Dynamic error diffusion-based quantitative vorticity accumulated gas flux estimation method

Abstract

The invention relates to the technical field of gas flux estimation, in particular to a quantitative vorticity accumulated gas flux estimation method based on dynamic error diffusion. The method comprises the following steps: quantifying local error distribution of a turbulence component signal, and judging whether the turbulence component signal meets a first flow direction wind speed calculation requirement or not based on the error distribution; and obtaining a to-be-corrected flow direction wind speed of the to-be-measured gas based on the turbulence component signal, obtaining a correction value of the temperature observation value and a turbulence disturbance correction value of the measurement system, and correcting the to-be-corrected flow direction wind speed of the to-be-measured gas based on the correction value of the temperature observation value and the turbulence disturbance correction value of the measurement system. Obtaining a first flow direction wind speed of the to-be-detected gas; the gas flux is estimated based on the first flow direction wind speed of the to-be-measured gas, system deviation caused by error accumulation in a traditional method can be solved, the VOCs flux measurement precision is improved, stability is still kept under the unsteady atmospheric condition, and the data missing rate is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of gas flux estimation, and particularly relates to a method for estimating the gas flux of quantized vorticity accumulation based on dynamic error diffusion. Background Technique

[0002] Gas flux estimation is a key link in the research of atmospheric science, ecology and environmental science, which helps us understand the process of material and energy exchange between the atmosphere and the earth's surface. The Eddy Covariance Method is one of the most commonly used techniques for estimating gas fluxes at present. It calculates the covariance by measuring the pulsations of high-frequency wind speed and scalars (such as temperature, humidity, gas concentration) to obtain the flux.

[0003] The existing Eddy Covariance Method and the Relaxed Eddy Accumulation (REA) method rely on the steady-state atmosphere assumption, and are susceptible to the influence of turbulent non-steadiness, instrument response delay and error accumulation. Especially in the measurement of low-concentration gases such as VOCs (Volatile Organic Compounds), there is a problem of insufficient measurement accuracy.

[0004] Therefore, there is an urgent need for a method for estimating the gas flux of quantized vorticity accumulation based on dynamic error diffusion to solve the above problems. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for estimating the gas flux of quantized vorticity accumulation based on dynamic error diffusion: to solve the technical problem of low measurement accuracy in the flux measurement of low-concentration gas VOCs by the existing Eddy Covariance Method and the Relaxed Eddy Accumulation (REA) method.

[0006] The method for estimating the gas flux of quantized vorticity accumulation based on dynamic error diffusion includes: Decompose the turbulent pulsation signal by the multi-scale decomposition technology based on high-frequency data, separate the non-turbulent component signal, and obtain the turbulent component signal; Quantify the local error distribution of the turbulent component signal, and judge whether the turbulent component signal meets the first requirement for calculating the flow direction wind speed based on the error distribution; Obtain the corrected flow direction wind speed of the gas to be measured based on the turbulent component signal, and obtain the corrected value of the temperature observation value and the turbulent perturbation correction value of the measurement system. Correct the corrected flow direction wind speed of the gas to be measured based on the corrected value of the temperature observation value and the turbulent perturbation correction value of the measurement system to obtain the first flow direction wind speed of the gas to be measured; Estimate the gas flux based on the first flow direction wind speed of the gas to be measured.

[0007] Furthermore, decomposing the turbulent pulsation signal by the multi-scale decomposition technology based on high-frequency data, separating the non-turbulent component signal, and obtaining the turbulent component signal specifically includes the following processes: Using the harmonic superposition method, a random signal that obeys the power spectrum is generated from the turbulent pulsation signal. Then, through Fourier high-pass filtering, the pulsation signal with a frequency greater than that corresponding to one times the atmospheric surface layer thickness is selected to obtain a preset small-scale pulsation signal, which is denoted as the signal to be separated. After performing a discrete Fourier transform on the signal to be separated, a frequency-domain equation is obtained. The difference in the natural frequencies between the adjacent amplitudes of each candidate frequency is calculated, and the candidate frequencies with equal differences in the natural frequencies between the adjacent amplitudes are retained and reconstructed into a turbulent component signal through an inverse wave transform.

