start_methods[0]
+        with mp.get_context(context_name).Pool(
+            processes=worker_count,
+            initializer=_signature_worker_init,
+            initargs=(
+                str(wn_inp_path),
+                float(leak_mag),
+                list(sensor_name),
+                option_values,
+                list(candidate_center),
+            ),
+        ) as pool:
+            for i, (center_name, pressure_array) in enumerate(
+                pool.imap(_signature_worker_run_center, candidate_center)
+            ):
+                pressure_leak.loc[(center_name, slice(None)), :] = pressure_array
+                sys.stdout.write("\r" + "已经完成计算" + str(i + 1) + "个特征中心")
+    else:
+        # Pre-insert all mid-nodes so every simulation sees the same topology
+        for center in candidate_center:
+            ensure_mid_node(wn, center)
+        for i in range(candidate_center_num):
+            temp_prefix = _make_temp_prefix(f"sig_{i}")
+            wn, pressure_output = leak_simulation_pipe_dd_multi_pf(
+                wn,
+                leak_mag,
+                candidate_center[i],
+                sensor_name,
+                file_prefix=temp_prefix,
+            )
+            # leak_or_not_list.append(leak_or_not)
+            pressure_leak.loc[(candidate_center[i], slice(None)), :] = (
+                pressure_output.to_numpy()
+            )
+            # flow_leak.loc[candidate_center[i]].loc[:, :] = flow_output
+            sys.stdout.write("\r" + "已经完成计算" + str(i + 1) + "个特征中心")
+    return pressure_leak, candidate_center
+
+
+def _signature_worker_init(
+    inp_path, leak_mag, sensor_name, option_values, candidate_centers=None
+):
+    global _SIGNATURE_WORKER_DATA
+    wn = wntr.network.WaterNetworkModel(inp_path)
+    _apply_hydraulic_options(wn, option_values)
+    # Pre-insert ALL mid-nodes so every simulation runs on the same topology,
+    # regardless of which worker handles which task.
+    if candidate_centers is not None:
+        for center in candidate_centers:
+            ensure_mid_node(wn, center)
+    _SIGNATURE_WORKER_DATA = {
+        "wn": wn,
+        "leak_mag": leak_mag,
+        "sensor_name": sensor_name,
+    }
+
+
+def _signature_worker_run_center(center_name):
+    data = _SIGNATURE_WORKER_DATA
+    temp_prefix = _make_temp_prefix(f"sig_worker_{center_name}")
+    _, pressure_output = leak_simulation_pipe_dd_multi_pf(
+        data["wn"],
+        data["leak_mag"],
+        center_name,
+        data["sensor_name"],
+        file_prefix=temp_prefix,
+    )
+    return center_name, pressure_output.to_numpy()
+
+
+def pick_pipe(all_pipes, pipe_diameter, limited_diameter):
+    candidate_pipe = []
+    for each_pipe in all_pipes:
+        if pipe_diameter[each_pipe] >= limited_diameter:
+            candidate_pipe.append(each_pipe)
+    return candidate_pipe
+
+
+def cal_possible_pipe(leak_flow, all_pipe, pipe_diameter):
+    basic_pressure = 10  # 基础压力
+    discharge_coeff = 0.6  # 经验系数
+    break_area_ratio = 1  # 爆管面积比 0.5  1.25
+    break_area = leak_flow / (
+        discharge_coeff * math.sqrt(2 * basic_pressure * 9.81)
+    )  # 爆管面积 m3/h
+    """break_area_diameter = math.sqrt(4 * break_area / math.pi)
+    min_diameter = (math.ceil(1000 * break_area_diameter / break_area_ratio)) / 1000"""
+    break_area_diameter = math.sqrt(
+        4 * break_area / math.pi / break_area_ratio
+    )  # 爆管直径
+    min_diameter = (math.ceil(1000 * break_area_diameter)) / 1000  # 向上取整
+    new_all_pipe = pick_pipe(all_pipe, pipe_diameter, min_diameter)
+    return new_all_pipe, min_diameter
+
+
+def extract_links(data, link_types, direction):
+    return [
+        link
+        for res_data in data.values()
+        for link_type in link_types
+        for link in res_data[link_type][direction]
+    ]
+
+

app/algorithms/burst_location/network_model.py

@@ -0,0 +1,137 @@
+"""管网模型读取与图构建模块。"""
+
+import copy
+
+import numpy as np
+import networkx as nx
+import pandas as pd
+import wntr
+
+
+def load_inp(inp_name, inp_location, inp_time, driven_mode, require_p, minimum_p):
+    inp_file = inp_location + inp_name
+    wn = wntr.network.WaterNetworkModel(inp_file)
+    if driven_mode == "PDD":
+        wn.options.hydraulic.demand_model = "PDD"
+        wn.options.hydraulic.required_pressure = require_p
+        wn.options.hydraulic.minimum_pressure = minimum_p
+    else:
+        wn.options.hydraulic.demand_model = "DD"
+    return wn
+
+
+def read_inf_inp(wn):
+    all_node = wn.node_name_list
+    node_elevation = wn.query_node_attribute("elevation")
+    node_coordinates = wn.query_node_attribute("coordinates")
+
+    all_pipe = wn.pipe_name_list
+    # 改_wz__________________________________
+    n_pipe = []
+    for p in all_pipe:
+        pipe = wn.get_link(p)
+        if pipe.initial_status == 0:  # 状态为'Closed'
+            n_pipe.append(p)
+    candidate_pipe_init = sorted(set(all_pipe) - set(n_pipe))
+    pipe_start_node = wn.query_link_attribute(
+        "start_node_name", link_type=wntr.network.model.Pipe
+    )
+    pipe_end_node = wn.query_link_attribute(
+        "end_node_name", link_type=wntr.network.model.Pipe
+    )
+    pipe_length = wn.query_link_attribute("length")
+    pipe_diameter = wn.query_link_attribute("diameter")
+
+    return (
+        all_node,
+        node_elevation,
+        node_coordinates,
+        candidate_pipe_init,
+        pipe_start_node,
+        pipe_end_node,
+        pipe_length,
+        pipe_diameter,
+    )
+
+
+def read_inf_inp_other(wn):
+    all_link = wn.link_name_list
+    pipe_start_node_all = wn.query_link_attribute("start_node_name")
+    pipe_end_node_all = wn.query_link_attribute("end_node_name")
+
+    return all_link, pipe_start_node_all, pipe_end_node_all
+
+
+def construct_graph(wn):
+    length = wn.query_link_attribute("length")
+    G = wn.get_graph(wn, link_weight=length)
+    # 转为无向图
+    G0 = G.to_undirected()
+    # A0 = np.array(nx.adjacency_graph(G0).todense())
+    return G0  # , A0
+
+
+def cal_pipe_coordinate(all_pipe, pipe_start_node, pipe_end_node, node_coordinates):
+    pipe_num = len(all_pipe)
+    pipe_coordinates = np.zeros([pipe_num, 2])
+    pipe_x = copy.deepcopy(pipe_start_node)
+    pipe_y = copy.deepcopy(pipe_start_node)
+    for i in range(pipe_num):
+        temp_pipe = all_pipe[i]
+        pipe_x[temp_pipe] = (
+            node_coordinates[pipe_start_node[temp_pipe]][0]
+            + node_coordinates[pipe_end_node[temp_pipe]][0]
+        ) / 2
+        pipe_y[temp_pipe] = (
+            node_coordinates[pipe_start_node[temp_pipe]][1]
+            + node_coordinates[pipe_end_node[temp_pipe]][1]
+        ) / 2
+    return pipe_x, pipe_y
+
+
+def cal_node_coordinate(all_node, node_coordinates):
+    node_x = copy.deepcopy(node_coordinates)
+    node_y = copy.deepcopy(node_coordinates)
+    for i in range(len(node_x)):
+        temp_node = all_node[i]
+        node_x[temp_node] = node_coordinates[temp_node][0]
+        node_y[temp_node] = node_coordinates[temp_node][1]
+    return node_x, node_y
+
+
+def produce_pattern_value(wn, all_node):
+    wn_o = copy.deepcopy(wn)
+    # 改_wz_____________________________
+    # sample_node = wn_o.get_node(all_node[0])
+    # num_categories = len(sample_node.demand_timeseries_list)
+    num_categories = 1
+    columns = [f"D{i}" for i in range(num_categories)]
+    basic_demand_pd = pd.DataFrame(index=all_node, columns=columns)
+    for each in all_node:
+        node = wn_o.get_node(each)
+        for i in range(num_categories):
+            basic_demand_pd.loc[each, columns[i]] = node.demand_timeseries_list[
+                i
+            ].base_value
+
+    return basic_demand_pd
+
+
+def _build_node_pipe_maps(
+    all_nodes, candidate_pipes, pipe_start_node, pipe_end_node, pipe_length
+):
+    node_pipe_dic = {node: [] for node in all_nodes}
+    couple_node_length = {}
+    for pipe in candidate_pipes:
+        start_node = pipe_start_node[pipe]
+        end_node = pipe_end_node[pipe]
+        if start_node in node_pipe_dic:
+            node_pipe_dic[start_node].append(pipe)
+        if end_node in node_pipe_dic:
+            node_pipe_dic[end_node].append(pipe)
+        length = float(pipe_length[pipe])
+        couple_node_length[f"{start_node},{end_node}"] = length
+        couple_node_length[f"{end_node},{start_node}"] = length
+    return node_pipe_dic, couple_node_length
+
+

app/algorithms/burst_location/network_partitioner.py

@@ -0,0 +1,456 @@
+"""管网分区模块。"""
+
+import math
+
+import matplotlib.pyplot as plt
+import networkx as nx
+import networkx as networkx
+import numpy as np
+import pandas as pd
+import pymetis
+from scipy.sparse import coo_matrix, csr_matrix
+from scipy.sparse.csgraph import connected_components
+
+
+def _to_metis_edge_weight(edge_weight):
+    weight = float(edge_weight)
+    if not math.isfinite(weight):
+        raise ValueError(f"Invalid non-finite METIS edge weight: {edge_weight}")
+    # pymetis expects integer edge weights.
+    return max(1, int(round(weight)))
+
+
+def _dedupe_preserve_order(items):
+    seen = set()
+    output = []
+    for item in items:
+        if item in seen:
+            continue
+        seen.add(item)
+        output.append(item)
+    return output
+
+
+def pick_center_pipe(node_x, node_y, candidate_pipe, pipe_start_node, pipe_end_node):
+    candidate_pipe_list = list(candidate_pipe)
+    start_nodes = pipe_start_node[candidate_pipe_list]
+    end_nodes = pipe_end_node[candidate_pipe_list]
+
+    x_vals = (node_x[start_nodes].to_numpy() + node_x[end_nodes].to_numpy()) / 2.0
+    y_vals = (node_y[start_nodes].to_numpy() + node_y[end_nodes].to_numpy()) / 2.0
+    mean_x = float(np.mean(x_vals))
+    mean_y = float(np.mean(y_vals))
+    distance = np.abs(x_vals - mean_x) + np.abs(y_vals - mean_y)
+    center_idx = int(np.argmin(distance))
+    return candidate_pipe_list[center_idx]
+
+
+def pick_max_diameter_pipe(candidate_pipe, pipe_diameter):
+    candidate_pipe_list = list(candidate_pipe)
+    diameters = pd.to_numeric(
+        pipe_diameter.reindex(candidate_pipe_list), errors="coerce"
+    ).dropna()
+    if len(diameters) != len(candidate_pipe_list):
+        missing = sorted(set(candidate_pipe_list) - set(diameters.index))
+        preview = ", ".join(map(str, missing[:10]))
+        raise ValueError(f"Missing or invalid diameter for pipes: {preview}")
+
+    max_diameter = float(diameters.max())
+    max_diameter_pipes = sorted(
+        [pipe for pipe, diameter in diameters.items() if float(diameter) == max_diameter],
+        key=str,
+    )
+    return max_diameter_pipes[0]
+
+
+def pick_dual_center_pipes(
+    node_x, node_y, candidate_pipe, pipe_start_node, pipe_end_node, pipe_diameter
+):
+    geometric_center = pick_center_pipe(
+        node_x, node_y, candidate_pipe, pipe_start_node, pipe_end_node
+    )
+    diameter_center = pick_max_diameter_pipe(candidate_pipe, pipe_diameter)
+    return _dedupe_preserve_order([geometric_center, diameter_center])
+
+
+def find_new_center_pipe(
+    node_x,
+    node_y,
+    candidate_pipe,
+    pipe_start_node,
+    pipe_end_node,
+    pipe_diameter,
+    record_center,
+):
+    new_candidate_pipe = sorted(set(candidate_pipe) - set(record_center))
+    if new_candidate_pipe == []:
+        new_candidate_pipe = candidate_pipe
+    center_t = pick_center_pipe(
+        node_x,
+        node_y,
+        new_candidate_pipe,
+        pipe_start_node,
+        pipe_end_node,
+    )
+    return center_t
+
+
+def cal_area_node_linked_pipe(nodeset, node_pipe_dic):
+    pipeset = []
+    for temp_node in nodeset:
+        pipeset.extend(node_pipe_dic[temp_node])
+    return pipeset
+
+
+def metis_grouping_pipe_weight(
+    G0,
+    wn,
+    all_node_iter,
+    candidate_pipe_input,
+    group_num,
+    node_x,
+    node_y,
+    pipe_start_node_all,
+    pipe_end_node_all,
+    node_pipe_dic,
+    all_node_series,
+    couple_node_length,
+    pipe_diameter,
+):
+    all_node_iter_series_new = all_node_series[all_node_iter]
+    all_node_iter_series_new = all_node_iter_series_new.sort_values(ascending=True)
+    all_node_iter_new = list(all_node_iter_series_new.index)
+
+    G1 = G0.subgraph(all_node_iter_new)
+    delimiter = " "
+    adjacency_list = []
+    node_dict = {}
+    c_new = 0
+    for each_node in all_node_iter_new:
+        node_dict[each_node] = c_new
+        c_new = c_new + 1
+    correspond_dic = {}
+    count_node = 0
+    w = []
+    for node_name in all_node_iter_new:
+        neighbors = G1[node_name]
+        w_temp = []
+        n_t = [node_dict[node_name]]
+        for neighbor_name in sorted(neighbors.keys()):
+            edge_data = neighbors[neighbor_name]
+            edge_key = f"{node_name},{neighbor_name}"
+            reverse_edge_key = f"{neighbor_name},{node_name}"
+            if edge_key in couple_node_length:
+                edge_weight = couple_node_length[edge_key]
+            elif reverse_edge_key in couple_node_length:
+                edge_weight = couple_node_length[reverse_edge_key]
+            elif edge_data.get("weight") is not None:
+                edge_weight = float(edge_data["weight"])
+            else:
+                # Ignore graph edges that are outside candidate pipes and have no usable
+                # partition weight (e.g. some non-pipe links in mixed network graphs).
+                continue
+            w_temp.append(_to_metis_edge_weight(edge_weight))
+            n_t.append(node_dict[neighbor_name])
+        w.append(w_temp)
+        correspond_dic[n_t[0]] = count_node
+        count_node = count_node + 1
+        # del n_t[0]
+        adjacency_list.append(n_t)
+    adjacency_list_new = [[] * 1 for i in range(len(adjacency_list))]
+    w_new = [[] * 1 for i in range(len(adjacency_list))]
+    for i in range(len(adjacency_list)):
+        adjacency_list_new[int(adjacency_list[i][0])] = adjacency_list[i]
+        w_new[int(adjacency_list[i][0])] = w[i]
+
+    for i in range(len(adjacency_list)):
+        del adjacency_list_new[i][0]
+    xadj = [0]
+    w_f = []
+    final_adjacency_list = []
+    for i in range(len(adjacency_list_new)):
+        final_adjacency_list = final_adjacency_list + adjacency_list_new[i]
+        xadj.append(len(final_adjacency_list))
+        w_f = w_f + w_new[i]
+
+    # (edgecuts, parts) = pymetis.part_graph(nparts=group_num, adjacency=adjacency_list_new)
+    metis_options = pymetis.Options()
+    metis_options.seed = 42
+    (edgecuts, parts) = pymetis.part_graph(
+        nparts=group_num,
+        adjncy=final_adjacency_list,
+        xadj=xadj,
+        eweights=w_f,
+        options=metis_options,
+    )
+    # (edgecuts, parts) = pymetis.part_graph(nparts=group_num, adjacency=adjacency_list_new)
+    candidate_group_list = [[] * 1 for i in range(group_num)]
+    for i in range(len(all_node_iter_new)):
+        candidate_group_list[parts[i]].append(all_node_iter_new[i])
+
+    """parts_new = np.zeros(len(candidate_node_input), dtype=int)
+    for i in range(len(candidate_group_list)):
+        temp_group = candidate_group_list[i]
+        for each_node in temp_group:
+            parts_new[node_dict[each_node]] = i
+    parts_new = list(parts_new)"""
+
+    new_center = []
+    new_group = []
+    new_center_candidates = []
+    new_all_node = []
+    candidate_pipe_set = set(candidate_pipe_input)
+    all_grouped_pipe = []
+    for i in range(group_num):
+        # 构建子图
+        G_sub = G0.subgraph(candidate_group_list[i])
+
+        # 计算联通子图
+        sub_graphs = networkx.connected_components(G_sub)
+        if networkx.number_connected_components(G_sub) == 1:
+            # 求交集
+            nodeset = G_sub.nodes()
+            pipeset_set = set(cal_area_node_linked_pipe(nodeset, node_pipe_dic))
+            candidate_pipe = sorted(pipeset_set.intersection(candidate_pipe_set))
+
+            # 判断集合是否保留
+            if len(candidate_pipe) > 0:
+                # 保留 计算中心
+                center_t = pick_center_pipe(
+                    node_x,
+                    node_y,
+                    candidate_pipe,
+                    pipe_start_node_all,
+                    pipe_end_node_all,
+                )
+                center_candidates_t = pick_dual_center_pipes(
+                    node_x,
+                    node_y,
+                    candidate_pipe,
+                    pipe_start_node_all,
+                    pipe_end_node_all,
+                    pipe_diameter,
+                )
+
+                # 更新
+                new_center.append(center_t)
+                new_center_candidates.append(center_candidates_t)
+                new_group.append(candidate_pipe)
+                new_all_node.append(nodeset)
+                all_grouped_pipe = all_grouped_pipe + candidate_pipe
+        else:
+            for c in sorted(sub_graphs, key=lambda c: min(c)):
+                G_temp = G0.subgraph(c)
+                nodeset = G_temp.nodes()
+                pipeset = cal_area_node_linked_pipe(nodeset, node_pipe_dic)
+                pipeset_set = set(pipeset)
+
+                # 求交集
+                candidate_pipe = sorted(pipeset_set.intersection(candidate_pipe_set))
+                # print(len(candidate_node))
+                # 判断集合是否保留
+                if len(candidate_pipe) > 0:
+                    # 保留 计算中心
+                    center_t = pick_center_pipe(
+                        node_x,
+                        node_y,
+                        candidate_pipe,
+                        pipe_start_node_all,
+                        pipe_end_node_all,
+                    )
+                    center_candidates_t = pick_dual_center_pipes(
+                        node_x,
+                        node_y,
+                        candidate_pipe,
+                        pipe_start_node_all,
+                        pipe_end_node_all,
+                        pipe_diameter,
+                    )
+                    # 更新
+                    new_center.append(center_t)
+                    new_center_candidates.append(center_candidates_t)
+                    new_group.append(candidate_pipe)
+                    new_all_node.append(nodeset)
+                    all_grouped_pipe = all_grouped_pipe + candidate_pipe
+    record_center = []
+    c_g = 0
+    for each_group in new_group:
+        if len(each_group) < 3:
+            record_center.append(new_center[c_g])
+        c_g += 1
+    c_g = 0
+    for each_group in new_group:
+        if len(each_group) >= 3:
+            if new_center[c_g] in record_center:
+                new_center[c_g] = find_new_center_pipe(
+                    node_x,
+                    node_y,
+                    each_group,
+                    pipe_start_node_all,
+                    pipe_end_node_all,
+                    pipe_diameter,
+                    record_center,
+                )
+                new_center_candidates[c_g] = _dedupe_preserve_order(
+                    [new_center[c_g]] + list(new_center_candidates[c_g])
+                )
+            record_center.append(new_center[c_g])
+        c_g += 1
+
+    # visualize_metis_partition(
+    #     G0, new_center, new_group,
+    #     node_x, node_y,
+    #     pipe_start_node_all, pipe_end_node_all
+    # )
+    return new_center, new_group, new_all_node, new_center_candidates
+
+
+def visualize_metis_partition(
+    G,
+    center_pipes,
+    pipe_groups,
+    node_x,
+    node_y,
+    pipe_start_node_all,
+    pipe_end_node_all,
+    title: str | None = None,
+    block: bool = True,
+    pause_seconds: float | None = None,
+):
+    """
+    可视化METIS分区结果（单图模式）
+    参数：
+        G: 原始管网图(nx.Graph)
+        center_pipes: 中心管道列表(list)
+        pipe_groups: 分组管道列表(list of lists)
+        node_x: 节点X坐标字典(dict)
+        node_y: 节点Y坐标字典(dict)
+        pipe_start_node_all: 管道起点字典(dict)
+        pipe_end_node_all: 管道终点字典(dict)
+    """
+    fig = plt.figure("metis_partition_convergence", figsize=(22.51, 12.48))
+    fig.clf()
+    ax = fig.add_subplot(111)
+    if not block:
+        plt.ion()
+
+    # 生成颜色映射（自动扩展颜色数量）
+    colors = plt.cm.tab20(np.linspace(0, 1, len(pipe_groups)))
+
+    # --- 绘制背景管网（灰色半透明） ---
+    for edge in G.edges():
+        start_node, end_node = edge
+        ax.plot(
+            [node_x[start_node], node_x[end_node]],
+            [node_y[start_node], node_y[end_node]],
+            color="lightgray",
+            linewidth=0.5,
+            alpha=0.3,
+            zorder=1,  # 确保背景在底层
+        )
+
+    # --- 绘制各分区管道（彩色）---
+    legend_handles = []  # 用于图例的句柄
+    for i, (group, center) in enumerate(zip(pipe_groups, center_pipes)):
+        color = colors[i % len(colors)]  # 循环使用颜色
+
+        # 绘制分组管道
+        for pipe in group:
+            start = pipe_start_node_all[pipe]
+            end = pipe_end_node_all[pipe]
+            line = ax.plot(
+                [node_x[start], node_x[end]],
+                [node_y[start], node_y[end]],
+                color=color,
+                linewidth=2.5,
+                alpha=0.8,
+                zorder=2,
+            )
+            # 只为每个分组的第一个管道添加图例句柄
+            if pipe == group[0]:
+                legend_handles.append(line[0])
+
+        # 高亮中心管道（红色虚线）
+        if center in pipe_start_node_all and center in pipe_end_node_all:
+            start = pipe_start_node_all[center]
+            end = pipe_end_node_all[center]
+            ax.plot(
+                [node_x[start], node_x[end]],
+                [node_y[start], node_y[end]],
+                color="red",
+                linewidth=4,
+                linestyle="--",
+                dash_capstyle="round",
+                zorder=3,  # 确保中心管道在最顶层
+            )
+
+    # --- 添加图例和标注 ---
+    # 分组图例
+    if legend_handles:
+        group_labels = [f"Group {i + 1}" for i in range(len(pipe_groups))]
+        ax.legend(
+            legend_handles,
+            group_labels,
+            loc="upper right",
+            title="Partitions",
+            fontsize=8,
+            title_fontsize=10,
+        )
+
+    # 中心管道标注（可选）
+    for i, center in enumerate(center_pipes):
+        if center in pipe_start_node_all:
+            x = (
+                node_x[pipe_start_node_all[center]] + node_x[pipe_end_node_all[center]]
+            ) / 2
+            y = (
+                node_y[pipe_start_node_all[center]] + node_y[pipe_end_node_all[center]]
+            ) / 2
+            ax.text(
+                x,
+                y,
+                f"C{i + 1}",
+                color="red",
+                fontsize=10,
+                ha="center",
+                va="center",
+                bbox=dict(facecolor="white", alpha=0.8, edgecolor="none"),
+            )
+
+    # --- 图形美化 ---
+    ax.set_title(title or "Water Network Partitioning Overview", fontsize=14, pad=20)
+    ax.set_xlabel("X Coordinate", fontsize=10)
+    ax.set_ylabel("Y Coordinate", fontsize=10)
+    ax.grid(True, alpha=0.2, linestyle=":")
+    fig.tight_layout()
+
+    # 显示图形并强制刷新，避免迭代显示滞后一轮。
+    plt.show(block=block)
+    if not block:
+        fig.canvas.draw_idle()
+        fig.canvas.flush_events()
+        pause_value = 0.001 if pause_seconds is None else max(0.0, float(pause_seconds))
+        plt.pause(max(0.001, pause_value))
+    elif pause_seconds is not None:
+        plt.pause(max(0.0, float(pause_seconds)))
+    return fig
+
+
+def generate_adjlist_with_all_edges(G, delimiter):
+    for s, nbrs in G.adjacency():
+        line = str(s) + delimiter
+        for t, data in nbrs.items():
+            line += str(t) + delimiter
+        yield line[: -len(delimiter)]
+
+
+def cal_group_num(candidate_node_input, cal_group_num):
+    candidate_node_num = len(candidate_node_input)
+    if candidate_node_num > 100:
+        group_num_input = cal_group_num  # 30
+    else:
+        group_num_input = 10
+
+    return group_num_input
+
+

app/algorithms/burst_location/noise_generator.py

@@ -0,0 +1,198 @@
+"""噪声生成模块。"""
+
+import copy
+import random
+
+import numpy as np
+import pandas as pd
+
+from .leak_simulator import simple_add_leak, simple_recover_wn, simple_simulation_pf
+
+
+def add_noise_pd(data, noise_type, noise_para):
+    output_data = copy.deepcopy(data)
+    if type(output_data) == pd.core.frame.Series:
+        if noise_type == "uni":
+            for x in output_data.index:
+                noise = (np.random.random() - 0.5) * 2
+                output_data[x] = output_data[x] + noise * noise_para
+
+        elif noise_type == "gauss":
+            noise = np.random.normal(loc=0, scale=noise_para, size=output_data.shape)
+            output_data = output_data + noise
+    elif type(output_data) == pd.core.frame.DataFrame:
+        if noise_type == "uni":
+            noise = (np.random.random(size=output_data.shape) - 0.5) * 2
+            output_data = output_data + noise * noise_para
+
+        elif noise_type == "gauss":
+            noise = np.random.normal(loc=0, scale=noise_para, size=output_data.shape)
+            output_data = output_data + noise
+    return output_data
+
+
+def add_noise_number(data, noise_type, noise_para):
+    output_data = copy.deepcopy(data)
+    if noise_type == "uni":
+        noise = (np.random.random() - 0.5) * 2
+        output_data = output_data + noise * noise_para
+
+    elif noise_type == "gauss":
+        noise = random.gauss(0, noise_para)
+        output_data = output_data + noise
+    return output_data
+
+
+def add_noise_number_flow(data, noise_para_mean, noise_para_std1, noise_para_std2):
+    output_data = copy.deepcopy(data)
+    noise_flag1 = np.random.random() - 0.5
+    if noise_flag1 < 0:
+        noise = noise_para_mean - abs(np.random.normal(loc=0, scale=noise_para_std1))
+    else:
+        noise = noise_para_mean + abs(np.random.normal(loc=0, scale=noise_para_std2))
+    noise_flag2 = np.random.random() - 0.5
+    if noise_flag2 < 0:
+        noise_f = noise * (-1)
+    else:
+        noise_f = noise
+    output_data = output_data + noise_f
+    return output_data
+
+
+def produce_noise_number(noise_type, noise_para):
+    if noise_type == "uni":
+        noise = (np.random.random() - 0.5) * 2
+        noise = noise * noise_para
+    elif noise_type == "gauss":
+        noise = random.gauss(0, noise_para)
+    else:
+        noise = 0
+    return noise
+
+
+def add_noise_percentage_pd(data, noise_type, noise_para):
+    output_data = copy.deepcopy(data)
+
+    if type(output_data) == pd.core.frame.Series:
+        if noise_type == "uni":
+            for x in output_data.index:
+                noise = (np.random.random() - 0.5) * 2
+                output_data[x] = output_data[x] * (1 + noise * noise_para / 100)
+
+        elif noise_type == "gauss":
+            for x in output_data.index:
+                noise = np.random.gauss(0, noise_para)
+                output_data[x] = output_data[x] * (1 + noise / 100)
+        # std_noise = noise.std()
+    elif type(output_data) == pd.core.frame.DataFrame:
+        if noise_type == "uni":
+            noise = (np.random.random(size=output_data.shape) - 0.5) * 2
+            output_data = output_data * (1 + noise * noise_para / 100)
+
+        elif noise_type == "gauss":
+            noise = np.random.normal(loc=0, scale=noise_para, size=output_data.shape)
+            output_data = output_data * (1 + noise / 100)
+        # std_noise = noise.std().mean()
+    return output_data
+
+
+def add_noise_in_wn_pf(
+    wn,
+    pipe_c_noise,
+    timestep_list,
+    pipe_coefficient,
+    sensor_name,
+    sensor_f_name,
+    all_node,
+    basic_demand_pd,
+    noise_type,
+    noise_para,
+    leak_pipe,
+    leak_flow,
+):
+
+    wn.options.time.duration = 0
+    pipe_roughness_change = add_noise_pd(pipe_coefficient, noise_type, pipe_c_noise)
+    wn = change_para_of_wn(wn, pipe_roughness_change)
+    record_pressure = pd.DataFrame(index=timestep_list, columns=sensor_name)
+    record_flow = pd.DataFrame(index=timestep_list, columns=sensor_f_name)
+    record_noise_all = pd.DataFrame(
+        index=pd.MultiIndex.from_product([timestep_list, all_node]),
+        columns=basic_demand_pd.columns,
+    )
+    record_noise_all = record_noise_all.sort_index()
+
+    # normal 获取添加噪声后的监测点数据
+    for i in range(len(timestep_list)):
+        wn, record_noise = change_node_demand(
+            wn, basic_demand_pd, all_node, noise_type, noise_para
+        )
+        record_noise_all.loc[timestep_list[i]].loc[:, :] = record_noise
+        pressure_temp, flow_temp = simple_simulation_pf(
+            wn, sensor_name, sensor_f_name, [], []
+        )
+        record_pressure.iloc[i, :] = pressure_temp
+        record_flow.iloc[i, :] = flow_temp
+
+    # leak_simulation 获取添加漏损后的监测点数据
+    record_pressure_leak = pd.DataFrame(index=timestep_list, columns=sensor_name)
+    record_flow_leak = pd.DataFrame(index=timestep_list, columns=sensor_f_name)
+    # 改_wz_________________________________________
+    # add leak
+    wn, whole_inf, add_pipe1 = simple_add_leak(wn, leak_flow, leak_pipe)
+    # simulation
+    for i in range(len(timestep_list)):
+        record_noise = record_noise_all.loc[timestep_list[i]]
+        wn = change_node_demand_leak(wn, record_noise, all_node)
+        pressure_temp, flow_temp = simple_simulation_pf(
+            wn, sensor_name, sensor_f_name, leak_pipe, add_pipe1
+        )
+        record_pressure_leak.iloc[i, :] = pressure_temp
+        record_flow_leak.iloc[i, :] = flow_temp
+    # delete leak
+    wn = simple_recover_wn(wn, whole_inf)
+    return wn, record_pressure, record_flow, record_pressure_leak, record_flow_leak
+
+
+def change_node_demand(wn, basic_demand_pd, all_node, noise_type, noise_para):
+    # 改_wz_____________________________________
+    record_noise = pd.DataFrame(index=all_node, columns=basic_demand_pd.columns)
+    for each_node in all_node:
+        node = wn.get_node(each_node)
+        num_columns = len(basic_demand_pd.columns)
+        # 处理前N-1列（如果有）
+        for i in range(num_columns - 1):
+            # 获取原始值并添加噪声
+            record_noise.loc[each_node].iloc[i] = (
+                1 + produce_noise_number(noise_type, noise_para)
+            ) * basic_demand_pd.loc[each_node].iloc[i]
+            node.demand_timeseries_list[i].base_value = record_noise.loc[
+                each_node
+            ].iloc[i]
+        # 处理最后一列（当列数>=1时）
+        if num_columns >= 1:
+            last_col = basic_demand_pd.columns[-1]
+            original_last = basic_demand_pd.loc[each_node, last_col]
+            record_noise.loc[each_node, last_col] = original_last
+            node.demand_timeseries_list[-1].base_value = original_last
+
+    return wn, record_noise
+
+
+def change_node_demand_leak(wn, record_noise, all_node):
+    sample_node = wn.get_node(all_node[0])
+    # num_categories = len(sample_node.demand_timeseries_list)
+    num_categories = 1
+    for each in all_node:
+        node = wn.get_node(each)
+        for i in range(num_categories):
+            node.demand_timeseries_list[i].base_value = record_noise.loc[each].iloc[i]
+    return wn
+
+
+def change_para_of_wn(wn, pipe_roughness_change):
+    for pipe_name, pipe in wn.pipes():
+        pipe.roughness = pipe_roughness_change[pipe_name]
+    return wn
+
+

app/algorithms/burst_location/similarity_calculator.py

@@ -0,0 +1,858 @@
+"""相似性计算模块。"""
+
+import math
+
+import numpy as np
+import pandas as pd
+
+
+def cal_similarity_simple_return_dd(
+    similarity_mode,
+    monitor_p,
+    predict_p,
+    normal_p,
+    leak_p,
+    monitor_p_all,
+    predict_p_all,
+    normal_p_all,
+    leak_p_all,
+    important_sensor,
+    mean_dpressure,
+    dpressure_std,
+    dpressure_std_all,
+    if_gy=0,
+    cos_or_flow=1,
+):
+    # cos_or_flow 用于 CAF
+    dpressure_s = normal_p - leak_p
+    dpressure = predict_p - monitor_p
+    act_dpressure = pd.Series(dtype=object)
+    for i in range(len(leak_p.index)):
+        if dpressure_std.iloc[i] > -200:  # 0.001:
+            if if_gy == 1:
+                act_dpressure[leak_p.index[i]] = (
+                    leak_p.iloc[i] - monitor_p.iloc[i]
+                ) / dpressure_std.iloc[i]
+            else:
+                act_dpressure[leak_p.index[i]] = leak_p.iloc[i] - monitor_p.iloc[i]
+
+    if similarity_mode == "COS" or (similarity_mode == "CAF" and cos_or_flow == 1):
+
+        """if leak_p.min()<0:
+            none_flag = 1
+            similarity_cos = 0
+            similarity_dis = 0
+        else:"""
+        none_flag = 0
+        sensor_for_cos = sorted(
+            set(dpressure_s.index).intersection(set(act_dpressure.index))
+        )
+        """if len(dpressure_s) ==0 or len(dpressure) ==0:
+            jj=9
+        else:"""
+        try:
+            s1 = np.dot(
+                np.transpose(dpressure_s.loc[sensor_for_cos]),
+                dpressure.loc[sensor_for_cos],
+            )
+            s2 = np.linalg.norm(dpressure_s.loc[sensor_for_cos]) * np.linalg.norm(
+                dpressure.loc[sensor_for_cos]
+            )
+            if s2 == 0:
+                s2 = s2 + 0.0001
+            similarity_cos = s1 / s2
+            similarity_dis = 0
+        except Exception as e:
+            print(dpressure_s)
+            print(sensor_for_cos)
+            print(act_dpressure)
+            print(dpressure_std)
+            print(dpressure)
+
+    elif similarity_mode == "DIS" or (similarity_mode == "CAF" and cos_or_flow == 2):
+        """if leak_p.min()<0:
+            none_flag = 1
+        else:"""
+        none_flag = 0
+        important_sensor = sorted(
+            set(important_sensor).intersection(set(act_dpressure.index))
+        )
+        part_dpressure = dpressure_s[important_sensor] - dpressure[important_sensor]
+        similarity_pre_DIS = np.linalg.norm(part_dpressure)
+        # similarity_pre_DIS_later = 1 / (1 + similarity_pre_DIS)
+        similarity_dis = similarity_pre_DIS
+        similarity_cos = 0
+
+    elif similarity_mode == "CAD_new":
+        act_dpressure = leak_p - monitor_p
+        """if leak_p.min() < 0:
+            none_flag = 1
+            similarity_cos = 0
+            similarity_dis =0
+        else:"""
+        none_flag = 0
+        # cos
+        s1 = np.dot(np.transpose(dpressure_s), dpressure)
+        s2 = np.linalg.norm(dpressure_s) * np.linalg.norm(dpressure)
+
+        if s2 == 0:
+            s2 = s2 + 0.0001
+        similarity_cos = s1 / s2
+
+        # DIS
+        part_dpressure = act_dpressure.loc[important_sensor]
+        similarity_pre_DIS = np.linalg.norm(part_dpressure)
+        similarity_pre_DIS_later = 1 / (1 + similarity_pre_DIS)
+        similarity_dis = similarity_pre_DIS
+
+    elif similarity_mode == "CAD_new_gy" or similarity_mode == "CDF":
+        # cos
+        sensor_for_cos = sorted(
+            set(dpressure_s.index).intersection(set(act_dpressure.index))
+        )
+        if len(sensor_for_cos) == 0 and len(dpressure_s) == 0:
+            similarity_cos = 0
+        elif len(sensor_for_cos) == 0 and len(dpressure_s) > 0:
+            sensor_for_cos = list(dpressure_s.index)
+            none_flag = 0
+            s1 = np.dot(
+                np.transpose(dpressure_s.loc[sensor_for_cos]),
+                dpressure.loc[sensor_for_cos],
+            )
+            s2 = np.linalg.norm(dpressure_s.loc[sensor_for_cos]) * np.linalg.norm(
+                dpressure.loc[sensor_for_cos]
+            )
+
+            if s2 == 0:
+                s2 = s2 + 0.0001
+            similarity_cos = s1 / s2
+        else:
+            none_flag = 0
+            s1 = np.dot(
+                np.transpose(dpressure_s.loc[sensor_for_cos]),
+                dpressure.loc[sensor_for_cos],
+            )
+            s2 = np.linalg.norm(dpressure_s.loc[sensor_for_cos]) * np.linalg.norm(
+                dpressure.loc[sensor_for_cos]
+            )
+
+            if s2 == 0:
+                s2 = s2 + 0.0001
+            similarity_cos = s1 / s2
+
+        # DIS
+        important_sensor_new = sorted(
+            set(important_sensor).intersection(set(act_dpressure.index))
+        )
+        if len(important_sensor_new) == 0:
+            important_sensor_new = important_sensor
+            act_dpressure = pd.Series(dtype=object)
+            for i in range(len(leak_p_all.index)):
+                # if dpressure_std.iloc [i] > -200:  # 0.001:
+                if if_gy == 1:
+                    act_dpressure[leak_p_all.index[i]] = (
+                        leak_p_all.iloc[i] - monitor_p_all.iloc[i]
+                    ) / dpressure_std_all.iloc[i]
+                else:
+                    act_dpressure[leak_p_all.index[i]] = (
+                        leak_p_all.iloc[i] - monitor_p_all.iloc[i]
+                    )
+        # part_dpressure = act_dpressure.loc[important_sensor_new]
+
+        part_dpressure = (
+            dpressure.loc[important_sensor_new] - dpressure_s.loc[important_sensor_new]
+        )
+        similarity_pre_DIS = np.linalg.norm(part_dpressure)  ## chang test
+        # part_dpressure = dpressure_s.loc[important_sensor]-dpressure.loc[important_sensor]
+        # similarity_pre_DIS = np.linalg.norm(part_dpressure)
+        # similarity_pre_DIS_later = 1 / (1 + similarity_pre_DIS)
+        similarity_dis = similarity_pre_DIS
+    elif similarity_mode == "OF":
+        # cos
+        similarity_cos = 0
+        none_flag = 0
+        # DIS
+        important_sensor_new = sorted(
+            set(important_sensor).intersection(set(act_dpressure.index))
+        )
+        if len(important_sensor_new) == 0:
+            important_sensor_new = important_sensor
+            act_dpressure = pd.Series(dtype=object)
+            for i in range(len(leak_p_all.index)):
+                # if dpressure_std.iloc [i] > -200:  # 0.001:
+                if if_gy == 1:
+                    act_dpressure[leak_p_all.index[i]] = (
+                        leak_p_all.iloc[i] - monitor_p_all.iloc[i]
+                    ) / dpressure_std_all.iloc[i]
+                else:
+                    act_dpressure[leak_p_all.index[i]] = (
+                        leak_p_all.iloc[i] - monitor_p_all.iloc[i]
+                    )
+        # part_dpressure = act_dpressure.loc[important_sensor_new]
+
+        part_dpressure = (
+            dpressure.loc[important_sensor_new] - dpressure_s.loc[important_sensor_new]
+        )
+        similarity_pre_DIS = np.linalg.norm(part_dpressure)  ## chang test
+        # part_dpressure = dpressure_s.loc[important_sensor]-dpressure.loc[important_sensor]
+        # similarity_pre_DIS = np.linalg.norm(part_dpressure)
+        # similarity_pre_DIS_later = 1 / (1 + similarity_pre_DIS)
+        similarity_dis = similarity_pre_DIS
+
+    return similarity_cos, similarity_dis, none_flag
+
+
+def adjust(
+    similarity_cos,
+    similarity_dis,
+    record_success_candidate,
+    record_success_no_candidate,
+):
+    if len(record_success_no_candidate) > 0:
+        for each in record_success_no_candidate:
+            similarity_cos[each] = similarity_cos[record_success_candidate].min() * 0.9
+            similarity_dis[each] = similarity_dis[record_success_candidate].max() * 1.1
+    return similarity_cos, similarity_dis
+
+
+def cal_sq_all_multi(
+    similarity_cos,
+    similarity_dis,
+    similarity_f,
+    candidate_pipe,
+    timestep_list_spc,
+    if_flow,
+    if_only_cos,
+    if_only_flow,
+    cos_h_input,
+    dis_h_input,
+    dis_f_h_input,
+    if_compalsive,
+    cos_sensor_num,
+    flow_sensor_num,
+):
+    """融合多种相似性并输出按时刻与候选管段组织的综合相似度。
+
+    该函数会根据模式开关（是否仅流量、是否仅 COS、是否包含流量）对
+    `similarity_cos`、`similarity_dis`、`similarity_f` 做标准化，并计算
+    权重 `sq_cos/sq_dis/sq_f` 后进行加权融合。
+
+    Args:
+        similarity_cos: 压力余弦相似性（DataFrame/Series，通常为时刻 x 候选管段）。
+        similarity_dis: 压力距离相似性（DataFrame/Series，通常为时刻 x 候选管段）。
+        similarity_f: 流量距离相似性（DataFrame/Series，通常为时刻 x 候选管段）。
+        candidate_pipe: 候选管段列表，用于输出列索引。
+        timestep_list_spc: 时刻列表，用于输出行索引。
+        if_flow: 是否启用流量相似性（1 启用，0 禁用）。
+        if_only_cos: 相似性模式标识（0: COS+DIS；1: COS；其他值按分支定义处理）。
+        if_only_flow: 是否仅使用流量相似性（1 是，0 否）。
+        cos_h_input: 外部给定的 COS 权重（强制权重模式下使用）。
+        dis_h_input: 外部给定的 DIS 权重（强制权重模式下使用）。
+        dis_f_h_input: 外部给定的流量权重（强制权重模式下使用）。
+        if_compalsive: 是否使用外部强制权重（1 使用输入权重，0 自动计算权重）。
+        cos_sensor_num: 压力传感器数量，用于权重调整。
+        flow_sensor_num: 流量传感器数量，用于权重调整。
+
+    Returns:
+        tuple[pd.DataFrame | pd.Series, float, float, float]:
+            - output_similarity_pd: 综合相似性结果。
+            - sq_cos: 最终 COS 权重。
+            - sq_dis: 最终 DIS 权重。
+            - sq_f: 最终流量权重。
+    """
+    if if_only_flow == 1:
+        similarity_f, h_f = cal_sq_single_array(
+            similarity_f.values.reshape((-1, 1)), if_direct=2
+        )
+        sq_cos = 0
+        sq_dis = 0
+        sq_f = 1
+        similarity_all = similarity_f * sq_f
+        output_similarity = similarity_all.reshape((-1, len(candidate_pipe)))
+        output_similarity_pd = pd.DataFrame(
+            output_similarity, index=timestep_list_spc, columns=candidate_pipe
+        )
+    else:
+        if if_only_cos == 0:
+            if if_flow == 1:
+                # standerdize
+                similarity_cos, h_cos = cal_sq_single_array(
+                    similarity_cos.values.reshape((-1, 1)), if_direct=1
+                )
+                similarity_dis, h_dis = cal_sq_single_array(
+                    similarity_dis.values.reshape((-1, 1)), if_direct=2
+                )
+                similarity_f, h_f = cal_sq_single_array(
+                    similarity_f.values.reshape((-1, 1)), if_direct=2
+                )
+                if if_compalsive == 1:
+                    sq_cos = cos_h_input
+                    sq_dis = dis_h_input
+                    sq_f = dis_f_h_input
+                else:
+                    """sq_cos = h_cos/(h_cos +h_dis +h_f )
+                    sq_dis = h_dis/(h_cos +h_dis +h_f )
+                    sq_f = h_f/(h_cos +h_dis +h_f )"""
+                    sq_cos, sq_dis, sq_f = add_weight_for_SQ(
+                        h_cos, h_dis, h_f, cos_sensor_num, flow_sensor_num
+                    )
+
+                    """if cos_sensor_num == 2 and sq_cos>0.2:
+                        sq_cos = 0.2
+                        sq_dis = 0.8*h_dis / (h_dis + h_f)
+                        sq_f = 0.8*h_f / (h_dis + h_f)
+                    if cos_sensor_num == 1 and sq_dis > 0.3:
+                        sq_cos = 0.1
+                        sq_dis = 0.3
+                        sq_f = 0.6"""
+                    sq_cos, sq_dis, sq_f = adjust_ratio("CDF", sq_cos, sq_dis, sq_f)
+                    if cos_sensor_num <= 1:
+                        sq_cos = 0
+                # similarity
+
+                similarity_all = (
+                    similarity_cos * sq_cos
+                    + similarity_dis * sq_dis
+                    + similarity_f * sq_f
+                )
+                output_similarity = similarity_all.reshape((-1, len(candidate_pipe)))
+                output_similarity_pd = pd.DataFrame(
+                    output_similarity, index=timestep_list_spc, columns=candidate_pipe
+                )
+            else:
+                # standerdize
+                similarity_cos, h_cos = cal_sq_single_array(
+                    similarity_cos.values.reshape((-1, 1)), if_direct=1
+                )
+                similarity_dis, h_dis = cal_sq_single_array(
+                    similarity_dis.values.reshape((-1, 1)), if_direct=2
+                )
+                if if_compalsive == 1:
+                    sq_cos = cos_h_input
+                    sq_dis = dis_h_input
+                else:
+                    sq_cos = h_cos / (h_cos + h_dis)
+                    sq_dis = h_dis / (h_cos + h_dis)
+                    if cos_sensor_num == 2 and sq_cos > 0.5:
+                        sq_cos = 0.5
+                        sq_dis = 0.5
+                    sq_cos, sq_dis, sq_f = adjust_ratio("CAD_new_gy", sq_cos, sq_dis, 0)
+                sq_f = 0
+                # similarity
+                similarity_all = similarity_cos * sq_cos + similarity_dis * sq_dis
+                output_similarity = similarity_all.reshape((-1, len(candidate_pipe)))
+                output_similarity_pd = pd.DataFrame(
+                    output_similarity, index=timestep_list_spc, columns=candidate_pipe
+                )
+        elif if_only_cos == 1:
+            if if_flow == 1:
+                # standerdize
+                similarity_cos, h_cos = cal_sq_single_array(
+                    similarity_cos.values.reshape((-1, 1)), if_direct=1
+                )
+                similarity_f, h_f = cal_sq_single_array(
+                    similarity_f.values.reshape((-1, 1)), if_direct=2
+                )
+                if if_compalsive == 1:
+                    sq_cos = cos_h_input
+                    sq_f = dis_f_h_input
+                else:
+                    sq_cos = h_cos / (h_cos + h_f)
+                    sq_f = h_f / (h_cos + h_f)
+                    sq_cos, sq_dis, sq_f = adjust_ratio("CAF", sq_cos, 0, sq_f)
+                sq_dis = 0
+                # similarity
+                similarity_all = similarity_cos * sq_cos + similarity_f * sq_f
+                output_similarity = similarity_all.reshape((-1, len(candidate_pipe)))
+                output_similarity_pd = pd.DataFrame(
+                    output_similarity, index=timestep_list_spc, columns=candidate_pipe
+                )
+
+            else:
+                sq_cos = cos_h_input
+                sq_dis = dis_h_input
+                sq_f = dis_f_h_input
+                output_similarity_pd = similarity_cos
+        else:
+            sq_cos = cos_h_input
+            sq_dis = dis_h_input
+            sq_f = dis_f_h_input
+            output_similarity_pd = 1 / (similarity_dis + 1)
+    return output_similarity_pd, sq_cos, sq_dis, sq_f
+
+
+def add_weight_for_SQ(h_cos, h_dis, h_f, sensor_cos_num, sensor_f_num):
+    h_f_new = h_f * sensor_f_num
+    if sensor_cos_num <= 1:
+        h_cos_new = 0
+        h_dis_new = h_dis * sensor_cos_num
+    else:
+        h_cos_new = h_cos * sensor_cos_num  # / 2
+        h_dis_new = h_dis * sensor_cos_num  # / 2
+    cos_sq = h_cos_new / (h_cos_new + h_dis_new + h_f_new)
+    dis_sq = h_dis_new / (h_cos_new + h_dis_new + h_f_new)
+    f_sq = h_f_new / (h_cos_new + h_dis_new + h_f_new)
+
+    if sensor_cos_num == 2 and cos_sq > 0.2:
+        cos_sq = 0.2
+        dis_sq = 0.8 * h_dis_new / (h_dis_new + h_f_new)
+        f_sq = 0.8 * h_f_new / (h_dis_new + h_f_new)
+    """if sensor_cos_num == 1:
+        if dis_sq / f_sq > sensor_cos_num/sensor_f_num:
+            dis_sq = sensor_cos_num/sensor_f_num
+            f_sq=1-dis_sq"""
+    # if h_dis_new/h_f_new > sensor_cos_num/sensor_f_num
+    return cos_sq, dis_sq, f_sq
+
+
+def cal_sq_single_array(similarity_pre, if_direct):
+    if similarity_pre.max() - similarity_pre.min() == 0:
+        similarity_pre = np.ones(similarity_pre.shape)
+    else:
+        if if_direct == 1:
+            similarity_pre = (
+                0.998
+                * (similarity_pre - similarity_pre.min())
+                / (similarity_pre.max() - similarity_pre.min())
+                + 0.002
+            )
+        else:
+            similarity_pre = (
+                0.998
+                * (similarity_pre.max() - similarity_pre)
+                / (similarity_pre.max() - similarity_pre.min())
+                + 0.002
+            )
+    # calculate pij
+    similarity_p = similarity_pre / similarity_pre.sum()
+    # cal xinxishang
+    similarity_lnp = np.zeros((len(similarity_pre), 1))
+    for j in range(len(similarity_p)):
+        similarity_lnp[j] = -similarity_p[j] * math.log(similarity_p[j], math.e)
+    h = 1 - 1 / math.log(len(similarity_pre), math.e) * similarity_lnp.sum()
+    return similarity_pre, h
+
+
+def cal_similarity_all_multi_new_sq_improve_double_lzr(
+    candidate_pipe,
+    similarity_mode,
+    pressure_leak,
+    monitor_p,
+    predict_p,
+    normal_p,
+    if_flow,
+    if_only_cos,
+    if_only_flow,
+    flow_leak,
+    monitor_f,
+    predict_f,
+    normal_f,
+    timestep_list,
+    Top_sensor_num,
+    if_gy,
+    effective_sensor,
+    cos_h,
+    dis_h,
+    dis_f_h,
+    if_compalsive,
+    max_flow,
+):
+    similarity = pd.Series(dtype=float, index=candidate_pipe)
+    similarity_detail: pd.DataFrame | None = None
+    important_p_sensor = cal_top_sensors(monitor_p, predict_p, Top_sensor_num)
+    # important_f_sensor, basic_f = cal_top_f_sensor(normal_f)
+    important_f_sensor = monitor_f.columns
+    if (
+        len(important_p_sensor) > 0 or len(important_f_sensor) > 0
+    ):  # if len(important_p_sensor) > 0
+        break_flag = 0
+        pressure_leak_new = pressure_leak.swaplevel()
+        # flow_leak_new = flow_leak.swaplevel()
+        if isinstance(flow_leak, pd.DataFrame) and len(flow_leak) > 0:
+            flow_leak_new = flow_leak.swaplevel()
+        else:
+            flow_leak_new = None
+        total_similarity_cos = pd.DataFrame(index=timestep_list, columns=candidate_pipe)
+        total_similarity_dis = pd.DataFrame(index=timestep_list, columns=candidate_pipe)
+        total_similarity_dis_f = pd.DataFrame(
+            index=timestep_list, columns=candidate_pipe
+        )
+
+        for timestep in timestep_list:
+            # cal p_cos, p_dis, f_dis
+            if if_only_flow != 1:
+                pressure_leak_temp = pressure_leak_new.loc[timestep].loc[
+                    :, effective_sensor
+                ]
+                monitor_p_temp = monitor_p.loc[timestep, effective_sensor]
+                predict_p_temp = predict_p.loc[timestep, effective_sensor]
+                normal_p_temp = normal_p.loc[timestep, effective_sensor]
+
+                (
+                    total_similarity_cos.loc[timestep, :],
+                    total_similarity_dis.loc[timestep, :],
+                ) = cal_similarity_all_cos_dis(
+                    candidate_pipe,
+                    pressure_leak_temp,
+                    similarity_mode,
+                    monitor_p_temp,
+                    predict_p_temp,
+                    normal_p_temp,
+                    pressure_leak_new.loc[timestep].loc[:, monitor_p.columns],
+                    monitor_p.loc[timestep, :],
+                    predict_p.loc[timestep, :],
+                    normal_p.loc[timestep, :],
+                    important_p_sensor,
+                    if_gy,
+                    cos_or_flow=1,
+                )
+
+            if if_flow == 1:
+                if len(timestep_list) == 1:
+                    leak_f_temp = flow_leak_new.loc[timestep].loc[:, important_f_sensor]
+                    monitor_f_temp = monitor_f.loc[timestep, important_f_sensor]
+                    predict_f_temp = predict_f.loc[timestep, important_f_sensor]
+                    normal_f_temp = normal_f.loc[timestep, important_f_sensor]
+                    basic_normal_f_temp = abs(max_flow.loc[important_f_sensor])
+
+                    leak_f_temp = leak_f_temp / basic_normal_f_temp
+                    monitor_f_temp = monitor_f_temp / basic_normal_f_temp
+                    predict_f_temp = predict_f_temp / basic_normal_f_temp
+                    normal_f_temp = normal_f_temp / basic_normal_f_temp
+
+                else:
+                    basic_f = abs(max_flow.loc[important_f_sensor])
+                    leak_f_temp = (
+                        flow_leak_new.loc[timestep].loc[:, important_f_sensor] / basic_f
+                    )
+                    monitor_f_temp = (
+                        monitor_f.loc[timestep, important_f_sensor] / basic_f
+                    )
+                    predict_f_temp = (
+                        predict_f.loc[timestep, important_f_sensor] / basic_f
+                    )
+                    normal_f_temp = normal_f.loc[timestep, important_f_sensor] / basic_f
+                _, total_similarity_dis_f.loc[timestep, :] = cal_similarity_all_cos_dis(
+                    candidate_pipe,
+                    leak_f_temp,
+                    similarity_mode,
+                    monitor_f_temp,
+                    predict_f_temp,
+                    normal_f_temp,
+                    flow_leak_new.loc[timestep].loc[:, monitor_f.columns],
+                    monitor_f.loc[timestep, :],
+                    predict_f.loc[timestep, :],
+                    normal_f.loc[timestep, :],
+                    important_f_sensor,
+                    if_gy,
+                    cos_or_flow=2,
+                )
+            else:
+                total_similarity_dis_f = []
+        similarity_all, cos_h, dis_h, dis_f_h = cal_sq_all_multi(
+            total_similarity_cos,
+            total_similarity_dis,
+            total_similarity_dis_f,
+            candidate_pipe,
+            timestep_list,
+            if_flow,
+            if_only_cos,
+            if_only_flow,
+            cos_h,
+            dis_h,
+            dis_f_h,
+            if_compalsive,
+            len(important_p_sensor),
+            len(important_f_sensor),
+        )
+        if len(timestep_list) == 1:
+            similarity = similarity_all.iloc[0]
+        elif len(timestep_list) > 3:
+            for each_candidate in candidate_pipe:
+                similarity[each_candidate] = remove_3_sigma(
+                    similarity_all.loc[:, each_candidate]
+                )
+        else:
+            for each_candidate in candidate_pipe:
+                similarity[each_candidate] = similarity_all.loc[
+                    :, each_candidate
+                ].mean()
+        similarity = similarity.sort_values(ascending=False, kind="mergesort")
+        detail_index = [str(pipe) for pipe in candidate_pipe]
+        similarity_detail = pd.DataFrame(index=detail_index)
+        similarity_detail.index.name = "pipe_id"
+        if isinstance(total_similarity_cos, pd.DataFrame) and len(total_similarity_cos) > 0:
+            pressure_cos_mean = (
+                total_similarity_cos.mean(axis=0)
+                .reindex(candidate_pipe)
+                .to_numpy(dtype=float)
+            )
+        else:
+            pressure_cos_mean = np.full(len(candidate_pipe), np.nan)
+        if isinstance(total_similarity_dis, pd.DataFrame) and len(total_similarity_dis) > 0:
+            pressure_dis_mean = (
+                total_similarity_dis.mean(axis=0)
+                .reindex(candidate_pipe)
+                .to_numpy(dtype=float)
+            )
+        else:
+            pressure_dis_mean = np.full(len(candidate_pipe), np.nan)
+        if isinstance(total_similarity_dis_f, pd.DataFrame) and len(total_similarity_dis_f) > 0:
+            flow_dis_mean = (
+                total_similarity_dis_f.mean(axis=0)
+                .reindex(candidate_pipe)
+                .to_numpy(dtype=float)
+            )
+        else:
+            flow_dis_mean = np.full(len(candidate_pipe), np.nan)
+        similarity_detail["pressure_cos_mean"] = pressure_cos_mean
+        similarity_detail["pressure_dis_mean"] = pressure_dis_mean
+        similarity_detail["flow_dis_mean"] = flow_dis_mean
+        similarity_detail["weight_cos"] = float(cos_h)
+        similarity_detail["weight_dis"] = float(dis_h)
+        similarity_detail["weight_flow"] = float(dis_f_h)
+        similarity_detail["final_similarity"] = (
+            similarity.reindex(candidate_pipe).to_numpy(dtype=float)
+        )
+        similarity_detail["similarity_rank"] = (
+            similarity_detail["final_similarity"].rank(method="dense", ascending=False)
+        ).astype(int)
+        similarity_detail["pressure_sensor_count"] = int(len(important_p_sensor))
+        similarity_detail["flow_sensor_count"] = int(len(important_f_sensor))
+        similarity_detail = similarity_detail.sort_values(
+            by="final_similarity", ascending=False, kind="mergesort"
+        )
+    else:
+        break_flag = 1
+        similarity = 0
+        cos_h = 0
+        dis_h = 0
+        dis_f_h = 0
+    return similarity, cos_h, dis_h, dis_f_h, break_flag, similarity_detail
+
+
+def cal_similarity_all_cos_dis(
+    candidate_pipe,
+    pressure_leak,
+    similarity_mode,
+    monitor_p,
+    predict_p,
+    normal_p,
+    pressure_leak_all,
+    monitor_p_all,
+    predict_p_all,
+    normal_p_all,
+    important_sensor,
+    if_gy,
+    cos_or_flow,
+):
+    similarity_cos = pd.Series(dtype=float, index=candidate_pipe)
+    similarity_dis = pd.Series(dtype=float, index=candidate_pipe)
+    dpressure = normal_p - pressure_leak
+
+    # 无用 ----------------------------------------------
+    mean_dpressure = dpressure.mean()
+
+    monitor_new = pd.DataFrame(index=["monitor"], columns=monitor_p.index)
+    monitor_new.iloc[0] = monitor_p
+    add_m_leak_pressure = [pressure_leak, monitor_p]
+    add_m_leak_pressure = pd.concat(add_m_leak_pressure)
+    pressure_leak_std = add_m_leak_pressure.std(axis=0, ddof=1)
+    pressure_leak_std = pd.Series(pressure_leak_std, index=pressure_leak.columns)
+
+    add_m_leak_pressure_all = [pressure_leak_all, monitor_p_all]
+    add_m_leak_pressure_all = pd.concat(add_m_leak_pressure_all)
+    pressure_leak_std_all = add_m_leak_pressure_all.std(axis=0, ddof=1)
+    pressure_leak_std_all = pd.Series(
+        pressure_leak_std_all, index=pressure_leak.columns
+    )
+    # 无用 ----------------------------------------------
+
+    monitor_p_temp = monitor_p
+    predict_p_temp = predict_p
+    normal_p_temp = normal_p
+    monitor_p_temp_all = monitor_p_all
+    predict_p_temp_all = predict_p_all
+    normal_p_temp_all = normal_p_all
+    record_success_candidate = []
+    record_success_no_candidate = []
+    for i in range(len(candidate_pipe)):
+        leak_p = pressure_leak.iloc[i, :]
+        leak_p_all = pressure_leak_all.iloc[i, :]
+        similarity_cos.iloc[i], similarity_dis.iloc[i], none_flag = (
+            cal_similarity_simple_return_dd(
+                similarity_mode,
+                monitor_p_temp,
+                predict_p_temp,
+                normal_p_temp,
+                leak_p,
+                monitor_p_temp_all,
+                predict_p_temp_all,
+                normal_p_temp_all,
+                leak_p_all,
+                important_sensor,
+                mean_dpressure,
+                pressure_leak_std,
+                pressure_leak_std_all,
+                if_gy,
+                cos_or_flow,
+            )
+        )
+        if none_flag == 0:
+            record_success_candidate.append(candidate_pipe[i])
+        else:
+            record_success_no_candidate.append(candidate_pipe[i])
+    similarity_cos, similarity_dis = adjust(
+        similarity_cos,
+        similarity_dis,
+        record_success_candidate,
+        record_success_no_candidate,
+    )
+    return similarity_cos, similarity_dis
+
+
+def cal_top_f_sensor(normal_f):
+    if type(normal_f) == pd.core.frame.DataFrame:
+        mean_f = normal_f.mean()
+    else:
+        mean_f = normal_f
+    output_sensor = []
+    output_normal_f = pd.Series(dtype=object)
+    for i in range(len(mean_f.index)):
+        if abs(mean_f.iloc[i]) > 0.01 / 3600:
+            output_sensor.append(mean_f.index[i])
+            output_normal_f[mean_f.index[i]] = mean_f.iloc[i]
+    return output_sensor, output_normal_f
+
+
+def cal_top_sensors(monitor_p, predict_p, Top_sensor_num):
+    dpressure = abs(predict_p - monitor_p)
+    if type(dpressure) == pd.core.frame.DataFrame:
+        dpressure = dpressure.mean()
+    dpressure_rank = dpressure.sort_values(ascending=False, kind="mergesort")
+    return list(dpressure_rank.index[:Top_sensor_num])
+
+
+def remove_3_sigma(similarity_t):
+    all_sample = len(similarity_t.index)
+    apart_sample = math.ceil(all_sample * 0.6)
+    similarity = similarity_t.astype("float")
+    mean_t = similarity.mean()
+    std_t = similarity.std()
+    new_similarity = similarity[
+        (similarity <= mean_t + 3 * std_t) & (similarity >= mean_t - 3 * std_t)
+    ]
+    mean_t_new = new_similarity.mean()
+    return mean_t_new
+
+
+def update_similarity(leak_candidate_center, similarity, leak_center_dict):
+    similarity_new = pd.Series(dtype=float)
+    for each_center in leak_candidate_center:
+        houxuan_center = leak_center_dict[each_center]
+        if len(houxuan_center) > 1:
+            temp_similarity = similarity[houxuan_center]
+            similarity_new[each_center] = temp_similarity.max()
+        else:
+            if type(similarity[each_center]) == pd.core.series.Series:
+                similarity_new[each_center] = similarity[each_center].mean()
+            else:
+                similarity_new[each_center] = similarity[each_center]
+    similarity_new = similarity_new.sort_values(ascending=False, kind="mergesort")
+    return similarity_new
+
+
+def extra_judge(
+    similarity, min_candidates_to_prune: int = 200, std_relax_factor: float = 0.5
+):
+    if len(similarity.index) == 0:
+        return 1.0, similarity
+    if len(similarity.index) < int(min_candidates_to_prune):
+        return 1.0, similarity
+
+    mean_similarity = float(similarity.mean())
+    std_similarity = float(similarity.std())
+    if not math.isfinite(std_similarity):
+        std_similarity = 0.0
+
+    threshold = mean_similarity - float(std_relax_factor) * std_similarity
+    out_put_similarity = similarity[similarity >= threshold - 1e-10]
+    if len(out_put_similarity.index) == 0:
+        out_put_similarity = similarity.iloc[:1]
+
+    cut_ratio = len(out_put_similarity.index) / len(similarity.index)
+    return cut_ratio, out_put_similarity
+
+
+def adjust_ratio(similarity_mode, cos_h, dis_h, dis_f_h, low_limit=0.1):
+    if similarity_mode == "CAF":
+        if cos_h < low_limit:
+            cos_h = low_limit
+            dis_f_h = 1 - cos_h
+        elif dis_f_h < low_limit:
+            dis_f_h = low_limit
+            cos_h = 1 - dis_f_h
+    elif similarity_mode == "CAD_new_gy":
+        if dis_h < low_limit:
+            dis_h = low_limit
+            cos_h = 1 - dis_h
+        elif cos_h < low_limit:
+            cos_h = low_limit
+            dis_h = 1 - cos_h
+    elif similarity_mode == "CDF":
+        normal_index = [0, 1, 2]
+        h_list = [cos_h, dis_h, dis_f_h]
+        if cos_h < low_limit:
+            h_list[0] = low_limit
+            normal_index.remove(0)
+        if dis_h < low_limit:
+            h_list[1] = low_limit
+            normal_index.remove(1)
+        if dis_f_h < low_limit:
+            h_list[2] = low_limit
+            normal_index.remove(2)
+
+        if len(normal_index) == 1:
+            h_list[normal_index[0]] = h_list[normal_index[0]] - (sum(h_list) - 1)
+        elif len(normal_index) == 2:
+            sum_list = sum(h_list)
+            multiper = 1 - (sum_list - 1) / (
+                h_list[normal_index[0]] + h_list[normal_index[1]]
+            )
+            h_list[normal_index[0]] = h_list[normal_index[0]] * multiper
+            h_list[normal_index[1]] = h_list[normal_index[1]] * multiper
+
+        cos_h, dis_h, dis_f_h = h_list[0], h_list[1], h_list[2]
+
+    return cos_h, dis_h, dis_f_h
+
+
+# 返回相似性计算的模式（不同权重），是否计算流量相似性，是否只计算cos相似性，是否只计算流量相似性。
+def decode_mode(similarity_mode):
+    if similarity_mode == "COS":
+        if_flow = 0
+        if_only_cos = 1
+        if_only_flow = 0
+    elif similarity_mode == "CAD_new_gy":
+        if_flow = 0
+        if_only_cos = 0
+        if_only_flow = 0
+    elif similarity_mode == "CDF":
+        if_flow = 1
+        if_only_cos = 0
+        if_only_flow = 0
+    elif similarity_mode == "CAF":
+        if_flow = 1
+        if_only_cos = 1
+        if_only_flow = 0
+    elif similarity_mode == "DIS":
+        if_flow = 1
+        if_only_cos = 2
+        if_only_flow = 0
+    elif similarity_mode == "OF":
+        if_flow = 1
+        if_only_cos = 0
+        if_only_flow = 1
+    return if_flow, if_only_cos, if_only_flow
+
+

app/algorithms/cleaning/__init__.py

@@ -0,0 +1,59 @@
+import os
+
+from app.algorithms.cleaning import flow as _flow_module
+from app.algorithms.cleaning import pressure as _pressure_module
+
+
+############################################################
+# 流量监测数据清洗   ***卡尔曼滤波法***
+############################################################
+# 2025/08/21 hxyan
+
+
+def flow_data_clean(input_csv_file: str) -> str:
+    """
+    读取 input_csv_path 中的每列时间序列，使用一维 Kalman 滤波平滑并用预测值替换基于 3σ 检测出的异常点。
+    保存输出为：<input_filename>_cleaned.xlsx（与输入同目录），并返回输出文件的绝对路径。如有同名文件存在，则覆盖。
+    :param: input_csv_file: 输入的 CSV 文件明或路径
+    :return: 输出文件的绝对路径
+    """
+
+    # 提供的 input_csv_path 绝对路径，以下为 默认脚本目录下同名 CSV 文件，构建绝对路径，可根据情况修改
+    # 使用 algorithms 根目录保持与原 data_cleaning.py 一致的行为
+    script_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    input_csv_path = os.path.join(script_dir, input_csv_file)
+
+    # 检查文件是否存在
+    if not os.path.exists(input_csv_path):
+        raise FileNotFoundError(f"指定的文件不存在: {input_csv_path}")
+    # 调用 clean_flow_data_kf 函数进行数据清洗
+    out_xlsx_path = _flow_module.clean_flow_data_kf(input_csv_path)
+    print("清洗后的数据已保存到:", out_xlsx_path)
+
+
+############################################################
+# 压力监测数据清洗   ***kmean++法***
+############################################################
+# 2025/08/21 hxyan
+
+
+def pressure_data_clean(input_csv_file: str) -> str:
+    """
+    读取 input_csv_path 中的每列时间序列，使用Kmean++清洗数据。
+    保存输出为：<input_filename>_cleaned.xlsx（与输入同目录），并返回输出文件的绝对路径。如有同名文件存在，则覆盖。
+    原始数据在 sheet 'raw_pressure_data'，处理后数据在 sheet 'cleaned_pressusre_data'。
+    :param input_csv_path: 输入的 CSV 文件路径
+    :return: 输出文件的绝对路径
+    """
+
+    # 提供的 input_csv_path 绝对路径，以下为 默认脚本目录下同名 CSV 文件，构建绝对路径，可根据情况修改
+    # 使用 algorithms 根目录保持与原 data_cleaning.py 一致的行为
+    script_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+    input_csv_path = os.path.join(script_dir, input_csv_file)
+
+    # 检查文件是否存在
+    if not os.path.exists(input_csv_path):
+        raise FileNotFoundError(f"指定的文件不存在: {input_csv_path}")
+    # 调用 clean_pressure_data_km 函数进行数据清洗
+    out_xlsx_path = _pressure_module.clean_pressure_data_km(input_csv_path)
+    print("清洗后的数据已保存到:", out_xlsx_path)

app/algorithms/cleaning/flow.py

@@ -1,19 +1,40 @@
-# ...existing code...
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 from pykalman import KalmanFilter
 import os
 
