Photoacoustic spectrum identification method based on gas photoacoustic effect

Abstract

The invention discloses a photoacoustic spectrum identification method based on a gas photoacoustic effect. The method comprises the following steps: S1, introducing a multi-component gas mixture into a closed photoacoustic cell; s2, based on the absorption characteristics of different gases, selecting corresponding laser wavelengths to irradiate the gas mixture in the photoacoustic cell; s3, collecting and recording a photoacoustic signal generated by the gas mixture through a high-sensitivity photoacoustic detector; s4, processing and separating the collected photoacoustic signals by using a multivariate analysis algorithm; s5, based on a separation result, identifying and quantifying the concentration of each component gas; according to the photoacoustic spectrum identification method based on the gas photoacoustic effect, separation and identification of signals are achieved, the problem that the equipment cost is greatly increased due to complexity of a light source is avoided, the application field of the photoacoustic spectrum technology is widened, and the method is suitable for popularization and application. The problem that separation is difficult due to overlapping of photoacoustic signals of different gases in multi-component gas mixture detection in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of photoacoustic spectrum detection, and in particular to a photoacoustic spectrum recognition method based on gas photoacoustic effect. Background Art

[0002] In the field of multi-component gas mixture detection, photoacoustic spectroscopy is widely used due to its high sensitivity and selectivity. This technology identifies and quantifies gas components by detecting the acoustic wave signal generated by the gas after absorbing light of a specific wavelength. However, in practical applications, when there are multiple components in the mixed gas, the absorption spectra of the photoacoustic signal of different gases may overlap, making it difficult to separate the signals. This signal overlap effect seriously affects the quantitative detection capability of photoacoustic spectroscopy in complex gas mixtures.

[0003] Traditional methods to solve this problem include using high-resolution light sources or complex mathematical decomposition algorithms. However, these methods have significant limitations: high-resolution light sources increase the complexity and cost of the equipment, while mathematical decomposition algorithms place higher demands on data processing capabilities and model accuracy, and are easily affected by background noise. In addition, for gas components with similar absorption spectrum characteristics within certain wavelength ranges, it is difficult to achieve effective signal separation even with optimized algorithms and equipment. Summary of the invention

[0004] The purpose of the present invention is to provide a photoacoustic spectrum identification method based on the photoacoustic effect of gas, so as to solve the problem of difficulty in separation caused by overlapping photoacoustic signals of different gases in the detection of multi-component gas mixtures in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a photoacoustic spectrum identification method based on gas photoacoustic effect, the method comprising:

[0006] S1. introducing a multi-component gas mixture into a closed photoacoustic cell;

[0007] S2. Based on the absorption characteristics of different gases, select the corresponding laser wavelength to irradiate the gas mixture in the photoacoustic cell;

[0008] S3, collecting and recording the photoacoustic signal generated by the gas mixture through a high-sensitivity photoacoustic detector;

[0009] S4. Process and separate the collected photoacoustic signals using a multivariate analysis algorithm. Specifically, the signals collected by the photoacoustic detector are converted into a digital signal matrix. The principal component analysis is applied to reduce the dimension to reduce the signal noise and extract the main characteristic signals. The principal component analysis formula is: Z = X·W;

[0010] Where Z represents the signal matrix after dimensionality reduction, X represents the original photoacoustic signal matrix, and W represents the principal component matrix;

[0011] Independent component analysis is used to separate the independent components in the signal, corresponding to the photoacoustic characteristic signals of each gas. The independent component analysis formula is: S = A·B;

[0012] Among them, S represents the independent signal matrix after separation, A represents the separation matrix, and B represents the mixed photoacoustic signal matrix;

[0013] S5. Based on the separation results, identify and quantify the concentration of each component gas. Specifically, the separated photoacoustic signal intensity is combined with the corresponding absorption coefficient and laser power to calculate the concentration of each gas. The specific formula is:

[0014] Among them, c i represents the concentration of the i-th gas, S i represents the corresponding photoacoustic signal intensity, α i represents the absorption coefficient, and P represents the laser power;

[0015] The calibration experiment results are used to verify the accuracy of the concentration calculation, and the parameters are adjusted to compensate for environmental interference. The identification results are output and a concentration distribution diagram or table of each component gas is generated.

