fix(api): export contract with CI dependencies

The legacy online simulation path still exported internal ACTIVE states as STATUS keywords, which EPANET 2 rejects. Preserve numeric settings and EPANET 3 output while passing the format through both exporters. Surface the original engine error inside the model transaction so failed runs roll back before result storage.

Regression guard: six failures reproduced on the deployed baseline, including native input validation. All 34 focused export, service and endpoint checks pass after the fix; the real project also returns native node and link results.

.dockerignore

@@ -0,0 +1,22 @@
+.git
+.github
+.gitea
+__pycache__/
+.pytest_cache/
+.mypy_cache/
+.venv/
+venv/
+build/
+dist/
+package/
+temp/
+data/
+db_inp/
+inp/
+.env
+.env.*
+logs/
+coverage/
+*.pyc
+*.dump
+app/algorithms/pipe_health_prediction/model/my_survival_forest_model_quxi.joblib

.env.example

@@ -0,0 +1,83 @@
+# TJWaterServerBinary 环境变量模板
+# 复制为 .env 后按启用的功能填写；不要提交 .env 或任何真实凭据。
+# 生产配置由部署主机的受控 backend.env 注入，不随镜像或 CI/CD 同步本地 .env。
+# 进程环境变量优先；修改 Compose env_file 后需重新创建容器。
+
+# 基础运行
+PROJECT_NAME="TJWater Server"
+ENVIRONMENT="production"
+API_V1_STR="/api/v1"
+NETWORK_NAME="tjwater"
+
+# Fernet 格式；用于加密项目数据库 DSN、GeoServer 管理密码等敏感配置。
+# 生成方式：python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())"
+DATABASE_ENCRYPTION_KEY="replace-with-generated-fernet-key"
+
+# 业务数据库（PostgreSQL）
+DB_NAME="tjwater"
+DB_HOST="localhost"
+DB_PORT="5432"
+DB_USER="tjwater"
+DB_PASSWORD="password"
+
+# 时序数据库（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
+METADATA_DB_NAME="system_hub"
+METADATA_DB_HOST="localhost"
+METADATA_DB_PORT="5432"
+METADATA_DB_USER="tjwater"
+METADATA_DB_PASSWORD="password"
+METADATA_DB_POOL_SIZE=5
+METADATA_DB_MAX_OVERFLOW=10
+
+# 项目数据库连接池、缓存与模板
+PROJECT_PG_CACHE_SIZE=16
+PROJECT_TS_CACHE_SIZE=16
+PROJECT_PG_POOL_MIN_SIZE=0
+PROJECT_PG_POOL_SIZE=4
+PROJECT_PG_MAX_OVERFLOW=2
+PROJECT_TS_POOL_MIN_SIZE=0
+PROJECT_TS_POOL_MAX_SIZE=4
+WNDB_SCHEMA_TEMPLATE_DB_NAME="tjwater_v2_schema_template"
+TIMESCALEDB_SCHEMA_TEMPLATE_DB_NAME="tjwater_v2_timescale_template"
+WNDB_TEMP_DB_MAX_COUNT=8
+
+# GeoServer 项目供应
+GEOSERVER_URL="http://localhost:8080/geoserver"
+GEOSERVER_USERNAME="admin"
+GEOSERVER_PASSWORD="password"
+# 留空时分别复用 DB_HOST、DB_PORT、DB_USER、DB_PASSWORD。
+# GeoServer 在容器内运行时，GEOSERVER_DB_HOST 通常应填写数据库服务名。
+GEOSERVER_DB_HOST=""
+GEOSERVER_DB_PORT=""
+GEOSERVER_DB_USER=""
+GEOSERVER_DB_PASSWORD=""
+# 客户端缓存时间，单位为秒；默认 30 天。
+GEOSERVER_CLIENT_CACHE_SECONDS=2592000
+
+# Keycloak JWT 校验
+# 未启用认证时可留空；生产环境应注入完整公钥和预期 audience。
+KEYCLOAK_PUBLIC_KEY=""
+KEYCLOAK_ALGORITHM="RS256"
+KEYCLOAK_AUDIENCE="account"
+KEYCLOAK_ACCESS_TOKEN_MAX_AGE_SECONDS=900
+
+# 浏览器跨域访问（主 API）
+# 必须填写明确来源（协议、域名或 IP、端口），英文逗号分隔；不允许 *。
+CORS_ALLOWED_ORIGINS=http://127.0.0.1:5173,http://localhost:5173,http://127.0.0.1:15173,http://localhost:15173
+
+# Bocha 网页搜索（旧版仍保留；未启用时令牌留空）
+BOCHA_API_KEY=""
+BOCHA_WEB_SEARCH_URL="https://api.bochaai.com/v1/web-search"
+BOCHA_WEB_SEARCH_TIMEOUT_SECONDS=30
+
+# 天地图地理编码（旧版使用 TIANDITU_GEOCODER_TOKEN）
+TIANDITU_GEOCODER_TOKEN=""
+TIANDITU_GEOCODER_URL="https://api.tianditu.gov.cn/geocoder"
+TIANDITU_GEOCODER_TIMEOUT_SECONDS=30

.gitea/workflows/package.yml

@@ -0,0 +1,22 @@
+name: Server CI/CD v2
+
+on:
+  push:
+    tags:
+      - "v*"
+  workflow_dispatch: {}
+
+jobs:
+  build-test-publish-and-deploy:
+    uses: OrgTJWater/ci-templates/.gitea/workflows/container-cd.yml@68c8a9855391baa31d31523674f5812cd24ec604
+    with:
+      image_name: gitea.waternetwork.cn/orgtjwater/tjwater-backend
+      dockerfile: Dockerfile
+      build_context: .
+      test_target: test
+      deploy_service: backend
+      deploy_host: 192.168.1.114
+    secrets:
+      REGISTRY_USERNAME: ${{ secrets.REGISTRY_USERNAME }}
+      REGISTRY_PASSWORD: ${{ secrets.REGISTRY_PASSWORD }}
+      DEV_DEPLOY_SSH_KEY: ${{ secrets.DEV_DEPLOY_SSH_KEY }}

.gitignore

@@ -1,7 +1,10 @@
-*.pyc
-.env
 db_inp/
 temp/
 data/
+build/
+*.pyc
+.env
 *.dump
-api_ex/model/my_survival_forest_model_quxi.joblib
+.vscode/
+app/algorithms/pipe_health_prediction/model/my_survival_forest_model_quxi.joblib
+/inp/

AGENTS.md

@@ -0,0 +1,38 @@
+# Repository Guidelines
+
+## Project Structure & Module Organization
+
+This repository contains the TJWater Python backend. Main application code lives in `app/`: API routes under `app/api`, authentication in `app/auth`, configuration in `app/core`, database and repository code in `app/infra`, domain models/schemas in `app/domain`, and business logic in `app/services` and `app/algorithms`.
+
+Tests are under `tests/`, split into `tests/unit`, `tests/api`, and `tests/auth`. SQL and sample assets are stored in `resources/`; deployment files are in `Dockerfile`, `.gitea/workflows/package.yml`, and `infra/docker/docker-compose.yml`. Local data directories such as `db_inp/`, `temp/`, `data/`, and `.env` are ignored and should not be committed.
+
+## Build, Test, and Development Commands
+
+Use the existing conda environment when available:
+
+```bash
+conda run -n server python -m pytest tests/unit tests/auth -q
+conda run -n server uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
+docker build -t tjwater-server:local .
+docker compose -f infra/docker/docker-compose.yml config
+```
+
+`pytest` runs backend tests. `uvicorn` starts the FastAPI app locally. `docker build` verifies the container image. `docker compose config` validates compose syntax and variable expansion.
+
+## Coding Style & Naming Conventions
+
+Use Python 3.12, four-space indentation, type hints for new public functions, and explicit imports. Keep API endpoint modules grouped by domain under `app/api/v1/endpoints`. Use `snake_case` for files, functions, and variables; `PascalCase` for classes and Pydantic models. Prefer existing repository/service patterns in `app/infra/db` and `app/services` over introducing new abstractions.
+
+## Testing Guidelines
+
+The project uses `pytest`. Name test files `test_*.py` and test functions `test_*`. Keep unit tests isolated with fakes or monkeypatching from `tests/conftest.py`. Some existing tests depend on local data outside the repository; avoid adding new tests that require untracked files. For API changes, add or update tests in `tests/api`.
+
+## Commit & Pull Request Guidelines
+
+History uses a mix of Conventional Commit prefixes and concise Chinese messages, for example `feat(api): add Tianditu geocoding` or `fix(auth): validate project context`. Prefer `feat(scope): ...`, `fix(scope): ...`, or a clear Chinese summary.
+
+Pull requests should describe the behavior change, list verification commands, mention configuration or migration impacts, and link related issues. Include API examples or screenshots only when they clarify user-facing behavior.
+
+## Security & Configuration Tips
+
+Do not commit `.env`, database dumps, generated caches, or local project data. Use `.env.example` as the configuration template. CI/CD only uses Gitea repository secrets `REGISTRY_USERNAME`, `REGISTRY_PASSWORD`, and `DEV_DEPLOY_SSH_KEY`; production application settings are injected on the Dev host.

AUTHENTICATION_AND_USER_MANAGEMENT.md

@@ -0,0 +1,123 @@
+# 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.
+
+## Fixed Project RBAC
+
+Project roles are stored directly in
+`user_project_membership.project_role`; there is no separate role table or
+user-defined permission editor in this delivery.
+
+| Role | Main access |
+| --- | --- |
+| `modeler` | Model upload/import, simulation, burst, risk, and optimization analysis |
+| `dispatcher` | SCADA cleaning, simulation and burst analysis |
+| `auditor` | Project read access and project-scoped audit logs |
+| `viewer` | WebGIS and read-only risk results |
+
+Legacy `owner`, `admin`, and `member` values remain supported for existing
+records. The backend is the authorization boundary; the frontend uses
+`GET /api/v1/access/context` only to hide unavailable menus and guard routes.
+System admins receive environment, membership, and global-audit permissions,
+but still need a project membership for project business APIs.
+
+## Login Snapshot Refresh
+
+Every authenticated metadata-user resolution validates the Keycloak access token
+and reads `sub`, `preferred_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` for active users.
+
+System administrators explicitly synchronize the Keycloak user directory from
+the system-management page. New identities are stored with role `user` and
+`is_active = false`; existing identities keep their TJWater role, active state,
+superuser flag, and project memberships. This makes pending accounts visible
+without requiring the user to log in and without granting access automatically.
+The administrator's Keycloak account must have the `realm-management`
+`query-users` and `view-users` client roles. The backend forwards that access
+token only to the same token issuer's Keycloak Admin API; no management
+credential is exposed to the browser.
+
+## 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`
+3. `resources/sql/006_metadata_rbac_roles.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. `006` extends existing membership constraints with
+the fixed delivery roles.
+
+## Frontend System Management
+
+`/system-admin` is shown only when `GET /api/v1/access/context` returns
+`environment.manage`. The page lets admins maintain metadata users, project
+members, projects, project database routing for `biz_data` and `iot_data`, and
+connection health checks. This replaces direct SQL editing for normal project
+onboarding.
+
+Hydraulic model authoring is outside the Web application. Models are prepared
+in the desktop modeling client and uploaded/imported by an authorized modeler;
+the system administrator configures the project environment and database
+routing.

BACKEND_NAMING_AUDIT.md

@@ -0,0 +1,77 @@
+# Backend Naming Audit
+
+DOC-003 audit for the internal `TJWaterServerBinary` 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:
+
+- Metadata/admin: `/api/v1/admin/projects`, `/api/v1/admin/users/sync`, `/api/v1/admin/projects/{project_id}/members`
+- Audit: `/api/v1/audit/logs`, `/api/v1/audit/logs/count`
+- Agent auth: `/api/v1/agent/auth/context`
+- 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 `{project_id}`, `{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/synchronization utilities: `takesnapshotforoperation`, `syncwithserver`
+- 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 `project_id`, `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`
+
+## 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.

Dockerfile

@@ -0,0 +1,42 @@
+FROM condaforge/miniforge3:latest AS 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
+
+# 本地数据目录和环境变量在运行时通过 Compose 挂载或注入，
+# 不应进入镜像构建上下文。
+COPY app ./app
+COPY contracts ./contracts
+COPY infra ./infra
+COPY resources ./resources
+RUN python -c "from pathlib import Path; from zipfile import ZipFile; model_dir = Path('app/algorithms/pipe_health_prediction/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/pipe_health_prediction/model/my_survival_forest_model_quxi.zip
+RUN mkdir -p db_inp temp data inp
+
+# 设置 PYTHONPATH 以便 uvicorn 找到 app 模块
+ENV PYTHONPATH=/app
+
+FROM base AS test
+COPY scripts ./scripts
+COPY tests ./tests
+RUN python -m compileall -q app && \
+    python -c "import app.main" && \
+    python scripts/check_openapi.py && \
+    pytest -q tests
+
+FROM base AS runner
+
+EXPOSE 8000
+
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000", "--workers", "4"]

README.md

@@ -0,0 +1,124 @@
+# TJWaterServerBinary 内部后端
+
+`TJWaterServerBinary` 是 TJWater 内部版 Python 后端，基于 FastAPI 提供认证、项目、管网、模拟、爆管、漏损、SCADA 和地图服务集成能力。该仓库用于内部开发和完整功能维护。
+
+## 技术栈
+
+- Python 3.12
+- FastAPI / Uvicorn
+- Pydantic / SQLAlchemy / psycopg
+- 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、EPANET 和外部集成
+app/services/         业务服务编排
+app/algorithms/       管网算法、模拟、爆管、漏损、清洗和健康分析
+app/native/           本地管网数据读写与转换
+tests/                后端测试
+resources/            SQL、模板和示例资源
+infra/docker/         Docker Compose 编排
+```
+
+## 本地开发
+
+推荐使用已有 conda 环境：
+
+```bash
+conda run -n server python -m pytest tests/unit tests/auth -q
+conda run -n server uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
+```
+
+如需要进入环境：
+
+```bash
+conda activate server
+```
+
+## CORS 配置
+
+后端通过 `.env` 或进程环境变量中的 `CORS_ALLOWED_ORIGINS` 设置允许的浏览器页面来源，
+进程环境变量优先。必须使用英文逗号分隔的明确来源，不能填写 `*`，例如：
+
+```dotenv
+CORS_ALLOWED_ORIGINS=https://demo.waternetwork.cn,http://192.168.1.133,https://192.168.1.133,http://192.168.1.110,https://192.168.1.110
+```
+
+未设置时默认只允许本机 `5173`、`15173` 开发来源。来源需包含协议、域名或 IP、端口，
+不带路径；非默认端口须逐项列出。CORS 匹配浏览器页面来源，不是 Nginx 连接 IP 或域名解析结果。
+保留跨域凭据支持及 `Authorization`、`X-Project-Id` 请求头，接口认证和权限校验继续生效。
+
+修改后需重启后端进程。容器部署需同步更新宿主机的受控 `backend.env`；
+使用 Compose `env_file` 注入时，需要重新创建容器才能读取新值，CI/CD 不会自动同步本地 `.env`。
+
+## 常用命令
+
+```bash
+conda run -n server python -m pytest tests -q
+conda run -n server python scripts/run_server.py
+docker build -t tjwater-server:local .
+docker compose -f infra/docker/docker-compose.yml config
+```
+
+- `pytest`：运行自动化测试。
+- `scripts/run_server.py`：使用项目脚本启动服务。
+- `docker build`：构建后端镜像。
+- `docker compose config`：检查 compose 配置和变量展开。
+
+## 开发规范
+
+- Python 文件、函数、变量、Pydantic 字段、JSON body 字段和 query 参数使用 `snake_case`。
+- Python 类和 Pydantic 模型使用 `PascalCase`。
+- 新 HTTP 路径使用 `kebab-case`，例如 `/api/v1/pressure-status/analyze`。
+- 优先复用现有 FastAPI/service/repository 边界。
+- 不要把临时数据、数据库 dump、日志或本地运行产物纳入提交。
+
+## 项目数据库路由
+
+项目级 REST 请求通过 `X-Project-Id` 解析元数据中的数据库配置：
+
+- `biz_data` DSN 用于管网业务数据。每个物理业务库使用同名 `_template` 数据库，通过逻辑订阅只同步 `network` schema；模拟临时库从该项目模板克隆。`WNDB_SCHEMA_TEMPLATE_DB_NAME`（当前为 `tjwater_v2_schema_template`）仅用于创建空业务库和 INP 导入暂存库。
+- `iot_data` DSN 用于 TimescaleDB，始终使用元数据配置的完整 DSN，不再从项目代码推导数据库名。
+- 元数据、业务库和 TimescaleDB 可以部署在同一主机，也可以分别部署。
+
+完整新建供水项目使用 `POST /api/v1/admin/project-provisions`，以
+`multipart/form-data` 同时提交 `name`、小写 `code`、可选的
+`description`、`gs_workspace`、`map_zoom` 和 INP `file`。工作流会按顺序完成：
+
+1. EPANET 校验 INP，并从空结构模板创建业务库、导入模型；
+2. 创建同名 `_template`，复制 32 张 `network` 表并建立逻辑订阅；
+3. 从 `TIMESCALEDB_SCHEMA_TEMPLATE_DB_NAME` 创建空时序库；
+4. 创建 GeoServer 工作空间、PostGIS 数据存储和 7 个 GIS 图层，将客户端缓存设为 `GEOSERVER_CLIENT_CACHE_SECONDS`；
+5. 最后在一个元数据事务中写入项目、两条加密数据库路由和创建者成员关系，并将项目设为 `active`。
+
+基础设施任一步失败时按 GeoServer、时序库、管网模板、业务库的逆序清理；元数据提交失败也执行同样清理。旧 `POST /admin/projects` 仅保留给已经由外部流程创建好的资源登记使用，并已标记为 deprecated。
+
+使用模板复制或临时方案库的模拟功能时，`biz_data` 账号必须具备数据库创建和删除权限；只有显式删除项目时才会终止该项目的现有数据库会话，普通复制不会主动中断复制源会话。
+
+## 测试与发布
+
+提交前根据改动范围运行最小有效测试：
+
+```bash
+conda run -n server python -m pytest tests/unit tests/auth -q
+```
+
+发布镜像前建议运行：
+
+```bash
+docker build -t tjwater-server:local .
+```
+
+Gitea 包工作流位于 `.gitea/workflows/package.yml`，通常由 tag 触发构建、推送镜像并通知部署 webhook。
+
+## 安全规则
+
+不要提交 `.env`、客户数据、数据库 dump、日志、生成缓存、`db_inp/`、`temp/`、`data/` 或本地密钥。CI/CD 凭据应放在 Gitea secrets 和仓库变量中。

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 +1,5 @@
+"""Pure water-network calculation packages.
+
+Application workflows belong in :mod:`app.services`; database and external
+system access belongs in :mod:`app.infra` or :mod:`app.native`.
+"""

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/api_ex/kmeans_sensor.py

@@ -1,109 +0,0 @@
-import wntr
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-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.monitor_nodes = []
-        self.coords = []
-        self.junction_nodes = {}  # Added missing initialization
-
-
-    def get_junctions_coordinates(self):
-       
-        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 )
-
-        # 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) 
-
-        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)  
-
-        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() 
-
-
-    
-    
-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)
-
-    # Select monitoring pointse
-    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)
-    print(sensorindex)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-

app/algorithms/burst_detection/__init__.py

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

app/algorithms/burst_detection/pressure_anomaly.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 PressureAnomalyDetector:
+    """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_localization/__init__.py

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

app/algorithms/burst_localization/candidate_ranking.py

@@ -0,0 +1,771 @@
+"""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_signature import cal_signature_pipe_multi_pf
+from .topology_partitioning import (
+    cal_group_num,
+    metis_grouping_pipe_weight,
+    visualize_metis_partition,
+)
+from .similarity_metrics 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_localization/leak_signature.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_localization/pipeline.py

@@ -0,0 +1,238 @@
+import logging
+from multiprocessing import cpu_count
+from pathlib import Path
+from typing import Any, Iterable
+
+import pandas as pd
+
+from app.algorithms.burst_localization import leak_signature
+
+from .candidate_ranking import (
+    DN_search_multi_simple_add_flow_count_new,
+)
+from .topology_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 _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_signature.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,
+    }

app/algorithms/burst_localization/similarity_metrics.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/burst_localization/synthetic_observations.py

@@ -0,0 +1,197 @@
+"""噪声生成模块。"""
+
+import copy
+import random
+
+import numpy as np
+import pandas as pd
+
+from .leak_signature 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_localization/topology_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_localization/topology_partitioning.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/demand_allocation/__init__.py

@@ -0,0 +1,5 @@
+"""Demand allocation calculations."""
+
+from .pipe_length_weighted import allocate_demand_by_pipe_length
+
+__all__ = ["allocate_demand_by_pipe_length"]

app/algorithms/demand_allocation/pipe_length_weighted.py

@@ -0,0 +1,36 @@
+"""Pipe-length-weighted demand allocation.
+
+This module deliberately accepts plain topology data and performs no database
+or file access. Application services are responsible for loading topology.
+"""
+
+from typing import Any, Mapping
+
+
+def allocate_demand_by_pipe_length(
+    demand: float,
+    topology_nodes: Mapping[str, Mapping[str, Any]],
+    topology_links: Mapping[str, Mapping[str, Any]],
+) -> dict[str, float]:
+    """Allocate total demand to junctions by half of each incident link length."""
+
+    if not topology_nodes or not topology_links or demand == 0.0:
+        return {}
+
+    total_link_length = sum(
+        abs(float(link["length"])) for link in topology_links.values()
+    )
+    if total_link_length <= 0.0:
+        return {}
+
+    demand_per_length = demand / total_link_length
+    result: dict[str, float] = {}
+    for node_id, node in topology_nodes.items():
+        if node["type"] != "junction":
+            continue
+        incident_length = sum(
+            abs(float(topology_links[link_id]["length"]))
+            for link_id in node["links"]
+        )
+        result[node_id] = incident_length * demand_per_length * 0.5
+    return result

app/algorithms/dma_leakage_estimation/__init__.py

@@ -0,0 +1,3 @@
+from app.algorithms.dma_leakage_estimation.genetic_optimizer import DmaLeakageOptimizer
+
+__all__ = ["DmaLeakageOptimizer"]

app/algorithms/dma_leakage_estimation/genetic_optimizer.py

@@ -0,0 +1,609 @@
+import wntr
+import numpy as np
+import pandas as pd
+import os
+import time
+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 = DmaLeakageOptimizer._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 DmaLeakageOptimizer:
+    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 = DmaLeakageOptimizer._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

app/algorithms/dma_leakage_estimation/topology_partitioning.py

@@ -0,0 +1,206 @@
+"""Pure topology partitioning used by DMA leakage estimation."""
+
+import math
+from collections import deque
+from typing import Any, Iterable, Mapping
+
+import numpy as np
+
+
+def build_dma_partitions(
+    sensor_nodes: list[str],
+    node_coords: Mapping[str, Mapping[str, Any]],
+    link_entries: Iterable[str],
+    dma_count: int | None,
+) -> tuple[dict[str, str], list[dict[str, Any]]]:
+    """Assign every topology node to a sensor-seeded virtual DMA."""
+
+    all_nodes = list(node_coords)
+    if not all_nodes:
+        raise ValueError("管网中未获取到可分区节点。")
+    available_sensors = [node for node in sensor_nodes if node in node_coords]
+    if not available_sensors:
+        raise ValueError("无可用压力传感器，无法生成虚拟分区。")
+
+    area_count = _resolve_dma_count(dma_count, available_sensors, all_nodes)
+    sensor_area_map = _cluster_sensors_to_areas(
+        available_sensors, node_coords, area_count
+    )
+    adjacency = _build_adjacency(link_entries, all_nodes)
+    distance_by_sensor = {
+        sensor: _bfs_distances(adjacency, sensor) for sensor in available_sensors
+    }
+
+    assignment_count = {sensor: 0 for sensor in available_sensors}
+    area_map: dict[str, str] = {}
+    for node_id in sorted(all_nodes):
+        sensor = _choose_sensor_for_node(
+            node_id,
+            available_sensors,
+            node_coords,
+            distance_by_sensor,
+            assignment_count,
+        )
+        assignment_count[sensor] += 1
+        area_map[node_id] = sensor_area_map[sensor]
+
+    return area_map, _build_area_meta(area_map, sensor_area_map)
+
+
+def _resolve_dma_count(
+    dma_count: int | None, sensor_nodes: list[str], all_nodes: list[str]
+) -> int:
+    if dma_count is None:
+        return min(len(sensor_nodes), len(all_nodes))
+    if dma_count <= 0:
+        raise ValueError("dma_count 必须大于 0。")
+    if dma_count > len(all_nodes):
+        raise ValueError("dma_count 不能大于可分区节点数量。")
+    if dma_count > len(sensor_nodes):
+        raise ValueError("dma_count 不能大于可用传感器数量。")
+    return dma_count
+
+
+def _cluster_sensors_to_areas(
+    sensor_nodes: list[str],
+    node_coords: Mapping[str, Mapping[str, Any]],
+    area_count: int,
+) -> dict[str, str]:
+    if area_count >= len(sensor_nodes):
+        return {sensor: str(index + 1) for index, sensor in enumerate(sensor_nodes)}
+
+    points = np.array(
+        [
+            [float(node_coords[sensor]["x"]), float(node_coords[sensor]["y"])]
+            for sensor in sensor_nodes
+        ],
+        dtype=float,
+    )
+    centers = points[:area_count].copy()
+    labels = np.full(points.shape[0], -1, dtype=int)
+    for _ in range(20):
+        distances_squared = (
+            (points[:, None, :] - centers[None, :, :]) ** 2
+        ).sum(axis=2)
+        next_labels = distances_squared.argmin(axis=1)
+        if np.array_equal(labels, next_labels):
+            break
+        labels = next_labels
+        for index in range(area_count):
+            cluster_points = points[labels == index]
+            if cluster_points.size > 0:
+                centers[index] = cluster_points.mean(axis=0)
+    labels = _restore_empty_area_labels(labels, points, centers, area_count)
+    return {
+        sensor: str(int(labels[index]) + 1)
+        for index, sensor in enumerate(sensor_nodes)
+    }
+
+
+def _restore_empty_area_labels(
+    labels: np.ndarray,
+    points: np.ndarray,
+    centers: np.ndarray,
+    area_count: int,
+) -> np.ndarray:
+    """Keep every requested area represented when coordinates are degenerate."""
+
+    labels = labels.copy()
+    for missing_area in sorted(set(range(area_count)) - set(labels.tolist())):
+        area_sizes = {
+            area: int(np.count_nonzero(labels == area)) for area in range(area_count)
+        }
+        donor_area = max(
+            (area for area, size in area_sizes.items() if size > 1),
+            key=lambda area: (area_sizes[area], -area),
+        )
+        donor_indices = np.flatnonzero(labels == donor_area)
+        replacement_index = max(
+            (int(index) for index in donor_indices),
+            key=lambda index: (
+                float(np.sum((points[index] - centers[donor_area]) ** 2)),
+                index,
+            ),
+        )
+        labels[replacement_index] = missing_area
+        centers[missing_area] = points[replacement_index]
+    return labels
+
+
+def _build_adjacency(
+    link_entries: Iterable[str], all_nodes: list[str]
+) -> dict[str, set[str]]:
+    adjacency: dict[str, set[str]] = {node: set() for node in all_nodes}
+    for link in link_entries:
+        parts = str(link).split(":")
+        if len(parts) < 4:
+            continue
+        node1, node2 = parts[-2], parts[-1]
+        if node1 in adjacency and node2 in adjacency:
+            adjacency[node1].add(node2)
+            adjacency[node2].add(node1)
+    return adjacency
+
+
+def _bfs_distances(adjacency: Mapping[str, set[str]], start: str) -> dict[str, int]:
+    distances = {start: 0}
+    queue: deque[str] = deque([start])
+    while queue:
+        node = queue.popleft()
+        for neighbor in adjacency.get(node, set()):
+            if neighbor in distances:
+                continue
+            distances[neighbor] = distances[node] + 1
+            queue.append(neighbor)
+    return distances
+
+
+def _choose_sensor_for_node(
+    node_id: str,
+    sensors: list[str],
+    node_coords: Mapping[str, Mapping[str, Any]],
+    distance_by_sensor: Mapping[str, Mapping[str, int]],
+    assignment_count: Mapping[str, int],
+) -> str:
+    min_distance: int | None = None
+    candidates: list[str] = []
+    for sensor in sensors:
+        distance = distance_by_sensor.get(sensor, {}).get(node_id)
+        if distance is None:
+            continue
+        if min_distance is None or distance < min_distance:
+            min_distance = distance
+            candidates = [sensor]
+        elif distance == min_distance:
+            candidates.append(sensor)
+    if not candidates:
+        node_coord = node_coords[node_id]
+        return min(
+            sensors,
+            key=lambda sensor: math.hypot(
+                float(node_coord["x"]) - float(node_coords[sensor]["x"]),
+                float(node_coord["y"]) - float(node_coords[sensor]["y"]),
+            ),
+        )
+    return min(candidates, key=lambda sensor: (assignment_count[sensor], sensor))
+
+
+def _build_area_meta(
+    area_map: Mapping[str, str], sensor_area_map: Mapping[str, str]
+) -> list[dict[str, Any]]:
+    nodes_by_area: dict[str, list[str]] = {}
+    for node_id, area_id in area_map.items():
+        nodes_by_area.setdefault(area_id, []).append(node_id)
+    sensors_by_area: dict[str, list[str]] = {}
+    for sensor, area_id in sensor_area_map.items():
+        sensors_by_area.setdefault(area_id, []).append(sensor)
+
+    return [
+        {
+            "area_id": area_id,
+            "sensor_nodes": sorted(sensors_by_area.get(area_id, [])),
+            "node_ids": sorted(nodes_by_area[area_id]),
+            "node_count": len(nodes_by_area[area_id]),
+        }
+        for area_id in sorted(nodes_by_area, key=int)
+    ]

