Mine pressure prediction model of fully mechanized mining face based on Improved Transformer

With the increase of mining depth, the frequency of mine pressure disasters on the comprehensive mining face also increases, which has a significant impact on the safety production of coal mines, so the accurate prediction of mine pressure on the comprehensive mining face is of great significance to the prevention of coal mine disasters. In order to improve the prediction accuracy of mine pressure, an improved Transformer mine pressure prediction model is proposed in this paper. Firstly, the gray correlation is used to analyze and rank the mine pressure monitoring data of multiple supports at the working face; secondly, the trend-seasonality decomposition method is combined with Transformer to build the improved Transformer prediction model and optimize it with the optimization algorithm to realize the prediction of mine pressure at the comprehensive mining working face. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are used to evaluate the prediction effect of the model. The experimental results show that the prediction result of the improved Transformer model is better than the traditional BP neural network, GRU, LSTM and the basic Transformer model, and has higher accuracy.