Spectral image recognition method for laser gas analyzer

Abstract

The invention relates to the technical field of spectral image data processing, in particular to a spectral image recognition method for a laser gas analyzer, which comprises the following steps: acquiring a gas absorption spectrum image, dividing the gas absorption spectrum image into a plurality of wave bands, and determining a low-frequency layer approximation coefficient and a high-frequency layer detail coefficient of each wave band through wavelet transform; calculating the signal score of the wave band according to the energy variance of the detail coefficient of the high-frequency layer of the highest layer and the approximation coefficient of the low-frequency layer of the lowest layer in the wave band; based on a comparison result of the signal score of the wave band and a score threshold value corresponding to the wave band, determining the possibility that the wave band is in an absorption peak region; calculating a hard threshold value of each high-frequency layer of each wave band; and in response to the comparison result of the detail coefficient of the high-frequency layer of each layer in the wave band and the corresponding hard threshold, denoising the gas absorption spectrum image to obtain the recognition result of the gas absorption spectrum image, so that the accuracy of the recognition result of the gas absorption spectrum image is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of spectral image data processing. More specifically, the present invention relates to a spectral image recognition method for a laser gas analyzer. Background Art

[0002] A laser analyzer is a high-precision gas detection device based on spectral absorption technology, mainly used in industrial process monitoring, environmental monitoring, safety protection and other fields, and can measure the concentration of various gases in real time and accurately. In industrial and environmental monitoring, the complex and changeable gas components and environmental interferences often cause deviations in the measurement data of the laser gas analyzer. The spectral image recognition method can extract spectral features, and through algorithm processing and analysis, it can eliminate environmental interferences and quickly identify subtle changes in the spectrum.

[0003] At present, differential optical absorption spectroscopy (DOAS) is mostly used for spectral image recognition of laser gas analyzers. This technology quantitatively analyzes the gas concentration by comparing the intensity attenuation at specific wavelengths in the absorption spectrum. The characteristic absorption peaks of gas molecules usually appear as high-frequency fluctuation signals. Before obtaining the gas analysis result, denoising processing needs to be carried out first to improve the detection sensitivity. However, in the low-concentration detection of spectral image gas recognition, the molar absorptivity of some gases is low. When the gas concentration decreases, its absorption intensity will also decrease accordingly, resulting in weak absorption peak signals in the collected gas absorption spectral image. During the wavelet threshold denoising process, they will be misjudged as noise and removed, resulting in inaccurate results of spectral image recognition for laser gas analyzers.

[0004] Therefore, how to effectively reduce the possibility that weak absorption peak signals are misjudged as noise and removed, so as to improve the accuracy of spectral image recognition results for laser gas analyzers, is an urgent problem to be solved at present. Summary of the Invention

[0005] To solve the above technical problem of how to effectively reduce the possibility that weak absorption peak signals are misjudged as noise and removed, so as to improve the accuracy of spectral image recognition results for laser gas analyzers, the present invention proposes a spectral image recognition method for a laser gas analyzer, and the method includes the following steps: Obtain a gas absorption spectrum image and divide it into multiple bands. Determine the low-frequency layer approximation coefficients and high-frequency layer detail coefficients of each band through wavelet transform; calculate the signal score of the band based on the energy variances of the high-frequency layer detail coefficients of the highest layer and the low-frequency layer approximation coefficients of the lowest layer in the band; determine the possibility that the band is in the absorption peak region based on the comparison result between the signal score of the band and the corresponding score threshold; calculate the hard threshold of each high-frequency layer of each band: ; 、 、 are the hard threshold, global noise standard deviation, and total number of detail coefficients of the th high-frequency layer of the th band respectively, is the possibility that the th band is in the absorption peak region, 、 are the energy variances of the high-frequency layer detail coefficients of the th band and the highest layer of the gas absorption spectrum image respectively, is the preset adjustment factor, is the natural logarithm, is the exponential function with base e; in response to the comparison result between the high-frequency layer detail coefficients of each layer in the band and the corresponding hard threshold, denoise the gas absorption spectrum image to obtain the recognition result of the gas absorption spectrum image.