[0008] Furthermore, the process of reconstructing into a turbulent component signal through an inverse wave transform specifically includes the following steps: Set the lowest frequency of the turbulent component signal, select the embedding dimension of the phase space reconstruction matrix, and construct the phase space reconstruction matrix ; Perform a singular value decomposition on the phase space reconstruction matrix ; Reconstruct each order of sub-signals using the anti-diagonal method ; Calculate the reconstruction information index of the sub-signal : Based on the reconstruction information index of the sub-signal perform a descending order arrangement, and select the sub-signals with a reconstruction information index greater than the preset index threshold for reconstruction into a turbulent component signal.

[0009] Furthermore, the process of quantifying the local error distribution of the turbulent component signal specifically includes the following steps: The local error refers to the deviation between the observed value and the true value of the turbulent component signal at a specific time. Statistical indicators such as the mean, variance, and root mean square error are used to quantify the magnitude and distribution characteristics of the local error, and the sum value of the mean, variance, and root mean square error is denoted as the local error characteristic index of the turbulent component signal.

[0010] Furthermore, the process of determining whether the turbulent component signal meets the first-flow velocity calculation requirement based on the error distribution specifically includes the following steps: Number each specific time to obtain the local error characteristic index for each specific time. Establish a rectangular coordinate system with the execution time of each specific time as the X-axis and the local error characteristic index as the Y-axis. Generate an error curve by plotting points. Then, draw perpendicular lines from the two endpoints of the error curve to the X-axis to obtain two perpendicular line segments. A figure is formed by the error curve, the two perpendicular line segments, and the X-axis. Calculate the area of the figure and record the area as the error characteristic value. Determine whether the error characteristic value exceeds a preset error characteristic threshold. If so, determine that the turbulent component signal does not meet the first flow direction wind speed calculation requirement. If not, determine that the turbulent component signal meets the first flow direction wind speed calculation requirement.

[0011] Furthermore, obtaining the to-be-corrected flow direction wind speed of the gas to be measured based on the turbulent component signal specifically includes the following process: Based on the turbulent component signal, use the spectrum analysis method to analyze the energy distribution of the turbulent signal at different frequencies and infer the wind speed direction angle of the gas to be measured. Calculate the dynamic pressure based on the wind speed direction of the gas to be measured and the static pressure of the surrounding environment. Calculate the to-be-corrected flow direction wind speed of the gas to be measured based on the dynamic pressure and the static pressure of the surrounding environment.

[0012] Furthermore, obtaining the correction value of the temperature observation value specifically includes the following process: Calculate the Mach number based on the dynamic pressure and the static pressure of the surrounding environment, and obtain the measured temperature value of the gas to be measured. Substitute the Mach number and the measured temperature value of the gas to be measured into the first correlation formula to calculate the correction value of the temperature observation value.

[0013] Furthermore, obtaining the turbulent perturbation correction value of the measurement system specifically includes: directly measuring the influence of turbulence on the measurement signal by comparing the measurement results with and without turbulent perturbation, and obtaining the turbulent perturbation correction value through multiple measurements and statistical analysis.

[0014] Compared with the existing solutions, the beneficial effects achieved by the present invention: The present invention decomposes the turbulent pulsation signal based on the multi-scale decomposition technology of high-frequency data, separates the non-turbulent component signal, and obtains the turbulent component signal; quantifies the local error distribution of the turbulent component signal, and determines whether the turbulent component signal meets the first flow velocity calculation requirement based on the error distribution; obtains the corrected flow velocity of the gas to be measured based on the turbulent component signal, and obtains the correction value of the temperature observation value and the turbulent disturbance correction value of the measurement system, and corrects the corrected flow velocity of the gas to be measured based on the correction value of the temperature observation value and the turbulent disturbance correction value of the measurement system to obtain the first flow velocity of the gas to be measured; estimates the gas flux based on the first flow velocity of the gas to be measured, which can solve the systematic deviation caused by error accumulation in the traditional method, improve the measurement accuracy of the VOCs flux, maintain stability under unsteady atmospheric conditions, and reduce the data missing rate.

