A 3D carbon map mapping method based on carbon data and spatial fusion

Abstract

This invention provides a 3D carbon map mapping method based on carbon data and spatial fusion, relating to the field of carbon emission monitoring and visualization technology. The method includes: acquiring end-user carbon emission data based on end-user energy consumption data; acquiring end-user map location data based on end-user attribute information; determining a mapping table between geographic information and carbon emission data; acquiring a 3D model of the end-user; and generating a 3D carbon map. According to this invention, based on end-user carbon emission data, by spatially mapping end-user map location data with individual buildings, carbon emission data can be accurately mapped to each plot or building. Combined with 3D building information, multi-dimensional fusion of carbon emission data with building vectors, terrain, and land use properties is achieved, generating a centimeter-resolution 3D carbon map that can be mapped to individual buildings, providing high-resolution, visualized data support for urban carbon governance, low-carbon planning, and carbon trading.

Description

Technical Field

[0001] This invention relates to the field of carbon emission monitoring and visualization technology, and in particular to a 3D carbon map mapping method based on carbon data and spatial fusion. Background Technology

[0002] In related technologies, most carbon emission data remains at the city or street level, failing to be refined to the plot or individual building scale. Furthermore, traditional carbon maps are mostly two-dimensional rasters, unable to reflect three-dimensional attributes such as building height, volume, and function, limiting the correlation analysis between carbon intensity and building spatial characteristics. Moreover, end-user carbon data (e.g., electricity, gas, and heat consumption) often lacks precise spatial coordinates. In addition, most carbon maps rely on annual statistics, unable to support high-frequency monitoring and dynamic governance. Therefore, they suffer from low spatial resolution, difficulties in data fusion, and low update frequency, making it difficult to effectively correlate with land plots or individual buildings, and hindering high-precision three-dimensional representation, resulting in inefficient identification of responsible parties.

[0003] The information disclosed in the background section of this application is intended only to enhance the understanding of the general background of this application and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0004] This invention provides a 3D carbon map mapping method based on carbon data and spatial fusion, which can solve the technical problems that related technologies are difficult to effectively associate with land parcels or individual buildings, and are difficult to express in high-precision three-dimensional form.

[0005] According to a first aspect of the present invention, a 3D carbon map mapping method based on carbon data and spatial fusion is provided, comprising: Obtain end-user carbon emission data based on end-user energy consumption data; Based on the end-user's attribute information, obtain the end-user's map location data; Based on end-user carbon emission data and map location data, determine a mapping table between geographic information and carbon emission data; By using geographic information data and carbon emission data, we can obtain a 3D model of the end user. A 3D carbon map is generated based on the 3D model, map location data, and mapping table.

[0006] According to the present invention, obtaining carbon emission data of end users includes: Based on the energy consumption data of end users, obtain the electricity consumption data, gas consumption data and heat consumption data of end users; Obtain carbon emission factors; Carbon emission data of end users is obtained based on carbon emission factors, electricity consumption data, gas consumption data, and heat consumption data.

[0007] According to the present invention, obtaining map location data of an end user includes: Based on the end-user's attribute information, obtain the end-user's coordinate information from the internet map; Clustering and deduplication of the coordinate information of each terminal user is performed to obtain the map location data of the terminal user.

[0008] According to the present invention, the method further includes: In the absence of coordinate information, the address text of the terminal user is determined based on the terminal user's attribute information; Obtain geocoding information based on the end user's address text; Based on geocoding information, obtain the map location data of the end user.

[0009] According to the present invention, determining a mapping table between geographic information and carbon emission data includes: Obtain land parcel boundary information; If the map location data is within the plot boundary information and there is a building outline within the plot boundary information, then a first mapping relationship is established between the map location data and the building. If the map location data is within the plot boundary information and there is no building outline within the plot boundary information, then the map location data will be mapped to the nearest neighbor building or the center of the plot. If the map location data is located in the intersection area of ​​multiple buildings or plots, a weighted allocation method is used to establish the first mapping relationship between the map location data and the buildings or plots; Based on the first mapping relationship and the carbon emission data of end users, a mapping table between geographic information and carbon emission data is determined.

[0010] According to the present invention, obtaining a 3D model of an end user includes: Based on geographic information data, obtain the building's height, number of floors, base area, and volume; Obtain the functional type information of the building; Obtain information on the building's age, structural type, and energy efficiency rating; Based on the height, number of floors, base area, volume, functional type information, age information, structural type and energy efficiency rating, a three-dimensional model architecture is obtained; Carbon emission data is added to the 3D model architecture to obtain a 3D model for the end user.

[0011] According to the present invention, generating a 3D carbon map includes: Determine the location information of the 3D model in the internet map by using map location data; Determine the hierarchical colors of the 3D model based on the mapping table; A 3D carbon map is obtained based on the graded color and location information.

[0012] According to a second aspect of the present invention, a 3D carbon map mapping system based on carbon data and spatial fusion is provided, comprising: The carbon emission data module acquires end-user carbon emission data based on end-user energy consumption data. The map location data module obtains the map location data of the end user based on the end user's attribute information; The mapping table module determines the mapping table between geographic information and carbon emission data based on the end user's carbon emission data and map location data; The 3D model module uses geographic information data and carbon emission data to obtain 3D models for end users; The 3D carbon map generation module generates a 3D carbon map based on the 3D model, map location data, and mapping table.

