基于规则链的网络协同制造数据融合方法研究

doi:10.11896/jsjkx.220300175

计算机科学 ›› 2022, Vol. 49 ›› Issue (11A): 220300175-7.doi: 10.11896/jsjkx.220300175

基于规则链的网络协同制造数据融合方法研究

胡楚阳¹, 柳先辉², 赵卫东²

1 同济大学电子与信息工程学院上海 201804
2 同济大学电子与信息工程学院CAD研究中心上海 201804

出版日期:2022-11-10 发布日期:2022-11-21
通讯作者: 柳先辉(xianhui_l@163.com)
作者简介:(2030813@tongji.edu.cn)
基金资助:
国家重点研发计划(2018YFB1703500)

Data Fusion Method of Network Collaborative Manufacturing Based on Rule Chain

HU Chu-yang¹, LIU Xian-hui², ZHAO Wei-dong²

1 Department of Electronic and Information Engineering,Tongji University,Shanghai 201804,China
2 CAD Research Center,Department of Electronic and Information Engineering,Tongji University,Shanghai 201804,China

Online:2022-11-10 Published:2022-11-21
About author:HU Chu-yang,born in 1998,postgra-duate.Her main research interests include microservices,Internet of things and network collaborative manufacturing.
LIU Xian-hui,born in 1979,Ph.D,associate researcher(associate professor).His main research interests include machine learning,data mining and big data,and networked manufacturing.
Supported by:
National Key Research and Development Program of China(2018YFB1703500).

摘要/Abstract

摘要： 对制造资源数据进行高效组织利用,施以有效的数据融合手段进而提取更多的有利信息,是当今智能制造领域的研究热点。同时,基于事件流的规则链技术开始崭露头角,其高自由度为与数据融合相结合提供了可能。针对当下制造资源组织模型较少、数据融合模型应用范围辖制在同架构系统内、与规则链结合的研究较少的问题,对复杂制造资源的组织方法进行建模,改进了基于MROM-VMC的调度流程,总结了制造资源数据存储链结构,并针对数据链中的数据处理环节提出了一种基于规则链的数据融合方法,以处理大量同构与异构的传感器数据,最终在网络协同制造资源平台中的数据处理环节得到验证,提升了制造资源数据利用的效率以及数据融合方法的可操作性,为用户提供辅助决策支持。

关键词: 网络协同制造, 数据融合, 规则链, 资源组织

Abstract: The efficient organization and utilization of manufacturing resource data with effective data fusion to extract more beneficial information is a hot research topic in the field of intelligent manufacturing today.Moreover,the rule chain technology based on event flow is emerging,and its high degree of freedom provides the possibility of combining with data fusion.In view of the problems that there are few manufacturing resource organization models,the application scope of data fusion models is governed within the same architecture system,and the combination with rule chains is less studied,we model the organization method of complex manufacturing resources,improve the scheduling process based on MROM-VMC,summarize the manufacturing resource data storage chain structure,and propose a rule chain-based data fusion for the data processing link in the data chain.A rule-based data fusion method is proposed for the data processing link in the data chain to handle a large amount of homogeneous and heterogeneous sensor data,which is finally validated in the data processing link in the network collaborative manufacturing resource platform.It improves the efficiency of manufacturing resource data utilization and the operability of the data fusion method,and provides users with auxiliary decision support.

Key words: Network collaborative manufacturing, Data fusion, Rule chains, Resource organization

中图分类号:

TP319

胡楚阳, 柳先辉, 赵卫东. 基于规则链的网络协同制造数据融合方法研究[J]. 计算机科学, 2022, 49(11A): 220300175-7. https://doi.org/10.11896/jsjkx.220300175

HU Chu-yang, LIU Xian-hui, ZHAO Wei-dong. Data Fusion Method of Network Collaborative Manufacturing Based on Rule Chain[J]. Computer Science, 2022, 49(11A): 220300175-7. https://doi.org/10.11896/jsjkx.220300175

