积累修改;

source/data_analysis.py

@@ -9,7 +9,7 @@ import pandas as pd
 import matplotlib.pyplot as plt
 import matplotlib.dates as mdates
 from sqlalchemy import create_engine
-from sqlalchemy import MetaData, Table, Column, String, Float, Integer
+from sqlalchemy import MetaData, Table, Column, String, Float, Integer, DateTime
 
 API_URL = "https://energy-iot.chinatowercom.cn/api/device/device/historyPerformance"
 
@@ -63,6 +63,8 @@ SemaMap_meter = {
 }
 
 API_Map = {
+    'refreshToken': ['https://energy-iot.chinatowercom.cn/api/auth/refreshToken', None],
+    'search_stn': ['https://energy-iot.chinatowercom.cn/api/device/station/list', None],
     'search_dev': ['https://energy-iot.chinatowercom.cn/api/device/device/page', None],
     'dev_info': ['https://energy-iot.chinatowercom.cn/api/device/device/devInfo', None],
     'perf_real': ['https://energy-iot.chinatowercom.cn/api/device/device/perfReal', None],
@@ -84,7 +86,7 @@ class Lamina_Data(object):
                 'history', metadata,
                 Column('dev', String(50)),
                 Column('mid', String(50)),
-                Column('time', Integer),
+                Column('time', DateTime),
                 Column('value', Float)
             )
             metadata.create_all(self.engine)
@@ -94,8 +96,8 @@ class Lamina_Data(object):
                 'log', metadata,
                 Column('dev', String(50)),
                 Column('mid', String(50)),
-                Column('Timestamp_start', Integer),
+                Column('Timestamp_start', DateTime),
-                Column('Timestamp_end', Integer),
+                Column('Timestamp_end', DateTime),
             )
             metadata.create_all(self.engine)
 
@@ -128,8 +130,8 @@ class Lamina_Data(object):
             self: Lamina_Data = args[0]
             result = func(*args, **kwds)
             if isinstance(result, pd.DataFrame):
+                if result.shape[0] != 0:
                     self.data['history'] = pd.concat([self.data['history'], result], ignore_index=True)
-                self.save_history_data()
             return result
         return wrapper
 
@@ -164,12 +166,13 @@ class Lamina_Data(object):
             """ 数据读取成功 """
             print(f"Get data success, mid={data_type[0]}, len={len(json_data['data'])}")
             table_data = pd.DataFrame(json_data['data'], columns=['collectTime', 'mid', 'midName', 'value'])
-            table_data['time'] = table_data['collectTime'].apply(lambda x: int(time.mktime(time.strptime(x, r"%Y-%m-%d %H:%M:%S"))))
             table_data['dev'] = device_id
+            table_data['time'] = pd.to_datetime(table_data.collectTime)
+            table_data['value'] = pd.to_numeric(table_data.value)
             return table_data[['dev', 'mid', 'time', 'value']]
         else:
             print(f"Get data fail, code={json_data['code']}, msg=\n\t{json_data['message']}")
-            return pd.DataFrame([], columns=['dev', 'mid', 'time', 'value'])
+            raise ValueError(f"{json_data['message']}")
 
     def get_real_data_by_net(self, device_id, fsu_id=None, header=None):
         """ 读取设备当前数据, 返回接口json数据 """
@@ -200,7 +203,7 @@ class Lamina_Data(object):
             return table_data
         else:
             print(f"Get data fail, code={json_data['code']}, msg=\n\t{json_data['message']}")
-            return pd.DataFrame([])
+            raise ValueError(f"{json_data['message']}")
 
     def get_devinfo_data_by_net(self, device_id, data_type, time_start:int, time_end:int, header=None):
         """ 读取设备信息, 返回接口json数据 """
@@ -221,17 +224,18 @@ class Lamina_Data(object):
             return table_data
         else:
             print(f"Get data fail, code={json_data['code']}, msg=\n\t{json_data['message']}")
-            return pd.DataFrame([], columns=['dev', 'mid', 'time', 'value'])
+            raise ValueError(f"{json_data['message']}")
 
     def spider_adapter(self, device_id:str, time_start:int, time_end:int):
         """ 爬取适配器数据 """
         result = {}
         columns_adapter = list(filter(lambda x: SemaMap_adapter[x][2], SemaMap_adapter.keys()))
-        data_adapter = pd.DataFrame([], columns=['time', *columns_adapter])
+        data_adapter = pd.DataFrame([], columns=['time', 'device', *columns_adapter])
         for k in columns_adapter:
             result[k] = self.get_history_data_by_net(device_id, SemaMap_adapter[k], time_start, time_end)
             if data_adapter.empty:
                 data_adapter.time = result[k].time
+                data_adapter.device = device_id
             data_adapter[k] = result[k].value.apply(float)
         return data_adapter
 
