Method and apparatus for determining co2 monitoring point, terminal device and storage medium

Abstract

The application provides a CO2 monitoring point determination method and device, terminal equipment and a storage medium. The method comprises the following steps: determining the simulation meteorological data of a target area and a plurality of target grids, taking the center point of each target grid as an initial monitoring point; inputting the simulation meteorological data of the target area and the target grid into a STILT model to obtain the hourly CO2 footprint of each initial monitoring point; calculating the error percentage of each initial monitoring point according to the hourly CO2 footprint; selecting a preset number of initial monitoring points as candidate monitoring points based on the error percentage; calculating the average footprint based on the hourly CO2 footprint of each candidate monitoring point, and judging whether the average footprint meets a preset condition; if yes, the preset number of candidate monitoring points are determined as target monitoring points. The application can accurately determine the target monitoring points to monitor CO2 at the target monitoring points, thereby improving the accuracy of subsequent inversion of the CO2 emission amount.

Description

Technical Field

[0001] This application relates to the field of atmospheric technology, specifically to a method, apparatus, terminal equipment, and storage medium for determining CO2 monitoring points. Background Technology

[0002] In recent years, the concentration of carbon dioxide (CO2) in the atmosphere has increased dramatically. Cities, as major CO2 emission areas, contribute 70% of global anthropogenic CO2 emissions. Therefore, it is essential to retrieve CO2 emissions from urban areas to study anthropogenic carbon emissions. To provide accurate and effective monitoring data for CO2 emission retrieval, it is necessary to establish a reasonable network of CO2 monitoring stations.

[0003] Existing methods for determining city-level CO2 monitoring stations typically rely on extensive historical wind direction data for the region, deploying monitoring stations based on experience. However, there is no quantifiable value to determine whether the determined CO2 monitoring stations are appropriate, which may lead to unreasonable deployment of CO2 monitoring stations and consequently affect the accuracy of subsequent CO2 emission calculations based on these monitoring stations. Summary of the Invention

[0004] In view of this, embodiments of this application provide a method, apparatus, terminal equipment, and storage medium for determining CO2 monitoring points, in order to solve the technical problem that existing methods for determining CO2 monitoring points may have unreasonable CO2 monitoring point layout, which in turn affects the accuracy of subsequent CO2 emission inversion.

[0005] In a first aspect, embodiments of this application provide a method for determining CO2 monitoring points, comprising: inputting reanalyzable meteorological data of a target area for a preset period into a WRF model to obtain simulated meteorological data of the target area; dividing the target area into multiple target grids, and using the center point of each target grid as an initial monitoring point; inputting the simulated meteorological data of the target area and the target grids into a STILT model to obtain the hourly CO2 footprint of each initial monitoring point; calculating the error percentage of each initial monitoring point based on the hourly CO2 footprint of each initial monitoring point and a preset prior error covariance matrix; selecting a preset number of initial monitoring points as candidate monitoring points based on the error percentage of each initial monitoring point; calculating the average footprint based on the hourly CO2 footprint of each candidate monitoring point, and determining whether the average footprint meets a preset condition; if so, determining the preset number of candidate monitoring points as target monitoring points, so that CO2 monitoring is carried out at the preset number of target monitoring points.

[0006] In one possible implementation of the first aspect, calculating the error percentage of each initial monitoring point based on the hourly CO2 footprint of each initial monitoring point and a preset prior error covariance matrix includes: calculating a posterior error covariance matrix based on the hourly matrix corresponding to the hourly CO2 footprint of each initial monitoring point and the preset prior error covariance matrix; and obtaining the error percentage of each initial monitoring point based on the preset prior error covariance matrix and the posterior error covariance matrix.

[0007] In one possible implementation of the first aspect, selecting a preset number of initial monitoring points as candidate monitoring points based on the error percentage of each initial monitoring point includes: sorting the error percentage of each initial monitoring point from smallest to largest; randomly selecting a preset number of initial monitoring points as candidate monitoring points from the initial monitoring points corresponding to the first X error percentages; wherein the value of X is greater than the preset number.

[0008] In one possible implementation of the first aspect, based on the hourly CO2 footprint of each candidate monitoring point, an average footprint is calculated, and it is determined whether the average footprint meets a preset condition; if so, a preset number of candidate monitoring points are determined as target monitoring points, including: calculating an average matrix corresponding to the average footprint based on the hourly matrix corresponding to the hourly CO2 footprint of each candidate monitoring point; determining whether the average matrix meets a preset condition; the preset condition is that the proportion of non-zero values ​​in the average matrix among all values ​​of the average matrix is ​​greater than a preset threshold; if so, a preset number of candidate monitoring points are determined as target monitoring points.

