用多智能体检索增强系统，让储能故障诊断有据可查。
Traceable Fault Diagnosis for Battery Energy Storage Systems via Retrieval-Augmented Multi-Agent O&M Assistant
- 通过多智能体协同，整合数据与知识库实现可追溯诊断。
- 支持文本图像混合检索，提升对异常现象的识别准确率。
- 适合电力运维人员和系统工程师快速定位储能故障根源。
大规模电池储能系统（BESS）的运维决策需融合告警信息、单体电芯数据、设备拓扑、诊断表、历史案例及维护文档。监测平台虽能识别阈值越限，却难以判断电压不均、电阻漂移、短路风险、容量差异或热异常是否需干预。本文提出一种可追溯的BESS故障诊断助手，采用检索增强的多智能体推理框架，连接运行数据、领域知识、视觉证据并自动生成报告。通过特定任务路由、受模式约束的自然语言数据库访问、混合文本-图像检索及基于证据的答案合成，提升了诊断可靠性。初步内部评估覆盖任务路由、数据库访问与诊断推理环节。
原文摘要 · Abstract (English)
Large-scale battery energy storage systems (BESSs) require O&M decisions that combine alarms, cell-level measurements, device topology, diagnostic tables, historical cases, and maintenance documents. Monitoring platforms can flag threshold violations, but they often cannot explain whether voltage inconsistency, resistance drift, short-circuit risk, capacity divergence, or thermal abnormality needs intervention. This digest presents a traceable BESS fault-diagnosis assistant that uses retrieval-augmented multi-agent reasoning to connect operational data, domain knowledge, visual evidence, and report generation. Reliability is improved through BESS-specific task routing, schema-constrained natural-language database access, hybrid text-image retrieval, and evidence-based answer synthesis. Preliminary internal evaluation is reported for routing, database access, and diagnostic reasoning.
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