计算机与数字工程2024，Vol.52Issue(6)：1714-1720,7.DOI:10.3969/j.issn.1672-9722.2024.06.021
基于CNN-LSTM混合模型的民航非计划事件分析方法
Analysis Method of Civil Aviation Unplanned Events Based on CNN-LSTM Hybrid Model
摘要
Abstract
Safety is the core theme of the civil aviation industry,and unplanned events are an important source of information for identifying safety hazards and improving aviation safety.The unstructured and large number of unplanned events make manual analysis difficult and inefficient.In order to improve the analysis efficiency and accuracy of unplanned events,this paper proposes a hybrid deep neural network model based on CNN-LSTM,which is used for the automated analysis of civil aviation unplanned events.It is compared with SVM,CNN and LSTM models,trained on the airline event log sample data set,and judged the event classification results.The experimental results show that the proposed CNN-LSTM hybrid model has the highest classification accu-racy,and has the most stable classification performance for unbalanced data samples.关键词
深度学习/民航安全/文本分析/卷积神经网络/长短时记忆神经网络Key words
deep learning/civil aviation safety/text analysis/convolutional neural network/long short-term memory分类
信息技术与安全科学引用本文复制引用
王捷,周迪,左洪福,陆扬..基于CNN-LSTM混合模型的民航非计划事件分析方法[J].计算机与数字工程,2024,52(6):1714-1720,7.基金项目
国家自然科学基金项目(编号:U1933202) （编号:U1933202）
民航大NSF重点基金项目(编号:U1733201)资助. （编号:U1733201）
