传感器 健康 监控 代理

import pandas as pd, numpy as np
from datetime import datetime, timedelta

sensors = pd.read_csv('sensor_historian.csv')
faulty = []

for tag, grp in sensors.groupby('tag'):
    drift = np.abs(grp['value'].diff()).mean()
    flatline = (grp['value'].std() < 0.01).any()
    noise = grp['value'].std() / grp['value'].mean()
    out_of_range = ((grp['value'] < grp['min']) | (grp['value'] > grp['max'])).any()
    health = 'good'
    if flatline or out_of_range:
        health = 'fault'
        faulty.append(tag)
    elif drift > 2.0 or noise > 0.15:
        health = 'drift'

print(f'Checked {len(sensors)} readings from {sensors["tag"].nunique()} sensors')
print(f'Faulty: {faulty}')