[0006] After denoising the gas absorption spectrum image, the present invention performs spectral image recognition, which can effectively reduce noise interference and thus accurately obtain the recognition result of the spectral image. During the process of denoising the gas absorption spectrum image, the present invention takes into account that the signals of some absorption peaks in the gas absorption spectrum image are relatively weak. When denoising through conventional wavelet transform, such absorption peaks may be removed as noise, affecting the accuracy of the denoising effect; therefore, the present invention analyzes the energy characteristics of each band in the gas absorption spectrum image to obtain the possibility that it is in the absorption peak region, and dynamically adjusts the hard threshold in wavelet transform denoising based on this, reducing the hard threshold in the absorption peak region of weak signals and improving the accuracy of denoising, thereby effectively improving the accuracy of the recognition result of the gas absorption spectrum image. When calculating the possibility that each band is in the absorption peak region, the present invention also determines the signal score according to the signal-to-noise ratio, and improves the possibility that the band in the weak absorption peak region is recognized as an absorption peak through the comparison result between the signal score and the score threshold, thereby effectively improving the accuracy of the recognition result of the gas absorption spectrum image obtained based on this.

[0007] A spectral image recognition method for a laser gas analyzer provided by the present invention. Before obtaining the gas absorption spectral image and dividing it into multiple bands, it further includes: collecting the wavelength and absorbance of the gas in the environment, constructing a curve graph with the wavelength of the gas as the horizontal axis and the absorbance of the gas as the vertical axis, and obtaining the gas absorption spectral image after preprocessing.

[0008] A spectral image recognition method for a laser gas analyzer provided by the present invention. The step of obtaining the gas absorption spectral image and dividing it into multiple bands includes: dividing the gas absorption spectral image into multiple bands according to a preset wavelength interval.

[0009] The present invention takes into account that the change trends of absorption peaks in the gas absorption spectral image are not the same. Therefore, by dividing the gas absorption spectral image into multiple bands and judging whether each absorption peak is an absorption peak according to the energy characteristics of the absorption peaks in each band, the possibility of diluting the absorption peak characteristics by global analysis is effectively avoided.

[0010] A spectral image recognition method for a laser gas analyzer provided by the present invention. The step of determining the low-frequency layer approximation coefficients and high-frequency layer detail coefficients of each band by wavelet transform includes: dividing the band into a preset number of layers by wavelet transform, and each layer corresponds to a low-frequency layer approximation coefficient and three high-frequency layer detail coefficients; wherein, the first layer is the lowest layer, the last layer is the highest layer, and each low-frequency layer approximation coefficient or high-frequency layer detail coefficient is a coefficient set.

[0011] A spectral image recognition method for a laser gas analyzer provided by the present invention. The step of calculating the signal score of the band includes: calculating the signal score of the th band ; is the logarithm with base 10, is the energy variance of the high-frequency layer detail coefficients of the highest layer of the th band, is the energy variance of the low-frequency layer approximation coefficients of the lowest layer of the th band.

[0012] A spectral image recognition method for a laser gas analyzer provided by the present invention. The method for obtaining the score threshold corresponding to the band includes: ; is the score threshold corresponding to the th band, is a preset score base threshold, , are respectively the The energy variance of the high-frequency layer detail coefficients of the highest layer of the gas absorption spectral image in a certain band is the exponential function with base e.

[0013] Considering that there are both strong signals and weak signals in some bands, in order to avoid the weak absorption peaks in such bands being identified as noise, the present invention reduces the threshold by the ratio of the energy variances of the high-frequency layer detail coefficients of the highest layer of the band and the gas absorption spectral image, so that the possibility of the band containing relatively weak but actually existing absorption peaks being in the absorption peak state is increased, improving the accuracy of threshold adjustment.

[0014] According to a spectral image recognition method for a laser gas analyzer provided by the present invention, determining the possibility that the band is in the absorption peak region according to the comparison result between the signal score of the band and the corresponding score threshold includes: if the signal score of the band is greater than or equal to the corresponding score threshold of the band, then taking the normalized difference between the signal score of the band and the corresponding score threshold as the possibility that the band is in the absorption peak region; otherwise, setting the possibility that the band is in the absorption peak region to 0.

[0015] By obtaining the comparison result between the signal score of the band and its corresponding score threshold, the present invention obtains the possibility that the band is in the absorption peak region, and can retain the continuous information of the signal intensity while filtering out noise, preparing for subsequent quantitative analysis.

[0016] According to a spectral image recognition method for a laser gas analyzer provided by the present invention, denoising the gas absorption spectral image in response to the comparison result between the high-frequency layer detail coefficients of each layer in the band and the corresponding hard threshold includes: if the high-frequency layer detail coefficients of each layer in the band are less than the corresponding hard threshold, then setting the high-frequency layer detail coefficients to 0, otherwise remaining unchanged to achieve denoising; performing inverse wavelet transform on the high-frequency layer detail coefficients of each layer in the denoised band to obtain the denoised gas absorption spectral image.

