Lijun Tang
Papers
1
Total Citations
10
H-Index
1
About
Lijun Tang is a leading researcher in intelligent power grid inspection, with a primary focus on the integration of robotics, multi-source data fusion, and knowledge graph technologies. His most-cited work, “Multi-source fusion of substation intelligent inspection robot based on knowledge graph: A overview and roadmap” (2022, 10 citations), provides a comprehensive framework for enhancing the autonomy and reliability of substation patrol robots. Tang’s major contribution lies in proposing a knowledge-graph-driven approach to fuse heterogeneous sensor data—such as visual, thermal, and acoustic inputs—enabling robots to interpret complex substation environments and detect equipment anomalies more effectively. This work addresses the critical challenge of expanding substation areas and increasing load demands, offering a roadmap for next-generation intelligent inspection systems. Tang’s research is highly relevant to the energy sector, directly supporting the safe and efficient operation of power grids. His achievements include advancing the practical deployment of robotics in high-stakes industrial settings, bridging the gap between theoretical AI methods and real-world engineering applications. For students and researchers, Tang’s work exemplifies how knowledge graphs can transform traditional inspection tasks into intelligent, data-driven processes.
Research Focus
Key Achievements
Top Papers
- 1