The Method Research For Mine Safety Warning Systems

Coal mine, as China's important energy source and fuel, has the proportion of the total energy demand, about 75% or so. Since the fundamental position, secure implementation, and efficient and sustainable development of coal industry are the reliable guarantee for attaining great economic strategic objectives of China. In recent years, the repeated occurrence of major coal accidents indicates that the coal security situation has not been improved fundamentally. The problem of coal mine security is the most remarkable and urgent one in China that should be solved at present.This article will give some methods to predict coal and gas outburst, reconstruction of underground coal delamination, and surface subsidence in mine security system, which utilizes the basic principles of mutation, reproducing kernel neural system and support vector machines. It provides the strong evidences for the safe prediction of mine based on theory. Catastrophe theory is a new subdivision of mathematics, a theoretical research studied by singularity theory and bifurcation theory about the discontinous change phenomenon. Coal and gas outburst is a catastrophic dynamic phenomena happening in the mine, coal and rock mass, also a form of violent energy releasing of coal-rock with gas. Based on the opinions of mechanics and energy, distinct mutation features are stressed from gestation to sartup and from development to the end. According to the conditions of producing coal and gas outburst, theroretical analysis is made systematically in this article to the synthetically effects and outburst process in the coal and gas outburst, on the other hand,catastrphe theory is applied, which are both used to build swallowtail catastrophe model and to fulfill secure prediction.Reproducing kernel originating from different branches, which has become an important tool of approximation currently. Combining reproducing kernel with neural networks organically aims to put forward a new type of reproducing kernel-neural network. his new type contributes network training to the problem solving linear equations, and set up a sparse solution of mathematical model with sufficient accurancy and performing of system behavior. Compared with the reconstruction of fault plane and spline, this model is more in line with the actual situation. This article will take advantage of reproducing kernel-neurual network in order to reconstruct the fault plane, learn changes on surface coal mining, prevent the accidents of well collapse and achieve the secure prediction.Precipitation produced by coal mining is effected by a number of factors. The relationship between each factor and the function it plays is hard to define. Therefore, the prediction of surface coal mining subsidence belongs to the complicated nonlinear system problems. Support vector machine theory is a new method to data mining and processing. Taking the probability viewpoint of statistics into consideration, it is more accurate than neurual network, and the best way to solve the coal mining problem. This article applies the methods for support vector machines and the analysis of hydro-geological conditions in mine area, settlement and other basic factors lement data, get the prediction model of surface settlement that is more realistic, give the prediction of mine area settlement and achieve security forecast.