[0017] According to a spectral image recognition method for a laser gas analyzer provided by the present invention, denoising the gas absorption spectral image to obtain the recognition result of the gas absorption spectral image includes: comparing the absorption peaks in the denoised gas absorption spectral image with the standard absorption peaks to obtain the recognition result of the denoised gas absorption spectral image.

[0018] According to a spectral image recognition method for a laser gas analyzer provided by the present invention, comparing the absorption peaks in the denoised gas absorption spectral image with the standard absorption peaks is achieved by the least squares method.

[0019] In view of the fact that the gas types in the denoised gas absorption spectral image are usually not single, the present invention can accurately identify various types of gases and improve the accuracy of detection results by fitting each absorption peak with the standard absorption peak using the least squares method.

[0020] The present invention has the following beneficial effects: Based on the above technical solution, for a spectral image recognition method for a laser gas analyzer provided by the present invention, when obtaining the recognition result, by performing spectral image recognition after denoising the gas absorption spectral image, the noise interference can be effectively reduced, and thus the recognition result of the spectral image can be accurately obtained. During the process of denoising the gas absorption spectral image, the present invention takes into account that the signals of some absorption peaks in the gas absorption spectral image are relatively weak. When denoising by conventional wavelet transform, such absorption peaks may be removed as noise, affecting the accuracy of the denoising effect. Therefore, the present invention analyzes the energy characteristics of each band in the gas absorption spectral image to obtain the possibility of it being in the absorption peak region, and dynamically adjusts the hard threshold in wavelet transform denoising based on this, reducing the hard threshold in the region of weak signal absorption peaks and improving the accuracy of denoising, thereby effectively improving the accuracy of the recognition result of the gas absorption spectral image. When calculating the possibility of each band being in the absorption peak region, the present invention also determines the signal score according to the signal-to-noise ratio, and improves the possibility of the band in the weak absorption peak region being recognized as an absorption peak through the comparison result of the signal score and the score threshold, thereby effectively improving the accuracy of the recognition result of the gas absorption spectral image obtained based on this. Description of the Drawings

[0021] Figure 1 It is a schematic flowchart of a spectral image recognition method for a laser gas analyzer provided by an embodiment of the present invention. Detailed Embodiments

[0022] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention.

[0023] The embodiment of the present invention discloses a spectral image recognition method for a laser gas analyzer. By dynamically adjusting the threshold for noise screening in each band, the method can avoid removing weak absorption peak signals as noise and improve the accuracy of the recognition result of the gas absorption spectral image.

[0024] Please refer to Figure 1 , Figure 1 It is a schematic flowchart of a spectral image recognition method for a laser gas analyzer provided by an embodiment of the present invention, and the method includes the following steps: S1: Obtain a gas absorption spectral image.

[0025] Specifically, after the environmental air flow stabilizes, the wavelength, optical path, and gas concentration are collected by a laser analyzer. The optical path and gas concentration are input into the Lambert-Beer formula to calculate its absorbance, and the unit of the wavelength can be set to nm.

[0026] Exemplarily, in the embodiment of the present invention, obtaining the gas absorption spectrum image includes: collecting the wavelength and absorbance of the gas in the environment, constructing a curve graph with the wavelength of the gas as the horizontal axis and the absorbance of the gas as the vertical axis, and obtaining the gas absorption spectrum image after preprocessing. There are multiple absorption peaks on the gas absorption spectrum image.

[0027] Among them, the preprocessing method can be spectral normalization, etc. The preprocessing method can be specifically set according to actual needs, and the embodiment of the present invention does not limit it too much here.

[0028] Before determining the gas recognition result based on the gas absorption spectrum image, it is necessary to denoise the gas absorption spectrum image. Noise is usually a high-frequency signal, and most of the effective information in the spectrum (such as characteristic absorption signals) is also contained in the high-frequency components. Therefore, the embodiment of the present invention mainly processes the high-frequency coefficients. After wavelet decomposition of the gas absorption spectrum image by wavelet transform, a set of wavelet coefficient information is extracted, and the small-amplitude high-frequency coefficients are removed by comparing the coefficients with a threshold, so as to achieve the purpose of denoising. In the process of obtaining the threshold comparison result, the hard threshold can set to zero the part of the high-frequency coefficients that is less than the hard threshold and retain the part that is greater than the hard threshold, retaining most of the characteristics of the signal while suppressing the noise, which is suitable for denoising the gas absorption spectrum image.

