Urban carbon emission analysis method, device, equipment and medium

Abstract

The invention provides an urban carbon emission analysis method, which comprises the following steps: constructing an urban carbon emission local partition system, and dividing a city into a plurality of types of regions by the urban carbon emission local partition system according to environmental characteristics and functional characteristics of the city; based on the spatial information and the ground feature height information of the target city, making a sample set of each type of region, and obtaining carbon emission local partition information of the target city according to the plurality of sample sets; acquiring carbon emission data of the target city to obtain carbon emission distribution information of the target city; and performing carbon emission analysis on the target city based on the carbon emission local partition information and the carbon emission distribution information. The invention further provides an urban carbon emission analysis device, equipment and a medium.

Description

Technical Field

[0001] The present disclosure relates to the field of carbon emission technology, and more specifically, to a method, device, equipment and medium for spatiotemporal analysis of urban carbon emissions. Background Art

[0002] With the acceleration of urbanization, urban carbon emissions are becoming increasingly serious and a significant factor in global climate change. Cities are a major source of greenhouse gas emissions, with buildings, transportation systems, and industrial activities consuming vast amounts of energy and emitting significant amounts of carbon dioxide. Research on urban carbon emissions helps understand the characteristics and spatial patterns of urban carbon emissions, promote the use of green buildings, clean transportation, and renewable energy, and provide a scientific basis for policymakers in developing carbon reduction policies and sustainable development strategies.

[0003] Urban pattern plays a central role in carbon emissions research, influencing energy consumption, transportation, land use, and other aspects, which in turn has a profound impact on a city's carbon emissions. Urban pattern includes both functional zones and morphology. Functional zones refer to areas within a city with different uses (such as residential, commercial, industrial, and public facilities), while urban morphology involves the spatial layout, building density, and overall design of these areas. The two influence each other and jointly determine the city's operation, resource utilization, and carbon emission characteristics. Generally speaking, residential, commercial, industrial, and transportation areas have higher carbon emissions, while green spaces and open spaces have lower carbon emissions. High-density cities generally mean less land use, and promoting public transportation and non-motorized transportation (such as walking and cycling) can effectively reduce carbon emissions per unit area. Low-density cities, on the other hand, tend to rely on private cars for travel, resulting in higher transportation emissions, which increases the overall carbon footprint.

[0004] Currently, scholars both domestically and internationally are still limited to studying urban structure and carbon emissions using a single functional zone or urban form. While carbon emission research based on functional zones is highly targeted, the delineation of functional zones relies heavily on human factors and is highly subjective. Furthermore, significant differences exist between functional zone settings and carbon emissions across cities, resulting in poor portability of research methods and models and non-universal applicability of findings. Carbon emission research based on urban form focuses more on building density, road layout, and green space allocation, while neglecting other factors such as economic and social factors. Therefore, considering both urban form and functional zones in urban carbon emission research is of great significance for carbon emission analysis. Summary of the Invention

[0005] In view of the above problems, the present disclosure provides a method for analyzing urban carbon emissions. It constructs a localized carbon emission zoning system that integrates urban morphology and functional zones. Based on this system and the city's land feature height information, localized carbon emission zoning information is obtained. This localized carbon emission zoning information fully considers the spatial heterogeneity of urban morphology and functional zones, as well as the city's three-dimensional structure. Based on this information, urban carbon emission analysis can achieve more accurate spatiotemporal analysis of carbon emissions, revealing the spatiotemporal variation characteristics of carbon emissions in different types of urban areas, and providing a scientific basis for urban planning and the formulation of carbon reduction policies. The present disclosure also provides an urban carbon emission analysis device, equipment, and medium.

[0006] According to one aspect of the present disclosure, a method for analyzing urban carbon emissions is provided, comprising:

[0007] Establishing a localized urban carbon emission zoning system that divides cities into multiple types of regions based on their environmental and functional characteristics;

[0008] Based on the spatial information and height information of the ground objects of the target city, a sample set of each type of area is prepared, and local zoning information of carbon emissions of the target city is obtained according to the plurality of sample sets;

[0009] Acquiring carbon emission data of the target city to obtain carbon emission distribution information of the target city;

[0010] Based on the carbon emission local zoning information and the carbon emission distribution information, a carbon emission analysis is performed on the target city.

