面向电网调度领域的实体识别技术

近年随着电网调度领域数据自动化、智能化管理需求的日益增长,知识图谱成为提供知识管理、智能查询、辅助决策等功能的重要技术。实体作为构成知识图谱的核心要素,识别的准确率将直接影响知识图谱的质量。针对电网调度领域,首先分析电网调度实体识别研究现状,明确了实体识别任务目标,然后根据电网调度领域文本数据特征,设计了同时满足局部特征与全局特征提取需求的算法结构,构建了基于双向长短期记忆网络(bi-directional long short-term memory, BiLSTM)-卷积神经网络(convolutional neural networks, CNN)-条件随机场(conditional random field,CRF)的电网调度领域实体识别模型。最后实验结果表明,所提方法识别准确率达到93.1%,F₁值达到86.05%,能够有效支撑电网调度领域实体识别工作的开展。

Research on Entity Recognition Technology in Power Grid Dispatching Field

In recent years, with the increasing demand of data automation and intelligent management in the field of power grid dispatching, knowledge graph has become an important technology to provide knowledge management, intelligent query, auxiliary decision-making and other functions. As the core element of knowledge graph, the accuracy of entity recognition will directly affect the quality of knowledge graph. Aiming at the field of power grid dispatching, this paper firstly analyzes the research status of entity recognition in power grid dispatching field, and defines the task objective of entity recognition. Then, according to the text data features of power grid dispatching, an algorithm structure is designed to meet the requirements of local and global feature extraction, and a named entity recognition model based on BiLSTM-CNN-CRF is constructed. Finally, the experimental results show that the recognition accuracy of this method reaches 93.1%, and the F₁ value reaches 86.05%, which can effectively support the development of entity recognition in the field of power grid dispatching.