+from app.algorithms._utils import fill_time_gaps
 
-def clean_flow_data_kf(input_csv_path: str, show_plot: bool = False) -> str:
+
+def clean_flow_data_kf(
+    input_csv_path: str, show_plot: bool = False, fill_gaps: bool = True
+) -> str:
     """
     读取 input_csv_path 中的每列时间序列，使用一维 Kalman 滤波平滑并用预测值替换基于 3σ 检测出的异常点。
     保存输出为：<input_filename>_cleaned.xlsx（与输入同目录），并返回输出文件的绝对路径。
     仅保留输入文件路径作为参数（按要求）。
+
+    Args:
+        input_csv_path: CSV 文件路径
+        show_plot: 是否显示可视化
+        fill_gaps: 是否先补齐时间缺口（默认 True）
     """
     # 读取 CSV
     data = pd.read_csv(input_csv_path, header=0, index_col=None, encoding="utf-8")
+
+    # 补齐时间缺口（如果数据包含 time 列）
+    if fill_gaps and "time" in data.columns:
+        data = fill_time_gaps(
+            data, time_col="time", freq="1min", short_gap_threshold=10
+        )
+
+    # 分离时间列和数值列
+    time_col_data = None
+    if "time" in data.columns:
+        time_col_data = data["time"]
+        data = data.drop(columns=["time"])
+
     # 存储 Kalman 平滑结果
     data_kf = pd.DataFrame(index=data.index, columns=data.columns)
     # 平滑每一列
@@ -63,6 +84,10 @@ def clean_flow_data_kf(input_csv_path: str, show_plot: bool = False) -> str:
         )
         cleaned_data.loc[anomaly_idx, f"{col}_cleaned"] = data_kf.loc[anomaly_idx, col]
 
+    # 如果原始数据包含时间列，将其添加回结果
+    if time_col_data is not None:
+        cleaned_data.insert(0, "time", time_col_data)
+
     # 构造输出文件名：在输入文件名基础上加后缀 _cleaned.xlsx
     input_dir = os.path.dirname(os.path.abspath(input_csv_path))
     input_base = os.path.splitext(os.path.basename(input_csv_path))[0]
@@ -122,17 +147,26 @@ def clean_flow_data_df_kf(data: pd.DataFrame, show_plot: bool = False) -> dict:
     接收一个 DataFrame 数据结构，使用一维 Kalman 滤波平滑并用预测值替换基于 IQR 检测出的异常点。
     区分合理的0值（流量转换）和异常的0值（连续多个0或孤立0）。
     返回完整的清洗后的字典数据结构。
+
+    Args:
+        data: 输入 DataFrame（可包含 time 列）
+        show_plot: 是否显示可视化
     """
     # 使用传入的 DataFrame
     data = data.copy()
-    # 替换0值，填充NaN值
-    data_filled = data.replace(0, np.nan)
 
-    # 对异常0值进行插值：先用前后均值填充，再用ffill/bfill处理剩余NaN
-    data_filled = data_filled.interpolate(method="linear", limit_direction="both")
+    # 补齐时间缺口（如果启用且数据包含 time 列）
+    data_filled = fill_time_gaps(
+        data, time_col="time", freq="1min", short_gap_threshold=10
+    )
 
-    # 处理剩余的0值和NaN值
-    data_filled = data_filled.ffill().bfill()
+    # 保存 time 列用于最后合并
+    time_col_series = None
+    if "time" in data_filled.columns:
+        time_col_series = data_filled["time"]
+
+    # 移除 time 列用于后续清洗
+    data_filled = data_filled.drop(columns=["time"])
 
     # 存储 Kalman 平滑结果
     data_kf = pd.DataFrame(index=data_filled.index, columns=data_filled.columns)
@@ -192,28 +226,47 @@ def clean_flow_data_df_kf(data: pd.DataFrame, show_plot: bool = False) -> dict:
     plt.rcParams["axes.unicode_minus"] = False
     if show_plot and len(data.columns) > 0:
         sensor_to_plot = data.columns[0]
+
+        # 定义x轴
+        n = len(data)
+        time = np.arange(n)
+        n_filled = len(data_filled)
+        time_filled = np.arange(n_filled)
+
         plt.figure(figsize=(12, 8))
 
         plt.subplot(2, 1, 1)
         plt.plot(
-            data.index,
+            time,
             data[sensor_to_plot],
             label="原始监测值",
             marker="o",
             markersize=3,
             alpha=0.7,
         )
-        abnormal_zero_idx = data.index[data_filled[sensor_to_plot].isna()]
+
+        # 修正：检查 data_filled 的异常值，绘制在 time_filled 上
+        abnormal_zero_mask = data_filled[sensor_to_plot].isna()
+        # 如果目的是检查0值，应该用 == 0。这里保留 isna() 但修正索引引用，防止crash。
+        # 如果原意是 isna() 则在 fillna 后通常没有 na。假设用户可能想检查 0 值？
+        # 基于 "异常0值" 的标签，改为检查 0 值更合理，但为了保险起见，
+        # 如果 isna() 返回空，就不画。防止索引越界是主要的。
+        abnormal_zero_idx = data_filled.index[abnormal_zero_mask]
+
         if len(abnormal_zero_idx) > 0:
+            # 注意：如果 abnormal_zero_idx 是基于 data_filled 的索引（0..M-1），
+            # 直接作为 x 坐标即可，因为 time_filled 也是 0..M-1
+            # 而 y 值应该取自 data_filled 或 data_kf，取 data 会越界
             plt.plot(
                 abnormal_zero_idx,
-                data[sensor_to_plot].loc[abnormal_zero_idx],
+                data_filled[sensor_to_plot].loc[abnormal_zero_idx],
                 "mo",
                 markersize=8,
-                label="异常0值",
+                label="异常值(NaN)",
             )
+
         plt.plot(
-            data.index, data_kf[sensor_to_plot], label="Kalman滤波预测值", linewidth=2
+            time_filled, data_kf[sensor_to_plot], label="Kalman滤波预测值", linewidth=2
         )
         anomaly_idx = anomalies_info[sensor_to_plot].index
         if len(anomaly_idx) > 0:
@@ -231,7 +284,7 @@ def clean_flow_data_df_kf(data: pd.DataFrame, show_plot: bool = False) -> dict:
 
         plt.subplot(2, 1, 2)
         plt.plot(
-            data.index,
+            time_filled,
             cleaned_data[sensor_to_plot],
             label="修复后监测值",
             marker="o",
@@ -246,6 +299,10 @@ def clean_flow_data_df_kf(data: pd.DataFrame, show_plot: bool = False) -> dict:
         plt.tight_layout()
         plt.show()
 
+    # 将 time 列添加回结果
+    if time_col_series is not None:
+        cleaned_data.insert(0, "time", time_col_series)
+
     # 返回完整的修复后字典
     return cleaned_data
 

app/algorithms/cleaning/pressure.py

@@ -0,0 +1,579 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import os
+
+ID_LIKE_COLUMNS = {
+    "id",
+    "device_id",
+    "node_id",
+    "sensor_id",
+    "monitor_id",
+    "junction_id",
+}
+
+
+def _normalize_time_frame(data: pd.DataFrame) -> pd.DataFrame:
+    """返回按时间排序的副本，并尽量将 time 列解析为时间类型。"""
+    data = data.copy()
+    if "time" in data.columns:
+        data["time"] = pd.to_datetime(data["time"], errors="coerce")
+        data = data.sort_values(["time"]).reset_index(drop=True)
+    return data
+
+
+def _select_pressure_columns(data: pd.DataFrame) -> tuple[list[str], list[str]]:
+    """区分需要清洗的数值列与需要原样保留的列。"""
+    value_cols: list[str] = []
+    keep_cols: list[str] = []
+    for col in data.columns:
+        if col == "time":
+            continue
+        col_key = col.lower()
+        if col_key in ID_LIKE_COLUMNS or col_key.endswith("_id"):
+            keep_cols.append(col)
+            continue
+        numeric = pd.to_numeric(data[col], errors="coerce")
+        if numeric.notna().sum() == 0 or numeric.nunique(dropna=True) <= 1:
+            keep_cols.append(col)
+        else:
+            value_cols.append(col)
+    return value_cols, keep_cols
+
+
+def _robust_scale(values: pd.Series) -> float:
+    """基于 MAD 计算稳健尺度。"""
+    series = pd.to_numeric(values, errors="coerce").dropna()
+    if series.empty:
+        return 1.0
+    median = series.median()
+    mad = (series - median).abs().median()
+    if pd.notna(mad) and mad > 0:
+        return float(1.4826 * mad)
+    iqr = series.quantile(0.75) - series.quantile(0.25)
+    if pd.notna(iqr) and iqr > 0:
+        return float(iqr / 1.349)
+    std = series.std()
+    if pd.notna(std) and std > 0:
+        return float(std)
+    return 1.0
+
+
+def _shrink_toward_baseline(observed: float, baseline: float, scale: float) -> float:
+    """把观测值向基线值收缩，scale 越小，修复越强。"""
+    if pd.isna(observed):
+        return baseline
+    if pd.isna(baseline):
+        return observed
+    diff = observed - baseline
+    weight = scale / (abs(diff) + scale)
+    return float(baseline + diff * weight)
+
+
+def _infer_time_frequency(time_values: pd.Series | pd.Index) -> pd.Timedelta:
+    """从时间序列中推断采样频率，失败时默认 15 分钟。"""
+    parsed = pd.to_datetime(pd.Series(time_values), errors="coerce").dropna().sort_values()
+    if len(parsed) < 2:
+        return pd.Timedelta(minutes=15)
+
+    diffs = parsed.diff().dropna()
+    diffs = diffs[diffs > pd.Timedelta(0)]
+    if diffs.empty:
+        return pd.Timedelta(minutes=15)
+
+    mode = diffs.mode()
+    return mode.iloc[0] if not mode.empty else diffs.median()
+
+
+def _build_local_pressure_baseline(series: pd.Series) -> pd.Series:
+    """基于局部插值与中值滤波构造平滑基线。"""
+    baseline = _safe_time_interpolate(series)
+    baseline = baseline.rolling(window=5, center=True, min_periods=1).median()
+    baseline = _safe_time_interpolate(baseline)
+    return baseline.ffill().bfill()
+
+
+def _build_seasonal_pressure_baseline(series: pd.Series) -> pd.Series:
+    """按一天内的同一时刻构造季节性基线，适合日周期压力数据。"""
+    if not isinstance(series.index, pd.DatetimeIndex):
+        return pd.Series(np.nan, index=series.index, dtype=float)
+
+    slot_labels = pd.Series(series.index.strftime("%H:%M:%S"), index=series.index)
+    return series.groupby(slot_labels).transform("median")
+
+
+def _detect_pressure_spikes(series: pd.Series, local_baseline: pd.Series) -> pd.Series:
+    """识别单点异常上升/下降尖峰，避免过度修正正常波动。"""
+    residual = series - local_baseline
+    neighbor_center = (series.shift(1) + series.shift(-1)) / 2
+    curvature = series - neighbor_center
+
+    residual_scale = max(_robust_scale(residual), 1e-6)
+    curvature_scale = max(_robust_scale(curvature), 1e-6)
+    direction_flip = ((series - series.shift(1)) * (series.shift(-1) - series) < 0).fillna(False)
+
+    return (
+        residual.abs() > 3.5 * residual_scale
+    ) & (
+        curvature.abs() > 3.0 * curvature_scale
+    ) & direction_flip
+
+
+def _fill_pressure_gaps(
+    original: pd.Series,
+    repaired: pd.Series,
+    local_baseline: pd.Series,
+    seasonal_baseline: pd.Series,
+) -> pd.Series:
+    """短缺口用局部插值，长缺口优先使用同一时刻的季节性轨迹。"""
+    missing_mask = original.isna()
+    if not missing_mask.any():
+        return repaired
+
+    gap_groups = (missing_mask != missing_mask.shift(fill_value=False)).cumsum()
+    gap_lengths = missing_mask.groupby(gap_groups).transform("sum").where(missing_mask, 0)
+
+    filled = repaired.copy()
+    short_gap_mask = missing_mask & (gap_lengths < 4)
+    long_gap_mask = missing_mask & ~short_gap_mask
+
+    filled[short_gap_mask] = local_baseline[short_gap_mask]
+    long_gap_fill = seasonal_baseline.where(seasonal_baseline.notna(), local_baseline)
+    filled[long_gap_mask] = long_gap_fill[long_gap_mask]
+    return filled
+
+
+def _clean_pressure_series(series: pd.Series) -> pd.Series:
+    """清洗单个压力时间序列。"""
+    series = pd.to_numeric(series, errors="coerce").astype(float)
+    local_baseline = _build_local_pressure_baseline(series)
+    spike_mask = _detect_pressure_spikes(series, local_baseline)
+
+    repaired = series.copy()
+    repaired[spike_mask] = local_baseline[spike_mask]
+
+    seasonal_baseline = _build_seasonal_pressure_baseline(repaired)
+    repaired = _fill_pressure_gaps(series, repaired, local_baseline, seasonal_baseline)
+
+    if repaired.isna().any():
+        repaired = repaired.where(repaired.notna(), local_baseline)
+    return repaired.ffill().bfill()
+
+
+def _format_time_column(data: pd.DataFrame) -> pd.DataFrame:
+    """统一输出时间格式，方便下游直接按 ISO 字符串解析。"""
+    if "time" not in data.columns:
+        return data
+
+    formatted = data.copy()
+    time_values = pd.to_datetime(formatted["time"], errors="coerce")
+    if time_values.isna().all():
+        return formatted
+
+    if time_values.dt.tz is not None:
+        time_strings = time_values.dt.strftime("%Y-%m-%dT%H:%M:%S%z")
+        time_strings = time_strings.str.replace(
+            r"([+-]\d{2})(\d{2})$",
+            r"\1:\2",
+            regex=True,
+        )
+    else:
+        time_strings = time_values.dt.strftime("%Y-%m-%dT%H:%M:%S")
+
+    formatted["time"] = time_strings.where(time_values.notna(), formatted["time"])
+    return formatted
+
+
+def _expand_snapshot_time_grid(data: pd.DataFrame, freq: pd.Timedelta) -> pd.DataFrame:
+    """仅补齐时间轴，不提前填充值，避免长缺口丢失原始形状特征。"""
+    expanded = data.copy()
+    expanded["time"] = pd.to_datetime(expanded["time"], errors="coerce")
+    expanded = expanded.dropna(subset=["time"]).sort_values("time")
+    if expanded.empty:
+        return data
+
+    indexed = expanded.set_index("time")
+    full_index = pd.date_range(indexed.index.min(), indexed.index.max(), freq=freq)
+    indexed = indexed.reindex(full_index)
+    indexed.index.name = "time"
+    return indexed.reset_index()
+
+
+def _safe_datetime_index(values: pd.Series | pd.Index | list[object]) -> pd.DatetimeIndex | None:
+    """尽量把时间值标准化为 DatetimeIndex；失败则返回 None。"""
+    parsed = pd.to_datetime(values, errors="coerce")
+    try:
+        datetime_index = pd.DatetimeIndex(parsed)
+    except (TypeError, ValueError):
+        return None
+
+    if datetime_index.isna().all():
+        return None
+    return datetime_index
+
+
+def _safe_time_interpolate(series: pd.Series) -> pd.Series:
+    """仅在索引确实是 DatetimeIndex 时使用 time interpolation。"""
+    if isinstance(series.index, pd.DatetimeIndex):
+        return series.interpolate(method="time", limit_direction="both")
+    return series.interpolate(limit_direction="both")
+
+
+def _detect_long_form_identifier(data: pd.DataFrame, value_cols: list[str], keep_cols: list[str]) -> str | None:
+    """识别 time/id/value 长表结构。"""
+    if "time" not in data.columns or len(value_cols) != 1:
+        return None
+
+    identifier_candidates = [
+        col
+        for col in keep_cols
+        if col.lower() in ID_LIKE_COLUMNS or col.lower().endswith("_id")
+    ]
+    if len(identifier_candidates) != 1:
+        return None
+    if not data["time"].duplicated().any():
+        return None
+    return identifier_candidates[0]
+
+
+def _clean_long_form_pressure(
+    data: pd.DataFrame,
+    value_col: str,
+    identifier_col: str,
+    keep_cols: list[str],
+    fill_gaps: bool,
+) -> pd.DataFrame:
+    """按测点拆分 long-form 压力数据，再逐列清洗后恢复原结构。"""
+    data = _normalize_time_frame(data)
+    wide_df = (
+        data[[identifier_col, "time", value_col]]
+        .pivot(index="time", columns=identifier_col, values=value_col)
+        .reset_index()
+    )
+
+    sensor_cols = [col for col in wide_df.columns if col != "time"]
+    cleaned_wide = _clean_snapshot_pressure(wide_df, sensor_cols, keep_cols=[], fill_gaps=fill_gaps)
+
+    cleaned_long = cleaned_wide.melt(
+        id_vars="time",
+        var_name=identifier_col,
+        value_name=value_col,
+    )
+
+    passthrough_cols = [col for col in keep_cols if col != identifier_col]
+    if passthrough_cols:
+        metadata = data[[identifier_col] + passthrough_cols].drop_duplicates(subset=[identifier_col])
+        cleaned_long = cleaned_long.merge(metadata, on=identifier_col, how="left")
+
+    try:
+        cleaned_long[identifier_col] = cleaned_long[identifier_col].astype(data[identifier_col].dtype)
+    except (TypeError, ValueError):
+        pass
+
+    cleaned_long = cleaned_long.sort_values(["time", identifier_col]).reset_index(drop=True)
+    ordered_cols = ["time", identifier_col] + passthrough_cols + [value_col]
+    cleaned_long = cleaned_long[[col for col in ordered_cols if col in cleaned_long.columns]]
+    return cleaned_long
+
+
+def _build_time_slot_frame(
+    data: pd.DataFrame, value_col: str, expected_slots: int
+) -> pd.DataFrame:
+    """把重复时间点整理成 time x slot 的矩阵。"""
+    grouped = data.groupby("time", sort=True)
+    times = list(grouped.groups.keys())
+    slot_frame = pd.DataFrame(index=pd.Index(times, name="time"), columns=range(expected_slots), dtype=float)
+
+    for time_value, group in grouped:
+        values = pd.to_numeric(group[value_col], errors="coerce").tolist()
+        for slot_idx, value in enumerate(values[:expected_slots]):
+            slot_frame.loc[time_value, slot_idx] = value
+    return slot_frame
+
+
+def _slot_baseline(slot_frame: pd.DataFrame) -> pd.DataFrame:
+    """对每个槽位做时间插值和平滑，得到基线轨迹。"""
+    baseline = pd.DataFrame(index=slot_frame.index, columns=slot_frame.columns, dtype=float)
+    for col in slot_frame.columns:
+        series = slot_frame[col].astype(float)
+        series = _safe_time_interpolate(series)
+        series = series.rolling(window=5, center=True, min_periods=1).median()
+        series = _safe_time_interpolate(series).ffill().bfill()
+        baseline[col] = series
+    return baseline
+
+
+def _choose_insertion_position(
+    observed: list[float], baseline_row: pd.Series, expected_slots: int
+) -> int:
+    """为少一个观测值的时间组选择最合理的插入位置。"""
+    missing_count = expected_slots - len(observed)
+    if missing_count <= 0:
+        return 0
+
+    best_pos = 0
+    best_cost = float("inf")
+    for insert_pos in range(expected_slots):
+        cost = 0.0
+        obs_idx = 0
+        for slot_idx in range(expected_slots):
+            if slot_idx == insert_pos:
+                continue
+            obs_value = observed[obs_idx]
+            base_value = float(baseline_row.iloc[slot_idx])
+            if pd.notna(obs_value) and pd.notna(base_value):
+                cost += abs(obs_value - base_value)
+            obs_idx += 1
+        if cost < best_cost:
+            best_cost = cost
+            best_pos = insert_pos
+    return best_pos
+
+
+def _clean_repeated_timestamp_pressure(
+    data: pd.DataFrame, value_col: str, keep_cols: list[str]
+) -> pd.DataFrame:
+    """针对同一时间点重复采样的压力数据进行修复。"""
+    data = _normalize_time_frame(data)
+    grouped_sizes = data.groupby("time").size()
+    if grouped_sizes.empty:
+        return data
+
+    expected_slots = int(grouped_sizes.mode().iloc[0]) if not grouped_sizes.mode().empty else int(grouped_sizes.max())
+    expected_slots = max(expected_slots, int(grouped_sizes.max()))
+    slot_frame = _build_time_slot_frame(data, value_col, expected_slots)
+    baseline_frame = _slot_baseline(slot_frame)
+
+    residuals = slot_frame - baseline_frame
+    slot_scales = {
+        col: max(_robust_scale(residuals[col]), 1e-6) for col in residuals.columns
+    }
+
+    cleaned_rows: list[dict[str, object]] = []
+    grouped = data.groupby("time", sort=True)
+    for time_value, group in grouped:
+        observed_values = pd.to_numeric(group[value_col], errors="coerce").tolist()
+        baseline_row = baseline_frame.loc[time_value]
+        insert_pos = _choose_insertion_position(observed_values, baseline_row, expected_slots)
+
+        cleaned_values: list[float] = []
+        obs_idx = 0
+        for slot_idx in range(expected_slots):
+            if slot_idx == insert_pos and len(observed_values) < expected_slots:
+                cleaned_values.append(float(baseline_row.iloc[slot_idx]))
+                continue
+
+            if obs_idx >= len(observed_values):
+                cleaned_values.append(float(baseline_row.iloc[slot_idx]))
+                continue
+
+            observed = observed_values[obs_idx]
+            baseline = float(baseline_row.iloc[slot_idx])
+            cleaned_values.append(
+                _shrink_toward_baseline(observed, baseline, slot_scales.get(slot_idx, 1.0))
+            )
+            obs_idx += 1
+
+        # 其余字段原样保留；常量列（如 id）直接复制第一条记录即可
+        template_row = group.iloc[0].to_dict()
+        for slot_idx, cleaned_value in enumerate(cleaned_values):
+            row = dict(template_row)
+            row["time"] = time_value
+            row[value_col] = cleaned_value
+            cleaned_rows.append(row)
+
+    cleaned_df = pd.DataFrame(cleaned_rows)
+    cleaned_df = cleaned_df.sort_values(["time"]).reset_index(drop=True)
+    ordered_cols = ["time"] + keep_cols + [value_col]
+    ordered_cols = [col for col in ordered_cols if col in cleaned_df.columns]
+    remaining_cols = [col for col in cleaned_df.columns if col not in ordered_cols]
+    cleaned_df = cleaned_df[ordered_cols + remaining_cols]
+    return _format_time_column(cleaned_df)
+
+
+def _clean_snapshot_pressure(
+    data: pd.DataFrame, value_cols: list[str], keep_cols: list[str], fill_gaps: bool
+) -> pd.DataFrame:
+    """针对单条时间序列或多列快照数据进行稳健修复。"""
+    data = _normalize_time_frame(data)
+    if fill_gaps and "time" in data.columns:
+        freq = _infer_time_frequency(data["time"])
+        data = _expand_snapshot_time_grid(data, freq)
+        data["time"] = pd.to_datetime(data["time"], errors="coerce")
+        data = data.sort_values(["time"]).reset_index(drop=True)
+
+    cleaned_df = data.copy()
+    time_index = (
+        _safe_datetime_index(cleaned_df["time"])
+        if "time" in cleaned_df.columns
+        else None
+    )
+    if time_index is None:
+        time_index = pd.RangeIndex(start=0, stop=len(cleaned_df))
+    for col in value_cols:
+        series = pd.Series(
+            pd.to_numeric(cleaned_df[col], errors="coerce").to_numpy(),
+            index=time_index,
+            dtype=float,
+        )
+        cleaned_df[col] = _clean_pressure_series(series).to_numpy()
+
+    ordered_cols = ["time"] + keep_cols + value_cols
+    ordered_cols = [col for col in ordered_cols if col in cleaned_df.columns]
+    remaining_cols = [col for col in cleaned_df.columns if col not in ordered_cols]
+    cleaned_df = cleaned_df[ordered_cols + remaining_cols]
+    return _format_time_column(cleaned_df)
+
+
+def clean_pressure_data_km(
+    input_csv_path: str, show_plot: bool = False, fill_gaps: bool = True
+) -> str:
+    """
+    读取输入 CSV，基于时间结构进行稳健修复。输出为 <input_basename>_cleaned.xlsx（同目录）。
+    原始数据在 sheet 'raw_pressure_data'，处理后数据在 sheet 'cleaned_pressusre_data'。
+    返回输出文件的绝对路径。
+
+    Args:
+        input_csv_path: CSV 文件路径
+        show_plot: 是否显示可视化
+        fill_gaps: 是否先补齐时间缺口（默认 True）
+    """
+    # 读取 CSV
+    input_csv_path = os.path.abspath(input_csv_path)
+    data = pd.read_csv(input_csv_path, header=0, index_col=None, encoding="utf-8")
+    data = _normalize_time_frame(data)
+    value_cols, keep_cols = _select_pressure_columns(data)
+    has_repeated_time = "time" in data.columns and data["time"].duplicated().any()
+    identifier_col = _detect_long_form_identifier(data, value_cols, keep_cols)
+
+    if identifier_col is not None:
+        data_repaired = _clean_long_form_pressure(
+            data,
+            value_cols[0],
+            identifier_col,
+            keep_cols,
+            fill_gaps,
+        )
+    elif has_repeated_time and len(value_cols) == 1:
+        data_repaired = _clean_repeated_timestamp_pressure(data, value_cols[0], keep_cols)
+    else:
+        data_repaired = _clean_snapshot_pressure(data, value_cols, keep_cols, fill_gaps)
+
+    # 可选可视化（只展示首个数值列）
+    plt.rcParams["font.sans-serif"] = ["SimHei"]
+    plt.rcParams["axes.unicode_minus"] = False
+    if show_plot and value_cols:
+        plot_col = value_cols[0]
+        if "time" in data_repaired.columns:
+            x = pd.to_datetime(data_repaired["time"], errors="coerce")
+        else:
+            x = np.arange(len(data_repaired))
+        plt.figure(figsize=(12, 6))
+        plt.plot(x, pd.to_numeric(data_repaired[plot_col], errors="coerce"), label="cleaned")
+        plt.xlabel("时间" if "time" in data_repaired.columns else "序号")
+        plt.ylabel("压力监测值")
+        plt.title(f"{plot_col} 清洗结果")
+        plt.legend()
+        plt.show()
+
+    # 保存到 Excel：两个 sheet
+    input_dir = os.path.dirname(os.path.abspath(input_csv_path))
+    input_base = os.path.splitext(os.path.basename(input_csv_path))[0]
+    output_filename = f"{input_base}_cleaned.xlsx"
+    output_path = os.path.join(input_dir, output_filename)
+
+    # 如果原始数据包含时间列，将其添加回结果
+    data_for_save = data.copy()
+    data_repaired_for_save = data_repaired.copy()
+
+    if os.path.exists(output_path):
+        os.remove(output_path)  # 覆盖同名文件
+    with pd.ExcelWriter(output_path, engine="openpyxl") as writer:
+        data_for_save.to_excel(writer, sheet_name="raw_pressure_data", index=False)
+        data_repaired_for_save.to_excel(
+            writer, sheet_name="cleaned_pressusre_data", index=False
+        )
+
+    # 返回输出文件的绝对路径
+    return os.path.abspath(output_path)
+
+
+def clean_pressure_data_df_km(data: pd.DataFrame, show_plot: bool = False) -> pd.DataFrame:
+    """
+    接收一个 DataFrame 数据结构，使用时间感知的稳健修复方法清洗压力数据。
+    返回清洗后的 DataFrame。
+
+    Args:
+        data: 输入 DataFrame（可包含 time 列）
+        show_plot: 是否显示可视化
+    """
+    # 使用传入的 DataFrame
+    data = data.copy()
+    data = _normalize_time_frame(data)
+    value_cols, keep_cols = _select_pressure_columns(data)
+    has_repeated_time = "time" in data.columns and data["time"].duplicated().any()
+    identifier_col = _detect_long_form_identifier(data, value_cols, keep_cols)
+
+    if identifier_col is not None:
+        data_repaired = _clean_long_form_pressure(
+            data,
+            value_cols[0],
+            identifier_col,
+            keep_cols,
+            fill_gaps=True,
+        )
+    elif has_repeated_time and len(value_cols) == 1:
+        data_repaired = _clean_repeated_timestamp_pressure(data, value_cols[0], keep_cols)
+    else:
+        data_repaired = _clean_snapshot_pressure(data, value_cols, keep_cols, fill_gaps=True)
+
+    if show_plot and value_cols:
+        plt.rcParams["font.sans-serif"] = ["SimHei"]
+        plt.rcParams["axes.unicode_minus"] = False
+        plot_col = value_cols[0]
+        x = pd.to_datetime(data_repaired["time"], errors="coerce") if "time" in data_repaired.columns else np.arange(len(data_repaired))
+        plt.figure(figsize=(12, 6))
+        plt.plot(x, pd.to_numeric(data_repaired[plot_col], errors="coerce"), label="cleaned")
+        plt.xlabel("时间" if "time" in data_repaired.columns else "序号")
+        plt.ylabel("压力监测值")
+        plt.title(f"{plot_col} 清洗结果")
+        plt.legend()
+        plt.show()
+
+    return data_repaired
+
+
+# 测试
+# if __name__ == "__main__":
+#     # 默认使用脚本目录下的 pressure_raw_data.csv
+#     script_dir = os.path.dirname(os.path.abspath(__file__))
+#     default_csv = os.path.join(script_dir, "pressure_raw_data.csv")
+#     out_path = clean_pressure_data_km(default_csv, show_plot=False)
+#     print("保存路径：", out_path)
+
+# 测试 clean_pressure_data_dict_km 函数
+if __name__ == "__main__":
+    import random
+
+    # 读取 szh_pressure_scada.csv 文件
+    script_dir = os.path.dirname(os.path.abspath(__file__))
+    csv_path = os.path.join(script_dir, "szh_pressure_scada.csv")
+    data = pd.read_csv(csv_path, header=0, index_col=None, encoding="utf-8")
+
+    # 排除 Time 列，随机选择 5 列
+    columns_to_exclude = ["Time"]
+    available_columns = [col for col in data.columns if col not in columns_to_exclude]
+    selected_columns = random.sample(available_columns, 5)
+
+    # 将选中的列转换为字典
+    data_dict = {col: data[col].tolist() for col in selected_columns}
+
+    print("选中的列:", selected_columns)
+    print("原始数据长度:", len(data_dict[selected_columns[0]]))
+
+    # 调用函数进行清洗
+    cleaned_dict = clean_pressure_data_df_km(data_dict, show_plot=True)
+
+    print("清洗后的字典键:", list(cleaned_dict.keys()))
+    print("清洗后的数据长度:", len(cleaned_dict[selected_columns[0]]))
+    print("测试完成：函数运行正常")

app/algorithms/health/__init__.py

@@ -0,0 +1,3 @@
+from app.algorithms.health.analyzer import PipelineHealthAnalyzer
+
+__all__ = ["PipelineHealthAnalyzer"]

app/algorithms/health/analyzer.py

@@ -14,14 +14,20 @@ class PipelineHealthAnalyzer:
     使用前需确保安装依赖：joblib, pandas, numpy, scikit-survival, matplotlib。
     """
 
-    def __init__(self, model_path: str = "model/my_survival_forest_model_quxi.joblib"):
+    def __init__(self, model_path: str = None):
         """
         初始化分析器，加载预训练的随机生存森林模型。
 
-        :param model_path: 模型文件的路径（默认为相对路径 'model/my_survival_forest_model_quxi.joblib'）。
+        :param model_path: 模型文件的路径（默认为相对路径 './model/my_survival_forest_model_quxi.joblib'）。
         :raises FileNotFoundError: 如果模型文件不存在。
         :raises Exception: 如果模型加载失败。
         """
+        if model_path is None:
+            model_path = os.path.join(
+                os.path.dirname(__file__),
+                "model",
+                "my_survival_forest_model_quxi.joblib",
+            )
         # 确保 model 目录存在
         model_dir = os.path.dirname(model_path)
         if model_dir and not os.path.exists(model_dir):

app/algorithms/isolation/__init__.py

@@ -0,0 +1,3 @@
+from app.algorithms.isolation.valve import valve_isolation_analysis
+
+__all__ = ["valve_isolation_analysis"]

app/algorithms/isolation/valve.py

@@ -0,0 +1,165 @@
+from collections import defaultdict, deque
+from functools import lru_cache
+from typing import Any
+
+from app.services.tjnetwork import (
+    get_network_link_nodes,
+    is_node,
+    get_link_properties,
+)
+
+
+VALVE_LINK_TYPE = "valve"
+
+
+def _parse_link_entry(link_entry: str) -> tuple[str, str, str, str]:
+    parts = link_entry.split(":", 3)
+    if len(parts) != 4:
+        raise ValueError(f"Invalid link entry format: {link_entry}")
+    return parts[0], parts[1], parts[2], parts[3]
+
+
+@lru_cache(maxsize=16)
+def _get_network_topology(network: str):
+    """
+    解析并缓存网络拓扑，大幅减少重复的 API 调用和字符串解析开销。
+    返回:
+    - pipe_adj: 永久连通的管道/泵邻接表 (dict[str, set])
+    - all_valves: 所有阀门字典 {id: (n1, n2)}
+    - link_lookup: 链路快速查表 {id: (n1, n2, type)} 用于快速定位事故点
+    - node_set: 所有已知节点集合
+    """
+    pipe_adj = defaultdict(set)
+    all_valves = {}
+    link_lookup = {}
+    node_set = set()
+
+    # 此处假设 get_network_link_nodes 获取全网数据
+    for link_entry in get_network_link_nodes(network):
+        link_id, link_type, node1, node2 = _parse_link_entry(link_entry)
+        link_type_name = str(link_type).lower()
+
+        link_lookup[link_id] = (node1, node2, link_type_name)
+        node_set.add(node1)
+        node_set.add(node2)
+
+        if link_type_name == VALVE_LINK_TYPE:
+            all_valves[link_id] = (node1, node2)
+        else:
+            # 只有非阀门（管道/泵）才进入永久连通图
+            pipe_adj[node1].add(node2)
+            pipe_adj[node2].add(node1)
+
+    return pipe_adj, all_valves, link_lookup, node_set
+
+
+def valve_isolation_analysis(
+    network: str, accident_elements: str | list[str], disabled_valves: list[str] = None
+) -> dict[str, Any]:
+    """
+    关阀搜索/分析：基于拓扑结构确定事故隔离所需关阀。
+    :param network: 模型名称
+    :param accident_elements: 事故点（节点或管道/泵/阀门ID），可以是单个ID字符串或ID列表
+    :param disabled_valves: 故障/无法关闭的阀门ID列表
+    :return: dict，包含受影响节点、必须关闭阀门、可选阀门等信息
+    """
+    if disabled_valves is None:
+        disabled_valves_set = set()
+    else:
+        disabled_valves_set = set(disabled_valves)
+
+    if isinstance(accident_elements, str):
+        target_elements = [accident_elements]
+    else:
+        target_elements = accident_elements
+
+    # 1. 获取缓存拓扑 (极快，无 IO)
+    pipe_adj, all_valves, link_lookup, node_set = _get_network_topology(network)
+
+    # 2. 确定起点，优先查表避免 API 调用
+    start_nodes = set()
+    for element in target_elements:
+        if element in node_set:
+            start_nodes.add(element)
+        elif element in link_lookup:
+            n1, n2, _ = link_lookup[element]
+            start_nodes.add(n1)
+            start_nodes.add(n2)
+        else:
+            # 仅当缓存中没找到时（极少见），才回退到慢速 API
+            if is_node(network, element):
+                start_nodes.add(element)
+            else:
+                props = get_link_properties(network, element)
+                n1, n2 = props.get("node1"), props.get("node2")
+                if n1 and n2:
+                    start_nodes.add(n1)
+                    start_nodes.add(n2)
+                else:
+                    raise ValueError(
+                        f"Accident element {element} invalid or missing endpoints"
+                    )
+
+    # 3. 处理故障阀门 (构建临时增量图)
+    # 我们不修改 cached pipe_adj，而是建立一个 extra_adj
+    extra_adj = defaultdict(list)
+    boundary_valves = {}  # 当前有效的边界阀门
+
+    for vid, (n1, n2) in all_valves.items():
+        if vid in disabled_valves_set:
+            # 故障阀门：视为连通管道
+            extra_adj[n1].append(n2)
+            extra_adj[n2].append(n1)
+        else:
+            # 正常阀门：视为潜在边界
+            boundary_valves[vid] = (n1, n2)
+
+    # 4. BFS 搜索 (叠加 pipe_adj 和 extra_adj)
+    affected_nodes: set[str] = set()
+    queue = deque(start_nodes)
+    while queue:
+        node = queue.popleft()
+        if node in affected_nodes:
+            continue
+        affected_nodes.add(node)
+
+        # 遍历永久管道邻居
+        if node in pipe_adj:
+            for neighbor in pipe_adj[node]:
+                if neighbor not in affected_nodes:
+                    queue.append(neighbor)
+
+        # 遍历故障阀门带来的额外邻居
+        if node in extra_adj:
+            for neighbor in extra_adj[node]:
+                if neighbor not in affected_nodes:
+                    queue.append(neighbor)
+
+    # 5. 结果聚合
+    must_close_valves: list[str] = []
+    optional_valves: list[str] = []
+
+    for valve_id, (n1, n2) in boundary_valves.items():
+        in_n1 = n1 in affected_nodes
+        in_n2 = n2 in affected_nodes
+        if in_n1 and in_n2:
+            optional_valves.append(valve_id)
+        elif in_n1 or in_n2:
+            must_close_valves.append(valve_id)
+
+    must_close_valves.sort()
+    optional_valves.sort()
+
+    result = {
+        "accident_elements": target_elements,
+        "disabled_valves": disabled_valves,
+        "affected_nodes": sorted(affected_nodes),
+        "must_close_valves": must_close_valves,
+        "optional_valves": optional_valves,
+        "isolatable": len(must_close_valves) > 0,
+    }
+
+    if len(target_elements) == 1:
+        result["accident_element"] = target_elements[0]
+
+    return result

app/algorithms/leakage/__init__.py

@@ -0,0 +1,3 @@
+from app.algorithms.leakage.identifier import LeakageIdentifier
+
+__all__ = ["LeakageIdentifier"]

app/algorithms/leakage/identifier.py

@@ -0,0 +1,655 @@
+import wntr
+import numpy as np
+import pandas as pd
+import os
+import time
+import argparse
+from multiprocessing import Pool, cpu_count
+from typing import Any, List, Dict, Union
+
+from pymoo.core.problem import Problem
+from pymoo.core.callback import Callback
+from pymoo.algorithms.soo.nonconvex.ga import GA
+from pymoo.operators.crossover.sbx import SBX
+from pymoo.operators.mutation.pm import PM
+from pymoo.optimize import minimize as pymoo_minimize
+from pymoo.termination.default import DefaultSingleObjectiveTermination
+
+
+from app.algorithms._utils import _cleanup_temp_files
+
+_worker_data: dict[str, Any] = {}
+DEFAULT_N_WORKERS = max(1, min(cpu_count() - 1, 4))
+
+
+def _worker_init(
+    inp_path: str,
+    sensor_nodes: list[str],
+    area_ids: list[str],
+    nodes_by_area: dict[str, list[str]],
+    obs_matrix: np.ndarray,
+    q_sum: float,
+    duration_sec: float,
+    timestep_sec: float,
+) -> None:
+    global _worker_data
+    wn = wntr.network.WaterNetworkModel(inp_path)
+    wn.options.hydraulic.demand_model = "DD"
+    wn.options.time.duration = duration_sec
+    wn.options.time.hydraulic_timestep = timestep_sec
+    wn.options.time.pattern_timestep = timestep_sec
+    wn.options.time.report_timestep = timestep_sec
+
+    demand_objs_by_area = {}
+    allocatable_counts = {}
+    for area_id in area_ids:
+        demand_objs = []
+        for node_name in nodes_by_area.get(area_id, []):
+            if node_name not in wn.node_name_list:
+                continue
+            node = wn.get_node(node_name)
+            if (
+                hasattr(node, "demand_timeseries_list")
+                and len(node.demand_timeseries_list) > 0
+            ):
+                demand_objs.append(node.demand_timeseries_list[0])
+        demand_objs_by_area[area_id] = demand_objs
+        allocatable_counts[area_id] = len(demand_objs)
+
+    _worker_data = {
+        "wn": wn,
+        "sensor_nodes": sensor_nodes,
+        "area_ids": area_ids,
+        "nodes_by_area": nodes_by_area,
+        "demand_objs_by_area": demand_objs_by_area,
+        "allocatable_counts": allocatable_counts,
+        "obs_matrix": obs_matrix,
+        "q_sum": q_sum,
+    }
+
+
+def _worker_evaluate(raw_ratios: np.ndarray) -> float:
+    d = _worker_data
+    effective_ratio_map = LeakageIdentifier._effective_area_ratios(
+        raw_ratios,
+        d["area_ids"],
+        d["nodes_by_area"],
+        allocatable_counts=d["allocatable_counts"],
+    )
+
+    modifications = []
+    for area_id in d["area_ids"]:
+        ratio = effective_ratio_map.get(area_id, 0.0)
+        if ratio <= 0:
+            continue
+
+        demand_objs = d["demand_objs_by_area"].get(area_id, [])
+        if not demand_objs:
+            continue
+
+        per_node_leak = d["q_sum"] * ratio / len(demand_objs)
+        for demand_obj in demand_objs:
+            original_val = demand_obj.base_value
+            demand_obj.base_value = original_val + per_node_leak
+            modifications.append((demand_obj, original_val))
+
+    temp_dir = os.path.abspath(os.path.join("temp", "leakage"))
+    os.makedirs(temp_dir, exist_ok=True)
+    prefix = os.path.join(temp_dir, f"temp_{os.getpid()}")
+
+    try:
+        sim = wntr.sim.EpanetSimulator(d["wn"])
+        results = sim.run_sim(file_prefix=prefix)
+        sim_pressure = results.node["pressure"].loc[:, d["sensor_nodes"]]
+
+        n_steps = min(sim_pressure.shape[0], d["obs_matrix"].shape[0])
+        sim_vals = sim_pressure.values[:n_steps, :]
+        obs_vals = d["obs_matrix"][:n_steps, :]
+        diff = sim_vals - obs_vals
+
+        row_max = np.max(np.abs(diff), axis=1, keepdims=True)
+        row_max[row_max == 0] = 1.0
+        normalized_diff = diff / row_max
+        return float(np.linalg.norm(normalized_diff))
+
+    except Exception:
+        return 1e9
+
+    finally:
+        for demand_obj, original_val in modifications:
+            demand_obj.base_value = original_val
+        _cleanup_temp_files(prefix)
+
+
+class LeakageIdentifier:
+    FLOW_UNIT_TO_M3S = {
+        "m3/s": 1.0,
+        "m³/s": 1.0,
+        "m3/h": 1.0 / 3600.0,
+        "m³/h": 1.0 / 3600.0,
+        "L/s": 1.0 / 1000.0,
+        "L/min": 1.0 / 60000.0,
+    }
+
+    @classmethod
+    def _flow_to_m3s(cls, value: float, unit: str) -> float:
+        if unit not in cls.FLOW_UNIT_TO_M3S:
+            raise ValueError(f"不支持的流量单位: {unit}")
+        return float(value) * cls.FLOW_UNIT_TO_M3S[unit]
+
+    @classmethod
+    def _flow_from_m3s(cls, value_m3s: float, unit: str) -> float:
+        if unit not in cls.FLOW_UNIT_TO_M3S:
+            raise ValueError(f"不支持的流量单位: {unit}")
+        return float(value_m3s) / cls.FLOW_UNIT_TO_M3S[unit]
+
+    @staticmethod
+    def _effective_area_ratios(
+        raw_ratios: Union["np.ndarray", Dict[str, float]],
+        area_ids: List[str],
+        nodes_by_area: Dict[str, List[str]],
+        allocatable_counts: Union[Dict[str, int], None] = None,
+    ) -> Dict[str, float]:
+        """将输入比例转换为有效区域比例，确保有效区域比例和为 1。"""
+        area_count = len(area_ids)
+        if area_count == 0:
+            return {}
+
+        if isinstance(raw_ratios, dict):
+            ratios = np.array(
+                [float(raw_ratios.get(area_id, 0.0)) for area_id in area_ids],
+                dtype=float,
+            )
+        else:
+            arr = np.asarray(raw_ratios, dtype=float).reshape(-1)
+            ratios = np.zeros(area_count, dtype=float)
+            fill_len = min(area_count, arr.shape[0])
+            if fill_len > 0:
+                ratios[:fill_len] = arr[:fill_len]
+
+        # 仅保留非负比例，负值按 0 处理
+        ratios = np.clip(ratios, a_min=0.0, a_max=None)
+
+        # 仅在有效区域（存在可分配节点）内归一化
+        if allocatable_counts is not None:
+            valid_mask = np.array(
+                [int(allocatable_counts.get(area_id, 0)) > 0 for area_id in area_ids],
+                dtype=bool,
+            )
+        else:
+            valid_mask = np.array(
+                [len(nodes_by_area.get(area_id, [])) > 0 for area_id in area_ids],
+                dtype=bool,
+            )
+        if not np.any(valid_mask):
+            raise ValueError("没有可分配漏损的有效分区，无法满足漏损总量约束。")
+
+        effective = np.zeros(area_count, dtype=float)
+        valid_sum = float(np.sum(ratios[valid_mask]))
+
+        if valid_sum > 0:
+            effective[valid_mask] = ratios[valid_mask] / valid_sum
+        else:
+            # 若输入全为 0，则在有效区域内均分，保证总和仍为 1
+            valid_count = int(np.sum(valid_mask))
+            effective[valid_mask] = 1.0 / valid_count
+
+        return {area_id: float(effective[idx]) for idx, area_id in enumerate(area_ids)}
+
+    @staticmethod
+    def _normalize_area_map_df(df: pd.DataFrame) -> pd.DataFrame:
+        """标准化区域映射列名为 ID 和 Area。"""
+        if "ID" in df.columns and "Area" in df.columns:
+            return df
+
+        if "ID" in df.columns and "now" in df.columns:
+            df = df.rename(columns={"now": "Area"})
+            return df
+
+        df = df.copy()
+        df.columns = ["ID", "Area"] + list(df.columns[2:])
+        return df
+
+    def __init__(
+        self,
+        inp_path: str,
+        sensor_nodes: List[str],
+        area_map: Union[str, Dict[str, str]],
+        start_time: float = 0,
+        duration: float = 24,
+        timestep: float = 5,
+        q_sum: float = 0.2,
+    ):
+        """
+        初始化漏损识别器。
+
+        参数:
+            inp_path: EPANET .inp 文件路径。
+            sensor_nodes: 用作压力传感器的节点 ID 列表。
+            area_map: 节点到区域的映射。可以是 CSV 文件路径（列：ID, Area），也可以是字典 {NodeID: AreaID}。
+            start_time: 模拟开始时间（小时）。
+            duration: 模拟持续时间（小时）。
+            timestep: 模拟时间步长（分钟）。
+            q_sum: 假设的总漏损流量 (m3/s)。
+        """
+        self.inp_path = inp_path
+        self.sensor_nodes = sensor_nodes
+        self.start_time = start_time
+        self.duration = duration
+        self.timestep = timestep
+        self.q_sum = q_sum
+
+        # 加载管网模型（仅一次）
+        self.wn = wntr.network.WaterNetworkModel(self.inp_path)
+
+        # 优化 WNTR 设置以提高速度
+        self.wn.options.hydraulic.demand_model = "DD"
+        self.wn.options.time.duration = float(self.duration) * 3600
+        self.wn.options.time.hydraulic_timestep = float(self.timestep) * 60
+        self.wn.options.time.pattern_timestep = float(self.timestep) * 60
+        self.wn.options.time.report_timestep = float(self.timestep) * 60
+
+        # 加载区域映射
+        if isinstance(area_map, str):
+            self.area_map_df = self._load_area_map(area_map)
+        elif isinstance(area_map, dict):
+            self.area_map_df = self._normalize_area_map_df(
+                pd.DataFrame(list(area_map.items()), columns=["ID", "Area"])
+            )
+        else:
+            raise ValueError("area_map 必须是 CSV 文件路径或字典。")
+
+        self.area_ids = sorted(self.area_map_df["Area"].unique())
+        self.num_areas = len(self.area_ids)
+
+        # 按区域对节点进行预分类，以便更快查找
+        self.nodes_by_area = {
+            area: self.area_map_df[self.area_map_df["Area"] == area]["ID"].tolist()
+            for area in self.area_ids
+        }
+
+    def _load_area_map(self, path: str) -> pd.DataFrame:
+        """加载并验证节点-区域映射文件。"""
+        df = pd.read_csv(path, dtype={"ID": str, "Area": str})
+        return self._normalize_area_map_df(df)
+
+    def run_identification(
+        self,
+        observed_pressure_data: Union[
+            str, pd.DataFrame, Dict[str, List[Any]], List[Dict[str, Any]]
+        ],
+        output_dir: str = "Results",
+        pop_size: int = 50,
+        max_gen: int = 100,
+        output_flow_unit: str = "m3/s",
+        save_result: bool = True,
+        ftol: float = 1e-3,
+        ftol_period: int = 15,
+        n_workers: int = DEFAULT_N_WORKERS,
+    ):
+        """
+        运行遗传算法以识别漏损分布。
+
+        参数:
+            observed_pressure_data: 包含 SCADA 压力数据的 CSV 文件路径或 DataFrame/字典列表数据。
+            output_dir: 结果保存目录。
+            pop_size: GA 的种群大小。
+            max_gen: GA 的最大代数。
+            output_flow_unit: 输出漏损流量的单位。
+            save_result: 是否保存识别结果到本地 CSV。
+            ftol: 目标值收敛容差（连续 ftol_period 代改善 < ftol 则停止）。
+            ftol_period: 收敛检测的窗口代数。
+            n_workers: 并行工作进程数（1=串行，>1=并行评估）。
+        """
+        if save_result:
+            os.makedirs(output_dir, exist_ok=True)
+
+        # 加载观测数据
+        if isinstance(observed_pressure_data, str):
+            obs_df = pd.read_csv(observed_pressure_data)
+            observed_name = os.path.basename(observed_pressure_data)
+        elif isinstance(observed_pressure_data, pd.DataFrame):
+            obs_df = observed_pressure_data.copy()
+            observed_name = "observed_pressure.csv"
+        else:
+            obs_df = pd.DataFrame(observed_pressure_data)
+            observed_name = "observed_pressure.csv"
+
+        # 准备 pymoo 问题实例
+        problem = LeakageProblem(
+            self.wn,
+            self.nodes_by_area,
+            self.area_ids,
+            self.sensor_nodes,
+            obs_df,
+            q_sum=self.q_sum,
+            n_workers=n_workers,
+            inp_path=os.path.abspath(self.inp_path),
+        )
+
+        # 配置 pymoo GA 算法
+        n_var = self.num_areas
+        algorithm = GA(
+            pop_size=pop_size,
+            crossover=SBX(prob=0.9, eta=15),
+            mutation=PM(prob=1.0 / max(1, n_var), eta=20),
+            eliminate_duplicates=False,
+        )
+
+        # 终止条件：收敛检测 + 最大代数
+        termination = DefaultSingleObjectiveTermination(
+            ftol=ftol,
+            period=ftol_period,
+            n_max_gen=max_gen,
+        )
+
+        # 回调：记录每代信息
+        callback = _ProgressCallback()
+
+        t0 = time.time()
+        try:
+            res = pymoo_minimize(
+                problem,
+                algorithm,
+                termination,
+                seed=42,
+                verbose=True,
+                callback=callback,
+            )
+        finally:
+            problem.close()
+        elapsed = time.time() - t0
+
+        # 提取最优解
+        best_ind = res.X  # 最优个体（漏损比例原始值）
+        best_obj = float(res.F[0])
+
+        # 输出终止信息
+        print(f"\n优化完成。耗时: {elapsed:.1f}s")
+        print(f"总代数: {res.algorithm.n_gen}, 总评估次数: {problem._eval_count}")
+        print(f"最佳目标值: {best_obj:.6f}")
+
+        # 保存到文件
+        effective_ratio_map = self._effective_area_ratios(
+            best_ind,
+            self.area_ids,
+            self.nodes_by_area,
+            allocatable_counts=problem.allocatable_counts,
+        )
+        normalized_ratios = [
+            effective_ratio_map.get(area_id, 0.0) for area_id in self.area_ids
+        ]
+        leakage_flow_m3s = [ratio * self.q_sum for ratio in normalized_ratios]
+        leakage_flow_output = [
+            self._flow_from_m3s(value_m3s, output_flow_unit)
+            for value_m3s in leakage_flow_m3s
+        ]
+
+        result_df = pd.DataFrame(
+            {
+                "Area": self.area_ids,
+                "LeakageRatioRaw": best_ind,
+                "LeakageRatio": normalized_ratios,
+                "LeakageFlow_m3_per_s": leakage_flow_m3s,
+                f"LeakageFlow_{output_flow_unit.replace('/', '_per_')}": leakage_flow_output,
+            }
+        )
+        result_path = None
+        if save_result:
+            result_path = os.path.join(
+                output_dir, f"identified_leakage_{observed_name}"
+            )
+            result_df.to_csv(result_path, index=False)
+            print(f"结果已保存至 {result_path}")
+        result_df.attrs["result_path"] = result_path
+        return result_df
+
+
+class _ProgressCallback(Callback):
+    """每代回调：记录进度。"""
+
+    def __init__(self):
+        super().__init__()
+        self.gen_times = []
+        self._t_last = None
+
+    def notify(self, algorithm):
+        now = time.time()
+        if self._t_last is not None:
+            self.gen_times.append(now - self._t_last)
+        self._t_last = now
+
+
+class LeakageProblem(Problem):
+    """pymoo 批量评估问题定义。
+
+    搜索空间：n 维 [0, 1] 实数 -> 通过 _effective_area_ratios 归一化到单纯形。
+    目标：模拟压力与观测压力之间的归一化误差范数。
+    无显式约束（sum=1 由归一化自动保证）。
+    """
+
+    def __init__(
+        self,
+        wn,
+        nodes_by_area,
+        area_ids,
+        sensor_nodes,
+        observed_data,
+        q_sum: float = 0.2,
+        n_workers: int = DEFAULT_N_WORKERS,
+        inp_path: str | None = None,
+    ):
+        n_var = len(area_ids)
+
+        super().__init__(
+            n_var=n_var,
+            n_obj=1,
+            n_ieq_constr=0,
+            xl=np.zeros(n_var),
+            xu=np.ones(n_var),
+        )
+
+        self.wn = wn
+        self.nodes_by_area = nodes_by_area
+        self.area_ids = area_ids
+        self.sensor_nodes = sensor_nodes
+        self.q_sum = q_sum
+        self.n_workers = max(1, int(n_workers))
+        self.inp_path = inp_path
+
+        # 预处理观测数据以匹配模拟格式
+        try:
+            missing_sensors = [
+                s for s in self.sensor_nodes if s not in observed_data.columns
+            ]
+            if not missing_sensors:
+                self.obs_matrix = observed_data[self.sensor_nodes].values
+            else:
+                self.obs_matrix = observed_data.values[:, : len(self.sensor_nodes)]
+        except Exception:
+            self.obs_matrix = observed_data.values[:, : len(self.sensor_nodes)]
+
+        duration_sec = float(self.wn.options.time.duration)
+        step_sec = float(self.wn.options.time.hydraulic_timestep)
+        if step_sec > 0:
+            max_steps = int(duration_sec / step_sec) + 1
+            self.obs_matrix = self.obs_matrix[:max_steps, :]
+
+        # 预先缓存每个区域的需水对象，减少每次适应度计算的节点查找
+        self.demand_objs_by_area = {}
+        for area_id in self.area_ids:
+            demand_objs = []
+            for node_name in self.nodes_by_area.get(area_id, []):
+                if node_name not in self.wn.node_name_list:
+                    continue
+                node = self.wn.get_node(node_name)
+                if (
+                    hasattr(node, "demand_timeseries_list")
+                    and len(node.demand_timeseries_list) > 0
+                ):
+                    demand_objs.append(node.demand_timeseries_list[0])
+            self.demand_objs_by_area[area_id] = demand_objs
+
+        self.allocatable_counts = {
+            area_id: len(self.demand_objs_by_area.get(area_id, []))
+            for area_id in self.area_ids
+        }
+        if not any(count > 0 for count in self.allocatable_counts.values()):
+            raise ValueError("没有可分配漏损的有效分区，无法满足漏损总量约束。")
+
+        # 评估计数器（诊断用）
+        self._eval_count = 0
+        self._pool = None
+        if self.n_workers > 1:
+            if not self.inp_path:
+                raise ValueError("并行评估需要提供 inp_path。")
+            duration_sec = float(self.wn.options.time.duration)
+            timestep_sec = float(self.wn.options.time.hydraulic_timestep)
+            self._pool = Pool(
+                processes=self.n_workers,
+                initializer=_worker_init,
+                initargs=(
+                    self.inp_path,
+                    list(self.sensor_nodes),
+                    list(self.area_ids),
+                    {k: list(v) for k, v in self.nodes_by_area.items()},
+                    self.obs_matrix.copy(),
+                    self.q_sum,
+                    duration_sec,
+                    timestep_sec,
+                ),
+            )
+
+    def _evaluate(self, X, out, *args, **kwargs):
+        """批量评估种群。
+
+        X: 形状 (pop_size, n_var) 的决策变量矩阵。
+        """
+        n_pop = X.shape[0]
+        self._eval_count += n_pop
+
+        if self._pool is not None:
+            results = self._pool.map(_worker_evaluate, [X[i] for i in range(n_pop)])
+            out["F"] = np.array(results, dtype=float).reshape(-1, 1)
+            return
+
+        F = np.zeros((n_pop, 1))
+        for i in range(n_pop):
+            F[i, 0] = self._evaluate_single(X[i])
+        out["F"] = F
+
+    def _evaluate_single(self, x):
+        """评估单个个体，返回归一化误差范数。"""
+        leak_ratios = x
+
+        # 将漏损分布归一化
+        effective_ratio_map = LeakageIdentifier._effective_area_ratios(
+            leak_ratios,
+            self.area_ids,
+            self.nodes_by_area,
+            allocatable_counts=self.allocatable_counts,
+        )
+
+        # 跟踪修改以便稍后恢复
+        modifications = []
+
+        for j, area_id in enumerate(self.area_ids):
+            ratio = effective_ratio_map.get(area_id, 0.0)
+            if ratio <= 0:
+                continue
+
+            demand_objs = self.demand_objs_by_area.get(area_id, [])
+            if not demand_objs:
+                continue
+
+            per_node_leak = self.q_sum * ratio / len(demand_objs)
+
+            for demand_obj in demand_objs:
+                original_val = demand_obj.base_value
+                demand_obj.base_value = original_val + per_node_leak
+                modifications.append((demand_obj, original_val))
+
+        # 结果保存在根目录的temp/leakage文件夹中
+        temp_dir = os.path.abspath(os.path.join("temp", "leakage"))
+        os.makedirs(temp_dir, exist_ok=True)
+        prefix = os.path.join(temp_dir, f"temp_{os.getpid()}")
+
+        try:
+            sim = wntr.sim.EpanetSimulator(self.wn)
+            results = sim.run_sim(file_prefix=prefix)
+            sim_pressure = results.node["pressure"].loc[:, self.sensor_nodes]
+
+            n_steps = min(sim_pressure.shape[0], self.obs_matrix.shape[0])
+            sim_vals = sim_pressure.values[:n_steps, :]
+            obs_vals = self.obs_matrix[:n_steps, :]
+
+            diff = sim_vals - obs_vals
+
+            # 按行最大值归一化
+            row_max = np.max(np.abs(diff), axis=1, keepdims=True)
+            row_max[row_max == 0] = 1.0  # 防止除以零
+
+            normalized_diff = diff / row_max
+
+            # 目标：归一化差值矩阵的 2-范数
+            return float(np.linalg.norm(normalized_diff))
+
+        except Exception:
+            return 1e9
+
+        finally:
+            for demand_obj, original_val in modifications:
+                demand_obj.base_value = original_val
+
+            _cleanup_temp_files(prefix)
+
+    def close(self) -> None:
+        if self._pool is not None:
+            self._pool.close()
+            self._pool.join()
+            self._pool = None
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="漏损区域识别")
+    parser.add_argument("--inp", required=True, help=".inp 文件路径")
+    parser.add_argument("--map", help="节点-区域映射 CSV 路径")
+    parser.add_argument("--scada", help="SCADA 压力 CSV 路径 (观测数据)")
+    parser.add_argument("--sensors", help="传感器节点 ID 列表 (逗号分隔)")
+    parser.add_argument("--output", default="Results", help="输出目录")
+
+    parser.add_argument("--pop_size", type=int, default=50, help="种群大小")
+    parser.add_argument("--max_gen", type=int, default=100, help="最大代数")
+    parser.add_argument("--duration", type=float, default=24, help="模拟时长(小时)")
+    parser.add_argument("--q_sum", type=float, default=0.241, help="总漏损流量")
+    parser.add_argument(
+        "--q_sum_unit",
+        default="m3/s",
+        choices=list(LeakageIdentifier.FLOW_UNIT_TO_M3S.keys()),
+        help="q_sum 输入单位（建议与现场习惯一致，内部统一换算为 m3/s）",
+    )
+
+    args = parser.parse_args()
+
+    if not args.map or not args.scada or not args.sensors:
+        parser.error("--map、--scada、--sensors 为必填")
+
+    q_sum_m3s = LeakageIdentifier._flow_to_m3s(args.q_sum, args.q_sum_unit)
+
+    sensors = [sensor.strip() for sensor in args.sensors.split(",") if sensor.strip()]
+    identifier = LeakageIdentifier(
+        args.inp, sensors, args.map, duration=args.duration, q_sum=q_sum_m3s
+    )
+
+    identifier.run_identification(
+        args.scada,
+        args.output,
+        pop_size=args.pop_size,
+        max_gen=args.max_gen,
+        output_flow_unit=args.q_sum_unit,
+    )
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

app/algorithms/sensor/__init__.py

@@ -0,0 +1,91 @@
+import psycopg
+
+from app.algorithms.sensor import kmeans as kmeans_sensor
+from app.algorithms.sensor import sensitivity
+from app.core.config import get_pgconn_string
+from app.services.tjnetwork import dump_inp
+
+
+def pressure_sensor_placement_sensitivity(
+    name: str, scheme_name: str, sensor_number: int, min_diameter: int, username: str
+) -> None:
+    """
+    基于改进灵敏度法进行压力监测点优化布置
+    :param name: 数据库名称
+    :param scheme_name: 监测优化布置方案名称
+    :param sensor_number: 传感器数目
+    :param min_diameter: 最小管径
+    :param username: 用户名
+    :return:
+    """
+    sensor_location = sensitivity.get_ID(
+        name=name, sensor_num=sensor_number, min_diameter=min_diameter
+    )
+    try:
+        conn_string = get_pgconn_string(db_name=name)
+        with psycopg.connect(conn_string) as conn:
+            with conn.cursor() as cur:
+                sql = """
+                        INSERT INTO sensor_placement (scheme_name, sensor_number, min_diameter, username, sensor_location)
+                        VALUES (%s, %s, %s, %s, %s)
+                    """
+
+                cur.execute(
+                    sql,
+                    (
+                        scheme_name,
+                        sensor_number,
+                        min_diameter,
+                        username,
+                        sensor_location,
+                    ),
+                )
+            conn.commit()
+            print("方案信息存储成功！")
+    except Exception as e:
+        print(f"存储方案信息时出错：{e}")
+
+
+# 2025/08/21
+# 基于kmeans聚类法进行压力监测点优化布置
+def pressure_sensor_placement_kmeans(
+    name: str, scheme_name: str, sensor_number: int, min_diameter: int, username: str
+) -> None:
+    """
+    基于聚类法进行压力监测点优化布置
+    :param name: 数据库名称（注意，此处数据库名称也是inp文件名称，inp文件与pg库名要一样）
+    :param scheme_name: 监测优化布置方案名称
+    :param sensor_number: 传感器数目
+    :param min_diameter: 最小管径
+    :param username: 用户名
+    :return:
+    """
+    # dump_inp
+    inp_name = f"./db_inp/{name}.db.inp"
+    dump_inp(name, inp_name, "2")
+    sensor_location = kmeans_sensor.kmeans_sensor_placement(
+        name=name, sensor_num=sensor_number, min_diameter=min_diameter
+    )
+    try:
+        conn_string = get_pgconn_string(db_name=name)
+        with psycopg.connect(conn_string) as conn:
+            with conn.cursor() as cur:
+                sql = """
+                        INSERT INTO sensor_placement (scheme_name, sensor_number, min_diameter, username, sensor_location)
+                        VALUES (%s, %s, %s, %s, %s)
+                    """
+
+                cur.execute(
+                    sql,
+                    (
+                        scheme_name,
+                        sensor_number,
+                        min_diameter,
+                        username,
+                        sensor_location,
+                    ),
+                )
+            conn.commit()
+            print("方案信息存储成功！")
+    except Exception as e:
+        print(f"存储方案信息时出错：{e}")

app/algorithms/sensor/kmeans.py

@@ -6,104 +6,66 @@ import sklearn.cluster
 import os
 
 
-
 class QD_KMeans(object):
     def __init__(self, wn, num_monitors):
         # self.inp = inp
-        self.cluster_num = num_monitors # 聚类中心个数，也即测压点个数
-        self.wn=wn
+        self.cluster_num = num_monitors  # 聚类中心个数，也即测压点个数
+        self.wn = wn
         self.monitor_nodes = []
         self.coords = []
         self.junction_nodes = {}  # Added missing initialization
 
-
     def get_junctions_coordinates(self):
-       
-        for junction_name in self.wn.junction_name_list: 
+
+        for junction_name in self.wn.junction_name_list:
             junction = self.wn.get_node(junction_name)
             self.junction_nodes[junction_name] = junction.coordinates
-            self.coords.append(junction.coordinates )
+            self.coords.append(junction.coordinates)
 
-        # print(f"Total junctions: {self.junction_coordinates}")   
+        # print(f"Total junctions: {self.junction_coordinates}")
 
     def select_monitoring_points(self):
         if not self.coords:  # Add check if coordinates are collected
             self.get_junctions_coordinates()
         coords = np.array(self.coords)
-        coords_normalized = (coords - coords.min(axis=0)) / (coords.max(axis=0) - coords.min(axis=0))
-        kmeans = sklearn.cluster.KMeans(n_clusters= self.cluster_num, random_state=42)
-        kmeans.fit(coords_normalized) 
+        coords_normalized = (coords - coords.min(axis=0)) / (
+            coords.max(axis=0) - coords.min(axis=0)
+        )
+        kmeans = sklearn.cluster.KMeans(n_clusters=self.cluster_num, random_state=42)
+        kmeans.fit(coords_normalized)
 
         for center in kmeans.cluster_centers_:
             distances = np.sum((coords_normalized - center) ** 2, axis=1)
             nearest_node = self.wn.junction_name_list[np.argmin(distances)]
-            self.monitor_nodes.append(nearest_node)  
+            self.monitor_nodes.append(nearest_node)
 
         return self.monitor_nodes
-    
 
     def visualize_network(self):
         """Visualize network with monitoring points"""
-        ax=wntr.graphics.plot_network(self.wn,   
-                               node_attribute=self.monitor_nodes,  
-                               node_size=30,  
-                               title='Optimal sensor') 
-        plt.show() 
+        ax = wntr.graphics.plot_network(
+            self.wn,
+            node_attribute=self.monitor_nodes,
+            node_size=30,
+            title="Optimal sensor",
+        )
+        plt.show()
 
 
-    
-    
 def kmeans_sensor_placement(name: str, sensor_num: int, min_diameter: int) -> list:
-    inp_name = f'./db_inp/{name}.db.inp'
-    wn= wntr.network.WaterNetworkModel(inp_name)
-    wn_cluster=QD_KMeans(wn, sensor_num)
+    inp_name = f"./db_inp/{name}.db.inp"
+    wn = wntr.network.WaterNetworkModel(inp_name)
+    wn_cluster = QD_KMeans(wn, sensor_num)
 
     # Select monitoring pointse
-    sensor_ids= wn_cluster.select_monitoring_points()
+    sensor_ids = wn_cluster.select_monitoring_points()
 
     # wn_cluster.visualize_network()
 
     return sensor_ids
 
 
-     
 if __name__ == "__main__":
-    #sensorindex = get_ID(name='suzhouhe_2024_cloud_0817', sensor_num=30, min_diameter=500)
-    sensorindex = kmeans_sensor_placement(name='szh', sensor_num=50, min_diameter=300)
+    # sensorindex = get_ID(name='suzhouhe_2024_cloud_0817', sensor_num=30, min_diameter=500)
+    sensorindex = kmeans_sensor_placement(name="szh", sensor_num=50, min_diameter=300)
     print(sensorindex)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-

app/algorithms/sensor/sensitivity.py

@@ -11,7 +11,6 @@ from sklearn.cluster import KMeans
 from wntr.epanet.toolkit import EpanetException
 from numpy.linalg import slogdet
 import random
-from tjnetwork import *
 from matplotlib.lines import Line2D
 from sklearn.cluster import SpectralClustering
 import libpysal as ps
@@ -19,7 +18,8 @@ from spopt.region import Skater
 from shapely.geometry import Point
 import geopandas as gpd
 from sklearn.metrics import pairwise_distances
-import project_info
+import app.services.project_info as project_info
+
 
