Add influxdb_api

influxdb_api.py

@@ -12,6 +12,9 @@ from tjnetwork import *
 import schedule
 import threading
 import globals
+import csv
+import pandas as pd
+import openpyxl
 import influxdb_info
 import time_api
 
@@ -139,6 +142,7 @@ def query_pg_scada_info_non_realtime(name: str) -> None:
     open_project(name)
     dic_time = get_time(name)
     globals.hydraulic_timestep = dic_time['HYDRAULIC TIMESTEP']
+
     close_project(name)
     # 连接数据库
     conn_string = f"dbname={name} host=127.0.0.1"
@@ -205,7 +209,7 @@ def query_pg_scada_info_non_realtime(name: str) -> None:
                 # print("Demand Non-Realtime IDs:", globals.demand_non_realtime_ids)
                 # print("Quality Non-Realtime IDs:", globals.quality_non_realtime_ids)
                 # print("Maximum Transmission Frequency:", globals.transmission_frequency)
-                # print("Hydraulic Timestep:", globals.hydraulic_timestep)
+                print("Hydraulic Timestep:", globals.hydraulic_timestep)
 
 
     except Exception as e:
@@ -220,17 +224,23 @@ def delete_buckets(client: InfluxDBClient, org_name: str) -> None:
     :param org_name: InfluxDB中organization的名称。
     :return: None
     """
+    # 定义需要删除的 bucket 名称列表
+    buckets_to_delete = ['SCADA_data', 'realtime_simulation_result', 'scheme_simulation_result']
+
     buckets_api = client.buckets_api()
     buckets_obj = buckets_api.find_buckets(org=org_name)
 
     # 确保 buckets_obj 拥有 buckets 属性
     if hasattr(buckets_obj, 'buckets'):
         for bucket in buckets_obj.buckets:
+            if bucket.name in buckets_to_delete:  # 只删除特定名称的 bucket
                 try:
                     buckets_api.delete_bucket(bucket)
                     print(f"Bucket {bucket.name} has been deleted successfully.")
                 except Exception as e:
                     print(f"Failed to delete bucket {bucket.name}: {e}")
+            else:
+                print(f"Skipping bucket {bucket.name}. Not in the deletion list.")
     else:
         print("未找到 buckets 属性，无法迭代 buckets。")
 
@@ -298,6 +308,8 @@ def create_and_initialize_buckets(client: InfluxDBClient, org_name: str) -> None
                 .field("status", None) \
                 .field("setting", 0.0) \
                 .field("quality", 0.0) \
+                .field("reaction", 0.0) \
+                .field("friction", 0.0) \
                 .time("2024-11-21T00:00:00Z")
 
             node_point = Point("node") \
@@ -380,7 +392,8 @@ def store_realtime_SCADA_data_to_influxdb(get_real_value_time: str, bucket: str
         try:
             try_count += 1
             if globals.reservoir_liquid_level_realtime_ids:
-                reservoir_liquid_level_realtime_data_list = get_realValue.get_realValue(ids=','.join(globals.reservoir_liquid_level_realtime_ids))
+                reservoir_liquid_level_realtime_data_list = get_realValue.get_realValue(
+                    ids=','.join(globals.reservoir_liquid_level_realtime_ids))
             if globals.tank_liquid_level_realtime_ids:
                 tank_liquid_level_realtime_data_list = get_realValue.get_realValue(ids=','.join(globals.tank_liquid_level_realtime_ids))
             if globals.fixed_pump_realtime_ids:
@@ -424,7 +437,7 @@ def store_realtime_SCADA_data_to_influxdb(get_real_value_time: str, bucket: str
 
