边-云协同下智能制造单元作业的数字孪生任务调度方法

doi:10.3901/JME.2024.06.137

机械工程学报 ›› 2024, Vol. 60 ›› Issue (6): 137-152.doi: 10.3901/JME.2024.06.137

• 特邀专栏：数据-知识混合驱动的智能制造系统 • 上一篇下一篇

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边-云协同下智能制造单元作业的数字孪生任务调度方法

王跃飞^1,2, 王超¹, 许于涛¹, 孙睿¹, 肖锴¹, 王凯林¹

1. 合肥工业大学机械工程学院合肥 230009;
2. 合肥工业大学安全关键工业测控技术教育部工程研究中心合肥 230009

收稿日期:2023-04-12 修回日期:2023-10-18 出版日期:2024-03-20 发布日期:2024-06-07
通讯作者: 王跃飞，男，1977年出生，博士，副教授，硕士研究生导师。主要研究方向为工业互联网系统、车联网与智能汽车、汽车能量智能管理，人机协同控制等。E-mail：yuefeiw@hfut.edu.cn
作者简介:王超，男，1998年出生。主要研究方向为智能制造系统监测控制与管理、工业互联网与车联网、计算机集成制造系统。E-mail：2243595845@qq.com;许于涛，男，1999年出生。主要研究方向为工业互联网系统。E-mail：764740790@qq.com
基金资助:
国家自然科学基金 (61202096)和安徽省重点研究与开发计划(202104a05020018)资助项目。

Digital Twin Task Scheduling Method for Jobs of Intelligent Manufacturing Unit under Edge-cloud Collaboration

WANG Yuefei^1,2, WANG Chao¹, XU Yutao¹, SUN Rui¹, XIAO Kai¹, WANG Kailin¹

1. School of Mechanical Engineering, Hefei University of Technology, Hefei 230009;
2. Engineering Research Center of Safety Critical Industrial Measurement and Control Technology of Ministry of Education, Hefei University of Technology, Hefei 230009

Received:2023-04-12 Revised:2023-10-18 Online:2024-03-20 Published:2024-06-07

摘要/Abstract

摘要： 智能制造单元作业数字孪生任务的高保真建模和调度优化是智能制造系统实现的关键问题之一。针对该问题，提出一种边-云协同下智能制造单元作业数字孪生任务的调度方法。基于智能制造系统端-边-云架构的虚拟现实交互框架，提出智能制造单元作业与数字孪生任务的映射方法，建立作业数字孪生任务调度的问题模型。考虑智能制造系统虚拟现实交互的快速响应性和偏差问题，提出一种基于端-边-云协同的数字孪生任务混合重调度策略。针对作业数字孪生任务调度的优化目标，设计环境自适应多因子优化遗传算法(Environmental adaptive multi-factor optimization genetic algorithm, EAMO-GA)。试验数据表明，EAMO-GA算法满足结果正确性验证，并且其有效性和收敛性都优于其他算法，可满足大规模、并行式数字孪生任务的调度场景需求。

关键词: 智能制造单元, 数字孪生, 端-边-云协同, 遗传算法

Abstract: High fidelity modeling and scheduling optimization of digital twin tasks for jobs in intelligent manufacturing unit is one of the key problems in the implementation of intelligent manufacturing systems. To solve this problem, a scheduling method of digital twin tasks for jobs in intelligent manufacturing unit under edge-cloud cooperation is proposed. Based on the virtual reality interactive framework of the end-edge-cloud architecture of intelligent manufacturing system, a mapping method between jobs of intelligent manufacturing unit and digital twin tasks is proposed, and a scheduling problem model of job digital twin tasks is established. Considering the problem of fast responsiveness and deviation of virtual reality interaction in intelligent manufacturing systems, a hybrid rescheduling strategy of digital twin tasks based on end-edge-cloud collaboration is proposed. Environmental adaptive multi-factor optimization genetic algorithm(EAMO-GA) is designed to optimize the scheduling of job digital twin tasks. The experimental data show that the EAMO-GA meets the correctness verification of the results, and its effectiveness and convergence are better than other algorithms, which can meet the requirements of large-scale and parallel digital twin task scheduling scenario.

Key words: intelligent manufacturing unit, digital twins, end-edge-cloud collaboration, genetic algorithm

中图分类号:

TN915

王跃飞, 王超, 许于涛, 孙睿, 肖锴, 王凯林. 边-云协同下智能制造单元作业的数字孪生任务调度方法[J]. 机械工程学报, 2024, 60(6): 137-152.

WANG Yuefei, WANG Chao, XU Yutao, SUN Rui, XIAO Kai, WANG Kailin. Digital Twin Task Scheduling Method for Jobs of Intelligent Manufacturing Unit under Edge-cloud Collaboration[J]. Journal of Mechanical Engineering, 2024, 60(6): 137-152.

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边-云协同下智能制造单元作业的数字孪生任务调度方法

Digital Twin Task Scheduling Method for Jobs of Intelligent Manufacturing Unit under Edge-cloud Collaboration

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