Research On Prediction Of Working Face Periodic Pressure Based On Recurrent Neural Network

Coal energy is the main source of energy in our country and plays an important role in maintaining the steady development of the national economy.In the production process of coal mines,the increasing trend of deep mining makes mine pressure hazards more likely to occur on the working surface,which has an impact on safe production and has brought serious threats to the lives of coal mine workers.The vast of mine pressure disasters occur during the pressure period,therefore,predicting the working face cycle to prevent mine pressure disasters and provide support guidance is of great significance.The main research contents of the paper on the prediction method of working face periodic pressure are as follows:A method for forecasting the working face periodic pressure based on Adamax-GRU is proposed.Perform data preprocessing on the original mine pressure data,use the mean square error as the loss function,and apply the Adamax optimization algorithm to optimize the gated recurrent unit,and establish a working face periodic pressure forecasting model based on Admax-GRU.The experimental results show that:Compared with BPNN,LSTM and GRU,the Adamax-GRU model reduces the errors of the test set by 27.8%,14.1%and 10.9%,respectively,and the prediction accuracy is improved.The prediction results reflect the periodicity,which is in line with the actual coal mine site,can reflect the maximum pressure.A method based on stacked GRU working face periodic pressure spatio-temporal prediction is proposed.The gray correlation degree is used to analyze the spatial correlation between the mining pressure time series of the working face,and the input of the time-space sequence is constructed;the stacked network structure is used to improve the calculation accuracy;and a working face periodic pressure time-space prediction model based on the stacked GRU is constructed.The experimental results show that:compared with the single-sequence Adamax-GRU model,the RMSE of the stacked GRU spatiotemporal prediction model test set is reduced by 84.77%;compared with the stacked LSTM model test set RMSE is reduced by 21.2%.Practical application of the proposed algorithm for forecasting working face periodic pressure is designed and implemented for coal mine dynamic pressure monitoring and intelligent early-warning analysis system,which realizes the system's intelligent early-warning function.The project was tested in a mine in Shaanxi,and the system was operated smoothly and achieved good monitoring and early warning functions.It can effectively provide support and guidance for coal mining face production and has a practical effect on reducing mine pressure disasters.