             # 创建Point对象
             point = (
-                Point('reservoir_liquid_level_realtime')        # measurement name
+                Point('reservoir_liquid_level_realtime')
                 .tag("date", datetime.fromisoformat(get_real_value_time).strftime('%Y-%m-%d'))
                 .tag("description", data['description'])
                 .tag("device_ID", data['device_ID'])
@@ -1158,15 +1171,16 @@ def query_latest_record_by_ID(ID: str, type: str, bucket: str="realtime_simulati
     :return: dict: 最新记录的数据，如果没有找到则返回 None。
     """
     if client.ping():
-        print("{} -- Successfully connected to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+        print("{} -- Successfully connected to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
     else:
-        print("{} -- Failed to connect to InfluxDB.".format(datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+        print("{} -- Failed to connect to InfluxDB.".format(
-
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
     query_api = client.query_api()
     if type == "node":
         flux_query = f'''
         from(bucket: "{bucket}")
-          |> range(start: -30d)  // 查找最近一月的记录
+          |> range(start: -7d)  // 查找最近七天的记录
           |> filter(fn: (r) => r["_measurement"] == "node")
           |> filter(fn: (r) => r["ID"] == "{ID}")
           |> pivot(
@@ -1174,6 +1188,7 @@ def query_latest_record_by_ID(ID: str, type: str, bucket: str="realtime_simulati
               columnKey:["_field"], 
               valueColumn:"_value"
           )
+          |> group()  // 将所有数据聚合到同一个 group  
           |> sort(columns: ["_time"], desc: true)
           |> limit(n: 1)
         '''
@@ -1195,7 +1210,7 @@ def query_latest_record_by_ID(ID: str, type: str, bucket: str="realtime_simulati
     elif type == "link":
         flux_query = f'''
         from(bucket: "{bucket}")
-          |> range(start: -30d)  // 查找最近一月的记录
+          |> range(start: -7d)  // 查找最近七天的记录
           |> filter(fn: (r) => r["_measurement"] == "link")
           |> filter(fn: (r) => r["ID"] == "{ID}")
           |> pivot(
@@ -1203,6 +1218,7 @@ def query_latest_record_by_ID(ID: str, type: str, bucket: str="realtime_simulati
               columnKey:["_field"], 
               valueColumn:"_value"
           )
+          |> group()  // 将所有数据聚合到同一个 group       
           |> sort(columns: ["_time"], desc: true)
           |> limit(n: 1)
         '''
@@ -1268,10 +1284,8 @@ def query_all_record_by_time(query_time: str, bucket: str="realtime_simulation_r
     # 将北京时间转换为 UTC 时间
     beijing_time = datetime.fromisoformat(query_time)
     utc_time = beijing_time.astimezone(timezone.utc)
-    # DingZQ, 2025-02-10, set delta time to 15, original is 1
+    utc_start_time = utc_time - timedelta(seconds=1)
-    utc_start_time = utc_time - timedelta(seconds=15)
+    utc_stop_time = utc_time + timedelta(seconds=1)
-    utc_stop_time = utc_time + timedelta(seconds=15)
-
     # 构建 Flux 查询语句
     flux_query = f'''
     from(bucket: "{bucket}")
@@ -1283,13 +1297,11 @@ def query_all_record_by_time(query_time: str, bucket: str="realtime_simulation_r
           valueColumn:"_value"
       )           
     '''
-
     # 执行查询
     tables = query_api.query(flux_query)
     node_records = []
     link_records = []
     # 解析查询结果
-
     for table in tables:
         for record in table.records:
             # print(record.values)  # 打印完整记录内容
@@ -1322,6 +1334,69 @@ def query_all_record_by_time(query_time: str, bucket: str="realtime_simulation_r
 
     return node_records, link_records
 
+# 2025/02/21
+def query_all_record_by_date(query_date: str, bucket: str="realtime_simulation_result", client: InfluxDBClient=client) -> tuple:
+    """
+    查询指定日期的所有记录，包括‘node’和‘link’，分别以指定的格式返回
+    :param query_date: 输入的日期，格式为‘2025-02-14’
+    :param bucket: 数据存储的bucket名称
+    :param client: 已初始化的InfluxDBClient 实例。
+    :return: dict: tuple: (node_records, link_records)
+    """
+    if client.ping():
+        print("{} -- Successfully connected to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    else:
+        print("{} -- Failed to connect to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    query_api = client.query_api()
+    # 构建 Flux 查询语句
+    flux_query = f'''
+    from(bucket: "{bucket}")
+      |> range(start: 2025-01-01T00:00:00Z)
+      |> filter(fn: (r) => r["_measurement"] == "node" or r["_measurement"] == "link")
+      |> filter(fn: (r) => r["date"] == "{query_date}")
+      |> pivot(
+          rowKey:["_time"], 
+          columnKey:["_field"], 
+          valueColumn:"_value"
+      )           
+    '''
+    # 执行查询
+    tables = query_api.query(flux_query)
+    node_records = []
+    link_records = []
+    # 解析查询结果
+    for table in tables:
+        for record in table.records:
+            # print(record.values)  # 打印完整记录内容
+            measurement = record["_measurement"]
+            # 处理 node 数据
+            if measurement == "node":
+                node_records.append({
+                    "time": record["_time"],
+                    "ID": record["ID"],
+                    "head": record["head"],
+                    "pressure": record["pressure"],
+                    "actualdemand": record["actualdemand"],
+                    "quality": record["quality"]
+                })
+            # 处理 link 数据
+            elif measurement == "link":
+                link_records.append({
+                    "time": record["_time"],
+                    "linkID": record["ID"],
+                    "flow": record["flow"],
+                    "velocity": record["velocity"],
+                    "headloss": record["headloss"],
+                    "quality": record["quality"],
+                    "status": record["status"],
+                    "setting": record["setting"],
+                    "reaction": record["reaction"],
+                    "friction": record["friction"]
+                })
+
+    return node_records, link_records
 
