Carbon emission monitoring point selection method and system based on space-air-ground integration

Abstract

The invention relates to the field of carbon emission monitoring, and provides a carbon emission monitoring point selection method and system based on space-air-ground integration. The method comprises the following steps: respectively extracting land type feature data, surface elevation feature data, surface temperature feature data and wind speed and wind direction feature data; obtaining carbon satellite remote sensing image data of a target research area, extracting a greenhouse gas concentration distribution diagram, combining land type feature data, surface elevation feature data, surface temperature feature data and wind speed and wind direction feature data, and extracting main influence factors by analyzing feature importance; performing hot spot analysis on the greenhouse gas concentration distribution diagram, and judging the range and diffusion influence of a hot spot region; taking the hot spot area as a center, combining the range and diffusion influence of the hot spot area, and adopting an iterative scanning mode to determine the arrangement position of monitoring equipment; according to the invention, comprehensive and accurate carbon emission monitoring can be realized.

Description

Technical Field

[0001] The present invention relates to the field of carbon emission monitoring technology, and in particular to a carbon emission monitoring point selection method and system based on air-space-ground integration. Background Art

[0002] The statements in this section merely provide 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 emissions monitoring due to its wide coverage, short revisit periods, and high spatial resolution. Platforms such as hyperspectral and microwave satellites can obtain distribution information based on the characteristic absorption 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 for accurately monitoring fixed point sources and small areas of high concentration.

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

[0005] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a carbon emission monitoring site selection method and system based on the integration of air, space and land. The present invention optimizes the site selection layout, integrates multi-source data, improves monitoring efficiency and accuracy, and realizes comprehensive and accurate carbon emission monitoring, providing scientific support for carbon emission accounting and management, and helping to achieve the carbon neutrality goal.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions: The first aspect of the present invention provides a carbon emission monitoring point selection method based on air-ground integration.

[0007] A carbon emission monitoring site selection method based on air-ground-space integration includes: Obtain the land cover type, surface digital elevation model, surface temperature, and surface wind speed and direction of the target study area, and extract land type characteristic data, surface elevation characteristic data, surface temperature characteristic data, and wind speed and direction characteristic data respectively; Obtain carbon satellite remote sensing image data for the target study area, extract greenhouse gas concentration distribution maps, combine land type characteristic data, surface elevation characteristic data, surface temperature characteristic data, and wind speed and direction characteristic data, analyze the importance of characteristics, and extract the main influencing factors; perform hotspot analysis on the greenhouse gas concentration distribution map to determine the scope and diffusion impact of the hotspot area; Taking the hotspot area as the center, taking into account the scope and diffusion impact of the hotspot area, an iterative scanning mode is used to determine the location of the monitoring equipment; Among them, the iterative scanning mode includes: inputting a coordinate table containing the center position and perimeter data of the building, generating a VBPI index map of the building distribution map, selecting valid candidate points for the best ground monitoring equipment scanning position, determining the final ground monitoring equipment scanning position, and determining whether additional scanning and iteration are required; until the iteration conditions are met, the monitoring equipment layout position is output.

[0008] Furthermore, the method of obtaining carbon satellite remote sensing image data of the target study area and extracting a greenhouse gas concentration distribution map includes: obtaining carbon satellite remote sensing image data of the target study area, preprocessing the carbon satellite remote sensing image data, including correcting the atmosphere; based on the preprocessed carbon satellite remote sensing image data, parsing different bands of data in the carbon satellite remote sensing image data, combining the atmospherically corrected data, identifying the concentration range of the gas, and determining the spatial distribution of the greenhouse gas; based on the spatial distribution of the greenhouse gas, obtaining a greenhouse gas concentration distribution map through the greenhouse gas concentration change trend in the carbon satellite remote sensing image data.

[0009] Furthermore, the main influencing factors are extracted by analyzing the importance of features; hotspot analysis is performed on the greenhouse gas concentration distribution map to determine the scope and diffusion impact of the hotspot area; the method includes: using a random forest algorithm to evaluate the contribution of land type characteristic data, surface elevation characteristic data, surface temperature characteristic data, and wind speed and wind direction characteristic data to gas concentration, and extracting the main influencing factors; using principal component analysis to reduce the dimension of the main influencing factors and determine the principal components; based on the principal components, a diffusion model established based on the atmospheric dynamics equation is used to determine the scope and diffusion impact of the hotspot area.

[0010] Furthermore, the input includes a coordinate table of the building's center position and perimeter data; the method includes: inputting a data set including the building's center position, perimeter data and environmental characteristics within the target research area, and using a perceptual quality model and a Gaussian process model to obtain monitoring position optimization results; performing range control calculations on the preliminary deployment points based on the monitoring position optimization results to ensure that the monitoring equipment covers hot spots and key diffusion paths, and generating a basic geographic spatial distribution map.

[0011] Furthermore, the VBPI index map of the building distribution map is generated using the following formula:

[0012] in, Indicates the i The position in the iteration The VBPI index, Indicates from position The number of buildings that can be seen, N i For the i The total number of buildings in the iteration is updated in each iteration.