[0015] Furthermore, the present invention can enhance the adaptability to unsteady conditions: it does not rely on the steady-state assumption, is applicable to complex meteorological conditions and underlying surface environments, and reduces data requirements: quantization processing can reduce the demand for high-frequency data sampling and lower the observation cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0017] Figure 1 It is the flowchart of the first method for estimating the gas flux of quantized vorticity accumulation based on dynamic error diffusion in the embodiments of the present invention; Figure 2 It is the flowchart of the second method for estimating the gas flux of quantized vorticity accumulation based on dynamic error diffusion in the embodiments of the present invention; Figure 3 It is the flowchart of the third method for estimating the gas flux of quantized vorticity accumulation based on dynamic error diffusion in the embodiments of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0019] In addition, the described features, structures, or characteristics may be combined in any suitable manner in one or more example embodiments. In the following description, numerous specific details are provided to give a thorough understanding of the example embodiments of the present disclosure. However, those skilled in the art will realize that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, steps, etc. may be adopted. In other cases, well-known structures, methods, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0020] This embodiment provides a method for estimating the gas flux of quantified vorticity accumulation based on dynamic error diffusion. Figure 1 It is the flowchart of the first method for estimating the gas flux of quantified vorticity accumulation based on dynamic error diffusion in the embodiments of the present invention, as Figure 1 shown. The method includes the following steps: Step S101: Decompose the turbulent pulsation signal based on the multi-scale decomposition technology of high-frequency data, separate the non-turbulent component signal, and obtain the turbulent component signal; Step S102: Quantify the local error distribution of the turbulent component signal, and determine whether the turbulent component signal meets the first calculation requirement of the flow direction wind speed based on the error distribution; Step S103: Obtain the corrected flow direction wind speed of the gas to be measured based on the turbulent component signal, obtain the corrected value of the temperature observation value and the turbulent perturbation correction value of the measurement system, and correct the corrected flow direction wind speed of the gas to be measured based on the corrected value of the temperature observation value and the turbulent perturbation correction value of the measurement system to obtain the first flow direction wind speed of the gas to be measured; Step S104: Estimate the gas flux based on the first flow direction wind speed of the gas to be measured.

[0021] In summary, decompose the turbulent pulsation signal based on the multi-scale decomposition technology of high-frequency data, separate the non-turbulent component signal, and obtain the turbulent component signal; quantify the local error distribution of the turbulent component signal, and determine whether the turbulent component signal meets the first calculation requirement of the flow direction wind speed based on the error distribution; obtain the corrected flow direction wind speed of the gas to be measured based on the turbulent component signal, obtain the corrected value of the temperature observation value and the turbulent perturbation correction value of the measurement system, and correct the corrected flow direction wind speed of the gas to be measured based on the corrected value of the temperature observation value and the turbulent perturbation correction value of the measurement system to obtain the first flow direction wind speed of the gas to be measured; estimate the gas flux based on the first flow direction wind speed of the gas to be measured, which can solve the systematic deviation caused by error accumulation in the traditional method, improve the measurement accuracy of the VOCs flux, maintain stability under non-steady atmospheric conditions, and reduce the data missing rate.

[0022] In some embodiments, Figure 2This is the flowchart of the second method for estimating gas flux by quantifying vorticity accumulation based on dynamic error diffusion in the embodiments of the present invention. As shown in Figure 2 the figure, the multi-scale decomposition technology based on high-frequency data is used to decompose the turbulent pulsation signal, separate the non-turbulent component signal, and obtain the turbulent component signal, which specifically includes the following processes: Step S201: Use the harmonic superposition method to generate a random signal that follows the power spectrum from the turbulent pulsation signal, and then perform Fourier high-pass filtering. Select the pulsation signal with a frequency greater than the corresponding pulsation signal of 1 times the atmospheric surface layer thickness to obtain a preset small-scale pulsation signal, and record the preset small-scale pulsation signal as the signal to be separated; Specifically, ; Among them, is the preset small-scale pulsation signal, t represents the time variable, H represents the corresponding high-pass filtering of the generated random signal that satisfies the target power spectrum, and only retains the high-frequency part corresponding to the wavelength less than the atmospheric surface layer thickness , represents the frequency, ; Among them, , , are the dimensional height and the average horizontal velocity at the corresponding height respectively, is the friction velocity, , and are three empirical parameters, is the random phase between different frequency pulsations uniformly distributed in the interval , , is the frequency upper limit, is the frequency lower limit, is the total number of frequencies.

[0023] Step S202: After performing the discrete Fourier transform on the signal to be separated, obtain the frequency domain equation, calculate the difference in the natural frequencies between the adjacent amplitudes of each candidate frequency, and retain the candidate frequencies with equal differences in the natural frequencies between the adjacent amplitudes; Specifically, after performing the discrete Fourier transform on the signal to be separated, the frequency domain equation is obtained: ; Among them, is the phase angle. When the difference in the natural frequencies between the adjacent amplitudes of the candidate frequency is equal, that is, , among which, is the preset frequency threshold, and retain the candidate frequencies with equal differences in the natural frequencies between the adjacent amplitudes.

