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| Title | Construction Of High-Precision Digital Twin Model And Real-Time Data Fusion Technology |
|---|---|
| ID_Doc | 15810 |
| Authors | Wang Y.; Zheng B.; Xing K. |
| Year | 2025 |
| Published | Proceedings - 2025 International Conference on Digital Analysis and Processing, Intelligent Computation, DAPIC 2025 |
| DOI | http://dx.doi.org/10.1109/DAPIC66097.2025.00103 |
| Abstract | High-precision digital twin model construction and real-time data fusion technology are the core of digital twin technology, which jointly support the whole life cycle simulation, verification, prediction and control of physical entities. In this paper, the construction process of high-precision digital twin model is deeply discussed, including four stages: data acquisition and processing, preliminary model establishment, model refinement and optimization, simulation verification and iterative update. In terms of key technologies and algorithms, this paper analyzes 3D modeling and geometric processing technology, physical properties and behavior simulation technology, and data fusion and real-time update technology. In particular, this paper discusses the role of data fusion algorithms such as Kalman filter in improving the accuracy and reliability of data. In the part of real-time data fusion technology, this paper discusses the data source and diversity, the fusion method and process, and the strategy of using clustering algorithm and machine learning model to improve the efficiency of data fusion. In the face of challenges, this paper points out some problems, such as data security and privacy protection, insufficient generalization ability of models, difficulties in data acquisition and integration, bottlenecks in real-time performance and lack of industry standards, and puts forward corresponding improvement measures. Finally, this paper looks forward to the future development of digital twin technology, and emphasizes the importance of data-driven intelligent modeling methods, cross-industry standardization and innovative applications in intelligent manufacturing, smart cities, smart medical care and other fields. © 2025 IEEE. |
| Author Keywords | digital twin model; high-precision; real-time data fusion |
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