A method and system for selecting carbon emission monitoring sites based on integrated air-space-ground systems

Abstract

This invention relates to the field of carbon emission monitoring, and provides a method and system for selecting monitoring sites based on an integrated space-air-ground approach. The method includes: extracting land type characteristic data, surface elevation characteristic data, surface temperature characteristic data, and wind speed and direction characteristic data; acquiring carbon satellite remote sensing image data of the target study area, extracting a greenhouse gas concentration distribution map, and combining the land type characteristic data, surface elevation characteristic data, surface temperature characteristic data, and wind speed and direction characteristic data to extract key influencing factors by analyzing the importance of these features; performing hotspot analysis on the greenhouse gas concentration distribution map to determine the extent and diffusion impact of hotspot areas; and using an iterative scanning mode centered on the hotspot areas and considering their extent and diffusion impact, determining the deployment locations of monitoring equipment. This invention enables comprehensive and accurate carbon emission monitoring.

Description

Technical Field

[0001] This invention relates to the field of carbon emission monitoring technology, and in particular to a method and system for selecting carbon emission monitoring sites based on an integrated air-space-ground system. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] In recent years, satellite remote sensing technology has been widely used in global carbon emission monitoring due to its advantages such as wide coverage, short revisit cycles, and high spatial resolution. Platforms such as hyperspectral satellites and microwave satellites can acquire distribution information based on the absorption characteristic bands of greenhouse gases. However, due to limitations such as atmospheric conditions, surface reflectivity, and satellite orbit design, single-satellite monitoring is insufficient for high-precision quantitative assessment, especially in meeting the needs for accurate monitoring of fixed point sources and small-scale high-concentration areas.

[0004] To address these shortcomings, ground-based monitoring networks have gradually developed, with fixed monitoring equipment deployed in key areas such as cities and industrial parks, including laser absorption spectrometers and infrared gas sensors. Existing technologies have constructed a regional greenhouse gas monitoring system that integrates a satellite subsystem, a ground hardware subsystem, a communication subsystem, and a monitoring service subsystem. The ground hardware subsystem integrates sensor devices with optimized chip structures. These ground-based devices provide high-precision data support for local carbon emission accounting, but due to their limited spatial distribution, large-scale, dynamic monitoring is difficult to achieve. Summary of the Invention

[0005] To address the technical problems mentioned above, this invention provides a carbon emission monitoring site selection method and system based on integrated air-space-ground technology. By optimizing site selection layout and integrating multi-source data, this invention improves monitoring efficiency and accuracy, achieving comprehensive and precise carbon emission monitoring, providing scientific support for carbon emission accounting and management, and contributing to the achievement of carbon neutrality goals.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] The first aspect of this invention provides a method for selecting monitoring sites for carbon emissions based on an integrated air-space-ground system.

[0008] A method for selecting monitoring sites for carbon emissions based on an integrated air-space-ground system includes:

[0009] Obtain the land cover type, digital elevation model of the land surface, land surface temperature, and surface wind speed and direction of the target study area, and extract land type feature data, surface elevation feature data, surface temperature feature data, and wind speed and direction feature data respectively;

[0010] Acquire carbon satellite remote sensing image data of the target study area, extract greenhouse gas concentration distribution maps, and combine land type characteristic data, surface elevation characteristic data, surface temperature characteristic data, and wind speed and direction characteristic data. By analyzing the importance of features, extract the main influencing factors; conduct hotspot analysis on the greenhouse gas concentration distribution maps to determine the range and diffusion impact of hotspot areas.

[0011] Centered on the hotspot area, and taking into account the scope and diffusion impact of the hotspot area, an iterative scanning mode is adopted to determine the location of monitoring equipment deployment;

[0012] The iterative scanning mode includes: inputting a coordinate table containing data on the center location and perimeter of buildings, generating a VBPI index map of the building distribution, selecting valid candidate points for the optimal scanning location of the ground monitoring equipment, determining the final scanning location of the ground monitoring equipment, determining whether additional scanning and iteration are needed; and outputting the deployment location of the monitoring equipment until the iteration conditions are met.

[0013] Furthermore, the method for acquiring carbon satellite remote sensing image data of the target study area and extracting a greenhouse gas concentration distribution map includes: acquiring carbon satellite remote sensing image data of the target study area; preprocessing the carbon satellite remote sensing image data, including atmospheric correction; based on the preprocessed carbon satellite remote sensing image data, analyzing different band data in the carbon satellite remote sensing image data, and combining the atmospherically corrected data to identify the concentration range of gases and determine the spatial distribution of greenhouse gases; and based on the spatial distribution of greenhouse gases, obtaining a greenhouse gas concentration distribution map by observing the greenhouse gas concentration change trend in the carbon satellite remote sensing image data.

[0014] Furthermore, the method involves analyzing the importance of features to extract key influencing factors; performing hotspot analysis on greenhouse gas concentration distribution maps to determine the extent and diffusion impact of hotspot areas; and employing a random forest algorithm to evaluate the contributions of land type characteristic data, surface elevation characteristic data, surface temperature characteristic data, and wind speed and direction characteristic data to gas concentrations, thereby extracting key influencing factors; using principal component analysis to reduce the dimensionality of key influencing factors and determine principal components; and based on the principal components, using a diffusion model established based on atmospheric dynamics equations to determine the extent and diffusion impact of hotspot areas.

[0015] Furthermore, the input includes a coordinate table containing the center location and perimeter data of the building; the method includes: inputting a dataset containing the center location, perimeter data of the building and environmental features within the target study area, using a perceived quality model and a Gaussian process model to obtain the monitoring location optimization result; based on the monitoring location optimization result, performing range control calculations on the preliminary deployment points to ensure that the monitoring equipment covers hotspot areas and key diffusion paths, and generating a basic geospatial distribution map.

