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知识图谱的预警探测体系探测效能贝叶斯评估方法
引用本文:袁博文,朱丰,刘兆鹏,王立伟,黄润宇.知识图谱的预警探测体系探测效能贝叶斯评估方法[J].现代防御技术,2022(1).
作者姓名:袁博文  朱丰  刘兆鹏  王立伟  黄润宇
作者单位:军事科学院战争研究院;国防大学联合作战学院
基金项目:国家自然科学基金青年科学基金项目(61703412)。
摘    要:战争中,预警探测体系作为典型的复杂系统,其探测效能发挥情况的有效评估一直是亟需人们研究解决的重要现实问题。突破了传统效能评估方法的局限,在分析了知识图谱技术、贝叶斯评估方法基本原理的基础上,提出了基于两者综合运用的预警探测体系探测效能评估新方法,利用专家知识和实验数据构建知识图谱,并进一步使用贝叶斯网络方法实施知识推理,从而实现了体系探测效能评估。通过构建具体作战场景,验证了所提方法的有效性,并基于推演数据分析了所提方法的评估情况。

关 键 词:预警探测体系  探测效能评估方法  复杂系统  知识图谱  贝叶斯网络

Bayesian Evaluation Method for Detection Efficiency of Early Warning Detection System Based on Knowledge Graph
YUAN Bo-wen,ZHU Feng,LIU Zhao-peng,WANG Li-wei,HUANG Run-yu.Bayesian Evaluation Method for Detection Efficiency of Early Warning Detection System Based on Knowledge Graph[J].Modern Defence Technology,2022(1).
Authors:YUAN Bo-wen  ZHU Feng  LIU Zhao-peng  WANG Li-wei  HUANG Run-yu
Institution:(Academy of Military Science,Institute of War,Beijing 100091,China;National Defense University,Joint Operation College,Beijing 100091,China)
Abstract:In war,early warning and detection system as a typical complex system,the effective evaluation of its detection effectiveness has always been an important practical problem that needs to be studied and solved.The limitations of traditional effectiveness evaluation methods are break-throughed.On the basis of analyzing the basic principles of knowledge graph technology and Bayesian evaluation method,a new method of detection efficiency evaluation of early warning detection system based on their comprehensive application is proposed.The knowledge graph is constructed by using expert knowledge and experimental data,and the Bayesian network method is further used to imple?ment knowledge reasoning,so as to realize the evaluation of system detection efficiency.The effective?ness of the proposed method by constructing specific combat scenarios is verified,and the evaluation of the proposed method based on deduced data is analyzed.
Keywords:early warning detection system  detection efficiency evaluation method  complex sys?tem  knowledge graph  Bayesian network
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