[0016] Preferably, the S1 comprises:

[0017] According to the volume concentration of the gas components, the total gas flow rate and the volume of the photoacoustic cell are determined, and the gas input flow rate and the cross-sectional area of ​​the input pipeline are adjusted to ensure that the gas mixture can be evenly introduced into the photoacoustic cell. The flow rate of the input gas is monitored by a flow meter. The gas flow calculation formula is:

[0018] Q = D·v;

[0019] Wherein, Q represents the gas flow rate, D represents the cross-sectional area of ​​the fluid channel, and v represents the flow velocity.

[0020] Preferably, S2 includes:

[0021] Using the absorption band data of the known target gas, the laser wavelength is selected to meet the maximum absorption characteristics, and the laser output parameters are adjusted to match the wavelength while ensuring that the incident light intensity is stable. A calibrated spectrometer is used to verify the matching degree between the laser wavelength and the gas absorption band.

[0022] Preferably, S3 includes:

[0023] According to the resonant frequency of the photoacoustic cell, the laser modulation frequency is set to enhance the photoacoustic signal. A high-sensitivity microphone or piezoelectric sensor is used to capture the pressure fluctuation signal in the photoacoustic cell. The high-frequency noise is filtered through a phase-locked amplifier to record a stable photoacoustic signal. The photoacoustic signal intensity formula is:

[0024] H ∝ P·α·f;

[0025] Where H represents the photoacoustic signal intensity, P represents the laser power, α represents the gas absorption coefficient, and f represents the modulation frequency.

[0026] Preferably, S2 further includes:

[0027] Measure the absorption peak positions of different gas components in the photoacoustic cell;

[0028] Determine the required laser wavelength range based on the absorption peak position;

[0029] Select multiple wavelengths within the laser wavelength range and irradiate them sequentially;

[0030] The optimal laser wavelength is determined based on how strongly each gas responds to a particular wavelength.

[0031] Preferably, determining the optimal laser wavelength based on the response intensity of each gas to a specific wavelength comprises:

[0032] Record the response intensity of each gas at different laser wavelengths to generate a response intensity spectrum;

[0033] Based on the response intensity spectrum, the wavelength with the highest response intensity is selected as the candidate wavelength;

[0034] Calculate the sum of the photoacoustic signal intensities S at different candidate wavelengths, where S = ΣI i , I i represents the photoacoustic signal intensity at each wavelength i;

[0035] The laser wavelength is selected based on the result of the formula S>T, where T is a pre-set threshold representing the minimum requirement for the photoacoustic signal intensity.

[0036] Preferably, the calculating the sum S of the photoacoustic signal intensities at different candidate wavelengths comprises:

[0037] Measure the photoacoustic signal intensity I at each candidate wavelength i ;

[0038] Calculate the sum of signal strengths S, where S = ΣI i , I i represents the photoacoustic signal intensity at each wavelength i;

[0039] Based on the sum of the photoacoustic signal intensities S, select the maximum value Smax As the optimal photoacoustic signal intensity;

[0040] When S < M×S max exclude this candidate wavelength, where S max is the maximum photoacoustic signal intensity at all candidate wavelengths, and M is a proportionality coefficient.

[0041] Preferably, when S < M×S max exclude this candidate wavelength includes:

[0042] Calculate the ratio of S to S max i.e., K = S / S max ;

[0043] Determine whether the ratio K is less than the set ratio M;

[0044] When K < M, exclude this candidate wavelength;

[0045] Use the remaining candidate wavelengths as the final selection objects.

[0046] Preferably, when calculating the concentration of each gas based on the photoacoustic signal intensity, the S5 further includes generating a calibration curve for the concentration of each component gas by using the normalization processing results of the separated signal intensity, absorption coefficient, and laser power.