@@ -239,17 +243,17 @@ class Lamina_Data(object):
         """ 爬取电表数据 """
         result = {}
         columns_meter = list(map(lambda x: x[4:], filter(lambda x: SemaMap_meter[x][2], SemaMap_meter.keys())))
-        data_meter = pd.DataFrame([], columns=['time', *columns_meter])
+        data_meter = pd.DataFrame([], columns=['time', 'device', *columns_meter])
         for k, v in SemaMap_meter.items():
             if v[2]:
                 result[k] = self.get_history_data_by_net(device_id, v, time_start, time_end)
                 if data_meter.empty:
                     data_meter.time = result[k].time
+                    data_meter.device = device_id
                 data_meter[k[4:]] = result[k].value.apply(float)
         return data_meter
 
-    def spider_station(self, device_id:str, time_start:int, time_end:int, header=None):
+    def spider_search_devices(self, device_id:str, header=None):
-        """ 爬取站点数据 """
         if header is None:
             header = self.api_origin['header']
 
@@ -268,22 +272,80 @@ class Lamina_Data(object):
         if json_data['code'] != 200:
             """ 数据读取失败 """
             print(f"Get data fail, code={json_data['code']}, msg=\n\t{json_data['message']}")
-            return pd.DataFrame([])
+            return ""
         elif search_dev := json_data['rows']:
             print(f"Search device success, len={len(search_dev)}")
-            station_id = search_dev[0]['stationCode']
+            return search_dev[0]['stationCode']
         else:
             print(f"Search device fail.")
-            return pd.DataFrame([])
+            return ""
+
+    def spider_search_station(self, name:str, header=None):
+        if header is None:
+            header = self.api_origin['header']
+
+        body = {
+            "pageNum": 1,
+            "pageSize": 10,
+            "provinceId": "",
+            "cityId": "",
+            "countId": "",
+            "name": name,
+            "code": "",
+            "rsSource": "",
+            "businessType": "7",
+            "status": "",
+            "onlineStatus": "",
+            "maintenancePerson": "",
+            "deptIds": []
+        }
+
+        req = requests.post(API_Map['search_stn'][0], json=body, headers=header)
+        json_data = req.json()
+        if json_data['code'] != 200:
+            """ 数据读取失败 """
+            print(f"Get data fail, code={json_data['code']}, msg=\n\t{json_data['message']}")
+            return ""
+        elif search_stn := json_data['rows']:
+            print(f"Search station success, len={len(search_stn)}")
+            return search_stn[0]['code']
+        else:
+            print(f"Search station fail.")
+            return ""
+
+    def spider_station(self, search:str, time_start:int, time_end:int, header=None):
+        """ 爬取站点数据 """
+        if header is None:
+            header = self.api_origin['header']
+        
+        if search[:3] == "TTE":
+            """ 设备编号 """
+            station_id = self.spider_search_devices(search, header=header)
+        else:
+            """ 站点名称 """
+            station_id = self.spider_search_station(search, header=header)
+
+        if station_id == "":
+            print(f"Search station fail.")
+            return {'result': False}
 
