大模型技术在存储管理中的应用性：开创智能存储的新纪元

# 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

# 读取存储管理数据
data = pd.read_csv('storage_management_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:])

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

# 构建深度神经网络模型
model = Sequential([
    Dense(64, activation='relu', input_shape=(scaled_data.shape[1],)),
    Dropout(0.2),
    Dense(64, activation='relu'),
    Dropout(0.2),
    Dense(1, activation='linear')
])
model.compile(optimizer='adam', loss='mean_squared_error')

# 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(scaled_data.drop(columns=['storage_usage']), scaled_data['storage_usage'], test_size=0.2, random_state=42)

# 训练模型
model.fit(X_train, y_train, epochs=50, batch_size=32, validation_data=(X_test, y_test))

# 进行预测
y_pred = model.predict(X_test)

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

mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)
print(f'均方误差: {mse:.4f}')
print(f'R^2: {r2:.4f}')

# 优化存储资源分配
def optimize_storage(predictions):
    # 根据预测结果优化存储资源分配策略
    optimized_allocations = []
    for pred in predictions:
        if pred > 0.8:
            optimized_allocations.append('增加存储空间')
        elif pred < 0.2:
            optimized_allocations.append('减少存储空间')
        else:
            optimized_allocations.append('保持不变')
    return optimized_allocations

optimized_allocations = optimize_storage(y_pred)
print("优化后的存储资源分配策略:", optimized_allocations)

import time

# 实时监控和优化存储资源使用情况
def monitor_and_optimize():
    while True:
        # 获取实时存储管理数据
        real_time_data = pd.read_csv('real_time_storage_log.csv')
        
        # 数据预处理
        real_time_data = real_time_data.fillna(method='ffill')
        scaled_real_time_data = scaler.transform(real_time_data.drop(columns=['timestamp']))
        scaled_real_time_data = pd.DataFrame(scaled_real_time_data, columns=real_time_data.columns[1:])
        
        # 进行预测
        real_time_predictions = model.predict(scaled_real_time_data)
        
        # 优化存储资源分配
        optimized_allocations = optimize_storage(real_time_predictions)
        print("实时优化后的存储资源分配策略:", optimized_allocations)
        
        # 间隔一定时间后再次监控和优化
        time.sleep(60)

# 启动实时监控和优化系统
monitor_and_optimize()