Freshwater aquaculture greenhouse gas monitoring equipment and system based on Gaussian diffusion model

Abstract

The invention relates to the technical field of greenhouse gas monitoring, in particular to freshwater aquaculture greenhouse gas monitoring equipment and system based on a Gaussian diffusion model. The method comprises the following steps: a sampling and control integration unit collects a gas sample through a sampling point, controls gas flow through an electromagnetic valve, and records state change in real time; gas analysis and meteorological monitoring are combined with functions of a gas analyzer and a meteorological monitoring station, and greenhouse gas concentration and environmental parameters are continuously measured; the coordinate calculation unit establishes a coordinate system based on meteorological monitoring data, adjusts the coordinate axis direction according to the wind direction and recalculates the coordinates of the sampling points; and the stability classification unit is used for classifying the atmospheric stability by using a Pasquill-Glifford method and calculating a diffusion coefficient at the same time. According to the method, the Gaussian diffusion model is adopted, and the model is a calculation model suitable for atmospheric pollutant emission under the uniform atmospheric condition, is widely applied to atmospheric pollutant diffusion research and has a good simulation effect.

Description

Technical Field

[0001] The present invention relates to the technical field of greenhouse gas monitoring, and in particular to a freshwater aquaculture greenhouse gas monitoring device and system based on a Gaussian diffusion model. Background Art

[0002] Freshwater aquaculture is one of the main modes of aquaculture in my country. The feed input and anaerobic environment of freshwater aquaculture make it an important source of greenhouse gas emissions in the atmosphere. At present, there have been many studies on greenhouse gas monitoring methods for freshwater aquaculture water interfaces, including: floating box method, boundary layer model method, inverted funnel method, etc. Among them, the floating box method is the most commonly used method for greenhouse gas monitoring at the water interface. However, the floating box method can only collect smaller areas in the water interface, and cannot cover the differences between aerated areas, non-aerated areas, feeding areas, and non-feeding areas. Therefore, it cannot fully represent the greenhouse gas emissions of all areas of the water interface, and this method cannot achieve continuous monitoring of greenhouse gas emissions. The diffusion model method can determine the content of greenhouse gases dissolved in the water body, thereby estimating the greenhouse gas emissions of the water body. However, this method is greatly affected by environmental and human interference, and does not include greenhouse gas emission methods such as bubbles, and the final estimate has a large error with the actual emission. The inverted funnel method can collect methane emitted in the form of bubbles, but it cannot effectively monitor the diffusion emissions of the water vapor interface and the temporal and spatial differences of the water area, and it cannot be monitored continuously. Therefore, a freshwater aquaculture greenhouse gas monitoring device and system based on a Gaussian diffusion model is provided. Summary of the invention

[0003] The purpose of the present invention is to provide a freshwater aquaculture greenhouse gas monitoring device and system based on a Gaussian diffusion model to solve the problems that the existing real-time monitoring method of greenhouse gas emissions at the interface of freshwater aquaculture waters proposed in the above background technology cannot cover the differences between aerated areas, non-aerated areas, feeding areas and non-feeding areas, has high interference and cannot be monitored continuously.

[0004] To achieve the above object, the present invention provides a freshwater aquaculture greenhouse gas monitoring device based on a Gaussian diffusion model, comprising:

[0005] A sampling and control integrated unit, which collects gas samples through sampling points, controls gas flow through solenoid valves, and records state changes in real time;

[0006] Gas analysis and meteorological monitoring, which combines the functions of a gas analyzer and a meteorological monitoring station to continuously measure greenhouse gas concentrations and environmental parameters;

[0007] A coordinate calculation unit, wherein the coordinate calculation unit establishes a coordinate system based on the data of meteorological monitoring, and adjusts the direction of the coordinate axis according to the wind direction to recalculate the coordinates of the sampling points;

[0008] A stability classification unit that uses the Pasquill-Gifford method to classify atmospheric stability and calculates diffusion coefficients;

[0009] An emission source estimation unit, wherein the emission source estimation unit applies a Gaussian diffusion model to screen qualified sampling points, calculate background concentrations, and ultimately estimate emission source rates.

