关键词: 大语言模型, 多源异构数据, 机载产品, 计量溯源, 知识图谱

Abstract: The diverse range of airborne products and the extensive industrial chain involve a complex testing system, necessitating comprehensive metrological work. However, airborne product data resources primarily exist in unstructured, fragmented, and multimodal forms, making it difficult to conduct holistic analysis of various testing elements or trace the standardization of testing and product quality under a unified framework, thereby posing challenges to metrological tasks.To address this issue, the construction of knowledge graph for Metrological Traceability of Airborne Products (MT-AP) was explored by leveraging generative Large Language Models (LLMs). First, the resource types and metrological traceability process were systematically categorized, and an ontological model for the Knowledge Graph (KG) was constructed. Second, LLMs-based workflow modules were designed and integrated into workflow chains, a method for constructing the MT-AP knowledge graph based on these chains and prompt templates was proposed. Experiments were conducted using airborne product case data and workflow chains. The knowledge comprehension and naming capability scored above 0.91, while the text segmentation and knowledge decoupling capability scored above 0.83. The complex parameter extraction and structuring capability maintained scores above 0.85. The results demonstrate that the proposed method exhibits satisfactory performance in key tasks of MT-AP knowledge graph construction, providing technical support for metrological engineering of airborne products.

Key words: Large Language Model (LLM), multi-source heterogeneous data, airborne product, metrological traceability, knowledge graph