[0024] Step S203: Reconstruct it into a turbulent component signal through the inverse wave transform.

[0025] Specifically, set the lowest frequency of the turbulent component signal, select the embedding dimension of the phase space reconstruction matrix, and construct the phase space reconstruction matrix ; Perform singular value decomposition on the phase space reconstruction matrix ; Reconstruct each order sub-signal by using the anti-diagonal method ; Calculate the reconstruction information index of the sub-signal : ; where ; is the amplitude corresponding to the sub-signal ; Based on the reconstruction information index of the sub-signal, perform a descending order arrangement, and select the sub-signals with the reconstruction information index greater than the preset index threshold for reconstruction into the turbulent component signal

[0026] In summary, the harmonic superposition method can more accurately simulate the randomness and multi-scale characteristics of turbulence by generating random signals that follow the power spectrum from the turbulent pulsation signal. This method is based on the information of the power spectral density, decomposes the turbulent signal into a series of sine waves with different frequencies and amplitudes, so as to more realistically reflect the physical process of turbulence. By Fourier high-pass filtering and selecting the pulsation signal with a frequency greater than 1 times the atmospheric surface layer thickness, the small-scale pulsation components in the turbulence can be effectively extracted. These small-scale pulsation signals are of great significance for understanding the processes of energy transfer, mixing, and diffusion in turbulence. After performing discrete Fourier transform on the signal to be separated, the frequency domain equation is obtained, and the difference in the natural frequency between the adjacent amplitudes of each candidate frequency is calculated. By retaining the candidate frequencies with equal differences in the natural frequency between the adjacent amplitudes, noise and interference can be further filtered out, and the accuracy of signal processing can be improved. After inverse Fourier transform to reconstruct the turbulent component signal, high-quality and low-noise turbulent signals can be obtained. These signals can be used for further analysis of the statistical characteristics, energy spectrum, and structure function of turbulence, providing strong support for turbulence research and applications

[0027] In some embodiments, quantifying the local error distribution of the turbulent component signal specifically includes the following process The local error refers to the deviation between the observed value and the true value of the turbulent component signal at a specific time. The mean, variance, and root mean square error statistical indicators are used to quantify the magnitude and distribution characteristics of the local error, and the sum value of the mean, variance, and root mean square error is denoted as the local error characteristic index of the turbulent component signal

[0028] In some embodiments Figure 3 ​is the flowchart of the third method for estimating gas flux by quantifying vorticity accumulation based on dynamic error diffusion according to an embodiment of the present invention. As shown in Figure 3 shown, determining whether the turbulent component signal meets the first cross-stream wind speed calculation requirement based on the error distribution specifically includes the following process: Step S301: Number each specific time, obtain the local error characteristic index of each specific time, establish a rectangular coordinate system with the execution time of each specific time as the X-axis and the local error characteristic index as the Y-axis, and generate an error curve by plotting points; Step S302: Draw perpendicular lines from the two endpoints of the error curve to the X-axis to obtain two perpendicular line segments. A figure is formed by the error curve, the two perpendicular line segments, and the X-axis. Calculate the area of the figure and denote the area of the figure as the error characteristic value; Step S303: Determine whether the error characteristic value exceeds a preset error characteristic threshold. If so, determine that the turbulent component signal does not meet the first cross-stream wind speed calculation requirement. If not, determine that the turbulent component signal meets the first cross-stream wind speed calculation requirement.

[0029] In summary, by combining the local error characteristic index of the time series with the execution time, establishing an error curve and calculating its area (error characteristic value), a quantitative evaluation of the error is achieved. This method avoids the limitations of relying only on a single error value or subjective judgment, and provides a more comprehensive and objective error representation.

[0030] Furthermore, obtaining the corrected cross-stream wind speed of the gas to be measured based on the turbulent component signal specifically includes the following process: Based on the turbulent component signal, use the spectrum analysis method to analyze the energy distribution of the turbulent signal at different frequencies, and infer the wind speed direction angle of the gas to be measured; Calculate the dynamic pressure based on the wind speed direction of the gas to be measured and the static pressure of the surrounding environment; ; is the dynamic pressure; ; is the static pressure of the surrounding environment, takes a value of 45°, is the wind speed direction angle; is an empirical constant of the influence of the shape of the turbulent signal probe on turbulence; Calculate the corrected cross-stream wind speed of the gas to be measured based on the dynamic pressure and the static pressure of the surrounding environment: Calculate the velocity pressure: The velocity pressure sd can be calculated by the difference between the measured dynamic pressure and static pressure, that is, sd = P_total - , where P_total is the total pressure (the sum of the dynamic pressure and static pressure), It is the static pressure.

