Research On Mine Pressure Prediction Method Of Working Face Based On Deep Learning

With the gradual exploitation of high-quality shallow buried coal seams and the resource integration of small and medium-sized coal mines,China’s coal mining began to show a deep and large-scale trend.The mine pressure disasters caused by the imbalance of mine pressure in the working face occur frequently,which seriously threatens the life safety of underground mining personnel.Therefore,in coal mining,it is of great significance to predict the mine pressure of the working face to ensure the safe production of coal mines.The paper focuses on improving the accuracy and efficiency of mine pressure prediction and supporting the monitoring and early warning of mine pressure in the working face.Based on deep learning,the research on the prediction method of mine pressure in the working face is carried out.The specific work is as follows:To solve the problem of low accuracy of mine pressure prediction,a mine pressure prediction method based on IWOA-Highway-BiLSTM is proposed.First,the Pauta criteria and k-nearest neighbor algorithm are used to preprocess the rock pressure monitoring data.Second,A bidirectional long-term and short-term memory network structure is constructed to extract the characteristics of mine pressure data,and highway networks is used as the hidden interlayer connection to reduce the training time of the mode.Finally,the improved whale algorithm is used to optimize the model parameters to realize the prediction of mine pressure data.The experimental results show that compared with SVR,RNN,LSTM and GRU,the prediction accuracy of IWOA-Highway-BiLSTM model on the test set is improved by 20.8%,16.7%,8.2%and 5.2%respectively;the running time is reduced by 39.8%,34.9%,38.1%and 8.1%,and achieved good prediction results.To solve the problem of reduced performance of cyclic neural network and its variant structure in processing large-scale data sets,a ground pressure prediction method based on Attention-TCN is proposed,which is suitable for large amount of data.By constructing a model based on the structure of time-series convolution network,the data features are extracted,and an attention layer is added at the top of each convolution to strengthen the key features and improve the prediction accuracy.The experimental results show that on the large data set,compared with RNN,LSTM,GRU and IWOA-Highway-BiLSTM models,the RMSE of the Attention-TCN model on the test set is reduced by 34.1%,24.1%,17.9%and 15.7%;the running time is reduced by 44.5%,50.4%,24.7%and 27.0%,achieved better prediction performance than the recurrent neural network and its variant structure..To support the mine pressure monitoring and disaster early warning of the working face.the intelligent monitoring and early warning system of mine pressure in working face is designed and realized through four links of system analysis,system design,function realization and system test.The field test results of the working face show that the system runs stably,with good monitoring and early warning effects,and has good practical application value for safe production in coal mines.