JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2020, Vol. 55 ›› Issue (7): 67-80.doi: 10.6040/j.issn.1671-9352.0.2019.585

Previous Articles    

Method of uncertain temporal event flow sequence reasoning based on multiple attribute decision making

ZHENG Huan-ke1, ZHANG Jing1,2,3*, YANG Ya-qi4, XIONG Mei-hui1   

  1. 1. Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, Yunnan, China;
    2. Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming 650500, Yunnan, China;
    3. Yunnan Xiaorun Technology Service Limited, Kunming 650500, Yunnan, China;
    4. Yunnan Administration for Market Regulation, Kunming 650228, Yunnan, China
  • Published:2020-07-08

PDF (PC)

558

Abstract: In the cyber-physical system, the scheduling sequence of uncertain temporal event flows has a single decision-making problem. To solve this problem, this paper firstly uses the advantage of D-S evidence theory in multi-evidence source probability fusion, considering the influence of multiple attributes such as event priority, deadline, urgency and event dependency, to construct a basic probability distribution model for fuzzy end time with multi-attribute features. Then, an association model with intuitionistic fuzzy sets and D-S evidence theory is established, and through this model, the membership and non-membership of the IFS at the fuzzy end time can be obtained. On this basis, negative temporal reasoning of IFS and correlation scoring function are used to derive the fuzzy start time probability score, and finally obtain the time series reasoning result, which is used to determine the uncertain temporal event flow scheduling order based on multi-attribute criterion. The experimental results show that when the number of events increases, the methods scheduling accuracy rate can keep more than 85%. And when the size of the fuzzy interval limit is expanded, the scheduling accuracy rate does not decrease more than 15%.

Key words: cyber-physical system, CPS scheduling, intuitionistic fuzzy sets, D-S evidence theory

CLC Number: 

