Research on health analysis and prediction algorithm of coal mine underground system

There are many types of electromechanical subsystems and hardware equipment in coal mines, and the cooperative working relationship is complicated, and it is difficult to make accurate judgments on the health of the system. In response to the above problems, a big data analysis and prediction method for the health of the underground system of coal mines is proposed. Through the processing results of the hardware equipment CPU usage, memory usage, temperature and other operating status data of the equipment layer, network transmission layer, system control layer and other links of the business system, the hidden Markov model and the BP neural network fusion algorithm are used to establish System health prediction model. Through test experiments, the test data shows that the method can accurately predict the operation status of the underground business system, with a correct rate of more than 93%, effectively improving the stability of the underground system operation and improving the level of intelligence in the coal mine.