留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于改进证据理论的LTE-R系统运营安全风险评估方法

高煜 武晓春

高煜, 武晓春. 基于改进证据理论的LTE-R系统运营安全风险评估方法[J]. 交通信息与安全, 2021, 39(5): 19-26. doi: 10.3963/j.jssn.1674-4861.2021.05.003
引用本文: 高煜, 武晓春. 基于改进证据理论的LTE-R系统运营安全风险评估方法[J]. 交通信息与安全, 2021, 39(5): 19-26. doi: 10.3963/j.jssn.1674-4861.2021.05.003
GAO Yu, WU Xiaochun. A Risk Assessment Method of LTE-R System Based on Improved Evidence Theory[J]. Journal of Transport Information and Safety, 2021, 39(5): 19-26. doi: 10.3963/j.jssn.1674-4861.2021.05.003
Citation: GAO Yu, WU Xiaochun. A Risk Assessment Method of LTE-R System Based on Improved Evidence Theory[J]. Journal of Transport Information and Safety, 2021, 39(5): 19-26. doi: 10.3963/j.jssn.1674-4861.2021.05.003

基于改进证据理论的LTE-R系统运营安全风险评估方法

doi: 10.3963/j.jssn.1674-4861.2021.05.003
基金项目: 

国家自然科学基金地区项目 61661027

详细信息
    作者简介:

    高煜(1997—),硕士研究生.研究方向: 交通信息工程控制.E-mail:949310024@qq.com

    通讯作者:

    武晓春(1973—),硕士,教授.研究方向:信号处理、交通信息工程及控制等.E-mail:369038806@qq.com

  • 中图分类号: 2021-06-28

A Risk Assessment Method of LTE-R System Based on Improved Evidence Theory

  • 摘要:

    针对LTE-R通信系统运营安全风险评估中存在专家主观评判差异与冲突的问题,提出基于AHP与改进证据理论相结合的方法对LTE-R系统进行评估研究。利用模糊数学将专家模糊评语转化为定量隶属度表示,采用改进证据理论,基于皮尔逊系数改进克服证据间冲突的过程,通过加权分配证据可信度权值改善信息主观性,构造冲突因子修正证据融合过程,得到基本概率分配(BPA)函数矩阵。选取最大隶属度作为判断原则,评估相应系统的风险等级并与其他方法进行对比。结果表明:LTE-R系统实例分析评估结果为“可忽略风险”,置信度为83.41%,较折扣证据理论、灰色模糊综合安全评估法分别提升3.12%和16.95%,评估结果一致的前提下,改进的方法能改善专家评判冲突无法正确处理及融合问题,提供了高置信度风险等级值,为LTE-R系统的运营评估提供参考。

     

  • 图  1  通信系统风险评估体系

    Figure  1.  Risk assessment system for communication system

    图  2  算法流程图

    Figure  2.  Algorithm flow

    图  3  各算法融合BPA结果

    Figure  3.  BPA results of fusing each algorithm

    表  1  9元组模糊评语

    Table  1.   9-tuple representation of fuzzy comments

    μrkv1 1 2 3 4 5 6 7 8 9
    特差v1 1 0.8 0 0 0 0 0 0 0
    很差v2 0.2 1 0.8 0 0 0 0 0 0
    v3 0 0.2 1 0. 80 0 0 0 0
    较差v4 0 0 0.2 1 0.8 0 0 0 0
    一般v5 0 0 0 0. 51 0.5 0 0 0
    较好v6 0 0 0 0 0.8 1 0.2 0 0
    v7 0 0 0 0 0 0.8 1 0.2 0
    很好v8 0 0 0 0 0 0 0.8 1 0.2
    优秀v9 0 0 0 0 0 0 0 0.8 1
    下载: 导出CSV

    表  2  9元组评语集

    Table  2.   9-tuple representation of comment sets

    μkRh 1 2 3 4 5 6 7 8 9
    可忽略风险R1 0 0 0 0 0 0 0 0.8 1
    可接受风险R2 0 0 0 0 0 0.8 1 0.2 0
    可容忍风险R3 0 0 0 0.2 1 0.2 0 0 0
    不期望风险R4 0 0.2 1 0.8 0 0 0 0 0
    不接受风险R5 1 0.8 0 0 0 0 0 0 0
    下载: 导出CSV