         body = {
             "businessType": "7",
             "stationCode": station_id,
         }
         time.sleep(0.5)
+        
         print(f"Get Data for Station: {station_id}")
         req = requests.post(API_Map['page'][0], json=body, headers=header)
         json_data = req.json()
+        if json_data['code'] != 200:
+            """ 数据读取失败 """
+            print(f"Get data fail, code={json_data['code']}, msg=\n\t{json_data['message']}")
+            return ""
+        
+        dev_meter = []
+        dev_adapter = []
+        try:
             for dev in sorted(json_data['rows'], key=lambda x: x['devCode']):
                 print(f"Dev: {dev['devTypeName']}, id={dev['devCode']}")
                 time.sleep(0.5)
@@ -294,10 +356,21 @@ class Lamina_Data(object):
                     case "0101":
                         fsu_id = dev['devCode']
                     case "0102":
-                    self.spider_adapter(dev['devCode'], time_start, time_end)
+                        dev_adapter.append(self.spider_adapter(dev['devCode'], time_start, time_end))
                     case "0103":
-                    self.spider_meter(dev['devCode'], time_start, time_end)
+                        dev_meter.append(self.spider_meter(dev['devCode'], time_start, time_end))
+            self.save_history_data()
+        except Exception as e:
+            print(f"Get data fail, msg=\n\t{e}")
+            return {'result': False, 'token': e.args[0]}
+        result = {
+            'result': True,
+            'station': station_id,
+            'adapter': pd.concat(dev_adapter, ignore_index=True),
+            'meter': pd.concat(dev_meter, ignore_index=True),
+        }
         print(f"Station Done.")
+        return result
  
     def spider(self, device_id:str, time_start:int, time_end:int):
         """ 通用爬虫 """
@@ -368,6 +441,102 @@ def sim_data_apt(times:tuple[int, int]):
     return pd.DataFrame(data)
 
