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面向军事群体的聚合及解聚可视化控制模型 被引量:1

Aggregation and Disaggregation Visual Control Model for Facing Military Group
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摘要 作战编队是军事领域特有的军事群体组织方式,具有广泛的军事应用,针对战场态势中作战编队的聚合/解聚可视化问题,基于知识图谱及模型-视图-控制器(MVC)设计模式,提出了面向作战编队的聚合/解聚可视化控制模型,主要研究作战编队基于MVC的架构设计、作战编队基于知识图谱语义建模、基于主成分分析(PCA)的作战编队区域几何辅助对象构建、作战编队解聚/聚合ADLOD显示及地图比例尺控制以及作战编队聚合/解聚可视化模型的向量形式表征,该模型既弥补了军事标绘相关标准中作战群体方面研究的空白,也为联合作战态势多分辨率显示优化提供了基于作战编队聚合简化的新技术途径,同时,提供的可视化手段可加深对战场综合态势的认知,提升作战指挥决策水平,军事应用前景良好。 The combat formation is a unique group organization mode in military fields,which has a wide range of military applications.For the aggregation and disaggregation visual research of the combat formation,based on the knowledge graph and mode-view-controller(MVC)design,an aggregation and disaggregation visual control model of facing combat formation is proposed.The researches mainly include the MVC-based architecture design,the semantic modeling based on the knowledge graph,the area geometry assist object construction of the combat formation based on principal components analysis(PCA),the aggregation and disaggregation levels of detail(ADLOD)display,the map scale control of combat formation and vector representation of the visual model.This model not only makes up for the research gaps of combat formation in the military plotting standards,but also provides a new technical approach based on the aggregation of the combat formation to optimize the multi-resolution display of the joint operational situations.At the same time,the visual model provides the visualization means to deepen the understanding of the battlefield integrated situation and improve the level of battle command decision.The research has a good military application prospect.
作者 杨帆 王家润 曹占广 YANG Fan;WANG Jiarun;CAO Zhanguang(North China Institute of Computing Technology(NCI),Beijing 100083,China;College of Joint Operation,National Defense University(NDU),Beijing 100091,China)
出处 《计算机测量与控制》 2023年第5期108-113,共6页 Computer Measurement &Control
基金 国防预研基金资助项目(31511070401)。
关键词 作战编队 聚合及解聚LOD 知识图谱 模型-视图-控制器MVC 可视化 combat formation ADLOD knowledge graph MV visualization
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