[0029] However, in the process of denoising the gas absorption spectrum image by wavelet transform, the absorption peaks with weak signals are extremely likely to be misidentified as noise and removed, affecting the accuracy of the subsequent analysis results obtained based on the gas absorption spectrum image.

[0030] In the gas absorption spectrum image, the energy of the absorption peak has a concentrated characteristic. Based on this, the embodiment of the present invention can divide the gas absorption spectrum image into multiple sub-bands, perform wavelet transform on each band respectively to obtain the energy corresponding to the high-frequency layer detail coefficients and the energy corresponding to the low-frequency layer approximation coefficients, analyze the energy concentration degree of the high-frequency layer detail coefficients and the low-frequency layer approximation coefficients, determine the quality of the band, set the bands with poor quality to 0, and then continue to judge the possibility that each band with better quality is in the absorption peak region, and dynamically adjust the hard threshold of each layer of the band based on the possibility that each band is in the absorption peak region, so as to effectively reduce the possibility that weak absorption peaks are judged as noise by the threshold, that is, continue to execute the following steps.

[0031] S2: Divide the gas absorption spectrum image into multiple bands, and determine the low-frequency layer approximation coefficients and high-frequency layer detail coefficients of each band through wavelet transform; calculate the signal score of the band according to the energy variances of the high-frequency layer detail coefficients of the highest layer and the low-frequency layer approximation coefficients of the lowest layer in the band.

[0032] Exemplarily, in the embodiments of the present invention, obtaining a gas absorption spectrum image and dividing it into multiple bands includes: dividing the gas absorption spectrum image into multiple bands according to a preset wavelength interval.

[0033] Among them, the wavelength interval can be set to 20 nm; the wavelength interval can be specifically set according to actual needs.

[0034] Specifically, when performing wavelet transform on each band, starting from the original image corresponding to the band according to the preset number of layers, first divide the original image into the first-layer low-frequency layer approximation coefficients and the three high-frequency layer detail coefficients of the first layer; then continue to layer the low-frequency layer approximation coefficients of the previous layer to obtain the low-frequency layer approximation coefficients and high-frequency layer detail coefficients of the next layer, and so on until all layers are obtained.

[0035] It should be noted that after processing each band through wavelet transform, high-frequency layer detail coefficients including gas information and noise and low-frequency layer approximation coefficients representing baseline drift can be obtained. The low-frequency layer approximation coefficients and high-frequency layer detail coefficients of each layer are not single values, but a series of coefficient sets. Among them, the first layer is the lowest layer, and the last layer is the highest layer. The higher the decomposition layer number, the smoother the low-frequency approximation layer coefficients and the less the noise. Therefore, the first layer retains the most noise, and the last layer has the highest signal effective component.

[0036] Therefore, when evaluating the quality by obtaining the signal-to-noise ratio score of each band in the embodiments of the present invention, the signal score of the band can be calculated by analyzing the energy variances of the high-frequency layer detail coefficients of the highest layer and the low-frequency layer approximation coefficients of the lowest layer. If the proportion of the high-frequency signal energy in a band is significantly higher than that of the noise region, the possibility of a true absorption peak appearing at this position is higher.

[0037] Exemplarily, in the embodiments of the present invention, determining the low-frequency layer approximation coefficients and high-frequency layer detail coefficients of each band through wavelet transform includes: dividing the band into a preset number of layers through wavelet transform, and each layer corresponds to a low-frequency layer approximation coefficient and three high-frequency layer detail coefficients; among them, each low-frequency layer approximation coefficient or high-frequency layer detail coefficient is a coefficient set.

[0038] Among them, the preset number can be 3; the number can be specifically set according to actual needs. It can be understood that if the number of layers is 3, the highest layer is the third layer.

[0039] It should be noted that the absorption peaks in the wavebands contain more high-frequency information and less noise, while the non-absorption peaks contain more noise and less high-frequency information. Therefore, in the embodiments of the present invention, the quality of each waveband can be determined by first obtaining the ratio of the signal to the noise in the waveband. The higher the ratio, the greater the possibility that the waveband is at an absorption peak. Since the effective part of the signal is concentrated in the detail coefficients of the high-frequency layer and the noise is concentrated in the approximation coefficients of the low-frequency layer, in the embodiments of the present invention, the signal score of the waveband is obtained by analyzing the ratio of the energy variance of the detail coefficients of the high-frequency layer of the highest layer to the energy variance of the approximation coefficients of the low-frequency layer of the lowest layer.

