统一scheme_type命名

app/infra/db/influxdb/api.py

@@ -404,7 +404,7 @@ def create_and_initialize_buckets(org_name: str) -> None:
                 Point("link")
                 .tag("date", None)
                 .tag("ID", None)
-                .tag("scheme_Type", None)
+                .tag("scheme_type", None)
                 .tag("scheme_name", None)
                 .field("flow", 0.0)
                 .field("leakage", 0.0)
@@ -420,7 +420,7 @@ def create_and_initialize_buckets(org_name: str) -> None:
                 Point("node")
                 .tag("date", None)
                 .tag("ID", None)
-                .tag("scheme_Type", None)
+                .tag("scheme_type", None)
                 .tag("scheme_name", None)
                 .field("head", 0.0)
                 .field("pressure", 0.0)
@@ -436,7 +436,7 @@ def create_and_initialize_buckets(org_name: str) -> None:
                 .tag("date", None)
                 .tag("description", None)
                 .tag("device_ID", None)
-                .tag("scheme_Type", None)
+                .tag("scheme_type", None)
                 .tag("scheme_name", None)
                 .field("monitored_value", 0.0)
                 .field("datacleaning_value", 0.0)
@@ -1811,7 +1811,7 @@ def query_SCADA_data_by_device_ID_and_time(
 def query_scheme_SCADA_data_by_device_ID_and_time(
     query_ids_list: List[str],
     query_time: str,
-    scheme_Type: str,
+    scheme_type: str,
     scheme_name: str,
     bucket: str = "scheme_simulation_result",
 ) -> Dict[str, float]:
@@ -1843,7 +1843,7 @@ def query_scheme_SCADA_data_by_device_ID_and_time(
         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"] == "monitored_value" and r["scheme_Type"] == "{scheme_Type}" and r["scheme_name"] == "{scheme_name}")
+             |> filter(fn: (r) => r["device_ID"] == "{device_id}" and r["_field"] == "monitored_value" and r["scheme_type"] == "{scheme_type}" and r["scheme_name"] == "{scheme_name}")
            """
         # 执行查询
         try:
@@ -3227,7 +3227,7 @@ def store_scheme_simulation_result_to_influxdb(
     link_result_list: List[Dict[str, any]],
     scheme_start_time: str,
     num_periods: int = 1,
-    scheme_Type: str = None,
+    scheme_type: str = None,
     scheme_name: str = None,
     bucket: str = "scheme_simulation_result",
 ):
@@ -3237,7 +3237,7 @@ def store_scheme_simulation_result_to_influxdb(
     :param link_result_list: (List[Dict[str, any]]): 包含连接和结果数据的字典列表。
     :param scheme_start_time: (str): 方案模拟开始时间。
     :param num_periods: (int): 方案模拟的周期数
-    :param scheme_Type: (str): 方案类型
+    :param scheme_type: (str): 方案类型
     :param scheme_name: (str): 方案名称
     :param bucket: (str): InfluxDB 的 bucket 名称，默认值为 "scheme_simulation_result"。
     :return:
@@ -3298,7 +3298,7 @@ def store_scheme_simulation_result_to_influxdb(
                         Point("node")
                         .tag("date", date_str)
                         .tag("ID", node_id)
-                        .tag("scheme_Type", scheme_Type)
+                        .tag("scheme_type", scheme_type)
                         .tag("scheme_name", scheme_name)
                         .field("head", data.get("head", 0.0))
                         .field("pressure", data.get("pressure", 0.0))
@@ -3322,7 +3322,7 @@ def store_scheme_simulation_result_to_influxdb(
                         Point("link")
                         .tag("date", date_str)
                         .tag("ID", link_id)
-                        .tag("scheme_Type", scheme_Type)
+                        .tag("scheme_type", scheme_type)
                         .tag("scheme_name", scheme_name)
                         .field("flow", data.get("flow", 0.0))
                         .field("velocity", data.get("velocity", 0.0))
@@ -3409,13 +3409,13 @@ def query_corresponding_query_id_and_element_id(name: str) -> None:
 
