Laser data optimization transmission method for photoacoustic spectrometry oil gas detection

Abstract

The invention discloses a laser data optimization transmission method for photoacoustic spectrum oil gas detection, and relates to the technical field of data transmission, and the method comprises the following steps: extracting an oil sample from a transformer, sending the oil sample into a degassing system, separating gas of the oil sample, sending the gas into a photoacoustic cell, carrying out laser irradiation, exciting a photoacoustic effect, generating pressure fluctuation, and forming a photoacoustic signal. The quantitative relation between the photoacoustic signal intensity and the gas concentration is established through data calibration, the accuracy of the detection result is ensured, the influence of temperature and humidity environmental factors on the photoacoustic signal is further considered in environmental compensation, the interference of the environmental factors on the detection result is weakened by introducing a compensation coefficient, a dual-guarantee mechanism is adopted, and the accuracy of the detection result is ensured. According to the invention, the optimized transmission laser data has higher precision and reliability, in the data transmission process, the analysis deviation caused by error accumulation is further reduced, and the accuracy of subsequent fault judgment and maintenance plans is improved.

Description

Technical Field

[0001] The invention relates to the technical field of data transmission, and in particular to a laser data optimization transmission method for photoacoustic spectroscopy oil and gas detection. Background Art

[0002] The transformer is the "heart" of the power grid load, and its insulating oil is an important medium for insulation and cooling, which is directly related to the safe operation of the power grid. During operation, the transformer may produce hydrogen, methane, carbon dioxide, acetylene and other fault gases due to internal thermal faults, discharge faults or component aging. The content of fault gas is closely related to the type and severity of transformer fault. By online monitoring of the content of fault gas, hidden faults inside the transformer can be discovered early, thereby avoiding major accidents.

[0003] In the prior art, during oil and gas detection, the photoacoustic effect of gas is easily disturbed by environmental factors such as temperature and humidity changes, and the interference of environmental factors will affect the accuracy of the detected signal information, and then affect the reliability of the data. Therefore, how to weaken the interference of environmental factors and improve the accuracy of laser data is the problem we need to solve. To this end, a laser data optimization transmission method for photoacoustic spectroscopy oil and gas detection is proposed. Summary of the invention

[0004] The present invention aims to provide a method for optimizing the transmission of laser data for photoacoustic spectroscopy oil and gas detection, so as to solve the problems raised in the above-mentioned background technology.

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

[0006] A laser data optimization transmission method for photoacoustic spectroscopy oil and gas detection comprises the following steps:

[0007] Step 1, extracting an oil sample from the transformer and sending it into a degassing system, separating the gas in the oil sample and sending it into a photoacoustic cell for laser irradiation, stimulating a photoacoustic effect, generating pressure fluctuations, and forming a photoacoustic signal;

[0008] Step 2, capturing and recording the photoacoustic signal generated by the gas in the photoacoustic cell based on a photoacoustic detector, and modulating and demodulating the photoacoustic signal to convert it into an electrical signal;

[0009] Step 3: Using digital signal processing algorithms, further filter out noise and extract signal features, analyze the frequency and amplitude parameters of the photoacoustic signal, and resolve the type and concentration of the gas based on known gas absorption spectra;

[0010] Step 4, calibrating the photoacoustic signal data and optimizing the environmental compensation, and remotely transmitting the processed data to the background analysis system for data analysis;

[0011] Step 5: The background analysis system conducts in-depth analysis of the data to identify the type, concentration and location of the fault gas. According to the analyzed fault gas information, combined with the operating status and historical data of the transformer, the system makes a fault judgment. Based on the judgment results, it formulates a corresponding maintenance plan to eliminate potential fault hazards inside the transformer.

[0012] A further improvement of the technical solution of the present invention is that in step 1, the photoacoustic signal is formed by:

[0013] Step 101, using a sampling device to safely and accurately extract an appropriate amount of oil sample from the transformer, during which it is necessary to ensure that the sampling process does not cause damage to the transformer itself and prevent the oil sample from being contaminated. After the oil sample is obtained, it is sealed and stored;

[0014] Step 102, sending the extracted oil sample to a degassing system for treatment, placing the oil sample under vacuum conditions to reduce the solubility of dissolved gas, and promoting the release and separation of gas from the oil;

[0015] Step 103, transporting the separated gas to the photoacoustic cell through a pipeline, and correctly aligning the laser and the photoacoustic detector;

[0016] Step 104, start the laser, set the wavelength, focus the laser beam on the window of the photoacoustic cell and irradiate the gas in the photoacoustic cell. After the laser energy is absorbed by the gas molecules, it will cause energy level transition and heat energy release inside the molecules.