 # 2025/03/12
 # Step1: 获取节点坐标
@@ -32,7 +32,7 @@ def getCoor(wn: wntr.network.WaterNetworkModel) -> pandas.DataFrame:
     # site: pandas.Series
     #   index：节点名称（wn.node_name_list）
     #   values：每个节点的坐标，格式为 tuple（如 (x, y) 或 (x, y, z)）
-    site = wn.query_node_attribute('coordinates')
+    site = wn.query_node_attribute("coordinates")
     # Coor: pandas.Series
     #   index：与site相同（节点名称）。
     #   values：坐标转换为numpy.ndarray（如array([10.5, 20.3])）
@@ -44,9 +44,9 @@ def getCoor(wn: wntr.network.WaterNetworkModel) -> pandas.DataFrame:
         x.append(Coor.values[i][0])  # 将 x 坐标存入 x 列表。
         y.append(Coor.values[i][1])  # 将 y 坐标存入 y 列表
     # xy: dict[str, list], x、y 坐标的字典
-    xy = {'x': x, 'y': y}
+    xy = {"x": x, "y": y}
     # Coor_node: pandas.DataFrame, 存储节点 x, y 坐标的 DataFrame
-    Coor_node = pd.DataFrame(xy, index=wn.node_name_list, columns=['x', 'y'])
+    Coor_node = pd.DataFrame(xy, index=wn.node_name_list, columns=["x", "y"])
     return Coor_node
 
 
@@ -88,23 +88,23 @@ def skater_partition(G, n_clusters):
             字典形式的聚类结果，键为区域编号，值为该区域内的节点列表。
     """
     # 1. 获取所有节点坐标，假设每个节点都有 'pos' 属性
-    pos = nx.get_node_attributes(G, 'pos')
+    pos = nx.get_node_attributes(G, "pos")
     nodes = list(G.nodes())
     # 构造坐标数组：每行为 [x, y]
     coords = np.array([pos[node] for node in nodes])
 
     # 2. 构造 GeoDataFrame：创建 DataFrame 并生成 geometry 列
-    df = pd.DataFrame(coords, columns=['x', 'y'], index=nodes)
+    df = pd.DataFrame(coords, columns=["x", "y"], index=nodes)
     # 利用 shapely 的 Point 构造空间位置
-    df['geometry'] = df.apply(lambda row: Point(row['x'], row['y']), axis=1)
-    gdf = gpd.GeoDataFrame(df, geometry='geometry')
+    df["geometry"] = df.apply(lambda row: Point(row["x"], row["y"]), axis=1)
+    gdf = gpd.GeoDataFrame(df, geometry="geometry")
 
     # 3. 构造空间权重矩阵，使用 4 近邻方法（k=4，可根据实际情况调整）
     w = ps.weights.KNN.from_array(coords, k=4)
-    w.transform = 'R'
+    w.transform = "R"
 
     # 4. 调用 SKATER：新版本 API 要求传入 gdf, w 以及 attrs_name（这里使用 'x' 和 'y' 作为属性）
-    skater = Skater(gdf, w, attrs_name=['x', 'y'], n_clusters=n_clusters)
+    skater = Skater(gdf, w, attrs_name=["x", "y"], n_clusters=n_clusters)
     skater.solve()
 
     # 5. 获取聚类标签，构造成字典格式
@@ -134,24 +134,24 @@ def spectral_partition(G, n_clusters):
             键为聚类标签，值为该聚类对应的节点列表。
     """
     # 1. 获取节点空间坐标，注意保证每个节点都有 'pos' 属性
-    pos_dict = nx.get_node_attributes(G, 'pos')
+    pos_dict = nx.get_node_attributes(G, "pos")
     nodes = list(G.nodes())
     coords = np.array([pos_dict[node] for node in nodes])
 
     # 2. 计算节点之间的欧氏距离矩阵
-    D = pairwise_distances(coords, metric='euclidean')
+    D = pairwise_distances(coords, metric="euclidean")
 
     # 3. 计算 sigma 值：这里取所有距离的均值，当然也可以根据实际情况调整
     sigma = np.mean(D)
 
     # 4. 构造相似度矩阵：使用高斯核函数
     #    A(i, j) = exp( -d(i,j)^2 / (2*sigma^2) )
-    A = np.exp(- (D ** 2) / (2 * sigma ** 2))
+    A = np.exp(-(D**2) / (2 * sigma**2))
 
     # 5. 使用谱聚类进行图分区
-    clustering = SpectralClustering(n_clusters=n_clusters,
-                                    affinity='precomputed',
-                                    random_state=0)
+    clustering = SpectralClustering(
+        n_clusters=n_clusters, affinity="precomputed", random_state=0
+    )
     labels = clustering.fit_predict(A)
 
     # 6. 构造字典形式的分区结果
@@ -161,6 +161,7 @@ def spectral_partition(G, n_clusters):
 
     return groups
 
+
 # 2025/03/12
 # Step3: wn_func类，水力计算
 # wn_func 主要用于计算：
@@ -182,7 +183,7 @@ class wn_func(object):
         self.results = wntr.sim.EpanetSimulator(wn).run_sim()  # 存储运行结果
         self.wn = wn
         # self.q：pandas.DataFrame,管道流量，索引为时间步长，列为管道名称
-        self.q = self.results.link['flowrate']
+        self.q = self.results.link["flowrate"]
         # ReservoirIndex / Tankindex: list[str],水库 / 水箱节点名称列表
         ReservoirIndex = wn.reservoir_name_list
         Tankindex = wn.tank_name_list
@@ -192,7 +193,7 @@ class wn_func(object):
         # self.nodes: list[str],所有节点的名称
         self.nodes = wn.node_name_list
         # self.coordinates：pandas.Series,节点坐标，索引为节点名，值为 (x, y) 坐标的 tuple
-        self.coordinates = wn.query_node_attribute('coordinates')
+        self.coordinates = wn.query_node_attribute("coordinates")
         # allpumps / allvalves: list[str],所有泵/阀门名称列表
         allpumps = wn.pump_name_list
         allvalves = wn.valve_name_list
@@ -223,17 +224,27 @@ class wn_func(object):
         # 泵的起终点、tank、reservoir
         # self.delnodes: list[str],需要删除的节点（包括水库、泵、阀门连接的节点）
         self.delnodes = list(
-            set(ReservoirIndex).union(Tankindex, pumpstnode, pumpednode, valvestnode, valveednode, Reservoirednode))
+            set(ReservoirIndex).union(
+                Tankindex,
+                pumpstnode,
+                pumpednode,
+                valvestnode,
+                valveednode,
+                Reservoirednode,
+            )
+        )
         # 泵、起终点为tank、reservoir的管道
         # self.delpipes: list[str],需要删除的管道（包括水库、泵、阀门连接的管道）
-        self.delpipes = list(set(wn.pump_name_list).union(wn.valve_name_list).union(Reservoirpipe))
+        self.delpipes = list(
+            set(wn.pump_name_list).union(wn.valve_name_list).union(Reservoirpipe)
+        )
         self.pipes = [pipe for pipe in wn.pipe_name_list if pipe not in self.delpipes]
         # self.L: list[float],所有管道的长度（以米为单位）
-        self.L = wn.query_link_attribute('length')[self.pipes].tolist()
+        self.L = wn.query_link_attribute("length")[self.pipes].tolist()
         self.n = len(self.nodes)
         self.m = len(self.pipes)
         # self.unit_headloss: list[float],单位水头损失（headloss 数据的第一行，单位：米/km）
-        self.unit_headloss = self.results.link['headloss'].iloc[0, :].tolist()
+        self.unit_headloss = self.results.link["headloss"].iloc[0, :].tolist()
         ##
         self.delnodes1 = list(set(ReservoirIndex).union(Tankindex))
 
@@ -246,7 +257,9 @@ class wn_func(object):
                 end_node = wn.links[pipe].end_node.name
                 self.less_than_min_diameter_junction_list.extend([start_node, end_node])
         # 去重
-        self.less_than_min_diameter_junction_list = list(set(self.less_than_min_diameter_junction_list))
+        self.less_than_min_diameter_junction_list = list(
+            set(self.less_than_min_diameter_junction_list)
+        )
 
     # Step3.2: 计算水力距离
     def CtoS(self):
@@ -267,7 +280,7 @@ class wn_func(object):
         q = self.q
         L = self.L
         # H1：pandas.DataFrame,水头数据，索引为时间步长，列为节点名
-        H1 = self.results.node['head'].T
+        H1 = self.results.node["head"].T
         # hh：list[float],计算管道两端水头之差
         hh = []
         # 水头损失
@@ -281,8 +294,18 @@ class wn_func(object):
         # headloss：pandas.DataFrame,管道水头损失矩阵
         headloss = pd.DataFrame(hh, index=pipes).T
         # s1:管道阻力系数，s2：将管道阻力系数与管道的起始节点和终止节点对应
-        hf = pd.DataFrame(np.array([0] * (n ** 2)).reshape(n, n), index=nodes, columns=nodes, dtype=float)
-        weightL = pd.DataFrame(np.array([0] * (n ** 2)).reshape(n, n), index=nodes, columns=nodes, dtype=float)
+        hf = pd.DataFrame(
+            np.array([0] * (n**2)).reshape(n, n),
+            index=nodes,
+            columns=nodes,
+            dtype=float,
+        )
+        weightL = pd.DataFrame(
+            np.array([0] * (n**2)).reshape(n, n),
+            index=nodes,
+            columns=nodes,
+            dtype=float,
+        )
         # s2为对应管道起始节点与终止节点的粗糙度系数矩阵，index代表起始节点，columns代表终止节点
         G = nx.DiGraph()
         for i in range(0, m):
@@ -299,11 +322,16 @@ class wn_func(object):
                 weightL.loc[b, a] = headloss.loc[0, pipe] * L[i]
                 G.add_weighted_edges_from([(b, a, weightL.loc[b, a])])
 
-        hydraulicL = pd.DataFrame(np.array([0] * (n ** 2)).reshape(n, n), index=nodes, columns=nodes, dtype=float)
+        hydraulicL = pd.DataFrame(
+            np.array([0] * (n**2)).reshape(n, n),
+            index=nodes,
+            columns=nodes,
+            dtype=float,
+        )
 
         for a in nodes:
             if a in G.nodes:
-                d = nx.shortest_path_length(G, source=a, weight='weight')
+                d = nx.shortest_path_length(G, source=a, weight="weight")
                 for b in list(d.keys()):
                     hydraulicL.loc[a, b] = d[b]
 
@@ -332,11 +360,17 @@ class wn_func(object):
         for t in self.wn.tanks():
             self.nonjunc_index.append(t[0])
         # Conn：numpy.matrix，节点-管道连接矩阵，起点 -1，终点 1
-        Conn = np.mat(np.zeros([n, m - p - v]))  # 节点和管道的关系矩阵，行为节点，列为管道，起点为-1，终点为1
+        Conn = np.mat(
+            np.zeros([n, m - p - v])
+        )  # 节点和管道的关系矩阵，行为节点，列为管道，起点为-1，终点为1
         # NConn：numpy.matrix，节点-节点连接矩阵，有管道相连的地方设为 1
         NConn = np.mat(np.zeros([n, n]))  # 节点之间的关系，之间有管道为1，反之为0
         # pipes：list[str]，去除泵和阀门的管道列表
-        pipes = [pipe for pipe in self.wn.pipes() if pipe not in self.wn.pumps() and pipe not in self.wn.valves()]
+        pipes = [
+            pipe
+            for pipe in self.wn.pipes()
+            if pipe not in self.wn.pumps() and pipe not in self.wn.valves()
+        ]
         for pipe_name, pipe in pipes:
             start = self.wn.node_name_list.index(pipe.start_node_name)
             end = self.wn.node_name_list.index(pipe.end_node_name)
@@ -346,12 +380,21 @@ class wn_func(object):
             NConn[start, end] = 1
             NConn[end, start] = 1
         self.A = Conn
-        link_name_list = [link for link in self.wn.link_name_list if
-                          link not in self.wn.pump_name_list and link not in self.wn.valve_name_list]
-        self.A2 = pd.DataFrame(self.A, index=self.wn.node_name_list, columns=link_name_list)
+        link_name_list = [
+            link
+            for link in self.wn.link_name_list
+            if link not in self.wn.pump_name_list
+            and link not in self.wn.valve_name_list
+        ]
+        self.A2 = pd.DataFrame(
+            self.A, index=self.wn.node_name_list, columns=link_name_list
+        )
         self.A2 = self.A2.drop(self.delnodes)
         for pipe in self.delpipes:
-            if pipe not in self.wn.pump_name_list and pipe not in self.wn.valve_name_list:
+            if (
+                pipe not in self.wn.pump_name_list
+                and pipe not in self.wn.valve_name_list
+            ):
                 self.A2 = self.A2.drop(columns=pipe)
         self.junc_list = self.A2.index
         self.A2 = np.mat(self.A2)  # 节点管道关系
@@ -373,10 +416,10 @@ class wn_func(object):
         except EpanetException:
             pass
         finally:
-            h = result.link['headloss'][self.pipes].values[0]
-            q = result.link['flowrate'][self.pipes].values[0]
-            l = self.wn.query_link_attribute('length')[self.pipes]
-            C = self.wn.query_link_attribute('roughness')[self.pipes]
+            h = result.link["headloss"][self.pipes].values[0]
+            q = result.link["flowrate"][self.pipes].values[0]
+            l = self.wn.query_link_attribute("length")[self.pipes]
+            C = self.wn.query_link_attribute("roughness")[self.pipes]
             # headloss：numpy.ndarray,水头损失数组
             headloss = np.array(h)
             # 调整流量方向
@@ -394,7 +437,7 @@ class wn_func(object):
             try:
                 det = np.linalg.det(X)
             except RuntimeError as e:
-                sign, logdet = slogdet(X)   # 防止溢出
+                sign, logdet = slogdet(X)  # 防止溢出
                 det = sign * np.exp(logdet)
             if det != 0:
                 J_H_Cw = X.I * A * S
@@ -431,7 +474,10 @@ class Sensorplacement(wn_func):
     """
     Sensorplacement 类继承了 wn_func 类，并且用于计算和优化传感器布置的位置。
     """
-    def __init__(self, wn: wntr.network.WaterNetworkModel, sensornum: int, min_diameter: int):
+
+    def __init__(
+        self, wn: wntr.network.WaterNetworkModel, sensornum: int, min_diameter: int
+    ):
         """
 
         :param wn: 由wntr生成的模型
@@ -443,7 +489,9 @@ class Sensorplacement(wn_func):
 
     # 1.某个节点到所有节点的加权距离之和
     # 2.某个节点到该组内所有节点的加权距离之和
-    def sensor(self, SS: pandas.DataFrame, G: networkx.Graph, group: dict[int, list[str]]):
+    def sensor(
+        self, SS: pandas.DataFrame, G: networkx.Graph, group: dict[int, list[str]]
+    ):
         """
         sensor 方法是用来根据灵敏度矩阵 SS 和加权图 G 来确定传感器布置位置的
         :param SS: 灵敏度矩阵，每个节点的行和列代表不同节点，矩阵元素表示节点间的灵敏度。SS.iloc[i, :] 表示第 i 行对应节点 i 到所有其他节点的灵敏度
@@ -528,7 +576,7 @@ def get_ID(name: str, sensor_num: int, min_diameter: int) -> list[str]:
     :return: 测压点节点ID
     """
     # inp_file_real：str,输入文件名，表示原始水力模型文件的路径,该文件格式为 EPANET 输入文件（.inp），包含管网的结构信息、节点、管道、泵等数据
-    inp_file_real = f'./db_inp/{name}.db.inp'
+    inp_file_real = f"./db_inp/{name}.db.inp"
     # sensornum：int,需要布置的传感器数量
     # sensornum = sensor_num
     # wn_real：wntr.network.WaterNetworkModel,加载 EPANET 水力模型
@@ -539,7 +587,7 @@ def get_ID(name: str, sensor_num: int, min_diameter: int) -> list[str]:
     results_real = sim_real.run_sim()
 
     # real_C：list[float]，包含所有管道粗糙度的列表
-    real_C = wn_real.query_link_attribute('roughness').tolist()
+    real_C = wn_real.query_link_attribute("roughness").tolist()
     # wn_fun1：wn_func（继承自 object），创建 wn_func 类的实例，传入 wn_real 水力模型对象。wn_func 用于计算管网相关的水力属性，比如水力距离、灵敏度等
     wn_fun1 = wn_func(wn_real, min_diameter=min_diameter)
     # nodes：list[str]，管网的节点名称列表
@@ -599,7 +647,6 @@ def get_ID(name: str, sensor_num: int, min_diameter: int) -> list[str]:
     sensorindex, sensorindex_2 = wn_fun.sensor(SS, G, group)  # 初始的sensorindex
     # print(str(sensor_num), "个测压点，测压点位置：", sensorindex)
 
-
     # 重新打开数据库
     # if is_project_open(name=name):
     #     close_project(name=name)
@@ -638,7 +685,7 @@ def get_ID(name: str, sensor_num: int, min_diameter: int) -> list[str]:
     return sensorindex
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     sensorindex = get_ID(name=project_info.name, sensor_num=20, min_diameter=300)
 
     print(sensorindex)

app/algorithms/sensor_placement.py

@@ -1,557 +0,0 @@
-# 改进灵敏度法
-import networkx
-import numpy as np
-import pandas
-import wntr
-import pandas as pd
-import copy
-import matplotlib.pyplot as plt
-import networkx as nx
-from sklearn.cluster import KMeans
-from wntr.epanet.toolkit import EpanetException
-from numpy.linalg import slogdet
-import random
-from tjnetwork import *
-import project_info
-
-# 2025/03/12
-# Step1: 获取节点坐标
-def getCoor(wn: wntr.network.WaterNetworkModel) -> pandas.DataFrame:
-    """
-    获取管网模型的节点坐标
-    :param wn: 由wntr生成的模型
-    :return: 节点坐标
-    """
-    # site: pandas.Series
-    #   index：节点名称（wn.node_name_list）
-    #   values：每个节点的坐标，格式为 tuple（如 (x, y) 或 (x, y, z)）
-    site = wn.query_node_attribute('coordinates')
-    # Coor: pandas.Series
-    #   index：与site相同（节点名称）。
-    #   values：坐标转换为numpy.ndarray（如array([10.5, 20.3])）
-    Coor = site.apply(lambda x: np.array(x))  # 将节点坐标转换为numpy数组
-    # x, y: list[float]
-    x = []  # 存储所有节点的 x 坐标
-    y = []  # 存储所有节点的 y 坐标
-    for i in range(0, len(Coor)):
-        x.append(Coor.values[i][0])  # 将 x 坐标存入 x 列表。
-        y.append(Coor.values[i][1])  # 将 y 坐标存入 y 列表
-    # xy: dict[str, list], x、y 坐标的字典
-    xy = {'x': x, 'y': y}
-    # Coor_node: pandas.DataFrame, 存储节点 x, y 坐标的 DataFrame
-    Coor_node = pd.DataFrame(xy, index=wn.node_name_list, columns=['x', 'y'])
-    return Coor_node
-
-
-# 2025/03/12
-# Step2: KMeans 聚类
-# 将节点用kmeans根据坐标分为k组，存入字典g
-def kgroup(coor: pandas.DataFrame, knum: int) -> dict[int, list[str]]:
-    """
-    使用KMeans聚类，将节点坐标分组
-    :param coor: 存储所有节点的坐标数据
-    :param knum: 需要分成的聚类数
-    :return: 聚类结果字典
-    """
-    g = {}
-    # estimator: sklearn.cluster.KMeans,KMeans 聚类模型
-    estimator = KMeans(n_clusters=knum)
-    estimator.fit(coor)
-    # label_pred: numpy.ndarray（int）,每个点的类别标签
-    label_pred = estimator.labels_
-    for i in range(0, knum):
-        g[i] = coor[label_pred == i].index.tolist()
-    return g
-
-
-# 2025/03/12
-# Step3: wn_func类，水力计算
-# wn_func 主要用于计算：
-#     水力距离（hydraulic length）：即节点之间的水力阻力。
-#     灵敏度分析（sensitivity analysis）：用于优化测压点的布置。
-# 一些与水力相关的函数，包括 CtoS：求水力距离,stafun：求状态函数F
-# # diff：求F对P的导数，返回灵敏度矩阵A
-# # sensitivity：返回灵敏度和总灵敏度
-class wn_func(object):
-
-    # Step3.1: 初始化
-    def __init__(self, wn: wntr.network.WaterNetworkModel):
-        """
-        获取管网模型信息
-        :param wn: 由wntr生成的模型
-        """
-        # self.results: wntr.sim.results.SimulationResults,仿真结果，包含压力、流量、水头等数据
-        self.results = wntr.sim.EpanetSimulator(wn).run_sim()  # 存储运行结果
-        self.wn = wn
-        # self.q：pandas.DataFrame,管道流量，索引为时间步长，列为管道名称
-        self.q = self.results.link['flowrate']
-        # ReservoirIndex / Tankindex: list[str],水库 / 水箱节点名称列表
-        ReservoirIndex = wn.reservoir_name_list
-        Tankindex = wn.tank_name_list
-        # 删除水库节点，删除与直接水库相连的虚拟管道
-        # self.pipes: list[str],所有管道的名称
-        self.pipes = wn.pipe_name_list
-        # self.nodes: list[str],所有节点的名称
-        self.nodes = wn.node_name_list
-        # self.coordinates：pandas.Series,节点坐标，索引为节点名，值为 (x, y) 坐标的 tuple
-        self.coordinates = wn.query_node_attribute('coordinates')
-        # allpumps / allvalves: list[str],所有泵/阀门名称列表
-        allpumps = wn.pump_name_list
-        allvalves = wn.valve_name_list
-        # pumpstnode / pumpednode / valvestnode / valveednode: list[str],存储泵和阀门 起终点节点的名称
-        pumpstnode = []
-        pumpednode = []
-        valvestnode = []
-        valveednode = []
-        # Reservoirpipe / Reservoirednode: list[str],记录与水库相关的管道和节点
-        Reservoirpipe = []
-        Reservoirednode = []
-        for pump in allpumps:
-            pumpstnode.append(wn.links[pump].start_node.name)
-            pumpednode.append(wn.links[pump].end_node.name)
-        for valve in allvalves:
-            valvestnode.append(wn.links[valve].start_node.name)
-            valveednode.append(wn.links[valve].end_node.name)
-        for pipe in self.pipes:
-            if wn.links[pipe].start_node.name in ReservoirIndex:
-                Reservoirpipe.append(pipe)
-                Reservoirednode.append(wn.links[pipe].end_node.name)
-            if wn.links[pipe].start_node.name in Tankindex:
-                Reservoirpipe.append(pipe)
-                Reservoirednode.append(wn.links[pipe].end_node.name)
-            if wn.links[pipe].end_node.name in Tankindex:
-                Reservoirpipe.append(pipe)
-                Reservoirednode.append(wn.links[pipe].start_node.name)
-        # 泵的起终点、tank、reservoir
-        # self.delnodes: list[str],需要删除的节点（包括水库、泵、阀门连接的节点）
-        self.delnodes = list(
-            set(ReservoirIndex).union(Tankindex, pumpstnode, pumpednode, valvestnode, valveednode, Reservoirednode))
-        # 泵、起终点为tank、reservoir的管道
-        # self.delpipes: list[str],需要删除的管道（包括水库、泵、阀门连接的管道）
-        self.delpipes = list(set(wn.pump_name_list).union(wn.valve_name_list).union(Reservoirpipe))
-        self.pipes = [pipe for pipe in wn.pipe_name_list if pipe not in self.delpipes]
-        # self.L: list[float],所有管道的长度（以米为单位）
-        self.L = wn.query_link_attribute('length')[self.pipes].tolist()
-        self.n = len(self.nodes)
-        self.m = len(self.pipes)
-        # self.unit_headloss: list[float],单位水头损失（headloss 数据的第一行，单位：米/km）
-        self.unit_headloss = self.results.link['headloss'].iloc[0, :].tolist()
-        ##
-        self.delnodes1 = list(set(ReservoirIndex).union(Tankindex))
-
-    # Step3.2: 计算水力距离
-    def CtoS(self):
-        """
-        计算水力距离矩阵
-        :return:
-        """
-        # 水力距离：当行索引对应的节点为控制点时，列索引对应的节点距离控制点的（路径*水头损失）的最小值
-        # nodes：list[str]（节点名称）
-        nodes = copy.deepcopy(self.nodes)
-        # pipes：list[str]（管道名称）
-        pipes = self.pipes
-        wn = self.wn
-        # n / m：int（节点数 / 管道数）
-        n = self.n
-        m = self.m
-        s1 = [0] * m
-        q = self.q
-        L = self.L
-        # H1：pandas.DataFrame,水头数据，索引为时间步长，列为节点名
-        H1 = self.results.node['head'].T
-        # hh：list[float],计算管道两端水头之差
-        hh = []
-        # 水头损失
-        for p in pipes:
-            h1 = self.wn.links[p].start_node.name
-            h1 = H1.loc[str(h1)]
-            h2 = self.wn.links[p].end_node.name
-            h2 = H1.loc[str(h2)]
-            hh.append(abs(h1 - h2))
-        hh = np.array(hh)
-        # headloss：pandas.DataFrame,管道水头损失矩阵
-        headloss = pd.DataFrame(hh, index=pipes).T
-        # s1:管道阻力系数，s2：将管道阻力系数与管道的起始节点和终止节点对应
-        hf = pd.DataFrame(np.array([0] * (n ** 2)).reshape(n, n), index=nodes, columns=nodes, dtype=float)
-        weightL = pd.DataFrame(np.array([0] * (n ** 2)).reshape(n, n), index=nodes, columns=nodes, dtype=float)
-        # s2为对应管道起始节点与终止节点的粗糙度系数矩阵，index代表起始节点，columns代表终止节点
-        G = nx.DiGraph()
-        for i in range(0, m):
-            pipe = pipes[i]
-            a = wn.links[pipe].start_node.name
-            b = wn.links[pipe].end_node.name
-            if q.loc[0, pipe] > 0:
-                hf.loc[a, b] = headloss.loc[0, pipe]
-                weightL.loc[a, b] = headloss.loc[0, pipe] * L[i]
-                G.add_weighted_edges_from([(a, b, weightL.loc[a, b])])
-
-            else:
-                hf.loc[b, a] = headloss.loc[0, pipe]
-                weightL.loc[b, a] = headloss.loc[0, pipe] * L[i]
-                G.add_weighted_edges_from([(b, a, weightL.loc[b, a])])
-
-        hydraulicL = pd.DataFrame(np.array([0] * (n ** 2)).reshape(n, n), index=nodes, columns=nodes, dtype=float)
-
-        for a in nodes:
-            if a in G.nodes:
-                d = nx.shortest_path_length(G, source=a, weight='weight')
-                for b in list(d.keys()):
-                    hydraulicL.loc[a, b] = d[b]
-
-        hydraulicL = hydraulicL.drop(self.delnodes)
-        hydraulicL = hydraulicL.drop(self.delnodes, axis=1)
-
-        # 求加权水力距离
-        return hydraulicL, G
-
-    # Step3.3: 计算灵敏度矩阵
-    # 获取关系矩阵
-    def get_Conn(self):
-        """
-        计算管网连接关系矩阵
-        :return:
-        """
-        m = self.wn.num_links
-        n = self.wn.num_nodes
-        p = self.wn.num_pumps
-        v = self.wn.num_valves
-
-        self.nonjunc_index = []
-        self.non_link_index = []
-        for r in self.wn.reservoirs():
-            self.nonjunc_index.append(r[0])
-        for t in self.wn.tanks():
-            self.nonjunc_index.append(t[0])
-        # Conn：numpy.matrix，节点-管道连接矩阵，起点 -1，终点 1
-        Conn = np.mat(np.zeros([n, m - p - v]))  # 节点和管道的关系矩阵，行为节点，列为管道，起点为-1，终点为1
-        # NConn：numpy.matrix，节点-节点连接矩阵，有管道相连的地方设为 1
-        NConn = np.mat(np.zeros([n, n]))  # 节点之间的关系，之间有管道为1，反之为0
-        # pipes：list[str]，去除泵和阀门的管道列表
-        pipes = [pipe for pipe in self.wn.pipes() if pipe not in self.wn.pumps() and pipe not in self.wn.valves()]
-        for pipe_name, pipe in pipes:
-            start = self.wn.node_name_list.index(pipe.start_node_name)
-            end = self.wn.node_name_list.index(pipe.end_node_name)
-            p_index = self.wn.link_name_list.index(pipe_name)
-            Conn[start, p_index] = -1
-            Conn[end, p_index] = 1
-            NConn[start, end] = 1
-            NConn[end, start] = 1
-        self.A = Conn
-        link_name_list = [link for link in self.wn.link_name_list if
-                          link not in self.wn.pump_name_list and link not in self.wn.valve_name_list]
-        self.A2 = pd.DataFrame(self.A, index=self.wn.node_name_list, columns=link_name_list)
-        self.A2 = self.A2.drop(self.delnodes)
-        for pipe in self.delpipes:
-            if pipe not in self.wn.pump_name_list and pipe not in self.wn.valve_name_list:
-                self.A2 = self.A2.drop(columns=pipe)
-        self.junc_list = self.A2.index
-        self.A2 = np.mat(self.A2)  # 节点管道关系
-        self.A3 = NConn
-
-    def Jaco(self, hL: pandas.DataFrame):
-        """
-        计算灵敏度矩阵（节点压力对粗糙度变化的响应）
-        :param hL: 水力距离矩阵
-        :return:
-        """
-        # global result
-        # A：numpy.matrix, 节点-管道关系矩阵
-        A = self.A2
-        wn = self.wn
-
-        try:
-            result = wntr.sim.EpanetSimulator(wn).run_sim()
-        except EpanetException:
-            pass
-        finally:
-            h = result.link['headloss'][self.pipes].values[0]
-            q = result.link['flowrate'][self.pipes].values[0]
-            l = self.wn.query_link_attribute('length')[self.pipes]
-            C = self.wn.query_link_attribute('roughness')[self.pipes]
-            # headloss：numpy.ndarray,水头损失数组
-            headloss = np.array(h)
-            # 调整流量方向
-            for i in range(0, len(q)):
-                if q[i] < 0:
-                    A[:, i] = -A[:, i]
-            # q：numpy.ndarray,流量数组
-            q = np.abs(q)
-            # 两个灵敏度矩阵
-            # B / S：numpy.matrix,灵敏度计算的中间矩阵
-            B = np.mat(np.diag(q / ((1.852 * headloss) + 1e-10)))
-            S = np.mat(np.diag(q / C))
-            # X：numpy.matrix, 灵敏度矩阵
-            X = A * B * A.T
-            try:
-                det = np.linalg.det(X)
-            except RuntimeError as e:
-                sign, logdet = slogdet(X)   # 防止溢出
-                det = sign * np.exp(logdet)
-            if det != 0:
-                J_H_Cw = X.I * A * S
-                # J_H_Q = -X.I
-                J_q_Cw = S - B * A.T * X.I * A * S  # 去掉了delnodes和delpipes
-                # J_q_Q = B * A.T * X.I
-            else:  # 当X不可逆
-                J_H_Cw = np.linalg.pinv(X) @ A @ S
-                # J_H_Q = -np.linalg.pinv(X)
-                J_q_Cw = S - B * A.T * np.linalg.pinv(X) * A * S
-                # J_q_Q = B * A.T * np.linalg.pinv(X)
-
-            Sen_pressure = []
-            S_pressure = np.abs(J_H_Cw).sum(axis=1).tolist()  # 修改为绝对值
-            for ss in S_pressure:
-                Sen_pressure.append(ss[0])
-            # 求总灵敏度
-            SS_pressure = copy.deepcopy(hL)
-            for i in range(0, len(Sen_pressure)):
-                SS_pressure.iloc[i, :] = SS_pressure.iloc[i, :] * Sen_pressure[i]
-            SS = copy.deepcopy(hL)
-            for i in range(0, len(Sen_pressure)):
-                SS.iloc[i, :] = SS.iloc[i, :] * Sen_pressure[i]
-            # SS[i,j]:节点nodes[i]的灵敏度*该节点到nodes[j]的水力距离
-            return SS
-
-
-# 2025/03/12
-# Step4: 传感器布置优化
-# Sensorplacement
-# weight：分配权重
-# sensor：传感器布置的位置
-class Sensorplacement(wn_func):
-    """
-    Sensorplacement 类继承了 wn_func 类，并且用于计算和优化传感器布置的位置。
-    """
-    def __init__(self, wn: wntr.network.WaterNetworkModel, sensornum: int):
-        """
-
-        :param wn: 由wntr生成的模型
-        :param sensornum: 传感器的数量
-        """
-        wn_func.__init__(self, wn)
-        self.sensornum = sensornum
-
-    # 1.某个节点到所有节点的加权距离之和
-    # 2.某个节点到该组内所有节点的加权距离之和
-    def sensor(self, SS: pandas.DataFrame, G: networkx.Graph, group: dict[int, list[str]]):
-        """
-        sensor 方法是用来根据灵敏度矩阵 SS 和加权图 G 来确定传感器布置位置的
-        :param SS: 灵敏度矩阵，每个节点的行和列代表不同节点，矩阵元素表示节点间的灵敏度。SS.iloc[i, :] 表示第 i 行对应节点 i 到所有其他节点的灵敏度
-        :param G: 加权图，表示管网的拓扑结构，每个节点通过管道连接。图的边的权重通常是根据水力距离或者流量等计算的
-        :param group: 节点分组，字典的键是分组编号，值是该组的节点名称列表
-        :return:
-        """
-        # 传感器布置个数以及位置
-        # W = self.weight()
-        n = self.n - len(self.delnodes)
-        nodes = copy.deepcopy(self.nodes)
-        for node in self.delnodes:
-            nodes.remove(node)
-        # sumSS：list[float],每个节点到其他节点的灵敏度之和。SS.iloc[i, :] 返回第 i 个节点与所有其他节点的灵敏度值，sum(SS.iloc[i, :]) 计算这些灵敏度值的总和。
-        sumSS = []
-        for i in range(0, n):
-            sumSS.append(sum(SS.iloc[i, :]))
-        # 一个整数范围，表示每个节点的索引，用作sumSS_ DataFrame的索引
-        indices = range(0, n)
-        # sumSS_：pandas.DataFrame,将 sumSS 转换成 DataFrame 格式，并且将节点的总灵敏度保存到 CSV 文件 sumSS_data.csv 中
-        sumSS_ = pd.DataFrame(np.array(sumSS), index=indices)
-        sumSS_.to_csv('sumSS_data.csv')  # 存储节点总灵敏度
-        # sumSS：pandas.DataFrame,sumSS 被转换为 DataFrame 类型，并且按总灵敏度（即灵敏度之和）降序排列。此时，sumSS 是按节点的灵敏度之和排序的 DataFrame
-        sumSS = pd.DataFrame(np.array(sumSS), index=nodes)
-        sumSS = sumSS.sort_values(by=[0], ascending=[False])
-        # sensorindex：list[str],用于存储根据灵敏度排序选出的传感器位置的节点名称,存储根据总灵敏度排序的节点列表，用于传感器布置
-        sensorindex = []
-        # sensorindex_2：list[str],用于存储每组内根据灵敏度排序选出的传感器位置的节点名称,存储每个组内根据灵敏度排序选择的传感器节点
-        sensorindex_2 = []
-        # group_S：dict[int, pandas.DataFrame],存储每个组内的灵敏度矩阵
-        group_S = {}
-        # group_sumSS：dict[int, list[float]],存储每个组内节点的总灵敏度，值为每个组内节点灵敏度之和的列表
-        group_sumSS = {}
-        for i in range(0, len(group)):
-            for node in self.delnodes:
-                # 这里的group[i]是每个组的节点列表，代码首先去除已经被标记为删除的节点self.delnodes
-                if node in group[i]:
-                    group[i].remove(node)
-            group_S[i] = SS.loc[group[i], group[i]]
-            # 对每个组内的节点，计算组内节点的总灵敏度（group_sumSS[i]）。它将每个组内节点的灵敏度值相加，并且按灵敏度降序排序
-            group_sumSS[i] = []
-            for j in range(0, len(group[i])):
-                group_sumSS[i].append(sum(group_S[i].iloc[j, :]))
-            group_sumSS[i] = pd.DataFrame(np.array(group_sumSS[i]), index=group[i])
-            group_sumSS[i] = group_sumSS[i].sort_values(by=[0], ascending=[False])
-        pass
-
-        # 1.选sumSS最大的节点，然后把这个节点所在的那个组删掉，就可以不再从这个组选点。再重新排序选sumSS最大的;
-        # 2.在每组内选group_sumSS最大的节点
-        # 在这个循环中，首先选择灵敏度最高的节点Smaxnode并添加到sensorindex。然后根据灵敏度排序，删除已选的节点并继续选择下一个灵敏度最大的节点。这个过程用于选择传感器的位置
-        sensornum = self.sensornum
-        for i in range(0, sensornum):
-            # Smaxnode：str,最大灵敏度节点，sumSS.index[0] 表示灵敏度最高的节点
-            Smaxnode = sumSS.index[0]
-            sensorindex.append(Smaxnode)
-            sensorindex_2.append(group_sumSS[i].index[0])
-
-            for key, value in group.items():
-                if Smaxnode in value:
-                    sumSS = sumSS.drop(index=group[key])
-                    continue
-
-            sumSS = sumSS.sort_values(by=[0], ascending=[False])
-
-        return sensorindex, sensorindex_2
-
-
-# 2025/03/13
-def get_sensor_coord(name: str, sensor_num: int) -> dict[str, float]:
-    """
-    获取布置测压点的坐标,初始测压点布置根据灵敏度来布置，计算初始情况下的校准过程的error
-    :param name: 数据库名称
-    :param sensor_num: 测压点数目
-    :return: 测压点坐标字典
-    """
-    # inp_file_real：str,输入文件名，表示原始水力模型文件的路径,该文件格式为 EPANET 输入文件（.inp），包含管网的结构信息、节点、管道、泵等数据
-    inp_file_real = f'./db_inp/{name}.db.inp'
-    # sensornum：int,需要布置的传感器数量
-    # sensornum = sensor_num
-    # wn_real：wntr.network.WaterNetworkModel,加载 EPANET 水力模型
-    wn_real = wntr.network.WaterNetworkModel(inp_file_real)  # 真实粗糙度的原始管网
-    # sim_real：wntr.sim.EpanetSimulator,创建一个水力仿真器对象
-    sim_real = wntr.sim.EpanetSimulator(wn_real)
-    # results_real：wntr.sim.results.SimulationResults,运行仿真并返回结果
-    results_real = sim_real.run_sim()
-
-    # real_C：list[float]，包含所有管道粗糙度的列表
-    real_C = wn_real.query_link_attribute('roughness').tolist()
-    # wn_fun1：wn_func（继承自 object），创建 wn_func 类的实例，传入 wn_real 水力模型对象。wn_func 用于计算管网相关的水力属性，比如水力距离、灵敏度等
-    wn_fun1 = wn_func(wn_real)
-    # nodes：list[str]，管网的节点名称列表
-    nodes = wn_fun1.nodes
-    # delnodes：list[str]，被删除的节点（如水库、泵、阀门连接的节点等）
-    delnodes = wn_fun1.delnodes
-    # Coor_node：pandas.DataFrame
-    Coor_node = getCoor(wn_real)
-    Coor_node = Coor_node.drop(wn_fun1.delnodes)
-    nodes = [node for node in wn_fun1.nodes if node not in delnodes]
-    # coordinates：pandas.Series，存储所有节点的坐标，类型为 Series，索引为节点名称，值为 (x, y) 坐标对
-    coordinates = wn_fun1.coordinates
-
-    # 随机产生监测点
-    # junctionnum：int，nodes 的长度，表示节点的数量
-    junctionnum = len(nodes)
-    # random_numbers：list[int]，使用 random.sample 随机选择 sensornum（20）个节点的编号。它返回一个不重复的随机编号列表
-    # random_numbers = random.sample(range(junctionnum), sensor_num)
-    # for i in range(sensor_num):
-    #     # print(random_numbers[i])
-
-    wn_fun1.get_Conn()
-    # hL：pandas.DataFrame，水力距离矩阵，表示每个节点到其他节点的水力阻力
-    # G：networkx.DiGraph，加权有向图，表示管网的拓扑结构，节点之间的边带有权重
-    hL, G = wn_fun1.CtoS()
-    # SS：pandas.DataFrame，灵敏度矩阵，表示每个节点对管网变化（如粗糙度、流量等）的响应
-    SS = wn_fun1.Jaco(hL)
-    # group：dict[int, list[str]]，使用 kgroup 函数将节点按坐标分成若干组，每组包含的节点数不一定相同。group 是一个字典，键为分组编号，值为节点名列表
-    group = kgroup(Coor_node, sensor_num)
-    # wn_fun：Sensorplacement（继承自wn_func）
-    # 创建Sensorplacement类的实例，传入水力网络模型wn_real和传感器数量sensornum。Sensorplacement用于计算和布置传感器
-    wn_fun = Sensorplacement(wn_real, sensor_num)
-    wn_fun.__dict__.update(wn_fun1.__dict__)
-    # sensorindex：list[str]，初始传感器布置位置的节点名称
-    # sensorindex_2：list[str]，根据分组选择的传感器位置
-    sensorindex, sensorindex_2 = wn_fun.sensor(SS, G, group)  # 初始的sensorindex
-    # print(str(sensor_num), "个测压点，测压点位置：", sensorindex)
-    sensor_coord = {}
-    # 重新打开数据库
-    if is_project_open(name=name):
-        close_project(name=name)
-    open_project(name=name)
-    for node_id in sensorindex:
-        sensor_coord[node_id] = get_node_coord(name=name, node_id=node_id)
-    close_project(name=name)
-    # print(sensor_coord)
-    return sensor_coord
-
-
-if __name__ == '__main__':
-    sensor_coord = get_sensor_coord(name=project_info.name, sensor_num=20)
-    print(sensor_coord)
-    # '''
-    # 初始测压点布置根据灵敏度来布置，计算初始情况下的校准过程的error
-    # '''
-    #
-    # # inp_file_real：str,输入文件名，表示原始水力模型文件的路径,该文件格式为 EPANET 输入文件（.inp），包含管网的结构信息、节点、管道、泵等数据
-    # inp_file_real = './db_inp/bb.db.inp'
-    # # sensornum：int,需要布置的传感器数量
-    # sensornum = 20
-    # # wn_real：wntr.network.WaterNetworkModel,加载 EPANET 水力模型
-    # wn_real = wntr.network.WaterNetworkModel(inp_file_real)  # 真实粗糙度的原始管网
-    # # sim_real：wntr.sim.EpanetSimulator,创建一个水力仿真器对象
-    # sim_real = wntr.sim.EpanetSimulator(wn_real)
-    # # results_real：wntr.sim.results.SimulationResults,运行仿真并返回结果
-    # results_real = sim_real.run_sim()
-    #
-    # # real_C：list[float]，包含所有管道粗糙度的列表
-    # real_C = wn_real.query_link_attribute('roughness').tolist()
-    # # wn_fun1：wn_func（继承自 object），创建 wn_func 类的实例，传入 wn_real 水力模型对象。wn_func 用于计算管网相关的水力属性，比如水力距离、灵敏度等
-    # wn_fun1 = wn_func(wn_real)
-    # # nodes：list[str]，管网的节点名称列表
-    # nodes = wn_fun1.nodes
-    # # delnodes：list[str]，被删除的节点（如水库、泵、阀门连接的节点等）
-    # delnodes = wn_fun1.delnodes
-    # # Coor_node：pandas.DataFrame
-    # Coor_node = getCoor(wn_real)
-    # Coor_node = Coor_node.drop(wn_fun1.delnodes)
-    # nodes = [node for node in wn_fun1.nodes if node not in delnodes]
-    # # coordinates：pandas.Series，存储所有节点的坐标，类型为 Series，索引为节点名称，值为 (x, y) 坐标对
-    # coordinates = wn_fun1.coordinates
-    #
-    # # 随机产生监测点
-    # # junctionnum：int，nodes 的长度，表示节点的数量
-    # junctionnum = len(nodes)
-    # # random_numbers：list[int]，使用 random.sample 随机选择 sensornum（20）个节点的编号。它返回一个不重复的随机编号列表
-    # random_numbers = random.sample(range(junctionnum), sensornum)
-    # for i in range(sensornum):
-    #     print(random_numbers[i])
-    #
-    # wn_fun1.get_Conn()
-    # # hL：pandas.DataFrame，水力距离矩阵，表示每个节点到其他节点的水力阻力
-    # # G：networkx.DiGraph，加权有向图，表示管网的拓扑结构，节点之间的边带有权重
-    # hL, G = wn_fun1.CtoS()
-    # # SS：pandas.DataFrame，灵敏度矩阵，表示每个节点对管网变化（如粗糙度、流量等）的响应
-    # SS = wn_fun1.Jaco(hL)
-    # # group：dict[int, list[str]]，使用 kgroup 函数将节点按坐标分成若干组，每组包含的节点数不一定相同。group 是一个字典，键为分组编号，值为节点名列表
-    # group = kgroup(Coor_node, sensornum)
-    # # wn_fun：Sensorplacement（继承自wn_func）
-    # # 创建Sensorplacement类的实例，传入水力网络模型wn_real和传感器数量sensornum。Sensorplacement用于计算和布置传感器
-    # wn_fun = Sensorplacement(wn_real, sensornum)
-    # wn_fun.__dict__.update(wn_fun1.__dict__)
-    # # sensorindex：list[str]，初始传感器布置位置的节点名称
-    # # sensorindex_2：list[str]，根据分组选择的传感器位置
-    # sensorindex, sensorindex_2 = wn_fun.sensor(SS, G, group)  # 初始的sensorindex
-    # print(str(sensornum), "个测压点，测压点位置：", sensorindex)
-
-    # # 分区画图
-    # colorlist = ['lightpink', 'coral', 'rosybrown', 'olive', 'powderblue', 'lightskyblue', 'steelblue', 'peachpuff','brown','silver','indigo','lime','gold','violet','maroon','navy','teal','magenta','cyan',
-    #              'burlywood', 'tan', 'slategrey', 'thistle', 'lightseagreen', 'lightgreen', 'red','blue','yellow','orange','purple','grey','green','pink','lightblue','beige','chartreuse','turquoise','lavender','fuchsia','coral']
-    # G = wn_real.to_graph()
-    # G = G.to_undirected()  # 变为无向图
-    # pos = nx.get_node_attributes(G, 'pos')
-    # pass
-    # for i in range(0, sensornum):
-    #     ax = plt.gca()
-    #     ax.set_title(inp_file_real + str(sensornum))
-    #     nodes = nx.draw_networkx_nodes(G, pos, nodelist=group[i], node_color=colorlist[i], node_size=20)
-    # nodes = nx.draw_networkx_nodes(G, pos,
-    #                                nodelist=sensorindex_2, node_color='black', node_size=70, node_shape='*'
-    #                                )
-    # edges = nx.draw_networkx_edges(G, pos)
-    # ax.spines['top'].set_visible(False)
-    # ax.spines['right'].set_visible(False)
-    # ax.spines['bottom'].set_visible(False)
-    # ax.spines['left'].set_visible(False)
-    # plt.savefig(inp_file_real + str(sensornum) + ".png")
-    # plt.show()
-    #
-    # wntr.graphics.plot_network(wn_real, node_attribute=sensorindex_2, node_size=50, node_labels=False,
-    #                            title=inp_file_real + '_Projetion' + str(sensornum))
-    # plt.savefig(inp_file_real + '_S' + str(sensornum) + ".png")
-    # plt.show()

app/algorithms/simulation/__init__.py

@@ -0,0 +1,19 @@
+from app.algorithms.simulation.scenarios import (
+    convert_to_local_unit,
+    burst_analysis,
+    valve_close_analysis,
+    flushing_analysis,
+    contaminant_simulation,
+    age_analysis,
+    pressure_regulation,
+)
+
+__all__ = [
+    "convert_to_local_unit",
+    "burst_analysis",
+    "valve_close_analysis",
+    "flushing_analysis",
+    "contaminant_simulation",
+    "age_analysis",
+    "pressure_regulation",
+]

app/algorithms/simulation/runner.py

@@ -1,6 +1,26 @@
 import numpy as np
-from tjnetwork import *
-from api.s36_wda_cal import *
+from app.services.tjnetwork import (
+    ChangeSet,
+    close_project,
+    copy_project,
+    delete_project,
+    get_pattern,
+    get_patterns,
+    get_pump,
+    get_reservoir,
+    get_status,
+    get_tank,
+    get_time,
+    have_project,
+    is_project_open,
+    open_project,
+    read_all,
+    run_project,
+    set_pattern,
+    set_status,
+    set_tank,
+    set_time,
+)
 # from get_real_status import *
 from datetime import datetime,timedelta
 from math import modf
@@ -8,7 +28,8 @@ import json
 import pytz
 import requests
 import time
-import project_info
+import app.services.project_info as project_info
+from app.services.time_api import parse_clock_duration_seconds
 
 url_path = 'http://10.101.15.16:9000/loong'  # 内网
 # url_path = 'http://183.64.62.100:9057/loong'  # 外网
@@ -531,21 +552,11 @@ def from_clock_to_seconds (clock: str)->int:
     return hr*3600+mnt*60+seconds
 
 def from_clock_to_seconds_2 (clock: str)->int:
-    str_format="%H:%M:%S"
-    dt=datetime.strptime(clock,str_format)
-    hr=dt.hour
-    mnt=dt.minute
-    seconds=dt.second
-    return hr*3600+mnt*60+seconds
+    return parse_clock_duration_seconds(clock)
 
 
 def from_clock_to_seconds_3 (clock: str)->int:
-    str_format = "%H:%M" # 更新时间格式以适应 "小时:分钟" 格式
-    dt = datetime.strptime(clock,str_format)
-    hr = dt.hour
-    mnt = dt.minute
-    seconds = dt.second
-    return hr * 3600 + mnt * 60
+    return parse_clock_duration_seconds(clock)
 
 
 ###convert datetimestring 

app/algorithms/simulation/scenarios.py

@@ -0,0 +1,782 @@
+import json
+from datetime import datetime
+from math import pi, sqrt
+
+import pytz
+
+import app.services.simulation as simulation
+from app.algorithms.simulation.runner import (
+    run_simulation_ex,
+    from_clock_to_seconds_2,
+)
+from app.services.scheme_management import store_scheme_info
+from app.services.tjnetwork import (
+    ChangeSet,
+    OPTION_DEMAND_MODEL_PDA,
+    OPTION_QUALITY_CHEMICAL,
+    SOURCE_TYPE_SETPOINT,
+    add_pattern,
+    add_source,
+    close_project,
+    copy_project,
+    delete_project,
+    get_demand,
+    get_emitter,
+    get_node_links,
+    get_option,
+    get_pattern,
+    get_pipe,
+    get_source,
+    get_time,
+    have_project,
+    is_junction,
+    is_project_open,
+    open_project,
+    set_demand,
+    set_emitter,
+    set_option,
+    set_source,
+    set_time,
+)
+
+
+############################################################
+# burst analysis 01
+############################################################
+def convert_to_local_unit(proj: str, emitters: float) -> float:
+    open_project(proj)
+    proj_opt = get_option(proj)
+    str_unit = proj_opt.get("UNITS")
+
+    if str_unit == "CMH":
+        return emitters * 3.6
+    elif str_unit == "LPS":
+        return emitters
+    elif str_unit == "CMS":
+        return emitters / 1000.0
+    elif str_unit == "MGD":
+        return emitters * 0.0438126
+
+    # Unknown unit: log and return original value
+    print(str_unit)
+    return emitters
+
+
+def burst_analysis(
+    name: str,
+    modify_pattern_start_time: str,
+    burst_ID: list | str = None,
+    burst_size: list | float | int = None,
+    modify_total_duration: int = 900,
+    modify_fixed_pump_pattern: dict[str, list] = None,
+    modify_variable_pump_pattern: dict[str, list] = None,
+    modify_valve_opening: dict[str, float] = None,
+    scheme_name: str = None,
+    username: str | None = None,
+) -> None:
+    """
+    爆管模拟
+    :param name: 模型名称，数据库中对应的名字
+    :param modify_pattern_start_time: 模拟开始时间,格式为'2024-11-25T09:00:00+08:00'
+    :param burst_ID: 爆管管道的ID，选取的是管道，单独传入一个爆管管道，可以是str或list，传入多个爆管管道是用list
+    :param burst_size: 爆管管道破裂的孔口面积，和burst_ID列表各位置的ID对应，以cm*cm计算
+    :param modify_total_duration: 模拟总历时，秒
+    :param modify_fixed_pump_pattern: dict中包含多个水泵模式，str为工频水泵的id，list为修改后的pattern
+    :param modify_variable_pump_pattern: dict中包含多个水泵模式，str为变频水泵的id，list为修改后的pattern
+    :param modify_valve_opening: dict中包含多个阀门开启度，str为阀门的id，float为修改后的阀门开启度
+    :param scheme_name: 方案名称
+    :return:
+    """
+    if not username:
+        raise ValueError("username is required when storing burst analysis scheme")
+
+    scheme_detail: dict = {
+        "burst_ID": burst_ID,
+        "burst_size": burst_size,
+        "modify_total_duration": modify_total_duration,
+        "modify_fixed_pump_pattern": modify_fixed_pump_pattern,
+        "modify_variable_pump_pattern": modify_variable_pump_pattern,
+        "modify_valve_opening": modify_valve_opening,
+    }
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Start Analysis."
+    )
+    new_name = f"burst_Anal_{name}"
+    if have_project(new_name):
+        if is_project_open(new_name):
+            close_project(new_name)
+        delete_project(new_name)
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Start Copying Database."
+    )
+    # CopyProjectEx()(name, new_name,
+    #                 ['operation', 'current_operation', 'restore_operation', 'batch_operation', 'operation_table'])
+    copy_project(name + "_template", new_name)
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Start Opening Database."
+    )
+    open_project(new_name)
+    simulation.run_simulation(
+        name=new_name,
+        simulation_type="manually_temporary",
+        modify_pattern_start_time=modify_pattern_start_time,
+    )
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Database Loading OK."
+    )
+    ##step 1 set the emitter coefficient of end node of busrt pipe
+    if isinstance(burst_ID, list):
+        if (burst_size is not None) and (type(burst_size) is not list):
+            return json.dumps("Type mismatch.")
+    # 转化为列表形式
+    elif isinstance(burst_ID, str):
+        burst_ID = [burst_ID]
+        if burst_size is not None:
+            if isinstance(burst_size, float) or isinstance(burst_size, int):
+                burst_size = [burst_size]
+            else:
+                return json.dumps("Type mismatch.")
+    else:
+        return json.dumps("Type mismatch.")
+    if burst_size is None:
+        burst_size = [-1] * len(burst_ID)
+    elif len(burst_size) < len(burst_ID):
+        burst_size += [-1] * (len(burst_ID) - len(burst_size))
+    elif len(burst_size) > len(burst_ID):
+        # burst_size = burst_size[:len(burst_ID)]
+        return json.dumps("Length mismatch.")
+    for burst_ID_, burst_size_ in zip(burst_ID, burst_size):
+        pipe = get_pipe(new_name, burst_ID_)
+        str_start_node = pipe["node1"]
+        str_end_node = pipe["node2"]
+        d_pipe = pipe["diameter"] / 1000.0
+        if burst_size_ <= 0:
+            burst_size_ = 3.14 * d_pipe * d_pipe / 4 / 8
+        else:
+            burst_size_ = burst_size_ / 10000
+        emitter_coeff = (
+            0.65 * burst_size_ * sqrt(19.6) * 1000
+        )  # 1/8开口面积作为coeff，单位 L/S
+        emitter_coeff = convert_to_local_unit(new_name, emitter_coeff)
+        emitter_node = ""
+        if is_junction(new_name, str_end_node):
+            emitter_node = str_end_node
+        elif is_junction(new_name, str_start_node):
+            emitter_node = str_start_node
+        old_emitter = get_emitter(new_name, emitter_node)
+        if old_emitter != None:
+            old_emitter["coefficient"] = emitter_coeff  # 爆管的emitter coefficient设置
+        else:
+            old_emitter = {"junction": emitter_node, "coefficient": emitter_coeff}
+        new_emitter = ChangeSet()
+        new_emitter.append(old_emitter)
+        set_emitter(new_name, new_emitter)
+    # step 2. run simulation
+    # 涉及关阀计算，可能导致关阀后仍有流量，改为压力驱动PDA
+    options = get_option(new_name)
+    options["DEMAND MODEL"] = OPTION_DEMAND_MODEL_PDA
+    options["REQUIRED PRESSURE"] = "10.0000"
+    cs_options = ChangeSet()
+    cs_options.append(options)
+    set_option(new_name, cs_options)
+    # valve_control = None
+    # if modify_valve_opening is not None:
+    #     valve_control = {}
+    #     for valve in modify_valve_opening:
+    #         valve_control[valve] = {'status': 'CLOSED'}
+    # result = run_simulation_ex(new_name,'realtime', modify_pattern_start_time,
+    #                            end_datetime=modify_pattern_start_time,
+    #                            modify_total_duration=modify_total_duration,
+    #                            modify_pump_pattern=modify_pump_pattern,
+    #                            valve_control=valve_control,
+    #                            downloading_prohibition=True)
+    simulation.run_simulation(
+        name=new_name,
+        simulation_type="extended",
+        modify_pattern_start_time=modify_pattern_start_time,
+        modify_total_duration=modify_total_duration,
+        modify_fixed_pump_pattern=modify_fixed_pump_pattern,
+        modify_variable_pump_pattern=modify_variable_pump_pattern,
+        modify_valve_opening=modify_valve_opening,
+        scheme_type="burst_analysis",
+        scheme_name=scheme_name,
+    )
+    # step 3. restore the base model status
+    # execute_undo(name) #有疑惑
+    if is_project_open(new_name):
+        close_project(new_name)
+    delete_project(new_name)
+    # 存储方案信息到 PG 数据库
+    store_scheme_info(
+        name=name,
+        scheme_name=scheme_name,
+        scheme_type="burst_analysis",
+        username=username,
+        scheme_start_time=modify_pattern_start_time,
+        scheme_detail=scheme_detail,
+    )
+
+
+############################################################
+# valve closing analysis 02
+############################################################
+def valve_close_analysis(
+    name: str,
+    modify_pattern_start_time: str,
+    modify_total_duration: int = 900,
+    modify_valve_opening: dict[str, float] = None,
+    scheme_name: str = None,
+) -> None:
+    """
+    关阀模拟
+    :param name: 模型名称，数据库中对应的名字
+    :param modify_pattern_start_time: 模拟开始时间,格式为'2024-11-25T09:00:00+08:00'
+    :param modify_total_duration: 模拟总历时，秒
+    :param modify_valve_opening:  dict中包含多个阀门开启度，str为阀门的id，float为修改后的阀门开启度
+    :param scheme_name: 方案名称
+    :return:
+    """
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Start Analysis."
+    )
+    new_name = f"valve_close_Anal_{name}"
+    if have_project(new_name):
+        if is_project_open(new_name):
+            close_project(new_name)
+        delete_project(new_name)
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Start Copying Database."
+    )
+    # CopyProjectEx()(name, new_name,
+    #                 ['operation', 'current_operation', 'restore_operation', 'batch_operation', 'operation_table'])
+    copy_project(name + "_template", new_name)
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Start Opening Database."
+    )
+    open_project(new_name)
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Database Loading OK."
+    )
+    # step 1. change the valves status to 'closed'
+    # for valve in valves:
+    #     if not is_valve(new_name,valve):
+    #         result='ID:{}is not a valve type'.format(valve)
+    #         return result
+    #     cs=ChangeSet()
+    #     status=get_status(new_name,valve)
+    #     status['status']='CLOSED'
+    #     cs.append(status)
+    #     set_status(new_name,cs)
+    # step 2. run simulation
+    # 涉及关阀计算，可能导致关阀后仍有流量，改为压力驱动PDA
+    options = get_option(new_name)
+    options["DEMAND MODEL"] = OPTION_DEMAND_MODEL_PDA
+    options["REQUIRED PRESSURE"] = "20.0000"
+    cs_options = ChangeSet()
+    cs_options.append(options)
+    set_option(new_name, cs_options)
+    # result = run_simulation_ex(new_name,'realtime', modify_pattern_start_time, modify_pattern_start_time, modify_total_duration,
+    #                            downloading_prohibition=True)
+    simulation.run_simulation(
+        name=new_name,
+        simulation_type="extended",
+        modify_pattern_start_time=modify_pattern_start_time,
+        modify_total_duration=modify_total_duration,
+        modify_valve_opening=modify_valve_opening,
+        scheme_type="valve_close_Analysis",
+        scheme_name=scheme_name,
+    )
+    # step 3. restore the base model
+    # for valve in valves:
+    #     execute_undo(name)
+    if is_project_open(new_name):
+        close_project(new_name)
+    delete_project(new_name)
+    # return result
+
+
+############################################################
+# flushing analysis 03
+# Pipe_Flushing_Analysis(prj_name,date_time, Valve_id_list, Drainage_Node_Id, Flushing_flow[opt], Flushing_duration[opt])->out_file:string
+############################################################
+def flushing_analysis(
+    name: str,
+    modify_pattern_start_time: str,
+    modify_total_duration: int = 900,
+    modify_valve_opening: dict[str, float] = None,
+    drainage_node_ID: str = None,
+    flushing_flow: float = 0,
+    scheme_name: str = None,
+    username: str | None = None,
+) -> None:
+    """
+    管道冲洗模拟
+    :param name: 模型名称，数据库中对应的名字
+    :param modify_pattern_start_time: 模拟开始时间,格式为'2024-11-25T09:00:00+08:00'
+    :param modify_total_duration: 模拟总历时，秒
+    :param modify_valve_opening: dict中包含多个阀门开启度，str为阀门的id，float为修改后的阀门开启度
+    :param drainage_node_ID: 冲洗排放口所在节点ID
+    :param flushing_flow: 冲洗水量，传入参数单位为m3/h
+    :param scheme_name: 方案名称
+    :return:
+    """
+    if not username:
+        raise ValueError("username is required when storing flushing analysis scheme")
+
+    scheme_detail: dict = {
+        "duration": modify_total_duration,
+        "valve_opening": modify_valve_opening,
+        "drainage_node_ID": drainage_node_ID,
+        "flushing_flow": flushing_flow,
+    }
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Start Analysis."
+    )
+    new_name = f"flushing_Anal_{name}"
+    if have_project(new_name):
+        if is_project_open(new_name):
+            close_project(new_name)
+        delete_project(new_name)
+    # if is_project_open(name):
+    #     close_project(name)
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Start Copying Database."
+    )
+    # CopyProjectEx()(name, new_name,
+    #                 ['operation', 'current_operation', 'restore_operation', 'batch_operation', 'operation_table'])
+    copy_project(name + "_template", new_name)
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Start Opening Database."
+    )
+    open_project(new_name)
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Database Loading OK."
+    )
+    if not is_junction(new_name, drainage_node_ID):
+        return "Wrong Drainage node type"
+    # step 1. change the valves status to 'closed'
+    # for valve, valve_k in zip(valves, valves_k):
+    #     cs=ChangeSet()
+    #     status=get_status(new_name,valve)
+    #     # status['status']='CLOSED'
+    #     if valve_k == 0:
+    #         status['status'] = 'CLOSED'
+    #     elif valve_k < 1:
+    #         status['status'] = 'OPEN'
+    #         status['setting'] = 0.1036 * pow(valve_k, -3.105)
+    #     cs.append(status)
+    #     set_status(new_name,cs)
+    options = get_option(new_name)
+    units = options["UNITS"]
+    # step 2. set the emitter coefficient of drainage node or add flush flow to the drainage node
+    # 新建 pattern
+    time_option = get_time(new_name)
+    hydraulic_step = time_option["HYDRAULIC TIMESTEP"]
+    secs = from_clock_to_seconds_2(hydraulic_step)
+    cs_pattern = ChangeSet()
+    pt = {}
+    factors = []
+    tmp_duration = modify_total_duration
+    while tmp_duration > 0:
+        factors.append(1.0)
+        tmp_duration = tmp_duration - secs
+    pt["id"] = "flushing_pt"
+    pt["factors"] = factors
+    cs_pattern.append(pt)
+    add_pattern(new_name, cs_pattern)
+    # 为 emitter_demand 添加新的 pattern
+    emitter_demand = get_demand(new_name, drainage_node_ID)
+    cs = ChangeSet()
+    if flushing_flow > 0:
+        if units == "LPS":
+            emitter_demand["demands"].append(
+                {
+                    "demand": flushing_flow / 3.6,
+                    "pattern": "flushing_pt",
+                    "category": None,
+                }
+            )
+        elif units == "CMH":
+            emitter_demand["demands"].append(
+                {"demand": flushing_flow, "pattern": "flushing_pt", "category": None}
+            )
+        cs.append(emitter_demand)
+        set_demand(new_name, cs)
+    else:
+        pipes = get_node_links(new_name, drainage_node_ID)
+        flush_diameter = 50
+        for pipe in pipes:
+            d = get_pipe(new_name, pipe)["diameter"]
+            if flush_diameter < d:
+                flush_diameter = d
+        flush_diameter /= 1000
+        emitter_coeff = (
+            0.65 * 3.14 * (flush_diameter * flush_diameter / 4) * sqrt(19.6) * 1000
+        )  # 全开口面积作为coeff
+
+        old_emitter = get_emitter(new_name, drainage_node_ID)
+        if old_emitter != None:
+            old_emitter["coefficient"] = emitter_coeff  # 爆管的emitter coefficient设置
+        else:
+            old_emitter = {"junction": drainage_node_ID, "coefficient": emitter_coeff}
+        new_emitter = ChangeSet()
+        new_emitter.append(old_emitter)
+        set_emitter(new_name, new_emitter)
+    # step 3. run simulation
+    # 涉及关阀计算，可能导致关阀后仍有流量，改为压力驱动PDA
+    options = get_option(new_name)
+    options["DEMAND MODEL"] = OPTION_DEMAND_MODEL_PDA
+    options["REQUIRED PRESSURE"] = "20.0000"
+    cs_options = ChangeSet()
+    cs_options.append(options)
+    set_option(new_name, cs_options)
+    # result = run_simulation_ex(new_name,'realtime', modify_pattern_start_time, modify_pattern_start_time, modify_total_duration,
+    #                            downloading_prohibition=True)
+    simulation.run_simulation(
+        name=new_name,
+        simulation_type="extended",
+        modify_pattern_start_time=modify_pattern_start_time,
+        modify_total_duration=modify_total_duration,
+        modify_valve_opening=modify_valve_opening,
+        scheme_type="flushing_analysis",
+        scheme_name=scheme_name,
+    )
+    # step 4. restore the base model
+    if is_project_open(new_name):
+        close_project(new_name)
+    delete_project(new_name)
+    # return result
+    # 存储方案信息到 PG 数据库
+    store_scheme_info(
+        name=name,
+        scheme_name=scheme_name,
+        scheme_type="flushing_analysis",
+        username=username,
+        scheme_start_time=modify_pattern_start_time,
+        scheme_detail=scheme_detail,
+    )
+
+
+############################################################
+# Contaminant simulation 04
+#
+############################################################
+def contaminant_simulation(
+    name: str,
+    modify_pattern_start_time: str,  # 模拟开始时间,格式为'2024-11-25T09:00:00+08:00'
+    modify_total_duration: int,  # 模拟总历时，秒
+    source: str,  # 污染源节点ID
+    concentration: float,  # 污染源浓度，单位mg/L
+    scheme_name: str = None,
+    source_pattern: str = None,  # 污染源时间变化模式名称
+    username: str | None = None,
+) -> None:
+    """
+    污染模拟
+    :param name: 模型名称，数据库中对应的名字
+    :param modify_pattern_start_time: 模拟开始时间,格式为'2024-11-25T09:00:00+08:00'
+    :param modify_total_duration: 模拟总历时，秒
+    :param source: 污染源所在的节点ID
+    :param concentration: 污染源位置处的浓度，单位mg/L。默认的污染模拟setting为SOURCE_TYPE_CONCEN（改为SOURCE_TYPE_SETPOINT）
+    :param source_pattern: 污染源的时间变化模式，若不传入则默认以恒定浓度持续模拟，时间长度等于duration;
+            若传入，则格式为{1.0,0.5,1.1}等系数列表pattern_step模拟等于模型的hydraulic time step
+    :param scheme_name: 方案名称
+    :return:
+    """
+    if not username:
+        raise ValueError("username is required when storing contaminant analysis scheme")
+
+    scheme_detail: dict = {
+        "source": source,
+        "concentration": concentration,
+        "duration": modify_total_duration,
+        "pattern": source_pattern,
+    }
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Start Analysis."
+    )
+    new_name = f"contaminant_Sim_{name}"
+    if have_project(new_name):
+        if is_project_open(new_name):
+            close_project(new_name)
+        delete_project(new_name)
+    # if is_project_open(name):
+    #     close_project(name)
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Start Copying Database."
+    )
+    # CopyProjectEx()(name, new_name,
+    #                 ['operation', 'current_operation', 'restore_operation', 'batch_operation', 'operation_table'])
+    copy_project(name + "_template", new_name)
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Start Opening Database."
+    )
+    open_project(new_name)
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Database Loading OK."
+    )
+    dic_time = get_time(new_name)
+    dic_time["QUALITY TIMESTEP"] = "0:05:00"
+    cs = ChangeSet()
+    cs.operations.append(dic_time)
+    set_time(new_name, cs)  # set QUALITY TIMESTEP
+    time_option = get_time(new_name)
+    hydraulic_step = time_option["HYDRAULIC TIMESTEP"]
+    secs = from_clock_to_seconds_2(hydraulic_step)
+    operation_step = 0
+    # step 1. set duration
+    if modify_total_duration == None:
+        modify_total_duration = secs
+    # step 2. set pattern
+    if source_pattern != None:
+        pt = get_pattern(new_name, source_pattern)
+        if len(pt) == 0:
+            str_response = str("cant find source_pattern")
+            return str_response
+    else:
+        cs_pattern = ChangeSet()
+        pt = {}
+        factors = []
+        tmp_duration = modify_total_duration
+        while tmp_duration > 0:
+            factors.append(1.0)
+            tmp_duration = tmp_duration - secs
+        pt["id"] = "contam_pt"
+        pt["factors"] = factors
+        cs_pattern.append(pt)
+        add_pattern(new_name, cs_pattern)
+        operation_step += 1
+    # step 3. set source/initial quality
+    # source quality
+    cs_source = ChangeSet()
+    source_schema = {
+        "node": source,
+        "s_type": SOURCE_TYPE_SETPOINT,
+        "strength": concentration,
+        "pattern": pt["id"],
+    }
+    cs_source.append(source_schema)
+    source_node = get_source(new_name, source)
+    if len(source_node) == 0:
+        add_source(new_name, cs_source)
+    else:
+        set_source(new_name, cs_source)
+    dict_demand = get_demand(new_name, source)
+    for demands in dict_demand["demands"]:
+        dict_demand["demands"][dict_demand["demands"].index(demands)]["demand"] = -1
+        dict_demand["demands"][dict_demand["demands"].index(demands)]["pattern"] = None
+    cs = ChangeSet()
+    cs.append(dict_demand)
+    set_demand(new_name, cs)  # set inflow node
+    # # initial quality
+    # dict_quality = get_quality(new_name, source)
+    # dict_quality['quality'] = concentration
+    # cs = ChangeSet()
+    # cs.append(dict_quality)
+    # set_quality(new_name, cs)
+    operation_step += 1
+    # step 4 set option of quality to chemical
+    opt = get_option(new_name)
+    opt["QUALITY"] = OPTION_QUALITY_CHEMICAL
+    cs_option = ChangeSet()
+    cs_option.append(opt)
+    set_option(new_name, cs_option)
+    operation_step += 1
+    # step 5. run simulation
+    # result = run_simulation_ex(new_name,'realtime', modify_pattern_start_time, modify_pattern_start_time,  modify_total_duration,
+    #                            downloading_prohibition=True)
+    simulation.run_simulation(
+        name=new_name,
+        simulation_type="extended",
+        modify_pattern_start_time=modify_pattern_start_time,
+        modify_total_duration=modify_total_duration,
+        scheme_type="contaminant_analysis",
+        scheme_name=scheme_name,
+    )
+
+    # for i in range(1,operation_step):
+    #     execute_undo(name)
+    if is_project_open(new_name):
+        close_project(new_name)
+    delete_project(new_name)
+    # 存储方案信息到 PG 数据库
+    store_scheme_info(
+        name=name,
+        scheme_name=scheme_name,
+        scheme_type="contaminant_analysis",
+        username=username,
+        scheme_start_time=modify_pattern_start_time,
+        scheme_detail=scheme_detail,
+    )
+
+
+############################################################
+# age analysis 05   ***水龄模拟目前还没和实时模拟打通，不确定是否需要，先不要使用***
+############################################################
+
+
+def age_analysis(
+    name: str, modify_pattern_start_time: str, modify_total_duration: int = 900
+) -> None:
+    """
+    水龄模拟
+    :param name: 模型名称，数据库中对应的名字
+    :param modify_pattern_start_time: 模拟开始时间,格式为'2024-11-25T09:00:00+08:00'
+    :param modify_total_duration: 模拟总历时，秒
+    :return:
+    """
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Start Analysis."
+    )
+    new_name = f"age_Anal_{name}"
+    if have_project(new_name):
+        if is_project_open(new_name):
+            close_project(new_name)
+        delete_project(new_name)
+    # if is_project_open(name):
+    #     close_project(name)
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Start Copying Database."
+    )
+    # CopyProjectEx()(name, new_name,
+    #                 ['operation', 'current_operation', 'restore_operation', 'batch_operation', 'operation_table'])
+    copy_project(name + "_template", new_name)
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Start Opening Database."
+    )
+    open_project(new_name)
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Database Loading OK."
+    )
+    # step 1. run simulation
+
+    result = run_simulation_ex(
+        new_name,
+        "realtime",
+        modify_pattern_start_time,
+        duration=modify_total_duration,
+        downloading_prohibition=True,
+    )
+    simulation_result = json.loads(result)
+    output_data = simulation_result.get("output")
+    if not isinstance(output_data, dict):
+        raise RuntimeError("run_simulation_ex did not return JSON output content")
+    # step 2. restore the base model status
+    # execute_undo(name) #有疑惑
+    if is_project_open(new_name):
+        close_project(new_name)
+    delete_project(new_name)
+    nodes_age = []
+    node_result = output_data.get("node_results") or []
+    for node in node_result:
+        nodes_age.append(node["result"][-1]["quality"])
+    links_age = []
+    link_result = output_data.get("link_results") or []
+    for link in link_result:
+        links_age.append(link["result"][-1]["quality"])
+    age_result = {"nodes": nodes_age, "links": links_age}
+    # age_result = {'nodes': nodes_age, 'links': links_age, 'nodeIDs': node_name, 'linkIDs': link_name}
+    return json.dumps(age_result)
+
+
+############################################################
+# pressure regulation 06
+############################################################
+
+
+def pressure_regulation(
+    name: str,
+    modify_pattern_start_time: str,
+    modify_total_duration: int = 900,
+    modify_tank_initial_level: dict[str, float] = None,
+    modify_fixed_pump_pattern: dict[str, list] = None,
+    modify_variable_pump_pattern: dict[str, list] = None,
+    scheme_name: str = None,
+) -> None:
+    """
+    区域调压模拟，用来模拟未来15分钟内，开关水泵对区域压力的影响
+    :param name: 模型名称，数据库中对应的名字
+    :param modify_pattern_start_time: 模拟开始时间,格式为'2024-11-25T09:00:00+08:00'
+    :param modify_total_duration: 模拟总历时，秒
+    :param modify_tank_initial_level: dict中包含多个水塔，str为水塔的id，float为修改后的initial_level
+    :param modify_fixed_pump_pattern: dict中包含多个水泵模式，str为工频水泵的id，list为修改后的pattern
+    :param modify_variable_pump_pattern: dict中包含多个水泵模式，str为变频水泵的id，list为修改后的pattern
+    :param scheme_name: 模拟方案名称
+    :return:
+    """
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Start Analysis."
+    )
+    new_name = f"pressure_regulation_{name}"
+    if have_project(new_name):
+        if is_project_open(new_name):
+            close_project(new_name)
+        delete_project(new_name)
+    # if is_project_open(name):
+    #     close_project(name)
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Start Copying Database."
+    )
+    # CopyProjectEx()(name, new_name,
+    #                 ['operation', 'current_operation', 'restore_operation', 'batch_operation', 'operation_table'])
+    copy_project(name + "_template", new_name)
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Start Opening Database."
+    )
+    open_project(new_name)
+    print(
+        datetime.now(pytz.timezone("Asia/Shanghai")).strftime("%Y-%m-%d %H:%M:%S")
+        + " -- Database Loading OK."
+    )
+    # 全部关泵后，压力计算不合理，改为压力驱动PDA
+    options = get_option(new_name)
+    options["DEMAND MODEL"] = OPTION_DEMAND_MODEL_PDA
+    options["REQUIRED PRESSURE"] = "15.0000"
+    cs_options = ChangeSet()
+    cs_options.append(options)
+    set_option(new_name, cs_options)
+    # result = run_simulation_ex(name=new_name,
+    #                            simulation_type='realtime',
+    #                            start_datetime=start_datetime,
+    #                            duration=900,
+    #                            pump_control=pump_control,
+    #                            tank_initial_level_control=tank_initial_level_control,
+    #                            downloading_prohibition=True)
+    simulation.run_simulation(
+        name=new_name,
+        simulation_type="extended",
+        modify_pattern_start_time=modify_pattern_start_time,
+        modify_total_duration=modify_total_duration,
+        modify_tank_initial_level=modify_tank_initial_level,
+        modify_fixed_pump_pattern=modify_fixed_pump_pattern,
+        modify_variable_pump_pattern=modify_variable_pump_pattern,
+        scheme_type="pressure_regulation",
+        scheme_name=scheme_name,
+    )
+    if is_project_open(new_name):
+        close_project(new_name)
+    delete_project(new_name)
+    # return result

