In the product design and manufacturing process, the effective management and representation of system requirements (SRs) are crucial for ensuring product quality and consistency. However, current methods are hindered by document ambiguity, weak requirement interdependencies, and limited semantic expressiveness in model-based systems engineering. To address these challenges, this paper proposes a prompt-driven integrated framework that synergizes large language models (LLMs) and knowledge graphs (KGs) to automate the visualization of SR text and structured knowledge extraction. Specifically, this paper introduces a template for information extraction tailored to arbitrary requirement documents, designed around five SysML-defined SR categories: functional requirements, interface requirements, performance requirements, physical requirements, and design constraints. By defining structured elements for each category and leveraging the GPT-4 model to extract key information from unstructured texts, the system can effectively extract and present the structured requirement information. Furthermore, the system constructs a knowledge graph to represent system requirements, visually illustrating the interdependencies and constraints between them. A case study applying this approach to Chapters 18–22 of the ‘Code for Design of Metro’ demonstrates the effectiveness of the proposed method in automating requirement representation, enhancing requirement traceability, and improving management. Moreover, a comparison of information extraction accuracy between GPT-4, GPT-3.5-turbo, BERT, and RoBERTa using the same dataset reveals that GPT-4 achieves an overall extraction accuracy of 84.76% compared to 79.05% for GPT-3.5-turbo and 59.05% for both BERT and RoBERTa. This proves the effectiveness of the proposed method in information extraction and provides a new technical pathway for intelligent requirement management.

Achieving high operational efficiency in modern manufacturing requires the seamless integration of production scheduling and intralogistics coordination. However, in flexible assembly shops, the decoupling between production sequencing and automated guided vehicle (AGV) routing often leads to resource conflicts, unbalanced workloads, and inefficient energy utilization. To address this challenge, this study proposes an improved genetic algorithm (IGA) for integrated production–logistics scheduling. The innovation lies in a triple-chain encoding strategy that concurrently represents production, transportation, and time-window constraints, coupled with adaptive crossover and mutation operators for enhanced population diversity. Furthermore, a time-window-constrained Dijkstra routing mechanism is incorporated to prevent AGV conflicts and improve synchronization between machines and logistics. Two representative shop-floor scenarios—baseline and disturbed conditions—were designed for validation. Comparative experiments against a standard genetic algorithm (GA) and a two-stage heuristic demonstrate that the IGA achieves 9.5% and 6.7% reductions in average makespan, respectively, while maintaining less than 1% deviation under 10% random disturbances. Statistical tests (p < 0.01, Cohen’s d > 1.4) confirm the method’s robustness and practical effectiveness. The proposed approach provides a reliable and implementable optimization framework that enhances coordination between production and AGV systems in flexible assembly environments and offers a practical reference for smart manufacturing scheduling and digital twin applications.