app/algorithms/pipe_health_prediction/__init__.py

@@ -0,0 +1,5 @@
+from app.algorithms.pipe_health_prediction.survival_predictor import (
+    PipeHealthSurvivalPredictor,
+)
+
+__all__ = ["PipeHealthSurvivalPredictor"]

app/algorithms/pipe_health_prediction/survival_predictor.py

@@ -4,7 +4,7 @@ import pandas as pd
 import matplotlib.pyplot as plt
 
 
-class PipelineHealthAnalyzer:
+class PipeHealthSurvivalPredictor:
     """
     管道健康分析器类，使用随机生存森林模型预测管道的生存概率。
 
@@ -14,19 +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。
         """
-        # 确保 model 目录存在
-        model_dir = os.path.dirname(model_path)
-        if model_dir and not os.path.exists(model_dir):
-            os.makedirs(model_dir, exist_ok=True)
-
+        if model_path is None:
+            model_path = os.path.join(
+                os.path.dirname(__file__),
+                "model",
+                "my_survival_forest_model_quxi.joblib",
+            )
         if not os.path.exists(model_path):
             raise FileNotFoundError(f"模型文件未找到: {model_path}")
 
@@ -96,7 +97,7 @@ class PipelineHealthAnalyzer:
 
 # 调用说明示例
 """
-在其他项目中使用PipelineHealthAnalyzer类的步骤：
+在其他项目中使用 PipeHealthSurvivalPredictor 类的步骤：
 
 1. 安装依赖（在requirements.txt中添加）：
    joblib==1.5.0
@@ -106,34 +107,29 @@ class PipelineHealthAnalyzer:
    matplotlib==3.9.4
 
 2. 导入类：
-   from pipeline_health_analyzer import PipelineHealthAnalyzer
+   from survival_predictor import PipeHealthSurvivalPredictor
 
 3. 初始化分析器（替换为实际模型路径）：
-   analyzer = PipelineHealthAnalyzer(model_path='path/to/my_survival_forest_model3-10.joblib')
+   predictor = PipeHealthSurvivalPredictor(model_path='path/to/model.joblib')
 
-4. 准备数据（pandas DataFrame，包含9个特征列）：
+4. 准备数据（pandas DataFrame，包含4个特征列）：
    import pandas as pd
    data = pd.DataFrame({
        'Material': [1, 2],  # 示例数据
        'Diameter': [100, 150],
        'Flow Velocity': [1.5, 2.0],
-       'Pressure': [50, 60],
-       'Temperature': [20, 25],
-       'Precipitation': [0.1, 0.2],
-       'Location': [1, 2],
-       'Structural Defects': [0, 1],
-       'Functional Defects': [0, 0]
+       'Pressure': [50, 60]
    })
 
 5. 进行预测：
-   survival_funcs = analyzer.predict_survival(data)
+   survival_funcs = predictor.predict_survival(data)
 
 6. 查看结果（每个样本的生存概率随时间变化）：
    for i, sf in enumerate(survival_funcs):
        print(f"样本 {i+1}: 时间点: {sf.x[:5]}..., 生存概率: {sf.y[:5]}...")
 
 7. 可视化（可选）：
-   analyzer.plot_survival(survival_funcs, save_path='survival_plot.png')
+   predictor.plot_survival(survival_funcs, save_path='survival_plot.png')
 
 注意：
 - 数据格式必须匹配特征列表，特征值为数值型。

app/algorithms/pressure_sensor_placement/__init__.py

@@ -0,0 +1 @@
+"""Pressure sensor placement calculation implementations."""

app/algorithms/pressure_sensor_placement/kmeans_placement.py

@@ -0,0 +1,96 @@
+import matplotlib.pyplot as plt
+import numpy as np
+import sklearn.cluster
+import wntr
+
+
+class KMeansPlacement:
+    def __init__(self, wn, num_monitors: int, min_diameter_mm: float):
+        self.cluster_num = num_monitors
+        self.wn = wn
+        self.monitor_nodes: list[str] = []
+        self.coords: list[tuple[float, float]] = []
+        self.candidate_nodes: list[str] = []
+        self.min_diameter_mm = min_diameter_mm
+
+    def get_junctions_coordinates(self) -> None:
+        eligible_nodes: set[str] = set()
+        junction_names = set(self.wn.junction_name_list)
+        for pipe_name in self.wn.pipe_name_list:
+            pipe = self.wn.get_link(pipe_name)
+            if float(pipe.diameter) * 1000 < self.min_diameter_mm:
+                continue
+            eligible_nodes.update(
+                node_id
+                for node_id in (pipe.start_node_name, pipe.end_node_name)
+                if node_id in junction_names
+            )
+
+        for junction_name in self.wn.junction_name_list:
+            if junction_name not in eligible_nodes:
+                continue
+            junction = self.wn.get_node(junction_name)
+            self.candidate_nodes.append(junction_name)
+            self.coords.append(junction.coordinates)
+
+    def select_monitoring_points(self) -> list[str]:
+        if not self.coords:
+            self.get_junctions_coordinates()
+        if self.cluster_num <= 0:
+            raise ValueError("sensor_count must be greater than zero")
+        if self.cluster_num > len(self.candidate_nodes):
+            raise ValueError("符合最小管径条件的候选节点数量少于请求的监测点数量")
+        coords = np.array(self.coords)
+        coordinate_span = coords.max(axis=0) - coords.min(axis=0)
+        coordinate_span[coordinate_span == 0] = 1.0
+        coords_normalized = (coords - coords.min(axis=0)) / coordinate_span
+        kmeans = sklearn.cluster.KMeans(n_clusters=self.cluster_num, random_state=42)
+        kmeans.fit(coords_normalized)
+
+        selected_indices: set[int] = set()
+        for cluster_index, center in enumerate(kmeans.cluster_centers_):
+            cluster_indices = np.flatnonzero(kmeans.labels_ == cluster_index)
+            available_indices = [
+                int(index)
+                for index in cluster_indices
+                if int(index) not in selected_indices
+            ]
+            if not available_indices:
+                available_indices = [
+                    index
+                    for index in range(len(self.candidate_nodes))
+                    if index not in selected_indices
+                ]
+            nearest_index = min(
+                available_indices,
+                key=lambda index: (
+                    float(np.sum((coords_normalized[index] - center) ** 2)),
+                    index,
+                ),
+            )
+            selected_indices.add(nearest_index)
+            nearest_node = self.candidate_nodes[nearest_index]
+            self.monitor_nodes.append(nearest_node)
+
+        return self.monitor_nodes
+
+    def visualize_network(self) -> None:
+        """Visualize network with monitoring points."""
+        wntr.graphics.plot_network(
+            self.wn,
+            node_attribute=self.monitor_nodes,
+            node_size=30,
+            title="Optimal sensor",
+        )
+        plt.show()
+
+
+def optimize_sensor_placement(
+    network_model: wntr.network.WaterNetworkModel,
+    sensor_count: int,
+    min_diameter_mm: float,
+) -> list[str]:
+    """Select sensor nodes from an already loaded network model."""
+
+    placement = KMeansPlacement(network_model, sensor_count, min_diameter_mm)
+    return placement.select_monitoring_points()