[0016] Furthermore, the VBPI index map generated from the building distribution map is expressed using the following formula:

[0017]

[0018] in, Indicates the first i Position in the next iteration The VBPI index, Indicates from position Number of buildings that can be seen N i For the first i The total number of buildings in each iteration is updated in each subsequent iteration.

[0019] Furthermore, the method for selecting effective candidate points for the optimal ground monitoring equipment scanning location includes: in each iteration, extracting high VBPI locations from the VBPI index map through a specific threshold for all candidate locations of the monitoring equipment; downsampling all candidate locations by downsampling the grid of the VBPI index map; and, based on the downsampling, using a particle swarm optimization algorithm to optimize the effective candidate locations, i.e., the effective candidate monitoring equipment locations.

[0020] Furthermore, the method for determining the final scanning location of the ground monitoring equipment includes:

[0021] The optimal monitoring device location is determined by quantifying the scan completeness of each valid candidate monitoring device location using the CDPC index.

[0022] The CDPC index is expressed using the following formula:

[0023]

[0024] in, i Indicates the first i iteration It is the first j A polygonal CDPC, This refers to the candidate locations scanned by the monitoring device in each iteration; subscript single and end Iterate through the candidate positions and the final optimal position separately.

[0025] A second aspect of the present invention provides a carbon emission monitoring site selection system based on an integrated air-space-ground system.

[0026] A carbon emission monitoring site selection system based on an integrated air-space-ground system includes:

[0027] The data acquisition module is configured to: acquire the land cover type, digital elevation model of the land surface, land surface temperature, and land surface wind speed and direction of the target study area, and extract land type feature data, land surface elevation feature data, land surface temperature feature data, and wind speed and direction feature data respectively;

[0028] The feature extraction and analysis module is configured to: acquire carbon satellite remote sensing image data of the target study area, extract greenhouse gas concentration distribution maps, combine land type feature data, surface elevation feature data, surface temperature feature data, and wind speed and direction feature data, extract the main influencing factors by analyzing feature importance, and perform hotspot analysis on the greenhouse gas concentration distribution map to determine the range and diffusion impact of hotspot areas.

[0029] The monitoring equipment deployment optimization module is configured to: determine the deployment location of monitoring equipment by taking the hotspot area as the center and combining the range and diffusion impact of the hotspot area with an iterative scanning mode;

[0030] The iterative scanning mode includes: inputting a coordinate table containing data on the center location and perimeter of buildings, generating a VBPI index map of the building distribution, selecting valid candidate points for the optimal scanning location of the ground monitoring equipment, determining the final scanning location of the ground monitoring equipment, determining whether additional scanning and iteration are needed; and outputting the deployment location of the monitoring equipment until the iteration conditions are met.

[0031] A third aspect of the present invention provides a computer device comprising:

[0032] A processor, adapted to execute computer programs;

[0033] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps in the space-air-ground integrated carbon emission monitoring site selection method described in the first aspect above.

[0034] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program adapted to be loaded by a processor and to execute the steps of the space-air-ground integrated carbon emission monitoring site selection method as described in the first aspect above.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] 1. Multi-source data fusion improves monitoring accuracy and timeliness: Existing technologies often rely on a single data source, making it difficult to comprehensively cover and accurately assess fixed point sources and small-scale high-concentration areas. This invention constructs a dynamic carbon emission distribution map by fusing large-scale monitoring data from satellite remote sensing, small-to-medium-scale hotspot detection data from UAVs, and high-precision verification data from ground equipment. This enables more refined monitoring and dynamic updates, overcoming the limitations of a single data source.

[0037] 2. Optimize site selection and layout to improve monitoring efficiency: This invention employs a site selection optimization method based on multi-source data, fully utilizing satellite and UAV hotspot detection results to guide the rational layout of ground monitoring equipment. By scientifically distributing monitoring points, it avoids the resource waste caused by unreasonable ground equipment deployment in existing technologies, and effectively compensates for the shortcomings of satellite and UAV monitoring in providing high-precision local coverage.

[0038] 3. Constructing an integrated air-space-ground system for comprehensive coverage and dynamic monitoring: In existing technologies, satellite remote sensing, UAVs, and ground equipment often operate independently. This application organically integrates these three elements to form a coordinated, three-dimensional monitoring network. When dealing with changes in atmospheric conditions, surface complexity, and diverse monitoring needs, the system exhibits greater synergy and adaptability, thereby ensuring the continuity, comprehensiveness, and accuracy of carbon emission monitoring. Attached Figure Description

[0039] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0040] Figure 1 This is a flowchart illustrating the carbon emission monitoring site selection method based on an integrated air-space-ground system, as shown in an embodiment of the present invention.

[0041] Figure 2 This is a flowchart of another embodiment of the carbon emission monitoring site selection method based on air-space-ground integration, as shown in the embodiments of the present invention;

[0042] Figure 3 This is a flowchart illustrating the deployment process of a monitoring device based on an iterative scanning mode, as shown in an embodiment of the present invention.

[0043] Figure 4 This is a structural diagram of a carbon emission monitoring site selection system based on an integrated air-space-ground system, as shown in an embodiment of the present invention.

[0044] Figure 5 This is a structural diagram of a computer device shown in an embodiment of the present invention. Detailed Implementation

[0045] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0046] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0047] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0048] As described in the background section, while satellite remote sensing possesses global coverage capabilities, its accuracy in precisely monitoring fixed point sources and small-scale high-concentration areas is limited by atmospheric conditions, surface reflectivity, and orbital design, resulting in insufficient precision. Ground-based monitoring equipment, although providing high-precision data support, is constrained by spatial distribution limitations, hindering large-scale dynamic monitoring. While unmanned aerial vehicles (UAVs) can compensate for small- to medium-scale monitoring needs, their limited endurance and coverage make them insufficient to independently meet comprehensive monitoring requirements. To address these technical problems, this invention provides a carbon emission monitoring site selection method and system based on an integrated air-space-ground approach. The technical solution of this invention will be described in detail below through several embodiments.

