TDLAS (Tunable Diode Laser Absorption Spectroscopy)-based regional carbon emission online monitoring system and method

Abstract

The invention provides a TDLAS (tunable diode laser absorption spectroscopy)-based regional carbon emission online monitoring system and method, and the system comprises a laser transmitter which transmits laser with a specific wavelength; the sampling and transmission module is used for introducing the laser into an emission source; the receiving and signal processing module is used for receiving the laser signal penetrating through the emission source and analyzing the laser signal to obtain the gas concentration; the high-precision monitoring module is used for analyzing the gas by adopting a TDLAS (Tunable Diode Laser Absorption Spectroscopy) technology and measuring the concentration of carbon dioxide; and the quick response module monitors the gas concentration in real time and feeds back the gas concentration in real time so as to adjust an emission strategy.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to a regional carbon emission online monitoring system and method based on TDLAS. Background Art

[0002] Carbon emission monitoring is a key link in achieving the goal of carbon neutrality. However, traditional monitoring methods have problems such as low accuracy and poor real-time performance. How to accurately and in real time monitor carbon emissions in a complex industrial environment and detect abnormal emissions in a timely manner has become a technical problem that needs to be solved urgently. Especially in large industrial parks or urban areas, the CO2 concentration is unevenly distributed and fluctuates greatly due to various factors, which brings huge challenges to monitoring. At the same time, it is not enough to just know the current emission data. How to analyze carbon emission trends based on historical data and make predictions is crucial to formulating emission reduction strategies. In addition, carbon emission data covers a wide range and has a large amount of data. How to efficiently process massive data and generate valuable analysis reports is also a thorny problem. Summary of the invention

[0003] The present invention provides a regional carbon emission online monitoring system based on TDLAS, which establishes an accurate, real-time, predictable and easy-to-supervise system in a complex and changeable actual environment, and reports to relevant departments in a timely manner.

[0004] To achieve the above object, the present invention provides the following technical solutions, which mainly include:

[0005] A regional carbon emission online monitoring system based on TDLAS, comprising:

[0006] The laser transmitter emits lasers of a specific wavelength. The sampling and transmission module introduces the laser into the emission source. The receiving and signal processing module receives the laser signal after passing through the emission source and analyzes it to obtain the gas concentration. The high-precision monitoring module uses TDLAS technology to analyze the gas and measure the carbon dioxide concentration. The rapid response module monitors the gas concentration in real time and provides immediate feedback to adjust the emission strategy.

[0007] Furthermore, the TDLAS detection module includes: a passive detection module, which is placed on the inner surface of the gas detection module, including a gas absorption cell and a guiding light path; an active detection module, which is placed on the outer surface of the gas detection module, including a driving demodulation module, an external guiding light path, a mid-infrared tunable semiconductor laser, a mid-infrared photodetector and a microcontroller.

[0008] Furthermore, the passive detection module and the active detection module are isolated from each other by a barium fluoride crystal window with an optical transmittance of 90%.

[0009] Furthermore, the rapid response module adjusts the emission strategy through calculation, wherein the calculation step includes: determining the closing condition of the light reciprocating reflection path according to the ratio of the distance between the two spherical cavity mirrors and the focal length; and making the reciprocating reflected light spots on the cavity mirror uniformly distributed according to the direction of the incident light.

[0010] Furthermore, the passive detection module is independently separated from the drive demodulation module, and the coupling connection between the optical path and the cable is achieved through the module interface.

[0011] Furthermore, the drive demodulation module includes: a laser and its drive and temperature control circuit; a photodetector and its temperature control circuit; and a microcontroller for controlling a stepper motor drive circuit and a communication interface circuit.

[0012] Furthermore, the method of detecting carbon dioxide concentration by the high-precision monitoring module using TDLAS technology includes: determining the output light intensity according to the Lambert-Beer law; calculating the carbon dioxide concentration according to the spectral absorption coefficient, gas pressure, gas component concentration, absorption line intensity and absorption line type; wherein the absorption line intensity is determined by the transition center frequency, the Einstein coefficient of stimulated absorption, the transition low-state energy level, the Boltzmann constant, the photon speed and the total internal partition function corresponding to the temperature.

[0013] Furthermore, the fast response module uses the damped least squares method to extract the intensity and broadening of the characteristic absorption spectral lines of the characteristic gas components, wherein a function expression is established according to the relationship between the intensity of the characteristic absorption spectral lines and the wavelength; the function expression is Taylor expanded and the higher-order terms are omitted; the parameter increment is calculated; the residual sum of squares is compared with the initial residual sum of squares, and the damping factor is adjusted until the iteration ends.

[0014] The present invention also provides a method for online monitoring of regional carbon emissions based on TDLAS, the method comprising:

[0015] Acquire carbon emission gas from the area to be tested, wherein the carbon emission gas includes CO2;

[0016] Acquiring absorption spectrum data of the CO2, wherein the absorption spectrum data includes light intensity information at multiple different wavelengths;

[0017] Get the preset CO2 absorption line parameters;

[0018] According to the CO2 absorption line parameters, the absorption spectrum data is fitted using the damped least squares method to obtain the absorption line intensity and broadening of CO2;

[0019] The concentration value of CO2 is calculated based on the intensity and broadening of the absorption spectrum of CO2;

[0020] If the CO2 concentration exceeds a preset threshold, it is determined that there is abnormal carbon emission in the test area, and an alarm message is triggered.

[0021] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0022] The present invention discloses a carbon emission monitoring method based on TDLAS technology. The method obtains the absorption spectrum data of CO2 in the area to be tested, combines the preset CO2 absorption line parameters, and uses the damped least squares method for fitting to calculate the concentration value of CO2. When the concentration exceeds the preset threshold, it is determined that there is abnormal carbon emission and an alarm is triggered. At the same time, the present invention can also predict future carbon emissions based on the trend of CO2 concentration changes, generate a carbon emission report and send it to the regulatory department. This method can not only monitor carbon emissions in real time, but also detect anomalies in time and predict future trends, providing a scientific basis for formulating carbon emission reduction strategies. The present invention has important application value in the field of carbon emission supervision, helps to improve the accuracy and real-time performance of carbon emission monitoring, and provides strong support for achieving the goal of carbon neutrality. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 A flow chart of a regional carbon emission online monitoring system based on TDLAS of the present invention;

[0024] Figure 2 It is a schematic diagram of the optical cavity simulation and uniform distribution of light spots on the spherical cavity mirror of a regional carbon emission online monitoring system based on TDLAS of the present invention;

[0025] Figure 3 A schematic diagram of an adjustable gas absorption pool scheme of a regional carbon emission online monitoring system based on TDLAS of the present invention;

[0026] Figure 4 It is a gas component characteristic absorption spectrum fitting analysis diagram based on L-M algorithm of a regional carbon emission online monitoring system based on TDLAS of the present invention;

[0027] Figure 5 A schematic diagram of a flow path of a regional carbon emission online monitoring system based on TDLAS of the present invention;

[0028] Figure 6 A distribution diagram of components of a drive demodulation module of a regional carbon emission online monitoring system based on TDLAS of the present invention;

[0029] Figure 7 A CO2 concentration detection flow chart of a regional carbon emission online monitoring system based on TDLAS of the present invention;

[0030] Figure 8 The present invention is a gas exhaust flow chart of a regional carbon emission online monitoring system based on TDLAS. DETAILED DESCRIPTION

[0031] The following will describe the technical solutions in the embodiments of the present invention in detail in conjunction with the accompanying drawings in the embodiments of the present invention. The described embodiments are only part of the embodiments of the present invention.

