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。
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.
作者
高艺平
李新宇
单杭冠
范晓晖
高亮
刘齐浩
GAO Yiping;LI Xinyu;SHAN Hangguan;FAN Xiaohui;GAO Liang;LIU Qihao(School of Mechanical Science and Technology,Huazhong University of Science and Technology,Wuhan 430074;College of Information Science and Electronic Engineering,Zhejiang University,Hangzhou 310058;Research Institute of China Mobile Communications Group Co.,Ltd.,Beijing 100032)
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2023年第12期38-46,共9页
Journal of Mechanical Engineering
基金
国家自然科学基金资助项目(U21B2029、52188102)。
关键词
离散制造
主动调度
5G互联
异常预测
运行调控
discrete manufacturing
proactive scheduling
5G interconnection
uncertainty prediction
operation control
