积累修改;
This commit is contained in:
何 泽隆
@@ -1,33 +1,36 @@
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import time
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import datetime
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import requests
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import numpy as np
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import pandas as pd
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from pathlib import Path
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from bs4 import BeautifulSoup
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import pandas as pd
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import matplotlib.pyplot as plt
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import matplotlib.dates as mdates
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from sqlalchemy import create_engine
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from sqlalchemy import MetaData, Table, Column, String, Float, Integer
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API_URL = "https://energy-iot.chinatowercom.cn/api/device/device/historyPerformance"
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headers = {
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"Accept-Encoding": "gzip, deflate, br, zstd",
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"Connection": "keep-alive",
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"Content-Length": "211",
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"Cookie": "HWWAFSESTIME=1732173820506; HWWAFSESID=1739685743c73769ff; dc04ed2361044be8a9355f6efb378cf2=WyIzNTI0NjE3OTgzIl0",
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"Host": "energy-iot.chinatowercom.cn",
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"Origin": "https://energy-iot.chinatowercom.cn",
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"Sec-Fetch-Dest": "empty",
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"Sec-Fetch-Mode": "cors",
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"Sec-Fetch-Site": "same-origin",
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36 Edg/131.0.0.0",
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"accept": "application/json, text/plain, */*",
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"accept-language": "zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6",
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"authorization": "Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJhdWQiOlsiIl0sInVzZXJfbmFtZSI6IndlYl9tYW5hZ2V8d2FuZ2xlaTQiLCJzY29wZSI6WyJhbGwiXSwiZXhwIjoxNzMyMjc4NDA5LCJ1c2VySWQiOjI0Mjg1LCJqdGkiOiJkODE0YTZhYy05YmJmLTQ0ZjQtYWRhYi0wMzAzNjUzNmNhNWIiLCJjbGllbnRfaWQiOiJ3ZWJfbWFuYWdlIn0.VhJaDKwzjekwOCsw_jOF_jvg7sX45okFcxkLyWtbfFVGWVWANhKNhVqj5Dn0Qb3wUXH3e-w74sDN1RI9QADngMOGP_H7aTwI_nukj6VmjpFA7kEtOBwa6ouvPZQMa1qa3UWl21Ac6GoLu14T4TIf4kQAMTdaYAMFrwDAXAkqvIDmKKjZbnDFUjUIcj-J_Y-LfHCEBjtcz7Rp_wMO-PMA5wII6kbcNoSFiYb0djcFQyeBcIUSUTRPixPcTYBkS-IhNrsOePIWlpNYMHbPxZdrZkV4M65BmBn4A9MUjWYHm7iIut8WVMdCXR4Sxp9m0mJHXR_IPWES4O7aBcuMkOmjyw",
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"content-type": "application/json;charset=UTF-8",
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"sec-ch-ua": "\"Microsoft Edge\";v=\"131\", \"Chromium\";v=\"131\", \"Not_A Brand\";v=\"24\"",
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"sec-ch-ua-mobile": "?0",
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"sec-ch-ua-platform": "Windows",
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API_HEADER = {
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"Accept-Encoding": "gzip, deflate, br, zstd",
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"Connection": "keep-alive",
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"Content-Length": "170",
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"Cookie": "HWWAFSESTIME=1732496660747; HWWAFSESID=880ee8eff3a2d23536; dc04ed2361044be8a9355f6efb378cf2=WyIzNTI0NjE3OTgzIl0",
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"authorization": "Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJhdWQiOlsiIl0sInVzZXJfbmFtZSI6IndlYl9tYW5hZ2V8d2FuZ2xlaTQiLCJzY29wZSI6WyJhbGwiXSwiZXhwIjoxNzMyNTQ4Nzc5LCJ1c2VySWQiOjI0Mjg1LCJqdGkiOiI1Y2I3ZDA2ZC1hNzczLTQ5ZDQtYmRiMy05ZjdiOWNmMjY3ZTYiLCJjbGllbnRfaWQiOiJ3ZWJfbWFuYWdlIn0.Gpk9eYc3W4gcGLTG4aLSEL7E-VD-qPYsELcyyPuZ29e9STfa18WNcbosdIDulbH-5LDqY6i6aFAWpVm41bIgwzLZpAhlp2dthJcoBn9a90wWO4nz-Xi-MK9MVhNR0C28fyxv7h8jP_LRQkBi-pAzm2j65gkSTL3Vakky6znawZ5pY42U74lF-SnYdGyNFK3Ryy3xeMG4N1Pu_OJFdjXdGANUJZcXg1gh8WrERPo2SvuiCzOVTuUQZO7lRc8qGyILTKC-snz5CoonHaCWMaRzcV5VtCGd-yBncpyMQN9rGPqCiiLmuyTG29aZh5UfR_mloGCQHacqRmYSq3uGkPodBg",
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"Host": "energy-iot.chinatowercom.cn",
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"Origin": "https://energy-iot.chinatowercom.cn",
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"Sec-Fetch-Dest": "empty",
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"Sec-Fetch-Mode": "cors",
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"Sec-Fetch-Site": "same-origin",
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36 Edg/131.0.0.0",
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"accept": "application/json, text/plain, */*",
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"accept-language": "zh-CN,zh;q=0.9,en;q=0.8,en-GB;q=0.7,en-US;q=0.6",
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"content-type": "application/json;charset=UTF-8",
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"sec-ch-ua": "\"Microsoft Edge\";v=\"131\", \"Chromium\";v=\"131\", \"Not_A Brand\";v=\"24\"",
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"sec-ch-ua-mobile": "?0",
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"sec-ch-ua-platform": "Windows",
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}
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SemaMap = {
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@@ -39,73 +42,148 @@ SemaMap = {
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'apt_power_out': ('0305122001', 'adapter', "输出功率"),
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}