[0047] Preferably, generating the calibration curve for the concentration of each component gas includes verifying the accuracy of the concentration calculation through standard gas experiments.

[0048] From the above technical solutions, it can be seen that the present invention has the following beneficial effects:

[0049] This photoacoustic spectroscopy recognition method based on the photoacoustic effect of gases imports a multi-component gas mixture into a closed photoacoustic cell, selects corresponding laser wavelengths to irradiate the gas mixture in the photoacoustic cell based on the absorption characteristics of different gases, collects and records the photoacoustic signals generated by the gas mixture through a high-sensitivity photoacoustic detector, processes and separates the collected photoacoustic signals by using a multivariate analysis algorithm, and based on the separation results, identifies and quantifies the concentrations of each component gas. It can accurately extract the characteristic signals of each component gas in the case of overlapping absorption spectra, improve the effectiveness and reliability of signal separation, significantly enhance the quantitative detection ability of the concentrations of each component in a complex mixed gas, ensure the high sensitivity and accuracy of the detection results, realize the separation and recognition of signals, avoid the problem of a large increase in equipment cost caused by the complication of the light source, broaden the application field of photoacoustic spectroscopy technology, and solve the problem of difficult separation in the detection of multi-component gas mixtures in the prior art due to the overlap of photoacoustic signals of different gases. Description of the Drawings

[0050] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0051] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] like Figure 1 As shown, the present invention provides a technical solution: a photoacoustic spectrum identification method based on gas photoacoustic effect, the method comprising:

[0053] S1. introducing a multi-component gas mixture into a closed photoacoustic cell;

[0054] S2. Based on the absorption characteristics of different gases, select the corresponding laser wavelength to irradiate the gas mixture in the photoacoustic cell;

[0055] S3, collecting and recording the photoacoustic signal generated by the gas mixture through a high-sensitivity photoacoustic detector;

[0056] S4. Process and separate the collected photoacoustic signals using a multivariate analysis algorithm. Specifically, the signals collected by the photoacoustic detector are converted into a digital signal matrix. The principal component analysis is used for dimensionality reduction to reduce signal noise and extract the main characteristic signals. The principal component analysis formula is: Z = X·W;

[0057] Where Z represents the signal matrix after dimensionality reduction, X represents the original photoacoustic signal matrix, and W represents the principal component matrix;

[0058] Independent component analysis is used to separate the independent components in the signal, corresponding to the photoacoustic characteristic signals of each gas. The independent component analysis formula is: S = A·B;

[0059] Among them, S represents the independent signal matrix after separation, A represents the separation matrix, and B represents the mixed photoacoustic signal matrix;

[0060] S5. Based on the separation results, identify and quantify the concentration of each component gas. Specifically, the separated photoacoustic signal intensity is combined with the corresponding absorption coefficient and laser power to calculate the concentration of each gas. The specific formula is:

[0061] Among them, c i represents the concentration of the i-th gas, S i represents the corresponding photoacoustic signal intensity, α i represents the absorption coefficient, and P represents the laser power;

[0062] The calibration experiment results are used to verify the accuracy of the concentration calculation, and the parameters are adjusted to compensate for environmental interference. The identification results are output and a concentration distribution diagram or table of each component gas is generated.

[0063] This method uses the photoacoustic effect of gas. Through the absorption of lasers of specific wavelengths by different gases, the gas molecules are stimulated to produce thermal expansion effects, thereby generating photoacoustic signals. The selectivity of the photoacoustic signal can be improved by adjusting the laser wavelength to match the absorption peak of the gas. After recording the signal using a photoacoustic detector, the principal component analysis and independent separation analysis techniques are used to decompose the signal, remove noise and extract gas characteristic signals. The concentration is calculated by combining the absorption coefficient and laser power, and finally the quantitative analysis results of the gas components are obtained. The noise interference is eliminated by the multivariate decomposition algorithm, which significantly improves the accuracy and resolution of signal processing; the method supports the simultaneous detection of multi-component gases and is suitable for the analysis of complex gas mixtures; the detection limit is improved by using high-sensitivity photoacoustic detectors and specific wavelength lasers; through calibration and parameter compensation, it can adapt to the detection needs of different environmental conditions.