[0032] like Figure 1 In this embodiment, a regional carbon emission online monitoring system based on TDLAS includes:

[0033] A laser transmitter that emits laser light of a specific wavelength;

[0034] The sampling and transmission module introduces the laser light into the emission source;

[0035] The receiving and signal processing module receives the laser signal after passing through the emission source and analyzes it to obtain the gas concentration;

[0036] High-precision monitoring module uses TDLAS technology to analyze gas and measure carbon dioxide concentration;

[0037] Rapid response module monitors gas concentration in real time and provides immediate feedback to adjust emission strategies.

[0038] Correspondingly, a control method of a regional carbon emission online monitoring system based on TDLAS may specifically include:

[0039] Step S101, obtaining carbon emission gas in a test area, wherein the carbon emission gas includes CO2.

[0040] The sampling and transmission module introduces the laser into the emission source to obtain the carbon emission gas in the area to be tested, and the carbon emission gas includes CO2. According to the obtained carbon emission gas, it is determined whether the gas contains CO2. If it contains CO2, it proceeds to the next step; if it does not contain CO2, it re-acquires the carbon emission gas.

[0041] The high-precision monitoring module uses the TDLAS detection module to analyze the gas containing CO2, and measures the CO2 concentration through laser absorption spectroscopy analysis technology. According to the measured CO2 concentration, the rapid response module monitors the CO2 concentration in real time to determine whether the CO2 concentration exceeds the preset threshold. If the CO2 concentration exceeds the preset threshold, the rapid response module immediately feedbacks the warning information, indicating that the emission strategy needs to be adjusted; if the CO2 concentration does not exceed the preset threshold, the CO2 concentration continues to be monitored in real time. According to the warning information of the rapid response module, the emission strategy that needs to be adjusted is obtained, and the working parameters of the emission equipment are adjusted in time through the control system to reduce the emission concentration of CO2.

[0042] After adjusting the working parameters of the emission equipment, the sampling and transmission module introduces the laser into the emission source again to obtain the adjusted carbon emission gas. The high-precision monitoring module measures the CO2 concentration of the adjusted carbon emission gas again to determine whether the CO2 concentration has dropped below the preset threshold. If the CO2 concentration drops below the preset threshold, the online carbon emission monitoring is completed; if the CO2 concentration is still higher than the preset threshold, return to step 6 and continue to adjust the working parameters of the emission equipment until the CO2 concentration meets the requirements.

[0043] Specifically, the sampling and transmission module uses laser to measure the absorption spectrum of CO2 at a wavelength of 1.57μm, and introduces the laser into the emission source to obtain carbon emission gas samples. The system determines whether the CO2 concentration in the gas exceeds the threshold of 400ppm. If it exceeds, it will enter the next step of analysis, otherwise it will resample. The high-precision monitoring module adopts TDLAS technology and uses the Beer-Lambert law to calculate the CO2 concentration by measuring the peak intensity of the CO2 absorption spectrum with an accuracy of up to 1ppm. The rapid response module monitors the change of CO2 concentration in real time. When the concentration exceeds 450ppm for 5 consecutive minutes, it will issue an early warning, indicating that the emission strategy needs to be adjusted. According to the early warning information, the control system increases the air-fuel ratio of the combustion equipment from 1.2 to 1.5 and reduces the fuel injection volume by 3% to reduce CO2 emissions. After adjustment, sampling and analysis are performed again. If the CO2 concentration drops below 400ppm, the monitoring is completed. Otherwise, the control system will further optimize the combustion parameters, such as increasing the combustion temperature by 50℃, extending the gas residence time by 2 seconds, etc., until the CO2 concentration stabilizes within the standard range. The entire process runs automatically without the need for human intervention, ensuring the real-time and accuracy of carbon emissions monitoring.

[0044] Step S102, using TDLAS technology to obtain absorption spectrum data of the CO2, wherein the absorption spectrum data includes light intensity information at multiple different wavelengths.

[0045] The laser of a specific wavelength is emitted, and the laser wavelength matches the absorption spectrum of the CO2 gas molecule to be measured. The emitted laser is introduced into the flue gas in the emission source to ensure that the laser can pass through the flue gas and fully interact with the CO2 gas molecules. After the laser passes through the flue gas, the CO2 gas molecules selectively absorb the laser of a specific wavelength, and the lasers of different wavelengths are absorbed to different degrees. The laser signal after passing through the flue gas is received, and the light intensity information at multiple different wavelengths is obtained to form the absorption spectrum data of the CO2 gas. According to the Lambert-Beer law, the CO2 gas concentration is logarithmically related to the intensity of the absorbed light, and the CO2 concentration is calculated by fitting the absorption light intensity of multiple different wavelengths. The TDLAS detection module is used to analyze and process the obtained CO2 absorption spectrum data to eliminate the interference of environmental factors and background gases and improve the measurement accuracy. By performing multiple regression analysis on the absorption spectrum data of different wavelengths, the quantitative relationship between the CO2 concentration and the absorption spectrum is established to obtain the accurate CO2 concentration value. The changes in the CO2 concentration in the emission source are monitored in real time, and instant feedback is obtained through rapid calculation, providing a basis for timely adjustment of emission control strategies. The measured CO2 concentration data is transmitted to the monitoring center to form a regional carbon emission database, providing data support for regional carbon emission management and carbon trading.

[0046] Specifically, the laser transmitter emits a laser with a wavelength of 1.57μm, which matches the absorption spectrum of CO2 molecules. After being collimated, the laser enters a 10-meter-long multi-pass absorption cell through optical fiber coupling to fully interact with CO2 in the exhaust gas. The laser signal received at the output end of the absorption cell contains light intensity data of 20 different wavelengths in the range of 1.56-1.58μm, forming the absorption spectrum of CO2. According to the Lambert-Beer law, the light intensity at the CO2 absorption peak is used as the independent variable, and the natural logarithm of the CO2 concentration is used as the dependent variable. The quantitative relationship between concentration and absorption light intensity is fitted by the least squares method. The TDLAS detection module uses wavelength modulation spectroscopy technology to quickly modulate the laser wavelength and synchronously demodulate the absorption signal, eliminating background light interference and increasing the minimum detectable concentration to 10-6mol / L. By performing partial least squares regression analysis on the absorption spectrum data of 20 wavelengths, a multivariate linear regression model of CO2 concentration and absorption spectrum was established, achieving high-precision measurement of CO2 concentration with a relative error of less than 1%. The system updates CO2 concentration data once a second and transmits it to the emission control system via industrial Ethernet, achieving real-time monitoring and optimized control of the emission process. At the same time, data from multiple monitoring points distributed in the region are aggregated to the cloud server. After big data analysis, a temporal and spatial distribution database of carbon emissions covering the entire region is formed, providing data support for regional carbon emission accounting and management decisions.

[0047] Step S103, obtaining preset CO2 absorption line parameters, wherein the CO2 absorption line parameters include line intensity, line width, center frequency, etc.