[0017] In step 105, the gas molecules absorb the laser energy and transition from the ground state to the excited state, releasing energy through a non-radiative relaxation process, thereby increasing the local temperature and the gas pressure, forming pressure fluctuations, and causing the pressure fluctuations to propagate in the photoacoustic cell, forming detectable photoacoustic signals.

[0018] A further improvement of the technical solution of the present invention is that in step 2, the conversion process of the electrical signal is:

[0019] Step 201, placing a photoacoustic detector, such as a microphone or a piezoelectric sensor, in the photoacoustic cell to scan the photoacoustic signal and capture the photoacoustic signal formed by the pressure fluctuation;

[0020] Step 202, using a low noise amplifier to preliminarily amplify the photoacoustic signal, and modulating the amplified signal using a frequency synchronized with the laser source to enhance the characteristics of the signal and suppress noise;

[0021] Step 203, transmitting the modulated signal through a transmission line, and demodulating the signal using a phase-locked amplifier to extract the photoacoustic signal component corresponding to the modulation frequency, removing the remaining high-frequency noise and low-frequency drift through a bandpass filter, and retaining the frequency component related to the photoacoustic signal;

[0022] Step 204, by transmitting the signal output by the photoacoustic detector to a signal processing device, the modulated photoacoustic signal is converted into an electrical signal, and an analog-to-digital converter is used to convert the analog electrical signal into a digital signal for subsequent digital signal processing.

[0023] A further improvement of the technical solution of the present invention is that in step 3, the analysis process of gas type and concentration is:

[0024] Step 301, applying a window function to perform windowing processing on the signal to reduce spectrum leakage, and using fast Fourier transform to analyze the spectrum characteristics of the photoacoustic signal, converting the time domain signal to the frequency domain. The Fourier transform can decompose the signal into complex amplitude and phase information of different frequency components;

[0025] Step 302, using a statistical method to estimate background noise, and using a Wiener filter to further remove noise to reduce noise in the signal;

[0026] Step 303, analyzing the fast Fourier transform result, extracting the main frequency components of the signal, making them correspond to the resonance frequency of the gas molecules, and calculating the amplitude of each frequency component;

[0027] Step 304, extracting peak frequency and amplitude features related to the type and concentration of the gas from the frequency domain signal, using a known gas absorption spectrum database, matching the extracted features with the spectrum in the database to identify the gas type;

[0028] Step 305, based on the intensity of the absorption line and the characteristics of the photoacoustic signal, the concentration of the gas is calculated using the Lambert-Beer law combined with an inversion algorithm;

[0029] Step 306: Output the final gas type and concentration results to the database for storage and recording.

[0030] A further improvement of the technical solution of the present invention is that the expression of the window function is:

[0031]

[0032] Where w(n) is the window function value, which indicates the value of the window function at sample point n, n is the sample index, N is the total number of signal samples, WT is the window function type, WL is the window function length, WL ≤ N, is the half length of the window function, which is used to control the period of the cosine function;

[0033] The calculation formula of the fast Fourier transform is:

[0034]

[0035] Where X(k) is the kth component in the frequency domain, x(n) is the time domain signal, N is the total number of signal samples, which is equal to the size of the FFT, which is a power of 2, and j is the imaginary unit such that j 2 =-1, is a constant factor in FFT, used to control the frequency scale, and kn is a factor that controls the phase change;

[0036] The expression for removing noise by the Wiener filter is:

[0037]

[0038] Among them, y(f) is the value of the filtered signal at frequency f, S(f) is the value of the power spectrum density of the signal at frequency f, is the noise power spectral density, which indicates the intensity of the noise.

