Cold region urban space operation carbon emission measuring and calculating method based on prototype construction

Abstract

The invention belongs to the field of environmental protection, and particularly relates to a cold region urban space operation carbon emission measuring and calculating method based on prototype construction. The objective of the invention is to solve the problem of large result deviation when the existing carbon emission measurement method is used for measurement. According to the cold region city space operation carbon emission measuring and calculating method based on prototype construction, organic connection and deduction of building data and city data in calculation are achieved based on the calculation thought of prototype construction, the building data and the city data of the calculated cold region city space area are used for reflecting the building carbon source, and the carbon emission of the city space operation carbon emission in the cold region is calculated. Greenbelt carbon sink is reflected by urban data, and large-scale cold region urban space operation carbon emission measurement and calculation can be realized. The carbon source and the carbon sink in the urban space in the cold region do not need to be analyzed and measured one by one, and only a certain number of building sample data need to be measured, so that the measurement and calculation steps are greatly simplified, and the problem of large result deviation when a carbon emission measurement and calculation method is used for measurement and calculation is solved.

Description

Technical Field

[0001] The present invention belongs to the field of environmental protection, and in particular relates to a carbon emission calculation method for urban space operations in cold regions based on a prototype. Background Art

[0002] Currently, carbon emission measurement in cold-region urban areas during the operational phase in my country faces challenges such as a lack of basic data, inadequate spatial analysis, and unclear carbon reduction indicators. The lack of basic data refers to the fact that currently available carbon emission data is mostly limited to the level of individual buildings. Building carbon emission measurement and calculation methods cannot measure carbon emissions across large urban areas. Existing urban carbon emission measurement methods are primarily based on scenario analysis and carbon dioxide content calculations, which often have large errors and a limited measurement scope. Inadequate spatial analysis refers to the fact that existing carbon emission measurement methods in cold-region urban areas often simplify the measured data, often ignoring the differences in carbon emissions between different types of buildings in the urban space during the operational phase, and providing a poor understanding of the carbon emission characteristics of different building types during the operational phase. Unclear carbon reduction indicators refer to the fact that existing methods, due to unclear boundary definitions and inadequate analysis of carbon reduction indicators, particularly those represented by carbon sinks, lead to differences in the definition of measurement content and the acquisition of data sources. This often results in significant deviations in the results when using these methods for measurement. Summary of the Invention

[0003] The purpose of this invention is to solve the problem of large deviations in the results of existing carbon emission calculation methods. It provides a prototype-based carbon emission calculation method for urban space operations in cold regions, including:

[0004] S1: Determine the calculation boundary of carbon emissions from urban space operations in cold regions to be measured;

[0005] The cold areas are defined as severely cold areas and cold areas.

[0006] The severely cold areas refer to those areas where the temperature in winter is below -20℃.

[0007] The cold areas refer to areas where the temperature in winter is around -10℃ to -20℃.

[0008] The urban space is defined as including buildings and green spaces in the city.

[0009] S2: Determine the types of building prototypes in the cold region urban space to be measured; the number of the building prototype types is N; N is a positive integer;

[0010] S3: Obtain a training dataset of urban spaces in cold regions;

[0011] S4: Construct a cold region city BP model based on building prototypes; train the cold region city BP model based on building prototypes according to the training data set of cold region city space to obtain a trained cold region city BP model based on building prototypes;

[0012] S5: Obtain the building distribution and volume data of all buildings in the cold region urban space to be measured,

[0013] S6: Input the building distribution and volume data of the cold region urban space to be measured into the trained cold region urban BP model based on building prototypes to obtain the daily carbon emission prediction data of all buildings in the cold region urban space to be measured;

[0014] S7: Obtain green space data of the cold region urban space to be measured; calculate the total carbon sink of the cold region urban space to be measured based on the green space data of the cold region urban space to be measured;

[0015] S8: Based on the calculation boundary of the operational carbon emissions of the cold-region urban space to be measured, the carbon emission prediction data of all buildings in the cold-region urban space to be measured obtained in S6, and the total carbon sink of the cold-region urban space to be measured, the total annual carbon emissions of the cold-region urban space to be measured are obtained;

[0016] S9: Integrate the building distribution and volume data of all buildings in the cold-region urban space to be measured obtained in S5, the carbon emission prediction data of all buildings in the cold-region urban space to be measured obtained in S6, the total carbon sink of the cold-region urban space to be measured obtained in S7, and the total annual carbon emission of the cold-region urban space to be measured obtained in S8 into the ArcGIS platform.

[0017] The kernel density tool in the ArcGIS platform is used to generate a visual representation of the spatiotemporal distribution of carbon emissions in cold-region cities, including heat maps of cold-region urban spaces during the heating period, non-heating period, and total carbon emissions throughout the year.

[0018] The beneficial effects of the present invention are:

[0019] Based on the computational idea of ​​prototype construction, the organic connection and deduction of building data and urban data in computation are realized. The building data and urban data of the calculated cold-region urban space area are used to reflect the building carbon source, and the urban data is used to reflect the green space carbon sink, which can realize the large-scale carbon emission measurement of cold-region urban space operations.

[0020] The "small data" of carbon emissions in the building operation stage can be used to reflect the "big data" of carbon emissions in the cold-region urban space operation stage. There is no need to analyze and measure the carbon sources and carbon sinks in the cold-region urban space one by one. Only a certain number of building sample data need to be measured, which greatly simplifies the measurement steps.

[0021] The proposed measurement and characterization methods can be organically linked with the ArcGIS platform to achieve data visualization, facilitating data analysis and subsequent applications. Furthermore, secondary development can form a scalable measurement toolkit for easy promotion and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is a schematic diagram of the process of the present invention;

[0023] Figure 2 A schematic diagram of a building vector diagram showing the building footprint and height information of the present invention;

[0024] Figure 3 A schematic diagram of a cadastral information map reflecting land use attributes according to the present invention;

[0025] Figure 4 It is a schematic diagram of the detailed process of the present invention;

[0026] Figure 5 This is a schematic diagram of the BP sub-model for predicting carbon emissions during the non-heating period in cold-region cities based on building prototypes of the present invention;

[0027] Figure 6 This is a schematic diagram of the BP sub-model for predicting carbon emissions during the heating period in cold-region cities based on building prototypes of the present invention. DETAILED DESCRIPTION

[0028] Specific implementation method 1: Combination Figures 1-6 The present invention is described, comprising:

[0029] S1: Determine the calculation boundary of carbon emissions from urban space operations in cold regions to be measured;

[0030] The cold regions are defined as severely cold regions and cold regions.

[0031] The severe cold areas refer to areas where the winter temperature is below -20℃

[0032] The cold areas refer to areas where the winter temperature is around -10℃ to -20℃

[0033] The urban space is defined as including buildings and green spaces in the city.

[0034] S2: Determine the types of building prototypes in the cold region urban space to be measured; the number of the building prototype types is N; N is a positive integer;

[0035] S3: Obtain a training dataset of urban spaces in cold regions;

[0036] S4: Construct a cold region city BP model based on building prototypes; train the cold region city BP model based on building prototypes according to the training data set of cold region city space to obtain a trained cold region city BP model based on building prototypes;

[0037] S5: Obtain the building distribution and volume data of all buildings in the cold region urban space to be measured,

[0038] S6: Input the building distribution and volume data of the cold region urban space to be measured into the trained cold region urban BP model based on building prototypes to obtain the daily carbon emission prediction data of all buildings in the cold region urban space to be measured;

[0039] S7: obtaining green space data of the cold region urban space to be measured; calculating the daily total carbon sink of the cold region urban space to be measured during the non-heating period based on the green space data of the cold region urban space to be measured;

[0040] S8: Based on the calculation boundary of the operational carbon emissions of the cold-region urban space to be measured, the daily carbon emission forecast data of all buildings in the cold-region urban space to be measured obtained in S6, and the daily total carbon sink of the cold-region urban space to be measured during the non-heating period, the total annual carbon emissions of the cold-region urban space to be measured are obtained;

[0041] S9: Integrate the building distribution and volume data of all buildings in the cold-region urban space to be measured obtained in S5, the daily carbon emission forecast data of all buildings in the cold-region urban space to be measured obtained in S6, the daily total carbon sink of the cold-region urban space to be measured during the non-heating period obtained in S7, and the total annual carbon emission of the cold-region urban space to be measured obtained in S8 into the ArcGIS platform;

[0042] The kernel density tool in the ArcGIS platform is used to generate a visual representation of the spatiotemporal distribution of carbon emissions in cold-region cities, including heat maps of cold-region urban spaces during the heating period, non-heating period, and total carbon emissions throughout the year.

[0043] This invention relates to a prototype-based method for calculating carbon emissions from urban space operations in cold regions. Specifically, it involves determining the carbon emission calculation boundary, acquiring building data sources, constructing building prototypes, calculating the carbon emissions of the building prototypes, and calculating and characterizing the total carbon emissions of urban spaces. The definition of urban space includes buildings and green spaces within the city, ignoring the impact of non-building factors such as public transportation.

[0044] The proposed calculation method, based on the computational concept of building prototype construction, is applicable to both severely cold and cold regions. It posits that the carbon emissions of urban spaces in cold regions during the year-round operation phase equal the heating-period operational carbon emissions of all building prototypes + the non-heating-period operational carbon emissions of all building prototypes minus the carbon sinks of green spaces during the non-heating period. This approach fully accounts for the computational specificities of carbon emissions in cold regions. The proposed characterization method can capture the differences in carbon emissions across urban spaces in cold regions and generate regional spatial carbon emission distribution maps, assisting practitioners in developing carbon reduction strategies.

[0045] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that:

[0046] The calculation boundary of carbon emissions from urban space operations in cold regions to be measured in S1 is expressed as follows:

[0047] A=B1+B2-C

[0048] A represents the carbon emissions of cold-region urban spaces during the year-round operation phase; B1 represents the carbon emissions of all buildings in cold-region urban spaces during the heating period; B2 represents the carbon emissions of all buildings in cold-region urban spaces during the non-heating period; and C represents the carbon sink of green spaces during the non-heating period.

[0049] Carbon emission calculation boundaries refer to the physical or conceptual boundaries selected when assessing and calculating the carbon emissions of a specific activity, product, organization, or country. These boundaries determine which emission sources are included in the calculation and which are excluded. This method sets the green space carbon sink to zero during the winter heating period in cold-region cities; the other steps and parameters are the same as those in Specific Implementation Method 1.

[0050] Specific embodiment three: This embodiment differs from specific embodiment one in that:

[0051] In S2, the architectural prototypes of all buildings in the cold region urban space to be measured are determined; the specific process is as follows:

[0052] S2.1: First, land use is divided into residential land, office land, commercial land, medical land, educational land and industrial land;

[0053] S2.2: Classify the buildings on the residential land into low-rise residential building prototypes, multi-story residential building prototypes, mid-rise residential building prototypes, and high-rise residential building prototypes based on the building height of the residential land;

[0054] S2.3: Classify the buildings on the office land into low-rise office building prototypes and high-rise office building prototypes based on the building height of the office land;

[0055] S2.4: Classify buildings on commercial land into low-rise commercial building prototypes and high-rise commercial building prototypes based on the building height of the commercial land;

[0056] S2.5: Classify buildings on medical land into low-rise medical building prototypes and high-rise medical building prototypes based on the building height of the medical land;

[0057] S2.6: Classify buildings on educational land into low-rise educational building prototypes and high-rise educational building prototypes based on the building height of the educational land;

[0058] S2.7: Take buildings in industrial land as industrial building prototypes; finally, divide all building prototypes in the cold region urban space to be measured into 13 types of building prototypes, i.e., N = 13;

[0059] The following table describes the specific division method:

[0060]

[0061]

[0062] The other steps and parameters are the same as those in the first and second embodiments.

[0063] Specific embodiment 4: This embodiment differs from specific embodiments 1 to 4 in that:

[0064] The training dataset of the cold region urban space to be measured in S3 includes:

[0065] Building volume datasets for 13 building prototype sampling areas, carbon emissions data for the non-heating period for 13 building prototype sampling areas, and carbon emissions data for the heating period for 13 building prototype sampling areas;

[0066] The 13 types of building prototype sampling areas include: low-rise residential building sampling area, multi-story residential building sampling area, mid-rise residential building sampling area, high-rise residential building sampling area; low-rise office building sampling area, high-rise office building sampling area; low-rise commercial building sampling area, high-rise commercial building sampling area; low-rise medical building sampling area, high-rise medical building sampling area; low-rise educational building sampling area, high-rise educational building sampling area; industrial building sampling area; each building prototype sampling area includes M sampling area building samples; M is a positive integer;

[0067] The other steps and parameters are the same as those in the first to third embodiments.

[0068] Specific embodiment 5: This embodiment differs from specific embodiments 1 to 4 in that:

[0069] The specific process of obtaining the training data set of the cold region urban space to be measured in S3 is as follows:

[0070] S3.1: Collect non-heating period datasets for 13 building prototype sampling areas, heating period datasets for 13 building prototype sampling areas, and building volume datasets for 13 building prototype sampling areas;

[0071] The non-heating period datasets of the 13 building prototype sampling areas include: energy consumption data of the 13 building prototype sampling areas during the non-heating period;

[0072] The heating period datasets of the 13 building prototype sampling areas include: energy consumption data of the 13 building prototype sampling areas during the heating period;

[0073] The energy data includes coal data, electricity data, and natural gas data;

[0074] The collection process can be conducted through self-testing and calculation, or obtained from universities, research institutes, relevant carbon emission calculation departments, commercial cooperation organizations, etc.

[0075] S3.2: Based on the collected non-heating period datasets and heating period datasets for the 13 building prototype sampling areas, calculate the non-heating period carbon emissions data for the 13 building prototype sampling areas. The specific process is as follows:

[0076]

[0077] in, represents the carbon emissions generated during the non-heating operation period of the sampling area of ​​the j-th building prototype, unit: kgCO2;

[0078] Represents the i-th energy data consumed by the j-th building prototype sampling area during the non-heating period;

[0079] k = 3, indicating the total number of energy types consumed in building operation;

[0080] Where i=1 represents coal (unit: kg), i=2 represents electricity (unit: kWh), and i=3 represents natural gas (unit: m 3 )

[0081] Represents the carbon emission coefficient of the j-th building prototype consuming the i-th energy, unit: kgCO2e·unit -1 ;

[0082] The carbon emission coefficient of the i-th energy source can be tested and calculated by oneself, or obtained from universities and research institutions, relevant carbon emission calculation departments, commercial cooperation institutions, etc., and is well known to people in this field.

[0083] S3.3: Based on the collected heating period datasets for the 13 building prototype sampling areas, calculate the heating period carbon emissions data for the 13 building prototype sampling areas; the specific process is as follows:

[0084]

[0085] in, represents the carbon emissions generated during the operation phase of the regional heating period of the j-th building prototype sampling area, unit: kgCO2;

[0086] f i j represents the i-th energy data consumed during the heating period of the j-th building prototype sampling area;

[0087] Where i=1 represents coal (unit: kg), i=2 represents electricity (unit: kWh), and i=3 represents natural gas (unit: m 3 )

[0088] Represents the carbon emission coefficient of the j-th building prototype consuming the i-th energy, unit: kgCO2e·unit -1 ;

[0089] The carbon emission coefficient of the i-th energy source can be tested and calculated by oneself, or obtained from universities, research institutions, relevant carbon emission calculation departments, commercial cooperation institutions, etc., and is well known to people in this field.

[0090] The other steps and parameters are the same as those in the first to fourth embodiments.

[0091] Specific embodiment 6: This embodiment differs from specific embodiments 1 to 5 in that:

[0092] The cold region city BP model based on building prototypes in S4 includes: 13 cold region city heating period carbon emission prediction BP sub-models based on building prototypes and 13 cold region city non-heating period carbon emission prediction BP sub-models based on building prototypes;

[0093] In said S4, a cold region city BP model based on building prototypes is constructed; the cold region city BP model based on building prototypes is trained according to a training data set of the cold region city space to be measured, to obtain a trained cold region city BP model based on building prototypes; the specific process is:

[0094] S4.1: Based on the 13 building prototype types, construct 13 building prototype-based BP sub-models for predicting carbon emissions during the heating period in cold-region cities. Each building prototype-based BP sub-model for predicting carbon emissions during the heating period in cold-region cities corresponds to one building prototype type.

[0095] According to the 13 types of building prototypes, 13 BP sub-models for predicting carbon emissions in cold-region cities during the non-heating period based on building prototypes were constructed. Each BP sub-model for predicting carbon emissions in cold-region cities during the non-heating period based on building prototypes corresponds to one building prototype type.

[0096] S4.2: Based on the building volume datasets and non-heating period carbon emission data of the 13 building prototype sampling areas in the training dataset of the cold-region urban space to be measured, 13 cold-region urban heating period carbon emission prediction BP sub-models based on building prototypes are trained to obtain 13 trained cold-region urban heating period carbon emission prediction BP sub-models based on building prototypes.

[0097] S4.3: Based on the building volume datasets and heating period carbon emission data of the 13 building prototype sampling areas in the training dataset of the cold-region urban space to be measured, train the 13 building prototype-based BP sub-models for predicting carbon emissions in the non-heating period of cold-region cities, and obtain the trained 13 building prototype-based BP sub-models for predicting carbon emissions in the non-heating period of cold-region cities.

[0098] ; Other steps and parameters are the same as those in one of the specific implementation methods one to five.

[0099] Specific embodiment 7: This embodiment differs from specific embodiments 1 to 6 in that:

[0100] Each of the BP sub-models for predicting carbon emissions in cold-region cities during the non-heating period based on building prototypes and the BP sub-models for predicting carbon emissions in cold-region cities during the heating period based on building prototypes in S4.1 includes: an input layer, a first hidden layer, a second hidden layer, and an output layer, wherein the input layer includes two input neurons; the output layer includes four output neurons; the first hidden layer includes N1 hidden neurons; the second hidden layer includes N2 hidden neurons; N1 and N2 are positive integers;

[0101] like Figure 5 As shown in the figure, the BP sub-model for predicting carbon emissions during the non-heating period in cold-region cities based on building prototypes sets the hidden layer to 2 layers, where V and n are input neurons, and C all 、C kg 、C kwh 、C m3 is the output neuron. V is the obtained building volume data; n is the nth building prototype, n = [1, 2, 3, ..., N]; Call 、C kg 、C kwh 、C m3 They are the total carbon emissions of buildings during the non-heating period, the total carbon emissions generated by the use of coal during the non-heating period, the total carbon emissions generated by the use of electricity during the non-heating period, and the total carbon emissions generated by the use of natural gas during the non-heating period.

[0102] like Figure 6 As shown in the figure, the BP sub-model for predicting carbon emissions during the heating period in cold regions based on building prototypes sets the hidden layer to 2 layers, where V and n are input neurons, and Ch all 、Ch kg 、Ch kwh 、Ch m3 is the output neuron. V is the obtained building volume data; n is the nth building prototype, n=[1,2,3,…,N]; Ch all 、Ch kg 、Ch kwh 、Ch m3 They are the total carbon emissions of the building during the heating period, the total carbon emissions generated by using coal during the heating period, the total carbon emissions generated by using electricity during the heating period, and the total carbon emissions generated by using natural gas during the heating period.

[0103] In said S4.2, based on the energy consumption data of the n-th building prototype sampling area during the non-heating period, the building volume data set of the n-th building prototype sampling area, and the carbon emission data of the n-th building prototype sampling area during the non-heating period in the training data set of the cold-region urban space to be measured, the n-th building prototype-based cold-region city non-heating period carbon emission prediction BP sub-model is trained to obtain the trained n-th building prototype-based cold-region city non-heating period carbon emission prediction BP sub-model; n = [1, 2, ..., N] The specific process is:

[0104] S4.2.1: The building volume data of the n-th building prototype sampling area is used as the input of the n-th building prototype-based cold-region city non-heating period carbon emission prediction BP sub-model, and the non-heating period carbon emission data of the n-th building prototype sampling area is used as the label. The n-th building prototype-based cold-region city non-heating period carbon emission prediction BP sub-model outputs the non-heating period carbon emission prediction data of the n-th building prototype sampling area;

[0105] S4.2.2: Iterate the training of the nth BP sub-model for predicting carbon emissions in the non-heating period in cold-region cities based on the building prototype according to the input and output of the nth BP sub-model for predicting carbon emissions in the non-heating period in cold-region cities based on the building prototype, to obtain a trained nth BP sub-model for predicting carbon emissions in the non-heating period in cold-region cities based on the building prototype;

[0106] In said S4.3, based on the building volume dataset of the n-th building prototype sampling area and the heating period carbon emission data of the n-th building prototype sampling area in the training dataset of the cold-region urban space to be measured, the n-th building prototype-based cold-region urban heating period carbon emission prediction BP sub-model is trained to obtain the trained n-th building prototype-based cold-region urban heating period carbon emission prediction BP sub-model; n = [1, 2, ..., N] The specific process is:

[0107] S4.3.1: The building volume data of the nth building prototype sampling area is used as the input variable of the nth building prototype-based cold-region city heating period carbon emission prediction BP sub-model, and the heating period carbon emission data of the nth building prototype sampling area is used as the label. The nth building prototype-based cold-region city heating period carbon emission prediction BP sub-model outputs the heating period carbon emission prediction data of the nth building prototype sampling area;

[0108] S4.3.2: The nth BP sub-model for predicting carbon emissions during the heating period in cold-region cities based on building prototypes is trained and iterated according to the input and output of the nth BP sub-model for predicting carbon emissions during the heating period in cold-region cities based on building prototypes to obtain a trained nth BP sub-model for predicting carbon emissions during the heating period in cold-region cities based on building prototypes; the other steps and parameters are the same as those in one of the specific implementation methods one to six.

[0109] Specific embodiment eight: This embodiment differs from specific embodiments one to seven in that:

[0110] In S4.2.2, the nth BP sub-model for predicting carbon emissions in cold-region cities during the non-heating period based on building prototypes is iterated based on the input and output to obtain a trained nth BP sub-model for predicting carbon emissions in cold-region cities during the non-heating period based on building prototypes. The specific process is as follows:

[0111] According to the input and output of the nth cold region city non-heating period carbon emission prediction BP sub-model based on building prototype, the determination coefficient R2n and mean square error MSEn of the nth cold region city non-heating period carbon emission prediction BP sub-model based on building prototype are calculated.

[0112] When the coefficient of determination R 2 When n is greater than 0.8 and the MSEn value is less than 0.2, the iteration is stopped, and the trained n-th BP sub-model for carbon emission prediction in the non-heating period of cold-region cities based on building prototypes is obtained;

[0113] In said S4.3.2, the nth BP sub-model for predicting carbon emissions during the heating period in cold regions cities based on building prototypes is trained and iterated according to the input and output of the nth BP sub-model for predicting carbon emissions during the heating period in cold regions cities based on building prototypes to obtain the trained nth BP sub-model for predicting carbon emissions during the heating period in cold regions cities based on building prototypes. The specific process is as follows:

[0114] The determination coefficient R is calculated based on the input and output of the nth cold region city heating period carbon emission prediction BP sub-model based on the building prototype 2 nheat and mean square error MSEnheat,

[0115] When the coefficient of determination R 2 When nheat is greater than 0.8 and MSEnheat is less than 0.2, the iteration is stopped, and the trained n-th BP sub-model for predicting carbon emissions during the heating period in cold-region cities based on building prototypes is obtained;

[0116] The coefficient of determination R 2 The calculation formula of the mean square error (MSE) is well known in the art and is as follows:

[0117]

[0118] Among them, H is the number of training samples of training data, h represents the hth training sample, y h represents the true value (label) of the h-th training sample, Represents the predicted output value when the BP sub-model inputs the hth training sample; is the average value of the training samples. It is clear to those skilled in the art that the training samples are divided according to the training data.

[0119] The other steps and parameters are the same as those in the first to seventh embodiments.

[0120] Specific embodiment 9: This embodiment differs from specific embodiments 1 to 8 in that:

[0121] The specific process of obtaining the building distribution and volume data of all buildings in the cold region urban space to be measured in S5 is as follows:

[0122] Obtain the basic building data and land use data of the cold-region urban space to be measured,

[0123] Obtaining building distribution and volume data of the cold-region urban space to be measured based on the building basic data and land use data of the cold-region urban space to be measured;

[0124] For example, basic building data for the area being calculated is obtained through platforms such as OpenStreetMap and Tianditu. Land use data for the area is also obtained from local planning authorities. This data is then transformed into a building vector map with building footprints and heights, and a cadastral map reflecting land use attributes and building types. This data is then imported into ArcGIS and overlaid. Buildings located in a specific land use area are assigned the same type, based on the assumption that each building type is defined by its land use.

[0125] The building prototypes described in step S2 above are determined based on building type and building height. By collecting basic building data and land use data, we can obtain building location information, building type information, and building height information in cold-region urban spaces. Buildings in the calculated area are divided into N building prototypes and their locations are determined.

[0126] These building prototypes are used as the total carbon source in the calculated cold-region urban space. During the calculation process, these building prototypes are treated as cylinders or cubes. According to the volume formula v = sh, the building distribution and volume data of N building prototypes in the calculated cold-region urban space can be obtained. This process is well known to those skilled in the art.

[0127] The specific process of obtaining the building distribution and volume data of all buildings in the cold region urban space to be measured in S5 is as follows:

[0128] Use ArcGIS software to obtain the basic building data of the cold region urban space to be measured;

[0129] The basic building data includes: building coordinates, building height, and building area;

[0130] The building basic data obtained by using ArcGIS software includes: the building basic data of the calculated area obtained from platforms such as OpenStreetMap and Tiandi Map,

[0131] Obtain the land use data of urban spaces in cold regions to be measured through local planning departments;

[0132] The daily carbon emission forecast data for all buildings in cold-region urban spaces to be calculated in S6 include:

[0133] The daily carbon emission forecast data of all buildings in the cold-region urban space during the heating period to be measured and the daily carbon emission forecast data of all buildings in the cold-region urban space during the non-heating period to be measured;

[0134] In said S7, the green space data of the cold region urban space to be measured is obtained; the daily total carbon sink of the cold region urban space to be measured during the non-heating period is obtained according to the cold region urban space to be measured; the specific process is as follows:

[0135] S7.1: Obtain land use data for the cold-region urban space to be measured through local planning departments; obtain the green space areas and green space types for the cold-region urban space to be measured;

[0136] S7.2: Use the plot inventory method to determine the carbon sequestration coefficients of different green space types in the cold-region urban space.

[0137] S7.3: Based on the green space area, green space type, and carbon sequestration coefficient of different green space types in the cold region urban space to be measured, calculate the total daily carbon sequestration of the cold region urban space during the non-heating period. The formula is:

[0138]

[0139] Among them, SG l represents the total area of ​​the lth type of green space, S l represents the carbon sequestration coefficient of the lth type of green space, and m represents the number of green space types;

[0140] l=[1,2,...,m];

[0141] The total green area is calculated based on the cadastral information map provided by the local planning department. l The unit is hectare (ha) or square meter (㎡) l The carbon absorption per unit area can be measured and calculated by the plot inventory method or obtained from universities, research institutions, relevant carbon emission calculation departments, commercial cooperation institutions, etc., which is well known to those in the art. The plot inventory method is a well-known forming method in the art.

[0142] Please refer to the table below for details. You can choose the method based on the actual situation of the measured area:

[0143]

[0144] The other steps and parameters are the same as those in the first to eighth embodiments.

[0145] Specific embodiment 10: This embodiment differs from specific embodiments 1 to 9 in that:

[0146] The total annual carbon emissions of the cold-region urban space to be measured are obtained in S8 based on the calculation boundary of the operational carbon emissions of the cold-region urban space to be measured, the carbon emission prediction data of all buildings in the cold-region urban space to be measured obtained in S6, and the total carbon sink of the cold-region urban space to be measured. The formula is:

[0147]

[0148] in, It represents the daily carbon emission forecast value of the j-th building prototype in the non-heating period of the cold-region city output by the trained BP sub-model for carbon emission forecasting in the non-heating period of the cold-region city based on the building prototype; T1 represents the number of days in the non-heating period of the cold-region city space to be measured, denoted as the daily carbon emission forecast value of the j-th building prototype during the cold-region city heating period output by the trained j-th BP sub-model for predicting carbon emissions during the cold-region city heating period based on the building prototype; T2 represents the number of days of the cold-region city space heating period to be measured; the other steps and parameters are the same as those in the specific implementation methods one to nine.

[0149] The present invention is based on the computational concept of prototype construction, which realizes the organic connection and deduction of building data and urban data in computation. The building data and urban data of the calculated cold-region urban space area are used to reflect the building carbon source, and the urban data are used to reflect the green space carbon sink, which can realize large-scale cold-region urban space operation carbon emission measurement. The "small data" of carbon emissions in the building operation stage can be used to reflect the "big data" of carbon emissions in the cold-region urban space operation stage. There is no need to analyze and measure the carbon sources and carbon sinks in the cold-region urban space one by one. Only a certain number of building sample data need to be measured, which greatly simplifies the measurement steps. The proposed measurement and characterization methods can be organically linked with the ArcGIS platform to realize data visualization, which is convenient for data analysis and subsequent applications. At the same time, a generalizable measurement tool set can be formed through secondary development to facilitate promotion and application.

[0150] The above only describes the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the above-mentioned specific implementation methods. Although the present invention has been disclosed as above with preferred embodiments, it is not intended to limit the present invention. Any technician familiar with this profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical content disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent replacements and improvements made to the above embodiments without departing from the content of the technical solution of the present invention, based on the technical essence of the present invention, within the spirit and principles of the present invention, still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A prototype-based method for calculating carbon emissions from urban space operations in cold regions, characterized by: include: S1: Determine the calculation boundary of carbon emissions from urban space operations in cold regions to be measured; S2: Determine the architectural prototypes of all buildings in the cold region urban space to be measured; The number of types of building prototypes is N; N is a positive integer; S3: Obtain a training dataset of urban spaces in cold regions; S4: Construct a cold region city BP model based on building prototypes; train the cold region city BP model based on building prototypes according to the training data set of cold region city space to obtain a trained cold region city BP model based on building prototypes; S5: Obtain the building distribution and volume data of all buildings in the cold region urban space to be measured, S6: Input the building distribution and volume data of the cold region urban space to be measured into the trained cold region urban BP model based on building prototypes to obtain the daily carbon emission prediction data of all buildings in the cold region urban space to be measured; S7: obtaining green space data of the cold region urban space to be measured; calculating the daily total carbon sink of the cold region urban space to be measured during the non-heating period based on the green space data of the cold region urban space to be measured; S8: Based on the calculation boundary of the operational carbon emissions of the cold-region urban space to be measured, the daily carbon emission forecast data of all buildings in the cold-region urban space to be measured obtained in S6, and the daily total carbon sink of the cold-region urban space to be measured during the non-heating period, the total annual carbon emissions of the cold-region urban space to be measured are obtained; S9: Integrate the building distribution and volume data of all buildings in the cold-region urban space to be measured obtained in S5, the daily carbon emission forecast data of all buildings in the cold-region urban space to be measured obtained in S6, the daily total carbon sink of the cold-region urban space to be measured during the non-heating period obtained in S7, and the total annual carbon emission of the cold-region urban space to be measured obtained in S8 into the ArcGIS platform. The kernel density tool in the ArcGIS platform is used to generate the spatiotemporal distribution of carbon emissions in cold-region cities for visualization.

2. The method for calculating carbon emissions from urban space operations in cold regions based on a prototype construction according to claim 1 is characterized in that: The calculation boundary of carbon emissions from urban space operations in cold regions to be measured in S1 is expressed as follows: A=B1+B2-C A represents the carbon emissions of cold-region urban spaces during the annual operation phase; B1 represents the carbon emissions of all buildings in cold-region urban spaces during the heating period; B2 represents the carbon emissions of all buildings in cold-region urban spaces during the non-heating period; and C represents the carbon sink of green spaces during the non-heating period.

3. The method for calculating carbon emissions from urban space operations in cold regions based on a prototype construction according to claim 2 is characterized in that: In S2, the architectural prototypes of all buildings in the cold region urban space to be measured are determined; the specific process is as follows: S2.1: First, land use is divided into residential land, office land, commercial land, medical land, educational land and industrial land; S2.2: Classify the buildings on the residential land into low-rise residential building prototypes, multi-story residential building prototypes, mid-rise residential building prototypes, and high-rise residential building prototypes based on the building height of the residential land; S2.3: Classify the buildings on the office land into low-rise office building prototypes and high-rise office building prototypes based on the building height of the office land; S2.4: Classify buildings on commercial land into low-rise commercial building prototypes and high-rise commercial building prototypes based on the building height of the commercial land; S2.5: Classify buildings on medical land into low-rise medical building prototypes and high-rise medical building prototypes based on the building height of the medical land; S2.6: Classify buildings on educational land into low-rise educational building prototypes and high-rise educational building prototypes based on the building height of the educational land; S2.7: Take the buildings in the industrial land as industrial building prototypes; eventually, divide the building prototypes of all the buildings in the cold region urban space to be measured into 13 building prototypes, that is, N = 13.

4. The method for calculating carbon emissions from urban space operations in cold regions based on a prototype construction according to claim 3 is characterized in that: The training dataset of cold region urban space in S3 includes: Building volume datasets for 13 building prototype sampling areas, carbon emissions data for the non-heating period for 13 building prototype sampling areas, and carbon emissions data for the heating period for 13 building prototype sampling areas; The 13 types of building prototype sampling areas include: low-rise residential building sampling area, multi-story residential building sampling area, mid-rise residential building sampling area, and high-rise residential building sampling area; low-rise office building sampling area, and high-rise office building sampling area; low-rise commercial building sampling area, and high-rise commercial building sampling area; low-rise medical building sampling area, and high-rise medical building sampling area; low-rise educational building sampling area, and high-rise educational building sampling area; industrial building sampling area; each building prototype sampling area includes M sampling area building samples; M is a positive integer.

5. The method for calculating carbon emissions from urban space operations in cold regions based on a prototype construction according to claim 4 is characterized in that: The specific process of obtaining the training dataset of cold region urban space in S3 is as follows: S3.1: Collect non-heating period datasets for 13 building prototype sampling areas, heating period datasets for 13 building prototype sampling areas, and building volume datasets for 13 building prototype sampling areas; The non-heating period datasets of the 13 building prototype sampling areas include: energy consumption data of the 13 building prototype sampling areas during the non-heating period; The heating period datasets of the 13 building prototype sampling areas include: energy consumption data of the 13 building prototype sampling areas during the heating period; The energy data includes coal data, electricity data, and natural gas data; S3.2: Based on the collected non-heating period datasets and heating period datasets for the 13 building prototype sampling areas, calculate the non-heating period carbon emissions data for the 13 building prototype sampling areas. The specific process is as follows: in, represents the carbon emissions generated during the non-heating operation period in the sampling area of ​​the j-th building prototype; Represents the i-th energy data consumed by the j-th building prototype sampling area during the non-heating period; k = 3, indicating the total number of energy types consumed in building operation; Where i=1 represents coal, i=2 represents electricity, and i=3 represents natural gas; represents the carbon emission coefficient of the j-th building prototype consuming the i-th energy; S3.3: Based on the collected heating period datasets for the 13 building prototype sampling areas, calculate the heating period carbon emissions data for the 13 building prototype sampling areas; the specific process is as follows: in, represents the carbon emissions generated during the operation phase of the regional heating period for the j-th building prototype sampling area; f i j represents the i-th energy data consumed during the heating period of the j-th building prototype sampling area; Where i=1 represents coal, i=2 represents electricity, and i=3 represents natural gas. It represents the carbon emission coefficient of the j-th building prototype consuming the i-th energy.

6. The method for calculating carbon emissions from urban space operations in cold regions based on a prototype construction according to claim 5 is characterized in that: The cold region city BP model based on building prototypes in S4 includes: 13 cold region city heating period carbon emission prediction BP sub-models based on building prototypes and 13 cold region city non-heating period carbon emission prediction BP sub-models based on building prototypes; In said S4, a cold region city BP model based on building prototypes is constructed; the cold region city BP model based on building prototypes is trained according to a training data set of cold region city space to obtain a trained cold region city BP model based on building prototypes; the specific process is: S4.1: Based on the 13 building prototype types, construct 13 building prototype-based BP sub-models for predicting carbon emissions during the heating period in cold-region cities. Each building prototype-based BP sub-model for predicting carbon emissions during the heating period in cold-region cities corresponds to one building prototype type. According to the 13 types of building prototypes, 13 BP sub-models for predicting carbon emissions in cold-region cities during the non-heating period based on building prototypes were constructed. Each BP sub-model for predicting carbon emissions in cold-region cities during the non-heating period based on building prototypes corresponds to one building prototype type. S4.2: Based on the building volume datasets and non-heating period carbon emission data of the 13 building prototype sampling areas in the cold region urban space training dataset, 13 cold region urban heating period carbon emission prediction BP sub-models based on building prototypes are trained to obtain 13 trained cold region urban heating period carbon emission prediction BP sub-models based on building prototypes. S4.3: Based on the building volume datasets of 13 building prototype sampling areas and the heating period carbon emission data of 13 building prototype sampling areas in the training dataset of cold-region urban space; train 13 cold-region city non-heating period carbon emission prediction BP sub-models based on building prototypes to obtain 13 trained cold-region city non-heating period carbon emission prediction BP sub-models based on building prototypes;.

7. The method for calculating carbon emissions from urban space operations in cold regions based on a prototype construction according to claim 6 is characterized in that: Each BP sub-model for predicting carbon emissions during the non-heating period in cold-region cities based on a building prototype and each BP sub-model for predicting carbon emissions during the heating period in cold-region cities based on a building prototype in S4.1 include: Input layer, first hidden layer, second hidden layer and output layer, The input layer includes two input neurons; the output layer includes four output neurons; the first hidden layer includes N1 hidden neurons; the second hidden layer includes N2 hidden neurons; N1 and N2 are positive integers; In said S4.2, based on the energy consumption data of the n-th building prototype sampling area during the non-heating period, the building volume data set of the n-th building prototype sampling area, and the carbon emission data of the n-th building prototype sampling area during the non-heating period in the training data set of the cold-region urban space to be measured, the n-th building prototype-based cold-region city non-heating period carbon emission prediction BP sub-model is trained to obtain the trained n-th building prototype-based cold-region city non-heating period carbon emission prediction BP sub-model; n = [1, 2, .., n, ., 13] The specific process is: S4.2.1: The building volume data of the n-th building prototype sampling area is used as the input of the n-th building prototype-based cold-region city non-heating period carbon emission prediction BP sub-model, and the non-heating period carbon emission data of the n-th building prototype sampling area is used as the label. The n-th building prototype-based cold-region city non-heating period carbon emission prediction BP sub-model outputs the non-heating period carbon emission prediction data of the n-th building prototype sampling area; S4.2.2: Iterate the training of the nth BP sub-model for predicting carbon emissions in the non-heating period in cold-region cities based on the building prototype according to the input and output of the nth BP sub-model for predicting carbon emissions in the non-heating period in cold-region cities based on the building prototype, to obtain a trained nth BP sub-model for predicting carbon emissions in the non-heating period in cold-region cities based on the building prototype; In S4.3, based on the building volume dataset of the n-th building prototype sampling area and the heating period carbon emission data of the n-th building prototype sampling area in the training dataset of the cold-region urban space to be measured, the n-th building prototype-based cold-region urban heating period carbon emission prediction BP sub-model is trained to obtain the trained n-th building prototype-based cold-region urban heating period carbon emission prediction BP sub-model; n = [1, 2, .., n, ., 13] The specific process is: S4.3.1: The building volume data of the nth building prototype sampling area is used as the input variable of the nth building prototype-based cold-region city heating period carbon emission prediction BP sub-model, and the heating period carbon emission data of the nth building prototype sampling area is used as the label. The nth building prototype-based cold-region city heating period carbon emission prediction BP sub-model outputs the heating period carbon emission prediction data of the nth building prototype sampling area; S4.3.2: The nth BP sub-model for predicting carbon emissions during the heating period in cold-region cities based on building prototypes is trained and iterated based on the input and output of the nth BP sub-model for predicting carbon emissions during the heating period in cold-region cities based on building prototypes to obtain a trained nth BP sub-model for predicting carbon emissions during the heating period in cold-region cities based on building prototypes.

8. The method for calculating carbon emissions from urban space operations in cold regions based on prototype construction according to claim 7 is characterized in that: In S4.2.2, the nth BP sub-model for predicting carbon emissions in cold-region cities during the non-heating period based on building prototypes is iterated based on the input and output to obtain a trained nth BP sub-model for predicting carbon emissions in cold-region cities during the non-heating period based on building prototypes. The specific process is as follows: Calculate the determination coefficient R of the nth BP sub-model for carbon emission prediction in cold region cities during non-heating period based on building prototypes according to the input and output of the nth BP sub-model for carbon emission prediction in cold region cities during non-heating period based on building prototypes. 2 n and mean square error MSEn, When the coefficient of determination R 2 When n is greater than 0.8 and the MSEn value is less than 0.2, the iteration is stopped, and the trained n-th BP sub-model for carbon emission prediction in the non-heating period of cold-region cities based on building prototypes is obtained; In said S4.3.2, the nth BP sub-model for predicting carbon emissions during the heating period in cold regions cities based on building prototypes is trained and iterated according to the input and output of the nth BP sub-model for predicting carbon emissions during the heating period in cold regions cities based on building prototypes to obtain the trained nth BP sub-model for predicting carbon emissions during the heating period in cold regions cities based on building prototypes. The specific process is as follows: The determination coefficient R is calculated based on the input and output of the nth cold region city heating period carbon emission prediction BP sub-model based on the building prototype 2 nheat and mean square error MSEnheat, When the coefficient of determination R 2 The iteration is stopped when nheat is greater than 0.8 and MSEnheat is less than 0.2, and the trained n-th BP sub-model for carbon emission prediction during the heating period in cold-region cities based on building prototypes is obtained.

9. The method for calculating carbon emissions from urban space operations in cold regions based on a prototype construction according to claim 8 is characterized in that: In S5, the building distribution and volume data of all buildings in the cold region urban space to be measured are obtained. The specific process is as follows: S5.1: Obtain the basic building data and land use data of the cold-region urban space to be measured. The specific process is as follows: Use ArcGIS software to obtain the basic building data of the cold region urban space to be measured; The basic building data includes: building coordinates, building height, and building area; Obtain the land use data of urban spaces in cold regions to be measured through local planning departments; S5.2: Obtain building distribution and volume data of the cold-region urban space to be measured based on the building basic data and land use data of the cold-region urban space to be measured; The daily carbon emission forecast data for all buildings in cold-region urban spaces to be calculated in S6 include: The daily carbon emission forecast data of all buildings in the cold-region urban space during the heating period to be measured and the daily carbon emission forecast data of all buildings in the cold-region urban space during the non-heating period to be measured; In said S7, green space data of the cold-region urban space to be measured is obtained; and the total carbon sink of the cold-region urban space to be measured is obtained based on the cold-region urban space to be measured. The specific process is S7.1: obtaining land use data of the cold-region urban space to be measured through the local planning department; obtaining the green space area and green space type of the cold-region urban space to be measured; S7.2: Use the plot inventory method to determine the carbon sequestration coefficients of different green space types in the cold-region urban space. S7.3: Calculate the total daily carbon sink of the cold region urban space to be measured based on the green space area, green space type, and carbon sink coefficient of each green space type. This can be expressed as: Among them, SG l represents the total area of ​​the lth type of green space, S l represents the carbon sequestration coefficient of the lth type of green space, and m represents the number of green space types; l＝[1,2,..,l,.,m]。 10. The method for calculating carbon emissions from urban space operations in cold regions based on a prototype construction according to claim 9 is characterized in that: The total annual carbon emissions of the cold-region urban space to be measured are obtained in S8 based on the calculation boundary of the operational carbon emissions of the cold-region urban space to be measured, the carbon emission prediction data of all buildings in the cold-region urban space to be measured obtained in S6, and the total carbon sink of the cold-region urban space to be measured. The formula is: Where Q all represents the total annual carbon emissions of cold-region urban spaces to be measured, It represents the daily carbon emission forecast value of the j-th building prototype in the non-heating period of the cold-region city output by the trained BP sub-model for carbon emission forecasting in the non-heating period of the cold-region city based on the building prototype; T1 represents the number of days in the non-heating period of the cold-region city space to be measured, denoted as the daily carbon emission forecast value of the j-th building prototype during the cold-region city heating period output by the trained BP sub-model for carbon emission forecasting during the cold-region city heating period based on the building prototype; T2 represents the number of days of the cold-region city space heating period to be measured.

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