Building carbon emission accounting method based on emission factor method and unmanned aerial vehicle surveying and mapping technology

Abstract

The invention provides a building carbon emission accounting method based on an emission factor method and an unmanned aerial vehicle surveying and mapping technology, and the method comprises the steps: developing a carbon emission accounting program, building and calling a carbon emission factor database, judging the type of a building through visual recognition and machine learning, and estimating the area of the building through unmanned aerial vehicle surveying and mapping. And the building material usage amount in the building construction stage and the energy consumption in the building construction, operation and demolition stages are further calculated, so that intelligent and efficient full-life-cycle carbon emission accounting for the constructed building or block is realized. According to the method, the carbon emission factor data is from domestic and overseas authoritative databases, and the uncertainty caused by adopting default values is reduced through data tracing, region and time identification, boundary information accounting and statistical induction of the data. Activity level data such as building material consumption and energy consumption in the whole life cycle of the building are estimated through unmanned aerial vehicle surveying and mapping, visual identification and machine learning, and carbon emission accounting errors caused by missing of statistical or monitoring data are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission accounting for completed buildings or blocks, and specifically to a building carbon emission accounting method based on emission factor method and drone mapping technology. Background Art

[0002] Currently, the carbon emission accounting software on the market often only focuses on products, and lacks specialized software that focuses on building carbon emissions. Existing building carbon emission accounting software is also mainly applicable to buildings to be constructed. Carbon emission accounting for completed buildings or blocks, especially for completed buildings or blocks that lack public data such as building materials usage and energy consumption data, needs to be developed urgently.

[0003] Complexity in accounting for completed buildings or blocks: For completed buildings, there is often a lack of complete building material usage and energy consumption data, which makes it difficult to efficiently and conveniently calculate the carbon emissions over its entire life cycle. Especially in old buildings, this data may never have been recorded in detail or lost, which brings huge challenges to carbon emissions accounting.

[0004] Data availability and accuracy: In many cases, the accounting of building carbon emissions relies on data from external databases, which usually contain carbon emission factors used in carbon emission accounting in various fields. However, existing databases often have problems such as too broad coverage, poor usability, inconsistent data structure, and inconsistent data units from different data sources, which greatly reduces the convenience, accuracy and comparability of the accounting results.

[0005] Standardization and consistency issues: Globally, countries have different standards for carbon emissions accounting, which makes it difficult to directly compare the building carbon emissions accounting results in different countries or regions. This diversity and lack of uniformity in standards further exacerbates the complexity of accounting. Summary of the invention

[0006] The purpose of the present invention is to provide a building carbon emission accounting method based on a carbon emission factor database and unmanned aerial vehicle surveying and modeling technology, which can perform carbon emission accounting on completed buildings or blocks.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A building carbon emission accounting method based on emission factor method and drone mapping technology is used to calculate carbon emission of completed buildings or blocks; it includes the following steps:

[0009] Step S1: Establishment of carbon emission factor database

[0010] Step S11: Filter carbon emission factor data related to the entire life cycle of the building from authoritative carbon emission databases at home and abroad;

[0011] Step S12: sort out and summarize the carbon emission factor data, unify the data units, and form a database. The database contains the following information of the carbon emission factor: category, name, unit, generation link, source type, source, applicable area and time.

[0012] Step S2: UAV mapping modeling to estimate building area

[0013] Step S21: using a drone to photograph and collect data on completed buildings or blocks;

[0014] Step S22: performing image processing and constructing a three-dimensional model based on the above data;

[0015] Step S23: obtaining a contour distribution map based on the three-dimensional model to estimate the building area of ​​the completed building or block;

[0016] Step S3: Machine learning to determine building type

[0017] Step S31: extracting shape parameters of the building in the three-dimensional model; using a data set of known building types for annotation, and associating each set of building shape parameters with its corresponding building type;

[0018] Step S32: using the model to classify and learn the building types;

[0019] Step S33: taking the extracted building shape parameters as independent variables, taking the building type as dependent variable, and using the labeled data set to train the model;

[0020] Step S34: input the newly collected building shape parameters into the trained model, and output the predicted building type;

[0021] Step S35: comparing the predicted building type with the actual building type to verify the judgment result of the model, and make adjustments and improvements;

[0022] Step S4: Estimation of carbon emission activity level data for the entire life cycle of the building

[0023] Step S41: estimating the amount of building materials used in the building construction phase based on the building area and building type;

[0024] Step S42: estimating energy consumption during the building construction phase based on the building area and building type, including electricity consumption and fossil energy consumption;

[0025] Step S43: estimating the energy consumption of the building during operation based on the building area and building type, including electricity consumption and fossil energy consumption;

[0026] Step S44: estimating energy consumption during the building demolition phase based on the building area and building type, including electricity consumption and fossil energy consumption;

[0027] The amount of building materials used during the building construction phase and the energy consumption during the building construction, operation and demolition phases constitute the activity level data of carbon emissions over the entire life cycle of the building.

