主管：中国科学院
主办：中国优选法统筹法与经济数学研究会
　　　中国科学院科技战略咨询研究院
Chinese Journal of Management Science

Chinese Journal of Management Science >

2023 , Vol. 31 >Issue 11: 332 - 340

DOI: https://doi.org/10.16381/j.cnki.issn1003-207x.2022.0287

Research on Digital Resource Aggregation and Recognition Method of Multi Value Chain Collaborative Data Space of Manufacturing Enterprises Based on Semantics

  • Jie-ping HAN ,
  • Dan ZHAO ,
  • Xiao-long YANG ,
  • Mei-ling GU ,
  • Huan-fen ZHANG
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  • 1.Northeast Electric Power University, Jilin 132012, China
    2.Beijing QingChang Power Technology Co. , Ltd, Beijing 100085, China

Received date: 2022-02-17

  Revised date: 2022-05-29

  Online published: 2023-11-20

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Abstract

In order to solve the problem of digital resource aggregation and identification caused by large data volume， low data value density， large data object granularity and fuzzy digital resource boundary in multi value chain collaborative data space of manufacturing enterprises， based on semantic theory， the digital resource aggregation and recognition method of manufacturing enterprise multi value chain collaborative data space is studied. Firstly， the digital resource aggregation model of manufacturing enterprise multi value chain collaborative data space is constructed； Secondly， a method for calculating the domain correlation of multi value chain collaborative data space of manufacturing enterprises is proposed； Then， based on the concept degree distribution and D-S evidence theory， the trust function and likelihood function are introduced， and the node discovery method of manufacturing enterprise multi value chain collaborative data space semantic network is proposed， forming a systematic digital resource aggregation and identification method. Taking the data of a power equipment manufacturing enterprise as the sample for simulation， the results show that the constructed manufacturing enterprise multi value chain collaborative data space semantic network has close connection， clear semantics and clear system. The proposed method has good aggregation effect and high purity， and can provide effective support for digital resource aggregation， knowledge resource discovery， knowledge service and intelligent decision-making of intelligent factory.

Key words： manufacturing enterprise; multi-value chain; data resources; semantics; polymerization

Cite this article

Jie-ping HAN , Dan ZHAO , Xiao-long YANG , Mei-ling GU , Huan-fen ZHANG . Research on Digital Resource Aggregation and Recognition Method of Multi Value Chain Collaborative Data Space of Manufacturing Enterprises Based on Semantics[J]. Chinese Journal of Management Science, 2023 , 31(11) : 332 -340 . DOI: 10.16381/j.cnki.issn1003-207x.2022.0287

