Digital twin system framework and information model for industry chain based on industrial Internet

Wenxuan WANG; Yongqin LIU; Xudong CHAI; Lin ZHANG

doi:10.1631/FITEE.2300123

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Digital twin system framework and information model for industry chain based on industrial Internet

Regular Papers | Updated：2024-07-28

- Digital twin system framework and information model for industry chain based on industrial Internet
- 基于工业互联网的产业链数字孪生系统框架及信息模型
- Frontiers of Information Technology & Electronic Engineering Vol. 25, Issue 7, Pages: 951-967(2024)
- Affiliations：
  
  1.School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
  2.Department of Control Science and Engineering, Jilin University, Changchun 130012, China
  3.CASICloud, China Aerospace Science and Industry Corporation Limited, Beijing 100080, China
- Author bio：
  
  E-mail: wangwenxuan0516@126.com
  liu_yq@buaa.edu.cn
  xdchai@263.net
  ‡Corresponding author
- Funds：
- DOI：10.1631/FITEE.2300123
  CLC： TP39
- Received：28 February 2023，
  
  Accepted：11 September 2023，
  
  Published：0 July 2024
- Accepted：
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Wenxuan WANG, Yongqin LIU, Xudong CHAI, et al. Digital twin system framework and information model for industry chain based on industrial Internet[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(7): 951-967.
DOI：

Wenxuan WANG, Yongqin LIU, Xudong CHAI, et al. Digital twin system framework and information model for industry chain based on industrial Internet[J]. Frontiers of Information Technology & Electronic Engineering, 2024, 25(7): 951-967. DOI： 10.1631/FITEE.2300123.

摘要

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。

Abstract

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.

关键词

Keywords

references

Al Faruque MA , Muthirayan D , Yu SY , et al. , 2021 . Cognitive digital twin for manufacturing systems . Proc Design, Automation & Test in Europe Conf & Exhibition , p. 440 - 445 . https://doi.org/10.23919/DATE51398.2021.9474166 https://doi.org/10.23919/DATE51398.2021.9474166

Bansal T , Juan DC , Ravi S , et al. , 2019 . A2N: attending to neighbors for knowledge graph inference . Proc 57 th Annual Meeting of the Association for Computational Linguistics , p. 4387 - 4392 . https://doi.org/10.18653/v1/P19-1431 https://doi.org/10.18653/v1/P19-1431

Bao JS , Guo DS , Li J , et al. , 2019 . The modelling and operations for the digital twin in the context of manufacturing . Enterp Inform Syst , 13 ( 4 ): 534 - 556 . https://doi.org/10.1080/17517575.2018.1526324 https://doi.org/10.1080/17517575.2018.1526324

Bordes A , Usunier N , Garcia-Durán A , et al. , 2013 . Translating embeddings for modeling multi-relational data . Proc 26 th Int Conf on Neural Information Processing Systems , p. 2787 - 2795 .

Chen HT , Jeremiah SR , Lee C , et al. , 2023 . A digital twin-based heuristic multi-cooperation scheduling framework for smart manufacturing in IIoT environment . Appl Sci , 13 ( 3 ): 1440 . https://doi.org/10.3390/app13031440 https://doi.org/10.3390/app13031440

Cheng JF , Zhang H , Tao F , et al. , 2020 . DT-II: digital twin enhanced Industrial Internet reference framework towards smart manufacturing . Robot Comput Integr Manuf , 62 : 101881 . https://doi.org/10.1016/j.rcim.2019.101881 https://doi.org/10.1016/j.rcim.2019.101881

Dai GQ , Wang XZ , Zou XY , et al. , 2022 . MRGAT: multi-relational graph attention network for knowledge graph completion . Neur Netw , 154 : 234 - 245 . https://doi.org/10.1016/j.neunet.2022.07.014 https://doi.org/10.1016/j.neunet.2022.07.014

Dettmers T , Minervini P , Stenetorp P , et al. , 2018 . Convolutional 2D knowledge graph embeddings . https://arxiv.org/abs/1707.01476 https://arxiv.org/abs/1707.01476

Devlin J , Chang MW , Lee K , et al. , 2019 . BERT: pre-training of deep bidirectional Transformers for language understanding . Proc Conf of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) , p. 4171 - 4186 . https://doi.org/10.18653/v1/N19-1423 https://doi.org/10.18653/v1/N19-1423

Guo L , Yan F , Li T , et al. , 2022 . An automatic method for constructing machining process knowledge base from knowledge graph . Robot Comput Integr Manuf , 73 : 102222 . https://doi.org/10.1016/j.rcim.2021.102222 https://doi.org/10.1016/j.rcim.2021.102222

Hamilton WL , Ying R , Leskovec J , 2017 . Inductive representation learning on large graphs . Proc 31 st Int Conf on Neural Information Processing Systems , p. 1025 - 1035 .

