An automatic recognition method and display platform for urban high-carbon emission spatial units

Abstract

The present invention discloses a method for automatically identifying urban high-carbon emission space units and a display platform, belonging to the field of urban planning, including a data acquisition and database construction module, an urban space unit division module, an urban space unit carbon emission generation module, an urban space unit high-carbon emission identification module, an online and offline interaction optimization module for urban high-carbon emission space units, and an interactive display module. The present invention solves the practical problems of difficult data collection, low accuracy, complex equipment, and lack of real-time interaction in current urban carbon emission data, responds to the intensive and sustainable requirements of urban planning and design, uses diversified data collection devices and intelligent algorithms to realize the identification of urban high-carbon emission space units, and combines hardware devices for optimization and interactive display. The present invention provides scientific support for energy conservation, emission reduction, and intensive layout in urban planning.

Description

Technical Field

[0001] The present invention relates to the field of urban planning, and particularly to a method for automatically identifying urban high-carbon emission spatial units and a display platform. Background Art

[0002] Identifying urban high-carbon emission spatial units requires identifying them according to different land use types in line with local conditions and implementing precise policies. At the same time, a large amount of urban carbon emission data needs to be collected, sorted out and analyzed. In the traditional design process, the identification and optimization of urban high-carbon emission spatial units are mostly manually completed by planning and design personnel, who need to collect and process data manually and propose optimization plans. The traditional identification, optimization and interactive display processes have the following limitations: First, due to the complexity, diversity and difficulty in collecting urban carbon emission data, a large amount of effort is required to complete data collection and sorting, consuming a large amount of human, economic and time costs in the process. Second, since the identification of urban high-carbon emission spatial units cannot be carried out according to land use types, the proposed optimization plans are disjointed from land use functions and lack practicality. Third, the identification and optimization results of urban high-carbon emission spatial units are often displayed relying on paper plans and texts, which cannot be interacted in real time and cannot be fully understood and applied by urban carbon emission control personnel without a professional background. Therefore, a method for automatically identifying urban high-carbon emission spatial units and a display platform are proposed. Summary of the Invention

[0003] In view of the deficiencies of the prior art, the present invention proposes a method for automatically identifying urban high-carbon emission spatial units and a display platform.

[0004] The object of the present invention can be achieved by the following technical solutions:

[0005] A method for automatically identifying urban high-carbon emission spatial units includes the following steps:

[0006] Obtaining the carbon emission data of the target city and constructing an urban carbon emission database, specifically including:

[0007] Obtain carbon emission data and construct an urban carbon emission database. Use the TanSat satellite, which has a 1 km resolution for carbon dioxide detection, to collect monthly frequency carbon dioxide concentration data in the urban built-up area. Use drones equipped with high-precision greenhouse gas monitors to obtain dynamic carbon emission data at 6 am, 9 am, 12 noon, 3 pm, 6 pm, and 12 am, based on the nature of urban land use. Use greenhouse gas mobile detection vehicles to obtain carbon emission data in key urban areas, including urban industrial parks and commercial central areas. Use ground-based high-resolution spectrometers at detection stations to obtain grid-based unorganized carbon emission data within a 500 m radius of the stations. Use handheld infrared gas imagers to conduct on-site verification and supplementation for point sources that have not been detected, and construct an urban carbon emission database.

[0008] Divide the urban built-up area into different types of urban spatial units according to the current land use nature, and establish an attribute table for urban spatial units; the attribute table includes data on the floor area, building area, land use nature, enterprise information, and population of urban spatial units, specifically including:

[0009] Obtain 3D urban vector data of the city from the local planning department, including road data, building data, population data, enterprise information data, and land use nature data. Divide urban spatial units based on the current land use nature, number them, and import them into the geographic information system. In the present invention, urban spatial units are divided according to the current land use, and each urban spatial unit has only one type of land use nature, which is the basic component unit of the city; enterprise information includes the output value of industrial enterprises above designated size and commercial sales.

[0010] Input the obtained 3D urban vector data, identify the land use nature, floor area A, building area S, and population quantity P of each urban spatial unit in the geographic information system, and embed the identification results into the attribute table. If the urban spatial unit is identified as a residential land unit, further identify the number of households H. If the urban spatial unit is identified as a public management and public service facilities land unit or a public utility land unit, further identify its service capacity F. If the urban spatial unit is identified as a commercial service facilities land unit, further identify its total sales S. If the urban spatial unit is identified as an industrial land unit, further identify its total output value Y. If the urban spatial unit is identified as a logistics and warehousing land, further identify its total community logistics volume P.