app/algorithms/pressure_sensor_placement/sensitivity_placement.py

@@ -0,0 +1,905 @@
+"""Pressure sensor placement based on scalable sensitivity analysis.
+
+The original implementation expanded a sparse water network into several dense
+``node x node``, ``node x pipe``, and ``pipe x pipe`` matrices.  That made the
+memory requirement quadratic and the explicit matrix inverse cubic in time.
+
+This module keeps one algorithm for every network size:
+
+* run EPANET once and reuse the first hydraulic state;
+* keep incidence and hydraulic graphs sparse;
+* estimate the row-wise L1 pressure sensitivity with deterministic Cauchy
+  projections and one sparse factorization;
+* estimate total directed hydraulic distance from a deterministic spatial
+  coreset without materialising an all-pairs distance matrix;
+* balance sensitivity score with geographic and pipe-network coverage without
+  materialising candidate-to-candidate distances.
+
+The random seed and sample counts are fixed, so the same model and request
+produce the same placement on every run.
+"""
+
+from __future__ import annotations
+
+import logging
+from dataclasses import dataclass
+from pathlib import Path
+from tempfile import TemporaryDirectory
+from time import perf_counter
+
+import numpy as np
+import wntr
+from scipy.sparse import csr_matrix, eye
+from scipy.sparse.csgraph import connected_components, dijkstra
+from scipy.sparse.linalg import splu
+from sklearn.cluster import MiniBatchKMeans
+
+
+logger = logging.getLogger(__name__)
+
+_RANDOM_SEED = 42
+_SENSITIVITY_PROJECTIONS = 256
+_HYDRAULIC_LANDMARKS = 256
+_PROJECTION_BLOCK_SIZE = 16
+_DIJKSTRA_BLOCK_SIZE = 16
+_HEADLOSS_EPSILON = 1e-10
+_DIAMETER_TOLERANCE_MM = 1e-9
+_COVERAGE_ELIGIBILITY_RATIO = 0.70
+_COVERAGE_EDGE_EPSILON = 1e-9
+
+
+@dataclass(frozen=True)
+class _PreparedNetwork:
+    """Sparse data required by the placement pipeline."""
+
+    node_names: tuple[str, ...]
+    full_node_indices: np.ndarray
+    candidate_indices: np.ndarray
+    coordinates: np.ndarray
+    incidence: csr_matrix
+    conductance: np.ndarray
+    roughness_response: np.ndarray
+    distance_graph: csr_matrix
+    coverage_graph: csr_matrix
+
+
+@dataclass(frozen=True)
+class _CandidatePool:
+    """Aligned candidate arrays consumed by the placement stage."""
+
+    full_indices: np.ndarray
+    coordinates: np.ndarray
+    names: np.ndarray
+    scores: np.ndarray
+
+
+def _run_hydraulic_simulation(
+    wn: wntr.network.WaterNetworkModel,
+):
+    """Run only the initial EPANET state without shared ``temp.*`` files."""
+
+    original_duration = wn.options.time.duration
+    try:
+        # Every downstream calculation reads ``iloc[0]``.  Running an extended
+        # simulation only allocates unused time-series results, which is
+        # especially expensive for daily models with tens of thousands of
+        # nodes.  Restore the caller's model even when EPANET fails.
+        wn.options.time.duration = 0
+        with TemporaryDirectory(prefix="tjwater-sensitivity-") as temp_dir:
+            file_prefix = str(Path(temp_dir) / "simulation")
+            return wntr.sim.EpanetSimulator(wn).run_sim(file_prefix=file_prefix)
+    finally:
+        wn.options.time.duration = original_duration
+
+
+def _excluded_elements(
+    wn: wntr.network.WaterNetworkModel,
+) -> tuple[set[str], set[str]]:
+    """Return nodes that cannot host sensors and source-connected pipes.
+
+    Reservoirs, tanks, pump/valve endpoints, and the junction immediately next
+    to a reservoir or tank are treated as hydraulic boundary nodes.  Pipes
+    connected directly to a source are removed from the perturbation set, as
+    in the legacy algorithm.
+    """
+
+    source_nodes = set(wn.reservoir_name_list) | set(wn.tank_name_list)
+    excluded_nodes = set(source_nodes)
+    source_pipes: set[str] = set()
+
+    for pipe_name, pipe in wn.pipes():
+        endpoints = {pipe.start_node_name, pipe.end_node_name}
+        if endpoints & source_nodes:
+            source_pipes.add(pipe_name)
+            excluded_nodes.update(endpoints)
+
+    for _link_name, link in list(wn.pumps()) + list(wn.valves()):
+        excluded_nodes.add(link.start_node_name)
+        excluded_nodes.add(link.end_node_name)
+
+    return excluded_nodes, source_pipes
+
+
+def _minimum_weight_csr(
+    rows: list[int],
+    columns: list[int],
+    weights: list[float],
+    *,
+    shape: tuple[int, int],
+) -> csr_matrix:
+    """Build a CSR graph while retaining the lightest parallel edge."""
+
+    if not rows:
+        return csr_matrix(shape, dtype=np.float64)
+
+    row_array = np.asarray(rows, dtype=np.int64)
+    column_array = np.asarray(columns, dtype=np.int64)
+    weight_array = np.asarray(weights, dtype=np.float64)
+    order = np.lexsort((column_array, row_array))
+    row_array = row_array[order]
+    column_array = column_array[order]
+    weight_array = weight_array[order]
+
+    group_start = np.empty(len(row_array), dtype=bool)
+    group_start[0] = True
+    group_start[1:] = (row_array[1:] != row_array[:-1]) | (
+        column_array[1:] != column_array[:-1]
+    )
+    starts = np.flatnonzero(group_start)
+    minimum_weights = np.minimum.reduceat(weight_array, starts)
+    return csr_matrix(
+        (minimum_weights, (row_array[starts], column_array[starts])),
+        shape=shape,
+    )
+
+
+def _node_coordinates(
+    wn: wntr.network.WaterNetworkModel,
+    node_names: tuple[str, ...],
+) -> np.ndarray:
+    coordinate_series = wn.query_node_attribute("coordinates")
+    coordinates = np.asarray(
+        [coordinate_series.loc[node_name] for node_name in node_names],
+        dtype=np.float64,
+    )
+    if coordinates.ndim != 2 or coordinates.shape[1] < 2:
+        raise ValueError("管网节点缺少二维坐标，无法进行监测点空间布置")
+    coordinates = coordinates[:, :2]
+    if not np.isfinite(coordinates).all():
+        raise ValueError("管网节点坐标包含非有限值，无法进行监测点空间布置")
+    return coordinates
+
+
+def _build_coverage_graph(
+    wn: wntr.network.WaterNetworkModel,
+    results,
+    full_node_index: dict[str, int],
+) -> csr_matrix:
+    """Build the active undirected physical graph used to spread sensors."""
+
+    status_series = results.link["status"].iloc[0]
+    rows: list[int] = []
+    columns: list[int] = []
+    weights: list[float] = []
+
+    for link_name, link in wn.links():
+        if float(status_series.loc[link_name]) <= 0:
+            continue
+
+        start = full_node_index[link.start_node_name]
+        end = full_node_index[link.end_node_name]
+        # Pipes carry their physical length.  Pumps and valves are point
+        # devices, so a tiny positive length preserves connectivity without
+        # dominating shortest-path distance.
+        weight = max(
+            float(getattr(link, "length", 0.0)),
+            _COVERAGE_EDGE_EPSILON,
+        )
+        rows.extend((start, end))
+        columns.extend((end, start))
+        weights.extend((weight, weight))
+
+    return _minimum_weight_csr(
+        rows,
+        columns,
+        weights,
+        shape=(len(full_node_index), len(full_node_index)),
+    )
+
+
+def _prepare_network(
+    wn: wntr.network.WaterNetworkModel,
+    results,
+    *,
+    min_diameter: int,
+) -> _PreparedNetwork:
+    excluded_nodes, source_pipes = _excluded_elements(wn)
+    full_node_names = tuple(wn.node_name_list)
+    full_node_index = {
+        node_name: index for index, node_name in enumerate(full_node_names)
+    }
+    node_names = tuple(
+        node_name for node_name in full_node_names if node_name not in excluded_nodes
+    )
+    if not node_names:
+        raise ValueError("管网中没有可参与灵敏度分析的节点")
+
+    node_index = {node_name: index for index, node_name in enumerate(node_names)}
+    full_node_indices = np.asarray(
+        [full_node_index[node_name] for node_name in node_names],
+        dtype=np.int64,
+    )
+    coordinates = _node_coordinates(wn, node_names)
+
+    flow_series = results.link["flowrate"].iloc[0]
+    headloss_series = results.link["headloss"].iloc[0]
+    head_series = results.node["head"].iloc[0]
+
+    candidate_nodes: set[str] = set()
+    for _pipe_name, pipe in wn.pipes():
+        diameter_mm = float(pipe.diameter) * 1000.0
+        if diameter_mm + _DIAMETER_TOLERANCE_MM < min_diameter:
+            continue
+        if pipe.start_node_name in node_index:
+            candidate_nodes.add(pipe.start_node_name)
+        if pipe.end_node_name in node_index:
+            candidate_nodes.add(pipe.end_node_name)
+
+    incidence_rows: list[int] = []
+    incidence_columns: list[int] = []
+    incidence_values: list[float] = []
+    conductance: list[float] = []
+    roughness_response: list[float] = []
+    distance_rows: list[int] = []
+    distance_columns: list[int] = []
+    distance_weights: list[float] = []
+
+    kept_pipe_count = 0
+    for pipe_name, pipe in wn.pipes():
+        if pipe_name in source_pipes:
+            continue
+
+        start_name = pipe.start_node_name
+        end_name = pipe.end_node_name
+        if start_name not in node_index and end_name not in node_index:
+            continue
+
+        flow = float(flow_series.loc[pipe_name])
+        absolute_flow = abs(flow)
+        headloss = abs(float(headloss_series.loc[pipe_name]))
+        roughness = float(pipe.roughness)
+        if roughness <= 0:
+            raise ValueError(f"管道 {pipe_name} 的粗糙度必须大于 0")
+
+        orientation = -1.0 if flow < 0 else 1.0
+        if start_name in node_index:
+            incidence_rows.append(node_index[start_name])
+            incidence_columns.append(kept_pipe_count)
+            incidence_values.append(-orientation)
+        if end_name in node_index:
+            incidence_rows.append(node_index[end_name])
+            incidence_columns.append(kept_pipe_count)
+            incidence_values.append(orientation)
+
+        conductance.append(
+            absolute_flow / (1.852 * headloss + _HEADLOSS_EPSILON)
+        )
+        roughness_response.append(absolute_flow / roughness)
+
+        if flow > 0:
+            upstream_name, downstream_name = start_name, end_name
+        else:
+            upstream_name, downstream_name = end_name, start_name
+        hydraulic_weight = (
+            abs(float(head_series.loc[start_name]) - float(head_series.loc[end_name]))
+            * float(pipe.length)
+        )
+        distance_rows.append(full_node_index[upstream_name])
+        distance_columns.append(full_node_index[downstream_name])
+        distance_weights.append(hydraulic_weight)
+        kept_pipe_count += 1
+
+    if kept_pipe_count == 0:
+        raise ValueError("管网中没有可用于灵敏度分析的管道")
+
+    incidence = csr_matrix(
+        (
+            np.asarray(incidence_values, dtype=np.float64),
+            (
+                np.asarray(incidence_rows, dtype=np.int64),
+                np.asarray(incidence_columns, dtype=np.int64),
+            ),
+        ),
+        shape=(len(node_names), kept_pipe_count),
+    )
+    conductance_array = np.asarray(conductance, dtype=np.float64)
+    response_array = np.asarray(roughness_response, dtype=np.float64)
+    if not np.isfinite(conductance_array).all() or not np.isfinite(
+        response_array
+    ).all():
+        raise ValueError("水力结果产生了非有限灵敏度系数")
+
+    distance_graph = _minimum_weight_csr(
+        distance_rows,
+        distance_columns,
+        distance_weights,
+        shape=(len(full_node_names), len(full_node_names)),
+    )
+    coverage_graph = _build_coverage_graph(wn, results, full_node_index)
+    candidate_indices = np.asarray(
+        [
+            index
+            for index, node_name in enumerate(node_names)
+            if node_name in candidate_nodes
+        ],
+        dtype=np.int64,
+    )
+
+    return _PreparedNetwork(
+        node_names=node_names,
+        full_node_indices=full_node_indices,
+        candidate_indices=candidate_indices,
+        coordinates=coordinates,
+        incidence=incidence,
+        conductance=conductance_array,
+        roughness_response=response_array,
+        distance_graph=distance_graph,
+        coverage_graph=coverage_graph,
+    )
+
+
+def _axis_normalized_coordinates(coordinates: np.ndarray) -> np.ndarray:
+    """Scale each axis independently for MiniBatchKMeans."""
+
+    minimum = coordinates.min(axis=0)
+    span = np.ptp(coordinates, axis=0)
+    span[span == 0] = 1.0
+    return (coordinates - minimum) / span
+
+
+def _isotropic_coordinates(coordinates: np.ndarray) -> np.ndarray:
+    """Normalize coordinates without distorting the network aspect ratio."""
+
+    minimum = coordinates.min(axis=0)
+    scale = float(np.max(np.ptp(coordinates, axis=0), initial=0.0))
+    if scale == 0:
+        scale = 1.0
+    return (coordinates - minimum) / scale
+
+
+def _cluster_labels(
+    coordinates: np.ndarray,
+    cluster_count: int,
+    *,
+    random_seed: int,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Cluster coordinates deterministically with one implementation at all sizes."""
+
+    normalized = _axis_normalized_coordinates(coordinates)
+    if cluster_count == 1:
+        return np.zeros(len(coordinates), dtype=np.int64), normalized[[0]]
+    if cluster_count >= len(coordinates):
+        return np.arange(len(coordinates), dtype=np.int64), normalized.copy()
+
+    model = MiniBatchKMeans(
+        n_clusters=cluster_count,
+        random_state=random_seed,
+        n_init=3,
+        batch_size=min(len(coordinates), max(1024, cluster_count * 3)),
+        max_iter=100,
+        max_no_improvement=20,
+        reassignment_ratio=0.0,
+    )
+    labels = model.fit_predict(normalized).astype(np.int64, copy=False)
+    return labels, np.asarray(model.cluster_centers_, dtype=np.float64)
+
+
+def _estimate_log_pressure_sensitivity(prepared: _PreparedNetwork) -> np.ndarray:
+    """Estimate each row's L1 sensitivity using streaming Cauchy projections."""
+
+    weighted_incidence = prepared.incidence.multiply(prepared.conductance)
+    laplacian = (weighted_incidence @ prepared.incidence.T).tocsc()
+    diagonal = np.asarray(laplacian.diagonal(), dtype=np.float64)
+    diagonal_scale = float(np.max(np.abs(diagonal), initial=0.0))
+    if diagonal_scale == 0:
+        raise ValueError("水力雅可比矩阵为空，无法计算压力灵敏度")
+
+    regularization = diagonal_scale * np.sqrt(np.finfo(np.float64).eps)
+    laplacian = laplacian + eye(
+        laplacian.shape[0], format="csc", dtype=np.float64
+    ) * regularization
+    factor = splu(
+        laplacian,
+        permc_spec="MMD_AT_PLUS_A",
+        diag_pivot_thresh=0.0,
+        options={"SymmetricMode": True},
+    )
+
+    random = np.random.default_rng(_RANDOM_SEED)
+    log_absolute_sum = np.zeros(len(prepared.node_names), dtype=np.float64)
+    projection_count = 0
+    float_epsilon = np.finfo(np.float64).eps
+    float_tiny = np.finfo(np.float64).tiny
+
+    while projection_count < _SENSITIVITY_PROJECTIONS:
+        block_size = min(
+            _PROJECTION_BLOCK_SIZE,
+            _SENSITIVITY_PROJECTIONS - projection_count,
+        )
+        uniform = random.random((prepared.incidence.shape[1], block_size))
+        np.clip(uniform, float_epsilon, 1.0 - float_epsilon, out=uniform)
+        cauchy_projection = np.tan(np.pi * (uniform - 0.5))
+        projected_response = prepared.incidence @ (
+            prepared.roughness_response[:, None] * cauchy_projection
+        )
+        solution = factor.solve(np.asarray(projected_response, dtype=np.float64))
+        log_absolute_sum += np.log(
+            np.maximum(np.abs(solution), float_tiny)
+        ).sum(axis=1)
+        projection_count += block_size
+
+    # For a standard Cauchy variable E[log(abs(X))] is zero.  Therefore this
+    # streaming geometric mean estimates log(||row||_1) without retaining the
+    # node-by-projection matrix.  A finite-sample bias is common to all rows and
+    # does not affect ranking.
+    return log_absolute_sum / _SENSITIVITY_PROJECTIONS
+
+
+def _landmark_coreset(prepared: _PreparedNetwork) -> tuple[np.ndarray, np.ndarray]:
+    landmark_count = min(_HYDRAULIC_LANDMARKS, len(prepared.node_names))
+    labels, centers = _cluster_labels(
+        prepared.coordinates,
+        landmark_count,
+        random_seed=_RANDOM_SEED + 1,
+    )
+    normalized = _axis_normalized_coordinates(prepared.coordinates)
+    landmarks: list[int] = []
+    weights: list[float] = []
+
+    for label in np.unique(labels):
+        members = np.flatnonzero(labels == label)
+        center = centers[int(label)]
+        squared_distance = np.square(normalized[members] - center).sum(axis=1)
+        landmarks.append(int(members[int(np.argmin(squared_distance))]))
+        weights.append(float(len(members)))
+
+    return (
+        np.asarray(landmarks, dtype=np.int64),
+        np.asarray(weights, dtype=np.float64),
+    )
+
+
+def _estimate_hydraulic_distance_sums(prepared: _PreparedNetwork) -> np.ndarray:
+    """Estimate outbound distance sums without an all-pairs distance matrix."""
+
+    landmark_indices, landmark_weights = _landmark_coreset(prepared)
+    full_landmark_indices = prepared.full_node_indices[landmark_indices]
+    reversed_graph = prepared.distance_graph.transpose().tocsr()
+    distance_sums = np.zeros(len(prepared.node_names), dtype=np.float64)
+
+    for start in range(0, len(landmark_indices), _DIJKSTRA_BLOCK_SIZE):
+        stop = min(start + _DIJKSTRA_BLOCK_SIZE, len(landmark_indices))
+        distances = dijkstra(
+            reversed_graph,
+            directed=True,
+            indices=full_landmark_indices[start:stop],
+            return_predecessors=False,
+        )
+        distances = np.atleast_2d(distances)[:, prepared.full_node_indices]
+        # The legacy matrix represented unreachable pairs as zero.  Retaining
+        # that convention prevents disconnected branches from receiving an
+        # artificial infinite score.
+        distances[~np.isfinite(distances)] = 0.0
+        distance_sums += landmark_weights[start:stop] @ distances
+
+    return distance_sums
+
+
+def _build_candidate_pool(
+    prepared: _PreparedNetwork,
+    log_sensitivity: np.ndarray,
+    hydraulic_distance_sums: np.ndarray,
+) -> _CandidatePool:
+    candidate_indices = prepared.candidate_indices
+    candidate_distance = hydraulic_distance_sums[candidate_indices]
+    with np.errstate(divide="ignore", invalid="ignore"):
+        scores = log_sensitivity[candidate_indices] + np.log(candidate_distance)
+    scores = np.nan_to_num(
+        scores,
+        nan=-np.inf,
+        neginf=-np.inf,
+        posinf=np.finfo(np.float64).max,
+    )
+    return _CandidatePool(
+        full_indices=prepared.full_node_indices[candidate_indices],
+        coordinates=_isotropic_coordinates(prepared.coordinates[candidate_indices]),
+        names=np.asarray(
+            [prepared.node_names[index] for index in candidate_indices],
+            dtype=str,
+        ),
+        scores=scores,
+    )
+
+
+def _highest_scoring_position(
+    names: np.ndarray,
+    scores: np.ndarray,
+    positions: np.ndarray,
+) -> int:
+    """Return the best position, breaking score ties by node name."""
+
+    order = np.lexsort((names[positions], -scores[positions]))
+    return int(positions[order[0]])
+
+
+def _relative_gap(
+    distances: np.ndarray,
+    available: np.ndarray,
+) -> np.ndarray:
+    """Normalize available distances to their current finite maximum."""
+
+    maximum = float(np.max(distances[available], initial=0.0))
+    if not np.isfinite(maximum) or maximum <= np.finfo(np.float64).eps:
+        return np.zeros(len(distances), dtype=np.float64)
+    return distances / maximum
+
+
+def _eligible_gap_positions(
+    relative_gap: np.ndarray,
+    available: np.ndarray,
+) -> np.ndarray:
+    """Return positions within the configured fraction of the largest gap."""
+
+    maximum = float(np.max(relative_gap[available], initial=0.0))
+    if maximum <= np.finfo(np.float64).eps:
+        return np.flatnonzero(available)
+    threshold = _COVERAGE_ELIGIBILITY_RATIO * maximum
+    return np.flatnonzero(
+        available & (relative_gap >= threshold - np.finfo(np.float64).eps)
+    )
+
+
+def _allocate_component_quotas(
+    coverage_graph: csr_matrix,
+    candidate_full_indices: np.ndarray,
+    candidate_scores: np.ndarray,
+    *,
+    sensor_num: int,
+) -> tuple[np.ndarray, np.ndarray]:
+    """Allocate sensor counts by active pipe length with candidate caps."""
+
+    component_count, node_components = connected_components(
+        coverage_graph,
+        directed=False,
+        return_labels=True,
+    )
+    candidate_components = node_components[candidate_full_indices]
+    capacities = np.bincount(
+        candidate_components,
+        minlength=component_count,
+    ).astype(np.int64, copy=False)
+
+    # The graph is symmetric.  Summed row weights count every physical edge
+    # twice, hence the division by two after aggregation by component.
+    node_lengths = np.asarray(coverage_graph.sum(axis=1)).ravel()
+    component_lengths = np.bincount(
+        node_components,
+        weights=node_lengths,
+        minlength=component_count,
+    ) / 2.0
+    component_best_scores = np.full(component_count, -np.inf, dtype=np.float64)
+    np.maximum.at(
+        component_best_scores,
+        candidate_components,
+        candidate_scores,
+    )
+
+    active_components = np.flatnonzero(capacities)
+    quotas = np.zeros(component_count, dtype=np.int64)
+    if len(active_components) > sensor_num:
+        order = np.lexsort(
+            (
+                active_components,
+                -component_best_scores[active_components],
+                -component_lengths[active_components],
+            )
+        )
+        quotas[active_components[order[:sensor_num]]] = 1
+        return candidate_components, quotas
+
+    quotas[active_components] = 1
+    remaining = sensor_num - len(active_components)
+    while remaining > 0:
+        available = active_components[
+            quotas[active_components] < capacities[active_components]
+        ]
+        if len(available) == 0:
+            raise ValueError("连通区域中的候选节点不足，无法分配监测点名额")
+
+        weights = component_lengths[available]
+        if float(weights.sum()) <= 0:
+            weights = (capacities[available] - quotas[available]).astype(
+                np.float64,
+                copy=False,
+            )
+        ideal = remaining * weights / float(weights.sum())
+        whole = np.minimum(
+            np.floor(ideal).astype(np.int64),
+            capacities[available] - quotas[available],
+        )
+        whole_count = int(whole.sum())
+        if whole_count:
+            quotas[available] += whole
+            remaining -= whole_count
+            continue
+
+        fractional = ideal - np.floor(ideal)
+        order = np.lexsort(
+            (
+                available,
+                -component_best_scores[available],
+                -weights,
+                -fractional,
+            )
+        )
+        for component in available[order]:
+            quotas[component] += 1
+            remaining -= 1
+            if remaining == 0:
+                break
+
+    return candidate_components, quotas
+
+
+def _select_component_positions(
+    coverage_graph: csr_matrix,
+    candidates: _CandidatePool,
+    component_positions: np.ndarray,
+    existing_positions: list[int],
+    *,
+    quota: int,
+) -> list[int]:
+    """Select one component's sensors with score-aware farthest-first search."""
+
+    local_coordinates = candidates.coordinates[component_positions]
+    local_names = candidates.names[component_positions]
+    local_scores = candidates.scores[component_positions]
+    nearest_geographic = np.full(len(component_positions), np.inf)
+    nearest_topological = np.full(len(component_positions), np.inf)
+
+    for position in existing_positions:
+        nearest_geographic = np.minimum(
+            nearest_geographic,
+            np.linalg.norm(
+                local_coordinates - candidates.coordinates[position],
+                axis=1,
+            ),
+        )
+
+    if existing_positions:
+        all_local = np.ones(len(component_positions), dtype=bool)
+        seed_eligible = _eligible_gap_positions(
+            _relative_gap(nearest_geographic, all_local),
+            all_local,
+        )
+    else:
+        seed_eligible = np.arange(len(component_positions), dtype=np.int64)
+
+    seed = _highest_scoring_position(
+        local_names,
+        local_scores,
+        seed_eligible,
+    )
+    selected_local = [seed]
+    remaining = np.ones(len(component_positions), dtype=bool)
+    remaining[seed] = False
+
+    while len(selected_local) < quota:
+        newest = selected_local[-1]
+        geographic_distance = np.linalg.norm(
+            local_coordinates - local_coordinates[newest],
+            axis=1,
+        )
+        nearest_geographic = np.minimum(
+            nearest_geographic,
+            geographic_distance,
+        )
+
+        source = int(candidates.full_indices[component_positions[newest]])
+        topological_distance = dijkstra(
+            coverage_graph,
+            directed=False,
+            indices=source,
+            return_predecessors=False,
+        )[candidates.full_indices[component_positions]]
+        nearest_topological = np.minimum(
+            nearest_topological,
+            topological_distance,
+        )
+
+        coverage_gap = np.maximum(
+            _relative_gap(nearest_geographic, remaining),
+            _relative_gap(nearest_topological, remaining),
+        )
+        eligible_local = _eligible_gap_positions(coverage_gap, remaining)
+        next_local = _highest_scoring_position(
+            local_names,
+            local_scores,
+            eligible_local,
+        )
+        selected_local.append(next_local)
+        remaining[next_local] = False
+
+    return [int(component_positions[position]) for position in selected_local]
+
+
+def _geographic_coverage_metrics(
+    candidate_coordinates: np.ndarray,
+    selected_positions: list[int],
+) -> tuple[float, float, float]:
+    normalized = _isotropic_coordinates(candidate_coordinates)
+    nearest = np.full(len(normalized), np.inf)
+    for position in selected_positions:
+        nearest = np.minimum(
+            nearest,
+            np.linalg.norm(normalized - normalized[position], axis=1),
+        )
+
+    selected_coordinates = normalized[selected_positions]
+    if len(selected_positions) < 2:
+        minimum_gap = 0.0
+    else:
+        pairwise = np.linalg.norm(
+            selected_coordinates[:, None, :] - selected_coordinates[None, :, :],
+            axis=2,
+        )
+        np.fill_diagonal(pairwise, np.inf)
+        minimum_gap = float(pairwise.min())
+    return (
+        float(nearest.max()),
+        float(np.quantile(nearest, 0.95)),
+        minimum_gap,
+    )
+
+
+def _select_sensor_nodes(
+    prepared: _PreparedNetwork,
+    log_sensitivity: np.ndarray,
+    hydraulic_distance_sums: np.ndarray,
+    *,
+    sensor_num: int,
+) -> list[str]:
+    candidate_indices = prepared.candidate_indices
+    if len(candidate_indices) < sensor_num:
+        raise ValueError(
+            "满足最小管径要求的候选节点少于请求的监测点数量："
+            f"候选 {len(candidate_indices)} 个，请求 {sensor_num} 个"
+        )
+
+    candidates = _build_candidate_pool(
+        prepared,
+        log_sensitivity,
+        hydraulic_distance_sums,
+    )
+    candidate_components, component_quotas = _allocate_component_quotas(
+        prepared.coverage_graph,
+        candidates.full_indices,
+        candidates.scores,
+        sensor_num=sensor_num,
+    )
+    selected_positions: list[int] = []
+    quota_components = np.flatnonzero(component_quotas)
+    component_order = np.lexsort(
+        (quota_components, -component_quotas[quota_components])
+    )
+    for component in quota_components[component_order]:
+        component_positions = np.flatnonzero(candidate_components == component)
+        selected_positions.extend(
+            _select_component_positions(
+                prepared.coverage_graph,
+                candidates,
+                component_positions,
+                selected_positions,
+                quota=int(component_quotas[component]),
+            )
+        )
+
+    selected_array = np.asarray(selected_positions, dtype=np.int64)
+    selected_order = np.lexsort(
+        (
+            candidates.names[selected_array],
+            -candidates.scores[selected_array],
+        )
+    )
+    selected_positions = selected_array[selected_order].tolist()
+    maximum_radius, p95_radius, minimum_gap = _geographic_coverage_metrics(
+        prepared.coordinates[candidate_indices],
+        selected_positions,
+    )
+    logger.info(
+        "Sensitivity placement coverage: components=%d max_radius=%.6f "
+        "p95_radius=%.6f min_sensor_gap=%.6f",
+        int(np.count_nonzero(component_quotas)),
+        maximum_radius,
+        p95_radius,
+        minimum_gap,
+    )
+    return [str(candidates.names[position]) for position in selected_positions]
+
+
+def optimize_sensor_placement(
+    wn: wntr.network.WaterNetworkModel,
+    sensor_num: int,
+    min_diameter: int,
+) -> list[str]:
+    """Return deterministic pressure monitoring nodes for a loaded network.
+
+    ``min_diameter`` is expressed in millimetres, matching the HTTP contract.
+    A node is a valid installation candidate when at least one incident pipe
+    meets the threshold.  All valid hydraulic nodes still participate in the
+    sensitivity calculation so small pipes continue to influence the result.
+    """
+
+    if sensor_num <= 0:
+        raise ValueError("监测点数量必须大于 0")
+    if min_diameter < 0:
+        raise ValueError("最小管径不能小于 0")
+
+    total_started = perf_counter()
+    simulation_started = total_started
+    results = _run_hydraulic_simulation(wn)
+    simulation_seconds = perf_counter() - simulation_started
+
+    preparation_started = perf_counter()
+    prepared = _prepare_network(wn, results, min_diameter=min_diameter)
+    preparation_seconds = perf_counter() - preparation_started
+
+    sensitivity_started = perf_counter()
+    log_sensitivity = _estimate_log_pressure_sensitivity(prepared)
+    sensitivity_seconds = perf_counter() - sensitivity_started
+
+    distance_started = perf_counter()
+    hydraulic_distance_sums = _estimate_hydraulic_distance_sums(prepared)
+    distance_seconds = perf_counter() - distance_started
+
+    selection_started = perf_counter()
+    selected = _select_sensor_nodes(
+        prepared,
+        log_sensitivity,
+        hydraulic_distance_sums,
+        sensor_num=sensor_num,
+    )
+    selection_seconds = perf_counter() - selection_started
+
+    logger.info(
+        "Sensitivity placement completed: nodes=%d pipes=%d candidates=%d "
+        "sensors=%d seconds=%.3f "
+        "(simulation=%.3f preparation=%.3f sensitivity=%.3f "
+        "distance=%.3f selection=%.3f)",
+        len(prepared.node_names),
+        prepared.incidence.shape[1],
+        len(prepared.candidate_indices),
+        len(selected),
+        perf_counter() - total_started,
+        simulation_seconds,
+        preparation_seconds,
+        sensitivity_seconds,
+        distance_seconds,
+        selection_seconds,
+    )
+    return selected
+
+
+def optimize_sensor_placement_from_inp(
+    inp_path: str | Path,
+    sensor_num: int,
+    min_diameter: int,
+) -> list[str]:
+    """Load an EPANET INP model and run the unified placement algorithm."""
+
+    wn = wntr.network.WaterNetworkModel(str(inp_path))
+    return optimize_sensor_placement(
+        wn,
+        sensor_num=sensor_num,
+        min_diameter=min_diameter,
+    )

app/algorithms/scada_cleaning/__init__.py

@@ -0,0 +1,6 @@
+"""SCADA time-series cleaning algorithms."""
+
+from .flow_series import clean_flow_data_df_kf
+from .pressure_series import clean_pressure_data_df_km
+
+__all__ = ["clean_flow_data_df_kf", "clean_pressure_data_df_km"]

app/algorithms/scada_cleaning/flow_series.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]
@@ -117,22 +142,33 @@ def clean_flow_data_kf(input_csv_path: str, show_plot: bool = False) -> str:
     return os.path.abspath(output_path)
 
 
-def clean_flow_data_df_kf(data: pd.DataFrame, show_plot: bool = False) -> dict:
+def clean_flow_data_df_kf(
+    data: pd.DataFrame, show_plot: bool = False
+) -> pd.DataFrame:
     """
     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。
+    返回完整的清洗后 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()
-    # 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 +228,47 @@ 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 +286,7 @@ 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",
@@ -246,44 +301,9 @@ 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
-
-
-# # 测试
-# if __name__ == "__main__":
-#     # 默认：脚本目录下同名 CSV 文件
-#     script_dir = os.path.dirname(os.path.abspath(__file__))
-#     default_csv = os.path.join(script_dir, "pipe_flow_data_to_clean2.0.csv")
-#     out = clean_flow_data_kf(default_csv)
-#     print("清洗后的数据已保存到:", out)
-
-# 测试 clean_flow_data_dict 函数
-if __name__ == "__main__":
-    import random
-
-    # 读取 szh_flow_scada.csv 文件
-    script_dir = os.path.dirname(os.path.abspath(__file__))
-    csv_path = os.path.join(script_dir, "szh_flow_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, 1)
-
-    # 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_flow_data_df_kf(data_dict, show_plot=True)
-    # 将清洗后的字典写回 CSV
-    out_csv = os.path.join(script_dir, f"{selected_columns[0]}_clean.csv")
-    pd.DataFrame(cleaned_dict).to_csv(out_csv, index=False, encoding="utf-8-sig")
-    print("已保存清洗结果到:", out_csv)
-    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/scada_cleaning/pressure_series.py

@@ -0,0 +1,543 @@
+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

app/algorithms/sensitivity.py

@@ -1,654 +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 *
-from matplotlib.lines import Line2D
-from sklearn.cluster import SpectralClustering
-import libpysal as ps
-from spopt.region import Skater
-from shapely.geometry import Point
-import geopandas as gpd
-from sklearn.metrics import pairwise_distances
-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
-
-
-def skater_partition(G, n_clusters):
-    """
-    使用 SKATER 算法对输入的无向图 G 进行区域划分，
-    保证每个划分区域在图论意义上是连通的，
-    同时依据节点坐标的空间信息进行划分。
-
-    参数:
-        G: networkx.Graph
-            带有节点坐标属性（键为 'pos'）的无向图。
-        n_clusters: int
-            希望划分的区域数量。
-
-    返回:
-        groups: dict
-            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')
-    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)
-    # 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')
-
-    # 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'
-
-    # 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.solve()
-
-    # 5. 获取聚类标签，构造成字典格式
-    labels = skater.labels_
-    groups = {}
-    for label, node in zip(labels, nodes):
-        groups.setdefault(label, []).append(node)
-
-    return groups
-
-
-def spectral_partition(G, n_clusters):
-    """
-    利用谱聚类算法对图 G 进行分区：
-      1. 根据所有节点的空间坐标计算欧氏距离矩阵；
-      2. 利用高斯核函数构造相似度矩阵；
-      3. 使用 SpectralClustering 进行归一化割，返回分区结果。
-
-    参数:
-        G: networkx.Graph
-            每个节点需要有 'pos' 属性，其值为 (x, y) 坐标。
-        n_clusters: int
-            希望划分的聚类数目。
-
-    返回:
-        groups: dict
-            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')
-    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')
-
-    # 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))
-
-    # 5. 使用谱聚类进行图分区
-    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
-    groups = {}
-    for label, node in zip(labels, nodes):
-        groups.setdefault(label, []).append(node)
-
-    return groups
-
-# 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, min_diameter: int):
-        """
-        获取管网模型信息
-        :param wn: 由wntr生成的模型
-        :param min_diameter: 安装的最小管径
-        """
-        # 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))
-
-        # === 改动新增部分：筛选管径小于 min_diameter 的管道节点 ===
-        self.less_than_min_diameter_junction_list = []
-        for pipe in self.pipes:
-            diameter = wn.links[pipe].diameter
-            if diameter < min_diameter:
-                start_node = wn.links[pipe].start_node.name
-                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))
-
-    # 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, min_diameter: int):
-        """
-
-        :param wn: 由wntr生成的模型
-        :param sensornum: 传感器的数量
-        :param min_diameter: 安装的最小管径
-        """
-        wn_func.__init__(self, wn, min_diameter=min_diameter)
-        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 = {}
-
-        # 改动
-        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])
-            for node in self.less_than_min_diameter_junction_list:
-                # 这里的group_sumSS[i]是每个分组的灵敏度节点排序列表，去除已经被标记为删除的节点self.less_than_min_diameter_junction_list
-                if node in group_sumSS[i]:
-                    group_sumSS[i].remove(node)
-        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_ID(name: str, sensor_num: int, min_diameter: int) -> list[str]:
-    """
-    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
-    :param min_diameter: 安装的最小管径
-    :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'
-    # 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, min_diameter=min_diameter)
-    # 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
-
-    G1 = wn_real.to_graph()
-    G1 = G1.to_undirected()  # 变为无向图
-
-    group = kgroup(Coor_node, sensor_num)
-    # group = skater_partition(G1, sensor_num)
-    # group = spectral_partition(G1, sensor_num)
-
-    # print(group)
-    # --------------------- 保存 group 数据 ---------------------
-    # 将 group 数据转换为一个“长格式”的 DataFrame，
-    # 每一行记录一个节点及其所属的分组
-    # group_data = []
-    # for group_id, node_list in group.items():
-    #     for node in node_list:
-    #         group_data.append({"Group": group_id, "Node": node})
-    #
-    # df_group = pd.DataFrame(group_data)
-    #
-    # # 保存为 Excel 文件，文件名为 "group.xlsx"；index=False 表示不保存行索引
-    # df_group.to_excel("group.xlsx", index=False)
-
-    # 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, min_diameter=min_diameter)
-    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)
-
-
-    # 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)
-    # # 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, sensor_num):
-    #     ax = plt.gca()
-    #     ax.set_title(inp_file_real + str(sensor_num))
-    #     nodes = nx.draw_networkx_nodes(G, pos, nodelist=group[i], node_color=colorlist[i], node_size=10)
-    # nodes = nx.draw_networkx_nodes(G, pos,
-    #                                nodelist=sensorindex_2, node_color='red', 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(sensor_num) + ".png", dpi=300)
-    # plt.show()
-    #
-    # wntr.graphics.plot_network(wn_real, node_attribute=sensorindex_2, node_size=50, node_labels=False,
-    #                            title=inp_file_real + '_Projetion' + str(sensor_num))
-    # plt.savefig(inp_file_real + '_S' + str(sensor_num) + ".png", dpi=300)
-    # plt.show()
-    return sensorindex
-
-
-if __name__ == '__main__':
-    sensorindex = get_ID(name=project_info.name, sensor_num=20, min_diameter=300)
-
-    print(sensorindex)
-    # 将 sensor_coord 字典转换为 DataFrame，
-    # 使用 orient='index' 表示字典的键作为 DataFrame 的行索引，
-    # 数据中每个键对应的 value 是一个子字典，其键 'x' 和 'y' 成为 DataFrame 的列名
-    # df_sensor_coord = pd.DataFrame.from_dict(sensor_coord, orient='index')
-    #
-    # # 将索引名称设为 'Node'
-    # df_sensor_coord.index.name = 'Node'
-    #
-    # # 保存到 Excel 文件
-    # df_sensor_coord.to_excel("sensor_coord.xlsx", index=True)

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/valve_isolation/__init__.py

