• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2023, Vol. 59 ›› Issue (12): 38-46.doi: 10.3901/JME.2023.12.038

• 特邀专栏:制造大数据分析与决策 • 上一篇    下一篇

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5G技术赋能的智能离散制造车间主动调度模式

高艺平1, 李新宇1, 单杭冠2, 范晓晖3, 高亮1, 刘齐浩1   

  1. 1. 华中科技大学机械科学与工程学院 武汉 430074;
    2. 浙江大学信息与电子工程学院 杭州 310058;
    3. 中国移动通信有限公司研究院 北京 100032
  • 收稿日期:2022-11-03 修回日期:2023-05-04 出版日期:2023-06-20 发布日期:2023-08-15
  • 通讯作者: 李新宇(通信作者),男,1985年出生,博士,教授,博士研究生导师。主要研究方向为智能制造系统、车间调度、智能优化。E-mail:lixinyu@hust.edu.cn
  • 作者简介:高艺平,男,1991年出生,博士。主要研究方向为制造大数据分析、深度学习。E-mail:gaoyiping@hust.edu.cn;单杭冠,男,1982年出生,博士,副教授,博士研究生导师。主要研究方向为与5G/B5G移动通信相关的网络设计和用户服务质量保障。E-mail:hshan@zju.edu.cn;范晓晖,女,1978年出生,硕士,工程师。主要研究方向为5G互联互通、物联网技术。E-mail:fanxiaohui@chinamobile.com;高亮,男,1974年出生,博士,教授,博士研究生导师。主要研究方向为智能制造系统、智能设计。E-mail:gaoliang@hust.edu.cn;刘齐浩,男,1994年出生,博士研究生。主要研究方向为车间调度,集成式工艺规划与车间调度。E-mail:lllqh@hust.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(U21B2029、52188102)。

Research on Proactive Scheduling Theory and Method Enabled by 5G Technology for Intelligent Discrete Manufacturing Shop

GAO Yiping1, LI Xinyu1, SHAN Hangguan2, FAN Xiaohui3, GAO Liang1, LIU Qihao1   

  1. 1. School of Mechanical Science and Technology, Huazhong University of Science and Technology, Wuhan 430074;
    2. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou 310058;
    3. Research Institute of China Mobile Communications Group Co., Ltd., Beijing 100032
  • Received:2022-11-03 Revised:2023-05-04 Online:2023-06-20 Published:2023-08-15

摘要: 分析了当前离散制造车间调度模式的不足和面临的挑战,在此基础上,提出一种5G技术赋能的智能离散制造车间主动调度模式,将生产过程的传统被动调度模式转化为“互联-预测-调控”的主动调度模式。针对实际生产中交货期变化、机器故障等异常工况,通过5G技术构建云-边-端协同的车间多源异构数据互联互通体系,打通各层级之间的通信壁垒,实现全生产要素的数据实时感知与互联以及生产指令的上传下达;基于感知数据,采用深度学习等人工智能技术实现车间运行状态与异常事件的精准预测;根据预测结果主动调控生产计划,优化资源配置,构建了云-边-端协同的主动调度机制,实现调度“规则+算法”的混合联动,降低异常事件对生产过程的影响,实现复杂动态制造环境下的车间性能优化。

关键词: 离散制造, 主动调度, 5G互联, 异常预测, 运行调控

Abstract: Based on the analysis of the shortcomings and challenges of current discrete manufacturing shop operation mode, an intelligent discrete shop proactive scheduling mode enabled by 5G is proposed. The proposed mode transformed the traditional passive scheduling into a proactive scheduling of "interconnection-prediction-regulation". For the uncertain events such as delivery date change and machine failure in actual workshops, a cloud-edge-terminal collaboration system for multi-source heterogeneous data interconnection and communication is constructed through 5G, which breaks down the communication barriers between different levels, realizes the real-time data perception of all production factors, and uploading and issuing of production instructions; Based on the sensory data, the proactive scheduling realizes accurate prediction of workshop running state and uncertain events with advanced artificial intelligence algorithms, such as deep learning. And the proposed mode builds a cloud-edge-terminal collaboration scheduling mechanism with hybridization of scheduling rules and algorithms, which proactively adjusts scheduling process according to the results of prediction and optimizes the allocation of resources. The proposed mode can reduce the impact of abnormal events on the production process, implement workshop performance optimization in the complicated dynamic manufacturing environment.

Key words: discrete manufacturing, proactive scheduling, 5G interconnection, uncertainty prediction, operation control

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