基于适用概率匹配与多维情境驱动的设计知识推送技术

张树有; 顾叶; 刘晓健; 谭建荣

doi:10.1631/FITEE.1700763

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基于适用概率匹配与多维情境驱动的设计知识推送技术

常规文章 | Updated：2022-05-19

- 基于适用概率匹配与多维情境驱动的设计知识推送技术
- A knowledge push technology based on applicable probability matching and multidimensional context driving
- 信息与电子工程前沿（英文） 2018年19卷第2期页码：235-245
- Affiliations：
  
  State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
- Author bio：
  
  Xiao-jian LIU, E-mail: liuxj@zju.edu.cn
- Funds：
  
  Project supported by the National Natural Science Foundation of China (No. 51675478), the Natural Science Foundation of Zhejiang Province, China (No. LY15E050004), and Youth Funds of the State Key Laboratory of Fluid Power & Mechatronic Systems, Zhejiang University
- DOI：10.1631/FITEE.1700763
  中图分类号： TP391
- 收稿：2017-11-16，
  
  修回：2018-;2-19，
  
  纸质出版：2018-02
- Accepted：
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张树有, 顾叶, 刘晓健, 等. 基于适用概率匹配与多维情境驱动的设计知识推送技术[J]. 信息与电子工程前沿（英文）, 2018,19(2):235-245.

Shu-you ZHANG, Ye GU, Xiao-jian LIU, et al. 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.
张树有, 顾叶, 刘晓健, 等. 基于适用概率匹配与多维情境驱动的设计知识推送技术[J]. 信息与电子工程前沿（英文）, 2018,19(2):235-245. DOI： 10.1631/FITEE.1700763.

Shu-you ZHANG, Ye GU, Xiao-jian LIU, et al. 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. DOI： 10.1631/FITEE.1700763.

摘要

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。

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.

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