Data Correction Method for Improving Measurement Accuracy

Abstract

The invention provides a data correction method for improving measurement precision. The data correction method comprises the steps of a, acquiring to-be-corrected continuous indirect measurement data, and establishing a segmented nonlinear correction model I and a segmented nonlinear correction model II; b, setting a hysteresis interval q-p, wherein critical thresholds satisfy a relation of q<p, and on the condition that the value of the measured continuous indirect data is lower than the critical threshold p, performing correction by means of the segmented nonlinear correction model I, and on the condition that the value of the measured continuous indirect data increase to higher than the critical threshold p, performing correction by means of the segmented nonlinear correction model II, and on the condition that the value of the measured continuous indirect data gradually reduces to lower than the critical threshold q, performing correction by means of the segmented nonlinear correction model I. According to the data correction method, data are corrected by means of a segmented nonlinear mode. Compared with a traditional segmented linear correction, the data correction method is advantageous in that less correction points in segmented nonlinear correction are realized; and furthermore after the hysteresis interval is introduced, high-frequency data jump caused by high-frequency correction model switching at critical points can be prevented.

Description

technical field [0001] The invention relates to the field of instrument measurement, in particular to a data correction method for improving measurement accuracy. Background technique [0002] In the process of instrumentation measurement, indirect measurement is to convert a measurand into several directly measurable quantities for measurement, and then calculate or draw according to the relationship formula (ie measurement formula) derived from the definition or law, so as to indirect The measurement method to obtain the measurement results will inevitably produce errors for indirect measurement. In order to eliminate the influence of this error and improve the accuracy of the measurement data, data correction is required. Currently, the existing data correction methods are mainly segmented linear correction and Non-segmented nonlinear correction, however, in order to ensure measurement accuracy with segmented linear correction, multiple correction points need to be set. I...

AI Technical Summary

Problems solved by technology

[0002] In the process of instrumentation measurement, indirect measurement is to convert a measurand into several directly measurable quantities for measurement, and then calculate or draw according to the relationship formula (ie measurement formula) derived from the definition or law, so as to indirect The measurement method to obtain the measurement results will inevitably produce errors for indirect measurement. In order to eliminate the influence of this error and improve the accuracy of the measurement data, data correction is required. Currently, the existing data correction methods are mainly segmented linear correction and Non-segmented nonlinear correction, however, in order to ensure measurement accuracy with segmented linear correction, multiple correction points need to be set. If multiple correction points are set, the efficiency of data correction will be greatly affected. If too few correction points are set, However, the accuracy of measurement cannot be guaranteed, and most of the non-segmented nonlinear corrections currently used are only suitable for monotonically rising or falling curves, which have poor adaptability and low measurement accuracy. Therefore, a new kind of data is urgently needed Revised algorithm to improve measurement accuracy

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