OPTIMIZATION OF COAL MINE ROCKBURST EARLY WARNING SYSTEM

As the main energy and important industrial raw materials, coal plays a vital role. With the deep development of coal mining, the risk of underground coal and rock dynamic disasters is rising, which seriously threatens the safety of coal mining. In this paper, the interference signals and precursory characteristic signals in acoustic emission (AE) and electromagnetic radiation (EMR) signals are analyzed. A multi classification model based on the fine KNN model is established to classify the jamming signal data in three different intervals. ARIMA model is used to summarize and analyze the trend characteristics of precursory characteristic signals. The method of random forest classification model is used to classify and identify the time interval of the precursor signal. And calculate the probability of precursory characteristic data at a specific time.