Abstract: To address domain shift issues in solar cell datasets, this thesis proposes a data normalization controller（DNC） tailored for photovoltaic defect detection in dynamic open environments, aiming to enhance the network’s domain adaptation capability. During the testing phase, the DNC method in this study adjusts model parameters based on small batches of sample data（less than 0.5%）, effectively rectifying domain statistics. DNC maps data experiencing domain shift in the target domain onto the same distribution space as the source domain data, without requiring prior labeling of data or access to the entire target domain dataset. Experimental results demonstrate that DNC significantly improves the target detection model’s adaptability to domain-shifted data. Using only a minimal amount of unlabeled target domain data（less than 0.5%）, substantial performance gains can be achieved on out-of-distribution data, while maintaining the model’s prediction speed（FPS）.