门控变关系图卷积网络的涉烟案件当事人预警  

Gated variable graph convolutional network for warning of parties in tobacco-related cases

作　　者：冯鹏程 张高豪 谢刚 FENG Pengcheng;ZHANG Gaohao;XIE Gang(School of Big Data and Computer Science,Guizhou Normal University,Guiyang 550025,China;Guiyang Branch,Guizhou Tobacco Company,Guiyang 550002,China)

机构地区：[1]贵州师范大学大数据与计算机科学学院,贵州贵阳550025 [2]贵州省烟草公司贵阳市公司,贵州贵阳550002

出　　处：《大数据》2024年第5期45-55,共11页Big Data Research

基　　金：贵州省烟草公司贵阳市公司科技项目(黔烟筑科[2020]3号)。

摘　　要：为了落实“精准监管”方针,烟草公司需要提高涉烟案件命中率。过往的方法缺少了对涉烟案件高危当事人的研究,阻碍了案件命中率的提高。基于烟草公司存有的大量历史数据,挖掘出准确的预警名单是提高案件命中率的有效途径。进行高危当事人特征分析后,提出门控变关系图卷积网络,以得到准确的高危当事人预警名单。首先,门控变关系图卷积网络使用变关系图卷积网络,捕捉当事人的关系与关键特征。然后,门控层进一步提取特征。最后,把特征输入Softmax层得到分类结果,进而得到预警名单。通过对比实验,证明构建的模型效果更佳。某市专卖局应用本项目的系列成果后,其案件命中率从约0.01%提升到了约0.5%,这证明构建的预警模型能满足真实监管的需求。In order to implement the"precision supervision"policy,tobacco companies need to increase the hit rate of tobacco-related cases.Past approaches had lacked research on high-risk parties in tobacco-related cases,which had hindered the improvement of case hit rates.Based on the large amount of historical data stored in tobacco companies,mining accurate warning lists is an effective way to improve case hit rates.After conducting the analysis of high-risk parties features,a gated variable relationship graph convolutional network was proposed to obtain an accurate high-risk parties warning list.Firstly,the gated variable relation graph convolutional network used variable relation graph convolutional network to capture the relationship and key features of the parties.Then,the gated layer was applied to further learn the features.Finally,the learned features were inputted to the Softmax layer to get the classification results,and then an alert list is obtained.Through comparison experiments,the constructed model is proved to be more effective.After a municipal monopoly bureau applied the results of this project,its case hit rate improved from about 0.01%to about 0.5%,which proves that the early warning model can meet the needs of real regulation.

关 键 词：烟草行业 涉烟案件 高危当事人识别 门控层 图卷积网络 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]