Curve real-time adaptive display method and system based on state judgment, storage medium and device

Abstract

The invention discloses a curve real-time adaptive display method and system based on state judgment, a storage medium and a device, and the method comprises the following steps: determining an image display area, the abscissa axis of the image display area being a time flow, and the ordinate axis of the image display area being gas turbine test data; determining the display upper and lower limits of the vertical coordinate axis of the image display area by predicting the full-range upper and lower limits of the data; reading test data from a gas turbine test data source; obtaining a steady-state data range in the test data, and updating the display upper and lower limits of the regional ordinate axis according to the steady-state data range; displaying an image within the display upper and lower limits of the region ordinate axis of the image display region. A self-adaptive display technology is added on the basis of gas turbine test data acquisition and display, high-resolution display of parameter variation and fluctuation under the conditions of a transition state and a steady state in the whole test process can be met along with the change of a test state, and a curve with a high data identification degree is obtained.

Description

technical field [0001] The invention relates to the field of gas turbine testing, in particular to a real-time self-adaptive display method, system, storage medium and device for curves based on state judgment. Background technique [0002] Gas turbines (hereinafter referred to as gas turbines) are widely used in energy, power and other fields. In order to improve its performance, it is necessary to test the gas turbine and collect relevant parameters. The gas turbine test is to obtain reliable performance data. During the test run, a large amount of data of the engine and test equipment will be measured, including the steady state data of the engine performance assessment point and the transition state data of the whole test process. During the test, in order to facilitate the test researchers to operate and analyze the engine and equipment intuitively with reference to the test data, it is necessary to visualize the steady-state data and transition-state data, and monitor...

AI Technical Summary

Problems solved by technology

For example: the range of the measured parameter Pi in the transition state during the test is 0%-100%, and the steady-state test points are 75%, 85%, 95%. According to the existing practice, the upper limit setting value of the parameter Pi is set is 100%, and the lower limit is set to 0%. To ensure that the maximum value of the transition state of parameter changes can be displayed in a single frame; when the engine enters the steady-state test points of 75%, 85%, and 95%, the measured parameters The change value of Pi is significantly reduced. When the engine steady-state test point is running normally, the normal fluctuation value of this parameter should be > 1%. During the operation of this steady-state test point, in order to evaluate the performance and safety monitoring of the engine, test researchers need to monitor and monitor in time. Find out whether the fluctuation of the measured parameter Pi exceeds the limit. If it exceeds, it needs to be combined with other related parameters for detailed analysis or troubleshooting. At this time, the fluctuation of about 1% is relative to the upper and lower limit settings of 0%-100%. The proportion is very small, and it is difficult to distinguish and monitor in the entire continuous screen display, so the data analysis and safety monitoring of the steady-state process cannot be carried out. By analogy, there are dozens to hundreds of pieces of data that need to be monitored for time history in the test , the current display method cannot meet the needs of detailed analysis of the fluctuations of the measured parameters at the steady-state test point and the need to troubleshoot hidden problems, that is, the problem of being unable to perform more fine-grained data analysis under the steady-state parameters

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