A power transmission equipment defect grading method and device based on a multi-modal large model
By constructing a high-quality dataset using a multimodal large model and performing progressive training, the reliability problem of intelligent classification of defects in power transmission equipment is solved, and accurate and interpretable classification results are achieved in scenarios with few samples.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- HUNAN UNIV
- Filing Date
- 2026-06-16
- Publication Date
- 2026-07-17
AI Technical Summary
Existing technologies have insufficient reliability in intelligent classification of defects in power transmission equipment, especially in scenarios with few samples, where it is difficult to effectively integrate the experience of domain experts and improve the generalization ability of the model.
We employ a multimodal large model, constructing a defect dataset, generating a corrective thought chain, and performing progressive training. Combining cross-modal feature fusion and an interpretable reasoning paradigm, we utilize both closed-source and open-source large models to perform cross-modal feature fusion and defect classification of images and text.
It improves the reliability of intelligent classification of defects in power transmission equipment, reduces annotation costs, enhances the model's reasoning ability and the interpretability of classification results, and ensures accuracy and reliability in scenarios with few samples.
Smart Images
Figure CN122415607A_ABST