[0013] Furthermore, the method for selecting valid candidate points for the optimal ground monitoring equipment scanning position includes: in each iteration, extracting high VBPI positions from the VBPI index map of all candidate positions of the monitoring equipment through a specific threshold; downsampling all candidate positions by grid downsampling the VBPI index map; and optimizing the valid candidate positions, i.e., the valid candidate monitoring equipment positions, using a particle swarm optimization algorithm based on the downsampling.

[0014] Furthermore, the method for determining the final scanning position of the ground monitoring equipment includes: The CDPC index is used to quantify the scanning completeness of each valid candidate monitoring device location and determine the optimal monitoring device location; The CDPC index is expressed by the following formula:

[0015] in, i Indicates the i iterations, It is j The CDPC of polygons, Refers to the candidate locations scanned by the monitoring device in each iteration; single and end Iterate over candidate positions and the final best position separately.

[0016] The second aspect of the present invention provides a carbon emission monitoring point selection system based on air-ground integration.

[0017] A carbon emission monitoring point selection system based on air-ground integration includes: A data acquisition module is configured to: obtain the land cover type, surface digital elevation model, surface temperature, and surface wind speed and direction of the target study area, and extract land type characteristic data, surface elevation characteristic data, surface temperature characteristic data, and wind speed and direction characteristic data respectively; The feature extraction and analysis module is configured to: obtain carbon satellite remote sensing image data of the target study area, extract the greenhouse gas concentration distribution map, combine the land type feature data, surface elevation feature data, surface temperature feature data, and wind speed and direction feature data, analyze the feature importance, and extract the main influencing factors; perform hotspot analysis on the greenhouse gas concentration distribution map to determine the scope and diffusion impact of the hotspot area; The monitoring equipment deployment optimization module is configured to: determine the deployment location of monitoring equipment using an iterative scanning mode based on the hotspot area and the scope and diffusion impact of the hotspot area; Among them, the iterative scanning mode includes: inputting a coordinate table containing the center position and perimeter data of the building, generating a VBPI index map of the building distribution map, selecting valid candidate points for the best ground monitoring equipment scanning position, determining the final ground monitoring equipment scanning position, and determining whether additional scanning and iteration are required; until the iteration conditions are met, the monitoring equipment layout position is output.

[0018] A third aspect of the present invention provides a computer device, comprising: a processor adapted to execute a computer program; Computer-readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, the steps of the carbon emission monitoring point selection method based on air-space-ground integration as described in the first aspect above are implemented.

[0019] The fourth aspect of the present invention provides a computer-readable storage medium, which stores a computer program, and the computer program is suitable for being loaded by a processor and executing the steps in the carbon emission monitoring point selection method based on integrated air-space-ground-ground as described in the first aspect above.

[0020] Compared with the prior art, the present invention has the following beneficial effects: 1. Multi-source data fusion improves monitoring accuracy and timeliness: Existing technologies often rely on a single data source, making it difficult to fully cover and accurately assess fixed point sources and small areas of high concentration. This invention fuses large-scale monitoring data from satellite remote sensing, small and medium-scale hotspot detection data from drones, and high-precision verification data from ground-based equipment to construct a dynamic carbon emissions distribution map, achieving more refined monitoring and dynamic updates, overcoming the limitations of a single data source.

[0021] 2. Optimize site selection and layout to improve monitoring efficiency: This invention uses a site selection optimization method based on multi-source data, leveraging satellite and drone hotspot detection results to guide the rational layout of ground monitoring equipment. By scientifically distributing monitoring points, it avoids the resource waste caused by the irrational layout of ground equipment in existing technologies and effectively compensates for the lack of local high-precision coverage provided by satellite and drone monitoring.

[0022] 3. Build an integrated air-ground-air system to achieve comprehensive coverage and dynamic monitoring: In existing technologies, satellite remote sensing, drones, and ground-based equipment often operate independently. This application integrates these three elements into a coordinated, three-dimensional monitoring network. This system offers enhanced synergy and adaptability in response to changing atmospheric conditions, surface complexity, and diverse monitoring needs, ensuring continuous, comprehensive, and accurate carbon emissions monitoring. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0024] Figure 1 1 is a flow chart of a method for selecting carbon emission monitoring points based on air-ground integration according to an embodiment of the present invention; Figure 2 is a flow chart of another embodiment of a method for selecting a carbon emission monitoring point based on air-ground integration according to an embodiment of the present invention; Figure 3 is a flowchart of a monitoring device deployment process based on an iterative scanning mode according to an embodiment of the present invention; Figure 4 is a structural diagram of a carbon emission monitoring point selection system based on air-ground integration according to an embodiment of the present invention; Figure 5 It is a structural diagram of a computer device shown in an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0026] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0027] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0028] As introduced in the background technology, although satellite remote sensing has global coverage capabilities, it is subject to atmospheric conditions, surface reflectivity and orbit design in the precise monitoring of fixed point sources and small-scale high-concentration areas, resulting in insufficient accuracy; although ground monitoring equipment can provide high-precision data support, it is subject to the limitations of spatial distribution and cannot achieve large-scale dynamic monitoring; although drones can make up for the needs of small and medium-scale monitoring, their endurance and coverage are limited, making it difficult to independently meet comprehensive monitoring requirements. In order to solve the above technical problems, the present invention provides a carbon emission monitoring point selection method and system based on air-space-ground integration. The technical solution of the present invention is described in detail below through several embodiments.