 
+def save_station_by_file1(data_lamina: Lamina_Data):
+    """ 依据文件爬取所需站点数据 """
+    time_start = datetime.datetime(2024, 12, 24, 0, 0, 0)
+    time_end   = datetime.datetime(2024, 12, 26, 0, 0, 0)
+    time_start_timestamp = time.mktime(time_start.timetuple())
+    time_end_timestamp   = time.mktime(time_end.timetuple())
+    stations = pd.read_excel(Path(r'C:\Users\wrqal\Documents\Obsidian Vault\附件\25号0发电适配器.xlsx'))
+    output_file = Path(r'result/output.xlsx')
+    if output_file.exists():
+        finished_station = pd.read_excel(output_file, sheet_name=None)
+        finished_station["Station"]['station'] = finished_station["Station"]['station'].astype('str')
+        finished_station["Adatper"]['station'] = finished_station["Adatper"]['station'].astype('str')
+        finished_station["Meter"]['station']   = finished_station["Meter"]['station'].astype('str')
+        merged_df = pd.merge(stations['点位名称'], finished_station['Station']['点位名称'], how='left', indicator=True)
+        remain_station = merged_df[merged_df['_merge'] == 'left_only'].drop(columns='_merge')
+    else:
+        remain_station = stations['点位名称']
+
+    dataset = []
+    for name in remain_station['点位名称']:
+        print(f"Station: {name}")
+        data = data_lamina.spider_station(name, time_start_timestamp, time_end_timestamp)
+        if data['result']:
+            dataset.append(data)
+    print(f"Done.")
+
+    # 使用 ExcelWriter 将多个 DataFrame 保存到不同的工作表中
+    df_station = pd.DataFrame([], columns=['station', '点位名称'])
+    df_station.station = [data['station'] for data in dataset]
+    df_station.点位名称 = remain_station['点位名称'][:len(dataset)].values
+    df_adapter = pd.concat([data['adapter'].assign(station=data['station']) for data in dataset], ignore_index=True)
+    df_meter = pd.concat([data['meter'].assign(station=data['station']) for data in dataset], ignore_index=True)
+    column_adapter = ['time', 'station', *df_adapter.columns[1:-1]]
+    column_meter = ['time', 'station', *df_meter.columns[1:-1]]
+    if output_file.exists():
+        """ 连接文件 """
+        df_station = pd.concat([finished_station['Station'], df_station], ignore_index=True)
+        df_adapter = pd.concat([finished_station['Adatper'], df_adapter], ignore_index=True)
+        df_meter   = pd.concat([finished_station['Meter'], df_meter], ignore_index=True)
+    with pd.ExcelWriter(output_file) as writer:
+        df_station.to_excel(writer, sheet_name='Station', index=False)
+        df_adapter.to_excel(writer, sheet_name='Adatper', index=False, columns=column_adapter)
+        df_meter.to_excel(writer, sheet_name='Meter', index=False, columns=column_meter)
+
+    print(f"数据已成功保存到 {output_file}")
+
+
+def save_station_by_file2(data_lamina: Lamina_Data, file_path):
+    """ 依据文件爬取所需站点数据 """
+    file_input = Path(file_path)
+    file_output = file_input.parent / (file_input.stem + '_output.xlsx')
+    df_input = pd.read_excel(file_input)
+    time_start_timestamp = df_input['开始时间'][0].tz_localize('Asia/Shanghai').timestamp()
+    time_end_timestamp   = df_input['结束时间'][0].tz_localize('Asia/Shanghai').timestamp()
+    if file_output.exists():
+        finished_station = pd.read_excel(file_output, sheet_name=None)
+        finished_station["Station"]['station'] = finished_station["Station"]['station'].astype('str')
+        finished_station["Adatper"]['station'] = finished_station["Adatper"]['station'].astype('str')
+        finished_station["Meter"]['station']   = finished_station["Meter"]['station'].astype('str')
+        merged_df = pd.merge(df_input['点位名称'], finished_station['Station']['点位名称'], how='left', indicator=True)
+        remain_station = merged_df[merged_df['_merge'] == 'left_only'].drop(columns='_merge')
+    else:
+        remain_station = df_input
+
+    dataset = []
+    for name in remain_station['点位名称']:
+        print(f"Station: {name}")
+        data = data_lamina.spider_station(name, time_start_timestamp, time_end_timestamp)
+        if data['result']:
+            dataset.append(data)
+        elif data['token']:
+            """ Token 失效 """
+            data_lamina.api_origin['header']['authorization'] = data['token']
+
+    print(f"Done.")
+
+    # 使用 ExcelWriter 将多个 DataFrame 保存到不同的工作表中
+    df_station = pd.DataFrame([], columns=['station', '点位名称'])
+    df_station.station = [data['station'] for data in dataset]
+    df_station.点位名称 = remain_station['点位名称'][:len(dataset)].values
+    df_adapter = pd.concat([data['adapter'].assign(station=data['station']) for data in dataset], ignore_index=True)
+    df_meter = pd.concat([data['meter'].assign(station=data['station']) for data in dataset], ignore_index=True)
+    column_adapter = ['time', 'station', *df_adapter.columns[1:-1]]
+    column_meter = ['time', 'station', *df_meter.columns[1:-1]]
+    if file_output.exists():
+        """ 连接文件 """
+        df_station = pd.concat([finished_station['Station'], df_station], ignore_index=True)
+        df_adapter = pd.concat([finished_station['Adatper'], df_adapter], ignore_index=True)
+        df_meter   = pd.concat([finished_station['Meter'], df_meter], ignore_index=True)
+    with pd.ExcelWriter(file_output) as writer:
+        df_station.to_excel(writer, sheet_name='Station', index=False)
+        df_adapter.to_excel(writer, sheet_name='Adatper', index=False, columns=column_adapter)
+        df_meter.to_excel(writer, sheet_name='Meter', index=False, columns=column_meter)
+
+    print(f"数据已成功保存到 {file_output}")
+
 if __name__=='__main__':
     """ 主体调用流程 """
     if hasattr(__builtins__,"__IPYTHON__"):
@@ -375,19 +544,38 @@ if __name__=='__main__':
     else:
         path_db = 'result/chinatowercom.db'
 
