Predicting model of stored-grain temperature based on an improved feature enhanced broad learning

The prediction of grain storage temperature is an important measure to ensure the safety of grain storage. The traditional machine learning model is limited by insufficient accuracy, and the deep learning method has high accuracy but long training time. The model makes full use of the advantage of broad learning training speed, and uses long short-term memory network to enhance the learning ability of the model for the time series characteristics of grain storage data. Combined with the multi-head self-attention mechanism, the important correlation information focusing of multi-dimensional features is enhanced, and the prediction test of grain storage temperature is carried out. The results show that the grain storage temperature prediction accuracy of the improved feature-enhanced breadth learning is higher than that of the unimproved broad learning based on some real grain storage data. At the same time, compared with the convolution-long short-term memory network and Transformer deep learning model, the proposed broad model has higher accuracy and less training time. The results of this study provide an effective alternative scheme for safe grain storage in technology.