Power Big Data Visualization Fusion

Information fusion is very important in the research and analysis of the power big data value,it can get a more comprehensive,systematic and accurate description of the data fusion of the same object,and form a unified description of the view,to achieve the goal of integration of chaotic data processing.By studying the basic characteristics of large power data,the efficiency and performance of power large data visualization preprocessing are analyzed emphatically,and the information fusion method is introduced in the visualization of power big data preprocessing stage.According to the characteristics of "large quantity" and "variety" of the power data,a large data set classification fusion model based on the combination of neural network and MapReduce is adopted.The design of the fusion model is mainly based on the clustering and nonlinear mapping ability of neural network,at the same time,according to the characteristics of large power data,combined with the MapReduce model,the parallel operation of large scale data sets is realized,the method applicable to classification of multi-source data fusion processing,so as to solve the problem of heterogeneous data fusion with large data in structured,semi-structured and unstructured hybrid forms,and satisfy the operating parameters of power network for the high quality of scene display requirements.Then combined with the theory of fuzzy mathematics,select the part of the data that needs to be displayed,and reduce the workload of the visual background.Finally,the different types of data can be visualized,the different types of data of the same object can be fused and processed,and the main features of the data are highlighted.The same attribute data of different objects can also be fused and displayed,and the correlation analysis of attributes is done,and it is possible to guess the intrinsic link between them,which makes it more convenient for people to make intuitive understanding and judgment of the data.