基于结构熵的云边缘协同制造系统复杂性评估框架

IF 2.5 Q2 ENGINEERING, INDUSTRIAL IET Collaborative Intelligent Manufacturing Pub Date : 2023-04-17 DOI:10.1049/cim2.12077
Jiajian Li, Yanjun Shi, Xueyan Sun, Dong Liu
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引用次数: 0

摘要

工业物联网(IIoT),以及5G及其他网络,正在推动智能制造革命的新时代。然而,更多异构实体和复杂通信协议的集成使增强型制造系统变得复杂,对其复杂性的定量评估提出了挑战。为解决这一问题,结合复杂网络和信息熵理论,提出了基于工业物联网的协同制造系统复杂性评估框架。首先,考虑各种接入通信的权值,将物理空间中的工业实体映射为二层复杂网络;其次,引入重要感知结构熵,从系统通信的角度捕捉工业网络的复杂性。在各种网络拓扑结构上进行的实验验证了该方法的有效性,为系统设计提供了指导。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A complexity assessment framework with structure entropy for a cloud-edge collaborative manufacturing system

The Industrial Internet of Things (IIoT), along with 5G and beyond networks, is driving a new era of revolution in intelligent manufacturing. However, the integration of more heterogeneous entities and intricate communication protocols complicates the enhanced manufacturing system, posing challenges for quantitatively assessing its complexity. To tackle this issue, a complexity assessment framework for the IIoT-enabled collaborative manufacturing system is proposed by combining the complex network and information entropy theory. Firstly, industrial entities in the physical space are mapped into a two-tier complex network taking into account the weights of various access communications. Secondly, an importance-aware structure entropy is introduced to capture the complexity of industrial networks from the communication perspective in the system. The experiments conducted on various network topological structures validate the proposed method and provide guidance for system design.

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来源期刊
IET Collaborative Intelligent Manufacturing
IET Collaborative Intelligent Manufacturing Engineering-Industrial and Manufacturing Engineering
CiteScore
9.10
自引率
2.40%
发文量
25
审稿时长
20 weeks
期刊介绍: IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly. The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).
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