[0029] Figure 1 This is a flow chart of a method for selecting a carbon emission monitoring point based on air-ground integration according to an embodiment of the present invention; Figure 1 , the method comprising: Obtain the land cover type, surface digital elevation model, surface temperature, and surface wind speed and direction of the target study area, and extract land type characteristic data, surface elevation characteristic data, surface temperature characteristic data, and wind speed and direction characteristic data respectively; Obtain carbon satellite remote sensing image data for the target study area, extract greenhouse gas concentration distribution maps, combine land type characteristic data, surface elevation characteristic data, surface temperature characteristic data, and wind speed and direction characteristic data, analyze the importance of characteristics, and extract the main influencing factors; perform hotspot analysis on the greenhouse gas concentration distribution map to determine the scope and diffusion impact of the hotspot area; Taking the hotspot area as the center, taking into account the scope and diffusion impact of the hotspot area, an iterative scanning mode is used to determine the location of the monitoring equipment; Among them, the iterative scanning mode includes: inputting a coordinate table containing the center position and perimeter data of the building, generating a VBPI index map of the building distribution map, selecting valid candidate points for the best ground monitoring equipment scanning position, determining the final ground monitoring equipment scanning position, and determining whether additional scanning and iteration are required; until the iteration conditions are met, the monitoring equipment layout position is output.

[0030] The present invention builds a multi-source, multi-dimensional monitoring network by integrating satellite remote sensing, drone observation, and ground equipment. Satellite remote sensing is responsible for global monitoring, drones are used to patrol small and medium-scale hotspot areas, and ground equipment provides high-precision data verification and supplementation. In particular, in terms of site selection and layout, the system focuses on combining the hotspot detection results of satellites and drones and rationally deploying ground equipment to improve monitoring efficiency and coverage accuracy. Through multi-level data fusion, a dynamic carbon emission distribution map is constructed to provide scientific support for site selection optimization and precision management, thereby contributing to the realization of global carbon neutrality goals.

[0031] Figure 2This is a flowchart illustrating another embodiment of a method for selecting carbon emission monitoring sites based on integrated space-ground and air-space systems, according to an embodiment of the present invention. This method aims to improve the accuracy and efficiency of carbon emission monitoring through carbon monitoring technology at multiple spatiotemporal scales. This method optimizes the existing layout of carbon emission monitoring sites through an integrated space-ground monitoring system, avoiding the irrational deployment issues caused by relying solely on visual judgment. The following are specific implementation steps of the present invention.

[0032] S1: Collect carbon satellite remote sensing images and conduct regional analysis of gas concentration monitoring Carbon satellite remote sensing image data is collected for the target study area. These satellite remote sensing image data are mainly used to analyze the spatial distribution of greenhouse gases (such as carbon dioxide, methane, nitrogen oxides, etc.) in the study area.

[0033] The concentration distribution of these greenhouse gases directly affects regional carbon emissions, providing critical data support for carbon emission monitoring and the development of environmental protection measures. Satellite remote sensing imagery provides a large-scale, continuous, and efficient observation platform that can effectively capture changes in greenhouse gas concentrations across a region at different temporal and spatial scales. This unique advantage is particularly evident in areas where ground-based monitoring is difficult to obtain information.

[0034] (1) Data collection in target areas: Target data collection targets are determined for the study area, and relevant carbon satellite imagery data are obtained through satellite remote sensing technology. This data primarily covers the concentrations of greenhouse gases such as carbon dioxide, methane, and nitrogen oxides. The absorption characteristics of atmospheric gases are obtained through meteorological satellites. Considering the satellite orbit and sensor observation capabilities, the data collection frequency is optimized to provide high-quality gas concentration estimates.

[0035] (2) Image data preprocessing: The collected satellite image data is preprocessed through radiation correction, atmospheric correction, and geometric correction to eliminate noise and interference in the data and improve the accuracy of subsequent analysis. Radiation correction is used to remove radiation deviations caused by the characteristics of satellite sensors to ensure that the acquired spectral data can truly reflect the physical properties of the surface. Atmospheric correction corrects the influence of the atmosphere on remote sensing signals, eliminates interference from factors such as atmospheric scattering and absorption, and ensures the accuracy of measurement data. Geometric correction adjusts the spatial geometric relationship of satellite images 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 greatly improved, providing a reliable basis for subsequent gas concentration inversion and analysis.

[0036] (3) Gas concentration inversion: Due to 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 at different wavelengths, thereby inferring their concentration distribution. Based on the physical model, the radiative transfer model (RTM) or data-driven algorithm analyzes the different bands of data in the remote sensing image. Combined with the atmospheric correction data, it can accurately identify the concentration range of the gas and distinguish the spatial distribution of different gases. This process makes the gas concentration information inverted from the remote sensing data more reliable and accurate by establishing a mathematical model between gas concentration and radiation data.

[0037] (4) Identification of hotspots: By analyzing the concentration trends in satellite imagery data, we can identify areas with high emissions and locate specific emission sources. For these hotspots, we can further analyze their spatial distribution characteristics to determine the density of emission sources and their impact on the surrounding environment. Combined with spatiotemporal trend analysis, we can identify seasonal and annual changes in hotspots, as well as emission fluctuations under special climatic conditions, providing a theoretical basis for the subsequent determination of small-scale research areas.

