AI加持的运维新视野：让系统状态监测不再掉链子

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout

# 构建一个简单的神经网络模型
model = Sequential([
    Dense(64, input_shape=(10,), activation='relu'),
    Dropout(0.2),
    Dense(32, activation='relu'),
    Dense(1, activation='sigmoid')
])

# 模拟训练数据进行模型训练
train_data = [[0.1, 0.2, 0.3, ..., 0.8], [0.2, 0.1, ..., 0.9]]
train_labels = [0, 1]
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(train_data, train_labels, epochs=10)

# 检测实时数据中的异常
real_time_data = [[0.3, 0.2, ..., 0.7]]
prediction = model.predict(real_time_data)
if prediction[0] > 0.5:
    print("系统状态异常！")

from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.cluster import KMeans

# 加载日志数据
log_data = ["Error: Disk failure", "Warning: High CPU usage", "Error: Network timeout"]

# 提取关键字并进行聚类分析
vectorizer = TfidfVectorizer(stop_words='english')
X = vectorizer.fit_transform(log_data)
kmeans = KMeans(n_clusters=2, random_state=0).fit(X)

# 输出聚类结果
for i, label in enumerate(kmeans.labels_):
    print(f"日志 {i} 属于类别 {label}")

import numpy as np
from sklearn.linear_model import LinearRegression

# 模拟时间序列数据
time_series_data = np.array([1, 2, 3, 4, 5, 6]).reshape(-1, 1)
fault_data = np.array([2, 4, 6, 8, 10, 12])

# 训练回归模型预测未来故障趋势
model = LinearRegression()
model.fit(time_series_data, fault_data)
future = np.array([7, 8, 9]).reshape(-1, 1)
predictions = model.predict(future)

print("预测故障趋势：", predictions)