[0009] In one possible implementation of the first aspect, the method further includes: after determining a preset number of candidate monitoring points as target monitoring points, selecting the next preset number of initial monitoring points as the next set of candidate monitoring points and repeating the target monitoring point determination step to obtain multiple sets of target monitoring points; calculating the sum of the error percentages corresponding to each set of target monitoring points as the sum of the error percentages of the set of target monitoring points; and selecting the set with the smallest sum of error percentages among the multiple sets of target monitoring points as the final target monitoring point.

[0010] In one possible implementation of the first aspect, the method further includes: if the average footprint does not meet the preset conditions, then selecting the next preset number of initial monitoring points as the next set of candidate monitoring points and repeating the target monitoring point judgment step; if the next preset number of initial monitoring points is selected as the next set of candidate monitoring points and the target monitoring point judgment step is repeated for a preset number of times, and the result of the target monitoring point judgment is negative each time, then the preset number is incremented by one as the new preset number, and the process jumps to the step of selecting the preset number of initial monitoring points as candidate monitoring points based on the error percentage of each initial monitoring point, and the process is repeated until the target monitoring point is determined.

[0011] In one possible implementation of the first aspect, the target area is divided into multiple target grids, including: determining a target urban area based on the CO2 emission inventory and population density of the target area; and dividing the target urban area according to latitude and longitude to obtain multiple target grids.

[0012] Secondly, embodiments of this application provide a CO2 monitoring point determination device, comprising:

[0013] The simulation module is used to input reanalyzable meteorological data of the target area for a preset time period into the WRF model to obtain simulated meteorological data of the target area;

[0014] The segmentation module is used to divide the target area into multiple target grids, and the center point of each target grid is used as the initial monitoring point.

[0015] The determination module is used to input simulated meteorological data of the target area and the target grid into the STILT model to obtain the hourly CO2 footprint of each initial monitoring point;

[0016] The calculation module is used to calculate the error percentage of each initial monitoring point based on the hourly CO2 footprint and the preset prior error covariance matrix of each initial monitoring point;

[0017] The judgment module is used to select a preset number of initial monitoring points as candidate monitoring points based on the error percentage of each initial monitoring point; calculate the average footprint based on the hourly CO2 footprint of each candidate monitoring point, and determine whether the average footprint meets the preset conditions; if so, determine the preset number of candidate monitoring points as target monitoring points, so that CO2 monitoring can be carried out at the preset number of target monitoring points.

[0018] Thirdly, embodiments of this application provide a terminal device, including a memory and a processor. The memory stores a computer program that can run on the processor. When the processor executes the computer program, it implements the CO2 monitoring point determination method as described in any of the first aspects.

[0019] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the CO2 monitoring point determination method as described in any of the first aspects.

[0020] Fifthly, embodiments of this application provide a computer program product that, when run on a terminal device, causes the terminal device to execute the CO2 monitoring point determination method described in any of the first aspects.

[0021] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0022] The CO2 monitoring point determination method, apparatus, terminal equipment, and storage medium provided in this application embodiment determine simulated meteorological data of a target area and multiple target grids. The center point of each target grid is used as an initial monitoring point. The simulated meteorological data of the target area and the target grids are input into the STILT model to obtain the hourly CO2 footprint of each initial monitoring point. The error percentage of each initial monitoring point is calculated based on its hourly CO2 footprint. Based on the error percentage, a preset number of initial monitoring points are selected as candidate monitoring points. The average footprint is calculated based on the hourly CO2 footprint of each candidate monitoring point, and it is determined whether the average footprint meets preset conditions. If so, the preset number of candidate monitoring points are determined as target monitoring points. The error percentage of each initial monitoring point is used as a quantifiable value to judge the quality of the initial monitoring point. This allows for accurate determination of the number and location of target monitoring points, ensuring effective and accurate CO2 monitoring at these target monitoring points and improving the accuracy of subsequent CO2 emission retrieval.