[0011] According to an embodiment of the present disclosure, the environmental features include building type features and land cover type features, wherein:

[0012] The building type characteristics include at least building density, average height, layout openness, average tree height, and the type and ratio of permeable and impermeable surfaces;

[0013] The land cover type characteristics include at least the normalized vegetation index, surface roughness and surface albedo.

[0014] According to an embodiment of the present disclosure, based on the functional characteristics of the city, the city is divided into at least residential emission areas, commercial emission areas, industrial emission areas, traffic emission areas, green areas and open spaces.

[0015] According to an embodiment of the present disclosure, generating a sample set for each type of region includes:

[0016] Acquiring remote sensing image data of the target city and performing preprocessing to obtain the spatial information;

[0017] Obtaining the height information of the ground feature by calculation or measurement;

[0018] A sample set for each type of area is generated based on the spatial information and the ground feature height information.

[0019] According to an embodiment of the present disclosure, obtaining the localized carbon emission information of the target city based on the plurality of sample sets includes:

[0020] Obtaining a local partition model of carbon emissions based on the plurality of sample sets;

[0021] Partitioning the target city based on the carbon emission local zoning model to obtain carbon emission local zoning information;

[0022] The carbon emission local partition information is verified.

[0023] According to an embodiment of the present disclosure, obtaining the carbon emission data of the target city includes: obtaining at least one of carbon emission remote sensing data, carbon emission verification data, and carbon emission site monitoring data.

[0024] According to an embodiment of the present disclosure, obtaining the carbon emission distribution information of the target city includes: combining the carbon emission verification data and the carbon emission site monitoring data, and performing image super-resolution reconstruction on the carbon emission remote sensing data.

[0025] According to an embodiment of the present disclosure, at least one of a time series analysis method, a spatial autocorrelation analysis method, and a hotspot analysis method is used to perform carbon emission analysis on the target city.

[0026] According to another aspect of the present disclosure, a device for analyzing urban carbon emissions is provided. The device is used to perform any of the above-mentioned methods for analyzing urban carbon emissions, comprising:

[0027] An acquisition module, configured to acquire the spatial information, the height information of the ground features, and the carbon emission data of the target city;

[0028] A partitioning module, configured to obtain the carbon emission local partitioning information according to the plurality of sample sets;

[0029] A distribution module, configured to obtain the carbon emission distribution information according to the carbon emission data;

[0030] The analysis module is used to perform carbon emission analysis on the target city.

[0031] According to another aspect of the present disclosure, there is provided an electronic device, comprising:

[0032] one or more processors;

[0033] a memory for storing one or more computer programs,

[0034] The one or more processors execute the one or more computer programs to implement the steps of any of the above methods.

[0035] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0036] One or more of the above embodiments have the following beneficial effects:

[0037] This paper proposes a system for localizing urban carbon emissions. This system integrates the environmental and functional characteristics of cities, fully accounting for the spatial heterogeneity of urban morphology and functional zones. This system significantly improves the accuracy of urban carbon emissions analysis. Furthermore, the system also considers the height of land features when obtaining localized carbon emission zoning information, i.e., the three-dimensional structure of the city. This system can further improve the accuracy of urban carbon emissions analysis and provide a scientific basis for urban planning and the formulation of carbon reduction policies. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The above contents and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0039] Figure 1 A diagram schematically illustrates an application scenario of the urban carbon emissions analysis method according to an embodiment of the present disclosure;

[0040] Figure 2 Schematically shows a step diagram of a method for analyzing urban carbon emissions according to an embodiment of the present disclosure;

[0041] Figure 3 Schematically illustrating the steps of preparing a sample set for each type of region in operation S220 according to an embodiment of the present disclosure;

[0042] Figure 4 Schematically illustrating a step diagram of obtaining carbon emission local zone information of a target city based on multiple sample sets in operation S220 according to an embodiment of the present disclosure;

[0043] Figure 5 The following schematically shows a structural block diagram of a device for analyzing urban carbon emissions according to an embodiment of the present disclosure;

[0044] Figure 6 The block diagram of an electronic device suitable for implementing the urban carbon emission analysis method according to an embodiment of the present disclosure is schematically shown.

