Research On Prediction Of Mine Radio Wave Field Strength Based On Deep Learning

With the development of intellectualization of coal mine production,establishing a reliable underground communication system is the key to ensure the safe mining of coal mines.Because of the narrow and long mine roadway,limited space and complex environment,radio waves are affected by many fading factors in the transmission process,resulting in strong field fluctuation and signal attenuation at the receiving point.In this thesis,a deep learning method is used to establish a radio wave field strength prediction model for complex mine roadways,and to predict the distribution trend of the signal field strength at the receiving end,which provides an important basis for the efficient construction of a mine wireless communication system.In this thesis,the rectangular roadway is taken as the research object,and the main factors affecting the transmission of radio waves in the roadway are summarized through the wave guide mode theory and formula derivation,such as the working frequency of the antenna,the width and height of the roadway,the inclination and roughness of the roadway wall,and the electrical parameters of coal and rock,and uses them as the input variable of the field strength prediction model.In order to verify the validity of the prediction model,a set of wireless channel measurement system for mine roadway with CC2530 chip as the core is designed and implemented by using ZigBee wireless data transmission technology,and the field measurement is completed in the rectangular roadway and semi-circular arch roadway,and real roadway field strength dataset are obtained.Aiming at the problems of high computational complexity,low prediction accuracy and poor universality of traditional field strength prediction modeling,a mine radio field strength prediction model based on deep learning is established in combination with the large-scale and small-scale decay characteristics of mine roadways.An improved convolution neural network(Convolutional Neural Network,CNN)prediction model is designed.A batch normalization layer is added after each convolution layer to speed up network convergence.To extract the implicit characteristics of time series,a bidirectional long-term and short-term memory(Bidirectional Long Short Term Memory Network,BiLSTM)network prediction model is designed.The improved CNN and BiLSTM models are applied to the prediction of mine roadway field strength.The experimental results show that both models have good performance and prediction accuracy in predicting field strength.To further improve the fitting effect and robustness of the model,and to enhance the mining ability of the model for data characteristics,on the basis of a single network,the improved CNN and BiLSTM network will be combined in series,the parameters of CNN-BiLSTM network are optimized,and the attention mechanism is introduced,and a CNN-BiLSTM-AM field strength prediction model is proposed.The model performance is validated by the measured data set.The results show that the correlation coefficient of the proposed model is 0.9989,and the prediction effect is the best.It shows that the combination of attention mechanism and deep learning method can effectively improve the accuracy of field intensity distribution prediction in mine roadway.