Coal mine electrical safety management and accident prevention based on neural network and signal processing

Abstract

Based on the upgrading of electronic components, communication and computer technology have made rapid progress. Neural network technology which mainly relies on the simulation of animal nerves, realize large-scale node information processing functions. Signal processing can complete the processing of various types of signals to obtain key data. With the further shortage of global energy, countries have begun to pay attention to the development of energy. As one of the traditional energy supply methods, coal mines have received a lot of financial support from the government. However, coal mine safety accidents have always been the focus of many enterprises and governments. In the process of coal mine development, there are many electrical equipment, and there are many changes in the underground environment. The problems of flooding, subsidence, and rodents biting off the line cause the paralysis of the electrical system and eventually lead to safety accidents in coal mine development. The neural network technology, can add all the electrical equipment in the mine operation, and to the network for analysis, and the signal processing technology can centrally process the signals of a variety of electrical equipment, and analyze the abnormal signals sent by the electrical equipment, and finally realize the prediction of electrical accidents. This paper firstly introduces the neural network and signal processing technology used in the text. Using the examples of hidden safety hazards in mining areas discovered during the team’s inspection as a basis, and combining with the actual situation in our country, these cases are classified. Next, using literature analysis and comparative research methods to analyze the problems existing in the current coal mine electrical safety management and accident prevention. A programme of safety management and accident prevention. The O-Net monitoring system constructed by the institute has its own unique advantage that it has more inputs than other networks, and its network structure is more complex and diverse, which of course also shows that it has more functions than other networks. The results of the scheme test show that the scheme can effectively reduce the probability of electrical safety accidents in coal mines, and at the same time effectively improve the efficiency of electrical safety management in coal mines. After multiple cycles of verification, the algorithm designed in this study has a very good effect in improving the safety of electrical equipment in coal mines, and the risk factors of each work have been effectively controlled, which is worthy of promotion in coal mines with similar environments.