AgenticTwin：集成数字孪生的智能体大语言模型异常检测框架

中文摘要 AI 辅助

数字孪生正越来越多地用于监测和模拟网络物理系统的行为。即使有熟练的操作人员，解释数字孪生管道中检测到的异常也颇具挑战性，因为原始传感器数据的复杂性和庞大的数量使得全面分析十分困难。大型语言模型（LLM）的最新进展提供了推理和解释的良好能力，但它们与数字孪生驱动的异常分析的整合仍未得到充分探索。在本研究中，我们提出了AgenticTwin，一个将LLM驱动的推理与基于数字孪生的异常检测管道相整合的智能体框架。该框架将LLM生成的解释建立在数字孪生驱动的异常分类器的输出之上，并使操作人员能够针对系统提出相关的自然语言问题。除了框架本身，我们还引入了一个面向基准的评估管道，该管道构建于注入了合成异常的真实世界天气传感器数据集之上，能够针对异常事件生成受控的操作人员查询。我们进一步评估了在实际网络物理环境中部署轻量级开源LLM的可行性。实验结果表明，结构化智能体协作和基于知识的推理在各种可能的异常场景中提升了诊断质量、上下文检索能力和缓解措施质量。

英文摘要

Digital twins are increasingly used to monitor and simulate the behavior of cyber-physical systems. Even with skilled operators, interpreting anomalies detected within digital twin pipelines is challenging, as the sheer complexity and volume of raw sensor data make thorough analysis difficult. Recent advances in large language models (LLMs) offer promising capabilities for reasoning and explanation, yet their integration into digital twin-driven anomaly analysis remains underexplored. In this work, we propose AgenticTwin, an agentic framework that integrates LLM-driven reasoning with a digital twin-based anomaly detection pipeline. The framework grounds LLM-generated explanations in outputs from a digital twin-driven anomaly classifier and enables human operators to ask relevant natural-language questions about the system. Beyond the framework itself, we introduce a benchmark-oriented evaluation pipeline constructed over synthetic anomalies injected into a real-world weather sensor dataset, enabling controlled generation of operator queries over anomaly events. We further evaluate the feasibility of deploying lightweight, open-source LLMs for practical cyber-physical environments. Experimental results demonstrate that structured agent collaboration and knowledge-grounded reasoning improve diagnosis quality, contextual retrieval, and mitigation quality across diverse possible anomaly scenarios.

发表机构

• School of Computing, Wichita State University（威奇托州立大学计算机学院）
• Auburn University（奥本大学）

机构由 AI 辅助整理，请以论文原文为准。