[0013] According to a third aspect of the present invention, a 3D carbon map mapping device based on carbon data and spatial fusion is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the 3D carbon map mapping method based on carbon data and spatial fusion.

[0014] According to a fourth aspect of the present invention, a computer-readable storage medium is provided having computer program instructions stored thereon, which, when executed by a processor, implement the 3D carbon map mapping method based on carbon data and spatial fusion.

[0015] By adopting the above technical solution, the present invention can achieve the following technical effects: According to this invention, carbon emission data of end-users can be obtained based on their energy consumption data, and map location data can be obtained based on their attribute information, thereby determining a mapping table between geographic information and carbon emission data. Furthermore, a three-dimensional model of the end-user can be obtained through geographic information data and carbon emission data, thus generating a 3D carbon map. This map can be effectively associated with land parcels or individual buildings, achieving multi-dimensional fusion of carbon emission data with building vectors, terrain, and land use properties, generating a high-precision 3D carbon map with centimeter-level resolution, detailing down to individual buildings, and integrating "carbon data-spatial morphology-three-dimensional scene." This provides high-precision three-dimensional representation, offering high-resolution, visualized data support for urban carbon governance, low-carbon planning, and carbon trading. Carbon emission data of end-users can be obtained based on their energy consumption data and carbon emission factors, providing basic data for generating the 3D carbon map. When determining the map location data of end-users, it can be obtained based on their attribute information. Considering the possibility of missing coordinate information, address text is used to supplement the missing coordinate information, improving the accuracy and objectivity of the end-user's map location data. When determining the mapping table between geographic information and carbon emission data, a primary mapping relationship can be established between end users and building blocks or land parcels. GIS software can then be used to spatially correlate carbon emission data with multi-source attribute data, constructing a comprehensive database of building carbon emissions in urban blocks to support subsequent analysis and model building. Furthermore, based on 3D models, map location data, and mapping tables, a 3D carbon map can be generated, intuitively displaying the relationship between carbon intensity and building spatial form. This achieves a 10-meter-level accurate mapping between end-user carbon data and land parcels or individual buildings, and establishes a data link connecting "carbon data-POI-land parcel-building." By integrating multi-source heterogeneous data, multi-dimensional fusion of carbon emission data with building vectors, topography, and land use properties can be achieved, generating a high-precision 3D carbon map with centimeter-level resolution, detailing down to individual buildings and integrating "carbon data-spatial form-3D scene." This provides high-precision 3D representation, offering high-resolution, visualized data support for urban carbon governance, low-carbon planning, and carbon trading.

[0016] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Other features and aspects of the invention will become clearer from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort. Figure 1 An exemplary flowchart of a 3D carbon map mapping method based on carbon data and spatial fusion according to an embodiment of the present invention is shown. Figure 2 An exemplary flowchart illustrating the acquisition of end-user carbon emission data according to an embodiment of the present invention is shown; Figure 3 A block diagram of a 3D carbon map mapping system based on carbon data and spatial fusion according to an embodiment of the present invention is shown as an example. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0020] Figure 1 An exemplary flowchart of a 3D carbon map mapping method based on carbon data and spatial fusion according to an embodiment of the present invention is shown, the method comprising: Step S1: Obtain the end-user's carbon emission data based on the end-user's energy consumption data; Step S2: Obtain the terminal user's map location data based on the terminal user's attribute information; Step S3: Determine the mapping table between geographic information and carbon emission data based on the end user's carbon emission data and map location data; Step S4: Obtain a 3D model of the end user using geographic information data and carbon emission data; Step S5: Generate a 3D carbon map based on the 3D model, map location data, and mapping table.

[0021] The 3D carbon map mapping method based on carbon data and spatial fusion according to embodiments of the present invention can obtain the end-user's carbon emission data based on the end-user's energy consumption data, and obtain the end-user's map location data based on the end-user's attribute information, thereby determining a mapping table between geographic information and carbon emission data. Furthermore, a three-dimensional model of the end-user can be obtained through geographic information data and carbon emission data, thereby generating a 3D carbon map. This map can be effectively associated with land parcels or individual buildings, achieving multi-dimensional fusion of carbon emission data with building vectors, terrain, and land use properties, generating a high-precision 3D carbon map with centimeter-level resolution, detailing down to individual buildings, and integrating "carbon data-spatial morphology-three-dimensional scene." This provides high-precision three-dimensional representation and offers high-resolution, visualized data support for urban carbon governance, low-carbon planning, and carbon trading.

[0022] Example 1: Figure 2 An exemplary flowchart illustrating the acquisition of end-user carbon emission data is shown according to an embodiment of the present invention.

[0023] According to an embodiment of the present invention, in step S1, obtaining the carbon emission data of the end user based on the end user's energy consumption data includes: step S11, obtaining the end user's electricity consumption data, gas consumption data, and heat consumption data based on the end user's energy consumption data; step S12, obtaining the carbon emission factor; and step S13, obtaining the end user's carbon emission data based on the carbon emission factor, electricity consumption data, gas consumption data, and heat consumption data.

