| Citation: | XUE Junjie, ZHOU Junhua, SHI Guoqiang, et al. Effective strategy of heterogeneous model data fusion in product collaborative design[J]. Journal of Beijing University of Aeronautics and Astronautics, 2022, 48(6): 995-1003. doi: 10.13700/j.bh.1001-5965.2020.0699(in Chinese)
|
Aiming at the problem of model data fusion among different design tools in complex product design, the research explores the fusion strategy of end-to-end heterogeneous model data between tools. A multi-layer collaborative strategy of heterogeneous data is proposed, which uses the dynamic characteristics of database management and model attribute sharing to realize the integration of heterogeneous model data. In the system integration environment of OpenMBEE, through the secondary development of the modeling tool CREO, the strategy is employed to obtain the dynamic model attribute information in the whole life cycle design. The effectiveness of the strategy is verified by 3D model editing and reuse function testing. In order to realize the fusion of heterogeneous data, an intelligent algorithm to automatically obtain the attribute information of visual model is explored, based on Transformer model and bi-directional LSTM (Bi-LSTM) model. Utilizing the multi-layer perceptual characteristics of neural network, the algorithm realizes automatic extraction of heterogeneous data attribute information through deep learning and feature analysis of the attribute information in the model. The effectiveness of the intelligent model information extraction is verified by the model data set that is established with the requirement analysis models designed by modeling tool CAMEO.
| [1] |
GRIEVES M W. Product lift cycle management: The new paradigm for enterprises[J]. International Journal of Product Development, 2005, 2(1-2): 71-84.
|
| [2] |
BOSCHERT S, ROSEN R. Digital Twin—The simulation aspect[M]. Berlin: Springer, 2016: 59-74.
|
| [3] |
MOUSAVI B A, AZZOUZ R, HEAVEY C, et al. A survey of model-based system engineering methods to analyse complex supply chains: A case study in semiconductor supply chain[J]. IFAC-PaperOnLine, 2019, 52(13): 1254-1259. doi: 10.1016/j.ifacol.2019.11.370
|
| [4] |
KRUSE B, BLACKBURN M. Collaborating with OpenMBEE as an authoritative source of truth environment[J]. Procedia Computer Science, 2019, 153: 277-284. doi: 10.1016/j.procs.2019.05.080
|
| [5] |
WEILKIENS T. SysML-The systems modeling language[M]//WEILKIENS T. Systems engineering with SysML/UML. Berlin: Springer, 2007: 223-270.
|
| [6] |
谢慧敏. 基于XML的数据转换和发布的实现[D]. 南京: 南京理工大学, 2007.
XIE H M. Implementation of data transformation and publishing based on XML[D]. Nanjing: Nanjing University of Technology, 2007(in Chinese).
|
| [7] |
TAN H, HADZIC F, DILLON T S, et al. Tree model guided candidate generation for mining frequent subtrees from XML documents[J]. ACM Transactions on Knowledge Discovery from Data, 2008, 2(2): 1-43.
|
| [8] |
郭丽红, 王箭. 基于PCA的XML文档特征提取方法[J]. 计算机工程与设计, 2011, 32(11): 3894-3896.
GUO L H, WANG J. Feature extraction method of XML document based on PCA[J]. Computer Engineering and Design, 2011, 32(11): 3894-3896(in Chinese).
|
| [9] |
潘有能. XML挖掘: 聚类, 分类与信息提取[M]. 杭州: 浙江大学出版社, 2012.
PAN Y N. XML mining: Clustering, classification and information extraction[M]. Hangzhou: Zhejiang University Press, 2012(in Chinese).
|
| [10] |
邱实, 袁晓艳, 裴非, 等. 基于配置文件对XML数据进行字段提取及结构化方法: CN109885569A[P]. 2019-06-14.