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def get_history_data(device_id, data_type, times: tuple[int, int]):
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""" 读取信号量历史数据, 返回接口json数据 """
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body = {
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"startTimestamp": times[0],
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"endTimestamp": times[1],
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"deviceCode": f"{device_id}",
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"mid": f"{data_type}",
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"businessType": "7",
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"pageNum": 2,
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"pageSize": 5,
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"total": 0
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}
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req = requests.post(API_URL, data=body, headers=headers)
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return req.json()
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class Lamina_Data(object):
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""" 叠光主站数据分析 """
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def __init__(self, database="sqlite:///:memory", header=None):
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""" 初始化 """
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self.engine = create_engine(database)
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def adapter_status_graphs(device_id, times: tuple[int, int]):
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""" 获取数据, 绘制图表 """
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data_volt_in = get_history_data(device_id, SemaMap["apt_volt_in"], times)
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data_curr_in = get_history_data(device_id, SemaMap["apt_curr_in"], times)
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data_volt_out = get_history_data(device_id, SemaMap["apt_volt_out"], times)
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data_power_out = get_history_data(device_id, SemaMap["apt_power_out"], times)
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metadata = MetaData()
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metadata.reflect(bind=self.engine)
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if 'history' not in metadata.tables:
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history_table = Table(
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'history', metadata,
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Column('dev', String(50)),
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Column('mid', String(50)),
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Column('time', Integer),
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Column('value', Float)
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)
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metadata.create_all(self.engine)
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data_apt = []
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for item in zip(data_volt_in['data'], data_volt_out['data'], data_curr_in['data'], data_power_out['data']):
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print(item)
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piont_time = time.mktime(time.strptime(item[0]['collectTime'], r"%Y-%m-%d %H:%M:%S"))
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point_apt = {
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'time': int(piont_time),
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'volt_in': item[0]['value'],
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'volt_out': item[1]['value'],
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'curr_in': item[2]['value'],
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'power_out': item[3]['value'],
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self.data = {
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'history': pd.read_sql_table('history', self.engine),
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}
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data_apt.append(point_apt)
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table_apt = pd.DataFrame(data_apt)
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# 图表绘制
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chart_apt(table_apt)
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self.api_origin = {
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'domain': 'https://energy-iot.chinatowercom.cn/api',
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'header': API_HEADER,
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}
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def chart_apt(table_apt):
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""" 绘制适配器信息图表 """
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fig, ax1 = plt.subplots(figsize=(12, 6))
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ax1.plot(table_apt['time'], table_apt['volt_in'], color='green', label='Input Voltage')
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ax1.plot(table_apt['time'], table_apt['volt_out'], color='red', label='Output Voltage')
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# ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M:%S'))
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def save_history_data(self):
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""" 保存历史数据 """
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# 设置x轴的主要刻度定位器为自动日期定位器，这使得x轴上的刻度根据数据自动选择最合适的日期格式
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ax1.xaxis.set_major_locator(mdates.AutoDateLocator())
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data_memory = self.data['history']
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data_file = pd.read_sql_table('history', self.engine)
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ax2 = ax1.twinx()
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# 绘制斜线阴影
