Application of fractional differential operator in coal mine detection data fusion processing

In the detection and collection of coal mine production information,there are inevitably differences in the performance and working environment between sensors,these unavoidable differences lead to great differences in the measurement data of the same production information from multiple sensors in the same working place. Therefore,the detection data collected by the data detection center can hardly reflect the real production information,which directly affects the normal operation order of the coal mine production management and the safety of life and property of the production system. Comparing with the traditional hardware performance improvement method,which consumes a lot of money for purchasing high-performance information detection equipment to improve the accuracy of information data,this paper proposes a software fusion processing method of multi-sensor detection data based on fractional differential filtering algorithm for coal mine production information monitoring data. After discussed the application characteristics of fractional differential operator in signal processing,then the model of coal mine monitoring data fusion based on frac- tional differential operator is established,and using 0. 5 order differential operator in the data fusion processing experi- ment of gas concentration detection in a coal mine in Huainan proves the superiority of the fractional differential filter algorithm. The experiment proves that the fractional differential operator has a high performance of differential detec- tion data fusion,it can effectively improve the accuracy of coal mine detection data and scientific decision-making of coal mine safety production management. Compared with the traditional method of purchasing high-performance infor- mation detection equipment to improve the accuracy of information data,the data fusion method based on fractional dif- ferential filter algorithm has the outstanding advantages of low cost and remarkable fusion effect.