参考文献

[1]JI F,HE W P,WANG D C,et al.Research on collaborativemanufacturing chain for complex parts in networked manufacturing environment[J].Computer Integrated Manufacturing Systems,2006(1):71-77.
[2]GUO B.On Intelligent IOT and Future Manufacturing－Embracing the Era of Human-Computer-Things Swarm Intelligence Computing[J].Frontiers,2020(13):32-42.
[3]YI S P,LIU M,WEN P H.Overview of cloud manufacturing service based on lifecycle theory[J].Computer Integrated Manufacturing Systems,2016,22(4):871-883.
[4]LI J,TAO F,CHENG Y,et al.Big Data in product lifecycle mana-gement[J].International Journal of Advanced Manufacturing Technology,2015(81):667-684.
[5]YAN Y Y,ZHANG B,FENG Z X.Research and application of multi-source data fusion and collaborative control methods [J].Electronic Technology & Software Engineering,2020(20):183-185.
[6]KONG L,PENG X,CHEN Y,et al.Multi-sensor measurement and data fusion technology for manufacturing process monito-ring:a literature review[J].International Journal of Extreme Manufacturing,2020,2(2):27.
[7]SHAO J F,HE X S,WANG J F,et al.Design of Textile Manufacturing Execution System Based on Big Data [J].Journal of Mechanical Engineering,2015,51(5):160-170.
[8]ZHANG A S.Research on data perception,fusion and visualization of digital workshop[D].Guiyang:Guizhou University,2019.
[9]LIN H,HU J,WANG X,et al.Toward Secure Data Fusion in Industrial IoT Using Transfer Learning [J].IEEE Transactions on Industrial Informatics,2021,17(10):7117-7122.
[10]LUO W C.Research on Key Technology of Predictive Maintenance of CNC Machine Tool Based on Digital Twin[D].Qing-dao:Shandong University,2020.
[11]WANG Y H,ZHENG L Y,FAN W,et al.Data collection and fusion of manufacturing workshop based on standard semantic model and complex event processing under cloud architecture[J].Computer Integrated Manufacturing Systems,2019,25(12):3103-3115.
[12]TSANOUSA A,BEKTSIS E,KYRIAKOPOULOS C,et al.A Review of Multisensor Data Fusion Solutions in Smart Manufacturing:Systems and Trends[J].Sensors,2022,22(5):1734.
[13]FAN L,ZHANG L.Multi-system fusion based on deep neuralnetwork and cloud edge computing and its application in intelligent manufacturing[J].Neural Computing and Applications,2021(2):1-10.
[14]TAO F,CHENG Y,CHENG J F,et al.Theories and technologies for cyber-physical fusion in digital twin shop-floor [J].Computer Integrated Manufacturing Systems,2017,23(8):1603-1611.
[15]HUANG S H,GUO Y,ZHA S S,et al.Review on Internet-of-manufacturing-things and key technologies for discrete workshop [J].Computer Integrated Manufacturing Systems,2019,25(2):284-302.
[16]GUO N,JIN T G,LIU W J,et al.Research on production sche-duling system based on a new manufacturing resources organization model[J].Control and Decision,2011,26(2):308-312.
[17]DROLET J,MARCOUX Y,ABDULNOUR G.Simulation-based performance comparison between dynamic cells,classical cells and job shops:a case study[J].International Journal of Production Research,2008,46(2):509-536.
[18]LI Y,ZHAO M,XU M Y,et al.A survey of research on multi-source information fusion technology [J].Intelligent Computer and Applications,2019,9(5):186-189.

