Automatic audit system based on intelligent mining of audit work order text

Guoyi Zhao; Mingming Liu; Li Gu; Haiyang Ren; Le Chang; Jingsong He; Zongwei Wang; Xinyue Jiang

doi:10.1117/12.3031339

19 July 2024 Automatic audit system based on intelligent mining of audit work order text

Guoyi Zhao, Mingming Liu, Li Gu, Haiyang Ren, Le Chang, Jingsong He, Zongwei Wang, Xinyue Jiang

Proceedings Volume 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024); 131817I (2024) https://doi.org/10.1117/12.3031339
Event: Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 2024, Beijing, China

Abstract

With the rapid development of electricity marketing business, the volume of business and data for power grid companies has significantly increased. Traditional manual auditing methods are unable to meet modern management demands. According to statistics, the manual misjudgment rate during the quality inspection process of audit work orders has reached 17.8%. This article establishes an intelligent audit model for audit work orders based on text mining technology. Firstly, pre-trained models are used to generate sentence vectors. Then, TextRank algorithm and MMR algorithm are integrated to generate summaries. Subsequently, using a Transformer model with incorporated sparse attention mechanism, the generated summaries are used as inputs for binary classification to obtain audit results. Experimental results demonstrate that the proposed audit model for work orders can achieve automated processing and fine-grained analysis of marketing audit monitoring tasks, effectively improving the efficiency and accuracy of the auditing process.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guoyi Zhao, Mingming Liu, Li Gu, Haiyang Ren, Le Chang, Jingsong He, Zongwei Wang, and Xinyue Jiang "Automatic audit system based on intelligent mining of audit work order text", Proc. SPIE 13181, Third International Conference on Electronic Information Engineering, Big Data, and Computer Technology (EIBDCT 2024), 131817I (19 July 2024); https://doi.org/10.1117/12.3031339

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KEYWORDS

Inspection

Data modeling

Semantics

Transformers

Feature extraction

Performance modeling

Education and training

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