    表  3  皮尔逊系数相关程度

    Table  3.   Correlation degree of Pearson coefficient

    ρXY的范围 相关程度
    0.00 ≤ ρXY<0.20 极弱相关或无关
    0.20 ≤ ρXY<0.40 弱相关
    0.40 ≤ ρXY<0.60 中等程度相关
    0.60 ≤ ρXY<0.80 强相关
    0.80 ≤ ρXY<1.00 极强相关
    下载: 导出CSV

    表  4  指标权重

    Table  4.   The weight of the indicator

    二级指标 权重
    B1 0.024 0
    B2 0.055 0
    B3 0.063 0
    B4 0.225 1
    B5 0.087 5
    B6 0.079 2
    B7 0.032 0
    B8 0.020 9
    B9 0.210 6
    B10 0.078 1
    B11 0.054 2
    B12 0.022 2
    B13 0.012 4
    B14 0.005 0
    B15 0.030 6
    下载: 导出CSV

    表  5  专家模糊评语表

    Table  5.   Fuzzy comment form of experts

    指标层 专家1 专家2 专家3 专家4 专家5 专家6 专家7 专家8 专家9 专家10
    B1 8 6 7 5 7 7 6 8 6 7
    B2 6 7 8 8 7 7 7 6 7 6
    B3 7 86 6 7 8 8 8 7 8 6
    B4 7 9 9 7 9 9 8 9 9 9
    B5 8 9 9 7 9 9 7 9 9 9
    B6 9 9 9 6 9 8 9 9 7 9
    B7 9 9 9 7 8 9 8 8 9 9
    B8 7 7 9 6 8 9 8 9 7 9
    B9 9 9 9 9 8 9 9 9 9 9
    B10 6 9 7 8 9 9 9 9 8 9
    B11 7 8 7 6 9 9 9 9 7 9
    B12 9 9 7 8 9 9 9 8 7 7
    B13 9 8 9 7 7 9 9 8 9 7
    B14 7 8 9 8 9 9 6 8 7 9
    B15 8 9 9 5 8 7 6 7 9 9
    下载: 导出CSV

    表  6  指标B6的BPA表

    Table  6.   BPA table for indicator B6

    专家 R1 R2 R3 R4 R5
    1 0.947 3 0.052 7 0 0 0
    2 0.947 3 0.052 7 0 0 0
    3 0.947 3 0.052 7 0 0 0
    4 0 0.444 4 0.555 6 0 0
    5 0.947 3 0.052 7 0 0 0
    6 0.517 2 0.482 8 0 0 0
    7 0.947 3 0.052 7 0 0 0
    8 0.947 3 0.052 7 0 0 0
    9 0.063 0 0.881 9 0.055 1 0 0
    10 0.947 3 0.052 7 0 0 0
    下载: 导出CSV

    表  7  风险因素BPA表

    Table  7.   BPA table of risk factors

    风险因素 R1 R2 R3 R4 R5
    B1 0.011 0 0.929 3 0.059 7 0 0
    B2 0.001 6 0.853 8 0.144 6 0 0
    B3 0.164 1 0.769 3 0.066 6 0 0
    B4 0.992 3 0.007 6 0.000 1 0 0
    B5 0.993 0 0.006 9 0.000 1 0 0
    B6 0.975 4 0.024 5 0.000 1 0 0
    B7 0.936 5 0.063 4 0.000 1 0 0
    B8 0.814 6 0.185 0 0.000 4 0 0
    B9 0.996 3 0.003 7 0 0 0
    B10 0.968 0 0.031 9 0.000 1 0 0
    B11 0.895 8 0.103 9 0.000 3 0 0
    B12 0.689 3 0.308 4 0.002 3 0 0
    B13 0.649 3 0.350 3 0.000 4 0 0
    B14 0.771 3 0.227 1 0.001 6 0 0
    B15 0.904 9 0.091 6 0.003 6 0 0
    下载: 导出CSV

