import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from scipy.optimize import minimize


# 解冻时间计算函数
def calculate_defrost_time(w, T_initial):
    # 物理参数
    rho_coal = 1400  # 煤的密度
    rho_ice = 917  # 冰的密度
    C_coal = 1000  # 煤的比热容
    C_ice = 2100
    L = 334000  # 物体长度（米）
    k_coal = 0.3  # 煤的导热系数
    k_ice = 2.2
    h = 10  # 近似导热系数（W/m²·K）
    T_air = 20  # 空气温度（摄氏度）
    T_m = 0  # 熔点温度（摄氏度）
    a, b, c = 13, 2.72, 1.6  # 物体的尺寸（长、宽、高）
    # 计算体积和表面积
    V = a * b * c
    A = 2 * (a * b) + 2 * (b * c) + 2 * (a * c)
    # 计算混合密度
    rho_mix = (1 - w) * rho_coal + w * rho_ice
    # 计算混合比热容
    C_mix = (1 - w) * C_coal + w * C_ice
    # 计算特征长度
    L_char = V / A
    # 计算体积分数
    phi = (w * rho_coal) / rho_ice
    # 计算有效导热系数
    k_eff = (k_coal * (2 * k_coal + k_ice - 2 * phi * (k_coal - k_ice)) /
             (2 * k_coal + k_ice + phi * (k_coal - k_ice))) + 1.1
    # 初步解冻时间 t1（小时）
    t1 = ((0.2 * rho_mix * C_mix * L_char ** 2) / k_eff *
          ((T_m - T_initial) / (T_air - T_initial))) / 3600
    # 进一步解冻时间 t2（小时）
    k_char = h  # 近似处理 k_char
    t2 = ((rho_mix * w * L * L_char * L_char / (k_eff * (T_air - T_m))) *
          ((0.5 + k_eff / (k_char * L_char)) / 6)) / 3600
    # 总解冻时间
    t_total = t1 + t2
    return t_total


# 模拟生成数据
np.random.seed(42)
n_samples = 500
# 含水量，范围 10% - 15%
w = np.random.uniform(0.1, 0.15, n_samples)
# 初始温度，范围 -10 到 20 摄氏度
T_initial = np.random.uniform(-10, 20, n_samples)
# 计算解冻时间
original_times = []
for i in range(n_samples):
    original_times.append(calculate_defrost_time(w[i], T_initial[i]))

# 模拟实际作业时长（实际时长和计算时长有一定随机误差）
y = np.array(original_times) + np.random.normal(0, 0.2, n_samples)

# 准备特征
X = np.column_stack((w, T_initial, original_times))

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 选择模型并训练
model = RandomForestRegressor(n_estimators=100, random_state=42)
model.fit(X_train, y_train)

# 模型评估
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error: {mse}")


# 反推函数
def reverse_calculate(time):
    def objective_function(vars):
        w, T_initial = vars
        calculated_time = calculate_defrost_time(w, T_initial)
        return abs(calculated_time - time)

    bounds = [(0.1, 0.15), (-10, 20)]  # 含水量和初始温度的范围
    num_trials = 5  # 尝试的次数
    best_result = None
    best_error = np.inf
    for _ in range(num_trials):
        # 随机生成初始猜测值
        initial_guess = [np.random.uniform(0.1, 0.15), np.random.uniform(-10, 20)]
        result = minimize(objective_function, initial_guess, bounds=bounds)
        error = result.fun
        if error < best_error:
            best_error = error
            best_result = result
    return best_result.x[0], best_result.x[1]


# 输入实际解冻时间
try:
    test_time = float(input("请输入实际解冻时间（小时）: "))
    w_result, T_result = reverse_calculate(test_time)
    print(f"反推得到的含水量: {w_result}")
    print(f"反推得到的初始温度: {T_result}")
    recalculated_time = calculate_defrost_time(w_result, T_result)
    print(f"重新计算的解冻时间: {recalculated_time}")
except ValueError:
    print("输入无效，请输入有效的数字。")