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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.