Jr Hedberg TD , Bajaj M , Camelio JA , 2020 . Using graphs to link data across the product lifecycle for enabling smart manufacturing digital threads . J Comput Inform Sci Eng , 20 ( 1 ): 011011 . https://doi.org/10.1115/1.4044921 https://doi.org/10.1115/1.4044921

Jia WJ , Wang W , Zhang ZZ , 2022 . From simple digital twin to complex digital twin part 1: a novel modeling method for multi-scale and multi-scenario digital twin . Adv Eng Inform , 53 : 101706 . https://doi.org/10.1016/j.aei.2022.101706 https://doi.org/10.1016/j.aei.2022.101706

Kamble SS , Gunasekaran A , Parekh H , et al. , 2022 . Digital twin for sustainable manufacturing supply chains: current trends, future perspectives, and an implementation framework . Technol Forecast Soc Change , 176 : 121448 . https://doi.org/10.1016/j.techfore.2021.121448 https://doi.org/10.1016/j.techfore.2021.121448

Kiel D , Arnold C , Voigt KI , 2017 . The influence of the Industrial Internet of Things on business models of established manufacturing companies—a business level perspective . Technovation , 68 : 4 - 19 . https://doi.org/10.1016/j.technovation.2017.09.003 https://doi.org/10.1016/j.technovation.2017.09.003

Kong TX , Hu TL , Zhou TT , et al. , 2021 . Data construction method for the applications of workshop digital twin system . J Manuf Syst , 58 : 323 - 328 . https://doi.org/10.1016/j.jmsy.2020.02.003 https://doi.org/10.1016/j.jmsy.2020.02.003

Li BH , Hou BC , Yu WT , et al. , 2017 . Applications of artificial intelligence in intelligent manufacturing: a review . Front Inform Technol Electron Eng , 18 ( 1 ): 86 - 96 . https://doi.org/10.1631/FITEE.1601885 https://doi.org/10.1631/FITEE.1601885

Liu MF , Li XY , Li J , et al. , 2022 . A knowledge graph-based data representation approach for IIoT-enabled cognitive manufacturing . Adv Eng Inform , 51 : 101515 . https://doi.org/10.1016/j.aei.2021.101515 https://doi.org/10.1016/j.aei.2021.101515

Liu YSY , Gu F , Gu XJ , et al. , 2022 . Resource recommendation based on industrial knowledge graph in low-resource conditions . Int J Comput Intell Syst , 15 ( 1 ): 42 . https://doi.org/10.1007/s44196-022-00097-2 https://doi.org/10.1007/s44196-022-00097-2

Lu YQ , Xu X , 2019 . Cloud-based manufacturing equipment and big data analytics to enable on-demand manufacturing services . Robot Comput Integr Manuf , 57 : 92 - 102 . https://doi.org/10.1016/j.rcim.2018.11.006 https://doi.org/10.1016/j.rcim.2018.11.006

Menon K , Kärkkäinen H , Wuest T , et al. , 2019 . Industrial Internet platforms: a conceptual evaluation from a product lifecycle management perspective . Proc Inst Mech Eng B , 233 ( 5 ): 1390 - 1401 . https://doi.org/10.1177/0954405418760651 https://doi.org/10.1177/0954405418760651

Min QF , Lu YG , Liu ZY , et al. , 2019 . Machine learning based digital twin framework for production optimization in petrochemical industry . Int J Inform Manag , 49 : 502 - 519 . https://doi.org/10.1016/j.ijinfomgt.2019.05.020 https://doi.org/10.1016/j.ijinfomgt.2019.05.020

Qin W , Chen SQ , Peng MG , 2020 . Recent advances in Industrial Internet: insights and challenges . Dig Commun Netw , 6 ( 1 ): 1 - 13 . https://doi.org/10.1016/j.dcan.2019.07.001 https://doi.org/10.1016/j.dcan.2019.07.001

Ren L , Li YJ , Wang XK , et al. , 2023 . An ABGE-aided manufacturing knowledge graph construction approach for heterogeneous IIoT data integration . Int J Prod Res , 61 ( 12 ): 4102 - 4116 . https://doi.org/10.1080/00207543.2022.2042416 https://doi.org/10.1080/00207543.2022.2042416

