计算机集成制造系统 ›› 2020, Vol. 26 ›› Issue (9): 2541-2551.DOI: 10.13196/j.cims.2020.09.023

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基于用户知识存量的3D打印云平台知识服务方法

成方敏,余隋怀,初建杰,樊佳爽,陈健   

  1. 西北工业大学陕西省工业设计工程实验室
  • 出版日期:2020-09-30 发布日期:2020-09-30
  • 基金资助:
    国家重点研发计划专项资助项目(2017YFB1104205);高等学校学科创新引智计划资助项目(B13044)。

Knowledge service method for 3D printing cloud platform based on user knowledge stock

  • Online:2020-09-30 Published:2020-09-30
  • Supported by:
    Project supported by the National Key Research and Development Program,China(No.2017YFB1104205),and the National 111 Program,China(No.B13044).

摘要: 为了提高3D打印云服务平台知识服务能力,满足平台上产品开发任务中用户个性化的知识需求,提出一种基于用户知识存量的知识服务方法。综合考虑了用户知识需求与任务知识需求,在对3D打印云服务平台任务流程与知识服务特点分析的基础上,建立了知识服务方法模型;构建了用户知识体系,根据用户任务表现定量测度知识存量与知识需求度;构建了3D打印知识领域本体,并基于本体对知识资源与任务进行建模;综合任务与知识资源之间的语义相似度和用户的知识需求度计算了知识推荐系数,通过知识资源过滤实现了用户个性化知识服务;最后,以3D打印云平台上的玩具汽车外观设计任务为例,验证了该方法的可行性与有效性

关键词: 知识服务, 3D打印云平台, 个性化知识需求, 用户知识存量, 知识推荐系数, 产品开发

Abstract: To improve the knowledge service capability of 3D printing cloud service platform,and to meet the personalized knowledge needs of users in product development tasks on the platform,a knowledge service method based on user knowledge stock was proposed.Considering users' knowledge needs and task knowledge needs comprehensively,a knowledge service method model was established based on the analysis of task flow and knowledge service characteristics of 3D printing cloud platform.The user knowledge system was constructed,and the knowledge inventory and knowledge demand were quantitatively measured according to the user task performance.The ontology of 3D printing knowledge domain was constructed.Based on the ontology,the knowledge resource and task were modeled.The semantic similarity between the task and the knowledge resource and the user's knowledge demand were used to calculate the knowledge recommendation coefficient to filter the knowledge resource and realize the user's personalized knowledge service.A design task of toy cars on 3D printing cloud platform was presented as an example to verify the feasibility and effectiveness of the proposed method.

Key words: knowledge service, 3D printing cloud platform, personalized knowledge needs, user knowledge stock, knowledge recommendation coefficient, product development

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