Controllable Structured Data Generation and Augmentation Mechanism for Power Grid Applications

Structured data is widely used in domains such as industry, finance, and healthcare. However, real-world datasets often suffer from issues such as data sparsity, distribution imbalance, and missing features, which significantly hinder the generalization performance of machine learning models. To address these challenges, this paper proposes a structured data augmentation method based on high-entropy sample masking and large language model-assisted completion. The method first employs a lightweight RandomForest classifier to estimate the prediction entropy of each sample, identifying high-uncertainty instances. For these samples, only key features—selected based on feature importance—are masked, and the masked inputs are then completed using DeepSeek-R1. To ensure the consistency of generated samples with the original data distribution, Kullback-Leibler (KL) divergence is introduced as a filtering criterion to retain only the most distribution-aligned samples. Furthermore, an iterative enhancement framework is established, which terminates automatically when the average KL divergence between generated datasets across two consecutive iterations falls below a defined threshold (0.01). Experimental results on multiple classification tasks demonstrate that the proposed method achieves superior performance in terms of accuracy, stability, and generation efficiency compared to conventional augmentation methods and full-field LLM generation. This work offers an efficient, controllable, and trustworthy solution for structured data augmentation.