基于云边协作的工业互联网排产方法：以钢铁热轧生产为例

丁婧伊; 金嘉晖; 杨丰赫; 熊润群; 单冯; 东方

doi:10.12263/DZXB.20221018

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基于云边协作的工业互联网排产方法：以钢铁热轧生产为例

学术论文 | 更新时间：2025-12-08

- 基于云边协作的工业互联网排产方法：以钢铁热轧生产为例
- Industrial Internet Scheduling Method Based on Cloud-Edge Collaboration: A Case Study of Steel Hot Rolling
- 电子学报 2024年52卷第9期页码：2988-2999
- 作者机构：
  
  1.东南大学计算机科学与工程学院，江苏南京 211189
  2.东南大学网络空间安全学院，江苏南京 211189
- 作者简介：
  
  [ "丁婧伊女，2000年1月出生于内蒙古根河市.现为东南大学计算机科学与工程学院硕士研究生.主要研究方向为分布式计算、工业互联网. E-mail: jingyi_ding@seu.edu.cn" ]
  [ "金嘉晖男，1986年2月出生于浙江省温州市.现为东南大学计算机科学与工程学院副教授、博士生导师.主要研究方向为分布式数据处理、工业互联网、智能物联网. E-mail: jjin@seu.edu.cn" ]
  杨丰赫男，1996年3月出生于浙江省丽水市.2021年毕业于东南大学网络空间安全学院.主要研究方向为工业互联网、云排产. E-mail: fhyang@aa.seu.edu.cn
  熊润群男，1982年9月出生于福建省龙岩市.现为东南大学计算机科学与工程学院副教授.主要研究方向涉及云计算、大数据、物联网、工业互联网. E-mail: xiong@seu.edu.cn
  单冯男，1985年5月出生于江苏省南通市.2016年于东南大学获得博士学位，现为东南大学计算机科学与工程学院副教授.研究方向为算法设计与分析、物联网. E-mail: shanfeng@seu.edu.cn
  东方男，1982年5月出生于江苏省南京市.现为东南大学计算机科学与工程学院教授、博士生导师.主要研究方向为边缘计算、云计算与工业互联网. E-mail: fdong@seu.edu.cn
- 基金信息：
  
  国家重点研发计划(2021YFB2900100);国家自然科学基金(62232004;62072099;61632008)
- DOI：10.12263/DZXB.20221018
  中图分类号： TP393
- 收稿：2022-09-05，
  
  修回：2023-03-23，
  
  纸质出版：2024-09-25
- 稿件说明：
移动端阅览
丁婧伊, 金嘉晖, 杨丰赫, 等. 基于云边协作的工业互联网排产方法：以钢铁热轧生产为例[J]. 电子学报, 2024, 52(09): 2988-2999.

DING Jing-yi, JIN Jia-hui, YANG Feng-he, et al. Industrial Internet Scheduling Method Based on Cloud-Edge Collaboration: A Case Study of Steel Hot Rolling[J]. Acta Electronica Sinica, 2024, 52(09): 2988-2999.
丁婧伊, 金嘉晖, 杨丰赫, 等. 基于云边协作的工业互联网排产方法：以钢铁热轧生产为例[J]. 电子学报, 2024, 52(09): 2988-2999. DOI：10.12263/DZXB.20221018

DING Jing-yi, JIN Jia-hui, YANG Feng-he, et al. Industrial Internet Scheduling Method Based on Cloud-Edge Collaboration: A Case Study of Steel Hot Rolling[J]. Acta Electronica Sinica, 2024, 52(09): 2988-2999. DOI：10.12263/DZXB.20221018

摘要

随着工业互联网的蓬勃发展，工业生产需要满足用户的个性化需求.由于个性化产品规格多样种类繁多，一个高效的智能排产方法对企业的生产制造尤为重要.从部署模式来看，现有的智能排产系统可分为企业本地部署和云排产服务两类.本地排产系统的计算与存储资源相对有限，难以满足精确排产算法的需求；而云排产系统需要大量工业核心排产数据的支撑并按需计费，计算存储与网络传输的开销使排产服务成本较高.此外，工业核心数据上传至云可能存在数据泄露的风险.针对以上问题，本文以钢铁热轧生产为例，将边缘计算技术引入智能排产，提出了一种云边协作的工业互联网排产框架（Production Scheduling based on Edge-Cloud-Collaboration，PSECC），本框架在边缘端预处理原始工业数据，保证核心生产数据保留在企业端；在云端进行算法求解，通过部署通用型求解算法又为框架赋予了可扩展性.本文基于PSECC框架设计实现了针对钢铁热轧排产任务的云边分解方法，实验证明本文提出的云边协作排产方法与常规求解器的性能相似，但是可以避免工业核心数据上云，且云端求解器的选择更加灵活.在性能方面，云排产的总排产时间是 PSECC 的1.4~3.7倍，其中网络传输时间是10~15倍.

Abstract

With the rapid development of the industrial Internet

industrial production needs to satisfy personalized user requirements. Due to the wide variety of personalized product specifications

an efficient and intelligent scheduling method is particularly important for manufacturing enterprises. From the perspective of deployment mode

existing intelligent scheduling systems can be divided into two categories: enterprise on-premises deployment and cloud scheduling services. The computing and storage resources of the local scheduling system are relatively limited

making it difficult to meet the needs of accurate scheduling algorithms. In contrast

cloud scheduling systems require the support of a large amount of industrial core scheduling data and charge on demand. The overhead of computing

storage

and network transmission makes scheduling service costs high. Additionally

uploading core industrial data to the cloud may carry the risk of data leakage. To address these issues

this paper takes the hot rolling production of iron and steel as an example

introduces edge computing technology into intelligent production scheduling

and proposes a cloud-edge collaborative industrial internet production scheduling framework (PSECC). The framework preprocesses the original industrial data at the edge to ensure that core production data is kept at the enterprise end

while the algorithm is solved in the cloud. The framework is also extended by deploying a general-purpose algorithm. Based on the PSECC framework

we designed and realized a cloud-edge decomposition method for hot rolling production scheduling tasks in steel. Experiments show that the performance of the cloud-edge collaborative production scheduling method proposed in this paper is similar to that of the conventional solver

but it can avoid uploading industrial core data to the cloud

and the choice of cloud solver is more flexible. In terms of performance

the total scheduling time of cloud scheduling is 1.4 to 3.7 times that of PSECC

and the network transmission time is 10 to 15 times..

关键词

Keywords

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