 # 2025/03/11
 def fill_scheme_simulation_result_to_SCADA(
-    scheme_Type: str = None,
+    scheme_type: str = None,
     scheme_name: str = None,
     query_date: str = None,
     bucket: str = "scheme_simulation_result",
 ):
     """
-    :param scheme_Type: 方案类型
+    :param scheme_type: 方案类型
     :param scheme_name: 方案名称
     :param query_date: 查询日期，格式为 'YYYY-MM-DD'
     :param bucket: InfluxDB 的 bucket 名称，默认值为 "scheme_simulation_result"
@@ -3457,7 +3457,7 @@ def fill_scheme_simulation_result_to_SCADA(
     # 查找associated_element_id的对应值
     for key, value in globals.scheme_source_outflow_ids.items():
         scheme_source_outflow_result = query_scheme_curve_by_ID_property(
-            scheme_Type=scheme_Type,
+            scheme_type=scheme_type,
             scheme_name=scheme_name,
             query_date=query_date,
             ID=value,
@@ -3470,7 +3470,7 @@ def fill_scheme_simulation_result_to_SCADA(
                 Point("scheme_source_outflow")
                 .tag("date", query_date)
                 .tag("device_ID", key)
-                .tag("scheme_Type", scheme_Type)
+                .tag("scheme_type", scheme_type)
                 .tag("scheme_name", scheme_name)
                 .field("monitored_value", data["value"])
                 .time(data["time"], write_precision="s")
@@ -3480,7 +3480,7 @@ def fill_scheme_simulation_result_to_SCADA(
 
     for key, value in globals.scheme_pipe_flow_ids.items():
         scheme_pipe_flow_result = query_scheme_curve_by_ID_property(
-            scheme_Type=scheme_Type,
+            scheme_type=scheme_type,
             scheme_name=scheme_name,
             query_date=query_date,
             ID=value,
@@ -3492,7 +3492,7 @@ def fill_scheme_simulation_result_to_SCADA(
                 Point("scheme_pipe_flow")
                 .tag("date", query_date)
                 .tag("device_ID", key)
-                .tag("scheme_Type", scheme_Type)
+                .tag("scheme_type", scheme_type)
                 .tag("scheme_name", scheme_name)
                 .field("monitored_value", data["value"])
                 .time(data["time"], write_precision="s")
@@ -3502,7 +3502,7 @@ def fill_scheme_simulation_result_to_SCADA(
 
     for key, value in globals.scheme_pressure_ids.items():
         scheme_pressure_result = query_scheme_curve_by_ID_property(
-            scheme_Type=scheme_Type,
+            scheme_type=scheme_type,
             scheme_name=scheme_name,
             query_date=query_date,
             ID=value,
@@ -3514,7 +3514,7 @@ def fill_scheme_simulation_result_to_SCADA(
                 Point("scheme_pressure")
                 .tag("date", query_date)
                 .tag("device_ID", key)
-                .tag("scheme_Type", scheme_Type)
+                .tag("scheme_type", scheme_type)
                 .tag("scheme_name", scheme_name)
                 .field("monitored_value", data["value"])
                 .time(data["time"], write_precision="s")
@@ -3524,7 +3524,7 @@ def fill_scheme_simulation_result_to_SCADA(
 
     for key, value in globals.scheme_demand_ids.items():
         scheme_demand_result = query_scheme_curve_by_ID_property(
-            scheme_Type=scheme_Type,
+            scheme_type=scheme_type,
             scheme_name=scheme_name,
             query_date=query_date,
             ID=value,
@@ -3536,7 +3536,7 @@ def fill_scheme_simulation_result_to_SCADA(
                 Point("scheme_demand")
                 .tag("date", query_date)
                 .tag("device_ID", key)
-                .tag("scheme_Type", scheme_Type)
+                .tag("scheme_type", scheme_type)
                 .tag("scheme_name", scheme_name)
                 .field("monitored_value", data["value"])
                 .time(data["time"], write_precision="s")
@@ -3546,7 +3546,7 @@ def fill_scheme_simulation_result_to_SCADA(
 
     for key, value in globals.scheme_quality_ids.items():
         scheme_quality_result = query_scheme_curve_by_ID_property(
-            scheme_Type=scheme_Type,
+            scheme_type=scheme_type,
             scheme_name=scheme_name,
             query_date=query_date,
             ID=value,
@@ -3558,7 +3558,7 @@ def fill_scheme_simulation_result_to_SCADA(
                 Point("scheme_quality")
                 .tag("date", query_date)
                 .tag("device_ID", key)
-                .tag("scheme_Type", scheme_Type)
+                .tag("scheme_type", scheme_type)
                 .tag("scheme_name", scheme_name)
                 .field("monitored_value", data["value"])
                 .time(data["time"], write_precision="s")
@@ -3629,14 +3629,14 @@ def query_SCADA_data_curve(
 
