Prediction And Study Of Coal Mine Gas Monitoring Based On Improved Particle Swarm Wavelet Neural Network Intelligent Algorithm

With the development of industrial society and the demand of coal industry in China's industrial production occupies an absolute dominant position, the coal safety accidents are following. In the past ten years, China has invested a lot of manpower and material resources in the prevention of coal mine accidents. Although China's coal mine safety accident has significantly decreased, there is still much improving space compared with foreign countries.The existence of gas Underground coal mine is threatening the lives of miners all the time.Therefore, the prediction of gas emission is of great significance to not only ensure the safety of miners' lives but also improve the coal quality and production benefit.Taking the coal mine gas monitoring system as the research background and combined with the actual production of coal mine, the paper puts forward an algorithm to improve the improved particle swarm wavelet neural network, which is used to predict the amount of gas emission in underground mine. This paper mainly studies the following aspects:Firstly, the paper analyzes the operation process of the underground monitoring system in the actual production of coal mine, and builds the gas collection system simulated mine working face roadway. The article analysis the mining working face gas emission amount of data will appear abnormal data and missing data and complex nonlinear data high degree and the initial data take the moving average method and whiten normalization method processing.Secondly, the article puts forward the gas prediction problem based on gas collection system, give the idea of gas prediction, and analysis and comparison of commonly used gas prediction algorithm, decided to use the particle swarm algorithm and wavelet neural network algorithm. In order to make up for the defects of these two kinds of intelligent algorithms and give full play to their respective advantages, the particle swarm algorithm and the wavelet neural network algorithm are combined, and the improved particle swarm optimizationalgorithm is proposed. The proposed algorithm is used to predict the amount of gas emission underground mined out area and the simulation was carried out in Matlab environment. The results show that the proposed algorithm is better than the traditional algorithm can be better used in gas prediction problem.Finally, in order to facilitate the operator to monitor the current state of the system, the system combined with the actual situation of coal mine production. The article designs PC interface by King View software and communicates with the whole system by serial port mode.Meanwhile, the simulation results of the proposed algorithm are drawn, and the on-line debugging of the software and hardware system is carried out. The operation result proves that the system can realize the required monitoring function.