jiang
be6317eb48
Add project-bound OAuth MCP queries and controlled analysis with scoped evidence, limits, timeouts and a nearby-model spatial query using EPSG:4326 inputs. Default nearby searches to 200 meters and five results. Integrate investigation messaging/presentation, health/readiness and typed history; remove duplicate web search and fix online simulation and compressed baseline writes. Include project migrations 023-025; keep local credentials and demo seeding excluded. Validation: 319 focused pytest checks; real PostGIS temporary-table regression; read-only Jinqian bridge radius comparison; cross-language MCP discovery/calls.
110 lines
3.9 KiB
Python
110 lines
3.9 KiB
Python
from fastapi import APIRouter, Depends, HTTPException, Query
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from datetime import datetime
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from psycopg import AsyncConnection
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from app.services.timeseries_analysis import TimeseriesAnalysisService
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from app.domain.schemas.timeseries_history import (
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ElementHistoryQuery,
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ElementHistoryResponse,
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)
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from app.services.timeseries_history import TimeseriesHistoryService
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from .dependencies import get_timescale_connection, get_postgres_connection
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router = APIRouter()
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history_router = APIRouter()
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@history_router.post(
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"/timeseries/views/element-history/query",
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summary="批量查询管网元素历史数据",
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response_model=ElementHistoryResponse,
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)
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async def query_element_history(
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payload: ElementHistoryQuery,
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timescale_conn: AsyncConnection = Depends(get_timescale_connection),
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postgres_conn: AsyncConnection = Depends(get_postgres_connection),
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) -> ElementHistoryResponse:
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try:
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return await TimeseriesHistoryService.query(
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timescale_conn, postgres_conn, payload
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)
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except ValueError as exc:
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raise HTTPException(status_code=422, detail=str(exc)) from exc
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@router.post("/timeseries/scada-cleaning-runs", summary="清洗SCADA监测数据")
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async def clean_scada_data(
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device_ids: str = Query(..., description="设备ID列表或 'all' 表示清洗所有设备"),
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start_time: datetime = Query(..., description="清洗数据的开始时间"),
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end_time: datetime = Query(..., description="清洗数据的结束时间"),
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timescale_conn: AsyncConnection = Depends(get_timescale_connection),
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postgres_conn: AsyncConnection = Depends(get_postgres_connection),
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):
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"""
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清洗SCADA监测数据
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根据device_ids查询monitored_value，清洗后更新cleaned_value。
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支持清洗指定设备或所有设备的数据。
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Args:
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device_ids: 设备ID列表，用逗号分隔，或 'all' 表示清洗所有设备
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start_time: 清洗数据的开始时间
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end_time: 清洗数据的结束时间
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timescale_conn: TimescaleDB连接
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postgres_conn: PostgreSQL连接
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Returns:
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清洗结果信息
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Raises:
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HTTPException: 当清洗过程出现错误时返回400错误
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"""
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try:
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if device_ids == "all":
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device_ids_list = []
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else:
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device_ids_list = (
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[id.strip() for id in device_ids.split(",") if id.strip()]
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if device_ids
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else []
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)
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return await TimeseriesAnalysisService.clean_scada_data(
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timescale_conn, postgres_conn, device_ids_list, start_time, end_time
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)
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except ValueError as e:
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raise HTTPException(status_code=400, detail=str(e))
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@router.get("/pipeline-health-predictions", summary="预测管道健康状况")
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async def predict_pipeline_health(
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query_time: datetime = Query(..., description="查询时间"),
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timescale_conn: AsyncConnection = Depends(get_timescale_connection),
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postgres_conn: AsyncConnection = Depends(get_postgres_connection),
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):
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"""
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预测管道健康状况
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根据管网名称和当前时间，查询管道信息和实时数据，
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使用随机生存森林模型预测管道的生存概率。
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Args:
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query_time: 查询时间
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timescale_conn: TimescaleDB连接
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Returns:
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预测结果列表，每个元素包含 link_id 和对应的生存函数
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Raises:
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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
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"""
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try:
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return await TimeseriesAnalysisService.predict_pipeline_health(
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timescale_conn, postgres_conn, query_time
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)
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except ValueError as e:
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raise HTTPException(status_code=400, detail=str(e))
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except FileNotFoundError as e:
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raise HTTPException(status_code=404, detail=str(e))
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except Exception as e:
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raise HTTPException(status_code=500, detail=f"内部服务器错误: {str(e)}")
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