wzy-warehouse
251 lines
9.6 KiB
Python
251 lines
9.6 KiB
Python
"""
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暴雨灾害链预测接口
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"""
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import asyncio
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from datetime import datetime
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from typing import List, Dict, Any, Optional
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from fastapi import APIRouter, HTTPException
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from app.schemas.api_schemas import (
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RainfallPredictRequest, PredictResponse, PredictData,
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UpdateMonitoringTimeRequest, DistrictSummaryRequest, DistrictSummaryItem
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)
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from app.utils.api_deps import get_rainfall_model, get_prediction_semaphore
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from app.repositories.dbn_repository import dbn_repository
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from app.repositories.rainfall_repository import rainfall_repository as rain_repo
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from app.core.rainfall_manager import rainfall_manager
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from app.config.paths import get_logger
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from app.utils.db_helper import db_helper
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from app.utils.time_converter import TimeConverter
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router = APIRouter(prefix="/rainfall", tags=["暴雨灾害链"])
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logger = get_logger("api.rainfall")
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SOURCE_TYPE_MAP = {1: "隐患点", 2: "风险点"}
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LEVEL_MAP = {"低": "低", "中": "中", "较高": "较高", "高": "高"}
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def _build_prediction_map(results: List[Dict[str, Any]]) -> Dict[str, float]:
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"""将模型原始结果转换为存储格式: {id_type: 概率百分比}"""
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result_map = {}
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for r in results:
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probs = r.get("disaster_probabilities", {})
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if not probs:
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continue
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source_id = r["source_id"]
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source_type = r.get("source_type")
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max_hazard = max(probs, key=probs.get)
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key = f"{source_id}_{source_type}"
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result_map[key] = round(probs[max_hazard] * 100, 2)
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return result_map
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def _build_prediction_map_with_location(results: List[Dict[str, Any]], threshold: float = 50.0) -> Dict[str, Dict[str, Any]]:
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"""将模型原始结果转换为返回格式: {id_type: {probability, lon, lat}}"""
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from config import settings
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threshold = getattr(settings, 'PREDICT_PROBABILITY_THRESHOLD', threshold)
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result_map = {}
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for r in results:
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probs = r.get("disaster_probabilities", {})
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if not probs:
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continue
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source_id = r["source_id"]
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source_type = r.get("source_type")
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max_hazard = max(probs, key=probs.get)
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prob_value = round(probs[max_hazard] * 100, 2)
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# 低于阈值不返回
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if prob_value < threshold:
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continue
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key = f"{source_id}_{source_type}"
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result_map[key] = {
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"probability": prob_value,
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"lon": r.get("lon"),
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"lat": r.get("lat")
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}
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return result_map
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def _fetch_points(point_ids: Optional[List[int]], region_code: Optional[str]) -> List[Dict[str, Any]]:
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"""获取点位列表"""
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if point_ids:
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return dbn_repository.get_points_by_ids(point_ids)
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return dbn_repository.get_all_points(region_code)
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def _predict_sync(point_ids: Optional[List[int]], region_code: Optional[str],
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rainfall: Optional[float], duration: Optional[float],
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operation_type: str, occurred_time: Optional[datetime] = None) -> tuple:
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"""
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同步执行暴雨预测（在线程池中运行）
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Args:
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occurred_time: 事件发生时间，用于查询降雨数据和DBN推理
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Returns:
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(存储用result_map, 返回用result_map_with_location, 传入的条件, 实际使用的事件时间)
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"""
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points = _fetch_points(point_ids, region_code)
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if not points:
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return {}, {}, {}, occurred_time or datetime.now()
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# 使用传入的时间，如果没有传则使用 rainfall_manager 中的全局查询时间，最后才用当前时间
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if occurred_time:
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query_time = occurred_time
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else:
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from app.core.rainfall_manager import rainfall_manager
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query_time = rainfall_manager.get_current_query_time() or datetime.now()
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model = get_rainfall_model()
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raw_results = model.predict_multiple_points(points, rainfall=rainfall, duration=duration, query_time=query_time)
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result_map = _build_prediction_map(raw_results) # 用于存储
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result_map_with_location = _build_prediction_map_with_location(raw_results) # 用于返回
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# 存储传入的原始条件（降雨量和持续时间可能每个点不同，所以存储传入值）
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condition = {
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"point_ids": point_ids,
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"region_code": region_code,
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"rainfall": rainfall,
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"duration": duration,
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"occurred_time": query_time.isoformat() if hasattr(query_time, 'isoformat') else str(query_time)
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}
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return result_map, result_map_with_location, condition, query_time
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@router.post("/update-monitoring-time", summary="更新降雨监测查询时间")
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async def update_monitoring_time(req: UpdateMonitoringTimeRequest):
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"""
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更新降雨站点监测的查询时间，触发重新计算
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- **query_time**: 新的查询时间，格式: YYYY-MM-DD HH:mm:ss
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"""