[0048] According to the expression of the relationship between the intensity and wavelength of the characteristic absorption line of the gas component to be measured, the parameters of the CO2 absorption line are determined, including the peak intensity of the characteristic absorption line and the common background intensity of the two overlapping characteristic lines. The damped least squares method (L-M algorithm) is used to quickly and real-time analyze the characteristic absorption line of CO2 to obtain the intensity and broadening parameters of the characteristic absorption line of CO2. In the range near the central wavelength of each CO2 characteristic line, the two points with the largest experimental values ​​are selected, and the initial peak intensity is calculated by calculating their average value, and the initial line intensity value of the CO2 absorption line is calculated from this. The average value of the wavelength corresponding to the maximum experimental value is obtained and set as the initial central frequency of the CO2 absorption line. The initial value of the half-height full width of the CO2 characteristic line is set to 0.1 as the estimated value of the initial line width of the CO2 absorption line. At the background at both ends of the CO2 characteristic line range, the average value of the four smallest points is selected and set as the initial background intensity value of the CO2 absorption line. Through Taylor expansion and ignoring high-order terms, the matrix expression of the relationship between the intensity and wavelength of the CO2 absorption line is established, and the damping factor is introduced to simplify the calculation. Iteratively calculate the incremental matrix of CO2 absorption line parameters, dynamically adjust the damping factor, reduce the requirements of the iteration process on the initial value, and ensure rapid convergence of the iteration. Continuously update the CO2 absorption line parameters until the set convergence conditions are met, and finally obtain accurate CO2 absorption line parameters, including line strength, line width, center frequency, etc.

[0049] Specifically, according to the relationship between the intensity of the characteristic absorption line of the CO2 gas component to be measured and the wavelength, I(λ)=I_0+A_1e^(-(λ-λ_01)^2 / (2σ_1^2))+A_2e^(-(λ-λ_02)^2 / (2σ_2^2)), where A_1, A_2 and σ_1, σ_2 are the peak intensities and line widths of the two characteristic absorption lines, respectively, and λ_01, λ_02 are the center frequencies of the two lines. , I_0 is the background intensity. The damped least squares method is used to fit the expression. In the range of ±0.1nm around the central wavelengths of the CO2 characteristic spectrum of 1572.335nm and 1572.02nm, the intensity values ​​of 0.632 and 0.589 at the two points with the largest experimental values ​​at 1572.331nm and 1572.025nm are selected. The average value of 0.6105 is calculated as the initial peak intensity, and the initial line intensity is estimated to be 3.42×10^(-21)cm^(-1) / (mol·cm^(-2)). The average wavelength of the two maximum values, 1572.178nm, is set as the initial center frequency, and the initial line width is estimated to be 0.1nm. The average intensity of the four points of 1572.08nm, 1572.12nm, 1572.58nm, and 1572.62nm is selected at the backgrounds at both ends of 1572.1nm and 1572.6nm. .021 is used as the initial background intensity, and the damping factor is introduced through Taylor expansion. The incremental matrix of CO2 absorption line parameters is iteratively calculated, and the damping factor is dynamically adjusted. After 8 iterations, the convergence condition is met. Finally, the line intensity of the CO2 absorption line is 3.58×10^(-21)cm^(-1) / (mol·cm^(-2)), the line width is 0.097nm, and the center frequencies are 1572.332nm and 1572.023nm.

[0050] Step S104, fitting the absorption spectrum data using the damped least square method according to the CO2 absorption line parameters to obtain the absorption line intensity and broadening of CO2.

[0051] Obtain the absorption spectrum data of the gas component CO2 to be measured, and record the wavelength and corresponding absorption intensity of each data point. According to the absorption spectrum data, determine the two points with the largest experimental values ​​in the range near the central wavelength of the characteristic absorption line, calculate their average value, and obtain the initial peak intensity. Set the initial characteristic line half-width to 0.1, and set the average value of the four points with the smallest background at both ends of the characteristic line range as the initial background intensity value. Construct the relationship expression between the intensity and wavelength of the characteristic absorption line, which contains the parameters of the characteristic absorption line: peak intensity, central wavelength, half-width and background intensity. Taylor expand the relationship expression for the parameters, ignore the high-order terms, and obtain a linear equation system about the parameter increment vector. Construct a matrix of the relationship between the line intensity and the wavelength, including the partial differential matrix, the fitting value matrix before and after the parameter change, and construct the parameter increment matrix based on the experimental value matrix and the damping factor. Dynamically adjust the damping factor through the damped least squares method (L-M algorithm), iteratively solve the parameter increment matrix, and update the parameters of the characteristic absorption line. Determine whether the parameter increment converges during the iteration process. If so, stop the iteration; if not, continue to adjust the damping factor and perform the next iteration. Finally, determine the peak intensity and broadening parameters of the CO2 absorption spectrum, and output the absorption spectrum intensity and broadening results of CO2.

[0052] Specifically, the absorption spectrum data of the gas component CO2 to be measured is obtained, and the wavelength and corresponding absorption intensity of each data point are recorded. For example, 50 data points are selected near 1.57μm, and the absorption intensity range is 0.1-0.9. According to the absorption spectrum data, the two points with the largest experimental values ​​in the range near the central wavelength of 1.573μm of the characteristic absorption spectrum are determined, such as 0.85 and 0.87, and their average value 0.86 is calculated to obtain the initial peak intensity. The initial characteristic spectrum line half-height full width is set to 0.1nm, and the average value of the four smallest points at the background at both ends of the characteristic spectrum line is 0.12 as the initial background intensity value. The relationship expression between the intensity of the characteristic absorption spectrum and the wavelength is constructed, which contains the parameters of the characteristic absorption spectrum: peak intensity 0.86, central wavelength 1.573μm, half-height full width 0.1nm and background intensity 0.12. Taylor expansion of the relationship expression for the parameters is performed, and the second-order and higher terms are omitted to obtain a linear equation system about the parameter increment vector. A 50×4 matrix of the relationship between the intensity and wavelength of the spectrum line was constructed, including a 4×4 partial differential matrix, a 50×1 matrix of fitting values ​​before and after the parameter change, and a 4×1 matrix of parameter increments was constructed based on the 50×1 matrix of experimental values ​​and the damping factor λ. The damping factor λ was dynamically adjusted by the damped least squares method (L-M algorithm), and the parameter increment matrix was iteratively solved to update the parameters of the characteristic absorption spectrum line. During the iteration process, it was determined whether the infinite norm of the parameter increment was less than the set convergence threshold of 0.001. If so, the iteration was stopped; if so, λ was multiplied by 10 and the next iteration was performed until convergence. Finally, the peak intensity of the CO2 absorption spectrum line was determined to be 0.882, and the broadening parameter was 0.095nm. The absorption spectrum line intensity and broadening results of CO2 were output.

[0053] Step S105, calculating the concentration value of CO2 according to the intensity and broadening of the absorption spectrum of CO2.

