Greenhouse gas monitoring station layout optimization method and device based on particle swarm optimization

Abstract

The invention provides a greenhouse gas monitoring station layout optimization method and device based on a particle swarm optimization algorithm, and the method comprises the steps: obtaining first greenhouse gas flux field raster data of an observation network, and carrying out the interpolation to obtain second greenhouse gas flux field raster data; n grid placement monitoring stations are extracted from the second greenhouse gas flux field grid data, first footprint field matrixes in one-to-one correspondence with the n grids are obtained, the n first footprint field matrixes are combined into a second footprint field matrix of the observation network, the overall efficiency of the second footprint field matrix is obtained until the maximum iteration round number is reached, and the maximum iteration round number is obtained; and taking n grids corresponding to the second footprint field matrix with the highest overall efficiency as n final grids for placing monitoring sites. According to the method, finer data support is provided for monitoring station layout optimization through interpolation, the footprint field matrixes of all monitoring stations are combined, then the overall observation efficiency is calculated, evaluation on the solution quality is provided, and the positions of the monitoring stations are more accurate.

Description

Technical Field

[0001] The present application relates to the technical field of greenhouse gas monitoring site layout optimization, and in particular to a greenhouse gas monitoring site layout optimization method and device based on a particle swarm algorithm. Background Art

[0002] The Carbon Monitoring and Assessment Pilot is a project that aims to promote sustainable development by monitoring and assessing carbon footprints. The project is jointly participated by government agencies, enterprises and non-governmental organizations, and aims to raise people's awareness of carbon emissions and take measures to reduce their impact on the environment. In the pilot project, a certain number of industrial, commercial and residential areas will be selected as monitoring objects. By installing advanced carbon monitoring equipment, carbon emission data in these areas will be collected. These data will be used to assess carbon emission levels and analyze the sources and influencing factors of carbon emissions. Through the Carbon Monitoring and Assessment Pilot, we will be able to better understand the status and trends of carbon emissions and provide valuable reference data for governments and enterprises. This will help promote the goal of sustainable development, reduce the impact of carbon emissions on the environment, and improve people's quality of life. Therefore, the optimization of the monitoring site layout has become an important design link.

[0003] However, the current site selection of greenhouse gas monitoring sites relies on qualitative analysis and personal experience, and the layout of existing monitoring sites is optimized directly based on the raw data without further processing the raw data, which may have low resolution and cannot provide sufficient spatial details. Summary of the invention

[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide a greenhouse gas monitoring site layout optimization method and device based on a particle swarm algorithm, which provides more refined data support for the optimization of the monitoring site layout through interpolation, and provides an evaluation of the solution quality by merging the footprint field matrices of each monitoring site and then calculating the overall observation efficiency, so as to make the monitoring site location more accurate.

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

[0006] The greenhouse gas monitoring site layout optimization method based on particle swarm algorithm includes:

[0007] Obtain the first greenhouse gas flux field grid data of the observation network;

[0008] Interpolating the first greenhouse gas flux field grid data to obtain second greenhouse gas flux field grid data having a resolution consistent with a preset footprint field matrix;

[0009] Extract n grids from the second greenhouse gas flux field grid data to place monitoring stations;

[0010] According to the grid position of the first greenhouse gas flux field grid data of each monitoring station, the corresponding first footprint field matrix is ​​obtained, and the center position coordinates of the first footprint field matrix are set as the spatial coordinates of the monitoring station;

[0011] Merge the n first footprint field matrices corresponding to the n grids into a second footprint field matrix of the observation network;

[0012] Obtaining the overall efficiency of the second footprint field matrix;

[0013] Jump to n grids from the second greenhouse gas flux field raster data until the maximum number of iterations is reached, and use the n grids corresponding to the second footprint field matrix with the highest overall efficiency as the n final grids to place monitoring sites.

[0014] Furthermore, the method of interpolating the first greenhouse gas flux field grid data to obtain the second greenhouse gas flux field grid data having the same resolution as the preset footprint field matrix is:

[0015] The first greenhouse gas flux field grid data is interpolated using the weighted summation method based on the Lanczos interpolation algorithm.

