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

doi:10.3901/JME.2024.06.137

Abstract

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

CLC Number:

TN915

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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