AI 中文总结
针对可重构制造中认知数字孪生调试难题，提出基于智能体和AI的工作流，利用LangGraph、RAG和MCP实现双路径自动调试，实验显示感知精度97.2%，部署时间从数周降至2小时。
AI 中文摘要
在可重构制造中，快速定制化调试认知数字孪生（CDT）是一项重大挑战。传统的数字孪生（DT）构建方法主要关注几何重建，往往忽视了自主推理所需的深层语义集成和功能互操作性。本文提出了一种基于智能体的、AI驱动的工作流，以自动化端到端的CDT调试。该系统利用LangGraph作为多智能体编排引擎，实现双路径综合：语义路径使用检索增强生成（RAG）从非结构化文档中提取技术规范，而功能路径使用模型上下文协议（MCP）自主发现并绑定实时工业遥测数据。在机器人加工单元中的实验验证表明，该系统在感知方面实现了97.2%的平均精度（mAP），并将部署周期从数周缩短至平均2小时，标志着从手动脚本编写到自主编排的范式转变。
英文摘要
Rapid bespoke commissioning of the Cognitive Digital Twin (CDT) is a major challenge in reconfigurable manufacturing. Traditional digital twin (DT) construction methods primarily focus on geometric reconstruction, often neglecting the deep semantic integration and functional interoperability necessary for autonomous reasoning. This paper proposes an agent-based, AI-driven workflow to automate end-to-end CDT debugging. The system utilises LangGraph as a multi-agent orchestration engine to achieve dual-path synthesis: the semantic path extracts technical specifications from unstructured documents using Retrieval Augmented Generation (RAG), while the functional path autonomously discovers and binds to real-time industrial telemetry data using Model Context Protocol (MCP). Experimental validation in a robotic machining cell demonstrates that the system achieves a mean average accuracy (mAP) of 97.2% in perception and reduces the deployment cycle from several weeks to an average of 2 hours, marking a paradigm shift from manual scripting to autonomous orchestration.
