The automatic verification device for energy meters conducts quality checks through periodic verifications and interim verifications

leading to issues such as low operational efficiency

long verification cycles

and the inability to promptly identify and rectify anomalies. Therefore

implementing online anomaly detection for the automatic verification device for electricity meters holds significant importance. In this study

a method for online anomaly detection based on a small number of unlabeled samples is proposed. It involves the online collection of positional error data from the automatic verification device for electricity meters

construction of error features

extraction of crucial data components using principal component analysis. Subsequently

it employs the isolation forest algorithm to mark anomalous meter positions. Following this

a support vector machine (SVM) model is trained based on the marked results

and the hyperparameters of the SVM are optimized using k-fold cross-validation and Bayesian optimization. With the use of only a small number of support vectors

the method achieves anomaly detection for meter positions with an accuracy rate of 99.44%

demonstrating effective anomaly detection.