A greenhouse gas monitoring device and system for freshwater aquaculture based on a Gaussian diffusion model

Abstract

This invention relates to the field of greenhouse gas monitoring technology, specifically to a freshwater aquaculture greenhouse gas monitoring device and system based on a Gaussian diffusion model. It includes the following steps: a sampling and control integration unit collects gas samples through sampling points, controls gas flow through solenoid valves, and records state changes in real time; a gas analysis and meteorological monitoring unit 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 establishes a coordinate system based on meteorological monitoring data, adjusts the coordinate axis direction according to wind direction, and recalculates the coordinates of the sampling points; a stability classification unit uses the Pasquill-Gifford method to classify atmospheric stability and calculates the diffusion coefficient. This invention employs a Gaussian diffusion model, which is a computational model for atmospheric pollutant emissions under homogeneous atmospheric conditions and has been widely used in atmospheric pollutant diffusion research, demonstrating good simulation results.

Description

Technical Field

[0001] This invention relates to the field of greenhouse gas monitoring technology, and more specifically, to a greenhouse gas monitoring device and system for freshwater aquaculture based on a Gaussian diffusion model. Background Technology

[0002] Freshwater aquaculture is one of the main aquaculture models in my country. The feed input and anaerobic environment of freshwater aquaculture make it a significant source of greenhouse gas emissions into the atmosphere. Currently, many studies have been conducted on greenhouse gas monitoring methods at the water interface of freshwater aquaculture, including the floating box method, boundary layer model method, and inverted funnel method. Among these, the floating box method is currently the most commonly used method for monitoring greenhouse gases at the water interface. However, the floating box method can only collect data from a relatively small area of ​​the water interface and cannot cover the differences between aerated, non-aerated, fed, and non-fed areas. Therefore, it cannot fully represent greenhouse gas emissions from the entire water interface, and this method cannot achieve continuous monitoring of greenhouse gas emissions. The diffusion model method can determine the dissolved greenhouse gas content in the water body, thereby estimating greenhouse gas emissions from the water body. However, this method is greatly affected by environmental and anthropogenic interference and does not include greenhouse gas emissions through bubbles, resulting in a significant error between the final estimate and the actual emissions. The inverted funnel method can collect methane emitted in the form of bubbles, but it cannot effectively monitor diffusion emissions at the water vapor interface and the spatiotemporal differences in the water body, and it cannot provide continuous monitoring. Therefore, a greenhouse gas monitoring device and system for freshwater aquaculture based on a Gaussian diffusion model is provided. Summary of the Invention

[0003] The purpose of this invention is to provide a greenhouse gas monitoring device and system for freshwater aquaculture based on a Gaussian diffusion model, in order to solve the problems of existing real-time monitoring methods for greenhouse gas emissions from freshwater aquaculture water interfaces, which cannot cover the differences between aeration zones, non-aeration zones, feeding zones, and non-feeding zones, as well as the high interference and inability to continuously monitor these areas, as mentioned in the background art.

[0004] To achieve the above objectives, the present invention aims to provide a greenhouse gas monitoring device for freshwater aquaculture based on a Gaussian diffusion model, comprising:

[0005] The sampling and control integrated unit 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, which establishes a coordinate system based on meteorological monitoring data and recalculates the coordinates of sampling points by adjusting the direction of the coordinate axes according to the wind direction;

[0008] A stability classification unit is provided, which uses the Pasquill-Gifford method to classify atmospheric stability and calculates the diffusion coefficient.

[0009] The emission source estimation unit applies a Gaussian diffusion model to screen sampling points that meet the conditions, calculates the background concentration, and finally estimates the emission source rate.

[0010] As a further improvement to this technical solution, the sampling and control integrated unit collects gas samples through sampling points, controls gas flow through solenoid valves, and records 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 the predetermined time t, the integrated controller issues a solenoid valve opening command, and the solenoid valve responds to the command, allowing gas to flow into the system from a specific sampling point;

[0013] S1.3 The controller continuously monitors the status and abnormal conditions of the solenoid valve, and records the timestamp, corresponding sampling point number and other relevant information for each solenoid valve opening and closing in real time.