[0045] It should be noted that, for the sake of clarity, in the drawings used to describe the embodiments of the present disclosure, the sizes of the overall / local structures or overall / local areas may be enlarged or reduced, that is, these drawings are not drawn according to the actual scale. DETAILED DESCRIPTION

[0046] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0047] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0048] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0049] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0050] Existing research on urban pattern and carbon emissions remains limited to single functional zones and urban forms. While highly targeted, carbon emission studies based on functional zones lack transferability, and the results are not universally applicable. Carbon emission studies based on urban form ignore other factors, such as economic and social factors. Furthermore, existing research primarily focuses on two-dimensional functional zoning, with incomplete consideration of three-dimensional form, which is also a significant factor influencing urban carbon emission patterns.

[0051] Embodiments of the present disclosure provide a method, apparatus, device, and medium for analyzing urban carbon emissions. The method includes: establishing a localized urban carbon emissions zoning system that divides a city into multiple types of regions based on its environmental and functional characteristics; generating a sample set for each type of region based on the spatial information and height information of a target city; and obtaining localized carbon emissions zoning information for the target city based on the multiple sample sets; acquiring carbon emissions data for the target city to obtain carbon emissions distribution information for the target city; and performing carbon emissions analysis on the target city based on the localized carbon emissions zoning information and the carbon emissions distribution information.

[0052] According to the embodiments of the present disclosure, a local zoning system for urban carbon emissions that integrates urban morphology and functional areas is constructed, and local zoning information of carbon emissions is obtained based on the system and the height information of urban features. The local zoning information of carbon emissions fully considers the spatial heterogeneity of urban morphology and functional areas, as well as the three-dimensional structure of the city. Urban carbon emissions analysis is carried out on this basis, which can achieve more accurate spatiotemporal pattern analysis of carbon emissions, reveal the spatiotemporal variation characteristics of carbon emissions in different types of urban areas, and provide a scientific basis for urban planning and the formulation of carbon emission reduction policies.

[0053] Figure 1 The following schematically illustrates an application scenario diagram applicable to the urban carbon emission analysis method according to an embodiment of the present disclosure.

[0054] like Figure 1 As shown, the application scenario 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is used as a medium for providing a communication link between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links or optical fiber cables.

[0055] Users can use a first terminal device 101, a second terminal device 102, and a third terminal device 103 to interact with a server 105 via a network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (for example only). The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with display screens and support web browsing, including but not limited to smartphones, tablet computers, laptop computers, and desktop computers.

[0056] Server 105 can be a server that provides various services, such as a backend management server (for example only) that supports the business systems browsed by users using first terminal device 101, second terminal device 102, and third terminal device 103. The backend management server can analyze and process received user requests and other data, and provide feedback (e.g., web pages, information, or data obtained or generated based on user requests) to the terminal devices. For example, server 105 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud computing, network services, and middleware services.

[0057] It should be noted that the city carbon emission analysis method provided in the embodiment of the present disclosure can generally be executed by a server. Accordingly, the city carbon emission analysis device provided in the embodiment of the present disclosure can generally be set in a server.

[0058] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0059] The following will be based on Figure 1 The described scenario provides a detailed description of the urban carbon emission analysis method according to an embodiment of the present disclosure.

[0060] Figure 2 The figure schematically shows the steps of the urban carbon emission analysis method according to an embodiment of the present disclosure.

[0061] like Figure 2 As shown, the urban carbon emission analysis method of this embodiment includes operations S210 to S240.

[0062] In operation S210 , a local zoning system for urban carbon emissions is constructed. The local zoning system for urban carbon emissions divides the city into multiple types of areas according to the environmental characteristics and functional characteristics of the city.

[0063] Local Climate Zones (LCZs) are a classification system used for climate studies in cities and surrounding areas, proposed by Stefan Grab et al. in 2012. The purpose of LCZs is to better understand and analyze urban microclimate characteristics and their impacts by dividing urban areas into different climate zones.

[0064] In existing technology, LCZs are generally constructed based on a city's environmental characteristics, dividing it into multiple zone types. For example, these environmental characteristics include building type characteristics and land cover type characteristics. Building type characteristics include at least building density, average height, layout openness, average tree height, and the type and ratio of permeable and impermeable surfaces. Land cover type characteristics include at least the Normalized Difference Vegetation Index, surface roughness, and surface albedo.

[0065] The urban carbon emissions zoning system provided in this disclosure considers not only the environmental characteristics of a city but also its functional characteristics. In this embodiment, based on the functional characteristics of a city, the city is divided into at least residential emission zones, commercial emission zones, industrial emission zones, transportation emission zones, green spaces, and open spaces.