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for i in range(len(table_apt) - 1):
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ax1.fill_between(
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[table_apt['time'].iloc[i], table_apt['time'].iloc[i + 1]],
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[table_apt['power_out'].iloc[i], table_apt['power_out'].iloc[i + 1]],
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color='red', alpha=0.5)
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# 合并新数据和现有数据
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combined_df = pd.concat([data_file, data_memory], ignore_index=True)
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# 通过 'dev', 'mid', 'time' 三列进行去重
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combined_df.drop_duplicates(subset=['dev', 'mid', 'time'], inplace=True)
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# 将去重后的数据插入数据库
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combined_df.to_sql('history', self.engine, if_exists='replace', index=False)
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lines, labels = ax1.get_legend_handles_labels()
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shadows, shadow_labels = ax2.get_legend_handles_labels()
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ax1.legend(lines + shadows, labels + shadow_labels, loc='upper left')
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print(f"成功插入 {len(combined_df)} 条数据")
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ax1.set_title('Device Data Visualization')
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ax1.set_xlabel('Time')
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ax1.set_ylabel('Voltage (V)')
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ax2.set_ylabel('Power (W)')
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return len(combined_df)
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plt.show()
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def get_histoty_data(self, device_id, data_type, time_start:int, time_end:int):
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""" 读取历史数据 """
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flag = 0
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time_Interval = 20 * 60
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database = self.data['history']
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filter_data = database[database['dev'] == device_id &
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database['mid'] == SemaMap[data_type][0] &
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database['time'].between(time_start, time_end)]
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if filter_data.shape[1] == 0 or (filter_data['time'].min() - time_start > time_Interval) or (time_end - filter_data['time'].max() > time_Interval):
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""" 需要从网页获取数据 """
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flag = 1
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return filter_data
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def get_history_data_by_net(self, device_id, data_type, time_start:int, time_end:int, header=None):
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""" 读取信号量历史数据, 返回接口json数据 """
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if header is None:
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header = self.api_origin['header']
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body = {
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"businessType": "7",
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"startTimestamp": int(time_start * 1000),
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"endTimestamp": int(time_end * 1000),
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"deviceCode": f"{device_id}",
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"mid": f"{data_type[0]}",
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"pageNum": 1,
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"pageSize": 10,
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"total": 0
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}
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req = requests.post(API_URL, json=body, headers=header)
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json_data = req.json()
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if json_data['code'] == 200:
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""" 数据读取成功 """
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print(f"Get data success, len={len(json_data['data'])}")
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table_data = pd.DataFrame(json_data['data'], columns=['collectTime', 'mid', 'midName', 'value'])
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table_data['time'] = table_data['collectTime'].apply(lambda x: int(time.mktime(time.strptime(x, r"%Y-%m-%d %H:%M:%S"))))
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table_data['dev'] = device_id
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return table_data[['dev', 'mid', 'time', 'value']]
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else:
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print(f"Get data fail, code={json_data['code']}, msg=\n\t{json_data['msg']}")
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return pd.DataFrame([], columns=['dev', 'mid', 'time', 'value'])
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def graphs_adapter(self, device_id, time_start:int|str, time_end:int|str):
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""" 绘制图表-适配器数据 """
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if type(time_start) is str:
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time_start = time.mktime(time.strptime(time_start, r"%Y-%m-%d %H:%M:%S"))
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if type(time_end) is str:
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time_end = time.mktime(time.strptime(time_end, r"%Y-%m-%d %H:%M:%S"))
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data_volt_in = self.get_history_data_by_net(device_id, SemaMap["apt_volt_in"], time_start, time_end)