[0031] Apply Bernoulli's equation: Bernoulli's equation describes the relationship among the velocity, pressure, and density of a fluid. By transforming the above equation, the calculation formula for wind speed can be obtained: V = sqrt( ) where ρ is the density of the gas to be measured, and V is the wind speed of the gas to be measured in the flow direction to be corrected.

[0032] Furthermore, obtaining the correction value of the temperature observation value specifically includes the following process: Calculate the Mach number based on the dynamic pressure and the static pressure of the surrounding environment, and obtain the measured temperature value of the gas to be measured. Substitute the Mach number and the measured temperature value of the gas to be measured into the first correlation formula to calculate the correction value of the temperature observation value.

[0033] Calculate the Mach number based on the dynamic pressure and the static pressure of the surrounding environment: ; where Ma is the Mach number, γ is the ratio of the specific heat capacity at constant pressure to the specific heat capacity at constant volume of dry air; The first correlation formula is: ; where T is the measured temperature value of the gas to be measured, cv is the specific heat capacity at constant volume of dry air, Rv is the gas constant of moist air, α is the dimensionless temperature constant coefficient.

[0034] Furthermore, obtaining the turbulence disturbance correction value of the measurement system specifically includes: directly measuring the influence of turbulence on the measurement signal by comparing the measurement results with and without turbulence disturbance, and obtaining the turbulence disturbance correction value through multiple measurements and statistical analysis.

[0035] In some embodiments, estimating the gas flux based on the first flow velocity of the gas to be measured includes: Completing the measurement of the BVOCs flux in the environment based on the first flow velocity through a relaxation eddy accumulation collection system includes the following steps: Step 1: After opening the gas mass flow controller for 15 minutes, start the pump to collect the BVOCs flux in the environment; Step 2: Obtain the wind speed and wind direction based on the wind speed data, and obtain the standard deviation σ corresponding to the wind speed perpendicular to the collector which is set and stored in the relaxation eddy accumulation collection system by the relaxation eddy accumulation collection system; Step 3: When the wind speed is greater than 0.6 and the wind direction is horizontally upward relative to the collector. The sample is collected into the UP sampling tube through the control of the solenoid valve. When the wind speed is greater than 0.6 and the wind direction is horizontally downward relative to the collector. The sample is collected into the down sampling tube through the control of the solenoid valve. When the wind speed is less than 0.6 , the sample is collected into the NeUtral collection tube through the control of the solenoid valve. Among them, the sampling tubes include the UP sampling tube, the down sampling tube, and the NeUtral collection tube; Step Four, after all sampling is completed, switch to the air outlet and do not collect any samples; Step Five, calculate the BVOCs flux of the samples in the collection tubes.

[0036] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center in a wired (such as infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that includes one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0037] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. A professional technician can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0038] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be repeated here.

[0039] In several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only for some logical function divisions. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0040] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0041] As mentioned above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for estimating gas flux by quantifying vorticity accumulation based on dynamic error diffusion, characterized in that, The method includes: Decompose the turbulent pulsation signal using a multi-scale decomposition technology based on high-frequency data, separate the non-turbulent component signal, and obtain the turbulent component signal; Quantify the local error distribution of the turbulent component signal, and judge whether the turbulent component signal meets the first cross-stream wind speed calculation requirement based on the error distribution; Obtain the cross-stream wind speed to be corrected of the gas to be measured based on the turbulent component signal, obtain the correction value of the temperature observation value and the turbulent perturbation correction value of the measurement system, and correct the cross-stream wind speed to be corrected of the gas to be measured based on the correction value of the temperature observation value and the turbulent perturbation correction value of the measurement system to obtain the first cross-stream wind speed of the gas to be measured; Estimate the gas flux based on the first cross-stream wind speed of the gas to be measured.

