Research On Fermented Grains Temperature Prediction Model Of Make Wine Robot Based On LSTM

Chinese liquor brewing technology is the inheritance of Chinese liquor culture for five thousand years.Both practical value and historical value are very important.Distillation is a very important step in wine making,and the quality of distillation lies in the quality of the upper steamer process.In the process of accurate gas exploration,accurate prediction of fermented grains temperature plays an important role.When there are several areas on the surface of fermented grains running off,the robot on the steamer can not complete the spreading operation in time,which is easy to cause wine loss and directly affect the quality of wine.If we can use the historical temperature information of fermented grains to predict the temperature change of fermented grains in the future,it will play an important role in the timely and reasonable operation of the robot.Based on the temperature prediction of fermented grains,the main work of this paper is as follows:Collect the relevant data of the steaming process in Jinpai distillery.The collected data include missing value,abnormal value and the edge of the retort.The reasonable data preprocess is designed,including using Hough transform to extract the temperature data of fermented grains,reasonably filling the missing value,removing the abnormal temperature value,rotating and aligning the temperature matrix,reasonably dividing the data,etc.Find out the important factors that affect the temperature of fermented grains,and extract the time and space related characteristics.After pretreatment,the temperature data of fermented grains were extracted accurately,which provided the basis for the subsequent input of the model.Based on the temperature prediction of fermented grains and the problems of LSTM,an attention mechanism based LSTM model(ATT-LSTM)was designed.In order to effectively utilize the output of LSTM middle hidden layer at all times,calculate the attention distribution,weight the output information,and fully mine the hidden law behind the fermented grains temperature data.In view of the complex structure of the LSTM algorithm,there are many super parameters need to be optimized.The Bayesian optimization algorithm is selected to optimize the parameters,and the influence of different parameters on the prediction effect of the model is studied.Select the appropriate optimizer,loss function,learning rate and other parameters to reduce errors and training time.The validity and accuracy of ATT-LSTM model are verified by using the constructed data set.The improved model uses two-layer stacking structure and the optimal parameters are used to train.The ATT-LSTM was compared with LSTM and RNN to observe the prediction effect,and the evaluation index was used to compare the error.The experimental results show that the MSE of ATT-LSTM is lower than that of RNN and LSTM.The improved model can accurately predict the temperature of fermented grains and provide the basis for the accurate gas exploration of the steamer robot.