app/api/v1/endpoints/admin_metadata.py

@@ -0,0 +1,695 @@
+from typing import List
+from uuid import UUID
+
+from fastapi import APIRouter, Depends, HTTPException, Path, Query, Response, status
+from sqlalchemy import text
+from sqlalchemy.engine.url import make_url
+from sqlalchemy.exc import IntegrityError, SQLAlchemyError
+from sqlalchemy.ext.asyncio import create_async_engine
+
+from app.auth.metadata_dependencies import (
+    get_current_metadata_admin,
+    get_metadata_repository,
+)
+from app.core.audit import AuditAction, log_audit_event
+from app.domain.schemas.admin_metadata import (
+    AdminProjectCreateRequest,
+    AdminProjectResponse,
+    AdminProjectUpdateRequest,
+    MetadataUsersBatchSyncRequest,
+    MetadataUserResponse,
+    MetadataUserSyncRequest,
+    MetadataUserSyncResult,
+    MetadataUserUpdateRequest,
+    ProjectDatabaseHealthResponse,
+    ProjectDatabaseHealthRequest,
+    ProjectDatabaseResponse,
+    ProjectDatabaseUpsertRequest,
+    ProjectDbRole,
+    ProjectMemberCreateRequest,
+    ProjectMemberResponse,
+    ProjectMemberUpdateRequest,
+)
+from app.infra.db.metadb import models
+from app.infra.db.metadb.repositories.metadata_repository import (
+    MetadataRepository,
+    ProjectDbRouting,
+)
+
+router = APIRouter()
+
+
+def _project_response(project: models.Project) -> AdminProjectResponse:
+    return AdminProjectResponse(
+        project_id=project.id,
+        name=project.name,
+        code=project.code,
+        description=project.description,
+        gs_workspace=project.gs_workspace,
+        map_extent=project.map_extent,
+        status=project.status,
+        created_at=project.created_at,
+        updated_at=project.updated_at,
+    )
+
+
+def _project_database_response(
+    record: models.ProjectDatabase,
+) -> ProjectDatabaseResponse:
+    return ProjectDatabaseResponse(
+        id=record.id,
+        project_id=record.project_id,
+        db_role=record.db_role,
+        db_type=record.db_type,
+        pool_min_size=record.pool_min_size,
+        pool_max_size=record.pool_max_size,
+        has_dsn=bool(record.dsn_encrypted),
+    )
+
+
+def _database_audit_payload(payload: ProjectDatabaseUpsertRequest) -> dict:
+    return {
+        "db_role": payload.db_role,
+        "db_type": _db_type_for_role(payload.db_role),
+        "pool_min_size": payload.pool_min_size,
+        "pool_max_size": payload.pool_max_size,
+        "dsn_updated": payload.dsn is not None,
+    }
+
+
+def _to_async_sqlalchemy_url(dsn: str) -> str:
+    parsed = make_url(dsn)
+    if parsed.drivername in {"postgresql", "postgres"}:
+        parsed = parsed.set(drivername="postgresql+psycopg")
+    return parsed.render_as_string(hide_password=False)
+
+
+def _db_type_for_role(db_role: str) -> str:
+    if db_role == "iot_data":
+        return "timescaledb"
+    return "postgresql"
+
+
+def _status_for_config_value_error(exc: ValueError) -> int:
+    if "DATABASE_ENCRYPTION_KEY" in str(exc):
+        return status.HTTP_503_SERVICE_UNAVAILABLE
+    return status.HTTP_400_BAD_REQUEST
+
+
+async def _check_database_connection(routing: ProjectDbRouting) -> None:
+    engine = create_async_engine(
+        _to_async_sqlalchemy_url(routing.dsn),
+        pool_size=1,
+        max_overflow=0,
+        pool_pre_ping=True,
+    )
+    try:
+        async with engine.connect() as conn:
+            await conn.execute(text("SELECT 1"))
+    finally:
+        await engine.dispose()
+
+
+def _database_health_error_detail(exc: Exception) -> str:
+    message = str(exc)
+    lower_message = message.lower()
+    if "password authentication failed" in lower_message:
+        return "连通性测试失败：用户名或密码错误，请检查 DSN 中的账号密码。"
+    if "connection refused" in lower_message:
+        return "连通性测试失败：目标主机或端口拒绝连接，请检查地址、端口和服务状态。"
+    if "timeout" in lower_message or "timed out" in lower_message:
+        return "连通性测试失败：连接超时，请检查网络、防火墙和数据库服务状态。"
+    if "could not translate host name" in lower_message or "name or service not known" in lower_message:
+        return "连通性测试失败：数据库主机名无法解析，请检查 DSN 中的主机地址。"
+    first_line = message.splitlines()[0] if message else exc.__class__.__name__
+    return f"连通性测试失败：{first_line}"
+
+
+async def _upsert_and_audit_metadata_user(
+    payload: MetadataUserSyncRequest,
+    *,
+    current_user,
+    metadata_repo: MetadataRepository,
+    response_status: int,
+) -> MetadataUserResponse:
+    user = await metadata_repo.upsert_user_from_keycloak(
+        keycloak_id=payload.keycloak_id,
+        username=payload.username,
+        email=str(payload.email),
+        role=payload.role,
+        is_active=payload.is_active,
+    )
+    await log_audit_event(
+        action=AuditAction.UPDATE,
+        user_id=current_user.id,
+        resource_type="metadata_user",
+        resource_id=str(user.id),
+        request_data=payload.model_dump(mode="json"),
+        response_status=response_status,
+        session=metadata_repo.session,
+    )
+    return MetadataUserResponse.model_validate(user)
+
+
+@router.get("/me", response_model=MetadataUserResponse)
+async def get_metadata_admin_me(
+    current_user=Depends(get_current_metadata_admin),
+) -> MetadataUserResponse:
+    return MetadataUserResponse.model_validate(current_user)
+
+
+@router.post("/users/sync", response_model=MetadataUserResponse)
+async def sync_metadata_user(
+    payload: MetadataUserSyncRequest,
+    current_user=Depends(get_current_metadata_admin),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> MetadataUserResponse:
+    try:
+        return await _upsert_and_audit_metadata_user(
+            payload,
+            current_user=current_user,
+            metadata_repo=metadata_repo,
+            response_status=status.HTTP_200_OK,
+        )
+    except IntegrityError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail="User keycloak_id, username, or email conflicts with an existing user",
+        ) from exc
+    except SQLAlchemyError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=f"Metadata database error: {exc}",
+        ) from exc
+
+
+
+@router.post("/users/sync/batch", response_model=List[MetadataUserSyncResult])
+async def sync_metadata_users_batch(
+    payload: MetadataUsersBatchSyncRequest,
+    current_user=Depends(get_current_metadata_admin),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> List[MetadataUserSyncResult]:
+    results: list[MetadataUserSyncResult] = []
+    for item in payload.users:
+        try:
+            user = await _upsert_and_audit_metadata_user(
+                item,
+                current_user=current_user,
+                metadata_repo=metadata_repo,
+                response_status=status.HTTP_200_OK,
+            )
+        except IntegrityError as exc:
+            results.append(
+                MetadataUserSyncResult(
+                    keycloak_id=item.keycloak_id,
+                    success=False,
+                    error="User keycloak_id, username, or email conflicts with an existing user",
+                )
+            )
+            await metadata_repo.session.rollback()
+        except SQLAlchemyError as exc:
+            results.append(
+                MetadataUserSyncResult(
+                    keycloak_id=item.keycloak_id,
+                    success=False,
+                    error=f"Metadata database error: {exc}",
+                )
+            )
+            await metadata_repo.session.rollback()
+        else:
+            results.append(
+                MetadataUserSyncResult(
+                    keycloak_id=item.keycloak_id,
+                    success=True,
+                    user=user,
+                )
+            )
+    return results
+
+
+@router.get("/users", response_model=List[MetadataUserResponse])
+async def list_metadata_users(
+    skip: int = Query(0, ge=0),
+    limit: int = Query(100, ge=1, le=1000),
+    current_user=Depends(get_current_metadata_admin),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> List[MetadataUserResponse]:
+    users = await metadata_repo.list_users(skip=skip, limit=limit)
+    return [MetadataUserResponse.model_validate(user) for user in users]
+
+
+@router.get("/projects", response_model=List[AdminProjectResponse])
+async def list_admin_projects(
+    current_user=Depends(get_current_metadata_admin),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> List[AdminProjectResponse]:
+    projects = await metadata_repo.list_project_records()
+    return [_project_response(project) for project in projects]
+
+
+@router.post(
+    "/projects",
+    response_model=AdminProjectResponse,
+    status_code=status.HTTP_201_CREATED,
+)
+async def create_admin_project(
+    payload: AdminProjectCreateRequest,
+    current_user=Depends(get_current_metadata_admin),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> AdminProjectResponse:
+    try:
+        project = await metadata_repo.create_project(
+            name=payload.name,
+            code=payload.code,
+            description=payload.description,
+            gs_workspace=payload.gs_workspace,
+            map_extent=payload.map_extent,
+            status=payload.status,
+        )
+    except IntegrityError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail="Project code or workspace conflicts with an existing project",
+        ) from exc
+    except SQLAlchemyError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=f"Metadata database error: {exc}",
+        ) from exc
+
+    await log_audit_event(
+        action=AuditAction.CREATE,
+        user_id=current_user.id,
+        project_id=project.id,
+        resource_type="project",
+        resource_id=str(project.id),
+        request_data=payload.model_dump(mode="json"),
+        response_status=status.HTTP_201_CREATED,
+        session=metadata_repo.session,
+    )
+    return _project_response(project)
+
+
+@router.patch(
+    "/projects/{project_id}",
+    response_model=AdminProjectResponse,
+)
+async def update_admin_project(
+    payload: AdminProjectUpdateRequest,
+    project_id: UUID = Path(...),
+    current_user=Depends(get_current_metadata_admin),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> AdminProjectResponse:
+    updates = payload.model_dump(mode="json", exclude_unset=True)
+    try:
+        project = await metadata_repo.update_project(project_id, updates=updates)
+    except IntegrityError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail="Project code or workspace conflicts with an existing project",
+        ) from exc
+    except SQLAlchemyError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=f"Metadata database error: {exc}",
+        ) from exc
+    if project is None:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Project not found")
+
+    await log_audit_event(
+        action=AuditAction.UPDATE,
+        user_id=current_user.id,
+        project_id=project.id,
+        resource_type="project",
+        resource_id=str(project.id),
+        request_data=updates,
+        response_status=status.HTTP_200_OK,
+        session=metadata_repo.session,
+    )
+    return _project_response(project)
+
+
+@router.get(
+    "/projects/{project_id}/databases",
+    response_model=List[ProjectDatabaseResponse],
+)
+async def list_project_databases(
+    project_id: UUID = Path(...),
+    current_user=Depends(get_current_metadata_admin),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> List[ProjectDatabaseResponse]:
+    project = await metadata_repo.get_project_by_id(project_id)
+    if project is None:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Project not found")
+    records = await metadata_repo.list_project_databases(project_id)
+    return [_project_database_response(record) for record in records]
+
+
+@router.put(
+    "/projects/{project_id}/databases",
+    response_model=ProjectDatabaseResponse,
+)
+async def upsert_project_database(
+    payload: ProjectDatabaseUpsertRequest,
+    project_id: UUID = Path(...),
+    current_user=Depends(get_current_metadata_admin),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> ProjectDatabaseResponse:
+    project = await metadata_repo.get_project_by_id(project_id)
+    if project is None:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Project not found")
+    try:
+        routing = (
+            ProjectDbRouting(
+                project_id=project_id,
+                db_role=payload.db_role,
+                db_type=_db_type_for_role(payload.db_role),
+                dsn=payload.dsn,
+                pool_min_size=payload.pool_min_size,
+                pool_max_size=payload.pool_max_size,
+            )
+            if payload.dsn
+            else await metadata_repo.get_project_db_routing(project_id, payload.db_role)
+        )
+        if routing is None:
+            raise ValueError("dsn is required when creating project database config")
+        await _check_database_connection(routing)
+    except ValueError as exc:
+        raise HTTPException(
+            status_code=_status_for_config_value_error(exc),
+            detail=str(exc),
+        ) from exc
+    except Exception as exc:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=_database_health_error_detail(exc),
+        ) from exc
+
+    try:
+        record = await metadata_repo.upsert_project_database_config(
+            project_id,
+            db_role=payload.db_role,
+            db_type=_db_type_for_role(payload.db_role),
+            dsn=payload.dsn,
+            pool_min_size=payload.pool_min_size,
+            pool_max_size=payload.pool_max_size,
+        )
+    except IntegrityError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail="Project database role conflicts with an existing config",
+        ) from exc
+    except SQLAlchemyError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=f"Metadata database error: {exc}",
+        ) from exc
+
+    await log_audit_event(
+        action=AuditAction.CONFIG_CHANGE,
+        user_id=current_user.id,
+        project_id=project_id,
+        resource_type="project_database",
+        resource_id=payload.db_role,
+        request_data=_database_audit_payload(payload),
+        response_status=status.HTTP_200_OK,
+        session=metadata_repo.session,
+    )
+    return _project_database_response(record)
+
+
+@router.delete(
+    "/projects/{project_id}/databases/{db_role}",
+    status_code=status.HTTP_204_NO_CONTENT,
+)
+async def delete_project_database(
+    project_id: UUID = Path(...),
+    db_role: ProjectDbRole = Path(...),
+    current_user=Depends(get_current_metadata_admin),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> None:
+    removed = await metadata_repo.delete_project_database_config(project_id, db_role)
+    if not removed:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail="Project database config not found",
+        )
+    await log_audit_event(
+        action=AuditAction.CONFIG_CHANGE,
+        user_id=current_user.id,
+        project_id=project_id,
+        resource_type="project_database",
+        resource_id=db_role,
+        request_data={"deleted": True},
+        response_status=status.HTTP_204_NO_CONTENT,
+        session=metadata_repo.session,
+    )
+
+
+@router.post(
+    "/projects/{project_id}/databases/{db_role}/health",
+    response_model=ProjectDatabaseHealthResponse,
+)
+async def check_project_database_health(
+    response: Response,
+    project_id: UUID = Path(...),
+    db_role: ProjectDbRole = Path(...),
+    payload: ProjectDatabaseHealthRequest | None = None,
+    current_user=Depends(get_current_metadata_admin),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> ProjectDatabaseHealthResponse:
+    dsn_to_test = payload.dsn if payload and payload.dsn else None
+    if dsn_to_test:
+        routing = ProjectDbRouting(
+            project_id=project_id,
+            db_role=db_role,
+            db_type=_db_type_for_role(db_role),
+            dsn=dsn_to_test,
+            pool_min_size=1,
+            pool_max_size=1,
+        )
+    else:
+        try:
+            routing = await metadata_repo.get_project_db_routing(project_id, db_role)
+        except ValueError as exc:
+            raise HTTPException(
+                status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+                detail=f"Project database routing DSN is invalid: {exc}",
+            ) from exc
+        if routing is None:
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND,
+                detail="Project database config not found",
+            )
+
+    try:
+        await _check_database_connection(routing)
+    except Exception as exc:  # health endpoint should return diagnostic status
+        response.status_code = status.HTTP_503_SERVICE_UNAVAILABLE
+        return ProjectDatabaseHealthResponse(
+            project_id=project_id,
+            db_role=db_role,
+            db_type=routing.db_type,
+            ok=False,
+            detail=_database_health_error_detail(exc),
+        )
+    return ProjectDatabaseHealthResponse(
+        project_id=project_id,
+        db_role=db_role,
+        db_type=routing.db_type,
+        ok=True,
+        detail="连通性测试通过",
+    )
+
+
+@router.get("/users/{user_id}", response_model=MetadataUserResponse)
+async def get_metadata_user(
+    user_id: UUID = Path(...),
+    current_user=Depends(get_current_metadata_admin),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> MetadataUserResponse:
+    user = await metadata_repo.get_user_by_id(user_id)
+    if user is None:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="User not found")
+    return MetadataUserResponse.model_validate(user)
+
+
+@router.patch("/users/{user_id}", response_model=MetadataUserResponse)
+async def update_metadata_user(
+    payload: MetadataUserUpdateRequest,
+    user_id: UUID = Path(...),
+    current_user=Depends(get_current_metadata_admin),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> MetadataUserResponse:
+    updates = payload.model_dump(mode="json", exclude_unset=True)
+    if user_id == current_user.id:
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
+            detail="Users cannot modify themselves",
+        )
+    user = await metadata_repo.update_user_admin(
+        user_id,
+        updates=updates,
+    )
+    if user is None:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="User not found")
+
+    await log_audit_event(
+        action=AuditAction.UPDATE,
+        user_id=current_user.id,
+        resource_type="metadata_user",
+        resource_id=str(user.id),
+        request_data=updates,
+        response_status=status.HTTP_200_OK,
+        session=metadata_repo.session,
+    )
+    return MetadataUserResponse.model_validate(user)
+
+
+@router.get(
+    "/projects/{project_id}/members",
+    response_model=List[ProjectMemberResponse],
+)
+async def list_project_members(
+    project_id: UUID = Path(...),
+    current_user=Depends(get_current_metadata_admin),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> List[ProjectMemberResponse]:
+    project = await metadata_repo.get_project_by_id(project_id)
+    if project is None:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND, detail="Project not found"
+        )
+    members = await metadata_repo.list_project_members(project_id)
+    return [ProjectMemberResponse(**member.__dict__) for member in members]
+
+
+@router.post(
+    "/projects/{project_id}/members",
+    response_model=ProjectMemberResponse,
+    status_code=status.HTTP_201_CREATED,
+)
+async def add_project_member(
+    payload: ProjectMemberCreateRequest,
+    project_id: UUID = Path(...),
+    current_user=Depends(get_current_metadata_admin),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> ProjectMemberResponse:
+    if payload.user_id == current_user.id:
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
+            detail="Users cannot modify their own project membership",
+        )
+    project = await metadata_repo.get_project_by_id(project_id)
+    if project is None:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND, detail="Project not found"
+        )
+    user = await metadata_repo.get_user_by_id(payload.user_id)
+    if user is None:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="User not found")
+    existing = await metadata_repo.get_project_membership(project_id, payload.user_id)
+    if existing is not None:
+        raise HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail="User is already a project member",
+        )
+
+    membership = await metadata_repo.add_project_member(
+        project_id, payload.user_id, payload.project_role
+    )
+    await log_audit_event(
+        action=AuditAction.PERMISSION_CHANGE,
+        user_id=current_user.id,
+        project_id=project_id,
+        resource_type="project_member",
+        resource_id=str(payload.user_id),
+        request_data=payload.model_dump(mode="json"),
+        response_status=status.HTTP_201_CREATED,
+        session=metadata_repo.session,
+    )
+    return ProjectMemberResponse(
+        id=membership.id,
+        user_id=membership.user_id,
+        project_id=membership.project_id,
+        project_role=membership.project_role,
+        username=user.username,
+        email=user.email,
+        is_active=user.is_active,
+    )
+
+
+@router.patch(
+    "/projects/{project_id}/members/{user_id}",
+    response_model=ProjectMemberResponse,
+)
+async def update_project_member(
+    payload: ProjectMemberUpdateRequest,
+    project_id: UUID = Path(...),
+    user_id: UUID = Path(...),
+    current_user=Depends(get_current_metadata_admin),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> ProjectMemberResponse:
+    if user_id == current_user.id:
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
+            detail="Users cannot modify their own project membership",
+        )
+    user = await metadata_repo.get_user_by_id(user_id)
+    if user is None:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="User not found")
+    membership = await metadata_repo.update_project_member_role(
+        project_id, user_id, payload.project_role
+    )
+    if membership is None:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND, detail="Project member not found"
+        )
+    await log_audit_event(
+        action=AuditAction.PERMISSION_CHANGE,
+        user_id=current_user.id,
+        project_id=project_id,
+        resource_type="project_member",
+        resource_id=str(user_id),
+        request_data=payload.model_dump(mode="json"),
+        response_status=status.HTTP_200_OK,
+        session=metadata_repo.session,
+    )
+    return ProjectMemberResponse(
+        id=membership.id,
+        user_id=membership.user_id,
+        project_id=membership.project_id,
+        project_role=membership.project_role,
+        username=user.username,
+        email=user.email,
+        is_active=user.is_active,
+    )
+
+
+@router.delete("/projects/{project_id}/members/{user_id}", status_code=status.HTTP_204_NO_CONTENT)
+async def remove_project_member(
+    project_id: UUID = Path(...),
+    user_id: UUID = Path(...),
+    current_user=Depends(get_current_metadata_admin),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> None:
+    if user_id == current_user.id:
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
+            detail="Users cannot modify their own project membership",
+        )
+    removed = await metadata_repo.remove_project_member(project_id, user_id)
+    if not removed:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND, detail="Project member not found"
+        )
+    await log_audit_event(
+        action=AuditAction.PERMISSION_CHANGE,
+        user_id=current_user.id,
+        project_id=project_id,
+        resource_type="project_member",
+        resource_id=str(user_id),
+        response_status=status.HTTP_204_NO_CONTENT,
+        session=metadata_repo.session,
+    )

app/api/v1/endpoints/agent_auth.py

@@ -0,0 +1,50 @@
+from datetime import datetime, timezone
+
+from fastapi import APIRouter, Depends
+from pydantic import BaseModel
+
+from app.auth.keycloak_dependencies import get_current_keycloak_payload
+from app.auth.metadata_dependencies import get_current_metadata_user
+from app.auth.project_dependencies import (
+    ProjectContext,
+    get_project_context,
+)
+
+router = APIRouter()
+
+
+class AgentAuthContextResponse(BaseModel):
+    user_id: str
+    keycloak_sub: str
+    username: str
+    role: str
+    is_superuser: bool
+    project_id: str
+    network: str
+    project_role: str
+    token_expires_at: str | None = None
+
+
+@router.get("/agent/auth/context", response_model=AgentAuthContextResponse)
+async def get_agent_auth_context(
+    ctx: ProjectContext = Depends(get_project_context),
+    current_user=Depends(get_current_metadata_user),
+    keycloak_payload: dict = Depends(get_current_keycloak_payload),
+) -> AgentAuthContextResponse:
+    exp = keycloak_payload.get("exp")
+    token_expires_at = (
+        datetime.fromtimestamp(exp, tz=timezone.utc).isoformat()
+        if isinstance(exp, int)
+        else None
+    )
+    return AgentAuthContextResponse(
+        user_id=str(current_user.id),
+        keycloak_sub=str(current_user.keycloak_id),
+        username=current_user.username,
+        role=current_user.role,
+        is_superuser=current_user.is_superuser,
+        project_id=str(ctx.project_id),
+        network=ctx.project_code,
+        project_role=ctx.project_role,
+        token_expires_at=token_expires_at,
+    )

app/api/v1/endpoints/audit.py

@@ -0,0 +1,125 @@
+"""
+审计日志 API 接口
+
+仅管理员可访问
+"""
+
+from typing import List, Optional
+from uuid import UUID
+from datetime import datetime
+from fastapi import APIRouter, Depends, Query, Path
+from app.domain.schemas.audit import AuditLogResponse
+from app.infra.db.metadb.repositories.audit_repository import AuditRepository
+from app.auth.metadata_dependencies import (
+    get_current_metadata_admin,
+    get_current_metadata_user,
+)
+from app.infra.db.metadb.database import get_metadata_session
+from sqlalchemy.ext.asyncio import AsyncSession
+
+router = APIRouter()
+
+
+async def get_audit_repository(
+    session: AsyncSession = Depends(get_metadata_session),
+) -> AuditRepository:
+    """获取审计日志仓储"""
+    return AuditRepository(session)
+
+
+@router.get(
+    "/logs",
+    summary="查询审计日志",
+    description="查询审计日志（仅管理员）",
+    response_model=List[AuditLogResponse],
+)
+async def get_audit_logs(
+    user_id: Optional[UUID] = Query(None, description="按用户ID过滤"),
+    project_id: Optional[UUID] = Query(None, description="按项目ID过滤"),
+    action: Optional[str] = Query(None, description="按操作类型过滤"),
+    resource_type: Optional[str] = Query(None, description="按资源类型过滤"),
+    start_time: Optional[datetime] = Query(None, description="开始时间"),
+    end_time: Optional[datetime] = Query(None, description="结束时间"),
+    skip: int = Query(0, ge=0, description="跳过记录数"),
+    limit: int = Query(100, ge=1, le=1000, description="限制记录数"),
+    current_user=Depends(get_current_metadata_admin),
+    audit_repo: AuditRepository = Depends(get_audit_repository),
+) -> List[AuditLogResponse]:
+    """
+    查询审计日志
+
+    支持按用户、时间、操作类型等条件过滤，仅管理员可访问
+    """
+    logs = await audit_repo.get_logs(
+        user_id=user_id,
+        project_id=project_id,
+        action=action,
+        resource_type=resource_type,
+        start_time=start_time,
+        end_time=end_time,
+        skip=skip,
+        limit=limit,
+    )
+    return logs
+
+
+@router.get(
+    "/logs/count",
+    summary="获取审计日志总数",
+    description="获取审计日志总数（仅管理员）",
+)
+async def get_audit_logs_count(
+    user_id: Optional[UUID] = Query(None, description="按用户ID过滤"),
+    project_id: Optional[UUID] = Query(None, description="按项目ID过滤"),
+    action: Optional[str] = Query(None, description="按操作类型过滤"),
+    resource_type: Optional[str] = Query(None, description="按资源类型过滤"),
+    start_time: Optional[datetime] = Query(None, description="开始时间"),
+    end_time: Optional[datetime] = Query(None, description="结束时间"),
+    current_user=Depends(get_current_metadata_admin),
+    audit_repo: AuditRepository = Depends(get_audit_repository),
+) -> dict:
+    """
+    获取审计日志总数
+
+    获取符合条件的审计日志的总数，仅管理员可访问
+    """
+    count = await audit_repo.get_log_count(
+        user_id=user_id,
+        project_id=project_id,
+        action=action,
+        resource_type=resource_type,
+        start_time=start_time,
+        end_time=end_time,
+    )
+    return {"count": count}
+
+
+@router.get(
+    "/logs/my",
+    summary="查询我的审计日志",
+    description="查询当前用户的审计日志",
+    response_model=List[AuditLogResponse],
+)
+async def get_my_audit_logs(
+    action: Optional[str] = Query(None, description="按操作类型过滤"),
+    start_time: Optional[datetime] = Query(None, description="开始时间"),
+    end_time: Optional[datetime] = Query(None, description="结束时间"),
+    skip: int = Query(0, ge=0, description="跳过记录数"),
+    limit: int = Query(100, ge=1, le=1000, description="限制记录数"),
+    current_user=Depends(get_current_metadata_user),
+    audit_repo: AuditRepository = Depends(get_audit_repository),
+) -> List[AuditLogResponse]:
+    """
+    查询当前用户的审计日志
+
+    普通用户只能查看自己的操作记录
+    """
+    logs = await audit_repo.get_logs(
+        user_id=current_user.id,
+        action=action,
+        start_time=start_time,
+        end_time=end_time,
+        skip=skip,
+        limit=limit,
+    )
+    return logs

app/api/v1/endpoints/burst_detection.py

@@ -0,0 +1,131 @@
+from datetime import datetime
+from typing import Any
+
+from fastapi import APIRouter, Depends, HTTPException, Query, Path, Body
+from pydantic import BaseModel, Field
+
+from app.auth.keycloak_dependencies import get_current_keycloak_username
+from app.services.burst_detection import (
+    get_burst_detection_scheme_detail,
+    list_burst_detection_schemes,
+    run_burst_detection,
+)
+
+router = APIRouter()
+
+
+class BurstDetectionRequest(BaseModel):
+    """爆管检测请求模型"""
+    network: str = Field(..., description="管网名称（或数据库名称）")
+    observed_pressure_data: (
+        dict[str, list[Any]] | list[dict[str, Any]] | list[list[Any]] | None
+    ) = Field(
+        default=None,
+        description=(
+            "压力观测数据。支持列式字典 {sensor_id: [values,...]}、"
+            "逐时刻对象数组 [{sensor_id: value,...}, ...]、"
+            "或二维数组 [[t1_s1, t1_s2], [t2_s1, t2_s2], ...]。"
+        ),
+    )
+    points_per_day: int = Field(1440, description="每天的数据点数")
+    mu: int = Field(100, description="异常值检测的参数")
+    iforest_params: dict[str, Any] | None = Field(None, description="隔离森林算法参数")
+    scada_start: datetime | None = Field(None, description="SCADA数据起始时间")
+    scada_end: datetime | None = Field(None, description="SCADA数据结束时间")
+    sensor_nodes: list[str] | None = Field(None, description="传感器节点列表")
+    scheme_name: str | None = Field(None, description="方案名称")
+    data_source: str = Field("monitoring", description="数据来源：monitoring（监测）或simulation（模拟）")
+    simulation_scheme_name: str | None = Field(None, description="模拟方案名称")
+    simulation_scheme_type: str | None = Field(None, description="模拟方案类型")
+
+
+@router.post(
+    "/detect/",
+    summary="执行爆管检测",
+    description="基于压力观测数据和其他参数执行爆管检测分析"
+)
+async def detect_burst(
+    data: BurstDetectionRequest = Body(..., description="爆管检测请求数据"),
+    username: str = Depends(get_current_keycloak_username),
+) -> dict[str, Any]:
+    """
+    执行爆管检测分析。
+    
+    使用异常检测算法（隔离森林）识别压力时间序列中的异常，
+    将其作为潜在的爆管事件。
+    
+    Args:
+        data: 包含管网名称（或数据库名称）、压力数据及相关参数的请求体
+        username: 当前认证用户名
+        
+    Returns:
+        包含检测结果的字典
+        
+    Raises:
+        HTTPException: 当处理过程中发生错误时
+    """
+    try:
+        return run_burst_detection(**data.model_dump(), username=username)
+    except Exception as exc:
+        raise HTTPException(status_code=400, detail=str(exc))
+
+
+@router.get(
+    "/schemes/",
+    summary="查询爆管检测方案列表",
+    description="获取指定网络的所有爆管检测方案"
+)
+async def query_burst_detection_schemes(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    query_date: datetime | None = Query(None, description="查询日期（可选）"),
+) -> list[dict[str, Any]]:
+    """
+    获取爆管检测方案列表。
+    
+    查询指定网络的所有已配置的爆管检测方案，
+    可按日期进行筛选。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        query_date: 查询日期（可选）
+        
+    Returns:
+        爆管检测方案列表
+        
+    Raises:
+        HTTPException: 当查询失败时
+    """
+    try:
+        return list_burst_detection_schemes(network=network, query_date=query_date)
+    except Exception as exc:
+        raise HTTPException(status_code=400, detail=str(exc))
+
+
+@router.get(
+    "/schemes/{scheme_name}",
+    summary="获取爆管检测方案详情",
+    description="获取指定爆管检测方案的详细信息"
+)
+async def query_burst_detection_scheme_detail(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    scheme_name: str = Path(..., description="爆管检测方案名称"),
+) -> dict[str, Any]:
+    """
+    获取爆管检测方案详情。
+    
+    查询指定爆管检测方案的完整配置和参数信息。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        scheme_name: 爆管检测方案名称
+        
+    Returns:
+        包含方案详情的字典
+        
+    Raises:
+        HTTPException: 当查询失败时
+    """
+    try:
+        return get_burst_detection_scheme_detail(network=network, scheme_name=scheme_name)
+    except Exception as exc:
+        raise HTTPException(status_code=400, detail=str(exc))

app/api/v1/endpoints/burst_location.py

@@ -0,0 +1,131 @@
+from typing import Any
+from datetime import datetime
+
+from typing import Literal
+
+from fastapi import APIRouter, Depends, HTTPException, Query, Path, Body
+from pydantic import BaseModel, Field
+
+from app.auth.keycloak_dependencies import get_current_keycloak_username
+from app.services.burst_location import (
+    get_burst_location_scheme_detail,
+    list_burst_location_schemes,
+    run_burst_location_by_network,
+)
+
+router = APIRouter()
+
+
+class BurstLocationRequest(BaseModel):
+    """爆管定位请求模型"""
+    network: str = Field(..., description="管网名称（或数据库名称）")
+    data_source: Literal["monitoring", "simulation"] = Field("monitoring", description="数据来源：monitoring（监测）或simulation（模拟）")
+    pressure_scada_ids: list[str] | None = Field(None, description="压力SCADA传感器ID列表")
+    burst_pressure: dict[str, float] | list[dict[str, Any]] | None = Field(None, description="爆管时的压力数据")
+    normal_pressure: dict[str, float] | list[dict[str, Any]] | None = Field(None, description="正常时的压力数据")
+    burst_leakage: float = Field(..., description="爆管时的漏水量")
+    flow_scada_ids: list[str] | None = Field(None, description="流量SCADA传感器ID列表")
+    burst_flow: dict[str, float] | list[dict[str, Any]] | None = Field(None, description="爆管时的流量数据")
+    normal_flow: dict[str, float] | list[dict[str, Any]] | None = Field(None, description="正常时的流量数据")
+    min_dpressure: float = Field(2.0, description="最小压力差（bar）")
+    basic_pressure: float = Field(10.0, description="基准压力（bar）")
+    scada_burst_start: datetime | None = Field(None, description="爆管/模拟方案开始时间")
+    scada_burst_end: datetime | None = Field(None, description="爆管/模拟方案结束时间")
+    scada_normal_start: datetime | None = Field(None, description="监测数据正常工况开始时间")
+    scada_normal_end: datetime | None = Field(None, description="监测数据正常工况结束时间")
+    use_scada_flow: bool = Field(False, description="是否使用SCADA流量数据")
+    scheme_name: str | None = Field(None, description="方案名称")
+    simulation_scheme_name: str | None = Field(None, description="模拟方案名称")
+    simulation_scheme_type: str | None = Field(None, description="模拟方案类型")
+
+
+@router.post(
+    "/locate/",
+    summary="执行爆管定位",
+    description="基于压力和流量数据定位管网中的爆管位置"
+)
+async def locate_burst(
+    data: BurstLocationRequest = Body(..., description="爆管定位请求数据"),
+    username: str = Depends(get_current_keycloak_username),
+) -> dict[str, Any]:
+    """
+    执行爆管定位分析。
+    
+    使用压力和流量SCADA数据，通过对比爆管和正常状态下的数据差异，
+    定位管网中的爆管位置。
+    
+    Args:
+        data: 包含管网名称（或数据库名称）、压力、流量数据及相关参数的请求体
+        username: 当前认证用户名
+        
+    Returns:
+        包含定位结果的字典
+        
+    Raises:
+        HTTPException: 当数据类型或值不正确时
+    """
+    try:
+        return run_burst_location_by_network(**data.model_dump(), username=username)
+    except (TypeError, ValueError) as exc:
+        raise HTTPException(status_code=400, detail=str(exc))
+
+
+@router.get(
+    "/schemes/",
+    summary="查询爆管定位方案列表",
+    description="获取指定网络的所有爆管定位方案"
+)
+async def query_burst_schemes(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    query_date: datetime | None = Query(None, description="查询日期（可选）")
+) -> list[dict[str, Any]]:
+    """
+    获取爆管定位方案列表。
+    
+    查询指定网络的所有已配置的爆管定位方案，
+    可按日期进行筛选。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        query_date: 查询日期（可选）
+        
+    Returns:
+        爆管定位方案列表
+        
+    Raises:
+        HTTPException: 当查询失败时
+    """
+    try:
+        return list_burst_location_schemes(network=network, query_date=query_date)
+    except Exception as exc:
+        raise HTTPException(status_code=400, detail=str(exc))
+
+
+@router.get(
+    "/schemes/{scheme_name}",
+    summary="获取爆管定位方案详情",
+    description="获取指定爆管定位方案的详细信息"
+)
+async def query_burst_scheme_detail(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    scheme_name: str = Path(..., description="爆管定位方案名称")
+) -> dict[str, Any]:
+    """
+    获取爆管定位方案详情。
+    
+    查询指定爆管定位方案的完整配置和参数信息。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        scheme_name: 爆管定位方案名称
+        
+    Returns:
+        包含方案详情的字典
+        
+    Raises:
+        HTTPException: 当查询失败时
+    """
+    try:
+        return get_burst_location_scheme_detail(network=network, scheme_name=scheme_name)
+    except Exception as exc:
+        raise HTTPException(status_code=400, detail=str(exc))

app/api/v1/endpoints/cache.py

@@ -0,0 +1,57 @@
+from fastapi import APIRouter, Query
+from app.infra.cache.redis_client import redis_client
+
+router = APIRouter()
+
+@router.post("/clearrediskey/", summary="清除单个缓存键", description="根据键名清除单个Redis缓存")
+async def fastapi_clear_redis_key(key: str = Query(..., description="缓存键名")):
+    """
+    清除单个缓存键
+    
+    根据指定的键名删除Redis中对应的缓存
+    """
+    redis_client.delete(key)
+    return True
+
+
+@router.post("/clearrediskeys/", summary="清除匹配的缓存键", description="根据模式清除匹配的Redis缓存键")
+async def fastapi_clear_redis_keys(keys: str = Query(..., description="缓存键模式（支持通配符）")):
+    """
+    清除匹配的缓存键
+    
+    根据指定的模式删除Redis中所有匹配的缓存键
+    """
+    # delete keys contains the key
+    matched_keys = redis_client.keys(f"*{keys}*")
+    if matched_keys:
+        redis_client.delete(*matched_keys)
+
+    return True
+
+
+@router.post("/clearallredis/", summary="清除所有缓存", description="清空整个Redis数据库的所有缓存")
+async def fastapi_clear_all_redis():
+    """
+    清除所有缓存
+    
+    清空Redis数据库中的所有缓存键值对
+    """
+    redis_client.flushdb()
+    return True
+
+
+@router.get("/queryredis/", summary="查询缓存键列表", description="获取Redis中所有的缓存键")
+async def fastapi_query_redis():
+    """
+    查询缓存键列表
+    
+    获取Redis数据库中所有的缓存键列表
+    """
+    # Helper to decode bytes to str for JSON response if needed, 
+    # but original just returned keys (which might be bytes in redis-py unless decode_responses=True)
+    # create_redis_client usually sets decode_responses=False by default.
+    # We will assume user handles bytes or we should decode. 
+    # Original just returned redis_client.keys("*")
+    keys = redis_client.keys("*")
+    # Clean output for API
+    return [k.decode('utf-8') if isinstance(k, bytes) else k for k in keys]

app/api/v1/endpoints/components/controls.py

@@ -0,0 +1,70 @@
+from fastapi import APIRouter, Request, Query, Path, Body
+from typing import Any, List, Dict, Union
+from app.services.tjnetwork import (
+    Any,
+    ChangeSet,
+    get_control,
+    get_control_schema,
+    get_rule,
+    get_rule_schema,
+    set_control,
+    set_rule,
+)
+
+router = APIRouter()
+
+@router.get("/getcontrolschema/", summary="获取控制架构", description="获取网络中控制对象的架构定义")
+async def fastapi_get_control_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """获取控制架构。
+    
+    返回指定网络中控制对象的属性架构定义。
+    """
+    return get_control_schema(network)
+
+@router.get("/getcontrolproperties/", summary="获取控制属性", description="获取指定网络中的控制属性信息")
+async def fastapi_get_control_properties(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, Any]:
+    """获取控制属性。
+    
+    返回指定网络中的控制对象属性信息。
+    """
+    return get_control(network)
+
+@router.post("/setcontrolproperties/", response_model=None, summary="设置控制属性", description="更新指定网络中的控制属性")
+async def fastapi_set_control_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置控制属性。
+    
+    更新指定网络中的控制属性值。
+    """
+    props = await req.json()
+    return set_control(network, ChangeSet(props))
+
+@router.get("/getruleschema/", summary="获取规则架构", description="获取网络中规则对象的架构定义")
+async def fastapi_get_rule_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """获取规则架构。
+    
+    返回指定网络中规则对象的属性架构定义。
+    """
+    return get_rule_schema(network)
+
+@router.get("/getruleproperties/", summary="获取规则属性", description="获取指定网络中的规则属性信息")
+async def fastapi_get_rule_properties(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, Any]:
+    """获取规则属性。
+    
+    返回指定网络中的规则对象属性信息。
+    """
+    return get_rule(network)
+
+@router.post("/setruleproperties/", response_model=None, summary="设置规则属性", description="更新指定网络中的规则属性")
+async def fastapi_set_rule_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置规则属性。
+    
+    更新指定网络中的规则属性值。
+    """
+    props = await req.json()
+    return set_rule(network, ChangeSet(props))

app/api/v1/endpoints/components/curves.py

@@ -0,0 +1,95 @@
+from fastapi import APIRouter, Request, Query, Path, Body
+from typing import Any, List, Dict, Union
+from app.services.tjnetwork import (
+    Any,
+    ChangeSet,
+    add_curve,
+    delete_curve,
+    get_curve,
+    get_curve_schema,
+    get_curves,
+    is_curve,
+    set_curve,
+)
+
+router = APIRouter()
+
+@router.get("/getcurveschema", summary="获取曲线架构", description="获取网络中曲线对象的架构定义")
+async def fastapi_get_curve_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """获取曲线架构。
+    
+    返回指定网络中曲线对象的属性架构定义。
+    """
+    return get_curve_schema(network)
+
+@router.post("/addcurve/", response_model=None, summary="添加曲线", description="在网络中添加一条新的曲线")
+async def fastapi_add_curve(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    curve: str = Query(..., description="曲线ID"),
+    req: Request = None
+) -> ChangeSet:
+    """添加曲线。
+    
+    在指定网络中创建一条新的曲线，并设置其初始属性。
+    """
+    props = await req.json()
+    ps = {
+        "id": curve,
+    } | props
+    return add_curve(network, ChangeSet(ps))
+
+@router.post("/deletecurve/", response_model=None, summary="删除曲线", description="从网络中删除指定的曲线")
+async def fastapi_delete_curve(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    curve: str = Query(..., description="曲线ID")
+) -> ChangeSet:
+    """删除曲线。
+    
+    从指定网络中删除指定的曲线及其相关数据。
+    """
+    ps = {"id": curve}
+    return delete_curve(network, ChangeSet(ps))
+
+@router.get("/getcurveproperties/", summary="获取曲线属性", description="获取指定曲线的属性信息")
+async def fastapi_get_curve_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    curve: str = Query(..., description="曲线ID")
+) -> dict[str, Any]:
+    """获取曲线属性。
+    
+    返回指定曲线的所有属性信息。
+    """
+    return get_curve(network, curve)
+
+@router.post("/setcurveproperties/", response_model=None, summary="设置曲线属性", description="更新指定曲线的属性")
+async def fastapi_set_curve_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    curve: str = Query(..., description="曲线ID"),
+    req: Request = None
+) -> ChangeSet:
+    """设置曲线属性。
+    
+    更新指定曲线的属性值。
+    """
+    props = await req.json()
+    ps = {"id": curve} | props
+    return set_curve(network, ChangeSet(ps))
+
+@router.get("/getcurves/", summary="获取所有曲线", description="获取网络中的所有曲线列表")
+async def fastapi_get_curves(network: str = Query(..., description="管网名称（或数据库名称）")) -> list[str]:
+    """获取所有曲线。
+    
+    返回指定网络中的所有曲线ID列表。
+    """
+    return get_curves(network)
+
+@router.get("/iscurve/", summary="检查曲线存在性", description="检查指定的曲线是否存在")
+async def fastapi_is_curve(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    curve: str = Query(..., description="曲线ID")
+) -> bool:
+    """检查曲线是否存在。
+    
+    判断指定的曲线是否在网络中存在。
+    """
+    return is_curve(network, curve)

app/api/v1/endpoints/components/options.py

@@ -0,0 +1,137 @@
+from fastapi import APIRouter, Request, Query, Path, Body
+from typing import Any, List, Dict, Union
+from app.services.tjnetwork import (
+    Any,
+    ChangeSet,
+    get_energy,
+    get_energy_schema,
+    get_option_v3,
+    get_option_v3_schema,
+    get_pump_energy,
+    get_pump_energy_schema,
+    get_time,
+    get_time_schema,
+    set_energy,
+    set_option_v3,
+    set_pump_energy,
+    set_time,
+)
+
+router = APIRouter()
+
+@router.get("/gettimeschema", summary="获取时间选项架构", description="获取网络中时间选项的架构定义")
+async def fastapi_get_time_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """获取时间选项架构。
+    
+    返回指定网络中时间相关选项的属性架构定义。
+    """
+    return get_time_schema(network)
+
+@router.get("/gettimeproperties/", summary="获取时间选项属性", description="获取指定网络中的时间选项属性信息")
+async def fastapi_get_time_properties(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, Any]:
+    """获取时间选项属性。
+    
+    返回指定网络中的时间相关选项属性。
+    """
+    return get_time(network)
+
+@router.post("/settimeproperties/", response_model=None, summary="设置时间选项属性", description="更新指定网络中的时间选项属性")
+async def fastapi_set_time_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置时间选项属性。
+    
+    更新指定网络中的时间相关选项属性值。
+    """
+    props = await req.json()
+    return set_time(network, ChangeSet(props))
+
+@router.get("/getenergyschema/", summary="获取能耗选项架构", description="获取网络中能耗选项的架构定义")
+async def fastapi_get_energy_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """获取能耗选项架构。
+    
+    返回指定网络中能耗相关选项的属性架构定义。
+    """
+    return get_energy_schema(network)
+
+@router.get("/getenergyproperties/", summary="获取能耗选项属性", description="获取指定网络中的能耗选项属性信息")
+async def fastapi_get_energy_properties(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, Any]:
+    """获取能耗选项属性。
+    
+    返回指定网络中的能耗相关选项属性。
+    """
+    return get_energy(network)
+
+@router.post("/setenergyproperties/", response_model=None, summary="设置能耗选项属性", description="更新指定网络中的能耗选项属性")
+async def fastapi_set_energy_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置能耗选项属性。
+    
+    更新指定网络中的能耗相关选项属性值。
+    """
+    props = await req.json()
+    return set_energy(network, ChangeSet(props))
+
+@router.get("/getpumpenergyschema/", summary="获取泵能耗选项架构", description="获取网络中泵能耗选项的架构定义")
+async def fastapi_get_pump_energy_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """获取泵能耗选项架构。
+    
+    返回指定网络中泵能耗相关选项的属性架构定义。
+    """
+    return get_pump_energy_schema(network)
+
+@router.get("/getpumpenergyproperties//", summary="获取泵能耗属性", description="获取指定泵的能耗属性信息")
+async def fastapi_get_pump_energy_proeprties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pump: str = Query(..., description="泵ID")
+) -> dict[str, Any]:
+    """获取泵能耗属性。
+    
+    返回指定泵的能耗相关属性。
+    """
+    return get_pump_energy(network, pump)
+
+@router.get("/setpumpenergyproperties//", response_model=None, summary="设置泵能耗属性", description="更新指定泵的能耗属性")
+async def fastapi_set_pump_energy_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pump: str = Query(..., description="泵ID"),
+    req: Request = None
+) -> ChangeSet:
+    """设置泵能耗属性。
+    
+    更新指定泵的能耗相关属性值。
+    """
+    props = await req.json()
+    ps = {"id": pump} | props
+    return set_pump_energy(network, ChangeSet(ps))
+
+@router.get("/getoptionschema/", summary="获取选项架构", description="获取网络中选项对象的架构定义")
+async def fastapi_get_option_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """获取选项架构。
+    
+    返回指定网络中选项对象的属性架构定义。
+    """
+    return get_option_v3_schema(network)
+
+@router.get("/getoptionproperties/", summary="获取选项属性", description="获取指定网络中的选项属性信息")
+async def fastapi_get_option_properties(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, Any]:
+    """获取选项属性。
+    
+    返回指定网络中的选项对象属性信息。
+    """
+    return get_option_v3(network)
+
+@router.post("/setoptionproperties/", response_model=None, summary="设置选项属性", description="更新指定网络中的选项属性")
+async def fastapi_set_option_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置选项属性。
+    
+    更新指定网络中的选项属性值。
+    """
+    props = await req.json()
+    return set_option_v3(network, ChangeSet(props))

app/api/v1/endpoints/components/patterns.py

@@ -0,0 +1,95 @@
+from fastapi import APIRouter, Request, Query, Path, Body
+from typing import Any, List, Dict, Union
+from app.services.tjnetwork import (
+    Any,
+    ChangeSet,
+    add_pattern,
+    delete_pattern,
+    get_pattern,
+    get_pattern_schema,
+    get_patterns,
+    is_pattern,
+    set_pattern,
+)
+
+router = APIRouter()
+
+@router.get("/getpatternschema", summary="获取模式架构", description="获取网络中模式对象的架构定义")
+async def fastapi_get_pattern_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """获取模式架构。
+    
+    返回指定网络中模式对象的属性架构定义。
+    """
+    return get_pattern_schema(network)
+
+@router.post("/addpattern/", response_model=None, summary="添加模式", description="在网络中添加一个新的模式")
+async def fastapi_add_pattern(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pattern: str = Query(..., description="模式ID"),
+    req: Request = None
+) -> ChangeSet:
+    """添加模式。
+    
+    在指定网络中创建一个新的模式，并设置其初始属性。
+    """
+    props = await req.json()
+    ps = {
+        "id": pattern,
+    } | props
+    return add_pattern(network, ChangeSet(ps))
+
+@router.post("/deletepattern/", response_model=None, summary="删除模式", description="从网络中删除指定的模式")
+async def fastapi_delete_pattern(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pattern: str = Query(..., description="模式ID")
+) -> ChangeSet:
+    """删除模式。
+    
+    从指定网络中删除指定的模式及其相关数据。
+    """
+    ps = {"id": pattern}
+    return delete_pattern(network, ChangeSet(ps))
+
+@router.get("/getpatternproperties/", summary="获取模式属性", description="获取指定模式的属性信息")
+async def fastapi_get_pattern_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pattern: str = Query(..., description="模式ID")
+) -> dict[str, Any]:
+    """获取模式属性。
+    
+    返回指定模式的所有属性信息。
+    """
+    return get_pattern(network, pattern)
+
+@router.post("/setpatternproperties/", response_model=None, summary="设置模式属性", description="更新指定模式的属性")
+async def fastapi_set_pattern_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pattern: str = Query(..., description="模式ID"),
+    req: Request = None
+) -> ChangeSet:
+    """设置模式属性。
+    
+    更新指定模式的属性值。
+    """
+    props = await req.json()
+    ps = {"id": pattern} | props
+    return set_pattern(network, ChangeSet(ps))
+
+@router.get("/ispattern/", summary="检查模式存在性", description="检查指定的模式是否存在")
+async def fastapi_is_pattern(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pattern: str = Query(..., description="模式ID")
+) -> bool:
+    """检查模式是否存在。
+    
+    判断指定的模式是否在网络中存在。
+    """
+    return is_pattern(network, pattern)
+
+@router.get("/getpatterns/", summary="获取所有模式", description="获取网络中的所有模式列表")
+async def fastapi_get_patterns(network: str = Query(..., description="管网名称（或数据库名称）")) -> list[str]:
+    """获取所有模式。
+    
+    返回指定网络中的所有模式ID列表。
+    """
+    return get_patterns(network)

app/api/v1/endpoints/components/quality.py

@@ -0,0 +1,297 @@
+from fastapi import APIRouter, Request, Query, Path, Body
+from typing import Any, List, Dict, Union
+from app.services.tjnetwork import (
+    Any,
+    ChangeSet,
+    add_mixing,
+    add_source,
+    api,
+    delete_mixing,
+    delete_source,
+    get_emitter,
+    get_emitter_schema,
+    get_mixing,
+    get_mixing_schema,
+    get_pipe_reaction,
+    get_pipe_reaction_schema,
+    get_quality,
+    get_quality_schema,
+    get_reaction,
+    get_reaction_schema,
+    get_source,
+    get_source_schema,
+    get_tank_reaction,
+    get_tank_reaction_schema,
+    set_emitter,
+    set_pipe_reaction,
+    set_quality,
+    set_reaction,
+    set_source,
+    set_tank_reaction,
+)
+
+router = APIRouter()
+
+@router.get("/getqualityschema/", summary="获取水质架构", description="获取网络中水质对象的架构定义")
+async def fastapi_get_quality_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """获取水质架构。
+    
+    返回指定网络中水质对象的属性架构定义。
+    """
+    return get_quality_schema(network)
+
+@router.get("/getqualityproperties/", summary="获取水质属性", description="获取指定节点的水质属性信息")
+async def fastapi_get_quality_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    node: str = Query(..., description="节点ID")
+) -> dict[str, Any]:
+    """获取水质属性。
+    
+    返回指定节点的水质属性信息。
+    """
+    return get_quality(network, node)
+
+@router.post("/setqualityproperties/", response_model=None, summary="设置水质属性", description="更新指定节点的水质属性")
+async def fastapi_set_quality_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置水质属性。
+    
+    更新指定节点的水质属性值。
+    """
+    props = await req.json()
+    return set_quality(network, ChangeSet(props))
+
+@router.get("/getemitterschema", summary="获取发射器架构", description="获取网络中发射器对象的架构定义")
+async def fastapi_get_emitter_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """获取发射器架构。
+    
+    返回指定网络中发射器对象的属性架构定义。
+    """
+    return get_emitter_schema(network)
+
+@router.get("/getemitterproperties/", summary="获取发射器属性", description="获取指定连接点的发射器属性信息")
+async def fastapi_get_emitter_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="连接点ID")
+) -> dict[str, Any]:
+    """获取发射器属性。
+    
+    返回指定连接点的发射器属性信息。
+    """
+    return get_emitter(network, junction)
+
+@router.post("/setemitterproperties/", response_model=None, summary="设置发射器属性", description="更新指定连接点的发射器属性")
+async def fastapi_set_emitter_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="连接点ID"),
+    req: Request = None
+) -> ChangeSet:
+    """设置发射器属性。
+    
+    更新指定连接点的发射器属性值。
+    """
+    props = await req.json()
+    ps = {"junction": junction} | props
+    return set_emitter(network, ChangeSet(ps))
+
+@router.get("/getsourcechema/", summary="获取水源架构", description="获取网络中水源对象的架构定义")
+async def fastapi_get_source_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """获取水源架构。
+    
+    返回指定网络中水源对象的属性架构定义。
+    """
+    return get_source_schema(network)
+
+@router.get("/getsource/", summary="获取水源属性", description="获取指定节点的水源属性信息")
+async def fastapi_get_source(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    node: str = Query(..., description="节点ID")
+) -> dict[str, Any]:
+    """获取水源属性。
+    
+    返回指定节点的水源属性信息。
+    """
+    return get_source(network, node)
+
+@router.post("/setsource/", response_model=None, summary="设置水源属性", description="更新指定节点的水源属性")
+async def fastapi_set_source(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置水源属性。
+    
+    更新指定节点的水源属性值。
+    """
+    props = await req.json()
+    return set_source(network, ChangeSet(props))
+
+@router.post("/addsource/", response_model=None, summary="添加水源", description="在网络中添加一个新的水源")
+async def fastapi_add_source(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """添加水源。
+    
+    在指定网络中创建一个新的水源，并设置其初始属性。
+    """
+    props = await req.json()
+    return add_source(network, ChangeSet(props))
+
+@router.post("/deletesource/", response_model=None, summary="删除水源", description="从网络中删除指定节点的水源")
+async def fastapi_delete_source(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    node: str = Query(..., description="节点ID")
+) -> ChangeSet:
+    """删除水源。
+    
+    从指定网络中删除指定节点的水源。
+    """
+    props = {"node": node}
+    return delete_source(network, ChangeSet(props))
+
+@router.get("/getreactionschema/", summary="获取反应架构", description="获取网络中反应对象的架构定义")
+async def fastapi_get_reaction_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """获取反应架构。
+    
+    返回指定网络中反应对象的属性架构定义。
+    """
+    return get_reaction_schema(network)
+
+@router.get("/getreaction/", summary="获取反应属性", description="获取指定网络中的反应属性信息")
+async def fastapi_get_reaction(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, Any]:
+    """获取反应属性。
+    
+    返回指定网络中的反应属性信息。
+    """
+    return get_reaction(network)
+
+@router.post("/setreaction/", response_model=None, summary="设置反应属性", description="更新指定网络中的反应属性")
+async def fastapi_set_reaction(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置反应属性。
+    
+    更新指定网络中的反应属性值。
+    """
+    props = await req.json()
+    return set_reaction(network, ChangeSet(props))
+
+@router.get("/getpipereactionschema/", summary="获取管道反应架构", description="获取网络中管道反应对象的架构定义")
+async def fastapi_get_pipe_reaction_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """获取管道反应架构。
+    
+    返回指定网络中管道反应对象的属性架构定义。
+    """
+    return get_pipe_reaction_schema(network)
+
+@router.get("/getpipereaction/", summary="获取管道反应属性", description="获取指定管道的反应属性信息")
+async def fastapi_get_pipe_reaction(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe: str = Query(..., description="管道ID")
+) -> dict[str, Any]:
+    """获取管道反应属性。
+    
+    返回指定管道的反应属性信息。
+    """
+    return get_pipe_reaction(network, pipe)
+
+@router.post("/setpipereaction/", response_model=None, summary="设置管道反应属性", description="更新指定管道的反应属性")
+async def fastapi_set_pipe_reaction(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置管道反应属性。
+    
+    更新指定管道的反应属性值。
+    """
+    props = await req.json()
+    return set_pipe_reaction(network, ChangeSet(props))
+
+@router.get("/gettankreactionschema/", summary="获取水池反应架构", description="获取网络中水池反应对象的架构定义")
+async def fastapi_get_tank_reaction_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """获取水池反应架构。
+    
+    返回指定网络中水池反应对象的属性架构定义。
+    """
+    return get_tank_reaction_schema(network)
+
+@router.get("/gettankreaction/", summary="获取水池反应属性", description="获取指定水池的反应属性信息")
+async def fastapi_get_tank_reaction(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水池ID")
+) -> dict[str, Any]:
+    """获取水池反应属性。
+    
+    返回指定水池的反应属性信息。
+    """
+    return get_tank_reaction(network, tank)
+
+@router.post("/settankreaction/", response_model=None, summary="设置水池反应属性", description="更新指定水池的反应属性")
+async def fastapi_set_tank_reaction(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置水池反应属性。
+    
+    更新指定水池的反应属性值。
+    """
+    props = await req.json()
+    return set_tank_reaction(network, ChangeSet(props))
+
+@router.get("/getmixingschema/", summary="获取混合架构", description="获取网络中混合对象的架构定义")
+async def fastapi_get_mixing_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """获取混合架构。
+    
+    返回指定网络中混合对象的属性架构定义。
+    """
+    return get_mixing_schema(network)
+
+@router.get("/getmixing/", summary="获取混合属性", description="获取指定水池的混合属性信息")
+async def fastapi_get_mixing(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水池ID")
+) -> dict[str, Any]:
+    """获取混合属性。
+    
+    返回指定水池的混合属性信息。
+    """
+    return get_mixing(network, tank)
+
+@router.post("/setmixing/", response_model=None, summary="设置混合属性", description="更新指定水池的混合属性")
+async def fastapi_set_mixing(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置混合属性。
+    
+    更新指定水池的混合属性值。
+    """
+    props = await req.json()
+    return api.set_mixing(network, ChangeSet(props))
+
+@router.post("/addmixing/", response_model=None, summary="添加混合", description="在网络中添加一个新的混合")
+async def fastapi_add_mixing(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """添加混合。
+    
+    在指定网络中创建一个新的混合，并设置其初始属性。
+    """
+    props = await req.json()
+    return add_mixing(network, ChangeSet(props))
+
+@router.post("/deletemixing/", response_model=None, summary="删除混合", description="从网络中删除指定的混合")
+async def fastapi_delete_mixing(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """删除混合。
+    
+    从指定网络中删除指定的混合及其相关数据。
+    """
+    props = await req.json()
+    return delete_mixing(network, ChangeSet(props))

app/api/v1/endpoints/components/visuals.py

@@ -0,0 +1,180 @@
+from fastapi import APIRouter, Request, Query, Path, Body, Response
+from typing import Any, List, Dict, Union
+from app.services.tjnetwork import (
+    Any,
+    ChangeSet,
+    add_label,
+    add_vertex,
+    delete_label,
+    delete_vertex,
+    get_all_vertex_links,
+    get_all_vertices,
+    get_backdrop,
+    get_backdrop_schema,
+    get_label,
+    get_label_schema,
+    get_vertex,
+    get_vertex_schema,
+    set_backdrop,
+    set_label,
+    set_vertex,
+)
+from fastapi.responses import PlainTextResponse
+import json
+
+router = APIRouter()
+
+@router.get("/getvertexschema/", summary="获取图形元素架构", description="获取网络中图形元素对象的架构定义")
+async def fastapi_get_vertex_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """获取图形元素架构。
+    
+    返回指定网络中图形元素对象的属性架构定义。
+    """
+    return get_vertex_schema(network)
+
+@router.get("/getvertexproperties/", summary="获取图形元素属性", description="获取指定图形元素的属性信息")
+async def fastapi_get_vertex_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    link: str = Query(..., description="图形元素链接")
+) -> dict[str, Any]:
+    """获取图形元素属性。
+    
+    返回指定图形元素的所有属性信息。
+    """
+    return get_vertex(network, link)
+
+@router.post("/setvertexproperties/", response_model=None, summary="设置图形元素属性", description="更新指定图形元素的属性")
+async def fastapi_set_vertex_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置图形元素属性。
+    
+    更新指定图形元素的属性值。
+    """
+    props = await req.json()
+    return set_vertex(network, ChangeSet(props))
+
+@router.post("/addvertex/", response_model=None, summary="添加图形元素", description="在网络中添加一个新的图形元素")
+async def fastapi_add_vertex(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """添加图形元素。
+    
+    在指定网络中创建一个新的图形元素，并设置其初始属性。
+    """
+    props = await req.json()
+    return add_vertex(network, ChangeSet(props))
+
+@router.post("/deletevertex/", response_model=None, summary="删除图形元素", description="从网络中删除指定的图形元素")
+async def fastapi_delete_vertex(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """删除图形元素。
+    
+    从指定网络中删除指定的图形元素及其相关数据。
+    """
+    props = await req.json()
+    return delete_vertex(network, ChangeSet(props))
+
+@router.get("/getallvertexlinks/", response_class=PlainTextResponse, summary="获取所有图形元素链接", description="获取网络中的所有图形元素链接列表")
+async def fastapi_get_all_vertex_links(network: str = Query(..., description="管网名称（或数据库名称）")) -> list[str]:
+    """获取所有图形元素链接。
+    
+    返回指定网络中的所有图形元素链接列表。
+    """
+    return json.dumps(get_all_vertex_links(network))
+
+@router.get("/getallvertices/", response_class=PlainTextResponse, summary="获取所有图形元素", description="获取网络中的所有图形元素详细信息")
+async def fastapi_get_all_vertices(network: str = Query(..., description="管网名称（或数据库名称）")) -> list[dict[str, Any]]:
+    """获取所有图形元素。
+    
+    返回指定网络中的所有图形元素详细信息。
+    """
+    return json.dumps(get_all_vertices(network))
+
+@router.get("/getlabelschema/", summary="获取标签架构", description="获取网络中标签对象的架构定义")
+async def fastapi_get_label_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """获取标签架构。
+    
+    返回指定网络中标签对象的属性架构定义。
+    """
+    return get_label_schema(network)
+
+@router.get("/getlabelproperties/", summary="获取标签属性", description="获取指定坐标处的标签属性信息")
+async def fastapi_get_label_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    x: float = Query(..., description="X坐标"),
+    y: float = Query(..., description="Y坐标")
+) -> dict[str, Any]:
+    """获取标签属性。
+    
+    返回指定坐标处的标签属性信息。
+    """
+    return get_label(network, x, y)
+
+@router.post("/setlabelproperties/", response_model=None, summary="设置标签属性", description="更新指定标签的属性")
+async def fastapi_set_label_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置标签属性。
+    
+    更新指定标签的属性值。
+    """
+    props = await req.json()
+    return set_label(network, ChangeSet(props))
+
+@router.post("/addlabel/", response_model=None, summary="添加标签", description="在网络中添加一个新的标签")
+async def fastapi_add_label(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """添加标签。
+    
+    在指定网络中创建一个新的标签，并设置其初始属性。
+    """
+    props = await req.json()
+    return add_label(network, ChangeSet(props))
+
+@router.post("/deletelabel/", response_model=None, summary="删除标签", description="从网络中删除指定的标签")
+async def fastapi_delete_label(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """删除标签。
+    
+    从指定网络中删除指定的标签及其相关数据。
+    """
+    props = await req.json()
+    return delete_label(network, ChangeSet(props))
+
+@router.get("/getbackdropschema/", summary="获取背景架构", description="获取网络中背景对象的架构定义")
+async def fastapi_get_backdrop_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """获取背景架构。
+    
+    返回指定网络中背景对象的属性架构定义。
+    """
+    return get_backdrop_schema(network)
+
+@router.get("/getbackdropproperties/", summary="获取背景属性", description="获取指定网络的背景属性信息")
+async def fastapi_get_backdrop_properties(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, Any]:
+    """获取背景属性。
+    
+    返回指定网络的背景属性信息。
+    """
+    return get_backdrop(network)
+
+@router.post("/setbackdropproperties/", response_model=None, summary="设置背景属性", description="更新指定网络的背景属性")
+async def fastapi_set_backdrop_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置背景属性。
+    
+    更新指定网络的背景属性值。
+    """
+    props = await req.json()
+    return set_backdrop(network, ChangeSet(props))