[0038] The present invention combines satellite remote sensing, drone 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.

[0039] S2: Multi-source data collection and feature data extraction Based on the carbon satellite remote sensing data, other auxiliary data of the study area are collected, including but not limited to land cover type, surface digital elevation model (DEM), surface temperature, surface wind speed and direction, etc. These data are used to further optimize the selection of monitoring points.

[0040] Based on the land type classification information, different land cover types are assigned numerical values. For example, forest is 1, grassland is 2, farmland is 3, urban is 4, and water bodies are 5. By calculating the numerical values ​​of pixels of different land types, the average value of each land type in the area is obtained. This average value represents the spatial distribution characteristics of each land type in the area and can be used as a spatial proportion characteristic of land type.

[0041] Obtain a digital elevation model of the study area, calculate the value of each pixel at the target resolution, and finally calculate the average elevation of the area as the surface elevation characteristic data of the area. This value represents the overall elevation level of the area and can be used to analyze the characteristics of the terrain.

[0042] After obtaining surface temperature data for the study area, cloud-affected areas are removed using a cloud detection algorithm to ensure accuracy. The annual average surface temperature for the region is calculated by applying this cloud-removed data to each year. This value represents the temperature characteristics of the region and is used to analyze temperature distribution and fluctuations.

[0043] The surface wind speed data of the study area was obtained, and statistical analysis was performed on the surface wind speed data to calculate the annual average wind speed in the area and obtain the wind speed characteristic data of the area.

[0044] Obtain wind direction data for the study area, process it, analyze the wind direction distribution in the area, calculate the wind frequency in each direction, and use statistical methods to determine the main wind direction in the area. This result can be used as the wind direction characteristic data for the area.

[0045] The present invention solves the problems of insufficient coverage and limited accuracy of single monitoring means in the existing technology through multi-source collaboration, and realizes accurate monitoring of fixed point sources and small-scale high-concentration areas.

[0046] S3: Data preprocessing and feature data generation After collecting the above data, data preprocessing is performed to ensure that the data from different sources can be in a unified format and generate feature data that can be used for analysis. These feature data include: Land type characteristic data: Based on the spatial distribution of land types, the spatial proportion of each land type is obtained to reflect the land use characteristics of the area.

[0047] Surface elevation characteristic data: By processing digital elevation model (DEM) data, the average surface elevation of the area is obtained to reflect the terrain characteristics of the area.

[0048] Surface temperature characteristic data: By processing temperature data, the temperature change characteristics of the region are obtained to provide support for subsequent analysis of the spatial distribution of carbon emissions.

[0049] Wind speed and direction characteristic data: Wind speed and direction data are used to calculate the spatial distribution of wind speed and main wind direction in the region, helping to predict the diffusion pattern of carbon emission sources.

[0050] S4: UAV high-precision monitoring and data fusion analysis Based on the characteristic data obtained in step S3, high-precision detection of greenhouse gas concentrations is performed on the drone. The drone carries high-precision sensors to monitor carbon emission hotspots in the area and obtain a greenhouse gas concentration distribution map.

[0051] After obtaining 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 superimposed to form a multidimensional dataset. During the data fusion process, a random forest algorithm is used to analyze feature importance to extract the main influencing factors and guide the layout of monitoring points. The random forest algorithm evaluates the contribution of each feature to the target variable (gas concentration) by constructing multiple decision trees. Its importance calculation formula is:

[0052] in, It is a feature i The importance of T is the number of decision trees, and They are the impurity of the nodes before and after the split, respectively, and are usually calculated using the Gini index or information gain.

[0053] In order to reduce data redundancy and extract the main information, principal component analysis (PCA) is used to reduce the dimension of the feature data. PCA determines the principal components of the data by decomposing the eigenvalues ​​of the covariance matrix. The formula is as follows:

[0054]

[0055] in, C is the covariance matrix, and Respectively k The eigenvectors and eigenvalues ​​of the principal components, Indicates the i samples, which is the , represents the sample mean.

[0056] To accurately identify areas with high gas concentrations, drones were used to collect data for hotspot analysis, and diffusion models were used to simulate the propagation paths of greenhouse gases. The diffusion model is based on atmospheric dynamics equations and primarily involves the following formulas:

[0057] in, is the vector differential operator, C is the gas concentration, u is the wind speed vector, D is the diffusion coefficient, S is the gas source term, representing the emission intensity. Finite difference methods are used to perform numerical calculations, combining wind speed and direction data to simulate the gas diffusion trajectory, thereby determining the scope of the hotspot area and its diffusion impact.

[0058] S5: Monitoring equipment deployment based on iterative scanning mode After completing drone detection and data fusion analysis, the deployment range of ground monitoring equipment was initially determined based on correlation and diffusion simulation results. This range was centered around high-concentration hotspots and optimized using diffusion pathways and a multi-factor influencing model, including deployment point selection and iterative optimization.

[0059] 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 building perimeter is assigned the length of its exterior walls or the building's outer boundary. Alternatively, the building perimeter can be represented by other variables based on an estimate of the outer boundary.