[0024] According to an embodiment of the present invention, in step S11, electricity consumption data, gas consumption data, and heat consumption data of the end user are obtained based on the end user's energy consumption data. The end user can be a building, enterprise, or merchant, etc. The energy consumption data refers to the consumption data of the end user's electricity, gas, and heat. The electricity consumption data (i.e., electricity consumption) is the electricity consumption data, the gas consumption data (i.e., gas consumption) is the gas consumption data, and the heat consumption data (i.e., heat consumption) is the heat consumption data. In the example, when obtaining electricity consumption data, methods such as direct accounting, itemized induction, and proportional allocation can be used to allocate the electricity consumption data. The direct accounting method involves directly reading or accumulating the readings of all electricity meters directly associated with the building to obtain the building's total electricity consumption data, and is suitable for single-function buildings with one or more electricity meters. The itemized summarization method includes calculating the power consumption data of each functional area (i.e., the power consumption data of each functional area is the sum of all meter readings in that area) and calculating the total power consumption data of the building (i.e., the total power consumption data of the building is the sum of the power consumption data of all functional areas). It is applicable to multi-functional buildings with multiple sub-meters and is needed to lay the foundation for subsequent functional carbon accounting (i.e., assigning meters to corresponding functional areas according to their actual power supply range). The proportional allocation method includes the load density method (i.e., calculating the ratio of the theoretical power consumption data of each building to the annual power consumption index per unit area of ​​different building types and the building area, and allocating the actual power consumption data of each building according to this weight, applicable to the situation where different types of buildings share a single meter in parks and communities), the household index method (i.e., proportional allocation based on the total number of households in each building, applicable to the situation where multiple residential buildings, apartments, and hotel guest rooms share a single meter), and the activity duration method (i.e., proportional allocation based on the effective operating hours of each building, applicable to the situation where multiple exhibition halls, stadiums, and other buildings with significantly different operating times share a single meter), applicable to the situation where one main meter supplies power to multiple buildings or users.

[0025] In the example, when acquiring gas consumption data, the gas consumption data of smart remote gas meters can be allocated according to the direct accounting method, the itemized induction method, and the proportional allocation method. Furthermore, the gas consumption data of pre-purchased gas meters can be allocated according to the direct accounting method and the itemized induction method. The direct accounting method for smart remote gas meters involves directly reading or summing the readings of all gas meters directly associated with the building to obtain the building's total gas consumption data. This method is suitable for single-function buildings with one or more gas meters. The itemized induction method for smart remote gas meters includes calculating gas consumption data by zone (i.e., the gas consumption data for each functional zone is the sum of all gas meter readings within that zone) and calculating the building's total gas consumption data (i.e., the building's total gas consumption data is the sum of gas consumption data from all functional zones). This method is suitable for multi-functional buildings with multiple sub-gas meters and is applicable when it is necessary to lay the foundation for subsequent functional carbon accounting. The proportional allocation methods for smart remote gas meters include the load density method (i.e., calculating the ratio of theoretical gas consumption data for each building based on the annual gas consumption per unit area of ​​different building types and the building area, and allocating the actual gas consumption data of each building according to this weight, applicable to situations where different types of buildings share a single meter in parks or communities), the household number index method (i.e., proportional allocation based on the total number of households in each building, applicable to situations where multiple residential buildings, apartments, or hotel rooms share a single gas meter), and the activity duration method (i.e., proportional allocation based on the effective operating hours of each building, applicable to situations where multiple buildings such as exhibition halls and stadiums with significantly different operating times share a single gas meter), suitable for situations where one main gas meter supplies gas to multiple buildings. The direct calculation method for prepaid gas meters directly treats the total gas purchase volume as the gas consumption data for that statistical period, suitable for situations where a single or multiple gas purchase records happen to cover a single statistical period (e.g., a calendar month or calendar year). The proportional allocation method for prepaid gas meters includes establishing a typical monthly gas consumption ratio model (i.e., collecting monthly gas consumption data from smart gas meters in similar buildings of the same type and scale in the local area for several consecutive years, and calculating the average proportion of gas consumption data for each natural month to the total annual amount), allocating gas purchases to the covered months (i.e., determining the set of month numbers actually covered by a single gas purchase, first calculating the total proportion of the covered months, and then allocating the gas purchase to each covered month according to the proportion coefficient of each month), and summarizing gas consumption data according to the statistical period (i.e., after allocating all gas purchase records to specific months, adding the allocated gas consumption data of all months within the statistical period to obtain the estimated gas consumption data for that statistical period). It is applicable to situations where gas purchase records cover two or more statistical periods (e.g., multiple natural months or natural years), and requires analysis of historical data from smart gas meters in similar buildings in the local area to derive typical monthly gas consumption patterns and decompose the total gas purchase over time.