 # 2025/02/01
 def query_curve_by_ID_property_daterange(ID: str, type: str, property: str, start_date: str, end_date: str, bucket: str="realtime_simulation_result", client: InfluxDBClient=client) -> list:
@@ -1383,7 +1458,6 @@ def query_curve_by_ID_property_daterange(ID: str, type: str, property: str, star
 
     return results
 
-
 def query_buckets(client: InfluxDBClient=client) -> list[str]:
     # 获取 Buckets API 实例
     buckets_api = client.buckets_api()
@@ -1411,6 +1485,642 @@ def query_measurements(bucket: str, client: InfluxDBClient=client) -> list[str]:
     return measurements
 
 
+
+
+# WMH 2025/02/13
+def store_scheme_simulation_result_to_influxdb(node_result_list: List[Dict[str, any]], link_result_list: List[Dict[str, any]],
+                                scheme_start_time: str, num_periods: int = 1, scheme_Type: str = None, scheme_Name: str = None,
+                                bucket: str = "scheme_simulation_result", client: InfluxDBClient = client):
+    """
+    将方案模拟计算结果存入 InfluxuDb 的scheme_simulation_result这个bucket中。
+    :param node_result_list: (List[Dict[str, any]]): 包含节点和结果数据的字典列表。
+    :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_Name: (str): 方案名称
+    :param bucket: (str): InfluxDB 的 bucket 名称，默认值为 "scheme_simulation_result"。
+    :param client: (InfluxDBClient): 已初始化的 InfluxDBClient 实例。
+    :return:
+    """
+    if client.ping():
+        print("{} -- Successfully connected to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    else:
+        print("{} -- Failed to connect to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+
+    try:
+        write_api = client.write_api()
+
+        date_str = scheme_start_time.split('T')[0]
+        time_beijing = datetime.strptime(scheme_start_time, '%Y-%m-%dT%H:%M:%S%z')
+
+        timestep_parts = globals.hydraulic_timestep.split(':')
+        timestep = timedelta(hours=int(timestep_parts[0]), minutes=int(timestep_parts[1]), seconds=int(timestep_parts[2]))
+
+        for node_result in node_result_list:
+            # 提取节点信息和数据结果
+            node_id = node_result.get('node')
+            # 从period 0 到 period num_period - 1
+            for period_index in range(num_periods):
+                scheme_time = (time_beijing + (timestep * period_index)).isoformat()
+                data_list = [node_result.get('result', [])[period_index]]
+                for data in data_list:
+                    # 构建 Point 数据，多个 field 存在于一个数据点中
+                    node_point = Point("node") \
+                        .tag("date", date_str) \
+                        .tag("ID", node_id) \
+                        .tag("scheme_Type", scheme_Type) \
+                        .tag("scheme_Name", scheme_Name) \
+                        .field("head", data.get('head', 0.0)) \
+                        .field("pressure", data.get('pressure', 0.0)) \
+                        .field("actualdemand", data.get('demand', 0.0)) \
+                        .field("demanddeficit", None) \
+                        .field("totalExternalOutflow", None) \
+                        .field("quality", data.get('quality', 0.0)) \
+                        .time(scheme_time)
+                    # 写入数据到 InfluxDB，多个 field 在同一个 point 中
+                    write_api.write(bucket=bucket, org=org_name, record=node_point)
+                    write_api.flush()
+
+        for link_result in link_result_list:
+            link_id = link_result.get('link')
+            for period_index in range(num_periods):
+                scheme_time = (time_beijing + (timestep * period_index)).isoformat()
+                data_list = [link_result.get('result', [])[period_index]]
+                for data in data_list:
+                    link_point = Point("link") \
+                        .tag("date", date_str) \
+                        .tag("ID", link_id) \
+                        .tag("scheme_Type", scheme_Type) \
+                        .tag("scheme_Name", scheme_Name) \
+                        .field("flow", data.get('flow', 0.0)) \
+                        .field("velocity", data.get('velocity', 0.0)) \
+                        .field("headloss", data.get('headloss', 0.0)) \
+                        .field("quality", data.get('quality', 0.0)) \
+                        .field("status", data.get('status', "UNKNOWN")) \
+                        .field("setting", data.get('setting', 0.0)) \
+                        .field("reaction", data.get('reaction', 0.0)) \
+                        .field("friction", data.get('friction', 0.0)) \
+                        .time(scheme_time)
+                    write_api.write(bucket=bucket, org=org_name, record=link_point)
+                    write_api.flush()
+
+    except Exception as e:
+        raise RuntimeError(f"数据写入 InfluxDB 时发生错误: {e}")
+
+
+# 2025/02/15
+def query_SCADA_data_curve(api_query_id: str, start_date: str, end_date: str, bucket: str="SCADA_data", client: InfluxDBClient=client) -> list:
+    """
+    根据SCADA设备的api_query_id和时间范围，查询得到曲线，查到的数据为0时区时间
+    :param api_query_id: SCADA设备的api_query_id
+    :param start_date: 查询开始的时间，格式为 'YYYY-MM-DD'
+    :param end_date: 查询结束的时间，格式为 'YYYY-MM-DD'
+    :param bucket: 数据存储的 bucket 名称，默认值为 "SCADA_data"
+    :param client: 已初始化的 InfluxDBClient 实例
+    :return: 查询结果的列表
+    """
+    if client.ping():
+        print("{} -- Successfully connected to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    else:
+        print("{} -- Failed to connect to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    query_api = client.query_api()
+    # 将 start_date 的北京时间转换为 UTC 时间范围
+    start_time = (datetime.strptime(start_date, "%Y-%m-%d") - timedelta(days=1)).replace(hour=16, minute=0, second=0, tzinfo=timezone.utc).isoformat()
+    stop_time = datetime.strptime(end_date, "%Y-%m-%d").replace(hour=15, minute=59, second=59, tzinfo=timezone.utc).isoformat()
+    # 构建 Flux 查询语句
+    flux_query = f'''
+    from(bucket: "{bucket}")
+      |> range(start: {start_time}, stop: {stop_time})
+      |> filter(fn: (r) => r["device_ID"] == "{api_query_id}")
+    '''
+    # 执行查询
+    tables = query_api.query(flux_query)
+    # 解析查询结果
+    results = []
+    for table in tables:
+        for record in table.records:
+            results.append({
+                "time": record["_time"],
+                "value": record["_value"]
+            })
+    return results
+
+
+# 2025/02/18
+def query_scheme_all_record_by_time(scheme_Type: str, scheme_Name: str, query_time: str,
+                                    bucket: str="scheme_simulation_result", client: InfluxDBClient=client) -> 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_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。