[0023] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this specification. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart illustrating a method for determining CO2 monitoring points according to an embodiment of this application;

[0026] Figure 2 This is a schematic diagram of a target mesh provided in an embodiment of this application;

[0027] Figure 3 This is a flowchart illustrating a method for determining CO2 monitoring points according to another embodiment of this application;

[0028] Figure 4 This is a flowchart illustrating a method for determining CO2 monitoring points according to another embodiment of this application;

[0029] Figure 5 This is a flowchart illustrating a method for determining CO2 monitoring points according to another embodiment of this application;

[0030] Figure 6 This is a schematic diagram of the CO2 monitoring point determination device provided in one embodiment of this application;

[0031] Figure 7 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. Detailed Implementation

[0032] The present application will be described more clearly below with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the function of the present application, but do not limit the present application in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present application. These all fall within the protection scope of the present application.

[0033] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0034] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0035] In the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0036] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0037] Furthermore, the term "multiple" mentioned in the embodiments of this application should be interpreted as two or more.

[0038] In recent years, the concentration of carbon dioxide (CO2) in the atmosphere has increased dramatically. Cities, as major CO2 emission areas, contribute 70% of global anthropogenic CO2 emissions. Therefore, it is essential to retrieve CO2 emissions from urban areas to study anthropogenic carbon emissions. To provide accurate and effective monitoring data for CO2 emission retrieval, it is necessary to establish a reasonable network of CO2 monitoring stations.

[0039] Existing methods for determining city-level CO2 monitoring stations typically rely on extensive historical wind direction data for the region, deploying monitoring stations based on experience. However, there is no quantifiable value to determine whether the determined CO2 monitoring stations are appropriate, which may lead to unreasonable deployment of CO2 monitoring stations and consequently affect the accuracy of subsequent CO2 emission calculations based on these monitoring stations.

[0040] To address the aforementioned issues, this application provides a method for determining CO2 monitoring points. This method involves determining simulated meteorological data for a target area and multiple target grids. The center point of each target grid is used as an initial monitoring point. The simulated meteorological data and target grids are input into a STILT model to obtain the hourly CO2 footprint of each initial monitoring point. The error percentage of each initial monitoring point is calculated based on its hourly CO2 footprint. Based on this error percentage, a preset number of initial monitoring points are selected as candidate monitoring points. An average footprint is calculated based on the hourly CO2 footprint of each candidate monitoring point, and it is determined whether the average footprint meets preset conditions. If so, the preset number of candidate monitoring points are determined as target monitoring points. The error percentage of each initial monitoring point is used as a quantifiable value to judge the quality of the initial monitoring points. This method accurately determines the number and location of target monitoring points, ensuring effective and accurate CO2 monitoring at these target monitoring points and improving the accuracy of subsequent CO2 emission retrieval.

[0041] Figure 1 This is a flowchart illustrating a method for determining CO2 monitoring points according to an embodiment of this application. Figure 1 As shown, the method in the embodiments of this application may include:

[0042] Step 101: Input the reanalyzable meteorological data of the target area for a preset time period into the WRF model to obtain the simulated meteorological data of the target area.

[0043] For example, Final Operational Global Analysis (FNL) data is global meteorological reanalysis data provided by institutions such as the U.S. National Center for Environmental Prediction. FNL data provides insights into real-world regional weather conditions. This FNL data is determined every 6 hours and includes data such as air pressure, geopotential height, temperature, and relative humidity. The Weather Research and Forecasting Model (WRF) is a mesoscale numerical model system developed by institutions such as the National Center for Atmospheric Research, which can obtain refined meteorological data for simulated target regions.

[0044] Optionally, the FNL data of the target area for a preset time period can be input into the WRF model. By utilizing the nested mode of the WRF model, simulated meteorological data with multiple resolutions can be obtained, such as simulated meteorological data with resolutions of 15 km, 10 km and 5 km. In this embodiment, the data with the finest resolution, namely the simulated meteorological data with a resolution of 5 km, is selected as the simulated meteorological data of the target area for subsequent calculations.

[0045] Step 102: Divide the target area into multiple target grids, and use the center point of each target grid as the initial monitoring point.

[0046] In one possible implementation, step 102, which involves dividing the target area into multiple target grids, may specifically include: determining the target urban area based on the CO2 emission inventory and population density of the target area; and dividing the target urban area according to latitude and longitude to obtain multiple target grids.

[0047] For example, based on the CO2 emission inventory and population density of the target area, areas with CO2 emission inventories exceeding a first threshold and population densities exceeding a second threshold are identified as target urban areas. This ensures the rationality and effectiveness of setting up target monitoring points for CO2 monitoring in target urban areas with high anthropogenic CO2 emissions. It is important to note that the target area covers the target urban area.

