Text2SQL model oriented to power grid information retrieval

Jia Du; Ying Shi; Xiaozhu Liu; Zhongjian Hu

doi:10.1117/12.3011261

8 November 2023 Text2SQL model oriented to power grid information retrieval

Jia Du, Ying Shi, Xiaozhu Liu, Zhongjian Hu

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 129232H (2023) https://doi.org/10.1117/12.3011261
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

The massive data in the power grid system is stored in various databases. However, when users want to query the contents of the databases, they need to write structured query language (SQL), which is difficult for non-professionals. This paper focuses on the research of Text2SQL technology in the power domain based on the Seq2Seq model. On one hand, the BERT model is introduced to classify the queries of power equipment defect texts, solving the problem of inaccurate prediction of aggregate functions in SQL statements. On the other hand, the decoding strategy with fused rule-constrained attention mechanism is employed to mine the data features of the input text, addressing the issue of information loss in input sequences. Finally, the improved Seq2Seq model is proposed by integrating the above strategies. Experimental results show that compared to the Seq2Seq model, the proposed algorithm achieves a 24.88% improvement in accuracy.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jia Du, Ying Shi, Xiaozhu Liu, and Zhongjian Hu "Text2SQL model oriented to power grid information retrieval", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 129232H (8 November 2023); https://doi.org/10.1117/12.3011261

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