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

app/algorithms/valve_isolation/topology_search.py

@@ -0,0 +1,142 @@
+"""Topology-only valve isolation search."""
+
+from collections import defaultdict, deque
+from typing import Any, Iterable, Literal
+
+
+VALVE_LINK_TYPE = "valve"
+AccidentElementType = Literal["auto", "link", "node"]
+
+
+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]
+
+
+def _reachable_nodes(
+    adjacency: dict[str, list[tuple[str, str]]],
+    start_nodes: Iterable[str],
+    blocked_links: set[str],
+) -> set[str]:
+    visited = set(start_nodes)
+    queue = deque(visited)
+    while queue:
+        for neighbor, link_id in adjacency.get(queue.popleft(), ()):
+            if link_id not in blocked_links and neighbor not in visited:
+                visited.add(neighbor)
+                queue.append(neighbor)
+    return visited
+
+
+def valve_isolation_analysis(
+    link_entries: Iterable[str],
+    accident_elements: str | list[str],
+    disabled_valves: list[str] | None = None,
+    *,
+    accident_element_type: AccidentElementType = "auto",
+    water_source_nodes: Iterable[str] | None = None,
+    closed_links: Iterable[str] = (),
+) -> dict[str, Any]:
+    """Find nearest valve boundaries, then check source connectivity.
+
+    Legacy untyped IDs prefer links; explicit node mode handles cross-type ID
+    collisions. Supply impact is a before/after connectivity difference, not
+    a hydraulic pressure or demand calculation. Without source metadata only
+    the provisional boundary search is available.
+    """
+
+    disabled_valves_set = set(disabled_valves or [])
+    target_elements = (
+        [accident_elements]
+        if isinstance(accident_elements, str)
+        else accident_elements
+    )
+    if not target_elements:
+        raise ValueError("At least one accident element is required")
+    if accident_element_type not in {"auto", "link", "node"}:
+        raise ValueError(f"Invalid accident element type: {accident_element_type}")
+    closed_links_set = set(closed_links)
+
+    adjacency: dict[str, list[tuple[str, str]]] = defaultdict(list)
+    all_valves: dict[str, tuple[str, str]] = {}
+    link_lookup: dict[str, tuple[str, str, str]] = {}
+    node_set: set[str] = set()
+    for link_entry in link_entries:
+        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.update((node1, node2))
+        if link_type_name == VALVE_LINK_TYPE:
+            all_valves[link_id] = (node1, node2)
+        if link_id not in closed_links_set:
+            adjacency[node1].append((node2, link_id))
+            adjacency[node2].append((node1, link_id))
+
+    unknown_disabled = disabled_valves_set - all_valves.keys()
+    if unknown_disabled:
+        raise ValueError(f"Disabled valve was not found in topology: {sorted(unknown_disabled)}")
+
+    start_nodes: set[str] = set()
+    for element in target_elements:
+        if accident_element_type != "node" and element in link_lookup:
+            node1, node2, _ = link_lookup[element]
+            start_nodes.update((node1, node2))
+        elif accident_element_type != "link" and element in node_set:
+            start_nodes.add(element)
+        else:
+            raise ValueError(f"Accident element {element} was not found in topology")
+
+    closable_valves = set(all_valves) - disabled_valves_set - closed_links_set
+    affected_nodes = _reachable_nodes(adjacency, start_nodes, closable_valves)
+
+    must_close_valves: list[str] = []
+    optional_valves: list[str] = []
+    for valve_id in closable_valves:
+        node1, node2 = all_valves[valve_id]
+        node1_affected = node1 in affected_nodes
+        node2_affected = node2 in affected_nodes
+        if node1_affected and node2_affected:
+            optional_valves.append(valve_id)
+        elif node1_affected or node2_affected:
+            must_close_valves.append(valve_id)
+
+    must_close_valves.sort()
+    optional_valves.sort()
+    isolatable = bool(must_close_valves)
+    failure_reason = None if isolatable else "no_boundary_valves"
+    sources = set(water_source_nodes or ())
+    source_check_performed = bool(sources)
+    supply_affected_nodes = None
+    if water_source_nodes is not None:
+        if not sources:
+            isolatable = False
+            failure_reason = "water_sources_unavailable"
+        else:
+            after_closure = _reachable_nodes(adjacency, sources, set(must_close_valves))
+            if affected_nodes & after_closure:
+                isolatable = False
+                failure_reason = "connected_to_water_source"
+            if isolatable:
+                before_closure = _reachable_nodes(adjacency, sources, set())
+                supply_affected_nodes = sorted(before_closure - after_closure)
+    result: dict[str, Any] = {
+        "accident_elements": target_elements,
+        "accident_element_type": accident_element_type,
+        "disabled_valves": disabled_valves,
+        "affected_nodes": sorted(affected_nodes) if isolatable else [],
+        "affected_node_count": len(affected_nodes),
+        "must_close_valves": must_close_valves,
+        "optional_valves": optional_valves,
+        "isolatable": isolatable,
+        "source_check_performed": source_check_performed,
+        "isolation_failure_reason": failure_reason,
+        "supply_affected_nodes": supply_affected_nodes,
+        "supply_affected_node_count": (
+            len(supply_affected_nodes) if supply_affected_nodes is not None else None
+        ),
+    }
+    if len(target_elements) == 1:
+        result["accident_element"] = target_elements[0]
+    return result

app/api/pagination.py

@@ -0,0 +1,14 @@
+from __future__ import annotations
+
+from collections.abc import Iterable
+from typing import Generic, TypeVar
+
+T = TypeVar("T")
+
+
+class PaginatedList(list[T], Generic[T]):
+    """A page of items carrying the total count from its data source."""
+
+    def __init__(self, items: Iterable[T], *, total: int) -> None:
+        super().__init__(items)
+        self.total = total

app/api/problem_details.py

@@ -0,0 +1,109 @@
+from __future__ import annotations
+
+from typing import Any
+from uuid import uuid4
+
+from fastapi import FastAPI, HTTPException, Request
+from fastapi.exceptions import RequestValidationError
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel, Field
+
+from app.native.wndb.core.database import MaterializedViewRefreshAfterCommitError
+
+
+class ProblemDetails(BaseModel):
+    """RFC 9457 compatible error response used by the REST contract."""
+
+    type: str
+    title: str
+    status: int
+    detail: str
+    instance: str
+    code: str
+    trace_id: str
+    errors: list[dict[str, Any]] = Field(default_factory=list)
+
+
+def _trace_id(request: Request) -> str:
+    return request.headers.get("X-Request-Id") or str(uuid4())
+
+
+def _problem_response(
+    request: Request,
+    *,
+    status_code: int,
+    title: str,
+    detail: str,
+    code: str,
+    errors: list[dict[str, Any]] | None = None,
+) -> JSONResponse:
+    problem = ProblemDetails(
+        type=f"https://tjwater.example/problems/{code.replace('_', '-')}",
+        title=title,
+        status=status_code,
+        detail=detail,
+        instance=request.url.path,
+        code=code,
+        trace_id=_trace_id(request),
+        errors=errors or [],
+    )
+    return JSONResponse(
+        status_code=status_code,
+        content=problem.model_dump(mode="json"),
+        media_type="application/problem+json",
+    )
+
+
+def install_problem_details_handlers(app: FastAPI) -> None:
+    @app.exception_handler(MaterializedViewRefreshAfterCommitError)
+    async def materialized_view_refresh_error_handler(
+        request: Request,
+        exc: MaterializedViewRefreshAfterCommitError,
+    ) -> JSONResponse:
+        response = _problem_response(
+            request,
+            status_code=503,
+            title="Materialized view refresh failed",
+            detail=(
+                f"Project {exc.project!r} changes were committed, but GIS query "
+                "views could not be refreshed. Do not repeat the write blindly."
+            ),
+            code="materialized_view_refresh_failed_after_commit",
+        )
+        response.headers["X-TJWater-Changes-Committed"] = "true"
+        return response
+
+    @app.exception_handler(RequestValidationError)
+    async def validation_error_handler(
+        request: Request,
+        exc: RequestValidationError,
+    ) -> JSONResponse:
+        return _problem_response(
+            request,
+            status_code=422,
+            title="Validation error",
+            detail="Request validation failed",
+            code="validation_error",
+            errors=exc.errors(),
+        )
+
+    @app.exception_handler(HTTPException)
+    async def http_error_handler(request: Request, exc: HTTPException) -> JSONResponse:
+        detail = exc.detail if isinstance(exc.detail, str) else str(exc.detail)
+        code_by_status = {
+            401: "unauthenticated",
+            403: "forbidden",
+            404: "not_found",
+            409: "conflict",
+            422: "validation_error",
+            503: "dependency_unavailable",
+        }
+        return _problem_response(
+            request,
+            status_code=exc.status_code,
+            title=code_by_status.get(exc.status_code, "request_error")
+            .replace("_", " ")
+            .title(),
+            detail=detail,
+            code=code_by_status.get(exc.status_code, "request_error"),
+        )

app/api/v1/endpoints/access.py

@@ -0,0 +1,39 @@
+from fastapi import APIRouter, Depends, Header
+
+from app.auth.metadata_dependencies import (
+    get_current_metadata_user,
+    get_metadata_repository,
+)
+from app.auth.permissions import resolve_permissions
+from app.auth.project_dependencies import resolve_project_context
+from app.domain.schemas.access import AccessContextResponse
+from app.infra.db.metadb.repositories.metadata_repository import MetadataRepository
+
+router = APIRouter()
+
+
+@router.get("/access-context", response_model=AccessContextResponse)
+async def get_access_context(
+    x_project_id: str | None = Header(default=None, alias="X-Project-Id"),
+    current_user=Depends(get_current_metadata_user),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> AccessContextResponse:
+    project_context = (
+        await resolve_project_context(x_project_id, current_user, metadata_repo)
+        if x_project_id
+        else None
+    )
+    permissions = resolve_permissions(
+        project_role=project_context.project_role if project_context else None,
+        system_role=current_user.role,
+        is_superuser=current_user.is_superuser,
+    )
+    return AccessContextResponse(
+        user_id=current_user.id,
+        username=current_user.username,
+        system_role=current_user.role,
+        is_system_admin=current_user.is_superuser or current_user.role == "admin",
+        project_id=project_context.project_id if project_context else None,
+        project_role=project_context.project_role if project_context else None,
+        permissions=sorted(permissions),
+    )

app/api/v1/endpoints/admin_metadata.py

@@ -0,0 +1,804 @@
+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.api.pagination import PaginatedList
+from app.auth.metadata_dependencies import (
+    get_current_metadata_admin,
+    get_metadata_repository,
+)
+from app.auth.keycloak_dependencies import (
+    get_current_keycloak_payload,
+    oauth2_optional,
+)
+from app.core.audit import AuditAction, log_audit_event
+from app.domain.schemas.admin_metadata import (
+    AdminProjectCreateRequest,
+    AdminProjectResponse,
+    AdminProjectUpdateRequest,
+    KeycloakUserSyncIssue,
+    KeycloakUserSyncResponse,
+    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,
+)
+from app.services.keycloak_user_sync import (
+    KeycloakUserSyncConfigurationError,
+    KeycloakUserSyncRequestError,
+    fetch_keycloak_users,
+)
+
+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("/admin/users/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("/admin/user-syncs", 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("/admin/user-syncs/batches", 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.post(
+    "/admin/keycloak-user-syncs",
+    response_model=KeycloakUserSyncResponse,
+)
+async def sync_keycloak_user_directory(
+    current_user=Depends(get_current_metadata_admin),
+    keycloak_payload: dict = Depends(get_current_keycloak_payload),
+    keycloak_access_token: str | None = Depends(oauth2_optional),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> KeycloakUserSyncResponse:
+    try:
+        keycloak_users = await fetch_keycloak_users(
+            issuer=str(keycloak_payload.get("iss") or ""),
+            bearer_token=keycloak_access_token or "",
+        )
+    except KeycloakUserSyncConfigurationError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=str(exc),
+        ) from exc
+    except KeycloakUserSyncRequestError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_502_BAD_GATEWAY,
+            detail=str(exc),
+        ) from exc
+
+    created = 0
+    updated = 0
+    issues: list[KeycloakUserSyncIssue] = []
+    for keycloak_user in keycloak_users:
+        if keycloak_user.service_account_client_id:
+            issues.append(
+                KeycloakUserSyncIssue(
+                    username=keycloak_user.username,
+                    reason="service_account",
+                )
+            )
+            continue
+        if not keycloak_user.email:
+            issues.append(
+                KeycloakUserSyncIssue(
+                    username=keycloak_user.username,
+                    reason="missing_email",
+                )
+            )
+            continue
+        try:
+            _, was_created = await metadata_repo.sync_user_snapshot_from_keycloak(
+                keycloak_id=keycloak_user.keycloak_id,
+                username=keycloak_user.username,
+                email=keycloak_user.email,
+            )
+        except IntegrityError:
+            await metadata_repo.session.rollback()
+            issues.append(
+                KeycloakUserSyncIssue(
+                    username=keycloak_user.username,
+                    reason="identity_conflict",
+                )
+            )
+        except SQLAlchemyError as exc:
+            await metadata_repo.session.rollback()
+            raise HTTPException(
+                status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+                detail="Metadata database error during Keycloak user synchronization",
+            ) from exc
+        else:
+            created += int(was_created)
+            updated += int(not was_created)
+
+    result = KeycloakUserSyncResponse(
+        scanned=len(keycloak_users),
+        created=created,
+        updated=updated,
+        skipped=len(issues),
+        issues=issues,
+    )
+    await log_audit_event(
+        action=AuditAction.UPDATE,
+        user_id=current_user.id,
+        resource_type="keycloak_user_directory",
+        request_data=result.model_dump(mode="json", exclude={"issues"}),
+        response_status=status.HTTP_200_OK,
+        session=metadata_repo.session,
+    )
+    return result
+
+
+@router.get("/admin/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)
+    total = await metadata_repo.count_users()
+    items = [MetadataUserResponse.model_validate(user) for user in users]
+    return PaginatedList(items, total=total)
+
+
+@router.get("/admin/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(
+    "/admin/projects",
+    response_model=AdminProjectResponse,
+    status_code=status.HTTP_201_CREATED,
+    deprecated=True,
+    summary="仅登记已有项目元数据",
+    description=(
+        "仅用于登记已经由外部流程完整创建的资源。新项目应调用 "
+        "POST /admin/project-provisions。"
+    ),
+)
+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,
+            creator_user_id=current_user.id,
+        )
+    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(
+    "/admin/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(
+    "/admin/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(
+    "/admin/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(
+    "/admin/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(
+    "/admin/projects/{project_id}/databases/{db_role}/health-checks",
+    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("/admin/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("/admin/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(
+    "/admin/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(
+    "/admin/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(
+    "/admin/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("/admin/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 +1,53 @@
+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,
+)
+from app.auth.permissions import permissions_for_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
+    permissions: list[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,
+        permissions=sorted(permissions_for_context(ctx)),
+        token_expires_at=token_expires_at,
+    )

app/api/v1/endpoints/audit.py

@@ -0,0 +1,146 @@
+from datetime import datetime
+from typing import Literal
+from uuid import UUID
+
+from fastapi import APIRouter, Depends, Query, Request, status
+from pydantic import BaseModel
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.auth.metadata_dependencies import (
+    get_current_metadata_admin,
+    get_current_metadata_user,
+)
+from app.api.pagination import PaginatedList
+from app.core.audit import AuditAction, log_audit_event
+from app.domain.schemas.audit import AuditLogResponse
+from app.infra.db.metadb.database import get_metadata_session
+from app.infra.db.metadb.repositories.audit_repository import AuditRepository
+
+router = APIRouter()
+
+
+class SessionAuditEventRequest(BaseModel):
+    event: Literal["login", "logout"]
+
+
+async def get_audit_repository(
+    session: AsyncSession = Depends(get_metadata_session),
+) -> AuditRepository:
+    return AuditRepository(session)
+
+
+@router.get(
+    "/audit-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: UUID | None = Query(None, description="按用户ID过滤"),
+    project_id: UUID | None = Query(None, description="按项目ID过滤"),
+    action: str | None = Query(None, description="按操作类型过滤"),
+    resource_type: str | None = Query(None, description="按资源类型过滤"),
+    start_time: datetime | None = Query(None, description="开始时间"),
+    end_time: datetime | None = 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]:
+    items = 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,
+    )
+    total = 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 PaginatedList(items, total=total)
+
+
+@router.get(
+    "/audit-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: UUID | None = Query(None, description="按用户ID过滤"),
+    project_id: UUID | None = Query(None, description="按项目ID过滤"),
+    action: str | None = Query(None, description="按操作类型过滤"),
+    resource_type: str | None = Query(None, description="按资源类型过滤"),
+    start_time: datetime | None = Query(None, description="开始时间"),
+    end_time: datetime | None = Query(None, description="结束时间"),
+    _current_user=Depends(get_current_metadata_admin),
+    audit_repo: AuditRepository = Depends(get_audit_repository),
+) -> dict:
+    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.post("/audit-events", status_code=status.HTTP_204_NO_CONTENT)
+async def record_session_event(
+    payload: SessionAuditEventRequest,
+    request: Request,
+    current_user=Depends(get_current_metadata_user),
+    session: AsyncSession = Depends(get_metadata_session),
+) -> None:
+    await log_audit_event(
+        action=AuditAction.LOGIN if payload.event == "login" else AuditAction.LOGOUT,
+        user_id=current_user.id,
+        resource_type="session",
+        resource_id=str(current_user.keycloak_id),
+        ip_address=request.client.host if request.client else None,
+        request_method=request.method,
+        request_path=request.url.path,
+        response_status=status.HTTP_204_NO_CONTENT,
+        session=session,
+    )
+
+
+@router.get(
+    "/audit-logs/mine",
+    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: str | None = Query(None, description="按操作类型过滤"),
+    start_time: datetime | None = Query(None, description="开始时间"),
+    end_time: datetime | None = 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]:
+    items = await audit_repo.get_logs(
+        user_id=current_user.id,
+        action=action,
+        start_time=start_time,
+        end_time=end_time,
+        skip=skip,
+        limit=limit,
+    )
+    total = await audit_repo.get_log_count(
+        user_id=current_user.id,
+        action=action,
+        start_time=start_time,
+        end_time=end_time,
+    )
+    return PaginatedList(items, total=total)

app/api/v1/endpoints/burst_detection.py

@@ -0,0 +1,83 @@
+from datetime import datetime
+from typing import Any
+from uuid import UUID
+
+from fastapi import APIRouter, Depends, HTTPException, Body
+from pydantic import BaseModel, Field
+from starlette.concurrency import run_in_threadpool
+
+from app.auth.keycloak_dependencies import get_current_keycloak_username
+from app.services.burst_detection import (
+    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")
+    target_time: datetime | None = Field(
+        None,
+        description="目标侦测时刻；为空时自动使用最近一个完整的监测时刻",
+    )
+    sampling_interval_minutes: int | None = Field(
+        None,
+        ge=1,
+        le=1440,
+        description="采样间隔（分钟）；为空时根据压力 SCADA 传输频率自动推断",
+    )
+    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_run_id: UUID | None = Field(None, description="分析模拟运行 ID")
+
+
+@router.post(
+    "/burst-detections",
+    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 await run_in_threadpool(
+            run_burst_detection,
+            **data.model_dump(),
+            username=username,
+        )
+    except Exception as exc:
+        raise HTTPException(status_code=400, detail=str(exc))

app/api/v1/endpoints/burst_location.py

@@ -0,0 +1,73 @@
+from typing import Any
+from datetime import datetime
+
+from typing import Literal
+from uuid import UUID
+
+from fastapi import APIRouter, Depends, HTTPException, Body
+from pydantic import BaseModel, Field
+from starlette.concurrency import run_in_threadpool
+
+from app.auth.keycloak_dependencies import get_current_keycloak_username
+from app.services.burst_location import (
+    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="爆管定位运行名称")
+    simulation_run_id: UUID | None = Field(None, description="分析模拟运行 ID")
+
+
+@router.post(
+    "/burst-locations",
+    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 await run_in_threadpool(
+            run_burst_location_by_network,
+            **data.model_dump(),
+            username=username,
+        )
+    except (TypeError, ValueError) as exc:
+        raise HTTPException(status_code=400, detail=str(exc))

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

@@ -0,0 +1,70 @@
+from typing import Any
+
+from fastapi import APIRouter, Body, Query
+from app.services.tjnetwork import (
+    ChangeSet,
+    get_control,
+    get_control_schema,
+    get_rule,
+    get_rule_schema,
+    set_control,
+    set_rule,
+)
+
+router = APIRouter()
+
+@router.get("/network-schemas/control", summary="获取控制架构", description="获取网络中控制对象的架构定义")
+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("/controls/properties", summary="获取控制属性", description="获取指定网络中的控制属性信息")
+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.patch("/controls/properties", response_model=None, summary="设置控制属性", description="更新指定网络中的控制属性")
+def fastapi_set_control_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> ChangeSet:
+    """设置控制属性。
+    
+    更新指定网络中的控制属性值。
+    """
+    props = payload
+    return set_control(network, ChangeSet(props))
+
+@router.get("/rule-schemas", summary="获取规则架构", description="获取网络中规则对象的架构定义")
+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("/rule-properties", summary="获取规则属性", description="获取指定网络中的规则属性信息")
+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.patch("/rule-properties", response_model=None, summary="设置规则属性", description="更新指定网络中的规则属性")
+def fastapi_set_rule_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> ChangeSet:
+    """设置规则属性。
+    
+    更新指定网络中的规则属性值。
+    """
+    props = payload
+    return set_rule(network, ChangeSet(props))

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

@@ -0,0 +1,95 @@
+from typing import Any
+
+from fastapi import APIRouter, Body, Query
+from app.services.tjnetwork import (
+    ChangeSet,
+    add_curve,
+    delete_curve,
+    get_curve,
+    get_curve_schema,
+    get_curves,
+    is_curve,
+    set_curve,
+)
+
+router = APIRouter()
+
+@router.get("/network-schemas/curve", summary="获取曲线架构", description="获取网络中曲线对象的架构定义")
+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("/curves", response_model=None, summary="添加曲线", description="在网络中添加一条新的曲线")
+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"),
+    payload: dict[str, Any] = Body(...)
+) -> 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 = payload
+    ps = {
+        "id": curve,
+    } | props
+    return add_curve(network, ChangeSet(ps))
+
+@router.delete("/curves", response_model=None, summary="删除曲线", description="从网络中删除指定的曲线")
+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("/curves/properties", summary="获取曲线属性", description="获取指定曲线的属性信息")
+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.patch("/curves/properties", response_model=None, summary="设置曲线属性", description="更新指定曲线的属性")
+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"),
+    payload: dict[str, Any] = Body(...)
+) -> ChangeSet:
+    """设置曲线属性。
+    
+    更新指定曲线的属性值。
+    """
+    props = payload
+    ps = {"id": curve} | props
+    return set_curve(network, ChangeSet(ps))
+
+@router.get("/curves", summary="获取所有曲线", description="获取网络中的所有曲线列表")
+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("/curves/existence", summary="检查曲线存在性", description="检查指定的曲线是否存在")
+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 typing import Any
+
+from fastapi import APIRouter, Body, Query
+from app.services.tjnetwork import (
+    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("/network-schemas/time", summary="获取时间选项架构", description="获取网络中时间选项的架构定义")
+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("/network-options/time", 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")
+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.patch("/time-properties", 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")
+def fastapi_set_time_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> 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 = payload
+    return set_time(network, ChangeSet(props))
+
+@router.get("/network-schemas/energy", summary="获取能耗选项架构", description="获取网络中能耗选项的架构定义")
+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("/network-options/energy", 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")
+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.patch("/energy-properties", 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")
+def fastapi_set_energy_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> 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 = payload
+    return set_energy(network, ChangeSet(props))
+
+@router.get("/network-schemas/pump-energy", 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")
+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("/network-options/pump-energy", summary="获取泵能耗属性", description="获取指定泵的能耗属性信息")
+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.patch("/network-options/pump-energy", response_model=None, summary="设置泵能耗属性", description="更新指定泵的能耗属性")
+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"),
+    payload: dict[str, Any] = Body(...)
+) -> ChangeSet:
+    """设置泵能耗属性。
+    
+    更新指定泵的能耗相关属性值。
+    """
+    props = payload
+    ps = {"id": pump} | props
+    return set_pump_energy(network, ChangeSet(ps))
+
+@router.get("/network-schemas/option", summary="获取选项架构", description="获取网络中选项对象的架构定义")
+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("/network-options", summary="获取选项属性", description="获取指定网络中的选项属性信息")
+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.patch("/network-options", response_model=None, summary="设置选项属性", description="更新指定网络中的选项属性")
+def fastapi_set_option_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> ChangeSet:
+    """设置选项属性。
+    
+    更新指定网络中的选项属性值。
+    """
+    props = payload
+    return set_option_v3(network, ChangeSet(props))

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

@@ -0,0 +1,95 @@
+from typing import Any
+
+from fastapi import APIRouter, Body, Query
+from app.services.tjnetwork import (
+    ChangeSet,
+    add_pattern,
+    delete_pattern,
+    get_pattern,
+    get_pattern_schema,
+    get_patterns,
+    is_pattern,
+    set_pattern,
+)
+
+router = APIRouter()
+
+@router.get("/network-schemas/pattern", summary="获取模式架构", description="获取网络中模式对象的架构定义")
+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("/patterns", response_model=None, summary="添加模式", description="在网络中添加一个新的模式")
+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"),
+    payload: dict[str, Any] = Body(...)
+) -> 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 = payload
+    ps = {
+        "id": pattern,
+    } | props
+    return add_pattern(network, ChangeSet(ps))
+
+@router.delete("/patterns", response_model=None, summary="删除模式", description="从网络中删除指定的模式")
+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("/patterns/properties", summary="获取模式属性", description="获取指定模式的属性信息")
+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.patch("/patterns/properties", response_model=None, summary="设置模式属性", description="更新指定模式的属性")
+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"),
+    payload: dict[str, Any] = Body(...)
+) -> ChangeSet:
+    """设置模式属性。
+    
+    更新指定模式的属性值。
+    """
+    props = payload
+    ps = {"id": pattern} | props
+    return set_pattern(network, ChangeSet(ps))
+
+@router.get("/patterns/existence", summary="检查模式存在性", description="检查指定的模式是否存在")
+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("/patterns", summary="获取所有模式", description="获取网络中的所有模式列表")
+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 typing import Any
+
+from fastapi import APIRouter, Body, Query
+from app.services.tjnetwork import (
+    ChangeSet,
+    add_mixing,
+    add_source,
+    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_mixing,
+    set_pipe_reaction,
+    set_quality,
+    set_reaction,
+    set_source,
+    set_tank_reaction,
+)
+
+router = APIRouter()
+
+@router.get("/network-schemas/quality", summary="获取水质架构", description="获取网络中水质对象的架构定义")
+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("/quality-configurations/properties", summary="获取水质属性", description="获取指定节点的水质属性信息")
+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.patch("/quality-configurations/properties", response_model=None, summary="设置水质属性", description="更新指定节点的水质属性")
+def fastapi_set_quality_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> ChangeSet:
+    """设置水质属性。
+    
+    更新指定节点的水质属性值。
+    """
+    props = payload
+    return set_quality(network, ChangeSet(props))
+
+@router.get("/network-schemas/emitter", summary="获取发射器架构", description="获取网络中发射器对象的架构定义")
+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("/emitters/properties", summary="获取发射器属性", description="获取指定连接点的发射器属性信息")
+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.patch("/emitters/properties", 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")
+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"),
+    payload: dict[str, Any] = Body(...)
+) -> ChangeSet:
+    """设置发射器属性。
+    
+    更新指定连接点的发射器属性值。
+    """
+    props = payload
+    ps = {"junction": junction} | props
+    return set_emitter(network, ChangeSet(ps))
+
+@router.get("/network-schemas/source", summary="获取水源架构", description="获取网络中水源对象的架构定义")
+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("/sources/detail", summary="获取水源属性", description="获取指定节点的水源属性信息")
+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.patch("/sources", response_model=None, summary="设置水源属性", description="更新指定节点的水源属性")
+def fastapi_set_source(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> ChangeSet:
+    """设置水源属性。
+    
+    更新指定节点的水源属性值。
+    """
+    props = payload
+    return set_source(network, ChangeSet(props))
+
+@router.post("/sources", response_model=None, summary="添加水源", description="在网络中添加一个新的水源")
+def fastapi_add_source(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> 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 = payload
+    return add_source(network, ChangeSet(props))
+
+@router.delete("/sources", response_model=None, summary="删除水源", description="从网络中删除指定节点的水源")
+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("/network-schemas/reaction", summary="获取反应架构", description="获取网络中反应对象的架构定义")
+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("/reactions/detail", summary="获取反应属性", description="获取指定网络中的反应属性信息")
+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.patch("/reactions", response_model=None, summary="设置反应属性", description="更新指定网络中的反应属性")
+def fastapi_set_reaction(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> ChangeSet:
+    """设置反应属性。
+    
+    更新指定网络中的反应属性值。
+    """
+    props = payload
+    return set_reaction(network, ChangeSet(props))
+
+@router.get("/network-schemas/pipe-reaction", 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")
+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("/pipe-reactions/detail", summary="获取管道反应属性", description="获取指定管道的反应属性信息")
+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.patch("/pipe-reactions", response_model=None, summary="设置管道反应属性", description="更新指定管道的反应属性")
+def fastapi_set_pipe_reaction(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> ChangeSet:
+    """设置管道反应属性。
+    
+    更新指定管道的反应属性值。
+    """
+    props = payload
+    return set_pipe_reaction(network, ChangeSet(props))
+
+@router.get("/network-schemas/tank-reaction", 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")
+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("/tank-reactions/detail", summary="获取水池反应属性", description="获取指定水池的反应属性信息")
+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.patch("/tank-reactions", response_model=None, summary="设置水池反应属性", description="更新指定水池的反应属性")
+def fastapi_set_tank_reaction(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> ChangeSet:
+    """设置水池反应属性。
+    
+    更新指定水池的反应属性值。
+    """
+    props = payload
+    return set_tank_reaction(network, ChangeSet(props))
+
+@router.get("/network-schemas/mixing", summary="获取混合架构", description="获取网络中混合对象的架构定义")
+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("/mixing-configurations/detail", summary="获取混合属性", description="获取指定水池的混合属性信息")
+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.patch("/mixing-configurations", response_model=None, summary="设置混合属性", description="更新指定水池的混合属性")
+def fastapi_set_mixing(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> ChangeSet:
+    """设置混合属性。
+    
+    更新指定水池的混合属性值。
+    """
+    props = payload
+    return set_mixing(network, ChangeSet(props))
+
+@router.post("/mixing-configurations", response_model=None, summary="添加混合", description="在网络中添加一个新的混合")
+def fastapi_add_mixing(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> 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 = payload
+    return add_mixing(network, ChangeSet(props))
+
+@router.delete("/mixing-configurations", response_model=None, summary="删除混合", description="从网络中删除指定的混合")
+def fastapi_delete_mixing(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> 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 = payload
+    return delete_mixing(network, ChangeSet(props))