 # 2025/02/18
 def query_scheme_all_record_by_time(
-    scheme_Type: str,
+    scheme_type: str,
     scheme_name: str,
     query_time: str,
     bucket: str = "scheme_simulation_result",
 ) -> tuple:
     """
     查询指定方案某一时刻的所有记录，包括‘node'和‘link’，分别以指定格式返回。
-    :param scheme_Type: 方案类型
+    :param scheme_type: 方案类型
     :param scheme_name: 方案名称
     :param query_time: 输入的北京时间，格式为 '2024-11-24T17:30:00+08:00'。
     :param bucket: 数据存储的 bucket 名称。
@@ -3660,7 +3660,7 @@ def query_scheme_all_record_by_time(
     flux_query = f"""
     from(bucket: "{bucket}")
       |> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()})
-      |> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}" and r["scheme_name"] == "{scheme_name}" and r["_measurement"] == "node" or r["_measurement"] == "link")
+      |> filter(fn: (r) => r["scheme_type"] == "{scheme_type}" and r["scheme_name"] == "{scheme_name}" and r["_measurement"] == "node" or r["_measurement"] == "link")
       |> pivot(
           rowKey:["_time"], 
           columnKey:["_field"], 
@@ -3710,7 +3710,7 @@ def query_scheme_all_record_by_time(
 
 # 2025/03/04
 def query_scheme_all_record_by_time_property(
-    scheme_Type: str,
+    scheme_type: str,
     scheme_name: str,
     query_time: str,
     type: str,
@@ -3719,7 +3719,7 @@ def query_scheme_all_record_by_time_property(
 ) -> list:
     """
     查询指定方案某一时刻‘node'或‘link’某一属性值，以指定格式返回。
-    :param scheme_Type: 方案类型
+    :param scheme_type: 方案类型
     :param scheme_name: 方案名称
     :param query_time: 输入的北京时间，格式为 '2024-11-24T17:30:00+08:00'。
     :param type: 查询的类型（决定 measurement）
@@ -3752,7 +3752,7 @@ def query_scheme_all_record_by_time_property(
     flux_query = f"""
     from(bucket: "{bucket}")
       |> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()})
-      |> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}" and r["scheme_name"] == "{scheme_name}" and r["_measurement"] == "{measurement}" and r["_field"] == "{property}")      
+      |> filter(fn: (r) => r["scheme_type"] == "{scheme_type}" and r["scheme_name"] == "{scheme_name}" and r["_measurement"] == "{measurement}" and r["_field"] == "{property}")      
     """
     # 执行查询
     tables = query_api.query(flux_query)
@@ -3767,7 +3767,7 @@ def query_scheme_all_record_by_time_property(
 
 # 2025/02/19
 def query_scheme_curve_by_ID_property(
-    scheme_Type: str,
+    scheme_type: str,
     scheme_name: str,
     query_date: str,
     ID: str,
@@ -3777,7 +3777,7 @@ def query_scheme_curve_by_ID_property(
 ) -> list:
     """
     根据scheme_Type和scheme_name,查询该模拟方案中，某一node或link的某一属性值的所有时间的结果
-    :param scheme_Type: 方案类型
+    :param scheme_type: 方案类型
     :param scheme_name: 方案名称
     :param query_date: 查询日期，格式为 'YYYY-MM-DD'
     :param ID: 元素的ID
@@ -3817,7 +3817,7 @@ def query_scheme_curve_by_ID_property(
     flux_query = f"""
     from(bucket: "{bucket}")
       |> range(start: {start_time}, stop: {stop_time})
-      |> filter(fn: (r) => r["_measurement"] == "{measurement}" and r["scheme_Type"] == "{scheme_Type}" and r["scheme_name"] == "{scheme_name}" and r["ID"] == "{ID}" and r["_field"] == "{property}")
+      |> filter(fn: (r) => r["_measurement"] == "{measurement}" and r["scheme_type"] == "{scheme_type}" and r["scheme_name"] == "{scheme_name}" and r["ID"] == "{ID}" and r["_field"] == "{property}")
     """
     # 执行查询
     tables = query_api.query(flux_query)
@@ -3832,14 +3832,14 @@ def query_scheme_curve_by_ID_property(
 