+    :param client: 已初始化的 InfluxDBClient 实例。
+    :return: dict: tuple: (node_records, link_records)
+    """
+    if client.ping():
+        print("{} -- Successfully connected to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    else:
+        print("{} -- Failed to connect to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    query_api = client.query_api()
+    # 将北京时间转换为 UTC 时间
+    beijing_time = datetime.fromisoformat(query_time)
+    utc_time = beijing_time.astimezone(timezone.utc)
+    utc_start_time = utc_time - timedelta(seconds=1)
+    utc_stop_time = utc_time + timedelta(seconds=1)
+    # 构建 Flux 查询语句
+    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}")
+      |> filter(fn: (r) => r["scheme_Name"] == "{scheme_Name}")
+      |> filter(fn: (r) => r["_measurement"] == "node" or r["_measurement"] == "link")
+      |> pivot(
+          rowKey:["_time"], 
+          columnKey:["_field"], 
+          valueColumn:"_value"
+      )           
+    '''
+    # 执行查询
+    tables = query_api.query(flux_query)
+    node_records = []
+    link_records = []
+    # 解析查询结果
+    for table in tables:
+        for record in table.records:
+            # print(record.values)  # 打印完整记录内容
+            measurement = record["_measurement"]
+            # 处理 node 数据
+            if measurement == "node":
+                node_records.append({
+                    "time": record["_time"],
+                    "ID": record["ID"],
+                    "head": record["head"],
+                    "pressure": record["pressure"],
+                    "actualdemand": record["actualdemand"],
+                    "quality": record["quality"]
+                })
+            # 处理 link 数据
+            elif measurement == "link":
+                link_records.append({
+                    "time": record["_time"],
+                    "linkID": record["ID"],
+                    "flow": record["flow"],
+                    "velocity": record["velocity"],
+                    "headloss": record["headloss"],
+                    "quality": record["quality"],
+                    "status": record["status"],
+                    "setting": record["setting"],
+                    "reaction": record["reaction"],
+                    "friction": record["friction"]
+                })
+
+    return node_records, link_records
+
+# 2025/02/19
+def query_scheme_curve_by_ID_property(scheme_Type: str, scheme_Name: str, ID: str, type: str, property: str,
+                                      bucket: str="scheme_simulation_result", client: InfluxDBClient=client) -> 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_Name: 方案名称
+    :param ID: 元素的ID
+    :param type: 元素的类型，node或link
+    :param property: 元素的属性值
+    :param bucket: 数据存储的 bucket 名称，默认值为 "scheme_simulation_result"
+    :param client: 已初始化的 InfluxDBClient 实例
+    :return: 查询结果的列表
+    """
+    if client.ping():
+        print("{} -- Successfully connected to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    else:
+        print("{} -- Failed to connect to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    query_api = client.query_api()
+    # 确定 measurement
+    if type == "node":
+        measurement = "node"
+    elif type == "link":
+        measurement = "link"
+    else:
+        raise ValueError(f"不支持的类型: {type}")
+    # 构建 Flux 查询语句
+    flux_query = f'''
+    from(bucket: "{bucket}")
+      |> range(start: 2025-01-01T00:00:00Z)
+      |> filter(fn: (r) => r["_measurement"] == "{measurement}")
+      |> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}")
+      |> filter(fn: (r) => r["scheme_Name"] == "{scheme_Name}")
+      |> filter(fn: (r) => r["ID"] == "{ID}")
+      |> filter(fn: (r) => r["_field"] == "{property}")
+    '''
+    # 执行查询
+    tables = query_api.query(flux_query)
+    # 解析查询结果
+    results = []
+    for table in tables:
+        for record in table.records:
+            results.append({
+                "time": record["_time"],
+                "value": record["_value"]
+            })
+    return results
+
+
+# 2025/02/21
+def query_scheme_all_record(scheme_Type: str, scheme_Name: str, bucket: str="scheme_simulation_result",
+                            client: InfluxDBClient=client) -> 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_Name: 方案名称
+    :param bucket: 数据存储的 bucket 名称。
+    :param client: 已初始化的 InfluxDBClient 实例。
+    :return: dict: tuple: (node_records, link_records)
+    """
+    if client.ping():
+        print("{} -- Successfully connected to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    else:
+        print("{} -- Failed to connect to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    query_api = client.query_api()
+    # 构建 Flux 查询语句
+    flux_query = f'''
+    from(bucket: "{bucket}")
+      |> range(start: 2025-01-01T00:00:00Z)
+      |> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}")
+      |> filter(fn: (r) => r["scheme_Name"] == "{scheme_Name}")
+      |> filter(fn: (r) => r["_measurement"] == "node" or r["_measurement"] == "link")
+      |> pivot(
+          rowKey:["_time"], 
+          columnKey:["_field"], 
+          valueColumn:"_value"
+      )           
+    '''
+    # 执行查询
+    tables = query_api.query(flux_query)
+    node_records = []
+    link_records = []
+    # 解析查询结果
+    for table in tables:
+        for record in table.records:
+            # print(record.values)  # 打印完整记录内容
+            measurement = record["_measurement"]
+            # 处理 node 数据
+            if measurement == "node":
+                node_records.append({
+                    "time": record["_time"],
+                    "ID": record["ID"],
+                    "head": record["head"],
+                    "pressure": record["pressure"],
+                    "actualdemand": record["actualdemand"],
+                    "quality": record["quality"]
+                })
+            # 处理 link 数据
+            elif measurement == "link":
+                link_records.append({
+                    "time": record["_time"],
+                    "linkID": record["ID"],
+                    "flow": record["flow"],
+                    "velocity": record["velocity"],
+                    "headloss": record["headloss"],
+                    "quality": record["quality"],
+                    "status": record["status"],
+                    "setting": record["setting"],
+                    "reaction": record["reaction"],
+                    "friction": record["friction"]
+                })
+
+    return node_records, link_records
+
+# 2025/02/16