[0049] Figure 1 This is a flowchart illustrating the site selection method for carbon emission monitoring based on an integrated air-space-ground system, as shown in an embodiment of the present invention; see also Figure 1 The method includes:

[0050] Obtain the land cover type, digital elevation model of the land surface, land surface temperature, and surface wind speed and direction of the target study area, and extract land type feature data, surface elevation feature data, surface temperature feature data, and wind speed and direction feature data respectively;

[0051] Acquire carbon satellite remote sensing image data of the target study area, extract greenhouse gas concentration distribution maps, and combine land type characteristic data, surface elevation characteristic data, surface temperature characteristic data, and wind speed and direction characteristic data. By analyzing the importance of features, extract the main influencing factors; conduct hotspot analysis on the greenhouse gas concentration distribution maps to determine the range and diffusion impact of hotspot areas.

[0052] Centered on the hotspot area, and taking into account the scope and diffusion impact of the hotspot area, an iterative scanning mode is adopted to determine the location of monitoring equipment deployment;

[0053] The iterative scanning mode includes: inputting a coordinate table containing data on the center location and perimeter of buildings, generating a VBPI index map of the building distribution, selecting valid candidate points for the optimal scanning location of the ground monitoring equipment, determining the final scanning location of the ground monitoring equipment, determining whether additional scanning and iteration are needed; and outputting the deployment location of the monitoring equipment until the iteration conditions are met.

[0054] This invention integrates satellite remote sensing, UAV observation, and ground equipment to construct a multi-source, multi-dimensional monitoring network. Satellite remote sensing is responsible for global monitoring, UAVs are used for small- to medium-scale hotspot area patrols, and ground equipment provides high-precision data verification and supplementation. Particularly in site selection and layout, the system emphasizes combining hotspot detection results from satellites and UAVs to rationally deploy ground equipment, thereby improving monitoring efficiency and coverage accuracy. Through multi-level data fusion, a dynamic carbon emission distribution map is constructed, providing scientific support for site selection optimization and precise management, thus contributing to the achievement of global carbon neutrality goals.

[0055] Figure 2 This is a flowchart illustrating another embodiment of the space-air-ground integrated carbon emission monitoring site selection method of the present invention. This method aims to improve the accuracy and efficiency of carbon emission monitoring through multi-temporal and spatial scale carbon monitoring technology. By using a space-air-ground integrated monitoring system, this method optimizes the existing carbon emission monitoring site layout, avoiding the problem of unreasonable deployment caused by relying solely on visual judgment. The following are the specific implementation steps of the present invention.

[0056] S1: Collect carbon satellite remote sensing images and conduct regional analysis of gas concentration monitoring.

[0057] Carbon satellite remote sensing imagery data was collected for the target study area. This satellite remote sensing imagery data was mainly used to analyze the spatial distribution of greenhouse gases (such as carbon dioxide, methane, and nitrogen oxides) within the study area.

[0058] The concentration distribution of these greenhouse gases directly affects regional carbon emissions, thus providing crucial data support for carbon emission monitoring and the formulation of environmental protection measures. Satellite remote sensing imagery provides a large-scale, continuous, and efficient observation platform, capable of effectively capturing changes in greenhouse gas concentrations at different time and spatial scales within a region. It has unique advantages, especially for areas where information is difficult to obtain through ground-based monitoring methods.

[0059] (1) Data collection in the target area: The data collection targets for the study area are determined, and relevant carbon satellite imagery data are acquired through satellite remote sensing technology. These data mainly cover the concentration information of greenhouse gases such as carbon dioxide, methane, and nitrogen oxides. The absorption characteristics of atmospheric gases are obtained through meteorological satellites, and the data acquisition frequency is optimized by considering satellite orbit and sensor observation capabilities to provide high-quality gas concentration estimates.

[0060] (2) Image data preprocessing: The collected satellite image data undergoes preprocessing such as radiometric correction, atmospheric correction, and geometric correction to eliminate noise and interference in the data and improve the accuracy of subsequent analysis. Radiometric correction removes radiometric bias caused by the characteristics of satellite sensors, ensuring that the acquired spectral data accurately reflects the physical properties of the Earth's surface. Atmospheric correction corrects the influence of the atmosphere on remote sensing signals, eliminating interference from atmospheric scattering, absorption, and other factors, ensuring the accuracy of the measurement data. Geometric correction adjusts the spatial geometric relationship of the satellite image to ensure that the image coordinates are consistent with the actual geographic coordinates on the ground, thereby achieving precise positioning. After these corrections, the data quality is significantly improved, providing a reliable foundation for subsequent gas concentration inversion and analysis.

[0061] (3) Gas concentration inversion: Based on the different absorption characteristics of different greenhouse gases (such as carbon dioxide and methane) in the atmosphere, satellite sensors can capture the absorption peaks of these gases through spectral data of different wavelengths, thereby inferring their concentration distribution. Radiative transfer models (RTM) based on physical models or data-driven algorithms are used to analyze data from different bands in remote sensing images. Combined with atmospherically corrected data, the concentration range of gases can be accurately identified, and the spatial distribution of different gases can be distinguished. This process establishes a mathematical model between gas concentration and radiation data, making the gas concentration information inverted from remote sensing data more reliable and accurate.

[0062] (4) Hotspot identification: By analyzing the concentration change trends in satellite imagery data, areas with high emissions are identified, and specific emission sources are located. For these hotspot areas, their spatial distribution characteristics can be further analyzed to determine the density of emission sources and their impact on the surrounding environment. Combined with spatiotemporal trend analysis, seasonal variations, annual variations, and emission fluctuations under special climatic conditions in hotspot areas can be identified, providing a theoretical basis for the determination of subsequent small-scale research areas.

