136 lines
5.8 KiB
Python
136 lines
5.8 KiB
Python
import time
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import datetime
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import requests
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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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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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}
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SemaMap = {
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'apt_temp': ('0305117001', 'adapter', "设备温度"),
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'apt_volt_in': ('0305118001', 'adapter', "输入电压"),
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'apt_curr_in': ('0305119001', 'adapter', "输入电流"),
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'apt_volt_out': ('0305120001', 'adapter', "输出电压"),
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'apt_curr_out': ('0305121001', 'adapter', "输出电流"),
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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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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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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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}
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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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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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# 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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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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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.show()
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def sim_data_apt(times:tuple[int, int]):
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""" 模拟数据 """
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t_start = time.mktime(time.strptime(times[0], r"%Y-%m-%d %H:%M:%S"))
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t_end = time.mktime(time.strptime(times[1], r"%Y-%m-%d %H:%M:%S"))
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count_data = (t_end - t_start) / (10 * 60)
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time_list = range(int(t_start), int(t_end), 20 * 60)
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time_list = tuple(map(lambda x: time.strftime(r"%Y-%m-%d %H:%M:%S", time.localtime(x)), time_list))
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data = {
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'time': time_list,
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'volt_in': 10 + 10 * np.random.random(len(time_list)),
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'curr_in': 1 + 2 * np.random.random(len(time_list)),
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'volt_out': 54 + 2 * np.random.random(len(time_list)),
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}
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data['power_out'] = tuple(map(lambda x: x[0] * x[1], zip(data['volt_in'],data['curr_in'])))
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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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chart_apt(data)
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plt.waitforbuttonpress()
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pass |