[0010] As a further improvement of the technical solution, the sampling and control integrated unit collects gas samples through the sampling point, controls the gas flow through the solenoid valve, and records the state changes in real time, including the following steps:

[0011] S1.1, configure the sampling point location, start the air pump, and initialize the integrated controller;

[0012] S1.2. According to a predetermined time schedule t, the integrated controller issues a solenoid valve opening command, and the solenoid valve responds to the command to allow gas to flow into the system from a specific sampling point;

[0013] S1.3. The controller continuously monitors the state and abnormality of the solenoid valve, and records the timestamp of each solenoid valve switch, the corresponding sampling point number and other relevant information in real time;

[0014] S1.4. After completing the sampling cycle, the controller closes all opened solenoid valves in sequence, and the system returns to the standby state, ready to start again according to the new schedule.

[0015] As a further improvement of the technical solution, the gas analysis and meteorological monitoring includes a gas analysis module and a meteorological monitoring module;

[0016] Wherein, the gas analysis module continuously measures the concentrations of methane, nitrous oxide and carbon dioxide in the gas sample;

[0017] The meteorological monitoring module monitors wind speed and direction in real time and collects environmental parameters regularly.

[0018] As a further improvement of the technical solution, the coordinate calculation unit establishes a coordinate system based on the meteorological monitoring data, adjusts the direction of the coordinate axis according to the wind direction and recalculates the coordinates of the sampling points, including the following steps:

[0019] S2.1. Establish a three-dimensional coordinate axis with the center of the fish pond surface as the origin and the north direction as the X axis;

[0020] S2.2. According to the distance of each sampling point from the origin, establish the coordinates of the sampling point in the coordinate axis (x i ,y i , z i );

[0021] S2.3. With due north as 0°, the angle between the wind direction and due north is recorded as the wind direction angle, and the average wind direction angle of all three-dimensional ultrasonic anemometers is recorded as θ;

[0022] S2.4. Take the average wind speed of all three-dimensional ultrasonic anemometers, record it as u, and measure the methane concentration c at each sampling point. i ;

[0023] S2.5, rotate the coordinate axis according to the wind direction θ so that the X axis coincides with the wind direction angle, and calculate the new coordinate (x i ',y i ', z i ').

[0024] As a further improvement of the technical solution, in S2.5, the new coordinates (x i ',y i ', z i ')for:

[0025] x i '=x i cos(θ)+y i sin(θ);

[0026] y i '=x i sin(θ)+y i cos(θ).

[0027] As a further improvement of the technical solution, the stability classification unit includes a stability classification module and a diffusion coefficient calculation module;

[0028] The stability classification module finds the corresponding stability category in the Pasquill-Gifford stability classification table according to the ground wind speed u and the net solar radiation level;

[0029] The diffusion coefficient calculation module searches for the corresponding parameter a in the predefined diffusion coefficient table according to the selected atmospheric stability category. y , b y , a z , b z , using the obtained parameters, the diffusion coefficient is calculated according to the Gaussian diffusion model.

[0030] As a further improvement of the technical solution, the method of using the obtained parameters to calculate the diffusion coefficient according to the Gaussian diffusion model includes the following steps:

[0031]

[0032] Among them, σy (x) represents the horizontal diffusion coefficient at distance x; σ z (x) represents the vertical diffusion coefficient at distance x; a y Represents the scaling factor of the diffusion coefficient in the horizontal direction; a z b represents the scaling factor of the diffusion coefficient in the vertical direction; y b represents the exponential factor of the horizontal diffusion coefficient; z Represents the exponential factor of the vertical diffusion coefficient; x represents the distance from the emission source to the monitoring point.

[0033] As a further improvement of the technical solution, the emission source estimation unit applies a Gaussian diffusion model to screen qualified sampling points, calculate the background concentration, and finally estimate the emission source rate, including the following steps:

[0034] S4.1. Check whether each coordinate is within the Gaussian diffusion wake based on the diffusion coefficient:

[0035] |y i '|≤2σ y (x);

[0036] |z|≤σ z (x);

[0037] Where z represents the horizontal coordinate of the sampling point relative to the emission source;

[0038] S4.2. Select all new coordinates x i '<0, take the average value of greenhouse gas concentration at the sampling point to get the atmospheric background concentration c a ;

[0039] S4.3. The original methane concentration c of each sampling point i Subtract the atmospheric background concentration c a , and obtain the adjusted concentration c' i , c' i =c i -c a ;

[0040] S4.4. For each sampling point that meets the Gaussian wake condition, estimate the emission source rate Q.