+def export_SCADA_data_to_csv(start_date: str, end_date: str, bucket: str="SCADA_data", client: InfluxDBClient=client) -> None:
+    """
+    导出influxdb中SCADA_data这个bucket的数据到csv中
+    :param start_date: 查询开始的时间，格式为 'YYYY-MM-DD'
+    :param end_date: 查询结束的时间，格式为 'YYYY-MM-DD'
+    :param bucket: 数据存储的 bucket 名称，默认值为 "SCADA_data"
+    :param client: 已初始化的 InfluxDBClient 实例
+    :return:
+    """
+    if client.ping():
+        print("{} -- Successfully connected to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    else:
+        print("{} -- Failed to connect to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    query_api = client.query_api()
+    # 将 start_date 的北京时间转换为 UTC 时间范围
+    start_time = (datetime.strptime(start_date, "%Y-%m-%d") - timedelta(days=1)).replace(hour=16, minute=0, second=0, tzinfo=timezone.utc).isoformat()
+    stop_time = datetime.strptime(end_date, "%Y-%m-%d").replace(hour=15, minute=59, second=59, tzinfo=timezone.utc).isoformat()
+    # 构建 Flux 查询语句
+    flux_query = f'''
+    from(bucket: "{bucket}")
+      |> range(start: {start_time}, stop: {stop_time})
+    '''
+    # 执行查询
+    tables = query_api.query(flux_query)
+    # 存储查询结果
+    rows = []
+    for table in tables:
+        for record in table.records:
+            row = {
+                'time': record.get_time(),
+                'measurement': record.get_measurement(),
+                'date': record.values.get('date', None),
+                'description': record.values.get('description', None),
+                'device_ID': record.values.get('device_ID', None),
+                'monitored_value': record.get_value() if record.get_field() == 'monitored_value' else None,
+                'datacleaning_value': record.get_value() if record.get_field() == 'datacleaning_value' else None,
+                'simulation_value': record.get_value() if record.get_field() == 'simulation_value' else None,
+            }
+            rows.append(row)
+    # 动态生成 CSV 文件名
+    csv_filename = f"SCADA_data_{start_date}至{end_date}.csv"
+    # 写入到 CSV 文件
+    with open(csv_filename, mode='w', newline='') as file:
+        writer = csv.DictWriter(file, fieldnames=['time', 'measurement', 'date', 'description', 'device_ID', 'monitored_value', 'datacleaning_value', 'simulation_value'])
+        writer.writeheader()
+        writer.writerows(rows)
+
+    print(f"Data exported to {csv_filename} successfully.")
+
+# 2025/02/17
+def export_realtime_simulation_result_to_csv(start_date: str, end_date: str, bucket: str="realtime_simulation_result", client: InfluxDBClient=client) -> None:
+    """
+    导出influxdb中realtime_simulation_result这个bucket的数据到csv中
+    :param start_date: 查询开始的时间，格式为 'YYYY-MM-DD'
+    :param end_date: 查询结束的时间，格式为 'YYYY-MM-DD'
+    :param bucket: 数据存储的 bucket 名称，默认值为 "SCADA_data"
+    :param client: 已初始化的 InfluxDBClient 实例
+    :return:
+    """
+    if client.ping():
+        print("{} -- Successfully connected to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    else:
+        print("{} -- Failed to connect to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    query_api = client.query_api()
+    # 将 start_date 的北京时间转换为 UTC 时间范围
+    start_time = (datetime.strptime(start_date, "%Y-%m-%d") - timedelta(days=1)).replace(hour=16, minute=0, second=0, tzinfo=timezone.utc).isoformat()
+    stop_time = datetime.strptime(end_date, "%Y-%m-%d").replace(hour=15, minute=59, second=59, tzinfo=timezone.utc).isoformat()
+    # 构建 Flux 查询语句，查询指定时间范围内的数据
+    flux_query_link = f'''
+    from(bucket: "{bucket}")
+      |> range(start: {start_time}, stop: {stop_time})
+      |> filter(fn: (r) => r["_measurement"] == "link")
+    '''
+    # 执行查询
+    link_tables = query_api.query(flux_query_link)
+    # 存储link类的数据
+    link_rows = []
+    link_data = {}
+    for table in link_tables:
+        for record in table.records:
+            key = (record.get_time(), record.values.get('ID', None))
+            if key not in link_data:
+                link_data[key] = {}
+            field = record.get_field()
+            link_data[key][field] = record.get_value()
+            link_data[key]['measurement'] = record.get_measurement()
+            link_data[key]['date'] = record.values.get('date', 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")
+    '''
+    # 执行查询
+    node_tables = query_api.query(flux_query_node)
+    # 存储node类的数据
+    node_rows = []
+    node_data = {}
+    for table in node_tables:
+        for record in table.records:
+            key = (record.get_time(), record.values.get('ID', None))
+            if key not in node_data:
+                node_data[key] = {}
+            field = record.get_field()
+            node_data[key][field] = record.get_value()
+            node_data[key]['measurement'] = record.get_measurement()
+            node_data[key]['date'] = record.values.get('date', None)
+
+    for key in set(link_data.keys()):
+        row = {'time': key[0], "ID": key[1]}
+        row.update(link_data.get(key, {}))
+        link_rows.append(row)
+    for key in set(node_data.keys()):
+        row = {'time': key[0], "ID": key[1]}
+        row.update(node_data.get(key, {}))
+        node_rows.append(row)
+
+    # 动态生成 CSV 文件名
+    csv_filename_link = f"realtime_simulation_link_result_{start_date}至{end_date}.csv"
+    csv_filename_node = f"realtime_simulation_node_result_{start_date}至{end_date}.csv"
+    # 写入到 CSV 文件
+    with open(csv_filename_link, mode='w', newline='') as file:
+        writer = csv.DictWriter(file, fieldnames=['time', 'measurement', 'date', 'ID', 'flow', 'leakage', 'velocity', 'headloss', 'status', 'setting', 'quality', 'friction', 'reaction'])
+        writer.writeheader()
+        writer.writerows(link_rows)
+    with open(csv_filename_node, mode='w', newline='') as file:
+        writer = csv.DictWriter(file, fieldnames=['time', 'measurement', 'date', 'ID', 'head', 'pressure', 'actualdemand',
+                                                  'demanddeficit', 'totalExternalOutflow', 'quality'])
+        writer.writeheader()
+        writer.writerows(node_rows)
+
+    print(f"Data exported to {csv_filename_link} and {csv_filename_node} successfully.")
+
+# 2025/02/18
+def export_scheme_simulation_result_to_csv_time(start_date: str, end_date: str, bucket: str="scheme_simulation_result", client: InfluxDBClient=client) -> None:
+    """
+    导出influxdb中scheme_simulation_result这个bucket的数据到csv中
+    :param start_date: 查询开始的时间，格式为 'YYYY-MM-DD'
+    :param end_date: 查询结束的时间，格式为 'YYYY-MM-DD'
+    :param bucket: 数据存储的 bucket 名称，默认值为 "SCADA_data"
+    :param client: 已初始化的 InfluxDBClient 实例
+    :return:
+    """
+    if client.ping():