[0016] Furthermore, the first greenhouse gas flux field raster data of the study area are obtained through the meteorological observation station and its website.

[0017] Furthermore, the method of merging the n first footprint field matrices corresponding to the n monitoring sites into the second footprint field matrix is:

[0018] The n first footprint field matrices corresponding to n grids are merged according to the spatial position. Specifically, for a spatial position, if the spatial position has different values, the maximum value is taken as the value at the spatial position. If there is only one value at the spatial position, the value is taken as the value at the spatial position to obtain the second footprint field matrix. The above operation is performed for all spatial positions until all spatial positions in the n first footprint field matrices are traversed to obtain the second footprint field matrix. The calculation formula is:

[0019]

[0020] Among them, H merged is the combined second footprint field matrix, H i is the first footprint field matrix corresponding to the i-th grid, H merged (x, y) is the value at the spatial position (x, y) in the merged second footprint field matrix, H i (x, y) represents the value at the spatial position (x, y) in the first footprint field matrix corresponding to the i-th grid.

[0021] Furthermore, the calculation formula for obtaining the overall efficiency of the second footprint field matrix is:

[0022]

[0023] Among them, H merged is the merged second footprint field matrix, X is the greenhouse gas flux field, The operation means multiplying the elements at corresponding positions of the matrix and then summing them.

[0024] Furthermore, the PSO algorithm is used to extract n grids from the second greenhouse gas flux field grid data to place monitoring stations. The specific method is as follows:

[0025] On the second greenhouse gas flux field grid data, randomly select n grids as a particle, and the particle group size is num_particles;

[0026] Calculate the overall efficiency of the observation network as the fitness of each particle, and the initial position of each particle as the individual optimal solution of the particle. Find the maximum fitness among all particles and use the position of the particle as the optimal solution of the particle swarm.

[0027] Set the maximum number of iterations and initialize the speed to 0. The speed of each particle is a 2n-dimensional vector.

[0028] Update the velocity and position of each particle;

[0029] Based on the updated particle speed and position, the particle speed and / or position is adjusted, specifically: if the particle position does not fall on the grid, the particle position is corrected to the grid closest to it; if the particle crosses the grid boundary, the particle speed and position are corrected;

[0030] Update the optimal position pbest of each particle and the optimal position gbest of the population;

[0031] Returns the speed and position of each particle updated until the maximum number of iterations is reached.

[0032] Furthermore, when a particle crosses the grid boundary, the calculation formula for correcting the particle's velocity and position is:

[0033] v(t+1)=v(t)×k

[0034] x(t+1)=x(t)+k×[x boundary -x(t)]

[0035] Where d is the distance between the current position and the grid boundary, k is the adjustment factor, and x boundaryare the boundary coordinates, v(t) is the velocity of the particle at time t, v(t+1) is the velocity of the particle at time t+1, x(t) is the position of the particle at time t, and x(t+1) is the position of the particle at time t.

[0036] A greenhouse gas monitoring site layout optimization device based on particle swarm algorithm, comprising:

[0037] A first acquisition module is used to acquire first greenhouse gas flux field grid data of the observation network;

[0038] An interpolation module, used for interpolating the first greenhouse gas flux field grid data to obtain second greenhouse gas flux field grid data having a resolution consistent with a preset footprint field matrix;

[0039] A location extraction module, used for extracting n grids from the second greenhouse gas flux field grid data to place monitoring sites;

[0040] A second acquisition module is used to acquire the corresponding first footprint field matrix according to the grid position of the first greenhouse gas flux field grid data of each monitoring site, and set the center position coordinates of the first footprint field matrix as the spatial coordinates of the monitoring site;

[0041] A merging module, used for merging n first footprint field matrices corresponding to n grids into a second footprint field matrix of the observation network;

[0042] A third acquisition module is used to acquire the overall efficiency of the second footprint field matrix;

[0043] The fourth acquisition module is used to jump from n grids in the second greenhouse gas flux field raster data until the maximum number of iterations is reached, and the n grids corresponding to the second footprint field matrix with the highest overall efficiency are used as n final grids to place monitoring sites.

[0044] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the greenhouse gas monitoring site layout optimization method based on the particle swarm algorithm is implemented.

