ChatTwin: Toward Automated Digital Twin Generation for Data Center via Large Language Models
Minghao Li, Ruihang Wang, Xin Zhou, Zhaomeng Zhu, Yonggang Wen, Rui Tan
Abstract
Digital twin has been applied in various industrial fields to represent physical systems. However, the design of high-fidelity digital scenes is challenging in that it often requires intensive manual processes and domain expertise to edit the 3D models or description documents. To reduce human efforts, this paper proposes ChatTwin, a conversational system that leverages the power of GPT-4 to automate the generation of scene description documents for digital twins. ChatTwin assists scene generation by i) segmenting user-input prompts, ii) generating scenes with segmented prompts, and iii) optimizing the generated content. Specifically, the Segment-and-Generate (SG) workflow decomposes the long-text generation into several subtasks and reduces the complexity of the original task. The evaluation through our data center digital twin system shows that ChatTwin outperforms other baselines in terms of generation accuracy and efficiency.