[0054] Obtain the characteristic absorption spectral line data of the gas component CO2 to be measured, and record the intensity and wavelength information of the spectral line. According to the light transmission theory under the paraxial approximation of the resonant cavity, by adjusting the spacing between the two spherical cavity mirrors and the focal length ratio, ensure that the path of the light reciprocating reflection in the cavity is closed, so that the light spots are evenly distributed on a certain circumference. By rotating the right cavity mirror, the exit light hole coincides with different light spots, and different transmission distances of the reflected light in the optical cavity are achieved, so as to obtain the spectral line data under different absorption optical paths. According to the Lambert-Beer law, the spectral line data under different absorption optical paths correspond to different gas component detection limits and ranges. The damped least squares method (L-M algorithm) is used to process the acquired spectral line data. Initialize the parameter vector, including the peak intensity, central wavelength, half-maximum full width and background intensity of the characteristic absorption spectral line. The initial value is to select two points with the largest experimental values ​​in the range near the central wavelength of each characteristic spectrum line, calculate their average value to obtain the initial peak intensity, set the average value of the wavelength corresponding to the maximum experimental value as the initial wavelength, set the initial full width at half maximum as 0.1, and set the average value of the four points with the smallest background at both ends of the characteristic spectrum line range as the initial background intensity. The relationship between the intensity of the characteristic absorption spectrum line and the wavelength is expressed as a function expression, and the parameters are Taylor expanded, and the high-order terms are omitted to obtain a simplified matrix form. Construct the partial differential matrix of the characteristic parameters of the spectrum line and the fitting value matrix of each data point before and after the parameter change. Calculate the parameter increment matrix and use the damping factor to dynamically adjust the iteration step size. Multiply the difference between the experimental value matrix of the spectrum line intensity and the fitting value matrix by the transpose of the partial differential matrix, and then multiply it by the damping factor to obtain the parameter increment matrix. Update the parameter vector, add the parameter increment matrix to the initial parameter vector, and obtain a new parameter vector. Repeat the iterative process until the parameter increment is less than the preset threshold, and the iteration converges. According to the parameter vector after the iteration converges, determine the peak intensity and broadening of the CO2 characteristic absorption spectrum line. The peak intensity and broadening correspond to the maximum absorption value and width of the spectrum line, respectively. Combined with the ambient temperature information, the acquired spectrum line intensity and broadening are substituted into the model using the pre-established binary function relationship model to calculate the CO2 concentration value. The binary function model is based on the proportional relationship between the absorption line intensity and broadening and the ambient temperature, as well as the functional relationship between the absorption line intensity and temperature. Output the CO2 concentration calculation results, and store and record the data for subsequent analysis and application. Through the real-time monitoring module, the spectrum line data is continuously updated, and the above steps are repeated to achieve dynamic monitoring of CO2 concentration.

[0055] Specifically, when obtaining the characteristic absorption spectrum data of CO2, the distance between the two spherical cavity mirrors is set to 50 mm and the focal length is set to 25 mm to ensure that the reciprocating reflection path of the light in the cavity is closed and the light spots are evenly distributed on a circle with a diameter of 10 mm. By rotating the right cavity mirror, the exit light hole is overlapped with the light spots on the circumference in turn, and the spectrum data at each exit is recorded, such as the first exit optical path is 100 mm, the second is 150 mm, and so on. According to the Lambert-Beer law, the absorption coefficient under different optical paths is calculated, and the detection lower limit is determined to be 0.1 ppm, and the range is 0-1000 ppm. The damped least squares method is used to process the spectrum data, initialize the parameter vector, assume that the central wavelength is 1570 nanometers, select the two points with the largest experimental values ​​near the wavelength, and the intensities are 0.8 and 0.9 respectively, and the initial peak intensity is calculated to be 0.85. The initial half-height full width is set to 0.1 nanometers, and the background intensity is taken as the average value of the minimum four points at both ends of the spectrum line, which is 0.05. The relationship between the spectral line intensity and wavelength is expressed as a function, and the parameters are Taylor expanded to construct a partial differential matrix, such as matrix elements including partial derivatives of peak intensity, central wavelength, half-maximum full width and background intensity. When calculating the parameter increment matrix, the damping factor is set to 0.01, and the iterative formula Δθ=(J^TJ+λI)^(-1)J^TΔy is used, where J is the partial differential matrix, Δy is the difference matrix between the experimental value and the fitting value, λ is the damping factor, and I is the unit matrix. Iteratively update the parameters, and if Δθ is less than 0.001, it is determined to be convergent. According to the converged parameters, the peak intensity of the CO2 spectral line is determined to be 0.87 and the broadening is 0.12 nanometers. Combined with the ambient temperature of 300K, the binary function model C=f(I, σ, T) is substituted, where I is the peak intensity, σ is the broadening, and T is the temperature, and the CO2 concentration is calculated to be 450ppm. The real-time monitoring module updates the spectral line data every second, repeats the above steps, dynamically monitors the changes in CO2 concentration, and stores the data in the database for subsequent analysis.

[0056] Step S106: If the CO2 concentration exceeds a preset threshold, it is determined that there is abnormal carbon emission in the test area, and an alarm message is triggered.

[0057] The laser transmitter starts to emit a laser of a specific wavelength, which matches the absorption spectrum of the gas molecule CO2 to be measured. The sampling and transmission module introduces the emitted laser into the emission source of the area to be measured to ensure that the laser can pass through the flue gas in the emission source. The receiving and signal processing module receives the laser signal after passing through the flue gas, analyzes the signal through a preset algorithm, and obtains the gas concentration data. The high-precision monitoring module uses the TDLAS detection module to analyze the analyzed gas concentration data and measure the concentration value of CO2. The rapid response module monitors the CO2 concentration value in real time and transmits the real-time data to the data processing unit. The data processing unit compares the CO2 concentration value monitored in real time according to the preset threshold. If the CO2 concentration value exceeds the preset threshold, the abnormal carbon emission judgment process is triggered. The abnormal carbon emission judgment process is started, and the system further analyzes the duration and change trend of the abnormal data to eliminate accidental errors. If the abnormal data continues to exceed the preset time threshold and the change trend conforms to the abnormal emission characteristics, the system determines that there is abnormal carbon emission in the area to be measured. The system generates an alarm message and sends the alarm message to the monitoring center or relevant management department through the communication module so that timely response measures can be taken.

[0058] Specifically, the laser transmitter is started to emit a laser with a wavelength of 1572 nanometers, which accurately matches the absorption peak of CO2 molecules in the infrared spectrum. The sampling and transmission module guides the laser to the emission source through optical fiber to ensure that the laser passes through the flue gas containing CO2. The optical path length is set to 10 meters to ensure sufficient absorption. The receiving and signal processing module uses a high-sensitivity photodetector to receive the laser signal that passes through the flue gas, and analyzes the signal through the Fourier transform algorithm to obtain gas concentration data with an analysis accuracy of 0.1ppm. The TDLAS detection module in the high-precision monitoring module uses a combination of a passive detection module and an active detection module.

[0059] The passive detection module has a built-in gas absorption cell, and the optical path guidance error is less than 1 mm;

[0060] The active detection module contains a mid-infrared tunable semiconductor laser and a microcontroller. The laser tuning range covers 1570-1575 nanometers, and the microcontroller operates at a frequency of 1GHz to ensure data processing speed.

[0061] The rapid response module monitors the CO2 concentration in real time, with a data update frequency of 10 times per second, and real-time data is transmitted to the data processing unit.

[0062] The data processing unit presets the CO2 concentration threshold at 500ppm. The real-time data is compared with the threshold. If the threshold is exceeded three times in a row, the abnormal carbon emission judgment process is triggered.

[0063] The abnormal carbon emission judgment process further analyzes the data and determines abnormal emissions if the duration exceeds 30 seconds and the concentration increase rate exceeds 5ppm / second.

[0064] The system generates alarm information and sends it to the monitoring center through the GPRS communication module. The information includes time, location, concentration value, etc. to ensure timely response from the management department.

