新增清洗数据 API

influxdb_api.py

@@ -2004,6 +2004,66 @@ def query_cleaning_SCADA_data_by_device_ID_and_timerange(
 
     return SCADA_dict
 
+# 查找 SCADA 模拟数据，返回的是一个list，list的内容是清洗后的正常值，表示为 time + value
+def query_simulation_SCADA_data_by_device_ID_and_timerange(
+    query_ids_list: List[str],
+    start_time: str,
+    end_time: str,
+    bucket: str = "SCADA_data",
+):
+    """
+    查询指定时间范围内，多个SCADA设备的修复的单个的数据
+    :param query_ids_list: SCADA设备ID的列表
+    :param start_time: 输入的北京时间，格式为 '2023-11-24T17:30:00+08:00'。
+    :param end_time: 输入的北京时间，格式为 '2023-11-24T17:30:00+08:00'。
+    :param bucket: InfluxDB 的 bucket 名称，默认值为 "SCADA_data"。
+    :return:
+    """
+    client = get_new_client()
+    if not client.ping():
+        print(
+            "{} -- Failed to connect to InfluxDB.".format(
+                datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+            )
+        )
+
+    query_api = client.query_api()
+    print("start_time", start_time)
+    print("end_time", end_time)
+    # 将北京时间转换为 UTC 时间
+    beijing_start_time = datetime.fromisoformat(start_time)
+    print("beijing_start_time", beijing_start_time)
+    utc_start_time = time_api.to_utc_time(beijing_start_time)
+    print("utc_start_time", utc_start_time)
+    beijing_end_time = datetime.fromisoformat(end_time)
+    print("beijing_end_time", beijing_end_time)
+    utc_stop_time = time_api.to_utc_time(beijing_end_time)
+    print("utc_stop_time", utc_stop_time)
+
+    SCADA_dict = {}
+    for device_id in query_ids_list:
+        flux_query = f"""
+        from(bucket: "{bucket}")
+        |> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()})
+        |> filter(fn: (r) => r["device_ID"] == "{device_id}" and r["_field"] == "datacleaning_value")
+        |> sort(columns: ["_time"])
+        """
+        # 执行查询，返回一个 FluxTable 列表
+        tables = query_api.query(flux_query)
+        print(tables)
+        records_list = []
+        for table in tables:
+            for record in table.records:
+                # 获取记录的时间和监测值
+                records_list.append(
+                    {"time": record["_time"], "value": record["_value"]}
+                )
+        SCADA_dict[device_id] = records_list
+
+    client.close()
+
+    return SCADA_dict
+
 
 # 2025/05/04 DingZQ
 # SCADA 数据原版缺失，根据历史数据的平均值补上缺失的部分

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
+        # # 只选择以 '_cleaned' 结尾的清洗数据列
-        cleaned_columns = [
+        # cleaned_columns = [
-            col for col in cleaned_value_df.columns if col.endswith("_cleaned")
+        #     col for col in cleaned_value_df.columns if col.endswith("_cleaned")
-        ]
+        # ]
-        cleaned_value_df = cleaned_value_df[cleaned_columns]
+        # cleaned_value_df = cleaned_value_df[cleaned_columns]
-        # 重命名列,移除 '_cleaned' 后缀
+        # # 重命名列,移除 '_cleaned' 后缀
-        cleaned_value_df = cleaned_value_df.rename(
+        # cleaned_value_df = cleaned_value_df.rename(
-            columns={
+        #     columns={
-                col: col.replace("_cleaned", "") for col in cleaned_value_df.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