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try:
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# 将字符串时间解析为 datetime 对象
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new_time = TimeConverter.parse_input_time(req.query_time)
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# 更新监测时间，触发重新计算
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result = rainfall_manager.update_query_time(new_time)
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logger.info(f"更新监测时间成功: {result}")
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return {
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"code": 200,
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"message": "success",
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"data": result
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}
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except ValueError as e:
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logger.error(f"时间格式错误: {e}")
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raise HTTPException(status_code=400, detail=f"时间格式错误: {e}")
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except Exception as e:
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logger.error(f"更新监测时间失败: {e}", exc_info=True)
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raise HTTPException(status_code=500, detail=f"更新监测时间失败: {e}")
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@router.post("/predict", response_model=PredictResponse, summary="暴雨灾害链预测")
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async def predict_rainfall(req: RainfallPredictRequest):
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"""
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批量预测隐患点/风险点的灾害概率。两种模式（二选一）：
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**自动推演模式**：rainfall、duration、region_code 全部不传
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→ 从气象表自动获取各点最近站降雨数据，不限区域
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**指定条件模式**：rainfall、duration、region_code 全部传入
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→ 按指定降雨条件和区域预测
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- **disaster_name**: 灾害名称
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- **point_ids**: 点位ID列表（可选）
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- **occurred_time**: 事件发生时间（可选，不传则为当前时间）
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- **operation_type**: 操作类型（如 '实时监测', '情景模拟', '应急评估'）
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"""
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semaphore = get_prediction_semaphore()
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async with semaphore:
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loop = asyncio.get_event_loop()
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try:
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result_map, result_map_with_location, condition, occurred_time = await loop.run_in_executor(
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None, _predict_sync, req.point_ids, req.region_code,
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req.rainfall, req.duration, req.operation_type, req.occurred_time
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)
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except Exception as e:
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logger.error(f"暴雨预测失败: {e}", exc_info=True)
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raise HTTPException(status_code=500, detail=f"预测失败: {e}")
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# 保存推理结果
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record_id = None
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if result_map:
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try:
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record_id = dbn_repository.save_inference_result(
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disaster_name=req.disaster_name,
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event_type="rainfall",
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occurred_time=occurred_time,
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operation_type=req.operation_type,
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condition=condition,
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result=result_map
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)
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logger.info(f"推理结果已保存，record_id={record_id}")
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except Exception as e:
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logger.error(f"保存推理结果失败: {e}", exc_info=True)
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return PredictResponse(code=200, message="success", data=PredictData(record_id=record_id, list=result_map_with_location))
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@router.post("/district-summary", summary="获取各区降雨概况")
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async def district_summary(req: DistrictSummaryRequest):
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"""
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根据推理结果ID获取各行政区的降雨概况（用于报告生成）。
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逻辑：
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- 如果预测时传了 rainfall → 直接用 condition 中的值
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- 如果预测时没传 rainfall → 从 xian_meteorology 按区聚合实际气象数据
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- **inference_id**: 推理结果ID
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"""
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# 1. 查推理结果
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record = dbn_repository.get_inference_result(req.inference_id)
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if not record:
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raise HTTPException(status_code=404, detail=f"推理结果不存在: {req.inference_id}")
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condition = record.get('condition') or {}
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occurred_time = record.get('occurred_time')
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# 2. 如果传了 rainfall → 直接用 condition 中的值
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if condition.get('rainfall') is not None:
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region_code = condition.get('region_code')
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rainfall_val = float(condition['rainfall'])
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duration_val = float(condition.get('duration', 0))
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if region_code:
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# 查 district 名称
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district_row = db_helper.execute_query_one(
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"SELECT name FROM xian_district WHERE code = %s AND is_delete = 0", (region_code,)
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)
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district_name = district_row['name'] if district_row else region_code
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items = [{
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'district_name': district_name,
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'district_code': region_code,
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'rainfall': rainfall_val,
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'duration_hours': duration_val
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}]
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else:
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items = [{
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'district_name': '全市',
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'district_code': '',
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'rainfall': rainfall_val,
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'duration_hours': duration_val
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}]
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else:
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# 3. 自动推演模式 → 从气象表按区聚合
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items = rain_repo.get_district_rainfall_summary(occurred_time)
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if not items:
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return {"code": 200, "message": "success", "data": []}
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# 转换为响应格式
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result_items = [
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DistrictSummaryItem(**item).model_dump()
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for item in items
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]
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return {"code": 200, "message": "success", "data": result_items}
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