[0065] Among them, it is necessary to add that:

[0066] The active detection module is placed on the outer surface of the simulated engine rear head, and is mainly composed of a drive demodulation module, an external guide optical path, a mid-infrared tunable semiconductor laser, a mid-infrared photodetector, a microcontroller, and corresponding drive and demodulation circuit devices;

[0067] The passive detection module and the active detection module are isolated by a barium fluoride (BaF2) crystal window with an optical transmittance of 90% in the spectral range of 0.2 to 9.5 microns and a thickness of 10 mm to ensure the airtightness of the gas detection module and meet the explosion-proof requirements during the experiment.

[0068] Furthermore, the calculation steps of adjusting the emission strategy of the quick response module are:

[0069] When two identical concave spherical mirrors are placed coaxially facing each other at a certain interval, the light beam can be reflected back and forth multiple times on the surfaces of the two cavity mirrors, thereby achieving a longer absorption optical path in a smaller volume of the optical cavity;

[0070] For each reciprocating reflection, the light spot on a cavity mirror will rotate with the center of the cavity mirror as the origin, where the angle satisfies:

[0071]

[0072] Where d is the distance between the two spherical cavity mirrors, and f is the focal length of the spherical mirror. According to the light transmission theory under the paraxial approximation of the resonant cavity, when the ratio between the distance between the two spherical cavity mirrors and the spherical focal length is reasonably set, the path closure condition of the reciprocating reflection of the light can be met:

[0073] 2nθ=2mπ

[0074] Where n is a positive integer. At this time, the direction of the incident light is further reasonably set to achieve that the light spots reciprocatingly reflected on the cavity mirror are evenly distributed on a certain circle.

[0075] like Figure 2 As shown, based on the above theory, a gas absorption cell is formed by two spherical reflectors placed at a certain distance and facing each other;

[0076] The incident light beam enters through the fixed incident light hole on the left cavity mirror of the optical cavity, and after multiple reflections, it exits through the exit light hole on the right cavity mirror (exit point); Figure 2 The “black dots” distributed on the edge of the cavity mirror on the middle right represent the incident points of the multiply reflected beams on it;

[0077] When the right cavity mirror rotates in a coaxial manner with the left cavity mirror, the exit light hole on it will coincide with different incident points of the multi-reflected light beam on it, and the multi-reflected light beam will emerge from the absorption cell at the corresponding incident points;

[0078] Since different incident points of the reflected light beam on the right cavity mirror determine the absorption optical path of the reflected light beam when it passes through this point, as the right cavity mirror rotates, the entire absorption optical path experienced by the light beam when it is emitted from different incident points will be different;

[0079] According to the Lambert-Beer law, the detection limit and range of the device for gas components will be different under different absorption optical path lengths. Therefore, the rotation of the right cavity mirror can effectively adjust the absorption optical path length of the multi-reflected light beam.

[0080] The right laparoscope is rotated by the adjusting screw through the transmission shaft;

[0081] Since the reflector M3 is fixed on the transmission shaft through the connecting frame, and the outgoing light is parallel to the optical axis of the absorption cell after being refracted by the reflector M3, as the cavity mirror rotates, all the outgoing light beams will remain parallel to the optical axis after passing through the reflector M3;

[0082] A hole is punched in the middle of the reflector M4, and the transmission shaft passes through the middle of the reflector M4 and is connected to the reflector M3;

[0083] The optical axis parallel light beam is guided out of the in-situ detection module through the reflector M4, the reflector M5, etc. and enters the drive demodulation module in sequence, thereby realizing the adjustment of the absorption optical path of the multi-reflected light beam in the absorption cell.

[0084] It is worth noting that the TDLAS detection module also includes a drive demodulation module, and the passive detection module is independently separated from the drive demodulation module, and the coupling and connection between the optical path and the cable are realized through the module interface;

[0085] The drive demodulation module has built-in ICL laser and its drive and temperature control circuit, photodetector and its temperature control circuit, microcontroller, preamplifier circuit, A / D conversion circuit, stepper motor drive circuit, communication interface circuit and communication interface;

[0086] ICL laser: located at the top, connected through the drive circuit and temperature control circuit;

[0087] Photodetector: located in the middle, connected through the preamplifier circuit and A / D conversion circuit;

[0088] Microcontroller: located at the bottom, controls the stepper motor drive circuit and communication interface circuit;

[0089] Since a mercury cadmium telluride mid-infrared photodetector is used, a photodetector temperature control circuit needs to be set up behind it;

[0090] The photocurrent of the photodetector is amplified by the preamplifier circuit and then sent to the A / D conversion circuit to be converted into a digital signal.

[0091] The stepper motor drive circuit is connected to the stepper motor through a cable and a module interface;

[0092] The drive demodulation module is provided with a communication interface, which can transmit the concentration value of the gas component to be measured in real time;

[0093] The microcontroller (MCU) is connected to each related circuit module to realize the corresponding control, calculation and communication functions.

[0094] Furthermore, since the light spot on the cavity mirror corresponds to the number of reciprocating reflections of the light beam in the optical cavity, a perforation is opened on one end of the cavity mirror and the perforation is made to coincide with different light spots, so that different transmission distances of the reflected light in the optical cavity can be achieved.

[0095] Step S107, predicting the future carbon emission status of the area to be tested according to the change trend of CO2 concentration, and generating a carbon emission report.

[0096] The method for detecting CO2 concentration using the TDLAS detection module is as follows:

[0097] According to the Lambert-Beer law, when a light beam with intensity I0 and frequency v passes through a gas medium with an optical path length of L, if the incident light frequency v resonates with the vibration frequency of the gas molecules, the gas will have an absorption effect on the incident light energy, and the outgoing light intensity I can be expressed as:

[0098] I=I0exp(-α(v,P,T)L)

[0099] Among them, α(v) is the spectral absorption coefficient, which is a function of frequency v, pressure P and temperature T, and can be further expressed as:

[0100]

[0101] Where P is the gas pressure, x i is the concentration of the i-th gas component, S i (T) is the absorption line intensity of the i-th gas component at temperature T, φ i (v) is the absorption line shape of the i-th gas component;

[0102] Absorption line strength S i (T) is a function of temperature T, which can be expressed as follows from statistical physics and Einstein's stimulated radiation theory:

[0103]

[0104] Where v0 is the transition center frequency, b is the Einstein coefficient of stimulated absorption, E is the low-state energy level in the transition, g is the degeneracy of the low-state energy level in the transition, and k B is the Boltzmann constant, c is the photon speed, and Q(T) is the total internal partition function corresponding to temperature T. The HITRAN database provides discrete internal partition function data in the temperature range of 70-3005K. Using the polynomial fitting algorithm, the partition function with continuous distribution along with temperature T can be obtained, which is generally expressed by a third-order polynomial:

[0105] Q(T)=aT 3 +bT 2 +cT+d

[0106] Where a, b, c and d are the fitting coefficients;

[0107] The absorption line type is a function of the photon frequency v. Due to the existence of the broadening mechanism, the absorption line type has a certain broadening.

[0108] Depending on whether the broadening mechanism acts uniformly on all gas molecules, the line broadening mechanism can be divided into uniform broadening and non-uniform broadening;

[0109] The natural broadening caused by the lifetime of gas molecule energy levels and the collision broadening between molecules both contribute to the uniform broadening. However, compared with the collision broadening, the natural broadening is usually negligible, and the absorption line shape after broadening is suitable for the Lorentz line shape description.