+        print("{} -- Successfully connected to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    else:
+        print("{} -- Failed to connect to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    query_api = client.query_api()
+    # 将 start_date 的北京时间转换为 UTC 时间范围
+    start_time = (datetime.strptime(start_date, "%Y-%m-%d") - timedelta(days=1)).replace(hour=16, minute=0, second=0, tzinfo=timezone.utc).isoformat()
+    stop_time = datetime.strptime(end_date, "%Y-%m-%d").replace(hour=15, minute=59, second=59, tzinfo=timezone.utc).isoformat()
+    # 构建 Flux 查询语句，查询指定时间范围内的数据
+    flux_query_link = f'''
+    from(bucket: "{bucket}")
+      |> range(start: {start_time}, stop: {stop_time})
+      |> filter(fn: (r) => r["_measurement"] == "link")
+    '''
+    # 执行查询
+    link_tables = query_api.query(flux_query_link)
+    # 存储link类的数据
+    link_rows = []
+    link_data = {}
+    for table in link_tables:
+        for record in table.records:
+            key = (record.get_time(), record.values.get('ID', None))
+            if key not in link_data:
+                link_data[key] = {}
+            field = record.get_field()
+            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_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")
+    '''
+    # 执行查询
+    node_tables = query_api.query(flux_query_node)
+    # 存储node类的数据
+    node_rows = []
+    node_data = {}
+    for table in node_tables:
+        for record in table.records:
+            key = (record.get_time(), record.values.get('ID', None))
+            if key not in node_data:
+                node_data[key] = {}
+            field = record.get_field()
+            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_Name'] = record.values.get('scheme_Name', None)
+
+    for key in set(link_data.keys()):
+        row = {'time': key[0], "ID": key[1]}
+        row.update(link_data.get(key, {}))
+        link_rows.append(row)
+    for key in set(node_data.keys()):
+        row = {'time': key[0], "ID": key[1]}
+        row.update(node_data.get(key, {}))
+        node_rows.append(row)
+
+    # 动态生成 CSV 文件名
+    csv_filename_link = f"scheme_simulation_link_result_{start_date}至{end_date}.csv"
+    csv_filename_node = f"scheme_simulation_node_result_{start_date}至{end_date}.csv"
+    # 写入到 CSV 文件
+    with open(csv_filename_link, mode='w', newline='') as file:
+        writer = csv.DictWriter(file, fieldnames=['time', 'measurement', 'date', 'scheme_Type', 'scheme_Name', 'ID', 'flow', 'leakage', 'velocity', 'headloss', 'status', 'setting', 'quality', 'friction', 'reaction'])
+        writer.writeheader()
+        writer.writerows(link_rows)
+    with open(csv_filename_node, mode='w', newline='') as file:
+        writer = csv.DictWriter(file, fieldnames=['time', 'measurement', 'date', 'scheme_Type', 'scheme_Name', 'ID', 'head', 'pressure', 'actualdemand',
+                                                  'demanddeficit', 'totalExternalOutflow', 'quality'])
+        writer.writeheader()
+        writer.writerows(node_rows)
+
+    print(f"Data exported to {csv_filename_link} and {csv_filename_node} successfully.")
+
+# 2025/02/18
+def export_scheme_simulation_result_to_csv_scheme(scheme_Type: str, scheme_Name: str, bucket: str="scheme_simulation_result", client: InfluxDBClient=client) -> 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_Name: 查询的方案名
+    :param bucket: 数据存储的 bucket 名称，默认值为 "SCADA_data"
+    :param client: 已初始化的 InfluxDBClient 实例
+    :return:
+    """
+    if client.ping():
+        print("{} -- Successfully connected to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    else:
+        print("{} -- Failed to connect to InfluxDB.".format(
+            datetime.now().strftime('%Y-%m-%d %H:%M:%S')))
+    query_api = client.query_api()
+    # 构建 Flux 查询语句，查询指定时间范围内的数据
+    flux_query_link = f'''
+    from(bucket: "{bucket}")
+      |> range(start: 2025-01-01T00:00:00Z)
+      |> filter(fn: (r) => r["_measurement"] == "link")
+      |> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}")
+      |> filter(fn: (r) => r["scheme_Name"] == "{scheme_Name}")
+    '''
+    # 执行查询
+    link_tables = query_api.query(flux_query_link)
+    # 存储link类的数据
+    link_rows = []
+    link_data = {}
+    for table in link_tables:
+        for record in table.records:
+            key = (record.get_time(), record.values.get('ID', None))
+            if key not in link_data:
+                link_data[key] = {}
+            field = record.get_field()
+            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_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: 2025-01-01T00:00:00Z)
+      |> filter(fn: (r) => r["_measurement"] == "node")
+      |> filter(fn: (r) => r["scheme_Type"] == "{scheme_Type}")
+      |> filter(fn: (r) => r["scheme_Name"] == "{scheme_Name}")
+    '''
+    # 执行查询
+    node_tables = query_api.query(flux_query_node)
+    # 存储node类的数据
+    node_rows = []
+    node_data = {}
+    for table in node_tables:
+        for record in table.records:
+            key = (record.get_time(), record.values.get('ID', None))
+            if key not in node_data:
+                node_data[key] = {}
+            field = record.get_field()
+            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_Name'] = record.values.get('scheme_Name', None)
+
+    for key in set(link_data.keys()):
+        row = {'time': key[0], "ID": key[1]}
+        row.update(link_data.get(key, {}))
+        link_rows.append(row)
+    for key in set(node_data.keys()):
+        row = {'time': key[0], "ID": key[1]}
+        row.update(node_data.get(key, {}))
+        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"
+    csv_filename_node = 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:
+        writer = csv.DictWriter(file, fieldnames=['time', 'measurement', 'date', 'scheme_Type', 'scheme_Name', 'ID', 'flow', 'leakage', 'velocity', 'headloss', 'status', 'setting', 'quality', 'friction', 'reaction'])
+        writer.writeheader()
+        writer.writerows(link_rows)
+    with open(csv_filename_node, mode='w', newline='') as file:
+        writer = csv.DictWriter(file, fieldnames=['time', 'measurement', 'date', 'scheme_Type', 'scheme_Name', 'ID', 'head', 'pressure', 'actualdemand',
+                                                  'demanddeficit', 'totalExternalOutflow', 'quality'])
+        writer.writeheader()
+        writer.writerows(node_rows)
+
+    print(f"Data exported to {csv_filename_link} and {csv_filename_node} successfully.")
+
 # 示例调用
 if __name__ == "__main__":
     url = influxdb_info.url
@@ -1438,20 +2148,19 @@ if __name__ == "__main__":
 