[0045] A non-transitory computer-readable storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned greenhouse gas monitoring site layout optimization method based on particle swarm algorithm.

[0046] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0047] The greenhouse gas monitoring site layout optimization method based on particle swarm algorithm provided by the present invention can improve the resolution of flux field data through interpolation, so as to more accurately evaluate the coverage effect of the monitoring site on the flux field, and can better retain the original characteristics of the data, reduce the information loss in the interpolation process, and provide more refined data support for the optimization of the monitoring site layout. In addition, the present invention places monitoring sites by repeatedly updating n grids until the maximum number of iterations is reached, and uses the n grids corresponding to the second footprint field matrix with the highest overall efficiency as the n final grids to place monitoring sites, which can further improve the optimization efficiency and accuracy of the monitoring site layout. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0049] Figure 1 It is a flow chart of the greenhouse gas monitoring site layout optimization method based on particle swarm algorithm of the present invention;

[0050] Figure 2 is a schematic diagram of an interpolated methane flux field in a specific embodiment of the present invention;

[0051] Figure 3 is a schematic diagram of the overall efficiency (fitness) of the optimal particles in a specific embodiment of the present invention;

[0052] Figure 4 It is a first schematic diagram of the monitoring station position corresponding to the optimal particle in a specific implementation manner of the present invention;

[0053] Figure 5 A second schematic diagram of the location of the monitoring site corresponding to the optimal particle in a specific embodiment of the present invention;

[0054] Figure 6 It is a schematic diagram of the merged second footprint field matrix corresponding to the optimal particle in a specific implementation manner of the present invention;

[0055] Figure 7 It is a schematic diagram of a greenhouse gas monitoring site layout optimization device based on a particle swarm algorithm according to the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0057] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present invention. However, it is apparent to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features well known in the art are not described.

[0058] It should be understood that the present invention can be implemented in different forms and should not be interpreted as limited to the embodiments set forth herein. On the contrary, these embodiments are provided to make the disclosure thorough and complete and to fully convey the scope of the present invention to those skilled in the art.

[0059] The present invention is described in detail below by taking the example of selecting 5 grids in a certain observation network to place monitoring stations to monitor methane emissions.

[0060] Example 1

[0061] Example 1 provides a greenhouse gas monitoring site layout optimization method based on a particle swarm algorithm, such as Figure 1 As shown, the following steps are included:

[0062] Step S1: Acquire the first greenhouse gas flux field grid data of the observation network;

[0063] Step S2: interpolating the first greenhouse gas flux field grid data to obtain second greenhouse gas flux field grid data having a resolution consistent with a preset footprint field matrix;

[0064] Step S3: extracting n grids from the second greenhouse gas flux field grid data to place monitoring sites;

[0065] Step S4: according to the grid position of the first greenhouse gas flux field grid data of each monitoring site, the corresponding first footprint field matrix is ​​obtained, and the center position coordinates of the first footprint field matrix are set as the spatial coordinates of the monitoring site;

[0066] Step S5: merging the n first footprint field matrices corresponding to the n grids into a second footprint field matrix of the observation network;

[0067] Step S6: obtaining the overall efficiency of the second footprint field matrix;

[0068] Step S7: Jump to step S3 until the maximum number of iterations is reached, and the n grids corresponding to the second footprint field matrix with the highest overall efficiency are used as the n final grids to place monitoring sites.

[0069] The greenhouse gas monitoring site layout optimization method based on the particle swarm algorithm provided in this embodiment can improve the resolution of the flux field data through interpolation, so as to more accurately evaluate the coverage effect of the monitoring site on the flux field, and can better retain the original characteristics of the data, reduce the information loss in the interpolation process, and provide more refined data support for the optimization of the monitoring site layout. In addition, this embodiment can further improve the optimization efficiency and accuracy of the monitoring site layout by repeatedly updating n grids to place monitoring sites until the maximum number of iterations is reached, and using the n grids corresponding to the second footprint field matrix with the highest overall efficiency as the n final grids to place monitoring sites.