[0048] Optional, a schematic diagram of the target mesh is shown below. Figure 2 As shown, see Figure 2 The target city area is divided into multiple target grids at equal intervals according to latitude and longitude. These target grids are then numbered, with the row and column number of each grid serving as its label. For example, S15,20 represents the target grid in row 15 and column 20.

[0049] Step 103: Input the simulated meteorological data of the target area and the target grid into the STILT model to obtain the hourly CO2 footprint of each initial monitoring point.

[0050] For example, the STILT (Stochastic Time Inverted Lagrangian Transport) model is an open-source Lagrangian particle diffusion model that is widely used to simulate the transport of pollution and greenhouse gases in the atmosphere.

[0051] Optionally, the latitude and longitude information of each target grid is sequentially input into the STILT model along with the simulated meteorological data of the target area to obtain the hourly CO2 footprint of each initial monitoring point, which is the hourly carbon footprint of each initial monitoring point. The hourly CO2 footprint of each initial monitoring point can be represented in matrix form, that is, the hourly CO2 footprint of each initial monitoring point corresponds to an hourly matrix H, which is an h×a×b matrix, where h is the number of hours, a is the number of rows of the target grid after dividing the target urban area, and b is the number of columns of the target grid after dividing the target urban area, that is, a×b is the number of target grids.

[0052] Step 104: Calculate the error percentage of each initial monitoring point based on the hourly CO2 footprint and the preset prior error covariance matrix.

[0053] In one possible implementation, see Figure 3 Step 104 may specifically include:

[0054] Step 1041: Calculate the posterior error covariance matrix based on the hourly CO2 footprint corresponding to each initial monitoring point and the preset prior error covariance matrix.

[0055] Step 1042: Obtain the error percentage of each initial monitoring point based on the preset prior error covariance matrix and posterior error covariance matrix.

[0056] Specifically, the Jacobian matrix M corresponding to all initial monitoring points is constructed based on the hourly CO2 footprint matrix H corresponding to all initial monitoring points. Here, M is an n×m matrix, n is the number of initial monitoring points, and m is defined as h×a×b.

[0057] Optionally, the optimal CO2 flux f can be determined by minimizing the cost J(f) according to Bayes' theorem. post To determine the posterior error covariance matrix, Bayes' theorem is:

[0058]

[0059] In the formula, z is an n×1 vector of CO2 observations at the initial monitoring points, M is an n×m Jacobian matrix, f is an m×1 vector of CO2 flux, f0 is an m×1 vector of prior CO2 flux, R is the error covariance matrix of the n×n STILT model (a diagonal matrix), and n is the number of initial monitoring points. The error covariance matrix R of this STILT model can be set according to different target areas, different STILT model parameters, and different CO2 fluxes, and is a known numerical matrix; C f0 It is an m×m preset prior error covariance matrix representing the uncertainty of the prior flux field, and is a known numerical matrix.

[0060] By calculating the extreme values ​​of Bayes' theorem, the formula for the posterior error covariance matrix is ​​obtained:

[0061]

[0062] In the formula, C f Let be the m×m posterior error covariance matrix.

[0063] In calculating the posterior error covariance matrix corresponding to each initial monitoring point, n=1, that is, the transpose of the hourly matrix H corresponding to the hourly CO2 footprint of each initial monitoring point is used. T (H T Substituting the position of M in the above formula for the posterior error covariance matrix (which is a 1×m matrix) yields the posterior error covariance matrix for each initial monitoring point.

[0064] The posterior error covariance matrix C f Convert to scalar J Ce And in order to cooperate with J Ce For comparison, the preset prior error covariance matrix C is used. f0 Convert to scalar J Ce0 The scalar conversion formula is:

[0065]

[0066]

[0067] In the formula, C fij Let C be the posterior error covariance matrix. f The value of the element in the i-th row and j-th column, C f0ij For the preset prior error covariance matrix C f0 The value of the element in the i-th row and j-th column.

[0068] The formula for the percentage of error is:

[0069]

[0070] In the formula, UR represents the percentage of error.

[0071] Based on the scalar transformation formula and the error percentage formula, the above scalar transformation and error percentage calculation steps are performed on the posterior error covariance matrix of each initial monitoring point to obtain the error percentage of each initial monitoring point.

[0072] Step 105: Based on the error percentage of each initial monitoring point, select a preset number of initial monitoring points as candidate monitoring points.

