An Online Operational Status Evaluation Method for Smart Meters in Power System Based on Cross-Modal Perception Using Large Language Models

With the large-scale deployment of smart electricity meters in China, power companies need online methods that can evaluate meter operating status and locate faulty units without field inspection. Meter data are inherently multimodal. They combine time-series measurements with textual event logs. The semantic gap between these modalities limits the accuracy of existing approaches. This paper proposes an online operational status evaluation method for smart meters based on cross-modal perception with large language models. A Bi-LSTM and TCN-Attention network with quantile regression first builds a robust district line-loss baseline that captures seasonal fluctuations and operational uncertainty. A cross-modal alignment module then fuses the two modalities. The measurement sequences are encoded by PatchTST, and the event logs are encoded by a LoRA-fine-tuned LLM. The fusion is performed through contrastive learning and gated fusion. A retrieval-augmented knowledge graph provides additional support. Finally, a multi-indicator health index grades meters into five condition levels, and a hidden Markov model estimates the remaining useful life. Case study results demonstrate that the proposed method achieves 85.7% accuracy, outperforming the strongest baseline.