    表  8  评价结果对比

    Table  8.   Comparison of evaluation results

    方法 R1 R2 R3 R4 R5
    D-S证据理论 0 1 0 0 0
    文献[10] 0.713 2 0.211 6 0.074 1 0.001 1 0
    文献[16] 0.802 9 0.189 5 0.007 6 0 0
    本文方法 0.834 1 0.152 1 0.013 9 0 0
    下载: 导出CSV
  • [1] 杨晓燕. 发展下一代铁路移动通信系统的探讨[J]. 高速铁路技术, 2017, 8 (1): 54-56. doi: 10.3969/j.issn.1674-8247.2017.01.012

    YANG Xiaoyan. Discussion on the development of next generation railway mobile communication system[J]. High Speed Railway Technology, 2017, 8 (1): 54-56. (in Chinese) doi: 10.3969/j.issn.1674-8247.2017.01.012
    [2] CHEN Mengjia, ZHAO Yisheng, GAO Jincheng, et al. Adaptive modulation and frame length method based on Moore state machine in LTE-R communication system[J]. IET Communications, 2020, 14 (8): 1327-1334. doi: 10.1049/iet-com.2018.5835
    [3] 杨树忠, 马良德, 杨吉, 等. 铁路下一代移动通信系统LTE-R检测技术研究[J]. 中国铁路, 2018, 667 (1): 70-73. https://www.cnki.com.cn/Article/CJFDTOTAL-TLZG201801014.htm

    YANG Shuzhong, MA Liangde, YANG Ji, et al. Research on LTE-R Detection technology of railway next generation mobile communication system[J]. China Railway, 2018, 667(1): 70-73. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-TLZG201801014.htm
    [4] 李春铎. 铁路LTE-R宽带移动通信系统高速适应性研究[D]. 北京: 中国铁道科学研究院, 2019.

    LI Chunduo. Research on high speed adaptability of railway LTE-R broadband mobile communication system[D]. Beijing: China Academy of Railway Sciences, 2019. (in Chinese)
    [5] 腾辉. LTE-R通信系统安全设计与评估技术研究[D]. 北京: 北京交通大学, 2012.

    TENG Hui. Research on security design and evaluation technology of LT-R communication system[D]. Beijing: Beijing Jiaotong University, 2012. (in Chinese)
    [6] 高志远. LTE-R通信系统可靠性分析与研究[D]. 北京: 北京交通大学, 2016.

    GAO Zhiyuan. Reliability analysis and research of LTE-R communication system[D]. Beijing: Beijing Jiaotong University, 2016. (in Chinese)
    [7] 陈永刚, 周净毓, 戴乾军, 等. 基于组合赋权-云模型的LTE-R系统的安全评估研究[J]. 铁道科学与工程学报, 2019, 16 (12): 3110-3118. https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD201912026.htm

    CHEN Yonggang, ZHOU Jingyu, DAI Qianjun, et al. Research on security assessment of LTE-R systems based on combined empowerment-cloud model[J]. Journal of Railway Science and Engineering, 2019, 16 (12): 3110-3118. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD201912026.htm
    [8] 虎丽丽, 徐岩, 陶慧青. 基于动态故障树的LTE-R通信系统可靠性分析[J]. 计算机工程, 2020, 46 (9): 205-212. https://www.cnki.com.cn/Article/CJFDTOTAL-JSJC202009028.htm

    HU Lili, XU Yan, TAO Huiqing. Reliability analysisLTE-R communication system based on dynamic fault tree[J]. Computer Engineering, 2020, 46 (9): 205-212. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJC202009028.htm
    [9] 向志华. 朔黄重载铁路TD-LTE系统QoS评价标准研究[J]. 高速铁路技术, 2016, 7 (3): 7-10. doi: 10.3969/j.issn.1674-8247.2016.03.002

    XIANG Zhihua. Research on QoS evaluation standard of TD-LTE system of Shuohuang heavy-haul railway[J]. High Speed Railway Technology, 2016, 7(3): 7-10. (in Chinese) doi: 10.3969/j.issn.1674-8247.2016.03.002
    [10] 马迪迪. 基于高速铁路LTE-R通信系统安全风险评估技术研究[D]. 兰州: 兰州交通大学, 2019.