QIU S, YUAN X Y, PEI F, et al. Field extraction and structure method for XML data based on configuration file: CN109885569A[P]. 2019-06-14(in Chinese).
|
| [11] |
SONG E, HAW S C. XML-REG: Transforming XML into relational using hybrid-based mapping approach[J]. IEEE Access, 2020, 8: 177623-177639. doi: 10.1109/ACCESS.2020.3026006
|
| [12] |
BRAHMIA Z, HAMROUNI H, BOUAZIZ R. XML data manipulation in conventional and temporal XML databases: A survey[J]. Computer Science Review, 2020, 36: 100231. doi: 10.1016/j.cosrev.2020.100231
|
| [13] |
MADNI A M, SIEVERS M. Model-based systems engineering: Motivation, current status, and needed advances[M]. Berlin: Springer, 2018: 311-325.
|
| [14] |
BONE M, BLACKBURN M, KRUSE B, et al. Toward an interoperability and integration framework to enable digital thread[J]. Systems, 2018, 6(4): 46. doi: 10.3390/systems6040046
|
| [15] |
BAYER T J, BENNETT M, DELP C L, et al. Update-concept of operations for integrated model-centric engineering at JPL[C]// 2011 Aerospace Conference. Piscataway: IEEE Press, 2011: 1-15.
|
| [16] |
CELLURA M, GUARINO F, LONGO S, et al. Modeling the energy and environmental life cycle of buildings: A co-simulation approach[J]. Renewable and Sustainable Energy Reviews, 2017, 80: 733-742. doi: 10.1016/j.rser.2017.05.273
|
| [17] |
GRAIGNIC P, VOSGIEN T, JANKOVIC M, et al. Complex system simulation: Proposition of a MBSE framework for design-analysis integration[J]. Procedia Computer Science, 2013, 16: 59-68. doi: 10.1016/j.procs.2013.01.007
|
| [18] |
VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Advances in Neural Information Processing Systems, 2017: 5998-6008.
|
| [19] |
SAHU S K, ANAND A. Drug-drug interaction extraction from biomedical texts using long short-term memory network[J]. Journal of Biomedical Informatics, 2018, 86: 15-24. doi: 10.1016/j.jbi.2018.08.005
|
| [20] |
LI F, JIN Y, LIU W, et al. Fine-tuning bidirectional encoder representations from transformers (BERT)-based models on large-scale electronic health record notes: An empirical study[J]. JMIR Medical Informatics, 2019, 7(3): 14830. doi: 10.2196/14830
|
| [21] |
GUAN W, SMETANNIKOV I, TIANXING M. Survey on automatic text summarization and transformer models applicability[C]//2020 International Conference on Control, Robotics and Intelligent System, 2020: 176-184.
|
| [22] |
ALAPARTHI S, MISHRA M. Bidirectional encoder representations from transformers (BERT): A sentiment analysis odyssey[EB/OL]. (2020-06-02)[2020-12-16].
|
| [23] |
DEVLIN J, CHANG M W, LEE K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[C]// NAACL-HLT 2019, 2019, 1: 4171-4186.
|
| [24] |
HUANG Z, XU W, YU K.Bidirectional LSTM-CRF models for sequence tagging[EB/OL]. (2015-08-09)[2020-12-16].
|
| [25] |
李丽双, 郭元凯. 基于CNN-BLSTM-CRF模型的生物医学命名实体识别[J]. 中文信息学报, 2018, 32(1): 116-122. doi: 10.3969/j.issn.1003-0077.2018.01.015
LI L S, GUO Y K. Biomedical named entity recognition based on CNN-BLSTM-CRF model[J]. Chinese Journal of Information, 2018, 32(1): 116-122(in Chinese). doi: 10.3969/j.issn.1003-0077.2018.01.015
|
Figures(10)
Copyright © Journal of Beijing University of Aeronautics and Astronautics
Address: Editorial Department of Journal of Beijing University of Aeronautics and Astronautics, 37 Xueyuan Road, Haidian District, Beijing Post Code: 100191 Email: jbuaa@cqjj8.com
Supported by:
Beijing Renhe Information Technology Co., Ltd.
DownLoad:
