Risk Identification Method of On-site Safety Supervision Database for Typical Operation Scenarios
In response to the high error rate and low identification efficiency of on-site safety supervision database risk identifica-tion,a risk identification method of on-site safety supervision database for typical operation scenarios is proposed.Aiming at the abnormal data in the on-site safety supervision database of typical operation scenarios,the moving average method and AR model are used to preprocess the data according to the type of abnormal data.The grey correlation clustering algorithm is used to extract data risk features.The dendritic cell algorithm is introduced,and MAP is used as an antigen comprehensive evalua-tion index to achieve risk identification in the on-site safety supervision database.The experimental results show that the pro-posed method has a lower risk identification error rate and higher recognition efficiency,and has good application value.
万方数据
维普
