基于线性调频盲卷积的大数据聚类控制方法

Large Data Clustering Control Method Based on Linear Frequency Modulation Bind Convolution

海量的大数据在大型电力系统中以电压脉冲、电流、用电功率等形式在传输和存储,需要对大型电力系统中的大数据进行优化聚类控制处理,提高大数据的调度和模式控制识别能力。传统方法采用FCM聚类方法,对电力系统的热噪声具有较强的敏感性,导致数据聚类效果不好。提出一种基于线性调频盲卷积的大数据聚类控制方法,构建了大型电力系统中的大数据分布结构模型,对大数据信息流进行线性调频信号拟合,采用线性调频盲卷积方法进行数据融合滤波,优化数据聚类性能。实验结果表明,采用该算法进行大数据聚类,数据聚集度较高,为模式识别和信号检测奠定基础,可提高电力系统中的数据聚类和控制能力。

In the large power network system,the large data in the system is based on voltage pulse,electric current,power and other forms of transmission and storage.It is necessary to optimize the large power system to optimize the clustering control,and improve the large data scheduling and mode control.This paper proposes a large data clustering control method based on linear frequency modulation blind deconvolution.The large data distribution structure model of large power system is constructed.The data stream is fitted by linear frequency modulation signal.The data fusion filtering is performed by using the linear frequency modulation blind convolution method.Experimental results show that the proposed algorithm is based on large data clustering and data aggregation,which lays the foundation for pattern recognition and signal detection.