增加温度诊断功能特征值为1时的前后50个数据点的查询功能

app/models/TemperatureDataQueryInput.py

@@ -0,0 +1,6 @@
+from pydantic import BaseModel
+
+class TemperatureDataQueryInput(BaseModel):
+    windCode: str
+    windTurbineNumber: str
+    timestamp: str

app/routers/temperature.py

@@ -10,6 +10,7 @@ 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
 
@@ -194,7 +195,54 @@ if __name__ == "__main__":
 
     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):
     """

app/services/MSET_Temp.py

@@ -9,7 +9,7 @@ from sklearn.neighbors import BallTree
 
 from app.config import dataBase
 from app.database import get_engine
-
+from typing import Dict
 
 class MSET_Temp:
     """
@@ -184,3 +184,98 @@ class MSET_Temp:
             metric=lambda a, b: 1.0 - inst.calcSimilarity(a, b)
         )
         return inst
+
+    def query_surrounding_data(self, timestamp: str, minutes_around: int = 250) -> Dict:
+        """
+        查询指定时间点前后50个点的数据
+        参数:
+            timestamp: 中心时间点，格式为 'yyyy-mm-dd HH:MM:SS'
+            minutes_around: 查询前后多少分钟的数据
+        返回:
+            {
+                'record_count': int,
+                'records': List[Dict],
+                'columns_mapping': Dict[str, str]  # 字段中英文映射
+            }
+        """
+        # 中英文映射字典
+        cn_map = {
+            'wind_turbine_name':'风机名称',
+            'time_stamp': '时间',
+            'active_power': '有功功率(kW)',
+            'rotor_speed': '风轮转速(rpm)',
+            'generator_speed':'发电机转速(rpm)',
+            'wind_velocity': '风速(m/s)',
+            'pitch_angle_blade_1':'桨距角1(°)',
+            'pitch_angle_blade_2':'桨距角2(°)',  
+            'pitch_angle_blade_3':'桨距角3(°)',
+            'cabin_position':'机舱位置(°)',   
+            'true_wind_direction':'绝对风向(°)',
+            'yaw_error1':'对风角度(°)',     
+            'set_value_of_active_power':'有功功率设定值(kW)',
+            '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(℃)',     
+            'cabin_temperature':'机舱内温度(℃)',
+            'twisted_cable_angle':'扭缆角度(°)',     
+            'outside_cabin_temperature':'环境温度(℃)',
+            'main_bearing_temperature':'主轴承轴承温度(℃)',     
+            'main_bearing_temperature_2': '主轴承轴承温度2(℃)',            
+            'gearbox_high_speed_shaft_bearing_temperature':'齿轮箱高速轴轴承温度(℃)',
+            'gearboxmedium_speed_shaftbearing_temperature':'齿轮箱中速轴轴承温度(℃)',     
+            'gearbox_low_speed_shaft_bearing_temperature':'齿轮箱低速轴轴承温度(℃)',
+            'generator_winding1_temperature':'发电机绕组1温度(℃)',     
+            'generator_winding2_temperature':'发电机绕组2温度(℃)',
+            'generator_winding3_temperature':'发电机绕组3温度(℃)',     
+            'grid_a_phase_current':'电网A相电流(A)',     
+            'grid_b_phase_current': '电网B相电流(A)',
+            'grid_c_phase_current': '电网C相电流(A)'
+        }
+
+        table = f"{self.windCode}_minute"
+        engine = get_engine(dataBase.DATA_DB)
+
+        # 查询数据
+        sql = text(f"""
+            SELECT *
+            FROM {table}
+            WHERE wind_turbine_number IN ({','.join([f"'{t}'" for t in self.windTurbineNumberList])})
+            AND time_stamp BETWEEN 
+                DATE_SUB(:timestamp, INTERVAL :minutes MINUTE) 
+                AND DATE_ADD(:timestamp, INTERVAL :minutes MINUTE)
+            ORDER BY time_stamp ASC
+        """)
+        
+        df = pd.read_sql(sql, engine, params={
+            "timestamp": timestamp,
+            "minutes": minutes_around
+        })
+
+        # 打印查询到的数据条数
+        record_count = len(df)
+        print(f"查询到 {record_count} 条数据")
+
+        if df.empty:
+            return {
+                'record_count': 0,
+                'records': [],
+                'columns_mapping': {}
+            }
+
+        # 删除空列和不需要的列
+        cols_to_drop = ['wind_turbine_number', 'reactive_power','lab', 'year', 'month','day','year_month','front_back_vibration_of_the_cabin','side_to_side_vibration_of_the_cabin',
+                        'actual_torque','given_torque','clockwise_yaw_count','counterclockwise_yaw_count','unusable','power_curve_available','required_gearbox_speed','inverter_speed_master_control',
+                        'wind_turbine_status','wind_turbine_status2','turbulence_intensity'
+                        ]
+        cols_to_drop = [col for col in cols_to_drop if col in df.columns]
+        df = df.drop(columns=cols_to_drop)
+        df = df.dropna(axis=1, how='all')
+
+        # 转换字段名和格式
+        df['time_stamp'] = df['time_stamp'].astype(str)
+        records = df.rename(columns=cn_map).to_dict('records')
+
+        return {
+            'record_count': record_count,
+            'records': records
+        }