A high-precision detection method for real-time multi-sensor temperature data fusion

Abstract

The invention discloses a high-accuracy detection method of real-time temperature data fusion of multiple sensors, which comprises the following steps of: step 1, remissness error removal based on relativity function sequence: (1) acquiring temperature data, (2) judging whether the number of acquisition times is larger than or equal to a set value C1 or not, if NO, continuing to acquire the temperature data, if YES, judging whether the number of the acquisition times is larger than the set value C1 or not, if NO, computing the variance and the mean value initial value of sensors, if YES, computing the variance and the mean value of the sensors in a recurrent way, (3) computing the fusion degree of the sensors, (4) computing the support degree of the sensors, and (5) selecting valid sensors, and deleting the temperature data obtained from invalid sensors; step 2, orthogonal neural network based temperature information fusion of the multiple sensors: (1) neural network training and weight vector recurrence, (2) computing neural network output, and (3) computing a temperature fusion value of the multiple sensors; and step 3, correcting variance and mean value of the invalid sensors. By the method, not only can the temperature detection accuracy and credibility be improved, related systems can be conveniently processed in real time. The method has the advantages that the stability is good, the compute is simple, the implementation is easy, and the like.

Description

technical field [0001] The invention belongs to the technical field of high-precision temperature detection, and in particular relates to a high-precision detection method for temperature data fusion that objectively evaluates the support between sensors, estimates sensor characteristic parameters in real time, and does not require any prior knowledge. The method can not only improve the accuracy and reliability of temperature detection, but also facilitate the real-time processing of related systems, and has the advantages of good stability, simple calculation, and easy implementation. Background technique [0002] In many automatic control, detection and scientific experiments, high-precision and high-reliability detection of temperature is required. For example, whether the temperature detection of vaccine cold chain can meet its special requirements not only affects the safe storage of vaccines, but also affects the system alarm. Credibility, which in turn seriously inte...

AI Technical Summary

Problems solved by technology

Regarding the estimation of the sensor variance, this method corrects the variance every time an observation value is added. Its essence is to use all the observation data to make the sensor variance estimate closer to reality, and overcome the existing methods that only use limited data to establish. Insufficiency of the temperature fusion model

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