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12 November 2024 Research on deep reinforcement learning-based power equipment event extraction technique
Bochuan Song, Xiaoxuan Fan, Xinghui Zhang, Dafeng Zhang, Qiang Zhang, Fei Zhou
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Proceedings Volume 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) ; 133952Z (2024) https://doi.org/10.1117/12.3048878
Event: International Conference on Optics, Electronics, and Communication Engineering, 2024, Wuhan, China
Abstract
Power equipment public opinion events can affect all aspects of the power industry, including equipment failures, power supply interruptions, environmental impacts, and policy changes. Through public opinion event extraction, electric power companies and related government agencies can monitor and analyze public concerns and feedbacks in real time, quickly respond to potential problems, improve equipment management and maintenance, increase power supply reliability, and reduce operational risks. In this paper, we propose a deep reinforcement learning-based public opinion event extraction framework for electric power equipment, and design a reward function based on the recognition of trigger words and the detection results of related event elements, and finally, through comparative experiments, we can see that the extraction results of the proposed framework are better, and it can meet the requirements of public opinion event extraction for electric power equipment.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Bochuan Song, Xiaoxuan Fan, Xinghui Zhang, Dafeng Zhang, Qiang Zhang, and Fei Zhou "Research on deep reinforcement learning-based power equipment event extraction technique", Proc. SPIE 13395, International Conference on Optics, Electronics, and Communication Engineering (OECE 2024) , 133952Z (12 November 2024); https://doi.org/10.1117/12.3048878
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KEYWORDS
Deep learning
Instrument modeling
Data modeling
Education and training
Machine learning
Transformers
Feature extraction
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