# 数据加载与预处理函数
|
# -------------------------------
|
def load_data(upstream_file, downstream_file, river_level_file=None, flow_file=None, source_name="青龙港"):
|
"""加载上游和下游数据并进行数据质量处理"""
|
pass
|
|
def filter_salinity_anomalies(df, threshold_ratio=0.5, window_size=5, max_days=1):
|
"""过滤盐度数据中的异常值"""
|
pass
|
|
def resample_to_hourly(df):
|
"""将分钟级数据重采样为小时级数据"""
|
pass
|
|
# 特征工程函数
|
# -------------------------------
|
def add_lunar_features(df):
|
"""添加农历（潮汐）特征"""
|
pass
|
|
def batch_create_delay_features(df, delay_hours):
|
"""为数据框中的特定列创建延迟特征"""
|
pass
|
|
def generate_features(df):
|
"""生成其他特征，包括历史数据、时间特征、统计特征和外部特征"""
|
pass
|
|
# 数据持久化函数
|
# -------------------------------
|
def save_processed_data(df, filename='processed_data.pkl'):
|
"""保存处理后的数据"""
|
pass
|
|
def load_processed_data(filename='processed_data.pkl'):
|
"""加载处理后的数据"""
|
pass
|
|
def load_both_datasets():
|
"""加载两个数据源的数据集"""
|
pass
|
|
# 特征构建与预测函数
|
# -------------------------------
|
def create_features_vectorized(df, look_back=168, forecast_horizon=1):
|
"""向量化构造训练样本"""
|
pass
|
|
def generate_prediction_features(df, current_date, look_back=168):
|
"""为预测生成特征"""
|
pass
|
|
# 模型训练与评估函数
|
# -------------------------------
|
def train_and_predict(df, start_time, force_retrain=False):
|
"""训练模型并进行预测"""
|
pass
|
|
def get_model_metrics():
|
"""获取保存在模型缓存中的准确度指标"""
|
pass
|
|
# GUI界面部分
|
# -------------------------------
|
def run_gui():
|
"""运行GUI界面"""
|
pass
|
|
# 主函数
|
# -------------------------------
|
def main():
|
"""主函数，程序入口"""
|
pass
|
|
# 程序入口
|
if __name__ == "__main__":
|
main()
|
