Monitoring System And Dangerous Gas Cloud Model Theory

Gas is a major cause of coal mine threat to security risk. Once gas explosion, it will be thestate property loss, but also threatens the lives of the workers.At present, the way of gas dataprocessing for coal mine monitoring system is mostly a single. The level of intelligentcommunity and decision making is not much high.Monitoring parameters like gan andtemperatuer use the independent processing sensor. it will cut the inner link of differentmonitoring data and will be lack of multisensor information fusionThis paper will combine multi-sensor information fusion technology, cloud model andimmune danger theory, and puts forward a coal mine gas monitoring system of multi-levelinformation fusion and intelligent decision.The data layer fusion is use the batch estimationtheory to reduce impact of shock signals and improve the accuracy of the sensor detectiondataUsing genetic algorithm produces anomaly detector, the use of safety state operation datatraining and optimization, and using the model as a dangerous perceptron, extraction danger levelsignal.Immune danger theory as the decision-making fusion in sensor information fusion and ituse coordinated stimulus characteristic to comprehensive abnormal signal and danger signal, theweighted average method for the global situation comprehensive decision.Simulation results show:The Batch estimation method can effectively reduce impact ofshock signals part of the homogeneous sensor signal.The coordination of immune anomaly sigaldetection, and the cloud model danger signal detection,realize gas monitoring effectively improvethe accuracy of the integrated decision.