Curve graph equal-proportion expansion method and device

Abstract

According to the curve graph equal-proportion expansion method, the expansion coefficient of each curve is calculated according to each curve data point, and each curve is expanded according to the expansion coefficient to form a new curve graph. According to the invention, the curve charts with different orders of magnitude and different variation trends are subjected to equal-proportion expansion processing, and the curves with large numerical value differences are integrated into the same coordinate system and are displayed through a software interface, so that a user can conveniently and clearly observe and analyze the mutual relation between the variation trends of the variables, and functions of reason analysis and tracing, big data prediction and the like are realized.

Description

technical field [0001] The invention belongs to the technical field of computer image processing, and in particular relates to a method and a device for expanding the function of a graph in equal proportion. Background technique [0002] Curve graph is a commonly used technology in the field of computer software. It can be applied to any industry software to represent the changing trend of data or variables through curves for comparison and analysis. [0003] When graphs are applied in many industries, there will be a need for multivariate analysis and comparison of changing trends, but there are problems with different orders of magnitude among many variables, resulting in large differences in the changing trends of their respective graphs. For example, in the environmental monitoring industry, people need Real-time monitoring of the values ​​of various pollutants, such as CO, SO 2 There are as many as dozens of pollutants, and the value of each pollutant needs to be uploa...

AI Technical Summary

Problems solved by technology

[0003] When graphs are applied in many industries, there will be a need for multivariate analysis and comparison of changing trends, but there are problems with different orders of magnitude among many variables, resulting in large differences in the changing trends of their respective graphs. For example, in the environmental monitoring industry, people need Real-time monitoring of the values ​​of various pollutants, such as CO, SO 2 There are as many as dozens of pollutants, and the value of each pollutant needs to be uploaded regularly. Analysts may need to observe the trend of the value of each pollutant over a period of time, find out the rules, and analyze which substances have positive or negative correlations. connect

However, the values ​​of different substances may vary greatly, some may be hundreds or thousands, and some may be as small as the negative power of 10

[0004] If the curves of the above-mentioned variables with large numerical differences are integrated and displayed in the same graph, some curves will have very large amplitudes, and some curves will approach a straight line, making it impossible to distinguish

If the method of generating multiple graphs is not intuitive enough, dozens of variables may generate dozens of graphs, which cannot be compared and analyzed

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