在船舶制造中，薄板产线工序复杂、设备多样且数据源异构，传统数字孪生建模依赖人工定义接口与属性，效率低且一致性不足。本文提出一种面向薄板产线的生成式数字孪生建模方法。通过构建涵盖工艺单元、设备、属性、感控单元与功能行为的5类核心本体，形成统一的语义结构；并设计本体到数字孪生定义语言（DTDL）的映射机制，实现领域知识的标准化表达。进一步引入大语言模型，利用其语义理解与生成能力，将自然语言描述自动转化为符合 DTDL v3 规范的结构化模型，并通过语义一致性校验确保模型逻辑与约束的正确性。实验结果表明，该方法在建模效率、语义准确性和属性覆盖度方面较人工方法分别提升约 79.6%、9.4% 和 28.5%，显著提升了数字孪生建模的自动化与规范化水平。

In shipbuilding, thin-plate production lines are characterized by complex processes, diverse equipment, and heterogeneous data sources. Traditional digital twin modeling relies heavily on manually defined interfaces and attributes, resulting in low efficiency and limited consistency. This paper proposes a generative digital twin modeling method tailored for thin-plate production lines. The method constructs five core ontologies—covering process units, equipment, attributes, sensing-control units, and functional behaviors—to establish a unified semantic structure. Furthermore, a mapping mechanism from ontology to the digital twins definition language (DTDL) is designed to enable standardized representation of domain knowledge. A large language model is then introduced to leverage its semantic understanding and generative capabilities, automatically transforming natural language descriptions into structured models compliant with the DTDL v3 specification. Semantic consistency validation is performed to ensure the correctness of model logic and constraints. Experimental results demonstrate that, compared with manual approaches, the proposed method improves modeling efficiency, semantic accuracy, and attribute coverage by approximately 79.6%, 9.4%, and 28.5%, respectively—significantly enhancing the automation and standardization of digital twin modeling.