[0039] A further improvement of the technical solution of the present invention is that in step 4, the process of data calibration and environmental compensation of the photoacoustic signal is as follows:

[0040] Step 401, extracting the photoacoustic signal of the historical database, calibrating the photoacoustic signal of the historical database using a gas of known standard concentration, establishing a quantitative relationship between the photoacoustic signal intensity and the gas concentration, and recording the output values ​​of the photoacoustic signal at different concentrations, and fitting a concentration-signal intensity calibration formula;

[0041] Step 402, by measuring the relationship between the internal working temperature and the output signal of the photoacoustic spectrum detection in advance, a stable temperature value is selected as a calibration point, and the ratio of the photoacoustic signal at other temperatures to the photoacoustic signal calibrated at the calibration temperature point is obtained as a compensation coefficient, and the compensation coefficient array is queried according to the current actual working temperature to perform temperature compensation on the photoacoustic signal;

[0042] Step 403, through the relationship between the internal working humidity and the output signal of the photoacoustic spectrum detected by the pre-experimental measurement, a stable humidity value is selected as a calibration point, and the ratio of the photoacoustic signal at other humidity to the photoacoustic signal at the calibration humidity point is obtained as a compensation coefficient, and the compensation coefficient array is queried according to the current actual working humidity to perform humidity compensation on the photoacoustic signal;

[0043] Step 404, comprehensively optimize the impact of environmental factors on the photoacoustic signal data, encapsulate the processed data into a standard format, encrypt it, and use a secure communication protocol to transmit the data to the background analysis system for data analysis.

[0044] A further improvement of the technical solution of the present invention is that the concentration-signal intensity calibration formula is obtained by polynomial regression fitting, and its fitting expression is:

[0045] C=a0+a1S+a2S 2 +…+a m S m ;

[0046] a i =(SA T SA) -1 SA T CA;

[0047] Where C is the concentration, S is the signal intensity, a0, a1, ..., a m is the regression coefficient, m is the order of the polynomial, and a i is the regression coefficient, SA is the matrix of signal strength, SA T is the transposed matrix of SA, T is the transpose operation, which means swapping the rows and columns of the matrix, and CA is the corresponding concentration vector.

[0048] A further improvement of the technical solution of the present invention is that the calculation formula for temperature compensation of the photoacoustic signal is:

[0049]

[0050] Among them, S c (W) is the signal strength after temperature compensation, S y is the original photoacoustic signal intensity, W is the current ambient temperature, and W r is the reference temperature, which is used for the specific temperature point of calibration, α and β are temperature-related fitting coefficients, which indicate the degree of influence of temperature change on signal strength, and ΔW is the temperature change range;

[0051] The calculation formula for humidity compensation of the photoacoustic signal is:

[0052]

[0053] Among them, S c (H) is the signal strength after humidity compensation, H is the current ambient humidity, and H r is the reference humidity, used for calibration of specific humidity points, γ is the humidity-related coefficient, indicating the degree of influence of humidity changes on signal strength, S y is the original photoacoustic signal intensity;

[0054] The calculation formula for the comprehensive temperature and humidity compensation of the photoacoustic signal is:

[0055]

[0056] Among them, S tc is the photoacoustic signal intensity after comprehensive temperature and humidity compensation, S yis the original photoacoustic signal intensity, p is the number of terms in the summation function, h is the index variable in the summation function, ΔW h is the change in the hth temperature interval, ΔH h is the change in the hth humidity interval, α h and γ h is the temperature and humidity influence coefficient corresponding to the hth interval.

[0057] A further improvement of the technical solution of the present invention is that in step 5, the process of fault judgment and maintenance plan formulation is as follows:

[0058] Step 501, the background analysis system receives the encrypted data transmitted by the front end, performs decryption processing, verifies the integrity and correctness of the data, obtains the original photoacoustic signal data, and performs format conversion on the decrypted data;

[0059] Step 502, using digital signal processing technology to extract the characteristics of the photoacoustic signal, and converting the signal characteristics into the quantitative concentration of the gas, and identifying the type of faulty gas according to the specific absorption spectrum and concentration data of the gas;

[0060] Step 503, matching the detected gas type and concentration with the known fault mode, and conducting a comprehensive analysis based on the transformer's operating status, historical data, and real-time monitoring data to determine the cause of the fault;

[0061] Step 504, assessing the severity and urgency of the fault according to the gas concentration level and change rate, and analyzing the specific location of the fault using the gas distribution and change trend, and formulating corresponding maintenance measures based on the fault judgment result;

[0062] Step 505, evaluate the potential risks and priorities of the maintenance measures, determine the maintenance schedule, schedule resources according to the maintenance plan, notify relevant personnel of the analysis results and maintenance plan, and generate a detailed fault analysis report.

