基于MEC的生产线任务分配系统与方法

Yuying Xue, Shen Yun, Du Huibin, Yaqi Song, M. Cheriet
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摘要

工业智能对资源的需求较高,边缘计算具有处理速度快、私密性强的特点,已成为推动工业智能发展的重要技术。本文针对工业场景,构建了一个基于MEC (Multi-access Edge Computing)的任务分配系统,支持MEC之间的任务迁移。生产线巡检模块实时记录任务信息,为任务分配提供依据。感知模块实时监控MEC资源和任务执行情况。为了提高边缘资源的利用率,任务分配模块综合考虑MEC状态和任务需求来构建优化模型。为了提高优化能力,本文提出了一种改进遗传算法(IGA)。IGA增加了基因突变过程中的MEC可信度因子,保证了基因的多样性,提高了收敛速度,最终实现了生产线任务的合理分配。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A System and Method of Production Line Task Allocation Based on MEC
Industrial intelligence has a high demand for resources, edge computing has the characteristics of fast processing speed and strong privacy, it has become an important technology to promote industrial intelligence. In this paper, a task distribution system based on MEC (Multi-access Edge Computing) is built for the industrial scenario, which supports the task migration between MECs. The production line inspection module records task information in real time and provides a basis for task assignment. The perception module monitors MEC resources and task execution in real time. In order to improve the utilization rate of edge resources, the task allocation module comprehensively considers MEC status and task requirements to build the optimization model. In order to improve the optimization ability, this paper proposes an IGA (Improved Genetic Algorithm). IGA increases the MEC credibility factor in the process of gene mutation, which can ensure the diversity of the gene and improve the convergence speed, and finally realizes a reasonable allocation of production line tasks.
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