[0026] In the example, when obtaining heat consumption data, the heat consumption data for regional heating networks can be allocated using the direct accounting method, the itemized induction method, and the proportional allocation method. The direct accounting method can also be used to allocate heat consumption data for self-built boiler room heating. The direct accounting method for regional heating networks directly uses the total heat consumption data provided by the heating company, calculated on an area-by-area basis, and is suitable for situations where the heating area covers a single building. The proportional allocation method for regional heating networks uses the total heat consumption data provided by the heating company, calculated on an area-by-area basis, and allocates it according to the proportion of each building's heating area; this is suitable for situations where the heating area covers multiple buildings. The direct accounting method for self-built boiler room heating multiplies the building's heating area, the heating heat index per unit area determined by local standards, the total number of heating days within the statistical period, and the duration of heating days (e.g., 24 hours) to obtain the total heat consumption data for the self-built boiler room.

[0027] In the example, when acquiring end-user energy consumption data, the system first uses the end-user's "responsible entity name" (e.g., owner, property management company) as the core search identifier. This is automatically compared with the energy company's official business system via the system interface (API) to directly identify the corresponding energy consumption account. If a precise match fails, a "standard address database" (i.e., a database containing street names, house numbers, etc.) is automatically activated for auxiliary searching. Potential accounts are filtered out based on address similarity for manual verification. After matching, for successfully matched accounts, data is collected according to a preset cycle, prioritizing real-time remote transmission data from deployed smart meters (e.g., smart meters, gas meters), followed by settlement data from the energy company's official business system. Each data entry's timestamp and source ID are recorded. When system data collection fails or data is severely missing, manual data entry is initiated. Priority is given to contacting the responsible entity to obtain official settlement statements or invoices, followed by organizing personnel to conduct on-site meter readings. All manually entered data must have a written record of the reasons for the missing data and supporting documentation.

[0028] In this example, to ensure the accuracy and consistency of subsequent allocation and accounting, the original energy consumption data needs to be standardized. This includes data cleaning: using statistical methods to identify and process outliers in the original energy consumption data; and for missing data, interpolation, averaging, or machine learning methods can be used to complete it based on its characteristics. Time scale alignment: unifying similar energy consumption data from different sources with different collection frequencies (e.g., 15 minutes, hours, months) to the same accounting time resolution to eliminate deviations caused by inconsistent settlement cycles. Data unit unification: standardizing the units of measurement for various energy consumption data (e.g., kWh, Nm³). 3The data (including GJ) is uniformly converted into the input units required by the subsequent carbon emission accounting formula to ensure that all data inputs are standardized and the accounting results are comparable. Furthermore, data spatial positioning is required. For example, using "responsible entity name" and "standard address" as dual verification criteria, the standardized energy consumption account information is automatically compared and associated with the building attribute database output from the "Building Information Standardization" stage. This directly establishes a mapping relationship between energy consumption data and building IDs. The core matching results are then spatially overlaid with the building GIS layer for analysis, and final verification is performed using geographical location to ensure that energy consumption data logically and spatially belongs to the correct end-user. For accounts with failed matching or ambiguities, a manual verification process is initiated for confirmation.

[0029] Example 2: According to an embodiment of the present invention, in step S12, a carbon emission factor is obtained. When obtaining the carbon emission factor, the carbon emission factor corresponding to the electricity consumption data can be the national power grid carbon emission factor determined according to the "Ministry of Ecology and Environment: 2023 National Electricity Carbon Footprint Factor," i.e., 0.6205 kgCO2e / kWh, or the power grid supply emission factor determined according to other standards, such as the recommended value established in DB11 / T 1784—2020, i.e., 0.604 tCO2 / MWh, or the power grid carbon emission factor determined according to the "Ministry of Ecology and Environment: 2023 Report on Research on Carbon Dioxide Emission Factors of Regional Power Grids in China," i.e., 0.595 kgCO2e / kWh. And it can be based on a unit of measurement of 10... 4 Nm 3 The lower heating value is 389.31 GJ / 10. 4 Nm 3 The carbon content per unit calorific value is 15.30 × 10⁻⁶. -3 Information such as GJ and fuel carbonization rate of 99% is used to determine the carbon dioxide emission factor of natural gas, which is the carbon emission factor corresponding to the gas consumption data. The carbon emission factor corresponding to the heat consumption data can be the carbon dioxide emission factor of heat determined according to the recommended value established in DB11 / T 1784—2020, i.e., 0.11tCO2 / GJ.

[0030] Example 3: According to an embodiment of the present invention, in step S13, carbon emission data of end users is obtained based on carbon emission factors, electricity consumption data, gas consumption data, and heat consumption data. The product of the total electricity consumption data of end users within the region during the statistical period and the corresponding carbon emission factor is the total carbon emission of electricity consumption by end users within the statistical period, which can be considered as the carbon emission data corresponding to the electricity consumption data of end users. The product of the total gas consumption data of end users within the region during the statistical period and the corresponding carbon emission factor is the total carbon emission of gas consumption by end users within the statistical period, which can be considered as the carbon emission data corresponding to the gas consumption data of end users. The product of the heat consumption data of the regional network of end users, the carbon emission factor corresponding to the heat consumption data, and the regional network heat loss rate index (i.e., the index determined with reference to GB / T 51161-2016 "Energy Consumption Standard for Civil Buildings [with Explanatory Notes]", for example, 0-5%) is the carbon emission corresponding to the heat consumption data of the regional network of end users. The product of the end-user's self-built boiler heat consumption data and the corresponding carbon emission factor is the carbon emission amount corresponding to the end-user's self-built boiler room heat consumption data. The sum of the carbon emission amounts corresponding to the end-user's regional grid heat consumption data and the carbon emission amounts corresponding to the self-built boiler room heat consumption data is the total carbon emission amount of the end-user's heat consumption within the statistical period, which can be considered as the carbon emission data corresponding to the end-user's heat consumption data. Further, by summing the carbon emission data corresponding to the end-user's electricity consumption data, gas consumption data, and heat consumption data, the end-user's carbon emission data (kgCO2) can be obtained. In the example, the output structure of the end-user carbon emission data can be: end-user ID, end-user name, industry type, carbon emission amount, energy category, and time granularity (hour / day / month).

