Analysis_Multi/app/routers/temperature.py

import glob
import os
from pathlib import Path

import pandas as pd
from fastapi import APIRouter, HTTPException
from fastapi.responses import JSONResponse

from app.logger import logger
from app.models.AutoDiagModel import AutoDiagInput
from app.models.TemperatureInput import TemperatureInput
from app.models.TemperatureThresholdInput import TemperatureThresholdInput
from app.models.TemperatureDataQueryInput import TemperatureDataQueryInput
from app.services.Auto_diag import Auto_diag
from app.services.MSET_Temp import MSET_Temp

router = APIRouter()

# 全局：{ windCode: { turbine: { channel: model, … }, … }, … }
MODEL_STORE: dict[str, dict[str, dict[str, MSET_Temp]]] = {}

# 英文→中文映射
cn_map = {
    'main_bearing_temperature': '主轴承温度',
    'gearbox_oil_temperature': '齿轮箱油温',
    'generatordrive_end_bearing_temperature': '发电机驱动端轴承温度',
    'generatornon_drive_end_bearing_temperature': '发电机非驱动端轴承温度'
}


@router.on_event("startup")
def load_all_models():

    for f in glob.glob("./app/temperature-models/*/*/*.pkl"):
        path = Path(f)
        ch = os.path.splitext(path.name)[0]
        MODEL_STORE.setdefault(path.parent.parent.name, {}).setdefault(path.parent.name, {})[ch] = MSET_Temp.load_model(f)
    logger.info(f"模型加载完成: { {k: list(v.keys()) for k, v in MODEL_STORE.items()} }")


@router.post("/temperature/threshold")
async def route_threshold(inp: TemperatureThresholdInput):
    """
    输入：
    {
      "windCode": "WOF091200030",
      "windTurbineNumberList": ["WOG01355"],
      "startTime": "2024-06-01 00:00",
      "endTime":   "2024-06-05 01:00",
      "pageNo":    1,
      "pageSize":  10
    }
    输出：
    {
      "data": {
        "type": "temperature_threshold",
        "records": [
          {
            "wind_turbine_number": "WOG01355",
            "time_stamp": "2025-06-01 00:05:00",
            "temp_channel": "主轴承温度",
            "SPRT_score": 0.12,
            "status": "正常"
          },
          ...
        ],
        "totalSize": 42
      },
      "code": 200,
      "message": "success"
    }
    """
    # 1) 校验模型是否存在
    if inp.windCode not in MODEL_STORE:
        raise HTTPException(404, f"无模型：{inp.windCode}")
    # 2) 为每台待分析风机，拉数据并推理
    records = []
    for turbine in inp.windTurbineNumberList:
        if turbine not in MODEL_STORE[inp.windCode]:
            continue
        analyzer = MSET_Temp(inp.windCode, [turbine], inp.startTime, inp.endTime)
        df = analyzer._get_data_by_filter()
        if df.empty:
            continue
        df['time_stamp'] = pd.to_datetime(df['time_stamp'])

        for eng, cn in cn_map.items():
            if eng not in df.columns:
                continue
            sub = df[['time_stamp', eng]].dropna()
            arr = sub[eng].values.reshape(-1, 1)
            ts = sub['time_stamp'].dt.strftime("%Y-%m-%d %H:%M:%S").tolist()

            model = MODEL_STORE[inp.windCode][turbine].get(eng)
            if not model:
                continue
            flags = model.predict_SPRT(arr, decisionGroup=1)

            for i, sc in enumerate(flags):
                records.append({
                    "wind_turbine_number": turbine,
                    "time_stamp": ts[i],
                    "temp_channel": cn,
                    "SPRT_score": sc,
                    "status": "危险" if sc > 0.99 else "正常"
                })

    # 分页返回
    total = len(records)
    start = (inp.pageNo - 1) * inp.pageSize
    end = start + inp.pageSize
    return {
        "data": {
            "type": "temperature_threshold",
            "records": records[start:end],
            "totalSize": total
        },
        "code": 200,
        "message": "success"
    }


@router.post("/SPRT/trend")
async def route_trend(inp: TemperatureInput):
    """
    输入：
    {
      "windCode": "WOF091200030",
      "windTurbineNumberList": ["WOG01355"],
      "startTime": "2024-06-01 00:00",
      "endTime":   "2024-06-05 01:00"
    }
    输出：
    {
      "data": {
        "type": "SPRT_trend",
        "main_bearing": {"timestamps": [...], "values": [...]},
        "gearbox_oil": {"timestamps": [...], "values": [...]},
        "generator_drive_end": {"timestamps": [...], "values": [...]},
        "generator_nondrive_end": {"timestamps": [...], "values": [...]}
      },
      "code": 200,
      "message": "success"
    }