[0064] S1 includes determining the total gas flow rate and the volume of the photoacoustic cell according to the volume concentration of the gas components, adjusting the gas input flow rate and the cross-sectional area of ​​the input pipeline to ensure that the gas mixture can be evenly introduced into the photoacoustic cell, and monitoring the flow rate of the input gas through a flow meter. The gas flow calculation formula is:

[0065] Q = D·υ;

[0066] Where Q represents the gas flow rate, D represents the cross-sectional area of ​​the fluid channel, and υ represents the flow velocity.

[0067] This step optimizes the acquisition of photoacoustic signals by regulating the gas input parameters to ensure the uniformity and stability of the gas mixture. The gas flow rate is measured and calculated by a flow meter, and the appropriate input conditions are determined in combination with the gas volume concentration and the photoacoustic cell volume parameters to avoid turbulence, dead zones or uneven flow. The formula Q=D·υ reflects the linear relationship between gas flow rate, flow velocity and the cross-sectional area of ​​the input pipeline, and can accurately control the amount of input gas. By adjusting the gas flow rate and pipeline design, the gas mixture is evenly distributed before entering the photoacoustic cell, thereby improving the stability of the detection signal; the gas flow rate is monitored using a flow meter and a calculation formula to avoid signal distortion caused by excessive or insufficient gas flow; this method can flexibly adjust the input conditions according to different gas component concentrations and photoacoustic cell volumes to meet a variety of experimental requirements; uniform gas flow reduces signal interference and improves the reliability and accuracy of photoacoustic detection.

[0068] S2 includes using the absorption band data of the known target gas, selecting the laser wavelength to match the maximum absorption characteristics, adjusting the laser output parameters to match the wavelength while ensuring the stability of the incident light intensity, and using a calibrated spectrometer to verify the matching degree between the laser wavelength and the gas absorption band.

[0069] This step is based on the absorption characteristics of different gases for lasers of specific wavelengths, and selects the wavelength at the peak of the absorption band of the target gas as the output wavelength of the laser source, thereby achieving high-sensitivity detection of the target gas. By adjusting the laser output power and stability parameters, the energy stability of the laser output at the set wavelength is guaranteed. At the same time, the laser is calibrated in combination with a high-resolution spectrometer to ensure the matching accuracy of the laser wavelength and the absorption peak of the target gas, reducing the detection error caused by wavelength offset. Select the wavelength of the maximum absorption characteristic of the target gas to enhance the intensity of the photoacoustic signal and improve the detection sensitivity; through the laser adjustment function, it can adapt to the absorption peak wavelength of different gases to expand the scope of detection; calibrate the spectrometer to ensure the matching of the laser wavelength and the absorption band, while ensuring the stability of the incident light intensity and reducing measurement fluctuations; by selecting the absorption wavelengths of different component gases one by one, multi-component identification of complex gas mixtures can be achieved.

[0070] S3 includes setting the laser modulation frequency to enhance the photoacoustic signal according to the resonant frequency of the photoacoustic cell, using a high-sensitivity microphone or piezoelectric sensor to capture the pressure fluctuation signal in the photoacoustic cell, filtering the high-frequency noise through a phase-locked amplifier, and recording a stable photoacoustic signal. The photoacoustic signal intensity formula is:

[0071] H ∝ P·α·f;

[0072] Where H represents the photoacoustic signal intensity, P represents the laser power, α represents the gas absorption coefficient, and f represents the modulation frequency.