[0031] In this way, carbon emission data of end users can be obtained based on their energy consumption data and carbon emission factors, providing basic data for generating 3D carbon maps.

[0032] Example 4: According to an embodiment of the present invention, in step S2, obtaining the map location data of the terminal user based on the terminal user's attribute information includes: obtaining the coordinate information of the terminal user in an Internet map based on the terminal user's attribute information; and clustering and deduplicating the coordinate information of each terminal user to obtain the map location data of the terminal user.

[0033] According to embodiments of the present invention, POI information (i.e., information including the user's name, address, latitude and longitude coordinates, etc.) of the end user can be obtained through multi-source data such as Internet map APIs, business registration, street view images, and IoT positioning. This information constitutes the attribute information of the end user. Among the aforementioned attribute information of the end user, the latitude and longitude coordinates of the end user obtained from the Internet map API constitute the coordinate information of the end user. Furthermore, by clustering and deduplicating the coordinate information of duplicate or offset end users (for example, by using algorithms such as hierarchical clustering based on distance thresholds or grid-based clustering), the coordinate information belonging to the same end user can be aggregated into one coordinate information, and their attributes can be merged to obtain the map location data of the end user, achieving a coordinate accuracy of ≤10 meters.

[0034] Example 5: According to an embodiment of the present invention, the method further includes: determining the terminal user's address text based on the terminal user's attribute information when coordinate information is missing; obtaining geocoding information based on the terminal user's address text; and obtaining the terminal user's map location data based on the geocoding information.

[0035] According to an embodiment of the present invention, in the case of missing coordinate information, the text of the terminal user's address in the terminal user's attribute information can be obtained, which is the terminal user's address text. For example, if the address in the attribute information of a terminal user lacking coordinate information is "No. 111, XXX Road", the terminal user's address text is "No. 111, XXX Road". Further, the above address text can be standardized (e.g., removing redundancy, unifying format, etc.) and input into a geocoding tool (e.g., Internet map API, commercial GIS platform, etc.) for address matching (i.e., calculating the similarity between the address text and each address text in the coding tool's database and matching them). This allows the acquisition of the coordinate information of the address text with the highest text similarity (i.e., the latitude and longitude coordinates corresponding to the terminal user's address text lacking coordinate information), which is the geocoding information. This completes the terminal user's coordinate information for those lacking coordinate information. Furthermore, by clustering and deduplicating the duplicate or offset geocoding information of terminal users, the terminal user's map location data can be obtained.

[0036] This method allows for the acquisition of map location data for end-users based on their attribute information. It also addresses the issue of missing coordinate information by using address text to fill in the gaps, thus improving the accuracy and objectivity of the map location data for end-users.

[0037] Example 6: According to an embodiment of the present invention, in step S3, determining a mapping table between geographic information and carbon emission data based on the end user's carbon emission data and map location data includes: obtaining land parcel boundary information; if the map location data is within the land parcel boundary information and there is a building outline within the land parcel boundary information, then establishing a first mapping relationship between the map location data and the building; if the map location data is within the land parcel boundary information and there is no building outline within the land parcel boundary information, then establishing a first mapping relationship between the map location data and the nearest neighbor building or the center of the land parcel; if the map location data is located in the intersection area of ​​multiple buildings or land parcels, then establishing a first mapping relationship between the map location data and the building or land parcel using a weighted allocation method; and determining a mapping table between geographic information and carbon emission data based on the first mapping relationship and the end user's carbon emission data.

[0038] According to an embodiment of the present invention, land parcel boundary information is obtained. Based on internet map APIs or official building survey data, urban land parcel boundaries (i.e., the red line of the national land use plan) and building outlines (e.g., the outlines of buildings in BIM or 3D reality) can be obtained, which constitutes the land parcel boundary information. If the map location data is located within the land parcel boundary information and a building outline exists within the land parcel boundary information, then the building within the land parcel boundary information can be considered the building of the end user corresponding to the map location data. The map location data can then be bound to the building within the land parcel boundary information to generate a map location data-building association pair, thereby establishing a first mapping relationship.

[0039] According to an embodiment of the present invention, if the map location data is located within the land parcel boundary information and there is no building outline within the land parcel boundary information (for example, there is only empty land within the land parcel boundary information), the nearest neighbor building (i.e., the building closest to the land parcel in a straight line) or the center of the land parcel (i.e., the center point of the land parcel) can be considered as the building of the terminal user corresponding to the map location data. Then the map location data can be bound to the nearest neighbor building or the center of the land parcel to establish a first mapping relationship.