[0063] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0064] 1. The present invention provides a method for optimizing the transmission of laser data for photoacoustic spectroscopy oil and gas detection. The quantitative relationship between the photoacoustic signal intensity and the gas concentration is established through data calibration to ensure the accuracy of the detection results. The environmental compensation further considers the influence of temperature and humidity environmental factors on the photoacoustic signal. The interference of environmental factors on the detection results is weakened by introducing a compensation coefficient. The dual protection mechanism makes the optimized transmitted laser data have higher accuracy and reliability. During the data transmission process, the analysis deviation caused by error accumulation is further reduced, and the accuracy of subsequent fault judgment and maintenance plan is improved.

[0065] 2. The present invention provides a laser data optimization transmission method for photoacoustic spectroscopy oil and gas detection. By using a high-precision analog-to-digital converter and digital signal processing technology, the signal-to-noise ratio of the photoacoustic signal is significantly improved, thereby improving the accuracy of the data. In addition, the spectrum leakage and cross-interference are further reduced through windowing processing to ensure the reliability of the detection results, accurately extract the characteristic frequency and amplitude of the gas, and provide an accurate data basis for the quantitative analysis of the gas type and concentration.

[0066] 3. The present invention provides a laser data optimization transmission method for photoacoustic spectroscopy oil and gas detection. Through data calibration and environmental compensation, not only the accuracy of single detection is improved, but also the robustness and adaptability of the entire detection system are enhanced. Through the relationship between environmental parameters and photoacoustic signals measured in advance, stable detection performance can be maintained under different environmental conditions, so that it can play a role in a wider range of application scenarios, improving the practicality and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] 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.

[0068] Figure 1 is a flow chart of the method of the present invention;

[0069] Figure 2 The analytical flow chart of the gas types and concentrations of the present invention;

[0070] Figure 3 The figure is a flow chart of data calibration and environmental compensation of photoacoustic signals of the present invention. DETAILED DESCRIPTION

[0071] 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.

[0072] Examples, such as Figure 1-3 As shown, the present invention provides a laser data optimization transmission method for photoacoustic spectroscopy oil and gas detection, comprising the following steps:

[0073] Step 1, extract oil samples from the transformer and send them into the degassing system, separate the gas in the oil samples and send them into the photoacoustic cell for laser irradiation, stimulate the photoacoustic effect, generate pressure fluctuations, and form photoacoustic signals. Use a sampling device to safely and accurately extract an appropriate amount of oil samples from the transformer. During the sampling process, it is necessary to ensure that the sampling process will not cause damage to the transformer itself, and at the same time avoid contamination of the oil samples. After obtaining the oil samples, they are sealed and stored to prevent external air contamination and gas loss. The extracted oil samples are sent to the degassing system for processing, and the oil samples are placed under vacuum conditions to reduce the solubility of dissolved gases and promote Release and separate the gas from the oil, and transport the separated gas to the photoacoustic cell through a pipeline, ensure that the photoacoustic cell is clean and undamaged, and correctly align the laser and the photoacoustic detector, start the laser, set the wavelength, focus the laser beam and irradiate the window of the photoacoustic cell onto the gas in the photoacoustic cell. After the laser energy is absorbed by the gas molecules, it will cause energy level transition and heat energy release inside the molecules. The gas molecules absorb the laser energy and transition from the ground state to the excited state, releasing energy through the non-radiative relaxation process, so that the local temperature rises, the gas pressure increases, and the pressure fluctuation is formed. The pressure fluctuation is propagated in the photoacoustic cell to form a detectable photoacoustic signal;

[0074] Step 2, based on the photoacoustic detector, capture and record the photoacoustic signal generated by the gas in the photoacoustic pool, modulate and demodulate the photoacoustic signal, and convert it into an electrical signal, place a photoacoustic detector such as a microphone or a piezoelectric sensor in the photoacoustic pool, scan the photoacoustic signal, capture the photoacoustic signal formed by pressure fluctuations, use a low-noise amplifier to preliminarily amplify the photoacoustic signal, and modulate the amplified signal using a frequency synchronized with the laser source to enhance the characteristics of the signal and suppress noise, transmit the modulated signal through a transmission line, and use a phase-locked amplifier to demodulate the signal to extract the photoacoustic signal component corresponding to the modulation frequency, remove the remaining high-frequency noise and low-frequency drift through a bandpass filter, retain the frequency component related to the photoacoustic signal, and convert the modulated photoacoustic signal into an electrical signal by transmitting the signal output by the photoacoustic detector to a signal processing device, and use an analog-to-digital converter to convert the analog electrical signal into a digital signal for subsequent digital signal processing;

