基于 SysML v2 的协作式基于模型的系统工程中 LLM 辅助的语义对齐与集成
LLM-Assisted Semantic Alignment and Integration in Collaborative Model-Based Systems Engineering Using SysML v2
AI总结：
研究基于 SysML v2 的跨组织协作中语义对齐挑战，提出由提示驱动的结构化方法，利用 SysML v2 构造，经迭代开发对齐方法与交互提示，以实现 LLM 辅助的语义对齐集成。
摘要：
基于模型的系统工程（MBSE）中的跨组织协作在跨独立开发的系统模型实现语义对齐方面面临诸多挑战。SysML v2 引入了增强的结构模块化和形式语义，为可互操作建模提供了更坚实的基础。同时，基于 GPT 的大语言模型（LLMs）为辅助模型理解和集成提供了新能力。本文提出了一种结构化的、由提示驱动的方法，用于基于 LLM 的 SysML v2 模型语义对齐。核心贡献在于对齐方法和交互提示的迭代开发，包括模型提取、语义匹配和验证。该方法利用 SysML v2 构造（如别名、导入和元数据扩展）来支持可追溯的、软对齐集成。通过一个测量系统的示例，使用基于 GPT 的 LLM 进行了演示。讨论了优点和局限性。
英文摘要：
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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