信息技术的快速发展和工业4.0的深入推进，正驱动数字孪生技术在制造业智能化转型中的广泛应用。然而现有制造业数字孪生模型在语义层面仍存在局限性，具体表现为：对物理资产的多维属性描述不够全面，动态交互能力不足且缺乏统一的语义标准规范。为此，本文提出一种基于大语言模型的数字孪生模型语义重构方法。该方法利用数字孪生定义语言构建语义信息资产库，并融合大语言模型与知识图谱技术，实时生成和补充资产语义信息，从而实现数字孪生的动态语义重构。最后，以活塞加工产线为对象进行试验验证，结果表明，相较于传统手动建模方法，基于大语言模型的语义重构方法在处理复杂任务时效率与稳定性显著提升，能够快速响应动态变化的生产需求，并在可视化展示方面具有优势。

The rapid development of information technology and the deep advancement of Industry 4.0 are driving the widespread application of digital twin technology in the intelligent transformation of manufacturing. However, existing digital twin models in manufacturing still have limitations at the semantic level, specifically manifested as: inadequate multidimensional attribute descriptions of physical assets, insufficient dynamic interaction capabilities, and lack of unified semantic standards and specifications. To address this, this paper proposes a semantic reconstruction method for digital twin models based on large language models (LLMs). This method utilizes a digital twin definition language to build a semantic information asset library and integrates large language models with knowledge graph technology to generate and supplement asset semantic information in real time, thereby achieving dynamic semantic reconstruction of digital twins. Finally, experiments are conducted on a piston processing production line for validation. The results show that compared to traditional manual modeling methods, the semantic reconstruction method based on large language model significantly improves efficiency and stability in handling complex tasks, can quickly respond to dynamically changing production demands, and has advantages in visual presentation.