AI 中文总结
针对民航网络边缘数据处理问题，介绍边缘人工智能通过压缩、协作推理和分割学习等技术，减少延迟、带宽和暴露风险，实现断连平稳运行，阐述其运行动机、技术、范式及应用趋势，为民航提供低延迟、隐私保护和弹性的人工智能服务。
AI 中文摘要
民航对安全要求极高，其从驾驶舱、塔台到停机坪和维护等运营在网络边缘产生大量异构数据。然而，以云为中心部署大型人工智能模型往往会导致高任务延迟，在通信受限环境中缺乏离线能力，且需要集中敏感数据，带来隐私和主权风险。边缘人工智能通过压缩、协作推理和分割学习将感知、预测和决策逻辑更靠近数据生产者，减少延迟、带宽和暴露风险，实现断连时的平稳运行。本文全面介绍了适用于民航的边缘智能，阐述其运行动机，回顾边缘推理和学习技术，介绍组织计算范式及民航环境配置，描述新兴应用和未来研究趋势，认为优化的边缘解决方案可补充云基础，为民航全生命周期提供低延迟、隐私保护和弹性的人工智能服务。
英文摘要
Civil aviation is safety critical and its operations, from flight decks and towers to ramps and maintenance, generate massive, heterogeneous data at the network edge. Yet cloud centric deployment of large Artificial Intelligence (AI) models often produces high task latency, lacks offline capability in communication denied environments, and requires centralizing sensitive data, raising privacy and sovereignty risks. Edge AI moves perception, prediction, and decision logic closer to the data producers via compression, collaborative inference, and split learning, thereby reducing latency, bandwidth, and exposure while enabling graceful operation during disconnections. This paper provides a panoramic view and a common understanding of edge intelligence tailored to civil aviation. We firstly articulate the operational motivations for edge AI, and then review recent techniques for edge inference and edge learning. We then introduce the organizational computing paradigms and the respective configurations in civil aviation environments; finally, we describe the emerging applications and the future research trends of edge intelligence in civil aviation. We argue that a refined edge solution can complement cloud foundations to deliver low latency, privacy preserving, and resilient AI services across the civil aviation lifecycle.
Comments8 pages, 2 figures, 2 tables
Journal ref2025 5th International Conference on Information Communication and Software Engineering (ICICSE), Chongqing, China, 12 to 14 December 2025, IEEE, 2026
DOI：10.1109/ICICSE66971.2025.11429888