[0040] Exemplarily, in the embodiments of the present invention, the signal score of the th waveband is calculated. Specifically, reference can be made to the following relational expression: ; is the signal score of the th waveband, is the logarithm to the base 10, is the energy variance of the detail coefficients of the high-frequency layer of the highest layer of the th waveband, is the rd waveband, and is the energy variance of the approximation coefficients of the low-frequency layer of the lowest layer.

[0041] In the above formula, The larger it is, the higher the degree of uneven energy distribution of the detail coefficients of the high-frequency layer of the highest layer corresponding to the th waveband, and the higher the degree of energy concentration; The smaller it is, the flatter the baseline and the less significant the trend change. At this time, The higher the possibility that the increase is caused by an absorption peak rather than baseline drift, and the higher the quality.

[0042] The signal score of the waveband is used to characterize the possibility that the waveband is in a high-energy region. The higher the waveband energy, The larger it is, the higher the proportion of high-frequency signals and the lower the proportion of low-frequency baseline noise, and the higher the possibility that it is in the absorption peak region; conversely, it indicates that the possibility that it is in the non-absorption peak region is higher.

[0043] Based on the above steps, the signal scores of each waveband in the gas absorption spectrum image can be obtained. When calculating the possibility that each waveband is at an absorption peak based on the signal scores of the wavebands, it is necessary to first screen out some wavebands with relatively low signal scores and poor quality. The screening can be achieved by setting an acceptable minimum signal quality threshold, so as to accurately obtain the possibility that each waveband is in the absorption peak region.

[0044] S3: Determine the possibility that the band is in the absorption peak region based on the comparison result between the band-based signal score and the corresponding score threshold of the band, and calculate the hard threshold of each high-frequency layer of each band.

[0045] It should be noted that the absorption peak corresponds to the local high-frequency signal. The change in the spectral slope near the absorption peak will be captured by the detail coefficients of the high-frequency layer, resulting in a drastic fluctuation in the coefficient values in this region and an increase in the energy variance. Therefore, the greater the energy variance of the detail coefficients of the high-frequency layer within the band, the more uneven the energy distribution within the band, the phenomenon of energy aggregation exists, and there may be an absorption peak. And the detail coefficients of the highest-level high-frequency layer of the entire image mainly reflect the global noise level and the overall spectral fluctuation trend. When setting the score threshold for each band, since there are strong absorption peaks and weak absorption peaks in the gas absorption spectral image, the spectral signal intensities of different bands may vary significantly.

[0046] Therefore, when setting the score threshold for each band, the signal intensities of different bands can be converted into relative values according to the ratio of the detail coefficients of the highest-level high-frequency layer in each band to the detail coefficients of the highest-level high-frequency layer of the entire gas absorption spectral image, which is convenient for cross-band comparison.

[0047] Specifically, when setting the score threshold corresponding to each band, for the band region with a higher energy concentration level, the possibility that it is in the absorption peak region is higher and the signal score is higher. Further, by reducing the signal score threshold, the bands with a medium energy concentration level can also participate in the subsequent threshold processing, improving the robustness of the algorithm while enhancing the recognition effect of weak gas absorption peaks. On the contrary, for the band region with a lower energy concentration level, the possibility that it is in the absorption peak region is lower. At this time, the signal score threshold can be increased synchronously to ensure that when the energy concentration level of the band is low, a higher threshold setting can be coordinated, thereby forming a double filtering mechanism to improve the discrimination requirements for the noise region and effectively suppressing the random noise interference in the low-energy bands.

[0048] Among them, the detail coefficients of the highest-level high-frequency layer of the entire gas absorption spectral image can be obtained through wavelet transform. The specific steps can be realized by the prior art, and the embodiments of the present invention will not elaborate here.

[0049] Exemplarily, in the embodiments of the present invention, calculating the score threshold corresponding to each band includes: ; is the score threshold corresponding to the th band, is the preset score base threshold, is the energy variance of the detail coefficients of the highest-level high-frequency layer of the th band, is the energy variance of the high-frequency layer detail coefficients of the highest layer of the gas absorption spectrum image, is the exponential function with base e.

