新增清洗数据 API

main.py

@@ -3120,6 +3120,18 @@ async def fastapi_query_cleaning_scada_data_by_device_id_and_time_range(
     return influxdb_api.query_cleaning_SCADA_data_by_device_ID_and_timerange(
         query_ids_list=query_ids, start_time=starttime, end_time=endtime
     )
+# 查询到的SCADA模拟数据（从 realtime_simulation bucket 中查找）
+@app.get("/querysimulationscadadatabydeviceidandtimerange/")
+async def fastapi_query_simulation_scada_data_by_device_id_and_time_range(
+    ids: str, starttime: str, endtime: str
+):
+
+    print(f"query_ids: {ids}, starttime: {starttime}, endtime: {endtime}")
+
+    query_ids = ids.split(",")
+    return influxdb_api.query_simulation_SCADA_data_by_device_ID_and_timerange(
+        query_ids_list=query_ids, start_time=starttime, end_time=endtime
+    )
 
 
 # 查询指定时间范围内，多个SCADA设备的清洗后的数据
@@ -4156,17 +4168,17 @@ async def fastapi_scada_device_data_cleaning(
 
         # 添加 time 列到首列
         cleaned_value_df = pd.DataFrame(cleaned_value_df)
-        # 只选择以 '_cleaned' 结尾的清洗数据列
-        cleaned_columns = [
-            col for col in cleaned_value_df.columns if col.endswith("_cleaned")
-        ]
-        cleaned_value_df = cleaned_value_df[cleaned_columns]
-        # 重命名列,移除 '_cleaned' 后缀
-        cleaned_value_df = cleaned_value_df.rename(
-            columns={
-                col: col.replace("_cleaned", "") for col in cleaned_value_df.columns
-            }
-        )
+        # # 只选择以 '_cleaned' 结尾的清洗数据列
+        # cleaned_columns = [
+        #     col for col in cleaned_value_df.columns if col.endswith("_cleaned")
+        # ]
+        # cleaned_value_df = cleaned_value_df[cleaned_columns]
+        # # 重命名列,移除 '_cleaned' 后缀
+        # cleaned_value_df = cleaned_value_df.rename(
+        #     columns={
+        #         col: col.replace("_cleaned", "") for col in cleaned_value_df.columns
+        #     }
+        # )
         cleaned_df = pd.concat([df["time"], cleaned_value_df], axis=1)
 
         # 调试输出,确认列名