用大模型辅助对齐跨组织系统模型，提升协同设计的语义一致性。
LLM-Assisted Semantic Alignment and Integration in Collaborative Model-Based Systems Engineering Using SysML v2
- 基于提示工程的迭代方法，结合模型提取与匹配实现语义对齐。
- 利用SysML v2的别名、导入和元数据扩展，支持可追溯的软对齐集成。
- 适合需要跨团队协作建模的系统工程领域，尤其关注模型互操作性。
在基于模型的系统工程（MBSE）中，跨组织协作面临独立开发系统模型之间语义不一致的挑战。SysML v2通过增强的结构模块化和形式化语义，为模型互操作提供了更坚实的基础。同时，基于GPT的大语言模型（LLM）为理解与整合模型提供了新能力。本文提出一种结构化、提示驱动的LLM辅助语义对齐方法，核心在于迭代开发对齐流程与交互提示，包含模型提取、语义匹配与验证。该方法利用SysML v2中的别名（alias）、导入（import）和元数据扩展等机制，支持可追溯的软对齐集成。通过一个测量系统示例验证了其有效性，讨论了优势与局限性。
原文摘要 · Abstract (English)
Cross-organizational collaboration in Model-Based Systems Engineering (MBSE) faces many challenges in achieving semantic alignment across independently developed system models. SysML v2 introduces enhanced structural modularity and formal semantics, offering a stronger foundation for interoperable modeling. Meanwhile, GPT-based Large Language Models (LLMs) provide new capabilities for assisting model understanding and integration. This paper proposes a structured, prompt-driven approach for LLM-assisted semantic alignment of SysML v2 models. The core contribution lies in the iterative development of an alignment approach and interaction prompts, incorporating model extraction, semantic matching, and verification. The approach leverages SysML v2 constructs such as alias, import, and metadata extensions to support traceable, soft alignment integration. It is demonstrated with a GPT-based LLM through an example of a measurement system. Benefits and limitations are discussed.
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