# DTL-GNN: Digital Twin Lightweight Method Based on Graph Neural Network
## 3 Virtual Model Lightweight Approach
### Method Main Process
Experiments have shown that the classification accuracy of DTL-GNN is improved by 25.4% on the overall class model compared to traditional algorithms and by 38.4% on independent models. DTL-GNN can increase the proportion of essential parts in the model, reduce file size and system burden, and ensure accurate output. DTL-GNN has demonstrated significant advantages in mechatronics integration but may face challenges in other domains, such as data privacy in the medical field, real-time processing demands in manufacturing, and data integration in traditional industries. Future work could focus on improving knowledge extraction from DTL databases, expanding DTL-GNN's applicability across a broader range of digital twin scenarios, and exploring its application in the construction industry. In particular, future research could investigate how DTL-GNN can be adapted to address the unique challenges of construction models, such as structural complexity, dynamic environmental factors, and real-time resource optimization. Such advancements would reinforce DTL-GNN's advantages in industrial and cross-disciplinary applications and foster ongoing innovation in digital twin technology. C.L. : Conceptualization, Methodology, Software, Formal analysis, Investigation, Data Curation, Writing--Original Draft, Visualization. L.Y. : Conceptualization, Writing--Review and Editing, Supervision, Project administration, Funding acquisition. Y.L. : Supervision, Project administration, Investigation. S.Z. : Validation, Investigation, Supervision. T.Z. : Conceptualization, Writing--Review and Editing, Supervision, Project administration, Funding acquisition. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant No.72061006), Guizhou Provincial Basic Research Program (Natural Science) (Grant No. Qiankehejichu-ZK[2024] General 424), and Academic New Seedling Foundation Project of Guizhou Normal University (Grant No. Qianshixinmiao-[2021]A30). The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding author. Author [PERSON] was employed by the company Guizhou Qunjian Precision Machinery Company, Zunyi. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## References

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