Adaptive convolution neural network algorithm of whole process learning rate for mine fire detection method

Coal energy plays a pillar role in the development of national economy. The safe mining of coal energy has always been an important research topic of domestic and foreign scholars. In view of the problems existing in the current mine fire detection methods, such as the number of measurement points, the difficulty of maintenance, the complexity of installation and the high rate of false alarm and missing alarm, an intelligent mine fire detection method based on convolution neural network is proposed. In view of the problem that the learning rate parameter selection is not suitable and easy to interfere with the convergence of the model, the selection of subjective factors is strong and it is not easy to find the best learning rate, this paper proposes a method of the whole process adaptive learning rate. This method takes the mine temperature, humidity, smoke concentration, CO concentration and O2 concentration as input, through the self-learning of the whole process adaptive learning rate convolution neural network, and outputs the prediction results respectively, namely, the probability values of open fire, smoldering fire and no fire. By using Anaconda environment to build model simulation results show that the recognition error of open fire, smoldering fire and no fire probability is less than 3%, which can greatly reduce the rate of missing and false alarm.