[0050] Among them, the scoring base threshold can be set to 10; the scoring base threshold can be set according to actual needs.

[0051] In the above formula, the higher, it indicates that the energy concentration degree of the th band is higher than the global noise level of the gas absorption spectrum image, and the threshold of its signal score needs to be reduced so that this band can be more easily determined as the potential absorption peak region. On the contrary, the smaller, the more the threshold needs to be increased to reduce noise interference.

[0052] After obtaining the signal score of each band and the corresponding scoring threshold of this band according to the above steps, the possibility that this band is in the absorption peak region can be determined according to the signal score of each band and the corresponding scoring threshold of this band.

[0053] It can be understood that when the signal score of the band is less than the corresponding scoring threshold of this band, it indicates that the energy fluctuation in this band does not significantly exceed the global noise level, and the possibility that this band is in the absorption peak region can be directly set to 0 to avoid misjudgment in the follow-up. On the contrary, when the signal score of the band is greater than or equal to the corresponding scoring threshold of this band, the larger the difference between the signal score and the threshold, the more significant the unevenness of the energy distribution in this band is higher than the noise level, and the higher the possibility of the corresponding absorption peak.

[0054] Exemplarily, in the embodiment of the present invention, based on the comparison result of the signal score of the band and the corresponding scoring threshold of this band, determining the possibility that this band is in the absorption peak region includes: if the signal score of the band is greater than or equal to the corresponding scoring threshold of this band, then taking the normalized difference between the signal score of this band and the corresponding scoring threshold as the possibility that this band is in the absorption peak region; otherwise, setting the possibility that this band is in the absorption peak region to 0.

[0055] It should be noted that the signal in the absorption peak region changes violently and is rich in a large number of high-frequency components representing key information. A fixed threshold is likely to misjudge the high-frequency effective signal as noise and filter it out, resulting in the loss of key information and weakening the accuracy of the analysis result; the signal in the non-absorption peak region is relatively stable, and background noise interference becomes the main factor affecting the signal quality. Then, due to improper threshold setting, it may lead to insufficient noise suppression or overprocessing, resulting in problems such as blurred details and noise residue.

[0056] Therefore, after obtaining the possibility of each band being in the absorption peak region based on the above steps, the hard thresholds of each layer in each band can be dynamically adjusted according to the possibility of the band being in the absorption peak region, so as to actively reduce the threshold in the absorption peak region to ensure the complete retention of high-frequency effective signals; and appropriately increase the threshold in the non-absorption peak region to specifically enhance the noise filtering ability.

[0057] Exemplarily, in the embodiment of the present invention, when calculating the hard threshold of the th high-frequency layer of the th band, the specific calculation can refer to the following relational expression: ; is the hard threshold of the th high-frequency layer of the th band, is the global noise standard deviation of the th high-frequency layer of the th band, is the total number of detail coefficients of the th high-frequency layer of the th band, is the possibility of the th band being in the absorption peak region, is the energy variance of the detail coefficients of the highest high-frequency layer of the th band, is the energy variance of the detail coefficients of the highest high-frequency layer of the gas absorption spectral image, is a preset adjustment factor, is the natural logarithm with base e, is the exponential function with base e.

[0058] Among them, the preset adjustment factor can be set to 1.2; specifically, it can be set according to actual needs.

[0059] In the above formula, is the basic hard threshold of the th high-frequency layer of the th band.

[0060] is the adjustment coefficient of the basic hard threshold. The higher is and the lower is, the higher the possibility of the band being in the absorption peak region. At this time, the basic hard threshold needs to be reduced according to the possibility of it being in the absorption peak region, so as to increase the possibility of the band being recognized as the absorption peak region. is negative. By weighting the negative exponential value of to , the adjustment coefficient of the basic hard threshold can be accurately obtained, so as to accurately adjust the basic hard threshold.

[0061] It is understandable that a band with a probability of 0 in the absorption peak region is a noise band. At this time, the basic hard threshold can be directly increased according to a preset adjustment factor, so as to accurately identify this band as a noise band.

[0062] After obtaining the hard threshold of each high-frequency layer of each band according to the above steps, denoising can be performed according to the hard threshold of each high-frequency layer of each band, and the inverse wavelet transform is performed on the denoised high-frequency layer detail coefficients, so as to obtain a complete denoised gas absorption spectrum image. By discriminating the absorption peaks of the signals retained in the denoised gas absorption spectrum image, the recognition result of the gas absorption spectrum image can be accurately obtained.