[0041] As a further improvement of this technical solution, in S4.4, the estimated emission source rate Q is:

[0042]

[0043] in, Indicates that at distance x i 'The horizontal diffusion coefficient at '; Indicates that at distance x i The vertical diffusion coefficient at '; i ' represents the distance of the i-th sampling point along the X axis of the new coordinate system; y i ' represents the distance of the i-th sampling point along the Y axis of the new coordinate system; z i ' represents the height position of the i-th sampling point; e represents the base of the natural logarithm; π represents the circumference of a circle; i represents the index of the sampling point.

[0044] On the other hand, the present invention provides a freshwater aquaculture greenhouse gas monitoring system based on a Gaussian diffusion model, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the freshwater aquaculture greenhouse gas monitoring devices based on a Gaussian diffusion model described above.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. The freshwater aquaculture greenhouse gas monitoring equipment and system based on the Gaussian diffusion model adopts the Gaussian diffusion model, which is a calculation model for atmospheric pollutant emissions under uniform atmospheric conditions. It has been widely used in atmospheric pollutant diffusion research and has a good simulation effect.

[0047] 2. In the freshwater aquaculture greenhouse gas monitoring equipment and system based on the Gaussian diffusion model, a complete set of equipment and systems are established for the freshwater aquaculture water interface, and calculations are based on the Gaussian diffusion model, so as to achieve real-time monitoring of greenhouse gas emissions in the entire freshwater aquaculture water interface. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a flow chart of the overall method of the present invention; Figure 2 This is the circuit diagram of the monitoring device of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] Example 1: Please refer to Figure 1 As shown, this embodiment provides a freshwater aquaculture greenhouse gas monitoring device based on a Gaussian diffusion model, comprising the following steps:

[0051] The sampling and control integrated unit 1 collects gas samples through the sampling points, controls the gas flow through the solenoid valve, and records the state changes in real time;

[0052] In this embodiment, according to the size of the fish pond, at least 16 sampling points are evenly arranged around the fish pond, the sampling points are between 2-10 meters from the water surface boundary and between 0.5-2 meters from the ground; each sampling point uses a separate PA material plastic hose, and a three-way valve is installed at the end; each sampling point uses a separate PA material plastic hose, and a three-way valve is installed at the end; one end of the three-way valve is connected to a gas diverter and connected to an air pump; the other end of the three-way valve is connected to a 1-input and 1-output normally closed solenoid valve, and the solenoid valve is installed on the manifold to form a solenoid valve group, and an integrated controller is installed on the manifold, which can control the switch of each solenoid valve in real time and can record data in real time (specific freshwater aquaculture water interface greenhouse gas monitoring equipment circuit as shown in FIG. Figure 2 shown);

[0053] Multiple sampling points are evenly set around the fish pond. The sampling points are connected to the air pump with PA pipes for continuous air extraction to achieve real-time update of the gas in the pipe. At the end of the pipe, it is connected to a solenoid valve group and a gas manifold through a three-way valve, and then connected to a greenhouse gas analyzer through the manifold to achieve automatic switching of multiple sampling points. By collecting meteorological data such as wind speed, wind direction, and light in real time, combining the measured values ​​of multiple sampling points, and applying the Gaussian diffusion model and nonlinear least squares method, the greenhouse gas emission flux of the fish pond is finally calculated. Real-time monitoring of greenhouse gas emissions at the entire water interface of freshwater aquaculture is achieved; multiple sampling points are connected to the air pump through PA hoses and three-way valves for continuous air extraction, so as to achieve real-time update of the gas in the pipe and avoid the lag of gas samples caused by the length of the pipe; the three-way valve is connected to the solenoid valve group and the integrated controller to achieve automatic switching of multiple sampling points;

[0054] The sampling and control integrated unit 1 collects gas samples through the sampling point, controls the gas flow through the solenoid valve, and records the state changes in real time, including the following steps:

[0055] S1.1. Configure the sampling point location and ensure that all hardware connections are correct. Start the gas pump to provide a continuous power source for gas collection, initialize the integrated controller, establish a communication link, and prepare to receive and send control instructions.

[0056] S1.2. According to a predetermined time schedule t, the integrated controller issues a solenoid valve opening command, and the solenoid valve responds to the command to allow gas to flow into the system from a specific sampling point;

[0057] S1.3. The controller continuously monitors the state and abnormality of the solenoid valve, and records the timestamp of each solenoid valve switch, the corresponding sampling point number and other relevant information in real time;

[0058] S1.4. After completing the sampling cycle, the controller closes all opened solenoid valves in sequence, and the system returns to the standby state, ready to start again according to the new schedule.