[0073] In one possible implementation, step 105 may specifically include: sorting the error percentage of each initial monitoring point from smallest to largest; and randomly selecting a preset number of initial monitoring points as candidate monitoring points from the first X initial monitoring points corresponding to the error percentages.

[0074] Where the value of X is greater than the preset number of values.

[0075] Optionally, to ensure the effectiveness and accuracy of the candidate monitoring points, the error percentages of the initial monitoring points are sorted from smallest to largest, and candidate monitoring points are randomly selected from the initial monitoring points corresponding to the top X error percentages. For example, after sorting the error percentages of the initial monitoring points from smallest to largest, 3 initial monitoring points are randomly selected from the top 20 error percentages as candidate monitoring points. The preset number can be set as needed; considering economic costs, a preset number of 3 can be used.

[0076] Step 106: Calculate the average footprint based on the hourly CO2 footprint of each candidate monitoring point, and determine whether the average footprint meets the preset conditions; if so, determine a preset number of candidate monitoring points as target monitoring points so that CO2 can be monitored at the preset number of target monitoring points.

[0077] In one possible implementation, see Figure 4 Step 106 may specifically include:

[0078] Step 1061: Calculate the average matrix corresponding to the average footprint based on the hourly CO2 footprint of each candidate monitoring point.

[0079] Step 1062: Determine whether the average matrix meets the preset conditions.

[0080] Step 1063: If yes, then determine the preset number of candidate monitoring points as target monitoring points.

[0081] The preset condition is that the proportion of non-zero values ​​in the average matrix is ​​greater than a preset threshold among all values ​​in the average matrix.

[0082] Optionally, for each candidate monitoring point, the hourly CO2 footprint corresponding to the hourly matrix H is averaged over time by dividing each hourly matrix H by the number of hours h, resulting in the time average matrix H′ for each candidate monitoring point. This time average matrix H′ is an a×b matrix. The time average matrices H′ corresponding to a preset number of candidate monitoring points are then averaged spatially by summing the time average matrices H′ of the candidate monitoring points and dividing by the preset number, resulting in the average matrix H. mean That is, to obtain the average matrix H corresponding to the average footprint. mean .

[0083] The process involves determining whether the average matrix satisfies a preset condition, specifically whether the proportion of non-zero values ​​in the average matrix exceeds a preset threshold. In other words, it checks whether the proportion of non-zero elements in the average matrix exceeds a preset threshold. If so, the average matrix H... mean If the preset conditions are met, a preset number of candidate monitoring points are selected as target monitoring points. The preset threshold can be set as needed; for example, it can be 95%.

[0084] A simple example is to sort the error percentages of the initial monitoring points from smallest to largest, select three initial monitoring points from the top 20 error percentages as candidate monitoring points, and calculate the time mean of the hourly CO2 footprint matrix H corresponding to each candidate monitoring point to obtain the time average matrix H′ for each candidate monitoring point. Then, calculate the spatial mean of these three time average matrices H′ to obtain the average matrix H′. mean Determine the average matrix H mean If the proportion of non-zero elements to the total number of elements is greater than 95%, then the above three candidate monitoring points are used as target monitoring points. This determines the number and location of target monitoring points in the target area, so that CO2 can be monitored at the above three target monitoring points.

[0085] In one possible implementation, see Figure 5 When the average matrix does not meet the preset conditions, the CO2 monitoring point determination method provided in this application embodiment further includes:

[0086] S1. If the average footprint does not meet the preset conditions, select the next preset number of initial monitoring points as the next set of candidate monitoring points and repeat the judgment steps for the target monitoring point.

[0087] S2. If the next set of preset number of initial monitoring points is selected as the next set of candidate monitoring points and the judgment step of the target monitoring point is repeated for a preset number of times, and the judgment result of the target monitoring point is negative each time, then the preset number is incremented by one as the new preset number, and the process jumps to the step of selecting a preset number of initial monitoring points as candidate monitoring points based on the error percentage of each initial monitoring point and repeats cyclically until the target monitoring point is determined.

[0088] Optionally, if the average footprint does not meet the preset conditions, a preset number of initial monitoring points are selected from the initial monitoring points corresponding to the first X error percentages as the next set of candidate monitoring points, and step 106 is executed. If the preset number of executions is repeated and the judgment result of the target monitoring point is negative each time, the preset number is incremented by one as the new preset number, and the process jumps to step 105. Steps 105 to 106 are executed repeatedly until the target monitoring point is determined. In other words, if the preset number of candidate monitoring points cannot meet the monitoring requirements at this time, the preset number is incremented by one as the new preset number until the target monitoring point is determined.

