Study On Gas Trend And Fusion Analysis Method Of Fully Mechanized Face In Huangling No.1 Mine

As we all know,mine gas disasters have been a serious threat to the safe production of coal mines in China.At present,the utilization of coal mine monitoring system is mainly for local monitoring and management.However,the analysis and processing of these monitoring data to complete the prediction of coal mine safety parameters and the early warning function are rare,and there is still a lack of intelligent data analysis.The purpose of this paper is to study the gas trend of coal mine fully mechanized mining face and its fusion early warning analysis to change the coal mine safety management fr-om the traditional after-the-fact tracking to the pre-existing prevention control to meet the needs of managers for decision-making.Firstly,this paper analyzes and studies the most important gas monitoring data in the environmental monitoring data of intelligent fully mechanized mining face.It starts to establish the prediction model of the change law of single gas parameters,and establishes ARIlMA prediction by random time series of gas historical monitoring data.Model and test the reliability of ARIMA model predictions.In order to eliminate the undulating cluster and thick-tailed distribution generated in the ARIMA modeling process,the residual sequence is subjected to autoregressive heteroscedasticity(GARCH)reprocessing,and use the simulation results as noise items of ARIMA prediction and re-predict them.Experiments show that the combined model has better prediction effect.Secondly,the paper analyzes the advantages and disadvantages of the current data fusion algorithm and its application,and proposes an early warning model for sensor heterogeneous data fusion based on multi-measurement parameters.Based on the gas data obtained by automatic sensor monitoring and manual detection,the comprehensive measurement values of each key position are obtained through fusion,and the correlation is analyzed.Under the guidance of the ventilation network settlement theory,the underground coal mine is reasonably divided into multiple regions.Different data fusion prediction models are established for different regions,and reasonable and high-precision data analysis results are obtained after synthesis.At the same time,this paper develops an integrated early warning system for working face.The successful application of the integrated early warning system in Huangling No.1 well proves that multi-dimensional heterogeneous data fusion technology is the deep implementation of information technology in coal mine ventilation safety management,which will raise the application of safety monitoring system to a new level.