wzy-warehouse
129 lines
4.7 KiB
Python
129 lines
4.7 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 RainfallPredictRequest, PredictResponse, PredictionItem
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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.config.paths import get_logger
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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_items(results: List[Dict[str, Any]]) -> List[PredictionItem]:
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"""将模型原始结果转换为接口返回格式"""
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items = []
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for r in results:
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probs = r.get("disaster_probabilities", {})
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levels = r.get("disaster_levels", {})
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if not probs:
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continue
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max_hazard = max(probs, key=probs.get)
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items.append(PredictionItem(
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id=r["source_id"], # 使用 source_id（隐患点/风险点ID）而非 xian_risk_factors.id
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type=SOURCE_TYPE_MAP.get(r.get("source_type"), "未知"),
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probability=round(probs[max_hazard], 4),
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level=LEVEL_MAP.get(levels.get(max_hazard, "none"), "无"),
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))
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return items
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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) -> 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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Returns:
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(预测结果列表, 原始结果, 输入条件, 当前时间)
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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 [], [], {}, 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)
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items = _build_prediction_items(raw_results)
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# 构建条件和结果用于保存
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now = datetime.now()
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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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}
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save_results = [
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{
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"point_id": r.get("source_id"), # 使用 source_id（隐患点/风险点ID）而非 xian_risk_factors.id
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"source_type": r.get("source_type"),
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"lon": r.get("lon"),
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"lat": r.get("lat"),
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"disaster_probabilities": r.get("disaster_probabilities", {}),
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"disaster_levels": r.get("disaster_levels", {})
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}
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for r in raw_results
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]
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return items, save_results, condition, now
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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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根据降雨量和持续时间，批量预测隐患点/风险点的灾害概率和等级。
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- **point_ids**: 点位ID列表（可选，不传则查询所有点）
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- **region_code**: 行政区划代码（可选，不传则不限区域）
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- **rainfall**: 累计降雨量(mm)，不传则从气象表自动获取
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- **duration**: 降雨持续时间(h)，不传则从气象表自动获取
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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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items, save_results, condition, now = 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
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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 save_results:
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try:
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record_id = dbn_repository.save_inference_result(
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event_type="rainfall",
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occurred_time=now,
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operation_type=req.operation_type,
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condition=condition,
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result=save_results
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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=items, record_id=record_id)
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