基于模糊图神经网络的工业互联网服务组件编排方法

刘岸鑫; 程良伦; 王涛

doi:10.12052/gdutxb.240068

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基于模糊图神经网络的工业互联网服务组件编排方法

计算机科学与技术 | 更新时间：2026-05-27

- 基于模糊图神经网络的工业互联网服务组件编排方法
- Fuzzy Graph Neural Network-based Industrial Internet Service Component Orchestration Method
- 广东工业大学学报 2025年42卷第5期页码：96-104, 120
- 作者机构：
  
  1. 广东工业大学计算机学院,广东,广州,510006
  2. 广东工业大学自动化学院,广东,广州,510006
- 作者简介：
  
  刘岸鑫(1997–)，男，硕士研究生，主要研究方向为工业软件，E-mail：liuanxin10337@163.com
  程良伦(1964–)，男，教授，博士，主要研究方向为信息物理融合系统，E-mail：llcheng@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金联合基金资助集成项目(U20A6003)
- DOI：10.12052/gdutxb.240068
  中图分类号： TP393
- 纸质出版：2025
- 稿件说明：
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刘岸鑫, 程良伦, 王涛. 基于模糊图神经网络的工业互联网服务组件编排方法[J]. 广东工业大学学报, 2025, 42(5): 96–104.

Liu Anxin, Cheng Lianglun, Wang Tao. Fuzzy Graph Neural Network-based Industrial Internet Service Component Orchestration Method[J]. 2025, 42(5): 96-104, 120.
刘岸鑫, 程良伦, 王涛. 基于模糊图神经网络的工业互联网服务组件编排方法[J]. 广东工业大学学报, 2025, 42(5): 96–104. DOI： 10.12052/gdutxb.240068

Liu Anxin, Cheng Lianglun, Wang Tao. Fuzzy Graph Neural Network-based Industrial Internet Service Component Orchestration Method[J]. 2025, 42(5): 96-104, 120. DOI： 10.12052/gdutxb.240068.

摘要

工业互联网环境工业软件系统具有规模庞大、涵盖的软件组件与设备实体繁杂异构等特点，在异构设备与软件对象统一的服务化组件化封装基础上，通过服务组件编排可实现多样化应用工业软件系统灵活敏捷构建，但对服务编排效率与服务质量保障方面具有较高要求。为提升工业互联网环境服务组件编排效率并保障服务质量，本文提出基于图神经网络与模糊理论的服务组件编排方法FGraphSAGE_GA，将工业互联网服务组件编排问题形式化建模为图结构中的链路预测问题，并采用监督学习方法实现预测模型训练。针对新的服务组件编排问题，训练后的模型进行概率预测缩小候选空间，随后使用遗传算法优化服务组件编排方案。在两个不同的数据集上，针对3种规模的服务组件编排问题开展实验与分析，证明了所提出的FGraphSAGE_GA算法在服务质量和编排效率方面具有良好性能。

Abstract

In industrial internet environments

industrial software systems are usually large-scale and with a complex and heterogeneous array of software systems and physical devices. Based on the unified service-oriented and component-based encapsulation of heterogeneous devices and software objects

diverse applications of industrial software systems can be flexibly constructed through the orchestration of service components. However

it usually requires high efficiency of service orchestration and the assurance of service quality. To enhance the efficiency of service component orchestration and ensure service quality in industrial internet environments

this paper proposes a service component orchestration method called FGraphSAGE_GA

which formalizes the convert the component orchestration problem in industrial internet environments into a link prediction problem within a graph structure. It employs a supervised learning approach to train the prediction model. For the new service component orchestration problem

the trained model performs probabilistic predictions to narrow down the candidate space

followed by the optimization of the service component orchestration plan using a genetic algorithm. Experiments were conducted on two different datasets for service component orchestration problems of three different scales

and experimental results show that the proposed FGraphSAGE_GA algorithm has good performance in terms of service quality and orchestration efficiency.

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references

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