[0066] Based on the environmental characteristics and functional characteristics of the above-mentioned cities, the urban carbon emission local zoning system provided in this disclosure divides cities into the following types of areas: (1) Residential emission areas include compact high-rise residential areas (high-rise buildings), compact middle-rise residential areas (multi-story buildings), compact low-rise residential areas, open high-rise residential areas, open middle-rise residential areas, open low-rise residential areas (villas and other low-rise buildings) and unconventional residential areas (urban villages, etc.); (2) Commercial emission areas include compact high-rise commercial areas (such as central business districts, etc.), compact middle-rise commercial areas, compact low-rise commercial areas, open high-rise commercial areas, open middle-rise commercial areas, and open low-rise commercial areas; (3) Industrial emission areas include heavy industrial areas, light industrial / technological parks, and warehousing and logistics areas; (4) Transportation emission areas include transportation hub areas (such as airports, railway stations, ports, etc.), highway / main road influence areas, and secondary road influence areas; (5) Green spaces and open spaces include agricultural areas, garden greening areas, and water areas.

[0067] The above-mentioned urban carbon emission local zoning system integrates the environmental and functional characteristics of the city, and fully considers the spatial heterogeneity of urban form and functional areas. Urban carbon emission analysis based on this system can significantly improve the analysis accuracy of carbon emissions.

[0068] In operation S220 , based on the spatial information and the height information of the ground objects of the target city, a sample set of each type of area is generated, and the carbon emission local zoning information of the target city is obtained according to the multiple sample sets.

[0069] First, in this embodiment, remote sensing image data of the target city is obtained as spatial information, and in addition, height information of ground objects is obtained by calculation or measurement.

[0070] Next, within each type of area, representative sample locations are selected. For example, in a commercial area, areas of different sizes (such as large shopping malls, small commercial streets, etc.) and different geographical locations (city center and suburban commercial areas) should be selected as samples to reflect the diversity and common characteristics of the area type. In addition, to ensure the objectivity of the sample, random sampling methods are used to select sample locations to a certain extent. For example, several points can be randomly selected as candidate sample locations within a designated area according to a certain grid spacing, and then the final sample locations can be determined based on the actual situation.

[0071] Finally, based on spatial information and ground feature height information, a sample set for each type of area is produced, and the local zoning information of carbon emissions in the target city is obtained based on multiple sample sets.

[0072] In operation S230 , carbon emission data of the target city is acquired to obtain carbon emission distribution information of the target city.

[0073] First, the carbon emission data of the target city is obtained. In this embodiment, the carbon emission data includes at least one of carbon emission remote sensing data, carbon emission verification data, and carbon emission site monitoring data.

[0074] Carbon emission remote sensing data refers to carbon dioxide emissions data acquired via remote sensing satellites and generally has a lower resolution. Carbon emission verification data includes detailed information about carbon emissions from companies or institutions and generally has a higher resolution. Carbon emission site monitoring data includes carbon emission data recorded by carbon and gas monitoring equipment deployed in various urban areas, such as the Total Carbon Column Observation Network (TCCON) and the Global Greenhouse Gas Reference Network (GGGRN), and generally has a higher resolution.

[0075] In addition, other auxiliary data can be obtained to improve the carbon emission distribution information of the target city, such as meteorological data (temperature, precipitation, etc.), population distribution data, lighting data, DEM data, etc.

[0076] Next, the carbon emission remote sensing data is combined with carbon emission verification data and carbon emission site monitoring data to perform image super-resolution reconstruction, thereby obtaining carbon emission data with fine spatial resolution. As an example, machine learning or deep learning methods can be used for image super-resolution reconstruction.

[0077] In operation S240 , carbon emission analysis is performed on the target city based on the carbon emission local zone information and the carbon emission distribution information.

[0078] As an example, based on the carbon emission local zoning information and the carbon emission distribution information, at least one of the time series analysis method, the spatial autocorrelation analysis method, and the hot spot analysis method is used to perform carbon emission analysis on the target city.

[0079] Time series analysis is a method used to analyze data sequences with a time sequence. It is mainly used to study the trends, seasonality, periodicity and other characteristics in the data, and can be used to analyze the temporal variation patterns of carbon emissions.

[0080] Spatial autocorrelation analysis is a method used to analyze whether spatial data has spatial autocorrelation. It uses methods such as the Moran index and geographic detectors to study the correlation and dependence of spatial data in geographic space. It can be used to analyze the spatial distribution characteristics and spatial correlation patterns of carbon emission data.

