人工智能在服务水平管理中的应用：提升运营效率的新途径

# 创建并激活虚拟环境
python3 -m venv venv
source venv/bin/activate

# 安装所需依赖库
pip install numpy pandas scikit-learn tensorflow matplotlib

import pandas as pd

# 读取服务水平指标数据
data = pd.read_csv('service_level_log.csv')

# 查看数据结构
print(data.head())

# 数据清洗：处理缺失值
data = data.fillna(method='ffill')

# 数据规范化
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
scaled_data = scaler.fit_transform(data.drop(columns=['timestamp']))
scaled_data = pd.DataFrame(scaled_data, columns=data.columns[1:])

from sklearn.ensemble import RandomForestRegressor

# 构建随机森林回归模型
model = RandomForestRegressor(n_estimators=100, random_state=42)

# 特征选择
features = ['cpu_usage', 'memory_usage', 'disk_io', 'network_io']
X = scaled_data[features]
y = scaled_data['response_time']

# 数据分割：划分训练集和测试集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 训练模型
model.fit(X_train, y_train)

# 进行预测
y_pred_train = model.predict(X_train)
y_pred_test = model.predict(X_test)

# 模型评估
from sklearn.metrics import mean_squared_error, r2_score

train_mse = mean_squared_error(y_train, y_pred_train)
test_mse = mean_squared_error(y_test, y_pred_test)
train_r2 = r2_score(y_train, y_pred_train)
test_r2 = r2_score(y_test, y_pred_test)

print(f'训练集均方误差: {train_mse:.4f}')
print(f'测试集均方误差: {test_mse:.4f}')
print(f'训练集R^2: {train_r2:.4f}')
print(f'测试集R^2: {test_r2:.4f}')

# 实时监控系统性能
import time
import numpy as np

def monitor_system():
    cpu_usage = np.random.random() * 100
    memory_usage = np.random.random() * 100
    disk_io = np.random.random() * 100
    network_io = np.random.random() * 100
    return [cpu_usage, memory_usage, disk_io, network_io]

# 自动调优系统配置
def auto_tune_system(predicted_response_time):
    if predicted_response_time > 500:
        print("优化策略: 增加服务器资源")
        # 具体的优化策略实现（例如增加CPU核心数、扩展内存等）
    else:
        print("系统性能正常，无需调整")

# 实时监控和调优
while True:
    system_metrics = monitor_system()
    predicted_response_time = model.predict([system_metrics])[0]
    auto_tune_system(predicted_response_time)
    time.sleep(60)