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基于工业互联网的产业链数字孪生系统框架及信息模型

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工业互联网、云计算、大数据技术的融合正在改变产业链的经营和管理模式.然而,产业链涉及领域广泛、发展环境复杂、影响因素众多,给工业大数据的高效整合与利用带来挑战.针对当前产业链物理空间与虚拟空间的融合,本文建立基于工业互联网的产业链数字孪生系统框架.进一步,本文提出一种基于知识图谱的产业链信息模型,以整合复杂异构的产业链数据并抽取产业知识.首先,建立产业链本体,提出基于科技成果的实体对齐方法.第二,提出基于Transformer的双向编码器表示(BERT)与多头选择模型的产业链信息实体关系联合抽取方法.第三,提出基于关系图卷积网络与图采样聚合网络的关系补全模型,该模型同时考虑了知识图谱的语义信息和图结构信息.实验结果表明,本文所提出的实体关系联合抽取模型和关系补全模型的性能明显优于其他基线模型.最后,本文基于基础机械领域的18条产业链数据建立了产业链信息模型,证明了该方法的可行性.
Digital twin system framework and information model for industry chain based on industrial Internet
The integration of industrial Internet,cloud computing,and big data technology is changing the business and management mode of the industry chain.However,the industry chain is characterized by a wide range of fields,complex environment,and many factors,which creates a challenge for efficient integration and leveraging of industrial big data.Aiming at the integration of physical space and virtual space of the current industry chain,we propose an industry chain digital twin(DT)system framework for the industrial Internet.In addition,an industry chain information model based on a knowledge graph(KG)is proposed to integrate complex and heterogeneous industry chain data and extract industrial knowledge.First,the ontology of the industry chain is established,and an entity alignment method based on scientific and technological achievements is proposed.Second,the bidirectional encoder representations from Transformers(BERT)based multi-head selection model is proposed for joint entity-relation extraction of industry chain information.Third,a relation completion model based on a relational graph convolutional network(R-GCN)and a graph sample and aggregate network(GraphSAGE)is proposed which considers both semantic information and graph structure information of KG.Experimental results show that the performances of the proposed joint entity-relation extraction model and relation completion model are significantly better than those of the baselines.Finally,an industry chain information model is established based on the data of 18 industry chains in the field of basic machinery,which proves the feasibility of the proposed method.

Industry chainDigital twinIndustrial InternetKnowledge graphGraph neural network

王文宣、刘永钦、柴旭东、张霖

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北京航空航天大学自动化科学与电气工程学院,中国 北京市,100191

吉林大学控制科学与工程系,中国长春市,130012

中国航天科工集团航天云网科技发展有限公司,中国 北京市,100080

产业链 数字孪生 工业互联网 知识图谱 图神经网络

Ministry of Industry and Information Technology of ChinaMinistry of Industry and Information Technology of China

TC200802CTC190A445

2024

10.1631/FITEE.2300123

信息与电子工程前沿(英文)
浙江大学

信息与电子工程前沿(英文)

CSTPCD
影响因子：0.371
ISSN：2095-9184
年,卷(期)：2024.25(7)
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