[0081] Hotspot analysis is a spatial analysis method used to identify clusters of high or low values ​​in data. It can visually demonstrate the spatial distribution characteristics and differences of geographical phenomena. Specifically, it can identify areas of high and low carbon emissions to assess their spatial distribution characteristics.

[0082] Figure 3 The diagram schematically illustrates the steps of preparing a sample set for each type of region in operation S220 according to an embodiment of the present disclosure.

[0083] like Figure 3 As shown, the process of preparing a sample set for each type of region in this embodiment includes operations S310 to S330.

[0084] In operation S310 , remote sensing image data of a target city is acquired and pre-processed to obtain spatial information.

[0085] As an example, high-resolution remote sensing image data of the target city is collected. In this embodiment, high-resolution remote sensing image data includes at least optical remote sensing images with a spatial resolution between 1m and 30m, including blue, green, red, and near-infrared bands, as well as images with stereoscopic observation capabilities such as lidar, SAR, drones, etc.

[0086] As an example, the remote sensing image data of the target city is preprocessed, including geometric correction, radiation correction and atmospheric correction, to obtain spatial information.

[0087] Geometric correction refers to the process of correcting the geometric distortion of remote sensing images, aiming to align the image's geometric position and shape with those in the actual geographic coordinate system. Specifically, a geometric transformation model is established between the image coordinate system and the geographic coordinate system. Transformation parameters are calculated using known control points (corresponding points on the image to points with known coordinates on the ground). A geometric transformation is then performed on each pixel in the image, converting it from the image coordinate system to the geographic coordinate system. Common geometric correction methods include polynomial correction, similarity transformation, and affine transformation.

[0088] Radiometric correction refers to the process of correcting the radiometric information of remote sensing images to eliminate the effects of the sensor's inherent characteristics and external environmental factors. Specifically, by studying the sensor's spectral response characteristics and analyzing its response differences across different wavelengths, a radiometric correction model is established. Furthermore, the effects of factors such as solar altitude, solar azimuth, and terrain obstruction on radiometric information are considered, and the image's radiometric brightness values ​​are corrected using a radiation transfer model and correction algorithm.

[0089] Atmospheric correction removes atmospheric influences from remote sensing images to obtain the true reflectivity and emissivity of ground objects. Gases (such as water vapor and carbon dioxide) and aerosols in the atmosphere absorb and scatter solar radiation and electromagnetic waves reflected by ground objects, resulting in deviations in the radiometric information of remote sensing images. By studying atmospheric radiation transfer models and developing an atmospheric correction algorithm, atmospheric parameters (such as aerosol optical depth and water vapor content) and surface reflectivity data are used to perform atmospheric correction on images.

[0090] In operation S320, the height information of the ground object is obtained by calculation or measurement.

[0091] In an optional embodiment, a method for calculating the height of building shadows based on high-resolution remote sensing images can be used to calculate the height of ground objects. Specifically, the following steps are included:

[0092] (1) Shadow feature extraction. Input high-resolution remote sensing images (multispectral bands) and construct shadow extraction rules based on geometric, texture, and spectral features (such as low grayscale values ​​in shadow areas and significant differences in infrared band radiation). Use grayscale values ​​or infrared band radiation differences to set thresholds and divide the image into shadow and non-shadow areas. Use morphological operations (such as denoising and edge smoothing) to optimize the shadow extraction results.

[0093] (2) Shadow vectorization and length calculation. Binarize the shadow map (shadow area is 1, non-shadow area is 0), convert the shadow map into a vector polygon (such as a Shp file), generate a cluster of parallel lines (along the direction of sunlight projection) according to the solar azimuth when the image was acquired, intersect the parallel lines with the shadow vector map, record the length of each intersection, and take the maximum value of the intersection lines in the same area as the visible length of the shadow of the building at that location.

[0094] (3) Inversion of ground object height: Input shadow length, solar altitude angle and azimuth, and satellite observation angle, and establish ground object height inversion models for the case where the satellite and the sun are on the same side of the target, and for the case where the satellite and the sun are on opposite sides of the target, to generate a ground object height distribution map.

[0095] In another optional embodiment, the height information of the ground object can also be obtained by direct measurement. For example, the height of the ground object can be reconstructed directly by using the acquired point cloud data using a laser radar mounted on an unmanned aerial vehicle.

