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()