基于迁移工作流的航班信息预取与推送方法

IF 1.3 4区 工程技术 Q2 ENGINEERING, AEROSPACE Journal of Aerospace Information Systems Pub Date : 2023-10-24 DOI:10.2514/1.i011197
Tao Xu, Youchao Sun
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引用次数: 0

摘要

随着飞机座舱仪表板结构的复杂化,大量的飞行数据被放置在仪表板上,受训飞行员在飞行训练中需要更多的时间来学习和理解飞行信息。这个问题延长了飞行训练时间,增加了成本。本文提出了一种预获取和推送飞行信息的机制,以方便见习飞行员的飞行训练。本文通过建立飞机座舱环境下的迁移工作流模型,以较短的时间间隔获取飞行数据,解决了飞行序列数据量大、不同数据间时间序列关系混乱等问题。然后将飞行数据输入多层感知机长短期记忆(Multilayer Perceptron Long - short - Memory, MLP-LSTM)预测算法,该算法通过分析当前飞行数据并预测下一阶段的飞行数据,生成提示操作信息和预测信息。给出了起飞阶段的实例分析。给出了预测算法的实验结果,证明了迁移工作流模型和MLP-LSTM算法对时间序列飞行数据的预测效果优于LSTM算法。
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Prefetch and Push Method of Flight Information Based on Migration Workflow
As the architecture of aircraft cockpit panels becomes more complicated and more flight data are placed onto the panels, trainee pilots require more time during flight training to learn and comprehend flight information. This problem lengthens flight training time and raises costs. This paper proposes a mechanism for prefetching and pushing flight information to facilitate flight training for trainee pilots. This paper addresses the challenges of a high quantity of data and the chaotic time-series relationship between distinct data in flight sequence data by building a migration workflow model in the aircraft cockpit environment and getting flight data with shorter time intervals. Then the flight data are input into the Multilayer Perceptron Long Short-Term Memory (MLP-LSTM) prediction algorithm, which generates the prompt operation information and prediction information by analyzing the current flight data and predicting flight data of next stage. A case study of the takeoff stage is given. The experimental results of the prediction algorithm are given, which prove that the time-series flight data refined by the migration workflow model and MLP-LSTM algorithm have a better prediction effect compared with the LSTM algorithm.
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来源期刊
CiteScore
3.70
自引率
13.30%
发文量
58
审稿时长
>12 weeks
期刊介绍: This Journal is devoted to the dissemination of original archival research papers describing new theoretical developments, novel applications, and case studies regarding advances in aerospace computing, information, and networks and communication systems that address aerospace-specific issues. Issues related to signal processing, electromagnetics, antenna theory, and the basic networking hardware transmission technologies of a network are not within the scope of this journal. Topics include aerospace systems and software engineering; verification and validation of embedded systems; the field known as ‘big data,’ data analytics, machine learning, and knowledge management for aerospace systems; human-automation interaction and systems health management for aerospace systems. Applications of autonomous systems, systems engineering principles, and safety and mission assurance are of particular interest. The Journal also features Technical Notes that discuss particular technical innovations or applications in the topics described above. Papers are also sought that rigorously review the results of recent research developments. In addition to original research papers and reviews, the journal publishes articles that review books, conferences, social media, and new educational modes applicable to the scope of the Journal.
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