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

@@ -0,0 +1,180 @@
+from typing import Any
+
+from fastapi import APIRouter, Body, Query
+from app.services.tjnetwork import (
+    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("/network-schemas/vertex", 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")
+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("/visual-elements/properties", summary="获取图形元素属性", description="获取指定图形元素的属性信息")
+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.patch("/visual-elements/properties", response_model=None, summary="设置图形元素属性", description="更新指定图形元素的属性")
+def fastapi_set_vertex_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> ChangeSet:
+    """设置图形元素属性。
+    
+    更新指定图形元素的属性值。
+    """
+    props = payload
+    return set_vertex(network, ChangeSet(props))
+
+@router.post("/visual-elements", 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")
+def fastapi_add_vertex(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> 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 = payload
+    return add_vertex(network, ChangeSet(props))
+
+@router.delete("/visual-elements", response_model=None, summary="删除图形元素", description="从网络中删除指定的图形元素")
+def fastapi_delete_vertex(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> 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 = payload
+    return delete_vertex(network, ChangeSet(props))
+
+@router.get("/visual-elements/links", 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")
+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("/all-vertices", 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")
+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("/network-schemas/label", summary="获取标签架构", description="获取网络中标签对象的架构定义")
+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("/labels/properties", summary="获取标签属性", description="获取指定坐标处的标签属性信息")
+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.patch("/labels/properties", response_model=None, summary="设置标签属性", description="更新指定标签的属性")
+def fastapi_set_label_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> ChangeSet:
+    """设置标签属性。
+    
+    更新指定标签的属性值。
+    """
+    props = payload
+    return set_label(network, ChangeSet(props))
+
+@router.post("/labels", response_model=None, summary="添加标签", description="在网络中添加一个新的标签")
+def fastapi_add_label(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> 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 = payload
+    return add_label(network, ChangeSet(props))
+
+@router.delete("/labels", response_model=None, summary="删除标签", description="从网络中删除指定的标签")
+def fastapi_delete_label(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> 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 = payload
+    return delete_label(network, ChangeSet(props))
+
+@router.get("/network-schemas/backdrop", summary="获取背景架构", description="获取网络中背景对象的架构定义")
+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("/backdrops/properties", summary="获取背景属性", description="获取指定网络的背景属性信息")
+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.patch("/backdrops/properties", response_model=None, summary="设置背景属性", description="更新指定网络的背景属性")
+def fastapi_set_backdrop_properties(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> ChangeSet:
+    """设置背景属性。
+    
+    更新指定网络的背景属性值。
+    """
+    props = payload
+    return set_backdrop(network, ChangeSet(props))

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(
+    "/geocoding-requests",
+    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 +1,87 @@
+import os
+from typing import Any
+from datetime import datetime
+
+from fastapi import APIRouter, Depends, HTTPException, Body
+from pydantic import BaseModel, ConfigDict, Field
+from starlette.concurrency import run_in_threadpool
+
+from app.auth.keycloak_dependencies import get_current_keycloak_username
+from app.services.dma_leakage_estimation import (
+    run_leakage_identification,
+)
+
+router = APIRouter()
+DEFAULT_N_WORKERS = max(1, min((os.cpu_count() or 1) - 1, 4))
+MAX_POPULATION_SIZE = 1_000
+MAX_GENERATIONS = 1_000
+MAX_DURATION_HOURS = 168
+
+
+class LeakageIdentifyRequest(BaseModel):
+    """漏损识别请求模型"""
+
+    model_config = ConfigDict(extra="forbid")
+
+    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]] | None = (
+        Field(None, description="观测的压力数据；文件路径不属于公共 API 输入")
+    )
+    start_time: float = Field(0, ge=0, description="起始时间（小时）")
+    duration: float = Field(
+        24, gt=0, le=MAX_DURATION_HOURS, description="持续时间（小时）"
+    )
+    timestep: float = Field(5, gt=0, le=1440, description="时间步长（分钟）")
+    q_sum: float = Field(0.2, ge=0, description="总流量（m3/s）")
+    q_sum_unit: str = Field("m3/s", description="流量单位")
+    pop_size: int = Field(
+        50, ge=2, le=MAX_POPULATION_SIZE, description="种群大小"
+    )
+    max_gen: int = Field(100, ge=1, le=MAX_GENERATIONS, description="最大代数")
+    n_workers: int = Field(
+        DEFAULT_N_WORKERS,
+        ge=1,
+        le=DEFAULT_N_WORKERS,
+        description="工作进程数",
+    )
+    output_flow_unit: str = Field("m3/s", description="输出流量单位")
+    dma_count: int | None = Field(None, ge=1, 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(
+    "/leakage-identifications",
+    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 await run_in_threadpool(
+            run_leakage_identification,
+            **data.model_dump(),
+            username=username,
+        )
+    except Exception as exc:
+        raise HTTPException(status_code=400, detail=str(exc))

app/api/v1/endpoints/meta.py

@@ -0,0 +1,119 @@
+import logging
+from fastapi import APIRouter, Depends, HTTPException, status, Query, Path
+import psycopg
+from psycopg import AsyncConnection
+
+from app.auth.project_dependencies import (
+    ProjectContext,
+    get_project_context,
+    get_project_pg_connection,
+    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("/projects/current/metadata", 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，包括项目基本信息和项目权限
+    """
+    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("/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("/projects/current/database-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_conn: AsyncConnection = Depends(get_project_pg_connection),
+    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:
+        async with pg_conn.cursor() as cur:
+            await cur.execute("SELECT 1")
+            await cur.fetchone()
+    except psycopg.Error 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/model_import.py

@@ -0,0 +1,391 @@
+import json
+from pathlib import Path
+from tempfile import NamedTemporaryFile
+from uuid import UUID, uuid4
+
+from fastapi import (
+    APIRouter,
+    Depends,
+    File,
+    Form,
+    HTTPException,
+    Path as ApiPath,
+    Request,
+    UploadFile,
+    status,
+)
+from sqlalchemy.exc import IntegrityError
+from starlette.concurrency import run_in_threadpool
+
+from app.auth.metadata_dependencies import (
+    get_current_metadata_admin,
+    get_metadata_repository,
+)
+from app.auth.project_dependencies import (
+    ProjectContext,
+    resolve_project_business_routing,
+)
+from app.core.audit import AuditAction, log_audit_event
+from app.core.encryption import is_database_encryption_configured
+from app.domain.schemas.admin_metadata import (
+    AdminProjectResponse,
+    ProjectProvisionResponse,
+)
+from app.infra.db.metadb.repositories.metadata_repository import MetadataRepository
+from app.infra.db.project_routing import activate_project_routing
+from app.native.wndb.core.database import MaterializedViewRefreshAfterCommitError
+from app.services.network_import import network_update
+from app.services.project_provisioning import (
+    ProjectProvisioningError,
+    ProvisionedProjectInfrastructure,
+    provision_project_infrastructure,
+    validate_project_code,
+)
+from app.services.tjnetwork import run_inp
+
+router = APIRouter()
+
+MAX_INP_FILE_BYTES = 50 * 1024 * 1024
+INP_SECTIONS = ("[TITLE]", "[JUNCTIONS]", "[RESERVOIRS]", "[TANKS]", "[PIPES]")
+
+
+async def _get_active_project(project_id: UUID, metadata_repo: MetadataRepository):
+    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",
+        )
+    if project.status != "active":
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
+            detail="Project is not active",
+        )
+    return project
+
+
+def _validate_inp_bytes(content: bytes, filename: str) -> str:
+    if Path(filename).suffix.lower() != ".inp":
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Only .inp model files are accepted",
+        )
+    if not content:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="INP file is empty",
+        )
+    if len(content) > MAX_INP_FILE_BYTES:
+        raise HTTPException(
+            status_code=status.HTTP_413_REQUEST_ENTITY_TOO_LARGE,
+            detail="INP file exceeds the 50 MiB limit",
+        )
+    for encoding in ("utf-8-sig", "gb18030"):
+        try:
+            text = content.decode(encoding)
+            break
+        except UnicodeDecodeError:
+            continue
+    else:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="INP file encoding is not supported",
+        )
+    upper_text = text.upper()
+    if not any(section in upper_text for section in INP_SECTIONS):
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Invalid INP file structure",
+        )
+    return text
+
+
+async def _read_upload(file: UploadFile) -> tuple[bytes, str]:
+    filename = Path(file.filename or "").name
+    content = await file.read(MAX_INP_FILE_BYTES + 1)
+    normalized = _validate_inp_bytes(content, filename).encode("utf-8")
+    return normalized, filename
+
+
+async def _audit_model_change(
+    *,
+    request: Request,
+    current_user,
+    metadata_repo: MetadataRepository,
+    project_id: UUID,
+    action: str,
+) -> None:
+    await log_audit_event(
+        action=AuditAction.UPDATE,
+        user_id=current_user.id,
+        project_id=project_id,
+        resource_type="hydraulic_model",
+        resource_id=action,
+        request_data={"operation": action},
+        ip_address=request.client.host if request.client else None,
+        request_method=request.method,
+        request_path=request.url.path,
+        response_status=status.HTTP_200_OK,
+        session=metadata_repo.session,
+    )
+
+
+def _run_uploaded_inp_sync(content: bytes) -> str:
+    target_dir = Path("inp")
+    target_dir.mkdir(parents=True, exist_ok=True)
+    model_name = f"admin_model_{uuid4().hex}"
+    target_path = target_dir / f"{model_name}.inp"
+    target_path.write_bytes(content)
+    return run_inp(model_name)
+
+
+async def _run_uploaded_inp(content: bytes) -> str:
+    return await run_in_threadpool(_run_uploaded_inp_sync, content)
+
+
+def _update_from_inp_sync(content: bytes, project_code: str) -> None:
+    temp_path: Path | None = None
+    try:
+        with NamedTemporaryFile(suffix=".inp", delete=False) as temp_file:
+            temp_file.write(content)
+            temp_path = Path(temp_file.name)
+        network_update(str(temp_path), project_code)
+    finally:
+        if temp_path is not None:
+            temp_path.unlink(missing_ok=True)
+
+
+async def _update_from_inp(content: bytes, project_code: str) -> None:
+    await run_in_threadpool(_update_from_inp_sync, content, project_code)
+
+
+async def _apply_model_update(content: bytes, project_code: str) -> None:
+    try:
+        await _update_from_inp(content, project_code)
+    except MaterializedViewRefreshAfterCommitError:
+        raise
+    except Exception as exc:
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail=f"数据库操作失败: {exc}",
+        ) from exc
+
+
+def _provision_from_inp_sync(
+    content: bytes,
+    *,
+    code: str,
+    workspace: str,
+) -> ProvisionedProjectInfrastructure:
+    temp_path: Path | None = None
+    try:
+        with NamedTemporaryFile(suffix=".inp", delete=False) as temp_file:
+            temp_file.write(content)
+            temp_path = Path(temp_file.name)
+        return provision_project_infrastructure(
+            code=code,
+            workspace=workspace,
+            inp_path=temp_path,
+        )
+    finally:
+        if temp_path is not None:
+            temp_path.unlink(missing_ok=True)
+
+
+@router.post(
+    "/admin/project-provisions",
+    response_model=ProjectProvisionResponse,
+    status_code=status.HTTP_201_CREATED,
+    summary="创建完整供水项目",
+)
+async def provision_project(
+    request: Request,
+    name: str = Form(..., min_length=1, max_length=100),
+    code: str = Form(..., min_length=1, max_length=50),
+    description: str | None = Form(default=None),
+    gs_workspace: str | None = Form(default=None, max_length=100),
+    map_zoom: int = Form(default=14, ge=1, le=22),
+    file: UploadFile = File(..., description="EPANET INP 模型文件"),
+    current_user=Depends(get_current_metadata_admin),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> ProjectProvisionResponse:
+    try:
+        normalized_code = validate_project_code(code)
+    except ValueError as exc:
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=str(exc),
+        ) from exc
+    workspace = gs_workspace or normalized_code
+    if await metadata_repo.get_project_by_code(normalized_code) is not None:
+        raise HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail="Project code already exists",
+        )
+    if not is_database_encryption_configured():
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail="DATABASE_ENCRYPTION_KEY is not configured",
+        )
+
+    content, filename = await _read_upload(file)
+    validation_result = await _run_uploaded_inp(content)
+    try:
+        validation_payload = json.loads(validation_result)
+    except (TypeError, json.JSONDecodeError) as exc:
+        raise HTTPException(
+            status_code=status.HTTP_500_INTERNAL_SERVER_ERROR,
+            detail="EPANET validation returned an invalid response",
+        ) from exc
+    if validation_payload.get("simulation_result") != "successful":
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="EPANET model validation failed",
+        )
+    try:
+        infrastructure = await run_in_threadpool(
+            _provision_from_inp_sync,
+            content,
+            code=normalized_code,
+            workspace=workspace,
+        )
+    except ProjectProvisioningError as exc:
+        if isinstance(exc.cause, ValueError):
+            response_status = status.HTTP_409_CONFLICT
+        elif exc.stage == "preflight":
+            response_status = status.HTTP_503_SERVICE_UNAVAILABLE
+        else:
+            response_status = status.HTTP_500_INTERNAL_SERVER_ERROR
+        raise HTTPException(
+            status_code=response_status,
+            detail={
+                "stage": exc.stage,
+                "message": str(exc.cause),
+                "cleanup_errors": exc.cleanup_errors,
+            },
+        ) from exc
+
+    map_extent = {"bbox": list(infrastructure.map_bbox), "zoom": map_zoom}
+    try:
+        project = await metadata_repo.create_provisioned_project(
+            name=name,
+            code=normalized_code,
+            description=description,
+            gs_workspace=workspace,
+            map_extent=map_extent,
+            creator_user_id=current_user.id,
+            business_dsn=infrastructure.business_dsn,
+            timescale_dsn=infrastructure.timescale_dsn,
+            pool_min_size=1,
+            pool_max_size=4,
+        )
+    except Exception as exc:
+        await metadata_repo.session.rollback()
+        cleanup_errors = await run_in_threadpool(infrastructure.cleanup)
+        if isinstance(exc, IntegrityError):
+            response_status = status.HTTP_409_CONFLICT
+            detail = "Project code or workspace conflicts with an existing project"
+        else:
+            response_status = status.HTTP_503_SERVICE_UNAVAILABLE
+            detail = f"Metadata database error: {exc}"
+        if cleanup_errors:
+            detail = f"{detail}; cleanup failures: {', '.join(cleanup_errors)}"
+        raise HTTPException(status_code=response_status, detail=detail) from exc
+
+    await log_audit_event(
+        action=AuditAction.CREATE,
+        user_id=current_user.id,
+        project_id=project.id,
+        resource_type="project_provision",
+        resource_id=str(project.id),
+        request_data={
+            "name": name,
+            "code": normalized_code,
+            "filename": filename,
+            "gs_workspace": workspace,
+            "layers": list(infrastructure.layers),
+        },
+        ip_address=request.client.host if request.client else None,
+        request_method=request.method,
+        request_path=request.url.path,
+        response_status=status.HTTP_201_CREATED,
+        session=metadata_repo.session,
+    )
+    return ProjectProvisionResponse(
+        project=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,
+        ),
+        business_database=normalized_code,
+        model_template_database=infrastructure.model_template,
+        timescale_database=normalized_code,
+        geoserver_workspace=workspace,
+        geoserver_layers=list(infrastructure.layers),
+    )
+
+
+@router.post(
+    "/admin/projects/{project_id}/model-imports",
+    summary="导入桌面端水力模型",
+)
+async def import_project_model(
+    request: Request,
+    project_id: UUID = ApiPath(...),
+    file: UploadFile = File(..., description="桌面端导出的 INP 模型文件"),
+    current_user=Depends(get_current_metadata_admin),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> dict:
+    project = await _get_active_project(project_id, metadata_repo)
+    content, filename = await _read_upload(file)
+    result = await _run_uploaded_inp(content)
+    await _audit_model_change(
+        request=request,
+        current_user=current_user,
+        metadata_repo=metadata_repo,
+        project_id=project.id,
+        action="import",
+    )
+    return {"project_id": str(project.id), "filename": filename, "result": result}
+
+
+@router.patch(
+    "/admin/projects/{project_id}/model-imports",
+    summary="更新桌面端水力模型",
+)
+async def update_project_model(
+    request: Request,
+    project_id: UUID = ApiPath(...),
+    file: UploadFile = File(..., description="桌面端导出的 INP 模型文件"),
+    current_user=Depends(get_current_metadata_admin),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> dict:
+    project = await _get_active_project(project_id, metadata_repo)
+    content, filename = await _read_upload(file)
+    routing = await resolve_project_business_routing(
+        ProjectContext(
+            project_id=project.id,
+            project_code=project.code,
+            user_id=current_user.id,
+            project_role="owner",
+            system_role=current_user.role,
+            is_superuser=current_user.is_superuser,
+        ),
+        metadata_repo,
+    )
+    with activate_project_routing(routing):
+        await _apply_model_update(content, project.code)
+    await _audit_model_change(
+        request=request,
+        current_user=current_user,
+        metadata_repo=metadata_repo,
+        project_id=project.id,
+        action="update",
+    )
+    return {"project_id": str(project.id), "filename": filename, "updated": True}

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

@@ -0,0 +1,139 @@
+from typing import Any
+
+from fastapi import APIRouter, Body, Query
+from app.services.tjnetwork import (
+    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(
+    "/network-schemas/demand",
+    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"
+)
+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(
+    "/demands/properties",
+    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"
+)
+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.patch(
+    "/demands/properties",
+    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"
+)
+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"),
+    payload: dict[str, Any] = Body(...)
+) -> 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 = payload
+    ps = {"junction": junction} | props
+    return set_demand(network, ChangeSet(ps))
+
+############################################################
+# water distribution 36.[Water Distribution]
+############################################################
+@router.post(
+    "/demands/to-nodes",
+    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"
+)
+def fastapi_calculate_demand_to_nodes(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> 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 = payload
+    demand = props["demand"]
+    nodes = props["nodes"]
+    return calculate_demand_to_nodes(network, demand, nodes)
+
+@router.post(
+    "/demands/to-region",
+    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"
+)
+def fastapi_calculate_demand_to_region(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> 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 = payload
+    demand = props["demand"]
+    region = props["region"]
+    return calculate_demand_to_region(network, demand, region)
+
+@router.post(
+    "/demands/to-network",
+    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"
+)
+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 typing import Any
+
+from fastapi import APIRouter, Body, Query
+from app.services.tjnetwork import (
+    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(
+    "/nodes/existence",
+    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"
+)
+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(
+    "/junctions/existence",
+    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)"
+)
+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(
+    "/reservoirs/existence",
+    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)"
+)
+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(
+    "/tanks/existence",
+    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"
+)
+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(
+    "/links/existence",
+    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"
+)
+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(
+    "/pipes/existence",
+    summary="检查是否为管道",
+    description="检查指定ID是否为水网中的管道"
+)
+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(
+    "/pumps/existence",
+    summary="检查是否为泵",
+    description="检查指定ID是否为水网中的泵"
+)
+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(
+    "/valves/existence",
+    summary="检查是否为阀门",
+    description="检查指定ID是否为水网中的阀门"
+)
+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(
+    "/node-types",
+    summary="获取节点类型",
+    description="获取指定节点的类型(接点/水源/蓄水池)"
+)
+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(
+    "/link-types",
+    summary="获取管线类型",
+    description="获取指定管线的类型(管道/泵/阀门)"
+)
+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(
+    "/element-types",
+    summary="获取元素类型",
+    description="获取指定元素的类型(节点或管线)"
+)
+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(
+    "/element-type-values",
+    summary="获取元素类型值",
+    description="获取指定元素的类型数值标识"
+)
+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(
+    "/nodes",
+    summary="获取所有节点",
+    description="获取指定水网中的所有节点ID列表"
+)
+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(
+    "/links",
+    summary="获取所有管线",
+    description="获取指定水网中的所有管线ID列表"
+)
+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(
+    "/node-links",
+    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(
+    "/node-properties",
+    summary="获取节点属性",
+    description="获取指定节点的所有属性信息"
+)
+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(
+    "/link-properties",
+    summary="获取管线属性",
+    description="获取指定管线的所有属性信息"
+)
+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(
+    "/scada-properties",
+    summary="获取SCADA点属性",
+    description="获取指定SCADA点的属性信息"
+)
+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(
+    "/all-scada-properties",
+    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"
+)
+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(
+    "/element-properties-with-types",
+    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"
+)
+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(
+    "/element-properties",
+    summary="获取元素属性",
+    description="获取指定元素的属性信息"
+)
+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(
+    "/title-schemas",
+    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"
+)
+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(
+    "/titles",
+    summary="获取水网标题属性",
+    description="获取指定水网的标题(Title)信息"
+)
+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.patch(
+    "/titles",
+    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"
+)
+def fastapi_set_title(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> ChangeSet:
+    """设置水网的标题属性。"""
+    props = payload
+    return set_title(network, ChangeSet(props))
+
+############################################################
+# status 10.[STATUS]
+############################################################
+
+@router.get(
+    "/status-schemas",
+    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"
+)
+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(
+    "/status",
+    summary="获取管线状态",
+    description="获取指定管线的状态信息"
+)
+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.patch(
+    "/status-properties",
+    response_model=None,
+    summary="设置管线状态",
+    description="设置指定管线的状态信息"
+)
+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"),
+    payload: dict[str, Any] = Body(...)
+) -> ChangeSet:
+    """设置管线的状态属性。"""
+    props = payload
+    ps = {"link": link} | props
+    return set_status(network, ChangeSet(ps))
+
+############################################################
+# General Deletion
+############################################################
+
+@router.delete(
+    "/nodes",
+    response_model=None,
+    summary="删除节点",
+    description="删除指定的节点(接点/水源/蓄水池)"
+)
+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.delete(
+    "/links",
+    response_model=None,
+    summary="删除管线",
+    description="删除指定的管线(管道/泵/阀门)"
+)
+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 +1,104 @@
+from typing import Any
+
+from fastapi import APIRouter, Query
+
+from app.services.tjnetwork import (
+    get_major_node_coords,
+    get_major_pipe_nodes,
+    get_network_in_extent,
+    get_network_link_nodes,
+    get_node_coord,
+)
+
+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 = payload
+#     return set_coord(network, ChangeSet(props))
+
+@router.get(
+    "/node-coords",
+    summary="获取节点坐标",
+    description="获取指定节点的地理坐标(X, Y)"
+)
+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(
+    "/network-in-extents",
+    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"
+)
+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(
+    "/majornode-coords",
+    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"
+)
+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(
+    "/major-pipe-nodes",
+    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"
+)
+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(
+    "/network-link-nodes",
+    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"
+)
+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 +1,360 @@
+from typing import Any
+
+from fastapi import APIRouter, Body, Query
+from app.services.tjnetwork import (
+    ChangeSet,
+    add_junction,
+    delete_junction,
+    get_all_junctions,
+    get_junction,
+    get_junction_schema,
+    set_junction,
+)
+
+router = APIRouter()
+
+@router.get("/network-schemas/junction", 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。")
+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("/junctions", 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。")
+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.delete("/junctions", response_model=None, summary="删除节点", description="从供水网络中删除指定的节点。")
+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("/junctions/elevation", summary="获取节点标高", description="获取指定节点的标高（海拔高度）。")
+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("/junctions/x", summary="获取节点 X 坐标", description="获取指定节点的 X 坐标值。")
+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("/junctions/y", summary="获取节点 Y 坐标", description="获取指定节点的 Y 坐标值。")
+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("/junctions/coord", summary="获取节点坐标", description="获取指定节点的 X 和 Y 坐标。")
+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("/junctions/demand", summary="获取节点需水量", description="获取指定节点的需水量。")
+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("/junctions/pattern", summary="获取节点需水模式", description="获取指定节点的需水模式标识。")
+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.patch("/junctions/elevation", response_model=None, summary="设置节点标高", description="设置指定节点的标高值。")
+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.patch("/junctions/x", response_model=None, summary="设置节点 X 坐标", description="设置指定节点的 X 坐标值。")
+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.patch("/junctions/y", response_model=None, summary="设置节点 Y 坐标", description="设置指定节点的 Y 坐标值。")
+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.patch("/junctions/coord", response_model=None, summary="设置节点坐标", description="设置指定节点的 X 和 Y 坐标。")
+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.patch("/junctions/demand", response_model=None, summary="设置节点需水量", description="设置指定节点的需水量。")
+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.patch("/junctions/pattern", 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。")
+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("/junctions/properties", summary="获取节点属性", description="获取指定节点的所有属性信息。")
+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("/junctions", 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。")
+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
+    results = get_all_junctions(network)
+    return results
+
+@router.patch("/junctions/properties", 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。")
+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"),
+    payload: dict[str, Any] = Body(...)
+) -> 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 = payload
+    ps = {"id": junction} | props
+    return set_junction(network, ChangeSet(ps))