[0070] In this embodiment, the footprint refers to the impact intensity of the upstream pollution source on a certain grid. Each element of the footprint field matrix corresponds to the footprint of each grid in the actual space.

[0071] The method for obtaining the footprint field matrix is ​​as follows: obtain atmospheric data such as wind speed, wind direction, and temperature from meteorological observation stations, determine several upstream locations (such as oil fields, industrial areas, etc.), use the WRF-STILT model to simulate the transmission and diffusion of tracer particles in the atmosphere, track the trajectory of these particles, take a specific time step, understand which upstream areas the air received by each grid comes from, and calculate the impact intensity of the upstream location on a certain location, that is, the footprint of the location. Calculate the footprint for each grid to form a footprint field matrix.

[0072] In step S1 of this embodiment, the first greenhouse gas flux field grid data of the study area is obtained through a meteorological observation station and its website.

[0073] In the specific implementation of this embodiment, the methane flux field raster data is clipped to the experimental range using the clipping grid by range function of QGIS software.

[0074] In step S2 of this embodiment, the resolution of the preset footprint field matrix is ​​set as needed, that is, it is determined according to the accuracy of the monitoring site location, and each grid in the second greenhouse gas flux field raster data obtained after interpolation is a candidate location. If the size of the matrix corresponding to the first greenhouse gas flux field raster data is 10×10, the resolution is 10km. If the required accuracy of the monitoring site location is 1km, then the size of the matrix corresponding to the second greenhouse gas flux field raster data after interpolation should be 100×100, and the resolution is 1km. At this time, each element (grid) in the matrix corresponding to the second greenhouse gas flux field raster data corresponds to a grid of 1km×1km in the actual space, and each grid is a candidate location. The final solution is to select several candidate locations as monitoring sites from many candidate locations.

[0075] In practice, the resolution of the first greenhouse gas flux field grid data may be several kilometers to tens of kilometers, while the resolution of the preset footprint field matrix may be within one kilometer. Through step S2, it can be ensured that the size of the actual space corresponding to one grid of the second greenhouse gas flux field grid data is equal to the size of the actual space corresponding to one grid of the preset footprint field matrix.

[0076] In step S2 of this embodiment, the method of interpolating the first greenhouse gas flux field grid data to obtain the second greenhouse gas flux field grid data having a resolution consistent with the preset footprint field matrix is:

[0077] The first greenhouse gas flux field grid data is interpolated using the weighted summation method based on the Lanczos interpolation algorithm.

[0078] The Lanczos interpolation method can improve the resolution of flux field data, thereby more accurately evaluating the coverage of the flux field by monitoring stations. Lanczos interpolation is a high-precision interpolation method that can better preserve the original characteristics of the data, reduce information loss during the interpolation process, and provide more refined data support for the optimization of monitoring station layout.

[0079] Specifically, the Lanczos interpolation algorithm uses the pixel values ​​around the interpolation point to calculate the value of the interpolation point by weighted summation; the formula is as follows:

[0080]

[0081] Among them, V(x, y) is the value of the point to be interpolated, f(i, j) is the pixel value around the point to be interpolated, L(·) is the Lanczos function, a is the Lanczos interpolation parameter, which is 4 in step S2 of this embodiment, and d is the interpolation multiple, which is the matrix size corresponding to the second greenhouse gas flux field grid data after interpolation divided by the matrix size corresponding to the first greenhouse gas flux field grid data before interpolation in step S2 of this embodiment, that is, d=350 / / 34.

[0082] The definition formula of the Lanczos function is:

[0083]

[0084] For example, the matrix corresponding to the first greenhouse gas flux field raster data is a 10×10 matrix, and each element corresponds to a 10km×10km grid in actual space (called a large grid, and the geographic coordinates of each large grid can be easily known).

[0085] Each large grid has a corresponding first footprint field matrix (a total of 100 first footprint field matrices). Each element in a first footprint field matrix corresponds to 1km×1km of actual space. The size of the first footprint field matrix can be 50×50, 200×200, etc., without limitation.