[0059] Gas analysis and weather monitoring 2 combines the functions of a gas analyzer and a weather monitoring station to continuously measure greenhouse gas concentrations (methane, nitrous oxide, carbon dioxide) and environmental parameters (wind speed, wind direction, temperature, etc.);

[0060] In this embodiment, the gas analysis and meteorological monitoring 2 includes a gas analysis module 21 and a meteorological monitoring module 22;

[0061] The gas analysis module 21 continuously measures the concentrations of methane, nitrous oxide and carbon dioxide in the gas sample, and leads to a total gas outlet through the manifold, which is connected to the greenhouse gas analyzer. The greenhouse gas analyzer can continuously measure the concentrations of methane, nitrous oxide and carbon dioxide, and requires that the measurement accuracy of methane is at least 0.01ppm, the measurement accuracy of nitrous oxide is at least 0.001ppm, and the measurement accuracy of carbon dioxide is at least 0.1ppm;

[0062] The meteorological monitoring module 22 monitors wind speed and wind direction in real time, and regularly collects other environmental parameters such as temperature and humidity. A three-dimensional anemometer is installed at each sampling port, and a small meteorological station is installed around the fish pond. The three-dimensional anemometer and the small meteorological station are connected to the data recorder via the 485 communication protocol to record wind speed, wind direction, meteorological and other indicators in real time.

[0063] The coordinate calculation unit 3 establishes a coordinate system based on the data of meteorological monitoring, recalculates the coordinates of the sampling points by adjusting the direction of the coordinate axis according to the wind direction, establishes a virtual coordinate system with the center of the fish pond as the origin, and digitizes the complex spatial distribution problem so as to facilitate calculation using a mathematical model;

[0064] In this embodiment, the coordinate calculation unit 3 establishes a coordinate system based on the meteorological monitoring data, adjusts the direction of the coordinate axis according to the wind direction, and recalculates the coordinates of the sampling points, including the following steps:

[0065] S2.1. Establish a three-dimensional coordinate axis with the center of the fish pond surface as the origin and the north direction as the X axis;

[0066] S2.2. According to the distance of each sampling point from the origin, establish the coordinates of the sampling point in the coordinate axis (x i ,y i , z i );

[0067] S2.3. With due north as 0°, the angle between the wind direction and due north is recorded as the wind direction angle, and the average wind direction angle of the three-dimensional ultrasonic anemometer at all sampling ports is recorded as θ;

[0068] S2.4. Take the average wind speed of all three-dimensional ultrasonic anemometers, record it as u, and measure the methane or nitrous oxide concentration c at each sampling point. i ;

[0069] S2.5, rotate the coordinate axis according to the wind direction θ so that the X axis coincides with the wind direction angle, and calculate the new coordinate (x i ',y i ', z i ');

[0070] Calculate the new coordinates (x i ',y i ', z i ')for:

[0071] x i '=x i cos(θ)+y i cos(θ);

[0072] y i '=x i sin(θ)+y i cos(θ).

[0073] Stability Classification Unit 4 uses the Pasquill-Gifford method to classify atmospheric stability and also calculates diffusion coefficients;

[0074] In this embodiment, the stability classification unit 4 includes a stability classification module 41 and a diffusion coefficient calculation module 42;

[0075] The stability classification module 41 finds the corresponding stability category (A to F) in the Pasquill-Gifford stability classification table according to the ground wind speed u and the net solar radiation level (the Pasquill-Gifford stability classification table is shown in Table 1). The Pasquill-Gifford stability classification is a widely used method that divides atmospheric conditions into several categories (from A to F), where A represents very unstable atmospheric conditions, which are conducive to the rapid diffusion of pollutants; and F represents very stable atmospheric conditions, which are not conducive to the diffusion of pollutants and may cause the accumulation of pollutants in local areas. By determining the atmospheric stability category, the diffusion behavior of pollutants (such as greenhouse gases) can be more accurately predicted: different stability categories correspond to different diffusion coefficients, which are used in the Gaussian diffusion model to calculate the change of pollutant concentration with distance;

[0076] Table 1:

[0077] The diffusion coefficient calculation module 42 searches for the corresponding parameter a in the predefined diffusion coefficient table according to the selected atmospheric stability category. y , b y , a z , b z (The diffusion coefficient table is shown in Table 2). Using the obtained parameters, the diffusion coefficient is calculated according to the Gaussian diffusion model. Different atmospheric stability conditions will affect the diffusion mode of pollutants in the air. By finding the diffusion coefficient applicable to a specific stability category, the horizontal and vertical diffusion characteristics of greenhouse gases and other pollutants under different meteorological conditions can be more accurately described and quantified. The Gaussian diffusion model combined with the specific diffusion coefficient can accurately model the gas concentration distribution around the emission source, which helps to predict the gas concentration at a specific location, thereby providing a scientific basis for environmental monitoring, pollution warning and control. Table 2: Further, using the obtained parameters, the diffusion coefficient is calculated according to the Gaussian diffusion model, including the following steps:

[0078]

[0079] Among them, σ y (x) represents the horizontal diffusion coefficient at distance x, which indicates the dispersion degree of pollutant concentration in the horizontal direction; σ z (x) represents the vertical diffusion coefficient at a distance x, which indicates the dispersion degree of pollutant concentration in the vertical direction; a y Represents the scaling factor of the diffusion coefficient in the horizontal direction; a z b represents the scaling factor of the diffusion coefficient in the vertical direction; y b represents the exponential factor of the horizontal diffusion coefficient; z Represents the exponential factor of the vertical diffusion coefficient; x represents the distance from the emission source to the monitoring point.

[0080] Emission source estimation unit 5 applies the Gaussian diffusion model to screen qualified sampling points, calculate the background concentration, and finally estimate the emission source rate. The Gaussian diffusion model is a mathematical model used to predict the diffusion and distribution of pollutants in the atmosphere. It describes the change of pollutant concentration with distance and time based on the Gaussian function. By using the Gaussian diffusion model, we can more accurately understand how greenhouse gases or other pollutants released by emission sources diffuse in the atmosphere, which helps to assess the potential impact of these emissions on the surrounding environment and ecosystems; not all sampling point data are suitable for estimating emission source rates. By screening out sampling points located in the tail of emission sources through the Gaussian diffusion model, it can ensure that the data used is directly related to emission activities, thereby improving the effectiveness and accuracy of data analysis; calculating the background concentration (i.e., the atmospheric concentration in an area not affected by a specific emission source) is a key step in understanding the contribution of emission sources. Subtracting the background concentration from the original measurement value to obtain the adjusted concentration can more truly reflect the impact of the emission source; based on the adjusted concentration and known meteorological conditions (such as wind speed, wind direction, etc.), the Gaussian diffusion model can be used to infer the emission rate of the emission source, which is very important for quantifying emission intensity, tracking changing trends, and evaluating the effectiveness of emission reduction measures;

[0081] In this embodiment, the emission source estimation unit 5 uses the Gaussian diffusion model to screen the sampling points that meet the conditions, calculate the background concentration, and finally estimate the emission source rate, including the following steps:

[0082] S4.1. Check whether each coordinate is in the Gaussian diffusion tail based on the diffusion coefficient (by judging whether the sampling point is in the Gaussian diffusion tail of the emission source, it can be determined which areas are directly or significantly affected by the emission source, thereby defining the effective range of pollution impact; only sampling points in the tail can capture the changes in pollutant concentrations generated by the emission source, so the data at these points are more relevant for estimating the characteristics of the emission source):

[0083] |y i '|≤2σ y (x);

[0084] |z|≤σ z (x);

[0085] Where z represents the coordinate of the sampling point in the horizontal direction (usually perpendicular to the wind direction) relative to the emission source;

[0086] S4.2. Select all new coordinates x i '<0, that is, the sampling point located on the upwind side of the emission source, the greenhouse gas concentration at the sampling point is averaged to obtain the atmospheric background concentration c a ;

[0087] S4.3. The original methane or nitrous oxide concentration c of each sampling point i Subtract the atmospheric background concentration c a , and obtain the adjusted concentration c' i , c' i =c i -c a ;

[0088] S4.4. For each sampling point that meets the Gaussian wake condition, estimate the emission source rate Q;

[0089] Furthermore, the emission source rate Q is estimated as:

[0090]

[0091] in, Indicates that at distance x i 'The horizontal diffusion coefficient at '; Indicates that at distance x i The vertical diffusion coefficient at '; i ' represents the distance of the i-th sampling point along the X-axis of the new coordinate system (consistent with the wind direction); y i ' represents the distance of the i-th sampling point along the Y axis of the new coordinate system; z i ' represents the height position of the i-th sampling point; e represents the base of the natural logarithm; π represents the circumference of a circle; i represents the index of the sampling point.

