Substation work ticket identification and extraction algorithm based on multi-task model
Authors: 
Renjie Sui, Danyong Li, Ruolan Li, Li Fan, Xiaoyi SunAuthors Info & Claims
Pages 520 - 527
Abstract
With the large-scale construction of new substations and the intelligent upgrading of existing substations, higher requirements have been put forward for efficient and intelligent maintenance of substations. In practical applications, the substation work ticket forms are complex and diverse, and the current mainstream work ticket key information extraction algorithms cannot meet the work needs. This paper proposed an intelligent recognition system for extracting safety measures from a work ticket in a substation. Based on the traditional text detection algorithm, a Multi-task network for text detection, frame extraction and form classification structure was proposed. The text recognition network used CRNN+CTC to train and tested the multi-model text recognition network on the work ticket interface text image data set. Advanced functions such as automatic generation, automatic execution, calibration and restoration of operation steps from a work ticket to safety measures in the substation were realized. In order to prove the effectiveness of the algorithm, an experimental study was carried out, and a comparative experiment was carried out in text detection and text recognition. The module has been tested in Yangzhou Power Supply Company of State Grid, and the results show that the scheme has good feasibility and effectively improves the work efficiency of operation and maintenance personnel.
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Index Terms
- Substation work ticket identification and extraction algorithm based on multi-task model
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Published In
October 2022
1999 pages
ISBN:9781450397148
DOI:10.1145/3573428
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Association for Computing Machinery
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Published: 15 March 2023
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EITCE 2022
EITCE 2022: 2022 6th International Conference on Electronic Information Technology and Computer Engineering
October 21 - 23, 2022
Xiamen, China
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Overall Acceptance Rate 508 of 972 submissions, 52%
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