[0086] Then the first greenhouse gas flux field raster data is interpolated, and the matrix size corresponding to the obtained second greenhouse gas flux field raster data becomes 100×100. At this time, each element corresponds to a 1km×1km grid in the actual space (called a small grid), but the area covered by the matrix of the entire second greenhouse gas flux field raster data in the actual space remains basically unchanged, except that the resolution is increased from 10km to 1km.

[0087] When solving the location of the monitoring site, all small grids are candidate locations. If a small grid is selected, find the first footprint field matrix of the large grid to which the small grid belongs, and synchronize the geographic coordinates of the center of the first footprint field matrix to the coordinates of the small grid (because interpolation ensures that the resolution of the first footprint field matrix is ​​the same as the resolution of the matrix corresponding to the second greenhouse gas flux field raster data, so that each element of the first footprint field matrix can correspond to the matrix corresponding to the second greenhouse gas flux field raster data after translation, and element-by-element multiplication can be performed). If 5 small grids are selected, there are 5 first footprint field matrices with determined geographic coordinates. Take a large matrix that just covers these 5 first footprint field matrices as the second footprint field matrix. If there are multiple first footprint field matrices overlapping in the second footprint field matrix, take the larger value, and if there is a spare, take 0.

[0088] In the specific implementation of this embodiment, the matrix corresponding to the cropped first greenhouse gas flux field grid data is a 34*34 matrix, and the matrix corresponding to the second greenhouse gas flux field grid data obtained by interpolation using the Lanczos method is a 350*350 matrix.

[0089] In step S2 of this embodiment, Figure 2 As shown, it is the interpolated methane flux field in a specific implementation manner, and each grid in the second greenhouse gas flux field raster data is a candidate location.

[0090] In step S3 and step S6 of this embodiment, 5 locations are randomly selected from all candidate locations, ie, all small grids in the second greenhouse gas flux field grid data, to place monitoring stations, ie, n=5.

[0091] In the specific implementation manner of this embodiment, in step S4, five first footprint field matrices are obtained.

[0092] In step S5 of this embodiment, the method of merging the n first footprint field matrices corresponding to the n monitoring sites into the second footprint field matrix is:

[0093] The n first footprint field matrices corresponding to n grids are merged according to the spatial position. Specifically, for a spatial position, if there are different values ​​at the spatial position, the maximum value is taken as the value at the spatial position. If there is only one value at the spatial position, the value is taken as the value at the spatial position to obtain the second footprint field matrix. The above operation is performed for all spatial positions until all spatial positions in the n first footprint field matrices are traversed to obtain the second footprint field matrix. The calculation formula is:

[0094]

[0095] Among them, H merged is the combined second footprint field matrix, H i is the first footprint field matrix corresponding to the i-th grid, H merged (x, y) is the value at the spatial position (x, y) in the merged second footprint field matrix, H i (x, y) represents the value at the spatial position (x, y) in the first footprint field matrix corresponding to the i-th grid.

[0096] In the specific implementation manner of this embodiment, 5 points are selected on the 70*70 grid in the second greenhouse gas flux field raster data, and the 5 first footprint field matrices corresponding to the 5 points are merged into 1 second footprint field matrix. Since the size of the first footprint field matrix is ​​280, the size of the merged second footprint field matrix is ​​70+280=350 (the 4 points corresponding to the four corners of the 70*70 grid must be extended outward by 140, so 280 must be added), and the matrix size of the second greenhouse gas flux field raster data (after interpolation) is also 350. The size of the second footprint field matrix is ​​always consistent with the size of the second greenhouse gas flux field raster data, so the size of the second footprint field matrix is ​​350*350, such as Figure 6 , which is a schematic diagram of the merged footprint field matrix corresponding to the optimal particle in a specific implementation manner.

[0097] In step S6 of this embodiment, the element values ​​of the overall second footprint field matrix and the distribution of these values ​​are involved when calculating the overall efficiency of the observation network according to the formula; according to the given formula, the overall second footprint field matrix is ​​calculated to obtain a value, which is the overall efficiency of the observation network. The calculation formula for obtaining the overall efficiency E of the second footprint field matrix is:

[0098]

[0099] Among them, H mergedis the merged second footprint field matrix, X is the greenhouse gas flux field, The operation means multiplying the elements at corresponding positions of the matrix and then summing them.

