基于大样本数据的不规范航行通告识别方法  

Method for Identifying Irregular NOTAMs Based on Large Sample Data

作　　者：董兵 罗创 郝宽公 李昕倩 刘安全 DONG Bing;LUO Chuang;HAO Kuan-gong;Li Xin-qian;LIU An-quan(Air Traffic Management College,Civil Aviation Flight University of China,Guanghan 618307,China)

机构地区：[1]中国民用航空飞行学院空中交通管理学院,广汉618307

出　　处：《科学技术与工程》2024年第23期9973-9979,共7页Science Technology and Engineering

基　　金：中国民用航空飞行学院重点科研项目(ZJ2021-09);中央高校基本科研业务费资助项目(J2023-050)。

摘　　要：航行通告(notice to airman,NOTAM)是通知航空情报信息变更的重要方式,航行通告中Q码和E项自由文本存在不对应以及表述不规范的问题。针对上述问题,基于情报中心2020年9月—2023年4月的105797条航行通告样本,提出改进后的基于双向编码器表示的深度金字塔卷积神经网络(bi-directional encoder representation technique-deep pyramidal convolutional neural network,BERT-DPCNN)航行通告要素相似度匹配识别方法,构建E码和Q项两类数据的文本数据集,标注文本正确性以及错误文本修正内容。采用基于变换器的双向编码器表示技术(BERT)将E项文本正则化预处理,提取全局文本特征,同时对Q码进行文本编码,生成预训练文本向量集。将训练好的向量集作为词嵌入层输入到深度金字塔卷积神经网络(DPCNN)模型中,随机选取60%训练集、20%测试集和20%验证集,用于模型训练,再使用训练好的模型进行文本相似度判别,模型评价指标结果显示各类航行通告平均精确率为88.77%,召回率为88.74%,F_(1)-score为89.50%,识别效果优于Text CNN、BERT、ERNIE、BERT-CNN模型。NOTAM is an important way to notify changes in aeronautical intelligence information,and there are problems of non-correspondence between Q-code and E-item free text as well as non-standardization of expression in NOTAM.Aiming at the above problems,the bi-directional encoder representation technique-deep pyramidal convolutional neural network(BERT-DPCNN)NOTAM element similarity matching identification method was proposed.To address the above problems,based on the flight information Centre s 105797 sample NOTAM from September 2020 to April 2023,the improved BERT-DPCNN navigational notice element similarity matching identification method was proposed to construct a text dataset with two types of data,E-code and Q-item,and to annotate the correctness of the text as well as the content of the wrong text correction.the E-item text was preprocessed by regularizing the E-item text using the bi-directional encoder representation technique(BERT)based on transformer,the global text features are extracted,and at the same time,the text encoding was carried out on the Q-code,and the pre-training text vector set was generated.The trained vector set was input into the deep pyramidal convolutional neural network(DPCNN)model as a word embedding layer,and 60%of the training set,20%of the testing set and 20%of the validation set are randomly selected for model training,and then the trained model is used to discriminate the text similarity,and the results of the model evaluation metrics show that the average precision of all types of navigational notices is 88.77%,the recall rate is 88.74%,the F_(1)-score is 89.50%,the recognition effect is better than Text CNN,BERT,ERNIE,BERT-CNN models.

关 键 词：航行通告 文本相似度 大样本数据 BERT DPCNN 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]