[0073] This step utilizes the resonant characteristics of the photoacoustic cell to amplify the pressure fluctuation signal caused by the absorption of light energy by matching the laser modulation frequency with the acoustic resonance frequency of the photoacoustic cell. A highly sensitive microphone or piezoelectric sensor is used to capture the acoustic signal in the photoacoustic cell, while the phase-locked amplifier eliminates high-frequency noise and extracts stable signal intensity through frequency selective processing. The formula H∝P·α·f shows that the intensity of the photoacoustic signal is proportional to the laser power, gas absorption coefficient and modulation frequency. Optimizing the modulation frequency can significantly improve the detection sensitivity and signal-to-noise ratio. By matching the resonant frequency, the intensity of the photoacoustic signal is increased, and the detection capability of trace gases is enhanced; the phase-locked amplifier effectively filters out interference signals to ensure that the extracted photoacoustic signal has high stability; the highly sensitive sensor adapts to low signal conditions and improves the capture efficiency of weak photoacoustic signals; the modulation frequency and laser power can be adjusted according to the resonance characteristics of different photoacoustic cells to adapt to a variety of experimental needs.

[0074] S2 also includes measuring the absorption peak positions of different gas components in the photoacoustic cell; determining the required laser wavelength range according to the absorption peak positions; selecting multiple wavelengths within the laser wavelength range and irradiating them in sequence; and determining the optimal laser wavelength based on the response intensity of each gas to a specific wavelength.

[0075] By measuring the absorption peak position of the gas, the absorption characteristics of each gas can be accurately located. Combined with the output range of the laser, multiple wavelengths covering the absorption peak are selected for irradiation one by one, and the photoacoustic signal response intensity at each wavelength is recorded. Based on the comparison of signal intensity, the most suitable laser wavelength for detection is screened out. This method optimizes the wavelength selection process, improves detection sensitivity and signal resolution, accurately locates the wavelength of gas absorption characteristics, improves the quality of photoacoustic signals, flexibly selects the wavelength range, adapts to the detection of multiple gas components, and improves the efficiency and accuracy of selecting the optimal laser wavelength.

[0076] Based on the response intensity of each gas to a specific wavelength, determining the optimal laser wavelength includes recording the response intensity of each gas at different laser wavelengths to generate a response intensity spectrum; based on the response intensity spectrum, selecting the wavelength with the highest response intensity as the candidate wavelength; calculating the sum S of the photoacoustic signal intensities at different candidate wavelengths, where S = ΣI i , I i Represents the photoacoustic signal intensity at each wavelength i; based on the result of the formula S>T, the laser wavelength is selected, where T is a pre-set threshold, indicating the minimum requirement for the photoacoustic signal intensity.

[0077] This method constructs a complete response intensity spectrum by sampling the photoacoustic response of the target gas at different laser wavelengths. By analyzing the spectrum, the maximum response intensity point of the gas at a specific wavelength is screened out as a candidate wavelength, and further combined with the sum S of the photoacoustic signal intensities at multiple candidate wavelengths, and judged by the set threshold T, so as to select the best laser wavelength suitable for detection. This method can comprehensively consider the signal intensity and noise ratio to improve the accuracy and stability of detection. By analyzing the response intensity spectrum, the wavelength with the highest response is screened out to ensure the sensitivity of detection. Based on the sum of the photoacoustic signal intensities and the threshold judgment, the selected laser wavelength can effectively balance the signal-to-noise ratio and sensitivity. The threshold T can be adjusted according to experimental requirements to adapt to different detection environments and target gases. The method is suitable for embedding algorithms for automatic wavelength selection, reducing manual intervention and improving detection efficiency.

[0078] Calculating the sum of the photoacoustic signal intensities S at different candidate wavelengths includes measuring the photoacoustic signal intensity I at each candidate wavelength i ; Calculate the sum of signal strengths S, where S = ΣI i , I i represents the photoacoustic signal intensity at each wavelength i; based on the sum of the photoacoustic signal intensities S, the maximum value S is selectedmax as the optimal photoacoustic signal intensity; when S < M×S max exclude this candidate wavelength, where S max is the maximum photoacoustic signal intensity at all candidate wavelengths, and M is a proportionality coefficient.