[0092] Furthermore, the above solution process is programmed in MATLAB to realize intelligent calculation, and finally realize the monitoring of greenhouse gas emissions in the entire water interface of freshwater aquaculture. Through MATLAB programming and design, a complete set of equipment and systems are established to realize real-time monitoring of greenhouse gas emissions in the entire water interface of freshwater aquaculture system;

[0093] The specific programming code is:

[0094] coords = readtable('coordinates.xlsx'); % Assuming it is coordinates.xlsx

[0095] % Read Table 2: Concentration, wind speed, wind direction, and weather stability level data

[0096] data=readtable('concentrations.xlsx');

[0097] %Assuming it is concentrations.xlsx

[0098] %Get wind direction angle and coordinates

[0099] theta = data.Wind_Direction; % Wind direction angle column

[0100] u = data.Wind_Speed; % wind speed

[0101] stability=data.Stability_Class;%Meteorological stability level

[0102] %Store new coordinates

[0103] coords_rotated = coords; % used to store the rotated coordinates

[0104] for i=1:16

[0105] % Get the original coordinates

[0106] x = coords{1,i};

[0107] y = coords{2,i};

[0108] %Convert wind direction angle to radians

[0109] theta_rad=deg2rad(theta(i));

[0110] % Calculate the coordinates after rotation

[0111] x_rot=x*cos(theta_rad)-y*sin(theta_rad);

[0112] y_rot=x*sin(theta_rad)+y*cos(theta_rad);

[0113] %Store the new coordinates

[0114] coords_rotated{1,i}=x_rot;

[0115] coords_rotated{2,i}=y_rot;

[0116] end

[0117] % Define a function to obtain the diffusion coefficient based on wind speed and stability level

[0118] function[sigma_y,sigma_z]=get_diffusion_coefficients(u,stability)

[0119] % Obtain diffusion coefficient based on stability level and wind speed

[0120] % Here we use the simplified diffusion coefficient formula

[0121] switch stability

[0122] case 'A'% is extremely unstable

[0123] sigma_y=0.22*x^0.87;

[0124] sigma_z=0.20*x^0.77;

[0125] case 'B'% is unstable

[0126] sigma_y=0.16*x^0.90;

[0127] sigma_z=0.12*x^0.75;

[0128] case 'C'% stable

[0129] sigma_y=0.11*x^0.92;

[0130] sigma_z=0.08*x^0.73;

[0131] case 'D'% is more stable

[0132] sigma_y=0.08*x^0.95;

[0133] sigma_z=0.06*x^0.70;

[0134] case 'E'% stable

[0135] sigma_y=0.06*x^0.97;

[0136] sigma_z=0.03*x^0.65;

[0137] case 'F' % extremely stable

[0138] sigma_y=0.04*x^1.00;

[0139] sigma_z=0.016*x^0.61;

[0140] end

[0141] end

[0142] % Determine whether the point is in the Gaussian wake

[0143] in_plume = zeros(1,16); %1 means in the wake, 0 means not

[0144] for i=1:16

[0145] %Get the rotated coordinates, wind speed and stability level

[0146] x_rot=coords_rotated{1,i};

[0147] y_rot=coords_rotated{2,i};

[0148] z_rot=coords{3,i}; %Assume that the z coordinate remains unchanged

[0149] u=data.Wind_Speed(i);% wind speed

[0150] stability=data.Stability_Class(i); % meteorological stability level

[0151] % Calculate diffusion coefficient

[0152] [sigma_y,sigma_z]=get_diffusion_coefficients(u,stability);

[0153] % Determine whether the point is in the Gaussian wake

[0154] if abs(y_rot)<=sigma_y&&abs(z_rot)<=sigma_z

[0155] in_plume(i)=1; % in the wake

[0156] else in_plume(i)=0;% not in the wake

[0157] end

[0158] end

[0159] % Correction of concentration at all points

[0160] corrected_concentrations=concentrations-ca;

[0161] %Define Gaussian model function

[0162] function C_model=gaussian_model(Q,x,y,z,u,sigma_y,sigma_z,h)