[0063] S4: In response to the comparison result between the high-frequency layer detail coefficients of each layer in the band and the corresponding hard threshold, denoise the gas absorption spectrum image to obtain the recognition result of the gas absorption spectrum image.

[0064] Exemplarily, in the embodiment of the present invention, in response to the comparison result between the high-frequency layer detail coefficients of each layer in the band and the corresponding hard threshold, denoising the gas absorption spectrum image includes: if the high-frequency layer detail coefficients of each layer in the band are less than the corresponding hard threshold, then set the high-frequency layer detail coefficients to 0, otherwise keep them unchanged to achieve denoising; perform the inverse wavelet transform on the high-frequency layer detail coefficients of each layer in the denoised band to obtain the denoised gas absorption spectrum image.

[0065] Exemplarily, after obtaining the comparison result between the high-frequency layer detail coefficients of each layer in the band and the corresponding hard threshold, the low-frequency layer approximation coefficients can be directly set to 0 to eliminate baseline interference.

[0066] Specifically, when performing the inverse wavelet transform on the high-frequency layer detail coefficients of each layer in the denoised band to obtain the denoised gas absorption spectrum image, the differential absorption spectrum of the high-frequency layer detail coefficients of each layer in each band can be obtained. After splicing the differential absorption spectra of each band in sequence, a complete denoised gas absorption spectrum image can be obtained.

[0067] Exemplarily, in the embodiment of the present invention, denoising the gas absorption spectrum image to obtain the recognition result of the gas absorption spectrum image includes: comparing the absorption peaks in the denoised gas absorption spectrum image with the standard absorption peaks to obtain the recognition result of the denoised gas absorption spectrum image.

[0068] Among them, through the DOAS technology, by analyzing and comparing the light intensity attenuation (i.e., absorbance) at specific wavelengths in the denoised gas absorption spectrum image, the position and approximate shape of the possible gas characteristic absorption peaks can be initially identified.

[0069] Specifically, the standard absorption peak is a measure of the light absorption ability of gas molecules at a specific wavelength. Different gas molecules have different standard absorption peaks. By matching the position and intensity of the absorption peaks, the gas species can be determined.

[0070] It should be noted that there are usually many gas species in the environment, and their absorption spectra will overlap. Therefore, the absorption spectrum can be fitted by the least squares method to decompose the measured spectrum into the absorption spectra of each gas component, and then compared with the standard absorption peak to accurately obtain the types and concentrations of each gas component, realizing the fine classification and identification of multi-component gases.

[0071] Exemplarily, in the embodiment of the present invention, the comparison between the absorption peak in the denoised gas absorption spectrum image and the standard absorption peak can be realized by the least squares method.

[0072] Among them, the specific steps of realizing the comparison between the absorption peak in the denoised gas absorption spectrum image and the standard absorption peak by the least squares method can be obtained through the prior art, and the embodiments of the present invention will not elaborate here.

[0073] It can be seen that in the embodiment of the present invention, when obtaining the recognition result of the gas absorption spectrum image, the gas absorption spectrum image can be obtained and divided into multiple bands. The low-frequency layer approximation coefficients and high-frequency layer detail coefficients of each band can be determined by wavelet transform; according to the energy variance of the high-frequency layer detail coefficients of the highest layer and the low-frequency layer approximation coefficients of the lowest layer in the band, the signal score of the band can be calculated; based on the comparison result between the signal score of the band and the corresponding score threshold, the possibility that the band is in the absorption peak region can be determined; calculate the hard threshold of each high-frequency layer of each band: ; 、 、 are the hard threshold, the global noise standard deviation, and the total number of detail coefficients of the th high-frequency layer of the th band respectively, is the possibility that the th band is in the absorption peak region, 、 are the energy variances of the high-frequency layer detail coefficients of the th band and the highest layer of the gas absorption spectrum image respectively, is the preset adjustment factor, is the natural logarithm, is the exponential function with e as the base; in response to the comparison result between the high-frequency layer detail coefficients of each layer in the band and the corresponding hard threshold, the gas absorption spectrum image is denoised to obtain the recognition result of the gas absorption spectrum image, effectively improving the accuracy of the recognition result of the gas absorption spectrum image.

