Study On Forecast Models Of The Mining Safety Production

Taken the modern forecasting theory and technology as the foundation and adopted the policy of "safety first, precaution crucial", the dissertation has an in-depth analysis of the current situation of the safety management and control within the mine safety production system. The present research focuses on the mine production system, safety state forecasting theory and technology, giving some basic concepts such as mine safety production system; the subsystem; the region; the safety state; the safety degree; the security rating and so on. In addition, the mine safety state transition model—SAFU, is put forward, which can be adopted to forecast the safety state of the mine region, the combination region and the function subsystem. The forecast safety state types can determine the factors which cause the changes of the mine region safety states. And this will eventually provide the policy-making basis for the forecast and the safety control of the mine safety.The present dissertation has carried out a series analyses on the following aspects: the regression analysis forecasting method, BP neural network binding forecasting model method, the grey forecasting method and the support vector forecasting model. Meanwhile the dissertation also discusses the ways the forecasting model adopts in the prediction of the mine safety in production, presenting the means to apply the regression forecasting method unify under different parameter numbers. The binding forecasting model reduces the relative number of the neural network, enhances the iterative speed and the forecasting efficiency of the neural model and accomplishes the mine safety state forecasting with less classified sample vectors. With the SAFU model, it not only describes five changing patterns of the mine safety states but also proposes the algorithm for danger factors of the mine safety in production, which is determined by the reversal safety value. As to the defined mine region and combined region, the paper analyzes the relevance of the regional safety state, suggesting the algorithm for the recognition of the connected region, putting forward the concept of the regional safety degree and the algorithm for the safety degree on the basis of the connected region. In addition, the methods to forecast the safety degree of the single-parameter and multi-parameter regions are proposed as well, which proves the efficiency of the unity forecasting in the mine regional safety in production and the existence and quantization of the mine safety degree and draws the conclusion that the mine regional safety degree is predictable. What's more, in the paper the author has set up the forecasting model based on the sample data drills and suggested the algorithm for the selection of the forecasting models. The longest match pattern presented in the paper, also known as the experienced model of the safety state, can be used to do the jobs of finding, description and prediction, which enables the regional safety state indescribable by the function to conduct the forecasting in model recognition. The paper also puts forward three basic combined patterns for the forecasting model, introducing the parameter projection so as to accomplish the combined construction of the forecasting model. The selection, combination as well as the model dispatching method of the mine safety in production region is also discussed in the paper. The author analyzes and designs the forecasting system of the mine production safety state for the analysis of the procedure, the prediction function and the data processing. The system is made further designation in its configuration which brings about the proposed processing method of monitoring sequential data synchronization. With the application of the noise processing technique, the present research proposes two algorithms: one for the cleaning of the monitoring data; the other is for the transaction of the sample data. Meanwhile, two modes for the information custom in the forecasting system have been postulated: the model parameter custom and the client information custom.