Safety Risk Assessment of Electric Power Operation Site Based On Variable Precision Rough Set

被引:7
作者
Chang, Zhengwei [1 ]
Deng, Yuanshi [1 ]
Wu, Jie [1 ]
Xiong, Xingzhong [2 ]
Chen, Mingju [2 ]
Wang, Hong [2 ]
Xie, Xiaona [3 ]
机构
[1] State Grid Sichuan Elect Power Res Inst, Chengdu, Sichuan, Peoples R China
[2] Sichuan Univ Sci & Engn, Sch Automat & Informat Engn, Yibin, Sichuan, Peoples R China
[3] Chengdu Univ Informat Technol, Sch Control Engn, Chengdu, Sichuan, Peoples R China
关键词
Dangerous situation awareness; variable precision rough set; electric power operation; sensor network;
D O I
10.1142/S0218126622502541
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
In order to improve the intelligent informatization level of electric power production safety and reduce the accidents, the paper constructs a dynamic perception scheme of electric power production site that utilizes multi-dimensional information such as personnel location, equipment status, and image information. This method uses a multi-sensor network to realize the real-time perception of the image and position information of dynamic power work objects, then uses object identification and intelligent analysis to acquire the dynamic factors. Static factors are selected through questionnaire and historical data, and variable precision fuzzy theory is used to calculate the weight of static factors at dynamic power operation sites. A comprehensive evaluation is established to perceive risk and estimate security probability based on static factors and dynamic scene information. The application system can present the situation of the operation scene, and then realize safety assessment and early warning of the dangerous situation in the case of dynamic power monitoring. This method can prevent safety accidents and enhance the overall safety of power operations.
引用
收藏
页数:20
相关论文
共 23 条
[1]  
Al-Ali A.R., 2019, J. Electron. Sci. Technol., V17, P332, DOI [10.1016/J.JNLEST.2020.100017, DOI 10.1016/J.JNLEST.2020.100017]
[2]  
Alontseva D.L., 2020, J ELECT SCI TECHNOL, DOI DOI 10.1016/J.JNLEST.2020.100057
[3]   Layerwise Security Protection for Deep Neural Networks in Industrial Cyber Physical Systems [J].
Jiang, Wei ;
Song, Ziwei ;
Zhan, Jinyu ;
Liu, Di ;
Wan, Jiafu .
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2022, 18 (12) :8797-8806
[4]   Interpretability-Guided Defense Against Backdoor Attacks to Deep Neural Networks [J].
Jiang, Wei ;
Wen, Xiangyu ;
Zhan, Jinyu ;
Wang, Xupeng ;
Song, Ziwei .
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2022, 41 (08) :2611-2624
[5]   Design optimization of confidentiality-critical cyber physical systems with fault detection [J].
Jiang, Wei ;
Wen, Liang ;
Zhan, Jinyu ;
Jiang, Ke .
JOURNAL OF SYSTEMS ARCHITECTURE, 2020, 107
[6]   Energy-Aware Design of Stochastic Applications With Statistical Deadline and Reliability Guarantees [J].
Jiang, Wei ;
Pan, Xiong ;
Jiang, Ke ;
Wen, Liang ;
Dong, Qi .
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2019, 38 (08) :1413-1426
[7]   Design optimization for security- and safety-critical distributed real-time applications [J].
Jiang, Wei ;
Pop, Paul ;
Jiang, Ke .
MICROPROCESSORS AND MICROSYSTEMS, 2017, 52 :401-415
[8]  
Li J.T., 2021, IND ENG, V24, P111
[9]  
Li S., 2020, COAL MINE SAF, V51, P250
[10]  
Liu Y. J, 2020, THESIS