[0075] Step 3, using a digital signal processing algorithm to further filter out noise and extract signal features, analyze the frequency and amplitude parameters of the photoacoustic signal, and resolve the type and concentration of the gas based on the known gas absorption spectrum, apply a window function to the signal to perform windowing processing to reduce spectrum leakage, and use fast Fourier transform to analyze the spectrum characteristics of the photoacoustic signal, convert the time domain signal to the frequency domain, the Fourier transform can decompose the signal into complex amplitude and phase information of different frequency components, use statistical methods to estimate background noise, and use Wiener filter to further remove noise and reduce noise in the signal, analyze the fast Fourier transform results, extract the main frequency components of the signal to make it correspond to the resonant frequency of the gas molecules, and calculate the amplitude of each frequency component, extract the peak frequency and amplitude characteristics related to the type and concentration of the gas from the frequency domain signal, use a known gas absorption spectrum database, match the extracted features with the spectrum in the database, identify the gas type, and calculate the gas concentration based on the intensity of the absorption line and the characteristics of the photoacoustic signal according to the Lambert-Beer law combined with the inversion algorithm, and output the final gas type and concentration results to the database for storage and recording;

[0076] Furthermore, the expression of the window function is:

[0077]

[0078] Where w(n) is the window function value, which indicates the value of the window function at sample point n, n is the sample index, N is the total number of signal samples, WT is the window function type, WL is the window function length, WL ≤ N, is the half length of the window function, which is used to control the period of the cosine function;

[0079] The calculation formula of fast Fourier transform is:

[0080]

[0081] Where X(k) is the kth component in the frequency domain, x(n) is the time domain signal, N is the total number of signal samples, which is equal to the size of the FFT, which is a power of 2, and j is the imaginary unit such that j 2 =-1, is a constant factor in FFT, used to control the frequency scale, and kn is a factor that controls the phase change;

[0082] The expression of Wiener filter to remove noise is:

[0083]

[0084] Among them, y(f) is the value of the filtered signal at frequency f, S(f) is the value of the power spectrum density of the signal at frequency f, is the noise power spectral density, which indicates the intensity of the noise;

[0085] Step 4, calibrate the data of the photoacoustic signal and optimize the environmental compensation, and remotely transmit the processed data to the background analysis system for data analysis, extract the photoacoustic signal of the historical database, use the gas of known standard concentration to calibrate the photoacoustic signal of the historical database, establish the quantitative relationship between the photoacoustic signal intensity and the gas concentration, and record the output value of the photoacoustic signal at different concentrations, fit the concentration-signal intensity calibration formula, select the stable temperature value as the calibration point through the relationship between the internal working temperature and the output signal of the photoacoustic spectrum detected by the pre-experimental measurement, and obtain the ratio of the photoacoustic signal at other temperatures to the calibrated photoacoustic signal at the calibration temperature point as the compensation point. compensation coefficient, query the compensation coefficient array according to the current actual working temperature, perform temperature compensation on the photoacoustic signal, select the stable humidity value as the calibration point through the relationship between the internal working humidity and the output signal of the photoacoustic spectrum detected by the pre-experimental measurement, obtain the ratio of the photoacoustic signal at other humidity to the photoacoustic signal at the calibration humidity point as the compensation coefficient, query the compensation coefficient array according to the current actual working humidity, perform humidity compensation on the photoacoustic signal, comprehensively optimize the impact of environmental factors on the photoacoustic signal data, encapsulate the processed data into a standard format, encrypt it, and use a secure communication protocol to transmit the data to the background analysis system for data analysis;

[0086] Furthermore, the concentration-signal intensity calibration formula is obtained using polynomial regression fitting, and its fitting expression is:

[0087] C=a0+a1S+a2S 2 +…+a m S m ;

[0088] a i =(SA T SA) -1 SA T CA;

