TOPIC：The study of risk monitoring and early warning model in coalmine based on data mining
ABSTRCT：Abnormal levels of mine ventilation system can reflect the risks and hidden dangers existing in the mine production system to some extent. By combining the basic information with the ventilation monitoring data and the production state, this paper studid the abnormal change characteristics of the mine ventilation system monitoring data in the production period, and then constructing the risk monitoring and early warning model. The adjacent algorithm was applied to realize the analysis of the effectively filtered ventilation data, and to extract the main disaster risk points shown in the coal mine ventilation system. The particle group algorithm was used to calculate the risk point monitoring range, and to construct the mine risk assessment standard and the risk early warning response model based on the evolution path of the risk point. The research of the paper can provide theoretical support for the realization of intelligent management of mine risk.