 # 2025/02/21
 def query_scheme_all_record(
-    scheme_Type: str,
+    scheme_type: str,
     scheme_name: str,
     query_date: str,
     bucket: str = "scheme_simulation_result",
 ) -> tuple:
     """
     查询指定方案的所有记录，包括‘node'和‘link’，分别以指定格式返回。
-    :param scheme_Type: 方案类型
+    :param scheme_type: 方案类型
     :param scheme_name: 方案名称
     :param query_date: 查询日期，格式为 'YYYY-MM-DD'
     :param bucket: 数据存储的 bucket 名称。
@@ -3867,7 +3867,7 @@ def query_scheme_all_record(
     flux_query = f"""
     from(bucket: "{bucket}")
       |> range(start: {utc_start_time.isoformat()}, stop: {utc_stop_time.isoformat()})
-      |> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}" and r["scheme_name"] == "{scheme_name}" and r["_measurement"] == "node" or r["_measurement"] == "link")
+      |> filter(fn: (r) => r["scheme_type"] == "{scheme_type}" and r["scheme_name"] == "{scheme_name}" and r["_measurement"] == "node" or r["_measurement"] == "link")
       |> pivot(
           rowKey:["_time"], 
           columnKey:["_field"], 
@@ -3917,7 +3917,7 @@ def query_scheme_all_record(
 
 # 2025/03/04
 def query_scheme_all_record_property(
-    scheme_Type: str,
+    scheme_type: str,
     scheme_name: str,
     query_date: str,
     type: str,
@@ -3926,7 +3926,7 @@ def query_scheme_all_record_property(
 ) -> list:
     """
     查询指定方案的‘node'或‘link’的某一属性值，以指定格式返回。
-    :param scheme_Type: 方案类型
+    :param scheme_type: 方案类型
     :param scheme_name: 方案名称
     :param query_date: 查询日期，格式为 'YYYY-MM-DD'
     :param type: 查询的类型（决定 measurement）
@@ -3964,7 +3964,7 @@ def query_scheme_all_record_property(
     flux_query = f"""
     from(bucket: "{bucket}")
       |> range(start: {start_time}, stop: {stop_time})
-      |> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}" and r["scheme_name"] == "{scheme_name}" and r["date"] == "{query_date}" and r["_measurement"] == "{measurement}" and r["_field"] == "{property}")        
+      |> filter(fn: (r) => r["scheme_type"] == "{scheme_type}" and r["scheme_name"] == "{scheme_name}" and r["date"] == "{query_date}" and r["_measurement"] == "{measurement}" and r["_field"] == "{property}")        
     """
     # 执行查询
     tables = query_api.query(flux_query)
@@ -4245,7 +4245,7 @@ def export_scheme_simulation_result_to_csv_time(
             link_data[key][field] = record.get_value()
             link_data[key]["measurement"] = record.get_measurement()
             link_data[key]["date"] = record.values.get("date", None)
-            link_data[key]["scheme_Type"] = record.values.get("scheme_Type", None)
+            link_data[key]["scheme_type"] = record.values.get("scheme_type", None)
             link_data[key]["scheme_name"] = record.values.get("scheme_name", None)
     # 构建 Flux 查询语句，查询指定时间范围内的数据
     flux_query_node = f"""
@@ -4267,7 +4267,7 @@ def export_scheme_simulation_result_to_csv_time(
             node_data[key][field] = record.get_value()
             node_data[key]["measurement"] = record.get_measurement()
             node_data[key]["date"] = record.values.get("date", None)
-            node_data[key]["scheme_Type"] = record.values.get("scheme_Type", None)
+            node_data[key]["scheme_type"] = record.values.get("scheme_type", None)
             node_data[key]["scheme_name"] = record.values.get("scheme_name", None)
     for key in set(link_data.keys()):
         row = {"time": key[0], "ID": key[1]}
@@ -4288,7 +4288,7 @@ def export_scheme_simulation_result_to_csv_time(
                 "time",
                 "measurement",
                 "date",
-                "scheme_Type",
+                "scheme_type",
                 "scheme_name",
                 "ID",
                 "flow",
@@ -4311,7 +4311,7 @@ def export_scheme_simulation_result_to_csv_time(
                 "time",
                 "measurement",
                 "date",
-                "scheme_Type",
+                "scheme_type",
                 "scheme_name",
                 "ID",
                 "head",
@@ -4330,14 +4330,14 @@ def export_scheme_simulation_result_to_csv_time(
 
