Bayesian information fusion-based measurement uncertainty evaluation and real-time updating method

Abstract

The invention discloses a Bayesian information fusion-based measurement uncertainty evaluation and real-time updating method. A core is that a measurement uncertainty dynamic evaluation model is established based on Bayesian information fusion and statistic inference principles. For the limitations that in existing detection, the measurement uncertainty is evaluated once in most cases, is used for a long term and cannot be updated continuously in real time along with daily measurement, and values of daily measurement data cannot be fully utilized, a maximum entropy principle and a hill-climbing search optimization algorithm are introduced; a prior distribution probability density function and a sample information likelihood function are determined by utilizing computer programming computation; a posteriori distribution probability density function is obtained in combination with a Bayesian formula; and the measurement uncertainty is subjected to optimization estimation, so that the measurement uncertainty evaluation and real-time updating are realized and latest information of a measurement system state is reflected in time.

Description

technical field [0001] The invention relates to the field of measurement uncertainty evaluation methods, in particular to a measurement uncertainty evaluation and real-time update method based on Bayesian information fusion. Background technique [0002] The purpose of measurement is to determine the measured value, and the quality of the measurement results is an important basis for measuring the reliability of the measurement results. Measurement uncertainty is an important indicator reflecting the quality of measurement results, and the reliability of measurement results largely depends on the size of its uncertainty. Measurement uncertainty is a parameter associated with a measurement result that characterizes the dispersion that can reasonably be assigned to the value being measured. Usually the quality of the measurement results is measured by the measurement error, but the measurement error can only reflect the short-term quality of the measurement. Whether the meas...

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Problems solved by technology

[0007] The measurement uncertainty evaluation and real-time update method based on Bayesian information fusion is characterized in that: based on Bayesian information fusion and statistical inference principles, a dynamic evaluation model of measurement uncertainty is established, aiming at the measurement uncertainty in existing detection It is often once assessed and used for a long time. It cannot be continuously updated in real time with daily measurement, and the value of daily measurement data cannot be fully utilized. The maximum entropy principle and hill-climbing search optimization algorithm are introduced to determine the prior distribution probability density function and The sample information likelihood function, combined with the Bayesian formula to obtain the probability density function of the posterior distribution, optimizes the estimation of the measurement uncertainty, realizes the measurement uncertainty evaluation and real-time update, and reflects the latest information of the measurement system status in a timely manner, including the following steps :

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