[0100] In the specific implementation manner of this embodiment, Figure 3 The figure shows the overall efficiency (fitness) of the optimal particles.

[0101] In step S3 and step S6 of this embodiment, the PSO algorithm is used to extract n grids from the second greenhouse gas flux field grid data to place monitoring sites. The specific method is:

[0102] Step S301: Randomly select n grids on the second greenhouse gas flux field grid data as a particle, and the particle group size is num_particles;

[0103] Step S302: Calculate the overall efficiency of the observation network as the fitness of each particle, and the initial position of each particle as the individual optimal solution pbest of the particle. Find the maximum fitness among all particles, and use the position of the particle as the particle swarm optimal solution gbest.

[0104] Step S303: Set the maximum number of iterations to run max , and the initialization speed is 0, the speed of each particle is a 2n-dimensional vector;

[0105] Step S304: Update the speed and position of each particle;

[0106] Step S305: Based on the updated particle speed and position, adjust the particle speed and / or position, specifically: if the particle position does not fall on the grid, correct the particle position to the grid closest to it; if the particle crosses the grid boundary, correct the particle speed and position;

[0107] Step S306: Update the optimal position pbest of each particle and the optimal position gbest of the population;

[0108] Step S307: Return to update the speed and position of each particle until the maximum number of iterations is reached.

[0109] In this specific implementation manner, in step S301, 5 grids are randomly selected on the 70*70 grid of the second greenhouse gas flux field grid data as a particle, and the particle group size num_particles=30.

[0110] In this specific implementation, in step S303, run max =500.

[0111] In step S304 of this embodiment, the speed of each particle is updated according to the following formula:

[0112] v(t+1)=ωv(t)+c1r1(pbest-x(t))+c2r2(gbest-x(t))

[0113] Wherein, v(t) is the velocity of the particle at time t, v(t+1) is the velocity of the particle at time t+1, x(t) is the position of the particle at time t, x(t+1) is the position of the particle at time t, r1 and r2 are two independent random numbers in the range [0,1], c1 and c2 are two constants. In this embodiment, c1=c1=2.

[0114] Where ω is the inertia weight factor, Run the current iteration number, ω max and ω min are the set maximum and minimum factors.

[0115] In this embodiment, when a particle crosses a grid boundary, the calculation formula for correcting the particle's velocity and position is:

[0116] v(t+1)=v(t)×k

[0117] x(t+1)=x(t)+k×[x boundary -x(t)]

[0118] Where d is the distance between the current position and the grid boundary, k is the adjustment factor, and x boundary are the grid boundary coordinates, v(t) is the velocity of the particle at time t, v(t+1) is the velocity of the particle at time t+1, x(t) is the position of the particle at time t, and x(t+1) is the position of the particle at time t.

[0119] In the specific implementation manner of this embodiment, Figure 4 As shown in FIG. 1 , it is a first schematic diagram of the monitoring station position corresponding to the optimal particle in a specific implementation manner, Figure 5 As shown, it is a second schematic diagram of the monitoring station positions corresponding to the optimal particles in a specific implementation manner.

[0120] In the specific implementation of this embodiment, in step S4, the footprint flux field matrix includes 49 csv files, which contain the central longitude and latitude coordinates of the pre-interpolation grid to which each first footprint field matrix belongs. The size of each first footprint field matrix is ​​280*280, which is approximately 280km*280km.

[0121] In summary, in the present invention, by using the particle swarm algorithm (PSO), the method can effectively find the optimal combination of observation station locations. The PSO algorithm is an optimization tool based on swarm intelligence, which simulates the behavior of bird flocks or fish schools, and finds the optimal solution to the problem through the mutual cooperation and competition of individuals in the group. In this method, each particle represents a possible monitoring site layout scheme. Through iterative search, the particles can gradually approach the optimal solution, thereby improving the optimization efficiency and accuracy of the monitoring site layout.

[0122] Moreover, the present invention uses the particle swarm algorithm, which can theoretically search the entire solution space. It does not need to select initial particles based on manual experience. It has randomness in iteration, can search for solutions that have not been tried before, can select points on a scale of hundreds of kilometers, has a stronger search capability for the solution space, and can select points for joint observations between cities on a larger scale.

