我有一段代码：import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler, StandardScaler

# 读取微测站数据
micro_station_data = pd.read_excel(r'C:\Users\123\Desktop\micro_station_data.xlsx', skiprows=1)

# 读取国测站数据
national_station_data = pd.read_excel(r'C:\Users\123\Desktop\national_station_data.xls', skiprows=1)

# 数据预处理
def preprocess_data(data):
    # 将“时段”列转换为datetime格式
    data['时段'] = pd.to_datetime(data['时段'], format ='%Y/%m/%d %H:%M:%S')
    # 对所有值为-99.0(E)和0.0(E)的数据进行空值处理
    columns_to_replace = ['SO2', 'CO(mg/m3)', 'NO2', 'O3-1H', 'PM10', 'PM2.5', 'NO', 'NOX', '湿度', '温度', '风速', '风向', '大气压']
    data.loc[:, columns_to_replace] = data.loc[:, columns_to_replace].replace([-99.0, 0.0], pd.NA)
    # 将非数字字符串替换为NaN值
    data.loc[:, columns_to_replace] = data.loc[:, columns_to_replace].apply(pd.to_numeric, errors='coerce')
     # 使用各列的非空值均值替换空值
    data = data.fillna(data.mean())
    # 处理第一列数据
    data['时段'] = data['时段'].dt.strftime('%Y-%m-%d %H:%M:%S')
    return data

# 对微测站数据进行预处理
preprocessed_micro_station_data = preprocess_data(micro_station_data)
# 对国测站数据进行预处理
preprocessed_national_station_data = preprocess_data(national_station_data)
# Z-Score异常值检测
def detect_outliers(data):
    # 移除非数值型数据
    numeric_data = data.select_dtypes(include=['int', 'float'])
    # 计算Z-Score
    z_scores = []
    threshold = 3
    # 检测异常值
    outliers = []
    for col in numeric_data.columns:
        try:
            z_score = stats.zscore(numeric_data[col])
            z_scores.append(z_score)
        except TypeError:
            continue
    z_scores = np.array(z_scores)
    for i, row in enumerate(z_scores):
        for j, score in enumerate(row):
            if not np.isnan(score) and abs(score) > threshold:
                outliers.append((i, j))

    return outliers
# 对预处理后的微测站数据进行异常值检测
micro_station_outliers = detect_outliers(preprocessed_micro_station_data)
# 归一化
def normalize_data(data):
    scaler = MinMaxScaler()
    columns_to_normalize = ['SO2', 'CO(mg/m3)', 'NO2', 'O3-1H', 'PM10', 'PM2.5', 'NO', 'NOX', '湿度', '温度', '风速', '风向', '大气压']
    normalized_data = scaler.fit_transform(data[columns_to_normalize])
    # 将归一化后的数据与原数据进行合并
    data[columns_to_normalize] = normalized_data
    return data

# 标准化
def standardize_data(data):
    scaler = StandardScaler()
    columns_to_standardize = ['SO2', 'CO(mg/m3)', 'NO2', 'O3-1H', 'PM10', 'PM2.5', 'NO', 'NOX', '湿度', '温度', '风速', '风向', '大气压']
    standardized_data = scaler.fit_transform(data[columns_to_standardize])
    # 将标准化后的数据与原数据进行合并
    data[columns_to_standardize] = standardized_data
    return data

# 对预处理后的微测站数据进行归一化
normalized_micro_station_data = normalize_data(preprocessed_micro_station_data)

# 对预处理后的国测站数据进行标准化
standardized_national_station_data = standardize_data(preprocessed_national_station_data)
import numpy as np
from sklearn.metrics import mean_squared_error


# 定义校准模型
class CalibrationModel:
    def init(self, num_coefficients):
        self.num_coefficients = 13
        self.coefficients = np.random.rand(num_coefficients)  # 初始化校准系数

    def calibrate_data(self, data):
        calibrated_data = data * self.coefficients  # 校准微测站数据
        return calibrated_data

    def evaluate(self, data1, data2):
        calibrated_data = self.calibrate_data(data1)
        mse = mean_squared_error(calibrated_data, data2)  # 均方误差作为评估指标
        return mse


# 遗传算法
def genetic_algorithm(data1, data2, num_coefficients, population_size, num_generations):
    population = []

    # 初始化种群
    for _ in range(population_size):
        model = CalibrationModel(num_coefficients)
        population.append(model)

    # 迭代演化
    for _ in range(num_generations):
        # 选择操作，选择适应度最高的个体
        population.sort(key=lambda x: x.evaluate(data1, data2))
        survivors = population[:population_size // 2]

        # 交叉操作，生成新个体
        offspring = []
        for i in range(population_size // 2):
            parent1 = np.random.choice(survivors)
            parent2 = np.random.choice(survivors)
            child = CalibrationModel(num_coefficients)
            child.coefficients = (parent1.coefficients + parent2.coefficients) / 2
            offspring.append(child)

        # 变异操作，引入新的基因变体
        for i in range(population_size // 2):
            model = np.random.choice(offspring)
            model.coefficients += np.random.normal(0, 0.1, num_coefficients)