[0063] This invention combines satellite remote sensing, UAV observation, and ground monitoring equipment to form a multi-source integrated three-dimensional monitoring network, thereby improving the coverage and accuracy of carbon emission monitoring.

[0064] S2: Multi-source data collection and feature data extraction

[0065] Based on carbon satellite remote sensing data, other auxiliary data for the study area were collected, including but not limited to land cover type, digital elevation model (DEM), surface temperature, surface wind speed, and wind direction. This data was used to further optimize the selection of monitoring points.

[0066] Based on land type classification information, different land cover types are numerically assigned. For example, forest is assigned 1, grassland 2, farmland 3, urban area 4, and water body 5. By calculating the pixel values ​​of different land types, the average value of each land type in the region is obtained. This average value represents the spatial distribution characteristics of various land types in the region and can be used as a feature of the spatial proportion of land types.

[0067] A digital elevation model of the study area is acquired, and the value of each pixel is calculated according to the target resolution. Finally, the average elevation value of the area is calculated as the surface elevation feature data of the region. This value represents the overall elevation level of the region and can be used to analyze the characteristics of topographic relief.

[0068] After acquiring surface temperature data for the study area, cloud-affected areas are removed using a cloud detection algorithm to ensure the accuracy of the surface temperature data. The annual average surface temperature for the region is then calculated by analyzing the annual data after cloud removal. This value represents the temperature characteristics of the region and is used to analyze temperature distribution and fluctuations.

[0069] Surface wind speed data of the study area is obtained, statistical analysis is performed on the surface wind speed data, the annual average wind speed of the area is calculated, and the wind speed characteristic data of the area is obtained.

[0070] Wind direction data for the study area is acquired, and the wind direction distribution in the region is analyzed through data processing. The wind frequency in each direction is calculated, and the dominant wind direction in the region is determined using statistical methods. This result can serve as wind direction characteristic data for the region.

[0071] This invention addresses the problems of insufficient coverage and limited accuracy of single monitoring methods in existing technologies by using multi-source collaboration, thereby achieving accurate monitoring of fixed point sources and small-scale high-concentration areas.

[0072] S3: Data Preprocessing and Feature Data Generation

[0073] After collecting the above data, data preprocessing was performed to ensure that data from different sources were formatted uniformly and to generate feature data that could be used for analysis. This feature data includes:

[0074] Land type characteristic data: Based on the spatial distribution of land types, the spatial proportion of each land type is obtained, reflecting the land use characteristics of the region.

[0075] Surface elevation characteristic data: By processing digital elevation model (DEM) data, the average surface elevation of the region is obtained, reflecting the topographic characteristics of the region.

[0076] Surface temperature characteristic data: By processing temperature data, the temperature variation characteristics of the region are obtained, which provides support for subsequent analysis of the spatial distribution of carbon emissions.

[0077] Wind speed and direction data: By using wind speed and direction data, the spatial distribution of wind speed and main wind direction in the region can be calculated, which helps to predict the diffusion patterns of carbon emission sources.

[0078] S4: High-precision monitoring and data fusion analysis of unmanned aerial vehicles (UAVs)

[0079] Based on the feature data obtained in step S3, high-precision detection of greenhouse gas concentrations is performed using UAVs. The UAVs, carrying high-precision sensors, monitor carbon emission hotspots within the region and obtain greenhouse gas concentration distribution maps.

[0080] After acquiring the gas concentration distribution map collected by the drone, other auxiliary data layers, including surface temperature, wind speed, wind direction, land cover type, and surface elevation, are overlaid to form a multidimensional dataset. During data fusion, to extract key influencing factors and guide monitoring point deployment, a random forest algorithm is used to analyze feature importance. Random forest constructs multiple decision trees to evaluate the contribution of each feature to the target variable (gas concentration), and its importance calculation formula is as follows:

[0081]

[0082] in, It is a feature i The importance of T For the number of decision trees, and These represent the impurities of nodes before and after the split, typically calculated using the Gini index or information gain.

[0083] To reduce data redundancy and extract key information, Principal Component Analysis (PCA) is used to reduce the dimensionality of the feature data. PCA determines the principal components of the data by decomposing the eigenvalues ​​of the covariance matrix, as shown in the following formula:

[0084]

[0085]

[0086] in, C It is the covariance matrix. and The first k The eigenvectors and eigenvalues ​​of each principal component Indicates the first i 10 samples, which are from the previous formula. , This represents the sample mean.

[0087] To accurately identify areas of high gas concentration, hotspot analysis was performed using data collected by drones, and the propagation paths of greenhouse gases were simulated using a diffusion model. The diffusion model is based on atmospheric dynamics equations and mainly involves the following formulas:

[0088]

[0089] in, It is a vector differential operator. C It refers to gas concentration. u It is a wind speed vector. D It is the diffusion coefficient. S This is the gas source term, representing emission intensity. Numerical calculations are performed using the finite difference method, combined with wind speed and direction data to simulate the gas diffusion trajectory, thereby determining the extent and diffusion impact of hotspot areas.

[0090] S5: Deployment of monitoring equipment based on iterative scanning mode

[0091] After completing UAV detection and data fusion analysis, the deployment range of ground monitoring equipment was initially determined based on correlation and diffusion simulation results. The deployment range was centered on high-concentration hotspot areas and optimized by combining diffusion paths and multi-factor influence models, including deployment point selection and iterative optimization.

[0092] The iterative scanning model assumes that a single building can be represented by a closed polygon in two-dimensional space. Depending on the application, the length of the building's outer walls or its outer boundary is assigned to the building's perimeter. Alternatively, based on an estimate of the outer boundary, the building's perimeter can also be represented by other variables.