[0040] According to embodiments of the present invention, if map location data is located in the intersection area of ​​multiple buildings or plots, a weighted allocation method (e.g., allocating carbon emission data by area weighting or functional similarity weighting) is used to establish a first mapping relationship between the map location data and the buildings or plots. For example, if the end user is an office, and the end user's map location data is located in the intersection area of ​​two office buildings, when allocating carbon emission data by area weighting, since the area of ​​office building A accounts for 3 / 10 of the total area of ​​the two office buildings, and the area of ​​office building B accounts for 7 / 10 of the total area of ​​the two office buildings, then 3 / 10 of the carbon emission data can be allocated to office building A, and 7 / 10 of the carbon emission data can be allocated to office building B. When allocating carbon emission data by area weighting, higher carbon emission data can be allocated to buildings with high functional similarity, and lower carbon emission data can be allocated to buildings with low functional similarity. For example, if the end user is a cement plant, and the end user's map location data is located in the intersection area of ​​the factory and office buildings, since the factory and cement plant have high functional similarity, then the factory can be allocated higher carbon emission data, and the office buildings can be allocated lower carbon emission data. Furthermore, using GIS software, the carbon emission data of each end-user corresponding to each map location data is assigned to buildings that have a primary mapping relationship with the same map location data, thus forming a mapping table between geographic information and carbon emission data. The output format of the above mapping table between geographic information and carbon emission data can be end-user ID → plot or building ID → carbon emission amount.

[0041] In this way, the first mapping relationship between end users and building blocks or land parcels can be established, and GIS software can be used to spatially correlate carbon emission data with multi-source attribute data to build a comprehensive database of building carbon emissions in urban blocks, providing support for subsequent analysis and model building.

[0042] Example 7: According to an embodiment of the present invention, in step S4, the height, number of floors, base area, and volume of the building are obtained based on geographic information data; the functional type information of the building is obtained; the age information, structural type, and energy-saving level of the building are obtained; a three-dimensional model architecture is obtained based on the height, number of floors, base area, volume, functional type information, age information, structural type, and energy-saving level; carbon emission data is added to the three-dimensional model architecture to obtain a three-dimensional model for the end user.

[0043] According to embodiments of the present invention, geographic information data of a building (e.g., data including the building's height, number of floors, etc.) can be obtained from channels such as internet map APIs and publicly available online data. From this geographic information, the building's height, number of floors, floor area, and volume (i.e., the product of floor area and height) can be extracted. The building's function, such as residential, office, commercial, or industrial, can be obtained from publicly available online data or internet map APIs; this represents the building's functional type information. The building's age (e.g., 1980s, 2000s), structure (e.g., brick-concrete structure, wood structure), and energy efficiency rating (e.g., 30% energy saving, 65% energy saving, etc.) can be obtained from channels such as real estate registration data or urban planning archives; this represents the building's age information, structural type, and energy efficiency rating.

[0044] According to embodiments of the present invention, data cleaning (i.e., removing obviously erroneous geometry from the building outline and merging fragmented parts of the same building) and spatial clipping (i.e., using plot boundary information to select and clip the building outline by location) are performed on the information of the end user's building, including height, number of floors, base area, volume, functional type, age, structural type, and energy efficiency rating. The information is then spatially correlated or table-linked with the building outline to create a block building base map corresponding to the end user. The building's height, number of floors, base area, volume, functional type, age, structural type, and energy efficiency rating are imported into a 3D GIS system (e.g., ArcGIS Pro). The block building base map is then 3D-extended to obtain a 3D building model of the end user's building (e.g., a model in BIM, CityGML, or real-scene 3D mesh format), which is the 3D model architecture. Furthermore, the corresponding carbon emission data of the end user can be added to the 3D model architecture to obtain the end user's 3D model. Using the same processing method, 3D models of each end user can be obtained. In the example, end-user POI data, such as map location data and energy consumption data, can also be added to the 3D model architecture. Furthermore, carbon emission data can be associated with end-user building IDs to calculate building carbon intensity (e.g., kgCO2 / m³). 2 ·a or kgCO2 / m 3 ·a).

[0045] Example 8: According to an embodiment of the present invention, in step S5, generating a 3D carbon map based on the 3D model, map location data, and mapping table includes: determining the location information of the 3D model in the Internet map using the map location data; determining the hierarchical color of the 3D model based on the mapping table; and obtaining the 3D carbon map based on the hierarchical color and location information.