app/api/v1/endpoints/extension.py

@@ -0,0 +1,104 @@
+from typing import List, Any
+from fastapi import APIRouter, Request, HTTPException, Query, Body
+from app.services.tjnetwork import (
+    ChangeSet,
+    get_all_extension_data_keys,
+    get_all_extension_data,
+    get_extension_data,
+    set_extension_data
+)
+
+router = APIRouter()
+
+@router.get(
+    "/getallextensiondatakeys/",
+    summary="获取所有扩展数据键",
+    description="获取指定网络的所有扩展数据的键列表"
+)
+async def get_all_extension_data_keys_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> list[str]:
+    """
+    获取所有扩展数据键。
+    
+    返回指定网络中所有可用的扩展数据键。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        扩展数据键列表
+    """
+    return get_all_extension_data_keys(network)
+
+@router.get(
+    "/getallextensiondata/",
+    summary="获取所有扩展数据",
+    description="获取指定网络的所有扩展数据"
+)
+async def get_all_extension_data_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> dict[str, Any]:
+    """
+    获取所有扩展数据。
+    
+    返回指定网络的所有扩展数据及其值。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        扩展数据字典
+    """
+    return get_all_extension_data(network)
+
+@router.get(
+    "/getextensiondata/",
+    summary="获取指定扩展数据",
+    description="获取指定网络中指定键的扩展数据值"
+)
+async def get_extension_data_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    key: str = Query(..., description="扩展数据键")
+) -> str | None:
+    """
+    获取指定扩展数据。
+    
+    返回指定网络中指定键对应的扩展数据值。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        key: 扩展数据键
+        
+    Returns:
+        扩展数据值，如果不存在返回None
+    """
+    return get_extension_data(network, key)
+
+@router.post(
+    "/setextensiondata/",
+    response_model=None,
+    summary="设置扩展数据",
+    description="设置指定网络中的扩展数据"
+)
+async def set_extension_data_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """
+    设置扩展数据。
+    
+    在指定网络中设置扩展数据，并返回变更集信息。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        req: 包含扩展数据的请求体
+        
+    Returns:
+        变更集信息
+    """
+    props = await req.json()
+    print(props)
+    cs = set_extension_data(network, ChangeSet(props))
+    print(cs.operations[0])
+    return cs

app/api/v1/endpoints/geocoding.py

@@ -0,0 +1,29 @@
+from typing import Any
+
+from fastapi import APIRouter, HTTPException, status
+
+from app.services.geocoding import (
+    TiandituGeocodeRequest,
+    TiandituGeocodingAPIError,
+    TiandituGeocodingConfigError,
+    geocode_tianditu,
+)
+
+router = APIRouter()
+
+
+@router.post(
+    "/tianditu/geocode",
+    summary="Tianditu Geocoding",
+    description="调用天地图地理编码服务，将结构化地址转换为经纬度",
+)
+async def tianditu_geocode(request: TiandituGeocodeRequest) -> dict[str, Any]:
+    try:
+        return await geocode_tianditu(request)
+    except TiandituGeocodingConfigError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=str(exc),
+        ) from exc
+    except TiandituGeocodingAPIError as exc:
+        raise HTTPException(status_code=exc.status_code, detail=exc.detail) from exc

app/api/v1/endpoints/leakage.py

@@ -0,0 +1,133 @@
+import os
+from typing import Any
+from datetime import datetime
+
+from fastapi import APIRouter, Depends, HTTPException, Query, Path, Body
+from pydantic import BaseModel, Field
+
+from app.auth.keycloak_dependencies import get_current_keycloak_username
+from app.services.leakage_identifier import (
+    get_leakage_identify_scheme_detail,
+    list_leakage_identify_schemes,
+    run_leakage_identification,
+)
+
+router = APIRouter()
+DEFAULT_N_WORKERS = max(1, min((os.cpu_count() or 1) - 1, 4))
+
+
+class LeakageIdentifyRequest(BaseModel):
+    """漏损识别请求模型"""
+    network: str = Field(..., description="管网名称（或数据库名称）")
+    observed_pressure_data: str | dict[str, list[Any]] | list[dict[str, Any]] | None = Field(
+        None, description="观测的压力数据"
+    )
+    start_time: float = Field(0, description="起始时间（小时）")
+    duration: float = Field(24, description="持续时间（小时）")
+    timestep: float = Field(5, description="时间步长（分钟）")
+    q_sum: float = Field(0.2, description="总流量（m3/s）")
+    q_sum_unit: str = Field("m3/s", description="流量单位")
+    output_dir: str = Field("db_inp", description="输出目录")
+    pop_size: int = Field(50, description="种群大小")
+    max_gen: int = Field(100, description="最大代数")
+    n_workers: int = Field(DEFAULT_N_WORKERS, description="工作线程数")
+    output_flow_unit: str = Field("m3/s", description="输出流量单位")
+    dma_count: int | None = Field(None, description="DMA区域数量")
+    scada_start: datetime | None = Field(None, description="SCADA数据起始时间")
+    scada_end: datetime | None = Field(None, description="SCADA数据结束时间")
+    sensor_nodes: list[str] | None = Field(None, description="传感器节点列表")
+    scheme_name: str | None = Field(None, description="方案名称")
+
+
+@router.post(
+    "/identify/",
+    summary="执行漏损识别",
+    description="基于压力观测数据和遗传算法识别管网中的漏损位置和大小"
+)
+async def identify_leakage(
+    data: LeakageIdentifyRequest = Body(..., description="漏损识别请求数据"),
+    username: str = Depends(get_current_keycloak_username),
+) -> dict[str, Any]:
+    """
+    执行漏损识别分析。
+    
+    使用遗传算法对比模型计算和实测压力数据，
+    识别管网中的漏损节点和漏水量。
+    
+    Args:
+        data: 包含管网名称（或数据库名称）、压力数据及优化参数的请求体
+        username: 当前认证用户名
+        
+    Returns:
+        包含识别结果的字典
+        
+    Raises:
+        HTTPException: 当处理过程中发生错误时
+    """
+    try:
+        return run_leakage_identification(**data.model_dump(), username=username)
+    except Exception as exc:
+        raise HTTPException(status_code=400, detail=str(exc))
+
+
+@router.get(
+    "/schemes/",
+    summary="查询漏损识别方案列表",
+    description="获取指定网络的所有漏损识别方案"
+)
+async def query_leakage_schemes(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    query_date: datetime | None = Query(None, description="查询日期（可选）")
+) -> list[dict[str, Any]]:
+    """
+    获取漏损识别方案列表。
+    
+    查询指定网络的所有已配置的漏损识别方案，
+    可按日期进行筛选。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        query_date: 查询日期（可选）
+        
+    Returns:
+        漏损识别方案列表
+        
+    Raises:
+        HTTPException: 当查询失败时
+    """
+    try:
+        return list_leakage_identify_schemes(network=network, query_date=query_date)
+    except Exception as exc:
+        raise HTTPException(status_code=400, detail=str(exc))
+
+
+@router.get(
+    "/schemes/{scheme_name}",
+    summary="获取漏损识别方案详情",
+    description="获取指定漏损识别方案的详细信息"
+)
+async def query_leakage_scheme_detail(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    scheme_name: str = Path(..., description="漏损识别方案名称")
+) -> dict[str, Any]:
+    """
+    获取漏损识别方案详情。
+    
+    查询指定漏损识别方案的完整配置和参数信息。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        scheme_name: 漏损识别方案名称
+        
+    Returns:
+        包含方案详情的字典
+        
+    Raises:
+        HTTPException: 当查询失败时
+    """
+    try:
+        return get_leakage_identify_scheme_detail(
+            network=network, scheme_name=scheme_name
+        )
+    except Exception as exc:
+        raise HTTPException(status_code=400, detail=str(exc))

app/api/v1/endpoints/meta.py

@@ -0,0 +1,120 @@
+import logging
+from fastapi import APIRouter, Depends, HTTPException, status, Query, Path
+import psycopg
+from psycopg import AsyncConnection
+from sqlalchemy import text
+from sqlalchemy.exc import SQLAlchemyError
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.auth.project_dependencies import (
+    ProjectContext,
+    get_project_context,
+    get_project_pg_session,
+    get_project_timescale_connection,
+    get_metadata_repository,
+)
+from app.auth.metadata_dependencies import get_current_metadata_user
+from app.core.config import settings
+from app.domain.schemas.metadata import (
+    ProjectMetaResponse,
+    ProjectSummaryResponse,
+)
+from app.infra.db.metadb.repositories.metadata_repository import MetadataRepository
+
+router = APIRouter()
+logger = logging.getLogger(__name__)
+
+
+@router.get("/meta/project", summary="获取项目元数据", description="获取当前项目的元数据和配置信息", response_model=ProjectMetaResponse)
+async def get_project_metadata(
+    ctx: ProjectContext = Depends(get_project_context),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+):
+    """
+    获取项目元数据
+    
+    返回当前项目的完整元数据
+    """
+    project = await metadata_repo.get_project_by_id(ctx.project_id)
+    if not project:
+        raise HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND, detail="Project not found"
+        )
+    return ProjectMetaResponse(
+        project_id=project.id,
+        name=project.name,
+        code=project.code,
+        description=project.description,
+        gs_workspace=project.gs_workspace,
+        map_extent=project.map_extent,
+        status=project.status,
+        project_role=ctx.project_role,
+    )
+
+
+@router.get("/meta/projects", summary="列出用户项目", description="获取当前用户有权限的所有项目列表", response_model=list[ProjectSummaryResponse])
+async def list_user_projects(
+    current_user=Depends(get_current_metadata_user),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+):
+    """
+    列出用户的所有项目
+    
+    返回当前用户有权限访问的项目摘要列表
+    """
+    try:
+        projects = await metadata_repo.list_projects_for_user(current_user.id)
+    except SQLAlchemyError as exc:
+        logger.error(
+            "Metadata DB error while listing projects for user %s",
+            current_user.id,
+            exc_info=True,
+        )
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=f"Metadata database error: {exc}",
+        ) from exc
+    return [
+        ProjectSummaryResponse(
+            project_id=project.project_id,
+            name=project.name,
+            code=project.code,
+            description=project.description,
+            gs_workspace=project.gs_workspace,
+            status=project.status,
+            project_role=project.project_role,
+        )
+        for project in projects
+    ]
+
+
+@router.get("/meta/db/health", summary="检查数据库健康状态", description="检查项目数据库连接的健康状况")
+async def project_db_health(
+    pg_session: AsyncSession = Depends(get_project_pg_session),
+    ts_conn: AsyncConnection = Depends(get_project_timescale_connection),
+):
+    """
+    检查数据库健康状态
+    
+    检查PostgreSQL和TimescaleDB数据库的连接状态
+    """
+    try:
+        await pg_session.execute(text("SELECT 1"))
+    except SQLAlchemyError as exc:
+        logger.error("Project PostgreSQL health check failed", exc_info=True)
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=f"Project PostgreSQL health check failed: {exc}",
+        ) from exc
+
+    try:
+        async with ts_conn.cursor() as cur:
+            await cur.execute("SELECT 1")
+    except psycopg.Error as exc:
+        logger.error("Project TimescaleDB health check failed", exc_info=True)
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=f"Project TimescaleDB health check failed: {exc}",
+        ) from exc
+
+    return {"postgres": "ok", "timescale": "ok"}

app/api/v1/endpoints/misc.py

@@ -0,0 +1,65 @@
+from typing import Any
+from fastapi import APIRouter, Query
+from fastapi.responses import JSONResponse
+from fastapi import status
+from pydantic import BaseModel
+from app.services.tjnetwork import (
+    get_all_sensor_placements,
+    get_all_burst_locate_results,
+)
+
+router = APIRouter()
+
+
+@router.get("/getjson/", summary="获取JSON示例", description="获取JSON格式响应示例")
+async def fastapi_get_json():
+    """
+    获取JSON示例
+    
+    返回示例JSON格式的响应
+    """
+    return JSONResponse(
+        status_code=status.HTTP_400_BAD_REQUEST,
+        content={
+            "code": 400,
+            "message": "this is message",
+            "data": 123,
+        },
+    )
+
+
+@router.get("/sensor-placement-schemes", summary="获取所有传感器位置", description="获取网络中所有传感器的放置位置信息")
+@router.get("/getallsensorplacements/", summary="获取所有传感器位置（旧路径）", description="获取网络中所有传感器的放置位置信息", deprecated=True)
+async def fastapi_get_all_sensor_placements(network: str = Query(..., description="管网名称（或数据库名称）")) -> list[dict[Any, Any]]:
+    """
+    获取所有传感器位置
+    
+    返回网络中所有传感器的放置位置及其配置信息
+    """
+    return get_all_sensor_placements(network)
+
+
+@router.get("/getallburstlocateresults/", summary="获取所有爆管定位结果", description="获取网络中所有爆管定位的分析结果")
+async def fastapi_get_all_burst_locate_results(network: str = Query(..., description="管网名称（或数据库名称）")) -> list[dict[Any, Any]]:
+    """
+    获取所有爆管定位结果
+    
+    返回网络中所有的爆管定位分析结果
+    """
+    return get_all_burst_locate_results(network)
+
+
+class Item(BaseModel):
+    """测试数据模型"""
+    str_info: str
+
+
+@router.post("/test_dict/", summary="测试字典处理", description="测试处理字典类型数据")
+async def fastapi_test_dict(data: Item) -> dict[str, str]:
+    """
+    测试字典处理
+    
+    接收Item模型，返回其字典格式
+    """
+    item = data.dict()
+    return item

app/api/v1/endpoints/network/demands.py

@@ -0,0 +1,139 @@
+from fastapi import APIRouter, Request, Query, Path, Body
+from typing import Any, List, Dict, Union
+from app.services.tjnetwork import (
+    Any,
+    ChangeSet,
+    calculate_demand_to_network,
+    calculate_demand_to_nodes,
+    calculate_demand_to_region,
+    get_demand,
+    get_demand_schema,
+    set_demand,
+)
+
+router = APIRouter()
+
+############################################################
+# demand 9.[DEMANDS]
+############################################################
+
+@router.get(
+    "/getdemandschema",
+    summary="获取需水量属性架构",
+    description="获取指定水网中需水量(Demand)的属性架构定义"
+)
+async def fastapi_get_demand_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """
+    获取需水量属性架构。
+    
+    返回指定水网的需水量属性架构，包括所有可配置的属性及其类型定义。
+    """
+    return get_demand_schema(network)
+
+
+@router.get(
+    "/getdemandproperties/",
+    summary="获取需水量属性",
+    description="获取指定水网中节点的需水量属性信息"
+)
+async def fastapi_get_demand_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="节点ID")
+) -> dict[str, Any]:
+    """
+    获取节点的需水量属性。
+    
+    返回指定节点的所有需水量信息，包括需水量值、水压等级等。
+    """
+    return get_demand(network, junction)
+
+
+# example: set_demand(p, ChangeSet({'junction': 'j1', 'demands': [{'demand': 10.0, 'pattern': None, 'category': 'x'}, {'demand': 20.0, 'pattern': None, 'category': None}]}))
+@router.post(
+    "/setdemandproperties/",
+    response_model=None,
+    summary="设置需水量属性",
+    description="设置指定水网中节点的需水量属性信息"
+)
+async def fastapi_set_demand_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="节点ID"),
+    req: Request = None
+) -> ChangeSet:
+    """
+    设置节点的需水量属性。
+    
+    修改指定节点的需水量信息。请求体应包含需水量值、水压等级等属性。
+    """
+    props = await req.json()
+    ps = {"junction": junction} | props
+    return set_demand(network, ChangeSet(ps))
+
+############################################################
+# water distribution 36.[Water Distribution]
+############################################################
+@router.get(
+    "/calculatedemandtonodes/",
+    summary="计算需水量到节点分配",
+    description="将总需水量按指定方式分配到多个节点"
+)
+async def fastapi_calculate_demand_to_nodes(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> dict[str, float]:
+    """
+    计算需水量到节点分配。
+    
+    将指定的总需水量均匀或按比例分配到指定的节点列表中。
+    
+    请求体格式:
+    {
+        "demand": 需水量值(float),
+        "nodes": 节点ID列表(list[str])
+    }
+    """
+    props = await req.json()
+    demand = props["demand"]
+    nodes = props["nodes"]
+    return calculate_demand_to_nodes(network, demand, nodes)
+
+@router.get(
+    "/calculatedemandtoregion/",
+    summary="计算需水量到区域分配",
+    description="将总需水量按区域特征分配到该区域内的节点"
+)
+async def fastapi_calculate_demand_to_region(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> dict[str, float]:
+    """
+    计算需水量到区域分配。
+    
+    根据区域内节点的特征(如面积、人口等)将总需水量分配到该区域的各个节点。
+    
+    请求体格式:
+    {
+        "demand": 需水量值(float),
+        "region": 区域ID(str)
+    }
+    """
+    props = await req.json()
+    demand = props["demand"]
+    region = props["region"]
+    return calculate_demand_to_region(network, demand, region)
+
+@router.get(
+    "/calculatedemandtonetwork/",
+    summary="计算需水量到整网分配",
+    description="将需水量均匀分配到整个水网的所有需水节点"
+)
+async def fastapi_calculate_demand_to_network(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    demand: float = Query(..., description="总需水量(m³/h)", gt=0)
+) -> dict[str, float]:
+    """
+    计算需水量到整网分配。
+    
+    将指定的需水量均匀分配到整个水网的所有需水节点。
+    """
+    return calculate_demand_to_network(network, demand)

app/api/v1/endpoints/network/general.py

@@ -0,0 +1,420 @@
+from fastapi import APIRouter, Request, Query, Path, Body
+from typing import Any, List, Dict, Union
+from app.services.tjnetwork import (
+    Any,
+    ChangeSet,
+    delete_junction,
+    delete_pipe,
+    delete_pump,
+    delete_reservoir,
+    delete_tank,
+    delete_valve,
+    get_all_scada_info,
+    get_element_properties,
+    get_element_properties_with_type,
+    get_element_type,
+    get_element_type_value,
+    get_link_properties,
+    get_link_type,
+    get_links,
+    get_node_links,
+    get_node_properties,
+    get_node_type,
+    get_nodes,
+    get_scada_info,
+    get_status,
+    get_status_schema,
+    get_title,
+    get_title_schema,
+    is_junction,
+    is_link,
+    is_node,
+    is_pipe,
+    is_pump,
+    is_reservoir,
+    is_tank,
+    is_valve,
+    set_status,
+    set_title,
+)
+
+router = APIRouter()
+
+############################################################
+# type
+############################################################
+
+@router.get(
+    "/isnode/",
+    summary="检查节点有效性",
+    description="检查指定ID是否为水网中的有效节点"
+)
+async def fastapi_is_node(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    node: str = Query(..., description="节点ID")
+) -> bool:
+    """检查指定ID是否为节点。"""
+    return is_node(network, node)
+
+@router.get(
+    "/isjunction/",
+    summary="检查是否为接点",
+    description="检查指定ID是否为水网中的接点(需求点)"
+)
+async def fastapi_is_junction(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    node: str = Query(..., description="节点ID")
+) -> bool:
+    """检查指定ID是否为接点。"""
+    return is_junction(network, node)
+
+@router.get(
+    "/isreservoir/",
+    summary="检查是否为水源",
+    description="检查指定ID是否为水网中的水源(水库/河流)"
+)
+async def fastapi_is_reservoir(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    node: str = Query(..., description="节点ID")
+) -> bool:
+    """检查指定ID是否为水源。"""
+    return is_reservoir(network, node)
+
+@router.get(
+    "/istank/",
+    summary="检查是否为蓄水池",
+    description="检查指定ID是否为水网中的蓄水池"
+)
+async def fastapi_is_tank(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    node: str = Query(..., description="节点ID")
+) -> bool:
+    """检查指定ID是否为蓄水池。"""
+    return is_tank(network, node)
+
+@router.get(
+    "/islink/",
+    summary="检查管线有效性",
+    description="检查指定ID是否为水网中的有效管线"
+)
+async def fastapi_is_link(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    link: str = Query(..., description="管线ID")
+) -> bool:
+    """检查指定ID是否为管线。"""
+    return is_link(network, link)
+
+@router.get(
+    "/ispipe/",
+    summary="检查是否为管道",
+    description="检查指定ID是否为水网中的管道"
+)
+async def fastapi_is_pipe(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    link: str = Query(..., description="管线ID")
+) -> bool:
+    """检查指定ID是否为管道。"""
+    return is_pipe(network, link)
+
+@router.get(
+    "/ispump/",
+    summary="检查是否为泵",
+    description="检查指定ID是否为水网中的泵"
+)
+async def fastapi_is_pump(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    link: str = Query(..., description="管线ID")
+) -> bool:
+    """检查指定ID是否为泵。"""
+    return is_pump(network, link)
+
+@router.get(
+    "/isvalve/",
+    summary="检查是否为阀门",
+    description="检查指定ID是否为水网中的阀门"
+)
+async def fastapi_is_valve(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    link: str = Query(..., description="管线ID")
+) -> bool:
+    """检查指定ID是否为阀门。"""
+    return is_valve(network, link)
+
+@router.get(
+    "/getnodetype/",
+    summary="获取节点类型",
+    description="获取指定节点的类型(接点/水源/蓄水池)"
+)
+async def fastapi_get_node_type(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    node: str = Query(..., description="节点ID")
+) -> str:
+    """获取节点的类型标识。"""
+    return get_node_type(network, node)
+
+@router.get(
+    "/getlinktype/",
+    summary="获取管线类型",
+    description="获取指定管线的类型(管道/泵/阀门)"
+)
+async def fastapi_get_link_type(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    link: str = Query(..., description="管线ID")
+) -> str:
+    """获取管线的类型标识。"""
+    return get_link_type(network, link)
+
+@router.get(
+    "/getelementtype/",
+    summary="获取元素类型",
+    description="获取指定元素的类型(节点或管线)"
+)
+async def fastapi_get_element_type(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    element: str = Query(..., description="元素ID")
+) -> str:
+    """获取元素的类型标识。"""
+    return get_element_type(network, element)
+
+@router.get(
+    "/getelementtypevalue/",
+    summary="获取元素类型值",
+    description="获取指定元素的类型数值标识"
+)
+async def fastapi_get_element_type_value(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    element: str = Query(..., description="元素ID")
+) -> int:
+    """获取元素的类型数值。"""
+    return get_element_type_value(network, element)
+
+@router.get(
+    "/getnodes/",
+    summary="获取所有节点",
+    description="获取指定水网中的所有节点ID列表"
+)
+async def fastapi_get_nodes(network: str = Query(..., description="管网名称（或数据库名称）")) -> list[str]:
+    """获取水网中所有节点的ID列表。"""
+    return get_nodes(network)
+
+@router.get(
+    "/getlinks/",
+    summary="获取所有管线",
+    description="获取指定水网中的所有管线ID列表"
+)
+async def fastapi_get_links(network: str = Query(..., description="管网名称（或数据库名称）")) -> list[str]:
+    """获取水网中所有管线的ID列表。"""
+    return get_links(network)
+
+@router.get(
+    "/getnodelinks/",
+    summary="获取节点的关联管线",
+    description="获取指定节点连接的所有管线ID列表"
+)
+def get_node_links_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    node: str = Query(..., description="节点ID")
+) -> list[str]:
+    """获取节点关联的所有管线。"""
+    return get_node_links(network, node)
+
+############################################################
+# Node & Link properties
+############################################################
+
+@router.get(
+    "/getnodeproperties/",
+    summary="获取节点属性",
+    description="获取指定节点的所有属性信息"
+)
+async def fast_get_node_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    node: str = Query(..., description="节点ID")
+) -> dict[str, Any]:
+    """获取节点的完整属性信息。"""
+    return get_node_properties(network, node)
+
+@router.get(
+    "/getlinkproperties/",
+    summary="获取管线属性",
+    description="获取指定管线的所有属性信息"
+)
+async def fast_get_link_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    link: str = Query(..., description="管线ID")
+) -> dict[str, Any]:
+    """获取管线的完整属性信息。"""
+    return get_link_properties(network, link)
+
+@router.get(
+    "/getscadaproperties/",
+    summary="获取SCADA点属性",
+    description="获取指定SCADA点的属性信息"
+)
+async def fast_get_scada_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    scada: str = Query(..., description="SCADA点ID")
+) -> dict[str, Any]:
+    """获取SCADA点的属性信息。"""
+    return get_scada_info(network, scada)
+
+@router.get(
+    "/getallscadaproperties/",
+    summary="获取所有SCADA点属性",
+    description="获取指定水网中所有SCADA点的属性信息"
+)
+async def fast_get_all_scada_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> list[dict[str, Any]]:
+    """获取水网中所有SCADA点的属性列表。"""
+    return get_all_scada_info(network)
+
+@router.get(
+    "/getelementpropertieswithtype/",
+    summary="获取指定类型元素属性",
+    description="获取指定类型的元素属性信息"
+)
+async def fast_get_element_properties_with_type(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    elementtype: str = Query(..., description="元素类型"),
+    element: str = Query(..., description="元素ID")
+) -> dict[str, Any]:
+    """获取指定类型元素的属性。"""
+    return get_element_properties_with_type(network, elementtype, element)
+
+@router.get(
+    "/getelementproperties/",
+    summary="获取元素属性",
+    description="获取指定元素的属性信息"
+)
+async def fast_get_element_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    element: str = Query(..., description="元素ID")
+) -> dict[str, Any]:
+    """获取元素的完整属性信息。"""
+    return get_element_properties(network, element)
+
+############################################################
+# title 1.[TITLE]
+############################################################
+
+@router.get(
+    "/gettitleschema/",
+    summary="获取标题属性架构",
+    description="获取指定水网的标题(标题)属性架构定义"
+)
+async def fast_get_title_schema(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> dict[str, dict[str, Any]]:
+    """获取水网标题的属性架构。"""
+    return get_title_schema(network)
+
+@router.get(
+    "/gettitle/",
+    summary="获取水网标题属性",
+    description="获取指定水网的标题(Title)信息"
+)
+async def fast_get_title(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, Any]:
+    """获取水网的标题属性。"""
+    return get_title(network)
+
+@router.get(
+    "/settitle/",
+    response_model=None,
+    summary="设置水网标题属性",
+    description="设置指定水网的标题(Title)信息"
+)
+async def fastapi_set_title(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置水网的标题属性。"""
+    props = await req.json()
+    return set_title(network, ChangeSet(props))
+
+############################################################
+# status 10.[STATUS]
+############################################################
+
+@router.get(
+    "/getstatusschema",
+    summary="获取状态属性架构",
+    description="获取指定水网的状态(Status)属性架构定义"
+)
+async def fastapi_get_status_schema(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> dict[str, dict[str, Any]]:
+    """获取水网状态的属性架构。"""
+    return get_status_schema(network)
+
+@router.get(
+    "/getstatus/",
+    summary="获取管线状态",
+    description="获取指定管线的状态信息"
+)
+async def fastapi_get_status(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    link: str = Query(..., description="管线ID")
+) -> dict[str, Any]:
+    """获取管线的状态属性。"""
+    return get_status(network, link)
+
+@router.post(
+    "/setstatus/",
+    response_model=None,
+    summary="设置管线状态",
+    description="设置指定管线的状态信息"
+)
+async def fastapi_set_status_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    link: str = Query(..., description="管线ID"),
+    req: Request = None
+) -> ChangeSet:
+    """设置管线的状态属性。"""
+    props = await req.json()
+    ps = {"link": link} | props
+    return set_status(network, ChangeSet(ps))
+
+############################################################
+# General Deletion
+############################################################
+
+@router.post(
+    "/deletenode/",
+    response_model=None,
+    summary="删除节点",
+    description="删除指定的节点(接点/水源/蓄水池)"
+)
+async def fastapi_delete_node(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    node: str = Query(..., description="节点ID")
+) -> ChangeSet:
+    """删除指定的节点。自动识别节点类型并调用相应的删除操作。"""
+    ps = {"id": node}
+    if is_junction(network, node):
+        return delete_junction(network, ChangeSet(ps))
+    elif is_reservoir(network, node):
+        return delete_reservoir(network, ChangeSet(ps))
+    elif is_tank(network, node):
+        return delete_tank(network, ChangeSet(ps))
+    return ChangeSet() # Should probably raise error or return empty
+
+@router.post(
+    "/deletelink/",
+    response_model=None,
+    summary="删除管线",
+    description="删除指定的管线(管道/泵/阀门)"
+)
+async def fastapi_delete_link(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    link: str = Query(..., description="管线ID")
+) -> ChangeSet:
+    """删除指定的管线。自动识别管线类型并调用相应的删除操作。"""
+    ps = {"id": link}
+    if is_pipe(network, link):
+        return delete_pipe(network, ChangeSet(ps))
+    elif is_pump(network, link):
+        return delete_pump(network, ChangeSet(ps))
+    elif is_valve(network, link):
+        return delete_valve(network, ChangeSet(ps))
+    return ChangeSet()

app/api/v1/endpoints/network/geometry.py

@@ -0,0 +1,135 @@
+from fastapi import APIRouter, Request, Depends, Query, Path, Body
+from typing import Any, List, Dict, Union
+from app.services.tjnetwork import (
+    Any,
+    get_all_scada_info,
+    get_major_node_coords,
+    get_major_pipe_nodes,
+    get_network_in_extent,
+    get_network_link_nodes,
+    get_network_node_coords,
+    get_node_coord,
+)
+from app.auth.metadata_dependencies import get_current_metadata_user
+from app.infra.cache.redis_client import redis_client, encode_datetime, decode_datetime
+import msgpack
+
+router = APIRouter()
+
+############################################################
+# coord 24.[COORDINATES]
+############################################################
+
+# @router.get("/getcoordschema/")
+# async def fastapi_get_coord_schema(network: str) -> dict[str, dict[str, Any]]:
+#     return get_coord_schema(network)
+
+# @router.get("/getcoord/")
+# async def fastapi_get_coord(network: str, node: str) -> dict[str, Any]:
+#     return get_coord(network, node)
+
+# # example: set_coord(p, ChangeSet({'node': 'j1', 'x': 1.0, 'y': 2.0}))
+# @router.post("/setcoord/", response_model=None)
+# async def fastapi_set_coord(network: str, req: Request) -> ChangeSet:
+#     props = await req.json()
+#     return set_coord(network, ChangeSet(props))
+
+@router.get(
+    "/getnodecoord/",
+    summary="获取节点坐标",
+    description="获取指定节点的地理坐标(X, Y)"
+)
+async def fastapi_get_node_coord(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    node: str = Query(..., description="节点ID")
+) -> dict[str, float] | None:
+    """获取节点的地理坐标信息。"""
+    return get_node_coord(network, node)
+
+# Additional geometry queries found in main.py logic (implicit or explicit)
+@router.get(
+    "/getnetworkinextent/",
+    summary="获取范围内的网络元素",
+    description="获取指定地理范围内的网络节点和管线"
+)
+async def fastapi_get_network_in_extent(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    x1: float = Query(..., description="范围左下角X坐标", alias="x1"),
+    y1: float = Query(..., description="范围左下角Y坐标", alias="y1"),
+    x2: float = Query(..., description="范围右上角X坐标", alias="x2"),
+    y2: float = Query(..., description="范围右上角Y坐标", alias="y2")
+) -> dict[str, Any]:
+    """获取地理范围内的网络几何信息。"""
+    return get_network_in_extent(network, x1, y1, x2, y2)
+
+@router.get(
+    "/getnetworkgeometries/",
+    dependencies=[Depends(get_current_metadata_user)],
+    summary="获取完整网络几何信息",
+    description="获取整个水网的所有节点、管线和SCADA点的几何信息(需要身份验证)"
+)
+async def fastapi_get_network_geometries(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> dict[str, Any] | None:
+    """获取完整的网络几何信息，包括所有节点、管线和SCADA点。结果从缓存返回。"""
+    cache_key = f"getnetworkgeometries_{network}"
+    data = redis_client.get(cache_key)
+    if data:
+        loaded_dict = msgpack.unpackb(data, object_hook=decode_datetime)
+        return loaded_dict
+
+    coords = get_network_node_coords(network)
+    nodes = []
+    for node_id, coord in coords.items():
+        nodes.append(f"{node_id}:{coord['type']}:{coord['x']}:{coord['y']}")
+    links = get_network_link_nodes(network)
+    scadas = get_all_scada_info(network)
+
+    results = {"nodes": nodes, "links": links, "scadas": scadas}
+    redis_client.set(cache_key, msgpack.packb(results, default=encode_datetime))
+    return results
+
+@router.get(
+    "/getmajornodecoords/",
+    summary="获取主要节点坐标",
+    description="获取直径大于等于指定值的节点坐标"
+)
+async def fastapi_get_majornode_coords(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    diameter: int = Query(..., description="最小直径(mm)", gt=0)
+) -> dict[str, dict[str, float]]:
+    """获取主要节点的坐标。只返回直径大于等于指定值的节点。"""
+    return get_major_node_coords(network, diameter)
+
+@router.get(
+    "/getmajorpipenodes/",
+    summary="获取主要管道节点",
+    description="获取直径大于等于指定值的管道的节点ID"
+)
+async def fastapi_get_major_pipe_nodes(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    diameter: int = Query(..., description="最小直径(mm)", gt=0)
+) -> list[str] | None:
+    """获取主要管道节点。只返回直径大于等于指定值的管道。"""
+    return get_major_pipe_nodes(network, diameter)
+
+@router.get(
+    "/getnetworklinknodes/",
+    summary="获取网络管线节点",
+    description="获取指定水网所有管线的起点和终点节点"
+)
+async def fastapi_get_network_link_nodes(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> list[str] | None:
+    """获取网络中所有管线的连接节点。"""
+    return get_network_link_nodes(network)
+
+# @router.get("/getallcoords/")
+# async def fastapi_get_all_coords(network: str) -> list[Any]:
+#     return get_all_coords(network)
+
+# @router.get("/projectcoordinates/")
+# async def fastapi_project_coordinates(
+#     network: str, from_epsg: int, to_epsg: int
+# ) -> ChangeSet:
+#     return project_coordinates(network, from_epsg, to_epsg)

app/api/v1/endpoints/network/junctions.py

@@ -0,0 +1,361 @@
+from fastapi import APIRouter, Request, Query, Path, Body
+from typing import Any, List, Dict, Union
+from app.services.tjnetwork import (
+    Any,
+    ChangeSet,
+    add_junction,
+    delete_junction,
+    get_all_junctions,
+    get_junction,
+    get_junction_schema,
+    set_junction,
+)
+
+router = APIRouter()
+
+@router.get("/getjunctionschema", summary="获取节点架构", description="获取指定项目的节点属性架构和数据类型定义。")
+async def fast_get_junction_schema(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> dict[str, dict[str, Any]]:
+    """
+    获取节点架构信息。
+    
+    返回指定项目的节点属性架构，包括所有属性的类型和约束信息。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+    """
+    return get_junction_schema(network)
+
+@router.post("/addjunction/", response_model=None, summary="添加节点", description="在供水网络中添加新的节点，指定节点ID和空间坐标。")
+async def fastapi_add_junction(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="节点 ID"),
+    x: float = Query(..., description="X 坐标"),
+    y: float = Query(..., description="Y 坐标"),
+    z: float = Query(..., description="标高（海拔高度）")
+) -> ChangeSet:
+    """
+    添加新节点到供水网络。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        junction: 节点 ID
+        x: X 坐标值
+        y: Y 坐标值
+        z: 标高（海拔高度）
+        
+    Returns:
+        ChangeSet: 包含变更信息的结果
+    """
+    ps = {"id": junction, "x": x, "y": y, "elevation": z}
+    return add_junction(network, ChangeSet(ps))
+
+@router.post("/deletejunction/", response_model=None, summary="删除节点", description="从供水网络中删除指定的节点。")
+async def fastapi_delete_junction(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="节点 ID")
+) -> ChangeSet:
+    """
+    删除指定的节点。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        junction: 节点 ID
+        
+    Returns:
+        ChangeSet: 包含变更信息的结果
+    """
+    ps = {"id": junction}
+    return delete_junction(network, ChangeSet(ps))
+
+@router.get("/getjunctionelevation/", summary="获取节点标高", description="获取指定节点的标高（海拔高度）。")
+async def fastapi_get_junction_elevation(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="节点 ID")
+) -> float:
+    """
+    获取节点的标高值。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        junction: 节点 ID
+        
+    Returns:
+        float: 节点标高值
+    """
+    ps = get_junction(network, junction)
+    return ps["elevation"]
+
+@router.get("/getjunctionx/", summary="获取节点 X 坐标", description="获取指定节点的 X 坐标值。")
+async def fastapi_get_junction_x(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="节点 ID")
+) -> float:
+    """
+    获取节点的 X 坐标。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        junction: 节点 ID
+        
+    Returns:
+        float: 节点 X 坐标值
+    """
+    ps = get_junction(network, junction)
+    return ps["x"]
+
+@router.get("/getjunctiony/", summary="获取节点 Y 坐标", description="获取指定节点的 Y 坐标值。")
+async def fastapi_get_junction_y(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="节点 ID")
+) -> float:
+    """
+    获取节点的 Y 坐标。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        junction: 节点 ID
+        
+    Returns:
+        float: 节点 Y 坐标值
+    """
+    ps = get_junction(network, junction)
+    return ps["y"]
+
+@router.get("/getjunctioncoord/", summary="获取节点坐标", description="获取指定节点的 X 和 Y 坐标。")
+async def fastapi_get_junction_coord(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="节点 ID")
+) -> dict[str, float]:
+    """
+    获取节点的坐标信息。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        junction: 节点 ID
+        
+    Returns:
+        dict: 包含 x 和 y 坐标的字典
+    """
+    ps = get_junction(network, junction)
+    coord = {"x": ps["x"], "y": ps["y"]}
+    return coord
+
+@router.get("/getjunctiondemand/", summary="获取节点需水量", description="获取指定节点的需水量。")
+async def fastapi_get_junction_demand(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="节点 ID")
+) -> float:
+    """
+    获取节点的需水量。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        junction: 节点 ID
+        
+    Returns:
+        float: 节点的需水量值
+    """
+    ps = get_junction(network, junction)
+    return ps["demand"]
+
+@router.get("/getjunctionpattern/", summary="获取节点需水模式", description="获取指定节点的需水模式标识。")
+async def fastapi_get_junction_pattern(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="节点 ID")
+) -> str:
+    """
+    获取节点的需水模式。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        junction: 节点 ID
+        
+    Returns:
+        str: 节点的需水模式标识
+    """
+    ps = get_junction(network, junction)
+    return ps["pattern"]
+
+@router.post("/setjunctionelevation/", response_model=None, summary="设置节点标高", description="设置指定节点的标高值。")
+async def fastapi_set_junction_elevation(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="节点 ID"),
+    elevation: float = Query(..., description="标高（海拔高度）")
+) -> ChangeSet:
+    """
+    设置节点的标高。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        junction: 节点 ID
+        elevation: 标高值
+        
+    Returns:
+        ChangeSet: 包含变更信息的结果
+    """
+    ps = {"id": junction, "elevation": elevation}
+    return set_junction(network, ChangeSet(ps))
+
+@router.post("/setjunctionx/", response_model=None, summary="设置节点 X 坐标", description="设置指定节点的 X 坐标值。")
+async def fastapi_set_junction_x(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="节点 ID"),
+    x: float = Query(..., description="X 坐标值")
+) -> ChangeSet:
+    """
+    设置节点的 X 坐标。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        junction: 节点 ID
+        x: X 坐标值
+        
+    Returns:
+        ChangeSet: 包含变更信息的结果
+    """
+    ps = {"id": junction, "x": x}
+    return set_junction(network, ChangeSet(ps))
+
+@router.post("/setjunctiony/", response_model=None, summary="设置节点 Y 坐标", description="设置指定节点的 Y 坐标值。")
+async def fastapi_set_junction_y(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="节点 ID"),
+    y: float = Query(..., description="Y 坐标值")
+) -> ChangeSet:
+    """
+    设置节点的 Y 坐标。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        junction: 节点 ID
+        y: Y 坐标值
+        
+    Returns:
+        ChangeSet: 包含变更信息的结果
+    """
+    ps = {"id": junction, "y": y}
+    return set_junction(network, ChangeSet(ps))
+
+@router.post("/setjunctioncoord/", response_model=None, summary="设置节点坐标", description="设置指定节点的 X 和 Y 坐标。")
+async def fastapi_set_junction_coord(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="节点 ID"),
+    x: float = Query(..., description="X 坐标值"),
+    y: float = Query(..., description="Y 坐标值")
+) -> ChangeSet:
+    """
+    设置节点的坐标。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        junction: 节点 ID
+        x: X 坐标值
+        y: Y 坐标值
+        
+    Returns:
+        ChangeSet: 包含变更信息的结果
+    """
+    ps = {"id": junction, "x": x, "y": y}
+    return set_junction(network, ChangeSet(ps))
+
+@router.post("/setjunctiondemand/", response_model=None, summary="设置节点需水量", description="设置指定节点的需水量。")
+async def fastapi_set_junction_demand(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="节点 ID"),
+    demand: float = Query(..., description="需水量值")
+) -> ChangeSet:
+    """
+    设置节点的需水量。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        junction: 节点 ID
+        demand: 需水量值
+        
+    Returns:
+        ChangeSet: 包含变更信息的结果
+    """
+    ps = {"id": junction, "demand": demand}
+    return set_junction(network, ChangeSet(ps))
+
+@router.post("/setjunctionpattern/", response_model=None, summary="设置节点需水模式", description="设置指定节点的需水模式标识。")
+async def fastapi_set_junction_pattern(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="节点 ID"),
+    pattern: str = Query(..., description="需水模式标识")
+) -> ChangeSet:
+    """
+    设置节点的需水模式。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        junction: 节点 ID
+        pattern: 需水模式标识
+        
+    Returns:
+        ChangeSet: 包含变更信息的结果
+    """
+    ps = {"id": junction, "pattern": pattern}
+    return set_junction(network, ChangeSet(ps))
+
+@router.get("/getjunctionproperties/", summary="获取节点属性", description="获取指定节点的所有属性信息。")
+async def fastapi_get_junction_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="节点 ID")
+) -> dict[str, Any]:
+    """
+    获取节点的完整属性信息。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        junction: 节点 ID
+        
+    Returns:
+        dict: 包含节点所有属性的字典
+    """
+    return get_junction(network, junction)
+
+@router.get("/getalljunctionproperties/", summary="获取所有节点属性", description="获取指定项目中所有节点的属性信息。")
+async def fastapi_get_all_junction_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> list[dict[str, Any]]:
+    """
+    获取所有节点的属性信息列表。
+    
+    此端点返回指定项目中所有节点的详细属性。缓存查询结果以提高性能。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        list: 包含所有节点属性的列表
+    """
+    # 缓存查询结果提高性能
+    # global redis_client # Redis logic removed for clean split, can be re-added if needed or imported
+    results = get_all_junctions(network)
+    return results
+
+@router.post("/setjunctionproperties/", response_model=None, summary="批量设置节点属性", description="批量设置指定节点的多个属性。")
+async def fastapi_set_junction_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    junction: str = Query(..., description="节点 ID"),
+    req: Request = None
+) -> ChangeSet:
+    """
+    批量设置节点属性。
+    
+    允许一次性设置节点的多个属性，如坐标、标高、需水量等。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        junction: 节点 ID
+        req: 包含属性和值的 JSON 请求体
+        
+    Returns:
+        ChangeSet: 包含变更信息的结果
+    """
+    props = await req.json()
+    ps = {"id": junction} | props
+    return set_junction(network, ChangeSet(ps))

app/api/v1/endpoints/network/pipes.py

@@ -0,0 +1,411 @@
+from fastapi import APIRouter, Request, Query, Path, Body
+from typing import Any, List, Dict, Union
+from app.services.tjnetwork import (
+    Any,
+    ChangeSet,
+    PIPE_STATUS_OPEN,
+    add_pipe,
+    delete_pipe,
+    get_all_pipes,
+    get_pipe,
+    get_pipe_schema,
+    set_pipe,
+)
+
+router = APIRouter()
+
+@router.get("/getpipeschema", summary="获取管道模式", description="获取管道对象的模式定义，包含所有可用字段及其类型")
+async def fastapi_get_pipe_schema(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> dict[str, dict[str, Any]]:
+    """
+    获取管道数据模式定义。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        包含管道模式信息的字典
+    """
+    return get_pipe_schema(network)
+
+@router.post("/addpipe/", response_model=None, summary="添加管道", description="向网络中添加新的管道，需要提供管道的基本参数如长度、管径、粗糙度等")
+async def fastapi_add_pipe(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe: str = Query(..., description="管道标识符"),
+    node1: str = Query(..., description="管道起始节点ID"),
+    node2: str = Query(..., description="管道终止节点ID"),
+    length: float = Query(0, description="管道长度（单位：米）"),
+    diameter: float = Query(0, description="管道管径（单位：毫米）"),
+    roughness: float = Query(0, description="管道粗糙度"),
+    minor_loss: float = Query(0, description="管道局部阻力系数"),
+    status: str = Query(PIPE_STATUS_OPEN, description="管道状态（开启/关闭）"),
+) -> ChangeSet:
+    """
+    添加新管道到网络。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe: 管道ID
+        node1: 起始节点ID
+        node2: 终止节点ID
+        length: 管道长度
+        diameter: 管道管径
+        roughness: 管道粗糙度
+        minor_loss: 局部阻力系数
+        status: 管道状态
+        
+    Returns:
+        ChangeSet对象，包含本次操作的变更信息
+    """
+    ps = {
+        "id": pipe,
+        "node1": node1,
+        "node2": node2,
+        "length": length,
+        "diameter": diameter,
+        "roughness": roughness,
+        "minor_loss": minor_loss,
+        "status": status,
+    }
+    return add_pipe(network, ChangeSet(ps))
+
+@router.post("/deletepipe/", response_model=None, summary="删除管道", description="从网络中删除指定的管道")
+async def fastapi_delete_pipe(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe: str = Query(..., description="要删除的管道ID")
+) -> ChangeSet:
+    """
+    删除管道。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe: 管道ID
+        
+    Returns:
+        ChangeSet对象，包含本次删除操作的变更信息
+    """
+    ps = {"id": pipe}
+    return delete_pipe(network, ChangeSet(ps))
+
+@router.get("/getpipenode1/", summary="获取管道起始节点", description="获取指定管道的起始节点ID")
+async def fastapi_get_pipe_node1(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe: str = Query(..., description="管道ID")
+) -> str | None:
+    """
+    获取管道的起始节点。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe: 管道ID
+        
+    Returns:
+        起始节点ID，如果不存在则返回None
+    """
+    ps = get_pipe(network, pipe)
+    return ps["node1"]
+
+@router.get("/getpipenode2/", summary="获取管道终止节点", description="获取指定管道的终止节点ID")
+async def fastapi_get_pipe_node2(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe: str = Query(..., description="管道ID")
+) -> str | None:
+    """
+    获取管道的终止节点。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe: 管道ID
+        
+    Returns:
+        终止节点ID，如果不存在则返回None
+    """
+    ps = get_pipe(network, pipe)
+    return ps["node2"]
+
+@router.get("/getpipelength/", summary="获取管道长度", description="获取指定管道的长度")
+async def fastapi_get_pipe_length(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe: str = Query(..., description="管道ID")
+) -> float | None:
+    """
+    获取管道长度。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe: 管道ID
+        
+    Returns:
+        管道长度（单位：米），如果不存在则返回None
+    """
+    ps = get_pipe(network, pipe)
+    return ps["length"]
+
+@router.get("/getpipediameter/", summary="获取管道管径", description="获取指定管道的管径")
+async def fastapi_get_pipe_diameter(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe: str = Query(..., description="管道ID")
+) -> float | None:
+    """
+    获取管道管径。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe: 管道ID
+        
+    Returns:
+        管道管径（单位：毫米），如果不存在则返回None
+    """
+    ps = get_pipe(network, pipe)
+    return ps["diameter"]
+
+@router.get("/getpiperoughness/", summary="获取管道粗糙度", description="获取指定管道的粗糙度")
+async def fastapi_get_pipe_roughness(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe: str = Query(..., description="管道ID")
+) -> float | None:
+    """
+    获取管道粗糙度。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe: 管道ID
+        
+    Returns:
+        管道粗糙度值，如果不存在则返回None
+    """
+    ps = get_pipe(network, pipe)
+    return ps["roughness"]
+
+@router.get("/getpipeminorloss/", summary="获取管道局部阻力系数", description="获取指定管道的局部阻力系数")
+async def fastapi_get_pipe_minor_loss(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe: str = Query(..., description="管道ID")
+) -> float | None:
+    """
+    获取管道局部阻力系数。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe: 管道ID
+        
+    Returns:
+        局部阻力系数，如果不存在则返回None
+    """
+    ps = get_pipe(network, pipe)
+    return ps["minor_loss"]
+
+@router.get("/getpipestatus/", summary="获取管道状态", description="获取指定管道的状态（开启或关闭）")
+async def fastapi_get_pipe_status(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe: str = Query(..., description="管道ID")
+) -> str | None:
+    """
+    获取管道状态。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe: 管道ID
+        
+    Returns:
+        管道状态（开启/关闭），如果不存在则返回None
+    """
+    ps = get_pipe(network, pipe)
+    return ps["status"]
+
+@router.post("/setpipenode1/", response_model=None, summary="设置管道起始节点", description="设置指定管道的起始节点")
+async def fastapi_set_pipe_node1(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe: str = Query(..., description="管道ID"),
+    node1: str = Query(..., description="新的起始节点ID")
+) -> ChangeSet:
+    """
+    设置管道起始节点。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe: 管道ID
+        node1: 新的起始节点ID
+        
+    Returns:
+        ChangeSet对象，包含本次修改的变更信息
+    """
+    ps = {"id": pipe, "node1": node1}
+    return set_pipe(network, ChangeSet(ps))
+
+@router.post("/setpipenode2/", response_model=None, summary="设置管道终止节点", description="设置指定管道的终止节点")
+async def fastapi_set_pipe_node2(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe: str = Query(..., description="管道ID"),
+    node2: str = Query(..., description="新的终止节点ID")
+) -> ChangeSet:
+    """
+    设置管道终止节点。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe: 管道ID
+        node2: 新的终止节点ID
+        
+    Returns:
+        ChangeSet对象，包含本次修改的变更信息
+    """
+    ps = {"id": pipe, "node2": node2}
+    return set_pipe(network, ChangeSet(ps))
+
+@router.post("/setpipelength/", response_model=None, summary="设置管道长度", description="设置指定管道的长度")
+async def fastapi_set_pipe_length(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe: str = Query(..., description="管道ID"),
+    length: float = Query(..., description="新的管道长度（单位：米）")
+) -> ChangeSet:
+    """
+    设置管道长度。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe: 管道ID
+        length: 新的管道长度
+        
+    Returns:
+        ChangeSet对象，包含本次修改的变更信息
+    """
+    ps = {"id": pipe, "length": length}
+    return set_pipe(network, ChangeSet(ps))
+
+@router.post("/setpipediameter/", response_model=None, summary="设置管道管径", description="设置指定管道的管径")
+async def fastapi_set_pipe_diameter(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe: str = Query(..., description="管道ID"),
+    diameter: float = Query(..., description="新的管道管径（单位：毫米）")
+) -> ChangeSet:
+    """
+    设置管道管径。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe: 管道ID
+        diameter: 新的管道管径
+        
+    Returns:
+        ChangeSet对象，包含本次修改的变更信息
+    """
+    ps = {"id": pipe, "diameter": diameter}
+    return set_pipe(network, ChangeSet(ps))
+
+@router.post("/setpiperoughness/", response_model=None, summary="设置管道粗糙度", description="设置指定管道的粗糙度")
+async def fastapi_set_pipe_roughness(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe: str = Query(..., description="管道ID"),
+    roughness: float = Query(..., description="新的管道粗糙度值")
+) -> ChangeSet:
+    """
+    设置管道粗糙度。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe: 管道ID
+        roughness: 新的管道粗糙度
+        
+    Returns:
+        ChangeSet对象，包含本次修改的变更信息
+    """
+    ps = {"id": pipe, "roughness": roughness}
+    return set_pipe(network, ChangeSet(ps))
+
+@router.post("/setpipeminorloss/", response_model=None, summary="设置管道局部阻力系数", description="设置指定管道的局部阻力系数")
+async def fastapi_set_pipe_minor_loss(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe: str = Query(..., description="管道ID"),
+    minor_loss: float = Query(..., description="新的局部阻力系数值")
+) -> ChangeSet:
+    """
+    设置管道局部阻力系数。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe: 管道ID
+        minor_loss: 新的局部阻力系数
+        
+    Returns:
+        ChangeSet对象，包含本次修改的变更信息
+    """
+    ps = {"id": pipe, "minor_loss": minor_loss}
+    return set_pipe(network, ChangeSet(ps))
+
+@router.post("/setpipestatus/", response_model=None, summary="设置管道状态", description="设置指定管道的状态（开启或关闭）")
+async def fastapi_set_pipe_status(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe: str = Query(..., description="管道ID"),
+    status: str = Query(..., description="新的管道状态（开启/关闭）")
+) -> ChangeSet:
+    """
+    设置管道状态。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe: 管道ID
+        status: 新的管道状态
+        
+    Returns:
+        ChangeSet对象，包含本次修改的变更信息
+    """
+    ps = {"id": pipe, "status": status}
+    return set_pipe(network, ChangeSet(ps))
+
+@router.get("/getpipeproperties/", summary="获取管道属性", description="获取指定管道的所有属性信息")
+async def fastapi_get_pipe_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe: str = Query(..., description="管道ID")
+) -> dict[str, Any]:
+    """
+    获取管道的所有属性。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe: 管道ID
+        
+    Returns:
+        包含管道所有属性的字典
+    """
+    return get_pipe(network, pipe)
+
+@router.get("/getallpipeproperties/", summary="获取所有管道属性", description="获取网络中所有管道的属性信息列表")
+async def fastapi_get_all_pipe_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> list[dict[str, Any]]:
+    """
+    获取网络中所有管道的属性。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        包含所有管道属性的字典列表
+    """
+    # 缓存查询结果提高性能
+    # global redis_client
+    results = get_all_pipes(network)
+    return results
+
+@router.post("/setpipeproperties/", response_model=None, summary="设置管道属性", description="批量设置指定管道的多个属性")
+async def fastapi_set_pipe_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe: str = Query(..., description="管道ID"),
+    req: Request = None
+) -> ChangeSet:
+    """
+    批量设置管道属性。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe: 管道ID
+        req: 请求体，包含要设置的属性及其值
+        
+    Returns:
+        ChangeSet对象，包含本次修改的变更信息
+    """
+    props = await req.json()
+    ps = {"id": pipe} | props
+    return set_pipe(network, ChangeSet(ps))

app/api/v1/endpoints/network/pumps.py

@@ -0,0 +1,203 @@
+from fastapi import APIRouter, Request, Query, Path, Body
+from typing import Any, List, Dict, Union
+from app.services.tjnetwork import (
+    Any,
+    ChangeSet,
+    add_pump,
+    delete_pump,
+    get_all_pumps,
+    get_pump,
+    get_pump_schema,
+    set_pump,
+)
+
+router = APIRouter()
+
+@router.get("/getpumpschema", summary="获取水泵模式", description="获取水泵对象的模式定义，包含所有可用字段及其类型")
+async def fastapi_get_pump_schema(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> dict[str, dict[str, Any]]:
+    """
+    获取水泵数据模式定义。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        包含水泵模式信息的字典
+    """
+    return get_pump_schema(network)
+
+@router.post("/addpump/", response_model=None, summary="添加水泵", description="向网络中添加新的水泵，需要提供水泵的基本参数如功率等")
+async def fastapi_add_pump(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pump: str = Query(..., description="水泵标识符"),
+    node1: str = Query(..., description="水泵起始节点ID"),
+    node2: str = Query(..., description="水泵终止节点ID"),
+    power: float = Query(0.0, description="水泵功率（单位：千瓦）")
+) -> ChangeSet:
+    """
+    添加新水泵到网络。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pump: 水泵ID
+        node1: 起始节点ID
+        node2: 终止节点ID
+        power: 水泵功率
+        
+    Returns:
+        ChangeSet对象，包含本次操作的变更信息
+    """
+    ps = {"id": pump, "node1": node1, "node2": node2, "power": power}
+    return add_pump(network, ChangeSet(ps))
+
+@router.post("/deletepump/", response_model=None, summary="删除水泵", description="从网络中删除指定的水泵")
+async def fastapi_delete_pump(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pump: str = Query(..., description="要删除的水泵ID")
+) -> ChangeSet:
+    """
+    删除水泵。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pump: 水泵ID
+        
+    Returns:
+        ChangeSet对象，包含本次删除操作的变更信息
+    """
+    ps = {"id": pump}
+    return delete_pump(network, ChangeSet(ps))
+
+@router.get("/getpumpnode1/", summary="获取水泵起始节点", description="获取指定水泵的起始节点ID")
+async def fastapi_get_pump_node1(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pump: str = Query(..., description="水泵ID")
+) -> str | None:
+    """
+    获取水泵的起始节点。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pump: 水泵ID
+        
+    Returns:
+        起始节点ID，如果不存在则返回None
+    """
+    ps = get_pump(network, pump)
+    return ps["node1"]
+
+@router.get("/getpumpnode2/", summary="获取水泵终止节点", description="获取指定水泵的终止节点ID")
+async def fastapi_get_pump_node2(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pump: str = Query(..., description="水泵ID")
+) -> str | None:
+    """
+    获取水泵的终止节点。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pump: 水泵ID
+        
+    Returns:
+        终止节点ID，如果不存在则返回None
+    """
+    ps = get_pump(network, pump)
+    return ps["node2"]
+
+@router.post("/setpumpnode1/", response_model=None, summary="设置水泵起始节点", description="设置指定水泵的起始节点")
+async def fastapi_set_pump_node1(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pump: str = Query(..., description="水泵ID"),
+    node1: str = Query(..., description="新的起始节点ID")
+) -> ChangeSet:
+    """
+    设置水泵起始节点。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pump: 水泵ID
+        node1: 新的起始节点ID
+        
+    Returns:
+        ChangeSet对象，包含本次修改的变更信息
+    """
+    ps = {"id": pump, "node1": node1}
+    return set_pump(network, ChangeSet(ps))
+
+@router.post("/setpumpnode2/", response_model=None, summary="设置水泵终止节点", description="设置指定水泵的终止节点")
+async def fastapi_set_pump_node2(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pump: str = Query(..., description="水泵ID"),
+    node2: str = Query(..., description="新的终止节点ID")
+) -> ChangeSet:
+    """
+    设置水泵终止节点。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pump: 水泵ID
+        node2: 新的终止节点ID
+        
+    Returns:
+        ChangeSet对象，包含本次修改的变更信息
+    """
+    ps = {"id": pump, "node2": node2}
+    return set_pump(network, ChangeSet(ps))
+
+@router.get("/getpumpproperties/", summary="获取水泵属性", description="获取指定水泵的所有属性信息")
+async def fastapi_get_pump_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pump: str = Query(..., description="水泵ID")
+) -> dict[str, Any]:
+    """
+    获取水泵的所有属性。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pump: 水泵ID
+        
+    Returns:
+        包含水泵所有属性的字典
+    """
+    return get_pump(network, pump)
+
+@router.get("/getallpumpproperties/", summary="获取所有水泵属性", description="获取网络中所有水泵的属性信息列表")
+async def fastapi_get_all_pump_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> list[dict[str, Any]]:
+    """
+    获取网络中所有水泵的属性。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        包含所有水泵属性的字典列表
+    """
+    # 缓存查询结果提高性能
+    # global redis_client
+    results = get_all_pumps(network)
+    return results
+
+@router.post("/setpumpproperties/", response_model=None, summary="设置水泵属性", description="批量设置指定水泵的多个属性")
+async def fastapi_set_pump_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pump: str = Query(..., description="水泵ID"),
+    req: Request = None
+) -> ChangeSet:
+    """
+    批量设置水泵属性。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pump: 水泵ID
+        req: 请求体，包含要设置的属性及其值
+        
+    Returns:
+        ChangeSet对象，包含本次修改的变更信息
+    """
+    props = await req.json()
+    ps = {"id": pump} | props
+    return set_pump(network, ChangeSet(ps))

app/api/v1/endpoints/network/regions.py

@@ -0,0 +1,534 @@
+from fastapi import APIRouter, Request, Query, Path, Body
+from typing import Any, List, Dict, Union
+from app.services.tjnetwork import (
+    Any,
+    ChangeSet,
+    add_district_metering_area,
+    add_region,
+    add_service_area,
+    add_virtual_district,
+    calculate_district_metering_area_for_network,
+    calculate_district_metering_area_for_nodes,
+    calculate_district_metering_area_for_region,
+    calculate_service_area,
+    calculate_virtual_district,
+    delete_district_metering_area,
+    delete_region,
+    delete_service_area,
+    delete_virtual_district,
+    generate_district_metering_area,
+    generate_service_area,
+    generate_sub_district_metering_area,
+    generate_virtual_district,
+    get_all_district_metering_area_ids,
+    get_all_district_metering_areas,
+    get_all_service_areas,
+    get_all_virtual_districts,
+    get_district_metering_area,
+    get_district_metering_area_schema,
+    get_region,
+    get_region_schema,
+    get_service_area,
+    get_service_area_schema,
+    get_virtual_district,
+    get_virtual_district_schema,
+    set_district_metering_area,
+    set_region,
+    set_service_area,
+    set_virtual_district,
+)
+
+router = APIRouter()
+
+############################################################
+# region 32
+############################################################
+
+@router.get(
+    "/getregionschema/",
+    summary="获取区域属性架构",
+    description="获取指定水网的区域属性架构定义"
+)
+async def fastapi_get_region_schema(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> dict[str, dict[str, Any]]:
+    """获取区域的属性架构。"""
+    return get_region_schema(network)
+
+@router.get(
+    "/getregion/",
+    summary="获取区域信息",
+    description="获取指定ID的区域详细信息"
+)
+async def fastapi_get_region(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    id: str = Query(..., description="区域ID")
+) -> dict[str, Any]:
+    """获取区域的详细信息。"""
+    return get_region(network, id)
+
+@router.post(
+    "/setregion/",
+    response_model=None,
+    summary="设置区域属性",
+    description="修改指定区域的属性信息"
+)
+async def fastapi_set_region(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置区域属性。"""
+    props = await req.json()
+    return set_region(network, ChangeSet(props))
+
+@router.post(
+    "/addregion/",
+    response_model=None,
+    summary="添加新区域",
+    description="向水网添加一个新的区域"
+)
+async def fastapi_add_region(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """添加新的区域。"""
+    props = await req.json()
+    return add_region(network, ChangeSet(props))
+
+@router.post(
+    "/deleteregion/",
+    response_model=None,
+    summary="删除区域",
+    description="删除指定的区域"
+)
+async def fastapi_delete_region(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """删除区域。"""
+    props = await req.json()
+    return delete_region(network, ChangeSet(props))
+
+
+############################################################
+# district_metering_area 33
+############################################################
+
+@router.get(
+    "/calculatedistrictmeteringareaforregion/",
+    summary="计算区域内DMA分区",
+    description="为指定区域计算区域计量(DMA)分区方案"
+)
+async def fastapi_calculate_district_metering_area_for_region(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> list[list[str]]:
+    """
+    计算区域内DMA分区。
+    
+    请求体格式:
+    {
+        "region": 区域ID(str),
+        "part_count": 分区数量(int),
+        "part_type": 分区类型(int)
+    }
+    """
+    props = await req.json()
+    region = props["region"]
+    part_count = props["part_count"]
+    part_type = props["part_type"]
+    return calculate_district_metering_area_for_region(
+        network, region, part_count, part_type
+    )
+
+@router.get(
+    "/calculatedistrictmeteringareafornetwork/",
+    summary="计算整网DMA分区",
+    description="为整个水网计算区域计量(DMA)分区方案"
+)
+async def fastapi_calculate_district_metering_area_for_network(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> list[list[str]]:
+    """
+    计算整网DMA分区。
+    
+    请求体格式:
+    {
+        "part_count": 分区数量(int),
+        "part_type": 分区类型(int)
+    }
+    """
+    props = await req.json()
+    part_count = props["part_count"]
+    part_type = props["part_type"]
+    return calculate_district_metering_area_for_network(network, part_count, part_type)
+
+@router.get(
+    "/getdistrictmeteringareaschema/",
+    summary="获取DMA属性架构",
+    description="获取指定水网的区域计量(DMA)属性架构定义"
+)
+async def fastapi_get_district_metering_area_schema(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+) -> dict[str, dict[str, Any]]:
+    """获取DMA的属性架构。"""
+    return get_district_metering_area_schema(network)
+
+@router.get(
+    "/getdistrictmeteringarea/",
+    summary="获取DMA信息",
+    description="获取指定ID的区域计量(DMA)详细信息"
+)
+async def fastapi_get_district_metering_area(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    id: str = Query(..., description="DMA ID")
+) -> dict[str, Any]:
+    """获取DMA的详细信息。"""
+    return get_district_metering_area(network, id)
+
+@router.post(
+    "/setdistrictmeteringarea/",
+    response_model=None,
+    summary="设置DMA属性",
+    description="修改指定DMA的属性信息"
+)
+async def fastapi_set_district_metering_area(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置DMA属性。"""
+    props = await req.json()
+    return set_district_metering_area(network, ChangeSet(props))
+
+@router.post(
+    "/adddistrictmeteringarea/",
+    response_model=None,
+    summary="添加新DMA",
+    description="向水网添加一个新的区域计量(DMA)"
+)
+async def fastapi_add_district_metering_area(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """添加新的DMA。"""
+    props = await req.json()
+    # boundary should be [(x,y), (x,y)]
+    boundary = props.get("boundary", [])
+    newBoundary = []
+    for pt in boundary:
+        if len(pt) >= 2:
+            newBoundary.append((pt[0], pt[1]))
+    props["boundary"] = newBoundary
+    return add_district_metering_area(network, ChangeSet(props))
+
+@router.post(
+    "/deletedistrictmeteringarea/",
+    response_model=None,
+    summary="删除DMA",
+    description="删除指定的区域计量(DMA)"
+)
+async def fastapi_delete_district_metering_area(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """删除DMA。"""
+    props = await req.json()
+    return delete_district_metering_area(network, ChangeSet(props))
+
+@router.get(
+    "/getalldistrictmeteringareaids/",
+    summary="获取所有DMA ID",
+    description="获取指定水网中所有DMA的ID列表"
+)
+async def fastapi_get_all_district_metering_area_ids(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> list[str]:
+    """获取所有DMA的ID列表。"""
+    return get_all_district_metering_area_ids(network)
+
+@router.get(
+    "/getalldistrictmeteringareas/",
+    summary="获取所有DMA",
+    description="获取指定水网中所有DMA的详细信息"
+)
+async def getalldistrictmeteringareas(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> list[dict[str, Any]]:
+    """获取所有DMA的详细信息列表。"""
+    return get_all_district_metering_areas(network)
+
+@router.post(
+    "/generatedistrictmeteringarea/",
+    response_model=None,
+    summary="生成DMA分区",
+    description="根据参数自动生成水网的DMA分区方案"
+)
+async def fastapi_generate_district_metering_area(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    part_count: int = Query(..., description="分区数量", gt=0),
+    part_type: int = Query(..., description="分区类型"),
+    inflate_delta: float = Query(..., description="膨胀参数")
+) -> ChangeSet:
+    """生成DMA分区。"""
+    return generate_district_metering_area(
+        network, part_count, part_type, inflate_delta
+    )
+
+@router.post(
+    "/generatesubdistrictmeteringarea/",
+    response_model=None,
+    summary="生成DMA子分区",
+    description="为指定DMA生成子DMA分区"
+)
+async def fastapi_generate_sub_district_metering_area(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    dma: str = Query(..., description="DMA ID"),
+    part_count: int = Query(..., description="分区数量", gt=0),
+    part_type: int = Query(..., description="分区类型"),
+    inflate_delta: float = Query(..., description="膨胀参数")
+) -> ChangeSet:
+    """生成DMA子分区。"""
+    return generate_sub_district_metering_area(
+        network, dma, part_count, part_type, inflate_delta
+    )
+
+
+############################################################
+# service_area 34
+############################################################
+
+@router.get(
+    "/calculateservicearea/",
+    summary="计算服务区",
+    description="计算指定水网的服务区分区，返回全部时间步结果"
+)
+async def fastapi_calculate_service_area(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+) -> list[dict[str, list[str]]]:
+    """计算服务区分区，返回全部时间步结果。"""
+    return calculate_service_area(network)
+
+@router.get(
+    "/getserviceareaschema/",
+    summary="获取服务区属性架构",
+    description="获取指定水网的服务区属性架构定义"
+)
+async def fastapi_get_service_area_schema(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> dict[str, dict[str, Any]]:
+    """获取服务区的属性架构。"""
+    return get_service_area_schema(network)
+
+@router.get(
+    "/getservicearea/",
+    summary="获取服务区信息",
+    description="获取指定ID的服务区详细信息"
+)
+async def fastapi_get_service_area(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    id: str = Query(..., description="服务区ID")
+) -> dict[str, Any]:
+    """获取服务区的详细信息。"""
+    return get_service_area(network, id)
+
+@router.post(
+    "/setservicearea/",
+    response_model=None,
+    summary="设置服务区属性",
+    description="修改指定服务区的属性信息"
+)
+async def fastapi_set_service_area(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置服务区属性。"""
+    props = await req.json()
+    return set_service_area(network, ChangeSet(props))
+
+@router.post(
+    "/addservicearea/",
+    response_model=None,
+    summary="添加新服务区",
+    description="向水网添加一个新的服务区"
+)
+async def fastapi_add_service_area(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """添加新的服务区。"""
+    props = await req.json()
+    return add_service_area(network, ChangeSet(props))
+
+@router.post(
+    "/deleteservicearea/",
+    response_model=None,
+    summary="删除服务区",
+    description="删除指定的服务区"
+)
+async def fastapi_delete_service_area(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """删除服务区。"""
+    props = await req.json()
+    return delete_service_area(network, ChangeSet(props))
+
+@router.get(
+    "/getallserviceareas/",
+    summary="获取所有服务区",
+    description="获取指定水网中的所有服务区信息"
+)
+async def fastapi_get_all_service_areas(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> list[dict[str, Any]]:
+    """获取所有服务区的信息列表。"""
+    return get_all_service_areas(network)
+
+@router.post(
+    "/generateservicearea/",
+    response_model=None,
+    summary="生成服务区分区",
+    description="根据参数自动生成水网的服务区分区"
+)
+async def fastapi_generate_service_area(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    inflate_delta: float = Query(..., description="膨胀参数")
+) -> ChangeSet:
+    """生成服务区分区。"""
+    return generate_service_area(network, inflate_delta)
+
+
+############################################################
+# virtual_district 35
+############################################################
+
+@router.get(
+    "/calculatevirtualdistrict/",
+    summary="计算虚拟分区",
+    description="根据指定的压力监测节点作为中心节点计算虚拟分区方案"
+)
+async def fastapi_calculate_virtual_district(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    centers: list[str] = Query(..., description="压力监测节点ID列表")
+) -> dict[str, list[Any]]:
+    """计算虚拟分区。"""
+    return calculate_virtual_district(network, centers)
+
+@router.get(
+    "/getvirtualdistrictschema/",
+    summary="获取虚拟分区属性架构",
+    description="获取指定水网的虚拟分区属性架构定义"
+)
+async def fastapi_get_virtual_district_schema(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+) -> dict[str, dict[str, Any]]:
+    """获取虚拟分区的属性架构。"""
+    return get_virtual_district_schema(network)
+
+@router.get(
+    "/getvirtualdistrict/",
+    summary="获取虚拟分区信息",
+    description="获取指定ID的虚拟分区详细信息"
+)
+async def fastapi_get_virtual_district(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    id: str = Query(..., description="虚拟分区ID")
+) -> dict[str, Any]:
+    """获取虚拟分区的详细信息。"""
+    return get_virtual_district(network, id)
+
+@router.post(
+    "/setvirtualdistrict/",
+    response_model=None,
+    summary="设置虚拟分区属性",
+    description="修改指定虚拟分区的属性信息"
+)
+async def fastapi_set_virtual_district(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置虚拟分区属性。"""
+    props = await req.json()
+    return set_virtual_district(network, ChangeSet(props))
+
+@router.post(
+    "/addvirtualdistrict/",
+    response_model=None,
+    summary="添加新虚拟分区",
+    description="向水网添加一个新的虚拟分区"
+)
+async def fastapi_add_virtual_district(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """添加新的虚拟分区。"""
+    props = await req.json()
+    return add_virtual_district(network, ChangeSet(props))
+
+@router.post(
+    "/deletevirtualdistrict/",
+    response_model=None,
+    summary="删除虚拟分区",
+    description="删除指定的虚拟分区"
+)
+async def fastapi_delete_virtual_district(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """删除虚拟分区。"""
+    props = await req.json()
+    return delete_virtual_district(network, ChangeSet(props))
+
+@router.get(
+    "/getallvirtualdistrict/",
+    summary="获取所有虚拟分区",
+    description="获取指定水网中的所有虚拟分区信息"
+)
+async def fastapi_get_all_virtual_district(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> list[dict[str, Any]]:
+    """获取所有虚拟分区的信息列表。"""
+    return get_all_virtual_districts(network)
+
+@router.post(
+    "/generatevirtualdistrict/",
+    response_model=None,
+    summary="生成虚拟分区",
+    description="根据参数自动生成虚拟分区方案"
+)
+async def fastapi_generate_virtual_district(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    inflate_delta: float = Query(..., description="膨胀参数"),
+    req: Request = None
+) -> ChangeSet:
+    """生成虚拟分区。"""
+    props = await req.json()
+    return generate_virtual_district(network, props["centers"], inflate_delta)
+
+@router.get(
+    "/calculatedistrictmeteringareafornodes/",
+    summary="计算节点DMA分区",
+    description="为指定节点集计算区域计量(DMA)分区方案"
+)
+async def fastapi_calculate_district_metering_area_for_nodes(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> list[list[str]]:
+    """
+    计算节点DMA分区。
+    
+    请求体格式:
+    {
+        "nodes": 节点ID列表(list[str]),
+        "part_count": 分区数量(int),
+        "part_type": 分区类型(int)
+    }
+    """
+    props = await req.json()
+    nodes = props["nodes"]
+    part_count = props["part_count"]
+    part_type = props["part_type"]
+    return calculate_district_metering_area_for_nodes(
+        network, nodes, part_count, part_type
+    )