[0096] By obtaining the height information of urban features, the three-dimensional structure of the city can be fully considered when subsequently obtaining the local zoning information of the city's carbon emissions. On this basis, urban carbon emissions analysis can be conducted, which is conducive to improving the analysis accuracy of carbon emissions.

[0097] In operation S330 , a sample set of each type of area is generated based on the spatial information and the object height information.

[0098] First, within each type of area in the urban carbon emission local zoning system obtained in step S210, representative sample locations are selected. For each selected sample location, detailed spatial information (such as the geographic coordinates of the location, description of the surrounding geographic environment, etc.) and ground feature height information (such as building height, vegetation height distribution, etc.) are collected.

[0099] Next, create sample labels, marking key information such as the sample type area, location number, collection time, etc., to facilitate subsequent analysis and management.

[0100] Finally, the relevant data of each sample are organized into a data set, including spatial location data (such as longitude and latitude coordinates), feature height data (such as building height values, vegetation height distribution maps, etc.), and corresponding type area labels, forming a complete sample set.

[0101] Figure 4 The diagram schematically illustrates a step of obtaining carbon emission local zone information of a target city based on multiple sample sets in operation S220 according to an embodiment of the present disclosure.

[0102] like Figure 4 As shown, obtaining the carbon emission local zone information of the target city in this embodiment includes operations S410 to S430.

[0103] In operation S410 , a carbon emission local partition model is obtained based on a plurality of sample sets.

[0104] In an optional embodiment, a random forest model can be used to obtain a localized carbon emissions partitioning model. Specifically, subsets are randomly selected from the sample set to construct multiple decision trees. Each decision tree is trained and split based on its corresponding subset. When constructing the decision tree, metrics such as information gain and the Gini coefficient are used to select the optimal features and split points. Finally, the random forest model integrates the prediction results of multiple decision trees, such as by majority voting (for classification tasks) or averaging (for regression tasks), to obtain a localized carbon emissions partitioning model.

[0105] In another optional embodiment, a deep learning model can be used to obtain a localized carbon emissions model. First, a suitable deep learning model architecture is selected, such as a multi-layer perceptron (MLP), convolutional neural network (CNN), or recurrent neural network (RNN). During training, an appropriate network structure (including the number of neurons in the input, hidden, and output layers, activation functions, etc.) is defined, and an optimization algorithm (such as gradient descent and its variants) is used to adjust the model parameters (weights and biases) to minimize the error between the predicted and actual values, thereby obtaining a localized carbon emissions model.

[0106] In operation S420, the target city is partitioned based on the carbon emission local partition model to obtain carbon emission local partition information.

[0107] As an example, the target city is partitioned using the carbon emission localization model obtained in step S410 to obtain carbon emission localization information for the target city. This carbon emission localization information not only integrates the city's environmental and functional characteristics but also takes into account the city's terrain height information (i.e., the city's three-dimensional structure), helping to improve the accuracy of subsequent carbon emission analysis.

[0108] In operation S430 , the carbon emission local zone information is verified.

[0109] In an optional embodiment, visual interpretation can be used to verify localized carbon emission information. Visual interpretation is a method for manually interpreting and analyzing remote sensing imagery. It primarily uses image characteristics such as color, texture, shape, size, and spatial location, combined with field research experience and prior knowledge, to identify, classify, and judge different objects or regions in the image.

[0110] In another optional embodiment, the confusion matrix method can be used to verify the localized carbon emission partition information. The confusion matrix method is a method for evaluating classification accuracy. By comparing the classification results with the actual classification situation, a two-dimensional matrix is ​​constructed. Each element in the matrix represents the number of samples in the corresponding category pair. The confusion matrix can be used to calculate indicators such as overall accuracy, Kappa coefficient, user accuracy, and producer accuracy, thereby evaluating the accuracy of the results.

[0111] Figure 5 The structural block diagram of the urban carbon emission analysis device according to an embodiment of the present disclosure is schematically shown.

[0112] like Figure 5 As shown, the urban carbon emission analysis device 500 of this embodiment includes an acquisition module 510 , a partitioning module 520 , a distribution module 530 and an analysis module 540 .

[0113] The acquisition module 510 may, for example, perform operations S220 to S230 to acquire spatial information, feature height information, and carbon emission data of the target city.

