Full Text:   <2046>

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CLC number: TP391

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2018-02-19

Cited: 0

Clicked: 6053

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Xiao-jian Liu

http://orcid.org/0000-0001-8147-9954

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Frontiers of Information Technology & Electronic Engineering  2018 Vol.19 No.2 P.235-245

http://doi.org/10.1631/FITEE.1700763


A knowledge push technology based on applicable probability matching and multidimensional context driving


Author(s):  Shu-you Zhang, Ye Gu, Xiao-jian Liu, Jian-rong Tan

Affiliation(s):  State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   liuxj@zju.edu.cn

Key Words:  Product design, Knowledge push, Applicable probability matching, Multidimensional context, Personalization


Shu-you Zhang, Ye Gu, Xiao-jian Liu, Jian-rong Tan. A knowledge push technology based on applicable probability matching and multidimensional context driving[J]. Frontiers of Information Technology & Electronic Engineering, 2018, 19(2): 235-245.

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Abstract: 
Actively pushing design knowledge to designers in the design process, what we call ‘knowledge push’, can help improve the efficiency and quality of intelligent product design. A knowledge push technology usually includes matching of related knowledge and proper pushing of matching results. Existing approaches on knowledge matching commonly have a lack of intelligence. Also, the pushing of matching results is less personalized. In this paper, we propose a knowledge push technology based on applicable probability matching and multidimensional context driving. By building a training sample set, including knowledge description vectors, case feature vectors, and the mapping Boolean matrix, two probability values, application and non-application, were calculated via a Bayesian theorem to describe the matching degree between knowledge and content. The push results were defined by the comparison between two probability values. The hierarchical design content models were built to filter the knowledge in push results. The rules of personalized knowledge push were sorted by multidimensional contexts, which include design knowledge, design context, design content, and the designer. A knowledge push system based on intellectualized design of CNC machine tools was used to confirm the feasibility of the proposed technology in engineering applications.

基于适用概率匹配与多维情境驱动的设计知识推送技术

概要:为了提高产品智能设计过程中设计知识的使用效率和质量,有必要向设计人员主动推送设计知识。知识推送主要包括知识匹配和匹配结果的合理推送两个方面。针对现有知识匹配通常缺乏智能性和匹配结果推送缺少个性化的问题,提出基于适用概率匹配和多维情境驱动的设计知识推送技术。构建包括设计知识表示向量、设计案例特征向量和映射布尔矩阵等的训练样本集,通过贝叶斯理论计算设计知识适用与不适用于设计内容的概率,即二者之间的匹配度,得到推送知识集。构建等级化设计内容模型对推送知识集进行过滤,通过设计知识、设计上下文、设计内容和设计人员等多维情境驱动,实现个性化的设计知识推送。在数控机床智能设计平台中的知识推送应用,证明了该技术的可行性和正确性。

关键词:产品设计;知识推送;适用概率匹配;多维情境;个性化

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