[0093] This invention employs a perceived quality model, Gaussian process, and particle swarm optimization algorithm to optimize the scanning location of ground monitoring equipment, ensuring that the equipment layout covers the main emission sources in the target area and improves monitoring efficiency. Based on the initial deployment range of the ground monitoring equipment obtained from the data fusion analysis in step S4, optimization design is carried out from two aspects: 1) the number of buildings covered by a single scan, and 2) the percentage of building angles scanned at each location. To this end, this invention introduces an index called the Visible Building Percentage Index (VBPI), which describes the percentage of buildings that can be scanned at a specific location based on visibility analysis. Simultaneously, the Cumulative Degree of Polygon Closure (CDPC) is introduced to evaluate the location scan completeness of each building during the iterative process. The proposed iterative pattern scanning design algorithm includes five main steps: 1) Inputting a coordinate table containing building center locations and perimeter data; 2) Generating a VBPI index map of building distribution; 3) Selecting valid candidate points for the optimal ground monitoring equipment scanning location; 4) Determining the final ground monitoring equipment scanning location; 5) Determining whether additional scans and iterations are needed. Each iteration includes these five steps. The step-by-step workflow is as follows: Figure 3 As shown, the specific steps are as follows:

[0094] (1) Data input and initialization

[0095] First, a dataset containing the building's center location, perimeter data, and environmental characteristics within the study area is input to generate a basic geospatial distribution map, which is used to subsequently generate the VBPI index map. The initialization process of this data utilizes a perceived quality model and a Gaussian process model, which form the basis for predicting the spatial distribution of gas concentrations. The mathematical formula for the perceived quality model is:

[0096]

[0097] in, For deployment points A Perceived quality This represents the total number of hotspot areas. Located in the center of the hotspot area, Candidate deployment point locations, Where is the diffusion radius, The standard deviation of the gas concentration, To prevent correction terms with a denominator of zero.

[0098] Gaussian processes help identify optimal monitoring locations by predicting gas concentration distributions at unmonitored sites. The formula for the covariance kernel function of a Gaussian process is:

[0099]

[0100] in, It is the covariance between two points. It is the signal variance. l It is a feature length scale.

[0101] Based on the perceived quality and Gaussian process model optimization results, range control calculations were performed on the initial deployment points to ensure that the monitoring equipment covered hotspot areas and key diffusion paths. Subsequently, the coverage efficiency of the monitoring network was verified by simulation, and the deployment points were adjusted as necessary to optimize sampling efficiency.

[0102] (2) Generate VBPI index map: The VBPI index is defined as the percentage of buildings that can be scanned from a specific location. It is introduced to assess how many buildings can be covered from a location scanned by ground monitoring equipment.

[0103]

[0104] in, Indicates the first i Position in the next iteration The VBPI index, Indicates from position Number of buildings that can be seen N i For the first i The total number of buildings in each iteration is calculated and updated in each subsequent iteration. In the first iteration, N i This equals the total number of all buildings in the diagram. In subsequent iterations, N i This refers to the number of buildings requiring additional scanning. Visibility analysis is used to calculate the VBPI index for all locations on the building distribution map. Visibility analysis determines the visible area by calculating the interaction between the line of sight and objects.

[0105] In this invention, instead of directly calculating the visible area of ​​each location, the visible area of ​​each building location is calculated, and then a VBPI index map is generated. First, the visible area is calculated for each building location, generating a corresponding visibility map. Each xy coordinate within the visible area is assigned a value of 1, and the xy coordinates of non-visible areas are assigned a value of 0. Next, the visibility maps of all building locations are aggregated into a global visibility map. Therefore, the value of each xy coordinate in the global visibility map represents the number of times that coordinate is visible from all building locations. This is equivalent to indicating how many buildings can be observed from that location. Finally, the VBPI index map is obtained by calculating the global visibility map with the total number of buildings in the current iteration.

[0106] The VBPI index map quantifies the occlusion effect at all spatial locations within the map. Clearly, a high VBPI value indicates a low occlusion effect, while a low VBPI value indicates a high occlusion effect. Furthermore, to mitigate the issue that high VBPI values ​​may be located within small adjacent regions with low VBPI values, the VBPI index map is convolved. The VBPI index map serves as the fundamental data for selecting the optimal location for ground monitoring equipment and is updated in each iteration.

[0107] (3) Selecting effective candidate locations for ground monitoring equipment: In each iteration, all candidate locations of the ground monitoring equipment are extracted from the VBPI index map using a specific threshold (Th, i.e., 85% of the maximum VBPI). Considering that adjacent locations have similar VBPI values, this invention downsamples all candidate locations by downsampling the grid of the VBPI index map.

[0108] Based on downsampling, Particle Swarm Optimization (PSO) is used to optimize the effective candidate positions. PSO gradually approaches the optimal position by simulating the motion trajectory of particles. The position update formula is as follows:

[0109]

[0110]

[0111] in, For the first i The particle in the first t The speed of generation For the first i The particle in the first t The position of the generation, For inertial weights, and As a learning factor, and It is a random number. This represents the optimal position for an individual particle. This is the globally optimal position.

[0112] From a technical perspective, a coarse grid array is created only for candidate regions, retaining the candidate with the highest VBPI value in each grid. These retained candidate locations are effective candidate locations for the optimal ground monitoring equipment in further processing. Furthermore, to avoid excessively close proximity between ground monitoring equipment and buildings, this invention sets a buffer zone for each building with a radius 1.3 times the building's perimeter. This invention also adds a circular buffer zone for the optimal location selected in previous iterations. This strategy helps prevent excessively short distances between ground monitoring equipment locations, thereby maximizing scan integrity.