-    API_HEADER['Cookie'] = "HWWAFSESTIME=1735108780906; HWWAFSESID=1c91597e07b0014c4d; dc04ed2361044be8a9355f6efb378cf2=WyIzNTI0NjE3OTgzIl0"
+    if not (file_db:= Path(path_db)).exists():
-    API_HEADER['authorization'] = 'Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJhdWQiOlsiIl0sInVzZXJfbmFtZSI6IndlYl9tYW5hZ2V8d2FuZ2xlaTQiLCJzY29wZSI6WyJhbGwiXSwiZXhwIjoxNzM1MjI2NDAyLCJ1c2VySWQiOjI0Mjg1LCJqdGkiOiJmNzA1ZTlkZC1mNTA5LTQwYzUtODFhNi0zYzdlMzhhZjE0ODgiLCJjbGllbnRfaWQiOiJ3ZWJfbWFuYWdlIn0.e8p-hKWoFyLJbtqKEvlIbLpz-_OB7Ak32d8qdTHNZEny12lUrUE0YYrWQTu0gGtT-eRNDJ62q51IUYOkM_5Ou0Qk2HLouR9-iygtgtjIno72466bv_ao5wvD2PZihXKaKet_c9mnpOqDpvaaApAU4_rk0u6Pg7uJG4stV-akaaMMqRLR-cK5ARePeyHophyGUx80kkSlnhYfGP2rJjEFva36iPaCzM6oiezObMoXWtAPw67vPS-5saTWnjYLrzxr3_s5Idk1pwWPNWfSa6Rl_YMKKiTdtWAEepyrxxOWVfMaeAQYt-ndHhxyBPjRluDTSwUViWmDidoFxkKPMQixVw'
+        file_db.touch()
+
+    API_HEADER['Cookie'] = "HWWAFSESID=ac80388ba903b6ae67; HWWAFSESTIME=1735517672864; dc04ed2361044be8a9355f6efb378cf2=WyIzNTI0NjE3OTgzIl0"
+    API_HEADER['authorization'] = 'Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJhdWQiOlsiIl0sInVzZXJfbmFtZSI6IndlYl9tYW5hZ2V8d2FuZ2xlaTQiLCJzY29wZSI6WyJhbGwiXSwiYXRpIjoiMzkzMTUzZGYtZDBkYS00YmM1LWJiZmMtZjdjMTQxYTEzOTU0IiwiZXhwIjoxNzM1OTcyNTA0LCJ1c2VySWQiOjI0Mjg1LCJqdGkiOiIwMTY1NDg1MC1mZjIwLTRkMzQtYTQ4ZC03NmRiZTk3MmQ3YWQiLCJjbGllbnRfaWQiOiJ3ZWJfbWFuYWdlIn0.diNffgdMwBp7_IsjTAwN-YYp8_zpRp5pGn7HJAar51R3jpJyeROFV1g8T6jC_d-bfqS59N649llL3kJPguFfTrwJSu7NS4oeD2YNPwuhpQIeMbszp48NZ3zKyct6COpMPTWRVMVygcfsKfkYnDJQSMyanfQCtLinfDhNq3ektLtPFxLsf54noBu8R_MJ-Pkdjiip3koA8Jhl2eL2Af4U-nd9UAjNaMT5HI0scC9ViEJshzgyxvOi4Pabzw-JI5ZRYzWd1rNZo7b1_s7OAYcqj2hteW1JdC7_6hTHZxpEjzRBvhuvpwHDtsBvAgGUI6hPoYkOpGNPG2QfW7Hyc7TWtA'
     data_lamina = Lamina_Data('sqlite:///' + path_db)
 
