A reference framework for the digital twin smart factory based on cloud-fog-edge computing collaboration

IF 5.9 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhiyuan Li, Xuesong Mei, Zheng Sun, Jun Xu, Jianchen Zhang, Dawei Zhang, Jingyi Zhu
{"title":"A reference framework for the digital twin smart factory based on cloud-fog-edge computing collaboration","authors":"Zhiyuan Li, Xuesong Mei, Zheng Sun, Jun Xu, Jianchen Zhang, Dawei Zhang, Jingyi Zhu","doi":"10.1007/s10845-024-02424-0","DOIUrl":null,"url":null,"abstract":"<p>Digital twin (DT) is an important approach for the factory to achieve intelligence. Due to the different scenarios and definitions, the generalization of frameworks for DT-based smart factories is weak, slowing down the overall process of industrial intelligence. Meanwhile, the pressure of data transmission and processing increases dramatically because of data explosion, which poses a challenge to the rational allocation of computing resources. In addition, more advanced strategies for training and running models are needed to support more sophisticated services. This paper proposes a reference framework that combines DT and cloud-fog-edge computing collaboration (CFE). First, the DT fuses physical and virtual spaces. The virtual-real fusion provides more information for operations, and the virtual space gives more accurate and timely decisions based on the constantly refreshed state. Secondly, by introducing CFE, suitable operating platforms for each layer of the DT-based smart factory are set, which enhances data interaction and reduces the dependence on cloud computing. The DT-CFE framework is well generalized. This paper first introduces the definition of the DT-based smart factory and its components. Then the methodology of the DT-CFE-based smart factory is proposed, and the network topology and operation mechanism are introduced. In this framework, the transmission and response performance of its data interaction is tested, and the interference of dynamic events occurring through scheduling is studied to illustrate the effectiveness and superiority of the framework.</p>","PeriodicalId":16193,"journal":{"name":"Journal of Intelligent Manufacturing","volume":"52 1","pages":""},"PeriodicalIF":5.9000,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Intelligent Manufacturing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s10845-024-02424-0","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Digital twin (DT) is an important approach for the factory to achieve intelligence. Due to the different scenarios and definitions, the generalization of frameworks for DT-based smart factories is weak, slowing down the overall process of industrial intelligence. Meanwhile, the pressure of data transmission and processing increases dramatically because of data explosion, which poses a challenge to the rational allocation of computing resources. In addition, more advanced strategies for training and running models are needed to support more sophisticated services. This paper proposes a reference framework that combines DT and cloud-fog-edge computing collaboration (CFE). First, the DT fuses physical and virtual spaces. The virtual-real fusion provides more information for operations, and the virtual space gives more accurate and timely decisions based on the constantly refreshed state. Secondly, by introducing CFE, suitable operating platforms for each layer of the DT-based smart factory are set, which enhances data interaction and reduces the dependence on cloud computing. The DT-CFE framework is well generalized. This paper first introduces the definition of the DT-based smart factory and its components. Then the methodology of the DT-CFE-based smart factory is proposed, and the network topology and operation mechanism are introduced. In this framework, the transmission and response performance of its data interaction is tested, and the interference of dynamic events occurring through scheduling is studied to illustrate the effectiveness and superiority of the framework.

Abstract Image

基于云-雾-边计算协作的数字孪生智能工厂参考框架
数字孪生(DT)是工厂实现智能化的重要方法。由于应用场景和定义不同,基于 DT 的智能工厂框架通用性较弱,延缓了工业智能化的整体进程。同时,数据爆炸带来的数据传输和处理压力剧增,对计算资源的合理分配提出了挑战。此外,还需要更先进的模型训练和运行策略,以支持更复杂的服务。本文提出了一个结合 DT 和云雾边缘计算协作(CFE)的参考框架。首先,DT 融合了物理空间和虚拟空间。虚实融合为操作提供了更多信息,虚拟空间根据不断刷新的状态做出更准确、更及时的决策。其次,通过引入 CFE,为基于 DT 的智能工厂各层设置了合适的操作平台,增强了数据交互,降低了对云计算的依赖。DT-CFE 框架具有良好的通用性。本文首先介绍了基于 DT 的智能工厂的定义及其组成部分。然后提出了基于 DT-CFE 的智能工厂的方法论,并介绍了网络拓扑结构和运行机制。在此框架下,测试了其数据交互的传输和响应性能,并研究了通过调度发生的动态事件的干扰,以说明该框架的有效性和优越性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing 工程技术-工程:制造
CiteScore
19.30
自引率
9.60%
发文量
171
审稿时长
5.2 months
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信