[0060] The present invention uses the perceptual quality model, Gaussian process and particle swarm optimization algorithm to optimize the scanning position of the ground monitoring equipment to ensure that the equipment layout covers the main emission sources in the target area and improve the monitoring efficiency. Based on the primary layout range of the ground monitoring equipment obtained by the data fusion analysis in step S4, the optimization design is performed from two aspects: 1) the number of buildings covered by a single scan, and 2) the percentage of building angles scanned by the position. To this end, the present invention introduces an indicator 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. At the same time, the cumulative degree of polygon closure (CDPC) is introduced to evaluate the position scanning completeness of each building during the iteration process. The proposed iterative pattern scanning design algorithm includes five main steps: 1) inputting a coordinate table containing the center position and perimeter data of the building; 2) generating a VBPI index map of the building distribution map; 3) selecting valid candidate points for the best ground monitoring equipment scanning position; 4) determining the final ground monitoring equipment scanning position; 5) determining whether additional scanning and iteration are required. Each iteration contains these five steps. The step-by-step workflow is as follows Figure 3 The specific steps are as follows: (1) Data input and initialization First, a dataset containing building center locations, perimeter data, and environmental characteristics within the study area is input to generate a basic geographic spatial distribution map, which is then used to generate the VBPI index map. The initialization process of this data uses the perceptual quality model and Gaussian process model, which are the basis for establishing the spatial distribution prediction of gas concentrations. The mathematical formula of the perceptual quality model is:

[0061] in, For deployment point A The perceived quality of is the total number of hotspot areas, The center of the hotspot area. is the candidate deployment point location, is the diffusion radius, is the standard deviation of gas concentration, A correction term to prevent the denominator from being zero.

[0062] The Gaussian process helps identify the best monitoring location by predicting the gas concentration distribution at unmonitored locations. The covariance kernel function formula of the Gaussian process is:

[0063] in, is the covariance between the two points, is the signal variance, l is the characteristic length scale.

[0064] 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 hot spots and key diffusion paths. The coverage efficiency of the monitoring network was then verified through simulations, and deployment points were adjusted as necessary to optimize sampling efficiency.

[0065] (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 evaluate how many buildings can be covered from the scanning location of ground monitoring equipment.

[0066]

[0067] in, Indicates the i The position in the iteration The VBPI index, Indicates from position The number of buildings that can be seen, N i For the i The total number of buildings in the iterations is updated in each iteration. N i is equal to the total number of buildings in the graph. In subsequent iterations, N i is the number of buildings that require additional scanning. The VBPI index is calculated for all locations in the building distribution map using the visibility analysis method. Visibility analysis determines the visible area by calculating the interaction between sight lines and objects.

[0068] In the present invention, instead of directly calculating the visible area of ​​each location, the visibility 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 and a corresponding visibility map is generated. Each xy coordinate in the visible area is assigned a value of 1, and the xy coordinate in the non-visible area is assigned a value of 0. Next, the visibility maps of all building locations are summarized into a global visibility map. Therefore, the value of each xy coordinate in the global visibility map indicates the number of times the coordinate can be seen from all building locations. This is equivalent to indicating how many buildings can be observed from this location. Finally, the VBPI index map is obtained by calculating the global visibility map with the total number of buildings in the current iteration.

[0069] The VBPI index map quantifies the occlusion effect at all spatial locations in the map. High VBPI values ​​indicate low occlusion effect, while low VBPI values ​​indicate high occlusion effect. Furthermore, given that high VBPI values ​​may be located within small areas with adjacent low VBPI values, a convolution process is performed on the VBPI index map to mitigate this issue. The VBPI index map serves as the fundamental data for selecting optimal ground monitoring device locations and is updated with each iteration.

[0070] (3) Selecting valid candidate locations for ground monitoring equipment: In each iteration, all candidate locations of the ground monitoring equipment are extracted from the VBPI index map with high VBPI values ​​passing a specific threshold (Th, i.e., 85% of the maximum VBPI). Considering that adjacent locations have similar VBPI values, the present invention downsamples all candidate locations by downsampling the grid of the VBPI index map.

[0071] On the basis of downsampling, the particle swarm optimization algorithm (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:

[0072]

[0073] in, For the i The particle in t The speed of generation, For the i The particle in t The position of the generation, is the inertia weight, and is the learning factor, and is a random number. is the optimal position of the individual particle, is the global optimal position.

[0074] Technically, a coarse grid array is created only for candidate areas, retaining the candidate with the highest VBPI value in each grid. These retained candidate locations serve as valid candidates for the optimal ground monitoring device for further processing. Furthermore, to prevent ground monitoring devices from coming too close to buildings, a buffer zone is set for each building, with a radius of 1.3 times the building's perimeter. A circular buffer zone is also added to the optimal location selected in the previous iteration. This strategy helps prevent the distance between ground monitoring device locations from being too close, thereby maximizing scan integrity.

[0075] (4) Determine the optimal placement of ground monitoring equipment by CDPC: CDPC is defined as the union angle of the building scanning area. When the area is not scanned, the CDPC value is 0°, and when the area is fully scanned, the CDPC value is 360°. The proposed CDPC index can quantify the scanning completeness of each valid candidate ground monitoring equipment location; therefore, this index can be used to determine the optimal ground monitoring equipment location. The optimal location of the 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.

[0076]

[0077] in, i Indicates the i iterations, It is j The CDPC of polygons, Refers to the candidate locations scanned by the ground monitoring equipment in each iteration. single and end represent the iterative candidate positions and the final optimal position respectively.