    MA Didi. Research on safety risk assessment technology based on high-speed railway LTE-R communication system[D]. Lanzhou: Lanzhou Jiaotong University, 2019. (in Chinese)
    [11] 张栋, 庄其建, 赖理文. 基于AHP和熵权法的地铁车站深基坑施工安全评价[J]. 现代交通技术, 2016, 13 (3): 80-86. doi: 10.3969/j.issn.1672-9889.2016.03.022

    ZHANG Dong, ZHUANG Qijian, LAI Liwen. Safety evaluation of deep foundation pit construction of subway station based on AHP and entropy weight method[J]. Modern Transportation Technology, 2016, 13 (3): 80-86. (in Chinese) doi: 10.3969/j.issn.1672-9889.2016.03.022
    [12] CHEN Luyuan, DENG Xinyang. A modified method for evaluating sustainable transport solutions based on AHP and Dempster-Shafer evidence theory[J]. Applied Sciences, 2018, 8(4): 563-580. doi: 10.3390/app8040563
    [13] SILVA M M, HIPEL K W, KILGOUR D M, et al. Strategic analysis of a regulatory conflict using Dempster Shafer theory and AHP for preference elicitation[J]. Journal of Systems Science and Systems Engineering, 2019, 28 (4): 415-433. doi: 10.1007/s11518-019-5420-1
    [14] GHOSH R, KUMAR P, ROY PP. A Dempster-Shafer theory based classifier combination for online Signature recognition and verification systems[J]. International Journal of Machine Learning and Cybernetics, 2019, 10 (9): 2467-2482. doi: 10.1007/s13042-018-0883-9
    [15] 宗芳, 于萍, 吴挺, 等. 常规公交风险的SEM与Bayesian Network组合评估方法研究[J]. 交通信息与安全, 2018, 36 (4): 22-28. doi: 10.3963/j.issn.1674-4861.2018.04.004

    ZONG Fang, YU Ping, WU Ting, et al. Research on the combined evaluation method of SEM and Bayesian network for conventional bus risk[J]. Journal of Transport Information and Safety, 2018, 36 (4): 22-28. (in Chinese) doi: 10.3963/j.issn.1674-4861.2018.04.004
    [16] 张友鹏, 王亚惠, 杨妮. 基于折扣证据理论的应答器系统风险评估[J]. 铁道科学与工程报, 2017, 14 (9): 1990-1997. https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD201709025.htm

    ZHANG Youpeng, WANG Yahui, YANG Ni. Risk assessment of transponder system based on discount evidence theory[J]. Journal of Railway Science and Engineering, 2017, 14(9): 1990-1997. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CSTD201709025.htm
    [17] LI Rongfei, CHEN Zhiyuan, LI Hao, et al. A new distance-based total uncertainty measure in Dempster-Shafer evidence theory[J]. Applied Intelligence, 2021, 1 (2): 1-29.
    [18] 毛一凡, 张多林, 王璐. 基于重合度的证据冲突度量方法[J]. 控制与决策, 2017, 32 (2): 293-298. https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201702014.htm

    MAO Yifan, ZHANG Duolin, WANG Lu. Evidence conflict measurement method based on coincidence degree[J]. Control and Decision, 2017, 32 (2): 293-298. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KZYC201702014.htm
    [19] ADANE D, KHANUJA H K. Monitor and detect suspicious transactions with database forensics and Dempster-Shafer theory of evidence[J]. International Journal of Electronic Security and Digital Forensics, 2020, 12 (2): 154-159. doi: 10.1504/IJESDF.2020.106302
  • 加载中
图(3) / 表(8)
计量
  • 文章访问数:  283
  • HTML全文浏览量:  222
  • PDF下载量:  22
  • 被引次数: 0
出版历程
  • 收稿日期:  2021-06-28

目录

    /

    返回文章
    返回