Cross Platform Lightweight and Efficient Digital Twin Model Interaction Component for Equipment Inventory Management
With the increasingly complex demand for equipment inventory management, traditional two-dimensional display methods are no longer able to meet users' requirements for information intuitiveness and interactivity. In addition, the geometric twin model of equipment and the product performance prediction model are important components of data elements in the digital space, so it is necessary to explore the interaction methods and linkage mechanisms. This article proposes a cross platform lightweight 3D scene interaction solution based on Vue framework and Three.js for equipment inventory management. Provide rich 3D scene interaction functions through track control, model drilling, highlight picking, explosion effects, and FMU model association. And through methods such as 3D model object picking, adaptive camera field of view, 3D model compression, page caching, and time-sharing function optimization of front-end loading speed, the interactive performance and user experience of 3D models on the web have been effectively improved. The experimental results show that these optimization methods significantly reduce model loading time, improve system response speed and stability, and provide new ideas and methods for visualizing equipment inventory management. At the same time, by combining the FMU model, the geometric twin model of inventory equipment can be combined with the product physical performance prediction model to provide component level performance prediction and visualization functions.
Authors
- Xihua Liu (ORCID: https://orcid.org/0000-0001-5996-3555)
- Yaohui Zhang
- Wenjie Gao (ORCID: https://orcid.org/0000-0001-9456-8884)
- Zhen Liu (ORCID: https://orcid.org/0000-0001-8317-1355)
- Yanmei Luo (ORCID: https://orcid.org/0000-0002-9432-1424)
- Guiyu Lai
Institutions
- China Academy of Engineering Physics (CN)
Publication Details
- Published
- 2025-04-25
- DOI
- https://doi.org/10.1109/iccea65460.2025.11103034
- Primary Topic
- Digital Transformation in Industry
- Type
- article
- Field-Weighted Citation Impact
- 0.00