A Multimodal Power Sample Feature Migration Method Based on Dual Cross-Modal Information Decoupling

With the rapid development of energy transition and power system informatization, the efficient integration and feature migration of multimodal power data have become critical challenges for intelligent power systems. Existing methods often overlook fine-grained semantic relationships in cross-modal alignment, leading to low information utilization. This paper proposes a multimodal power sample feature migration method based on dual cross-modal information decoupling. By introducing a fine-grained image–text alignment strategy and a dual-stream attention mechanism, deep integration and efficient migration of multimodal features are achieved. Experiments demonstrate that the proposed method outperforms baseline models (e.g., LLaVA, Qwen) in power scenario description (CSD), event localization (CELC), and knowledge question answering (CKQ), with significant improvements of up to 12.8% in key metrics such as image captioning (IC) and grounded captioning (GC). The method provides a robust solution for multimodal feature migration in power inspection and real-time monitoring, showing high practical value in industrial applications.