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data_curr_in = self.get_history_data_by_net(device_id, SemaMap["apt_curr_in"], time_start, time_end)
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data_volt_out = self.get_history_data_by_net(device_id, SemaMap["apt_volt_out"], time_start, time_end)
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data_power_out = self.get_history_data_by_net(device_id, SemaMap["apt_power_out"], time_start, time_end)
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combined_df = pd.concat([data_volt_in, data_curr_in, data_volt_out, data_power_out], ignore_index=True)
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self.data['history'] = pd.concat([self.data['history'], combined_df], ignore_index=True)
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self.save_history_data()
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data_adapter = pd.DataFrame([], columns=['time', 'volt_in', 'volt_out', 'curr_in', 'power_out'])
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data_adapter.time = data_volt_in.time
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data_adapter.volt_in = data_volt_in.value
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data_adapter.volt_out = data_volt_out.value
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data_adapter.curr_in = data_curr_in.value
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data_adapter.power_out = data_power_out.value
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return data_adapter
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def chart_adapter(self, data_adapter):
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""" 绘制适配器信息图表 """
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fig, ax1 = plt.subplots()
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ax1.plot(data_adapter['time'], data_adapter['volt_in'], color='green', label='Input Voltage')
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ax1.plot(data_adapter['time'], data_adapter['volt_out'], color='red', label='Output Voltage')
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ax2 = ax1.twinx()
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ax2.plot(data_adapter['time'], data_adapter['power_out'], color='gray', label='Output Power')
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# # 绘制斜线阴影
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# for i in range(len(table_apt) - 1):
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# ax1.fill_between(
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# [table_apt['time'].iloc[i], table_apt['time'].iloc[i + 1]],
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# [table_apt['power_out'].iloc[i], table_apt['power_out'].iloc[i + 1]],
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# color='red', alpha=0.5)
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lines, labels = ax1.get_legend_handles_labels()
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shadows, shadow_labels = ax2.get_legend_handles_labels()
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ax1.legend(lines + shadows, labels + shadow_labels, loc='upper left')
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ax1.set_title('Device Data Visualization')
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ax1.set_xlabel('Time')
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ax1.set_ylabel('Voltage (V)')
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ax2.set_ylabel('Power (W)')
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plt.ioff()
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plt.show()
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plt.savefig('output.png')
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# plt.close()
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plt.ion()
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def sim_data_apt(times:tuple[int, int]):
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""" 模拟数据 """
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@@ -124,13 +202,17 @@ def sim_data_apt(times:tuple[int, int]):
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return pd.DataFrame(data)
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if __name__=='__main__':
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import numpy as np
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data = sim_data_apt(('2024-10-1 00:00:00', '2024-10-1 12:00:00'))
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# API_HEADER['authorization'] = 'Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9.eyJhdWQiOlsiIl0sInVzZXJfbmFtZSI6IndlYl9tYW5hZ2V8d2FuZ2xlaTQiLCJzY29wZSI6WyJhbGwiXSwiZXhwIjoxNzMyNTQzMzIyLCJ1c2VySWQiOjI0Mjg1LCJqdGkiOiI2MGZlZjYxNC0zM2QyLTQ5NjMtYjM5Mi1mNTJlYzJhM2RmYTEiLCJjbGllbnRfaWQiOiJ3ZWJfbWFuYWdlIn0.jumI9T8swSPCKITrCU06DWRea0tpvwCidw6jFP-F26JH0fRCp801fbzPs5kHkrgh-sm_ayzO2mD9NCkC2hoC-YDQs8HCvXHZXwtYpn3qJ6vpWRQrMr8Vwk8HnuxbFUn-8Ccs88hHOa8rtrEVsglfl0gqU1C8LL1rsHxgYI3MAwwU_eHo74jNjSHuIn79iq4Gyxc6lial6Pb6WWOw1erqKRGRQPcfUtAXMjwXbQ03vFk6G-As_u353yKvznsGR4A70hFHkJJGugK3en8kJO3_g3sE6ddcx0CYicucRLckro4Md_S8mIPkw2RgGEK38vvEo2fqiyT8E67dC5D4_zMw5A'
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data_lamina = Lamina_Data('sqlite:///result/chinatowercom.db')
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chart_apt(data)
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# data = sim_data_apt(('2024-10-1 00:00:00', '2024-10-1 12:00:00'))
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# chart_apt(data)
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plt.waitforbuttonpress()
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table_apt = data_lamina.graphs_adapter('TTE0102DX2406180988', '2024-11-23 00:00:00', '2024-11-26 00:00:00')
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pass
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data_lamina.chart_adapter(table_apt)
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while True:
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plt.waitforbuttonpress()
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