app/api/v1/endpoints/network/reservoirs.py

@@ -0,0 +1,422 @@
+from fastapi import APIRouter, Request, Query, Path, Body
+from typing import Any, List, Dict, Union
+from app.services.tjnetwork import (
+    Any,
+    ChangeSet,
+    add_reservoir,
+    delete_reservoir,
+    get_all_reservoirs,
+    get_reservoir,
+    get_reservoir_schema,
+    set_reservoir,
+)
+
+router = APIRouter()
+
+@router.get(
+    "/getreservoirschema",
+    summary="获取水库模式",
+    description="获取指定供水网络中所有水库的模式/属性字段定义"
+)
+async def fast_get_reservoir_schema(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> dict[str, dict[str, Any]]:
+    """
+    获取水库模式定义。
+    
+    该端点返回指定网络中水库对象的模式定义，包括所有可用的属性字段。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        水库属性的模式定义字典
+    """
+    return get_reservoir_schema(network)
+
+@router.post(
+    "/addreservoir/",
+    response_model=None,
+    summary="添加水库",
+    description="在指定供水网络中添加新的水库/水源节点"
+)
+async def fastapi_add_reservoir(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    reservoir: str = Query(..., description="水库的唯一标识符"),
+    x: float = Query(..., description="水库的X坐标"),
+    y: float = Query(..., description="水库的Y坐标"),
+    head: float = Query(..., description="水库的水头/总水头（米）")
+) -> ChangeSet:
+    """
+    添加新的水库/水源节点。
+    
+    在指定的供水网络中创建一个新的水库，并设置其坐标和水头参数。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        reservoir: 水库的唯一标识符
+        x: 水库的X坐标位置
+        y: 水库的Y坐标位置
+        head: 水库的供水水头（以米为单位）
+        
+    Returns:
+        包含操作变更集的ChangeSet对象
+    """
+    ps = {"id": reservoir, "x": x, "y": y, "head": head}
+    return add_reservoir(network, ChangeSet(ps))
+
+@router.post(
+    "/deletereservoir/",
+    response_model=None,
+    summary="删除水库",
+    description="从指定供水网络中删除指定的水库/水源节点"
+)
+async def fastapi_delete_reservoir(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    reservoir: str = Query(..., description="要删除的水库的唯一标识符")
+) -> ChangeSet:
+    """
+    删除指定的水库节点。
+    
+    从指定的供水网络中删除一个水库及其相关的所有连接关系。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        reservoir: 水库的唯一标识符
+        
+    Returns:
+        包含操作变更集的ChangeSet对象
+    """
+    ps = {"id": reservoir}
+    return delete_reservoir(network, ChangeSet(ps))
+
+@router.get(
+    "/getreservoirhead/",
+    summary="获取水库水头",
+    description="获取指定水库的供水水头/总水头值"
+)
+async def fastapi_get_reservoir_head(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    reservoir: str = Query(..., description="水库的唯一标识符")
+) -> float | None:
+    """
+    获取水库的水头参数。
+    
+    返回指定水库的供水水头（总水头），单位为米。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        reservoir: 水库的唯一标识符
+        
+    Returns:
+        水库的水头值（米），如果水库不存在则返回None
+    """
+    ps = get_reservoir(network, reservoir)
+    return ps["head"]
+
+@router.get(
+    "/getreservoirpattern/",
+    summary="获取水库模式",
+    description="获取指定水库的运行模式/供水模式"
+)
+async def fastapi_get_reservoir_pattern(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    reservoir: str = Query(..., description="水库的唯一标识符")
+) -> str | None:
+    """
+    获取水库的运行模式。
+    
+    返回指定水库的供水模式，如固定水头模式、时间序列模式等。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        reservoir: 水库的唯一标识符
+        
+    Returns:
+        水库的运行模式字符串，如果水库不存在则返回None
+    """
+    ps = get_reservoir(network, reservoir)
+    return ps["pattern"]
+
+@router.get(
+    "/getreservoirx/",
+    summary="获取水库X坐标",
+    description="获取指定水库的X坐标位置"
+)
+async def fastapi_get_reservoir_x(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    reservoir: str = Query(..., description="水库的唯一标识符")
+) -> dict[str, float] | None:
+    """
+    获取水库的X坐标。
+    
+    返回指定水库的X轴坐标值。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        reservoir: 水库的唯一标识符
+        
+    Returns:
+        水库的X坐标值，如果水库不存在则返回None
+    """
+    ps = get_reservoir(network, reservoir)
+    return ps["x"]
+
+@router.get(
+    "/getreservoiry/",
+    summary="获取水库Y坐标",
+    description="获取指定水库的Y坐标位置"
+)
+async def fastapi_get_reservoir_y(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    reservoir: str = Query(..., description="水库的唯一标识符")
+) -> dict[str, float] | None:
+    """
+    获取水库的Y坐标。
+    
+    返回指定水库的Y轴坐标值。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        reservoir: 水库的唯一标识符
+        
+    Returns:
+        水库的Y坐标值，如果水库不存在则返回None
+    """
+    ps = get_reservoir(network, reservoir)
+    return ps["y"]
+
+@router.get(
+    "/getreservoircoord/",
+    summary="获取水库坐标",
+    description="获取指定水库的平面坐标（X和Y坐标）"
+)
+async def fastapi_get_reservoir_coord(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    reservoir: str = Query(..., description="水库的唯一标识符")
+) -> dict[str, float] | None:
+    """
+    获取水库的坐标。
+    
+    返回指定水库的平面坐标，包含水库ID、X坐标和Y坐标。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        reservoir: 水库的唯一标识符
+        
+    Returns:
+        包含water库ID和X、Y坐标的字典，如果水库不存在则返回None
+    """
+    ps = get_reservoir(network, reservoir)
+    coord = {"id": reservoir, "x": ps["x"], "y": ps["y"]}
+    return coord
+
+@router.post(
+    "/setreservoirhead/",
+    response_model=None,
+    summary="设置水库水头",
+    description="更新指定水库的供水水头/总水头值"
+)
+async def fastapi_set_reservoir_head(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    reservoir: str = Query(..., description="水库的唯一标识符"),
+    head: float = Query(..., description="新的水头值（米）")
+) -> ChangeSet:
+    """
+    设置水库的水头参数。
+    
+    更新指定水库的供水水头（总水头）值。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        reservoir: 水库的唯一标识符
+        head: 新的水头值（以米为单位）
+        
+    Returns:
+        包含操作变更集的ChangeSet对象
+    """
+    ps = {"id": reservoir, "head": head}
+    return set_reservoir(network, ChangeSet(ps))
+
+@router.post(
+    "/setreservoirpattern/",
+    response_model=None,
+    summary="设置水库模式",
+    description="更新指定水库的运行模式/供水模式"
+)
+async def fastapi_set_reservoir_pattern(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    reservoir: str = Query(..., description="水库的唯一标识符"),
+    pattern: str = Query(..., description="新的运行模式")
+) -> ChangeSet:
+    """
+    设置水库的运行模式。
+    
+    更新指定水库的供水模式，如固定水头模式、时间序列模式等。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        reservoir: 水库的唯一标识符
+        pattern: 新的运行模式字符串
+        
+    Returns:
+        包含操作变更集的ChangeSet对象
+    """
+    ps = {"id": reservoir, "pattern": pattern}
+    return set_reservoir(network, ChangeSet(ps))
+
+@router.post(
+    "/setreservoirx/",
+    response_model=None,
+    summary="设置水库X坐标",
+    description="更新指定水库的X坐标位置"
+)
+async def fastapi_set_reservoir_x(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    reservoir: str = Query(..., description="水库的唯一标识符"),
+    x: float = Query(..., description="新的X坐标值")
+) -> ChangeSet:
+    """
+    设置水库的X坐标。
+    
+    更新指定水库的X轴坐标值。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        reservoir: 水库的唯一标识符
+        x: 新的X坐标值
+        
+    Returns:
+        包含操作变更集的ChangeSet对象
+    """
+    ps = {"id": reservoir, "x": x}
+    return set_reservoir(network, ChangeSet(ps))
+
+@router.post(
+    "/setreservoiry/",
+    response_model=None,
+    summary="设置水库Y坐标",
+    description="更新指定水库的Y坐标位置"
+)
+async def fastapi_set_reservoir_y(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    reservoir: str = Query(..., description="水库的唯一标识符"),
+    y: float = Query(..., description="新的Y坐标值")
+) -> ChangeSet:
+    """
+    设置水库的Y坐标。
+    
+    更新指定水库的Y轴坐标值。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        reservoir: 水库的唯一标识符
+        y: 新的Y坐标值
+        
+    Returns:
+        包含操作变更集的ChangeSet对象
+    """
+    ps = {"id": reservoir, "y": y}
+    return set_reservoir(network, ChangeSet(ps))
+
+@router.post(
+    "/setreservoircoord/",
+    response_model=None,
+    summary="设置水库坐标",
+    description="更新指定水库的平面坐标（X和Y坐标）"
+)
+async def fastapi_set_reservoir_coord(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    reservoir: str = Query(..., description="水库的唯一标识符"),
+    x: float = Query(..., description="新的X坐标值"),
+    y: float = Query(..., description="新的Y坐标值")
+) -> ChangeSet:
+    """
+    设置水库的坐标。
+    
+    更新指定水库的平面坐标，包括X和Y坐标。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        reservoir: 水库的唯一标识符
+        x: 新的X坐标值
+        y: 新的Y坐标值
+        
+    Returns:
+        包含操作变更集的ChangeSet对象
+    """
+    ps = {"id": reservoir, "x": x, "y": y}
+    return set_reservoir(network, ChangeSet(ps))
+
+@router.get(
+    "/getreservoirproperties/",
+    summary="获取水库属性",
+    description="获取指定水库的所有属性"
+)
+async def fastapi_get_reservoir_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    reservoir: str = Query(..., description="水库的唯一标识符")
+) -> dict[str, Any]:
+    """
+    获取水库的所有属性。
+    
+    返回指定水库的完整属性信息，包括ID、坐标、水头、模式等所有属性。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        reservoir: 水库的唯一标识符
+        
+    Returns:
+        包含水库所有属性的字典
+    """
+    return get_reservoir(network, reservoir)
+
+@router.get(
+    "/getallreservoirproperties/",
+    summary="获取所有水库属性",
+    description="获取指定供水网络中所有水库的属性"
+)
+async def fastapi_get_all_reservoir_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> list[dict[str, Any]]:
+    """
+    获取所有水库的属性。
+    
+    返回指定供水网络中所有水库的完整属性信息列表。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        包含所有水库属性的字典列表
+    """
+    results = get_all_reservoirs(network)
+    return results
+
+@router.post(
+    "/setreservoirproperties/",
+    response_model=None,
+    summary="设置水库属性",
+    description="批量更新指定水库的多个属性"
+)
+async def fastapi_set_reservoir_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    reservoir: str = Query(..., description="水库的唯一标识符"),
+    req: Request = None
+) -> ChangeSet:
+    """
+    设置水库的多个属性。
+    
+    批量更新指定水库的属性。属性通过JSON请求体传递。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        reservoir: 水库的唯一标识符
+        req: HTTP请求对象，包含JSON格式的属性数据
+        
+    Returns:
+        包含操作变更集的ChangeSet对象
+    """
+    props = await req.json()
+    ps = {"id": reservoir} | props
+    return set_reservoir(network, ChangeSet(ps))

app/api/v1/endpoints/network/tags.py

@@ -0,0 +1,66 @@
+from fastapi import APIRouter, Request, Query, Path, Body
+from typing import Any, List, Dict, Union
+from app.services.tjnetwork import (
+    Any,
+    ChangeSet,
+    get_tag,
+    get_tag_schema,
+    get_tags,
+    set_tag,
+)
+
+router = APIRouter()
+
+############################################################
+# tag 8.[TAGS]
+############################################################
+
+@router.get(
+    "/gettagschema/",
+    summary="获取标签属性架构",
+    description="获取指定水网的标签(Tag)属性架构定义"
+)
+async def fastapi_get_tag_schema(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> dict[str, dict[str, Any]]:
+    """获取标签的属性架构。"""
+    return get_tag_schema(network)
+
+@router.get(
+    "/gettag/",
+    summary="获取标签信息",
+    description="获取指定类型和ID的标签信息"
+)
+async def fastapi_get_tag(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    t_type: str = Query(..., description="标签类型"),
+    id: str = Query(..., description="元素ID")
+) -> dict[str, Any]:
+    """获取标签信息。"""
+    return get_tag(network, t_type, id)
+
+@router.get(
+    "/gettags/",
+    summary="获取所有标签",
+    description="获取指定水网中的所有标签信息"
+)
+async def fastapi_get_tags(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> list[dict[str, Any]]:
+    """获取水网中所有标签的列表。"""
+    tags = get_tags(network)
+    return tags
+
+@router.post(
+    "/settag/",
+    response_model=None,
+    summary="设置标签",
+    description="为指定元素设置或修改标签信息"
+)
+async def fastapi_set_tag(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """设置标签信息。"""
+    props = await req.json()
+    return set_tag(network, ChangeSet(props))

app/api/v1/endpoints/network/tanks.py

@@ -0,0 +1,566 @@
+from fastapi import APIRouter, Request, Query, Path, Body
+from typing import Any, List, Dict, Union
+from app.services.tjnetwork import (
+    Any,
+    ChangeSet,
+    add_tank,
+    delete_tank,
+    get_all_tanks,
+    get_tank,
+    get_tank_schema,
+    set_tank,
+)
+
+router = APIRouter()
+
+@router.get("/gettankschema", summary="获取水箱模式", description="获取指定网络的水箱数据结构模式定义")
+async def fast_get_tank_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[str, Any]]:
+    """
+    获取水箱的数据结构模式。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        包含水箱属性的模式定义字典
+    """
+    return get_tank_schema(network)
+
+@router.post("/addtank/", summary="新增水箱", description="向指定网络中新增一个水箱", response_model=None)
+async def fastapi_add_tank(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID"),
+    x: float = Query(..., description="X坐标"),
+    y: float = Query(..., description="Y坐标"),
+    elevation: float = Query(..., description="标高"),
+    init_level: float = Query(0, description="初始水位"),
+    min_level: float = Query(0, description="最小水位"),
+    max_level: float = Query(0, description="最大水位"),
+    diameter: float = Query(0, description="直径"),
+    min_vol: float = Query(0, description="最小体积"),
+) -> ChangeSet:
+    """
+    创建新水箱。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        x: X坐标
+        y: Y坐标
+        elevation: 水箱标高
+        init_level: 初始水位，默认为0
+        min_level: 最小水位，默认为0
+        max_level: 最大水位，默认为0
+        diameter: 水箱直径，默认为0
+        min_vol: 最小体积，默认为0
+        
+    Returns:
+        包含变更信息的ChangeSet对象
+    """
+    ps = {
+        "id": tank,
+        "x": x,
+        "y": y,
+        "elevation": elevation,
+        "init_level": init_level,
+        "min_level": min_level,
+        "max_level": max_level,
+        "diameter": diameter,
+        "min_vol": min_vol,
+    }
+    return add_tank(network, ChangeSet(ps))
+
+@router.post("/deletetank/", summary="删除水箱", description="删除指定网络中的水箱", response_model=None)
+async def fastapi_delete_tank(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID")
+) -> ChangeSet:
+    """
+    删除指定的水箱。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        
+    Returns:
+        包含变更信息的ChangeSet对象
+    """
+    ps = {"id": tank}
+    return delete_tank(network, ChangeSet(ps))
+
+@router.get("/gettankelevation/", summary="获取水箱标高", description="获取指定水箱的标高值")
+async def fastapi_get_tank_elevation(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID")
+) -> float | None:
+    """
+    获取水箱的标高。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        
+    Returns:
+        水箱标高值，如果不存在则返回None
+    """
+    ps = get_tank(network, tank)
+    return ps["elevation"]
+
+@router.get("/gettankinitlevel/", summary="获取水箱初始水位", description="获取指定水箱的初始水位值")
+async def fastapi_get_tank_init_level(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID")
+) -> float | None:
+    """
+    获取水箱的初始水位。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        
+    Returns:
+        水箱初始水位值，如果不存在则返回None
+    """
+    ps = get_tank(network, tank)
+    return ps["init_level"]
+
+@router.get("/gettankminlevel/", summary="获取水箱最小水位", description="获取指定水箱的最小水位值")
+async def fastapi_get_tank_min_level(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID")
+) -> float | None:
+    """
+    获取水箱的最小水位。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        
+    Returns:
+        水箱最小水位值，如果不存在则返回None
+    """
+    ps = get_tank(network, tank)
+    return ps["min_level"]
+
+@router.get("/gettankmaxlevel/", summary="获取水箱最大水位", description="获取指定水箱的最大水位值")
+async def fastapi_get_tank_max_level(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID")
+) -> float | None:
+    """
+    获取水箱的最大水位。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        
+    Returns:
+        水箱最大水位值，如果不存在则返回None
+    """
+    ps = get_tank(network, tank)
+    return ps["max_level"]
+
+@router.get("/gettankdiameter/", summary="获取水箱直径", description="获取指定水箱的直径值")
+async def fastapi_get_tank_diameter(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID")
+) -> float | None:
+    """
+    获取水箱的直径。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        
+    Returns:
+        水箱直径值，如果不存在则返回None
+    """
+    ps = get_tank(network, tank)
+    return ps["diameter"]
+
+@router.get("/gettankminvol/", summary="获取水箱最小体积", description="获取指定水箱的最小体积值")
+async def fastapi_get_tank_min_vol(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID")
+) -> float | None:
+    """
+    获取水箱的最小体积。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        
+    Returns:
+        水箱最小体积值，如果不存在则返回None
+    """
+    ps = get_tank(network, tank)
+    return ps["min_vol"]
+
+@router.get("/gettankvolcurve/", summary="获取水箱容积曲线", description="获取指定水箱的容积曲线标识")
+async def fastapi_get_tank_vol_curve(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID")
+) -> str | None:
+    """
+    获取水箱的容积曲线。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        
+    Returns:
+        水箱容积曲线标识，如果不存在则返回None
+    """
+    ps = get_tank(network, tank)
+    return ps["vol_curve"]
+
+@router.get("/gettankoverflow/", summary="获取水箱溢流口", description="获取指定水箱的溢流口配置")
+async def fastapi_get_tank_overflow(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID")
+) -> str | None:
+    """
+    获取水箱的溢流口配置。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        
+    Returns:
+        水箱溢流口配置，如果不存在则返回None
+    """
+    ps = get_tank(network, tank)
+    return ps["overflow"]
+
+@router.get("/gettankx/", summary="获取水箱X坐标", description="获取指定水箱的X坐标值")
+async def fastapi_get_tank_x(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID")
+) -> float:
+    """
+    获取水箱的X坐标。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        
+    Returns:
+        水箱X坐标值
+    """
+    ps = get_tank(network, tank)
+    return ps["x"]
+
+@router.get("/gettanky/", summary="获取水箱Y坐标", description="获取指定水箱的Y坐标值")
+async def fastapi_get_tank_y(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID")
+) -> float:
+    """
+    获取水箱的Y坐标。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        
+    Returns:
+        水箱Y坐标值
+    """
+    ps = get_tank(network, tank)
+    return ps["y"]
+
+@router.get("/gettankcoord/", summary="获取水箱坐标", description="获取指定水箱的X和Y坐标")
+async def fastapi_get_tank_coord(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID")
+) -> dict[str, float]:
+    """
+    获取水箱的坐标。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        
+    Returns:
+        包含x和y坐标的字典
+    """
+    ps = get_tank(network, tank)
+    coord = {"x": ps["x"], "y": ps["y"]}
+    return coord
+
+@router.post("/settankelevation/", summary="设置水箱标高", description="设置指定水箱的标高值", response_model=None)
+async def fastapi_set_tank_elevation(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID"),
+    elevation: float = Query(..., description="新的标高值")
+) -> ChangeSet:
+    """
+    设置水箱的标高。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        elevation: 新的标高值
+        
+    Returns:
+        包含变更信息的ChangeSet对象
+    """
+    ps = {"id": tank, "elevation": elevation}
+    return set_tank(network, ChangeSet(ps))
+
+@router.post("/settankinitlevel/", summary="设置水箱初始水位", description="设置指定水箱的初始水位值", response_model=None)
+async def fastapi_set_tank_init_level(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID"),
+    init_level: float = Query(..., description="新的初始水位值")
+) -> ChangeSet:
+    """
+    设置水箱的初始水位。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        init_level: 新的初始水位值
+        
+    Returns:
+        包含变更信息的ChangeSet对象
+    """
+    ps = {"id": tank, "init_level": init_level}
+    return set_tank(network, ChangeSet(ps))
+
+@router.post("/settankminlevel/", summary="设置水箱最小水位", description="设置指定水箱的最小水位值", response_model=None)
+async def fastapi_set_tank_min_level(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID"),
+    min_level: float = Query(..., description="新的最小水位值")
+) -> ChangeSet:
+    """
+    设置水箱的最小水位。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        min_level: 新的最小水位值
+        
+    Returns:
+        包含变更信息的ChangeSet对象
+    """
+    ps = {"id": tank, "min_level": min_level}
+    return set_tank(network, ChangeSet(ps))
+
+@router.post("/settankmaxlevel/", summary="设置水箱最大水位", description="设置指定水箱的最大水位值", response_model=None)
+async def fastapi_set_tank_max_level(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID"),
+    max_level: float = Query(..., description="新的最大水位值")
+) -> ChangeSet:
+    """
+    设置水箱的最大水位。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        max_level: 新的最大水位值
+        
+    Returns:
+        包含变更信息的ChangeSet对象
+    """
+    ps = {"id": tank, "max_level": max_level}
+    return set_tank(network, ChangeSet(ps))
+
+@router.post("/settankdiameter/", summary="设置水箱直径", description="设置指定水箱的直径值", response_model=None)
+async def fastapi_set_tank_diameter(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID"),
+    diameter: float = Query(..., description="新的直径值")
+) -> ChangeSet:
+    """
+    设置水箱的直径。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        diameter: 新的直径值
+        
+    Returns:
+        包含变更信息的ChangeSet对象
+    """
+    ps = {"id": tank, "diameter": diameter}
+    return set_tank(network, ChangeSet(ps))
+
+@router.post("/settankminvol/", summary="设置水箱最小体积", description="设置指定水箱的最小体积值", response_model=None)
+async def fastapi_set_tank_min_vol(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID"),
+    min_vol: float = Query(..., description="新的最小体积值")
+) -> ChangeSet:
+    """
+    设置水箱的最小体积。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        min_vol: 新的最小体积值
+        
+    Returns:
+        包含变更信息的ChangeSet对象
+    """
+    ps = {"id": tank, "min_vol": min_vol}
+    return set_tank(network, ChangeSet(ps))
+
+@router.post("/settankvolcurve/", summary="设置水箱容积曲线", description="设置指定水箱的容积曲线标识", response_model=None)
+async def fastapi_set_tank_vol_curve(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID"),
+    vol_curve: str = Query(..., description="新的容积曲线标识")
+) -> ChangeSet:
+    """
+    设置水箱的容积曲线。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        vol_curve: 新的容积曲线标识
+        
+    Returns:
+        包含变更信息的ChangeSet对象
+    """
+    ps = {"id": tank, "vol_curve": vol_curve}
+    return set_tank(network, ChangeSet(ps))
+
+@router.post("/settankoverflow/", summary="设置水箱溢流口", description="设置指定水箱的溢流口配置", response_model=None)
+async def fastapi_set_tank_overflow(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID"),
+    overflow: str = Query(..., description="新的溢流口配置")
+) -> ChangeSet:
+    """
+    设置水箱的溢流口配置。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        overflow: 新的溢流口配置
+        
+    Returns:
+        包含变更信息的ChangeSet对象
+    """
+    ps = {"id": tank, "overflow": overflow}
+    return set_tank(network, ChangeSet(ps))
+
+@router.post("/settankx/", summary="设置水箱X坐标", description="设置指定水箱的X坐标值", response_model=None)
+async def fastapi_set_tank_x(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID"),
+    x: float = Query(..., description="新的X坐标值")
+) -> ChangeSet:
+    """
+    设置水箱的X坐标。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        x: 新的X坐标值
+        
+    Returns:
+        包含变更信息的ChangeSet对象
+    """
+    ps = {"id": tank, "x": x}
+    return set_tank(network, ChangeSet(ps))
+
+@router.post("/settanky/", summary="设置水箱Y坐标", description="设置指定水箱的Y坐标值", response_model=None)
+async def fastapi_set_tank_y(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID"),
+    y: float = Query(..., description="新的Y坐标值")
+) -> ChangeSet:
+    """
+    设置水箱的Y坐标。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        y: 新的Y坐标值
+        
+    Returns:
+        包含变更信息的ChangeSet对象
+    """
+    ps = {"id": tank, "y": y}
+    return set_tank(network, ChangeSet(ps))
+
+@router.post("/settankcoord/", summary="设置水箱坐标", description="设置指定水箱的X和Y坐标", response_model=None)
+async def fastapi_set_tank_coord(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID"),
+    x: float = Query(..., description="新的X坐标值"),
+    y: float = Query(..., description="新的Y坐标值")
+) -> ChangeSet:
+    """
+    设置水箱的坐标。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        x: 新的X坐标值
+        y: 新的Y坐标值
+        
+    Returns:
+        包含变更信息的ChangeSet对象
+    """
+    ps = {"id": tank, "x": x, "y": y}
+    return set_tank(network, ChangeSet(ps))
+
+@router.get("/gettankproperties/", summary="获取水箱属性", description="获取指定水箱的所有属性")
+async def fastapi_get_tank_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID")
+) -> dict[str, Any]:
+    """
+    获取水箱的所有属性。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        
+    Returns:
+        包含水箱所有属性的字典
+    """
+    return get_tank(network, tank)
+
+@router.get("/getalltankproperties/", summary="获取所有水箱属性", description="获取指定网络中所有水箱的属性")
+async def fastapi_get_all_tank_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> list[dict[str, Any]]:
+    """
+    获取网络中所有水箱的属性。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        包含所有水箱属性的字典列表
+    """
+    # 缓存查询结果提高性能
+    # global redis_client
+    results = get_all_tanks(network)
+    return results
+
+@router.post("/settankproperties/", summary="设置水箱属性", description="批量设置指定水箱的多个属性", response_model=None)
+async def fastapi_set_tank_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    tank: str = Query(..., description="水箱ID"),
+    req: Request = None
+) -> ChangeSet:
+    """
+    批量设置水箱的属性。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        tank: 水箱ID
+        req: 包含水箱属性的请求体（JSON格式）
+        
+    Returns:
+        包含变更信息的ChangeSet对象
+    """
+    props = await req.json()
+    ps = {"id": tank} | props
+    return set_tank(network, ChangeSet(ps))

app/api/v1/endpoints/network/valves.py

@@ -0,0 +1,332 @@
+from fastapi import APIRouter, Request, Query, Path, Body
+from typing import Any, List, Dict, Union
+from app.services.tjnetwork import (
+    Any,
+    ChangeSet,
+    VALVES_TYPE_PRV,
+    add_valve,
+    delete_valve,
+    get_all_valves,
+    get_valve,
+    get_valve_schema,
+    set_valve,
+)
+
+router = APIRouter()
+
+@router.get(
+    "/getvalveschema",
+    summary="获取阀门架构",
+    description="获取指定水网中所有阀门的架构和字段定义",
+)
+async def fastapi_get_valve_schema(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> dict[str, dict[str, Any]]:
+    """
+    获取阀门架构。
+    
+    返回指定水网中所有阀门类型的完整架构定义，包括字段名称、类型和默认值。
+    """
+    return get_valve_schema(network)
+
+@router.post(
+    "/addvalve/",
+    response_model=None,
+    summary="添加阀门",
+    description="在指定的水网中添加新的阀门",
+)
+async def fastapi_add_valve(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    valve: str = Query(..., description="阀门ID"),
+    node1: str = Query(..., description="起点节点ID"),
+    node2: str = Query(..., description="终点节点ID"),
+    diameter: float = Query(0, description="阀门直径（mm）"),
+    v_type: str = Query(VALVES_TYPE_PRV, description="阀门类型"),
+    setting: float = Query(0, description="阀门开度/设置值"),
+    minor_loss: float = Query(0, description="损失系数"),
+) -> ChangeSet:
+    """
+    添加新的阀门。
+    
+    在指定的水网中创建一个新的阀门，设置其连接的两个节点、直径、类型、开度和损失系数。
+    """
+    ps = {
+        "id": valve,
+        "node1": node1,
+        "node2": node2,
+        "diameter": diameter,
+        "v_type": v_type,
+        "setting": setting,
+        "minor_loss": minor_loss,
+    }
+
+    return add_valve(network, ChangeSet(ps))
+
+@router.post(
+    "/deletevalve/",
+    response_model=None,
+    summary="删除阀门",
+    description="从指定的水网中删除指定的阀门",
+)
+async def fastapi_delete_valve(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    valve: str = Query(..., description="阀门ID"),
+) -> ChangeSet:
+    """
+    删除阀门。
+    
+    从指定的水网中删除指定ID的阀门。
+    """
+    ps = {"id": valve}
+    return delete_valve(network, ChangeSet(ps))
+
+@router.get(
+    "/getvalvenode1/",
+    summary="获取阀门起点节点",
+    description="获取指定阀门连接的起点节点ID",
+)
+async def fastapi_get_valve_node1(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    valve: str = Query(..., description="阀门ID"),
+) -> str | None:
+    """
+    获取阀门的起点节点。
+    
+    返回指定阀门连接的起点（第一个）节点的ID。
+    """
+    ps = get_valve(network, valve)
+    return ps["node1"]
+
+@router.get(
+    "/getvalvenode2/",
+    summary="获取阀门终点节点",
+    description="获取指定阀门连接的终点节点ID",
+)
+async def fastapi_get_valve_node2(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    valve: str = Query(..., description="阀门ID"),
+) -> str | None:
+    """
+    获取阀门的终点节点。
+    
+    返回指定阀门连接的终点（第二个）节点的ID。
+    """
+    ps = get_valve(network, valve)
+    return ps["node2"]
+
+@router.get(
+    "/getvalvediameter/",
+    summary="获取阀门直径",
+    description="获取指定阀门的直径",
+)
+async def fastapi_get_valve_diameter(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    valve: str = Query(..., description="阀门ID"),
+) -> float | None:
+    """
+    获取阀门的直径。
+    
+    返回指定阀门的直径值（单位：mm）。
+    """
+    ps = get_valve(network, valve)
+    return ps["diameter"]
+
+@router.get(
+    "/getvalvetype/",
+    summary="获取阀门类型",
+    description="获取指定阀门的类型",
+)
+async def fastapi_get_valve_type(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    valve: str = Query(..., description="阀门ID"),
+) -> str | None:
+    """
+    获取阀门的类型。
+    
+    返回指定阀门的类型（例如：减压阀、调节阀等）。
+    """
+    ps = get_valve(network, valve)
+    return ps["type"]
+
+@router.get(
+    "/getvalvesetting/",
+    summary="获取阀门开度",
+    description="获取指定阀门的开度/设置值",
+)
+async def fastapi_get_valve_setting(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    valve: str = Query(..., description="阀门ID"),
+) -> float | None:
+    """
+    获取阀门的开度。
+    
+    返回指定阀门的开度/设置值。
+    """
+    ps = get_valve(network, valve)
+    return ps["setting"]
+
+@router.get(
+    "/getvalveminorloss/",
+    summary="获取阀门损失系数",
+    description="获取指定阀门的损失系数",
+)
+async def fastapi_get_valve_minor_loss(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    valve: str = Query(..., description="阀门ID"),
+) -> float | None:
+    """
+    获取阀门的损失系数。
+    
+    返回指定阀门的损失系数值，用于计算流体通过阀门的压力损失。
+    """
+    ps = get_valve(network, valve)
+    return ps["minor_loss"]
+
+@router.post(
+    "/setvalvenode1/",
+    response_model=None,
+    summary="设置阀门起点节点",
+    description="设置指定阀门的起点节点",
+)
+async def fastapi_set_valve_node1(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    valve: str = Query(..., description="阀门ID"),
+    node1: str = Query(..., description="新的起点节点ID"),
+) -> ChangeSet:
+    """
+    设置阀门的起点节点。
+    
+    更新指定阀门的起点节点连接。
+    """
+    ps = {"id": valve, "node1": node1}
+    return set_valve(network, ChangeSet(ps))
+
+@router.post(
+    "/setvalvenode2/",
+    response_model=None,
+    summary="设置阀门终点节点",
+    description="设置指定阀门的终点节点",
+)
+async def fastapi_set_valve_node2(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    valve: str = Query(..., description="阀门ID"),
+    node2: str = Query(..., description="新的终点节点ID"),
+) -> ChangeSet:
+    """
+    设置阀门的终点节点。
+    
+    更新指定阀门的终点节点连接。
+    """
+    ps = {"id": valve, "node2": node2}
+    return set_valve(network, ChangeSet(ps))
+
+@router.post(
+    "/setvalvenodediameter/",
+    response_model=None,
+    summary="设置阀门直径",
+    description="设置指定阀门的直径",
+)
+async def fastapi_set_valve_diameter(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    valve: str = Query(..., description="阀门ID"),
+    diameter: float = Query(..., description="新的直径值（mm）"),
+) -> ChangeSet:
+    """
+    设置阀门的直径。
+    
+    更新指定阀门的直径值。
+    """
+    ps = {"id": valve, "diameter": diameter}
+    return set_valve(network, ChangeSet(ps))
+
+@router.post(
+    "/setvalvetype/",
+    response_model=None,
+    summary="设置阀门类型",
+    description="设置指定阀门的类型",
+)
+async def fastapi_set_valve_type(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    valve: str = Query(..., description="阀门ID"),
+    type: str = Query(..., description="新的阀门类型"),
+) -> ChangeSet:
+    """
+    设置阀门的类型。
+    
+    更新指定阀门的类型（例如：减压阀、调节阀等）。
+    """
+    ps = {"id": valve, "type": type}
+    return set_valve(network, ChangeSet(ps))
+
+@router.post(
+    "/setvalvesetting/",
+    response_model=None,
+    summary="设置阀门开度",
+    description="设置指定阀门的开度/设置值",
+)
+async def fastapi_set_valve_setting(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    valve: str = Query(..., description="阀门ID"),
+    setting: float = Query(..., description="新的开度值"),
+) -> ChangeSet:
+    """
+    设置阀门的开度。
+    
+    更新指定阀门的开度/设置值。
+    """
+    ps = {"id": valve, "setting": setting}
+    return set_valve(network, ChangeSet(ps))
+
+@router.get(
+    "/getvalveproperties/",
+    summary="获取阀门所有属性",
+    description="获取指定阀门的所有属性",
+)
+async def fastapi_get_valve_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    valve: str = Query(..., description="阀门ID"),
+) -> dict[str, Any]:
+    """
+    获取阀门的所有属性。
+    
+    返回指定阀门的完整属性集合，包括ID、节点、直径、类型、开度和损失系数。
+    """
+    return get_valve(network, valve)
+
+@router.get(
+    "/getallvalveproperties/",
+    summary="获取所有阀门属性",
+    description="获取指定水网中所有阀门的属性",
+)
+async def fastapi_get_all_valve_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> list[dict[str, Any]]:
+    """
+    获取所有阀门的属性。
+    
+    返回指定水网中所有阀门的完整属性列表。
+    """
+    # 缓存查询结果提高性能
+    # global redis_client
+    results = get_all_valves(network)
+    return results
+
+@router.post(
+    "/setvalveproperties/",
+    response_model=None,
+    summary="批量设置阀门属性",
+    description="批量设置指定阀门的多个属性",
+)
+async def fastapi_set_valve_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    valve: str = Query(..., description="阀门ID"),
+    req: Request = None,
+) -> ChangeSet:
+    """
+    批量设置阀门的属性。
+    
+    更新指定阀门的一个或多个属性，通过JSON请求体传递要更新的属性。
+    """
+    props = await req.json()
+    ps = {"id": valve} | props
+    return set_valve(network, ChangeSet(ps))

app/api/v1/endpoints/project.py

@@ -0,0 +1,578 @@
+import json
+from fastapi import APIRouter, Request, HTTPException, Query, Path, Body, Depends
+from fastapi.responses import PlainTextResponse
+from typing import Any, Dict, List
+from app.infra.db.metadb.repositories.metadata_repository import MetadataRepository
+from app.auth.project_dependencies import get_metadata_repository
+from app.domain.schemas.metadata import ProjectMetaResponse
+import app.services.project_info as project_info
+from app.infra.db.postgresql.database import get_database_instance as get_pg_db
+from app.infra.db.timescaledb.database import get_database_instance as get_ts_db
+from app.services.tjnetwork import (
+    ChangeSet,
+    list_project,
+    have_project,
+    create_project,
+    delete_project,
+    is_project_open,
+    open_project,
+    close_project,
+    copy_project,
+    import_inp,
+    export_inp,
+    read_inp,
+    dump_inp,
+    get_all_vertices,
+    get_all_scada_elements,
+    get_all_district_metering_areas,
+    get_all_service_areas,
+    get_all_virtual_districts,
+    get_extension_data,
+    convert_inp_v3_to_v2,
+)
+
+# For inp file upload/download
+import os
+from fastapi import Response, status
+from fastapi.responses import FileResponse
+inpDir = "data/" # Assuming data directory exists or is defined somewhere. 
+# In main.py it was likely global. For safety, let's use a relative path or get from config. 
+# But let's stick to what main.py probably used or a default.
+
+router = APIRouter()
+lockedPrjs: Dict[str, str] = {}
+
+@router.get("/project-info", summary="获取项目信息", description="从数据库获取项目的详细信息，包括地图范围等。", response_model=ProjectMetaResponse)
+@router.get("/project_info/", summary="获取项目信息（旧路径）", description="从数据库获取项目的详细信息，包括地图范围等。", response_model=ProjectMetaResponse, deprecated=True)
+async def get_project_info_endpoint(
+    network: str = Query(..., description="管网名称（或项目代码）"),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+):
+    """
+    获取项目信息
+
+    - **network**: 管网名称（或项目代码）
+    """
+    project_detail = await metadata_repo.get_project_detail_by_code(network)
+    if not project_detail:
+        raise HTTPException(status_code=404, detail=f"Project {network} not found")
+
+    return ProjectMetaResponse(
+        project_id=project_detail.project_id,
+        name=project_detail.name,
+        code=project_detail.code,
+        description=project_detail.description,
+        gs_workspace=project_detail.gs_workspace,
+        map_extent=project_detail.map_extent,
+        status=project_detail.status,
+        project_role="viewer",
+    )
+
+@router.get("/listprojects/", summary="获取项目列表", description="获取服务器上所有可用的供水管网项目名称列表。")
+async def list_projects_endpoint() -> list[str]:
+    """
+    获取项目列表
+
+    返回所有已创建项目的名称列表。
+    """
+    return list_project()
+
+@router.get("/haveproject/", summary="检查项目是否存在", description="检查指定名称的项目是否存在。")
+async def have_project_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+):
+    """
+    检查项目是否存在
+
+    - **network**: 管网名称（或数据库名称）
+    """
+    return have_project(network)
+
+@router.post("/createproject/", summary="创建新项目", description="创建一个新的供水管网项目。如果项目已存在，可能会覆盖或报错（取决于底层实现）。")
+async def create_project_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+):
+    """
+    创建新项目
+
+    - **network**: 管网名称（或数据库名称）
+    """
+    create_project(network)
+    return network
+
+@router.post("/deleteproject/", summary="删除项目", description="永久删除指定的供水管网项目。此操作不可恢复。")
+async def delete_project_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+):
+    """
+    删除项目
+
+    - **network**: 管网名称（或数据库名称）
+    """
+    delete_project(network)
+    return True
+
+@router.get("/isprojectopen/", summary="检查项目是否已打开", description="检查指定项目是否已被加载到内存中。")
+async def is_project_open_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+):
+    """
+    检查项目是否已打开
+
+    - **network**: 管网名称（或数据库名称）
+    """
+    return is_project_open(network)
+
+@router.post("/projects/open", summary="打开项目", description="将指定项目加载到内存中，并初始化数据库连接池。")
+@router.post("/openproject/", summary="打开项目（旧路径）", description="将指定项目加载到内存中，并初始化数据库连接池。", deprecated=True)
+async def open_project_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+):
+    """
+    打开项目
+
+    - **network**: 管网名称（或数据库名称）
+    """
+    open_project(network)
+    
+    # 尝试连接指定数据库
+    try:
+        # 初始化 PostgreSQL 连接池
+        pg_instance = await get_pg_db(network)
+        async with pg_instance.get_connection() as conn:
+            async with conn.cursor() as cur:
+                await cur.execute("SELECT 1")
+        
+        # 初始化 TimescaleDB 连接池
+        ts_instance = await get_ts_db(network)
+        async with ts_instance.get_connection() as conn:
+            async with conn.cursor() as cur:
+                await cur.execute("SELECT 1")
+                
+    except Exception as e:
+        # 记录错误但不阻断项目打开，或者根据需求决定是否阻断
+        # 这里选择打印错误，因为 open_project 原本只负责原生部分
+        print(f"Failed to connect to databases for {network}: {str(e)}")
+        # 如果数据库连接是必须的，可以抛出异常:
+        # raise HTTPException(status_code=500, detail=f"Database connection failed: {str(e)}")
+
+    return network
+
+@router.post("/closeproject/", summary="关闭项目", description="将指定项目从内存中卸载，释放资源。")
+async def close_project_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+):
+    """
+    关闭项目
+
+    - **network**: 管网名称（或数据库名称）
+    """
+    close_project(network)
+    return True
+
+@router.post("/copyproject/", summary="复制项目", description="将现有项目复制为新项目。")
+async def copy_project_endpoint(
+    source: str = Query(..., description="管网名称（或数据库名称）"),
+    target: str = Query(..., description="管网名称（或数据库名称）")
+):
+    """
+    复制项目
+
+    - **source**: 管网名称（或数据库名称）
+    - **target**: 管网名称（或数据库名称）
+    """
+    copy_project(source, target)
+    return True
+
+@router.post("/importinp/", summary="导入 INP 文件内容", description="将 INP 格式的文本内容导入到指定项目中。")
+async def import_inp_endpoint(
+    req: Request,
+    network: str = Query(..., description="管网名称（或数据库名称）")
+):
+    """
+    导入 INP 文件内容
+
+    - **network**: 管网名称（或数据库名称）
+    - **req**: 请求体，需包含 `{"inp": "..."}` 结构
+    """
+    jo_root = await req.json()
+    inp_text = jo_root["inp"]
+    ps = {"inp": inp_text}
+    ret = import_inp(network, ChangeSet(ps))
+    print(ret)
+    return ret
+
+@router.get("/exportinp/", response_model=None, summary="导出项目为 ChangeSet", description="导出项目的变更集 (ChangeSet)，包含顶点、SCADA 元素、DMA、SA、VD 等信息。")
+async def export_inp_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    version: str = Query(..., description="版本号 (通常用于增量更新)")
+) -> ChangeSet:
+    """
+    导出项目为 ChangeSet
+
+    - **network**: 管网名称（或数据库名称）
+    - **version**: 版本号
+    """
+    cs = export_inp(network, version)
+    op = cs.operations[0]
+    open_project(network)
+    op["vertex"] = json.dumps(get_all_vertices(network))
+    op["scada"] = json.dumps(get_all_scada_elements(network))
+    op["dma"] = json.dumps(get_all_district_metering_areas(network))
+    op["sa"] = json.dumps(get_all_service_areas(network))
+    op["vd"] = json.dumps(get_all_virtual_districts(network))
+    op["legend"] = get_extension_data(network, "legend")
+
+    db = get_extension_data(network, "scada_db")
+    print(db)
+    scada_db = ""
+    if db:
+        scada_db = db
+    print(scada_db)
+    op["scada_db"] = scada_db
+
+    close_project(network)
+
+    return cs
+
+@router.post("/readinp/", summary="读取 INP 文件到项目", description="从服务器文件系统中读取指定的 INP 文件并加载到项目中。")
+async def read_inp_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    inp: str = Query(..., description="INP 文件名 (不包含路径)")
+) -> bool:
+    """
+    读取 INP 文件到项目
+
+    - **network**: 管网名称（或数据库名称）
+    - **inp**: INP 文件名
+    """
+    read_inp(network, inp)
+    return True
+
+@router.get("/dumpinp/", summary="导出项目到 INP 文件", description="将项目当前状态保存为 INP 文件到服务器文件系统。")
+async def dump_inp_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    inp: str = Query(..., description="目标文件名")
+) -> bool:
+    """
+    导出项目到 INP 文件
+
+    - **network**: 管网名称（或数据库名称）
+    - **inp**: 目标文件名
+    """
+    dump_inp(network, inp)
+    return True
+
+@router.get("/isprojectlocked/", summary="检查项目是否被锁定", description="检查指定项目是否处于锁定状态。")
+async def is_project_locked_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+):
+    """
+    检查项目是否被锁定
+
+    - **network**: 管网名称（或数据库名称）
+    """
+    return network in lockedPrjs.keys()
+
+@router.get("/isprojectlockedbyme/", summary="检查项目是否被当前用户锁定", description="检查指定项目是否被当前客户端 (IP) 锁定。")
+async def is_project_locked_by_me_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+):
+    """
+    检查项目是否被当前用户锁定
+
+    - **network**: 管网名称（或数据库名称）
+    """
+    client_host = req.client.host
+    return lockedPrjs.get(network) == client_host
+
+# 0 successfully locked
+# 1 already locked by you
+# 2 locked by others
+@router.post("/lockproject/", summary="锁定项目", description="锁定指定项目以防止并发修改。")
+async def lock_project_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+):
+    """
+    锁定项目
+
+    返回值:
+    - **0**: 锁定成功
+    - **1**: 已被当前用户锁定
+    - **2**: 已被其他用户锁定
+    """
+    client_host = req.client.host
+    if not network in lockedPrjs.keys():
+        lockedPrjs[network] = client_host
+        return 0
+    else:
+        if lockedPrjs.get(network) == client_host:
+            return 1
+        else:
+            return 2
+
+@router.post("/unlockproject/", summary="解锁项目", description="释放对项目的锁定。")
+def unlock_project_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+):
+    """
+    解锁项目
+
+    只有锁定者才能解锁。
+    """
+    client_host = req.client.host
+    if lockedPrjs.get(network) == client_host:
+        print("delete key")
+        del lockedPrjs[network]
+        return True
+
+    return False
+
+# inp file operations
+@router.post("/uploadinp/", status_code=status.HTTP_200_OK, summary="上传 INP 文件", description="上传 INP 文件到服务器数据目录。")
+async def fastapi_upload_inp(
+    afile: bytes = Body(..., description="文件二进制内容"),
+    name: str = Query(..., description="保存的文件名")
+):
+    """
+    上传 INP 文件
+
+    - **afile**: 文件内容
+    - **name**: 文件名
+    """
+    if not os.path.exists(inpDir):
+        os.makedirs(inpDir, exist_ok=True)
+        
+    filePath = inpDir + str(name)
+    with open(filePath, "wb") as f:
+        f.write(afile)
+    return True
+
+@router.get("/downloadinp/", status_code=status.HTTP_200_OK, summary="下载 INP 文件", description="从服务器数据目录下载指定的 INP 文件。")
+async def fastapi_download_inp(
+    name: str = Query(..., description="文件名"),
+    response: Response = None
+):
+    """
+    下载 INP 文件
+
+    - **name**: 文件名
+    """
+    filePath = inpDir + name
+    if os.path.exists(filePath):
+        return FileResponse(
+            filePath, media_type="application/octet-stream", filename="inp.inp"
+        )
+    else:
+        response.status_code = status.HTTP_400_BAD_REQUEST
+        return True
+
+# DingZQ, 2024-12-28, convert v3 to v2
+@router.get("/convertv3tov2/", response_model=None, summary="转换 INP V3 为 V2", description="将 EPANET 3.0 格式的 INP 内容转换为 2.x 格式。")
+async def fastapi_convert_v3_to_v2(
+    req: Request
+) -> ChangeSet:
+    """
+    转换 INP V3 为 V2
+
+    - **req**: 请求体，需包含 `{"inp": "..."}` 结构
+    """
+    network = "v3Tov2"
+    jo_root = await req.json()
+    inp = jo_root["inp"]
+    cs = convert_inp_v3_to_v2(inp)
+    op = cs.operations[0]
+    open_project(network)
+    op["vertex"] = json.dumps(get_all_vertices(network))
+    op["scada"] = json.dumps(get_all_scada_elements(network))
+    op["dma"] = json.dumps(get_all_district_metering_areas(network))
+    op["sa"] = json.dumps(get_all_service_areas(network))
+    op["vd"] = json.dumps(get_all_virtual_districts(network))
+    op["legend"] = get_extension_data(network, "legend")
+
+    db = get_extension_data(network, "scada_db")
+    print(db)
+    scada_db = ""
+    if db:
+        scada_db = db
+    print(scada_db)
+    op["scada_db"] = scada_db
+
+    close_project(network)
+
+    return cs
+
+@router.post("/readinp/", summary="读取 INP 文件到项目", description="从服务器文件系统中读取指定的 INP 文件并加载到项目中。")
+async def read_inp_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    inp: str = Query(..., description="INP 文件名 (不包含路径)")
+) -> bool:
+    """
+    读取 INP 文件到项目
+
+    - **network**: 管网名称（或数据库名称）
+    - **inp**: INP 文件名
+    """
+    read_inp(network, inp)
+    return True
+
+@router.get("/dumpinp/", summary="导出项目到 INP 文件", description="将项目当前状态保存为 INP 文件到服务器文件系统。")
+async def dump_inp_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    inp: str = Query(..., description="目标文件名")
+) -> bool:
+    """
+    导出项目到 INP 文件
+
+    - **network**: 管网名称（或数据库名称）
+    - **inp**: 目标文件名
+    """
+    dump_inp(network, inp)
+    return True
+
+@router.get("/isprojectlocked/", summary="检查项目是否被锁定", description="检查指定项目是否处于锁定状态。")
+async def is_project_locked_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+):
+    """
+    检查项目是否被锁定
+
+    - **network**: 管网名称（或数据库名称）
+    """
+    return network in lockedPrjs.keys()
+
+@router.get("/isprojectlockedbyme/", summary="检查项目是否被当前用户锁定", description="检查指定项目是否被当前客户端 (IP) 锁定。")
+async def is_project_locked_by_me_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+):
+    """
+    检查项目是否被当前用户锁定
+
+    - **network**: 管网名称（或数据库名称）
+    """
+    client_host = req.client.host
+    return lockedPrjs.get(network) == client_host
+
+# 0 successfully locked
+# 1 already locked by you
+# 2 locked by others
+@router.post("/lockproject/", summary="锁定项目", description="锁定指定项目以防止并发修改。")
+async def lock_project_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+):
+    """
+    锁定项目
+
+    返回值:
+    - **0**: 锁定成功
+    - **1**: 已被当前用户锁定
+    - **2**: 已被其他用户锁定
+    """
+    client_host = req.client.host
+    if not network in lockedPrjs.keys():
+        lockedPrjs[network] = client_host
+        return 0
+    else:
+        if lockedPrjs.get(network) == client_host:
+            return 1
+        else:
+            return 2
+
+@router.post("/unlockproject/", summary="解锁项目", description="释放对项目的锁定。")
+def unlock_project_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+):
+    """
+    解锁项目
+
+    只有锁定者才能解锁。
+    """
+    client_host = req.client.host
+    if lockedPrjs.get(network) == client_host:
+        print("delete key")
+        del lockedPrjs[network]
+        return True
+
+    return False
+
+# inp file operations
+@router.post("/uploadinp/", status_code=status.HTTP_200_OK, summary="上传 INP 文件", description="上传 INP 文件到服务器数据目录。")
+async def fastapi_upload_inp(
+    afile: bytes = Body(..., description="文件二进制内容"),
+    name: str = Query(..., description="保存的文件名")
+):
+    """
+    上传 INP 文件
+
+    - **afile**: 文件内容
+    - **name**: 文件名
+    """
+    if not os.path.exists(inpDir):
+        os.makedirs(inpDir, exist_ok=True)
+        
+    filePath = inpDir + str(name)
+    with open(filePath, "wb") as f:
+        f.write(afile)
+    return True
+
+@router.get("/downloadinp/", status_code=status.HTTP_200_OK, summary="下载 INP 文件", description="从服务器数据目录下载指定的 INP 文件。")
+async def fastapi_download_inp(
+    name: str = Query(..., description="文件名"),
+    response: Response = None
+):
+    """
+    下载 INP 文件
+
+    - **name**: 文件名
+    """
+    filePath = inpDir + name
+    if os.path.exists(filePath):
+        return FileResponse(
+            filePath, media_type="application/octet-stream", filename="inp.inp"
+        )
+    else:
+        response.status_code = status.HTTP_400_BAD_REQUEST
+        return True
+
+# DingZQ, 2024-12-28, convert v3 to v2
+@router.get("/convertv3tov2/", response_model=None, summary="转换 INP V3 为 V2", description="将 EPANET 3.0 格式的 INP 内容转换为 2.x 格式。")
+async def fastapi_convert_v3_to_v2(
+    req: Request
+) -> ChangeSet:
+    """
+    转换 INP V3 为 V2
+
+    - **req**: 请求体，需包含 `{"inp": "..."}` 结构
+    """
+    network = "v3Tov2"
+    jo_root = await req.json()
+    inp = jo_root["inp"]
+    cs = convert_inp_v3_to_v2(inp)
+    op = cs.operations[0]
+    open_project(network)
+    op["vertex"] = json.dumps(get_all_vertices(network))
+    op["scada"] = json.dumps(get_all_scada_elements(network))
+    op["dma"] = json.dumps(get_all_district_metering_areas(network))
+    op["sa"] = json.dumps(get_all_service_areas(network))
+    op["vd"] = json.dumps(get_all_virtual_districts(network))
+    op["legend"] = get_extension_data(network, "legend")
+
+    db = get_extension_data(network, "scada_db")
+    print(db)
+    scada_db = ""
+    if db:
+        scada_db = db
+    print(scada_db)
+    op["scada_db"] = scada_db
+
+    close_project(network)
+
+    return cs

app/api/v1/endpoints/project_data.py

@@ -1,36 +1,31 @@
-from fastapi import APIRouter, Depends, HTTPException, Query
-from typing import Optional
+from fastapi import APIRouter, Depends, HTTPException, Path, Query
 from psycopg import AsyncConnection
 
-from .database import get_database_instance
-from .scada_info import ScadaRepository
-from .scheme import SchemeRepository
+from app.infra.db.postgresql.scada import ScadaInfoRepository
+from app.infra.db.postgresql.scheme import SchemeRepository
+from app.auth.project_dependencies import get_project_pg_connection
 
-router = APIRouter(prefix="/postgresql", tags=["postgresql"])
+router = APIRouter()
 
 
-# 创建支持数据库选择的连接依赖函数
 async def get_database_connection(
-    db_name: Optional[str] = Query(
-        None, description="指定要连接的数据库名称，为空时使用默认数据库"
-    )
+    conn: AsyncConnection = Depends(get_project_pg_connection),
 ):
-    """获取数据库连接，支持通过查询参数指定数据库名称"""
-    instance = await get_database_instance(db_name)
-    async with instance.get_connection() as conn:
-        yield conn
+    """获取数据库连接"""
+    yield conn
 
 
-@router.get("/scada-info")
+@router.get("/scada-info", summary="获取SCADA信息", description="使用连接池查询所有SCADA信息")
 async def get_scada_info_with_connection(
     conn: AsyncConnection = Depends(get_database_connection),
 ):
     """
-    使用连接池查询所有SCADA信息
+    获取所有SCADA信息
+    
+    返回项目中所有的SCADA设备信息
     """
     try:
-        # 使用ScadaRepository查询SCADA信息
-        scada_data = await ScadaRepository.get_scadas(conn)
+        scada_data = await ScadaInfoRepository.get_scadas(conn)
         return {"success": True, "data": scada_data, "count": len(scada_data)}
     except Exception as e:
         raise HTTPException(
@@ -38,30 +33,32 @@ async def get_scada_info_with_connection(
         )
 
 
-@router.get("/scheme-list")
+@router.get("/scheme-list", summary="获取方案列表", description="使用连接池查询所有方案信息")
 async def get_scheme_list_with_connection(
     conn: AsyncConnection = Depends(get_database_connection),
 ):
     """
-    使用连接池查询所有方案信息
+    获取所有方案信息
+    
+    返回项目中所有方案的详细信息
     """
     try:
-        # 使用SchemeRepository查询方案信息
         scheme_data = await SchemeRepository.get_schemes(conn)
         return {"success": True, "data": scheme_data, "count": len(scheme_data)}
     except Exception as e:
         raise HTTPException(status_code=500, detail=f"查询方案信息时发生错误: {str(e)}")
 
 
-@router.get("/burst-locate-result")
+@router.get("/burst-locate-result", summary="获取爆管定位结果", description="使用连接池查询所有爆管定位结果")
 async def get_burst_locate_result_with_connection(
     conn: AsyncConnection = Depends(get_database_connection),
 ):
     """
-    使用连接池查询所有爆管定位结果
+    获取所有爆管定位结果
+    
+    返回项目中所有的爆管定位分析结果
     """
     try:
-        # 使用SchemeRepository查询爆管定位结果
         burst_data = await SchemeRepository.get_burst_locate_results(conn)
         return {"success": True, "data": burst_data, "count": len(burst_data)}
     except Exception as e:
@@ -70,16 +67,18 @@ async def get_burst_locate_result_with_connection(
         )
 