[0046] According to an embodiment of the present invention, based on map location data that has a first mapping relationship with the buildings corresponding to the 3D models of end users, the 3D models of end users are located to the corresponding locations on the Internet map, thereby obtaining the location information of the 3D models on the Internet map, which can be used to locate the positions of the 3D models of various end users in the 3D carbon map. Further, based on the mapping table (i.e., the street building carbon emission database), the graded colors of the 3D models are determined in a 3D GIS system (e.g., ArcGIS system) using the "Quantity → Graded Color → Natural Discontinuity Graded Method" in the symbol system toolbox, thus achieving 2D visualization. For example, in two case street blocks, there are a total of 1277 polygons, each polygon representing a building, i.e., 1277 buildings. Carbon emission data can be used as a graded field, and the natural discontinuity method can be used to grade the carbon emission data of all end users' buildings (e.g., divided into 7 levels). Gradient color mapping (e.g., cool colors represent low emissions, warm colors represent high emissions) is then applied to determine the graded colors of each end user's 3D model. In the example, you can insert hierarchical legends, a north arrow, a scale bar (1:3000), and the mapping date in the Layout view, and export a 300 dpi PNG for reporting and public display.

[0047] According to an embodiment of the present invention, a 3D carbon map is obtained based on hierarchical color and location information. For example, the building outline shapefiles of each end user are imported into a "local scene," a unified coordinate system is established, and the carbon emission data fields and building layer number segments of each end user are associated. Then, in ArcScene, the building outline layer is opened, and "Based on Expression" is selected: Height=Floor×2.8 m (default floor height). "Stretch texture" can be enabled to maintain the realism of the walls, and "Vertical exaggeration" can be disabled to avoid distortion. Further, it is published as a Scene Layer Package (slpk) in ArcGIS Pro, uploaded to ArcGIS Online, and a rotatable, zoomable, and clickable Web3D carbon map scene of the street buildings is generated based on hierarchical color and location information, which is the 3D carbon map. In the example, a 360° surround video (1080p, 30fps) can be exported for planning reports, and a PDF 3D bookmark can be generated simultaneously for offline browsing on mobile devices. The 3D carbon map can be automatically updated based on IoT meters and POIs, supporting daily or even hourly dynamic updates.

[0048] In the example, when "Stretch Texture" is enabled, the stretched volume fill can use a hierarchical gradient color consistent with the 2D model. Carbon emission fields are bound to the top and sides respectively to achieve consistent color within the voxel. The background can be set to white, the road network to pure black (RGB 0,0,0), and the water body to light blue (#B3E5FC) to enhance the contrast of the high carbon volume. At the same time, a simulated sun position (14:00 on December 22) can be added to generate shadows, which can intuitively reflect the differences in building volume among different end users.

[0049] In this way, 3D carbon maps can be generated based on 3D models, map location data, and mapping tables. These maps can intuitively display the relationship between carbon intensity and building spatial form, achieving 10-meter-level accurate mapping between end-user carbon data and land parcels or individual buildings. Furthermore, they can establish a data link between "carbon data-POI-land parcel-building," integrate multi-source heterogeneous data, and achieve multi-dimensional fusion of carbon emission data with building vectors, terrain, and land use properties. This generates a high-precision 3D carbon map with centimeter-level resolution, capable of being applied to individual buildings, and integrating "carbon data-spatial form-3D scene." This allows for high-precision 3D representation, providing high-resolution and visualized data support for urban carbon governance, low-carbon planning, and carbon trading.

[0050] According to embodiments of the present invention, a 3D carbon map mapping method based on carbon data and spatial fusion can obtain the end-user's carbon emission data based on the end-user's energy consumption data and obtain the end-user's map location data based on the end-user's attribute information, thereby determining a mapping table between geographic information and carbon emission data. Furthermore, a 3D model of the end-user can be obtained through geographic information data and carbon emission data, thereby generating a 3D carbon map. It can be effectively associated with land parcels or individual buildings, achieving multi-dimensional fusion of carbon emission data with building vectors, terrain, and land use properties, generating a high-precision 3D carbon map with centimeter-level resolution, detailing down to individual buildings, and integrating "carbon data-spatial morphology-3D scene," thus providing high-precision 3D representation and high-resolution, visualized data support for urban carbon governance, low-carbon planning, and carbon trading. It can also obtain the end-user's carbon emission data based on the end-user's energy consumption data and carbon emission factors, providing basic data for generating the 3D carbon map. When determining the end-user's map location data, it can be obtained based on the end-user's attribute information. Considering the case of missing coordinate information, address text is used to complete the missing coordinate information. This improves the accuracy and objectivity of map location data for end users. When determining the mapping table between geographic information and carbon emission data, a primary mapping relationship can be established between end users and building blocks or land parcels. GIS software is then used to spatially correlate carbon emission data with multi-source attribute data, constructing a comprehensive database of building carbon emissions in urban blocks, providing support for subsequent analysis and model building. Furthermore, based on 3D models, map location data, and mapping tables, 3D carbon maps can be generated, intuitively displaying the relationship between carbon intensity and building spatial morphology. This achieves 10-meter-level accurate mapping between end-user carbon data and land parcels or individual buildings, and establishes a data link connecting "carbon data-POI-land parcel-building." By integrating multi-source heterogeneous data, multi-dimensional fusion of carbon emission data with building vectors, topography, and land use properties is achieved, generating a high-precision 3D carbon map with centimeter-level resolution, detailing individual buildings and integrating "carbon data-spatial morphology-3D scene." This provides high-precision 3D representation, offering high-resolution, visualized data support for urban carbon governance, low-carbon planning, and carbon trading.

