重构现代化 FastAPI 后端项目框架

scripts/get_data.py

@@ -0,0 +1,167 @@
+import requests
+from datetime import datetime
+import pytz
+from typing import List, Dict, Union, Optional
+import csv
+
+# get_data 是用来获取 历史数据，也就是非实时数据的接口
+# get_realtime 是用来获取 实时数据
+
+def convert_timestamp_to_beijing_time(timestamp: Union[int, float]) -> datetime:
+    # 将毫秒级时间戳转换为秒级时间戳
+    timestamp_seconds = timestamp / 1000
+
+    # 将时间戳转换为datetime对象
+    utc_time = datetime.utcfromtimestamp(timestamp_seconds)
+
+    # 设定UTC时区
+    utc_timezone = pytz.timezone('UTC')
+
+    # 转换为北京时间
+    beijing_timezone = pytz.timezone('Asia/Shanghai')
+    beijing_time = utc_time.replace(tzinfo=utc_timezone).astimezone(beijing_timezone)
+
+    return beijing_time
+
+
+def beijing_time_to_utc(beijing_time_str: str) -> str:
+    # 定义北京时区
+    beijing_timezone = pytz.timezone('Asia/Shanghai')
+
+    # 将字符串转换为datetime对象
+    beijing_time = datetime.strptime(beijing_time_str, '%Y-%m-%d %H:%M:%S')
+
+    # 本地化时间对象
+    beijing_time = beijing_timezone.localize(beijing_time)
+
+    # 转换为UTC时间
+    utc_time = beijing_time.astimezone(pytz.utc)
+
+    # 转换为ISO 8601格式的字符串
+    return utc_time.strftime('%Y-%m-%dT%H:%M:%SZ')
+
+
+def get_history_data(ids: str, begin_date: str, end_date: str, downsample: Optional[str]) -> List[Dict[str, Union[str, datetime, int, float]]]:
+# def get_history_data(ids: str, begin_date: str, end_date: str, downsample: Optional[str]) -> None:
+    # 转换输入的北京时间为UTC时间
+    begin_date_utc = beijing_time_to_utc(begin_date)
+    end_date_utc = beijing_time_to_utc(end_date)
+
+    # 数据接口的地址
+    url = 'http://183.64.62.100:9057/loong/api/curves/data'
+    # url = 'http://10.101.15.16:9000/loong/api/curves/data'
+    # url_path = 'http://10.101.15.16:9000/loong'  # 内网
+
+    # 设置 GET 请求的参数
+    params = {
+        'ids': ids,
+        'beginDate': begin_date_utc,
+        'endDate': end_date_utc,
+        'downsample': downsample
+    }
+
+    history_data_list =[]
+
+    try:
+        # 发送 GET 请求获取数据
+        response = requests.get(url, params=params)
+
+        # 检查响应状态码，200 表示请求成功
+        if response.status_code == 200:
+            # 解析响应的 JSON 数据
+            data = response.json()
+            # 这里可以对获取到的数据进行进一步处理
+
+            # 打印 'mpointId' 和 'mpointName'
+            for item in data['items']:
+                mpoint_id = str(item['mpointId'])
+                mpoint_name = item['mpointName']
+                # print("mpointId:", item['mpointId'])
+                # print("mpointName:", item['mpointName'])
+
+                # 打印 'dataDate' 和 'dataValue'
+                for item_data in item['data']:
+                    # 将时间戳转换为北京时间
+                    beijing_time = convert_timestamp_to_beijing_time(item_data['dataDate'])
+                    data_value = item_data['dataValue']
+                    # 创建一个字典存储每条数据
+                    data_dict = {
+                        'time': beijing_time,
+                        'device_ID': str(mpoint_id),
+                        'description': mpoint_name,
+                        # 'dataDate (Beijing Time)': beijing_time.strftime('%Y-%m-%d %H:%M:%S'),
+                        'monitored_value': data_value  # 保留原有类型
+                    }
+
+                    history_data_list.append(data_dict)
+        else:
+            # 如果请求不成功，打印错误信息
+            print("请求失败，状态码:", response.status_code)
+
+    except Exception as e:
+        # 捕获异常
+        print("发生异常:", e)
+
+    return history_data_list
+
+
+# 使用示例
+# data_list = get_history_data(ids='9572',
+#                              begin_date='2025-02-08 06:00:00',
+#                              end_date='2025-02-08 12:00:00',
+#                              downsample='1m')
+#
+# # 打印数据列表
+# for data in data_list:
+#     print(data)
+
+# # 定义 CSV 文件的路径
+# csv_file_path = './influxdb_data_4984.csv'
+# # 将数据写入 CSV 文件
+# # with open(csv_file_path, mode='w', newline='') as file:
+# #     writer = csv.writer(file)
+# #
+# #     # 写入表头
+# #     writer.writerow(['measurement', 'mpointId', 'date', 'dataValue', 'datetime'])
+# #
+# #     # 写入数据
+# #     for data in data_list:
+# #         measurement = data['mpointName']
+# #         mpointId = data['mpointId']
+# #         date = data['datetime'].strftime('%Y-%m-%d')
+# #         dataValue = data['dataValue']
+# #         datetime_str = data['datetime']
+# #
+# #         # 写入一行
+# #         writer.writerow([measurement, mpointId, date, dataValue, datetime_str])
+# #
+# #
+# # print(f"数据已保存到 {csv_file_path}")
+#
+# filtered_csv_file_path = './filtered_influxdb_data_4984.csv'
+# #
+# # # # 读取并筛选数据
+# data_list1 = []
+#
+# with open(csv_file_path, mode='r') as file:
+#     csv_reader = csv.DictReader(file)
+#     for row in csv_reader:
+#         # 将 datetime 列解析为 datetime 对象
+#         datetime_value = datetime.strptime(row['datetime'], '%Y-%m-%d %H:%M:%S%z')
+#
+#         # 只保留时间为 15 分钟倍数的行
+#         if datetime_value.minute % 15 == 0:
+#             data_list1.append(row)
+#
+# # 将筛选后的数据写入新的 CSV 文件
+# with open(filtered_csv_file_path, mode='w', newline='') as file:
+#     writer = csv.writer(file)
+#
+#     # 写入表头
+#     writer.writerow(['measurement', 'mpointId', 'date', 'dataValue', 'datetime'])
+#
+#     # 写入筛选后的数据
+#     for data in data_list1:
+#         writer.writerow([data['measurement'], data['mpointId'], data['date'], data['dataValue'], data['datetime']])
+#
+# print(f"筛选后的数据已保存到 {filtered_csv_file_path}")