Intelligent Analysis Model for Civil Aviation Employees Safety Supervision Based on Knowledge Graph
DOI:
https://doi.org/10.54097/vc50pt64Keywords:
Civil aviation safety; Knowledge Graph; Civil Aviation Employees Safety Supervision.Abstract
Ensuring the safety of civil aviation employees is crucial for the overall safety and quality of the aviation industry. However, traditional methods of safety supervision are inefficient. This research proposes a regulatory approach based on knowledge graph technology, aiming to extract relationships from safety incidents involving civil aviation employees, establish a framework for safety risk analysis, and utilize the Neo4j graph database to provide comprehensive visualized data support for the safety supervision of civil aviation employees. This approach has the potential to enhance the safety supervision of civil aviation employees and contribute to overall aviation safety.
Downloads
References
General Aviation Security and Protection Regulations [J]. Gazette of the State Council of the People's Republic of China, 2022(17): 47-51. (In Chinese)
Han Dongwei. Research on Smuggling Crimes by Civil Aviation Employees in China [D]. Civil Aviation University of China, 2021. DOI: 10.27627/d.cnki.gzmhy.2021.000013. (In Chinese)
Zeng Yang, Luo Shuyi, Cheng Jie. Research on Background Qualification Assessment of Civil Aviation Flight Attendants [J]. Journal of Civil Aviation Flight University of China, 2021, 32(01): 22-24. (In Chinese)
Cheng Y, Jiao Y, Wei W, et al. Research on construction method of knowledge graph in the civil aviation security field[C]//2019 IEEE 1st International Conference on Civil Aviation Safety and Information Technology (ICCASIT). IEEE, 2019: 556-559.
Tian L, Zhou X, Wu Y P, et al. Knowledge graph and knowledge reasoning: A systematic review[J]. Journal of Electronic Science and Technology, 2022, 20(2): 100159.
Zhou H J, Shen T T, Liu X L, et al. Survey of knowledge graph approaches and applications [J]. Journal on Artificial Intelligence, 2020, 2(2): 89-101.
Uyar A, Aliyu F M. Evaluating search features of Google Knowledge Graph and Bing Satori: entity types, list searches and query interfaces[J]. Online Information Review, 2015, 39(2): 197-213.
Xu H, Li S, Xing B. Intelligent analysis of operational risk events in rail transit based on knowledge graph[J/OL]. Railway Standard Design, 1-12 [2023-12-26]. Available from: https://doi.org/10.13238/j.issn.1004-2954.202301280001. (In Chinese)
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Academic Journal of Science and Technology

This work is licensed under a Creative Commons Attribution 4.0 International License.








