Analysis model of distribution network equipment based on unstructured data and CNN-BiLSTM
To determine the defect level of distribution network equipment,a method for automatic defect analysis of distribution network equipment based on unstructured data and CNN-BiLSTM is proposed.Firstly,propose the overall idea of automatic classifi-cation of equipment defects in this distribution network;Then,unstructured data is processed using word2Vec and a CNN-BiLSTM defect level analysis model based on attention mechanism is constructed for data analysis;Finally,the feasibility of the above method was verified through simulation.The results showed that compared with a single CNN and CNN BiLSTM model,the proposed method in this study increased the weight of important information in the semantic features of power equipment defect description text by utili-zing attention mechanism,thereby improving the performance indicators of the analysis model.Among them,the accuracy index of Acc was improved by 4％～5％,the MF1 index was improved by 5％～6％,and the WF1 index was improved by 3％～4％.From this,it can be concluded that this defect analysis model can accurately analyze the defect level of power defect description texts,and has certain feasibility and effectiveness.
unstructured datadefects in power equipmentCNN-BiLSTMattention mechanism
万方数据
维普
