Photoacoustic cell detection and adjustment method capable of adjusting resonant frequency

Abstract

The invention discloses a method for detecting and adjusting a photoacoustic cell with adjustable resonant frequency, which relates to the technical field of gas detection, and comprises the following steps: collecting environmental parameters of the photoacoustic cell, transmitting the environmental parameters to a control center, and dividing the collected environmental parameters into historical data and real-time data, the method comprises the following steps: collecting historical data and real-time data, preprocessing the collected historical data and real-time data, establishing a photoacoustic cell evaluation model to predict the resonant frequency of a photoacoustic cell under different environmental conditions, measuring the real-time resonant frequency of the photoacoustic cell by adopting a resonance sound spectrum method, recording the initial resonant frequency, analyzing the change trend of the resonant frequency, and obtaining environmental interference factors at the same time. According to the invention, the resonant frequency is monitored in real time, compared with the preset value, accurately identified and adjusted to the optimal matching state, so that the maximum output of the photoacoustic signal is ensured, the signal capturing capability of the system is enhanced, the interference of background noise is reduced, and the accuracy of the detection result is greatly improved.

Description

Technical Field

[0001] The invention relates to the technical field of gas detection, and in particular to a photoacoustic cell detection and adjustment method with adjustable resonant frequency. Background Art

[0002] Photoacoustic cell detection technology is a non-destructive testing technology based on the photoacoustic effect. When gas molecules absorb light of a specific wavelength, they are excited to a high energy state, and then convert the absorbed light energy into heat energy through a non-radiative transition, generating sound waves. By detecting these sound wave signals, the properties and concentration of the gas can be analyzed. The photoacoustic cell with adjustable resonant frequency is based on this. By adjusting the resonant frequency of the photoacoustic cell to match the modulation frequency of the laser light source, the photoacoustic signal is enhanced and the detection sensitivity is improved.

[0003] In the prior art, due to changes in environmental factors such as temperature, humidity, and air pressure, the resonant frequency of the photoacoustic cell may drift, making it difficult to accurately predict the drift amount and drift speed of the resonant frequency, affecting the accuracy of gas concentration detection. Therefore, how to adjust the system parameters to compensate for the drift of the resonant frequency and reduce the impact of environmental factors on the photoacoustic cell detection is the problem we want to solve. For this purpose, a photoacoustic cell detection adjustment method with adjustable resonant frequency is proposed. Summary of the invention

[0004] The present invention aims to provide a method for adjusting the detection of a photoacoustic cell with adjustable resonant frequency, so as to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A detection and adjustment method for a photoacoustic cell with adjustable resonant frequency, step 1, collecting environmental parameters of the photoacoustic cell and transmitting them to a control center, and dividing the collected environmental parameters into historical data and real-time data for subsequent analysis and processing, wherein the environmental parameters include temperature, humidity and air pressure;

[0007] Step 2, preprocessing the collected historical data and real-time data, establishing a photoacoustic pool evaluation model of environmental factors and resonant frequency changes, so as to predict the resonant frequency of the photoacoustic pool under different environmental conditions;

[0008] Step 3, using resonance acoustic spectroscopy to measure the real-time resonant frequency of the photoacoustic cell, record the initial resonant frequency, analyze the change trend of the resonant frequency for comparison with subsequent measurement results, and simultaneously obtain environmental interference factors, wherein the environmental interference factors include temperature interference factors, humidity interference factors, and air pressure interference factors;

[0009] Step 4, analyzing the preprocessed historical data and combining it with the acquired environmental interference factors to obtain a temperature interference index, a humidity interference index, and an air pressure interference index to predict changes in the resonant frequency, including a drift amount and a drift speed of the resonant frequency;

[0010] Step 5, obtaining a prediction evaluation coefficient based on the temperature interference index, the humidity interference index and the air pressure interference index, and using the prediction evaluation coefficient to analyze the deviation between the prediction result and the resonant frequency under different environmental conditions to obtain an evaluation result;

[0011] Step 6, measure the resonant frequency in real time and compare it with the preset resonant frequency, analyze the difference, and combine it with the evaluation results to formulate and implement corresponding adjustment plans, such as adjusting the ambient temperature, humidity or air pressure, or optimizing the working parameters of the photoacoustic cell to reduce the deviation of the resonant frequency. At the same time, compare the adjusted resonant frequency with the standard resonant frequency again to form a closed-loop feedback system. If there is a deviation, continue to adjust until the ideal resonant frequency is reached.