 # 2025/02/18
 def export_scheme_simulation_result_to_csv_scheme(
-    scheme_Type: str,
+    scheme_type: str,
     scheme_name: str,
     query_date: str,
     bucket: str = "scheme_simulation_result",
 ) -> None:
     """
     导出influxdb中scheme_simulation_result这个bucket的数据到csv中
-    :param scheme_Type: 查询的方案类型
+    :param scheme_type: 查询的方案类型
     :param scheme_name: 查询的方案名
     :param query_date: 查询日期，格式为 'YYYY-MM-DD'
     :param bucket: 数据存储的 bucket 名称，默认值为 "SCADA_data"
@@ -4366,7 +4366,7 @@ def export_scheme_simulation_result_to_csv_scheme(
     flux_query_link = f"""
     from(bucket: "{bucket}")
       |> range(start: {start_time}, stop: {stop_time})
-      |> filter(fn: (r) => r["_measurement"] == "link" and r["scheme_Type"] == "{scheme_Type}" and r["scheme_name"] == "{scheme_name}")
+      |> filter(fn: (r) => r["_measurement"] == "link" and r["scheme_type"] == "{scheme_type}" and r["scheme_name"] == "{scheme_name}")
     """
     # 执行查询
     link_tables = query_api.query(flux_query_link)
@@ -4382,13 +4382,13 @@ def export_scheme_simulation_result_to_csv_scheme(
             link_data[key][field] = record.get_value()
             link_data[key]["measurement"] = record.get_measurement()
             link_data[key]["date"] = record.values.get("date", None)
-            link_data[key]["scheme_Type"] = record.values.get("scheme_Type", None)
+            link_data[key]["scheme_type"] = record.values.get("scheme_type", None)
             link_data[key]["scheme_name"] = record.values.get("scheme_name", None)
     # 构建 Flux 查询语句，查询指定时间范围内的数据
     flux_query_node = f"""
     from(bucket: "{bucket}")
       |> range(start: {start_time}, stop: {stop_time})
-      |> filter(fn: (r) => r["_measurement"] == "node" and r["scheme_Type"] == "{scheme_Type}" and r["scheme_name"] == "{scheme_name}")
+      |> filter(fn: (r) => r["_measurement"] == "node" and r["scheme_type"] == "{scheme_type}" and r["scheme_name"] == "{scheme_name}")
     """
     # 执行查询
     node_tables = query_api.query(flux_query_node)
@@ -4404,7 +4404,7 @@ def export_scheme_simulation_result_to_csv_scheme(
             node_data[key][field] = record.get_value()
             node_data[key]["measurement"] = record.get_measurement()
             node_data[key]["date"] = record.values.get("date", None)
-            node_data[key]["scheme_Type"] = record.values.get("scheme_Type", None)
+            node_data[key]["scheme_type"] = record.values.get("scheme_type", None)
             node_data[key]["scheme_name"] = record.values.get("scheme_name", None)
     for key in set(link_data.keys()):
         row = {"time": key[0], "ID": key[1]}
@@ -4416,10 +4416,10 @@ def export_scheme_simulation_result_to_csv_scheme(
         node_rows.append(row)
     # 动态生成 CSV 文件名
     csv_filename_link = (
-        f"scheme_simulation_link_result_{scheme_name}_of_{scheme_Type}.csv"
+        f"scheme_simulation_link_result_{scheme_name}_of_{scheme_type}.csv"
     )
     csv_filename_node = (
-        f"scheme_simulation_node_result_{scheme_name}_of_{scheme_Type}.csv"
+        f"scheme_simulation_node_result_{scheme_name}_of_{scheme_type}.csv"
     )
     # 写入到 CSV 文件
     with open(csv_filename_link, mode="w", newline="") as file:
@@ -4429,7 +4429,7 @@ def export_scheme_simulation_result_to_csv_scheme(
                 "time",
                 "measurement",
                 "date",
-                "scheme_Type",
+                "scheme_type",
                 "scheme_name",
                 "ID",
                 "flow",
@@ -4452,7 +4452,7 @@ def export_scheme_simulation_result_to_csv_scheme(
                 "time",
                 "measurement",
                 "date",
-                "scheme_Type",
+                "scheme_type",
                 "scheme_name",
                 "ID",
                 "head",
@@ -4878,15 +4878,15 @@ if __name__ == "__main__":
     # export_scheme_simulation_result_to_csv_time(start_date='2025-02-13', end_date='2025-02-15')
 
