A Knowledge Graph-Driven Approach for Architecture Design Space Generation and Reduction

Yanshao Sun, Ru Wang*, Yu Huang, Zhendong Liu

*此作品的通讯作者

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

The process of architecture design for complex systems relies on domain knowledge, which involves identifying architecture decision issues from text-based requirements and generating the architecture design space. As the complexity of the system increases, higher requirement is proposed for the storage and reuse of design knowledge. To address this challenge, this paper proposes a knowledge graph-driven approach for architecture design space generating and reduction. To represent and store knowledge in architecture decisions, a decision-oriented design knowledge graph (DDKG) is constructed by analyzing the relationship among requirements, functions, and knowledge in architecture decisions. The nodes in DDKG to generate the architecture design space are matched with decision issue keywords extracted from the user’s requirement text. Moreover, the design space is generated from nodes matched in DDKG and reduced by forming a Constraint Satisfaction Problem (CSP) model. The method proposed in this paper has been validated in the generation and reduction of architectural design space for the first-stage separation systems of launch vehicles.

源语言英语
主期刊名Advances in Mechanical Design - The Proceedings of the 2023 International Conference on Mechanical Design, ICMD 2023
编辑Jianrong Tan, Yu Liu, Hong-Zhong Huang, Jingjun Yu, Zequn Wang
出版商Springer Science and Business Media B.V.
129-143
页数15
ISBN(印刷版)9789819709212
DOI
出版状态已出版 - 2024
活动International Conference on Mechanical Design, ICMD 2023 - Chengdu, 中国
期限: 20 10月 202322 10月 2023

出版系列

姓名Mechanisms and Machine Science
155 MMS
ISSN(印刷版)2211-0984
ISSN(电子版)2211-0992

会议

会议International Conference on Mechanical Design, ICMD 2023
国家/地区中国
Chengdu
时期20/10/2322/10/23

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