[0012] A further improvement of the technical solution of the present invention is that in step 1, the process of collecting the environmental parameters of the photoacoustic cell is:

[0013] Step 101, deploying temperature sensors, humidity sensors, air pressure sensors and data acquisition equipment around the photoacoustic pool to ensure that the temperature, humidity and air pressure of the environment where the photoacoustic pool is located can be accurately measured, and wireless transmission is used to transmit environmental parameter data to a control center, and the received environmental parameter data is stored in a database of the control center to facilitate subsequent historical data query and analysis;

[0014] Step 102, setting parameters such as sampling frequency and data format of the data acquisition device to meet the needs of subsequent data analysis and processing, wherein the data format includes data type, data unit and data precision;

[0015] Step 103, divide the collected environmental parameter data into historical data and real-time data, and encapsulate the collected environmental parameter data, including adding timestamp and data identification information, to facilitate subsequent processing and analysis, where timestamp: records the specific time of data collection, so that the data collection time can be accurately known during subsequent analysis and processing; data identification information: includes identification information such as sensor type, data type, and collection location, so that the control center can accurately identify and process data.

[0016] A further improvement of the technical solution of the present invention is that in step 2, the construction process of the photoacoustic cell evaluation model is:

[0017] Step 201, cleaning and preprocessing the collected historical data and real-time data to remove noise, outliers and redundant information;

[0018] Step 202, extracting features from the preprocessed historical data, extracting temperature, humidity and air pressure features respectively, and performing feature dimensionality reduction and screening, using principal component analysis, linear discriminant analysis and other methods to perform feature dimensionality reduction, reduce the number of features, improve model training efficiency, and screen out the features that have the greatest impact on resonant frequency prediction through correlation analysis, mutual information and other methods, and construct a photoacoustic cell evaluation model;

[0019] Step 203, integrating the preprocessed historical data and real-time data, and dividing them into a training set, a validation set, and a test set, wherein the training set is used to train the photoacoustic pool evaluation model; the validation set is used to adjust model parameters and perform model selection; the test set is used to evaluate the final performance of the model, the photoacoustic pool evaluation model is trained using the training set data, and the trained photoacoustic pool evaluation model is verified using the validation set and the test set data, so as to evaluate the performance of the photoacoustic pool evaluation model and ensure the generalization ability of the model;

[0020] Step 204, input the preprocessed real-time data into the photoacoustic cell evaluation model to predict the resonant frequency under the current environmental conditions, analyze the change trend of the resonant frequency, and compare it with subsequent measurement results.

[0021] A further improvement of the technical solution of the present invention is that in step 3, the process of obtaining the environmental interference factor is:

[0022] Step 301, build a photoacoustic spectrum detection system and a resonance acoustic spectrum detection system, the photoacoustic cell adopts a first-order longitudinal resonance mode, ensure that the gas inlet and outlet are set in the buffer chambers at both ends to reduce the interference of gas flow on the photoacoustic signal detection, set the frequency range and resolution of the sweep signal to accurately capture the resonance frequency of the photoacoustic cell, and use an equal-amplitude sinusoidal sweep signal to perform a frequency scan on the photoacoustic cell, and record the intensity change of the photoacoustic signal during the sweep process, find the frequency point with the strongest photoacoustic signal, that is, the resonance frequency of the photoacoustic cell, and repeat the measurement multiple times under stable environmental conditions, and take the average value as the initial resonance frequency of the photoacoustic cell;

[0023] Step 302, real-time acquisition of photoacoustic signals, and calculation of the resonance frequency at the current moment, comparison of the real-time resonance frequency with the initial resonance frequency, analysis of the variation trend of the resonance frequency, plotting a variation curve of the resonance frequency over time, observing whether there are periodic changes or abnormal fluctuations, and analyzing the causes and laws of the resonance frequency variation in combination with changes in environmental factors;

[0024] Step 303, obtaining temperature interference factor, humidity interference factor and air pressure interference factor according to the pre-processed historical data and real-time data, and analyzing the cause of the change of the resonant frequency;

[0025] Step 304, record the environmental conditions, temperature, humidity, air pressure and corresponding resonant frequency of each measurement, use statistical methods to analyze the impact of environmental factors on the resonant frequency, and determine the specific value and change law of the environmental interference factor based on the analysis results.

