The power distribution network is critical to reliable electricity delivery, yet traditional inspection methods face limitations in semantic understanding, generalization, and closed-loop automation. To address these challenges, this paper proposes a Multi-Modal Agent framework specifically for power distribution defect detection. Central to this study is the systematic evaluation of multimodal foundation models as unified cognitive engines. We rigorously assess their integrated performance across three critical capabilities: (1) Perception, where the model must accurately identify equipment and generate expert-level descriptions of defects; (2) Reasoning, where the model interprets visual findings to diagnose causes, assess severity, and plan maintenance strategies based on domain knowledge; and (3) Tool Usage, where the model acts as an autonomous operator to execute actions -- such as querying knowledge bases or generating work orders -- to achieve closed-loop maintenance. To support this evaluation, a domain-specific evaluation dataset and a comprehensive benchmark are developed. Experimental results demonstrate the strengths and limitations of current foundation models in these three dimensions, providing empirical evidence for deploying autonomous agents in high-stakes industrial environments.

翻译：配电网络是可靠电力输送的关键基础设施，但传统检测方法在语义理解、泛化能力和闭环自动化方面存在局限性。针对这些挑战，本文提出一种专用于配电缺陷检测的多模态智能体框架。本研究的核心在于系统评估将多模态基础模型作为统一认知引擎的有效性。我们严格评估了其在三大关键能力上的综合表现：（1）感知能力——模型需准确识别设备并生成符合专家标准的缺陷描述；（2）推理能力——模型需解读视觉发现以诊断故障原因、评估严重程度并基于领域知识规划维护策略；（3）工具使用能力——模型作为自主操作者执行查询知识库或生成工单等动作，实现闭环维护。为支撑该评估，我们构建了领域专用评估数据集与综合基准测试。实验结果揭示了当前基础模型在这三个维度的优势与局限，为高风险工业环境中部署自主智能体提供了实证依据。