2. The method for estimating gas flux by quantifying vorticity accumulation based on dynamic error diffusion according to claim 1, wherein Decompose the turbulent pulsation signal using a multi-scale decomposition technology based on high-frequency data, separate the non-turbulent component signal, and obtain the turbulent component signal specifically Includes the following processes: Use the harmonic superposition method to generate a random signal that follows the power spectrum from the turbulent pulsation signal, then perform Fourier high-pass filtering, select the pulsation signal with a frequency greater than 1 times the atmospheric surface layer thickness to obtain a preset small-scale pulsation signal, denote the preset small-scale pulsation signal as the signal to be separated, perform a discrete Fourier transform on the signal to be separated to obtain a frequency-domain equation, calculate the difference in the natural frequency between the adjacent amplitudes of each candidate frequency, retain the candidate frequencies with equal differences in the natural frequency between the adjacent amplitudes, and reconstruct them into a turbulent component signal through an inverse wave transform.

3. The method for estimating gas flux by quantized vorticity accumulation based on dynamic error diffusion according to claim 2, characterized in that, Reconstruct into a turbulent component signal through an inverse wave transform specifically Includes the following processes: Set the lowest frequency of the turbulent component signal, select the embedding dimension of the phase space reconstruction matrix, and construct the phase space reconstruction matrix ; Perform singular value decomposition on the phase space reconstruction matrix ; Reconstruct each order sub-signal using the anti-diagonal method ; Calculated sub-signal of the reconstruction information index : Based on the sub-signal The reconstructed information index is sorted in descending order, and the sub-signals with the reconstructed information index greater than the preset index threshold are selected for reconstruction into turbulent component signals.

4. The method for estimating gas flux by quantized vorticity accumulation based on dynamic error diffusion according to claim 1, wherein Quantify the local error distribution of the turbulent component signal specifically Includes the following processes: The local error refers to the deviation between the observed value and the true value of the turbulent component signal at a specific time. Use statistical indicators such as the mean, variance, and root mean square error to quantify the magnitude and distribution characteristics of the local error, and denote the sum value of the mean, variance, and root mean square error as the local error characteristic index of the turbulent component signal.

5. The method for estimating gas flux by quantized vorticity accumulation based on dynamic error diffusion according to claim 4, wherein Judging whether the turbulent component signal meets the first cross-stream wind speed calculation requirement based on the error distribution specifically includes the following processes: Number each specific time, obtain the local error characteristic index of each specific time, establish a rectangular coordinate system with the execution time of each specific time as the X-axis and the local error characteristic index as the Y-axis, generate an error curve by plotting points, and then draw perpendicular lines from the two endpoints of the error curve to the X-axis to obtain two perpendicular line segments. A figure is formed by the error curve, the two perpendicular line segments, and the X-axis. Calculate the area of the figure and denote the area of the figure as the error characteristic value. Judge whether the error characteristic value exceeds the preset error characteristic threshold. If so, judge that the turbulent component signal does not meet the first cross-stream wind speed calculation requirement. If not, judge that the turbulent component signal meets the first cross-stream wind speed calculation requirement.

6. The method for estimating gas flux by quantifying vorticity accumulation based on dynamic error diffusion according to claim 1, wherein Obtain the cross-stream wind speed to be corrected of the gas to be measured based on the turbulent component signal specifically Includes the following processes: Based on the turbulent component signal, use the spectrum analysis method to analyze the energy distribution of the turbulent signal at different frequencies, and infer the wind speed direction angle of the gas to be measured; Calculate the dynamic pressure based on the wind speed direction of the gas to be measured and the static pressure of the surrounding environment; Calculate the cross-stream wind speed to be corrected of the gas to be measured based on the dynamic pressure and the static pressure of the surrounding environment.

7. The method for estimating gas flux by quantifying vorticity accumulation based on dynamic error diffusion according to claim 6, wherein The process of obtaining the correction value of the temperature observation specifically includes the following steps: Calculate the Mach number based on the dynamic pressure and the static pressure of the surrounding environment, and obtain the measured temperature value of the gas to be measured. Substitute the Mach number and the measured temperature value of the gas to be measured into the first correlation formula to calculate the correction value of the temperature observation.

8. The method for estimating gas flux by quantifying vorticity accumulation based on dynamic error diffusion according to claim 6, wherein The process of obtaining the correction value of the turbulence disturbance of the measurement system specifically includes: directly measuring the influence of turbulence on the measurement signal by comparing the measurement results with and without turbulence disturbance, and obtaining the correction value of the turbulence disturbance through multiple measurements and statistical analysis.

Images