Analysis Of Causes Of Coal And Gas Accident Based On Big Data Theory

As China's basic energy and important industrial raw materials,coal is a powerful support for the development of the national economy and maintains national economic security.Coal and gas outbursts are common mine disasters in coal mines.They are extremely sudden and devastating and seriously endanger the safety production of coal mines in China.The study of the main influencing factors of coal and gas outburst accidents is the basis for preventing coal and gas outburst accidents,and has important practical significance for preventing and controlling outstanding accidents.The appearance of big data provides a new method for highlighting the factors that affect the accident.Therefore,based on the theory of big data,this paper studied in detail the factors affecting coal and gas outburst accidents in China.The main research contents are as follows:(1)Based on the network data acquisition method to obtain coal and gas outburst accident cases and build an accident case system.The accident case system includes 27 factors such as the time,location,gas content,and gas pressure of the accident.According to its characteristics,it is divided into three modules that highlight the inherent properties of the accident,highlight the factors that affect the accident,and the consequences of the accident;(2)The correlation analysis method was used to quantitatively study the modules that highlight the influencing factors.Too many influencing factors make the invalid calculation volume increase.Therefore,the independent factors such as gas pressure,coal thickness and geological structure are selected as the main influencing factors.(3)Analysis of coal and gas outbursts based on grouping and graph analysis.A systematic analysis of coal and gas outburst accidents was conducted from the three dimensions of time,area,and accident level,summarizing the characteristics of accidents occurring at different times or regions in China's coal and gas outburst accidents.China's coal and gas outburst accidents occurred in the second place.In the quarter,there were more prominent accidents in the south.(4)Use data mining techniques to cross-couple analysis of influencing factors.The main influencing factors were coupled with the analysis of gas content and gas pressure.It was found that when the gas content reached 5.76m3/t~7m3/t,coal and gas outburst prevention measures should be taken to ensure the safe production of the mine;the Cramer's V correlation coefficient was used.The correlation degree between coal and gas outburst risk and influencing factors was analyzed.It was found that not only the gas content and gas pressure have a great influence on coal and gas outburst accidents,but also the factors such as geological structure,coal seam thickness and mining depth have a great influence on the outburst.(5)Based on the influencing factors of outstanding accidents,Logistic regression analysis was used to construct a probabilistic model for predicting coal and gas outburst accidents.The development method,coal mining technology,operation method and excavation technology were taken as the influencing factors for the calculation of the production system's outburst risk,and the influencing factors were quantified and modeled based on the coal mine mechanization calculation method.Based on the example verification,the accuracy rate of the model was high.Applied to coal and gas outburst accident prediction.The analysis of coal and gas outburst accidents based on big data theory provides a basis for forecasting and warning of accidents and can effectively improve the reliability of coal and gas outburst predictions.