[0014] S1.4 After completing the sampling cycle, the controller sequentially closes all the opened solenoid valves, and the system returns to standby mode, ready to restart according to the new schedule.

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

[0016] 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 periodically.

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

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

[0020] S2.2. Based on the distance of each sampling point from the origin, establish the coordinates (x, y) of the sampling points on the coordinate axes. 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. The average value of the wind direction angles of all three-dimensional ultrasonic anemometers is recorded as θ.

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

[0023] S2.5. Rotate the coordinate axes according to the wind direction θ so that the X-axis coincides with the wind direction angle, and calculate the new coordinates (x, y, θ) based on the rotation matrix. i ',y i ',z i ').

[0024] As a further improvement to this technical solution, in step S2.5, the new coordinates (x, y, z) are calculated based on the rotation matrix. 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 to this 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 based on the ground wind speed u and the net solar radiation level.

[0029] The diffusion coefficient calculation module looks up the corresponding parameter 'a' in a predefined diffusion coefficient table based on the selected atmospheric stability category. y b y a z b z Using the obtained parameters, the diffusion coefficient is calculated based on the Gaussian diffusion model.

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

[0031]

[0032] Where, σy (x) represents the horizontal diffusion coefficient at a distance x; σ z (x) represents the vertical diffusion coefficient at a distance x; a y The scaling factor representing the horizontal diffusion coefficient; a z The scaling factor representing the vertical diffusion coefficient; b y The exponential factor representing the horizontal diffusion coefficient; b z The exponential factor represents the vertical diffusion coefficient; x represents the distance from the emission source to the monitoring point.

[0033] As a further improvement to this technical solution, the emission source estimation unit applies a Gaussian diffusion model to screen sampling points that meet the conditions, calculates the background concentration, and finally estimates the emission source rate, including the following steps:

[0034] S4.1, Based on the diffusion coefficient, check whether each coordinate is within the Gaussian diffusion wake:

[0035] |y i '|≤2σ y (x);

[0036] |z|≤σ z (x);

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

[0038] S4.2 Select all new coordinates x i For sampling points where '<0', the average value of the greenhouse gas concentration at each sampling point is taken to obtain the atmospheric background concentration c. a ;

[0039] S4.3, The original methane concentration c at each sampling point i Subtract the atmospheric background concentration c a The adjusted concentration c' was obtained. 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 to this technical solution, in S4.4, the estimated emission source rate Q is:

[0042]

[0043] in, Indicates distance x i The horizontal diffusion coefficient at point '; Indicates distance x i Vertical diffusion coefficient at '; x 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 pi; 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, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the freshwater aquaculture greenhouse gas monitoring device based on the Gaussian diffusion model described above.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0046] 1. The freshwater aquaculture greenhouse gas monitoring equipment and system based on the Gaussian diffusion model employs the Gaussian diffusion model, a calculation model applicable to atmospheric pollutant emissions under homogeneous atmospheric conditions. It has been widely used in atmospheric pollutant diffusion research and exhibits good simulation results.

[0047] 2. In this freshwater aquaculture greenhouse gas monitoring equipment and system based on the Gaussian diffusion model, a complete set of equipment and systems is established for the freshwater aquaculture water interface, and the greenhouse gas emissions of the entire freshwater aquaculture water interface are monitored in real time based on the Gaussian diffusion model. Attached Figure Description

[0048] Figure 1 This is a flowchart illustrating the overall method of the present invention;

[0049] Figure 2 This is a circuit diagram of the monitoring equipment of the present invention. Detailed Implementation

[0050] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0051] Example 1: Please refer to Figure 1 As shown, this embodiment provides a greenhouse gas monitoring device for freshwater aquaculture based on a Gaussian diffusion model, including the following steps:

[0052] The sampling and control integrated unit 1 collects gas samples through sampling points, controls gas flow through solenoid valves, and records state changes in real time;

