A marine observation big data visualization analysis method based on a complex network

Abstract

A marine observation big data visualization analysis method based on a complex network comprises the steps of performing grid division on original marine observation big data, constructing daily average data in a grid into a single Gaussian model and a mixed Gaussian model, and obtaining nodes represented by probability feature vectors; Determining the similarity between any two nodes in the single Gaussian network and the multi-Gaussian network to obtain a similarity matrix; And setting a threshold value to obtain an adjacent matrix, calculating the degree, the clustering coefficient and thenode betweenness of each node according to the adjacent matrix, and visualizing or drawing the degree, the clustering coefficient and the node betweenness on double logarithm coordinates or on a map.According to the invention, the Gaussian mixture model is combined with the complex network theory for the first time; The invention provides a marine observation big data analysis and visualization method, the fluctuation of ocean motion reflected on the data is restored to the maximum extent, and model parameters are used for expressing high-dimensional ocean data, so that the defect that a network model constructed on the basis of Pearson similarity can only measure time sequence data is overcome, and the calculation speed is also improved.

Description

technical field [0001] The invention relates to a method for visualization and analysis of ocean observation big data based on a complex network, in particular to modeling and visualization analysis of a complex network of long-scale time series data, and belongs to the field of big data analysis. Background technique [0002] The ocean has a non-negligible impact on the global climate, and the abnormal interaction between it and the atmosphere can cause global extreme climate events. For example, the interaction between abnormal sea wind and sea water causes the famous El Niño and La Niña events, the correlation between sea surface pressure and atmospheric circulation causes the North American Pacific Oscillation (PNA), and the interaction between atmospheric pressure and ocean currents causes the North Atlantic Oscillation (NAO), etc. The abnormal climate events caused by the above-mentioned climate mode will not only cause serious floods and droughts globally, but also se...

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Problems solved by technology

[0010] The disadvantage of constructing a network model based on the Pearson correlation coefficient is that using the Pearson correlation coefficient to measure the similarity between two sea areas simulates the ocean system as a complex system

[0012] However, constructing a complex ocean network by measuring the nonlinear correlation between two sea areas through mutual information restores the nonlinear dynamics of the ocean system, but the accuracy of mutual information requires too much data

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