Application Research On Improved CBA Algorithm In Coal Mine Safety Early Warning

Coal mine accidents early warning is an important work for coal mine safety. At present, almost all coal mine accidents early warning are basically realized by monitoring a single factor whether meets the critical value or not. The system generally is not warning when each single factor not reaches the critical value, however, it is also a very dangerous state of the accident when multiple factors are close to the critical value. According to the analysis of coal mine explosion accidents, the main factors affecting the coal mine explosion are gas concentration, coal power content, carbon monoxide content, temperature and wind speed, etc. This paper explores the method of comprehensive warning for coal mine explosion disaster through synthetically analyze the factors of impact coal mine explosion.CBA (Classification Based on Association) is a classification algorithm based on class association rules. Its main feature is to get classification by the rule of covering the smaller support degree rules in the training samples based on the lager support degree rules which are generated by the association rules. As the coal mine safety warning is a complicated work, the single CBA algorithm can’t reach a good effect.The paper uses an improved CBA algorithm to provide an optimized training samples data set and the corresponding metric for real-time warning analysis of the coal mine explosion disaster. The improved CBA algorithm gets the classification rules data set through the correlation analysis of the training set in which the data is preprocessed. Then, the PFCM(Fuzzy C-Means Based on Solving Polynomial) algorithm is used. Finally, the membership value generated by the PFCM algorithm is used as the weight of the classified train sample data to do the weighted Bayesian classification. In order to increase the classification accuracy, feedback the wrong classification samples, and the weights are recalculated by the PFCM algorithm and the weighted Bayesian classification is done, until the classification accuracy rate does not change. So an optimized training samples data set and its corresponding weights can be determined through analyzing lots of historical monitoring data, which can improve the efficiency and accuracy in the real-time warning analysis for the coal mine explosion disaster.