Improvement of Power Equipment Defect Text Quality Based on Improved BI-LSTM

The power equipment defect text is important data generated by the operation and maintenance of the power grid system. It is characterized by having many professional words from the power field and high complexity. During model training, there are some problems, such as difficulty in semantic understanding, gradient disappearance, negative information loss, and data imbalance, which hurt the text quality and defect analysis effect. To deal with these issues, this paper proposes a ULF-BI-LSTM text quality improvement algorithm integrating UCNN, LeakyRelu activation function, and Focal Loss function. Then, we correctly separate professional vocabulary, delete invalid vocabulary, and normalize the object description by text data preprocessing. Finally, we fill in missed data and correct the wrong data using the ULF-BI-LSTM algorithm. Experimental results show three improvement strategies that effectively improve the accuracy, precision, F1, and recall of the algorithm. The proposed algorithm outperforms the mainstream algorithm TextCNN, SVM, and BI-LSTM, which alleviates the above problems.