相关文章 15

[1]	陈明鑫, 张钧波, 李天瑞. 联邦学习攻防研究综述 Survey on Attacks and Defenses in Federated Learning 计算机科学, 2022, 49(7): 310-323. https://doi.org/10.11896/jsjkx.211000079
[2]	杨斐斐, 沈思妤, 申德荣, 聂铁铮, 寇月. 面向数据融合的多粒度数据溯源方法 Method on Multi-granularity Data Provenance for Data Fusion 计算机科学, 2022, 49(5): 120-128. https://doi.org/10.11896/jsjkx.210300092
[3]	霍甜媛, 顾晶晶. 基于多源健康感知数据动静态关系融合的疾病诊断 Dynamic and Static Relationship Fusion of Multi-source Health Perception Data for Disease Diagnosis 计算机科学, 2022, 49(11A): 211100241-9. https://doi.org/10.11896/jsjkx.211100241
[4]	马冀, 林尚静, 李月颖, 庄琲, 贾睿, 田锦. 多源跨域数据融合的无线通信网络流量预测 Traffic Prediction for Wireless Communication Networks with Multi-source and Cross-domain Data Fusion 计算机科学, 2022, 49(11A): 210800165-7. https://doi.org/10.11896/jsjkx.210800165
[5]	潘登, 蔡梦云, 王振宇, 吕家亮. 阵列光幕测试系统目标识别方法 Testing System of Target Recognition Method of Array Screen 计算机科学, 2022, 49(11A): 211000109-4. https://doi.org/10.11896/jsjkx.211000109
[6]	周新民, 胡宜桂, 刘文洁, 孙荣俊. 基于多模态多层级数据融合方法的城市功能识别研究 Research on Urban Function Recognition Based on Multi-modal and Multi-level Data Fusion Method 计算机科学, 2021, 48(9): 50-58. https://doi.org/10.11896/jsjkx.210500220
[7]	张俊, 王杨, 李坤豪, 李昌, 赵传信. 基于流形学习的多源传感器体域网数据融合模型 Multi-source Sensor Body Area Network Data Fusion Model Based on Manifold Learning 计算机科学, 2020, 47(8): 323-328. https://doi.org/10.11896/jsjkx.191000012
[8]	马虹. 基于5G的视觉辅助BDS移动机器人融合定位算法 Fusion Localization Algorithm of Visual Aided BDS Mobile Robot Based on 5G 计算机科学, 2020, 47(6A): 631-633. https://doi.org/10.11896/JsJkx.190400156
[9]	黄婷婷, 冯锋. 无线传感器网络异构数据融合模型优化研究 Study on Optimization of Heterogeneous Data Fusion Model in Wireless Sensor Network 计算机科学, 2020, 47(11A): 339-344. https://doi.org/10.11896/jsjkx.200100109
[10]	蔡莉, 李英姿, 江芳, 梁宇. 面向城市热点区域的不平衡数据聚类挖掘研究 Study on Clustering Mining of Imbalanced Data Fusion Towards Urban Hotspots 计算机科学, 2019, 46(8): 16-22. https://doi.org/10.11896/j.issn.1002-137X.2019.08.003
[11]	杨思星, 郭艳, 李宁, 孙保明, 钱鹏. 基于数据融合的压缩感知多目标定位算法 Compressive Sensing Multi-target Localization Algorithm Based on Data Fusion 计算机科学, 2018, 45(9): 161-165. https://doi.org/10.11896／j.issn.1002-137X.2018.09.026
[12]	琚春华, 邹江波, 傅小康. 融入区块链技术的大数据征信平台的设计与应用研究 Design and Application of Big Data Credit Reporting Platform Integrating Blockchain Technology 计算机科学, 2018, 45(11A): 522-526.
[13]	高迪,徐峥,刘云淮. 面向公共安全数据处理的浪涌模型研究应用 Data Surge Models for Public Security Data Processing and Its Application in Unity of Security System 计算机科学, 2017, 44(Z6): 342-347. https://doi.org/10.11896/j.issn.1002-137X.2017.6A.078
[14]	陈燕俐,张乾,许建,王梦涵. 无线传感器网络多应用场景下的安全数据融合方案 Secure Data Aggregation Scheme for Multiple Applications in Wireless Sensor Networks 计算机科学, 2017, 44(9): 162-167. https://doi.org/10.11896/j.issn.1002-137X.2017.09.031
[15]	杨月平,王箭,薛明富. 面向敏感值的层次化多源数据融合隐私保护 Hierarchical Privacy Protection of Multi-source Data Fusion for Sensitive Value 计算机科学, 2017, 44(9): 156-161. https://doi.org/10.11896/j.issn.1002-137X.2017.09.030

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

基于规则链的网络协同制造数据融合方法研究

Data Fusion Method of Network Collaborative Manufacturing Based on Rule Chain

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

Metrics

本文评价

推荐阅读 0