[0053] In this embodiment, at least 16 sampling points are evenly set around the fishpond according to its size. The sampling points are 2-10 meters from the water surface boundary and 0.5-2 meters above the ground. Each sampling point uses a separate PA plastic hose with a three-way valve at the end. One end of the three-way valve is connected to a gas distributor and an air pump. The other end of the three-way valve is connected to a normally closed solenoid valve (1 inlet, 1 outlet). These solenoid valves are mounted on a manifold to form a solenoid valve group. An integrated controller is installed on the manifold to control the opening and closing of each solenoid valve in real time and to record data in real time (the specific wiring diagram for the freshwater aquaculture interface greenhouse gas monitoring equipment is shown below). Figure 2 (as shown);

[0054] Multiple sampling points are evenly distributed around the fishpond. These points are connected to air pumps via PA pipes for continuous gas extraction, ensuring real-time gas updates within the pipes. At the end of the pipes, a three-way valve connects to a solenoid valve assembly and a gas manifold, which in turn connects to a greenhouse gas analyzer, enabling automatic switching between multiple sampling points. By collecting real-time meteorological data such as wind speed, wind direction, and sunlight, combined with measurements from multiple sampling points, and employing a Gaussian diffusion model and nonlinear least squares method, the greenhouse gas emission flux from the fishpond is calculated. This achieves real-time monitoring of greenhouse gas emissions across the entire freshwater aquaculture area. Connecting multiple sampling points to air pumps via PA hoses and three-way valves ensures continuous gas extraction within the pipes, avoiding sample delays caused by lengthy pipework. Connecting the three-way valves to the solenoid valve assembly and then to an integrated controller enables automatic switching between multiple sampling points.

[0055] The sampling and control integrated unit 1 collects gas samples through sampling points, controls gas flow through solenoid valves, and records state changes in real time, including the following steps:

[0056] S1.1 Configure the sampling point location and ensure that all hardware connections are correct. Start the air pump to provide a continuous power source for gas collection, and initialize the integrated controller to establish a communication link and prepare to receive and send control commands.

[0057] S1.2 According to the predetermined time t, the integrated controller issues a solenoid valve opening command, and the solenoid valve responds to the command, allowing gas to flow into the system from a specific sampling point;

[0058] S1.3 The controller continuously monitors the status and abnormal conditions of the solenoid valve, and records the timestamp, corresponding sampling point number and other relevant information for each solenoid valve opening and closing in real time.

[0059] S1.4 After completing the sampling cycle, the controller sequentially closes all the opened solenoid valves, and the system returns to standby mode, ready to restart according to the new schedule.

[0060] Gas Analysis and Meteorological Monitoring 2 combines the functions of a gas analyzer and a meteorological monitoring station to continuously measure the concentration of greenhouse gases (methane, nitrous oxide, carbon dioxide) and environmental parameters (wind speed, wind direction, temperature, etc.).

[0061] In this embodiment, the gas analysis and meteorological monitoring 2 includes a gas analysis module 21 and a meteorological monitoring module 22;

[0062] The gas analysis module 21 continuously measures the concentrations of methane, nitrous oxide, and carbon dioxide in the gas sample, and outputs a total gas outlet through a manifold to connect to a greenhouse gas analyzer. The greenhouse gas analyzer is capable of continuously measuring the concentrations of methane, nitrous oxide, and carbon dioxide, and requires a methane measurement accuracy of at least 0.01 ppm, a nitrous oxide measurement accuracy of at least 0.001 ppm, and a carbon dioxide measurement accuracy of at least 0.1 ppm.

[0063] The meteorological monitoring module 22 monitors wind speed and direction in real time and collects other environmental parameters such as temperature and humidity periodically. A three-dimensional anemometer is installed at each sampling point, and a small weather station is installed around the fishpond. The three-dimensional anemometer and the small weather station are connected to the data logger via the 485 communication protocol to record wind speed, wind direction and meteorological indicators in real time.

[0064] The coordinate calculation unit 3 establishes a coordinate system based on meteorological monitoring data, adjusts the direction of the coordinate axes according to the wind direction, recalculates the coordinates of the sampling points, and establishes a virtual coordinate system with the center of the fishpond as the origin, digitizing the complex spatial distribution problem so that it can be calculated using a mathematical model.