[0089] Where C is the concentration, S is the signal intensity, a0, a1, ..., a m is the regression coefficient, m is the order of the polynomial, and a i is the regression coefficient, SA is the matrix of signal strength, SA T is the transposed matrix of SA, T is the transpose operation, which means swapping the rows and columns of the matrix, and CA is the corresponding concentration vector;

[0090] Furthermore, the calculation formula for temperature compensation of photoacoustic signal is:

[0091]

[0092] Among them, Sc (W) is the signal strength after temperature compensation, S y is the original photoacoustic signal intensity, W is the current ambient temperature, and W r is the reference temperature, which is used for the specific temperature point of calibration, α and β are temperature-related fitting coefficients, which indicate the degree of influence of temperature change on signal strength, and ΔW is the temperature change range;

[0093] The calculation formula for humidity compensation of photoacoustic signal is:

[0094]

[0095] Among them, S c (H) is the signal strength after humidity compensation, H is the current ambient humidity, and H r is the reference humidity, used for calibration of specific humidity points, γ is the humidity-related coefficient, indicating the degree of influence of humidity changes on signal strength, S y is the original photoacoustic signal intensity;

[0096] The calculation formula for comprehensive temperature and humidity compensation of photoacoustic signals is:

[0097]

[0098] Among them, S tc is the photoacoustic signal intensity after comprehensive temperature and humidity compensation, S y is the original photoacoustic signal intensity, p is the number of terms in the summation function, h is the index variable in the summation function, ΔW h is the change in the hth temperature interval, ΔH h is the change in the hth humidity interval, α h and γ h is the temperature and humidity influence coefficient corresponding to the hth interval;

[0099] Step 5: The background analysis system conducts in-depth analysis of the data to identify the type, concentration and location of the faulty gas. According to the analyzed faulty gas information, combined with the operating status and historical data of the transformer, a fault judgment is made. Based on the judgment result, a corresponding maintenance plan is formulated to eliminate potential fault hazards inside the transformer. The background analysis system receives the encrypted data transmitted by the front end, decrypts it, verifies the integrity and correctness of the data, obtains the original photoacoustic signal data, and converts the format of the decrypted data. The characteristics of the photoacoustic signal are extracted using digital signal processing technology, and the signal characteristics are converted into the quantitative concentration of the gas. According to the specific absorption spectrum and concentration data of the gas, the type of faulty gas is identified, and the detected gas type and concentration are matched with the known fault mode. Combined with the operating status, historical data and real-time monitoring data of the transformer, a comprehensive analysis is conducted to determine the cause of the fault. According to the gas concentration level and change rate, the severity and urgency of the fault are evaluated, and the specific location of the fault is analyzed using the gas distribution and change trend. Based on the fault judgment result, corresponding maintenance measures are formulated, the potential risks and priorities of the maintenance measures are evaluated, and the maintenance schedule is determined. According to the maintenance plan, resource scheduling is performed, and the analysis results and maintenance plan are notified to relevant personnel to generate a detailed fault analysis report.

[0100] 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 laser data optimization transmission method for photoacoustic spectroscopy oil and gas detection, characterized in that: The following steps are involved: Step 1, extracting an oil sample from the transformer and sending it into a degassing system, separating the gas in the oil sample and sending it into a photoacoustic cell for laser irradiation, stimulating a photoacoustic effect, generating pressure fluctuations, and forming a photoacoustic signal; Step 2, capturing and recording the photoacoustic signal generated by the gas in the photoacoustic cell based on a photoacoustic detector, and modulating and demodulating the photoacoustic signal to convert it into an electrical signal; Step 3: Using digital signal processing algorithms, further filter out noise and extract signal features, analyze the frequency and amplitude parameters of the photoacoustic signal, and resolve the type and concentration of the gas based on known gas absorption spectra; Step 4, calibrating the photoacoustic signal data and optimizing the environmental compensation, and remotely transmitting the processed data to the background analysis system for data analysis; Step 5: The background analysis system conducts in-depth analysis of the data to identify the type, concentration and location of the fault gas. According to the analyzed fault gas information, combined with the operating status and historical data of the transformer, the fault judgment is made. Based on the judgment results, a corresponding maintenance plan is formulated to eliminate potential fault hazards inside the transformer.

