深度学习，如何让运维工作从“救火队”变身“先知”？

import numpy as np
import pandas as pd
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense
from sklearn.preprocessing import MinMaxScaler

# 加载日志数据
data = pd.read_csv('logs.csv')  # 日志格式需包含时间戳和相关指标
data['timestamp'] = pd.to_datetime(data['timestamp'])
data = data.set_index('timestamp')

# 数据归一化
scaler = MinMaxScaler()
data_scaled = scaler.fit_transform(data)

# 创建时间序列数据
def create_sequences(data, seq_length):
    sequences, labels = [], []
    for i in range(len(data) - seq_length):
        sequences.append(data[i:i+seq_length])
        labels.append(data[i+seq_length])
    return np.array(sequences), np.array(labels)

seq_length = 10
X, y = create_sequences(data_scaled, seq_length)

# 构建LSTM模型
model = Sequential([
    LSTM(50, activation='relu', input_shape=(seq_length, data.shape[1])),
    Dense(1)
])
model.compile(optimizer='adam', loss='mse')

# 模型训练
model.fit(X, y, epochs=10, batch_size=32)

# 异常检测：预测与实际值的偏差
predictions = model.predict(X)
errors = np.abs(predictions - y)
threshold = np.percentile(errors, 95)  # 设置阈值
anomalies = errors > threshold
print(f"发现的异常点数量: {np.sum(anomalies)}")