不同宽度,厚度,重量,车间温度,冷却方式下,物料温度随时间衰减,请使用python机器学习,生成模拟数据,数据预处理,选择模型,划分数据集,训练模型,调整超参数,预测和评估,并绘图

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor

# 1. 生成模拟数据
np.random.seed(0)
n_samples = 1000

width = np.random.uniform(5, 20, n_samples)
thickness = np.random.uniform(1, 5, n_samples)
weight = np.random.uniform(100, 500, n_samples)
workshop_temp = np.random.uniform(20, 30, n_samples)
cooling_method = np.random.choice(['Air', 'Water'], n_samples)
time = np.random.uniform(0, 10, n_samples)
decay_rate = 0.2

temperature = (
    100 - (width + thickness + weight) +
    2 * workshop_temp +
    (cooling_m服务器托管网ethod == 'Water') * 10 -
    decay_rate * time
)

data = pd.DataFrame({
    '宽度': width,
    '厚度': thickness,
    '重量': weight,
    '车间温度': workshop_temp,
    '冷却方式': cooling_method,
    '时间': time,
    '物料温度': temperature
})

# 2. 数据预处理
# 对类别特征进行独热编码
data = pd.get_dummies(data, columns=['冷却方式'], drop_first=True)

# 3. 划分数据集
X = data.drop('物料温度', axis=1)
y = data['物料温度']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 4. 选择模型
model = LinearRegression()

# 5. 训练模型
model.fit(X_train, y_train)

# 6. 预测
y_pred = model.predict(X_test)

# 7. 评估模型
rmse = np.sqrt(mean_squared_error(y_test, y_pred))
print(f"均方根误差（RMSE）: {rmse}")

# 8. 绘图
plt.scatter(y_test, y_pred)
plt.xlabel("实际物料温度")
plt.ylabel("预测物料温度")
plt.title("实际温度 vs 预测温度")
服务器托管网plt.show()