2. The laser data optimization transmission method for photoacoustic spectroscopy oil and gas detection according to claim 1 is characterized by: In step 1, the photoacoustic signal is formed by: Step 101, using a sampling device to extract an appropriate amount of oil sample from the transformer, and after obtaining the oil sample, sealing and storing it; Step 102, sending the extracted oil sample to a degassing system for treatment, placing the oil sample under vacuum conditions to reduce the solubility of dissolved gas, and promoting the release and separation of gas from the oil; Step 103, transporting the separated gas to the photoacoustic cell through a pipeline, and correctly aligning the laser and the photoacoustic detector; Step 104, start the laser, set the wavelength, focus the laser beam on the window of the photoacoustic cell and irradiate the laser beam onto the gas in the photoacoustic cell; In step 105, the gas molecules absorb the laser energy and transition from the ground state to the excited state, releasing energy through a non-radiative relaxation process, thereby increasing the local temperature and the gas pressure, forming pressure fluctuations, and causing the pressure fluctuations to propagate in the photoacoustic cell, forming detectable photoacoustic signals.

3. The laser data optimization transmission method for photoacoustic spectroscopy oil and gas detection according to claim 2 is characterized by: In step 2, the conversion process of the electrical signal is: Step 201, placing a photoacoustic detector in the photoacoustic cell, scanning the photoacoustic signal, and capturing the photoacoustic signal formed by the pressure fluctuation; Step 202, using a low noise amplifier to preliminarily amplify the photoacoustic signal, and modulating the amplified signal using a frequency synchronized with the laser source to enhance the characteristics of the signal and suppress noise; Step 203, transmitting the modulated signal through a transmission line, and demodulating the signal using a phase-locked amplifier to extract the photoacoustic signal component corresponding to the modulation frequency, removing the remaining high-frequency noise and low-frequency drift through a bandpass filter, and retaining the frequency component related to the photoacoustic signal; Step 204 , converting the modulated photoacoustic signal into an electrical signal by transmitting the signal output by the photoacoustic detector to a signal processing device, and converting the analog electrical signal into a digital signal using an analog-to-digital converter.

4. The laser data optimization transmission method for photoacoustic spectroscopy oil and gas detection according to claim 3 is characterized by: In step 3, the analysis process of gas types and concentrations is as follows: Step 301, applying a window function to perform windowing processing on the signal to reduce spectrum leakage, and using fast Fourier transform to analyze the spectrum characteristics of the photoacoustic signal to convert the time domain signal into the frequency domain; Step 302, using a statistical method to estimate background noise, and using a Wiener filter to further remove noise to reduce noise in the signal; Step 303, analyzing the fast Fourier transform result, extracting the main frequency components of the signal, making them correspond to the resonance frequency of the gas molecules, and calculating the amplitude of each frequency component; Step 304, extracting peak frequency and amplitude features related to the type and concentration of the gas from the frequency domain signal, using a known gas absorption spectrum database, matching the extracted features with the spectrum in the database to identify the gas type; Step 305, based on the intensity of the absorption line and the characteristics of the photoacoustic signal, the concentration of the gas is calculated using the Lambert-Beer law combined with an inversion algorithm; Step 306: Output the final gas type and concentration results to the database for storage and recording.

5. The laser data optimization transmission method for photoacoustic spectroscopy oil and gas detection according to claim 4 is characterized by: The expression of the window function is: Where w(n) is the window function value, which indicates the value of the window function at sample point n, n is the sample index, N is the total number of signal samples, WT is the window function type, WL is the window function length, WL ≤ N, is the half length of the window function, which is used to control the period of the cosine function; The calculation formula of the fast Fourier transform is: Where X(k) is the kth component in the frequency domain, x(n) is the time domain signal, N is the total number of signal samples, which is equal to the size of the FFT, which is a power of 2, and j is the imaginary unit such that j 2 =-1, is a constant factor in FFT, used to control the frequency scale, and kn is a factor that controls the phase change; The expression for removing noise by the Wiener filter is: Among them, y(f) is the value of the filtered signal at frequency f, S(f) is the value of the power spectrum density of the signal at frequency f, is the noise power spectral density, which indicates the intensity of the noise.