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

@@ -0,0 +1,410 @@
+from typing import Any
+
+from fastapi import APIRouter, Body, Query
+from app.services.tjnetwork import (
+    ChangeSet,
+    PIPE_STATUS_OPEN,
+    add_pipe,
+    delete_pipe,
+    get_all_pipes,
+    get_pipe,
+    get_pipe_schema,
+    set_pipe,
+)
+
+router = APIRouter()
+
+@router.get("/network-schemas/pipe", 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")
+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("/pipes", 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")
+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.delete("/pipes", response_model=None, summary="删除管道", description="从网络中删除指定的管道")
+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("/pipes/node1", summary="获取管道起始节点", description="获取指定管道的起始节点ID")
+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("/pipes/node2", summary="获取管道终止节点", description="获取指定管道的终止节点ID")
+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("/pipes/length", summary="获取管道长度", description="获取指定管道的长度")
+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("/pipes/diameter", summary="获取管道管径", description="获取指定管道的管径")
+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("/pipes/roughness", summary="获取管道粗糙度", description="获取指定管道的粗糙度")
+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("/pipes/minor-loss", 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")
+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("/pipes/status", summary="获取管道状态", description="获取指定管道的状态（开启或关闭）")
+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.patch("/pipes/node1", response_model=None, summary="设置管道起始节点", description="设置指定管道的起始节点")
+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.patch("/pipes/node2", response_model=None, summary="设置管道终止节点", description="设置指定管道的终止节点")
+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.patch("/pipes/length", response_model=None, summary="设置管道长度", description="设置指定管道的长度")
+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.patch("/pipes/diameter", response_model=None, summary="设置管道管径", description="设置指定管道的管径")
+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.patch("/pipes/roughness", response_model=None, summary="设置管道粗糙度", description="设置指定管道的粗糙度")
+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.patch("/pipes/minor-loss", 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")
+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.patch("/pipes/status", 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）")
+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("/pipes/properties", summary="获取管道属性", description="获取指定管道的所有属性信息")
+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("/pipes", 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")
+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
+    results = get_all_pipes(network)
+    return results
+
+@router.patch("/pipes/properties", response_model=None, summary="设置管道属性", description="批量设置指定管道的多个属性")
+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"),
+    payload: dict[str, Any] = Body(...)
+) -> 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 = payload
+    ps = {"id": pipe} | props
+    return set_pipe(network, ChangeSet(ps))

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

@@ -0,0 +1,202 @@
+from typing import Any
+
+from fastapi import APIRouter, Body, Query
+from app.services.tjnetwork import (
+    ChangeSet,
+    add_pump,
+    delete_pump,
+    get_all_pumps,
+    get_pump,
+    get_pump_schema,
+    set_pump,
+)
+
+router = APIRouter()
+
+@router.get("/network-schemas/pump", 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")
+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("/pumps", 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")
+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.delete("/pumps", response_model=None, summary="删除水泵", description="从网络中删除指定的水泵")
+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("/pumps/node1", summary="获取水泵起始节点", description="获取指定水泵的起始节点ID")
+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("/pumps/node2", summary="获取水泵终止节点", description="获取指定水泵的终止节点ID")
+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.patch("/pumps/node1", response_model=None, summary="设置水泵起始节点", description="设置指定水泵的起始节点")
+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.patch("/pumps/node2", response_model=None, summary="设置水泵终止节点", description="设置指定水泵的终止节点")
+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("/pumps/properties", summary="获取水泵属性", description="获取指定水泵的所有属性信息")
+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("/pumps", 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")
+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
+    results = get_all_pumps(network)
+    return results
+
+@router.patch("/pumps/properties", response_model=None, summary="设置水泵属性", description="批量设置指定水泵的多个属性")
+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"),
+    payload: dict[str, Any] = Body(...)
+) -> 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 = payload
+    ps = {"id": pump} | props
+    return set_pump(network, ChangeSet(ps))

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

@@ -0,0 +1,72 @@
+from typing import Any
+
+from fastapi import APIRouter, Body, Query
+
+from app.services.tjnetwork import (
+    ChangeSet,
+    add_region,
+    delete_region,
+    get_nodes_in_region,
+    get_region,
+    get_region_schema,
+    get_regions,
+    set_region,
+)
+
+
+router = APIRouter()
+
+
+@router.get("/network-schemas/region", summary="获取区域属性架构")
+def get_region_schema_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+) -> dict[str, dict[str, Any]]:
+    return get_region_schema(network)
+
+
+@router.get("/regions", summary="获取区域列表")
+def get_regions_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+) -> list[dict[str, Any]]:
+    return [get_region(network, region_id) for region_id in get_regions(network)]
+
+
+@router.get("/regions/detail", summary="获取区域信息")
+def get_region_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    id: str = Query(..., description="区域 ID"),
+) -> dict[str, Any]:
+    return get_region(network, id)
+
+
+@router.get("/regions/nodes", summary="获取区域节点")
+def get_region_nodes_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    id: str = Query(..., description="区域 ID"),
+) -> list[str]:
+    return get_nodes_in_region(network, id)
+
+
+@router.patch("/regions", summary="修改区域", response_model=None)
+def set_region_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...),
+) -> ChangeSet:
+    return set_region(network, ChangeSet(payload))
+
+
+@router.post("/regions", summary="添加区域", response_model=None)
+def add_region_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...),
+) -> ChangeSet:
+    payload["boundary"] = [tuple(point[:2]) for point in payload.get("boundary", [])]
+    return add_region(network, ChangeSet(payload))
+
+
+@router.delete("/regions", summary="删除区域", response_model=None)
+def delete_region_endpoint(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...),
+) -> ChangeSet:
+    return delete_region(network, ChangeSet(payload))

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

@@ -0,0 +1,422 @@
+from typing import Any
+
+from fastapi import APIRouter, Body, Query
+from app.services.tjnetwork import (
+    ChangeSet,
+    add_reservoir,
+    delete_reservoir,
+    get_all_reservoirs,
+    get_reservoir,
+    get_reservoir_schema,
+    set_reservoir,
+)
+
+router = APIRouter()
+
+@router.get(
+    "/network-schemas/reservoir",
+    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"
+)
+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(
+    "/reservoirs",
+    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"
+)
+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.delete(
+    "/reservoirs",
+    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"
+)
+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(
+    "/reservoirs/head",
+    summary="获取水库水头",
+    description="获取指定水库的供水水头/总水头值"
+)
+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(
+    "/reservoirs/pattern",
+    summary="获取水库模式",
+    description="获取指定水库的运行模式/供水模式"
+)
+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(
+    "/reservoirs/x",
+    summary="获取水库X坐标",
+    description="获取指定水库的X坐标位置"
+)
+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(
+    "/reservoirs/y",
+    summary="获取水库Y坐标",
+    description="获取指定水库的Y坐标位置"
+)
+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(
+    "/reservoirs/coord",
+    summary="获取水库坐标",
+    description="获取指定水库的平面坐标（X和Y坐标）"
+)
+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.patch(
+    "/reservoirs/head",
+    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"
+)
+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.patch(
+    "/reservoirs/pattern",
+    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"
+)
+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.patch(
+    "/reservoirs/x",
+    response_model=None,
+    summary="设置水库X坐标",
+    description="更新指定水库的X坐标位置"
+)
+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.patch(
+    "/reservoirs/y",
+    response_model=None,
+    summary="设置水库Y坐标",
+    description="更新指定水库的Y坐标位置"
+)
+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.patch(
+    "/reservoirs/coord",
+    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）"
+)
+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(
+    "/reservoirs/properties",
+    summary="获取水库属性",
+    description="获取指定水库的所有属性"
+)
+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(
+    "/reservoirs",
+    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"
+)
+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.patch(
+    "/reservoirs/properties",
+    response_model=None,
+    summary="设置水库属性",
+    description="批量更新指定水库的多个属性"
+)
+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"),
+    payload: dict[str, Any] = Body(...)
+) -> 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 = payload
+    ps = {"id": reservoir} | props
+    return set_reservoir(network, ChangeSet(ps))

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

@@ -0,0 +1,66 @@
+from typing import Any
+
+from fastapi import APIRouter, Body, Query
+from app.services.tjnetwork import (
+    ChangeSet,
+    get_tag,
+    get_tag_schema,
+    get_tags,
+    set_tag,
+)
+
+router = APIRouter()
+
+############################################################
+# tag 8.[TAGS]
+############################################################
+
+@router.get(
+    "/network-schemas/tag",
+    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"
+)
+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(
+    "/tags/detail",
+    summary="获取标签信息",
+    description="获取指定类型和ID的标签信息"
+)
+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(
+    "/tags",
+    summary="获取所有标签",
+    description="获取指定水网中的所有标签信息"
+)
+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.patch(
+    "/tags",
+    response_model=None,
+    summary="设置标签",
+    description="为指定元素设置或修改标签信息"
+)
+def fastapi_set_tag(
+    network: str = Query(..., description="管网名称（或数据库名称）"),
+    payload: dict[str, Any] = Body(...)
+) -> ChangeSet:
+    """设置标签信息。"""
+    props = payload
+    return set_tag(network, ChangeSet(props))

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

@@ -0,0 +1,565 @@
+from typing import Any
+
+from fastapi import APIRouter, Body, Query
+from app.services.tjnetwork import (
+    ChangeSet,
+    add_tank,
+    delete_tank,
+    get_all_tanks,
+    get_tank,
+    get_tank_schema,
+    set_tank,
+)
+
+router = APIRouter()
+
+@router.get("/network-schemas/tank", 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")
+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("/tanks", summary="新增水箱", description="向指定网络中新增一个水箱", response_model=None)
+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.delete("/tanks", summary="删除水箱", description="删除指定网络中的水箱", response_model=None)
+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("/tanks/elevation", summary="获取水箱标高", description="获取指定水箱的标高值")
+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("/tanks/init-level", summary="获取水箱初始水位", description="获取指定水箱的初始水位值")
+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("/tanks/min-level", summary="获取水箱最小水位", description="获取指定水箱的最小水位值")
+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("/tanks/max-level", summary="获取水箱最大水位", description="获取指定水箱的最大水位值")
+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("/tanks/diameter", summary="获取水箱直径", description="获取指定水箱的直径值")
+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("/tanks/min-vol", summary="获取水箱最小体积", description="获取指定水箱的最小体积值")
+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("/tanks/vol-curve", summary="获取水箱容积曲线", description="获取指定水箱的容积曲线标识")
+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("/tanks/overflow", summary="获取水箱溢流口", description="获取指定水箱的溢流口配置")
+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("/tanks/x", summary="获取水箱X坐标", description="获取指定水箱的X坐标值")
+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("/tanks/y", summary="获取水箱Y坐标", description="获取指定水箱的Y坐标值")
+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("/tanks/coord", summary="获取水箱坐标", description="获取指定水箱的X和Y坐标")
+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.patch("/tanks/elevation", summary="设置水箱标高", description="设置指定水箱的标高值", response_model=None)
+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.patch("/tanks/init-level", 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)
+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.patch("/tanks/min-level", 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)
+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.patch("/tanks/max-level", 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)
+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.patch("/tanks/diameter", summary="设置水箱直径", description="设置指定水箱的直径值", response_model=None)
+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.patch("/tanks/min-vol", 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)
+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.patch("/tanks/vol-curve", 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)
+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.patch("/tanks/overflow", summary="设置水箱溢流口", description="设置指定水箱的溢流口配置", response_model=None)
+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.patch("/tanks/x", summary="设置水箱X坐标", description="设置指定水箱的X坐标值", response_model=None)
+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.patch("/tanks/y", summary="设置水箱Y坐标", description="设置指定水箱的Y坐标值", response_model=None)
+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.patch("/tanks/coord", summary="设置水箱坐标", description="设置指定水箱的X和Y坐标", response_model=None)
+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("/tanks/properties", summary="获取水箱属性", description="获取指定水箱的所有属性")
+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("/tanks", summary="获取所有水箱属性", description="获取指定网络中所有水箱的属性")
+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
+    results = get_all_tanks(network)
+    return results
+
+@router.patch("/tanks/properties", summary="设置水箱属性", description="批量设置指定水箱的多个属性", response_model=None)
+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"),
+    payload: dict[str, Any] = Body(...)
+) -> 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 = payload
+    ps = {"id": tank} | props
+    return set_tank(network, ChangeSet(ps))

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

@@ -0,0 +1,331 @@
+from typing import Any
+
+from fastapi import APIRouter, Body, Query
+from app.services.tjnetwork import (
+    ChangeSet,
+    VALVES_TYPE_PRV,
+    add_valve,
+    delete_valve,
+    get_all_valves,
+    get_valve,
+    get_valve_schema,
+    set_valve,
+)
+
+router = APIRouter()
+
+@router.get(
+    "/network-schemas/valve",
+    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",
+)
+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(
+    "/valves",
+    response_model=None,
+    summary="添加阀门",
+    description="在指定的水网中添加新的阀门",
+)
+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.delete(
+    "/valves",
+    response_model=None,
+    summary="删除阀门",
+    description="从指定的水网中删除指定的阀门",
+)
+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(
+    "/valves/node1",
+    summary="获取阀门起点节点",
+    description="获取指定阀门连接的起点节点ID",
+)
+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(
+    "/valves/node2",
+    summary="获取阀门终点节点",
+    description="获取指定阀门连接的终点节点ID",
+)
+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(
+    "/valves/diameter",
+    summary="获取阀门直径",
+    description="获取指定阀门的直径",
+)
+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(
+    "/valves/type",
+    summary="获取阀门类型",
+    description="获取指定阀门的类型",
+)
+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(
+    "/valves/setting",
+    summary="获取阀门开度",
+    description="获取指定阀门的开度/设置值",
+)
+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(
+    "/valves/minor-loss",
+    summary="获取阀门损失系数",
+    description="获取指定阀门的损失系数",
+)
+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.patch(
+    "/valves/node1",
+    response_model=None,
+    summary="设置阀门起点节点",
+    description="设置指定阀门的起点节点",
+)
+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.patch(
+    "/valves/node2",
+    response_model=None,
+    summary="设置阀门终点节点",
+    description="设置指定阀门的终点节点",
+)
+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.patch(
+    "/valves/diameter",
+    response_model=None,
+    summary="设置阀门直径",
+    description="设置指定阀门的直径",
+)
+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.patch(
+    "/valves/type",
+    response_model=None,
+    summary="设置阀门类型",
+    description="设置指定阀门的类型",
+)
+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.patch(
+    "/valves/setting",
+    response_model=None,
+    summary="设置阀门开度",
+    description="设置指定阀门的开度/设置值",
+)
+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(
+    "/valves/properties",
+    summary="获取阀门所有属性",
+    description="获取指定阀门的所有属性",
+)
+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(
+    "/valves",
+    summary="获取所有阀门属性",
+    description="获取指定水网中所有阀门的属性",
+)
+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
+    results = get_all_valves(network)
+    return results
+
+@router.patch(
+    "/valves/properties",
+    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",
+)
+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"),
+    payload: dict[str, Any] = Body(...),
+) -> 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 = payload
+    ps = {"id": valve} | props
+    return set_valve(network, ChangeSet(ps))

app/api/v1/endpoints/project.py

@@ -0,0 +1,57 @@
+import json
+from fastapi import APIRouter, HTTPException, Query, Depends
+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
+from app.services.tjnetwork import (
+    ChangeSet,
+    export_inp,
+    get_all_vertices,
+    get_all_scada_info,
+)
+
+router = APIRouter()
+
+@router.get("/projects/current", 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)
+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", # Default role for public access
+    )
+
+@router.get("/projects/current/exports/change-set", 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。")
+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]
+    op["vertex"] = json.dumps(get_all_vertices(network))
+    op["scada"] = json.dumps(get_all_scada_info(network))
+
+    return cs

app/api/v1/endpoints/project_data.py

@@ -0,0 +1,76 @@
+from uuid import UUID
+
+from fastapi import APIRouter, Depends, HTTPException, Query
+from psycopg import AsyncConnection
+
+from app.infra.db.postgresql.analysis import AnalysisRepository
+from app.auth.project_dependencies import get_project_pg_connection
+
+router = APIRouter()
+
+
+async def get_database_connection(
+    conn: AsyncConnection = Depends(get_project_pg_connection),
+):
+    """获取数据库连接"""
+    yield conn
+
+
+@router.get("/analysis/runs", summary="获取分析运行列表")
+async def get_analysis_runs(
+    conn: AsyncConnection = Depends(get_database_connection),
+):
+    """
+    获取所有方案信息
+    
+    返回项目中所有方案的详细信息
+    """
+    try:
+        runs = await AnalysisRepository.list_runs(conn)
+        return {"success": True, "data": runs, "count": len(runs)}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"查询分析运行时发生错误: {str(e)}")
+
+
+@router.get("/analysis/runs/{run_id}", summary="获取分析运行")
+async def get_analysis_run(
+    run_id: UUID,
+    conn: AsyncConnection = Depends(get_database_connection),
+):
+    """
+    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:
+        run = await AnalysisRepository.get_run(conn, run_id)
+        if run is None:
+            raise HTTPException(status_code=404, detail="分析运行不存在")
+        return run
+    except HTTPException:
+        raise
+    except Exception as e:
+        raise HTTPException(
+            status_code=500, detail=f"查询分析运行时发生错误: {str(e)}"
+        )
+
+
+@router.get("/analysis/runs/{run_id}/results", summary="获取分析结果")
+async def get_analysis_results(
+    run_id: UUID,
+    result_type: str | None = Query(default=None, description="结果类型"),
+    conn: AsyncConnection = Depends(get_database_connection),
+):
+    """
+    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:
+        return await AnalysisRepository.list_results(conn, run_id, result_type)
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=f"查询分析结果时发生错误: {str(e)}",
+        )

app/api/v1/endpoints/scada.py

@@ -0,0 +1,42 @@
+from typing import Any
+
+from fastapi import APIRouter, Depends, HTTPException
+from psycopg import AsyncConnection
+
+from app.auth.project_dependencies import get_project_pg_connection
+from app.domain.schemas.scada import ScadaDeviceResponse
+from app.infra.db.postgresql.scada import ScadaInfoRepository, get_scada_info_schema
+
+
+router = APIRouter()
+
+
+@router.get("/network-schemas/scada-device", summary="获取 SCADA 设备结构")
+def get_scada_device_schema() -> dict[str, dict[str, Any]]:
+    return get_scada_info_schema("")
+
+
+@router.get(
+    "/scada-devices",
+    summary="获取 SCADA 设备列表",
+    response_model=list[ScadaDeviceResponse],
+)
+async def get_scada_devices(
+    conn: AsyncConnection = Depends(get_project_pg_connection),
+) -> list[dict[str, Any]]:
+    return await ScadaInfoRepository.get_scadas(conn)
+
+
+@router.get(
+    "/scada-devices/{device_id}",
+    summary="获取 SCADA 设备",
+    response_model=ScadaDeviceResponse,
+)
+async def get_scada_device(
+    device_id: str,
+    conn: AsyncConnection = Depends(get_project_pg_connection),
+) -> dict[str, Any]:
+    device = await ScadaInfoRepository.get_scada(conn, device_id)
+    if device is None:
+        raise HTTPException(status_code=404, detail="SCADA 设备不存在")
+    return device

app/api/v1/endpoints/sensor_placement.py

@@ -0,0 +1,286 @@
+import logging
+from typing import Any
+from urllib.parse import quote
+from uuid import UUID
+
+from fastapi import APIRouter, Depends, HTTPException, Path, Query, status
+from fastapi.responses import StreamingResponse
+from starlette.concurrency import run_in_threadpool
+
+from app.auth.metadata_dependencies import get_current_metadata_user
+from app.auth.project_dependencies import (
+    ProjectContext,
+    get_project_context,
+    use_project_business_routing,
+)
+from app.infra.db.project_routing import ActiveProjectRouting
+from app.domain.schemas.sensor_placement import (
+    SensorPointResponse,
+    SensorPlacementExportRequest,
+    SensorPlacementOptimizeRequest,
+    SensorPlacementSchemeResponse,
+    SensorPlacementUpdateRequest,
+)
+from app.services.sensor_placement import (
+    SensorPlacementConflictError,
+    SensorPlacementNotFoundError,
+    SensorPlacementValidationError,
+    build_sensor_placement_workbook,
+    can_edit_sensor_placement,
+    get_sensor_placement_candidate,
+    get_sensor_placement_run,
+    list_sensor_placement_runs,
+    optimize_sensor_placement_by_kmeans,
+    optimize_sensor_placement_by_sensitivity,
+    update_sensor_placement_run,
+)
+
+router = APIRouter()
+logger = logging.getLogger(__name__)
+
+
+def _project_network(network: str, project_context: ProjectContext) -> str:
+    if network != project_context.project_code:
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
+            detail="请求的管网不属于当前项目",
+        )
+    return project_context.project_code
+
+
+def _can_modify_project(project_context: ProjectContext) -> bool:
+    return project_context.project_role == "member"
+
+
+def _require_project_write(
+    project_context: ProjectContext,
+) -> None:
+    if not _can_modify_project(project_context):
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
+            detail="当前项目角色为只读，不能修改监测点方案",
+        )
+
+
+def _service_http_error(exc: Exception) -> HTTPException:
+    if isinstance(exc, SensorPlacementNotFoundError):
+        return HTTPException(
+            status_code=status.HTTP_404_NOT_FOUND,
+            detail=str(exc),
+        )
+    if isinstance(exc, SensorPlacementConflictError):
+        return HTTPException(
+            status_code=status.HTTP_409_CONFLICT,
+            detail=str(exc),
+        )
+    return HTTPException(
+        status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
+        detail=str(exc),
+    )
+
+
+def _get_run_response(
+    network: str,
+    run_id: UUID,
+    current_user: Any,
+    project_context: ProjectContext,
+) -> dict[str, Any]:
+    try:
+        run = get_sensor_placement_run(network, run_id)
+        return {
+            **run,
+            "can_edit": (
+                _can_modify_project(project_context)
+                and can_edit_sensor_placement(current_user, run)
+            ),
+        }
+    except (
+        SensorPlacementNotFoundError,
+        SensorPlacementValidationError,
+    ) as exc:
+        raise _service_http_error(exc) from exc
+
+
+@router.get(
+    "/sensor-placement-candidates/{node_id}",
+    response_model=SensorPointResponse,
+    summary="获取监测点候选节点详情",
+)
+async def get_sensor_placement_candidate_detail(
+    node_id: str = Path(..., min_length=1, max_length=32),
+    project_context: ProjectContext = Depends(get_project_context),
+    _routing: ActiveProjectRouting = Depends(use_project_business_routing),
+) -> dict[str, Any]:
+    try:
+        return await run_in_threadpool(
+            get_sensor_placement_candidate,
+            project_context.project_code,
+            node_id,
+        )
+    except SensorPlacementValidationError as exc:
+        raise _service_http_error(exc) from exc
+
+
+@router.post(
+    "/sensor-placement-runs",
+    response_model=SensorPlacementSchemeResponse,
+    summary="创建并返回监测点优化方案",
+)
+async def optimize_sensor_placement_scheme(
+    payload: SensorPlacementOptimizeRequest,
+    project_context: ProjectContext = Depends(get_project_context),
+    current_user=Depends(get_current_metadata_user),
+) -> dict[str, Any]:
+    network = _project_network(payload.network, project_context)
+    _require_project_write(project_context)
+    optimizer = (
+        optimize_sensor_placement_by_sensitivity
+        if payload.method == "sensitivity"
+        else optimize_sensor_placement_by_kmeans
+    )
+    try:
+        created = await run_in_threadpool(
+            optimizer,
+            project_code=network,
+            run_name=payload.run_name,
+            sensor_count=payload.sensor_count,
+            min_diameter=payload.min_diameter,
+            created_by=current_user.username,
+        )
+        run = get_sensor_placement_run(network, created["run_id"])
+        return {**run, "can_edit": True}
+    except (
+        SensorPlacementConflictError,
+        SensorPlacementValidationError,
+        ValueError,
+    ) as exc:
+        raise _service_http_error(exc) from exc
+    except Exception as exc:
+        logger.exception("Sensor placement optimization failed")
+        raise HTTPException(
+            status_code=500,
+            detail="监测点优化失败，请稍后重试",
+        ) from exc
+
+
+@router.get(
+    "/sensor-placement-runs",
+    response_model=list[SensorPlacementSchemeResponse],
+    summary="获取监测点优化运行",
+)
+async def get_sensor_placement_runs(
+    project_context: ProjectContext = Depends(get_project_context),
+    _routing: ActiveProjectRouting = Depends(use_project_business_routing),
+) -> list[dict[str, Any]]:
+    return await run_in_threadpool(
+        list_sensor_placement_runs,
+        project_context.project_code,
+    )
+
+
+@router.get(
+    "/sensor-placement-runs/{run_id}",
+    response_model=SensorPlacementSchemeResponse,
+    summary="获取监测点方案详情",
+)
+def get_sensor_placement_run_detail(
+    run_id: UUID,
+    network: str = Query(..., min_length=1),
+    project_context: ProjectContext = Depends(get_project_context),
+    current_user=Depends(get_current_metadata_user),
+) -> dict[str, Any]:
+    return _get_run_response(
+        _project_network(network, project_context),
+        run_id,
+        current_user,
+        project_context,
+    )
+
+
+@router.put(
+    "/sensor-placement-runs/{run_id}",
+    response_model=SensorPlacementSchemeResponse,
+    summary="覆盖保存监测点方案",
+)
+def overwrite_sensor_placement_run(
+    run_id: UUID,
+    payload: SensorPlacementUpdateRequest,
+    network: str = Query(..., min_length=1),
+    project_context: ProjectContext = Depends(get_project_context),
+    current_user=Depends(get_current_metadata_user),
+) -> dict[str, Any]:
+    network = _project_network(network, project_context)
+    _require_project_write(project_context)
+    run = _get_run_response(
+        network,
+        run_id,
+        current_user,
+        project_context,
+    )
+    if not run["can_edit"]:
+        raise HTTPException(status_code=403, detail="无权修改该监测点优化运行")
+
+    try:
+        updated = update_sensor_placement_run(
+            network,
+            run_id,
+            expected_sensor_locations=payload.expected_sensor_locations,
+            sensor_locations=payload.sensor_locations,
+        )
+        return {**updated, "can_edit": True}
+    except (
+        SensorPlacementConflictError,
+        SensorPlacementNotFoundError,
+        SensorPlacementValidationError,
+    ) as exc:
+        raise _service_http_error(exc) from exc
+
+
+@router.post(
+    "/sensor-placement-runs/{run_id}/exports/excel",
+    summary="导出监测点工程清单",
+)
+async def export_sensor_placement_excel(
+    run_id: UUID,
+    payload: SensorPlacementExportRequest,
+    network: str = Query(..., min_length=1),
+    project_context: ProjectContext = Depends(get_project_context),
+    current_user=Depends(get_current_metadata_user),
+) -> StreamingResponse:
+    network = _project_network(network, project_context)
+    run = _get_run_response(
+        network,
+        run_id,
+        current_user,
+        project_context,
+    )
+    if (
+        payload.sensor_locations != run["sensor_locations"]
+        and not run["can_edit"]
+    ):
+        raise HTTPException(status_code=403, detail="无权导出该方案的未保存草稿")
+
+    try:
+        workbook = await run_in_threadpool(
+            build_sensor_placement_workbook,
+            network=network,
+            scheme=run,
+            sensor_location=payload.sensor_locations,
+            adjustment_status=payload.adjustment_status,
+        )
+    except SensorPlacementValidationError as exc:
+        raise _service_http_error(exc) from exc
+
+    filename = f"{run['name']}_监测点清单.xlsx"
+    encoded_filename = quote(filename)
+    return StreamingResponse(
+        workbook,
+        media_type=(
+            "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+        ),
+        headers={
+            "Content-Disposition": (
+                f"attachment; filename*=UTF-8''{encoded_filename}"
+            )
+        },
+    )