[0079] By measuring the photoacoustic signal intensity I at each candidate wavelength i and accumulating it to calculate the sum S of the photoacoustic signal intensities corresponding to each group of candidate wavelengths. Then compare all S values and select the maximum value S max as the reference standard for the optimal photoacoustic signal intensity. At the same time, with M as the proportionality coefficient, calculate the ratio of the current wavelength signal intensity to S max and exclude the wavelengths lower than M×S max This method can ensure that the selected wavelengths have high signal intensities and improve the stability and reliability of the detected signals. By comparing the sum of signal intensities and judging with the proportionality coefficient, it effectively screens out the wavelengths with the strongest and most stable signals, excludes low-intensity wavelengths, and avoids the influence of weak signals or noise on the final detection results. The proportionality coefficient M can be adjusted according to experimental conditions, flexibly adapting to different detection environments and target gases. Through clear calculation and screening rules, it realizes the automation and high efficiency of the wavelength selection process.

[0080] When S < M×S max exclude this candidate wavelength, including calculating the ratio of S to S max i.e., K = S / S max ; judge whether the ratio K is less than the set ratio M; when K < M, exclude this candidate wavelength; take the remaining candidate wavelengths as the final selection objects.

[0081] In this step, by calculating the ratio K of the sum S of the photoacoustic signal intensities of each candidate wavelength to the maximum photoacoustic signal intensity S max , the signal proportion is evaluated. Set a proportionality threshold M, screen out the candidate wavelengths that satisfy K≥M, and exclude the wavelengths with weak signal intensities. This method can effectively eliminate the noise interference wavelengths and retain the wavelengths with stable signal responses and high intensities as the final detection wavelengths, ensuring the accuracy and reliability of the detection. Through the quantitative judgment of the ratio K and the threshold M, it ensures that the selected wavelengths have sufficient signal intensities, excludes low-signal wavelengths, reduces interference, and improves the sensitivity and reliability of the detection. The threshold M can be adjusted according to actual detection requirements to adapt to different experimental scenarios and target gases. The calculation of the ratio and the screening process can be automatically completed by the algorithm, improving efficiency and reducing human intervention.

[0082] S5 also includes, when calculating the concentration of each gas based on the photoacoustic signal intensity, using the normalized processing results of the separated signal intensity, absorption coefficient, and laser power to generate a calibration curve for the concentration of each component gas.

[0083] The separation signal intensity I of each gas 分离 Normalized with the corresponding absorption coefficient α and laser power P, calculate the normalized signal intensity I 归一化 , the formula is: 归一化 =I 分离 / α×P;

[0084] By detecting the standard gas of known concentration, the corresponding normalized signal intensity I is recorded. 归一化 and gas concentration C, and plot the normalized signal intensity I according to the standard gas experimental data 归一化 The corresponding relationship between the concentration of each gas component C is used to generate the concentration calibration curve of each component gas, and the normalized signal intensity I of the unknown gas sample is calculated using the calibration curve. 归一化 Fitting is performed to calculate the concentration of each component gas.

[0085] This step eliminates the influence of absorption coefficient and laser power on the intensity of photoacoustic signal through normalization processing, ensuring that the signal intensity only reflects the change of gas concentration. Combined with the standard gas experiment, the concentration calibration curve is generated using the calibration data to achieve accurate calculation of gas concentration. By fitting the relationship between the signal intensity of the unknown sample and the calibration curve, the concentration of each component gas can be quickly determined to ensure the accuracy and repeatability of the test results. Through normalization processing, the interference of laser power and absorption coefficient is removed, and the reliability of concentration calculation is improved; the use of calibration curves ensures the consistency of test results under different experimental conditions; calibration curves can be generated separately for different gas components, supporting multi-component concentration calculations of complex gas mixtures; calibration curves can be generated in advance before the experiment, reducing the complexity of on-site calculations and improving detection efficiency.

