Study On Gas Anomaly Characteristics And Diagnosis Criteria Of Ding6-31030 Working Face In Pingdingshan Mine

Gas concentration in coal mine is the most important concern of monitoring and control system.However,for the problem of abnormal gas monitoring data,it mainly depends on the judgment and decision of personnel at present.The analysis and utilization of data characteristics are insufficient,and there is a certain lag.There is still great research space for massive gas monitoring data.This paper takes Pingdingshan No.1 Coal Mine as an example to analyze and mine its gas monitoring data.Through analyzing the stability of monitoring data and preprocessing it,the data characteristics are found,the identification method of gas anomaly types is studied,and an auxiliary decision-making system based on temporal-spatial correlation is designed.The stability of the gas monitoring data is analyzed after the abnormal monitoring data are processed by the cubic smoothing method,the moving average line method and the AR model method,and the optimal stability period 30 d and the shortest stability period 16 d are determined.By analyzing the characteristics of the gas monitoring data,seven performance modes and their establishment conditions are determined.After 7 kinds of gas types which are easy to occur in the working face are determined,according to further research,the corresponding performance modes of each anomaly are found,and the diagnostic criteria of each anomaly are determined.The method of neural network is used to identify abnormal gas monitoring data,and the accuracy of diagnostic criteria is verified by its 100% accuracy.Furthermore,the real-time diagnosis of gas anomaly type is realized by programming,with high accuracy.By looking for the method of maximum correlation,the lag time of gas monitoring data in different positions is determined,and the spatio-temporal correlation of gas monitoring data is determined,which is applied to assist decision-making.On the one hand,the accuracy of diagnostic criteria is improved.On the other hand,it improves the assistant decision-making for the unrecognized gas anomaly,and finally forms the online identification and assistant decision-making for the gas anomaly type.The research results have certain practical significance and scientific value for ground personnel to judge the safety situation of mining face and underground operation activities.