Substation equipment health state assessment method, system and equipment based on few-sample multi-modal fusion, and storage medium

Abstract

The invention relates to the technical field of power grid equipment state monitoring, in particular to a transformer substation equipment health state assessment method, system and equipment based on few-sample multi-modal fusion and a storage medium. The method comprises the following steps: acquiring image, sound, text, structuralization and other multi-modal operation data of transformer substation power distribution equipment, preprocessing the data, and establishing a comprehensive equipment state data basis; cLIP, BERT and Wave2vec pre-training models are selected and finely adjusted, multi-modal features are deeply fused through a cross attention mechanism by using the transfer learning ability of large-scale pre-training knowledge, pairwise interaction and information complementation among different modals are realized, information redundancy is effectively eliminated, and a complex association relationship among the modals is mined; a hierarchical structured model is constructed, a structured data health state result is calculated in combination with a discrimination matrix, and traditional power system expert experience and quantitative analysis are organically combined; and carrying out weighted fusion on the multi-modal health state result and the structured data health state result.

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