[0089] The preset number of times can be set as needed. It can also be equal to the total number of combinations of initial monitoring points selected from the initial monitoring points corresponding to the previous X error percentages. For example, if 3 initial monitoring points are selected as candidate monitoring points from the initial monitoring points corresponding to the previous 20 error percentages, then the preset number of times can be... Each time, the preset number of initial monitoring points selected is not exactly the same as those previously selected.

[0090] In another possible implementation, to further improve the rationality and effectiveness of the target monitoring points, multiple sets of candidate monitoring points can be selected for comparison, and the optimal set of candidate monitoring points can be selected as the optimal monitoring point. Specifically, the CO2 monitoring point determination method provided in this application embodiment further includes:

[0091] S11. After determining a preset number of candidate monitoring points as target monitoring points, select the next preset number of initial monitoring points as the next set of candidate monitoring points and repeat the judgment steps for target monitoring points to obtain multiple sets of target monitoring points.

[0092] S12. Calculate the sum of the error percentages corresponding to each group of target monitoring points, and use it as the sum of the error percentages of the target monitoring points in that group.

[0093] S13. Select the group with the smallest sum of error percentages among multiple target monitoring points as the final target monitoring point.

[0094] Specifically, after determining the first set of predetermined number of candidate monitoring points as target monitoring points, the next set of predetermined number of initial monitoring points is selected from the first X initial monitoring points corresponding to the error percentages as the next set of candidate monitoring points. Step 106 is repeated to obtain multiple sets of target monitoring points. The sum of the error percentages corresponding to the predetermined number of target monitoring points in each set is calculated as the sum of the error percentages of the target monitoring points in that set. From the multiple sets of target monitoring points, the set with the smallest sum of error percentages is selected as the final target monitoring point. The optimal target monitoring point that has low economic cost and meets the monitoring requirements is determined by traversal method to further improve the accuracy of subsequent CO2 emission inversion.

[0095] This application provides a method for determining CO2 monitoring points. It involves determining simulated meteorological data of a target area and multiple target grids, using the center point of each target grid as an initial monitoring point, and inputting the simulated meteorological data and target grids into a STILT model to obtain the hourly CO2 footprint of each initial monitoring point. The method calculates the error percentage of each initial monitoring point based on its hourly CO2 footprint, selects a preset number of initial monitoring points as candidate monitoring points based on the error percentage, calculates the average footprint based on the hourly CO2 footprint of each candidate monitoring point, and determines whether the average footprint meets preset conditions. If so, the preset number of candidate monitoring points are determined as target monitoring points. The error percentage of each initial monitoring point is used as a quantifiable value to judge the quality of the initial monitoring point, accurately determining the number and location of target monitoring points. Monitoring CO2 at these target monitoring points can meet the requirements for effective and accurate monitoring, improving the accuracy of subsequent CO2 emission retrieval.

[0096] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0097] Figure 6 This is a schematic diagram of the CO2 monitoring point determination device provided in one embodiment of this application. Figure 6 As shown, the CO2 monitoring point determination device provided in this embodiment may include: a simulation module 201, a division module 202, a determination module 203, a calculation module 204, and a judgment module 205.

[0098] The simulation module 201 is used to input reanalyzable meteorological data of the target area for a preset period into the WRF model to obtain simulated meteorological data of the target area.

[0099] The segmentation module 202 is used to segment the target area to obtain multiple target grids, and the center point of each target grid is used as the initial monitoring point.

[0100] The determination module 203 is used to input the simulated meteorological data of the target area and the target grid into the STILT model to obtain the hourly CO2 footprint of each initial monitoring point.

[0101] The calculation module 204 is used to calculate the error percentage of each initial monitoring point based on the hourly CO2 footprint of each initial monitoring point and the preset prior error covariance matrix.

[0102] The judgment module 205 is used to select a preset number of initial monitoring points as candidate monitoring points based on the error percentage of each initial monitoring point; calculate the average footprint based on the hourly CO2 footprint of each candidate monitoring point, and determine whether the average footprint meets the preset conditions; if so, determine the preset number of candidate monitoring points as target monitoring points, so that CO2 monitoring can be carried out at the preset number of target monitoring points.

[0103] Optionally, the calculation module 204 is specifically used to: calculate the posterior error covariance matrix based on the hourly CO2 footprint corresponding to each initial monitoring point and the preset prior error covariance matrix; and obtain the error percentage of each initial monitoring point based on the preset prior error covariance matrix and the posterior error covariance matrix.

