Named Entity Recognition for power data refers to the identification of key designative contents, such as equipment names, operation data, etc., from the text of power domain data to achieve the extraction and classification of key information from the perspective of power expertise. Deep neural networks have shown great effectiveness in power system data NER. However, the Chinese power domain data NER suffers from problems such as insufficient training data and a wide variety of data entities. To solve these problems, we propose a power data entity recognition model based on Lexical enhancement and Global pointer. Firstly, the model uses the Lexical enhancement method to merge the lexical information into the vector representation of each character, then uses the RoBERTa pre-trained model to receive the vectors from the input representation layer and further extract the features, and finally uses the Global pointer method for the entity recognition. In addition, we have experimented on a self-constructed Chinese power named entity recognition dataset, the result of the experiment indicated that the F1 values were higher than several other named entity recognition methods, such as Lattice- LSTM, SoftLexicon+BiLSTM+CRF, CAN and RoBERTa+GP, with improvements of 2.54%, 0.13%, 3.80%, and 0.42%, respectively.
With the development of 6G communication technology, satellite Internet will become an important means of achieving global network coverage and high-speed network transmission. The non-uniform distribution of ground equipment and the high-speed mobile characteristics of non-geostationary orbit (NGSO) satellites have created obstacles to the provision of services from satellites to the ground. Reasonable resource management in the multibeam satellite technology system can effectively guarantee that the service demand of ground users can be responded to in time and the resource utilisation rate of users can be improved as much as possible. In this paper, the multibeam satellite system is sorted out from the overall point of view. We also discuss the current work of Artificial Intelligence (AI) in the multibeam satellite processing process to illustrate the key role played by AI in it. Finally, we discuss potential open issues with the aim of providing some insights into the development of multibeam satellite communication systems and their application in AI techniques.
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