A Smart Factory Prediction Method Combining Big Data Experience Feedback and Deep Learning

In recent years, with the further development of deep learning and high-performance computing technologies, more intelligent algorithms have been applied to the actual production processes. The main research content of this paper is the prediction of element yield in the steel industry. First of all, considering that there is a lot of noise in the original data, it will affect the prediction accuracy, so this paper uses the "3-σ" principle and wavelet threshold denoising to preprocess the original data. Subsequently, this article starts from the effect of influencing factors on the yield, and uses a convolutional neural network (CNN) to predict the yield. By analyzing the prediction results, it is found that the prediction effect of CNN for some samples is not good. In order to optimize the model, this article starts from the two perspectives of the model and the algorithm. On the one hand, the LSTM neural network is introduced to take the historical data information of the yield into account, on the other hand, the CNN network and the LSTM are integrated through the Adaboost method. The advantages of the network are combined to establish an integrated learning algorithm of CNNLSTM-Adaboost, which further improves the prediction accuracy of the model. By simulating the model, a high-precision prediction result is obtained.