[0026] A further improvement of the technical solution of the present invention is that in step 4, the process of obtaining the temperature interference index, the humidity interference index and the air pressure interference index is as follows:

[0027] Step 401, using the pre-processed historical data, including temperature, humidity, air pressure and corresponding resonant frequency, to perform statistical analysis;

[0028] Step 402, measuring the resonant frequency of the photoacoustic cell under different temperature conditions, analyzing the correlation between the temperature and the resonant frequency, and calculating the temperature interference index; measuring the resonant frequency of the photoacoustic cell under different humidity conditions, analyzing the correlation between the humidity and the resonant frequency, and calculating the humidity interference index; measuring the resonant frequency of the photoacoustic cell under different air pressure conditions, analyzing the correlation between the air pressure and the resonant frequency, and calculating the air pressure interference index;

[0029] Step 403, using the temperature interference index, humidity interference index and air pressure interference index in combination with the photoacoustic cell evaluation model, predicting the change of the resonant frequency, and determining the change trend of the resonant frequency and the reason for the change;

[0030] Step 404, compare the predicted resonant frequency with the actually measured resonant frequency, analyze the prediction error of the photoacoustic pool evaluation model, and adjust the parameters of the photoacoustic pool evaluation model to improve the prediction accuracy, such as adding feature variables, changing the model structure, etc.

[0031] A further improvement of the technical solution of the present invention is that the calculation formula of the temperature interference index is:

[0032]

[0033] Where TDI is the temperature interference index, T is the current temperature, and T std is the standard temperature, σ T is the standard deviation of temperature, a is the coefficient of temperature on the resonant frequency, f res (T) is the resonant frequency at the current temperature T, f std is the resonant frequency at standard temperature;

[0034] The calculation formula of the humidity interference index is:

[0035]

[0036] Among them, HDI is the humidity interference index, H is the current humidity, and H std is the standard humidity, Hmid is the midpoint value of humidity, b is the coefficient of humidity on the resonant frequency, f res (H) is the resonant frequency at the current humidity H, f avg is the average resonant frequency under all humidity conditions;

[0037] The calculation formula of the air pressure interference index is:

[0038]

[0039] Among them, PDI is the pressure disturbance index, P is the current air pressure, and P std is the standard atmospheric pressure, γ is the exponential factor of atmospheric pressure, f res (P) is the resonant frequency at the current air pressure P, f std is the resonant frequency at standard atmospheric pressure, and c is a constant term used to adjust the sensitivity of PDI.

[0040] A further improvement of the technical solution of the present invention is that in step 5, the process of obtaining the prediction evaluation coefficient and the evaluation result is:

[0041] Step 501, traverse the pre-processed historical data, combine the temperature interference index, humidity interference index and air pressure interference index with the resonant frequency in the historical data, and analyze the correlation between the resonant frequency deviation and the temperature interference index, humidity interference index and air pressure interference index:

[0042] Step 502, assigning different weights to the temperature interference index, the humidity interference index and the air pressure interference index, performing weighted calculations on them, and obtaining a prediction evaluation coefficient;

[0043] Step 503, measuring the actual value of the resonant frequency under different environmental conditions, and using the photoacoustic cell evaluation model to predict the resonant frequency under these conditions, to obtain a predicted value of the resonant frequency, and calculating the deviation between the predicted value and the actual value;

[0044] Step 504, evaluate the deviation under each environmental condition and the corresponding prediction evaluation coefficient, determine the degree of deviation between the prediction result and the actual resonant frequency, and output the evaluation result.

[0045] The smaller the deviation rate, the more accurate the prediction result. The deviation between the predicted value and the actual value is calculated, and the reliability of the prediction result is comprehensively analyzed in combination with the prediction evaluation coefficient. The smaller the prediction evaluation coefficient, the smaller the impact of the current environmental conditions on the prediction result, and the more reliable the prediction result; conversely, the larger the prediction evaluation coefficient, the greater the impact of the current environmental conditions on the prediction result, and the lower the reliability of the prediction result.

[0046] A further improvement of the technical solution of the present invention is that the calculation formula of the prediction evaluation coefficient is:

[0047]

[0048] Among them, PAC is the prediction evaluation coefficient, TDI is the temperature interference index, which indicates the interference degree of the current ambient temperature on the resonant frequency, HDI is the humidity interference index, which indicates the interference degree of the current ambient humidity on the resonant frequency, PDI is the air pressure interference index, which indicates the interference degree of the current ambient air pressure on the resonant frequency, and TDI std is the standard value of the temperature interference index, HDI std is the standard value of humidity interference index, PDI std is the standard value of the air pressure interference index, σ TDI is the standard deviation of the temperature interference index, σ HDI is the standard deviation of the humidity interference index, σ PDI is the standard deviation of the pressure interference index.