[0123] Through the Lanczos interpolation method, the method can improve the resolution of greenhouse gas flux field data, so as to more accurately evaluate the coverage effect of the monitoring site on the flux field. Lanczos interpolation is a high-precision interpolation method that can better preserve the original characteristics of the data, reduce information loss in the interpolation process, and provide more refined data support for the optimization of monitoring site layout.

[0124] Furthermore, the present invention obtains a second footprint field matrix by combining the first footprint field matrices of each monitoring site and then calculates the overall efficiency, thereby providing an evaluation of the solution quality.

[0125] By correcting the velocity and position of particles in the PSO algorithm when they do not fall on the grid points or cross the grid boundaries, the rationality of the solution and the sustainability of the algorithm are guaranteed.

[0126] Example 2

[0127] Embodiment 2 provides a greenhouse gas monitoring site layout optimization device based on a particle swarm algorithm, such as Figure 7 As shown, including:

[0128] A first acquisition module is used to acquire first greenhouse gas flux field grid data of the observation network;

[0129] An interpolation module, used for interpolating the first greenhouse gas flux field grid data to obtain second greenhouse gas flux field grid data having a resolution consistent with a preset footprint field matrix;

[0130] A location extraction module, used for extracting n grids from the second greenhouse gas flux field grid data to place monitoring sites;

[0131] A second acquisition module is used to acquire the corresponding first footprint field matrix according to the grid position of the first greenhouse gas flux field grid data of each monitoring site, and set the center position coordinates of the first footprint field matrix as the spatial coordinates of the monitoring site;

[0132] A merging module, used for merging n first footprint field matrices corresponding to n grids into a second footprint field matrix of the observation network;

[0133] A third acquisition module is used to acquire the overall efficiency of the second footprint field matrix;

[0134] The fourth acquisition module is used to jump from n grids in the second greenhouse gas flux field raster data until the maximum number of iterations is reached, and the n grids corresponding to the second footprint field matrix with the highest overall efficiency are used as n final grids to place monitoring sites.

[0135] Example 3

[0136] Embodiment 3 provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned greenhouse gas monitoring site layout optimization method based on particle swarm algorithm when executing the program.

[0137] Example 4

[0138] Embodiment 4 provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the greenhouse gas monitoring site layout optimization method based on particle swarm algorithm is implemented.

[0139] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0140] The technical features of the above embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0141] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A greenhouse gas monitoring site layout optimization method based on particle swarm algorithm, characterized in that: include: Obtain the first greenhouse gas flux field grid data of the observation network; Interpolating the first greenhouse gas flux field grid data to obtain second greenhouse gas flux field grid data having a resolution consistent with a preset footprint field matrix; Extract n grids from the second greenhouse gas flux field grid data to place monitoring stations; According to the grid position of the first greenhouse gas flux field grid data of each monitoring station, the corresponding first footprint field matrix is ​​obtained, and the center position coordinates of the first footprint field matrix are set as the spatial coordinates of the monitoring station; Merge the n first footprint field matrices corresponding to the n grids into a second footprint field matrix of the observation network; Obtaining the overall efficiency of the second footprint field matrix; Jump to n grids from the second greenhouse gas flux field raster data until the maximum number of iterations is reached, and use the n grids corresponding to the second footprint field matrix with the highest overall efficiency as the n final grids to place monitoring sites.

2. The greenhouse gas monitoring site layout optimization method based on particle swarm algorithm according to claim 1 is characterized in that: The method for interpolating the first greenhouse gas flux field grid data to obtain the second greenhouse gas flux field grid data having the same resolution as the preset footprint field matrix is: The first greenhouse gas flux field grid data is interpolated using the weighted summation method based on the Lanczos interpolation algorithm.

3. The greenhouse gas monitoring site layout optimization method based on particle swarm algorithm according to claim 1 is characterized in that: The first greenhouse gas flux field raster data of the study area were obtained through meteorological observation stations and their websites.

