针对陆域智能化无人作战体系效能评估中存在的系统性不足、关联性缺乏以及高度复杂性考虑不足等问题，提出了一种基于图卷积神经网络（Graph Convolutional Networks，GCN）的陆域智能化无人作战体系效能评估框架，旨在通过利用GCN技术对智能化无人作战体系效能进行精确评估。针对陆域智能化作战特点建立了一套智能化作战的评估指标体系，并将该体系映射到图网络结构上，实现对无人作战体系在复杂作战环境中的高度抽象表示；采用大数据分析技术与专家经验知识对初始数据集进行预处理和特征工程，以优化输入数据的质量；通过应用GCN的半监督学习模式，深入挖掘指标体系的层次结构及其各组成部分之间的相互关联，实现对陆域智能化无人作战体系效能的综合评估。该评估框架针对目前陆域智能化无人作战体系效能评估中存在的诸多问题，提供了一种动态性强、系统化全面的解决方案，展示了GCN在军事科技领域的应用潜力。
Abstract
To address the issues of systemic inadequacies, lack of correlation, and insufficient consideration of complexity in the effectiveness evaluation of land-based intelligent unmanned combat systems, a graph convolutional network (GCN)-based effectiveness evaluationframework is proposed. The framework aims to leverage GCN technology to precisely evaluate the performance of intelligent unmanned combat systems. A comprehensive set of evaluation index systemis established according to the characteristics of land-based intelligent combat, and this system is mapped onto a graph network structure, enabling a highly abstract representation of the unmanned combat system in complex operational environments. The big data analytics and expert knowledge areused to preprocess and engineer the initial dataset for optimizing the quality of input data. The hierarchical structure of the evaluation index system and the interrelationships among its components are deeply explored by applying GCN's semi-supervised learning mode, thereby achieving a comprehensive evaluation of the effectiveness of land-based intelligent unmanned combat systems. This evaluation framework addresses numerous issues existingin the current evaluation of these systems, offering a dynamic, systematic, and comprehensive solution that demonstrates the application potential of GCN in the field of military technology.
关键词
无人作战 /
陆域智能化 /
效能评估 /
图卷积神经网络 /
大数据
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Key words
unmannedcombat /
land-basedintelligentcombat /
effectivenessevaluation /
graphconvolutionalnetwork /
bigdata
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第45卷增刊12024年10月
兵工学报ACTA ARMAMENTARII
Vol.45Suppl.1Oct.2024
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)， 胡建刚， 李俊杰， 陈励， 毛余琨， 叶梦雅
