Application of BERT-Free Pre-Training Model in Identifying Power Safety Risk Points

Abstract

In intelligent power systems operating under increasingly complex electromagnetic environments, accurate identification of safety risk points is essential for ensuring reliable equipment operation and supporting electromagnetic compatibility assessment. Traditional rule-based methods suffer from limited semantic understanding and poor generalization, making them insufficient for processing complex operation and maintenance texts. To address this issue, this paper proposes a lightweight CNN-based pre-training model built on a BERT-Free architecture for efficient and accurate power safety risk identification. A professional dataset containing 58,000 power operation and maintenance texts is constructed, and a high-quality multi-label corpus covering four categories and 17 subcategories is established through dictionary-enhanced word segmentation and expert cross-annotation. The model is exported in ONNX format to facilitate flexible deployment in engineering applications. Experimental results demonstrate that the proposed model achieves an overall accuracy of 89.1% and an F1-score of 87.3%. Under class imbalance, the F1-score reaches 0.901 for majority classes and 0.784 for minority classes, exhibiting strong robustness. The average inference time per sample is only 8.3 ms, indicating high computational efficiency. These results demonstrate that the proposed model provides an effective and practical solution for intelligent power safety risk identification while offering valuable support for electromagnetic infrastructure monitoring and reliable operation in modern power systems.