Research On Risk Identification And Early Warning Of Coal Mine Gas Explosion Based On Bayesian Network

As one of the types of coal mine disaster accidents,gas explosion is an important resistance that affects the safety production of coal mines.With the development of artificial intelligence and machine learning technologies,underground monitoring systems have been continuously improved,but there are problems such as low comprehensive utilization of monitoring data and low accuracy in identifying the evolutionary trend of risk sources.How to effectively identify the risk of gas explosion and make accurate predictions of accidents in a timely manner are the key issues that the coal mine industry needs to solve urgently.Based on this,data-driven research mode is adopted,collecting the relevant data of gas explosion accident investigation report of large and above coal mines,extracting the keywords of the accident cause;combining relevant literature research,national coal mine safety risk assessment indicators,and gas explosion accident mechanism,constructs the risk identification variable system of coal mine gas explosion accident.Taking Bayesian network as the research method,using the Bayesian network construction tool GeNie,combining expert knowledge and data learning,constructing a Bayesian network risk identification model for coal mine gas explosion,to assess the risks of gas explosion accident,and to identify potential sources of risk that lead to accidents;by setting the early warning interval of the accident risk,the early warning analysis of the disaster accident level is carried out based on the Bayesian network.It is hoped to provide technical support for timely and accurate identification of accident risk sources and improve the accuracy and sensitivity of disaster prevention.Based on the constructed Bayesian network model,through reverse reasoning,sensitivity analysis,the risk of gas explosion accident is accurately identified;through the analysis of the maximum cause chain,the key cause path leading to the gas explosion accident is found.Combined with the risk early warning theory,using the risk level matrix measurement and the expected value theory,the risk level early warning interval of gas explosion accident is defined;applying the Bayesian network model to specific examples of gas explosion accident,predicting the accident level,and the prediction result is compared with the actual situation.The results show that the total accuracy of the nodes in the constructed Bayesian network is 0.8936,the node accuracy is quite high,and the Bayesian network model has strong feasibility;illegal command,illegal operation,underground safety management chaos,blasting flame,local Ventilator ventilation management issues are the weak links to prevent gas explosion accidents;blasting flames and downhole machinery and equipment failures generate electrical sparks are the key factors leading to the occurrence of detonation fire sources;local ventilator ventilation problems are the main cause of underground gas accumulation.Figure 26 Table 17 Reference 73...