[0078] (5) Determine additional deployment locations or terminate the mission: The present invention uses the detection rate (δ) as an indicator to decide whether to stop the operation or continue to perform additional ground monitoring equipment scans. The detection rate is defined as the number of buildings in the figure ( N total ), meeting the operator's expected scan integrity requirements ( T E ) is the percentage of the number of buildings. It should be noted that I(j)is an indicator function. If the detection rate is greater than 95%, the program stops and the best ground monitoring device position for each iteration is derived. The program also stops if the difference in detection rate between two consecutive iterations is less than 5%, and if the detection rate is greater than 80%, indicating that the subsequent scans have little contribution to the results; therefore, all best ground monitoring device positions except the result of the last iteration are output. Otherwise, a new iteration is performed, performing additional scans, calculated as:

[0079]

[0080] It should be noted that T E It is the key parameter to determine whether to stop the iteration. j The CDPC value of the building reaches the operator defined T E , then no additional scan is required for this building. Otherwise, this building needs further sampling. In the new iteration, the buildings with CDPC values ​​less than T E At the same time, the VBPI index map is updated by aggregating the visibility maps of these buildings.

[0081] In addition, in the first iteration T E The values ​​are usually different from subsequent iterations because the CDPC value of the building is usually less than 180° in the first iteration, so 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°, so T E The value is usually set to a value greater than 180°, such as 200°. The present invention also suggests setting a higher value for areas with larger building perimeters. T E value and vice versa. In addition, T E The value can also be adjusted according to the building properties that need to be exported.

[0082] The present invention uses the hotspot detection results of satellites and UAVs to rationally deploy ground equipment and achieve dynamic optimization and efficient layout of monitoring points.

[0083] As an alternative, the present invention considers using drone monitoring and ground-based equipment monitoring as the primary data sources, incorporating satellite remote sensing only when needed. This approach is suitable for localized or specific areas (such as carbon emission hotspots such as industrial parks and cities) and does not require 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 a target area.

[0084] As an alternative, the present invention combines laser radar (LiDAR) technology with drones to replace some ground-based equipment for high-precision monitoring. Drones equipped with LiDAR sensors can scan the surface using lasers, measuring the height and location of carbon emission sources in real time and accurately determining the distribution of carbon emissions within a specific area. Compared to traditional ground-based equipment, LiDAR combined with drones offers flexible coverage and adaptability to diverse geographical environments, making it particularly suitable for areas where ground-based equipment is difficult to deploy.

[0085] As an alternative, the present 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 IoT technology) within a specific area. By collecting carbon emissions data in real time through distributed sensors and transmitting and processing the data over a network, the ground network can provide highly accurate carbon emissions data, making it particularly suitable for precise monitoring of fixed point sources and small areas. While this approach is limited by the localized monitoring range, it can achieve higher spatial resolution through network expansion, making it suitable for monitoring key areas and long-term continuous monitoring.

[0086] As an alternative, the present invention can employ automated data processing methods based on artificial intelligence (AI) and machine learning, using algorithms to automatically analyze and optimize the data fusion process. AI technology can accurately identify and track carbon emission sources, thereby reducing the need for manual intervention and improving monitoring accuracy and efficiency. AI systems can process data from various sources in real time and dynamically optimize data, enabling more accurate identification of hotspots and changing trends. Especially in complex environments, AI technology can better address uncertainties and enhance monitoring effectiveness.

[0087] Combination of the above Figure 1 The carbon emission monitoring point selection method based on air-space-ground integration provided by an embodiment of the present invention is introduced in detail. Next, the carbon emission monitoring point selection system based on air-space-ground integration provided by an embodiment of the present invention will be introduced with reference to the accompanying drawings.

[0088] Figure 4 This is a structural diagram of a carbon emission monitoring point selection system based on air-ground integration shown in an embodiment of the present invention. Figure 4 , the system of the present invention comprises: A data acquisition module is configured to: obtain the land cover type, surface digital elevation model, surface temperature, and surface wind speed and direction of the target study area, and extract land type characteristic data, surface elevation characteristic data, surface temperature characteristic data, and wind speed and direction characteristic data respectively; The feature extraction and analysis module is configured to: obtain carbon satellite remote sensing image data of the target study area, extract the greenhouse gas concentration distribution map, combine the land type feature data, surface elevation feature data, surface temperature feature data, and wind speed and direction feature data, analyze the feature importance, and extract the main influencing factors; perform hotspot analysis on the greenhouse gas concentration distribution map to determine the scope and diffusion impact of the hotspot area; The monitoring equipment deployment optimization module is configured to: determine the deployment location of monitoring equipment using an iterative scanning mode based on the hotspot area and the scope and diffusion impact of the hotspot area; Among them, the iterative scanning mode includes: inputting a coordinate table containing the center position and perimeter data of the building, generating a VBPI index map of the building distribution map, selecting valid candidate points for the best ground monitoring equipment scanning position, determining the final ground monitoring equipment scanning position, and determining whether additional scanning and iteration are required; until the iteration conditions are met, the monitoring equipment layout position is output.