     # 示例9：export_scheme_simulation_result_to_csv_scheme
-    # export_scheme_simulation_result_to_csv_scheme(scheme_Type='burst_Analysis', scheme_name='scheme1', query_date='2025-03-10')
+    # export_scheme_simulation_result_to_csv_scheme(scheme_type='burst_Analysis', scheme_name='scheme1', query_date='2025-03-10')
 
     # 示例10：query_scheme_all_record_by_time
-    # node_records, link_records = query_scheme_all_record_by_time(scheme_Type='burst_Analysis', scheme_name='scheme1', query_time="2025-02-14T10:30:00+08:00")
+    # node_records, link_records = query_scheme_all_record_by_time(scheme_type='burst_Analysis', scheme_name='scheme1', query_time="2025-02-14T10:30:00+08:00")
     # print("Node 数据：", node_records)
     # print("Link 数据：", link_records)
 
     # 示例11：query_scheme_curve_by_ID_property
-    # curve_result = query_scheme_curve_by_ID_property(scheme_Type='burst_Analysis', scheme_name='scheme1', ID='ZBBDTZDP000022',
+    # curve_result = query_scheme_curve_by_ID_property(scheme_type='burst_Analysis', scheme_name='scheme1', ID='ZBBDTZDP000022',
     #                                                  type='node', property='head')
     # print(curve_result)
 
@@ -4896,7 +4896,7 @@ if __name__ == "__main__":
     # print("Link 数据：", link_records)
 
     # 示例13：query_scheme_all_record
-    # node_records, link_records = query_scheme_all_record(scheme_Type='burst_Analysis', scheme_name='scheme1', query_date='2025-03-10')
+    # node_records, link_records = query_scheme_all_record(scheme_type='burst_Analysis', scheme_name='scheme1', query_date='2025-03-10')
     # print("Node 数据：", node_records)
     # print("Link 数据：", link_records)
 
@@ -4909,16 +4909,16 @@ if __name__ == "__main__":
     # print(result_records)
 
     # 示例16：query_scheme_all_record_by_time_property
-    # result_records = query_scheme_all_record_by_time_property(scheme_Type='burst_Analysis', scheme_name='scheme1',
+    # result_records = query_scheme_all_record_by_time_property(scheme_type='burst_Analysis', scheme_name='scheme1',
     #                                                           query_time='2025-02-14T10:30:00+08:00', type='node', property='head')
     # print(result_records)
 
     # 示例17：query_scheme_all_record_property
-    # result_records = query_scheme_all_record_property(scheme_Type='burst_Analysis', scheme_name='scheme1', query_date='2025-03-10', type='node', property='head')
+    # result_records = query_scheme_all_record_property(scheme_type='burst_Analysis', scheme_name='scheme1', query_date='2025-03-10', type='node', property='head')
     # print(result_records)
 
     # 示例18：fill_scheme_simulation_result_to_SCADA
-    # fill_scheme_simulation_result_to_SCADA(scheme_Type='burst_Analysis', scheme_name='burst0330', query_date='2025-03-30')
+    # fill_scheme_simulation_result_to_SCADA(scheme_type='burst_Analysis', scheme_name='burst0330', query_date='2025-03-30')
 
     # 示例19：query_SCADA_data_by_device_ID_and_timerange
     # result = query_SCADA_data_by_device_ID_and_timerange(query_ids_list=globals.pressure_non_realtime_ids, start_time='2025-04-16T00:00:00+08:00',
@@ -4926,7 +4926,7 @@ if __name__ == "__main__":
     # print(result)
 
     # 示例：manually_get_burst_flow
-    # leakage = manually_get_burst_flow(scheme_Type='burst_Analysis', scheme_name='burst_scheme', scheme_start_time='2025-03-10T12:00:00+08:00')
+    # leakage = manually_get_burst_flow(scheme_type='burst_Analysis', scheme_name='burst_scheme', scheme_start_time='2025-03-10T12:00:00+08:00')
     # print(leakage)
 
     # 示例：upload_cleaned_SCADA_data_to_influxdb