[0086] Generating the calibration curve of each component gas concentration includes verifying the accuracy of concentration calculation through standard gas experiments, selecting standard gas with known concentration as experimental sample, and the concentration range covers the possible concentration range of target gas; introducing the standard gas into the photoacoustic detection device, and recording the photoacoustic signal intensity I of each gas under the condition of predetermined laser wavelength. 分离 ; Normalize the experimental data and calculate the normalized signal intensity I 归一化 , the formula is: 归一化 =I 分离 / α×P, where α represents the absorption coefficient and P represents the laser power;

[0087] According to the normalized signal intensity I 归一化and the corresponding standard gas concentration C, and fit the calibration curve through regression analysis; introduce additional standard gas samples into the device, calculate their concentrations through the calibration curve, and compare them with the known concentration values ​​to verify the accuracy of the calibration curve; if a large error occurs during the verification process, adjust the fitting model or sampling parameters and correct the calibration curve to ensure the accuracy of the concentration calculation.

[0088] The corresponding data of the normalized photoacoustic signal intensity I normalized and the concentration C are collected through the standard gas experiment, and the calibration curve is fitted. The calibration curve is used to convert the photoacoustic signal intensity of the unknown gas into the actual concentration. During the verification process, by introducing additional standard gas samples, the calculated concentration is compared with the known concentration value, the accuracy of the calibration curve is checked, and the curve is corrected as needed to ensure that the final concentration calculation result is reliable and accurate. The calibration curve is verified and corrected through experiments to ensure the accuracy of the concentration calculation; the verification process reduces the error risk in the use of the calibration curve and enhances the credibility of the test results; through verification and correction, it can adapt to the concentration calculation needs of a variety of complex gas mixtures; the calibration curve verified in advance can be directly used for subsequent on-site detection, reducing additional calibration work.

[0089] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A photoacoustic spectrum identification method based on gas photoacoustic effect, characterized in that: The method comprises: S1. introducing a multi-component gas mixture into a closed photoacoustic cell; S2. Based on the absorption characteristics of different gases, select the corresponding laser wavelength to irradiate the gas mixture in the photoacoustic cell; S3, collecting and recording the photoacoustic signal generated by the gas mixture through a high-sensitivity photoacoustic detector; S4. Process and separate the collected photoacoustic signals using a multivariate analysis algorithm. Specifically, the signals collected by the photoacoustic detector are converted into a digital signal matrix. The principal component analysis is used for dimensionality reduction to reduce signal noise and extract the main characteristic signals. The principal component analysis formula is: Z = X·W; Where Z represents the signal matrix after dimensionality reduction, X represents the original photoacoustic signal matrix, and W represents the principal component matrix; Independent component analysis is used to separate the independent components in the signal, corresponding to the photoacoustic characteristic signals of each gas. The independent component analysis formula is: S = A·B; Among them, S represents the independent signal matrix after separation, A represents the separation matrix, and B represents the mixed photoacoustic signal matrix; S5. Based on the separation results, identify and quantify the concentration of each component gas. Specifically, the separated photoacoustic signal intensity is combined with the corresponding absorption coefficient and laser power to calculate the concentration of each gas. The specific formula is: Among them, c i represents the concentration of the i-th gas, S i represents the corresponding photoacoustic signal intensity, α i represents the absorption coefficient, and P represents the laser power; The calibration experiment results are used to verify the accuracy of the concentration calculation, and the parameters are adjusted to compensate for environmental interference. The identification results are output and a concentration distribution diagram or table of each component gas is generated.

2. The photoacoustic spectrum identification method based on the gas photoacoustic effect according to claim 1, characterized in that: The S1 includes: According to the volume concentration of the gas components, the total gas flow rate and the volume of the photoacoustic cell are determined, and the gas input flow rate and the cross-sectional area of ​​the input pipeline are adjusted to ensure that the gas mixture can be evenly introduced into the photoacoustic cell. The flow rate of the input gas is monitored by a flow meter. The gas flow calculation formula is: Q = D·v; Wherein, Q represents the gas flow rate, D represents the cross-sectional area of ​​the fluid channel, and v represents the flow velocity.