[0104] Optionally, the judgment module 205 is specifically used to: sort the error percentage of each initial monitoring point from smallest to largest; randomly select a preset number of initial monitoring points as candidate monitoring points from the initial monitoring points corresponding to the first X error percentages; wherein, the value of X is greater than the preset number of values.

[0105] Optionally, the judgment module 205 is further specifically used to: calculate the average matrix corresponding to the average footprint based on the hourly CO2 footprint corresponding to each candidate monitoring point; determine whether the average matrix meets the preset conditions; the preset condition is that the proportion of non-zero values ​​in the average matrix to all values ​​in the average matrix is ​​greater than a preset threshold; if so, determine a preset number of candidate monitoring points as target monitoring points.

[0106] Optionally, the judgment module 205 is further specifically used for: after determining a preset number of candidate monitoring points as target monitoring points, selecting the next preset number of initial monitoring points as the next set of candidate monitoring points and repeating the judgment steps for target monitoring points to obtain multiple sets of target monitoring points; calculating the sum of the error percentages corresponding to each set of target monitoring points as the sum of the error percentages of the target monitoring points in that set; and selecting the set with the smallest sum of error percentages among the multiple sets of target monitoring points as the final target monitoring point.

[0107] Optionally, the judgment module 205 is further configured to: if the average footprint does not meet the preset conditions, select the next set of preset number of initial monitoring points as the next set of candidate monitoring points and repeat the judgment step of the target monitoring point; if the next set of preset number of initial monitoring points is selected as the next set of candidate monitoring points and the judgment step of the target monitoring point is repeated for a preset number of times, and the judgment result of the target monitoring point is negative each time, then the preset number is incremented by one as the new preset number, and the process jumps to the step of selecting the preset number of initial monitoring points as candidate monitoring points based on the error percentage of each initial monitoring point and executes iteratively until the target monitoring point is determined.

[0108] Optionally, the division module 202 is specifically used to: determine the target urban area based on the CO2 emission inventory and population density of the target area; divide the target urban area according to latitude and longitude to obtain multiple target grids, and use the center point of each target grid as the initial monitoring point.

[0109] It should be noted that the information interaction and execution process between the above-mentioned devices / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0110] Figure 7 This is a schematic diagram of the structure of a terminal device provided in an embodiment of this application. For example... Figure 7 As shown, the terminal device 300 of this embodiment includes a processor 310 and a memory 320, wherein the memory 320 stores a computer program 321 that can run on the processor 310. When the processor 310 executes the computer program 321, it implements the steps in any of the above method embodiments, for example... Figure 1 Steps 101 to 106 are shown. Alternatively, when processor 310 executes computer program 321, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 6 The functions of modules 201 to 205 are shown.

[0111] For example, computer program 321 may be divided into one or more modules / units, one or more of which are stored in memory 320 and executed by processor 310 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of computer program 321 in terminal device 300.

[0112] Those skilled in the art will understand that Figure 7This is merely an example of a terminal device and does not constitute a limitation on the terminal device. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0113] The processor 310 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0114] The memory 320 can be an internal storage unit of the terminal device, such as the hard drive or memory of the terminal device, or an external storage device of the terminal device, such as a plug-in hard drive, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card. The memory 320 can also include both internal and external storage units of the terminal device. The memory 320 is used to store computer programs and other programs and data required by the terminal device. The memory 320 can also be used to temporarily store data that has been output or will be output.

[0115] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0116] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0117] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0118] In the embodiments provided by this invention, it should be understood that the disclosed apparatus / terminal devices and methods can be implemented in other ways. For example, the apparatus / terminal device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0119] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0120] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0121] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0122] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for determining CO2 monitoring points, characterized in that, include: Input the reanalyzable meteorological data of the target area for a preset time period into the WRF model to obtain the simulated meteorological data of the target area; The target area is divided into multiple target grids, and the center point of each target grid is used as the initial monitoring point. The simulated meteorological data of the target area and the target grid are input into the STILT model to obtain the hourly CO2 footprint of each initial monitoring point; The error percentage of each initial monitoring point is calculated based on the hourly CO2 footprint of each initial monitoring point and the preset prior error covariance matrix. Based on the error percentage of each initial monitoring point, a preset number of initial monitoring points are selected as candidate monitoring points; Based on the hourly CO2 footprint of each candidate monitoring point, the average footprint is calculated, and it is determined whether the average footprint meets the preset conditions. If so, the preset number of candidate monitoring points are determined as target monitoring points so that CO2 can be monitored at the preset number of target monitoring points.

