Research On Mine Gas Accident Risk Prediction Technology Based On Support Vector Machine

With the continuous development of machine learning and data mining technology,big data processing technology has gradually played a driving role in many industries,which has had a great impact and reformed on the traditional management mode of enterprises.Because of the limitation of the information construction level of coal mine,it starts late.Gas concentration monitoring datas has basically achieved the stages of collecting,summarizing,storing,and sharing,but it has lagged behind in deep mining research.If these data with strong correlation characteristics in gas concentration monitoring database are used effectively,combined with relevant factors such as coal mining volume,airflow velocity,gas drainage volume,and working surface acceleration speed during the same time period,We can using SVM regression and classification forecasting technology to dug out the hidden internal relations among these factors and the influence of these factors on the gas concentration in the high-prone areas of gas accidents,such as the Working Face,Heading Face,and Gob-Side Entry Retaining.Therefore,these laws can be applied to mine gas concentration anomaly analysis,gas accident risk prediction and other aspects,providing reference for mine safety decision-making,risk pre-assessment,and gas accident prevention.In the research process,this article mainly completed the following aspects:(1)The nonlinear prediction theory of support vector machine is studied,and the superiority of the theory in mine gas accident risk prediction is analyzed,which provides a theoretical basis for the follow-up research work.(2)By analyzing the influencing factors of gas concentration in the gas accumulation area,and combining the relevant regional gas concentration monitoring data,the sample data of gas concentration prediction was formed.Based on the nonlinear support vector machine regression prediction theory,the gas concentration SVR prediction model was established.(3)Combining the analysis of hazard sources of gas accidents in mines and gas concentration prediction results,a gas accident risk prediction system was established,and the risk of gas accidents was classified using a fuzzy comprehensive assessment method.And the gas accident Risk prediction model was established based on the nonlinear SVM classification prediction theory.(4)Based on relevant data collected from Gao He Mining of Lu An Group,prediction samples of gas concentration and gas accident risk was established.Then,using the established prediction model,the gas concentration and the gas accidents risk are predicted in Working Face,Heading Face,and Gob-Side Entry Retaining.