面向盗窃威胁的石窟寺安防系统效能评估研究
Research on the Effectiveness Evaluation of Cave Temple Security Prevention Systems Facing Theft Threats
陈成瑜 1张帅 2谢娟 2贾岩鹏 2宋旭升 1张漂洋 1金华1
作者信息
- 1. 中国人民公安大学信息网络安全学院,北京 102600
- 2. 敦煌研究院,甘肃酒泉 736200
- 折叠
摘要
石窟寺作为我国重要的不可移动文物,不仅在学术领域具有极高的价值,其稀缺性也赋予了它们显著的经济价值,从而面临着盗窃非法活动的风险.安全防范系统是基于人防、物防、技防的综合性防护系统,石窟寺安全防范系统效能评估对有效保护石窟寺文物安全具有重要作用.为兼顾石窟寺安全防范系统效能评估中评分的模糊性与不确定性和解决评估过程中指标权重不一致的问题,基于证据理论与云贝叶斯网络提出了一种效能评估方法.利用云模型获得贝叶斯网络根节点先验概率,结合证据理论得到贝叶斯网络条件概率,最后实地选取评估对象,根据评估结果给出防护建议,为石窟寺安防系统效能评估研究提供新的思路.
Abstract
Cave temples,as significant immovable cultural relics in our country,hold immense value in the academic field.Meanwhile,their rarity also confers them considerable economic worth,which makes them face the risk of theft and other illegal activities.The security prevention system is an integrated pro-tective system based on human defense,physical defense and technical defense.The effective evaluation of the cave temple security prevention system plays a crucial role in the effectively safeguarding the secur-ity of cultural relics within cave temples.An effectiveness evaluation method based on evidence theory and cloud Bayesian networks is proposed to address the fuzziness and uncertainty in the scoring of the ef-fectiveness evaluation of the cave temple security prevention system,as well as to resolve the issue of in-consistent indicator weights during the evaluation process.The cloud model is utilized to obtain the prior probabilities of the root nodes in the Bayesian network,and the conditional probability of Bayesian net-work is derived from the combination of evidence theory.Finally,this method is applied in the field to select the evaluation subjects.Protective recommendations are then put forward according to the evalua-tion results,offering a novel approach to the research on the effectiveness evaluation of security and de-fense systems for cave temples.
关键词
安全防范系统/效能评估/石窟寺/贝叶斯网络/盗窃威胁Key words
security prevention system/effectiveness evaluation/cave temple/Bayesian network/theft threats引用本文复制引用
出版年
2024
NETL
NSTL
万方数据