为制造质量管理系统设计人本化大模型助手架构
A Human-Centred Architecture for Large Language Models-Cognitive Assistants in Manufacturing within Quality Management Systems
- 基于组件化设计，融合需求分析与开发流程
- 通过专家焦点小组验证，支持灵活扩展与工作增强
- 适合制造业质量管理人员及系统集成开发者
大语言模型认知助手（LLM-CAs）可提升制造领域的质量管理系统（QMS），促进持续改进与知识管理。然而现有文献中缺乏聚焦于QMS的人本化软件架构，无法有效支持LLM-CAs在制造场景中的集成。本研究通过需求分析与软件开发流程，设计了一种基于组件的架构，经多轮专家焦点小组迭代验证。该架构具备灵活性、可扩展性、模块化与工作增强能力，为工业伙伴落地应用提供可行路径，展现出推动制造流程优化的潜力。
原文摘要 · Abstract (English)
Large Language Models-Cognitive Assistants (LLM-CAs) can enhance Quality Management Systems (QMS) in manufacturing, fostering continuous process improvement and knowledge management. However, there is no human-centred software architecture focused on QMS that enables the integration of LLM-CAs into manufacturing in the current literature. This study addresses this gap by designing a component-based architecture considering requirement analysis and software development process. Validation was conducted via iterative expert focus groups. The proposed architecture ensures flexibility, scalability, modularity, and work augmentation within QMS. Moreover, it paves the way for its operationalization with industrial partners, showcasing its potential for advancing manufacturing processes.
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