Gas Emergence Big Data and neural network filter

The gas supervision is a safety core of the coal mine production, which widespread existent a trouble, gas emergence big data, namely the pulse interference, the cause of the gas signal mistake alarm, is hardly resolved very often. In this paper, the reason and characteristics of gas emergence big data are analyzed to establish a kind of filter based on BP Neural Network. Through great quantities monitor data as training sample to train the network model, and tested by test samples, we get the needed network model. Results show that the model can guarantee alarm occurrence while the gas density is beyond the limit, meanwhile preventing supervision system from mistake alarm caused by pulse interference. Applying this research can promote the robustness of existing coal mine safety supervision system without update any device, which can improve the safety production in both social meaning and economic value.