[0049] A further improvement of the technical solution of the present invention is that in step 6, the process of formulating and implementing the corresponding adjustment plan is:

[0050] Step 601, compare the real-time measured resonant frequency value with the preset resonant frequency value, calculate the difference, and analyze the range and trend of the difference. According to the size and direction of the difference, analyze the possible causes, such as component aging, environmental changes, circuit parameter changes, etc.;

[0051] Step 602, combining the difference with the evaluation result, determining the adjustment target, formulating a specific adjustment plan, including adjusting the modulation frequency of the excitation light source and the cavity length of the modulated photoacoustic cell, and after the adjustment, verifying the matching effect of the final excitation light source modulation frequency and the photoacoustic cell resonant frequency to ensure that the photoacoustic signal reaches the maximum value and the detection sensitivity meets the requirements;

[0052] Step 603, after the adjustment is completed, the resonant frequency is measured again, and the adjusted resonant frequency value is compared with the preset resonant frequency value to verify the adjustment effect.

[0053] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0054] 1. The present invention provides a detection and adjustment method for a photoacoustic pool with adjustable resonant frequency. The resonant frequency is monitored in real time and compared with a preset value, and accurately identified and adjusted to the best matching state, thereby ensuring the maximum output of the photoacoustic signal. This not only enhances the signal capture capability of the system, but also reduces the interference of background noise, thereby greatly improving the accuracy of the detection results, and helping to improve the accuracy and reliability of the research.

[0055] 2. The present invention provides a detection and adjustment method for a photoacoustic pool with adjustable resonant frequency. The stability of the system is ensured by calculating and adjusting the resonant frequency in real time, and corresponding adjustments are made immediately to ensure that the resonant frequency is always kept in an optimal state, thereby minimizing errors and improving the accuracy of the detection results. In addition, the method can also monitor the performance of the photoacoustic pool in real time, promptly discover and solve potential problems, thereby improving the overall reliability of the system.

[0056] 3. The present invention provides a detection and adjustment method for a photoacoustic pool with adjustable resonant frequency. By dynamically adjusting the modulation frequency of the excitation light source and the cavity length of the photoacoustic pool, the system performance fluctuations caused by external factors such as component aging and environmental changes can be effectively addressed, ensuring that the system can maintain a stable resonant state under different working conditions, thereby improving the overall stability and reliability of the system. In addition, during long-term use, the system can automatically compensate for frequency deviations caused by component wear or environmental changes, thereby extending the service life of the system and reducing maintenance costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0058] Figure 1 is a flow chart of the method of the present invention;

[0059] Figure 2 The present invention is a flow chart for obtaining the temperature interference index, the humidity interference index and the air pressure interference index;

[0060] Figure 3 This is a flow chart for obtaining the prediction evaluation coefficient of the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0062] Embodiment 1, as Figure 1-3As shown, the present invention provides a detection and adjustment method for a photoacoustic pool with adjustable resonant frequency, step 1, collecting environmental parameters of the photoacoustic pool and transmitting them to a control center, and dividing the collected environmental parameters into historical data and real-time data for subsequent analysis and processing, wherein the environmental parameters include temperature, humidity and air pressure; the collection process of the environmental parameters of the photoacoustic pool is: deploying a temperature sensor, a humidity sensor, an air pressure sensor and a data acquisition device around the photoacoustic pool to ensure that the temperature, humidity and air pressure of the environment where the photoacoustic pool is located can be accurately measured, and using a wireless transmission method to transmit the environmental parameter data to the control center, and storing the received environmental parameter data in a database of the control center for subsequent Continuous historical data query and analysis, set the sampling frequency and data format of the data acquisition equipment and other parameters to meet the needs of subsequent data analysis and processing, where the data format includes data type, data unit and data accuracy, divide the collected environmental parameter data into historical data and real-time data, and encapsulate the collected environmental parameter data, including adding timestamp and data identification information, for subsequent processing and analysis, where timestamp: records the specific time of data collection, so that the data collection time can be accurately known in subsequent analysis and processing; data identification information: includes identification information such as sensor type, data type, collection location, etc., so that the control center can accurately identify and process data;

