云制造模式下面向加工设备的服务聚类与初选方法
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摘要
云制造模式下,加工设备量大类多,难以实现服务供给与服务请求的快速匹配。针对该问题,首先对加工设备的制造属性进行描述,改进了K-means算法随机选取初始聚类中心与提前确定聚类数目的不足,提出了基于相似度的加工设备云服务聚类方法。在对云服务进行聚类预处理并形成多个服务类簇的基础上,基于可拓论建立了服务请求与云服务类簇的物元模型,提出了加工设备云服务集合的初选方法。以云平台上加工中心云服务为例,验证了所提模型和方法的有效性。
It is difficult to select the service supplies of processing equipments with rich varieties and huge quantities to match the service demands quickly under the cloud manufacturing mode.To solve this problem,the manufacturing capacities of processing equipments were described,the traditional K-means algorithm with the initial cluster centers randomly and the number of clusters in advance was improved,and the similarity-based clustering method for cloud services of processing equipments was proposed.On the basis of clustering preprocessing of cloud services and generation of a plurality of class clusters,the extension theory-based matter-element models of service demands and cloud service class clusters were established,and the initial selection method for cloud service sets of processing equipments was presented.The cloud services of machining center on the cloud platform was taken as an example to illustrate the effectiveness of the proposed models and methods.
It is difficult to select the service supplies of processing equipments with rich varieties and huge quantities to match the service demands quickly under the cloud manufacturing mode.To solve this problem,the manufacturing capacities of processing equipments were described,the traditional K-means algorithm with the initial cluster centers randomly and the number of clusters in advance was improved,and the similarity-based clustering method for cloud services of processing equipments was proposed.On the basis of clustering preprocessing of cloud services and generation of a plurality of class clusters,the extension theory-based matter-element models of service demands and cloud service class clusters were established,and the initial selection method for cloud service sets of processing equipments was presented.The cloud services of machining center on the cloud platform was taken as an example to illustrate the effectiveness of the proposed models and methods.
引文
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[19]LIU Jingqing,GUO Dongjin,YE Ping.Improved K average spatial clustering method for nodes of water distribution system[J].Journal of Zhejiang University,2015,49(11):2128-2134(in Chinese).[柳景青,郭东进,叶萍.改进的给水管网节点K均值空间聚类[J].浙江大学学报,2015,49(11):2128-2134.]
[20]CHEN Xue,HUANG Zhili,LUO Jian.Approach for triangular fuzzy number-based uncertain multi-attribute decision making based on relative similarity degree relation[J].Control and Decision,2016,31(12):2232-2240(in Chinese).[陈雪,黄智力,罗键.基于相对相似度关系的三角模糊数型不确定多属性决策法[J].控制与决策,2016,31(12):2232-2240.]
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[2]WANG Shilong,SONG Wenyan,KANG Ling,et al.Manufacturing resource allocation based on cloud manufacturing[J].Computer Integrated Manufacturing Systems,2012,18(7):1396-1405(in Chinese).[王时龙,宋文艳,康玲,等.云制造环境下的制造资源优化配置研究[J].计算机集成制造系统,2012,18(7):1396-1405.]
[3]CHENG Ying,TAO Fei,ZHAO Dongming,et al.Modeling of manufacturing service supply-demand matching hypernetwork in service-oriented manufacturing systems[J].Robotics and Computer-Integrated Manufacturing,2017,45:59-72.
[4]JIAO Hejun,ZHANG Jing,LI Junhuai,et al.Research on cloud manufacturing service discovery based on latent semantic preference about OWL-S[J].International Journal of Computer Integrated Manufacturing,2017,30(4/5):433-441.
[5]CHEN Youling,LIU Chuanbiao,YANG Weiqi,et al.Assessment and selection for capability resource demand in cloud manufacturing environment[J].Computer Integrated Manufacturing Systems,2017,23(10):2304-2312(in Chinese).[陈友玲,刘传彪,阳玮琦,等.云制造环境下能力资源需求的评价与选择[J].计算机集成制造系统,2017,23(10):2304-2312.]
[6]MA Wenlong,ZHU Linan,WANG Wanliang.Cloud service selection model based on QoS-aware in cloud manufacturing environment[J].Computer Integrated Manufacturing Systems,2014,20(5):1246-1254(in Chinese).[马文龙,朱李楠,王万良.云制造环境下基于QoS感知的云服务选择模型[J].计算机集成制造系统,2014,20(5):1246-1254.]
[7]HE Ketai,ZHU Daoyun.Quality evaluation of cloud manufacturing service[J].Computer Integrated Manufacturing Systems,2018,24(1):53-62(in Chinese).[贺可太,朱道云.云制造服务质量评价[J].计算机集成制造系统,2018,24(1):53-62.]
