运维人的AI革命：人工智能如何让策略制定更聪明、更高效？

import pandas as pd
from sklearn.ensemble import IsolationForest

# 生成服务器日志数据
data = pd.DataFrame({
    "CPU_Usage": [20, 22, 21, 90, 23, 25, 24, 95, 26, 27, 100],
    "Memory_Usage": [30, 31, 32, 85, 33, 35, 36, 89, 38, 40, 92]
})

# 训练异常检测模型
model = IsolationForest(contamination=0.1)
data["Anomaly"] = model.fit_predict(data)

# 发现异常点
print(data[data["Anomaly"] == -1])

import re

# 模拟日志数据
logs = [
    "[ERROR] Connection timeout at 12:30:15",
    "[INFO] User login successful",
    "[WARNING] High memory usage detected",
    "[ERROR] Database connection failed at 12:32:47"
]

# 识别关键错误日志
error_logs = [log for log in logs if re.search(r"\[ERROR\]", log)]

print("发现的错误日志:", error_logs)

import pandas as pd
from statsmodels.tsa.arima_model import ARIMA

# 生成硬盘健康数据
data = pd.DataFrame({
    "Time": list(range(1, 11)),
    "Disk_Health_Score": [98, 95, 93, 90, 85, 80, 75, 70, 60, 50]
})

# 训练预测模型
model = ARIMA(data["Disk_Health_Score"], order=(2, 1, 2))
model_fit = model.fit()
future = model_fit.forecast(steps=3)

print("未来3次硬盘健康状况预测:", future)

import numpy as np

# 模拟服务器负载数据
past_load = np.array([30, 35, 40, 45, 50, 55, 60, 65])

# 预测下一个时刻的负载
predicted_load = np.mean(past_load[-3:])

print("预测的服务器负载:", predicted_load)