基于改进DTW算法的高维时空数据关联挖掘方法  

High dimensional spatiotemporal data association mining method based on improved DTW algorithm

作　　者：周春雷 董新微 季良 张璧君 许中平 ZHOU Chunlei;DONG Xinwei;JI Liang;ZHANG Bijun;XU Zhongping(Big Data Center of State Grid Corporation of China,Beijing 100052,China;Anhui Jiyuan Software Co.,Ltd.,Hefei 230088,China;Beijing Sgitg Accenture Information Technology Co.,Ltd.,Beijing 100052,China)

机构地区：[1]国家电网有限公司大数据中心,北京100052 [2]安徽继远软件有限公司,安徽合肥230088 [3]北京国网信通埃森哲信息技术有限公司,北京100052

出　　处：《电子设计工程》2023年第24期141-144,149,共5页Electronic Design Engineering

基　　金：国网大数据中心科技项目(52999019000800K0000000)。

摘　　要：电网数据体系较为庞大,挖掘其中的高维时空数据之间的关联性有助于提高电网运行质量。为此,利用改进后的DTW算法关联挖掘高维时空数据。采用改进DTW算法计算高维时空采样数据之间的距离矩阵,明确距离矩阵累积结果,完成数据预处理。按照降序循环项头表中的频繁项,计算空间数据和时间数据的支持度并与设定的阈值对比。以此为依据挖掘高维时空数据的关联性。实验结果证明,该方法挖掘结果较完整,平均误差为1.6%,可以为电网运行提供可靠依据。The power grid data system is relatively large,and mining the correlation between the high dimensional spatiotemporal data can help to improve the quality of power grid operation.Therefore,a high dimensional spatiotemporal data association mining method based on improved DTW algorithm is proposed.The improved DTW algorithm is used to calculate the distance matrix between the high dimensional spatiotemporal data,to clarify the cumulative result of the distance matrix,and to complete the data preprocessing.According to the frequent items in the recursive item header table in descending order,we calculated the support of spatial data and temporal data and compared them with the set threshold.On this basis,it mines the correlation of high dimensional spatiotemporal data.The experimental results show that the mining results of this method are relatively complete,and the average error is 1.6%,which can provide a reliable basis for the operation of the power grid.

关 键 词：改进DTW算法 高维时空数据 关联挖掘 模糊属性集 

分 类 号：TN912.34[电子电信—通信与信息系统]