Hongbo Wu, Yiming Li, Linjuan Zhang, Chao Qiu, Qi Li
{"title":"基于多分支剩余关注网络的电力营销检测模型","authors":"Hongbo Wu, Yiming Li, Linjuan Zhang, Chao Qiu, Qi Li","doi":"10.1109/ECIE52353.2021.00026","DOIUrl":null,"url":null,"abstract":"In recent years, the scale of smart grid is becoming larger and larger. Big data, multi-dimensional, high intelligence and strong reliability have become the significant characteristics of modern power grid. The traditional inspection and monitoring work method is difficult to adapt to the needs of the new form. The traditional inspection method has the problems of low efficiency, limited scope and insufficient depth. Intelligent methods represented by deep learning are becoming more and more effective tools to solve the above problems. This paper proposes a power marketing audit model based on multi branch residual attention network. Compared with the existing models, this model has high accuracy, wide range and strong operability.","PeriodicalId":219763,"journal":{"name":"2021 International Conference on Electronics, Circuits and Information Engineering (ECIE)","volume":"73 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Power marketing inspection model based on multi-branch residual attention network\",\"authors\":\"Hongbo Wu, Yiming Li, Linjuan Zhang, Chao Qiu, Qi Li\",\"doi\":\"10.1109/ECIE52353.2021.00026\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In recent years, the scale of smart grid is becoming larger and larger. Big data, multi-dimensional, high intelligence and strong reliability have become the significant characteristics of modern power grid. The traditional inspection and monitoring work method is difficult to adapt to the needs of the new form. The traditional inspection method has the problems of low efficiency, limited scope and insufficient depth. Intelligent methods represented by deep learning are becoming more and more effective tools to solve the above problems. This paper proposes a power marketing audit model based on multi branch residual attention network. Compared with the existing models, this model has high accuracy, wide range and strong operability.\",\"PeriodicalId\":219763,\"journal\":{\"name\":\"2021 International Conference on Electronics, Circuits and Information Engineering (ECIE)\",\"volume\":\"73 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 International Conference on Electronics, Circuits and Information Engineering (ECIE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ECIE52353.2021.00026\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 International Conference on Electronics, Circuits and Information Engineering (ECIE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ECIE52353.2021.00026","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Power marketing inspection model based on multi-branch residual attention network
In recent years, the scale of smart grid is becoming larger and larger. Big data, multi-dimensional, high intelligence and strong reliability have become the significant characteristics of modern power grid. The traditional inspection and monitoring work method is difficult to adapt to the needs of the new form. The traditional inspection method has the problems of low efficiency, limited scope and insufficient depth. Intelligent methods represented by deep learning are becoming more and more effective tools to solve the above problems. This paper proposes a power marketing audit model based on multi branch residual attention network. Compared with the existing models, this model has high accuracy, wide range and strong operability.