  • TP311
[1] LEE E A. Cyber physical systems:design challenges[C] // PETTIT R, BRINKSCHULTE U, DILLON T. Proceedings of the Eleventh IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing. Orlando: IEEE Computer Society, 2008: 363-369.
[2] 温景容, 武穆清, 宿景芳.信息物理融合系统[J]. 自动化学报, 2012, 38(4):507-517. WEN Jingrong, WU Muqing, SU Jingfang. Cyber-physical system[J]. Acta Automatica Sinica, 2012, 38(4):507-517.
[3] 李洪阳, 魏慕恒, 黄洁,等. 信息物理系统技术综述[J]. 自动化学报, 2019, 45(1):39-52. LI Hongyang, WEI Muheng, HUANG Jie, et al. Survey on cyber-physical systems[J]. Acta Automatica Sinica, 2019, 45(1):39-52.
[4] ATANASSOV K T. Intuitionistic fuzzy sets[J]. Fuzzy Sets & Systems, 1986, 20(1):87-96.
[5] DEMPSTER A P. Upper and lower probabilities induced by a multivalued mapping[J]. Annals of Mathematical Statistics, 1967, 38(2):325-339.
[6] 耿少峰. 面向信息物理系统的主动式复杂事件处理技术研究[D]. 长沙: 湖南大学, 2018. GENG Shaofeng. The research on proactive complex event processing for cyber-physical systems[D]. Changsha: Hunan University, 2018.
[7] WANG Yongheng, CAO Kening, ZHANG Xiaoming. Complex event processing over distributed probabilistic event streams[J]. Computers & Mathematics with Applications, 2013, 66(10):1808-1821.
[8] WU E, DIAO Y, RIZVI S. High-performance complex event processing over streams[C] // YU C, SCHEUERMANN P. Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data. New York:ACM, 2006:407-412.
[9] 曹科宁, 李仁发, 张小明, 等. 面向CPS复杂事件流的不确定性研究[J]. 计算机工程与科学, 2015, 37(3):415-421. CAO Kening, LI Renfa, ZHANG Xiaoming, et al. Research on uncertain CEP for CPS[J]. Computer Engineering and Science, 2015, 37(3):415-421.
[10] 李芳芳, 刘冲, 于戈. 面向CPS的时间戳不确定事件调度算法[J]. 计算机科学与探索, 2017, 11(6):887-896. LI Fangfang, LIU Chong, YU Ge. Scheduling algorithm of events with imprecise timestamps for CPS[J]. Journal of Frontiers of Computer Science and Technology, 2017, 11(6):887-896.
[11] 张晶, 熊梅惠, 陈垚,等. 面向信息物理系统的时间不确定任务流动态实时调度算法[J]. 信息与控制, 2018, 47(1):81-89. ZHANG Jing, XIONG Meihui, CHEN Yao, et al. Dynamic real-time scheduling algorithm with uncertain-time task flow for cyber-physical system[J]. Information and Control, 2018, 47(1):81-89.
[12] ZHANG H, DIAO Y, IMMERMAN N. Recognizing patterns in streams with imprecise timestamps[J]. Information Systems, 2013, 38(8):1187-1211.
[13] WANG D, RUNDENSTEINER E A, ELLISON R T. Active complex event processing over event streams[J]. Proceedings of the VLDB Endowment, 2011, 4(10):634-645.
[14] 刘红蕾, 李芳芳, 谷峪, 等. 面向时间不确定事件流的嵌套查询处理技术[J]. 计算机学报, 2017, 40(10):77-91. LIU Honglei, LI Fangfang, GU Yu, et al. Processing nested query over event streams with uncertain timestamps[J]. Chinese Journal of Computers, 2017, 40(10):77-91.
[15] DYMOVA L, SEVASTJANOV P. The operations on interval-valued intuitionistic fuzzy values in the framework of dempster-shafer theory[J]. Information Sciences, 2016, 360(10):256-272.
[16] 邢清华, 刘付显. 直觉模糊集隶属度与非隶属度函数的确定方法[J]. 控制与决策, 2009, 24(3):393-397. XING Qinghua, LIU Fuxian. Method of determining membership and nonmembership function in intuitionistic fuzzy sets[J]. Control and Decision, 2009, 24(3):393-397.
[17] 江红莉, 何建敏, 庄亚明, 等. 基于直觉模糊集和证据理论的群决策方法[J]. 控制与决策, 2012, 27(5):752-756. JIANG Hongli, HE Jianmin, ZHUANG Yaming, et al. Approach to group decision making based on intuitionistic fuzzy sets and evidence theory[J]. Control and Decision, 2012, 27(5):752-756.
[18] 宋亚飞, 王晓丹, 雷蕾. 基于直觉模糊集的时域证据组合方法研究[J]. 自动化学报, 2016, 42(9):1322-1338. SONG Yafei, WANG Xiaodan, LEI Lei. Combination of temporal evidence sources based on intuitionistic fuzzy sets[J]. Acta Automatica Sinica, 2016, 42(9):1322-1338.
[19] 申晓勇, 雷英杰, 周创明, 等. 基于直觉模糊集的不确定时序逻辑模型[J]. 计算机科学, 2010, 37(5):187-189. SHEN Xiaoyong, LEI Yingjie, ZHOU Chuangming, et al. Uncertain temporal logic model based on intuitionistic fuzzy sets[J]. Computer Science, 2010, 37(5):187-189.
[20] 申晓勇, 雷英杰, 华继学, 等. 基于IFTPN的不确定时间知识描述和推理方法[J]. 控制与决策, 2010, 25(10):1457-1462. SHEN Xiaoyong, LEI Yingjie, HUA Jixue, et al. Description and reasoning method of uncertain temporal knowledge based on IFTPN[J]. Control and Decision, 2010, 25(10):1457-1462.
[21] 郑寇全, 雷英杰, 王睿, 等. 基于IFTL的不确定时间推理方法[J]. 控制与决策, 2013, 28(7):1002-1006. ZHENG Kouquan, LEI Yingjie, WANG Rui, et al. Method of uncertain temporal reasoning based on IFTL[J]. Control and Decision, 2013, 28(7):1002-1006.
[22] 李芳芳, 刘红蕾, 于戈. 事件约束的时间不确定事件流查询处理[J]. 北京邮电大学学报, 2017, 40(2):53-60. LI Fangfang, LIU Honglei, YU Ge. Query processing over constraint event stream with uncertain timestamps [J]. Journal of Beijing University of Posts and Telecommunications, 2017, 40(2):53-60.
[23] 夏家莉, 陈辉, 杨兵. 一种动态优先级实时任务调度算法[J]. 计算机学报, 2012, 35(12):2685-2695. XIA Jiali, CHEN Hui, YANG Bing. A real-time scheduling algorithm based on dynamic priority[J]. Chinese Journal of Computers, 2012, 35(12):2685-2695.
[24] 杨茂林, 雷航, 廖勇. 一种共享资源敏感的实时任务分配算法[J]. 计算机学报, 2014, 37(7):1455-1465. YANG Maolin, LEI Hang, LIAO Yong. A shared resource-aware real-time task allocation algorithm[J]. Chinese Journal of Computers, 2014, 37(7):1455-1465.
[1] SHI Su-wei, LI Jin-jin, TAN An-hui. Fuzzy roughness and rough entropy of covering based generalized rough intuitionistic fuzzy set model [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(08): 86-91.
[2] LI Xiao-ping1， SUN Gang2. Intuitionistic fuzzy fully invariant subgroups and  characteristic subgroups [J]. J4, 2012, 47(2): 119-122.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE)
Supported by Beijing Magtech Co.Ltd