[0114] According to an embodiment of the present disclosure, acquisition module 510 may be configured to perform operation S310 to acquire remote sensing image data of a target city and perform preprocessing to obtain spatial information. For example, high-resolution remote sensing image data of the target city may be collected and preprocessed, including geometric correction, radiometric correction, and atmospheric correction, to obtain spatial information.

[0115] According to an embodiment of the present disclosure, the acquisition module 510 can be used to perform operation S320 to obtain ground object height information using a calculation or measurement method. In an optional embodiment, the ground object height information can be obtained using a method for calculating the height of building shadows based on high-resolution remote sensing images. In another optional embodiment, the ground object height information can also be obtained through direct measurement.

[0116] According to an embodiment of the present disclosure, the acquisition module 510 can be used to perform operation S230 to acquire carbon emission data of the target city. In this embodiment, the carbon emission data includes at least one of carbon emission remote sensing data, carbon emission verification data, and carbon emission site monitoring data.

[0117] The partitioning module 520 may, for example, perform operation S220 to obtain local partitioning information of carbon emissions according to multiple sample sets.

[0118] According to an embodiment of the present disclosure, the partitioning module 520 may be used to perform operations S410 to S430, which specifically include: obtaining a carbon emission local partitioning model based on multiple sample sets; partitioning the target city based on the carbon emission local partitioning model to obtain carbon emission local partitioning information; and verifying the carbon emission local partitioning information.

[0119] The distribution module 530 may, for example, perform operation S230 to obtain carbon emission distribution information based on the carbon emission data, specifically comprising: combining carbon emission verification data and carbon emission site monitoring data to perform image super-resolution reconstruction on the carbon emission remote sensing data, thereby obtaining carbon emission data with fine spatial resolution.

[0120] The analysis module 540 may, for example, perform operation S240 to analyze carbon emissions of the target city. Specifically, based on the carbon emission local partition information and carbon emission distribution information, the carbon emissions of the target city may be analyzed using at least one of time series analysis, spatial autocorrelation analysis, and hotspot analysis.

[0121] For the parts not mentioned in the apparatus part, they can be understood with reference to the various embodiments of the above-mentioned method. That is, the apparatus part includes modules for executing the various steps of any one of the method embodiments described above. In addition, the implementation methods, technical problems solved, functions achieved, and technical effects achieved of each module / unit / subunit, etc. in the apparatus part embodiment are respectively the same or similar to the implementation methods, technical problems solved, functions achieved, and technical effects achieved of each corresponding step in the method part embodiment, and will not be repeated here.

[0122] According to an embodiment of the present disclosure, any multiple modules among the data type determination module 910, the data acquisition and transmission module 920, and the data encapsulation module 930 can be combined into a single module, or any one of these modules can be split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules can be combined with at least part of the functionality of other modules and implemented in a single module.

[0123] According to an embodiment of the present disclosure, at least one of the acquisition module 510, the partitioning module 520, the distribution module 530, and the analysis module 540 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application-specific integrated circuit (ASIC), or may be implemented in hardware or firmware by any other reasonable means of integrating or packaging circuits, or implemented in any one of the three implementation methods of software, hardware, and firmware, or in an appropriate combination of any of them. Alternatively, at least one of the acquisition module 510, the partitioning module 520, the distribution module 530, and the analysis module 540 may be at least partially implemented as a computer program module, which, when executed, may perform the corresponding function.

[0124] Figure 6 The block diagram of an electronic device suitable for implementing the urban carbon emission analysis method according to an embodiment of the present disclosure is schematically shown.

[0125] like Figure 6As shown, the electronic device 1000 according to an embodiment of the present disclosure includes a processor 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage portion 1008 into a random access memory (RAM) 1003. The processor 1001 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or a related chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1001 may also include onboard memory for caching purposes. The processor 1001 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0126] Various programs and data required for the operation of the electronic device 1000 are stored in the RAM 1003. The processor 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. The processor 1001 performs various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 1002 and / or the RAM 1003. It should be noted that the programs may also be stored in one or more memories other than the ROM 1002 and the RAM 1003. The processor 1001 may also perform various operations of the method flow according to the embodiment of the present disclosure by executing the programs stored in the one or more memories.

