Research On Prediction Of Mine Surrounding Rock Stress Based On Deep Learning

With the increasing deepening of the coal mining depth,the intensity and frequency of coal mine production accidents caused by the enhancement of coal mine surrounding rock stress,which seriously threatens the safety of coal production.Therefore,in order to avoid such accidents,it is necessary to take essential means to the monitor and early-warning of the surrounding rock stress and ensure the safe and efficient production of the coal mine.This thesis is developed on the prediction methods of coal mine surrounding rock stress based on deep learning in order to solve the problems of low data reliability and unsatisfactory prediction precision in mine surrounding rock stress monitoring.The main researching content of this thesis is as follows:(1)A data fusion algorithm of surrounding rock stress based on support degree and adaptive weighted(SDAWS)of multi sensors is proposed in this thesis.Aiming at the problems of the mine surrounding rock stress monitoring data with multi-source and low data reliability,the algorithm fused the data of working yield of support in working faces in multiple positions.The surrounding rock stress data before fusion and after fusion were predicted respectively through the long-term and short-term memory network model.The results indicate that t he mean square error is reduced by 31.16%and 33.77%in the training set and the test set,respectively.(2)The stress prediction method of coal mine surrounding rock based on Highway Network Integrated with Attention mechanism and Convolutional Neural Network is proposed in this thesis.A prediction m odel of surrounding rock stress based on highway neural network is established,and the mean square error is used as the loss function to determine the hyper parameters of the model.Firstly,Convolutional Neural Network is used to automatically extract the characteristic information of surrounding rock stress.Then,the extracted features are input into the attention layer to assign weights to different input features.Finally,the model of surrounding rock stress prediction is established by the application of highway neural network to the time series data prediction of surrounding rock stress.The experimental results show that Attention-CNN-HN model achieves less mean square error on the test set by 37.06%and 12.17%respectively,compared with Attention-CNN-LSTM and Attention-CNN-GRU model.In addition,the operating efficiency of Attention-CNN-HN model is increased by 17.36%and 5.85%respectively.(3)The early-warning system of mine surrounding rock stress based on SDAWS algorithm and Attention-CNN-HN model is designed and implemented in this thesis.The system is analyzed and designed with the idea of object-oriented.Under the Java platform,four functions of dynamic surrounding rock stress monitoring,data statistical query,early-warning of surrounding rock stress and system setting are realized.Through the analysis,design,realization and test of the early-warning system of mine surrounding rock stress,the application of the system has been realized.The system has the features of beautiful interface and simple operation.The field application effect shows that the software has good application value and provides a good guarantee for the safety of underground production.