关键词: 物联网, 知识图谱, 大模型微调, 非智能传感器, 实体发现

Abstract: In the field of Industrial Internet of Things, device entity discovery constitutes a critical component of device management. Compared to intelligent sensors, the discovery process of non-intelligent sensors is particularly complex due to their lack of inherent communication protocols, making efficient and accurate identification of non-intelligent devices a pressing technical challenge. The paper proposes a knowledge graph and large language model (LLM)-based efficient recognition scheme for non-intelligent sensors. The solution first constructs a comprehensive knowledge graph by extracting attribute values from non-intelligent sensors. Subsequently, the knowledge graph information is fed into a large language model for fine-tuning, with optimal model parameters obtained through experimental optimization. Test results demonstrate that the method achieves a recognition accuracy of 96%, significantly enhancing identification efficiency. The proposed scheme is recognized as applicable to large-scale IoT application scenarios due to its high efficiency and accuracy, demonstrating broad application value across various Internet of Things environments.

Key words: Keywords: Internet of Things, knowledge graph, large model fine-tuning, non-smart sensors, entity discovery