[0089] In some embodiments, the obtaining of carbon satellite remote sensing image data of the target study area and the extraction of a greenhouse gas concentration distribution map include: obtaining carbon satellite remote sensing image data of the target study area, preprocessing the carbon satellite remote sensing image data, including correcting the atmosphere; based on the preprocessed carbon satellite remote sensing image data, parsing different bands of data in the carbon satellite remote sensing image data, combining the atmospherically corrected data, identifying the concentration range of the gas, and determining the spatial distribution of the greenhouse gas; based on the spatial distribution of the greenhouse gas, obtaining a greenhouse gas concentration distribution map through the greenhouse gas concentration change trend in the carbon satellite remote sensing image data.

[0090] In some embodiments, the method comprises: extracting the main influencing factors by analyzing the importance of features; performing hotspot analysis on the greenhouse gas concentration distribution map to determine the scope and diffusion impact of the hotspot area; including: using a random forest algorithm to evaluate the contribution of land type characteristic data, surface elevation characteristic data, surface temperature characteristic data, and wind speed and wind direction characteristic data to gas concentration, and extracting the main influencing factors; using principal component analysis to reduce the dimension of the main influencing factors and determine the principal components; and judging the scope and diffusion impact of the hotspot area based on the diffusion model established based on the atmospheric dynamics equation according to the principal components.

[0091] In some embodiments, the input includes a coordinate table of the building center position and perimeter data; including: inputting a data set including the building center position, perimeter data and environmental characteristics within the target study area, using a perceptual quality model and a Gaussian process model to obtain monitoring position optimization results; performing range control calculations on the preliminary deployment points based on the monitoring position optimization results to ensure that the monitoring equipment covers hot spots and key diffusion paths, and generating a basic geographic spatial distribution map.

[0092] In some embodiments, the generating of the VBPI index map of the building distribution map is represented by the following formula:

[0093] in, Indicates the i The position in the iteration The VBPI index, Indicates from position The number of buildings that can be seen, N i For the i The total number of buildings in the iteration is updated in each iteration.

[0094] In some embodiments, the method of selecting valid candidate points for the optimal ground monitoring device scanning position includes: in each iteration, extracting high VBPI positions from the VBPI index map of all candidate positions of the monitoring device through a specific threshold; downsampling all candidate positions by grid downsampling the VBPI index map; and optimizing the valid candidate positions, i.e., the valid candidate monitoring device positions, using a particle swarm optimization algorithm based on the downsampling.

[0095] In some embodiments, determining the final ground monitoring equipment scanning position includes: The CDPC index is used to quantify the scanning completeness of each valid candidate monitoring device location and determine the optimal monitoring device location; The CDPC index is expressed by the following formula:

[0096] in, i Indicates the i iterations, It is j The CDPC of polygons, Refers to the candidate locations scanned by the monitoring device in each iteration; single and end Iterate over candidate positions and the final best position separately.

[0097] According to an embodiment of the present invention, the carbon emission monitoring and point selection system based on the integration of air, space and land can correspond to the method described in the embodiment of the present invention, and the above and other operations and / or functions of each module of the carbon emission monitoring and point selection system based on the integration of air, space and land are respectively to achieve Figure 1 For the sake of brevity, the corresponding processes of each method in are not repeated here.

[0098] See also Figure 5 The structural diagram of the computer device shown in FIG. 1 shows a computer device including a processor, a communication interface, and a computer-readable storage medium. The processor, the communication interface, and the computer-readable storage medium can be 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 memory of the computer device, and the computer-readable storage medium is used to store a computer program. The computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer-readable storage medium. The processor (or CPU (Central Processing Unit)) is the computing core and control core of the computer device, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding steps in the embodiment of the carbon emission monitoring point selection method based on air-space-ground integration.

[0099] This embodiment provides a computer-readable storage medium (Memory). This computer-readable storage medium is a memory device within a computer device that is used to store programs and data. It should be understood that the computer-readable storage medium herein may include both built-in storage media within the computer device and, of course, 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. Furthermore, the storage space also stores one or more instructions suitable for being loaded and executed by the processor. These instructions may be one or more computer programs (including program code). It should be noted that the computer-readable storage medium herein may be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device; alternatively, it may be at least one computer-readable storage medium located remotely from the processor.

[0100] In one embodiment, one or more instructions are stored in the computer-readable storage medium; the processor loads and executes the one or more instructions stored in the computer-readable storage medium to implement the corresponding steps in the above-mentioned embodiment of the carbon emission monitoring point selection method based on integrated air-space-ground integration.

[0101] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) containing computer-usable program code.

[0102] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts 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, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0103] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0104] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0105] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium. When executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0106] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for selecting carbon emission monitoring points based on air-ground integration, characterized in that: include: Obtain the land cover type, surface digital elevation model, surface temperature, and surface wind speed and direction of the target study area, and extract land type characteristic data, surface elevation characteristic data, surface temperature characteristic data, and wind speed and direction characteristic data respectively; Obtain carbon satellite remote sensing image data for the target study area, extract greenhouse gas concentration distribution maps, combine land type characteristic data, surface elevation characteristic data, surface temperature characteristic data, and wind speed and direction characteristic data, analyze the importance of characteristics, and extract the main influencing factors; perform hotspot analysis on the greenhouse gas concentration distribution map to determine the scope and diffusion impact of the hotspot area; Taking the hotspot area as the center, taking into account the scope and diffusion impact of the hotspot area, an iterative scanning mode is used to determine the location of the monitoring equipment; Among them, the iterative scanning mode includes: inputting a coordinate table containing the center position and perimeter data of the building, generating a VBPI index map of the building distribution map, selecting valid candidate points for the best ground monitoring equipment scanning position, determining the final ground monitoring equipment scanning position, and determining whether additional scanning and iteration are required; until the iteration conditions are met, the monitoring equipment layout position is output.

