awg commit algorithm components

data_processing/processing_limit_power/processing_limit_power_by_statistics_wind.py

@@ -24,8 +24,8 @@ def wind_statistics_judgement(df_power,args):
       假设误差大致呈现标准正态分布 mean + N*std   
     """
 
-    col_ws, col_power, eps, min_samples, ws_in, ws_out, ws_rated, cap = (args['col_ws'], args['col_power'], args['eps'],
-                                    args['min_samples'], args['ws_in'], args['ws_out'], args['ws_rated'], args['cap'])
+    col_ws, col_power, eps, min_samples, ws_in, ws_out, ws_rated, cap = (args['col_ws'], args['col_power'], float(args['eps']),
+                                    int(args['min_samples']), float(args['ws_in']), float(args['ws_out']), float(args['ws_rated']), float(args['cap']))
     print("min_sample", min_samples)
 
     df_power[col_ws] = df_power[col_ws].apply(

models_processing/model_train/model_training_lstm.py

@@ -13,9 +13,10 @@ from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
 import tensorflow as tf
 from common.database_dml import get_data_from_mongo,insert_h5_model_into_mongo
 from common.processing_data_common import missing_features,str_to_list
-
+import threading
 app = Flask('model_training_lightgbm——service')
-
+# 创建一个锁对象
+lock = threading.Lock()
 def rmse(y_true, y_pred):
     return tf.math.sqrt(tf.reduce_mean(tf.square(y_true - y_pred)))
 
@@ -35,6 +36,7 @@ def create_sequences(data_features,data_target,time_steps):
 
 
 def build_model(data, args):
+    tf.keras.backend.clear_session()  # 清除当前的图和会话
     col_time, time_steps,features,target = args['col_time'], int(args['time_steps']), str_to_list(args['features']),args['target']
     if 'is_limit' in data.columns:
         data = data[data['is_limit']==False]
@@ -120,5 +122,5 @@ if __name__=="__main__":
     logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
     logger = logging.getLogger("model_training_lightgbm log")
     from waitress import serve
-    serve(app, host="0.0.0.0", port=10096)
+    serve(app, host="0.0.0.0", port=10096,threads=4)
     print("server start!")