螺栓是高空设备稳固连接的关键，但易受多种因素影响而出现松动等异常，威胁设备安全。当前基于深度学习的螺栓检测方法面临类不平衡和标签缺失问题。提出了基于半监督伪标签学习的历史动态加权损失（historical dynamic weighted loss, HDWL）模型。通过动态加权一致性正交化和类自适应公平惩罚，利用历史数据评估模型分类效果，引入自适应惩罚防止过拟合，增强对难分类样本的关注，提升模型性能。实验表明，HDWL模型的准确率相比其他方法有显著提高，在少数类训练和特征关注方面具有优势。

Bolts are the key to the stable connection of high-altitude equipment, but they are prone to abnormalities such as loosening under the influence of various factors, threatening the safety of the equipment. Currently, bolt detection methods based on deep learning are faced with the problems of class imbalance and label missing. Existing deep-learning-based bolt detection methods suffer from class imbalance and missing labels. A HDWL(historical dynamic weighted loss) model based on semi-supervised pseudo-label learning was proposed. By dynamic weighted orthogonality and class-adaptive fair punishment, the model classification was evaluated with historical data. Adaptive punishment was introduced to prevent overfitting and focus more on hard-to-classify samples, boosting model performance. Experiments showed that the HDWL model achieved significantly higher accuracy than other methods, with advantages in minority-class training and feature focus.