Method and device for improving measurement precision of gas analyzer

Abstract

The invention discloses a device capable of improving measurement precision of gas analysis instrument, which comprises a filter wheel which is provided with a plurality of infrared filters; an electric motor which is used to drive the an infrared stationary light source and a monolithic system, wherein, a plurality of infrared filters are arranged on the filter wheel, the electric motor is used to drive the filter wheel to rotate against a preset frequency; an infrared stable light source which is used to irradiate a detecting gas cell through the infrared filters and is provided with an infrared sensor behind the detecting gas cell to receive the beam from the infrared stable light source; a single chip system which is used to control the drive of the electric motor and conduct wavelet contraction thresholds filtering on the signals received by the infrared sensor. The method and device of the invention are optimized by decomposing Haar wavelet and rebuilding wavelet, multiplication is transferred to addition in wavelet transform and meanwhile section wavelet filtering is adopted, which reduces operation volume, can be applied on a single chip system or imbedded system, effectively reducing the noise signals of gas analysis instrument, improving the measurement precision and decreasing the dependence on the hard circuit filter and model selection.

Description

technical field [0001] The invention relates to a method and a device for measuring precision of a gas analyzer, in particular to a method and a device for improving the measuring precision of a medical gas analyzer. Background technique [0002] The medical gas analyzer in the prior art is a device dedicated to monitoring the patient's breath, especially before and after the patient is seriously ill or undergoing surgery, such as in the ICU intensive care unit, the medical gas analyzer monitors the gas composition and concentration in the patient's breath Changes in the patient's vital signs can be monitored. [0003] In existing medical gas analyzers, the spectral range of the respiratory gas analysis measurement signal is 0-4Hz. When other noises are suppressed, the noise in the same frequency band as the signal will become the main noise of the analyzer. This part of the noise limits the The improvement of the accuracy of the gas analyzer is difficult to eliminate by us...

AI Technical Summary

Problems solved by technology

[0006] However, the above-mentioned methods in the prior art can only suppress part of the noise, and cannot have a good suppression effect on the noise in the same frequency band as the spectrum and the signal spectrum, and are too pursuing the use of low-noise amplifier circuits, detectors, and low-fluctuation infrared light sources. , will greatly increase the complexity of the circuit and hardware cost

[0007] For some gases with weak infrared light absorption characteristics, such as N2O, halothane, etc., only reducing noise by hardware methods cannot meet the measurement accuracy requirements, and software filtering must be used

However, the traditional software filtering method has no good suppression effect on the noise in the same frequency band as the spectrum and the signal spectrum, such as the average filtering method, FIR (finite impulse response filtering) and other methods.

The more complex filtering methods in the prior art, such as the wavelet threshold filtering method proposed by Donoho-Johnstone, cannot be directly applied to the single-chip microcomputer system at all because of its complexity and large amount of calculation, and the real-time performance of the instrument cannot be guaranteed (the calculation time is too long). long)

However, gas concentration monitors in the field of medical equipment usually use single-chip microcomputer systems from the perspective of cost and application, which limits the practical application of this method.

The theory and practice of wavelet decomposition in the prior art are relatively mature, as can be seen in "Digital Signal Processing-Time Domain Discrete Random Signal Processing" (Publisher: Xidian University, Author: Ding Yumei, etc., the first edition in December 2002 ) pages 240-241, but because of the large amount of calculation, the existing wavelet decomposition has not been applied to the gas analyzer of the single-chip system

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