[0011] Based on the urban carbon emission database, obtain the urban carbon emission value of each urban spatial unit, link the carbon emission value to the corresponding urban spatial unit, and calculate the average carbon emission of each urban spatial unit to form an urban carbon emission average database; jointly construct a 3D urban carbon emission information sand table based on the urban carbon emission database, the attribute table of urban spatial units, and the urban carbon emission average database, specifically including:

[0012] Verify the accuracy of the obtained carbon emission data through cross - checking. Use a handheld infrared gas imager to conduct on - site verification and supplementation for the suspicious plots and blank plots, integrate the data, and use the atmospheric inversion method to obtain the urban carbon flux, which is linked to urban spatial units. Classify urban spatial units into seven categories according to the "Classification and Standard of Urban Land Use Nature", namely residential land units, public management and public service facility land units, commercial service facility land units, industrial land units, logistics and warehousing land units, public utility land units, and other land units, and then obtain the carbon emission values of urban spatial units of each land use type;

[0013] Calculate the average carbon emissions of urban spatial units through conversion formulas, including specific calculations for general - type indicators and special - type indicators; among them, the general - type indicators are carbon emissions per capita, carbon emissions per unit land area, and carbon emissions per unit building area; the special - type indicators are carbon emissions per household for residential use, carbon emissions per unit service capacity, carbon emissions per unit sales volume, carbon emissions per unit industrial output value, and carbon emissions per unit total logistics volume. The conversion formulas are as follows,

[0014]

[0015] Among them, Ci refers to the carbon flux of the urban spatial unit, and Pi refers to the permanent population of the i - th urban spatial unit.

[0016]

[0017] Among them, Ci refers to the carbon flux of the urban spatial unit, and Ai refers to the floor area of the i - th urban spatial unit.

[0018]

[0019] Among them, Ci refers to the carbon flux of the urban spatial unit, and Si refers to the total building area of the i - th urban spatial unit.

[0020]

[0021] Among them, Ri refers to the carbon flux of the urban residential space unit, and Hi refers to the total number of households in the i - th urban spatial unit.

[0022]

[0023] Among them, Ui refers to the carbon flux of urban public management and public service facility land units and urban public utility land units, and Fi refers to the number of people served by the i - th urban spatial unit.

[0024]

[0025] Among them, Bi refers to the carbon flux of the urban commercial service facility land unit, and Si refers to the total sales of the i-th urban spatial unit.

[0026]

[0027] Among them, Mi refers to the carbon flux of the urban industrial land unit, and Yi refers to the total industrial output value above designated size of the i-th urban spatial unit.

[0028]

[0029] Among them, Ui refers to the carbon flux of the urban logistics and warehousing land unit, and Pi refers to the total logistics volume of the i-th urban spatial unit.

[0030] Embed the obtained carbon emission mean value into the urban spatial unit attribute table, perform standardization processing and import it into the geographic information platform to construct a three-dimensional information sand table of urban carbon emissions.

[0031] Through the spatial link algorithm, spatially link the carbon emission data of urban spatial units according to the current land use types, respectively; divide the carbon emission data of different land use types of urban spatial units by day, and based on this, calculate the maximum and average values of the carbon emission per day of different land use types in each season of the urban spatial unit, specifically including:

[0032] In the geographic information system, classify the carbon emission data of urban spatial units through the spatial link algorithm. The classification is based on the "Standard for Classification of Urban Land Use and Planning Construction Land" (GB 50137-2011). The classification results include carbon emissions from residential land, carbon emissions from public management and public service facilities land, carbon emissions from commercial service facilities land, carbon emissions from industrial land, carbon emissions from logistics and warehousing land, carbon emissions from public utility land, and carbon emissions from other land, a total of seven types;

[0033] In the geographic information platform, divide the carbon emission data of urban spatial units of different land use types by day;

[0034] Through the geographic information platform, calculate the average value of the daily carbon emissions of different types of carbon emissions in each season of the urban spatial unit; the results are input into the intelligent interactive desktop display terminal. During the quarterly division, spring is from March to May, summer is from June to August, autumn is from September to November, and winter is from December to February.

[0035] Judge whether the urban spatial unit is a high-carbon emission spatial unit according to the K-means clustering algorithm; if the urban spatial unit identified as a high-carbon emission spatial unit, optimize the emissions of the urban spatial unit according to the land use type and make adjustments in the three-dimensional information sand table of urban carbon emissions, specifically including:

[0036] In the SPSS Statistics data statistical analysis software, the daily carbon emission data of urban spatial units are clustered by type and season respectively through the K-means clustering algorithm. The number of clusters is 3. Check and statistically analyze the initial cluster centers in the options, and select to exclude cases by column for missing values. In the output results of different seasons, each type contains 3 types of clustering results. Among them, the spatial unit with the highest cluster center value is the high-carbon emission spatial unit, the second highest is the medium-carbon emission spatial unit, and the lowest is the low-carbon emission spatial unit. The maximum value, minimum value, and the number of cases in the cluster of each type of clustering result can be viewed. The medium-carbon emission spatial units and low-carbon emission spatial units are directly output, and the high-carbon emission spatial units are optimized;

[0037] The data of high-carbon emission spatial units are input into the carbon emission optimization system. The data specifically includes land use nature information, land use boundary information, building information, and carbon emission mean information. The intelligent processing end of the carbon emission intelligent optimization system proposes preset optimization suggestions for urban spatial units with excessive carbon emissions according to land use types. For urban spatial units with high carbon emissions in residential land, the optimization suggestions are to increase the proportion of clean energy use and advocate a green and low-carbon lifestyle; for urban spatial units with high carbon emissions in public management and public service facility land, the optimization suggestions are to optimize the block form and increase the proportion of clean energy use; for urban spatial units with high carbon emissions in commercial service industry land, the optimization suggestions are to optimize the block form, formulate carbon emission reduction targets, and increase the proportion of clean energy use; for urban spatial units with high carbon emissions in industrial land, the optimization suggestions are to formulate carbon emission reduction targets and industrial renewal and upgrading plans, and increase the proportion of clean energy use; for urban spatial units with high carbon emissions in logistics and warehousing land, the optimization suggestions are to formulate carbon emission reduction targets and increase the proportion of clean energy use; for urban spatial units with high carbon emissions in public utility land, the optimization suggestions are to formulate carbon emission reduction targets and increase the distribution of high-carbon sink plants; for urban spatial units with high carbon emissions in other land uses, increase the green space carbon sink area and increase the proportion of clean energy use.

[0038] At the human-computer interaction end of the carbon emission intelligent optimization system, technicians verify and adjust the optimization suggestions for high-carbon emission spatial units proposed by the intelligent processing end, and output the optimization plan for urban high-carbon emission spatial units;

[0039] The carbon emission data information of urban spatial units is imported into the urban carbon emission three-dimensional information sand table. The three-dimensional display and real-scene interaction of urban high-carbon emission units are realized through the urban carbon emission three-dimensional information sand table equipment and virtual reality equipment; the drawings of urban high-carbon emission units are printed through 3D printing equipment, specifically including:

[0040] Integrate three types of data, namely, the carbon emission layout plan of urban spatial units, the carbon emission data of high-carbon emission spatial units in each season, and the optimization suggestions for high-carbon emission spatial units, using data integration and translation devices, and display them in the 3D information sandbox of urban carbon emissions. The 3D information sandbox of urban carbon emissions is generated after unifying the urban 3D vector data to the 2000 National Geodetic Coordinate System, and includes urban geographical elevation, road network, land use layout, and building information;

[0041] Perform human-computer interaction on the urban spatial unit information in the 3D information sandbox of urban carbon emissions through 3D holographic projection devices, VR glasses, and virtual reality data gloves. Through 3D holographic projection devices, the three-dimensional spatial form of urban spatial units, carbon emission average data, and carbon emission optimization solutions can be perceived; through VR glasses, one can roam in the virtual 3D model at the human scale. By using the virtual reality data gloves to click on the ground of a building or a block, one can obtain the carbon emission layout situation, its carbon emission average data, and carbon emission optimization solutions of the urban spatial unit to which it belongs;

[0042] Output the carbon emission layout plan of urban spatial units at a scale of 1:1000, the bird's-eye view of the carbon emission layout of urban spatial units at a scale of 1:1000, the plan grading index file, the daily carbon emission statistical table of each level of urban spatial units, the carbon emission optimization solution, and the comparison table of carbon emission data before and after optimization through the drawing data integration device, and print the above content into a design manual through a printing device. 3D print the solution 3D model through an industrial 3D printer.

[0043] The beneficial effects of the present invention:

[0044] The present invention comprehensively uses a variety of carbon emission detection devices to collect data and establish a carbon emission database, calculates the average carbon emission of each urban spatial unit through a conversion formula, and based on the K-means clustering algorithm module, reduces the data statistics and sorting work that previously required at least four to six weeks to be completed within one day, realizes the rapid identification of high-carbon emission units in a short time, and from the need to invest a large number of carbon emission detection devices and dozens of analysts to only need to invest a set of fixed devices and one data analyst to complete the identification work of urban high-carbon emission units, effectively reducing the time cost, economic cost, and labor cost, and improving the design efficiency.

[0045] The present invention divides urban spatial units according to the current land use type, improves the accuracy of urban carbon emission data, clarifies the carbon emission types of each urban spatial unit, unifies the dimension of carbon emission spatial units by constructing three types of general-purpose and five types of special-purpose conversion formulas, and can calculate the carbon emission values of each urban spatial unit under the same efficiency, promoting the reliability and objectivity of high-carbon emission spatial unit identification.

[0046] Through the online-offline interaction optimization module and the interaction display module, and by means of the urban carbon emission three-dimensional digital holographic sand table device and virtual reality device, the present invention enables managers and decision-makers to intuitively identify high-carbon emission spatial units and perform human-computer interactive optimization and adjustment, providing a three-dimensional display and real-scene interaction experience of urban high-carbon emission units to staff and urban public. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The present invention will be further described below in conjunction with the accompanying drawings.

[0048] Figure 1 is the method flowchart of the present application;

[0049] Figure 2 is the schematic diagram of the data acquisition device of the present application;

[0050] Figure 3 is the identification and display diagram of the high-carbon emission spatial unit of the present application;

[0051] Figure 4 is the schematic diagram of interaction display and printing of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0053] An automatic identification method and display platform for urban high-carbon emission spatial units, as Figure 1 shown, includes the following steps:

[0054] Step S1: Use the TanSat, a scientific experimental satellite for carbon dioxide detection with a resolution of 1 km, to collect monthly frequency carbon dioxide concentration data in urban built-up areas. Use drones equipped with high-precision greenhouse gas monitors to obtain dynamic carbon emission data at 6 am, 9 am, 12 noon, 3 pm, 6 pm, and 12 am in the morning for each urban street. Use greenhouse gas mobile detection vehicles to obtain carbon emission data in key urban areas, where the key areas include urban industrial parks and commercial central areas. Use ground-based high-resolution spectrometers at detection stations to obtain grid-based unorganized carbon emission data within a range of 500 m around the stations. Use handheld infrared gas imagers to conduct on-site verification and supplementation for point sources that have not been detected, and construct an urban carbon emission database. In the implementation process of this case, a total of 2,451 pixels of carbon dioxide concentration data were obtained, dynamic carbon emission data were obtained for a total of 7 streets within the jurisdiction, carbon emission data were obtained for 5 national and provincial parks, 4 scientific research and innovation bases, 2 commercial centers, and 6 logistics parks using mobile detection vehicles. A total of 1,058 grid-based monitoring stations were set up, and on-site verification or data supplementation was carried out for 18 point sources with anomalies or blanks.

[0055] Step S2: First, conduct urban spatial unit division. Obtain the three-dimensional urban vector data of the city from the local planning department, including road data, building data, population data, enterprise information data, and land use nature data. Divide urban spatial units based on the current land use nature, number them, and import them into the geographic information system. In the implementation process of this case, there were a total of 877 urban spatial units. Then, input the data obtained in S2-1, and identify the land use nature, floor area A, building area S, and population quantity P of each urban spatial unit in the geographic information system, and embed the identification results into the attribute table. If the urban spatial unit is identified as a residential land unit, further identify the number of households H. If the urban spatial unit is identified as a public management and public service facilities land unit or a public utility land unit, further identify its service capacity F. If the urban spatial unit is identified as a commercial service facilities land unit, further identify its total sales amount S. If the urban spatial unit is identified as an industrial land unit, further identify its total output value Y. If the urban spatial unit is identified as a logistics and warehousing land, further identify its total community logistics amount P. During the implementation process of this case, 368 residential land units, 86 public management and public service facilities land units, 129 commercial service facilities land units, 64 industrial land units, 7 logistics and warehousing land units, 68 public utility land units, and 155 other land units were identified.

[0056] Step S3: First, generate the carbon emission values of urban spatial units. Verify the accuracy of the carbon emission data obtained in S1 through cross-checking, integrate the data, and use the atmospheric inversion method to obtain the urban carbon flux, which is linked to the urban spatial units in S2. Classify the urban spatial units into seven categories according to the Classification and Standard of Urban Land Use Nature, and then obtain the carbon emission values of urban spatial units of each land use type. Next, calculate the average carbon emission of urban spatial units through a conversion formula, including 3 general types and 5 special types of land use. The general type indicators are carbon emission per capita, carbon emission per unit land area, and carbon emission per unit building area, and the special type indicators are carbon emission per household for residential use, carbon emission per unit service capacity, carbon emission per unit sales volume, carbon emission per unit industrial output value, and carbon emission per unit total logistics volume. Finally, embed the obtained average carbon emission into the urban spatial unit attribute table, perform standardization processing and import it into the geographic information platform to construct a three-dimensional information sand table of urban carbon emissions. During the implementation of this case, 1,104 general type carbon emission values and 722 special type carbon emission values were obtained.

[0057] Step S4: First, in the geographic information system, classify the urban spatial unit carbon emission data through a spatial linking algorithm. The classification is based on the Standard for Classification of Urban Land Use and Planning Construction Land (GB50137-2011). The classification results include carbon emissions from residential land, land for public management and public service facilities, land for commercial service facilities, industrial land, logistics warehousing land, public utility land, and other land, a total of seven types. During the implementation of this case, there are 368 with residential land, 86 with land for public management and public service facilities, 129 with land for commercial service facilities, 64 with industrial land, 7 with logistics warehousing land, 68 with public utility land, and 28 with other land. Secondly, in the geographic information platform, divide the carbon emission data of urban spatial units of different land use types by day. Then, through the geographic information platform, calculate the average emissions of different types of carbon emission days in each season of urban spatial units. The results are input into the intelligent interactive desktop display terminal. During the quarterly division, spring is from March to May, summer is from June to August, autumn is from September to November, and winter is from December to February.

[0058] Step S5: First, determine whether the urban spatial unit is a high-carbon emission spatial unit. In the SPSS Statistics data statistical analysis software, cluster the daily urban spatial unit carbon emission data by type and season respectively through the K-means clustering algorithm. The number of clusters is 3. Check the statistical initial cluster centers in the options and select to exclude cases by column for missing values. In the output results of different seasons, each type contains 3 types of clustering results. Among them, the one with the highest cluster center value is the high-carbon emission spatial unit, the second highest is the medium-carbon emission spatial unit, and the lowest is the low-carbon emission spatial unit. The maximum value, minimum value, and the number of cases in the cluster can be viewed. During the implementation of this case, 226 high-carbon emission spatial units, 431 medium-carbon emission spatial units, and 220 low-carbon emission spatial units were obtained. Secondly, output the medium-carbon emission spatial units and low-carbon emission spatial units to S7, and the high-carbon emission spatial units enter S6 for optimization.

[0059] Step S6: First, input the high-carbon emission spatial unit data into the carbon emission optimization system. The data specifically includes land use nature information, land use boundary information, building information, and carbon emission mean information. The intelligent processing end of the carbon emission intelligent optimization system puts forward preset optimization suggestions for urban spatial units by type for the types with excessive carbon emissions. For high-carbon emission spatial units of residential land, increase the proportion of clean energy use and advocate a green and low-carbon lifestyle; for high-carbon emission spatial units of public management and public service facility land, optimize the block form and increase the proportion of clean energy use; for high-carbon emission spatial units of commercial service industry land, optimize the block form, formulate carbon emission reduction indicators, and increase the proportion of clean energy use; for high-carbon emission spatial units of industrial land, formulate carbon emission reduction indicators and industrial renewal and upgrading plans, and increase the proportion of clean energy use; for high-carbon emission spatial units of logistics and warehousing land, formulate carbon emission reduction indicators and increase the proportion of clean energy use; for high-carbon emission spatial units of public utility land, formulate carbon emission reduction indicators and increase the distribution of high-carbon sink plants; for high-carbon emission spatial units of other land, increase the green space carbon sink area and increase the proportion of clean energy use. Then, at the human-computer interaction end of the carbon emission intelligent optimization system, technicians verify and adjust the optimization suggestions for high-carbon emission spatial units put forward by the intelligent processing end, and output the optimization plan for urban high-carbon emission spatial units. During the implementation of this case, the optimization suggestions for 826 high-carbon emission spatial units put forward by the intelligent processing end passed the verification at one time, and the remaining 51 were manually adjusted.

[0060] Step S7: First, use the data integration and translation device to integrate three types of data: the carbon emission layout plan of urban spatial units, the carbon emission data of high-carbon emission spatial units in each season, and the optimization suggestions for high-carbon emission spatial units, and display them in the 3D information sandbox of urban carbon emissions. The 3D information sandbox of urban carbon emissions is generated after unifying the urban 3D vector data to the 2000 National Geodetic Coordinate System, and includes urban geographical elevation, road network, land use layout, and building information. Secondly, conduct human-computer interaction on the urban spatial unit information in the 3D information sandbox of urban carbon emissions through 3D holographic projection equipment, VR glasses, and virtual reality data gloves. Through the 3D holographic projection equipment, the three-dimensional spatial form, average carbon emission data, and carbon emission optimization plan of urban spatial units can be perceived; through VR glasses, one can roam in the virtual 3D model at the human scale, and by using the virtual reality data gloves to click on the ground of a building or a block, the carbon emission layout and its average carbon emission data and carbon emission optimization plan of the urban spatial unit to which it belongs can be obtained. After that, based on S7-1, output the carbon emission layout plan of urban spatial units at a scale of 1:1000, the bird's-eye view of the carbon emission layout of urban spatial units at a scale of 1:1000, the plan grading index file, the daily carbon emission statistical table at all levels of the carbon emission layout of urban spatial units, the carbon emission optimization plan, and the comparison table of carbon emission data before and after optimization through the drawing data integration device, print the above content into a design manual through the printing device, and at the same time conduct 3D printing on the plan 3D model through an industrial 3D printer, and output the actual carbon emission video based on the virtual reality device combined with the influencing device.

[0061] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art of this industry should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed.

Claims

1. An automatic recognition method for urban high-carbon emission spatial units, characterized in that, it includes: Obtaining the carbon emission data of the target city and constructing an urban carbon emission database; Dividing the urban built-up area into different types of urban spatial units according to the current land use nature, and establishing an attribute table for urban spatial units; the attribute table includes the floor area data, building area data, land use nature data, enterprise information data and population data of urban spatial units; Obtaining the urban carbon emission value of each urban spatial unit based on the urban carbon emission database, linking the carbon emission value to the corresponding urban spatial unit, and calculating the average carbon emission of each urban spatial unit to form an urban carbon emission average database; jointly constructing an urban carbon emission three-dimensional information sand table based on the urban carbon emission database, the attribute table of urban spatial units and the urban carbon emission average database; Performing spatial linking on the carbon emission data of urban spatial units according to the current land use type through a spatial linking algorithm; dividing the carbon emission data of different land use types of urban spatial units by day, and calculating the maximum, minimum and average values of the carbon emissions of different land use types of each season of the urban spatial unit based on this; Judging whether the urban spatial unit is a high-carbon emission spatial unit according to the K-means clustering algorithm; if the urban spatial unit is identified as a high-carbon emission spatial unit, optimizing the emissions of the urban spatial unit according to the land use type and making adjustments in the urban carbon emission three-dimensional information sand table; The step of dividing the urban built-up area into different types of urban spatial units according to the current land use nature and establishing an attribute table for urban spatial units includes the following steps: Obtaining the three-dimensional urban vector data of the target city, including road data, building data, population data, enterprise information data, land use nature data, dividing urban spatial units based on the current land use nature, numbering them and importing them into the geographic information system; among them, the enterprise information data includes the output value of above-scale industries and commercial sales; Identifying and obtaining the land use nature, floor area A, building area S and population quantity P of each urban spatial unit from the three-dimensional urban vector data through the geographic information system, and embedding the identification results into the attribute table; if the urban spatial unit is identified as a residential land unit, further identifying its number of households H, if the urban spatial unit is identified as a public management and public service facility land unit or a public utility land unit, further identifying its service capacity F, if the urban spatial unit is identified as a commercial service facility land unit, further identifying its total sales S, if the urban spatial unit is identified as an industrial land unit, further identifying its total output value Y, if the urban spatial unit is identified as a logistics and warehousing land, further identifying its total community logistics volume P.

2. The automatic recognition method for urban high-carbon emission spatial units according to claim 1, characterized in that, the urban spatial units include residential land units, public management and public service facility land units, commercial service facility land units, industrial land units, logistics and warehousing land units, public utility land units and other land units.

3. The automatic recognition method for urban high-carbon emission spatial units according to claim 2, characterized in that, The urban carbon emission value of each urban spatial unit is obtained based on the urban carbon emission database, the carbon emission value is linked to the corresponding urban spatial unit, and the average carbon emission of each urban spatial unit is calculated to form an urban carbon emission average database; an urban carbon emission three-dimensional information sand table is jointly constructed based on the urban carbon emission database, the attribute table of the urban spatial unit, and the urban carbon emission average database, including the following steps: The carbon emission data in the urban carbon emission database is verified for accuracy through cross-checking, on-site verification and supplementation are carried out for the suspected plots and blank plots, the data is integrated, and the atmospheric inversion method is used to obtain the urban carbon flux; The average carbon emission of the urban spatial unit is calculated through a conversion formula, including specific calculations for general indicators and special indicators; among them, the general indicators are per capita carbon emission value, carbon emission value per unit land area, and carbon emission value per unit building area; the special indicators are carbon emission value per household for residence, carbon emission value per unit service capacity, carbon emission value per unit sales volume, carbon emission value per unit industrial output value, and carbon emission value per unit total logistics volume, and the conversion formula is as follows: Among them, Ci refers to the carbon flux of the urban spatial unit, and Pi refers to the permanent population of the i-th urban spatial unit; Among them, Ci refers to the carbon flux of the urban spatial unit, and Ai refers to the floor area of the i-th urban spatial unit; Among them, Ci refers to the carbon flux of the urban spatial unit, and Si refers to the total building area of the i-th urban spatial unit; Among them, Ri refers to the carbon flux of the urban residential spatial unit, and Hi refers to the total number of households in the i-th urban spatial unit; Among them, Ui refers to the carbon flux of the urban public management and public service facility land unit and the urban public utility facility land unit, and Fi refers to the number of people served by the i-th urban spatial unit; Among them, Bi refers to the carbon flux of the urban commercial service facility land unit, and Si refers to the total sales volume of the i-th urban spatial unit; Among them, Mi refers to the carbon flux of the urban industrial land unit, and Yi refers to the total industrial output value above designated size of the i-th urban spatial unit; Among them, Ui refers to the carbon flux of the urban logistics and warehousing land unit, and Pi refers to the total logistics volume of the i-th urban spatial unit; The average carbon emission of the urban spatial unit is embedded in the attribute table of the urban spatial unit, imported into the geographic information platform, and an urban carbon emission three-dimensional information sand table is constructed.

4. The method for automatically identifying urban high-carbon emission spatial units according to claim 3, characterized in that the carbon emission data of the urban spatial unit is spatially linked respectively according to the current land use type through the spatial link algorithm; the carbon emission data of different land use types of the urban spatial unit is divided by day, and based on this, the maximum and minimum values and the average value of the carbon emissions of different land use types in each season of the urban spatial unit are calculated, including the following steps: Classify the carbon emission data of urban spatial units through a spatial link algorithm; the classification types include carbon emission data of residential land, carbon emission data of land for public management and public service facilities, carbon emission data of land for commercial service facilities, carbon emission data of industrial land, carbon emission data of land for logistics and warehousing, carbon emission data of public utility land, and carbon emission data of other land. Divide the carbon emission data of urban spatial units of different land use types by day through a geographic information platform. Through a geographic information platform, calculate the average emission of different types of carbon emission days in each season of urban spatial units; during the quarterly division, spring is from March to May; summer is from June to August; autumn is from September to November; winter is from December to February of the following year.

5. The automatic identification method of urban high-carbon emission spatial units according to claim 4, characterized in that the judgment of whether an urban spatial unit is a high-carbon emission spatial unit according to the K-means clustering algorithm includes the following steps: Cluster the daily carbon emission data of urban spatial units by type and season respectively through the K-means clustering algorithm, and the number of clusters is 3; each type contains 3 clustering results, among which the one with the highest cluster center value is the high-carbon emission spatial unit, the second highest is the medium-carbon emission spatial unit, and the lowest is the low-carbon emission spatial unit.

6. The automatic identification method of urban high-carbon emission spatial units according to claim 5, characterized in that for the urban spatial units identified as high-carbon emission spatial units, optimize the emissions of urban spatial units according to the land use type and make adjustments in the 3D information sand table of urban carbon emissions, including the following steps: Input the data of the urban spatial units identified as high-carbon emission spatial units into the carbon emission optimization system; the data of high-carbon emission spatial units includes land use property information data, land boundary information data, building information data, and carbon emission average information data; the carbon emission optimization system executes the preset optimization plan for high-carbon emission spatial units according to the land use type respectively; Manually verify and adjust the preset optimization plan executed by the carbon emission optimization system, and adjust the 3D information sand table of urban carbon emissions based on the optimized plan of the high-carbon emission spatial units after verification and adjustment.

7. The automatic identification method of urban high-carbon emission spatial units according to claim 6, characterized in that Import the data information of urban carbon emission spatial units into the 3D information sand table of urban carbon emissions, and realize the 3D display and real-scene interaction of urban high-carbon emission units through the 3D information sand table equipment of urban carbon emissions and virtual reality equipment; realize the drawing printing of urban high-carbon emission units through 3D printing equipment.

8. The automatic identification method of urban high-carbon emission spatial units according to claim 1, characterized in that The carbon emission data of the target city includes the carbon dioxide concentration data of the urban built-up area collected by the scientific experiment satellite for carbon dioxide detection with a resolution of 1 km on a monthly basis, the dynamic carbon emission data of different time periods of each day for each urban street unit obtained by the unmanned aerial vehicle equipped with a high-precision greenhouse gas monitor, the carbon emission data of key urban areas obtained by the greenhouse gas mobile detection vehicle, the grid-based unorganized carbon emission data within a range of 500 m from the detection site obtained by the ground-based high-resolution spectrometer, and the supplementary data obtained by the handheld infrared gas imager for on-site verification of the undetected point sources; among them, the key areas include urban residential areas, urban industrial parks and commercial central areas.

9. A display platform for implementing the automatic identification method of urban high-carbon emission space units according to any one of claims 1-8, characterized in that , comprising: A data acquisition and database construction module: acquiring the carbon emission data of the target city and constructing an urban carbon emission database; An urban space unit division module: dividing the urban built-up area into different types of urban space units according to the current land use nature, and establishing an attribute table for the urban space units; the attribute table includes the floor area data, building area data, land use nature data, enterprise information data and population data of the urban space units; An urban space unit carbon emission generation module: obtaining the urban carbon emission value of each urban space unit based on the urban carbon emission database, linking the carbon emission value to the corresponding urban space unit, and calculating the average carbon emission of each urban space unit to form an urban carbon emission average database; jointly constructing a three-dimensional information sand table of urban carbon emissions based on the urban carbon emission database, the attribute table of urban space units and the urban carbon emission average database; An urban space unit carbon emission data clustering module: performing spatial linking on the carbon emission data of urban space units according to the current land use type through a spatial linking algorithm; dividing the carbon emission data of different land use types of urban space units by day, and calculating the maximum, minimum and average values of the carbon emissions of different land use types of each season of the urban space unit based on this; An urban high-carbon emission space unit identification module: judging whether the urban space unit is a high-carbon emission space unit according to the K-means clustering algorithm; if the urban space unit identified as a high-carbon emission space unit, optimizing the emissions of the urban space unit according to the land use type and making adjustments in the three-dimensional information sand table of urban carbon emissions; An interactive display module: importing the data information of urban carbon emission space units into the three-dimensional information sand table of urban carbon emissions, and realizing the three-dimensional display and real-scene interaction of urban high-carbon emission units through the three-dimensional information sand table equipment of urban carbon emissions and virtual reality equipment; realizing the drawing printing of urban high-carbon emission units through 3D printing equipment.

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