app/api/v1/endpoints/simulation.py

@@ -0,0 +1,448 @@
+from typing import Any, List, Literal, Optional
+from datetime import datetime, timedelta
+from fastapi import APIRouter, Body, Depends, HTTPException, Query
+from fastapi.responses import PlainTextResponse
+from app.auth.keycloak_dependencies import get_current_keycloak_username
+import app.services.simulation as simulation
+from app.services.tjnetwork import (
+    run_project,
+    run_project_return_dict,
+)
+from app.services.simulation_scenarios import (
+    burst_analysis,
+    valve_close_analysis,
+    flushing_analysis,
+    contaminant_simulation,
+    pressure_regulation,
+)
+from app.services.valve_isolation import analyze_valve_isolation
+from app.domain.time 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 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: str = Field(..., min_length=1, description="方案名称")
+
+
+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)
+    scada_mappings = simulation.query_corresponding_element_id_and_query_id(
+        network_name
+    )
+    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"),
+            scada_mappings=scada_mappings,
+        )
+        current_time += hydraulic_step
+
+
+# 必须用这个PlainTextResponse，不然每个key都有引号
+@router.post("/project-runs", 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。")
+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.post("/project-return-dict-runs", 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。")
+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)
+
+
+# Analysis Endpoints
+@router.post("/burst-analyses", 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。")
+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(..., min_length=1, description="爆管节点/管段ID列表"),
+    burst_size: list[float] = Query(..., min_length=1, description="对应各爆管点的爆管流量大小列表（L/s）"),
+    modify_total_duration: int = Query(..., gt=0, description="模拟总时长（秒）"),
+    scheme_name: str = Query(..., min_length=1, 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。
+    """
+    if len(burst_ID) != len(burst_size):
+        raise HTTPException(
+            status_code=422,
+            detail="burst_id 与 burst_size 的数量必须一致",
+        )
+    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.post("/valve-closure-analyses", 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。")
+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.post("/valve-isolation-analyses", 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。")
+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）"),
+    accident_element_type: Literal["auto", "link", "node"] = Query(
+        "auto", description="事故要素类型；auto优先管段再节点；分析同名节点须指定node，纯管段分析可指定link",
+    ),
+):
+    """
+    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）
+    - **accident_element_type**: auto优先管段；link仅匹配管段；node仅匹配节点
+    
+    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 / affected_node_count: 最近阀门围成的直接隔离区；不可隔离时列表为空，计数保留
+    - supply_affected_nodes / supply_affected_node_count: 按建议关阀后新增失去水源连接的节点；未验证或不可隔离时为null
+    - source_check_performed: 是否已执行水源连通检查
+    - isolation_failure_reason: 不可隔离原因；可隔离时为null
+    - isolatable: 存在关阀边界，且直接隔离区不再连通任何水源
+
+    使用模型初始开关状态进行无向拓扑分析，水池按潜在水源处理。
+    不包含时变控制、水池蓄水量或压力流量计算，不是现场停水范围的水力证明。
+    """
+    # 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": [],
+    # }
+    try:
+        result = analyze_valve_isolation(
+            network, accident_element, disabled_valves,
+            accident_element_type=accident_element_type,
+        )
+    except ValueError as exc:
+        raise HTTPException(status_code=422, detail=str(exc)) from exc
+    return result
+
+
+@router.post("/flushing-analyses", response_class=PlainTextResponse, summary="冲洗分析（高级）", description="高级版本的冲洗分析，支持按状态和设置值控制多个可选阀门，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。")
+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] | None = Query(None, description="参与控制的阀门ID列表（可选）"),
+    valves_k: List[float] | None = Query(
+        None, description="对应各阀门的开度列表（0-1，可选，与valves同时提供）"
+    ),
+    valve_statuses: List[Literal["OPEN", "CLOSED", "ACTIVE"]] | None = Query(
+        None, description="对应各阀门的开关状态列表（OPEN、CLOSED或ACTIVE，可选）"
+    ),
+    valve_settings: List[str] | None = Query(
+        None, description="对应各阀门的设置值列表（ACTIVE状态下必填）"
+    ),
+    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**: 参与控制的阀门ID列表（可选）
+    - **valves_k**: 各阀门的开度列表（0-1，可选，与valves同时提供）
+    - **valve_statuses**: 各阀门的开关状态列表（OPEN、CLOSED或ACTIVE，可选）
+    - **valve_settings**: 各阀门的设置值列表（ACTIVE状态下必填）
+    - **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 = None
+    valve_control = None
+    if valve_statuses is not None and valves_k is not None:
+        raise HTTPException(
+            status_code=422,
+            detail="valve_statuses 和 valves_k 不能同时提供",
+        )
+    if valve_settings is not None and valve_statuses is None:
+        raise HTTPException(
+            status_code=422,
+            detail="valve_settings 必须与 valve_statuses 同时提供",
+        )
+    if valves is None:
+        if (
+            valves_k is not None
+            or valve_statuses is not None
+            or valve_settings is not None
+        ):
+            raise HTTPException(
+                status_code=422,
+                detail="阀门控制参数必须与 valves 同时提供",
+            )
+    elif valve_statuses is not None:
+        if len(valves) != len(valve_statuses):
+            raise HTTPException(
+                status_code=422, detail="valves 和 valve_statuses 的数量必须一致"
+            )
+        if valve_settings is not None and len(valves) != len(valve_settings):
+            raise HTTPException(
+                status_code=422, detail="valves 和 valve_settings 的数量必须一致"
+            )
+
+        settings = valve_settings or [""] * len(valves)
+        valve_control = {}
+        for valve_id, raw_status, raw_setting in zip(
+            valves, valve_statuses, settings
+        ):
+            status = raw_status
+            setting = raw_setting.strip()
+            if status == "ACTIVE" and not setting:
+                raise HTTPException(
+                    status_code=422,
+                    detail=f"ACTIVE 状态的阀门 {valve_id} 必须提供设置值",
+                )
+
+            control: dict[str, str] = {"status": status}
+            if status == "ACTIVE":
+                control["setting"] = setting
+            valve_control[valve_id] = control
+    elif valves_k is not None:
+        if len(valves) != len(valves_k):
+            raise HTTPException(
+                status_code=422, detail="valves 和 valves_k 的数量必须一致"
+            )
+        valve_opening = {
+            valve_id: float(valve_k)
+            for valve_id, valve_k in zip(valves, valves_k)
+        }
+    else:
+        raise HTTPException(
+            status_code=422,
+            detail="提供 valves 时必须同时提供 valve_statuses 或 valves_k",
+        )
+    result = flushing_analysis(
+        name=network,
+        modify_pattern_start_time=start_time,
+        modify_total_duration=duration or 900,
+        modify_valve_opening=valve_opening,
+        valve_control=valve_control,
+        drainage_node_ID=drainage_node_ID,
+        flushing_flow=flush_flow,
+        scheme_name=scheme_name,
+        username=username,
+    )
+    return result or "success"
+
+
+@router.post("/contaminant-simulations", 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。")
+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.post("/pressure-regulation-analyses", 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。")
+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
+    
+    支持固定泵和变速泵的独立控制。
+    """
+    item = data.model_dump()
+    scada_mappings = simulation.query_corresponding_element_id_and_query_id(
+        item["network"]
+    )
+    fixed_pumps = set(scada_mappings.fixed_pumps)
+    variable_pumps = set(scada_mappings.variable_pumps)
+    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"],
+        scada_mappings=scada_mappings,
+    )
+    return "success"
+
+
+@router.post("/simulation-runs", 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。")
+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:
+        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/timeseries/analysis.py

@@ -0,0 +1,61 @@
+from datetime import datetime
+from uuid import UUID
+
+from fastapi import APIRouter, Depends, HTTPException, Query
+from psycopg import AsyncConnection
+
+from app.infra.db.timescaledb.repositories.analysis import AnalysisResultsRepository
+
+from .dependencies import get_timescale_connection
+
+router = APIRouter()
+
+
+@router.get("/timeseries/analysis/runs/{run_id}/nodes/{node_id}")
+async def get_analysis_node_series(
+    run_id: UUID,
+    node_id: str,
+    start_time: datetime = Query(...),
+    end_time: datetime = Query(...),
+    field: str = Query(...),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    try:
+        return await AnalysisResultsRepository.get_node_series(
+            conn, run_id, node_id, start_time, end_time, field
+        )
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+
+@router.get("/timeseries/analysis/runs/{run_id}/links/{link_id}")
+async def get_analysis_link_series(
+    run_id: UUID,
+    link_id: str,
+    start_time: datetime = Query(...),
+    end_time: datetime = Query(...),
+    field: str = Query(...),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    try:
+        return await AnalysisResultsRepository.get_link_series(
+            conn, run_id, link_id, start_time, end_time, field
+        )
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+
+@router.get("/timeseries/analysis/runs/{run_id}/values")
+async def get_analysis_values_at_time(
+    run_id: UUID,
+    result_time: datetime = Query(...),
+    element_type: str = Query(..., pattern="^(node|link)$"),
+    field: str = Query(...),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+):
+    try:
+        return await AnalysisResultsRepository.get_values_at_time(
+            conn, run_id, element_type, result_time, field
+        )
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc

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

@@ -0,0 +1,108 @@
+from fastapi import APIRouter, Depends, HTTPException, Query
+from datetime import datetime
+from psycopg import AsyncConnection
+
+from app.services.timeseries_analysis import TimeseriesAnalysisService
+from app.domain.schemas.timeseries_history import (
+    ElementHistoryQuery,
+    ElementHistoryResponse,
+)
+from app.services.timeseries_history import TimeseriesHistoryService
+from .dependencies import get_timescale_connection, get_postgres_connection
+
+router = APIRouter()
+
+
+@router.post(
+    "/timeseries/views/element-history/query",
+    summary="批量查询管网元素历史数据",
+    response_model=ElementHistoryResponse,
+)
+async def query_element_history(
+    payload: ElementHistoryQuery,
+    timescale_conn: AsyncConnection = Depends(get_timescale_connection),
+    postgres_conn: AsyncConnection = Depends(get_postgres_connection),
+) -> ElementHistoryResponse:
+    try:
+        return await TimeseriesHistoryService.query(
+            timescale_conn, postgres_conn, payload
+        )
+    except ValueError as exc:
+        raise HTTPException(status_code=422, detail=str(exc)) from exc
+
+
+@router.post("/timeseries/scada-cleaning-runs", 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 TimeseriesAnalysisService.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("/pipeline-health-predictions", summary="预测管道健康状况")
+async def predict_pipeline_health(
+    query_time: datetime = Query(..., description="查询时间"),
+    timescale_conn: AsyncConnection = Depends(get_timescale_connection),
+    postgres_conn: AsyncConnection = Depends(get_postgres_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
+        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 TimeseriesAnalysisService.predict_pipeline_health(
+            timescale_conn, postgres_conn, 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 +1,316 @@
+from fastapi import APIRouter, Depends, HTTPException, Query, Path, Body
+from typing import List
+from datetime import datetime
+from psycopg import AsyncConnection
+from pydantic import BaseModel
+
+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}"
+
+
+class RealtimeLinkBatchItem(BaseModel):
+    time: datetime
+    id: str
+    flow: float | None = None
+    friction: float | None = None
+    headloss: float | None = None
+    quality: float | None = None
+    reaction: float | None = None
+    setting: float | None = None
+    status: float | None = None
+    velocity: float | None = None
+
+
+class RealtimeNodeBatchItem(BaseModel):
+    time: datetime
+    id: str
+    actual_demand: float | None = None
+    total_head: float | None = None
+    pressure: float | None = None
+    quality: float | None = None
+
+
+@router.post("/timeseries/realtime/links/batches", status_code=201, summary="批量插入实时管道数据")
+async def insert_realtime_links(
+    data: List[RealtimeLinkBatchItem] = Body(..., description="同一时间点的管道快照数据"),
+    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, [item.model_dump() for item in data]
+    )
+    return {"message": f"Inserted {len(data)} records"}
+
+
+@router.get(
+    "/timeseries/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(
+    "/timeseries/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("/timeseries/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("/timeseries/realtime/nodes/batches", status_code=201, summary="批量插入实时节点数据")
+async def insert_realtime_nodes(
+    data: List[RealtimeNodeBatchItem] = Body(..., description="同一时间点的节点快照数据"),
+    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, [item.model_dump() for item in data]
+    )
+    return {"message": f"Inserted {len(data)} records"}
+
+
+@router.get(
+    "/timeseries/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(
+    "/timeseries/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("/timeseries/realtime/simulation-results", 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(
+    "/timeseries/realtime/records",
+    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(
+    "/timeseries/realtime/simulation-results",
+    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 +1,196 @@
+from fastapi import APIRouter, Depends, HTTPException, Query, Path, Body
+from typing import List
+from datetime import datetime
+from psycopg import AsyncConnection
+from pydantic import BaseModel, Field, field_validator
+
+from app.infra.db.postgresql.scada import ScadaInfoRepository
+from app.infra.db.timescaledb.repositories.scada import ScadaRepository
+from .dependencies import get_postgres_connection, get_timescale_connection
+
+router = APIRouter()
+SCADA_BATCH_MAX_ITEMS = 10_000
+
+
+class ScadaReadingBatchItem(BaseModel):
+    time: datetime
+    device_id: str = Field(min_length=1)
+    monitored_value: float | None = None
+    cleaned_value: float | None = None
+
+    @field_validator("device_id")
+    @classmethod
+    def normalize_device_id(cls, value: str) -> str:
+        normalized = value.strip()
+        if not normalized:
+            raise ValueError("device_id must not be blank")
+        return normalized
+
+
+@router.post("/timeseries/scada-readings/batches", status_code=201, summary="批量插入SCADA监测数据")
+async def insert_scada_data(
+    data: List[ScadaReadingBatchItem] = Body(
+        ...,
+        min_length=1,
+        max_length=SCADA_BATCH_MAX_ITEMS,
+        description="SCADA设备监测数据列表",
+    ),
+    conn: AsyncConnection = Depends(get_timescale_connection),
+    postgres_conn: AsyncConnection = Depends(get_postgres_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
+    """
+    rows = [item.model_dump() for item in data]
+    requested_ids = list(dict.fromkeys(item["device_id"] for item in rows))
+    existing_ids = await ScadaInfoRepository.get_existing_device_ids(
+        postgres_conn, requested_ids
+    )
+    missing_ids = [
+        device_id for device_id in requested_ids if device_id not in existing_ids
+    ]
+    if missing_ids:
+        raise HTTPException(
+            status_code=422,
+            detail=f"SCADA devices do not exist in BizDB: {', '.join(missing_ids)}",
+        )
+    await ScadaRepository.insert_scada_batch(conn, rows)
+    return {"message": f"Inserted {len(data)} records"}
+
+
+@router.get("/timeseries/scada-readings", 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(
+    "/timeseries/scada-readings/fields", 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("/timeseries/scada-readings/{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("/timeseries/scada-readings", 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/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-searches",
+    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/rest_router.py

@@ -0,0 +1,434 @@
+from __future__ import annotations
+
+import inspect
+import re
+from collections.abc import Iterable
+from copy import copy
+from functools import wraps
+from typing import Any, Generic, TypeVar, get_args, get_origin
+
+from fastapi import APIRouter, Depends, Query
+from fastapi.encoders import jsonable_encoder
+from fastapi.routing import APIRoute
+from pydantic import BaseModel, JsonValue, create_model
+from starlette.concurrency import run_in_threadpool
+from starlette.responses import Response
+
+from app.api.problem_details import ProblemDetails
+from app.api.pagination import PaginatedList
+from app.api.v1.router import api_router as handler_api_router
+from app.auth.metadata_dependencies import get_current_metadata_user
+from app.auth.project_dependencies import (
+    ProjectContext,
+    get_project_business_routing,
+    get_project_context,
+    get_project_simulation_routing,
+)
+from app.infra.db.project_routing import (
+    ActiveProjectRouting,
+    activate_project_routing,
+)
+
+T = TypeVar("T")
+
+
+class Page(BaseModel, Generic[T]):
+    items: list[T]
+    total: int
+    limit: int
+    offset: int
+
+
+async def _call_endpoint(endpoint, *args, **kwargs):
+    """Call async handlers directly and offload synchronous handlers."""
+    if inspect.iscoroutinefunction(endpoint):
+        return await endpoint(*args, **kwargs)
+    result = await run_in_threadpool(endpoint, *args, **kwargs)
+    return await result if inspect.isawaitable(result) else result
+
+
+_NAME_IS_NETWORK = {
+    "pressure_sensor_placement_sensitivity_endpoint",
+    "pressure_sensor_placement_kmeans_endpoint",
+}
+_DERIVE_USERNAME = {
+    "pressure_sensor_placement_sensitivity_endpoint": "username",
+    "pressure_sensor_placement_kmeans_endpoint": "username",
+    "fastapi_pressure_sensor_placement": "user_name",
+}
+_PUBLIC_PARAMETER_RENAMES = {
+    "burst_ID": "burst_id",
+    "drainage_node_ID": "drainage_node_id",
+}
+_MODEL_NAME_IS_NETWORK = {"RunSimulationManuallyByDate", "PressureSensorPlacement"}
+_MODEL_USERNAME_FROM_AUTH = {"PressureSensorPlacement"}
+_TIMESCALE_ROUTED_ENDPOINT_MODULES = {
+    "app.api.v1.endpoints.burst_detection",
+    "app.api.v1.endpoints.burst_location",
+    "app.api.v1.endpoints.leakage",
+    "app.api.v1.endpoints.simulation",
+}
+
+
+def _clean_name(name: str) -> str:
+    for prefix in ("fastapi_", "fast_"):
+        if name.startswith(prefix):
+            name = name[len(prefix) :]
+            break
+    if name.endswith("_endpoint"):
+        name = name[: -len("_endpoint")]
+    return name
+
+
+def _rest_body_model(annotation):
+    if not inspect.isclass(annotation) or not issubclass(annotation, BaseModel):
+        return None
+    project_fields = {
+        name
+        for name in ("network", "network_name")
+        if name in annotation.model_fields
+    }
+    if annotation.__name__ in _MODEL_NAME_IS_NETWORK and "name" in annotation.model_fields:
+        project_fields.add("name")
+    username_fields = (
+        {
+            name
+            for name in ("username", "user_name")
+            if name in annotation.model_fields
+        }
+        if annotation.__name__ in _MODEL_USERNAME_FROM_AUTH
+        else set()
+    )
+    excluded_fields = project_fields | username_fields
+    if not excluded_fields:
+        return None
+
+    public_fields = {
+        name: (field.annotation, copy(field))
+        for name, field in annotation.model_fields.items()
+        if name not in excluded_fields
+    }
+    public_model = create_model(
+        f"{annotation.__name__}Rest",
+        __module__=annotation.__module__,
+        **public_fields,
+    )
+    return annotation, public_model, project_fields, username_fields
+
+
+def _with_header_project_context(endpoint, route_name: str):
+    signature = inspect.signature(endpoint)
+    network_parameters = [
+        name for name in ("network", "network_name") if name in signature.parameters
+    ]
+    if route_name in _NAME_IS_NETWORK and "name" in signature.parameters:
+        network_parameters.append("name")
+    username_parameter = _DERIVE_USERNAME.get(route_name)
+    parameter_renames = {
+        internal: public
+        for internal, public in _PUBLIC_PARAMETER_RENAMES.items()
+        if internal in signature.parameters
+    }
+    body_models = {
+        name: body_model
+        for name, parameter in signature.parameters.items()
+        if (body_model := _rest_body_model(parameter.annotation)) is not None
+    }
+    model_has_username = any(model[3] for model in body_models.values())
+    if (
+        not network_parameters
+        and not username_parameter
+        and not parameter_renames
+        and not body_models
+    ):
+        return endpoint
+
+    existing_context_parameter = next(
+        (
+            name
+            for name, parameter in signature.parameters.items()
+            if parameter.annotation is ProjectContext
+        ),
+        None,
+    )
+    injected_context_name = existing_context_parameter or "_rest_project_context"
+    injected_routing_name = "_rest_project_routing"
+    injected_user_name = "_rest_current_user"
+
+    @wraps(endpoint)
+    async def wrapper(*args, **kwargs):
+        project_routing = kwargs.pop(injected_routing_name, None)
+        if not isinstance(project_routing, ActiveProjectRouting):
+            raise RuntimeError("REST project database routing was not resolved")
+        project_context = kwargs.get(injected_context_name)
+        if not isinstance(project_context, ProjectContext):
+            raise RuntimeError("REST project context was not resolved")
+        if not existing_context_parameter:
+            kwargs.pop(injected_context_name, None)
+        for parameter_name in network_parameters:
+            kwargs[parameter_name] = project_context.project_code
+        if username_parameter:
+            kwargs[username_parameter] = kwargs[injected_user_name].username
+            kwargs.pop(injected_user_name, None)
+        for internal_name, public_name in parameter_renames.items():
+            kwargs[internal_name] = kwargs.pop(public_name)
+        for parameter_name, (
+            original_model,
+            _public_model,
+            project_fields,
+            username_fields,
+        ) in body_models.items():
+            data = kwargs[parameter_name].model_dump()
+            data.update(
+                {field_name: project_context.project_code for field_name in project_fields}
+            )
+            if username_fields:
+                current_user = kwargs[injected_user_name]
+                data.update(
+                    {field_name: current_user.username for field_name in username_fields}
+                )
+            kwargs[parameter_name] = original_model.model_validate(data)
+        if model_has_username:
+            kwargs.pop(injected_user_name, None)
+        with activate_project_routing(project_routing):
+            return await _call_endpoint(endpoint, *args, **kwargs)
+
+    parameters = []
+    for name, parameter in signature.parameters.items():
+        if name in network_parameters or name == username_parameter:
+            continue
+        public_name = parameter_renames.get(name, name)
+        if public_name != name:
+            default = copy(parameter.default)
+            default.alias = public_name
+            default.validation_alias = public_name
+            default.serialization_alias = public_name
+            parameter = parameter.replace(name=public_name, default=default)
+        if name in body_models:
+            parameter = parameter.replace(annotation=body_models[name][1])
+        parameters.append(parameter)
+    routing_dependency = (
+        get_project_simulation_routing
+        if (
+            endpoint.__module__ in _TIMESCALE_ROUTED_ENDPOINT_MODULES
+        )
+        else get_project_business_routing
+    )
+    if not existing_context_parameter:
+        parameters.append(
+            inspect.Parameter(
+                injected_context_name,
+                kind=inspect.Parameter.KEYWORD_ONLY,
+                annotation=ProjectContext,
+                default=Depends(get_project_context),
+            )
+        )
+    parameters.append(
+        inspect.Parameter(
+            injected_routing_name,
+            kind=inspect.Parameter.KEYWORD_ONLY,
+            annotation=ActiveProjectRouting,
+            default=Depends(routing_dependency),
+        )
+    )
+    if username_parameter or model_has_username:
+        parameters.append(
+            inspect.Parameter(
+                injected_user_name,
+                kind=inspect.Parameter.KEYWORD_ONLY,
+                default=Depends(get_current_metadata_user),
+            )
+        )
+    wrapper.__signature__ = signature.replace(parameters=parameters)
+    return wrapper
+
+
+def _with_pagination(endpoint):
+    signature = inspect.signature(endpoint)
+    handler_limit_parameter = "limit" if "limit" in signature.parameters else None
+    handler_offset_parameter = next(
+        (
+            parameter_name
+            for parameter_name in ("offset", "skip")
+            if parameter_name in signature.parameters
+        ),
+        None,
+    )
+    handler_handles_pagination = bool(
+        handler_limit_parameter or handler_offset_parameter
+    )
+
+    @wraps(endpoint)
+    async def wrapper(*args, **kwargs):
+        if handler_handles_pagination:
+            limit = kwargs.get(handler_limit_parameter, 0)
+            offset = kwargs.get(handler_offset_parameter, 0)
+        else:
+            limit = kwargs.pop("_rest_limit")
+            offset = kwargs.pop("_rest_offset")
+        result = await _call_endpoint(endpoint, *args, **kwargs)
+        if not isinstance(result, list):
+            return result
+        if handler_handles_pagination:
+            if not isinstance(result, PaginatedList):
+                raise RuntimeError(
+                    f"Paginated handler {endpoint.__name__!r} must return "
+                    "PaginatedList with the real total"
+                )
+            return Page(
+                items=result,
+                total=result.total,
+                limit=limit or len(result),
+                offset=offset,
+            )
+        return Page(
+            items=result[offset : offset + limit],
+            total=len(result),
+            limit=limit,
+            offset=offset,
+        )
+
+    parameters = list(signature.parameters.values())
+    if not handler_handles_pagination:
+        parameters.extend(
+            [
+                inspect.Parameter(
+                    "_rest_limit",
+                    kind=inspect.Parameter.KEYWORD_ONLY,
+                    annotation=int,
+                    default=Query(100, ge=1, le=1000, alias="limit"),
+                ),
+                inspect.Parameter(
+                    "_rest_offset",
+                    kind=inspect.Parameter.KEYWORD_ONLY,
+                    annotation=int,
+                    default=Query(0, ge=0, alias="offset"),
+                ),
+            ]
+        )
+    wrapper.__signature__ = signature.replace(parameters=parameters)
+    return wrapper
+
+
+def _with_jsonable_response(endpoint):
+    """Normalize untyped handler results before JsonValue validation."""
+
+    @wraps(endpoint)
+    async def wrapper(*args, **kwargs):
+        result = await _call_endpoint(endpoint, *args, **kwargs)
+        if isinstance(result, Response):
+            return result
+        return jsonable_encoder(result)
+
+    return wrapper
+
+
+def _adapt_route(route: APIRoute) -> APIRoute:
+    methods = route.methods or set()
+    if len(methods) != 1:
+        raise RuntimeError(
+            f"REST route {route.name!r} must declare exactly one HTTP method"
+        )
+    method = next(iter(methods))
+    responses = dict(route.responses or {})
+    for status_code, description in (
+        (401, "Authentication required"),
+        (403, "Insufficient permission"),
+        (404, "Resource not found"),
+        (409, "Resource conflict"),
+        (422, "Validation error"),
+        (503, "Dependency unavailable"),
+    ):
+        responses.setdefault(
+            status_code,
+            {"model": ProblemDetails, "description": description},
+        )
+
+    endpoint = _with_header_project_context(route.endpoint, route.name)
+    response_model = route.response_model
+    has_untyped_response = response_model is None
+    if has_untyped_response:
+        endpoint = _with_jsonable_response(endpoint)
+    if get_origin(response_model) is list:
+        item_type = get_args(response_model)[0] if get_args(response_model) else JsonValue
+        response_model = Page[item_type]
+        endpoint = _with_pagination(endpoint)
+
+    clean_name = _clean_name(route.name)
+    creates_resource = clean_name.startswith(
+        ("add_", "create_", "copy_", "import_", "insert_", "store_", "take_", "upload_")
+    ) or route.name == "fastapi_pressure_sensor_placement"
+    status_code = (
+        204
+        if method == "DELETE"
+        else 201
+        if method == "POST" and creates_resource
+        else route.status_code
+    )
+    if status_code == 204:
+        response_model = None
+    elif response_model is None:
+        response_model = JsonValue
+
+    return APIRoute(
+        path=route.path,
+        endpoint=endpoint,
+        response_model=response_model,
+        status_code=status_code,
+        tags=route.tags,
+        dependencies=route.dependencies,
+        summary=route.summary,
+        description=route.description,
+        response_description=route.response_description,
+        responses=responses,
+        deprecated=False,
+        name=route.name,
+        methods={method},
+        operation_id=f"{method.lower()}_{re.sub(r'[^a-z0-9]+', '_', route.path).strip('_')}",
+        response_model_include=route.response_model_include,
+        response_model_exclude=route.response_model_exclude,
+        response_model_by_alias=route.response_model_by_alias,
+        response_model_exclude_unset=route.response_model_exclude_unset,
+        response_model_exclude_defaults=route.response_model_exclude_defaults,
+        response_model_exclude_none=route.response_model_exclude_none,
+        include_in_schema=route.include_in_schema,
+        response_class=route.response_class,
+        callbacks=route.callbacks,
+        openapi_extra=route.openapi_extra,
+    )
+
+
+def build_rest_router(routes: Iterable[Any]) -> APIRouter:
+    router = APIRouter()
+    seen: dict[tuple[str, str], APIRoute] = {}
+    operation_ids: set[str] = set()
+
+    for route in routes:
+        if not isinstance(route, APIRoute):
+            continue
+
+        methods = route.methods or set()
+        if len(methods) != 1:
+            raise RuntimeError(
+                f"REST route {route.name!r} must declare exactly one HTTP method"
+            )
+        method = next(iter(methods))
+        key = (method, route.path)
+        if key in seen:
+            previous = seen[key]
+            raise RuntimeError(
+                "REST route collision for "
+                f"{method} {route.path}: {previous.name!r} and {route.name!r}."
+            )
+
+        adapted = _adapt_route(route)
+        if adapted.operation_id in operation_ids:
+            adapted.operation_id = f"{adapted.operation_id}_{route.name}"
+        seen[key] = route
+        operation_ids.add(adapted.operation_id or "")
+        router.routes.append(adapted)
+
+    return router
+
+
+api_router = build_rest_router(handler_api_router.routes)

