用大模型和知识图谱自动生成系统设计矩阵，提升复杂系统架构设计效率。
Retrieval Augmented (Knowledge Graph), and Large Language Model-Driven Design Structure Matrix (DSM) Generation of Cyber-Physical Systems
- 结合大模型与知识图谱检索生成设计结构矩阵
- 在螺丝刀和立方体卫星案例中准确识别组件关系
- 适合系统工程与智能设计领域研究者参考
我们探索大型语言模型（LLMs）、检索增强生成（RAG）以及基于图的RAG（GraphRAG）在生成设计结构矩阵（DSM）方面的潜力。在两个不同应用场景——具有已知架构参考的电钻和立方体卫星上进行了测试，评估了这些方法在两项关键任务上的表现：确定预定义组件之间的关系，以及更复杂的组件识别及其后续关系推断。通过评估每个DSM元素及整体架构来衡量性能。尽管存在设计和计算挑战，但仍发现自动化生成DSM的可行路径，所有代码均已公开，便于复现并接收领域专家反馈。
原文摘要 · Abstract (English)
We explore the potential of Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Graph-based RAG (GraphRAG) for generating Design Structure Matrices (DSMs). We test these methods on two distinct use cases -- a power screwdriver and a CubeSat with known architectural references -- evaluating their performance on two key tasks: determining relationships between predefined components, and the more complex challenge of identifying components and their subsequent relationships. We measure the performance by assessing each element of the DSM and overall architecture. Despite design and computational challenges, we identify opportunities for automated DSM generation, with all code publicly available for reproducibility and further feedback from the domain experts.
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