2. The method for determining CO2 monitoring points according to claim 1, characterized in that, The step of calculating the error percentage for each initial monitoring point based on the hourly CO2 footprint and the preset prior error covariance matrix includes: The posterior error covariance matrix is ​​calculated based on the hourly matrix corresponding to the hourly CO2 footprint of each initial monitoring point and the preset prior error covariance matrix. The error percentage of each initial monitoring point is obtained based on the preset prior error covariance matrix and the posterior error covariance matrix.

3. The method for determining CO2 monitoring points according to claim 1, characterized in that, The step of selecting a preset number of initial monitoring points as candidate monitoring points based on the error percentage of each initial monitoring point includes: The error percentages of each initial monitoring point are sorted from smallest to largest. From the first X initial monitoring points corresponding to the error percentage, a preset number of initial monitoring points are randomly selected as candidate monitoring points; wherein, the value of X is greater than the value of the preset number.

4. The method for determining CO2 monitoring points according to claim 3, characterized in that, The average footprint is calculated based on the hourly CO2 footprint of each candidate monitoring point, and it is determined whether the average footprint meets the preset conditions. If so, then the preset number of candidate monitoring points are determined as target monitoring points, including: Based on the hourly matrix corresponding to the hourly CO2 footprint of each candidate monitoring point, calculate the average matrix corresponding to the average footprint. Determine whether the average matrix satisfies a preset condition; the preset condition is that the proportion of non-zero values ​​in the average matrix is ​​greater than a preset threshold. If so, then the preset number of candidate monitoring points are determined as target monitoring points.

5. The method for determining CO2 monitoring points according to claim 4, characterized in that, The method further includes: After determining the preset number of candidate monitoring points as target monitoring points, select the next preset number of initial monitoring points as the next set of candidate monitoring points and repeat the target monitoring point determination steps to obtain multiple sets of target monitoring points; Calculate the sum of the error percentages corresponding to each group of target monitoring points, and use this sum as the error percentage of the target monitoring points in that group; The group with the smallest sum of error percentages among multiple target monitoring points is selected as the final target monitoring point.

6. The method for determining CO2 monitoring points according to claim 1, characterized in that, The method further includes: If the average footprint does not meet the preset condition, then select the next preset number of initial monitoring points as the next set of candidate monitoring points and repeat the judgment step of the target monitoring point; If the step of selecting a preset number of initial monitoring points as the next set of candidate monitoring points and repeating the judgment step of the target monitoring point is executed for a preset number of times, and the judgment result of the target monitoring point is negative each time, then the preset number is incremented by one to become the new preset number, and the process jumps to the step of selecting a preset number of initial monitoring points as candidate monitoring points based on the error percentage of each initial monitoring point, and the process is repeated until the target monitoring point is determined.

7. The method for determining CO2 monitoring points according to any one of claims 1 to 6, characterized in that, The process of dividing the target region into multiple target grids includes: The target urban area is determined based on the CO2 emission inventory and population density of the target area; The target city area is divided according to latitude and longitude to obtain multiple target grids.

8. A device for determining CO2 monitoring points, characterized in that, include: The simulation module is used to input reanalyzable meteorological data of the target area for a preset time period into the WRF model to obtain simulated meteorological data of the target area; The segmentation module is used to divide the target area into multiple target grids, and use the center point of each target grid as the initial monitoring point; The determination module is used to input the simulated meteorological data of the target area and the target grid into the STILT model to obtain the hourly CO2 footprint of each initial monitoring point; The calculation module is used to calculate the error percentage of each initial monitoring point based on the hourly CO2 footprint of each initial monitoring point and the preset prior error covariance matrix; The judgment module is used to select a preset number of initial monitoring points as candidate monitoring points based on the error percentage of each initial monitoring point. Based on the hourly CO2 footprint of each candidate monitoring point, the average footprint is calculated, and it is determined whether the average footprint meets the preset conditions. If so, the preset number of candidate monitoring points are determined as target monitoring points so that CO2 can be monitored at the preset number of target monitoring points.

9. A terminal device, comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, characterized in that, When the processor executes the computer program, it implements the CO2 monitoring point determination method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the CO2 monitoring point determination method as described in any one of claims 1 to 7.

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