[0113] (4) Determining the optimal deployment location of ground monitoring equipment using CDPC: CDPC is defined as the union angle of the building scan area. The CDPC value is 0° when the area is not scanned and 360° when the area is fully scanned. The proposed CDPC index quantifies the scan completeness of each valid candidate ground monitoring equipment location; therefore, this index can be used to determine the optimal ground monitoring equipment location. Optimal location of ground monitoring equipment (x...) o , y o () refers to the location where the sum of CDPC of all buildings in the study area reaches its maximum value.

[0114]

[0115] in, i Indicates the first i iteration It is the first j A polygonal CDPC, This refers to the candidate locations scanned by the ground monitoring equipment in each iteration. (Subscript) single and end These represent the candidate positions in the iteration and the final optimal position, respectively.

[0116] (5) Determining Additional Deployment Locations or Terminating the Task: This invention uses the detection rate (δ) as an indicator to determine whether to stop operation or continue additional ground monitoring equipment scanning. The detection rate is defined as the number of all buildings in the diagram ( N total In ), the scan integrity requirements expected by the operator are met. T E The percentage of buildings. It should be noted that... I(j) This is an indicator function. If the detection rate is greater than 95%, the program stops and outputs the optimal ground monitoring device location for each iteration. The program also stops if the difference in detection rate between two adjacent iterations is less than 5%, and the detection rate is greater than 80%, indicating that subsequent scans contribute little to the results; therefore, all optimal ground monitoring device locations will be output except for the result of the last iteration. Otherwise, a new iteration will be performed, executing additional scans, calculated as follows:

[0117]

[0118]

[0119] It is important to note that T E This is a key parameter for determining whether to stop the iteration. If the... jThe CDPC value of each building reached the operator-defined value. T E If the CDPC value is less than a certain value, no additional scanning of the building is required. Otherwise, the building needs further sampling. In the new iteration, buildings with CDPC values ​​less than a certain value will be used. T E The buildings are scanned. Simultaneously, the VBPI index map is updated by summarizing the visibility map of these buildings.

[0120] In addition, in the first iteration T E The values ​​are usually different from those in subsequent iterations because the CDPC value of the building is typically less than 180° in the first iteration. T E It can be set to a value less than 180°, such as 120°. For subsequent iterations, the CDPC of the building may exceed 180°, therefore... T E The value is typically set to a value greater than 180°, such as 200°. This invention also suggests setting a higher value for areas with large building perimeters. T E The value is the same, and vice versa. Furthermore, T E The value can also be adjusted based on the building properties exported as needed.

[0121] This invention utilizes satellite and UAV hotspot detection results to rationally deploy ground equipment, achieving dynamic optimization and efficient layout of monitoring sites.

[0122] As an alternative, this invention can consider using drone monitoring and ground equipment monitoring as the primary data sources, introducing satellite remote sensing only when needed. This approach is suitable for localized or specific areas (such as certain industrial parks, cities, and other carbon emission hotspots) without requiring satellite coverage. By deploying high-precision ground sensors and drones equipped with high-resolution cameras, combined with data fusion algorithms, carbon emission sources can be monitored. While this approach reduces reliance on satellites, it limits the monitoring range and is suitable for precisely monitoring carbon emissions in target areas.

[0123] As an alternative, this invention combines LiDAR technology with drones to replace some ground-based equipment for high-precision monitoring. Drones equipped with LiDAR sensors can scan the Earth's surface with lasers to measure the height and location of carbon emission sources in real time, accurately obtaining the distribution of carbon emissions within a local area. Compared to traditional ground-based equipment, LiDAR combined with drones offers flexible coverage and can adapt to different geographical environments, making it particularly suitable for areas where it is difficult to deploy ground-based equipment.

[0124] As an alternative, this invention replaces some of the monitoring functions of satellite remote sensing by deploying a dense network of ground sensors (such as low-power sensors based on Internet of Things technology) within a specific area. By collecting carbon emission data in real time through distributed sensors and transmitting and processing it via the network, the ground network can provide high-precision carbon emission data, making it particularly suitable for accurate monitoring of fixed point sources and small areas. While this approach is limited by the locality of the monitoring range, it can achieve higher spatial resolution through network expansion, making it suitable for key areas and long-term continuous monitoring.

[0125] As an alternative, this invention can employ automated data processing methods based on artificial intelligence (AI) and machine learning, utilizing algorithms to automatically analyze and optimize the data fusion process. AI technology enables precise identification and tracking of carbon emission sources, thereby reducing the need for manual intervention and improving monitoring accuracy and efficiency. The AI ​​system can process data from different sources in real time and perform dynamic optimization, thus more accurately identifying hotspots and trends. Especially in complex environments, AI technology can better cope with uncertainties and improve monitoring effectiveness.

[0126] The above combination Figure 1 The method for selecting carbon emission monitoring sites based on the integrated air-space-ground system provided in the embodiments of the present invention has been described in detail. Next, the carbon emission monitoring site selection system based on the integrated air-space-ground system provided in the embodiments of the present invention will be described in conjunction with the accompanying drawings.

[0127] Figure 4 This is a schematic diagram of the structure of a carbon emission monitoring site selection system based on an integrated air-space-ground system, as shown in an embodiment of the present invention. Figure 4 The system described in this invention includes:

[0128] The data acquisition module is configured to: acquire the land cover type, digital elevation model of the land surface, land surface temperature, and land surface wind speed and direction of the target study area, and extract land type feature data, land surface elevation feature data, land surface temperature feature data, and wind speed and direction feature data respectively;

[0129] The feature extraction and analysis module is configured to: acquire carbon satellite remote sensing image data of the target study area, extract greenhouse gas concentration distribution maps, combine land type feature data, surface elevation feature data, surface temperature feature data, and wind speed and direction feature data, extract the main influencing factors by analyzing feature importance, and perform hotspot analysis on the greenhouse gas concentration distribution map to determine the range and diffusion impact of hotspot areas.