[8]YIN Chao,LUO Peng,LI Xiaobin,et al.Optimal selection method for machine tool resource based on multi-agent[J].Computer Integrated Manufacturing Systems,2016,22(6):1474-1484(in Chinese).[尹超,罗鹏,李孝斌,等.基于Multi-Agent的机床装备资源优化选择方法[J].计算机集成制造系统,2016,22(6):1474-1484.]
[9]KOTEKAR S,KAMATH S S.Enhancing service discovery using cat swarm optimisation based Web service clustering[J].Perspectives in Science,2016,8(1):715-717.
[10]WANG Yongming,ZHANG Yingjun,XIE Binhong,et al.Semantic web service discovery based on fuzzy clustering optimization[J].Computer Engineering,2013,39(7):219-223(in Chinese).[王永明,张英俊,谢斌红,等.基于模糊聚类优化的语义Web服务发现[J].计算机工程,2013,39(7):219-223.]
[11]LIU Bocheng,CHEN Qingkui.Fuzzy clustering partition model for computer cluster in cloud computing[J].Computer Science,2011,38(10):157-168(in Chinese).[刘伯成,陈庆奎.云计算中的集群资源模糊聚类划分模型[J].计算机科学,2011,38(10):157-168.]
[12]TONG Yifei,LI Dongbo.Attribute-based dynamic clustering of grid resources[J].Computer Integrated Manufacturing Systems,2008,14(4):813-820(in Chinese).[童一飞,李东波.基于属性的网格资源动态聚类研究[J].计算机集成制造系统,2008,14(4):813-820.]
[13]ZHANG Xianquan,WANG Liang,CHEN Weiru.Research on similar resources based on fuzzy clustering in grid environment[J].Computer Systems&Applications,2008,17(6):27-32(in Chinese).[张险全,王亮,陈未如.网格环境中相似资源的模糊聚类研究[J].计算机系统应用,2008,17(6):27-32.]
[14]HUANG Xing,LIU Xiaoqing,CAO Buqing,et al.MSCA:Mashup service clustering approach integrating K-means and Agnes algorithms[J].Journal of Chinese Computer Systems,2015,36(11):2492-2497(in Chinese).[黄兴,刘小青,曹步清,等.融合K-Means与Agnes的Mashup服务聚类方法[J].小型微型计算机系统,2015,36(11):2492-2497.]
[15]CAO B,LIU X,LIU J,et al.Domain-aware Mashup service clustering based on LDA topic model from multiple data sources[J].Information&Software Technology,2017,90(1):40-54.
[16]ARMSTRONG J J,ZHU M,HIRDES J P,et al.K-means cluster analysis of rehabilitation service users in the home health care system of Ontario:examining the heterogeneity of a complex geriatric population[J].Archives of Physical Medicine&Rehabilitation,2012,93(12):2198-2205.
[17]HUANG Dafu,REN Jingzheng,JIANG Hanmei,et al.Prediction of natural gas demand based on extension classified model[J].Computers and Applied Chemistry,2011,28(5):617-619(in Chinese).[黄大富,任竞争,江寒梅,等.用可拓聚类模型预测天然气的需求[J].计算机与应用化学,2011,28(5):617-619.]
[18]LIU Nianping,ZHAO Chunxia,WANG Hongtu,et al.Research on extension clustering prediction method for coal seam floor water inrush risk[J].Mining Safety&Environmental Protection,2016,43(2):63-66(in Chinese).[刘年平,赵春侠,王宏图,等.煤层底板突水危险性的可拓聚类预测方法研究[J].矿业安全与环保,2016,43(2):63-66.]
[19]LIU Jingqing,GUO Dongjin,YE Ping.Improved K average spatial clustering method for nodes of water distribution system[J].Journal of Zhejiang University,2015,49(11):2128-2134(in Chinese).[柳景青,郭东进,叶萍.改进的给水管网节点K均值空间聚类[J].浙江大学学报,2015,49(11):2128-2134.]
[20]CHEN Xue,HUANG Zhili,LUO Jian.Approach for triangular fuzzy number-based uncertain multi-attribute decision making based on relative similarity degree relation[J].Control and Decision,2016,31(12):2232-2240(in Chinese).[陈雪,黄智力,罗键.基于相对相似度关系的三角模糊数型不确定多属性决策法[J].控制与决策,2016,31(12):2232-2240.]
[21]CAI Wen.Extension theory and its applications[J].Chinese Science Bulletin,1999,44(7):673-682(in Chinese).[蔡文.可拓论及其应用[J].科学通报,1999,44(7):673-682.]
[22]YANG Chunyan,ZHANG Yongjun,CAI Wen.Study on extension set and its applications[J].Mathematics in Practice and Theory,2002,32(2):301-308(in Chinese).[杨春燕,张拥军,蔡文.可拓集合及其应用研究[J].数学的实践与认识,2002,32(2):301-308.]
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