A Model of Safety Monitoring and Early Warning for Coal Mine based on Ontology and Association Rules

In coal mine safety monitoring system, there are many problems, such as poor reliability, information management confusion, low utilization rate of mass monitoring data. In order to solve these problems, a new monitoring and early warning model of coal mine safety is put forward. The model uses the ontology to systematize the information in the field of coal mine safety monitor, so it makes mine knowledge benefit sharing and reusing; then, the association rule is used to mine the mass monitoring data, and a large number of hidden reasoning rules which can be used for early-warning are obtained; finally, the coal mine safety monitoring ontology and reasoning rules are combined with the Jena reasoning machine, a safety monitoring and warning model with reasoning mechanism is formed. The validity of the model is verified by reasoning experiments with real- time data. The results show that combined with ontology and association rules, the coal mine safety monitoring and early warning model effectively integrate information in monitoring field and improve the accuracy of early-warning to some extent.