Identification and Resolution Technology Based on Multi-Layer Knowledge Graph for Industrial Internet of Things
Qidi Wang, Jinlong Sun, Gaohe Liu, Jiaji Zhang, Shuqiang Gui, Qiankun Wang · 2024
With the advancement of Industrial Internet of Things (IIoT) technology, there is an increasing demand for efficient data storage solutions and robust data processing capabilities. The IIoT involves numerous devices and sensors that generate massive data streams, which require efficient storage and processing for real-time analysis and decision-making. This paper proposes an information management architecture based on multi-layer knowledge graphs combined with identification and resolution technology. The approach utilizes knowledge graph technology to structure multi-layer information within the IIoT and introduces identification and resolution technology, using Handle encoding to uniquely identify entities for cross-layer querying and data exchange. The BiLSTM-CRF (Bidirectional Long Short-Term Memory - Conditional Random Field) algorithm is employed for hierarchical entity classification, enhancing data consistency and accuracy. This study provides new insights and methods for data management in the IIoT, significantly improving the efficiency and quality of data management.