4. The greenhouse gas monitoring site layout optimization method based on particle swarm algorithm according to claim 1 is characterized in that: The method of merging the n first footprint field matrices corresponding to the n monitoring sites into the second footprint field matrix is: The n first footprint field matrices corresponding to n grids are merged according to the spatial position. Specifically, for a spatial position, if the spatial position has different values, the maximum value is taken as the value at the spatial position. If the spatial position has only one value, the value is taken as the value at the spatial position to obtain the second footprint field matrix. The above operation is performed on all spatial positions until all spatial positions in the n first footprint field matrices are traversed to obtain the second footprint field matrix. The calculation formula is: Among them, H merged is the combined second footprint field matrix, H i is the first footprint field matrix corresponding to the i-th grid, H merged (x, y) is the value at the spatial position (x, y) in the merged second footprint field matrix, H i (x,y) represents the value of the spatial position (x,y) in the first footprint field matrix corresponding to the i-th grid.

5. The greenhouse gas monitoring site layout optimization method based on particle swarm algorithm according to claim 1 is characterized in that: The calculation formula for obtaining the overall efficiency of the second footprint field matrix is: Among them, H merged is the merged second footprint field matrix, X is the greenhouse gas flux field, The operation means multiplying the elements at corresponding positions of the matrix and then summing them.

6. The greenhouse gas monitoring site layout optimization method based on particle swarm algorithm according to claim 1, characterized in that: The PSO algorithm is used to extract n grids from the second greenhouse gas flux field grid data to place monitoring stations. The specific method is as follows: On the second greenhouse gas flux field grid data, randomly select n grids as a particle, and the particle group size is num_particles; Calculate the overall efficiency of the observation network as the fitness of each particle, and the initial position of each particle as the individual optimal solution of the particle. Find the maximum fitness among all particles and use the position of the particle as the optimal solution of the particle swarm. Set the maximum number of iterations and initialize the speed to 0. The speed of each particle is a 2n-dimensional vector. Update the velocity and position of each particle; Based on the updated particle speed and position, the particle speed and / or position is adjusted, specifically: if the particle position does not fall on the grid, the particle position is corrected to the grid closest to it; if the particle crosses the grid boundary, the particle speed and position are corrected; Update the optimal position pbest of each particle and the optimal position gbest of the population; Returns the speed and position of each particle updated until the maximum number of iterations is reached.

7. The greenhouse gas monitoring site layout optimization method based on particle swarm algorithm according to claim 6 is characterized in that: When a particle crosses the grid boundary, the calculation formula for correcting the particle's velocity and position is: v(t+1)=v(t)×k x(t+1)=x(t)+k×[x boundary -x(t)] Where d is the distance between the current position and the grid boundary, k is the adjustment factor, and x boundary are the boundary coordinates, v(t) is the velocity of the particle at time t, v(t+1) is the velocity of the particle at time t+1, x(t) is the position of the particle at time t, and x(t+1) is the position of the particle at time t.

8. A greenhouse gas monitoring site layout optimization device based on particle swarm algorithm, characterized in that: include: A first acquisition module is used to acquire first greenhouse gas flux field grid data of the observation network; An interpolation module, used for interpolating the first greenhouse gas flux field grid data to obtain second greenhouse gas flux field grid data having a resolution consistent with a preset footprint field matrix; A location extraction module, used for extracting n grids from the second greenhouse gas flux field grid data to place monitoring sites; A second acquisition module is used to acquire the corresponding first footprint field matrix according to the grid position of the first greenhouse gas flux field grid data of each monitoring site, and set the center position coordinates of the first footprint field matrix as the spatial coordinates of the monitoring site; A merging module, used for merging n first footprint field matrices corresponding to n grids into a second footprint field matrix of the observation network; A third acquisition module is used to acquire the overall efficiency of the second footprint field matrix; The fourth acquisition module is used to jump from n grids in the second greenhouse gas flux field raster data until the maximum number of iterations is reached, and the n grids corresponding to the second footprint field matrix with the highest overall efficiency are used as n final grids to place monitoring sites.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the greenhouse gas monitoring site layout optimization method based on the particle swarm algorithm as described in any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the greenhouse gas monitoring site layout optimization method based on the particle swarm algorithm as described in any one of claims 1 to 7 is implemented.

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