     # step3: 查询测试示例
     with InfluxDBClient(url=url, token=token, org=org_name) as client:
-        # 示例1：query_latest_record_by_ID
-        bucket_name = "realtime_simulation_result"  # 数据存储的 bucket 名称
-        node_id = "ZBBDTZDP000022"  # 查询的节点 ID
-        link_id = "ZBBGXSZW000002"
 
+    #     # 示例1：query_latest_record_by_ID
+    #     bucket_name = "realtime_simulation_result"  # 数据存储的 bucket 名称
+    #     node_id = "ZBBDTZDP000022"  # 查询的节点 ID
+    #     link_id = "ZBBGXSZW000002"
+    #
     #     latest_record = query_latest_record_by_ID(ID=node_id, type="node", bucket=bucket_name, client=client)
-        # latest_record = query_latest_record_by_ID(ID=link_id, type="link", bucket=bucket_name, client=client)
+    #     # # latest_record = query_latest_record_by_ID(ID=link_id, type="link", bucket=bucket_name, client=client)
-        
+    #     #
-        all_records = query_all_record_by_time('2025-02-09T17:30:00+08:00')
+    #     if latest_record:
-        
+    #         print("最新记录：", latest_record)
-        if latest_record:
+    #     else:
-            print("最新记录：", latest_record)
+    #         print("未找到符合条件的记录。")
-        else:
-            print("未找到符合条件的记录。")
 