[0063] Step 2, preprocessing the collected historical data and real-time data, establishing a photoacoustic pool evaluation model of environmental factors and resonant frequency changes, so as to predict the resonant frequency of the photoacoustic pool under different environmental conditions; the construction process of the photoacoustic pool evaluation model is: data cleaning and preprocessing of the collected historical data and real-time data, removing noise, outliers and redundant information, extracting features from the preprocessed historical data, extracting temperature, humidity and air pressure features respectively, and performing feature dimensionality reduction and screening, using principal component analysis, linear discriminant analysis and other methods to perform feature dimensionality reduction, reduce the number of features, improve model training efficiency, and screen out the features that have the greatest impact on resonant frequency prediction through correlation analysis, mutual information and other methods, Construct a photoacoustic pool evaluation model, integrate the preprocessed historical data and real-time data, and divide them into training set, validation set and test set, where the training set is used to train the photoacoustic pool evaluation model; the validation set is used to adjust model parameters and perform model selection; the test set is used to evaluate the final performance of the model, use the training set data to train the photoacoustic pool evaluation model, and use the validation set and test set data to verify the trained photoacoustic pool evaluation model, evaluate the performance of the photoacoustic pool evaluation model, ensure the generalization ability of the model, input the preprocessed real-time data into the photoacoustic pool evaluation model to predict the resonant frequency under the current environmental conditions, analyze the change trend of the resonant frequency, and compare it with subsequent measurement results;

[0064] Step 3, using the resonance acoustic spectrum method to measure the real-time resonant frequency of the photoacoustic pool, record the initial resonant frequency, analyze the change trend of the resonant frequency for comparison with subsequent measurement results, and obtain environmental interference factors, wherein the environmental interference factors include temperature interference factors, humidity interference factors and air pressure interference factors; the process of obtaining environmental interference factors is as follows: building a photoacoustic spectrum detection system and a resonance acoustic spectrum detection system, the photoacoustic pool adopts a first-order longitudinal resonance mode, ensuring that the gas inlet and outlet are set in the buffer chambers at both ends to reduce the interference of gas flow on the photoacoustic signal detection, setting the frequency range and resolution of the sweep signal to accurately capture the resonant frequency of the photoacoustic pool, and using an equal-amplitude sinusoidal sweep signal to perform a frequency scan on the photoacoustic pool, and recording the intensity change of the photoacoustic signal during the sweep process, and finding the frequency point with the strongest photoacoustic signal, which is the resonance of the photoacoustic pool. Frequency, and under stable environmental conditions, repeat the measurement for multiple times, take the average value as the initial resonant frequency of the photoacoustic pool, collect photoacoustic signals in real time, calculate the resonant frequency at the current moment, compare the real-time resonant frequency with the initial resonant frequency, analyze the change trend of the resonant frequency, draw a curve of the resonant frequency change over time, observe whether there are periodic changes or abnormal fluctuations, and analyze the causes and laws of the resonant frequency changes in combination with the changes in environmental factors. According to the pre-processed historical data and real-time data, obtain the temperature interference factor, humidity interference factor and air pressure interference factor, analyze the causes of the resonant frequency changes, record the environmental conditions of each measurement, such as temperature, humidity, air pressure and the corresponding resonant frequency, use statistical methods to analyze the influence of environmental factors on the resonant frequency, and determine the specific values ​​and change laws of the environmental interference factors according to the analysis results;

[0065] Step 4, analyze the pre-processed historical data and combine it with the acquired environmental interference factors to obtain the temperature interference index, humidity interference index and air pressure interference index to predict the change of the resonant frequency, including the drift amount and drift speed of the resonant frequency; the acquisition process of the temperature interference index, humidity interference index and air pressure interference index is: using the pre-processed historical data, including temperature, humidity, air pressure and corresponding resonant frequency, to perform statistical analysis, measure the resonant frequency of the photoacoustic pool under different temperature conditions, analyze the correlation between temperature and resonant frequency, calculate the temperature interference index, measure the resonant frequency of the photoacoustic pool under different humidity conditions, analyze the correlation between humidity and resonant frequency, calculate the humidity interference index, measure the resonant frequency of the photoacoustic pool under different air pressure conditions, analyze the correlation between air pressure and resonant frequency, calculate the air pressure interference index, use the temperature interference index, humidity interference index and air pressure interference index in combination with the photoacoustic pool evaluation model to predict the change of the resonant frequency, judge the change trend of the resonant frequency and the reason for its change, compare the predicted resonant frequency with the actually measured resonant frequency, analyze the prediction error of the photoacoustic pool evaluation model, and adjust the parameters of the photoacoustic pool evaluation model to improve the prediction accuracy, such as adding characteristic variables, changing the model structure, etc.;