        # 更新种群
        population = survivors + offspring

    # 选择适应度最高的个体作为最终模型
    population.sort(key=lambda x: x.evaluate(data1, data2))
    best_model = population[0]

    return best_model


# 粒子群算法
def particle_swarm_algorithm(data1, data2, num_coefficients, num_particles, num_iterations):
    particles = []
    best_global_position = np.random.rand(num_coefficients)
    best_global_mse = float('inf')

    # 初始化粒子群
    for _ in range(num_particles):
        model = CalibrationModel(num_coefficients)
        particles.append(model)

    # 迭代更新
    for _ in range(num_iterations):
        for particle in particles:
            mse = particle.evaluate(data1, data2)
            if mse < best_global_mse:
                best_global_mse = mse
                best_global_position = particle.coefficients

            # 更新粒子位置和速度
            particle_velocity = np.random.rand(num_coefficients)
            particle_position = particle.coefficients + particle_velocity
            particle.coefficients = particle_position

    # 创建全局最优模型
    best_model = CalibrationModel(num_coefficients)
    best_model.coefficients = best_global_position

    return best_model


# 蚁群算法
def ant_colony_algorithm(data1, data2, num_coefficients, num_ants, num_iterations):
    pheromone = np.random.rand(num_coefficients)
    best_global_position = np.random.rand(num_coefficients)
    best_global_mse = float('inf')

    # 迭代搜索
    for _ in range(num_iterations):
        for _ in range(num_ants):
            model = CalibrationModel(num_coefficients)
            mse = model.evaluate(data1, data2)
            if mse < best_global_mse:
                best_global_mse = mse
                best_global_position = model.coefficients

        # 更新信息素
        pheromone = pheromone + (1 / best_global_mse)

    # 创建全局最优模型
    best_model = CalibrationModel(num_coefficients)
    best_model.coefficients = best_global_position

    return best_model


# 选择进化学习算法
def select_calibration_algorithm(algorithm, data1, data2, num_coefficients, num_individuals, num_generations):
    if algorithm == 'genetic_algorithm':
        return genetic_algorithm(data1, data2, num_coefficients, num_individuals, num_generations)
    elif algorithm == 'particle_swarm_algorithm':
        return particle_swarm_algorithm(data1, data2, num_coefficients, num_individuals, num_generations)
    elif algorithm == 'ant_colony_algorithm':
        return ant_colony_algorithm(data1, data2, num_coefficients, num_individuals, num_generations)
    else:
        raise ValueError(f'Invalid calibratio algorithm: {algorithm}')

        # 使用进化学习算法对微测站数据进行校准
        calibration_algorithm = 'particle_swarm_algorithm'# 设置选择的进化学习算法
        num_coefficients = 13  # 校准系数个数等于微测站数据的列数

        calibration_model = select_calibration_algorithm(calibration_algorithm, preprocessed_micro_station_data.values,
                                                         preprocessed_national_station_data.values, num_coefficients,
                                                         50, 100)

        # 使用校准模型对微测站数据进行校准
        calibrated_micro_station_data = calibration_model.calibrate_data(preprocessed_micro_station_data.values)

        # 进一步校正数据（根据非常规气态污染物交叉干扰和天气因素）
        def further_calibration(data1, data2, num_coefficients):
            # 计算校正系数
            calibration_factors = data2.mean() / data1.mean()

            # 应用校正系数
            calibrated_data = data1 * calibration_factors

            return calibrated_data

        # 对微测站数据进行进一步校正
        further_calibrated_micro_station_data = pd.DataFrame(further_calibrated_micro_station_data,
                                                     columns=['SO2', 'CO(mg/m3)', 'NO2', 'O3-1H', 'PM10', 'PM2.5', 'NO', 'NOX', '湿度', '温度', '风速', '风向', '大气压'])

        # 输出进一步校正后的微测站数据
        calibrated_micro_station_data = pd.DataFrame(calibrated_micro_station_data,
                                                     columns=preprocessed_micro_station_data.columns)
        calibrated_micro_station_data.to_excel('C:/Users/123/Desktop/further_calibrated_micro_station_data.xlsx', index = False)

         # 输出校准后的微测站数据
        calibrated_micro_station_data = pd.DataFrame(calibrated_micro_station_data,
                                                     columns=['SO2', 'CO(mg/m3)', 'NO2', 'O3-1H', 'PM10', 'PM2.5', 'NO', 'NOX', '湿度', '温度', '风速', '风向', '大气压'])
        calibrated_micro_station_data.to_excel('C:/Users/123/Desktop/calibrated_micro_station_data.xlsx', index=False)
# 统计校准后微测站数据的均值和标准差
mean_values = normalized_micro_station_data[['SO2', 'CO(mg/m3)', 'NO2', 'O3-1H', 'PM10', 'PM2.5', 'NO', 'NOX', '湿度', '温度', '风速', '风向', '大气压']].mean()
std_values = normalized_micro_station_data[['SO2', 'CO(mg/m3)', 'NO2', 'O3-1H', 'PM10', 'PM2.5', 'NO', 'NOX', '湿度', '温度', '风速', '风向', '大气压']].std()

# 可视化均值和标准差
mean_values.plot(kind='bar', title='Mean Values of Further Calibrated Data')
plt.xlabel('Variables')
plt.ylabel('Mean')
plt.show()

std_values.plot(kind='bar', title='Standard Deviation of Further Calibrated Data')
plt.xlabel('Variables')
plt.ylabel('Standard Deviation')
plt.show()