Research on a CNN-BiGRU disk fault prediction method integrating attention mechanism

Disk, as a crucial storage medium, can result in significant data loss if it malfunctions, causing immeasurable losses for individuals and businesses. Existing models for predicting disk failures have problems such as imbalanced disk data samples and underutilization of the temporal characteristics of the data. In this study, we focused on real disk data provided by the Backblaze cloud storage company and proposed a disk failure prediction model that combines a convolutional neural network (CNN) with a bidirectional gated recurrent unit(BiGRU) network, incorporating an attention mechanism.In terms of data preprocessing, we employed negative sampling and a focal loss function to balance positive and negative samples. Subsequently, we utilized CNN for feature extraction and combined it with BiGRU to effectively handle temporal data. The integration of an attention mechanism enables the model to quickly capture more critical feature informations. The selected features were then trained with the input data into the model. Compared to other fault prediction models, the proposed model in this paper demonstrates a performance improvement of 1% to 7% on four evaluation indicators, such as precision. This provides a robust support for enhancing disk storage reliability.