         # 示例2：query_all_record_by_time
         # node_records, link_records = query_all_record_by_time(query_time="2024-11-25T06:00:00+08:00")
@@ -1464,5 +2173,43 @@ if __name__ == "__main__":
         # print(curve_result)
 
         # 示例4：query_SCADA_data_by_device_ID_and_time
-        # SCADA_result_dict = query_SCADA_data_by_device_ID_and_time(globals.variable_pump_realtime_ids, query_time='2025-02-08T10:30:00+08:00')
+        # SCADA_result_dict = query_SCADA_data_by_device_ID_and_time(globals.variable_pump_realtime_ids, query_time='2025-02-14T23:58:00+08:00')
         # print(SCADA_result_dict)
+
+        # 示例5：query_SCADA_data_curve
+        # SCADA_result = query_SCADA_data_curve(api_query_id='3853', start_date='2025-02-14', end_date='2025-02-16')
+        # print(SCADA_result)
+
+        # 示例6：export_SCADA_data_to_csv
+        # export_SCADA_data_to_csv(start_date='2025-02-13', end_date='2025-02-15')
+
+        # 示例7：export_realtime_simulation_result_to_csv
+        # export_realtime_simulation_result_to_csv(start_date='2025-02-13', end_date='2025-02-15')
+
+        # 示例8：export_scheme_simulation_result_to_csv_time
+        # 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')
+
+        # 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")
+        # 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',
+        #                                                  type='node', property='head')
+        # print(curve_result)
+
+        # 示例12：query_all_record_by_date
+        # node_records, link_records = query_all_record_by_date(query_date='2025-02-14')
+        # print("Node 数据：", node_records)
+        # 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')
+        print("Node 数据：", node_records)
+        print("Link 数据：", link_records)
+
+