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.

基于工业互联网的产业链数字孪生系统框架及信息模型

[1]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.

[2]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.

[3]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.

[4]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.

[5]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.

[6]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.

[7]Dai GQ, Wang XZ, Zou XY, et al., 2022. MRGAT: multi-relational graph attention network for knowledge graph completion. Neur Netw, 154:234-245.

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

[9]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.

[10]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.

[11]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.

[12]Hedberg TDJr, 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.

[13]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.

[14]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.

[15]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.

[16]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.

[17]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.

[18]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.

[19]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.

[20]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.

[21]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.

[22]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.

[23]Qin W, Chen SQ, Peng MG, 2020. Recent advances in Industrial Internet: insights and challenges. Dig Commun Netw, 6(1):1-13.

[24]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.

[25]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.

[26]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.

[27]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.

[28]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

[29]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.

[30]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.

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

[32]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.

[33]Wang Y, Hu L, Gao WF, et al., 2023. AdaNS: adaptive negative sampling for unsupervised graph representation learning. Patt Recogn, 136:109266.

[34]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.

[35]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.

[36]Zhang L, Zhou LF, Horn BKP, 2021. Building a right digital twin with model engineering. J Manuf Syst, 59:151-164.

[37]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.

[38]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.

[39]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.

[40]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.