References

1 郭一彬.习近平“以人民为中心”的发展思想与“以顾客为中心”的质量发展理念的理论探析与实践路径——关于政府质量治理和企业质量发展的若干思考： 第十六届中国标准化论坛［C］.中国郑州，2019： 13.
1 Guo Yibin. The theoretical exploration and practical path of Xi Jinping's “people-centered” development thought and the “customer-centered” quality development concept： Thoughts on government quality management and enterprise quality development： The 16th China Standardization Forum［C］. Zhengzhou， China， 2019： 13.
2 解季非. 制造企业服务化路径选择研究［J］. 中国管理科学，2018，26（12）： 135-145.
2 Xie Jifei. Research on service path servitization of manufacturing enterprise ［J］. China Journal of Management Science， 2018， 26（12）： 135-145.
3 罗政，李玉纳.企业价值链协同知识创新影响因素的系统动力学建模与仿真［J］. 现代图书情报技术，2016（5）： 80-90.
3 Luo Zheng， Li Yuna. Influencing factors of collaborative knowledge creation in enterprise value Chain［J］. New Technology of Library and Information Service， 2016（5）： 80-90.
4 张云中. 从整合到聚合： 国内数字资源再组织模式的变革［J］. 数字图书馆论坛，2014 （6）： 16-20.
4 Zhang Yunzhong. From integration to aggregation： the change of digital resource re-organization pattern in China［J］. Digital Library Forum， 2014（6）： 16-20.
5 王露，易平涛，李伟伟 ．多源不确定信息的随机模拟聚合评价方法及应用［J/OL］．中国管理科学. .
5 Wang Lu， Yi Pingtao， Li Weiwei. Evaluation method and application of stochastic simulation aggregation of multi-source uncertain information［J/OL］. China Journal of Management Science. .
6 苏寒，胡笑旋. 基于群体智慧的复杂问题决策模式［J］. 中国管理科学，2012，20（S2）： 783-789.
6 Su Han， Hu Xiaoxuan. Decision-making patterns Complex issues based on collective intelligence［J］. China Journal of Management Science， 2012， 20（S2）： 783-789.
7 何建敏，赵巍. 股市波动长记忆性聚合效应的半参数检验［J］. 中国管理科学，2008，16（4）： 12-17.
7 He Jianmin， Zhao Wei. The semiparametric test for aggregated effect of long memory property in stock markets volatility［J］. China Journal of Management Science， 2008 16（4）： 12-17.
8 Sinha A， Lobiyal D K. Probabilistic Data Aggregation in Information-based Clustered Sensor Network［J］. Wireless Personal Communications， 2014， 77（2）： 1287-1310.
9 Wei Ping， Liu Yuehu， Zheng Nanning， et al. Semantic propagation network with robust spatial context descriptors for multi-class object labeling［J］. Neural Computing and Applications， 2014， 24（5）： 1003-1018.
10 Chen Yaxi， Santamaría R， Butz A， et al. Tagclusters： semantic aggregation of collaborative tags beyond tagclouds［C］//International Symposium on Smart Graphics， Berlin， Germany， 2009： 56-67.
11 Ruttenberg A， Clark T， Bug W， et al. Advancing translational research with the Semantic Web［J］. BMC bioinformatics， 2007， 8（3）： 1-16.
12 Porcel C， López-Herrera A G， Herrera-Viedma E. A recommender system for research resources based on fuzzy linguistic modeling［J］. Expert Systems with Applications， 2009， 36（3）： 5173-5183.
13 Schulz S， Blochinger W， Hannak H. Capability-aware information aggregation in peer-to-peer grids［J］. Journal of Grid Computing， 2009， 7（2）： 135-167.
14 Chang Y C， Hsieh Y L， Chen C C， et al. Semantic frame-based natural language understanding for intelligent topic detection agent［C］//International Conference on Industrial， Engineering and Other Applications of Applied Intelligent Systems， Springer， Cham， 2014： 339-348.
15 Xiong Zenggang， Yang Yang， Zhang Xuemin， et al. Integrated agent and semantic p2p grid resource discovery model［C］//Eighth ACIS International Conference on Software Engineering， Artificial Intelligence， Networking， and Parallel/Distributed Computing， Qingdao， China， 2007， 2： 216-220.
16 Kim J S， Choi D O. Mapping method for design digital content of SCORM model-for a content design of mind humanities［J］. Journal of Korea Entertainment Industry Association， 2012， 6 （1）： 34-43.
17 Wen Bin， He Keqing， Wang Jian. Connecting Ontologies： Semantic Carrier of Requirements for Networked Software［C］//2009 Fifth International Conference on Semantics， Knowledge and Grid， Zhuhai， China， 2009： 258-261.
18 Goebel P M， Vincze M. A cognitive modeling approach for the semantic aggregation of object prototypes from geometric primitives： toward understanding implicit object topology［C］//International Conference on Advanced Concepts for Intelligent Vision Systems， Berlin， Germany， 2007： 84-96.
19 De Maio C， Fenza G， Loia V， et al. Ontology-based knowledge structuring： an application on RSS Feeds［C］//2009 2nd Conference on Human System Interactions， Catania， Italy， 2009： 467-473.
20 Ravat F， Teste O， Tournier R， et al. Top_keyword： An aggregation function for textual document OLAP［C］//International Conference on Data Warehousing and Knowledge Discovery， Berlin， Germany， 2008： 55-64.
21 Della Rocca P， Senatore S， Loia V. A semantic-grained perspective of latent knowledge modeling［J］. Information Fusion， 2017， 36： 52-67.
22 Warner K S R， W?ger M. Building dynamic capabilities for digital transformation： an ongoing process of strategic renewal［J］. Long Range Planning， 2019，52（3）：326-349.
23 Nambisan S， Lyytinen K， Majchrzak A， et al. Digital innovation management： reinventing innovation management research in a digital world［J］. MIS Quarterly， 2017，41（1）：223-238.
24 Henfridsson O， Nandhakumar J， Scarbrough H， et al. Recombination in the open-ended value landscape of digital innovation［J］. Information and organization， 2018，28（2）：89-100.
25 魏江， 刘嘉玲， 刘洋. 新组织情境下创新战略理论新趋势和新问题［J］. 管理世界， 2021，37（7）：182-197.
25 Wei Jiang， Liu Jialing， and Liuyang. New trends and problems of innovation strategy theory in the context of new organization［J］. Management World， 2021，37（7）：182-197.
26 Li Yan， Sun Huiying， Huang Jincheng， et al. Low-end lock-in of Chinese equipment manufacturing industry and the global value chain［J］. Sustainability， 2020， 12（7）：2981-3006.
27 Hakanen T， Jaakkola E. Co‐creating customer‐focused solutions within business networks： a service perspective［J］. Journal of Service Management， 2012，23（4）：593-611.
28 Li B R， Wang Yi， Dai Guohong， et al. Framework and case study of cognitive maintenance in Industry 4.0［J］. Frontiers of Information Technology & Electronic Engineering， 2019， 20（11）：1493-1504.
29 王宁. 数据资产对独角兽企业成长性的影响研究［J］. 价格理论与实践， 2021（11）：123-128.
29 Wang Ning， Research on the influence of data assets on the growth of unicorn enterprises［J］. Price：Theory & Practice， 2021（11）：123-128.
30 Leydesdorff L. On the normalization and visualization of author co-citation data： Salton's cosineversus the jaccard index［J］. Journal of the American Society for Information Science and Technology， 2008，59（1）：77-85.
31 Dice L R. Measures of the amount of ecologic association between species［J］. Ecology （Durham）， 1945，26（3）：297-302.
32 McAuley J， Leskovec J. Hidden factors and hidden topics： understanding rating dimensions with review text［C］//Proceedings of the 7th ACM conference on Recommender systems， 2013： 165-172.
33 王周伟，刘少伟，魏伟，等. 中国地方政府债务风险关联网络的空间特征与影响因素［J］. 统计与信息论坛，2019，34 （12）： 22-31.
33 Wang Zhouwei， Liu Shaowei， Wei Wei， et al. Spatial characteristics and influencing factors of Local Government debt risk association network in China ［J］. Statistics and Information Forum， 2019， 34（12）： 22-31.
34 Noy N F， Musen M A. The PROMPT suite： interactive tools for ontology merging and mapping［J］. International Journal of Human-Computer Studies， 2003，59（6）：983-1024.
35 Brandes U， Erlebach T. Network analysis［M］. Springer Berlin Heidelberg， Berlin， 2005.
36 Petturiti D， Vantaggi B. Upper and lower conditional probabilities induced by a multivalued mapping［J］. Journal of Mathematical Analysis and Applications， 2018，458（2）：1214-1235.
37 Ng A， Jordan M， Weiss Y. On spectral clustering： analysis and an algorithm［J］. Advances in Neural Information Processing Systems， 2001： 1-5.
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