[0065] In this embodiment, the coordinate calculation unit 3 establishes a coordinate system based on meteorological monitoring data, and recalculates the coordinates of the sampling points by adjusting the coordinate axis direction according to the wind direction, including the following steps:

[0066] S2.1. Establish a three-dimensional coordinate axis with the center of the fishpond surface as the origin and the due north direction as the X-axis;

[0067] S2.2. Based on the distance of each sampling point from the origin, establish the coordinates (x, y) of the sampling points on the coordinate axes. i y i , z i );

[0068] S2.3. With due north as 0°, the angle between the wind direction and due north is recorded as the wind direction angle. The average value of the wind direction angles of the three-dimensional ultrasonic anemometers at all sampling port locations is recorded as θ.

[0069] S2.4. Take the average wind speed from all three-dimensional ultrasonic anemometers, denoted as u, and measure the methane or nitrous oxide concentration c at each sampling point. i ;

[0070] S2.5. Rotate the coordinate axes according to the wind direction θ so that the X-axis coincides with the wind direction angle, and calculate the new coordinates (x, y, θ) based on the rotation matrix. i ',y i ',z i ');

[0071] Calculate the new coordinates (x) based on the rotation matrix. i ',y i ',z i ')for:

[0072] x i '=x i cos(θ)+y i cos(θ);

[0073] y i '=x i sin(θ)+y i cos(θ).

[0074] Stability classification unit 4 uses the Pasquill-Gifford method to classify atmospheric stability and calculates the diffusion coefficient at the same time;

[0075] In this embodiment, the stability classification unit 4 includes a stability classification module 41 and a diffusion coefficient calculation module 42;

[0076] The stability classification module 41 finds the corresponding stability category (A to F) in the Pasquill-Gifford stability classification table based on 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; while F represents very stable atmospheric conditions, which are not conducive to the diffusion of pollutants and may lead to the accumulation of pollutants in local areas. By determining the atmospheric stability category, the diffusion behavior of pollutants (such as greenhouse gases) can be predicted more accurately: different stability categories correspond to different diffusion coefficients, which are used in Gaussian diffusion models to calculate the change of pollutant concentration with distance.

[0077] Table 1:

[0078]

[0079] The diffusion coefficient calculation module 42 searches for the corresponding parameter 'a' in a predefined diffusion coefficient table based on 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 pollutants such as greenhouse gases under different meteorological conditions can be described and quantified more accurately. By using the Gaussian diffusion model in combination with the specific diffusion coefficient, the gas concentration distribution around the emission source can be accurately modeled. This helps to predict the gas concentration at a specific location, thereby providing a scientific basis for environmental monitoring, pollution early warning and control.

[0080] Table 2:

[0081]

[0082] Furthermore, using the obtained parameters, the diffusion coefficient is calculated according to the Gaussian diffusion model, including the following steps:

[0083]

[0084] Where, σ y (x) represents the horizontal diffusion coefficient at a distance x, indicating the degree of dispersion of pollutant concentration in the horizontal direction; σ z (x) represents the vertical diffusion coefficient at a distance x, indicating the degree of dispersion of pollutant concentration in the vertical direction; a y The scaling factor representing the horizontal diffusion coefficient; a z The scaling factor representing the vertical diffusion coefficient; b y The exponential factor representing the horizontal diffusion coefficient; b z The exponential factor represents the vertical diffusion coefficient; x represents the distance from the emission source to the monitoring point.

[0085] Emission source estimation unit 5 uses a Gaussian diffusion model to screen eligible sampling points, calculate background concentrations, and ultimately estimate emission source rates. The Gaussian diffusion model is a mathematical model used to predict the diffusion and distribution of pollutants in the atmosphere. It describes the change in pollutant concentration with distance and time based on a Gaussian function. By using the Gaussian diffusion model, we can more accurately understand how greenhouse gases or other pollutants released from emission sources diffuse in the atmosphere, which helps 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. Screening sampling points located within the emission source wake using the Gaussian diffusion model ensures 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 areas unaffected by specific emission sources) is a key step in understanding the contribution of emission sources. Subtracting the background concentration from the original measurement yields an adjusted concentration that more accurately reflects the impact of emission sources. Based on the adjusted concentration and known meteorological conditions (such as wind speed and direction), the emission rate of the emission source can be inferred using the Gaussian diffusion model. This is crucial for quantifying emission intensity, tracking trends, and evaluating the effectiveness of emission reduction measures.

