A Correlation Evaluation Method of Predictors and Solar Flare Occurrence

Abstract

The present invention relates to the research technology of solar activity, in particular to a method for evaluating the correlation between predictors and solar flare occurrences. The specific steps are as follows: Step 1: Extract all predictors and the data corresponding to whether flare events occur, and define each predictor The correlation index; Step 2: draw a correlation diagram between any one of the "predictors" and "data on whether flares occur"; Step 3: count the occurrence probability of flares of the predictors in each data segment, and obtain the forecast The correlation index of the factor; Step 4: According to the numerical value of the correlation index, give the correlation conclusion between the predictor and whether a flare event occurs; Step 5: Perform steps 2 to 4 for other predictors, Get the correlation conclusion of each predictor. The present invention adopts the evaluation method of multiple data segments, and can obtain the correlation conclusion relatively completely and accurately.

Description

technical field [0001] The invention relates to a research technology of solar activity, in particular to a method for evaluating the correlation between a predictor based on a density statistics method and the occurrence of solar flares. [0002] Background of the invention [0003] A solar flare is a sudden and violent energy release process, and when a flare occurs, it will also endanger human survival. Accurately forecasting the situation of solar eruption activities in the future can prevent and deal with disasters in a timely manner. [0004] Prediction of solar flares requires accurate predictors, and there are two main methods for extracting predictors. One is to observe sunspots and then perform sunspot group classification: the most commonly used is the McIntosh classification method, which reduces the 9 types in the traditional Zurich classification to 7 types, and uses them as the characteristic parameters of the solar flare prediction process The other is to di...

AI Technical Summary

Problems solved by technology

[0005] At present, under the condition of obtaining a large amount of open source data, a set of linear or nonlinear mappings can be obtained by reducing the dimensionality of the predictors; however, although this method can eliminate redundant information in the predictors, it cannot directly Determining whether the predictors used are complete

As for how to build a relationship model between predictors and solar flares, and effectively evaluate the nonlinear relationship between predictors and flares, there is no authoritative public literature that elaborates on it

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