[0163] C_model=(Q / (2*pi*u*sigma_y*sigma_z))*exp(-y^2 / (2*sigma_y^2))*exp(-(zh)^2 / (2*sigma_z^2));

[0164] end

[0165] % Set initial guess

[0166] initial_guess =

[100] ; % The initial guess emission intensity Q is 100 (can be set according to actual conditions)

[0167] % Objective function

[0168] function error=objective(Q)

[0169] error=0;

[0170] for i=1:16

[0171] if in_plume(i) == 1% only fit the points in the wake

[0172] % Get data

[0173] x_rot=coords_rotated{1,i};

[0174] y_rot=coords_rotated{2,i};

[0175] z_rot = coords{3,i};

[0176] concentration_obs=corrected_concentrations(i);

[0177] u = data.Wind_Speed(i);

[0178] stability=data.Stability_Class(i);

[0179] % Calculate diffusion coefficient

[0180] [sigma_y,sigma_z]=get_diffusion_coefficients(u,stability);

[0181] % Calculate concentration using Gaussian model

[0182] C_model = gaussian_model (Q, x_rot, y_rot, z_rot, u, sigma_y, sigma_z, 0); % Assume that the source height h is 0

[0183] % Calculate the square of the error

[0184] error=error+(concentration_obs-C_model)^2;

[0185] end

[0186] end

[0187] end

[0188] %Use nonlinear least squares method to solve Q

[0189] options=optimset('MaxIter',1000,'Display','off');

[0190] Q_optimal=fminsearch(@objective,initial_guess,options);

[0191] Example 2: This example provides a freshwater aquaculture greenhouse gas monitoring system based on a Gaussian diffusion model, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the freshwater aquaculture greenhouse gas monitoring devices based on a Gaussian diffusion model described above.

[0192] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A freshwater aquaculture greenhouse gas monitoring device based on Gaussian diffusion model, characterized in that: The following steps are involved: A sampling and control integrated unit (1), wherein the sampling and control integrated unit (1) collects gas samples through a sampling point, controls the gas flow through a solenoid valve, and records state changes in real time; Gas analysis and meteorological monitoring (2), wherein the gas analysis and meteorological monitoring (2) combines the functions of a gas analyzer and a meteorological monitoring station to continuously measure greenhouse gas concentrations and environmental parameters; A coordinate calculation unit (3), wherein the coordinate calculation unit (3) establishes a coordinate system based on meteorological monitoring data, and adjusts the direction of the coordinate axis according to the wind direction to recalculate the coordinates of the sampling points; A stability classification unit (4), wherein the stability classification unit (4) classifies the atmospheric stability using the Pasquil-Gifford method and calculates the diffusion coefficient; An emission source estimation unit (5), wherein the emission source estimation unit (5) applies a Gaussian diffusion model to screen sampling points that meet the conditions, calculate the background concentration, and ultimately estimate the emission source rate.

2. The freshwater aquaculture greenhouse gas monitoring device based on the Gaussian diffusion model according to claim 1, characterized in that: The sampling and control integrated unit (1) collects gas samples through a sampling point, controls the gas flow through a solenoid valve, and records state changes in real time, comprising the following steps: S1.1, configure the sampling point location, start the air pump, and initialize the integrated controller; S1.

2. According to a predetermined time schedule t, the integrated controller issues a solenoid valve opening command, and the solenoid valve responds to the command to allow gas to flow into the system from a specific sampling point; S1.

3. The controller continuously monitors the state and abnormality of the solenoid valve, and records the timestamp of each solenoid valve switch, the corresponding sampling point number and other relevant information in real time; S1.

4. After completing the sampling cycle, the controller closes all opened solenoid valves in sequence, and the system returns to the standby state, ready to start again according to the new schedule.

3. The freshwater aquaculture greenhouse gas monitoring device based on the Gaussian diffusion model according to claim 2, characterized in that: The gas analysis and meteorological monitoring (2) comprises a gas analysis module (21) and a meteorological monitoring module (22); Wherein, the gas analysis module (21) continuously measures the concentrations of methane, nitrous oxide and carbon dioxide in the gas sample; The meteorological monitoring module (22) monitors wind speed and wind direction in real time, and collects environmental parameters regularly.