[0086] In this embodiment, the emission source estimation unit 5 applies a Gaussian diffusion model to screen sampling points that meet the conditions, calculates the background concentration, and finally estimates the emission source rate, including the following steps:

[0087] S4.1. Based on the diffusion coefficient, check whether each coordinate is within the Gaussian diffusion wake (by determining whether the sampling point is located within the Gaussian diffusion wake of the emission source, it can be determined which areas are directly or significantly affected by the emission source, thus defining the effective range of pollution impact; only sampling points in the wake can capture the changes in pollutant concentrations caused by the emission source, therefore the data from these points are more relevant for estimating the characteristics of the emission source):

[0088] |y i '|≤2σ y (x);

[0089] |z|≤σ z (x);

[0090] Where z represents the coordinates of the sampling point in the horizontal direction (usually perpendicular to the wind direction) relative to the emission source;

[0091] S4.2 Select all new coordinates x i For sampling points where '<0', i.e., sampling points located upwind of the emission source, the average value of the greenhouse gas concentration at each sampling point is used to obtain the atmospheric background concentration c. a ;

[0092] S4.3, The original methane or nitrous oxide concentration c at each sampling point i Subtract the atmospheric background concentration c a The adjusted concentration c' was obtained. i ,c' i =c i -c a ;

[0093] S4.4 For each sampling point that meets the Gaussian wake condition, estimate the emission source rate Q;

[0094] Furthermore, the emission source rate Q is estimated as follows:

[0095]

[0096] in, Indicates distance x i The horizontal diffusion coefficient at point '; Indicates distance x i Vertical diffusion coefficient at '; x i ' represents the distance of the i-th sampling point along the X-axis of the new coordinate system (aligned 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 pi; i represents the index of the sampling point.

[0097] Furthermore, the above solution process was programmed in MATLAB to achieve intelligent calculation, and finally greenhouse gas emission monitoring of the entire water interface of freshwater aquaculture was realized. Through MATLAB programming design, a complete set of equipment and systems was established to realize real-time monitoring of greenhouse gas emissions of the entire water interface of the freshwater aquaculture system.

[0098] The specific programming code is as follows:

[0099] coords = readtable('coordinates.xlsx'); % Assuming it's coordinates.xlsx

[0100] Read Table 2: Concentration, Wind Speed, Wind Direction, and Weather Stability Level Data

[0101] data=readtable('concentrations.xlsx');

[0102] %Assuming it's concentrations.xlsx

[0103] % Get wind direction angle and coordinates

[0104] theta = data.Wind_Direction; %Wind direction angle column

[0105] u = data.Wind_Speed; %wind speed

[0106] stability = data.Stability_Class; %Weather stability level

[0107] % Store new coordinates

[0108] coords_rotated = coords; % Used to store the rotated coordinates.

[0109] for i = 1:16

[0110] % Get raw coordinates

[0111] x = coords{1,i};

[0112] y = coords{2,i};

[0113] % wind direction angle converted to radians

[0114] theta_rad=deg2rad(theta(i));

[0115] % Calculate the coordinates after rotation

[0116] x_rot=x*cos(theta_rad)-y*sin(theta_rad);

[0117] y_rot=x*sin(theta_rad)+y*cos(theta_rad);

[0118] % Store new coordinates

[0119] coords_rotated{1,i}=x_rot;

[0120] coords_rotated{2,i}=y_rot;

[0121] end

[0122] Define a function to obtain the diffusion coefficient based on wind speed and stability level.

[0123] function[sigma_y,sigma_z]=get_diffusion_coefficients(u,stability)

[0124] The diffusion coefficient is obtained based on the stability level and wind speed.

[0125] The simplified diffusion coefficient formula is used here.

