基于知识图谱的空管特情处置决策支持方法

doi:10.12305/j.issn.1001-506X.2024.12.20

Abstract

Abstract:

Air traffic control (ATC) special situation is a special emergency situation encountered in the process of civil aviation transportation, and the principle of ATC disposal is as accurate and efficient as possible. If it is not disposed effectively, it will lead to major flight accidents. However, the traditional special situation disposal relies on manual labor, which is difficult to meet the requirements of precision and efficiency. Knowledge graph technology is used to extract, represent and manage ATC special situation information, and to assist ATC personnel in special situation disposal, which can effectively improve the emergency disposal efficiency of ATC special situation. Therefore, a top-down knowledge graph construction method for ATC special situation disposal is proposed. Firstly, the concept, relationship and knowledge structure of knowledge graph are defined from top to bottom to form a pattern layer. Then, considering the small amount of training in ATC special situation case records and the large number of domain entities, the entity extraction model which combined bi-directional long short-term memory (BiLSTM) network deep learning model and rule knowledge entity extraction model of bi-directional encoder representations from transformers (BERT)-BiLSTM-conditional radom fields (CRF)+regular expression (RE) is adopted to extract entities. On this basis, the BiLSTM+self-attention (SA) model is used to extract the relationships among entities. After that, the Jaccard correlation coefficient is used for knowledge fusion. Finally, the knowledge graph of the constructed ATC special situation disposal is visualized by using Neo4j graph database, and its application prospect in decision support of ATC special situation disposal in civil aviation is analyzed, providing references for the practical application of ATC department.

Key words: knowledge graph, special situation disposal, air traffic control (ATC), deep learning, decision support

CLC Number:

V355.1+1

Ke PENG, Huawei WANG, Zhaoguo HOU, Xiaohan ZENG, Tong LUO. Decision support method of ATC special situation disposal based on knowledge graph[J]. Systems Engineering and Electronics, 2024, 46(12): 4116-4127.

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数据名称	数据类型	选用部分
规章	半结构化	空管一般规定
检查单	半结构化	人员、处置措施、涉及部门等
特情案例记录	非结构化	特情名称、特情原因、人员、设备、指令、设备指令、飞行状态等

模型参数	模型参数取值
批尺寸	64
随机失活率	0.5
训练轮次	50
学习率	0.000 3
优化器	Adam
BERT读取的最大序列长度	128

实体抽取模型	P	R	F₁值
BiLSTM	0.754 3	0.737 7	0.745 9
BiLSTM-CRF	0.842 8	0.842 5	0.822 7
BERT-BiLSTM-CRF	0.895 8	0.903 1	0.899 4
BERT-BiLSTM-CRF+RE	0.920 1	0.952 1	0.935 8

模型参数	模型参数取值
嵌入方式	100维字向量
随机失活率	0.5
BiLSTM隐藏层单元数	200
训练轮次	100
学习率	0.000 5

序号	语料整理结果
1	自动化系统短期冲突预警出现自动化系统出现短期冲突预警, 两机水平间隔29.6 km
2	自动化系统水平间隔监测自动化系统出现短期冲突预警, 两机水平间隔29.6 km
3	水平间隔短期冲突预警引起自动化系统出现短期冲突预警, 两机水平间隔29.6 km

Decision support method of ATC special situation disposal based on knowledge graph

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关系类别	P	R	F₁值
导致	0.978 6	0.977 6	0.978 1
显示	0.831 2	0.882 6	0.856 1
监测	0.993 8	0.998 7	0.996 2
察觉	0.846 2	0.896 1	0.870 4
引起	0.965 7	0.918 5	0.941 5
报告	0.970 1	0.865 3	0.914 7
出现	0, 923 7	0.980 6	0.951 3
触发	0.828 9	0.813 3	0.821 0
指挥	0.982 4	0.986 0	0.984 2
实施	0.975 9	0.866 3	0.917 8
产生	0.813 4	0.846 1	0.829 4
同事	0.960 5	0.985 8	0.973 0
平均值	0.922 5	0.918 1	0.919 5

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