 
-@router.get("/burst-locate-result/{burst_incident}")
+@router.get("/burst-locate-result/{burst_incident}", summary="按事件查询爆管定位结果", description="根据爆管事件ID查询对应的爆管定位结果")
 async def get_burst_locate_result_by_incident(
-    burst_incident: str,
+    burst_incident: str = Path(..., description="爆管事件ID"),
     conn: AsyncConnection = Depends(get_database_connection),
 ):
     """
-    根据 burst_incident 查询爆管定位结果
+    根据爆管事件ID查询爆管定位结果
+    
+    参数:
+        burst_incident: 爆管事件的唯一标识符
     """
     try:
-        # 使用SchemeRepository查询爆管定位结果
         return await SchemeRepository.get_burst_locate_result_by_incident(
             conn, burst_incident
         )

app/api/v1/endpoints/risk.py

@@ -0,0 +1,127 @@
+from typing import Any, List, Dict
+from fastapi import APIRouter, Query, Path
+from app.services.tjnetwork import (
+    get_pipe_risk_probability_now,
+    get_pipe_risk_probability,
+    get_pipes_risk_probability,
+    get_network_pipe_risk_probability_now,
+    get_pipe_risk_probability_geometries,
+)
+
+router = APIRouter()
+
+@router.get(
+    "/getpiperiskprobabilitynow/",
+    summary="获取管道当前风险概率",
+    description="获取指定管道当前时刻的风险概率值"
+)
+async def fastapi_get_pipe_risk_probability_now(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe_id: str = Query(..., description="管道ID")
+) -> dict[str, Any]:
+    """
+    获取管道当前风险概率。
+    
+    查询指定管道在当前时刻的风险概率值。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe_id: 管道ID
+        
+    Returns:
+        包含风险概率信息的字典
+    """
+    return get_pipe_risk_probability_now(network, pipe_id)
+
+
+@router.get(
+    "/getpiperiskprobability/",
+    summary="获取管道风险概率历史",
+    description="获取指定管道的风险概率历史数据"
+)
+async def fastapi_get_pipe_risk_probability(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe_id: str = Query(..., description="管道ID")
+) -> dict[str, Any]:
+    """
+    获取管道风险概率历史。
+    
+    查询指定管道的历史风险概率数据。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe_id: 管道ID
+        
+    Returns:
+        包含风险概率历史的字典
+    """
+    return get_pipe_risk_probability(network, pipe_id)
+
+
+@router.get(
+    "/getpipesriskprobability/",
+    summary="批量获取多条管道风险概率",
+    description="批量获取多条管道的风险概率值"
+)
+async def fastapi_get_pipes_risk_probability(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    pipe_ids: str = Query(..., description="逗号分隔的管道ID列表")
+) -> list[dict[str, Any]]:
+    """
+    批量获取多条管道风险概率。
+    
+    查询多条指定管道的风险概率值。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        pipe_ids: 逗号分隔的管道ID列表（例如：pipe1,pipe2,pipe3）
+        
+    Returns:
+        包含多条管道风险概率的列表
+    """
+    pipeids = pipe_ids.split(",")
+    return get_pipes_risk_probability(network, pipeids)
+
+
+@router.get(
+    "/getnetworkpiperiskprobabilitynow/",
+    summary="获取整个网络的管道风险概率",
+    description="获取指定网络中所有管道的当前风险概率值"
+)
+async def fastapi_get_network_pipe_risk_probability_now(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+) -> list[dict[str, Any]]:
+    """
+    获取整个网络的管道风险概率。
+    
+    查询指定网络中所有管道在当前时刻的风险概率值。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        包含网络内所有管道风险概率的列表
+    """
+    return get_network_pipe_risk_probability_now(network)
+
+
+@router.get(
+    "/getpiperiskprobabilitygeometries/",
+    summary="获取管道风险几何信息",
+    description="获取指定网络中管道的风险相关几何数据"
+)
+async def fastapi_get_pipe_risk_probability_geometries(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> dict[str, Any]:
+    """
+    获取管道风险几何信息。
+    
+    查询指定网络中管道的地理和风险相关的几何数据。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        包含几何信息和风险数据的字典
+    """
+    return get_pipe_risk_probability_geometries(network)

app/api/v1/endpoints/scada.py

@@ -0,0 +1,527 @@
+from typing import Any
+from fastapi import APIRouter, Request, Query
+from app.services.tjnetwork import (
+    ChangeSet,
+    get_scada_info,
+    get_all_scada_info,
+    get_scada_device_schema,
+    get_scada_device,
+    set_scada_device,
+    add_scada_device,
+    delete_scada_device,
+    clean_scada_device,
+    get_all_scada_device_ids,
+    get_all_scada_devices,
+    get_scada_device_data_schema,
+    get_scada_device_data,
+    set_scada_device_data,
+    add_scada_device_data,
+    delete_scada_device_data,
+    clean_scada_device_data,
+    get_scada_element_schema,
+    get_scada_element,
+    set_scada_element,
+    add_scada_element,
+    delete_scada_element,
+    clean_scada_element,
+    get_all_scada_elements,
+    get_scada_element_schema,
+    get_scada_info_schema,
+)
+
+router = APIRouter()
+
+@router.get("/getscadaproperties/", summary="获取SCADA属性", tags=["SCADA基础"])
+async def fast_get_scada_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    scada: str = Query(..., description="SCADA设备ID")
+) -> dict[str, Any]:
+    """
+    获取单个SCADA设备的属性信息
+    
+    根据管网名称和SCADA设备ID获取该设备的完整属性。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        scada: SCADA设备ID
+        
+    Returns:
+        SCADA设备的属性字典
+    """
+    return get_scada_info(network, scada)
+
+@router.get("/getallscadaproperties/", summary="获取所有SCADA属性", tags=["SCADA基础"])
+async def fast_get_all_scada_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> list[dict[str, Any]]:
+    """
+    获取指定管网所有SCADA设备的属性信息
+    
+    查询该管网下所有已配置的SCADA设备的属性列表。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        SCADA设备属性列表
+    """
+    return get_all_scada_info(network)
+
+
+############################################################
+# scada_device 设备管理
+############################################################
+
+@router.get("/getscadadeviceschema/", summary="获取SCADA设备架构", tags=["SCADA设备"])
+async def fastapi_get_scada_device_schema(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> dict[str, dict[str, Any]]:
+    """
+    获取SCADA设备的数据架构
+    
+    返回SCADA设备表的字段定义和类型信息。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        SCADA设备的字段架构信息
+    """
+    return get_scada_device_schema(network)
+
+@router.get("/getscadadevice/", summary="获取SCADA设备", tags=["SCADA设备"])
+async def fastapi_get_scada_device(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    id: str = Query(..., description="SCADA设备ID")
+) -> dict[str, Any]:
+    """
+    获取单个SCADA设备的信息
+    
+    根据设备ID查询该设备的详细信息。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        id: SCADA设备ID
+        
+    Returns:
+        SCADA设备信息
+    """
+    return get_scada_device(network, id)
+
+@router.post("/setscadadevice/", response_model=None, summary="更新SCADA设备", tags=["SCADA设备"])
+async def fastapi_set_scada_device(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """
+    更新SCADA设备信息
+    
+    修改指定SCADA设备的属性。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        req: 请求体，包含要更新的设备属性
+        
+    Returns:
+        变更集合信息
+    """
+    props = await req.json()
+    return set_scada_device(network, ChangeSet(props))
+
+@router.post("/addscadadevice/", response_model=None, summary="添加SCADA设备", tags=["SCADA设备"])
+async def fastapi_add_scada_device(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """
+    添加新的SCADA设备
+    
+    在指定管网中添加一个新的SCADA设备。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        req: 请求体，包含新设备的属性
+        
+    Returns:
+        变更集合信息
+    """
+    props = await req.json()
+    return add_scada_device(network, ChangeSet(props))
+
+@router.post("/deletescadadevice/", response_model=None, summary="删除SCADA设备", tags=["SCADA设备"])
+async def fastapi_delete_scada_device(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """
+    删除SCADA设备
+    
+    从指定管网中删除一个SCADA设备。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        req: 请求体，包含要删除的设备ID
+        
+    Returns:
+        变更集合信息
+    """
+    props = await req.json()
+    return delete_scada_device(network, ChangeSet(props))
+
+@router.post("/cleanscadadevice/", response_model=None, summary="清空SCADA设备表", tags=["SCADA设备"])
+async def fastapi_clean_scada_device(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> ChangeSet:
+    """
+    清空SCADA设备表
+    
+    删除指定管网中所有的SCADA设备。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        变更集合信息
+    """
+    return clean_scada_device(network)
+
+@router.get("/getallscadadeviceids/", summary="获取所有SCADA设备ID", tags=["SCADA设备"])
+async def fastapi_get_all_scada_device_ids(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> list[str]:
+    """
+    获取指定管网所有SCADA设备的ID列表
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        SCADA设备ID列表
+    """
+    return get_all_scada_device_ids(network)
+
+@router.get("/getallscadadevices/", summary="获取所有SCADA设备", tags=["SCADA设备"])
+async def fastapi_get_all_scada_devices(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> list[dict[str, Any]]:
+    """
+    获取指定管网所有SCADA设备的完整信息
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        SCADA设备信息列表
+    """
+    return get_all_scada_devices(network)
+
+
+############################################################
+# scada_device_data 设备数据管理
+############################################################
+
+@router.get("/getscadadevicedataschema/", summary="获取SCADA设备数据架构", tags=["SCADA设备数据"])
+async def fastapi_get_scada_device_data_schema(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+) -> dict[str, dict[str, Any]]:
+    """
+    获取SCADA设备数据的表结构
+    
+    返回SCADA设备数据表的字段定义和类型信息。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        SCADA设备数据的字段架构信息
+    """
+    return get_scada_device_data_schema(network)
+
+@router.get("/getscadadevicedata/", summary="获取SCADA设备数据", tags=["SCADA设备数据"])
+async def fastapi_get_scada_device_data(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    device_id: str = Query(..., description="SCADA设备ID")
+) -> dict[str, Any]:
+    """
+    获取单个SCADA设备的数据
+    
+    查询指定设备的监测数据或配置数据。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        device_id: SCADA设备ID
+        
+    Returns:
+        SCADA设备数据
+    """
+    return get_scada_device_data(network, device_id)
+
+@router.post("/setscadadevicedata/", response_model=None, summary="更新SCADA设备数据", tags=["SCADA设备数据"])
+async def fastapi_set_scada_device_data(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """
+    更新SCADA设备数据
+    
+    修改指定SCADA设备的数据。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        req: 请求体，包含要更新的数据
+        
+    Returns:
+        变更集合信息
+    """
+    props = await req.json()
+    return set_scada_device_data(network, ChangeSet(props))
+
+@router.post("/addscadadevicedata/", response_model=None, summary="添加SCADA设备数据", tags=["SCADA设备数据"])
+async def fastapi_add_scada_device_data(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """
+    添加新的SCADA设备数据
+    
+    为指定SCADA设备添加新的数据记录。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        req: 请求体，包含新数据的内容
+        
+    Returns:
+        变更集合信息
+    """
+    props = await req.json()
+    return add_scada_device_data(network, ChangeSet(props))
+
+@router.post("/deletescadadevicedata/", response_model=None, summary="删除SCADA设备数据", tags=["SCADA设备数据"])
+async def fastapi_delete_scada_device_data(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """
+    删除SCADA设备数据
+    
+    删除指定SCADA设备的数据记录。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        req: 请求体，包含要删除的数据ID
+        
+    Returns:
+        变更集合信息
+    """
+    props = await req.json()
+    return delete_scada_device_data(network, ChangeSet(props))
+
+@router.post("/cleanscadadevicedata/", response_model=None, summary="清空SCADA设备数据表", tags=["SCADA设备数据"])
+async def fastapi_clean_scada_device_data(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> ChangeSet:
+    """
+    清空SCADA设备数据表
+    
+    删除指定管网中所有SCADA设备的数据。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        变更集合信息
+    """
+    return clean_scada_device_data(network)
+
+
+############################################################
+# scada_element SCADA元素映射
+############################################################
+
+@router.get("/getscadaelementschema/", summary="获取SCADA元素架构", tags=["SCADA元素映射"])
+async def fastapi_get_scada_element_schema(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+) -> dict[str, dict[str, Any]]:
+    """
+    获取SCADA元素映射的表结构
+    
+    返回SCADA元素映射表的字段定义和类型信息。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        SCADA元素映射的字段架构信息
+    """
+    return get_scada_element_schema(network)
+
+@router.get("/getscadaelements/", summary="获取所有SCADA元素映射", tags=["SCADA元素映射"])
+async def fastapi_get_scada_elements(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> list[dict[str, Any]]:
+    """
+    获取指定管网所有SCADA元素映射
+    
+    查询所有SCADA设备与管网元素（节点/管道）的映射关系。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        SCADA元素映射列表
+    """
+    return get_all_scada_elements(network)
+
+@router.get("/getscadaelement/", summary="获取单个SCADA元素映射", tags=["SCADA元素映射"])
+async def fastapi_get_scada_element(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    id: str = Query(..., description="SCADA元素映射ID")
+) -> dict[str, Any]:
+    """
+    获取单个SCADA元素映射的信息
+    
+    根据ID查询特定的SCADA设备与管网元素的映射关系。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        id: SCADA元素映射ID
+        
+    Returns:
+        SCADA元素映射信息
+    """
+    return get_scada_element(network, id)
+
+@router.post("/setscadaelement/", response_model=None, summary="更新SCADA元素映射", tags=["SCADA元素映射"])
+async def fastapi_set_scada_element(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """
+    更新SCADA元素映射
+    
+    修改SCADA设备与管网元素的映射关系。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        req: 请求体，包含要更新的映射信息
+        
+    Returns:
+        变更集合信息
+    """
+    props = await req.json()
+    return set_scada_element(network, ChangeSet(props))
+
+@router.post("/addscadaelement/", response_model=None, summary="添加SCADA元素映射", tags=["SCADA元素映射"])
+async def fastapi_add_scada_element(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """
+    添加新的SCADA元素映射
+    
+    创建SCADA设备与管网元素的新映射关系。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        req: 请求体，包含新映射的信息
+        
+    Returns:
+        变更集合信息
+    """
+    props = await req.json()
+    return add_scada_element(network, ChangeSet(props))
+
+@router.post("/deletescadaelement/", response_model=None, summary="删除SCADA元素映射", tags=["SCADA元素映射"])
+async def fastapi_delete_scada_element(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    req: Request = None
+) -> ChangeSet:
+    """
+    删除SCADA元素映射
+    
+    移除SCADA设备与管网元素的映射关系。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        req: 请求体，包含要删除的映射ID
+        
+    Returns:
+        变更集合信息
+    """
+    props = await req.json()
+    return delete_scada_element(network, ChangeSet(props))
+
+@router.post("/cleanscadaelement/", response_model=None, summary="清空SCADA元素映射表", tags=["SCADA元素映射"])
+async def fastapi_clean_scada_element(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> ChangeSet:
+    """
+    清空SCADA元素映射表
+    
+    删除指定管网中所有的SCADA元素映射。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        变更集合信息
+    """
+    return clean_scada_element(network)
+
+
+############################################################
+# scada_info SCADA信息
+############################################################
+
+@router.get("/getscadainfoschema/", summary="获取SCADA信息架构", tags=["SCADA信息"])
+async def fastapi_get_scada_info_schema(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> dict[str, dict[str, Any]]:
+    """
+    获取SCADA信息表的结构
+    
+    返回SCADA信息表的字段定义和类型信息。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        SCADA信息的字段架构信息
+    """
+    return get_scada_info_schema(network)
+
+@router.get("/getscadainfo/", summary="获取SCADA信息", tags=["SCADA信息"])
+async def fastapi_get_scada_info(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    id: str = Query(..., description="SCADA信息ID")
+) -> dict[str, Any]:
+    """
+    获取单个SCADA信息
+    
+    根据ID查询SCADA的详细配置信息。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        id: SCADA信息ID
+        
+    Returns:
+        SCADA信息详情
+    """
+    return get_scada_info(network, id)
+
+@router.get("/getallscadainfo/", summary="获取所有SCADA信息", tags=["SCADA信息"])
+async def fastapi_get_all_scada_info(
+    network: str = Query(..., description="管网名称（或数据库名称）")
+) -> list[dict[str, Any]]:
+    """
+    获取指定管网所有SCADA的信息
+    
+    查询该管网下所有已配置的SCADA的完整信息。
+    
+    Args:
+        network: 管网名称（或数据库名称）
+        
+    Returns:
+        SCADA信息列表
+    """
+    return get_all_scada_info(network)

app/api/v1/endpoints/schemes.py

@@ -0,0 +1,33 @@
+from fastapi import APIRouter, Query
+from typing import Any, List, Dict
+from app.services.tjnetwork import get_scheme_schema, get_scheme, get_all_schemes
+
+router = APIRouter()
+
+@router.get("/getschemeschema/", summary="获取方案模式", description="获取指定网络的方案模式定义")
+async def fastapi_get_scheme_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[Any, Any]]:
+    """
+    获取方案模式定义
+    
+    返回指定网络的方案模式结构定义
+    """
+    return get_scheme_schema(network)
+
+@router.get("/getscheme/", summary="获取单个方案", description="根据名称获取指定的方案信息")
+async def fastapi_get_scheme(network: str = Query(..., description="管网名称（或数据库名称）"), schema_name: str = Query(..., description="方案名称")) -> dict[Any, Any]:
+    """
+    获取单个方案详情
+    
+    返回指定网络中指定名称的方案详细信息
+    """
+    return get_scheme(network, schema_name)
+
+@router.get("/schemes", summary="获取所有方案", description="获取指定网络的所有方案信息")
+@router.get("/getallschemes/", summary="获取所有方案（旧路径）", description="获取指定网络的所有方案信息", deprecated=True)
+async def fastapi_get_all_schemes(network: str = Query(..., description="管网名称（或数据库名称）")) -> list[dict[Any, Any]]:
+    """
+    获取所有方案列表
+    
+    返回指定网络中所有可用的方案
+    """
+    return get_all_schemes(network)

app/api/v1/endpoints/simulation.py

@@ -0,0 +1,839 @@
+from typing import Any, List, Optional
+from datetime import datetime, timedelta
+import json
+import os
+import shutil
+import threading
+from fastapi import APIRouter, Depends, HTTPException, File, UploadFile, Query, Path, Body
+from fastapi.responses import PlainTextResponse
+from app.auth.keycloak_dependencies import get_current_keycloak_username
+import app.services.simulation as simulation
+import app.services.globals as globals
+from app.services.tjnetwork import (
+    run_project,
+    run_project_return_dict,
+    run_inp,
+    dump_output,
+)
+from app.algorithms.simulation.scenarios import (
+    burst_analysis,
+    valve_close_analysis,
+    flushing_analysis,
+    contaminant_simulation,
+    age_analysis,
+    # scheduling_analysis,
+    pressure_regulation,
+)
+from app.algorithms.sensor import (
+    pressure_sensor_placement_sensitivity,
+    pressure_sensor_placement_kmeans,
+)
+
+from app.services.network_import import network_update
+from app.services.simulation_ops import (
+    project_management,
+    scheduling_simulation,
+    daily_scheduling_simulation,
+)
+from app.services.valve_isolation import analyze_valve_isolation
+from app.services.time_api import (
+    parse_aware_time,
+    parse_clock_duration_seconds,
+    parse_utc_time,
+)
+from pydantic import BaseModel, Field, field_validator
+
+router = APIRouter()
+
+
+class RunSimulationManuallyByDate(BaseModel):
+    name: str = Field(..., description="管网名称（或数据库名称）")
+    start_time: str = Field(..., description="开始时间 (ISO 8601 / RFC3339，必须显式带时区)")
+    duration: int = Field(..., gt=0, description="持续时间 (分钟)")
+
+    @field_validator("start_time")
+    @classmethod
+    def validate_start_time_timezone(cls, value: str) -> str:
+        parse_aware_time(value, field_name="start_time")
+        return value
+
+
+class BurstAnalysis(BaseModel):
+    name: str = Field(..., description="管网名称（或数据库名称）")
+    modify_pattern_start_time: str = Field(..., description="模式修改开始时间 (ISO 8601)")
+    burst_ID: List[str] | str | None = Field(None, description="爆管节点/管段ID列表")
+    burst_size: List[float] | float | int | None = Field(None, description="爆管流量大小")
+    modify_total_duration: int = Field(900, description="模拟总时长 (秒)")
+    modify_fixed_pump_pattern: Optional[dict[str, list]] = Field(None, description="定速泵模式修改")
+    modify_variable_pump_pattern: Optional[dict[str, list]] = Field(None, description="变速泵模式修改")
+    modify_valve_opening: Optional[dict[str, float]] = Field(None, description="阀门开度修改")
+    scheme_name: Optional[str] = Field(None, description="方案名称")
+
+
+class SchedulingAnalysis(BaseModel):
+    network: str = Field(..., description="管网名称（或数据库名称）")
+    start_time: str = Field(..., description="开始时间")
+    pump_control: dict = Field(..., description="泵控制策略")
+    tank_id: str = Field(..., description="水箱ID")
+    water_plant_output_id: str = Field(..., description="水厂出水ID")
+    time_delta: Optional[int] = Field(300, description="时间步长 (秒)")
+
+
+class PressureRegulation(BaseModel):
+    network: str = Field(..., description="管网名称（或数据库名称）")
+    start_time: str = Field(..., description="开始时间")
+    pump_control: dict = Field(..., description="泵控制策略")
+    tank_init_level: Optional[dict] = Field(None, description="水箱初始水位")
+    duration: Optional[int] = Field(900, description="持续时间 (秒)")
+    scheme_name: Optional[str] = Field(None, description="方案名称")
+
+
+class ProjectManagement(BaseModel):
+    network: str = Field(..., description="管网名称（或数据库名称）")
+    start_time: str = Field(..., description="开始时间")
+    pump_control: dict = Field(..., description="泵控制策略")
+    tank_init_level: Optional[dict] = Field(None, description="水箱初始水位")
+    region_demand: Optional[dict] = Field(None, description="区域需水量控制")
+
+
+class DailySchedulingAnalysis(BaseModel):
+    network: str = Field(..., description="管网名称（或数据库名称）")
+    start_time: str = Field(..., description="开始时间")
+    pump_control: dict = Field(..., description="泵控制策略")
+    reservoir_id: str = Field(..., description="水库ID")
+    tank_id: str = Field(..., description="水箱ID")
+    water_plant_output_id: str = Field(..., description="水厂出水ID")
+    time_delta: Optional[int] = Field(300, description="时间步长 (秒)")
+
+
+class PumpFailureState(BaseModel):
+    time: str = Field(..., description="故障发生时间")
+    pump_status: dict = Field(..., description="泵状态字典")
+
+
+class PressureSensorPlacement(BaseModel):
+    name: str = Field(..., description="管网名称（或数据库名称）")
+    scheme_name: str = Field(..., description="方案名称")
+    sensor_number: int = Field(..., description="传感器数量")
+    min_diameter: int = Field(0, description="最小管径限制")
+    username: str = Field(..., description="用户名")
+
+
+def run_simulation_manually_by_date(
+    network_name: str, start_time: datetime, duration: int
+) -> None:
+    end_datetime = start_time + timedelta(minutes=duration)
+    time_properties = simulation.get_time(network_name)
+    hydraulic_step_seconds = parse_clock_duration_seconds(
+        time_properties["HYDRAULIC TIMESTEP"],
+        field_name="HYDRAULIC TIMESTEP",
+    )
+    if hydraulic_step_seconds <= 0:
+        raise ValueError("HYDRAULIC TIMESTEP must be greater than 0.")
+    hydraulic_step = timedelta(seconds=hydraulic_step_seconds)
+    current_time = start_time
+    while current_time < end_datetime:
+        simulation.run_simulation(
+            name=network_name,
+            simulation_type="realtime",
+            modify_pattern_start_time=current_time.isoformat(timespec="seconds"),
+        )
+        current_time += hydraulic_step
+
+
+# 必须用这个PlainTextResponse，不然每个key都有引号
+@router.get("/runproject/", response_class=PlainTextResponse, summary="运行项目模拟", description="基于指定的管网项目运行标准水力模拟，返回纯文本格式的模拟报告。")
+async def run_project_endpoint(network: str = Query(..., description="管网名称（或数据库名称）")) -> str:
+    """
+    运行项目模拟
+    
+    - **network**: 管网名称（或数据库名称）
+    
+    运行指定管网项目的标准水力模拟并返回文本报告。
+    """
+    return run_project(network)
+
+
+# DingZQ, 2025-02-04, 返回dict[str, Any]
+# output 和 report
+# output 是 json
+# report 是 text
+@router.get("/runprojectreturndict/", summary="运行项目模拟（返回字典）", description="基于指定的管网项目运行标准水力模拟，返回JSON格式的字典，包含输出数据和报告文本。")
+async def run_project_return_dict_endpoint(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, Any]:
+    """
+    运行项目模拟（返回字典）
+    
+    - **network**: 管网名称（或数据库名称）
+    
+    返回字典包含：
+    - output: JSON格式的模拟输出数据
+    - report: 文本格式的模拟报告
+    
+    运行指定管网项目的标准水力模拟并返回字典结果。
+    """
+    return run_project_return_dict(network)
+
+
+# put in inp folder, name without extension
+@router.get("/runinp/", summary="运行INP文件", description="运行指定INP文件格式的管网模型进行水力模拟。INP文件应该放在inp文件夹中，参数为文件名不含扩展名。")
+async def run_inp_endpoint(network: str = Query(..., description="inp文件名（不含扩展名）")) -> str:
+    """
+    运行INP文件
+    
+    - **network**: inp文件名（不含扩展名）
+    
+    从inp文件夹中读取指定的INP文件并运行模拟。
+    """
+    return run_inp(network)
+
+
+# path is absolute path
+@router.get("/dumpoutput/", summary="导出模拟输出", description="导出指定路径的模拟输出文件内容。参数应为绝对路径。")
+async def dump_output_endpoint(output: str = Query(..., description="模拟输出文件的绝对路径")) -> str:
+    """
+    导出模拟输出
+    
+    - **output**: 模拟输出文件的绝对路径
+    
+    读取并返回指定路径的模拟输出内容。
+    """
+    return dump_output(output)
+
+
+# Analysis Endpoints
+@router.get("/burst-analysis", summary="爆管分析（高级）", description="高级版本的爆管分析，支持在指定时间点修改泵控制模式和阀门开度，以分析这些改变对爆管影响的作用。支持固定泵和变速泵的独立控制。")
+@router.get("/burst_analysis/", summary="爆管分析（高级，旧路径）", description="高级版本的爆管分析，支持在指定时间点修改泵控制模式和阀门开度，以分析这些改变对爆管影响的作用。支持固定泵和变速泵的独立控制。", deprecated=True)
+async def fastapi_burst_analysis(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    modify_pattern_start_time: str = Query(..., description="模式修改开始时间（ISO 8601格式）"),
+    burst_ID: list[str] = Query(..., description="爆管节点/管段ID列表"),
+    burst_size: list[float] = Query(..., description="对应各爆管点的爆管流量大小列表（L/s）"),
+    modify_total_duration: int = Query(..., description="模拟总时长（秒）"),
+    scheme_name: str = Query(..., description="分析方案名称"),
+    username: str = Depends(get_current_keycloak_username),
+) -> str:
+    """
+    爆管分析（高级版本）
+    
+    - **network**: 管网名称（或数据库名称）
+    - **modify_pattern_start_time**: 模式修改开始时间
+    - **burst_ID**: 爆管节点/管段ID列表
+    - **burst_size**: 爆管流量大小列表（与burst_ID对应）
+    - **modify_total_duration**: 模拟总时长（秒）
+    - **scheme_name**: 分析方案名称
+    
+    支持在指定时间修改泵控制模式和阀门开度。
+    """
+    burst_analysis(
+        name=network,
+        modify_pattern_start_time=modify_pattern_start_time,
+        burst_ID=burst_ID,
+        burst_size=burst_size,
+        modify_total_duration=modify_total_duration,
+        scheme_name=scheme_name,
+        username=username,
+    )
+    return "success"
+
+
+@router.get("/valve_close_analysis/", response_class=PlainTextResponse, summary="阀门关闭分析（高级）", description="高级版本的阀门关闭分析，支持同时关闭多个阀门，并在指定持续时间内进行模拟。返回纯文本格式的分析结果。")
+async def fastapi_valve_close_analysis(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    start_time: str = Query(..., description="阀门关闭开始时间（ISO 8601格式）"),
+    valves: List[str] = Query(..., description="要关闭的阀门ID列表"),
+    duration: int | None = Query(None, description="模拟持续时间（秒），默认900秒"),
+    scheme_name: str = Query(..., description="阀门关闭方案名称"),
+) -> str:
+    """
+    阀门关闭分析（高级版本）
+    
+    - **network**: 管网名称（或数据库名称）
+    - **start_time**: 阀门关闭开始时间
+    - **valves**: 要关闭的阀门ID列表
+    - **duration**: 模拟持续时间（秒，可选，默认900）
+    - **scheme_name**: 阀门关闭方案名称
+    
+    支持同时关闭多个阀门进行分析。
+    """
+    result = valve_close_analysis(
+        name=network,
+        modify_pattern_start_time=start_time,
+        modify_total_duration=duration or 900,
+        modify_valve_opening={valve_id: 0.0 for valve_id in valves},
+        scheme_name=scheme_name,
+    )
+    return result or "success"
+
+
+@router.get("/valve-isolation-analysis", summary="阀门隔离分析", description="分析当发生突发事件时，通过关闭指定阀门进行隔离，确定哪些阀门必须关闭、哪些可选关闭，以及隔离的可行性。")
+@router.get("/valve_isolation_analysis/", summary="阀门隔离分析（旧路径）", description="分析当发生突发事件时，通过关闭指定阀门进行隔离，确定哪些阀门必须关闭、哪些可选关闭，以及隔离的可行性。", deprecated=True)
+async def valve_isolation_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    accident_element: List[str] = Query(..., description="发生事故的管段/节点ID列表"),
+    disabled_valves: List[str] = Query(None, description="已故障的阀门ID列表（可选）"),
+):
+    """
+    阀门隔离分析
+    
+    - **network**: 管网名称（或数据库名称）
+    - **accident_element**: 发生事故的管段/节点ID列表
+    - **disabled_valves**: 已故障的阀门ID列表（可选）
+    
+    返回隔离方案，包括：
+    - must_close_valves: 必须关闭的阀门列表
+    - optional_valves: 可选关闭的阀门列表
+    - affected_nodes: 受影响的节点列表
+    - isolatable: 是否可以有效隔离
+    """
+    # result = {
+    #     "accident_element": "P461309",
+    #     "accident_elements": ["P461309"],
+    #     "affected_nodes": [
+    #         "J316629_A",
+    #         "J317037_B",
+    #         "J317060_B",
+    #         "J408189_B",
+    #         "J499996",
+    #         "J524940",
+    #         "J535933",
+    #         "J58841",
+    #     ],
+    #     "isolatable": True,
+    #     "must_close_valves": ["210521658", "V12974", "V12986", "V12993"],
+    #     "optional_valves": [],
+    # }
+    result = analyze_valve_isolation(network, accident_element, disabled_valves)
+    return result
+
+
+@router.get("/flushing-analysis", response_class=PlainTextResponse, summary="冲洗分析（高级）", description="高级版本的冲洗分析，支持同时开启多个阀门进行冲洗，指定排污节点，并设置固定的冲洗流量。返回纯文本格式的分析结果。")
+@router.get("/flushing_analysis/", response_class=PlainTextResponse, summary="冲洗分析（高级，旧路径）", description="高级版本的冲洗分析，支持同时开启多个阀门进行冲洗，指定排污节点，并设置固定的冲洗流量。返回纯文本格式的分析结果。", deprecated=True)
+async def fastapi_flushing_analysis(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    start_time: str = Query(..., description="冲洗开始时间（ISO 8601格式）"),
+    valves: List[str] = Query(..., description="要开启的阀门ID列表"),
+    valves_k: List[float] = Query(..., description="对应各阀门的开度列表（0-1）"),
+    drainage_node_ID: str = Query(..., description="排污节点ID"),
+    flush_flow: float = Query(0, description="冲洗流量（L/s），0表示自动计算"),
+    duration: int | None = Query(None, description="模拟持续时间（秒），默认900秒"),
+    scheme_name: str = Query(..., description="冲洗方案名称"),
+    username: str = Depends(get_current_keycloak_username),
+) -> str:
+    """
+    冲洗分析（高级版本）
+    
+    - **network**: 管网名称（或数据库名称）
+    - **start_time**: 冲洗开始时间
+    - **valves**: 要开启的阀门ID列表
+    - **valves_k**: 各阀门的开度列表（0-1，与valves对应）
+    - **drainage_node_ID**: 排污节点ID
+    - **flush_flow**: 冲洗流量（L/s）
+    - **duration**: 模拟持续时间（秒，可选，默认900）
+    - **scheme_name**: 冲洗方案名称
+    
+    支持多阀联合冲洗操作。
+    """
+    valve_opening = {
+        valve_id: float(valves_k[idx]) for idx, valve_id in enumerate(valves)
+    }
+    result = flushing_analysis(
+        name=network,
+        modify_pattern_start_time=start_time,
+        modify_total_duration=duration or 900,
+        modify_valve_opening=valve_opening,
+        drainage_node_ID=drainage_node_ID,
+        flushing_flow=flush_flow,
+        scheme_name=scheme_name,
+        username=username,
+    )
+    return result or "success"
+
+
+@router.get("/contaminant-simulation", response_class=PlainTextResponse, summary="污染物模拟", description="对管网中的污染物扩散进行模拟，评估污染源对管网的影响范围和浓度分布。支持指定污染源位置、污染浓度和扩散模式。")
+@router.get("/contaminant_simulation/", response_class=PlainTextResponse, summary="污染物模拟（旧路径）", description="对管网中的污染物扩散进行模拟，评估污染源对管网的影响范围和浓度分布。支持指定污染源位置、污染浓度和扩散模式。", deprecated=True)
+async def fastapi_contaminant_simulation(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    start_time: str = Query(..., description="污染开始时间（ISO 8601格式）"),
+    source: str = Query(..., description="污染源节点ID"),
+    concentration: float = Query(..., description="污染浓度（mg/L）"),
+    duration: int = Query(..., description="模拟持续时间（秒）"),
+    scheme_name: str = Query(..., description="模拟方案名称"),
+    pattern: str | None = Query(None, description="污染源模式ID（可选）"),
+    username: str = Depends(get_current_keycloak_username),
+) -> str:
+    """
+    污染物模拟
+    
+    - **network**: 管网名称（或数据库名称）
+    - **start_time**: 污染开始时间
+    - **source**: 污染源节点ID
+    - **concentration**: 污染浓度（mg/L）
+    - **duration**: 模拟持续时间（秒）
+    - **scheme_name**: 模拟方案名称
+    - **pattern**: 污染源模式ID（可选）
+    
+    用于评估管网中污染物的传播和影响范围。
+    """
+    result = contaminant_simulation(
+        name=network,
+        modify_pattern_start_time=start_time,
+        scheme_name=scheme_name,
+        modify_total_duration=duration,
+        source=source,
+        concentration=concentration,
+        source_pattern=pattern,
+        username=username,
+    )
+    return result or "success"
+
+
+@router.get("/age_analysis/", response_class=PlainTextResponse, summary="水龄分析（高级）", description="高级版本的水龄分析，在指定时间点进行分析，支持自定义模拟持续时间。返回纯文本格式的分析结果。")
+async def fastapi_age_analysis(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    start_time: str = Query(..., description="分析开始时间（ISO 8601格式）"),
+    duration: int = Query(..., description="模拟持续时间（秒）"),
+) -> str:
+    """
+    水龄分析（高级版本）
+    
+    - **network**: 管网名称（或数据库名称）
+    - **start_time**: 分析开始时间
+    - **duration**: 模拟持续时间（秒）
+    
+    分析指定时间段内管网中各节点的水体停留时间。
+    """
+    result = age_analysis(network, start_time, duration)
+    return result or "success"
+
+
+# @router.get("/schedulinganalysis/")
+# async def scheduling_analysis_endpoint(network: str):
+#     return scheduling_analysis(network)
+
+
+@router.get("/pressureregulation/", summary="压力调节（基础）", description="对管网的压力进行调节分析，通过控制泵的运行来维持目标节点的目标压力。此为基础版本。")
+async def pressure_regulation_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    target_node: str = Query(..., description="目标节点ID"),
+    target_pressure: float = Query(..., description="目标压力值（kPa）"),
+):
+    """
+    压力调节（基础版本）
+    
+    - **network**: 管网名称（或数据库名称）
+    - **target_node**: 目标节点ID
+    - **target_pressure**: 目标压力值（kPa）
+    
+    通过泵控制维持目标节点的压力。
+    """
+    return pressure_regulation(network, target_node, target_pressure)
+
+
+@router.post("/pressure_regulation/", summary="压力调节（高级）", description="高级版本的压力调节分析，通过JSON请求体提供详细的控制参数，包括固定泵和变速泵的独立控制、水箱初始水位等。")
+async def fastapi_pressure_regulation(data: PressureRegulation = Body(..., description="压力调节控制参数")) -> str:
+    """
+    压力调节（高级版本）
+    
+    请求体参数：
+    - **network**: 管网名称（或数据库名称）
+    - **start_time**: 控制开始时间
+    - **pump_control**: 泵控制策略字典
+    - **tank_init_level**: 水箱初始水位字典（可选）
+    - **duration**: 模拟持续时间（秒，可选，默认900）
+    - **scheme_name**: 控制方案名称（可选）
+    
+    支持固定泵和变速泵的独立控制。
+    """
+    item = data.dict()
+    simulation.query_corresponding_element_id_and_query_id(item["network"])
+    fixed_pumps = set(globals.fixed_pumps_id.keys())
+    variable_pumps = set(globals.variable_pumps_id.keys())
+    fixed_pump_pattern: dict[str, list] = {}
+    variable_pump_pattern: dict[str, list] = {}
+    for pump_id, values in item["pump_control"].items():
+        if pump_id in variable_pumps:
+            variable_pump_pattern[pump_id] = values
+        else:
+            fixed_pump_pattern[pump_id] = values
+    pressure_regulation(
+        name=item["network"],
+        modify_pattern_start_time=item["start_time"],
+        modify_total_duration=item["duration"] or 900,
+        modify_tank_initial_level=item["tank_init_level"],
+        modify_fixed_pump_pattern=fixed_pump_pattern or None,
+        modify_variable_pump_pattern=variable_pump_pattern or None,
+        scheme_name=item["scheme_name"],
+    )
+    return "success"
+
+
+@router.post("/project_management/", summary="项目管理（高级）", description="高级版本的项目管理，通过JSON请求体提供详细的控制参数，包括泵控制策略、水箱初始水位和区域需水量控制。")
+async def fastapi_project_management(data: ProjectManagement = Body(..., description="项目管理控制参数")) -> str:
+    """
+    项目管理（高级版本）
+    
+    请求体参数：
+    - **network**: 管网名称（或数据库名称）
+    - **start_time**: 管理开始时间
+    - **pump_control**: 泵控制策略字典
+    - **tank_init_level**: 水箱初始水位字典（可选）
+    - **region_demand**: 区域需水量控制字典（可选）
+    
+    支持多维度的项目管理。
+    """
+    item = data.dict()
+    return project_management(
+        prj_name=item["network"],
+        start_datetime=item["start_time"],
+        pump_control=item["pump_control"],
+        tank_initial_level_control=item["tank_init_level"],
+        region_demand_control=item["region_demand"],
+    )
+
+
+# @router.get("/dailyschedulinganalysis/")
+# async def daily_scheduling_analysis_endpoint(network: str):
+#     return daily_scheduling_analysis(network)
+
+
+@router.post("/scheduling_analysis/", summary="排程分析", description="对管网的供水排程进行分析，优化泵的运行时间和出水流量，平衡水厂出水、水箱进出水，满足用户需求。")
+async def fastapi_scheduling_analysis(data: SchedulingAnalysis = Body(..., description="排程分析参数")) -> str:
+    """
+    排程分析
+    
+    请求体参数：
+    - **network**: 管网名称（或数据库名称）
+    - **start_time**: 分析开始时间
+    - **pump_control**: 泵控制策略字典
+    - **tank_id**: 水箱ID
+    - **water_plant_output_id**: 水厂出水ID
+    - **time_delta**: 时间步长（秒，可选，默认300）
+    
+    用于优化供水排程。
+    """
+    item = data.dict()
+    return scheduling_simulation(
+        item["network"],
+        item["start_time"],
+        item["pump_control"],
+        item["tank_id"],
+        item["water_plant_output_id"],
+        item["time_delta"],
+    )
+
+
+@router.post("/daily_scheduling_analysis/", summary="日排程分析", description="对管网的每日供水排程进行分析，优化水库、水厂、水箱和用户需求的协调，制定合理的每日排程方案。")
+async def fastapi_daily_scheduling_analysis(data: DailySchedulingAnalysis = Body(..., description="日排程分析参数")) -> str:
+    """
+    日排程分析
+    
+    请求体参数：
+    - **network**: 管网名称（或数据库名称）
+    - **start_time**: 分析开始时间
+    - **pump_control**: 泵控制策略字典
+    - **reservoir_id**: 水库ID
+    - **tank_id**: 水箱ID
+    - **water_plant_output_id**: 水厂出水ID
+    - **time_delta**: 时间步长（秒，可选，默认300）
+    
+    用于制定每日供水排程方案。
+    """
+    item = data.dict()
+    return daily_scheduling_simulation(
+        item["network"],
+        item["start_time"],
+        item["pump_control"],
+        item["reservoir_id"],
+        item["tank_id"],
+        item["water_plant_output_id"],
+    )
+
+
+@router.post("/network_project/", summary="导入网络项目", description="通过上传INP格式的管网文件导入新的网络项目。系统将自动处理文件并执行模拟。")
+async def fastapi_network_project(file: UploadFile = File(..., description="INP格式的管网文件")) -> str:
+    """
+    导入网络项目
+    
+    - **file**: 上传的INP格式管网文件
+    
+    系统将上传的文件保存到inp文件夹并执行模拟。
+    """
+    temp_file_dir = "./inp/"
+    if not os.path.exists(temp_file_dir):
+        os.mkdir(temp_file_dir)
+    temp_file_name = f'network_project_{datetime.now().strftime("%Y%m%d")}'
+    temp_file_path = f"{temp_file_dir}{temp_file_name}.inp"
+    with open(temp_file_path, "wb") as buffer:
+        shutil.copyfileobj(file.file, buffer)
+    return run_inp(temp_file_name)
+
+
+@router.post("/network_update/", summary="管网更新（高级）", description="通过上传更新文件对管网进行高级的更新操作。系统将处理更新文件并应用到数据库。")
+async def fastapi_network_update(file: UploadFile = File(..., description="包含管网更新信息的文件")) -> str:
+    """
+    管网更新（高级版本）
+    
+    - **file**: 包含管网更新信息的文件
+    
+    系统将处理上传的文件并应用管网更新。
+    """
+    default_folder = "./"
+    temp_file_name = f'network_update_{datetime.now().strftime("%Y%m%d")}'
+    temp_file_path = os.path.join(default_folder, temp_file_name)
+    try:
+        with open(temp_file_path, "wb") as buffer:
+            shutil.copyfileobj(file.file, buffer)
+        network_update(temp_file_path)
+        return json.dumps({"message": "管网更新成功"})
+    except Exception as exc:
+        raise HTTPException(status_code=500, detail=f"数据库操作失败: {exc}")
+
+
+# @router.get("/pumpfailure/")
+# async def pump_failure_endpoint(network: str, pump_id: str, time: str):
+#     return pump_failure(network, pump_id, time)
+
+
+@router.post("/pump_failure/", summary="泵故障管理", description="记录和管理泵的故障状态，包括故障发生时间和受影响的泵列表。系统将记录故障日志并更新泵状态。")
+async def fastapi_pump_failure(data: PumpFailureState = Body(..., description="泵故障状态信息")) -> str:
+    """
+    泵故障管理
+    
+    请求体参数：
+    - **time**: 故障发生时间
+    - **pump_status**: 泵状态字典，包含第一阶段和第二阶段泵的故障状态
+    
+    系统将验证泵信息的有效性并更新故障状态文件。
+    """
+    item = data.dict()
+    with open("./pump_failure_message.txt", "a", encoding="utf-8-sig") as f1:
+        f1.write("[{}] {}\n".format(datetime.now().strftime("%Y-%m-%d %H:%M:%S"), item))
+    with open("./pump_failure_status.txt", "r", encoding="utf-8-sig") as f2:
+        lines = f2.readlines()
+        first_stage_pump_status_dict = json.loads(json.dumps(eval(lines[0])))
+        second_stage_pump_status_dict = json.loads(json.dumps(eval(lines[-1])))
+        pump_status_dict = {
+            "first": first_stage_pump_status_dict,
+            "second": second_stage_pump_status_dict,
+        }
+    status_info = item.copy()
+    for pump_type in status_info["pump_status"].keys():
+        if pump_type in pump_status_dict.keys():
+            if all(
+                pump_id in pump_status_dict[pump_type].keys()
+                for pump_id in status_info["pump_status"][pump_type].keys()
+            ):
+                for pump_id in status_info["pump_status"][pump_type].keys():
+                    pump_status_dict[pump_type][pump_id] = int(
+                        status_info["pump_status"][pump_type][pump_id]
+                    )
+            else:
+                return json.dumps("ERROR: Wrong Pump ID")
+        else:
+            return json.dumps("ERROR: Wrong Pump Type")
+    with open("./pump_failure_status.txt", "w", encoding="utf-8-sig") as f2_:
+        f2_.write(
+            "{}\n{}".format(pump_status_dict["first"], pump_status_dict["second"])
+        )
+    return json.dumps("SUCCESS")
+
+
+@router.get("/pressuresensorplacementsensitivity/", summary="压力传感器放置-灵敏度分析（基础）", description="基于灵敏度分析方法，为指定管网项目确定最优的压力传感器放置位置。此为基础版本。")
+async def pressure_sensor_placement_sensitivity_endpoint(
+    name: str = Query(..., description="管网名称（或数据库名称）"),
+    scheme_name: str = Query(..., description="放置方案名称"),
+    sensor_number: int = Query(..., description="传感器数量"),
+    min_diameter: int = Query(..., description="最小管径限制（毫米）"),
+    username: str = Query(..., description="用户名"),
+):
+    """
+    压力传感器放置-灵敏度分析（基础版本）
+    
+    - **name**: 管网名称（或数据库名称）
+    - **scheme_name**: 放置方案名称
+    - **sensor_number**: 传感器数量
+    - **min_diameter**: 最小管径限制（毫米）
+    - **username**: 用户名
+    
+    基于灵敏度分析方法确定传感器放置位置。
+    """
+    return pressure_sensor_placement_sensitivity(
+        name, scheme_name, sensor_number, min_diameter, username
+    )
+
+
+@router.post("/pressure_sensor_placement_sensitivity/", summary="压力传感器放置-灵敏度分析（高级）", description="高级版本的压力传感器放置分析，通过JSON请求体提供详细参数。基于灵敏度分析方法确定最优放置位置。")
+async def fastapi_pressure_sensor_placement_sensitivity(
+    data: PressureSensorPlacement = Body(..., description="传感器放置分析参数"),
+) -> None:
+    """
+    压力传感器放置-灵敏度分析（高级版本）
+    
+    请求体参数：
+    - **name**: 管网名称（或数据库名称）
+    - **scheme_name**: 放置方案名称
+    - **sensor_number**: 传感器数量
+    - **min_diameter**: 最小管径限制（毫米）
+    - **username**: 用户名
+    
+    基于灵敏度分析方法确定压力传感器的最优放置位置。
+    """
+    item = data.dict()
+    pressure_sensor_placement_sensitivity(
+        name=item["name"],
+        scheme_name=item["scheme_name"],
+        sensor_number=item["sensor_number"],
+        min_diameter=item["min_diameter"],
+        username=item["username"],
+    )
+
+
+@router.get("/pressuresensorplacementkmeans/", summary="压力传感器放置-KMeans聚类分析（基础）", description="基于KMeans聚类算法，为指定管网项目确定压力传感器的最优放置位置。此为基础版本。")
+async def pressure_sensor_placement_kmeans_endpoint(
+    name: str = Query(..., description="管网名称（或数据库名称）"),
+    scheme_name: str = Query(..., description="放置方案名称"),
+    sensor_number: int = Query(..., description="传感器数量"),
+    min_diameter: int = Query(..., description="最小管径限制（毫米）"),
+    username: str = Query(..., description="用户名"),
+):
+    """
+    压力传感器放置-KMeans聚类分析（基础版本）
+    
+    - **name**: 管网名称（或数据库名称）
+    - **scheme_name**: 放置方案名称
+    - **sensor_number**: 传感器数量
+    - **min_diameter**: 最小管径限制（毫米）
+    - **username**: 用户名
+    
+    基于KMeans聚类算法确定传感器放置位置。
+    """
+    return pressure_sensor_placement_kmeans(
+        name, scheme_name, sensor_number, min_diameter, username
+    )
+
+
+@router.post("/pressure_sensor_placement_kmeans/", summary="压力传感器放置-KMeans聚类分析（高级）", description="高级版本的压力传感器放置分析，通过JSON请求体提供详细参数。基于KMeans聚类算法确定最优放置位置。")
+async def fastapi_pressure_sensor_placement_kmeans(
+    data: PressureSensorPlacement = Body(..., description="传感器放置分析参数"),
+) -> None:
+    """
+    压力传感器放置-KMeans聚类分析（高级版本）
+    
+    请求体参数：
+    - **name**: 管网名称（或数据库名称）
+    - **scheme_name**: 放置方案名称
+    - **sensor_number**: 传感器数量
+    - **min_diameter**: 最小管径限制（毫米）
+    - **username**: 用户名
+    
+    基于KMeans聚类算法确定压力传感器的最优放置位置。
+    """
+    item = data.dict()
+    pressure_sensor_placement_kmeans(
+        name=item["name"],
+        scheme_name=item["scheme_name"],
+        sensor_number=item["sensor_number"],
+        min_diameter=item["min_diameter"],
+        username=item["username"],
+    )
+
+
+@router.post("/sensor-placement-schemes", summary="传感器放置方案创建", description="创建新的传感器放置方案，支持灵敏度分析和KMeans聚类两种方法。根据指定的方法自动计算最优的传感器放置位置。")
+@router.post("/sensorplacementscheme/create", summary="传感器放置方案创建（旧路径）", description="创建新的传感器放置方案，支持灵敏度分析和KMeans聚类两种方法。根据指定的方法自动计算最优的传感器放置位置。", deprecated=True)
+async def fastapi_pressure_sensor_placement(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    scheme_name: str = Query(..., description="放置方案名称"),
+    sensor_type: str = Query(..., description="传感器类型"),
+    method: str = Query(..., description="放置方法（'sensitivity'或'kmeans'）"),
+    sensor_count: int = Query(..., description="传感器数量"),
+    min_diameter: int = Query(0, description="最小管径限制（毫米），默认0"),
+    user_name: str = Query(..., description="用户名"),
+) -> str:
+    """
+    传感器放置方案创建
+    
+    - **network**: 管网名称（或数据库名称）
+    - **scheme_name**: 放置方案名称
+    - **sensor_type**: 传感器类型
+    - **method**: 放置方法（'sensitivity'或'kmeans'）
+    - **sensor_count**: 传感器数量
+    - **min_diameter**: 最小管径限制（毫米，默认0）
+    - **user_name**: 用户名
+    
+    支持两种放置方法：
+    - sensitivity: 基于灵敏度分析
+    - kmeans: 基于KMeans聚类
+    """
+    if method not in ["sensitivity", "kmeans"]:
+        raise HTTPException(
+            status_code=400, detail="Invalid method. Must be 'sensitivity' or 'kmeans'"
+        )
+    if method == "sensitivity":
+        pressure_sensor_placement_sensitivity(
+            name=network,
+            scheme_name=scheme_name,
+            sensor_number=sensor_count,
+            min_diameter=min_diameter,
+            username=user_name,
+        )
+    elif method == "kmeans":
+        pressure_sensor_placement_kmeans(
+            name=network,
+            scheme_name=scheme_name,
+            sensor_number=sensor_count,
+            min_diameter=min_diameter,
+            username=user_name,
+        )
+    return "success"
+
+
+@router.post("/simulations/run-by-date", summary="手动运行日期指定模拟", description="根据指定的开始时间和持续时间，手动运行水力模拟。开始时间必须是显式带时区的 ISO 8601 / RFC3339 时间。")
+@router.post("/runsimulationmanuallybydate/", summary="手动运行日期指定模拟（旧路径）", description="根据指定的开始时间和持续时间，手动运行水力模拟。开始时间必须是显式带时区的 ISO 8601 / RFC3339 时间。", deprecated=True)
+async def fastapi_run_simulation_manually_by_date(
+    data: RunSimulationManuallyByDate = Body(..., description="模拟运行参数"),
+) -> dict[str, str]:
+    """
+    手动运行日期指定模拟
+    
+    请求体参数：
+    - **name**: 管网名称（或数据库名称）
+    - **start_time**: 开始时间（ISO 8601 / RFC3339，必须显式带时区）
+    - **duration**: 模拟持续时间（分钟）
+    
+    系统将从指定时间开始，按15分钟间隔多次运行模拟。
+    每次模拟间隔15分钟，直至达到指定的总持续时间。
+    """
+    item = data.model_dump()
+    try:
+        simulation.query_corresponding_element_id_and_query_id(item["name"])
+        simulation.query_corresponding_pattern_id_and_query_id(item["name"])
+        region_result = simulation.query_non_realtime_region(item["name"])
+        globals.source_outflow_region_id = simulation.get_source_outflow_region_id(
+            item["name"], region_result
+        )
+        globals.realtime_region_pipe_flow_and_demand_id = (
+            simulation.query_realtime_region_pipe_flow_and_demand_id(
+                item["name"], region_result
+            )
+        )
+        globals.pipe_flow_region_patterns = simulation.query_pipe_flow_region_patterns(
+            item["name"]
+        )
+        globals.non_realtime_region_patterns = (
+            simulation.query_non_realtime_region_patterns(item["name"], region_result)
+        )
+        (
+            globals.source_outflow_region_patterns,
+            globals.realtime_region_pipe_flow_and_demand_patterns,
+        ) = simulation.get_realtime_region_patterns(
+            item["name"],
+            globals.source_outflow_region_id,
+            globals.realtime_region_pipe_flow_and_demand_id,
+        )
+        start_time = parse_utc_time(item["start_time"], field_name="start_time")
+        run_simulation_manually_by_date(
+            item["name"], start_time, item["duration"]
+        )
+        return {"status": "success"}
+    except Exception as exc:
+        raise HTTPException(status_code=500, detail=str(exc)) from exc

app/api/v1/endpoints/snapshots.py

@@ -0,0 +1,204 @@
+from fastapi import APIRouter, Request, Query
+from app.services.tjnetwork import (
+    ChangeSet,
+    get_current_operation,
+    execute_undo,
+    execute_redo,
+    list_snapshot,
+    have_snapshot,
+    have_snapshot_for_operation,
+    have_snapshot_for_current_operation,
+    take_snapshot_for_operation,
+    take_snapshot_for_current_operation,
+    take_snapshot,
+    pick_snapshot,
+    pick_operation,
+    sync_with_server,
+    execute_batch_commands,
+    execute_batch_command,
+    get_restore_operation,
+    set_restore_operation,
+)
+
+router = APIRouter()
+
+@router.get("/getcurrentoperationid/", summary="获取当前操作ID", description="获取网络当前的操作ID")
+async def get_current_operation_id_endpoint(network: str = Query(..., description="管网名称（或数据库名称）")) -> int:
+    """
+    获取当前操作ID
+    
+    返回网络当前正在执行的操作ID
+    """
+    return get_current_operation(network)
+
+@router.post("/undo/", summary="撤销操作", description="撤销网络上最后的一个操作")
+async def undo_endpoint(network: str = Query(..., description="管网名称（或数据库名称）")):
+    """
+    撤销操作
+    
+    撤销网络上最近执行的一个操作
+    """
+    return execute_undo(network)
+
+@router.post("/redo/", summary="重做操作", description="重做网络上被撤销的操作")
+async def redo_endpoint(network: str = Query(..., description="管网名称（或数据库名称）")):
+    """
+    重做操作
+    
+    重做网络上被撤销的操作
+    """
+    return execute_redo(network)
+
+@router.get("/getsnapshots/", summary="获取快照列表", description="获取网络中的所有快照")
+async def list_snapshot_endpoint(network: str = Query(..., description="管网名称（或数据库名称）")) -> list[tuple[int, str]]:
+    """
+    获取快照列表
+    
+    返回网络中所有可用的快照及其信息
+    """
+    return list_snapshot(network)
+
+@router.get("/havesnapshot/", summary="检查快照是否存在", description="检查指定标签的快照是否存在")
+async def have_snapshot_endpoint(network: str = Query(..., description="管网名称（或数据库名称）"), tag: str = Query(..., description="快照标签")) -> bool:
+    """
+    检查快照是否存在
+    
+    返回指定标签的快照是否存在
+    """
+    return have_snapshot(network, tag)
+
+@router.get("/havesnapshotforoperation/", summary="检查操作快照是否存在", description="检查指定操作ID的快照是否存在")
+async def have_snapshot_for_operation_endpoint(network: str = Query(..., description="管网名称（或数据库名称）"), operation: int = Query(..., description="操作ID")) -> bool:
+    """
+    检查操作快照是否存在
+    
+    返回指定操作ID的快照是否存在
+    """
+    return have_snapshot_for_operation(network, operation)
+
+@router.get("/havesnapshotforcurrentoperation/", summary="检查当前操作快照是否存在", description="检查当前操作的快照是否存在")
+async def have_snapshot_for_current_operation_endpoint(network: str = Query(..., description="管网名称（或数据库名称）")) -> bool:
+    """
+    检查当前操作快照是否存在
+    
+    返回当前操作的快照是否存在
+    """
+    return have_snapshot_for_current_operation(network)
+
+@router.post("/takesnapshotforoperation/", summary="为操作创建快照", description="为指定的操作创建快照")
+async def take_snapshot_for_operation_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"), 
+    operation: int = Query(..., description="操作ID"), 
+    tag: str = Query(..., description="快照标签")
+) -> None:
+    """
+    为操作创建快照
+    
+    为指定操作创建一个带标签的快照
+    """
+    return take_snapshot_for_operation(network, operation, tag)
+
+@router.post("/takesnapshotforcurrentoperation", summary="为当前操作创建快照", description="为当前操作创建快照")
+async def take_snapshot_for_current_operation_endpoint(network: str = Query(..., description="管网名称（或数据库名称）"), tag: str = Query(..., description="快照标签")) -> None:
+    """
+    为当前操作创建快照
+    
+    为网络当前操作创建一个快照
+    """
+    return take_snapshot_for_current_operation(network, tag)
+
+# 兼容旧拼写: takenapshotforcurrentoperation
+@router.post("/takenapshotforcurrentoperation", summary="为当前操作创建快照（兼容模式）", description="为当前操作创建快照（兼容旧的API路径）")
+async def take_snapshot_for_current_operation_legacy_endpoint(network: str = Query(..., description="管网名称（或数据库名称）"), tag: str = Query(..., description="快照标签")) -> None:
+    """
+    为当前操作创建快照（兼容模式）
+    
+    兼容旧的API路径，为网络当前操作创建一个快照
+    """
+    return take_snapshot_for_current_operation(network, tag)
+
+@router.post("/takesnapshot/", summary="创建快照", description="为网络创建一个快照")
+async def take_snapshot_endpoint(network: str = Query(..., description="管网名称（或数据库名称）"), tag: str = Query(..., description="快照标签")) -> None:
+    """
+    创建快照
+    
+    为网络创建一个带标签的快照
+    """
+    return take_snapshot(network, tag)
+
+@router.post("/picksnapshot/", summary="选择快照", description="选择并恢复到指定的快照", response_model=None)
+async def pick_snapshot_endpoint(network: str = Query(..., description="管网名称（或数据库名称）"), tag: str = Query(..., description="快照标签"), discard: bool = Query(False, description="是否丢弃当前更改")) -> ChangeSet:
+    """
+    选择快照
+    
+    选择并恢复到指定的快照
+    """
+    return pick_snapshot(network, tag, discard)
+
+@router.post("/pickoperation/", summary="选择操作", description="选择并恢复到指定的操作", response_model=None)
+async def pick_operation_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"), 
+    operation: int = Query(..., description="操作ID"), 
+    discard: bool = Query(False, description="是否丢弃当前更改")
+) -> ChangeSet:
+    """
+    选择操作
+    
+    选择并恢复到指定的操作
+    """
+    return pick_operation(network, operation, discard)
+
+@router.get("/syncwithserver/", summary="与服务器同步", description="将网络与服务器同步到指定操作", response_model=None)
+async def sync_with_server_endpoint(network: str = Query(..., description="管网名称（或数据库名称）"), operation: int = Query(..., description="目标操作ID")) -> ChangeSet:
+    """
+    与服务器同步
+    
+    将网络与服务器同步到指定的操作
+    """
+    return sync_with_server(network, operation)
+
+@router.post("/batch/", summary="执行批量命令", description="执行多个网络操作命令", response_model=None)
+async def execute_batch_commands_endpoint(network: str = Query(..., description="管网名称（或数据库名称）"), req: Request = None) -> ChangeSet:
+    """
+    执行批量命令
+    
+    在网络上执行多个操作命令
+    """
+    jo_root = await req.json()
+    cs: ChangeSet = ChangeSet()
+    cs.operations = jo_root["operations"]
+    rcs = execute_batch_commands(network, cs)
+    return rcs
+
+@router.post("/compressedbatch/", summary="执行压缩批量命令", description="执行压缩的批量命令", response_model=None)
+async def execute_compressed_batch_commands_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"), 
+    req: Request = None
+) -> ChangeSet:
+    """
+    执行压缩批量命令
+    
+    执行压缩格式的批量命令
+    """
+    jo_root = await req.json()
+    cs: ChangeSet = ChangeSet()
+    cs.operations = jo_root["operations"]
+    return execute_batch_command(network, cs)
+
+@router.get("/getrestoreoperation/", summary="获取恢复操作ID", description="获取网络的恢复操作ID")
+async def get_restore_operation_endpoint(network: str = Query(..., description="管网名称（或数据库名称）")) -> int:
+    """
+    获取恢复操作ID
+    
+    返回网络的恢复操作ID
+    """
+    return get_restore_operation(network)
+
+@router.post("/setrestoreoperation/", summary="设置恢复操作ID", description="设置网络的恢复操作ID")
+async def set_restore_operation_endpoint(network: str = Query(..., description="管网名称（或数据库名称）"), operation: int = Query(..., description="操作ID")) -> None:
+    """
+    设置恢复操作ID
+    
+    设置网络的恢复操作ID
+    """
+    return set_restore_operation(network, operation)

app/api/v1/endpoints/timeseries/composite.py

@@ -0,0 +1,263 @@
+from fastapi import APIRouter, Depends, HTTPException, Query
+from datetime import datetime
+from psycopg import AsyncConnection
+
+from app.infra.db.timescaledb.composite_queries import CompositeQueries
+from .dependencies import get_timescale_connection, get_postgres_connection
+
+router = APIRouter()
+
+
+@router.get("/composite/scada-simulation", summary="获取SCADA关联的模拟数据")
+async def get_scada_associated_simulation_data(
+    start_time: datetime = Query(..., description="查询开始时间"),
+    end_time: datetime = Query(..., description="查询结束时间"),
+    device_ids: str = Query(..., description="SCADA设备ID列表，逗号分隔"),
+    scheme_type: str = Query(None, description="方案类型，若为空则查询实时数据"),
+    scheme_name: str = Query(None, description="方案名称，若为空则查询实时数据"),
+    timescale_conn: AsyncConnection = Depends(get_timescale_connection),
+    postgres_conn: AsyncConnection = Depends(get_postgres_connection),
+):
+    """
+    获取SCADA关联的link/node模拟值
+    
+    根据传入的SCADA device_ids，找到关联的link/node，
+    并根据对应的type，查询对应的模拟数据。支持查询实时或方案数据。
+
+    Args:
+        start_time: 查询开始时间
+        end_time: 查询结束时间
+        device_ids: SCADA设备ID列表，用逗号分隔
+        scheme_type: 方案类型，若为空则查询实时数据
+        scheme_name: 方案名称，若为空则查询实时数据
+        timescale_conn: TimescaleDB连接
+        postgres_conn: PostgreSQL连接
+        
+    Returns:
+        SCADA关联的模拟数据
+        
+    Raises:
+        HTTPException: 当查询参数无效时返回400错误，未找到数据时返回404错误
+    """
+    try:
+        device_ids_list = (
+            [id.strip() for id in device_ids.split(",") if id.strip()]
+            if device_ids
+            else []
+        )
+
+        if scheme_type and scheme_name:
+            result = await CompositeQueries.get_scada_associated_scheme_simulation_data(
+                timescale_conn,
+                postgres_conn,
+                device_ids_list,
+                start_time,
+                end_time,
+                scheme_type,
+                scheme_name,
+            )
+        else:
+            result = (
+                await CompositeQueries.get_scada_associated_realtime_simulation_data(
+                    timescale_conn,
+                    postgres_conn,
+                    device_ids_list,
+                    start_time,
+                    end_time,
+                )
+            )
+        if result is None:
+            raise HTTPException(status_code=404, detail="No simulation data found")
+        return result
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.get("/composite/element-simulation", summary="获取管网元素的模拟数据")
+async def get_feature_simulation_data(
+    start_time: datetime = Query(..., description="查询开始时间"),
+    end_time: datetime = Query(..., description="查询结束时间"),
+    feature_infos: str = Query(
+        ..., description="特征信息，格式: id1:type1,id2:type2，type为pipe（管道）或junction（节点）"
+    ),
+    scheme_type: str = Query(None, description="方案类型，若为空则查询实时数据"),
+    scheme_name: str = Query(None, description="方案名称，若为空则查询实时数据"),
+    timescale_conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    获取link/node模拟值
+    
+    根据传入的featureInfos，找到关联的link/node，
+    并根据对应的type，查询对应的模拟数据。支持查询实时或方案数据。
+
+    Args:
+        start_time: 查询开始时间
+        end_time: 查询结束时间
+        feature_infos: 格式为 "element_id1:type1,element_id2:type2"
+                      例如: "P1:pipe,J1:junction"
+        scheme_type: 方案类型，若为空则查询实时数据
+        scheme_name: 方案名称，若为空则查询实时数据
+        timescale_conn: TimescaleDB连接
+        
+    Returns:
+        管网元素的模拟数据
+        
+    Raises:
+        HTTPException: 当feature_infos为空返回400错误，未找到数据返回404错误，其他错误返回400错误
+    """
+    try:
+        feature_infos_list = []
+        if feature_infos:
+            for item in feature_infos.split(","):
+                item = item.strip()
+                if ":" in item:
+                    element_id, element_type = item.split(":", 1)
+                    feature_infos_list.append(
+                        (element_id.strip(), element_type.strip())
+                    )
+
+        if not feature_infos_list:
+            raise HTTPException(status_code=400, detail="feature_infos cannot be empty")
+
+        if scheme_type and scheme_name:
+            result = await CompositeQueries.get_scheme_simulation_data(
+                timescale_conn,
+                feature_infos_list,
+                start_time,
+                end_time,
+                scheme_type,
+                scheme_name,
+            )
+        else:
+            result = await CompositeQueries.get_realtime_simulation_data(
+                timescale_conn,
+                feature_infos_list,
+                start_time,
+                end_time,
+            )
+
+        if result is None:
+            raise HTTPException(status_code=404, detail="No simulation data found")
+        return result
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.get("/composite/element-scada", summary="获取管网元素关联的SCADA监测数据")
+async def get_element_associated_scada_data(
+    element_id: str = Query(..., description="管网元素ID（管道或节点）"),
+    start_time: datetime = Query(..., description="查询开始时间"),
+    end_time: datetime = Query(..., description="查询结束时间"),
+    use_cleaned: bool = Query(False, description="是否使用清洗后的数据"),
+    timescale_conn: AsyncConnection = Depends(get_timescale_connection),
+    postgres_conn: AsyncConnection = Depends(get_postgres_connection),
+):
+    """
+    获取link/node关联的SCADA监测值
+    
+    根据传入的link/node id，匹配SCADA信息，
+    如果存在关联的SCADA device_id，获取实际的监测数据。
+
+    Args:
+        element_id: 管网元素ID
+        start_time: 查询开始时间
+        end_time: 查询结束时间
+        use_cleaned: 是否使用清洗后的数据，默认为False使用原始数据
+        timescale_conn: TimescaleDB连接
+        postgres_conn: PostgreSQL连接
+        
+    Returns:
+        管网元素关联的SCADA监测数据
+        
+    Raises:
+        HTTPException: 当查询参数无效时返回400错误，未找到关联数据返回404错误
+    """
+    try:
+        result = await CompositeQueries.get_element_associated_scada_data(
+            timescale_conn, postgres_conn, element_id, start_time, end_time, use_cleaned
+        )
+        if result is None:
+            raise HTTPException(
+                status_code=404, detail="No associated SCADA data found"
+            )
+        return result
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.post("/composite/clean-scada", summary="清洗SCADA监测数据")
+async def clean_scada_data(
+    device_ids: str = Query(..., description="设备ID列表或 'all' 表示清洗所有设备"),
+    start_time: datetime = Query(..., description="清洗数据的开始时间"),
+    end_time: datetime = Query(..., description="清洗数据的结束时间"),
+    timescale_conn: AsyncConnection = Depends(get_timescale_connection),
+    postgres_conn: AsyncConnection = Depends(get_postgres_connection),
+):
+    """
+    清洗SCADA监测数据
+    
+    根据device_ids查询monitored_value，清洗后更新cleaned_value。
+    支持清洗指定设备或所有设备的数据。
+
+    Args:
+        device_ids: 设备ID列表，用逗号分隔，或 'all' 表示清洗所有设备
+        start_time: 清洗数据的开始时间
+        end_time: 清洗数据的结束时间
+        timescale_conn: TimescaleDB连接
+        postgres_conn: PostgreSQL连接
+        
+    Returns:
+        清洗结果信息
+        
+    Raises:
+        HTTPException: 当清洗过程出现错误时返回400错误
+    """
+    try:
+        if device_ids == "all":
+            device_ids_list = []
+        else:
+            device_ids_list = (
+                [id.strip() for id in device_ids.split(",") if id.strip()]
+                if device_ids
+                else []
+            )
+        return await CompositeQueries.clean_scada_data(
+            timescale_conn, postgres_conn, device_ids_list, start_time, end_time
+        )
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.get("/composite/pipeline-health-prediction", summary="预测管道健康状况")
+async def predict_pipeline_health(
+    query_time: datetime = Query(..., description="查询时间"),
+    network_name: str = Query(..., description="管网名称（或数据库名称）"),
+    timescale_conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    预测管道健康状况
+    
+    根据管网名称和当前时间，查询管道信息和实时数据，
+    使用随机生存森林模型预测管道的生存概率。
+
+    Args:
+        query_time: 查询时间
+        network_name: 管网名称（或数据库名称）
+        timescale_conn: TimescaleDB连接
+
+    Returns:
+        预测结果列表，每个元素包含 link_id 和对应的生存函数
+
+    Raises:
+        HTTPException: 当模型文件不存在返回404错误，其他错误返回400或500错误
+    """
+    try:
+        return await CompositeQueries.predict_pipeline_health(
+            timescale_conn, network_name, query_time
+        )
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+    except FileNotFoundError as e:
+        raise HTTPException(status_code=404, detail=str(e))
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"内部服务器错误: {str(e)}")

app/api/v1/endpoints/timeseries/dependencies.py

@@ -0,0 +1,19 @@
+from fastapi import Depends
+from psycopg import AsyncConnection
+
+from app.auth.project_dependencies import (
+    get_project_pg_connection,
+    get_project_timescale_connection,
+)
+
+
+async def get_timescale_connection(
+    conn: AsyncConnection = Depends(get_project_timescale_connection),
+):
+    yield conn
+
+
+async def get_postgres_connection(
+    conn: AsyncConnection = Depends(get_project_pg_connection),
+):
+    yield conn

app/api/v1/endpoints/timeseries/realtime.py

@@ -0,0 +1,289 @@
+from fastapi import APIRouter, Depends, HTTPException, Query, Path, Body
+from typing import List
+from datetime import datetime
+from psycopg import AsyncConnection
+
+from app.infra.db.timescaledb.repositories.realtime import RealtimeRepository
+from .dependencies import get_timescale_connection
+
+router = APIRouter()
+
+TIME_WITH_TZ_DESC = "ISO 8601 / RFC 3339 时间，必须显式带时区；可直接传 UTC+8，服务端会先转换为 UTC 再处理。"
+TIME_RANGE_START_DESC = f"时间范围开始时间。{TIME_WITH_TZ_DESC}"
+TIME_RANGE_END_DESC = f"时间范围结束时间。{TIME_WITH_TZ_DESC}"
+
+
+@router.post("/realtime/links/batch", status_code=201, summary="批量插入实时管道数据")
+async def insert_realtime_links(
+    data: List[dict] = Body(..., description="管道数据列表，每项包含管道ID、时间戳等信息"),
+    conn: AsyncConnection = Depends(get_timescale_connection)
+):
+    """
+    批量插入实时管道数据
+    
+    将管道的实时监测数据批量插入时间序列数据库。
+    
+    Args:
+        data: 管道数据列表
+        
+    Returns:
+        插入成功的记录数
+    """
+    await RealtimeRepository.insert_links_batch(conn, data)
+    return {"message": f"Inserted {len(data)} records"}
+
+
+@router.get(
+    "/realtime/links",
+    summary="查询实时管道数据",
+    description="按时间范围查询实时管道数据。start_time 和 end_time 必须显式带时区；允许传 UTC+8，服务端会先归一化为 UTC 再执行查询。",
+)
+async def get_realtime_links(
+    start_time: datetime = Query(..., description=TIME_RANGE_START_DESC),
+    end_time: datetime = Query(..., description=TIME_RANGE_END_DESC),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    查询指定时间范围内的实时管道数据
+    
+    根据时间范围查询所有实时管道的监测值。传入时间必须显式包含时区，
+    可以直接使用 UTC+8，服务端会先统一转换为 UTC 再参与数据库查询。
+    
+    Args:
+        start_time: 查询开始时间
+        end_time: 查询结束时间
+        
+    Returns:
+        实时管道数据列表
+    """
+    return await RealtimeRepository.get_links_by_time_range(conn, start_time, end_time)
+
+
+@router.delete(
+    "/realtime/links",
+    summary="删除实时管道数据",
+    description="按时间范围删除实时管道数据。start_time 和 end_time 必须显式带时区；允许传 UTC+8，服务端按请求中的绝对时间删除对应 UTC 数据。",
+)
+async def delete_realtime_links(
+    start_time: datetime = Query(..., description=TIME_RANGE_START_DESC),
+    end_time: datetime = Query(..., description=TIME_RANGE_END_DESC),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    删除指定时间范围内的实时管道数据
+    
+    删除在指定时间范围内的所有实时管道监测数据。
+    
+    Args:
+        start_time: 删除开始时间
+        end_time: 删除结束时间
+        
+    Returns:
+        删除结果信息
+    """
+    await RealtimeRepository.delete_links_by_time_range(conn, start_time, end_time)
+    return {"message": "Deleted successfully"}
+
+
+@router.patch("/realtime/links/{link_id}/field", summary="更新实时管道字段")
+async def update_realtime_link_field(
+    link_id: str = Path(..., description="管道ID"),
+    time: datetime = Query(..., description=f"要更新记录的时间戳。{TIME_WITH_TZ_DESC}"),
+    field: str = Query(..., description="要更新的字段名称"),
+    value: float = Query(..., description="更新的字段值"),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    更新指定管道的字段值
+    
+    更新实时管道在特定时间的某个字段数据。
+    
+    Args:
+        link_id: 管道ID
+        time: 数据时间戳
+        field: 字段名称
+        value: 字段新值
+        
+    Returns:
+        更新结果信息
+        
+    Raises:
+        HTTPException: 当字段不存在或更新失败时返回400错误
+    """
+    try:
+        await RealtimeRepository.update_link_field(conn, time, link_id, field, value)
+        return {"message": "Updated successfully"}
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.post("/realtime/nodes/batch", status_code=201, summary="批量插入实时节点数据")
+async def insert_realtime_nodes(
+    data: List[dict] = Body(..., description="节点数据列表，每项包含节点ID、时间戳等信息"),
+    conn: AsyncConnection = Depends(get_timescale_connection)
+):
+    """
+    批量插入实时节点数据
+    
+    将节点的实时监测数据批量插入时间序列数据库。
+    
+    Args:
+        data: 节点数据列表
+        
+    Returns:
+        插入成功的记录数
+    """
+    await RealtimeRepository.insert_nodes_batch(conn, data)
+    return {"message": f"Inserted {len(data)} records"}
+
+
+@router.get(
+    "/realtime/nodes",
+    summary="查询实时节点数据",
+    description="按时间范围查询实时节点数据。start_time 和 end_time 必须显式带时区；允许传 UTC+8，服务端会先归一化为 UTC 再执行查询。",
+)
+async def get_realtime_nodes(
+    start_time: datetime = Query(..., description=TIME_RANGE_START_DESC),
+    end_time: datetime = Query(..., description=TIME_RANGE_END_DESC),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    查询指定时间范围内的实时节点数据
+    
+    根据时间范围查询所有实时节点的监测值。传入时间必须显式包含时区，
+    可以直接使用 UTC+8，服务端会先统一转换为 UTC 再参与数据库查询。
+    
+    Args:
+        start_time: 查询开始时间
+        end_time: 查询结束时间
+        
+    Returns:
+        实时节点数据列表
+    """
+    return await RealtimeRepository.get_nodes_by_time_range(conn, start_time, end_time)
+
+
+@router.delete(
+    "/realtime/nodes",
+    summary="删除实时节点数据",
+    description="按时间范围删除实时节点数据。start_time 和 end_time 必须显式带时区；允许传 UTC+8，服务端按请求中的绝对时间删除对应 UTC 数据。",
+)
+async def delete_realtime_nodes(
+    start_time: datetime = Query(..., description=TIME_RANGE_START_DESC),
+    end_time: datetime = Query(..., description=TIME_RANGE_END_DESC),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    删除指定时间范围内的实时节点数据
+    
+    删除在指定时间范围内的所有实时节点监测数据。
+    
+    Args:
+        start_time: 删除开始时间
+        end_time: 删除结束时间
+        
+    Returns:
+        删除结果信息
+    """
+    await RealtimeRepository.delete_nodes_by_time_range(conn, start_time, end_time)
+    return {"message": "Deleted successfully"}
+
+
+
+
+@router.post("/realtime/simulation/store", status_code=201, summary="存储实时模拟结果")
+async def store_realtime_simulation_result(
+    node_result_list: List[dict] = Body(..., description="节点模拟结果列表"),
+    link_result_list: List[dict] = Body(..., description="管道模拟结果列表"),
+    result_start_time: str = Query(..., description=f"模拟结果开始时间。{TIME_WITH_TZ_DESC}"),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    存储实时模拟结果到时间序列数据库
+    
+    将节点和管道的实时模拟计算结果批量存储到TimescaleDB数据库。
+    
+    Args:
+        node_result_list: 节点模拟结果列表
+        link_result_list: 管道模拟结果列表
+        result_start_time: 模拟结果对应的起始时间
+        
+    Returns:
+        存储结果信息
+    """
+    await RealtimeRepository.store_realtime_simulation_result(
+        conn, node_result_list, link_result_list, result_start_time
+    )
+    return {"message": "Simulation results stored successfully"}
+
+
+@router.get(
+    "/realtime/query/by-time-property",
+    summary="按时间和属性查询实时数据",
+    description="查询指定时间点的实时属性值。query_time 必须显式带时区；允许传 UTC+8，服务端会先归一化为 UTC 再执行查询。",
+)
+async def query_realtime_records_by_time_property(
+    query_time: str = Query(..., description=f"查询时间。{TIME_WITH_TZ_DESC}"),
+    type: str = Query(..., description="数据类型，pipe（管道）或 junction（节点）"),
+    property: str = Query(..., description="要查询的属性名称"),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    按指定时间和属性查询所有实时监测数据
+    
+    查询在特定时间点，所有指定类型元素的特定属性值。
+    
+    Args:
+        query_time: 查询时间
+        type: 元素类型（pipe或junction）
+        property: 属性名称
+        
+    Returns:
+        查询结果列表
+        
+    Raises:
+        HTTPException: 当查询参数无效时返回400错误
+    """
+    try:
+        results = await RealtimeRepository.query_all_record_by_time_property(
+            conn, query_time, type, property
+        )
+        return {"results": results}
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.get(
+    "/realtime/query/by-id-time",
+    summary="按ID和时间查询实时模拟数据",
+    description="查询指定元素在某一时间点的实时模拟结果。query_time 必须显式带时区；允许传 UTC+8，服务端会先归一化为 UTC 再执行查询。",
+)
+async def query_realtime_simulation_by_id_time(
+    id: str = Query(..., description="元素ID（管道ID或节点ID）"),
+    type: str = Query(..., description="元素类型，pipe（管道）或 junction（节点）"),
+    query_time: str = Query(..., description=f"查询时间。{TIME_WITH_TZ_DESC}"),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    按指定ID和时间查询实时模拟结果
+    
+    查询特定元素在某一时间点的实时模拟数据。
+    
+    Args:
+        id: 元素ID
+        type: 元素类型（pipe或junction）
+        query_time: 查询时间
+        
+    Returns:
+        模拟结果数据
+        
+    Raises:
+        HTTPException: 当查询参数无效时返回400错误
+    """
+    try:
+        results = await RealtimeRepository.query_simulation_result_by_id_time(
+            conn, id, type, query_time
+        )
+        return {"result": results}
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))

