Study On Prediction And Pre-warning Method Of Gas Concentration In Fully Mechanized Mining Face Based On Deep Learning

Gas disaster was a major safety issue in the coal mine field.With the continuous improvement of the level of coal mine intelligence,gas concentration prediction and pre-warning technology plays a vital role in the prevention and control of coal mine disasters.Making full use of a large amount of gas data underground in coal mines for gas concentration prediction and pre-warning can effectively improve the pre-warning capabilities of gas disasters.Therefore,accurate and reliable gas concentration prediction and pre-warning are of great significance to coal mine safety production.This paper takes the gas monitoring data of a fully mechanized mining face in a mine in Shaanxi as the research object,and conducts the research on gas concentration prediction and pre-warning methods.In order to improve the prediction accuracy and pre-warning efficiency of gas concentration in fully mechanized mining face,this paper combines the prediction model in deep learning with the distributed processing framework,and builds a pre-warning framework of gas concentration based on Spark Streaming.Firstly,the factors affecting the gas concentration in the working face are analyzed,and the influencing factors of the gas concentration are verified by the grey relational degree method,and seven influencing factors of the gas concentration are selected to construct the influencing factor system of the gas concentration.Based on mine gas measured data at the same time,to create a single gated recurrent unit model(GRU),using the method of principal component analysis and the model for data dimension reduction GRU helped to improve the predictive accuracy of the model and the model for prediction of hidden layer neurons number,hidden layer,batch size and time step four aspects for parameter optimization.Secondly,in order to improve the prediction accuracy of the single GRU model,genetic algorithm(GA)and particle swarm optimization algorithm(PSO)were used to optimize the model.The PCA-GA-GRU model and PCA-PSO-GRU model were established respectively.After comparative analysis,the average absolute error of the PCA-PSO-GRU model was 0.0121.The root mean square error is 0.0159,the model fitting degree is 0.974,and the model training time is 65s.The model has high prediction accuracy and good prediction effect.Finally,based on the monitoring data of gas concentration,the pre-warning threshold of gas concentration was determined by statistical analysis,and the abnormal value of gas concentration was judged and the early warning level was divided by comparing the predicted value with the measured value,and the PCA-PSO GRU prediction model was combined with Kafka system and RDD data set.The pre-warning framework of gas concentration based on Spark Streaming was established,and the pre-warning efficiency of the framework was verified by the measured data of gas concentration.The experimental results show that the warning accuracy rate based on the Spark Streaming gas concentration warning framework is above 90%,and the whole pre-warning processing time is about 7s.This framework has greatly improved the speed of early warning of gas concentration,and provided support and decision-making for mine gas safety production and disaster prevention.