Mass data drawing optimization method based on maximum triangle three-segment algorithm

Abstract

The invention discloses a mass data drawing optimization method based on a maximum triangle three-segment algorithm. The method comprises the steps of firstly performing downsampling on data based on a maximum triangle three-segment algorithm, trying to compress original data and keeping detail features as much as possible; and for locally steep segmented data, the improvement of dynamically adjusting the segments is carried out. Then, the data with the magnitude exceeding a threshold value is sliced, and for each piece of sliced data, the ECharts can independently create an instance for drawing, so that a chart of sub-sliced data is obtained; and finally, positioning the obtained sub-charts to the same position by using a cascading style sheet language, and superposing the sub-charts to form a unique complete graph. By the adoption of the method, data points used for drawing can be greatly reduced on the premise that original data details are not lost as far as possible, and therefore visual drawing can be conducted on industrial mass data efficiently and with high performance.

Description

technical field [0001] The invention belongs to the technical field of data visualization, and in particular relates to a massive data drawing optimization method based on the largest triangle three-segment algorithm. Background technique [0002] In recent years, industrial remote operation and maintenance and online monitoring systems have been widely used, and in industrial monitoring scenarios, the visualization of time series data is an unavoidable topic. Because many monitoring systems need to frequently collect and report the data of field devices, and display them in real-time visual charts. These data are often updated frequently, and the amount of data updated each time is relatively large, which will result in a time-series data set with a large amount of data. [0003] There are some problems in the use of these massive data: First, the characteristics are not obvious. The magnitude of the data is large, the fluctuations are frequent, and the data points are ov...

AI Technical Summary

Problems solved by technology

[0003] There are some problems in the use of these massive data: First, the characteristics are not obvious

The magnitude of the data is large, the fluctuations are frequent, and the data points are overlapped after being connected by broken lines. Whether it is used for subsequent data mining or directly drawn, it is difficult to obtain significant trend characteristics, which is not conducive to data analysis and visualization.

Second, too many data points bring challenges to the display performance of graphics

The idea of ​​segmentation is correct, but the reason for ignoring the spikes with the time difference as the weight is due to the sudden change of the value, not the characteristics of the excessive time difference

Moreover, the selection of the threshold is not rigorous, there is no suitable fixed value, and it is not easy to select a suitable threshold in actual settings.

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