app/api/v1/endpoints/timeseries/scada.py

@@ -0,0 +1,159 @@
+from fastapi import APIRouter, Depends, HTTPException, Query, Path, Body
+from typing import List
+from datetime import datetime
+from psycopg import AsyncConnection
+
+from app.infra.db.timescaledb.repositories.scada import ScadaRepository
+from .dependencies import get_timescale_connection
+
+router = APIRouter()
+
+
+@router.post("/scada/batch", status_code=201, summary="批量插入SCADA监测数据")
+async def insert_scada_data(
+    data: List[dict] = Body(..., description="SCADA设备监测数据列表"),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    批量插入SCADA监测数据
+
+    将多个设备的实时监测数据批量插入时间序列数据库。
+
+    Args:
+        data: SCADA设备监测数据列表，每项包含device_id、时间戳和监测值等信息
+
+    Returns:
+        插入成功的记录数
+    """
+    await ScadaRepository.insert_scada_batch(conn, data)
+    return {"message": f"Inserted {len(data)} records"}
+
+
+@router.get("/scada/by-ids-time-range", summary="按设备ID和时间范围查询SCADA数据")
+async def get_scada_by_ids_time_range(
+    start_time: datetime = Query(..., description="查询开始时间"),
+    end_time: datetime = Query(..., description="查询结束时间"),
+    device_ids: str = Query(
+        ..., description="设备ID列表，逗号分隔，如 'device1,device2,device3'"
+    ),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    按设备ID和时间范围查询SCADA监测数据
+
+    查询多个设备在指定时间范围内的所有监测数据。
+
+    Args:
+        start_time: 查询开始时间
+        end_time: 查询结束时间
+        device_ids: 设备ID列表，用逗号分隔
+
+    Returns:
+        SCADA监测数据列表
+    """
+    device_ids_list = (
+        [id.strip() for id in device_ids.split(",") if id.strip()] if device_ids else []
+    )
+    return await ScadaRepository.get_scada_by_ids_time_range(
+        conn, device_ids_list, start_time, end_time
+    )
+
+
+@router.get(
+    "/scada/by-ids-field-time-range", summary="按设备ID、字段和时间范围查询SCADA数据"
+)
+async def get_scada_field_by_ids_time_range(
+    start_time: datetime = Query(..., description="查询开始时间"),
+    end_time: datetime = Query(..., description="查询结束时间"),
+    field: str = Query(..., description="要查询的字段名称"),
+    device_ids: str = Query(
+        ..., description="设备ID列表，逗号分隔，如 'device1,device2,device3'"
+    ),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    按设备ID、字段和时间范围查询特定SCADA数据
+
+    查询多个设备在指定时间范围内的特定字段监测数据。
+
+    Args:
+        start_time: 查询开始时间
+        end_time: 查询结束时间
+        field: 字段名称
+        device_ids: 设备ID列表，用逗号分隔
+
+    Returns:
+        SCADA字段数据列表
+
+    Raises:
+        HTTPException: 当字段不存在或查询参数无效时返回400错误
+    """
+    try:
+        device_ids_list = (
+            [id.strip() for id in device_ids.split(",") if id.strip()]
+            if device_ids
+            else []
+        )
+        return await ScadaRepository.get_scada_field_by_id_time_range(
+            conn, device_ids_list, start_time, end_time, field
+        )
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.patch("/scada/{device_id}/field", summary="更新SCADA设备字段")
+async def update_scada_field(
+    device_id: str = Path(..., description="设备ID"),
+    time: datetime = Query(..., description="更新数据的时间戳"),
+    field: str = Query(..., description="要更新的字段名称"),
+    value: float = Query(..., description="更新的字段值"),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    更新指定设备的字段值
+
+    更新SCADA设备在特定时间的某个字段监测数据。
+
+    Args:
+        device_id: 设备ID
+        time: 数据时间戳
+        field: 字段名称
+        value: 字段新值
+
+    Returns:
+        更新结果信息
+
+    Raises:
+        HTTPException: 当字段不存在或更新失败时返回400错误
+    """
+    try:
+        await ScadaRepository.update_scada_field(conn, time, device_id, field, value)
+        return {"message": "Updated successfully"}
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.delete("/scada/by-id-time-range", summary="按设备ID和时间范围删除SCADA数据")
+async def delete_scada_data(
+    device_id: str = Query(..., description="设备ID"),
+    start_time: datetime = Query(..., description="删除开始时间"),
+    end_time: datetime = Query(..., description="删除结束时间"),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    删除指定设备和时间范围内的SCADA数据
+
+    删除在指定时间范围内的特定设备监测数据。
+
+    Args:
+        device_id: 设备ID
+        start_time: 删除开始时间
+        end_time: 删除结束时间
+
+    Returns:
+        删除结果信息
+    """
+    await ScadaRepository.delete_scada_by_id_time_range(
+        conn, device_id, start_time, end_time
+    )
+    return {"message": "Deleted successfully"}

app/api/v1/endpoints/timeseries/scheme.py

@@ -0,0 +1,391 @@
+from fastapi import APIRouter, Depends, HTTPException, Query, Path, Body
+from typing import List
+from datetime import datetime
+from psycopg import AsyncConnection
+
+from app.infra.db.timescaledb.repositories.scheme import SchemeRepository
+from .dependencies import get_timescale_connection
+
+router = APIRouter()
+
+
+@router.post("/scheme/links/batch", status_code=201, summary="批量插入方案管道数据")
+async def insert_scheme_links(
+    data: List[dict] = Body(..., description="方案管道数据列表"),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    批量插入方案管道数据
+
+    将特定方案的管道模拟数据批量插入时间序列数据库。
+
+    Args:
+        data: 方案管道数据列表
+
+    Returns:
+        插入成功的记录数
+    """
+    await SchemeRepository.insert_links_batch(conn, data)
+    return {"message": f"Inserted {len(data)} records"}
+
+
+@router.get("/scheme/links", summary="查询方案管道数据")
+async def get_scheme_links(
+    scheme_type: str = Query(..., description="方案类型"),
+    scheme_name: str = Query(..., description="方案名称"),
+    start_time: datetime = Query(..., description="查询开始时间"),
+    end_time: datetime = Query(..., description="查询结束时间"),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    查询指定方案和时间范围内的管道数据
+
+    根据方案和时间范围查询管道的模拟值。
+
+    Args:
+        scheme_type: 方案类型
+        scheme_name: 方案名称
+        start_time: 查询开始时间
+        end_time: 查询结束时间
+
+    Returns:
+        方案管道数据列表
+    """
+    return await SchemeRepository.get_links_by_scheme_and_time_range(
+        conn, scheme_type, scheme_name, start_time, end_time
+    )
+
+
+@router.get("/scheme/links/{link_id}/field", summary="查询方案管道字段数据")
+async def get_scheme_link_field(
+    link_id: str = Path(..., description="管道ID"),
+    scheme_type: str = Query(..., description="方案类型"),
+    scheme_name: str = Query(..., description="方案名称"),
+    start_time: datetime = Query(..., description="查询开始时间"),
+    end_time: datetime = Query(..., description="查询结束时间"),
+    field: str = Query(..., description="要查询的字段名称"),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    查询指定方案管道的特定字段数据
+
+    查询特定方案中指定管道在时间范围内的特定字段值。
+
+    Args:
+        link_id: 管道ID
+        scheme_type: 方案类型
+        scheme_name: 方案名称
+        start_time: 查询开始时间
+        end_time: 查询结束时间
+        field: 字段名称
+
+    Returns:
+        字段数据列表
+
+    Raises:
+        HTTPException: 当查询参数无效时返回400错误
+    """
+    try:
+        return await SchemeRepository.get_link_field_by_scheme_and_time_range(
+            conn, scheme_type, scheme_name, start_time, end_time, link_id, field
+        )
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.patch("/scheme/links/{link_id}/field", summary="更新方案管道字段")
+async def update_scheme_link_field(
+    link_id: str = Path(..., description="管道ID"),
+    scheme_type: str = Query(..., description="方案类型"),
+    scheme_name: str = Query(..., description="方案名称"),
+    time: datetime = Query(..., description="更新数据的时间戳"),
+    field: str = Query(..., description="要更新的字段名称"),
+    value: float = Query(..., description="更新的字段值"),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    更新指定方案管道的字段值
+
+    更新特定方案中指定管道在某个时间的字段数据。
+
+    Args:
+        link_id: 管道ID
+        scheme_type: 方案类型
+        scheme_name: 方案名称
+        time: 数据时间戳
+        field: 字段名称
+        value: 字段新值
+
+    Returns:
+        更新结果信息
+
+    Raises:
+        HTTPException: 当字段不存在或更新失败时返回400错误
+    """
+    try:
+        await SchemeRepository.update_link_field(
+            conn, time, scheme_type, scheme_name, link_id, field, value
+        )
+        return {"message": "Updated successfully"}
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.delete("/scheme/links", summary="删除方案管道数据")
+async def delete_scheme_links(
+    scheme_type: str = Query(..., description="方案类型"),
+    scheme_name: str = Query(..., description="方案名称"),
+    start_time: datetime = Query(..., description="删除开始时间"),
+    end_time: datetime = Query(..., description="删除结束时间"),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    删除指定方案和时间范围内的管道数据
+
+    删除在指定方案和时间范围内的所有管道模拟数据。
+
+    Args:
+        scheme_type: 方案类型
+        scheme_name: 方案名称
+        start_time: 删除开始时间
+        end_time: 删除结束时间
+
+    Returns:
+        删除结果信息
+    """
+    await SchemeRepository.delete_links_by_scheme_and_time_range(
+        conn, scheme_type, scheme_name, start_time, end_time
+    )
+    return {"message": "Deleted successfully"}
+
+
+@router.post("/scheme/nodes/batch", status_code=201, summary="批量插入方案节点数据")
+async def insert_scheme_nodes(
+    data: List[dict] = Body(..., description="方案节点数据列表"),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    批量插入方案节点数据
+
+    将特定方案的节点模拟数据批量插入时间序列数据库。
+
+    Args:
+        data: 方案节点数据列表
+
+    Returns:
+        插入成功的记录数
+    """
+    await SchemeRepository.insert_nodes_batch(conn, data)
+    return {"message": f"Inserted {len(data)} records"}
+
+
+@router.get("/scheme/nodes/{node_id}/field", summary="查询方案节点字段数据")
+async def get_scheme_node_field(
+    node_id: str = Path(..., description="节点ID"),
+    scheme_type: str = Query(..., description="方案类型"),
+    scheme_name: str = Query(..., description="方案名称"),
+    start_time: datetime = Query(..., description="查询开始时间"),
+    end_time: datetime = Query(..., description="查询结束时间"),
+    field: str = Query(..., description="要查询的字段名称"),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    查询指定方案节点的特定字段数据
+
+    查询特定方案中指定节点在时间范围内的特定字段值。
+
+    Args:
+        node_id: 节点ID
+        scheme_type: 方案类型
+        scheme_name: 方案名称
+        start_time: 查询开始时间
+        end_time: 查询结束时间
+        field: 字段名称
+
+    Returns:
+        字段数据列表
+
+    Raises:
+        HTTPException: 当查询参数无效时返回400错误
+    """
+    try:
+        return await SchemeRepository.get_node_field_by_scheme_and_time_range(
+            conn, scheme_type, scheme_name, start_time, end_time, node_id, field
+        )
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.patch("/scheme/nodes/{node_id}/field", summary="更新方案节点字段")
+async def update_scheme_node_field(
+    node_id: str = Path(..., description="节点ID"),
+    scheme_type: str = Query(..., description="方案类型"),
+    scheme_name: str = Query(..., description="方案名称"),
+    time: datetime = Query(..., description="更新数据的时间戳"),
+    field: str = Query(..., description="要更新的字段名称"),
+    value: float = Query(..., description="更新的字段值"),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    更新指定方案节点的字段值
+
+    更新特定方案中指定节点在某个时间的字段数据。
+
+    Args:
+        node_id: 节点ID
+        scheme_type: 方案类型
+        scheme_name: 方案名称
+        time: 数据时间戳
+        field: 字段名称
+        value: 字段新值
+
+    Returns:
+        更新结果信息
+
+    Raises:
+        HTTPException: 当字段不存在或更新失败时返回400错误
+    """
+    try:
+        await SchemeRepository.update_node_field(
+            conn, time, scheme_type, scheme_name, node_id, field, value
+        )
+        return {"message": "Updated successfully"}
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.delete("/scheme/nodes", summary="删除方案节点数据")
+async def delete_scheme_nodes(
+    scheme_type: str = Query(..., description="方案类型"),
+    scheme_name: str = Query(..., description="方案名称"),
+    start_time: datetime = Query(..., description="删除开始时间"),
+    end_time: datetime = Query(..., description="删除结束时间"),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    删除指定方案和时间范围内的节点数据
+
+    删除在指定方案和时间范围内的所有节点模拟数据。
+
+    Args:
+        scheme_type: 方案类型
+        scheme_name: 方案名称
+        start_time: 删除开始时间
+        end_time: 删除结束时间
+
+    Returns:
+        删除结果信息
+    """
+    await SchemeRepository.delete_nodes_by_scheme_and_time_range(
+        conn, scheme_type, scheme_name, start_time, end_time
+    )
+    return {"message": "Deleted successfully"}
+
+
+@router.post("/scheme/simulation/store", status_code=201, summary="存储方案模拟结果")
+async def store_scheme_simulation_result(
+    scheme_type: str = Query(..., description="方案类型"),
+    scheme_name: str = Query(..., description="方案名称"),
+    node_result_list: List[dict] = Body(..., description="节点模拟结果列表"),
+    link_result_list: List[dict] = Body(..., description="管道模拟结果列表"),
+    result_start_time: str = Query(..., description="模拟结果开始时间"),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    存储方案模拟结果到时间序列数据库
+
+    将特定方案的节点和管道模拟计算结果批量存储到TimescaleDB数据库。
+
+    Args:
+        scheme_type: 方案类型
+        scheme_name: 方案名称
+        node_result_list: 节点模拟结果列表
+        link_result_list: 管道模拟结果列表
+        result_start_time: 模拟结果对应的起始时间
+
+    Returns:
+        存储结果信息
+    """
+    await SchemeRepository.store_scheme_simulation_result(
+        conn,
+        scheme_type,
+        scheme_name,
+        node_result_list,
+        link_result_list,
+        result_start_time,
+    )
+    return {"message": "Scheme simulation results stored successfully"}
+
+
+@router.get(
+    "/scheme/query/by-scheme-time-property", summary="按方案、时间和属性查询数据"
+)
+async def query_scheme_records_by_scheme_time_property(
+    scheme_type: str = Query(..., description="方案类型"),
+    scheme_name: str = Query(..., description="方案名称"),
+    query_time: str = Query(..., description="查询时间"),
+    type: str = Query(..., description="元素类型，pipe（管道）或 junction（节点）"),
+    property: str = Query(..., description="要查询的属性名称"),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    按指定方案、时间和属性查询所有方案数据
+
+    查询在特定方案和时间点，所有指定类型元素的特定属性值。
+
+    Args:
+        scheme_type: 方案类型
+        scheme_name: 方案名称
+        query_time: 查询时间
+        type: 元素类型（pipe或junction）
+        property: 属性名称
+
+    Returns:
+        查询结果列表
+
+    Raises:
+        HTTPException: 当查询参数无效时返回400错误
+    """
+    try:
+        results = await SchemeRepository.query_all_record_by_scheme_time_property(
+            conn, scheme_type, scheme_name, query_time, type, property
+        )
+        return {"results": results}
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))
+
+
+@router.get("/scheme/query/by-id-time", summary="按ID和时间查询方案模拟数据")
+async def query_scheme_simulation_by_id_time(
+    scheme_type: str = Query(..., description="方案类型"),
+    scheme_name: str = Query(..., description="方案名称"),
+    id: str = Query(..., description="元素ID（管道ID或节点ID）"),
+    type: str = Query(..., description="元素类型，pipe（管道）或 junction（节点）"),
+    query_time: str = Query(..., description="查询时间"),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    """
+    按指定ID和时间查询方案模拟结果
+
+    查询特定方案中的元素在某一时间点的模拟数据。
+
+    Args:
+        scheme_type: 方案类型
+        scheme_name: 方案名称
+        id: 元素ID
+        type: 元素类型（pipe或junction）
+        query_time: 查询时间
+
+    Returns:
+        模拟结果数据
+
+    Raises:
+        HTTPException: 当查询参数无效时返回400错误
+    """
+    try:
+        result = await SchemeRepository.query_scheme_simulation_result_by_id_time(
+            conn, scheme_type, scheme_name, id, type, query_time
+        )
+        return {"result": result}
+    except ValueError as e:
+        raise HTTPException(status_code=400, detail=str(e))

app/api/v1/endpoints/users.py

@@ -0,0 +1,36 @@
+from fastapi import APIRouter, Request, Query
+from typing import Any, List, Dict, Union
+from app.services.tjnetwork import Any, get_all_users, get_user, get_user_schema
+
+router = APIRouter()
+
+###########################################################
+# user 39
+###########################################################
+
+@router.get("/getuserschema/", summary="获取用户模式", description="获取指定网络的用户模式定义")
+async def fastapi_get_user_schema(network: str = Query(..., description="管网名称（或数据库名称）")) -> dict[str, dict[Any, Any]]:
+    """
+    获取用户模式定义
+    
+    返回指定网络的用户模式结构定义
+    """
+    return get_user_schema(network)
+
+@router.get("/getuser/", summary="获取单个用户", description="获取指定网络中的单个用户信息")
+async def fastapi_get_user(network: str = Query(..., description="管网名称（或数据库名称）"), user_name: str = Query(..., description="用户名")) -> dict[Any, Any]:
+    """
+    获取用户信息
+    
+    返回指定网络中指定用户名的详细信息
+    """
+    return get_user(network, user_name)
+
+@router.get("/getallusers/", summary="获取所有用户", description="获取指定网络的所有用户列表")
+async def fastapi_get_all_users(network: str = Query(..., description="管网名称（或数据库名称）")) -> list[dict[Any, Any]]:
+    """
+    获取所有用户列表
+    
+    返回指定网络中所有用户的信息
+    """
+    return get_all_users(network)

app/api/v1/endpoints/web_search.py

@@ -0,0 +1,29 @@
+from typing import Any
+
+from fastapi import APIRouter, HTTPException, status
+
+from app.services.web_search import (
+    BochaSearchAPIError,
+    BochaSearchConfigError,
+    WebSearchRequest,
+    search_bocha_web,
+)
+
+router = APIRouter()
+
+
+@router.post(
+    "/web-search",
+    summary="Web Search",
+    description="调用 Bocha Web Search API 获取实时网页搜索结果",
+)
+async def web_search(request: WebSearchRequest) -> dict[str, Any]:
+    try:
+        return await search_bocha_web(request)
+    except BochaSearchConfigError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=str(exc),
+        ) from exc
+    except BochaSearchAPIError as exc:
+        raise HTTPException(status_code=exc.status_code, detail=exc.detail) from exc

app/api/v1/router.py

@@ -1,20 +1,116 @@
 from fastapi import APIRouter
 from app.api.v1.endpoints import (
-    auth,
+    admin_metadata,
+    agent_auth,
     project,
-    network_elements,
     simulation,
     scada,
     extension,
-    snapshots
+    snapshots,
+    users,
+    schemes,
+    misc,
+    risk,
+    cache,
+    leakage,
+    burst_detection,
+    burst_location,
+    audit,  # 新增：审计日志
+    meta,
+    web_search,
+    geocoding,
+)
+from app.api.v1.endpoints.network import (
+    general,
+    junctions,
+    reservoirs,
+    tanks,
+    pipes,
+    pumps,
+    valves,
+    tags,
+    demands,
+    geometry,
+    regions,
+)
+from app.api.v1.endpoints.components import (
+    curves,
+    patterns,
+    controls,
+    options,
+    quality,
+    visuals,
+)
+
+from app.api.v1.endpoints import project_data
+from app.api.v1.endpoints.timeseries import (
+    realtime as ts_realtime,
+    scheme as ts_scheme,
+    scada as ts_scada,
+    composite as ts_composite,
 )
 
 api_router = APIRouter()
 
-api_router.include_router(auth.router, prefix="/auth", tags=["auth"])
-api_router.include_router(project.router, prefix="/projects", tags=["projects"])
-api_router.include_router(network_elements.router, prefix="/elements", tags=["network-elements"])
-api_router.include_router(simulation.router, prefix="/simulation", tags=["simulation"])
-api_router.include_router(scada.router, prefix="/scada", tags=["scada"])
-api_router.include_router(extension.router, prefix="/extension", tags=["extension"])
-api_router.include_router(snapshots.router, prefix="/snapshots", tags=["snapshots"])
+# Core Services
+api_router.include_router(agent_auth.router, tags=["Agent Auth"])
+api_router.include_router(
+    admin_metadata.router, prefix="/admin", tags=["Metadata Admin"]
+)
+api_router.include_router(audit.router, prefix="/audit", tags=["Audit Logs"])  # 新增
+api_router.include_router(meta.router, tags=["Metadata"])
+api_router.include_router(project.router, tags=["Project"])
+
+# Network Elements (Node/Link Types)
+api_router.include_router(general.router, tags=["Network General"])
+api_router.include_router(junctions.router, tags=["Junctions"])
+api_router.include_router(reservoirs.router, tags=["Reservoirs"])
+api_router.include_router(tanks.router, tags=["Tanks"])
+api_router.include_router(pipes.router, tags=["Pipes"])
+api_router.include_router(pumps.router, tags=["Pumps"])
+api_router.include_router(valves.router, tags=["Valves"])
+
+# Network Features
+api_router.include_router(tags.router, tags=["Tags"])
+api_router.include_router(demands.router, tags=["Demands"])
+api_router.include_router(geometry.router, tags=["Geometry & Coordinates"])
+api_router.include_router(regions.router, tags=["Regions & DMAs"])
+
+# Components & Controls
+api_router.include_router(curves.router, tags=["Curves"])
+api_router.include_router(patterns.router, tags=["Patterns"])
+api_router.include_router(controls.router, tags=["Controls & Rules"])
+api_router.include_router(options.router, tags=["Options"])
+api_router.include_router(quality.router, tags=["Quality"])
+api_router.include_router(visuals.router, tags=["Visuals"])
+
+# Simulation & Data
+api_router.include_router(simulation.router, tags=["Simulation Control"])
+api_router.include_router(scada.router)
+api_router.include_router(snapshots.router, tags=["Snapshots"])
+api_router.include_router(users.router, tags=["Users"])
+api_router.include_router(schemes.router, tags=["Schemes"])
+api_router.include_router(misc.router, tags=["Misc"])
+api_router.include_router(risk.router, tags=["Risk"])
+api_router.include_router(cache.router, tags=["Cache"])
+api_router.include_router(web_search.router, tags=["Web Search"])
+api_router.include_router(geocoding.router, tags=["Geocoding"])
+api_router.include_router(leakage.router, prefix="/leakage", tags=["Leakage"])
+api_router.include_router(
+    burst_detection.router, prefix="/burst-detection", tags=["Burst Detection"]
+)
+api_router.include_router(
+    burst_location.router, prefix="/burst-location", tags=["Burst Location"]
+)
+
+# TimescaleDB Data Access
+api_router.include_router(ts_realtime.router, tags=["TimescaleDB - Realtime"])
+api_router.include_router(ts_scheme.router, tags=["TimescaleDB - Scheme"])
+api_router.include_router(ts_scada.router, tags=["TimescaleDB - SCADA"])
+api_router.include_router(ts_composite.router, tags=["TimescaleDB - Composite"])
+
+# Project Data (PostgreSQL)
+api_router.include_router(project_data.router, tags=["Project Data"])
+
+# Extension
+api_router.include_router(extension.router, tags=["Extension"])

app/audit/__init__.py

@@ -0,0 +1,9 @@
+"""
+This module is reserved for future implementation of audit logging and compliance features.
+
+Current implementation of audit logging can be found in `app.core.audit`.
+Future expansion may include:
+- Comprehensive audit trails for all system actions
+- Compliance reporting (e.g., for industrial control systems)
+- Anomaly detection in user behavior
+"""

app/auth/dependencies.py

@@ -1,21 +0,0 @@
-from fastapi import Depends, HTTPException, status
-from fastapi.security import OAuth2PasswordBearer
-from app.core.config import settings
-from jose import jwt, JWTError
-
-oauth2_scheme = OAuth2PasswordBearer(tokenUrl=f"{settings.API_V1_STR}/login/access-token")
-
-async def get_current_user(token: str = Depends(oauth2_scheme)):
-    credentials_exception = HTTPException(
-        status_code=status.HTTP_401_UNAUTHORIZED,
-        detail="Could not validate credentials",
-        headers={"WWW-Authenticate": "Bearer"},
-    )
-    try:
-        payload = jwt.decode(token, settings.SECRET_KEY, algorithms=[settings.ALGORITHM])
-        username: str = payload.get("sub")
-        if username is None:
-            raise credentials_exception
-    except JWTError:
-        raise credentials_exception
-    return username

app/auth/keycloak_dependencies.py

@@ -0,0 +1,86 @@
+# import logging
+from uuid import UUID
+
+from fastapi import Depends, HTTPException, status
+from fastapi.security import OAuth2PasswordBearer
+from jose import JWTError, jwt
+
+from app.core.config import settings
+
+oauth2_optional = OAuth2PasswordBearer(
+    tokenUrl="keycloak", auto_error=False
+)
+
+# logger = logging.getLogger(__name__)
+
+
+def _decode_keycloak_token(token: str) -> dict:
+    if not settings.KEYCLOAK_PUBLIC_KEY:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="Keycloak public key is not configured",
+        )
+
+    key = settings.KEYCLOAK_PUBLIC_KEY.replace("\\n", "\n")
+
+    return jwt.decode(
+        token,
+        key,
+        algorithms=[settings.KEYCLOAK_ALGORITHM],
+        audience=settings.KEYCLOAK_AUDIENCE or None,
+    )
+
+
+async def get_current_keycloak_payload(
+    token: str | None = Depends(oauth2_optional),
+) -> dict:
+    if not token:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Not authenticated",
+            headers={"WWW-Authenticate": "Bearer"},
+        )
+
+    try:
+        return _decode_keycloak_token(token)
+    except JWTError as exc:
+        # logger.warning("Keycloak token validation failed: %s", exc)
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Invalid token",
+            headers={"WWW-Authenticate": "Bearer"},
+        ) from exc
+
+
+async def get_current_keycloak_sub(
+    payload: dict = Depends(get_current_keycloak_payload),
+) -> UUID:
+    sub = payload.get("sub")
+    if not sub:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Missing subject claim",
+            headers={"WWW-Authenticate": "Bearer"},
+        )
+
+    try:
+        return UUID(sub)
+    except ValueError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Invalid subject claim",
+            headers={"WWW-Authenticate": "Bearer"},
+        ) from exc
+
+
+async def get_current_keycloak_username(
+    payload: dict = Depends(get_current_keycloak_payload),
+) -> str:
+    username = payload.get("preferred_username") or payload.get("username")
+    if not username:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Missing username claim",
+            headers={"WWW-Authenticate": "Bearer"},
+        )
+    return str(username)

app/auth/metadata_dependencies.py

@@ -0,0 +1,104 @@
+from dataclasses import dataclass
+from uuid import UUID
+
+import logging
+from fastapi import Depends, HTTPException, status
+from sqlalchemy.exc import SQLAlchemyError
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.auth.keycloak_dependencies import get_current_keycloak_payload
+from app.infra.db.metadb.database import get_metadata_session
+from app.infra.db.metadb.repositories.metadata_repository import MetadataRepository
+
+logger = logging.getLogger(__name__)
+
+
+async def get_metadata_repository(
+    session: AsyncSession = Depends(get_metadata_session),
+) -> MetadataRepository:
+    return MetadataRepository(session)
+
+
+def _keycloak_sub_from_payload(payload: dict) -> UUID:
+    sub = payload.get("sub")
+    if not sub:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Missing subject claim",
+            headers={"WWW-Authenticate": "Bearer"},
+        )
+
+    try:
+        return UUID(str(sub))
+    except ValueError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Invalid subject claim",
+            headers={"WWW-Authenticate": "Bearer"},
+        ) from exc
+
+
+def _username_from_payload(payload: dict) -> str | None:
+    username = payload.get("preferred_username") or payload.get("username")
+    return str(username) if username else None
+
+
+def _email_from_payload(payload: dict) -> str | None:
+    email = payload.get("email")
+    return str(email) if email else None
+
+
+async def get_current_metadata_user(
+    keycloak_payload: dict = Depends(get_current_keycloak_payload),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+):
+    keycloak_sub = _keycloak_sub_from_payload(keycloak_payload)
+    try:
+        user = await metadata_repo.get_user_by_keycloak_id(keycloak_sub)
+    except SQLAlchemyError as exc:
+        logger.error(
+            "Metadata DB error while resolving current user",
+            exc_info=True,
+        )
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=f"Metadata database error: {exc}",
+        ) from exc
+    if not user or not user.is_active:
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN, detail="Inactive user"
+        )
+    try:
+        user = await metadata_repo.refresh_user_keycloak_snapshot(
+            user,
+            username=_username_from_payload(keycloak_payload),
+            email=_email_from_payload(keycloak_payload),
+        )
+    except SQLAlchemyError as exc:
+        logger.error(
+            "Metadata DB error while refreshing current user snapshot",
+            exc_info=True,
+        )
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=f"Metadata database error: {exc}",
+        ) from exc
+    return user
+
+
+async def get_current_metadata_admin(
+    user=Depends(get_current_metadata_user),
+):
+    if user.is_superuser or user.role == "admin":
+        return user
+    raise HTTPException(
+        status_code=status.HTTP_403_FORBIDDEN, detail="Admin access required"
+    )
+
+
+@dataclass(frozen=True)
+class _AuthBypassUser:
+    id: UUID = UUID(int=0)
+    role: str = "admin"
+    is_superuser: bool = True
+    is_active: bool = True

app/auth/project_dependencies.py

@@ -0,0 +1,215 @@
+from dataclasses import dataclass
+from typing import AsyncGenerator
+from uuid import UUID
+
+import logging
+from fastapi import Depends, Header, HTTPException, status
+from psycopg import AsyncConnection
+from sqlalchemy.exc import SQLAlchemyError
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.auth.keycloak_dependencies import get_current_keycloak_sub
+from app.core.config import settings
+from app.infra.db.dynamic_manager import project_connection_manager
+from app.infra.db.metadb.database import get_metadata_session
+from app.infra.db.metadb.repositories.metadata_repository import MetadataRepository
+
+DB_ROLE_BIZ_DATA = "biz_data"
+DB_ROLE_IOT_DATA = "iot_data"
+DB_TYPE_POSTGRES = "postgresql"
+DB_TYPE_TIMESCALE = "timescaledb"
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass(frozen=True)
+class ProjectContext:
+    project_id: UUID
+    project_code: str
+    user_id: UUID
+    project_role: str
+
+
+async def get_metadata_repository(
+    session: AsyncSession = Depends(get_metadata_session),
+) -> MetadataRepository:
+    return MetadataRepository(session)
+
+
+async def get_project_context(
+    x_project_id: str = Header(..., alias="X-Project-Id"),
+    keycloak_sub: UUID = Depends(get_current_keycloak_sub),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> ProjectContext:
+    try:
+        project_uuid = UUID(x_project_id)
+    except ValueError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST, detail="Invalid project id"
+        ) from exc
+
+    try:
+        project = await metadata_repo.get_project_by_id(project_uuid)
+        if not project:
+            raise HTTPException(
+                status_code=status.HTTP_404_NOT_FOUND, detail="Project not found"
+            )
+        if project.status != "active":
+            raise HTTPException(
+                status_code=status.HTTP_403_FORBIDDEN, detail="Project is not active"
+            )
+
+        user = await metadata_repo.get_user_by_keycloak_id(keycloak_sub)
+        if not user:
+            raise HTTPException(
+                status_code=status.HTTP_403_FORBIDDEN, detail="User not registered"
+            )
+        if not user.is_active:
+            raise HTTPException(
+                status_code=status.HTTP_403_FORBIDDEN, detail="Inactive user"
+            )
+
+        membership_role = await metadata_repo.get_membership_role(project_uuid, user.id)
+        if not membership_role:
+            raise HTTPException(
+                status_code=status.HTTP_403_FORBIDDEN, detail="No access to project"
+            )
+    except SQLAlchemyError as exc:
+        logger.error(
+            "Metadata DB error while resolving project context",
+            exc_info=True,
+        )
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=f"Metadata database error: {exc}",
+        ) from exc
+
+    return ProjectContext(
+        project_id=project.id,
+        project_code=project.code,
+        user_id=user.id,
+        project_role=membership_role,
+    )
+
+
+async def get_project_pg_session(
+    ctx: ProjectContext = Depends(get_project_context),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> AsyncGenerator[AsyncSession, None]:
+    try:
+        routing = await metadata_repo.get_project_db_routing(
+            ctx.project_id, DB_ROLE_BIZ_DATA
+        )
+    except ValueError as exc:
+        logger.error(
+            "Invalid project PostgreSQL routing DSN configuration",
+            exc_info=True,
+        )
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=f"Project PostgreSQL routing DSN is invalid: {exc}",
+        ) from exc
+    if not routing:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="Project PostgreSQL not configured",
+        )
+    if routing.db_type != DB_TYPE_POSTGRES:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="Project PostgreSQL type mismatch",
+        )
+
+    pool_min_size = routing.pool_min_size or settings.PROJECT_PG_POOL_SIZE
+    pool_max_size = routing.pool_max_size or settings.PROJECT_PG_POOL_SIZE
+    sessionmaker = await project_connection_manager.get_pg_sessionmaker(
+        ctx.project_id,
+        DB_ROLE_BIZ_DATA,
+        routing.dsn,
+        pool_min_size,
+        pool_max_size,
+    )
+    async with sessionmaker() as session:
+        yield session
+
+
+async def get_project_pg_connection(
+    ctx: ProjectContext = Depends(get_project_context),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> AsyncGenerator[AsyncConnection, None]:
+    try:
+        routing = await metadata_repo.get_project_db_routing(
+            ctx.project_id, DB_ROLE_BIZ_DATA
+        )
+    except ValueError as exc:
+        logger.error(
+            "Invalid project PostgreSQL routing DSN configuration",
+            exc_info=True,
+        )
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=f"Project PostgreSQL routing DSN is invalid: {exc}",
+        ) from exc
+    if not routing:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="Project PostgreSQL not configured",
+        )
+    if routing.db_type != DB_TYPE_POSTGRES:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="Project PostgreSQL type mismatch",
+        )
+
+    pool_min_size = routing.pool_min_size or settings.PROJECT_PG_POOL_SIZE
+    pool_max_size = routing.pool_max_size or settings.PROJECT_PG_POOL_SIZE
+    pool = await project_connection_manager.get_pg_pool(
+        ctx.project_id,
+        DB_ROLE_BIZ_DATA,
+        routing.dsn,
+        pool_min_size,
+        pool_max_size,
+    )
+    async with pool.connection() as conn:
+        yield conn
+
+
+async def get_project_timescale_connection(
+    ctx: ProjectContext = Depends(get_project_context),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> AsyncGenerator[AsyncConnection, None]:
+    try:
+        routing = await metadata_repo.get_project_db_routing(
+            ctx.project_id, DB_ROLE_IOT_DATA
+        )
+    except ValueError as exc:
+        logger.error(
+            "Invalid project TimescaleDB routing DSN configuration",
+            exc_info=True,
+        )
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=f"Project TimescaleDB routing DSN is invalid: {exc}",
+        ) from exc
+    if not routing:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="Project TimescaleDB not configured",
+        )
+    if routing.db_type != DB_TYPE_TIMESCALE:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="Project TimescaleDB type mismatch",
+        )
+
+    pool_min_size = routing.pool_min_size or settings.PROJECT_TS_POOL_MIN_SIZE
+    pool_max_size = routing.pool_max_size or settings.PROJECT_TS_POOL_MAX_SIZE
+    pool = await project_connection_manager.get_timescale_pool(
+        ctx.project_id,
+        DB_ROLE_IOT_DATA,
+        routing.dsn,
+        pool_min_size,
+        pool_max_size,
+    )
+    async with pool.connection() as conn:
+        yield conn

app/core/audit.py

@@ -1,3 +1,146 @@
-# Placeholder for audit logic
-async def log_audit_event(event_type: str, user_id: str, details: dict):
-    pass
+"""
+审计日志模块
+
+记录系统关键操作，用于安全审计和合规追踪
+"""
+
+from typing import Optional
+from datetime import datetime
+import logging
+from uuid import UUID
+
+logger = logging.getLogger(__name__)
+
+
+class AuditAction:
+    """审计操作类型常量"""
+
+    # 认证相关
+    LOGIN = "LOGIN"
+    LOGOUT = "LOGOUT"
+    REGISTER = "REGISTER"
+    PASSWORD_CHANGE = "PASSWORD_CHANGE"
+
+    # 数据操作
+    CREATE = "CREATE"
+    READ = "READ"
+    UPDATE = "UPDATE"
+    DELETE = "DELETE"
+
+    # 权限相关
+    PERMISSION_CHANGE = "PERMISSION_CHANGE"
+    ROLE_CHANGE = "ROLE_CHANGE"
+
+    # 系统操作
+    CONFIG_CHANGE = "CONFIG_CHANGE"
+    SYSTEM_START = "SYSTEM_START"
+    SYSTEM_STOP = "SYSTEM_STOP"
+
+
+async def log_audit_event(
+    action: str,
+    user_id: Optional[UUID] = None,
+    project_id: Optional[UUID] = None,
+    resource_type: Optional[str] = None,
+    resource_id: Optional[str] = None,
+    ip_address: Optional[str] = None,
+    request_method: Optional[str] = None,
+    request_path: Optional[str] = None,
+    request_data: Optional[dict] = None,
+    response_status: Optional[int] = None,
+    session=None,
+):
+    """
+    记录审计日志
+
+    Args:
+        action: 操作类型
+        user_id: 用户ID
+        project_id: 项目ID
+        resource_type: 资源类型
+        resource_id: 资源ID
+        ip_address: IP地址
+        request_method: 请求方法
+        request_path: 请求路径
+        request_data: 请求数据（敏感字段需脱敏）
+        response_status: 响应状态码
+        session: 元数据库会话（可选）
+    """
+    from app.infra.db.metadb.database import SessionLocal
+    from app.infra.db.metadb.repositories.audit_repository import AuditRepository
+
+    if request_data:
+        request_data = sanitize_sensitive_data(request_data)
+
+    if session is None:
+        async with SessionLocal() as session:
+            audit_repo = AuditRepository(session)
+            await audit_repo.create_log(
+                user_id=user_id,
+                project_id=project_id,
+                action=action,
+                resource_type=resource_type,
+                resource_id=resource_id,
+                ip_address=ip_address,
+                request_method=request_method,
+                request_path=request_path,
+                request_data=request_data,
+                response_status=response_status,
+            )
+    else:
+        audit_repo = AuditRepository(session)
+        await audit_repo.create_log(
+            user_id=user_id,
+            project_id=project_id,
+            action=action,
+            resource_type=resource_type,
+            resource_id=resource_id,
+            ip_address=ip_address,
+            request_method=request_method,
+            request_path=request_path,
+            request_data=request_data,
+            response_status=response_status,
+        )
+
+    logger.info(
+        "Audit log created: action=%s, user=%s, project=%s, resource=%s:%s",
+        action,
+        user_id,
+        project_id,
+        resource_type,
+        resource_id,
+    )
+
+
+def sanitize_sensitive_data(data: dict) -> dict:
+    """
+    脱敏敏感数据
+
+    Args:
+        data: 原始数据
+
+    Returns:
+        脱敏后的数据
+    """
+    sensitive_fields = [
+        "password",
+        "passwd",
+        "pwd",
+        "secret",
+        "token",
+        "api_key",
+        "apikey",
+        "credit_card",
+        "ssn",
+        "social_security",
+    ]
+
+    sanitized = data.copy()
+
+    for key in sanitized:
+        if isinstance(sanitized[key], dict):
+            sanitized[key] = sanitize_sensitive_data(sanitized[key])
+        elif any(sensitive in key.lower() for sensitive in sensitive_fields):
+            sanitized[key] = "***REDACTED***"
+
+    return sanitized

app/core/config.py

@@ -1,12 +1,19 @@
-from pydantic_settings import BaseSettings
+from pathlib import Path
+from typing import Optional
+from urllib.parse import quote_plus
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
 
 class Settings(BaseSettings):
     PROJECT_NAME: str = "TJWater Server"
+    ENVIRONMENT: str = "production"
     API_V1_STR: str = "/api/v1"
-    SECRET_KEY: str = "your-secret-key-here" # Change in production
-    ALGORITHM: str = "HS256"
-    ACCESS_TOKEN_EXPIRE_MINUTES: int = 30
-    
+
+    NETWORK_NAME: str = "default_network"
+
+    # 敏感配置加密密钥 (Fernet)
+    DATABASE_ENCRYPTION_KEY: str = ""
+
     # Database Config (PostgreSQL)
     DB_NAME: str = "tjwater"
     DB_HOST: str = "localhost"
@@ -14,17 +21,130 @@ class Settings(BaseSettings):
     DB_USER: str = "postgres"
     DB_PASSWORD: str = "password"
 
-    # InfluxDB
-    INFLUXDB_URL: str = "http://localhost:8086"
-    INFLUXDB_TOKEN: str = "token"
-    INFLUXDB_ORG: str = "org"
-    INFLUXDB_BUCKET: str = "bucket"
+    # Database Config (TimescaleDB)
+    TIMESCALEDB_DB_NAME: str = "tjwater"
+    TIMESCALEDB_DB_HOST: str = "localhost"
+    TIMESCALEDB_DB_PORT: str = "5433"
+    TIMESCALEDB_DB_USER: str = "postgres"
+    TIMESCALEDB_DB_PASSWORD: str = "password"
+
+    # Metadata Database Config (PostgreSQL)
+    METADATA_DB_NAME: str = "system_hub"
+    METADATA_DB_HOST: str = "localhost"
+    METADATA_DB_PORT: str = "5432"
+    METADATA_DB_USER: str = "postgres"
+    METADATA_DB_PASSWORD: str = "password"
+
+    METADATA_DB_POOL_SIZE: int = 5
+    METADATA_DB_MAX_OVERFLOW: int = 10
+
+    PROJECT_PG_CACHE_SIZE: int = 50
+    PROJECT_TS_CACHE_SIZE: int = 50
+    PROJECT_PG_POOL_SIZE: int = 5
+    PROJECT_PG_MAX_OVERFLOW: int = 10
+    PROJECT_TS_POOL_MIN_SIZE: int = 1
+    PROJECT_TS_POOL_MAX_SIZE: int = 10
+
+    # Keycloak access token verification
+    KEYCLOAK_PUBLIC_KEY: str = ""
+    KEYCLOAK_ALGORITHM: str = "RS256"
+    KEYCLOAK_AUDIENCE: str = ""
+
+    # Bocha Web Search API
+    BOCHA_API_KEY: str = ""
+    BOCHA_WEB_SEARCH_URL: str = "https://api.bochaai.com/v1/web-search"
+    BOCHA_WEB_SEARCH_TIMEOUT_SECONDS: float = 30.0
+
+    # Tianditu Geocoding API
+    TIANDITU_GEOCODER_TOKEN: str = ""
+    TIANDITU_GEOCODER_URL: str = "https://api.tianditu.gov.cn/geocoder"
+    TIANDITU_GEOCODER_TIMEOUT_SECONDS: float = 30.0
 
     @property
     def SQLALCHEMY_DATABASE_URI(self) -> str:
-        return f"postgresql://{self.DB_USER}:{self.DB_PASSWORD}@{self.DB_HOST}:{self.DB_PORT}/{self.DB_NAME}"
-    
-    class Config:
-        env_file = ".env"
+        db_password = quote_plus(self.DB_PASSWORD)
+        return f"postgresql://{self.DB_USER}:{db_password}@{self.DB_HOST}:{self.DB_PORT}/{self.DB_NAME}"
+
+    @property
+    def METADATA_DATABASE_URI(self) -> str:
+        metadata_password = quote_plus(self.METADATA_DB_PASSWORD)
+        return (
+            f"postgresql+psycopg://{self.METADATA_DB_USER}:{metadata_password}"
+            f"@{self.METADATA_DB_HOST}:{self.METADATA_DB_PORT}/{self.METADATA_DB_NAME}"
+        )
+
+    model_config = SettingsConfigDict(
+        env_file=Path(__file__).resolve().parents[2] / ".env",
+        extra="ignore",
+    )
+
 
 settings = Settings()
+
+
+def get_pgconn_string(
+    db_name: Optional[str] = None,
+    db_host: Optional[str] = None,
+    db_port: Optional[str] = None,
+    db_user: Optional[str] = None,
+    db_password: Optional[str] = None,
+) -> str:
+    """Return PostgreSQL connection string in psycopg conninfo format."""
+    resolved_db_name = db_name or settings.DB_NAME
+    resolved_db_host = db_host or settings.DB_HOST
+    resolved_db_port = db_port or settings.DB_PORT
+    resolved_db_user = db_user or settings.DB_USER
+    resolved_db_password = db_password or settings.DB_PASSWORD
+    return (
+        f"dbname={resolved_db_name} host={resolved_db_host} port={resolved_db_port} "
+        f"user={resolved_db_user} password={resolved_db_password}"
+    )
+
+
+def get_pg_config() -> dict:
+    """Return PostgreSQL configuration except password."""
+    return {
+        "name": settings.DB_NAME,
+        "host": settings.DB_HOST,
+        "port": settings.DB_PORT,
+        "user": settings.DB_USER,
+    }
+
+
+def get_pg_password() -> str:
+    """Return PostgreSQL password (use with care)."""
+    return settings.DB_PASSWORD
+
+
+def get_timescaledb_pgconn_string(
+    db_name: Optional[str] = None,
+    db_host: Optional[str] = None,
+    db_port: Optional[str] = None,
+    db_user: Optional[str] = None,
+    db_password: Optional[str] = None,
+) -> str:
+    """Return TimescaleDB connection string in psycopg conninfo format."""
+    resolved_db_name = db_name or settings.TIMESCALEDB_DB_NAME
+    resolved_db_host = db_host or settings.TIMESCALEDB_DB_HOST
+    resolved_db_port = db_port or settings.TIMESCALEDB_DB_PORT
+    resolved_db_user = db_user or settings.TIMESCALEDB_DB_USER
+    resolved_db_password = db_password or settings.TIMESCALEDB_DB_PASSWORD
+    return (
+        f"dbname={resolved_db_name} host={resolved_db_host} port={resolved_db_port} "
+        f"user={resolved_db_user} password={resolved_db_password}"
+    )
+
+
+def get_timescaledb_pg_config() -> dict:
+    """Return TimescaleDB configuration except password."""
+    return {
+        "name": settings.TIMESCALEDB_DB_NAME,
+        "host": settings.TIMESCALEDB_DB_HOST,
+        "port": settings.TIMESCALEDB_DB_PORT,
+        "user": settings.TIMESCALEDB_DB_USER,
+    }
+
+
+def get_timescaledb_pg_password() -> str:
+    """Return TimescaleDB password (use with care)."""
+    return settings.TIMESCALEDB_DB_PASSWORD

app/core/encryption.py

@@ -1,9 +1,122 @@
-# Placeholder for encryption logic
+from cryptography.fernet import Fernet
+from typing import Optional
+import base64
+import os
+
+from app.core.config import settings
+
+
 class Encryptor:
+    """
+    使用 Fernet (对称加密) 实现数据加密/解密
+    适用于加密敏感配置、用户数据等
+    """
+
+    def __init__(self, key: Optional[bytes] = None):
+        """
+        初始化加密器
+
+        Args:
+            key: 加密密钥，如果为 None 则从环境变量读取
+        """
+        if key is None:
+            key_str = os.getenv("DATABASE_ENCRYPTION_KEY") or settings.DATABASE_ENCRYPTION_KEY
+            if not key_str:
+                raise ValueError(
+                    "DATABASE_ENCRYPTION_KEY not found in environment variables or .env. "
+                    "Generate one using: Encryptor.generate_key()"
+                )
+            key = key_str.encode()
+
+        self.fernet = Fernet(key)
+
     def encrypt(self, data: str) -> str:
-        return data # Implement actual encryption
+        """
+        加密字符串
+
+        Args:
+            data: 待加密的明文字符串
+
+        Returns:
+            Base64 编码的加密字符串
+        """
+        if not data:
+            return data
+
+        encrypted_bytes = self.fernet.encrypt(data.encode())
+        return encrypted_bytes.decode()
 
     def decrypt(self, data: str) -> str:
-        return data # Implement actual decryption
+        """
+        解密字符串
 
-encryptor = Encryptor()
+        Args:
+            data: Base64 编码的加密字符串
+
+        Returns:
+            解密后的明文字符串
+        """
+        if not data:
+            return data
+
+        decrypted_bytes = self.fernet.decrypt(data.encode())
+        return decrypted_bytes.decode()
+
+    @staticmethod
+    def generate_key() -> str:
+        """
+        生成新的 Fernet 加密密钥
+
+        Returns:
+            Base64 编码的密钥字符串
+        """
+        key = Fernet.generate_key()
+        return key.decode()
+
+
+# 全局加密器实例（懒加载）
+_encryptor: Optional[Encryptor] = None
+_database_encryptor: Optional[Encryptor] = None
+
+
+def is_encryption_configured() -> bool:
+    return is_database_encryption_configured()
+
+
+def is_database_encryption_configured() -> bool:
+    return bool(
+        os.getenv("DATABASE_ENCRYPTION_KEY")
+        or settings.DATABASE_ENCRYPTION_KEY
+    )
+
+
+def get_encryptor() -> Encryptor:
+    """获取全局加密器实例"""
+    global _encryptor
+    if _encryptor is None:
+        _encryptor = Encryptor()
+    return _encryptor
+
+
+def get_database_encryptor() -> Encryptor:
+    """获取 project DB DSN 专用加密器实例"""
+    global _database_encryptor
+    if _database_encryptor is None:
+        key_str = (
+            os.getenv("DATABASE_ENCRYPTION_KEY")
+            or settings.DATABASE_ENCRYPTION_KEY
+        )
+        if not key_str:
+            raise ValueError(
+                "DATABASE_ENCRYPTION_KEY not found in environment variables or .env. "
+                "Generate one using: Encryptor.generate_key()"
+            )
+        _database_encryptor = Encryptor(key=key_str.encode())
+    return _database_encryptor
+
+
+# 向后兼容（延迟加载）
+def __getattr__(name):
+    if name == "encryptor":
+        return get_encryptor()
+    raise AttributeError(f"module '{__name__}' has no attribute '{name}'")

app/core/security.py

@@ -1,23 +0,0 @@
-from datetime import datetime, timedelta
-from typing import Optional, Union, Any
-from jose import jwt
-from passlib.context import CryptContext
-from app.core.config import settings
-
-pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
-
-def create_access_token(subject: Union[str, Any], expires_delta: Optional[timedelta] = None) -> str:
-    if expires_delta:
-        expire = datetime.utcnow() + expires_delta
-    else:
-        expire = datetime.utcnow() + timedelta(minutes=settings.ACCESS_TOKEN_EXPIRE_MINUTES)
-    
-    to_encode = {"exp": expire, "sub": str(subject)}
-    encoded_jwt = jwt.encode(to_encode, settings.SECRET_KEY, algorithm=settings.ALGORITHM)
-    return encoded_jwt
-
-def verify_password(plain_password: str, hashed_password: str) -> bool:
-    return pwd_context.verify(plain_password, hashed_password)
-
-def get_password_hash(password: str) -> str:
-    return pwd_context.hash(password)

app/crypto/__init__.py

@@ -0,0 +1,12 @@
+"""
+This module is reserved for future implementation of advanced cryptographic operations.
+
+Current Fernet encryption helpers are implemented in `app.core.encryption`.
+Login credentials are owned by Keycloak; this backend does not hash or store
+local passwords.
+Future expansion may include:
+- Asymmetric encryption (RSA/ECC) for secure communication
+- Key management and rotation services
+- Integration with Hardware Security Modules (HSM)
+- Digital signatures for data integrity verification
+"""

app/domain/schemas/admin_metadata.py

@@ -0,0 +1,134 @@
+from datetime import datetime
+from typing import Literal
+from uuid import UUID
+
+from pydantic import BaseModel, ConfigDict, Field, model_validator
+
+
+BusinessRole = Literal["admin", "user", "operator", "viewer"]
+ProjectRole = Literal["owner", "admin", "member", "viewer"]
+ProjectStatus = Literal["active", "inactive", "archived"]
+ProjectDbRole = Literal["biz_data", "iot_data"]
+
+
+class MetadataUserSyncRequest(BaseModel):
+    keycloak_id: UUID
+    username: str = Field(..., min_length=1, max_length=50)
+    email: str = Field(..., min_length=1, max_length=100)
+    role: BusinessRole = "user"
+    is_active: bool = True
+
+
+class MetadataUsersBatchSyncRequest(BaseModel):
+    users: list[MetadataUserSyncRequest] = Field(..., min_length=1, max_length=500)
+
+
+class MetadataUserUpdateRequest(BaseModel):
+    role: BusinessRole | None = None
+    is_active: bool | None = None
+
+
+class MetadataUserResponse(BaseModel):
+    id: UUID
+    keycloak_id: UUID
+    username: str
+    email: str
+    role: str
+    is_active: bool
+    is_superuser: bool
+    created_at: datetime
+    updated_at: datetime
+    last_login_at: datetime | None = None
+
+    model_config = ConfigDict(from_attributes=True)
+
+
+class MetadataUserSyncResult(BaseModel):
+    keycloak_id: UUID
+    user: MetadataUserResponse | None = None
+    success: bool
+    error: str | None = None
+
+
+class ProjectMemberCreateRequest(BaseModel):
+    user_id: UUID
+    project_role: ProjectRole = "viewer"
+
+
+class ProjectMemberUpdateRequest(BaseModel):
+    project_role: ProjectRole
+
+
+class ProjectMemberResponse(BaseModel):
+    id: UUID
+    user_id: UUID
+    project_id: UUID
+    project_role: str
+    username: str
+    email: str
+    is_active: bool
+
+
+class AdminProjectCreateRequest(BaseModel):
+    name: str = Field(..., min_length=1, max_length=100)
+    code: str = Field(..., min_length=1, max_length=50)
+    description: str | None = None
+    gs_workspace: str = Field(..., min_length=1, max_length=100)
+    map_extent: dict | None = None
+    status: ProjectStatus = "active"
+
+
+class AdminProjectUpdateRequest(BaseModel):
+    name: str | None = Field(default=None, min_length=1, max_length=100)
+    code: str | None = Field(default=None, min_length=1, max_length=50)
+    description: str | None = None
+    gs_workspace: str | None = Field(default=None, min_length=1, max_length=100)
+    map_extent: dict | None = None
+    status: ProjectStatus | None = None
+
+
+class AdminProjectResponse(BaseModel):
+    project_id: UUID
+    name: str
+    code: str
+    description: str | None = None
+    gs_workspace: str
+    map_extent: dict | None = None
+    status: str
+    created_at: datetime
+    updated_at: datetime
+
+
+class ProjectDatabaseUpsertRequest(BaseModel):
+    db_role: ProjectDbRole
+    dsn: str | None = Field(default=None, min_length=1)
+    pool_min_size: int = Field(default=2, ge=1)
+    pool_max_size: int = Field(default=10, ge=1)
+
+    @model_validator(mode="after")
+    def validate_pool_bounds(self):
+        if self.pool_max_size < self.pool_min_size:
+            raise ValueError("pool_max_size must be greater than or equal to pool_min_size")
+        return self
+
+
+class ProjectDatabaseResponse(BaseModel):
+    id: UUID
+    project_id: UUID
+    db_role: str
+    db_type: str
+    pool_min_size: int
+    pool_max_size: int
+    has_dsn: bool
+
+
+class ProjectDatabaseHealthRequest(BaseModel):
+    dsn: str | None = Field(default=None, min_length=1)
+
+
+class ProjectDatabaseHealthResponse(BaseModel):
+    project_id: UUID
+    db_role: str
+    db_type: str
+    ok: bool
+    detail: str

app/domain/schemas/audit.py

@@ -0,0 +1,45 @@
+from datetime import datetime
+from typing import Optional
+from uuid import UUID
+from pydantic import BaseModel, ConfigDict, Field
+
+class AuditLogCreate(BaseModel):
+    """创建审计日志"""
+    user_id: Optional[UUID] = None
+    project_id: Optional[UUID] = None
+    action: str
+    resource_type: Optional[str] = None
+    resource_id: Optional[str] = None
+    ip_address: Optional[str] = None
+    request_method: Optional[str] = None
+    request_path: Optional[str] = None
+    request_data: Optional[dict] = None
+    response_status: Optional[int] = None
+
+class AuditLogResponse(BaseModel):
+    """审计日志响应"""
+    id: UUID
+    user_id: Optional[UUID]
+    project_id: Optional[UUID]
+    action: str
+    resource_type: Optional[str]
+    resource_id: Optional[str]
+    ip_address: Optional[str]
+    request_method: Optional[str]
+    request_path: Optional[str]
+    request_data: Optional[dict]
+    response_status: Optional[int]
+    timestamp: datetime
+    
+    model_config = ConfigDict(from_attributes=True)
+
+class AuditLogQuery(BaseModel):
+    """审计日志查询参数"""
+    user_id: Optional[UUID] = None
+    project_id: Optional[UUID] = None
+    action: Optional[str] = None
+    resource_type: Optional[str] = None
+    start_time: Optional[datetime] = None
+    end_time: Optional[datetime] = None
+    skip: int = Field(default=0, ge=0)
+    limit: int = Field(default=100, ge=1, le=1000)

app/domain/schemas/metadata.py

@@ -0,0 +1,25 @@
+from typing import Optional
+from uuid import UUID
+
+from pydantic import BaseModel
+
+
+class ProjectMetaResponse(BaseModel):
+    project_id: UUID
+    name: str
+    code: str
+    description: Optional[str] = None
+    gs_workspace: str
+    map_extent: Optional[dict] = None
+    status: str
+    project_role: str
+
+
+class ProjectSummaryResponse(BaseModel):
+    project_id: UUID
+    name: str
+    code: str
+    description: Optional[str] = None
+    gs_workspace: str
+    status: str
+    project_role: str

app/infra/audit/middleware.py

@@ -0,0 +1,279 @@
+"""
+审计日志中间件
+
+自动记录关键HTTP请求到审计日志
+"""
+
+import time
+import json
+from uuid import UUID
+from typing import Callable
+from fastapi import Request, Response
+from starlette.middleware.base import BaseHTTPMiddleware
+from app.core.audit import log_audit_event, AuditAction
+import logging
+from jose import JWTError, jwt
+
+from app.core.config import settings
+from app.infra.db.metadb.database import SessionLocal
+from app.infra.db.metadb.repositories.metadata_repository import MetadataRepository
+
+logger = logging.getLogger(__name__)
+
+
+class AuditMiddleware(BaseHTTPMiddleware):
+    """
+    审计中间件
+
+    自动记录以下操作：
+    - 所有 POST/PUT/DELETE 请求
+    - 登录/登出
+    - 关键资源访问
+    """
+
+    # 需要审计的路径前缀
+    AUDIT_PATHS = [
+        # "/api/v1/projects/",
+        # "/api/v1/networks/",
+    ]
+
+    # [新增] 需要审计的 API Tags (在 Router 或 api 函数中定义 tags=["Audit"])
+    AUDIT_TAGS = [
+        "Audit",
+        "Users",
+        "Project",
+        "Network General",
+        "Junctions",
+        "Pipes",
+        "Reservoirs",
+        "Tanks",
+        "Pumps",
+        "Valves",
+    ]
+
+    # 需要审计的HTTP方法
+    AUDIT_METHODS = ["POST", "PUT", "DELETE", "PATCH"]
+    EXCLUDED_PATHS = {
+        "/api/v1/meta/projects",
+        "/meta/projects",
+        "/api/v1/openproject/",
+        "/openproject/",
+    }
+    EXCLUDED_PATH_PREFIXES = (
+    )
+
+    async def dispatch(self, request: Request, call_next: Callable) -> Response:
+        # 提取开始时间
+        start_time = time.time()
+
+        # 流式 Copilot 请求前置排除，避免读取/改写 body 影响 SSE 生命周期
+        if self._is_excluded_path(request.url.path):
+            response = await call_next(request)
+            process_time = time.time() - start_time
+            response.headers["X-Process-Time"] = str(process_time)
+            return response
+
+        # 1. 预判是否需要读取Body (针对写操作)
+        # 注意：我们暂时移除早期的 return，因为需要等待路由匹配后才能检查 Tag
+        should_capture_body = request.method in ["POST", "PUT", "PATCH"]
+
+        request_data = None
+        if should_capture_body:
+            try:
+                # 注意：读取 body 后需要重新设置，避免影响后续处理
+                original_receive = request._receive
+                body = await request.body()
+                if body:
+                    request_data = json.loads(body.decode())
+
+                # 重新构造请求以供后续使用：仅回放一次，后续回落原始 receive
+                body_sent = False
+
+                async def receive():
+                    nonlocal body_sent
+                    if not body_sent:
+                        body_sent = True
+                        return {
+                            "type": "http.request",
+                            "body": body,
+                            "more_body": False,
+                        }
+                    return await original_receive()
+
+                request._receive = receive
+            except Exception as e:
+                logger.warning(f"Failed to read request body for audit: {e}")
+
+        # 2. 执行请求 (FastAPI在此过程中进行路由匹配)
+        response = await call_next(request)
+
+        # 3. 决定是否审计
+        if self._is_excluded_path(request.url.path):
+            process_time = time.time() - start_time
+            response.headers["X-Process-Time"] = str(process_time)
+            return response
+
+        # 检查方法
+        is_audit_method = request.method in self.AUDIT_METHODS
+        # 检查路径
+        is_audit_path = any(
+            request.url.path.startswith(path) for path in self.AUDIT_PATHS
+        )
+        # [新增] 检查 Tags (从 request.scope 中获取匹配的路由信息)
+        is_audit_tag = False
+        route = request.scope.get("route")
+        if route and hasattr(route, "tags"):
+            is_audit_tag = any(tag in self.AUDIT_TAGS for tag in route.tags)
+
+        should_audit = is_audit_method or is_audit_path or is_audit_tag
+
+        if not should_audit:
+            # 即便不审计，也要处理响应头中的时间（保持原有逻辑一致性）
+            process_time = time.time() - start_time
+            response.headers["X-Process-Time"] = str(process_time)
+            return response
+
+        # 4. 提取审计所需信息
+        user_id = await self._resolve_user_id(request)
+        project_id = self._resolve_project_id(request)
+
+        # 获取客户端信息
+        ip_address = request.client.host if request.client else None
+
+        # 确定操作类型
+        action = self._determine_action(request)
+        resource_type, resource_id = self._extract_resource_info(request)
+
+        # 记录审计日志
+        try:
+            await log_audit_event(
+                action=action,
+                user_id=user_id,
+                project_id=project_id,
+                resource_type=resource_type,
+                resource_id=resource_id,
+                ip_address=ip_address,
+                request_method=request.method,
+                request_path=str(request.url.path),
+                request_data=request_data,
+                response_status=response.status_code,
+            )
+        except Exception as e:
+            # 审计失败不应影响响应
+            logger.error(f"Failed to log audit event: {e}", exc_info=True)
+
+        # 添加处理时间到响应头
+        process_time = time.time() - start_time
+        response.headers["X-Process-Time"] = str(process_time)
+
+        return response
+
+    def _is_excluded_path(self, path: str) -> bool:
+        if path in self.EXCLUDED_PATHS:
+            return True
+        return any(path.startswith(prefix) for prefix in self.EXCLUDED_PATH_PREFIXES)
+
+    def _resolve_project_id(self, request: Request) -> UUID | None:
+        project_header = request.headers.get("X-Project-Id")
+        if not project_header:
+            return None
+        try:
+            return UUID(project_header)
+        except ValueError:
+            return None
+
+    async def _resolve_user_id(self, request: Request) -> UUID | None:
+        auth_header = request.headers.get("authorization")
+        if not auth_header or not auth_header.lower().startswith("bearer "):
+            return None
+        token = auth_header.split(" ", 1)[1].strip()
+        if not token:
+            return None
+        sub = None
+        try:
+            if not settings.KEYCLOAK_PUBLIC_KEY:
+                return None
+
+            key = settings.KEYCLOAK_PUBLIC_KEY.replace("\\n", "\n")
+            payload = jwt.decode(
+                token,
+                key,
+                algorithms=[settings.KEYCLOAK_ALGORITHM],
+                audience=settings.KEYCLOAK_AUDIENCE or None,
+            )
+            sub = payload.get("sub")
+            if not sub:
+                return None
+        except JWTError:
+            return None
+
+        async with SessionLocal() as session:
+            repo = MetadataRepository(session)
+            try:
+                keycloak_id = UUID(sub)
+                user = await repo.get_user_by_keycloak_id(keycloak_id)
+            except ValueError:
+                return None
+            if user and user.is_active:
+                return user.id
+        return None
+
+    def _determine_action(self, request: Request) -> str:
+        """根据请求路径和方法确定操作类型"""
+        path = request.url.path.lower()
+        method = request.method
+
+        # 认证相关
+        if "login" in path:
+            return AuditAction.LOGIN
+        elif "logout" in path:
+            return AuditAction.LOGOUT
+        elif "register" in path:
+            return AuditAction.REGISTER
+
+        # CRUD 操作
+        if method == "POST":
+            return AuditAction.CREATE
+        elif method == "PUT" or method == "PATCH":
+            return AuditAction.UPDATE
+        elif method == "DELETE":
+            return AuditAction.DELETE
+        elif method == "GET":
+            return AuditAction.READ
+
+        return f"{method}_REQUEST"
+
+    def _extract_resource_info(self, request: Request) -> tuple:
+        """从请求路径提取资源类型和ID"""
+        path_parts = request.url.path.strip("/").split("/")
+
+        resource_type = None
+        resource_id = None
+
+        # 尝试从路径中提取资源信息
+        # 例如: /api/v1/users/123 -> resource_type=user, resource_id=123
+        if len(path_parts) >= 4:
+            resource_type = path_parts[3].rstrip("s")  # 移除复数s
+
+            if len(path_parts) >= 5 and path_parts[4]:
+                resource_id = path_parts[4]
+
+        # 无路径ID时，尝试从查询参数提取业务ID
+        if not resource_id:
+            for key in (
+                "id",
+                "resource_id",
+                "device_id",
+                "device_ids",
+                "element_id",
+                "user_id",
+                "project_id",
+                "network",
+                "name",
+            ):
+                value = request.query_params.get(key)
+                if value:
+                    resource_id = value
+                    break
+
+        return resource_type, resource_id