用大模型把法规文件变数字孪生的智能规则，让基建规划更合规、可解释。
LSDTs: LLM-Augmented Semantic Digital Twins for Adaptive Knowledge-Intensive Infrastructure Planning
- 用大模型从法规文档中提取知识，构建结构化语义框架
- 在飓风背景下实现符合规范的海上风电场布局优化
- 适合需要合规性与可解释性的复杂基建规划场景
数字孪生（DTs）为管理复杂基础设施系统提供了强大工具，但其效果常受限于非结构化知识的整合难题。大语言模型（LLMs）在提取和组织多样化文本信息方面表现出色，为此我们提出LSDTs（LLM-Augmented Semantic Digital Twins）框架，利用LLMs从环境法规、技术指南等非结构化文档中提取规划知识，并组织为正式本体。该本体构成语义层，驱动数字孪生——物理系统的虚拟模型——实现符合监管要求的真实场景模拟。我们在马里兰州海上风电场规划案例中评估LSDTs，包括飓风桑迪期间的应用。结果表明，LSDTs支持可解释的、符合规范的布局优化，实现高保真度仿真，并提升规划适应性。本研究展示了生成式AI与数字孪生结合，在复杂知识驱动规划任务中的潜力。
原文摘要 · Abstract (English)
Digital Twins (DTs) offer powerful tools for managing complex infrastructure systems, but their effectiveness is often limited by challenges in integrating unstructured knowledge. Recent advances in Large Language Models (LLMs) bring new potential to address this gap, with strong abilities in extracting and organizing diverse textual information. We therefore propose LSDTs (LLM-Augmented Semantic Digital Twins), a framework that helps LLMs extract planning knowledge from unstructured documents like environmental regulations and technical guidelines, and organize it into a formal ontology. This ontology forms a semantic layer that powers a digital twin-a virtual model of the physical system-allowing it to simulate realistic, regulation-aware planning scenarios. We evaluate LSDTs through a case study of offshore wind farm planning in Maryland, including its application during Hurricane Sandy. Results demonstrate that LSDTs support interpretable, regulation-aware layout optimization, enable high-fidelity simulation, and enhance adaptability in infrastructure planning. This work shows the potential of combining generative AI with digital twins to support complex, knowledge-driven planning tasks.
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