[0051] Example 9: Figure 3 An exemplary block diagram of a 3D carbon map mapping system based on carbon data and spatial fusion according to an embodiment of the present invention is shown, the system comprising: The carbon emission data module acquires end-user carbon emission data based on end-user energy consumption data. The map location data module obtains the map location data of the end user based on the end user's attribute information; The mapping table module determines the mapping table between geographic information and carbon emission data based on the end user's carbon emission data and map location data; The 3D model module uses geographic information data and carbon emission data to obtain 3D models for end users; The 3D carbon map generation module generates a 3D carbon map based on the 3D model, map location data, and mapping table.

[0052] According to one embodiment of the present invention, a 3D carbon map mapping device based on carbon data and spatial fusion is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to execute the 3D carbon map mapping method based on carbon data and spatial fusion.

[0053] According to one embodiment of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored, which, when executed by a processor, implement the 3D carbon map mapping method based on carbon data and spatial fusion.

[0054] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0055] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and explained in the embodiments, and any variations or modifications may be made to the implementation of the present invention without departing from the stated principles.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A 3D carbon map mapping method based on carbon data and spatial fusion, characterized in that, include: Obtain end-user carbon emission data based on end-user energy consumption data; Based on the end-user's attribute information, obtain the end-user's map location data; Based on end-user carbon emission data and map location data, determine a mapping table between geographic information and carbon emission data; By using geographic information data and carbon emission data, we can obtain a 3D model of the end user. A 3D carbon map is generated based on the 3D model, map location data, and mapping table.

2. The 3D carbon map mapping method based on carbon data and spatial fusion according to claim 1, characterized in that, Based on end-user energy consumption data, obtain end-user carbon emission data, including: Based on the energy consumption data of end users, obtain the electricity consumption data, gas consumption data and heat consumption data of end users; Obtain carbon emission factors; Carbon emission data of end users is obtained based on carbon emission factors, electricity consumption data, gas consumption data, and heat consumption data.

3. The 3D carbon map mapping method based on carbon data and spatial fusion according to claim 1, characterized in that, Based on the end-user's attribute information, obtain the end-user's map location data, including: Based on the end-user's attribute information, obtain the end-user's coordinate information from the internet map; Clustering and deduplicating the coordinate information of each terminal user yields the terminal user's map location data.

4. The 3D carbon map mapping method based on carbon data and spatial fusion according to claim 3, characterized in that, The method further includes: In the absence of coordinate information, the address text of the terminal user is determined based on the terminal user's attribute information; Obtain geocoding information based on the end user's address text; Based on geocoding information, obtain the map location data of the end user.

5. The 3D carbon map mapping method based on carbon data and spatial fusion according to claim 1, characterized in that, Based on end-user carbon emission data and map location data, a mapping table between geographic information and carbon emission data is determined, including: Obtain land parcel boundary information; If the map location data is within the plot boundary information and there is a building outline within the plot boundary information, then a first mapping relationship is established between the map location data and the building. If the map location data is within the plot boundary information and there is no building outline within the plot boundary information, then the map location data will be mapped to the nearest neighbor building or the center of the plot. If the map location data is located in the intersection area of ​​multiple buildings or plots, a weighted allocation method is used to establish the first mapping relationship between the map location data and the buildings or plots; Based on the first mapping relationship and the carbon emission data of end users, a mapping table between geographic information and carbon emission data is determined.

6. The 3D carbon map mapping method based on carbon data and spatial fusion according to claim 1, characterized in that, Using geographic information data and carbon emission data, obtain a 3D model of the end user, including: Based on geographic information data, obtain the building's height, number of floors, base area, and volume; Obtain the functional type information of the building; Obtain information on the building's age, structural type, and energy efficiency rating; Based on the height, number of floors, base area, volume, functional type information, age information, structural type and energy efficiency rating, a three-dimensional model architecture is obtained; Carbon emission data is added to the 3D model architecture to obtain a 3D model for the end user.

7. The 3D carbon map mapping method based on carbon data and spatial fusion according to claim 1, characterized in that, Based on the 3D model, map location data, and mapping table, a 3D carbon map is generated, including: The location information of the 3D model in the Internet map is determined by map location data; Determine the hierarchical colors of the 3D model based on the mapping table; A 3D carbon map is obtained based on the graded color and location information.

8. A 3D carbon map mapping system based on carbon data and spatial fusion, characterized in that, include: The carbon emission data module acquires end-user carbon emission data based on end-user energy consumption data. The map location data module obtains the map location data of the end user based on the end user's attribute information; The mapping table module determines the mapping table between geographic information and carbon emission data based on the end user's carbon emission data and map location data; The 3D model module uses geographic information data and carbon emission data to obtain 3D models for end users; The 3D carbon map generation module generates a 3D carbon map based on the 3D model, map location data, and mapping table.

9. A 3D carbon map mapping device based on carbon data and spatial fusion, characterized in that, include: processor; A memory for storing processor-executable instructions; wherein the processor is configured to invoke the instructions stored in the memory to perform the method as described in any one of claims 1-7.

10. A computer-readable storage medium having computer program instructions stored thereon, characterized in that, When the computer program instructions are executed by the processor, they implement the method as described in any one of claims 1-7.

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