[0066] Step 5, based on the temperature interference index, humidity interference index and air pressure interference index, obtain the prediction evaluation coefficient, and use the prediction evaluation coefficient to analyze the deviation between the prediction result and the resonant frequency under different environmental conditions to obtain the evaluation result; the process of obtaining the prediction evaluation coefficient and the evaluation result is: traversing the pre-processed historical data, combining the temperature interference index, humidity interference index and air pressure interference index with the resonant frequency in the historical data, analyzing the correlation between the resonant frequency deviation and the temperature interference index, humidity interference index and air pressure interference index, assigning different weights to the temperature interference index, humidity interference index and air pressure interference index, performing weighted calculation on them, and obtaining the prediction evaluation coefficient, measuring the actual value of the resonant frequency under different environmental conditions, and using the photoacoustic pool evaluation model to predict the resonant frequency under these conditions to obtain the predicted value of the resonant frequency, and calculating the deviation between the predicted value and the actual value, evaluating the deviation under each environmental condition with the corresponding prediction evaluation coefficient, judging the degree of deviation between the prediction result and the actual resonant frequency, and outputting the evaluation result at the same time, the smaller the deviation rate, the more accurate the prediction result, calculating the deviation between the predicted value and the actual value, and combining the prediction evaluation coefficient to comprehensively analyze the reliability of the prediction result. The smaller the prediction evaluation coefficient, the smaller the impact of the current environmental conditions on the prediction results, and the more reliable the prediction results; conversely, the larger the prediction evaluation coefficient, the greater the impact of the current environmental conditions on the prediction results, and the lower the reliability of the prediction results;

[0067] Step 6, measure the resonant frequency in real time and compare it with the preset resonant frequency, analyze the difference, and combine it with the evaluation results to formulate and implement corresponding adjustment plans, such as adjusting the ambient temperature, humidity or air pressure, or optimizing the working parameters of the photoacoustic cell to reduce the deviation of the resonant frequency, and compare the adjusted resonant frequency with the standard resonant frequency again to form a closed-loop feedback system. If there is a deviation, continue to adjust until the ideal resonant frequency is reached; wherein the process of formulating and implementing the corresponding adjustment plan is: compare the real-time measured resonant frequency value with the preset resonant frequency value, calculate the difference, and analyze the range and trend of the difference, analyze the possible reasons according to the size and direction of the difference, such as component aging, environmental changes, circuit parameter changes, etc., combine the difference with the evaluation results, determine the adjustment target, formulate a specific adjustment plan, including adjusting the modulation frequency of the excitation light source and the cavity length of the modulated photoacoustic cell, and after the adjustment is completed, verify the matching effect of the final excitation light source modulation frequency and the resonant frequency of the photoacoustic cell to ensure that the photoacoustic signal reaches the maximum value and the detection sensitivity meets the requirements. After the adjustment is completed, measure the resonant frequency again, compare the adjusted resonant frequency value with the preset resonant frequency value, and verify the adjustment effect.

[0068] Embodiment 2, as Figure 1-3As shown, based on Example 1, the present invention provides a technical solution: Preferably, the calculation formula of the temperature interference index is:

[0069]

[0070] Where TDI is the temperature interference index, T is the current temperature, and T std is the standard temperature, σ T is the standard deviation of temperature, a is the coefficient of temperature on the resonant frequency, f res (T) is the resonant frequency at the current temperature T, f std is the resonant frequency at standard temperature;

[0071] The calculation formula of the humidity interference index is:

[0072]

[0073] Among them, HDI is the humidity interference index, H is the current humidity, and H std is the standard humidity, H mid is the midpoint value of humidity, b is the coefficient of humidity on the resonant frequency, f res (H) is the resonant frequency at the current humidity H, f avg is the average resonant frequency under all humidity conditions;

[0074] The calculation formula of the air pressure interference index is:

[0075]

[0076] Among them, PDI is the pressure disturbance index, P is the current air pressure, and P std is the standard atmospheric pressure, γ is the exponential factor of atmospheric pressure, f res (P) is the resonant frequency at the current air pressure P, f std is the resonant frequency at standard atmospheric pressure, c is a constant term used to adjust the sensitivity of PDI;

[0077] The calculation formula of the prediction evaluation coefficient is:

[0078]

[0079] Among them, PAC is the prediction evaluation coefficient, TDI is the temperature interference index, which indicates the interference degree of the current ambient temperature on the resonant frequency, HDI is the humidity interference index, which indicates the interference degree of the current ambient humidity on the resonant frequency, PDI is the air pressure interference index, which indicates the interference degree of the current ambient air pressure on the resonant frequency, and TDI std is the standard value of the temperature interference index, HDI std is the standard value of humidity interference index, PDIstd is the standard value of the air pressure interference index, σ TDI is the standard deviation of the temperature interference index, σ HDI is the standard deviation of the humidity interference index, σ PDI is the standard deviation of the pressure interference index.