[0130] The monitoring equipment deployment optimization module is configured to: determine the deployment location of monitoring equipment by taking the hotspot area as the center and combining the range and diffusion impact of the hotspot area with an iterative scanning mode;

[0131] The iterative scanning mode includes: inputting a coordinate table containing data on the center location and perimeter of buildings, generating a VBPI index map of the building distribution, selecting valid candidate points for the optimal scanning location of the ground monitoring equipment, determining the final scanning location of the ground monitoring equipment, determining whether additional scanning and iteration are needed; and outputting the deployment location of the monitoring equipment until the iteration conditions are met.

[0132] In some embodiments, acquiring carbon satellite remote sensing image data of the target study area and extracting a greenhouse gas concentration distribution map includes: acquiring carbon satellite remote sensing image data of the target study area; preprocessing the carbon satellite remote sensing image data, including atmospheric correction; based on the preprocessed carbon satellite remote sensing image data, analyzing different band data in the carbon satellite remote sensing image data, and combining the atmospherically corrected data to identify the concentration range of gases and determine the spatial distribution of greenhouse gases; and based on the spatial distribution of greenhouse gases, obtaining a greenhouse gas concentration distribution map by observing the greenhouse gas concentration variation trend in the carbon satellite remote sensing image data.

[0133] In some embodiments, the step of extracting key influencing factors by analyzing feature importance and performing hotspot analysis on greenhouse gas concentration distribution maps to determine the extent and diffusion impact of hotspot areas includes: using a random forest algorithm to evaluate the contributions of land type characteristic data, surface elevation characteristic data, surface temperature characteristic data, and wind speed and direction characteristic data to gas concentrations, and extracting key influencing factors; using principal component analysis to reduce the dimensionality of key influencing factors and determine principal components; and based on the principal components, using a diffusion model established based on atmospheric dynamics equations to determine the extent and diffusion impact of hotspot areas.

[0134] In some embodiments, the input includes a coordinate table containing the center location and perimeter data of buildings; including: inputting a dataset containing the center location, perimeter data and environmental features within the target study area, using a perceived quality model and a Gaussian process model to obtain the monitoring location optimization result; performing range control calculations on the preliminary deployment points based on the monitoring location optimization result to ensure that the monitoring equipment covers hotspot areas and key diffusion paths, and generating a basic geospatial distribution map.

[0135] In some embodiments, the VBPI index map used to generate the building distribution map is represented by the following formula:

[0136]

[0137] in, Indicates the first i Position in the next iteration The VBPI index, Indicates from position Number of buildings that can be seen N i For the first i The total number of buildings in each iteration is updated in each subsequent iteration.

[0138] In some embodiments, selecting the effective candidate points for the optimal ground monitoring device scanning location includes: in each iteration, extracting high VBPI locations from the VBPI index map of all candidate locations of the monitoring device through a specific threshold; downsampling all candidate locations by grid downsampling the VBPI index map; and optimizing the effective candidate locations, i.e., the effective candidate monitoring device locations, based on the downsampling using a particle swarm optimization algorithm.

[0139] In some embodiments, determining the final scanning location of the ground monitoring equipment includes:

[0140] The optimal monitoring device location is determined by quantifying the scan completeness of each valid candidate monitoring device location using the CDPC index.

[0141] The CDPC index is expressed using the following formula:

[0142]

[0143] in, i Indicates the first i iteration It is the first j A polygonal CDPC, This refers to the candidate locations scanned by the monitoring device in each iteration; subscript single and end Iterate through the candidate positions and the final optimal position separately.

[0144] According to embodiments of the present invention, the integrated air-space-ground carbon emission monitoring site selection system can correspond to the execution of the methods described in the embodiments of the present invention, and the above and other operations and / or functions of each module of the integrated air-space-ground carbon emission monitoring site selection system are respectively for implementing Figure 1 For the sake of brevity, the corresponding processes of each method in the code will not be elaborated here.

[0145] See Figure 5The diagram shows the structure of a computer device, which includes a processor, a communication interface, and a computer-readable storage medium. The processor, communication interface, and computer-readable storage medium are connected via a bus or other means. The communication interface is used to receive and send data. The computer-readable storage medium can be stored in the computer device's memory. The computer-readable storage medium stores computer programs, including program instructions, and the processor executes the program instructions stored in the computer-readable storage medium. The processor (or CPU, Central Processing Unit) is the computing and control core of the computer device, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to implement the corresponding steps in the embodiment of the space-air-ground integrated carbon emission monitoring site selection method.

[0146] This embodiment provides a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the processing system of the computer device.

[0147] Furthermore, this storage space also contains one or more instructions suitable for loading and execution by the processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM memory or non-volatile memory, such as at least one disk storage device; optionally, it can also be at least one computer-readable storage medium located remotely from the aforementioned processor.

[0148] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes one or more instructions stored in the computer-readable storage medium to implement the corresponding steps in the above-described embodiment of the carbon emission monitoring site selection method based on integrated air-space-ground systems.