-    today = datetime.datetime.today()
-    yesterday = today - datetime.timedelta(days=1)
-    today_midnight = today.replace(hour=0, minute=0, second=0, microsecond=0)
-    yesterday_midnight = yesterday.replace(hour=0, minute=0, second=0, microsecond=0)
-    today_midnight_timestamp = time.mktime(today_midnight.timetuple())
-    yesterday_midnight_timestamp = time.mktime(yesterday_midnight.timetuple())
-
     # 依据站点内设备爬取整个站点的实时与历史数据
-    data = data_lamina.spider_station('TTE0102DX2406272727', yesterday_midnight_timestamp, today_midnight_timestamp)
+    # today = datetime.datetime.today()
+    # yesterday = today - datetime.timedelta(days=1)
+    # today_midnight = today.replace(hour=0, minute=0, second=0, microsecond=0)
+    # yesterday_midnight = yesterday.replace(hour=0, minute=0, second=0, microsecond=0)
+    # today_midnight_timestamp = time.mktime(today_midnight.timetuple())
+    # yesterday_midnight_timestamp = time.mktime(yesterday_midnight.timetuple())
+    # data = data_lamina.spider_station('TTE0102DX2410091439', yesterday_midnight_timestamp, today_midnight_timestamp)
+
+    # 读取站点历史数据
+    save_station_by_file1(data_lamina)
+    # save_station_by_file2(data_lamina, "result\station_Q0103.xlsx")
+
+    # 网站令牌更新
+    body = {
+    "appId": "pjxNHUmFrMuj82pJenTmEc3Uvzj1cAO/qXs3zKMTjsG7Quk59cyjBCQM4miupyXv1At4e3deTn1cF9c4/WveDaeJCwEB+Dslom9yufrVPziOmRrQj1iAo8QVWSUnT1k70soDst+JN6japzOt7vjibru0uS/xezHrhuLSyNxkqzs=",
+    "refreshToken": "eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJhdWQiOlsiIl0sInVzZXJfbmFtZSI6IndlYl9tYW5hZ2V8d2FuZ2xlaTQiLCJzY29wZSI6WyJhbGwiXSwiYXRpIjoiNGU4NTY1NTAtZDE5Ni00YjY4LWI1OGYtMzBkOTY0YjIyOGNkIiwiZXhwIjoxNzM1OTcyNTA0LCJ1c2VySWQiOjI0Mjg1LCJqdGkiOiIwMTY1NDg1MC1mZjIwLTRkMzQtYTQ4ZC03NmRiZTk3MmQ3YWQiLCJjbGllbnRfaWQiOiJ3ZWJfbWFuYWdlIn0.PFT8JlTvWay1GUI5TC2Ht25rZWkAnQT3nxs-dOcAVIN9To06rG8EDspZ5eFxmNuEraurNxHCOLPfQZ-bCzJ8ywlA747PyJxyMPBhRhgXSDHYHX7ZqHEUdQdQo_Wkf75I8ko8_szchyhItjtgDUCzud9TlxKeuBQuerpYV8tkUVWobp4ulnnHEg0kqZFDeXrI-84Lyy-kodCDI-r3KuMBC5Rvbce0hqMcs2l-2U7M-V7LUT2VhBEvQd8l_Agx8hqWcK-d-dMVhlNjcvcb0AKmcX845D0bD5tKVKim_5JX4Er9-NANzSmgO0SRnsFVuxHhXiNqSkTB7pIdyi9r-ui23Q",
+    "accessToken": API_HEADER['authorization']
+    }
+    body1 = {
+    "appId": "ePUxoRrHClb7+Wxk7NAJpiJhoVAcJbZ5NPJEak8ZTFrETrfA0JAIjbqiDuaow1Jdyg1FLjUAwlBXrLoKh514oTTZSp1U91ewVj+8ZvNi2vtbQkU03WdyxyHXiyTNjC88O1JRm13hRnIm1vRMoxsudm8CPCpUIsU9yYABZ+/w3A4=",
+    "refreshToken": "eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJhdWQiOlsiIl0sInVzZXJfbmFtZSI6IndlYl9tYW5hZ2V8d2FuZ2xlaTQiLCJzY29wZSI6WyJhbGwiXSwiYXRpIjoiNGU4NTY1NTAtZDE5Ni00YjY4LWI1OGYtMzBkOTY0YjIyOGNkIiwiZXhwIjoxNzM1OTcyNTA0LCJ1c2VySWQiOjI0Mjg1LCJqdGkiOiIwMTY1NDg1MC1mZjIwLTRkMzQtYTQ4ZC03NmRiZTk3MmQ3YWQiLCJjbGllbnRfaWQiOiJ3ZWJfbWFuYWdlIn0.PFT8JlTvWay1GUI5TC2Ht25rZWkAnQT3nxs-dOcAVIN9To06rG8EDspZ5eFxmNuEraurNxHCOLPfQZ-bCzJ8ywlA747PyJxyMPBhRhgXSDHYHX7ZqHEUdQdQo_Wkf75I8ko8_szchyhItjtgDUCzud9TlxKeuBQuerpYV8tkUVWobp4ulnnHEg0kqZFDeXrI-84Lyy-kodCDI-r3KuMBC5Rvbce0hqMcs2l-2U7M-V7LUT2VhBEvQd8l_Agx8hqWcK-d-dMVhlNjcvcb0AKmcX845D0bD5tKVKim_5JX4Er9-NANzSmgO0SRnsFVuxHhXiNqSkTB7pIdyi9r-ui23Q",
+    "accessToken": "eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJhdWQiOlsiIl0sInVzZXJfbmFtZSI6IndlYl9tYW5hZ2V8d2FuZ2xlaTQiLCJzY29wZSI6WyJhbGwiXSwiZXhwIjoxNzM1OTA5NTE1LCJ1c2VySWQiOjI0Mjg1LCJqdGkiOiI0ZTg1NjU1MC1kMTk2LTRiNjgtYjU4Zi0zMGQ5NjRiMjI4Y2QiLCJjbGllbnRfaWQiOiJ3ZWJfbWFuYWdlIn0.KxGBpvuPIP3CHfVT41wxE_v9vlHNC9GL6sfaIta8cI2qlMpTCVg9dg-4DgPlXuMrtI0YzrSbAywCQmFLGcBgh3HD_UuIAH-k3Y8__osZEgc4bUcJ58W-uukuEu3MEwbV6ZcxTq7dxf3iqu9aXGrawYY_iL-jIRH1v8Zcr4qUPA9Mlzl8LvZdzZ05XgntbxE8IQRmt1M5rWdWLV4tvbUEYR5eDGs3az0w-MFXQ8qNHo8KLJc68WvbilmOMWkhK2k_xQQTdNx_jPktjYfClZa6l9-6rYAb5MMqwt77fY0_JE87u3w5YbU_GRyBI2mjnJe1qKdMjUEpQwWqt3DLJWLe7Q"
+    }
+    req = requests.post(API_Map['refreshToken'][0], json=body1, headers=API_HEADER)
 