[0110] The non-uniform broadening of gas molecules is mainly caused by the Doppler effect, and the absorption line shape after broadening is suitable for the description of Gaussian line shape;

[0111] In gas detection at room temperature and pressure, collision broadening between gas molecules is dominant, so the Lorentz line type is often used to describe it, which is specifically expressed as follows:

[0112]

[0113] Where Δv is the half-height full width of the spectrum, and v0 is the central wavelength of the molecular transition;

[0114] In collision broadening, Δv is proportional to the gas pressure and can be expressed as:

[0115]

[0116] where xi is the concentration of the i-th component in the gas, γ i (T) is the collision broadening full width at half maximum coefficient between the i-th component in the gas and the absorbing gas component;

[0117] The formula shows that at normal temperature and pressure, the broadening of the Lorentzian line spectrum, which is mainly caused by collision broadening, is proportional to the ambient pressure;

[0118] On the other hand, the ideal gas state equation is: PV = nRT

[0119] Where V is the gas volume, T is the ambient temperature, and n is the amount of gas substance. The formula shows that when the gas concentration is a certain value and the gas volume is a constant value, the gas environment pressure is proportional to the ambient temperature;

[0120] Since the simulated engine is a relatively closed environment, combined with the formula, the broadening of the absorption spectrum of the gas component to be measured obtained in this closed environment is proportional to the ambient temperature;

[0121] On the other hand, as described in the formula, the absorption line intensity is a function of temperature. Therefore, the concentration of the gas component to be measured and the absorption line intensity and absorption line broadening constitute a binary function. Obtaining the absorption characteristic lines of the characteristic gas component to be measured and analyzing their line intensity and broadening in real time are the keys to achieving accurate gas concentration inversion.

[0122] It is worth mentioning that the fast response module monitors the gas in real time by using the damped least squares method (L-M algorithm) to achieve fast real-time extraction of the intensity and broadening of the characteristic absorption spectral lines of the characteristic gas components to be measured. The damped least squares method introduces a damping factor for controlling the iteration step on the basis of Gauss-Newton iteration. During the iteration process, the damping factor dynamically adjusts the iteration step, which reduces the requirements of the iteration process on the initial value of the parameters and ensures the rapid convergence of the iteration. It is very suitable for fast real-time analysis of the spectral line. The specific calculation method is:

[0123] The relationship between the intensity and wavelength of the characteristic absorption line of the gas component to be measured can be expressed as:

[0124]

[0125] Where A, ω and λ0 are the parameters of the characteristic absorption line, I peak is the peak intensity of the characteristic absorption line, I0 is the common background intensity of the two overlapping characteristic lines, and the formula is the function expression to be fitted for the characteristic absorption line:

[0126] Taylor expansion of the equation for parameters A, ω, λ0, and I0 and ignoring higher-order terms yields:

[0127]

[0128] Among them, C (0) =(A (0) ,ω (0) ,λ0 (0) ,I0 (0) ) is the given initial parameter vector, and ΔC=(ΔA,Δω,Δλ0,ΔI0) T The parameter increment vector;

[0129] For the fitting of n data points, the matrix of the relationship between spectral line intensity and wavelength can be expressed as:

[0130] I=I (0) +F (0) ΔC

[0131] Where F is the partial differential matrix of I with respect to the characteristic parameters of the spectral line, is the fitted value matrix of each data point before the parameter changes, is the fitting value matrix of each data point after the parameter changes ΔC. The parameter increment matrix ΔC can be expressed as:

[0132] ΔC=(F T F+D) -1 F T (YI (0) )

[0133] Where Y is the experimental value matrix of characteristic spectral line intensity, D is the damping factor, which is a 4th-order diagonal matrix;

[0134] In order to facilitate the value of the damping factor D, the formula can be simplified as:

[0135] ΔC=(F T F+B -1 DB -1 ) -1 F T (YI (0) )

[0136] Let A=F T F, then B is a diagonal matrix;

[0137] That is, to find the expression of parameter increment, the initial values ​​of absorption line characteristic parameters and damping factor are first determined before iteration, and then according to the initial fitting value matrix I (0) Calculate the parameter increment ΔC to obtain the new fitting value matrix I, adjust the damping factor, and calculate the new parameter increment with the new fitting value matrix. Repeat the above process until the iteration converges. At this time, the residual of the spectral line fitting value and the experimental value will be within a reasonable range.

[0138] It is worth emphasizing that the initial value C (0)Select two points with the largest experimental values ​​in the range near the central wavelength of each characteristic spectral line, calculate their average value and obtain the initial peak intensity I peak , thereby calculating the initial value A, the average value of the wavelength corresponding to the maximum experimental value is set as the initial wavelength λ0 of each spectral line, the full width at half maximum of the initial characteristic spectral line ω is 0.1, and the average value of the four smallest points at the background at both ends of the characteristic spectral line range is set as the initial background intensity value I0;

[0139] Step 1: The diagonal elements of the initial damping factor D are 0.001; a sufficiently small constant ε = 0.01 is given;

[0140] Step 2: Calculate the parameter increment ΔC by the formula;

[0141] Step 3: Compare the residual sum of squares χ 2 =(YI) T (YI) and the initial residual sum of squares

[0142] χ0 2 =(YI (0) ) T (YI (0) ) size, if χ 2 Less than χ0 2 , then let C=C (0) +ΔC, if the absolute value of each element in the parameter increment ΔC is less than ε, the iteration ends, otherwise the damping factor D is reduced by 10 times and go to step 2 to continue the iteration;

[0143] Step 4: If χ 2 Greater than χ0 2 , then the damping factor D is enlarged by 10 times and go to step 1) to continue iterating;

[0144] According to the above steps, until the iteration is finished, the fitting or separation extraction of the characteristic spectrum line can be completed;

[0145] The above algorithm is used to analyze the original characteristic absorption spectrum of the gas component to be measured represented by 13 discrete points, and the correlation coefficient obtained by fitting can reach more than 0.99;

[0146] like Figure 4 As shown, preliminary experiments show that the L-M algorithm has a small time overhead and high fitting correlation, and can achieve accurate real-time analysis of the characteristic absorption spectra of the characteristic gas components to be measured;

[0147] Based on the absorption line intensity and absorption line broadening of the gas component to be measured obtained by the above-mentioned spectral line fitting analysis, considering that the absorption line of the gas component is approximately linearly related to the gas component concentration and the ambient temperature within a certain range, a multivariate linear regression algorithm is used to construct a linear model for the concentration, line intensity and line broadening of the gas component to be measured. After the detection device is calibrated with concentration and temperature, it is trained to obtain accurate corrections for the inversion of gas components at different ambient temperatures, and accurately compensates for the concentration calculation drift introduced by the change of ambient temperature.

[0148] During the operation of the device, the original absorption spectrum data of the gas components to be tested in the aging container is obtained, and ultimately high-precision real-time detection of the concentrations of the gas components to be tested under different ambient temperatures is achieved.

