Collaborative visualization method for long-term trend and local fluctuation of durability index

Abstract

The invention discloses a collaborative visualization method for long-term trend and local fluctuation of a durability index, and the method specifically comprises the following steps: 1, collecting test data of the durability index, and sorting the test data according to a time sequence to obtain a test data set; 2, calculating a trend value of the test data at each moment; 3, segmenting the test data according to time intervals, and calculating a variable coefficient and skewness of each segment of test data; 4, drawing a curve by taking the duration of the test process as an abscissa and the trend value of the test data as an ordinate, drawing a segmentation line on the curve according to the skewness and positive and negative values, and visually displaying the durability index test data; according to the invention, the test process of each durability index can be visually displayed, and the variation trend of the durability in the test process and the fluctuation condition of the performance in each test period can be conveniently known, so that the durability can be specifically analyzed, and a more accurate test result can be obtained.

Description

technical field [0001] The invention belongs to the technical field of data visualization processing, and in particular relates to a collaborative visualization method of long-term trends and local fluctuations of durability performance indicators. Background technique [0002] In recent years, Industry 4.0 has become a major topic in the world. With the wide application of emerging technologies such as the Internet of Things and CPS, industrial process data from various sources has grown exponentially. People are facing unprecedented challenges in processing data analysis. Visualization technology as An important technology for interpreting and understanding complex data, it is increasingly being introduced into industrial data analysis scenarios to improve work efficiency and promote process innovation. [0003] Based on the durability test of automobile starter, a visual analysis method is proposed to understand the long-term trend and local fluctuation of large-scale lon...

AI Technical Summary

Problems solved by technology

[0003] Based on the durability test of automobile starter, a visual analysis method is proposed to understand the long-term trend and local fluctuation of large-scale long-term durability test data; durability test refers to the long-term or replicated service conditions of components and subsystems , to verify the product design, and determine the service life process in the early stage of product development, the durability test usually generates a large amount of test data, which contains rich information related to the working load of the test piece, durability performance, Internet of Things, CPS, etc. The wide application of these data makes it easy to obtain, transmit and share these data from smart factories, however, due to the lack of effective data analysis methods and tools, these data are often overlooked

[0004] When the testers and engineers analyze and understand the durability test data, they cannot compare the test data of multiple sets of test indicators, nor can they understand the overall change trend and local fluctuations of the durability performance, and cannot find out the abnormal conditions in the test in time. Therefore, it is urgent to provide a visual interface to help testers complete analysis tasks, so as to fully understand the durability test data

Images