app/api/v1/router.py

@@ -1,20 +1,196 @@
-from fastapi import APIRouter
+from fastapi import APIRouter, Depends
+
 from app.api.v1.endpoints import (
-    auth,
+    access,
+    admin_metadata,
+    agent_auth,
+    audit,
+    burst_detection,
+    burst_location,
+    geocoding,
+    leakage,
+    meta,
+    model_import,
     project,
-    network_elements,
-    simulation,
+    project_data,
     scada,
-    extension,
-    snapshots
+    sensor_placement,
+    simulation,
+    web_search,
+)
+from app.api.v1.endpoints.components import (
+    controls,
+    curves,
+    options,
+    patterns,
+    quality,
+    visuals,
+)
+from app.api.v1.endpoints.network import (
+    demands,
+    general,
+    geometry,
+    junctions,
+    pipes,
+    pumps,
+    regions,
+    reservoirs,
+    tags,
+    tanks,
+    valves,
+)
+from app.api.v1.endpoints.timeseries import (
+    analysis as ts_analysis,
+    composite as ts_composite,
+    realtime as ts_realtime,
+    scada as ts_scada,
+)
+from app.auth.permissions import (
+    BURST_RUN,
+    OPTIMIZATION_RUN,
+    SCADA_CLEAN,
+    SCADA_VIEW,
+    SIMULATION_RUN,
+    SIMULATION_VIEW,
+    WEBGIS_EDIT,
+    WEBGIS_VIEW,
+    require_method_permission,
+    require_permission,
 )
 
 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"])
+webgis_access = Depends(
+    require_method_permission(
+        read_permission=WEBGIS_VIEW,
+        write_permission=WEBGIS_EDIT,
+    )
+)
+scada_access = Depends(
+    require_method_permission(
+        read_permission=SCADA_VIEW,
+        write_permission=SCADA_CLEAN,
+    )
+)
+simulation_access = Depends(
+    require_method_permission(
+        read_permission=SIMULATION_VIEW,
+        write_permission=SIMULATION_RUN,
+    )
+)
+
+webgis_view_access = Depends(require_permission(WEBGIS_VIEW))
+simulation_run_access = Depends(require_permission(SIMULATION_RUN))
+burst_run_access = Depends(require_permission(BURST_RUN))
+optimization_run_access = Depends(require_permission(OPTIMIZATION_RUN))
+
+# Core services
+api_router.include_router(access.router, tags=["Access Control"])
+api_router.include_router(agent_auth.router, tags=["Agent Auth"])
+api_router.include_router(
+    admin_metadata.router,
+    tags=["Metadata Admin"],
+)
+api_router.include_router(model_import.router, tags=["Model Administration"])
+api_router.include_router(audit.router, tags=["Audit Logs"])
+api_router.include_router(meta.router, tags=["Metadata"])
+api_router.include_router(
+    project.router,
+    tags=["Project"],
+    dependencies=[webgis_access],
+)
+
+# WebGIS data
+for endpoint_router, tag in (
+    (general.router, "Network General"),
+    (junctions.router, "Junctions"),
+    (reservoirs.router, "Reservoirs"),
+    (tanks.router, "Tanks"),
+    (pipes.router, "Pipes"),
+    (pumps.router, "Pumps"),
+    (valves.router, "Valves"),
+    (tags.router, "Tags"),
+    (demands.router, "Demands"),
+    (geometry.router, "Geometry & Coordinates"),
+    (regions.router, "Regions & DMAs"),
+    (curves.router, "Curves"),
+    (patterns.router, "Patterns"),
+    (controls.router, "Controls & Rules"),
+    (options.router, "Options"),
+    (quality.router, "Quality"),
+    (visuals.router, "Visuals"),
+):
+    api_router.include_router(
+        endpoint_router,
+        tags=[tag],
+        dependencies=[webgis_access],
+    )
+
+# Simulation and analysis
+api_router.include_router(
+    simulation.router,
+    tags=["Simulation Control"],
+    dependencies=[simulation_run_access],
+)
+api_router.include_router(
+    scada.router,
+    tags=["SCADA Metadata"],
+    dependencies=[scada_access],
+)
+api_router.include_router(
+    sensor_placement.router,
+    tags=["Sensor Placement"],
+    dependencies=[optimization_run_access],
+)
+api_router.include_router(
+    web_search.router,
+    tags=["Web Search"],
+    dependencies=[webgis_view_access],
+)
+api_router.include_router(
+    geocoding.router,
+    tags=["Geocoding"],
+    dependencies=[webgis_view_access],
+)
+api_router.include_router(
+    leakage.router,
+    tags=["Leakage"],
+    dependencies=[burst_run_access],
+)
+api_router.include_router(
+    burst_detection.router,
+    tags=["Burst Detection"],
+    dependencies=[burst_run_access],
+)
+api_router.include_router(
+    burst_location.router,
+    tags=["Burst Location"],
+    dependencies=[burst_run_access],
+)
+
+# TimescaleDB data
+for endpoint_router, tag in (
+    (ts_realtime.router, "TimescaleDB - Realtime"),
+    (ts_analysis.router, "TimescaleDB - Analysis"),
+):
+    api_router.include_router(
+        endpoint_router,
+        tags=[tag],
+        dependencies=[simulation_access],
+    )
+
+for endpoint_router, tag in (
+    (ts_scada.router, "TimescaleDB - SCADA"),
+    (ts_composite.router, "TimescaleDB - Composite"),
+):
+    api_router.include_router(
+        endpoint_router,
+        tags=[tag],
+        dependencies=[scada_access],
+    )
+
+api_router.include_router(
+    project_data.router,
+    tags=["Project Data"],
+    dependencies=[webgis_view_access],
+)

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 +1,100 @@
+import time
+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")
+
+    payload = jwt.decode(
+        token,
+        key,
+        algorithms=[settings.KEYCLOAK_ALGORITHM],
+        audience=settings.KEYCLOAK_AUDIENCE or None,
+    )
+    if settings.KEYCLOAK_ACCESS_TOKEN_MAX_AGE_SECONDS <= 0:
+        return payload
+
+    issued_at = payload.get("iat")
+    if not isinstance(issued_at, (int, float)) or (
+        time.time() >= issued_at + settings.KEYCLOAK_ACCESS_TOKEN_MAX_AGE_SECONDS
+    ):
+        raise JWTError("Keycloak access token is older than the allowed maximum age")
+
+    return payload
+
+
+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
+
+
+def get_keycloak_preferred_username(payload: dict) -> str:
+    username = payload.get("preferred_username")
+    if not username:
+        raise HTTPException(
+            status_code=status.HTTP_401_UNAUTHORIZED,
+            detail="Missing preferred_username claim",
+            headers={"WWW-Authenticate": "Bearer"},
+        )
+    return str(username)
+
+
+async def get_current_keycloak_username(
+    payload: dict = Depends(get_current_keycloak_payload),
+) -> str:
+    return get_keycloak_preferred_username(payload)

app/auth/metadata_dependencies.py

@@ -0,0 +1,105 @@
+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,
+    get_keycloak_preferred_username,
+)
+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 _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)
+    username = get_keycloak_preferred_username(keycloak_payload)
+    email = _email_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="Metadata database is unavailable",
+        ) from exc
+    if not user or not user.is_active:
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
+            detail="User authorization pending",
+        )
+    try:
+        user = await metadata_repo.refresh_user_keycloak_snapshot(
+            user,
+            username=username,
+            email=email,
+        )
+    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="Metadata database is unavailable",
+        ) 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/permissions.py

@@ -0,0 +1,162 @@
+from collections.abc import Awaitable, Callable
+from typing import Any
+
+from fastapi import Depends, HTTPException, Request, status
+
+from app.auth.project_dependencies import ProjectContext, get_project_context
+
+WEBGIS_VIEW = "webgis.view"
+WEBGIS_EDIT = "webgis.edit"
+SCADA_VIEW = "scada.view"
+SCADA_CLEAN = "scada.clean"
+SIMULATION_VIEW = "simulation.view"
+SIMULATION_RUN = "simulation.run"
+BURST_VIEW = "burst.view"
+BURST_RUN = "burst.run"
+RISK_VIEW = "risk.view"
+RISK_RUN = "risk.run"
+OPTIMIZATION_VIEW = "optimization.view"
+OPTIMIZATION_RUN = "optimization.run"
+MODEL_IMPORT = "model.import"
+AUDIT_VIEW = "audit.view"
+ENVIRONMENT_MANAGE = "environment.manage"
+MEMBERSHIP_MANAGE = "membership.manage"
+
+PROJECT_MEMBER_PERMISSIONS = frozenset(
+    {
+        WEBGIS_VIEW,
+        WEBGIS_EDIT,
+        SCADA_VIEW,
+        SCADA_CLEAN,
+        SIMULATION_VIEW,
+        SIMULATION_RUN,
+        BURST_VIEW,
+        BURST_RUN,
+        RISK_VIEW,
+        RISK_RUN,
+        OPTIMIZATION_VIEW,
+        OPTIMIZATION_RUN,
+    }
+)
+
+PROJECT_VIEWER_PERMISSIONS = frozenset(
+    {
+        WEBGIS_VIEW,
+        SCADA_VIEW,
+        SIMULATION_VIEW,
+    }
+)
+
+SYSTEM_ADMIN_PERMISSIONS = frozenset(
+    {
+        MODEL_IMPORT,
+        AUDIT_VIEW,
+        ENVIRONMENT_MANAGE,
+        MEMBERSHIP_MANAGE,
+    }
+)
+
+PROJECT_ROLE_PERMISSIONS: dict[str, frozenset[str]] = {
+    "member": PROJECT_MEMBER_PERMISSIONS,
+    "viewer": PROJECT_VIEWER_PERMISSIONS,
+}
+
+
+def resolve_permissions(
+    *,
+    project_role: str | None,
+    system_role: str,
+    is_superuser: bool,
+) -> frozenset[str]:
+    permissions = set(PROJECT_ROLE_PERMISSIONS.get(project_role or "", frozenset()))
+    if is_superuser or system_role == "admin":
+        permissions.update(SYSTEM_ADMIN_PERMISSIONS)
+    return frozenset(permissions)
+
+
+def permissions_for_context(ctx: ProjectContext) -> frozenset[str]:
+    return resolve_permissions(
+        project_role=ctx.project_role,
+        system_role=ctx.system_role,
+        is_superuser=ctx.is_superuser,
+    )
+
+
+def _permission_denied(permission: str) -> HTTPException:
+    return HTTPException(
+        status_code=status.HTTP_403_FORBIDDEN,
+        detail={
+            "code": "permission_denied",
+            "permission": permission,
+        },
+    )
+
+
+async def _enforce_project_scope(request: Request, ctx: ProjectContext) -> None:
+    requested_network = (
+        request.path_params.get("network")
+        or request.query_params.get("network")
+    )
+    if not requested_network:
+        content_type = request.headers.get("content-type", "")
+        if content_type.startswith("application/json"):
+            try:
+                payload = await request.json()
+            except (ValueError, RuntimeError):
+                payload = None
+            if isinstance(payload, dict):
+                requested_network = payload.get("network")
+
+    if requested_network and str(requested_network) != ctx.project_code:
+        raise HTTPException(
+            status_code=status.HTTP_403_FORBIDDEN,
+            detail={
+                "code": "project_scope_denied",
+                "project_id": str(ctx.project_id),
+            },
+        )
+
+
+def require_permission(
+    permission: str,
+) -> Callable[..., Awaitable[ProjectContext]]:
+    async def dependency(
+        request: Request,
+        ctx: ProjectContext = Depends(get_project_context),
+    ) -> ProjectContext:
+        if permission not in permissions_for_context(ctx):
+            raise _permission_denied(permission)
+        await _enforce_project_scope(request, ctx)
+        return ctx
+
+    return dependency
+
+
+def require_method_permission(
+    *,
+    read_permission: str,
+    write_permission: str,
+) -> Callable[..., Awaitable[ProjectContext]]:
+    async def dependency(
+        request: Request,
+        ctx: ProjectContext = Depends(get_project_context),
+    ) -> ProjectContext:
+        permission = (
+            read_permission
+            if request.method.upper() in {"GET", "HEAD", "OPTIONS"}
+            else write_permission
+        )
+        if permission not in permissions_for_context(ctx):
+            raise _permission_denied(permission)
+        await _enforce_project_scope(request, ctx)
+        return ctx
+
+    return dependency
+
+
+def has_permission(user: Any, project_role: str | None, permission: str) -> bool:
+    return permission in resolve_permissions(
+        project_role=project_role,
+        system_role=str(getattr(user, "role", "user")),
+        is_superuser=bool(getattr(user, "is_superuser", False)),
+    )

app/auth/project_dependencies.py

@@ -0,0 +1,261 @@
+import logging
+from collections.abc import AsyncGenerator
+from dataclasses import dataclass
+from uuid import UUID
+
+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.metadata_dependencies import get_current_metadata_user
+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,
+    ProjectDbRouting,
+)
+from app.infra.db.project_routing import ActiveProjectRouting, activate_project_routing
+
+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
+    system_role: str = "user"
+    is_superuser: bool = False
+
+
+async def get_metadata_repository(
+    session: AsyncSession = Depends(get_metadata_session),
+) -> MetadataRepository:
+    return MetadataRepository(session)
+
+
+async def resolve_project_context(
+    x_project_id: str,
+    current_user,
+    metadata_repo: MetadataRepository,
+) -> 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"
+            )
+
+        membership_role = await metadata_repo.get_membership_role(
+            project_uuid, current_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="Metadata database is unavailable",
+        ) from exc
+
+    return ProjectContext(
+        project_id=project.id,
+        project_code=project.code,
+        user_id=current_user.id,
+        project_role=membership_role,
+        system_role=current_user.role,
+        is_superuser=current_user.is_superuser,
+    )
+
+
+async def get_project_context(
+    x_project_id: str = Header(..., alias="X-Project-Id"),
+    current_user=Depends(get_current_metadata_user),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> ProjectContext:
+    return await resolve_project_context(x_project_id, current_user, metadata_repo)
+
+
+async def get_project_business_routing(
+    ctx: ProjectContext = Depends(get_project_context),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> ActiveProjectRouting:
+    return await resolve_project_business_routing(ctx, metadata_repo)
+
+
+async def use_project_business_routing(
+    routing: ActiveProjectRouting = Depends(get_project_business_routing),
+) -> AsyncGenerator[ActiveProjectRouting, None]:
+    """Keep the routed BizDB active for an entire endpoint invocation."""
+    with activate_project_routing(routing):
+        yield routing
+
+
+async def resolve_project_business_routing(
+    ctx: ProjectContext,
+    metadata_repo: MetadataRepository,
+) -> ActiveProjectRouting:
+    business = await _get_project_routing(
+        metadata_repo,
+        ctx.project_id,
+        DB_ROLE_BIZ_DATA,
+        DB_TYPE_POSTGRES,
+        "PostgreSQL",
+    )
+    return ActiveProjectRouting(
+        project_code=ctx.project_code,
+        business_dsn=business.dsn,
+    )
+
+
+async def get_project_simulation_routing(
+    ctx: ProjectContext = Depends(get_project_context),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> ActiveProjectRouting:
+    return await resolve_project_simulation_routing(ctx, metadata_repo)
+
+
+async def resolve_project_simulation_routing(
+    ctx: ProjectContext,
+    metadata_repo: MetadataRepository,
+) -> ActiveProjectRouting:
+    business = await _get_project_routing(
+        metadata_repo,
+        ctx.project_id,
+        DB_ROLE_BIZ_DATA,
+        DB_TYPE_POSTGRES,
+        "PostgreSQL",
+    )
+    timescale = await _get_project_routing(
+        metadata_repo,
+        ctx.project_id,
+        DB_ROLE_IOT_DATA,
+        DB_TYPE_TIMESCALE,
+        "TimescaleDB",
+    )
+    return ActiveProjectRouting(
+        project_code=ctx.project_code,
+        business_dsn=business.dsn,
+        timescale_dsn=timescale.dsn,
+    )
+
+
+async def _get_project_routing(
+    metadata_repo: MetadataRepository,
+    project_id: UUID,
+    db_role: str,
+    expected_db_type: str,
+    database_label: str,
+) -> ProjectDbRouting:
+    try:
+        routing = await metadata_repo.get_project_db_routing(project_id, db_role)
+    except ValueError as exc:
+        logger.error(
+            "Invalid project %s routing DSN configuration",
+            database_label,
+            exc_info=True,
+        )
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=f"Project {database_label} routing DSN is invalid: {exc}",
+        ) from exc
+
+    if not routing:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=f"Project {database_label} not configured",
+        )
+    if routing.db_type != expected_db_type:
+        raise HTTPException(
+            status_code=status.HTTP_503_SERVICE_UNAVAILABLE,
+            detail=f"Project {database_label} type mismatch",
+        )
+    return routing
+
+
+async def get_project_pg_connection(
+    ctx: ProjectContext = Depends(get_project_context),
+    metadata_repo: MetadataRepository = Depends(get_metadata_repository),
+) -> AsyncGenerator[AsyncConnection, None]:
+    routing = await _get_project_routing(
+        metadata_repo,
+        ctx.project_id,
+        DB_ROLE_BIZ_DATA,
+        DB_TYPE_POSTGRES,
+        "PostgreSQL",
+    )
+
+    pool_min_size = (
+        routing.pool_min_size
+        if routing.pool_min_size is not None
+        else settings.PROJECT_PG_POOL_MIN_SIZE
+    )
+    pool_max_size = (
+        routing.pool_max_size
+        if routing.pool_max_size is not None
+        else settings.PROJECT_PG_POOL_SIZE
+    )
+    async with project_connection_manager.pg_connection(
+        ctx.project_id,
+        DB_ROLE_BIZ_DATA,
+        routing.dsn,
+        pool_min_size,
+        pool_max_size,
+    ) 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]:
+    routing = await _get_project_routing(
+        metadata_repo,
+        ctx.project_id,
+        DB_ROLE_IOT_DATA,
+        DB_TYPE_TIMESCALE,
+        "TimescaleDB",
+    )
+
+    pool_min_size = (
+        routing.pool_min_size
+        if routing.pool_min_size is not None
+        else settings.PROJECT_TS_POOL_MIN_SIZE
+    )
+    pool_max_size = (
+        routing.pool_max_size
+        if routing.pool_max_size is not None
+        else settings.PROJECT_TS_POOL_MAX_SIZE
+    )
+    async with project_connection_manager.timescale_connection(
+        ctx.project_id,
+        DB_ROLE_IOT_DATA,
+        routing.dsn,
+        pool_min_size,
+        pool_max_size,
+    ) as conn:
+        yield conn

app/core/audit.py

@@ -1,3 +1,147 @@
-# 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",
+        "dsn",
+        "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 +1,18 @@
-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 +20,157 @@ 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 = 16
+    PROJECT_TS_CACHE_SIZE: int = 16
+    PROJECT_PG_POOL_MIN_SIZE: int = 0
+    PROJECT_PG_POOL_SIZE: int = 4
+    PROJECT_PG_MAX_OVERFLOW: int = 2
+    PROJECT_TS_POOL_MIN_SIZE: int = 0
+    PROJECT_TS_POOL_MAX_SIZE: int = 4
+    WNDB_SCHEMA_TEMPLATE_DB_NAME: str = "tjwater_v2_schema_template"
+    TIMESCALEDB_SCHEMA_TEMPLATE_DB_NAME: str = "tjwater_v2_timescale_template"
+    WNDB_TEMP_DB_MAX_COUNT: int = 8
+
+    # GeoServer project provisioning
+    GEOSERVER_URL: str = "http://localhost:8080/geoserver"
+    GEOSERVER_USERNAME: str = ""
+    GEOSERVER_PASSWORD: str = ""
+    GEOSERVER_DB_HOST: str = ""
+    GEOSERVER_DB_PORT: str = ""
+    GEOSERVER_DB_USER: str = ""
+    GEOSERVER_DB_PASSWORD: str = ""
+    GEOSERVER_CLIENT_CACHE_SECONDS: int = 30 * 24 * 60 * 60
+
+    # Keycloak access token verification
+    KEYCLOAK_PUBLIC_KEY: str = ""
+    KEYCLOAK_ALGORITHM: str = "RS256"
+    KEYCLOAK_AUDIENCE: str = ""
+    KEYCLOAK_ACCESS_TOKEN_MAX_AGE_SECONDS: int = 900
+
+    # Browser cross-origin access; process environment overrides .env.
+    CORS_ALLOWED_ORIGINS: str = (
+        "http://127.0.0.1:5173,http://localhost:5173,"
+        "http://127.0.0.1:15173,http://localhost:15173"
+    )
+
+    @property
+    def cors_allowed_origins(self) -> list[str]:
+        origins = [value.strip() for value in self.CORS_ALLOWED_ORIGINS.split(",") if value.strip()]
+        if "*" in origins:
+            raise ValueError("CORS_ALLOWED_ORIGINS must list explicit origins")
+        return origins
+
+    # 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}'")