基于深度长短记忆模型的民航安保事件分析

Research on civil aviation security event analysis based on deep LSTM model

为辅助公安民警分析民航安保事件,采用深度长短记忆(LSTM)模型,研究民航安保事件行为主体识别问题。通过搭建民航安保事件数据库,对民航安保事件概念信息进行多模态信息表示,提取安保事件时序特征,构建深度LSTM模型,进而实现安保事件行为主体的学习与预测。结果表明:该模型可基于事件时序特征分析事件行为主体,预测精度更优,且在有噪声情况下也可得出良好结果,相关研究成果已在SZX机场成功应用。

In order to assist police officers to analyze civil aviation security events,deep LSTM model was used to study subject identification of these events. Firstly,multi-modal information of them was presented by establishing a database,and their time series characteristics were extracted. Then,a deep LSTM model was developed to study and predict subjects of security events. The results show that the proposed model can predict subjects more accurately based on time series characteristics of the events,and even in the presence of noise,it can obtain better prediction results. Moreover,related research results have been successfully applied in SZX international airport.