Understanding method of power dispatch professional language based on multi-model fusion

Shaoqing Xi; Lianfei Shan; Licheng Sha; Bin Li; Ke Zhang; Yue Zhang; Yongtian Qiao; Yu Wang; Fuquan Kang

doi:10.1117/12.3004944

9 October 2023 Understanding method of power dispatch professional language based on multi-model fusion

Shaoqing Xi, Lianfei Shan, Licheng Sha, Bin Li, Ke Zhang, Yue Zhang, Yongtian Qiao, Yu Wang, Fuquan Kang

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127912M (2023) https://doi.org/10.1117/12.3004944
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

In order to improve the accuracy of power dispatch professional language understanding, the professional language understanding method of power dispatching based on multi-model fusion is proposed. First, the dispatch professional language is represented as a low-dimensional feature vector based on the pre-trained word vector model. Then the mapping relationship between scheduling professional language and scheduling intention is trained based on text convolutional neural network (TextCNN). The relation relationship between professional language slot feature and information labels are trained based on the bidirectional long-term short-term memory network-conditional random field (BiLSTM-CRF), the dispatch professional language understanding is realized by the joint multi-model recognition results. Finally, through the verification of power dispatch professional language of a control center, compared with other methods, the proposed professional language understanding method has higher accuracy.

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

Citation Download Citation

Shaoqing Xi, Lianfei Shan, Licheng Sha, Bin Li, Ke Zhang, Yue Zhang, Yongtian Qiao, Yu Wang, and Fuquan Kang "Understanding method of power dispatch professional language based on multi-model fusion", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127912M (9 October 2023); https://doi.org/10.1117/12.3004944

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