[0126] switch stability

[0127] case 'A' is extremely unstable

[0128] sigma_y = 0.22 * x^0.87;

[0129] sigma_z = 0.20 * x^0.77;

[0130] case'B'% unstable

[0131] sigma_y = 0.16 * x^0.90;

[0132] sigma_z = 0.12 * x^0.75;

[0133] case 'C'% stable

[0134] sigma_y = 0.11 * x^0.92;

[0135] sigma_z = 0.08 * x^0.73;

[0136] case'D'% is relatively stable

[0137] sigma_y = 0.08 * x^0.95;

[0138] sigma_z = 0.06 * x^0.70;

[0139] case'E'% stable

[0140] sigma_y = 0.06 * x^0.97;

[0141] sigma_z = 0.03 * x^0.65;

[0142] case'F'% is extremely stable

[0143] sigma_y = 0.04 * x^1.00;

[0144] sigma_z = 0.016 * x^0.61;

[0145] end

[0146] end

[0147] % Determine if the point is within the Gaussian wake

[0148] in_plume = zeros(1, 16); %1 means within the wake, 0 means not.

[0149] for i = 1:16

[0150] % Obtain the rotated coordinates, wind speed, and stability level

[0151] x_rot=coords_rotated{1,i};

[0152] y_rot=coords_rotated{2,i};

[0153] z_rot = coords{3,i}; % Assuming the z-coordinate remains constant.

[0154] u=data.Wind_Speed(i);% wind speed

[0155] stability = data.Stability_Class(i); % Weather stability level

[0156] % Calculation of diffusion coefficient

[0157] [sigma_y,sigma_z]=get_diffusion_coefficients(u,stability);

[0158] % Determine if the point is within the Gaussian wake

[0159] if abs(y_rot)<=sigma_y&&abs(z_rot)<=sigma_z

[0160] in_plume(i) = 1; % In the wake

[0161] else in_plume(i) = 0; % Not in the wake

[0162] end

[0163] end

[0164] % Correction of concentration at all points

[0165] corrected_concentrations=concentrations-ca;

[0166] % Define the Gaussian model function

[0167] function C_model=gaussian_model(Q,x,y,z,u,sigma_y,sigma_z,h)

[0168] C_model=(Q / (2*pi*u*sigma_y*sigma_z))*exp(-y^2 / (2*sigma_y^2))*exp(-(zh)^2 / (2*sigma_z^2));

[0169] end

[0170] % Set initial guess

[0171] initial_guess =

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

[0172] %Objective Function

[0173] function error = objective(Q)

[0174] error = 0;

[0175] for i = 1:16

[0176] If in_plume(i) == 1%, only the points within the wake are fitted.

[0177] % Data Acquisition

[0178] x_rot=coords_rotated{1,i};

[0179] y_rot=coords_rotated{2,i};

[0180] z_rot = coords{3,i};

[0181] concentration_obs=corrected_concentrations(i);

[0182] u = data.Wind_Speed(i);

[0183] stability=data.Stability_Class(i);

[0184] % Calculation of diffusion coefficient

[0185] [sigma_y,sigma_z]=get_diffusion_coefficients(u,stability);

[0186] Concentration was calculated using a Gaussian model.

[0187] C_model = gaussian_model(Q, x_rot, y_rot, z_rot, u, sigma_y, sigma_z, 0); % Assuming the source height h is 0

[0188] % Calculate the squared error

[0189] error=error+(concentration_obs-C_model)^2;

[0190] end

[0191] end

[0192] end

[0193] % Solve Q using the nonlinear least squares method

[0194] options=optimset('MaxIter',1000,'Display','off');

[0195] Q_optimal=fminsearch(@objective,initial_guess,options);

[0196] Example 2: This example provides a freshwater aquaculture greenhouse gas monitoring system based on a Gaussian diffusion model, including a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the computer program to implement the freshwater aquaculture greenhouse gas monitoring device based on the Gaussian diffusion model described above.

[0197] The foregoing has shown and described 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 to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A greenhouse gas monitoring device for freshwater aquaculture based on a Gaussian diffusion model, characterized in that, Includes the following steps: The sampling and control integrated unit (1) collects gas samples through sampling points, controls gas flow through solenoid valves, and records state changes in real time. Gas analysis and meteorological monitoring (2), which combines the functions of a gas analyzer and a meteorological monitoring station to continuously measure greenhouse gas concentrations and environmental parameters; The coordinate calculation unit (3) establishes a coordinate system based on meteorological monitoring data and recalculates the coordinates of the sampling points by adjusting the direction of the coordinate axes according to the wind direction. Among them, the coordinate calculation unit (3) 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, including the following steps: S2.

