As key transportation hubs and economic engines,airports must implement security risk man-agement to ensure safe operations.Therefore,this study proposes an evaluation model for the effectiveness of hazard control measures,which firstly collects hazard source data from multiple airports,quantitatively analyzes them,and then uses GloVe to process the text of control measures into word vectors,and uses the text convolutional neural network(TextCNN)model to realize text classification and evaluate the effect of risk control measures.Finally,the advantages and disadvantages of the efficacy evaluation model of hazard control measures were determined.The results show that the accuracy of the performance evaluation model based on TextCNN is 74.36%,while that of the model based on TextRNN is 69.24%.Therefore,TextC-NN is more suitable for such evaluation tasks.This study provides a new perspective and approach for air-port authorities to more effectively identify,and manage various sources of hazard,thereby improving the safety performance of the entire airport.
关键词
机场风险管控/卷积神经网络/危险源/效能评估/自然语言处理/效能评估
Key words
airport risk control/convolutional neural network/hazard source/efficacy evaluation/natural language processing/effectiveness evaluation
维普
万方数据