[0149] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0150] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0151] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0152] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0153] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0154] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for selecting monitoring sites for carbon emissions based on an integrated air-space-ground system, characterized in that, include: Obtain the land cover type, digital elevation model of the land surface, land surface temperature, and surface wind speed and direction of the target study area, and extract land type feature data, surface elevation feature data, surface temperature feature data, and wind speed and direction feature data respectively; Acquire carbon satellite remote sensing image data of the target study area, extract greenhouse gas concentration distribution maps, and combine land type characteristic data, surface elevation characteristic data, surface temperature characteristic data, and wind speed and direction characteristic data. By analyzing the importance of features, extract the main influencing factors; conduct hotspot analysis on the greenhouse gas concentration distribution maps to determine the range and diffusion impact of hotspot areas. Centered on the hotspot area, and taking into account the scope and diffusion impact of the hotspot area, an iterative scanning mode is adopted to determine the location of monitoring equipment; The iterative scanning mode includes: inputting a coordinate table containing data on the center location and perimeter of buildings, generating a VBPI index map of the building distribution, selecting valid candidate points for the optimal scanning location of the ground monitoring equipment, determining the final scanning location of the ground monitoring equipment, determining whether additional scanning and iteration are needed; and outputting the deployment location of the monitoring equipment until the iteration conditions are met. The input includes a coordinate table containing the center location and perimeter data of buildings; the method includes: inputting a dataset containing the center location, perimeter data of buildings and environmental features within the target study area, using a perception quality model and a Gaussian process model to obtain the monitoring location optimization results; based on the monitoring location optimization results, performing range control calculations on the preliminary deployment points to ensure that the monitoring equipment covers hotspot areas and key diffusion paths, and generating a basic geospatial distribution map; The mathematical formula for the perceived quality model is as follows: in, For deployment points A Perceived quality This represents the total number of hotspot areas. Located in the center of the hotspot area, Candidate deployment point locations, Where is the diffusion radius, The standard deviation of the gas concentration, To prevent correction terms with a denominator of zero; Gaussian processes help identify optimal monitoring locations by predicting gas concentration distributions at unmonitored sites. The Gaussian process model formula is as follows: in, It is the covariance between two points. It is the signal variance. l It is a feature length scale; The generated building distribution map uses the VBPI index map, expressed by the following formula: in, Indicates the first i Position in the next iteration The VBPI index, Indicates from position Number of buildings that can be seen N i For the first i The total number of buildings in each iteration, and updated in each subsequent iteration; The method for selecting effective candidate points for the optimal scanning location of ground monitoring equipment includes: in each iteration, extracting high VBPI locations from the VBPI index map of all candidate locations of the monitoring equipment through a specific threshold; downsampling all candidate locations by downsampling the grid of the VBPI index map; and optimizing the effective candidate locations, i.e., the effective candidate monitoring equipment locations, based on the downsampling using a particle swarm optimization algorithm. The method for determining the final scanning location of the ground monitoring equipment includes: The optimal monitoring device location is determined by quantifying the scan completeness of each valid candidate monitoring device location using the CDPC index. The CDPC index is expressed using the following formula: in, i Indicates the first i iteration It is the first j A polygonal CDPC, This refers to the candidate locations scanned by the monitoring device in each iteration; subscript single and end Iterate through the candidate positions and the final optimal position separately; VBPI represents the percentage of buildings scanned at a specific location based on visibility analysis; CDPC represents the cumulative degree of polygon closure, used to assess the location scan integrity of each building during the iteration process.

2. The carbon emission monitoring site selection method based on integrated air-space-ground systems according to claim 1, characterized in that, The method for acquiring carbon satellite remote sensing image data of the target study area and extracting a greenhouse gas concentration distribution map includes: acquiring carbon satellite remote sensing image data of the target study area; preprocessing the carbon satellite remote sensing image data, including atmospheric correction; based on the preprocessed carbon satellite remote sensing image data, analyzing different band data in the carbon satellite remote sensing image data, and combining the atmospherically corrected data to identify the concentration range of gases and determine the spatial distribution of greenhouse gases; and based on the spatial distribution of greenhouse gases, obtaining a greenhouse gas concentration distribution map by observing the trend of greenhouse gas concentration changes in the carbon satellite remote sensing image data.

3. The carbon emission monitoring site selection method based on integrated air-space-ground system according to claim 1, characterized in that, The main influencing factors are extracted by analyzing the importance of features; Hotspot analysis was performed on greenhouse gas concentration distribution maps to determine the extent and diffusion impact of hotspot areas. The methods included: using a random forest algorithm to evaluate the contributions of land type characteristics, surface elevation characteristics, surface temperature characteristics, and wind speed and direction characteristics to gas concentrations, extracting key influencing factors; using principal component analysis to reduce the dimensionality of the key influencing factors and determine the principal components; and using a diffusion model based on atmospheric dynamics equations, determining the extent and diffusion impact of hotspot areas based on the principal components.

4. A carbon emission monitoring site selection system based on integrated air-space-ground systems, characterized in that, The carbon emission monitoring site selection method based on the integrated air-space-ground system as described in any one of claims 1-3 includes: The data acquisition module is configured to: acquire the land cover type, digital elevation model of the land surface, land surface temperature, and land surface wind speed and direction of the target study area, and extract land type feature data, land surface elevation feature data, land surface temperature feature data, and wind speed and direction feature data respectively; The feature extraction and analysis module is configured to: acquire carbon satellite remote sensing image data of the target study area, extract greenhouse gas concentration distribution maps, combine land type feature data, surface elevation feature data, surface temperature feature data, and wind speed and direction feature data, extract the main influencing factors by analyzing feature importance, and perform hotspot analysis on the greenhouse gas concentration distribution map to determine the range and diffusion impact of hotspot areas. The monitoring equipment deployment optimization module is configured to: determine the deployment location of monitoring equipment by taking the hotspot area as the center and combining the range and diffusion impact of the hotspot area with an iterative scanning mode; The iterative scanning mode includes: inputting a coordinate table containing data on the center location and perimeter of buildings, generating a VBPI index map of the building distribution, selecting valid candidate points for the optimal scanning location of the ground monitoring equipment, determining the final scanning location of the ground monitoring equipment, determining whether additional scanning and iteration are needed; and outputting the deployment location of the monitoring equipment until the iteration conditions are met.

5. A computer device, characterized in that, A processor, adapted to execute computer programs; A computer-readable storage medium storing a computer program, which, when executed by the processor, implements the steps of the carbon emission monitoring site selection method based on the integrated air-space-ground system as described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program adapted to be loaded by a processor and execute the steps of the carbon emission monitoring site selection method based on the integrated air-space-ground system as described in any one of claims 1-3.

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