[0149] The flow path of this device is mainly composed of a measuring flow path and a backflush flow path. The specific flow path diagram is as follows: Figure 5 as follows:

[0150] After the temperature of the CO2 measuring pool reaches 105°C, the high-temperature sampling pump will start. Under the action of the sampling pump, the measured gas enters the measuring gas chamber through the probe rod, probe, pneumatic valve, and filter. The analyzer uses TDLAS technology to analyze the gas, measure the concentration of CO2, and finally empties it. The device will enter the backflush state at regular intervals to backflush the sampling probe. At this time, the solenoid valve switches to the backflush gas path, the backflush solenoid valve opens, and the device automatically controls the backflush solenoid valve to open or close in a pulsed manner to achieve the purge of the probe gas path and the filter element;

[0151] Flowchart Description:

[0152] 1) Compressed air supply: The process starts with the supply of compressed air, which is the power source of the entire measurement process;

[0153] 2) V1 valve control: controls the flow and switch of compressed air to ensure that the gas enters the system in the required amount;

[0154] 3) Filter: Remove impurities and particles from compressed air to ensure the purity of the gas entering the measurement system.

[0155] 4) Sampling pump: pumps the filtered gas into the CO2 measurement cell;

[0156] 5) CO2 measuring cell: Here, the gas is measured to determine the concentration of CO2;

[0157] 6) Empty 2: After the measurement is completed, the gas is discharged from the system through empty 2;

[0158] Flowchart Description:

[0159] 1) Compressed air supply: The backflush process starts with the supply of compressed air, which is the power source of the entire backflush process;

[0160] 2) V2 valve control: controls the flow and switch of compressed air to ensure that the gas enters the backflush process in the required amount;

[0161] 3) Backflush: Compressed air enters the system through the V2 valve to remove residual gas in the measuring cell to prepare for the next measurement or maintain the system;

[0162] 4) Exhaust 1: The gas after backflushing is discharged from the system through exhaust 1.

[0163] It should be noted that although TDLAS is the core, in some cases, FTIR (Fourier transform infrared spectroscopy) or NDIR (non-dispersive infrared) technology can also be considered as an alternative. These technologies are also based on the principle of spectral absorption, but the implementation methods and applicable conditions are different. For example, FTIR can monitor multiple gases at the same time, while NDIR may be more suitable for low-cost, large-scale monitoring needs. However, they may not be as good as TDLAS technology in terms of accuracy, response speed and environmental adaptability.

[0164] The CO2 concentration data measured by the TDLAS detection module is obtained, and the data is transmitted to the signal processing module for analysis and processing. The CO2 concentration data is preprocessed by filtering and denoising through the signal processing module to obtain the optimized CO2 concentration data sequence. According to the optimized CO2 concentration data sequence, the CO2 concentration change trend prediction model is established by using the time series analysis method. If the goodness of fit of the prediction model reaches the set threshold, the model is applied to the prediction of future CO2 concentration; otherwise, the model parameters are adjusted and the model is retrained. Based on the established CO2 concentration change trend prediction model, the CO2 concentration change in the area to be tested in the future is predicted. According to the predicted CO2 concentration change, combined with the area of ​​the area to be tested, the total carbon emissions in the future are estimated. The estimated total carbon emissions in the future are compared and analyzed with the current actual emissions to determine whether the emission trend is in line with expectations. The CO2 concentration prediction results and carbon emission estimation results are combined to generate a carbon emission report for the area to be tested, including current emission status, future emission forecasts, emission trend analysis, etc. The generated carbon emission report is fed back in real time through the rapid response module to provide a basis for relevant departments to adjust emission strategies in a timely manner.

[0165] Specifically, the TDLAS detection module measures 10 CO2 concentration data per second to form a time series data stream, which is transmitted to the signal processing module in real time through a high-speed data transmission channel. The signal processing module uses the Kalman filter algorithm to denoise the CO2 concentration data, effectively eliminating the high-frequency noise interference in the measurement process and obtaining a smooth CO2 concentration curve. Based on the optimized CO2 concentration sequence data, the ARIMA time series model is applied to fit the CO2 concentration change trend by the least squares method. The model's goodness of fit R reaches more than 0.95, meeting the prediction accuracy requirements. The model is used to make a rolling prediction of the CO2 concentration in the next 24 hours to obtain the hourly concentration prediction value. According to the parameters that the area of ​​the measured area is 1 square kilometer and the distribution density of emission sources is 2 per square kilometer, the total carbon emissions in the next 24 hours are estimated to be 12 tons. Comparing the predicted carbon emissions with the current actual emissions of 10 tons, it is found that the emissions show a gradual upward trend and are expected to exceed the daily emission threshold. The comprehensive analysis results form a carbon emission report, which is sent to the environmental protection department in real time through the quick response module in the form of push notifications, providing a decision-making basis for them to take emission restriction measures in a timely manner.

[0166] Step S108, sending the carbon emission report to the regulatory department, so as to monitor the carbon emission situation of the area to be tested in real time and formulate carbon emission reduction strategies in time.

[0167] Collect carbon emission data of the area to be tested, analyze the gas through the TDLAS detection module, measure the concentration of CO2, and obtain real-time carbon emission concentration data. Preprocess the obtained carbon emission concentration data, and ensure the accuracy and consistency of the data through data cleaning and standardization operations to obtain standardized carbon emission concentration data. According to the standardized carbon emission concentration data, combined with the area, meteorological conditions and other parameters of the area to be tested, calculate the total carbon emissions of the area and generate a carbon emission report. Compare the generated carbon emission report with the preset carbon emission threshold to determine whether the current carbon emissions exceed the standard. If the standard is exceeded, the early warning mechanism is triggered and a carbon emission early warning signal is issued. According to the carbon emission early warning signal, a carbon emission exceeding standard report is automatically generated, detailing the time, location, value and possible reasons of the exceeding standard, forming a complete carbon emission exceeding standard analysis report. The carbon emission report and the carbon emission exceeding standard analysis report are packaged in accordance with the standard format, and the security and transmission efficiency of the report data are ensured through data encryption and compression processing to generate a carbon emission report data package. Through the network communication module, the carbon emission report data packet is sent to the data center of the regulatory department in real time, uploaded to the designated data receiving port, and the real-time reporting of carbon emission data is completed. After receiving the carbon emission report data packet, the data center of the regulatory department automatically decompresses and decrypts it, analyzes and stores the data, updates the carbon emission monitoring database, and realizes real-time control of regional carbon emissions. Based on the updated carbon emission monitoring database, combined with regional environmental capacity, emission reduction targets and other factors, the data center automatically generates carbon emission reduction strategy recommendations, provides data support for the regulatory department's carbon emission reduction decision-making, and assists in the formulation of targeted carbon emission reduction measures.

[0168] Specifically, the TDLAS detection module monitors the CO2 concentration in the test area in real time, collects 10 data points per second, and obtains high-precision concentration values ​​at the ppb level. The collected concentration data is Kalman filtered to eliminate outliers, and then the concentration value is mapped to between 0 and 1 through linear normalization to obtain a standardized concentration data sequence. According to the parameters such as the area of ​​the test area of ​​10 square kilometers, the temperature of 25 degrees Celsius, and the air pressure of 1 standard atmosphere, the mass conservation equation is used to calculate the total carbon emissions of the area per hour to be 1,000 tons. The calculated total carbon emissions are compared with the preset threshold of 800 tons / hour, and it is determined that the current carbon emissions exceed the standard by 25%, triggering the early warning mechanism. The system automatically generates a carbon emission excess report, which lists in detail the start time, duration, maximum excess value, and possible causes of the excess, such as a sudden increase in production load in the industrial park leading to a surge in emissions. The carbon emission report and the excess analysis report are packaged and encrypted using the AES-256 algorithm, with a compression ratio set to 50% to ensure data security and transmission efficiency. The data packet is sent to the data center of the regulatory department through the TCP / IP protocol and uploaded to the designated port 8888. After receiving the data packet, the data center decompresses and decrypts it, stores the data in the MongoDB database, and identifies carbon emission hotspots and key emission sources through cluster analysis and association rule mining. Finally, the data center combines the regional environmental capacity model and carbon emission prediction algorithm to give carbon emission reduction strategy recommendations for the next week, such as optimizing the industrial structure, improving energy efficiency, and strengthening carbon sink construction, to provide support for the realization of regional carbon peak and carbon neutrality goals.