    """
    if inp.windCode not in MODEL_STORE:
        raise HTTPException(404, f"无模型：{inp.windCode}")

    turbines_out = []
    for turbine in inp.windTurbineNumberList:
        if turbine not in MODEL_STORE[inp.windCode]:
            continue
        analyzer = MSET_Temp(inp.windCode, [turbine], inp.startTime, inp.endTime)
        df = analyzer._get_data_by_filter()
        if df.empty:
            continue
        df['time_stamp'] = pd.to_datetime(df['time_stamp'])

        ch_data = {}
        for eng, key in {
            'main_bearing_temperature': 'main_bearing',
            'gearbox_oil_temperature': 'gearbox_oil',
            'generatordrive_end_bearing_temperature': 'generator_drive_end',
            'generatornon_drive_end_bearing_temperature': 'generator_nondrive_end'
        }.items():
            if eng not in df.columns:
                ch_data[key] = {"timestamps": [], "values": []}
                continue
            sub = df[['time_stamp', eng]].dropna()
            ts = sub['time_stamp'].dt.strftime("%Y-%m-%d %H:%M:%S").tolist()
            arr = sub[eng].values.reshape(-1, 1)

            model = MODEL_STORE[inp.windCode][turbine].get(eng)
            vals = model.predict_SPRT(arr, decisionGroup=1) if model else []
            ch_data[key] = {"timestamps": ts, "values": vals}

        # turbines_out.append({
        #   "wind_turbine_number": turbine,
        #     **ch_data
        # })

    return {
        "data": {
            "type": "SPRT_trend",
            **ch_data
        },
        "code": 200,
        "message": "success"
    }


if __name__ == "__main__":
    import uvicorn

    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)

@router.post("/temperature/dataquery")
async def query_data(inp: TemperatureDataQueryInput):
    """
    查询指定风机在特定时间点前后各50个时间点的数据
    输入：
    {
      "windCode": "WOF091200030",
      "windTurbineNumber": "WOG01355",
      "timestamp": "2024-06-01 00:00:00"
    }
    输出：
    {
      "data": {
        "wind_turbine_number": "WOG01355",
        "record_count": 101,
        "records": [
          {"时间戳": "2024-05-31 23:10:00", "主轴承温度": 65.2, ...},
          {"时间戳": "2024-05-31 23:15:00", "主轴承温度": 65.5, ...},
          ...
          {"时间戳": "2024-06-01 00:50:00", "主轴承温度": 66.1, ...}
        ]
      },
      "code": 200,
      "message": "success"
    }
    """
    try:
        analyzer = MSET_Temp(inp.windCode, [inp.windTurbineNumber], "", "")
        result = analyzer.query_surrounding_data(inp.timestamp,minutes_around = 250)
        if result['record_count'] == 0:
            return JSONResponse(
                content={"code": 405, "message": "未找到数据"},
                status_code=200
            )

        return {
            "data": {
                "wind_turbine_number": inp.windTurbineNumber,
                "records": result['records']
            },
            "code": 200,
            "message": "success"
        }
        
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))
    
    
@router.post("/autodiag/{autodiagType}")
async def perform_diagnosis(autodiagType: str, input_data: AutoDiagInput):
    """
    执行自动诊断分析
    参数:
        autodiagType: 诊断类型
        input_data: 包含ids, windCode, engine_code的输入数据
    返回:
        诊断结果，包含状态码列表和统计信息
    """
    autodiag_map = {
        "Unbalance": "Unbalance_diag",  # 不平衡诊断
        "Misalignment": "Misalignment_diag",  # 不对中诊断
        "Looseness": "Looseness_diag",  # 松动诊断
        "Bearing": "Bearing_diag",  # 轴承诊断
        "Gear": "Gear_diag"  # 齿轮诊断
    }

    if autodiagType not in autodiag_map:
        raise HTTPException(status_code=400, detail="非可用的诊断类型")

    try:
        # 初始化诊断类
        autodiag = Auto_diag(input_data.ids, input_data.windCode, input_data.engine_code)

        # 获取诊断方法
        func = getattr(autodiag, autodiag_map[autodiagType])

        # 执行诊断
        if callable(func):
            result = func()  # 直接返回格式化后的结果
            return JSONResponse(content=result)

    except ValueError as e:
        # 专门捕获齿轮诊断的错误
        if "Can not perform gearbox diagnosis" in str(e):
            return JSONResponse(
                status_code=200,
                content={
                    "code": 400,
                    "message": str(e)
                }
            )
        elif "当前采集频率不适合进行诊断分析" in str(e):
            return JSONResponse(
                status_code=200,
                content={
                    "code": 405,
                    "message": str(e)
                }
            )
            # 其他ValueError
        raise HTTPException(status_code=400, detail=str(e))
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))