[0080] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for adjusting the detection of a photoacoustic cell with adjustable resonant frequency, characterized in that: Step 1, collecting environmental parameters of the photoacoustic pool and transmitting them to the control center, and dividing the collected environmental parameters into historical data and real-time data, wherein the environmental parameters include temperature, humidity and air pressure; Step 2, preprocessing the collected historical data and real-time data, and establishing a photoacoustic pool evaluation model to predict the resonant frequency of the photoacoustic pool under different environmental conditions; Step 3, using resonance acoustic spectroscopy to measure the real-time resonant frequency of the photoacoustic cell, record the initial resonant frequency, analyze the change trend of the resonant frequency, and simultaneously obtain environmental interference factors, wherein the environmental interference factors include temperature interference factors, humidity interference factors, and air pressure interference factors; Step 4, analyzing the preprocessed historical data and combining it with the acquired environmental interference factors to obtain a temperature interference index, a humidity interference index, and an air pressure interference index to predict changes in the resonant frequency; Step 5, obtaining a prediction evaluation coefficient based on the temperature interference index, the humidity interference index and the air pressure interference index, and using the prediction evaluation coefficient to analyze the deviation between the prediction result and the resonant frequency under different environmental conditions to obtain an evaluation result; Step 6, measure the resonant frequency in real time and compare it with the preset resonant frequency, analyze the difference, and combine it with the evaluation results to formulate and implement a corresponding adjustment plan.

2. The method for detecting and adjusting a photoacoustic cell with adjustable resonant frequency according to claim 1, characterized in that: In step 1, the process of collecting the environmental parameters of the photoacoustic cell is as follows: Step 101, deploying temperature sensors, humidity sensors, air pressure sensors and data acquisition equipment around the photoacoustic pool, and transmitting environmental parameter data to a control center using wireless transmission; Step 102, setting parameters such as sampling frequency and data format of the data acquisition device, wherein the data format includes data type, data unit and data precision; Step 103, dividing the collected environmental parameter data into historical data and real-time data, and encapsulating the collected environmental parameter data, including adding timestamp and data identification information.

3. The method for detecting and adjusting a photoacoustic cell with adjustable resonant frequency according to claim 2, characterized in that: In step 2, the construction process of the photoacoustic cell evaluation model is as follows: Step 201, cleaning and preprocessing the collected historical data and real-time data to remove noise, outliers and redundant information; Step 202, extracting features from the pre-processed historical data, extracting temperature, humidity and air pressure features respectively, performing feature dimension reduction and screening, and constructing a photoacoustic pool evaluation model; Step 203, integrating the preprocessed historical data and real-time data, and dividing them into a training set, a validation set, and a test set, using the training set data to train the photoacoustic pool evaluation model, and using the validation set and the test set data to verify the trained photoacoustic pool evaluation model, and evaluating the performance of the photoacoustic pool evaluation model; Step 204 , input the preprocessed real-time data into the photoacoustic cell evaluation model to predict the resonant frequency under the current environmental conditions.

4. The method for detecting and adjusting a photoacoustic cell with adjustable resonant frequency according to claim 3, characterized in that: In step 3, the process of obtaining the environmental interference factor is as follows: Step 301, build a photoacoustic spectrum detection system and a resonance acoustic spectrum detection system, set the frequency range and resolution of the sweep signal, and use a constant-amplitude sinusoidal sweep signal to perform a frequency scan on the photoacoustic cell, and record the intensity change of the photoacoustic signal during the sweep process, find the frequency point with the strongest photoacoustic signal, and repeat the measurement multiple times under stable environmental conditions, and take the average value as the initial resonance frequency of the photoacoustic cell; Step 302, collecting photoacoustic signals in real time, calculating the resonance frequency at the current moment, comparing the real-time resonance frequency with the initial resonance frequency, and analyzing the change trend of the resonance frequency; Step 303, obtaining temperature interference factor, humidity interference factor and air pressure interference factor according to the pre-processed historical data and real-time data, and analyzing the cause of the change of the resonant frequency; Step 304, recording the environmental conditions and corresponding resonant frequencies of each measurement, using statistical methods to analyze the impact of environmental factors on the resonant frequency, and determining the specific value and change pattern of the environmental interference factor based on the analysis results.