4. The freshwater aquaculture greenhouse gas monitoring device based on the Gaussian diffusion model according to claim 3 is characterized in that: The coordinate calculation unit (3) establishes a coordinate system based on the meteorological monitoring data, adjusts the direction of the coordinate axis according to the wind direction and recalculates the coordinates of the sampling points, comprising the following steps: S2.

1. Establish a three-dimensional coordinate axis with the center of the fish pond surface as the origin and the north direction as the X axis; S2.

2. According to the distance of each sampling point from the origin, establish the coordinates of the sampling point in the coordinate axis (x i ,y i , z i ); S2.

3. With due north as 0°, the angle between the wind direction and due north is recorded as the wind direction angle, and the average wind direction angle of all three-dimensional ultrasonic anemometers is recorded as θ; S2.

4. Take the average wind speed of all three-dimensional ultrasonic anemometers, record it as u, and measure the methane concentration c at each sampling point. i ; S2.5, rotate the coordinate axis according to the wind direction θ so that the X axis coincides with the wind direction angle, and calculate the new coordinate (x i ',y i ', z i ').

5. The freshwater aquaculture greenhouse gas monitoring device based on Gaussian diffusion model according to claim 4, characterized in that: In S2.5, the new coordinates (x i ',y i ', z i ')for: x i '=x i cos(θ)+y i sin(θ); and i '=x i sin(θ)+y i cos(θ).

6. The freshwater aquaculture greenhouse gas monitoring device based on the Gaussian diffusion model according to claim 5, characterized in that: The stability classification unit (4) comprises a stability classification module (41) and a diffusion coefficient calculation module (42); The stability classification module (41) finds the corresponding stability category in the Pasquill-Gifford stability classification table according to the ground wind speed u and the net solar radiation level; The diffusion coefficient calculation module (42) searches for the corresponding parameter a in a predefined diffusion coefficient table according to the selected atmospheric stability category. y , b y , a z , b z , using the obtained parameters, the diffusion coefficient is calculated according to the Gaussian diffusion model.

7. The freshwater aquaculture greenhouse gas monitoring device based on the Gaussian diffusion model according to claim 6, characterized in that: The method of using the obtained parameters to calculate the diffusion coefficient according to the Gaussian diffusion model comprises the following steps: Among them, σ y (x) represents the horizontal diffusion coefficient at distance x; σ z (x) represents the vertical diffusion coefficient at distance x; a y Represents the scaling factor of the diffusion coefficient in the horizontal direction; a z b represents the scaling factor of the diffusion coefficient in the vertical direction; y b represents the exponential factor of the horizontal diffusion coefficient; z Represents the exponential factor of the vertical diffusion coefficient; x represents the distance from the emission source to the monitoring point.

8. The freshwater aquaculture greenhouse gas monitoring device based on the Gaussian diffusion model according to claim 7, characterized in that: The emission source estimation unit (5) uses a Gaussian diffusion model to screen sampling points that meet the conditions, calculate the background concentration, and finally estimate the emission source rate, including the following steps: S4.

1. Check whether each coordinate is within the Gaussian diffusion wake based on the diffusion coefficient: |and i '|≤2σ y (x); |z|≤σ z (x); Where z represents the horizontal coordinate of the sampling point relative to the emission source; S4.

2. Select all new coordinates x i '<0, take the average value of greenhouse gas concentration at the sampling point to get the atmospheric background concentration c a ; S4.

3. The original methane concentration c of each sampling point i Subtract the atmospheric background concentration c a , and obtain the adjusted concentration c' i , c' i =c i -c a ; S4.

4. For each sampling point that meets the Gaussian wake condition, estimate the emission source rate Q.

9. The freshwater aquaculture greenhouse gas monitoring device based on Gaussian diffusion model according to claim 8, characterized in that: In S4.4, the estimated emission source rate Q is: in, Indicates that at distance x i 'The horizontal diffusion coefficient at '; Indicates that at distance x i The vertical diffusion coefficient at '; i ' represents the distance of the i-th sampling point along the X axis of the new coordinate system; y i ' represents the distance of the i-th sampling point along the Y axis of the new coordinate system; z i ' represents the height position of the i-th sampling point; e represents the base of the natural logarithm; π represents the circumference of a circle; i represents the index of the sampling point.

10. A freshwater aquaculture greenhouse gas monitoring system based on a Gaussian diffusion model, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: The processor executes a computer program to implement the freshwater aquaculture greenhouse gas monitoring device based on the Gaussian diffusion model as described in any one of claims 1-9.

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