Sarazin A , Bascans J , Sciau JB , et al. , 2021 . Expert system dedicated to condition-based maintenance based on a knowledge graph approach: application to an aeronautic system . Expert Syst Appl , 186 : 115767 . https://doi.org/10.1016/j.eswa.2021.115767 https://doi.org/10.1016/j.eswa.2021.115767

Schlichtkrull M , Kipf TN , Bloem P , et al. , 2018 . Modeling relational data with graph convolutional networks . 15 th Int Conf on the Semantic Web , p. 593 - 607 . https://doi.org/10.1007/978-3-319-93417-4_38 https://doi.org/10.1007/978-3-319-93417-4_38

Sun XM , Bao JS , Li J , et al. , 2020 . A digital twin-driven approach for the assembly-commissioning of high precision products . Robot Comput Integr Manuf , 61 : 101839 . https://doi.org/10.1016/j.rcim.2019.101839 https://doi.org/10.1016/j.rcim.2019.101839

Sun Z , Deng ZH , Nie JY , et al. , 2019 . RotatE: knowledge graph embedding by relational rotation in complex space . https://arxiv.org/abs/1902.10197v1 https://arxiv.org/abs/1902.10197v1

Tan YP , Xu HF , Yan D , et al. , 2021 . Automatic construction of knowledge graph and its application in electric power system . 3 rd Asia Energy and Electrical Engineering Symp , p. 725 - 729 . https://doi.org/10.1109/AEEES51875.2021.9403122 https://doi.org/10.1109/AEEES51875.2021.9403122

Trouillon T , Welbl J , Riedel S , et al. , 2016 . Complex embeddings for simple link prediction . Proc 33 rd Int Conf on Machine Learning , p. 2071 - 2080 .

Vashishth S , Sanyal S , Nitin V , et al. , 2020 . Composition-based multi-relational graph convolutional networks . Proc 8 th Int Conf on Learning Representations .

Wang JL , Xu CQ , Zhang J , et al. , 2020 . A collaborative architecture of the industrial Internet platform for manufacturing systems . Robot Comput Integr Manuf , 61 : 101854 . https://doi.org/10.1016/j.rcim.2019.101854 https://doi.org/10.1016/j.rcim.2019.101854

Wang Y , Hu L , Gao WF , et al. , 2023 . AdaNS: adaptive negative sampling for unsupervised graph representation learning . Patt Recogn , 136 : 109266 . https://doi.org/10.1016/j.patcog.2022.109266 https://doi.org/10.1016/j.patcog.2022.109266

Yang BS , Yih WT , He XD , et al. , 2015 . Embedding entities and relations for learning and inference in knowledge bases . Proc 3 rd Int Conf on Learning Representations , p. 141 - 153 .

Yin S , Zhang N , Dong HM , 2020 . Preventing COVID-19 from the perspective of industrial information integration: evaluation and continuous improvement of information networks for sustainable epidemic prevention . J Ind Inform Integr , 19 : 100157 . https://doi.org/10.1016/j.jii.2020.100157 https://doi.org/10.1016/j.jii.2020.100157

Zhang L , Zhou LF , Horn BKP , 2021 . Building a right digital twin with model engineering . J Manuf Syst , 59 : 151 - 164 . https://doi.org/10.1016/j.jmsy.2021.02.009 https://doi.org/10.1016/j.jmsy.2021.02.009

Zhang XY , Ming XG , Bao YG , et al. , 2022 . System construction for comprehensive industrial ecosystem oriented networked collaborative manufacturing platform (NCMP) based on three chains . Adv Eng Inform , 52 : 101538 . https://doi.org/10.1016/j.aei.2022.101538 https://doi.org/10.1016/j.aei.2022.101538

Zhou B , Shen XW , Lu YQ , et al. , 2023 . Semantic-aware event link reasoning over industrial knowledge graph embedding time series data . Int J Prod Res , 61 ( 12 ): 4117 - 4134 . https://doi.org/10.1080/00207543.2021.2022803 https://doi.org/10.1080/00207543.2021.2022803

Zhou GH , Zhang C , Li Z , et al. , 2020 . Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing . Int J Prod Res , 58 ( 4 ): 1034 - 1051 . https://doi.org/10.1080/00207543.2019.1607978 https://doi.org/10.1080/00207543.2019.1607978

Zhuang CB , Liu JH , Xiong H , 2018 . Digital twin-based smart production management and control framework for the complex product assembly shop-floor . Int J Adv Manuf Technol , 96 ( 1-4 ): 1149 - 1163 . https://doi.org/10.1007/s00170-018-1617-6 https://doi.org/10.1007/s00170-018-1617-6

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