The invention relates to a GCN-based manned and unmanned aerial vehicle fusion operation airspace key node identification method, which comprises the steps of manned and unmanned aerial vehicle fusion operation airspace leg network construction, manned and unmanned aerial vehicle heterogeneous traffic flow leg network weighted assignment, leg network node importance index calculation, and leg network key node identification. And constructing and training a key node recognition model based on the graph convolutional neural network, and performing key node recognition based on the trained graph convolutional neural network. According to the key node identification method, based on two factors of manned/unmanned aircraft flow and manned/unmanned traffic flow operation time between legs in a fusion operation airspace, the weight of the edge in the leg network is assigned, and effective construction of the fusion airspace undirected weighted leg network can be realized; the key node recognition model based on the graph convolutional neural network can reasonably calculate the importance degree of the nodes in combination with multiple importance indexes; the key nodes in the fusion airspace network can be quickly identified.

    本发明涉及一种基于GCN的有人无人航空器融合运行空域关键节点识别方法,包括步骤:有人与无人航空器融合运行空域航段网络构建,有人与无人航空器异质交通流航段网络加权赋值,航段网络节点重要性指标计算,基于图卷积神经网络的关键节点识别模型构建与训练,基于训练完成的图卷积神经网络进行关键节点识别。本发明的关键节点识别方法基于融合运行空域下航段间有人/无人航空器流量和有人/无人交通流运行时间两个因素,为航段网络中边的权重进行赋值,能实现融合空域无向加权航段网络的有效构建;所提出的基于图卷积神经网络的关键节点识别模型,能结合多种重要性指标合理计算节点的重要度;能实现迅速识别融合空域网络内的关键节点。


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    Titel :

    GCN-based manned and unmanned aircraft fusion operation airspace key node identification method


    Weitere Titelangaben:

    基于GCN的有人无人航空器融合运行空域关键节点识别方法


    Beteiligte:
    ZHANG YING (Autor:in) / XU SHIMIN (Autor:in) / ZHOU XIAOTONG (Autor:in) / CHEN HAIYAN (Autor:in) / XIE HUA (Autor:in) / YANG LEI (Autor:in) / WANG BING (Autor:in)

    Erscheinungsdatum :

    2024-02-27


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Chinesisch


    Klassifikation :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen






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