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

2. Based on the distance of each sampling point from the origin, establish the coordinates (x, y) of the sampling points on the coordinate axes. 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. The average value of the wind direction angles of all three-dimensional ultrasonic anemometers is recorded as θ. S2.

4. Take the average wind speed from all three-dimensional ultrasonic anemometers, denoted as u, and measure the methane concentration c at each sampling point. i ; S2.

5. Rotate the coordinate axes according to the wind direction θ so that the X-axis coincides with the wind direction angle, and calculate the new coordinates (x, y) based on the rotation matrix. i ′,y i ′,z i ′); Stability classification unit (4), which uses the Pasquill-Gifford method to classify atmospheric stability and calculates the diffusion coefficient at the same time; Among them, the stability classification unit (4) includes 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 based on the ground wind speed u and the net solar radiation level. 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 Using the obtained parameters, the diffusion coefficient is calculated based on the Gaussian diffusion model; The emission source estimation unit (5) applies a Gaussian diffusion model to screen sampling points that meet the conditions, calculates the background concentration, and finally estimates the emission source rate. Among them, the emission source estimation unit (5) applies a Gaussian diffusion model to screen sampling points that meet the conditions, calculates the background concentration, and finally estimates the emission source rate, including the following steps: S4.1, Based on the diffusion coefficient, check whether each coordinate is within the Gaussian diffusion wake: |and i ′|≤2σ y (x); |z|≤σ z (x); Where z represents the horizontal coordinates of the sampling point relative to the emission source; S4.2 Select all new coordinates x i For sampling points where ′<0, the average value of the greenhouse gas concentration at each sampling point is taken to obtain the atmospheric background concentration c. a ; S4.3, The original methane concentration c at each sampling point i Subtract the atmospheric background concentration c a The adjusted concentration c′ was obtained. 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.

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 sampling points, controls gas flow through solenoid valves, and records state changes in real time, including the following steps: S1.1 Configure the sampling point location, start the air pump, and initialize the integrated controller; S1.2 According to the predetermined time t, the integrated controller issues a solenoid valve opening command, and the solenoid valve responds to the command, allowing gas to flow into the system from a specific sampling point; S1.3 The controller continuously monitors the status and abnormal conditions of the solenoid valve, and records the timestamp, corresponding sampling point number and other relevant information for each solenoid valve opening and closing in real time. S1.4 After completing the sampling cycle, the controller sequentially closes all the opened solenoid valves, and the system returns to standby mode, ready to restart 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) includes a gas analysis module (21) and a meteorological monitoring module (22); 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 direction in real time and collects environmental parameters periodically.

4. The freshwater aquaculture greenhouse gas monitoring device based on the Gaussian diffusion model according to claim 1, characterized in that: In step S2.5, the new coordinates (x) are calculated based on the rotation matrix. 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)。 5. The freshwater aquaculture greenhouse gas monitoring device based on the Gaussian diffusion model according to claim 1, characterized in that: The calculation of the diffusion coefficient using the obtained parameters according to the Gaussian diffusion model includes the following steps: Where, σ y (x) represents the horizontal diffusion coefficient at a distance x; σ z (x) represents the vertical diffusion coefficient at a distance x; a y The scaling factor representing the horizontal diffusion coefficient; a z The scaling factor representing the vertical diffusion coefficient; b y The exponential factor representing the horizontal diffusion coefficient; b z The exponential factor represents the vertical diffusion coefficient; x represents the distance from the emission source to the monitoring point.

6. The freshwater aquaculture greenhouse gas monitoring device based on the Gaussian diffusion model according to claim 1, characterized in that: In S4.4, the emission source rate Q is estimated as follows: in, Indicates distance x i The horizontal diffusion coefficient at point ′; Indicates distance x i Vertical diffusion coefficient at point ′; x 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 pi; i represents the index of the sampling point.

7. A greenhouse gas monitoring system for freshwater aquaculture 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 1-6.

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