面向配电网数据的命名实体识别

Named Entity Recognition for Power Distribution Network Data

在电力系统中,配电调度是一个复杂且统筹性较强的工作,大多依赖于工作人员的经验和主观判断,极易出现纰漏.所以急需利用智能化手段来帮助检修计划的分析与生成.命名实体识别是构建配电网知识图谱以及问答系统等任务的关键技术,它能够将非结构化数据中的命名实体识别出来.针对配电检修数据的复杂性及强关联性等特点,本文采用BERT-IDCNN-BiLSTM-CRF深度学习模型.该模型相较于传统的BERT-BiLSTM-CRF模型,融入IDCNN神经网络模型,更好地利用GPU的性能,在保证识别准确率的前提下,提高效率.通过对标注好的检修计划数据进行训练,并与其他常用模型对比,在召回率、精确率以及F1值3个指标上,本文提出的模型均达到最优的效果,F1值可以达到83.1%,该模型在配电网数据识别任务上取得了很好的效果.

In the power system,distribution scheduling is complex and well-coordinated,which mostly depends on the experience and subjective judgment of staff and is prone to mistakes.Therefore,it is urgent to use intelligent means to help analyze and generate maintenance plans.Named entity recognition is a key technology in the construction of the knowledge graph of power distribution networks and the question answering system,which can recognize named entities in unstructured data.In view of the complexity and strong correlation of distribution maintenance data,this study adopts the deep learning model BERT-IDCNN-BiLSM-CRF.Compared with the traditional model BERT-BiLSTM-CRF,this model integrates the neural network model IDCNN,makes better use of the performance of GPU,and improves the efficiency on the premise of ensuring recognition accuracy.The labeled maintenance plan data are trained,and the proposed model is compared with other commonly used models.The results reveal that the proposed model achieves the best effect in terms of the recall rate,accuracy rate,and F1 value,and its F1 value can reach 83.1%.The model has achieved good results in the recognition of distribution network data.