Abstract
In the process of gas prediction and early warning, outliers in the data series are often discarded. There is also a likelihood of missing key information in the analysis process. To this end, this paper proposes an early warning model of coal face gas multifactor coupling relationship analysis. The model contains the k-means algorithm based on initial cluster center optimization and an Apriori algorithm based on weight optimization. Optimizing the initial cluster center of all data is achieved using the cluster center of the preorder data subset, so as to optimize the k-means algorithm. The optimized algorithm is used to filter out the outliers in the collected data set to obtain the data set of outliers. Then, the Apriori algorithm is optimized so that it can identify more important information that appears less frequently in the events. It is also used to mine and analyze the association rules of abnormal values and obtain interesting association rule events among the gas outliers in different dimensions. Finally, four warning levels of gas risk are set according to different confidence intervals, the truth and reliable warning results are obtained. By mining association rules between abnormal data in different dimensions, the validity and effectiveness of the gas early warning model proposed in this paper are verified. Realizing the classification of early warning of gas risks has important practical significance for improving the safety of coal mines.
Highlights
In most countries, coal mines are threatened by natural disasters such as gas, coal dust, fire, roof collapse and water inrush to varying degrees during the mining process [1,2,3].Among the many accidents in coal mines, gas accidents are the most prominent
The Apriori algorithm was used to analyze the coupling relationship between the working face, upper corner and mining coalbed gas concentrations and the abnormal value detected by the support pressure
This paper studied the dynamic changes in the gas in a mine and provided an indepth analysis of the abnormal values of the gas monitoring data
Summary
Coal mines are threatened by natural disasters such as gas, coal dust, fire, roof collapse and water inrush to varying degrees during the mining process [1,2,3]. Among the many accidents in coal mines, gas accidents are the most prominent. From 2013 to 2020, a total of 225 gas accidents of various types occurred in China, with 1304 deaths accounting for 8.3% of the total accidents and 28.05% of the total deaths. The need for coal mine gas control remains urgent [4,5]. Many scholars around the world have conducted research on gas prediction and early warning [6,7]. Song et al [8] used the
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