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云制造环境下知识云能力量化评估方法研究
引用本文:阴艳超,牛红伟. 云制造环境下知识云能力量化评估方法研究[J]. 农业机械学报, 2016, 47(8): 325-332,355
作者姓名:阴艳超  牛红伟
作者单位:昆明理工大学,昆明理工大学
基金项目:国家自然科学基金项目(51365022)和云南省教育厅重点项目(2014Z032)
摘    要:云制造是一种智能化制造新模式,知识是支撑云制造服务系统运行的核心。但对知识云及其服务能力、影响因素和评估方法的研究尚不够深入。在建立知识云能力整体特征评估体系基础上,提出了一种基于云推理的量化评估方法。通过对知识能力评估指标的云化处理,采用知识能力云的期望和超熵对服务效能满意度进行定量评价,并构建了知识能力服务效能评估云推理发生器,实现通过知识能力变化云描述服务满意度的变化情况,为最大限度保障云制造系统知识资源与知识能力共享和按需使用提供智能化支持。实验表明,该方法较好地实现了知识云能力的最大效能服务过程,对云制造环境下知识云能力服务效能综合评估进行了有益的探索。

关 键 词:云制造  知识云能力  服务效能  量化评估  云推理
收稿时间:2015-12-28

Quantitative Evaluation Approach of Cloud Capability Service for Knowledge in Cloud Manufacturing
Yin Yanchao and Niu Hongwei. Quantitative Evaluation Approach of Cloud Capability Service for Knowledge in Cloud Manufacturing[J]. Transactions of the Chinese Society for Agricultural Machinery, 2016, 47(8): 325-332,355
Authors:Yin Yanchao and Niu Hongwei
Affiliation:Kunming University of Science and Technology and Kunming University of Science and Technology
Abstract:Cloud manufacturing is a new service-oriented intelligent manufacturing model, which contains plentiful knowledge to support service system operating, and the manufacturing resources and capabilities are shared (as services) through the internet. However, there is a lack of in-depth research in knowledge cloud capability service, influence factors and its evaluation approach. The comprehensive evaluation system of the whole features was established for knowledge cloud capability service, and a new quantificational knowledge cloud capability evaluation approach was presented based on cloud reasoning. The expected value and hyper entropy of knowledge capability cloud were used to evaluate the satisfaction of service performance by cloud processing the evaluation indicators, and a cloud reasoning controller was designed to depict the satisfaction change style of knowledge capability, which provided support for the intelligent shared service of manufacturing resources and capability in cloud manufacturing system, e.g. design as a service, simulation as a service, production as a service, assembling as a service, and testing as a service. The result of simulating experiments showed that the validity and effectiveness of the presented approach provided beneficial exploration for evaluating knowledge cloud capability service of cloud manufacturing.
Keywords:cloud manufacturing  cloud capability service of knowledge  service performance  comprehensive evaluation  cloud reasoning
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