A boiler weld defect identification method, electronic equipment and storage medium
By combining multi-source information from defect detection models and large language models, and utilizing consistency scoring and strategy correction, the problems of missed detection and false alarm rate in boiler weld defect identification were solved, achieving a balance between high recall and low false alarm rate, and improving the accuracy and reliability of identification results.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- CHONGQING SPECIAL EQUIP TESTING & RES INST (CHONGQING SPECIAL EQUIP ACCIDENT EMERGENCY INVESTIGATION & PROCESSING CENT)
- Filing Date
- 2026-04-30
- Publication Date
- 2026-07-24
AI Technical Summary
Existing boiler weld defect identification technologies suffer from high risk of missed detection, high false alarm rate, inconsistent defect judgment, and difficulty in meeting industrial safety requirements. In particular, when using deep learning models, it is difficult to achieve a balance between high recall rate and low false alarm rate.
By acquiring X-ray digital images and metadata, a defect detection model is used to obtain the probability and features of defect types. Combined with a large language model, a search intent is generated. Input is then fed into industry standards and a historical case database for consistency scoring and strategy correction, ensuring the accuracy and reliability of defect identification.
It effectively reduces the false alarm rate without lowering the recall rate, improves the accuracy and reliability of defect identification, and meets industrial safety requirements.
Smart Images

Figure CN122134720B_ABST