DTL-GNN: Digital Twin Lightweight Method Based on Graph Neural Network

Chengjun Li, Liguo Yao, Yao Lu, Songsong Zhang and Taihua Zhang ()
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Chengjun Li: School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang 550025, China
Liguo Yao: School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang 550025, China
Yao Lu: School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang 550025, China
Songsong Zhang: Guizhou Qunjian Precision Machinery Company, Zunyi 563099, China
Taihua Zhang: School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang 550025, China

Future Internet, 2025, vol. 17, issue 2, 1-24

Abstract: In the digital twin system of mechatronics engineering, the scale and accuracy of models are continually improving. Nevertheless, this growth can hinder real-time interaction and decision-making accuracy within digital twins. The resulting delay impacts the entire system’s reliability by reducing its response speed and real-time decision-making. Consequently, there is an imperative demand for a lightweight approach to tackle the challenges arising from the escalating scale of digital twin virtual entity models. This paper presents a digital twin methodology that is lightweight. The procedure comprises three primary phases: graph data modeling, graph neural network analysis, and hierarchical simplification of virtual entities. Specifically, the graph neural network method proposed in this article is used to classify the graph data of virtual entities. Then, the model is hierarchically simplified based on the classification. Finally, experiments were conducted on factory and robotic arm datasets to evaluate the proposed method. The experimental results indicate that the DTL-GNN method can reduce system redundancy while preserving the essential features of virtual entities.

Keywords: digital twin; graph neural networks; lightweight methods; virtual model (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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