     # data = sim_data_apt(('2024-10-1 00:00:00', '2024-10-1 12:00:00'))
     # chart_apt(data)

source/dev_station.py

@@ -231,8 +231,20 @@ if __name__ == '__main__':
         "dev16": {'device_id': 'TTE0101DX2409210027',    # 2017-YD：定州北只东
                   'frame_print': True, 
                   'time_out': 6, 'retry': 1},
+        "dev17": {'device_id': 'TTE0101DX2406280009',    # 壮志村委会-光伏
+                  'frame_print': True, 
+                  'time_out': 6, 'retry': 1},
+        "dev18": {'device_id': 'TTE0101DX2406260013',    # 开发区竹行机房-光伏
+                  'frame_print': True, 
+                  'time_out': 6, 'retry': 1},
+        "dev19": {'device_id': 'TTE0101DX2409210093',    # 2016-YD:定州北木庄
+                  'frame_print': True, 
+                  'time_out': 6, 'retry': 1},
+        "dev20": {'device_id': 'TTE0101DX2409270062',    # 内丘中张村北
+                  'frame_print': True, 
+                  'time_out': 6, 'retry': 1},
     }
-    dev_lamina = LaminaStation(**mode_config["dev16"])
+    dev_lamina = LaminaStation(**mode_config["dev19"])
 
     dev_lamina.frame_read(0x0000, 0x20)
     time.sleep(2)

source/device/function/protocols.py

@@ -429,14 +429,20 @@ def display_data(address: int, data: bytes, modbus_map: dict=modbus_map) -> dict
 
 def print_display(output_data: dict):
     """ 格式化表示输出数据 """
+    bank_chars = ' \t\000'
     print("Parse Result:")
     label_len_max = max(map(lambda x: len(x[0]), output_data.values()))
     data_len_max = max(map(lambda x: len(str(x[1])), output_data.values()))
     for key, value in output_data.items():
-        label = value[0]
+        match value:
-        data  = "-".join(map(str, value[1])) if type(value) == list else value[1]
+            case (str() as label, list() as data):
+                print(f"{tools.ByteConv.display_hex(key, 4)}: {'-'.join(map(str, data)):<{data_len_max}} {label:<{label_len_max}}")
+            case (str() as label, str() as data):
+                print(f"{tools.ByteConv.display_hex(key, 4)}: {data.rstrip(bank_chars):<{data_len_max}} {label:<{label_len_max}}")
+            case (str() as label, data):
                 print(f"{tools.ByteConv.display_hex(key, 4)}: {data:<{data_len_max}} {label:<{label_len_max}}")
-
+            case _:
+                raise ValueError(f"Key: {key}, Unkown Value: {value}")