6. The laser data optimization transmission method for photoacoustic spectroscopy oil and gas detection according to claim 5 is characterized by: In step 4, the process of data calibration and environmental compensation of the photoacoustic signal is as follows: Step 401, extracting the photoacoustic signal of the historical database, calibrating the photoacoustic signal of the historical database using a gas of known standard concentration, establishing a quantitative relationship between the photoacoustic signal intensity and the gas concentration, and recording the output values ​​of the photoacoustic signal at different concentrations, and fitting a concentration-signal intensity calibration formula; Step 402, by measuring the relationship between the internal working temperature and the output signal of the photoacoustic spectrum detection in advance, a stable temperature value is selected as a calibration point, and the ratio of the photoacoustic signal at other temperatures to the photoacoustic signal calibrated at the calibration temperature point is obtained as a compensation coefficient, and the compensation coefficient array is queried according to the current actual working temperature to perform temperature compensation on the photoacoustic signal; Step 403, through the relationship between the internal working humidity and the output signal of the photoacoustic spectrum detected by the pre-experimental measurement, a stable humidity value is selected as a calibration point, and the ratio of the photoacoustic signal at other humidity to the photoacoustic signal at the calibration humidity point is obtained as a compensation coefficient, and the compensation coefficient array is queried according to the current actual working humidity to perform humidity compensation on the photoacoustic signal; Step 404, comprehensively optimizing the impact of environmental factors on the photoacoustic signal data, encapsulating the processed data into a standard format, encrypting the data, and transmitting the data to the background analysis system using a secure communication protocol.

7. The laser data optimization transmission method for photoacoustic spectroscopy oil and gas detection according to claim 6 is characterized by: The concentration-signal intensity calibration formula is obtained by polynomial regression fitting, and its fitting expression is: C=a0+a1S+a2S 2 +…+a m S m ; A i =(SA T SADDLE)- 1 SADDLE T THAT; Where C is the concentration, S is the signal intensity, a0, a1, ..., a m is the regression coefficient, m is the order of the polynomial, and a i is the regression coefficient, SA is the matrix of signal strength, SA T is the transposed matrix of SA, T is the transpose operation, which means swapping the rows and columns of the matrix, and CA is the corresponding concentration vector.

8. The laser data optimization transmission method for photoacoustic spectroscopy oil and gas detection according to claim 7 is characterized by: The calculation formula for temperature compensation of the photoacoustic signal is: Among them, S c (W) is the signal strength after temperature compensation, S y is the original photoacoustic signal intensity, W is the current ambient temperature, and W r is the reference temperature, which is used for the specific temperature point of calibration, α and β are the temperature-related fitting coefficients, and ΔW is the temperature variation range; The calculation formula for humidity compensation of the photoacoustic signal is: Among them, S c (H) is the signal strength after humidity compensation, H is the current ambient humidity, and H r is the reference humidity, used for calibration of specific humidity points, γ is the humidity-related coefficient, S y is the original photoacoustic signal intensity; The calculation formula for the comprehensive temperature and humidity compensation of the photoacoustic signal is: Among them, S tc is the photoacoustic signal intensity after comprehensive temperature and humidity compensation, S y is the original photoacoustic signal intensity, p is the number of terms in the summation function, h is the index variable in the summation function, ΔW h is the change in the hth temperature interval, ΔH h is the change in the hth humidity interval, α h and γ h is the temperature and humidity influence coefficient corresponding to the hth interval.

9. The laser data optimization transmission method for photoacoustic spectroscopy oil and gas detection according to claim 8, characterized in that: In step 5, the process of fault diagnosis and maintenance plan formulation is as follows: Step 501, the background analysis system receives the encrypted data transmitted by the front end, performs decryption processing, obtains the original photoacoustic signal data, and performs format conversion on the decrypted data; Step 502, using digital signal processing technology to extract the characteristics of the photoacoustic signal, and converting the signal characteristics into the quantitative concentration of the gas, and identifying the type of faulty gas according to the specific absorption spectrum and concentration data of the gas; Step 503, matching the detected gas type and concentration with the known fault mode, and conducting a comprehensive analysis based on the transformer's operating status, historical data, and real-time monitoring data to determine the cause of the fault; Step 504, assessing the severity and urgency of the fault according to the gas concentration level and change rate, and analyzing the specific location of the fault using the gas distribution and change trend, and formulating corresponding maintenance measures based on the fault judgment result; Step 505, evaluate the potential risks and priorities of the maintenance measures, determine the maintenance schedule, schedule resources according to the maintenance plan, notify relevant personnel of the analysis results and maintenance plan, and generate a detailed fault analysis report.

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