5. The method for detecting and adjusting a photoacoustic cell with adjustable resonant frequency according to claim 4, characterized in that: In step 4, the process of obtaining the temperature interference index, the humidity interference index and the air pressure interference index is as follows: Step 401, using the pre-processed historical data, including temperature, humidity, air pressure and corresponding resonant frequency, to perform statistical analysis; Step 402, measuring the resonant frequency of the photoacoustic cell under different temperature conditions, analyzing the correlation between the temperature and the resonant frequency, and calculating the temperature interference index; measuring the resonant frequency of the photoacoustic cell under different humidity conditions, analyzing the correlation between the humidity and the resonant frequency, and calculating the humidity interference index; measuring the resonant frequency of the photoacoustic cell under different air pressure conditions, analyzing the correlation between the air pressure and the resonant frequency, and calculating the air pressure interference index; Step 403, using the temperature interference index, humidity interference index and air pressure interference index in combination with the photoacoustic cell evaluation model, predicting the change of the resonant frequency, and determining the change trend of the resonant frequency and the reason for the change; Step 404 , comparing the predicted resonant frequency with the actually measured resonant frequency, analyzing the prediction error of the photoacoustic cell evaluation model, and adjusting the parameters of the photoacoustic cell evaluation model to improve the prediction accuracy.

6. The method for detecting and adjusting a photoacoustic cell with adjustable resonant frequency according to claim 5, characterized in that: The calculation formula of the temperature interference index is: Where TDI is the temperature interference index, T is the current temperature, and T std is the standard temperature, σ T is the standard deviation of temperature, a is the coefficient of temperature on the resonant frequency, f res (T) is the resonant frequency at the current temperature T, f std is the resonant frequency at standard temperature; The calculation formula of the humidity interference index is: Among them, HDI is the humidity interference index, H is the current humidity, and H std is the standard humidity, H mid is the midpoint value of humidity, b is the coefficient of humidity on the resonant frequency, f res (H) is the resonant frequency at the current humidity H, f avg is the average resonant frequency under all humidity conditions; The calculation formula of the air pressure interference index is: Among them, PDI is the pressure disturbance index, P is the current air pressure, and P std is the standard atmospheric pressure, γ is the exponential factor of atmospheric pressure, f res (P) is the resonant frequency at the current air pressure P, f std is the resonant frequency at standard pressure, and c is a constant.

7. The method for detecting and adjusting a photoacoustic cell with adjustable resonant frequency according to claim 6, characterized in that: In step 5, the process of obtaining the prediction evaluation coefficient and the evaluation result is as follows: Step 501, traversing the pre-processed historical data, combining the temperature interference index, humidity interference index and air pressure interference index with the resonant frequency in the historical data, and analyzing the correlation between the resonant frequency deviation and the temperature interference index, humidity interference index and air pressure interference index; Step 502, assigning different weights to the temperature interference index, the humidity interference index and the air pressure interference index, performing weighted calculations on them, and obtaining a prediction evaluation coefficient; Step 503, measuring the actual value of the resonant frequency under different environmental conditions, and using the photoacoustic cell evaluation model to predict the resonant frequency under these conditions, to obtain a predicted value of the resonant frequency, and calculating the deviation between the predicted value and the actual value; Step 504 , evaluating the deviation under each environmental condition and the corresponding prediction evaluation coefficient, determining the degree of deviation between the prediction result and the actual resonant frequency, and outputting the evaluation result.

8. The method for detecting and adjusting a photoacoustic cell with adjustable resonant frequency according to claim 7, characterized in that: The calculation formula of the prediction evaluation coefficient is: Among them, PAC is the prediction assessment coefficient, TDI is the temperature interference index, HDI is the humidity interference index, PDI is the pressure interference index, TDI std is the standard value of the temperature interference index, HDI std is the standard value of humidity interference index, PDI std is the standard value of the air pressure interference index, σ TDI is the standard deviation of the temperature interference index, σ HDI is the standard deviation of the humidity interference index, σ PDI is the standard deviation of the pressure interference index.

9. The method for detecting and adjusting a photoacoustic cell with adjustable resonant frequency according to claim 8, characterized in that: In step 6, the process of formulating and implementing the corresponding adjustment plan is as follows: Step 601, comparing the real-time measured resonant frequency value with the preset resonant frequency value, calculating the difference, and analyzing the range and trend of the difference; Step 602, combining the difference with the evaluation result, determining the adjustment target, formulating a specific adjustment plan, including adjusting the modulation frequency of the excitation light source and the cavity length of the modulated photoacoustic cell, and after the adjustment, verifying the matching effect between the final modulation frequency of the excitation light source and the resonant frequency of the photoacoustic cell; Step 603, after the adjustment is completed, the resonant frequency is measured again, and the adjusted resonant frequency value is compared with the preset resonant frequency value to verify the adjustment effect.

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