[0127] According to an embodiment of the present disclosure, electronic device 1000 may further include an input / output (I / O) interface 1005, which is also connected to bus 1004. Electronic device 1000 may also include one or more of the following components connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including devices such as a cathode ray tube (CRT), liquid crystal display (LCD), and speakers; a storage section 1008 including a hard disk; and a communication section 1009 including a network interface card such as a LAN card or modem. Communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. Removable media 1011, such as a magnetic disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed in drive 1010 as needed, so that computer programs read from the removable media can be installed into storage section 1008 as needed.

[0128] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments, or may exist independently and not be incorporated into the device / apparatus / system. The computer-readable storage medium carries one or more programs, and when executed, implements the method according to the embodiments of the present disclosure.

[0129] According to an embodiment of the present disclosure, a computer-readable storage medium may be a non-volatile computer-readable storage medium, and may include, for example, but is not limited to: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, a computer-readable storage medium may include the ROM 1002 and / or RAM 1003 described above, and / or one or more memories other than ROM 1002 and RAM 1003.

[0130] Those skilled in the art will appreciate that the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways, even if such combinations or couplings are not explicitly described in the present disclosure. In particular, the features described in the various embodiments of the present disclosure may be combined and / or coupled in various ways without departing from the spirit and teachings of the present disclosure. All such combinations and / or couplings fall within the scope of the present disclosure.

[0131] The embodiments of the present disclosure are described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be used in combination to advantage. The scope of the present disclosure is defined by the appended claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present disclosure.

Claims

1. A method for analyzing urban carbon emissions, characterized in that: include: Establishing a localized urban carbon emission zoning system that divides cities into multiple types of regions based on their environmental and functional characteristics; Based on the spatial information and height information of the ground objects of the target city, a sample set of each type of area is prepared, and local zoning information of carbon emissions of the target city is obtained according to the plurality of sample sets; Acquiring carbon emission data of the target city to obtain carbon emission distribution information of the target city; Based on the carbon emission local zoning information and the carbon emission distribution information, a carbon emission analysis is performed on the target city.

2. The urban carbon emission analysis method according to claim 1, characterized in that: The environmental characteristics include building type characteristics and land cover type characteristics, wherein: The building type characteristics include at least building density, average height, layout openness, average tree height, and the type and ratio of permeable and impermeable surfaces; The land cover type characteristics include at least the normalized vegetation index, surface roughness and surface albedo.

3. The urban carbon emission analysis method according to claim 1, characterized in that: According to the functional characteristics of the city, the city is divided into at least residential emission areas, commercial emission areas, industrial emission areas, traffic emission areas, green areas and open spaces.

4. The urban carbon emission analysis method according to claim 1, characterized in that: Producing a sample set of each of the described types of regions involves: Acquiring remote sensing image data of the target city and performing preprocessing to obtain the spatial information; Obtaining the height information of the ground feature by calculation or measurement; A sample set for each type of area is generated based on the spatial information and the ground feature height information.

5. The urban carbon emission analysis method according to claim 1, characterized in that: Obtaining the carbon emission local zone information of the target city based on the plurality of sample sets includes: Obtaining a local partition model of carbon emissions based on the plurality of sample sets; Partitioning the target city based on the carbon emission local zoning model to obtain carbon emission local zoning information; The carbon emission local partition information is verified.

6. The urban carbon emission analysis method according to claim 1, characterized in that: Acquiring the carbon emission data of the target city includes: acquiring at least one of carbon emission remote sensing data, carbon emission verification data, and carbon emission site monitoring data.

7. The urban carbon emission analysis method according to claim 6, characterized in that: Obtaining the carbon emission distribution information of the target city includes: combining the carbon emission verification data and the carbon emission site monitoring data, and performing image super-resolution reconstruction on the carbon emission remote sensing data.

8. The urban carbon emission analysis method according to claim 1, characterized in that: Carbon emissions analysis of the target city is performed using at least one of a time series analysis method, a spatial autocorrelation analysis method, and a hotspot analysis method.

9. An urban carbon emission analysis device, characterized in that: The device is used to perform the urban carbon emission analysis method according to any one of claims 1 to 8, comprising: An acquisition module, configured to acquire the spatial information, the height information of the ground features, and the carbon emission data of the target city; A partitioning module, configured to obtain the carbon emission local partitioning information according to the plurality of sample sets; A distribution module, configured to obtain the carbon emission distribution information according to the carbon emission data; The analysis module is used to perform carbon emission analysis on the target city.

10. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more computer programs, The one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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