3. The photoacoustic spectrum identification method based on the gas photoacoustic effect according to claim 1, characterized in that: The S2 includes: Using the absorption band data of the known target gas, the laser wavelength is selected to meet the maximum absorption characteristics, and the laser output parameters are adjusted to match the wavelength while ensuring that the incident light intensity is stable. A calibrated spectrometer is used to verify the matching degree between the laser wavelength and the gas absorption band.

4. The photoacoustic spectrum identification method based on the gas photoacoustic effect according to claim 1 is characterized in that: The S3 includes: According to the resonant frequency of the photoacoustic cell, the laser modulation frequency is set to enhance the photoacoustic signal. A high-sensitivity microphone or piezoelectric sensor is used to capture the pressure fluctuation signal in the photoacoustic cell. The high-frequency noise is filtered through a phase-locked amplifier to record a stable photoacoustic signal. The photoacoustic signal intensity formula is: H ∝ P·α·f; Where H represents the photoacoustic signal intensity, P represents the laser power, α represents the gas absorption coefficient, and f represents the modulation frequency.

5. The photoacoustic spectrum identification method based on gas photoacoustic effect according to claim 1, characterized in that: The S2 further includes: Measure the absorption peak positions of different gas components in the photoacoustic cell; Determine the required laser wavelength range based on the absorption peak position; Select multiple wavelengths within the laser wavelength range and irradiate them sequentially; The optimal laser wavelength is determined based on how strongly each gas responds to a particular wavelength.

6. The photoacoustic spectrum identification method based on the gas photoacoustic effect according to claim 5, characterized in that: Determining the optimal laser wavelength based on the response intensity of each gas to a specific wavelength includes: Record the response intensity of each gas at different laser wavelengths to generate a response intensity spectrum; Based on the response intensity spectrum, the wavelength with the highest response intensity is selected as the candidate wavelength; Calculate the sum of the photoacoustic signal intensities S at different candidate wavelengths, where S = ΣI i , I i represents the photoacoustic signal intensity at each wavelength i; The laser wavelength is selected based on the result of the formula S>T, where T is a pre-set threshold value representing the minimum requirement for the photoacoustic signal intensity.

7. The photoacoustic spectrum identification method based on gas photoacoustic effect according to claim 6, characterized in that: The calculation of the sum S of the photoacoustic signal intensities at different candidate wavelengths comprises: Measure the photoacoustic signal intensity I at each candidate wavelength i ; Calculate the sum of signal strengths S, where S = ΣI i , I i represents the photoacoustic signal intensity at each wavelength i; Based on the sum of the photoacoustic signal intensities S, select the maximum value S max as the optimal photoacoustic signal intensity; When S <M×S max When , the candidate wavelength is excluded, where S max is the maximum photoacoustic signal intensity under all candidate wavelengths, and M is the proportionality coefficient.

8. The photoacoustic spectrum identification method based on the gas photoacoustic effect according to claim 7, characterized in that: When S <M×S max When , the candidate wavelengths excluded include: Calculate S and S max The ratio of K = S / S max ; Determine whether the ratio K is less than the set ratio M; When K < M, exclude this candidate wavelength; Use the remaining candidate wavelengths as the final selection objects.

9. The photoacoustic spectrum identification method based on the gas photoacoustic effect according to claim 8, characterized in that: The S5 further includes, when calculating the concentration of each gas based on the photoacoustic signal intensity, generating a calibration curve for the concentration of each component gas by using the normalization processing results of the separated signal intensity, the absorption coefficient, and the laser power.

10. The photoacoustic spectrum identification method based on gas photoacoustic effect according to claim 9, characterized in that: Generating the calibration curve for the concentration of each component gas includes verifying the accuracy of the concentration calculation through standard gas experiments.

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