2. The carbon emission monitoring point selection method based on air-ground integration according to claim 1 is characterized in that: The method for obtaining carbon satellite remote sensing image data of a target study area and extracting a greenhouse gas concentration distribution map includes: obtaining carbon satellite remote sensing image data of the target study area, preprocessing the carbon satellite remote sensing image data, including correcting the atmosphere; parsing different bands of data in the carbon satellite remote sensing image data based on the preprocessed carbon satellite remote sensing image data, and identifying the concentration range of the gas in combination with the atmospherically corrected data to determine the spatial distribution of the greenhouse gas; and obtaining a greenhouse gas concentration distribution map based on the spatial distribution of the greenhouse gas by observing the greenhouse gas concentration change trend in the carbon satellite remote sensing image data.

3. The carbon emission monitoring point selection method based on air-ground integration according to claim 1 is characterized in that: By analyzing the importance of features, the main influencing factors are extracted; Hotspot analysis is conducted on the greenhouse gas concentration distribution map to determine the scope and diffusion impact of the hotspot area. The method includes: using the random forest algorithm to evaluate the contribution of land type characteristic data, surface elevation characteristic data, surface temperature characteristic data, and wind speed and direction characteristic data to gas concentration, and extracting the main influencing factors; using principal component analysis to reduce the dimension of the main influencing factors and determine the principal components; based on the principal components, a diffusion model established based on the atmospheric dynamics equation is used to determine the scope and diffusion impact of the hotspot area.

4. The method for selecting carbon emission monitoring points based on air-ground integration according to claim 1 is characterized in that: The input includes a coordinate table of the building center position and perimeter data; the method includes: inputting a data set including the building center position, perimeter data and environmental characteristics within the target research area, using a perceptual quality model and a Gaussian process model to obtain monitoring position optimization results; based on the monitoring position optimization results, performing range control calculations on preliminary deployment points to ensure that monitoring equipment covers hot spots and key diffusion paths, and generating a basic geographic spatial distribution map.

5. The carbon emission monitoring point selection method based on air-ground integration according to claim 1 is characterized in that: The VBPI index diagram of the building distribution map is generated using the following formula: in, Indicates the i The position in the iteration The VBPI index, Indicates from position The number of buildings that can be seen, N i For the i The total number of buildings in the iteration is updated in each iteration.

6. The carbon emission monitoring site selection method based on air-ground integration according to claim 1 is characterized in that: The method for selecting valid candidate points for the optimal ground monitoring device scanning position includes: in each iteration, extracting high VBPI positions from a VBPI index map of all candidate positions of the monitoring device through a specific threshold; downsampling all candidate positions by downsampling the grid of the VBPI index map; and optimizing the valid candidate positions, i.e., the valid candidate monitoring device positions, using a particle swarm optimization algorithm based on the downsampling.

7. The method for selecting carbon emission monitoring points based on air-ground integration according to claim 1 is characterized in that: The method for determining the final scanning position of the ground monitoring equipment includes: The CDPC index is used to quantify the scanning completeness of each valid candidate monitoring device location and determine the optimal monitoring device location; The CDPC index is expressed by the following formula: in, i Indicates the i iterations, It is j The CDPC of polygons, Refers to the candidate locations scanned by the monitoring device in each iteration; single and end Iterate over candidate positions and the final best position separately.

8. A carbon emission monitoring point selection system based on air-ground integration, characterized in that: include: A data acquisition module is configured to: obtain the land cover type, surface digital elevation model, surface temperature, and surface wind speed and direction of the target study area, and extract land type characteristic data, surface elevation characteristic data, surface temperature characteristic data, and wind speed and direction characteristic data respectively; The feature extraction and analysis module is configured to: obtain carbon satellite remote sensing image data of the target study area, extract the greenhouse gas concentration distribution map, combine the land type feature data, surface elevation feature data, surface temperature feature data, and wind speed and direction feature data, analyze the feature importance, and extract the main influencing factors; perform hotspot analysis on the greenhouse gas concentration distribution map to determine the scope and diffusion impact of the hotspot area; The monitoring equipment deployment optimization module is configured to: determine the deployment location of monitoring equipment using an iterative scanning mode based on the hotspot area and the scope and diffusion impact of the hotspot area; Among them, the iterative scanning mode includes: inputting a coordinate table containing the center position and perimeter data of the building, generating a VBPI index map of the building distribution map, selecting valid candidate points for the best ground monitoring equipment scanning position, determining the final ground monitoring equipment scanning position, and determining whether additional scanning and iteration are required; until the iteration conditions are met, the monitoring equipment layout position is output.

9. A computer device, characterized in that: a processor adapted to execute a computer program; A computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by the processor, the steps in the method for selecting a carbon emission monitoring point based on air-space-ground integration as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which is suitable for being loaded by a processor and executing the steps in the carbon emission monitoring point selection method based on air-space-ground integration as described in any one of claims 1 to 7.

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