[0169] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the concept of the present application. For example, the above features are replaced with the technical features with similar functions disclosed in the present application (but not limited to) to form a technical solution.

Claims

1. A regional carbon emission online monitoring system based on TDLAS, characterized in that: include: A laser transmitter that emits laser light of a specific wavelength; The sampling and transmission module introduces the laser light into the emission source; The receiving and signal processing module receives the laser signal after passing through the emission source and analyzes it to obtain the gas concentration; High-precision monitoring module uses TDLAS technology to analyze gas and measure carbon dioxide concentration; Rapid response module monitors gas concentration in real time and provides immediate feedback to adjust emission strategies.

2. The regional carbon emission online monitoring system based on TDLAS according to claim 1, characterized in that: The TDLAS detection module comprises: The passive detection module is placed on the inner surface of the gas detection module and includes a gas absorption cell and a guiding light path; The active detection module is placed on the outer surface of the gas detection module, and includes a driving demodulation module, an external guiding optical path, a mid-infrared tunable semiconductor laser, a mid-infrared photodetector and a microcontroller.

3. The regional carbon emission online monitoring system based on TDLAS as claimed in claim 2, characterized in that: The passive detection module is isolated from the active detection module by a barium fluoride crystal window with an optical transmittance of 90%.

4. The regional carbon emission online monitoring system based on TDLAS according to claim 1, characterized in that: The quick response module adjusts the emission strategy by calculation, wherein the calculation step includes: The closing condition of the reciprocating reflection path of the light is determined according to the ratio of the distance between the two spherical cavity mirrors and the focal length; According to the direction of the incident light, the light spots reflected back and forth on the cavity mirror are evenly distributed.

5. The regional carbon emission online monitoring system based on TDLAS as claimed in claim 1, characterized in that: When two identical concave spherical mirrors are placed coaxially facing each other at a certain interval, the light beam can be reflected back and forth multiple times on the surfaces of the two cavity mirrors, thereby achieving a longer absorption optical path in a smaller volume of the optical cavity; For each reciprocating reflection, the light spot on a cavity mirror will rotate with the center of the cavity mirror as the origin, where the angle satisfies: Where d is the distance between the two spherical cavity mirrors, and f is the focal length of the spherical mirror. According to the light transmission theory under the paraxial approximation of the resonant cavity, when the ratio between the distance between the two spherical cavity mirrors and the spherical focal length is reasonably set, the path closure condition of the reciprocating reflection of the light can be met: 2nθ=2mπ Where n is a positive integer. At this time, the direction of the incident light is further reasonably set to achieve that the light spots reflected back and forth on the cavity mirror are evenly distributed on a certain circle, which satisfies the uniform distribution of the light field and ensures that the light can evenly illuminate the gas to be measured.

6. The regional carbon emission online monitoring system based on TDLAS as claimed in claim 2, characterized in that: The passive detection module is independently separated from the drive demodulation module, and the coupling connection between the optical path and the cable is realized through the module interface.

7. The regional carbon emission online monitoring system based on TDLAS as claimed in claim 5, characterized in that: The drive demodulation module comprises: Laser and its driving and temperature control circuits; Photoelectric detector and its temperature control circuit; Microcontroller, used to control stepper motor drive circuit and communication interface circuit.

8. The regional carbon emission online monitoring system based on TDLAS according to claim 1, characterized in that: The method for the high-precision monitoring module to detect the carbon dioxide concentration using TDLAS technology includes: Assuming that the incident light intensity is I0, when a light beam with a frequency of v passes through a gas medium with an optical path of L, if the incident light frequency v resonates with the vibration frequency of the gas molecules, the gas will have an absorption effect on the incident light energy, and the outgoing light intensity I can be expressed as: I=I0exp(-α(v,P,T)L) Among them, α(v) is the spectral absorption coefficient, which is a function of frequency v, pressure P and temperature T, and can be further expressed as: Where P is the gas pressure, x i is the concentration of the i-th gas component, S i (T) is the absorption line intensity of the i-th gas component at temperature T, φ i (v) is the absorption line shape of the i-th gas component; Absorption line strength S i (T) is a function of temperature T, which can be expressed as follows from statistical physics and Einstein's stimulated radiation theory: Where v0 is the transition center frequency, b is the Einstein coefficient of stimulated absorption, E is the low-state energy level in the transition, g is the degeneracy of the low-state energy level in the transition, and k B is the Boltzmann constant, c is the speed of photons, Q(T) is the total internal partition function corresponding to temperature T; Q(T) uses a polynomial fitting algorithm to obtain a partition function that is continuously distributed with temperature T, expressed as a third-order polynomial: Q(T)=aT 3 +bT 2 +cT+d Where a, b, c and d are the fitting coefficients; In gas detection at room temperature and pressure, collision broadening between gas molecules is dominant, so the Lorentz line type is often used to describe it, which is specifically expressed as follows: Where Δv is the half-height full width of the spectrum, v0 is the central wavelength of the molecular transition, and in collision broadening, Δv is proportional to the gas pressure and can be expressed as: where x i is the concentration of the i-th component in the gas, γ i (T) is the collision broadening full width at half maximum coefficient between the i-th component in the gas and the absorbing gas component; The formula shows that at room temperature and pressure, the broadening of the Lorentzian line spectrum caused mainly by collision broadening is proportional to the ambient pressure. On the other hand, the ideal gas state equation is: PV = nRT Where V is the volume of the gas, T is the ambient temperature, and n is the amount of gas substance.

9. The regional carbon emission online monitoring system based on TDLAS according to claim 8, characterized in that: The fast response module uses the damped least square method to extract the intensity and broadening of the characteristic absorption spectral line of the characteristic gas component, wherein a function expression is established according to the relationship between the intensity and wavelength of the characteristic absorption spectral line; Taylor expand the function expression and ignore the higher-order terms; Calculate parameter increments; Compare the residual sum of squares with the initial residual sum of squares and adjust the damping factor until the iteration ends.

10. A regional carbon emission online monitoring method based on TDLAS, applicable to claims 1-8, characterized in that: The method comprises: Acquire carbon emission gas from the area to be tested, wherein the carbon emission gas includes CO2; Acquiring absorption spectrum data of the CO2, wherein the absorption spectrum data includes light intensity information at multiple different wavelengths; Get the preset CO2 absorption line parameters; According to the CO2 absorption line parameters, the absorption spectrum data is fitted using the damped least squares method to obtain the absorption line intensity and broadening of CO2; The concentration value of CO2 is calculated based on the intensity and broadening of the absorption spectrum of CO2; If the CO2 concentration exceeds a preset threshold, it is determined that there is abnormal carbon emission in the test area, and an alarm message is triggered.

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