Modular design of complex engineering systems is a universal technology for rapid system design in the modern industrial field. Generating assembly schemes for modular engineering systems in industrial production is the core of achieving automation in system design. Traditional system design methods based on human experience suffer from low efficiency and poor adaptability. To enable the automatic assembly of modules for engineering systems， the performance matching and correlation between modules need to be accurately identified. Currently， general large language models， with their strong language analysis capabilities， have been applied to multiple industries. However， limited by data barriers in specific industrial field， their application in assembly of system modules is rare. Therefore， constructing high-quality domain data， fine-tuning professional large language models， and developing domain frameworks for automatic design of modular system constitute an important research direction to be explored at present. Based on the Qwen2.5 open-source large model， we utilize industrial system knowledge to construct a component library and propose a large language model for design of complex industrial system （DCI-LLM）. Through the proposed low-rank adaptation （LoRA）-freeze-based “local-collaborative” fine-tuning method which combines local module and global knowledge of the system， DCI-LLM automatically generates system composition schemes and produces the related engineering drawings given system design requirements. We use the scheme design of heating systems as an example to verify the effectiveness of the proposed framework. Experimental results show that the fine-tuned DCI-LLM model achieves accuracy rates of 93.4% and 89.3% in answering questions about module knowledge and global knowledge， respectively. Moreover， scores from professional engineers indicate that DCI-LLM has practical application potential in scheme design of complex modular systems. Our work demonstrates that LLMs have significant application prospects in the field of automatic scheme design for complex industrial systems.
Xin et al. (Wed,) studied this question.