[0074] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A spectral image recognition method for a laser gas analyzer, characterized in that Including: Obtain a gas absorption spectrum image and divide it into multiple bands, and determine the low-frequency layer approximation coefficients and high-frequency layer detail coefficients of each band through wavelet transform; Calculate the signal score of the band based on the energy variances of the high-frequency layer detail coefficients of the highest layer and the low-frequency layer approximation coefficients of the lowest layer in the band; Determine the possibility that the band is in the absorption peak region based on the comparison result between the signal score of the band and the score threshold corresponding to the band; Calculate the hard threshold of each high-frequency layer of each band: ； , , are respectively the hard threshold, global noise standard deviation, and total number of detail coefficients of the th high-frequency layer of the th band. is the possibility that the th band is in the absorption peak region. , are respectively the energy variances of the detail coefficients of the high-frequency layers of the th band and the highest layer of the gas absorption spectrum image. is a preset adjustment factor. is the natural logarithm. is the exponential function with base e. In response to the comparison results between the detail coefficients of the high-frequency layers of each layer in the band and the corresponding hard thresholds, denoising is performed on the gas absorption spectrum image to obtain the recognition result of the gas absorption spectrum image.

2. The spectral image recognition method for a laser gas analyzer according to claim 1, characterized in that, Before the step of obtaining a gas absorption spectrum image and dividing it into multiple bands, it further includes: Collect the wavelength and absorbance of the gas in the environment, construct a curve graph with the wavelength of the gas as the horizontal axis and the absorbance of the gas as the vertical axis, and obtain the gas absorption spectrum image after preprocessing.

3. A spectral image recognition method for a laser gas analyzer according to claim 1, characterized in that, The step of obtaining a gas absorption spectrum image and dividing it into multiple bands includes: Divide the gas absorption spectrum image into multiple bands according to a preset wavelength interval.

4. A spectral image recognition method for a laser gas analyzer according to claim 1, characterized in that, The step of determining the low-frequency layer approximation coefficients and high-frequency layer detail coefficients of each band through wavelet transform includes: Divide the band into a preset number of layers through wavelet transform, and each layer corresponds to a low-frequency layer approximation coefficient and three high-frequency layer detail coefficients; Among them, the first layer is the lowest layer, the last layer is the highest layer, and each low-frequency layer approximation coefficient or high-frequency layer detail coefficient is a coefficient set.

5. A spectral image recognition method for a laser gas analyzer according to claim 1, characterized in that, The step of calculating the signal score of the band includes: Calculate the signal score for the th band : ； is the logarithm to the base 10, is the energy variance of the high-frequency layer detail coefficients at the highest layer of the is the energy variance of the low-frequency layer approximation coefficients at the lowest layer of the 6. The spectral image recognition method for a laser gas analyzer according to claim 1, characterized in that, The method for obtaining the score threshold corresponding to the band includes: ； is the scoring threshold corresponding to the th band, is the preset basic scoring threshold, , are respectively the energy variances of the detail coefficients of the high-frequency layer of the th band and the top layer of the gas absorption spectral image, is the exponential function with base e.

7. A spectral image recognition method for a laser gas analyzer according to claim 1, characterized in that The step of determining the possibility that the band is in the absorption peak region based on the comparison result between the signal score of the band and the score threshold corresponding to the band includes: If the signal score of the band is greater than or equal to the score threshold corresponding to the band, use the normalized difference between the signal score of the band and the corresponding score threshold as the possibility that the band is in the absorption peak region; Otherwise, set the possibility that the band is in the absorption peak region to 0.

8. A spectral image recognition method for a laser gas analyzer according to claim 1, characterized in that, The step of denoising the gas absorption spectrum image in response to the comparison result between the high-frequency layer detail coefficients of each layer in the band and the corresponding hard threshold includes: If the high-frequency layer detail coefficients of each layer in the band are less than the corresponding hard threshold, set the high-frequency layer detail coefficient to 0, otherwise keep it unchanged to achieve denoising; Perform inverse wavelet transform on the high-frequency layer detail coefficients of each layer in the denoised band to obtain the denoised gas absorption spectrum image.

9. A spectral image recognition method for a laser gas analyzer according to claim 1, characterized in that, The step of denoising the gas absorption spectrum image to obtain the recognition result of the gas absorption spectrum image includes: Compare the absorption peaks in the denoised gas absorption spectrum image with the standard absorption peaks to obtain the recognition result of the denoised gas absorption spectrum image.

10. A spectral image recognition method for a laser gas analyzer according to claim 9, characterized in that The comparison between the absorption peaks in the denoised gas absorption spectrum image and the standard absorption peaks is achieved through the least squares method.

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