Cross-Modal Semantic Alignment and Dynamic Routing Enhancement for Inspection and Supervision Scenarios

Traditional inspection and supervision in power grid operations suffer from heterogeneous multi-source data (text, tables, and images), low policy retrieval efficiency, difficult issue characterization, non-standardized reporting, and weak closed-loop rectification. To address these challenges in Guangdong Power Grid scenarios, this paper proposes CSA-DR, a Cross-modal Semantic Alignment and Dynamic Routing enhancement method. CSA-DR retrieves relevant policy documents, structured tables, and inspection-related images from an external regulatory knowledge base and encodes them via a tri-modal into a unified semantic space, achieving precise cross-modal alignment between inspection descriptions and supporting evidence. A dynamic routing mechanism is introduced to adaptively allocate modality importance according to task requirements, significantly improving key information extraction, violation detection, and causal analysis. Additionally, the framework integrates an external regulatory knowledge base. For each inspection task, relevant policy documents, structured tables, and evidence images are retrieved from this knowledge base and used as the tri-modal input to the model. This knowledge-grounded design enables cross-modal semantic alignment, evidence traceability, and standardized inspection report generation. Experiments on a real multi-source inspection dataset from Guangdong Power Grid show that CSA-DR consistently outperforms the compared baseline methods and ablation variant across all applicable metrics, with notable improvements in cross-modal MRR and image-to-image Recall@5.