Domain modeling for scenario sensing and edge decision-making

被引:1
作者
Shi, Haoran [1 ]
Liu, Shijun [1 ]
Pan, Li [1 ]
机构
[1] Shandong Univ, Sch Software, Jinan, Peoples R China
来源
2023 IEEE INTERNATIONAL CONFERENCE ON EDGE COMPUTING AND COMMUNICATIONS, EDGE | 2023年
关键词
Scenario modeling; Edge computing; Flexible manufacturing; Meta-model; Knowledge graph;
D O I
10.1109/EDGE60047.2023.00028
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The introduction of numerous edge computing nodes allows application systems to sense and make decisions in real-time but also brings new challenges. The diversity of application scenarios and the complexity of application processes can be effectively addressed through modeling. This paper proposes a modeling approach for manufacturing scenario sensing and edge decision-making. Firstly, an abstract meta-model (SMM) is defined, which provides a unified description of the resources and processes in the scenario and the interaction between the scenario and the edge. Based on the meta-model, an application scenario model (ASM) can be constructed for a specific scenario to support edge data feedback and decision-making for abnormal events. In addition, the model is constructed in a scenario modeling tool and validated in a simulated manufacturing production line, that is, whether the models can provide effective support for decision-making of abnormal events. The results demonstrate that mapping normalized models into codes at the edge computing nodes can improve the accuracy and real-time performance of decision-making.
引用
收藏
页码:118 / 125
页数:8
相关论文
共 24 条
[1]  
[Anonymous], 2015, Machine and Unit States: An Implementation Example of ANSI/ISA- 88.00.01
[2]  
Barbieri Giacomo, 2015, IFAC - Papers Online, V48, P178, DOI 10.1016/j.ifacol.2015.08.128
[3]   Industrial Edge Computing Enabling Embedded Intelligence [J].
Dai, Wenbin ;
Nishi, Hiroaki ;
Vyatkin, Valeriy ;
Huang, Victor ;
Shi, Yang ;
Guan, Xinping .
IEEE INDUSTRIAL ELECTRONICS MAGAZINE, 2019, 13 (04) :48-56
[4]   Complexity in Manufacturing Processes and Systems [J].
Domingo, Rosario ;
Blanco-Fernandez, Julio ;
Luis Garcia-Alcaraz, Jorge ;
Rivera, Leonardo .
COMPLEXITY, 2018,
[5]  
Le DM, 2016, 2016 IEEE RIVF INTERNATIONAL CONFERENCE ON COMPUTING & COMMUNICATION TECHNOLOGIES, RESEARCH, INNOVATION, AND VISION FOR THE FUTURE (RIVF), P247, DOI 10.1109/RIVF.2016.7800302
[6]  
Evans E., 2004, Domain-Driven Design:Tackling Complexity in the Heart of Software
[7]  
Huang H.-M., 2019, Proposed Expansion of Quality Information Framework (QIF) Standard Schema with Potential Failure Mode and Effects Analysis (FMEA) Information Model
[8]   Combining complex event models and timing constraints [J].
Jersak, M ;
Richter, K ;
Ernst, R .
SIXTH IEEE INTERNATIONAL HIGH-LEVEL DESIGN VALIDATION AND TEST WORKSHOP, PROCEEDINGS, 2001, :89-94
[9]   A Survey on Knowledge Graphs: Representation, Acquisition, and Applications [J].
Ji, Shaoxiong ;
Pan, Shirui ;
Cambria, Erik ;
Marttinen, Pekka ;
Yu, Philip S. .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 33 (02) :494-514
[10]   Learning-Based Edge Sensing and Control Co-Design for Industrial Cyber-Physical System [J].
Ji, Zhiduo ;
Chen, Cailian ;
He, Jianping ;
Zhu, Shanying ;
Guan, Xinping .
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2023, 20 (01) :59-73