[0028] Step S5: Data import and carbon emission accounting

[0029] Develop a calculation program to calculate carbon emissions using the following formula:

[0030] Carbon emissions = ∑(activity level × carbon emission factor);

[0031] In the formula, the carbon emission factors are provided by the database, including the carbon emission factors of main energy sources, electricity, building materials, and building materials transportation, etc.;

[0032] The activity level data is estimated based on the building area and building type, and includes the amount of building materials used during the building construction phase and the amount of energy consumed during the building construction, operation, and demolition phases.

[0033] Preferably, in step S11, the carbon emission factor database selects high-quality data from authoritative carbon emission factor databases at home and abroad, such as CPCD China Product Life Cycle Greenhouse Gas Emission Coefficient Set, IPCC Carbon Emission Factor Library, and "Building Carbon Emission Calculation Standard" GB / T 51366-2019.

[0034] Preferably, in step S12, the carbon emission factor data is in accordance with the unified data unit of the "Building Carbon Emission Calculation Standard" GB / T51366-2019, and through data traceability, elements such as region, time and accounting boundaries are identified. The carbon emission factor database contains the following information of the carbon emission factor: category, name, unit, generation link, source type, source, applicable area and time.

[0035] Preferably, in step S23, based on the elevation data of the building extracted from the three-dimensional model, a digital elevation model is generated, and a contour distribution map of the building is drawn;

[0036] The floor area of ​​the building is calculated by projecting the bottom surface of the three-dimensional model onto a two-dimensional plane; based on the three-dimensional model, the total surface area of ​​the building, including walls, roof and other parts, is calculated.

[0037] Preferably, in step S31, the shape parameters of the building, including height, width, length, surface area, volume and contour distribution, are extracted from the three-dimensional model, and the corresponding building type is associated based on the building shape parameters.

[0038] Preferably, in step S32, the model includes a vector machine, a decision tree, a random forest, or a neural network.

[0039] Preferably, in step S35, the performance of the model is evaluated by a cross-validation method, and the model parameters are adjusted or the feature extraction method is improved according to the results to ensure the accuracy and robustness of the model.

[0040] Preferably, the activity level data in step S4 covers carbon emission activity level data for the entire life cycle of the building, including the amount of building materials used during the building construction phase and the energy consumption during the building construction, operation and demolition phases.

[0041] Preferably, the accounting program in step S5 is developed based on SQL-Server Management Studio database and C# language.

[0042] Preferably, in step S5, the amount of building materials used in the building construction phase and the energy consumption in the building construction, operation and demolition phases are summarized; and the activity level data constituting the carbon emissions of the building throughout its life cycle are imported into the accounting program for carbon emissions accounting, and finally a detailed carbon emissions accounting report is generated.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] (1) This product fills the gap in the current market for convenient and efficient building carbon emission accounting tools for the general public. It is intelligent, fast and large-scale, and can be used to calculate carbon emissions for completed buildings or blocks.

[0045] (2) The database used in the present invention is more authoritative, has a more unified data structure, is of higher quality, and has higher credibility.

[0046] (3) The present invention can identify different types of buildings and can also modify data according to different regions, etc.

[0047] The present invention realizes intelligent and efficient carbon emission accounting for the entire life cycle of completed buildings or blocks through drone mapping modeling, visual recognition, machine learning, establishment and calling of carbon emission factor database, and development of application programs. For completed buildings with material lists, carbon emission accounting can be directly performed through the accounting program; and for completed buildings or blocks that lack public data such as building material usage and energy consumption data, this project can be fully applied to perform carbon emission accounting.

[0048] The present invention uses drone technology to estimate the building area, providing an efficient and accurate method that avoids the workload of traditional manual measurement. The accounting program can effectively integrate the carbon emission factor database, and based on the building type and building area, estimate the amount of building materials used in the building construction phase and the energy consumption in the building construction, operation, and demolition phases. The database in this patent is derived from authoritative open source databases at home and abroad, and is converted, classified, and unified in data structure in accordance with the "Building Carbon Emission Calculation Standard" (GB / T 51366). Through data tracing, the region, time, and accounting boundary information are identified, and the data is statistically summarized to reduce the uncertainty caused by the use of default values ​​and improve the credibility of the data. Use drone mapping, visual recognition, and machine learning to estimate activity level data such as building materials usage and energy consumption throughout the life cycle of a building, reducing carbon emission accounting errors caused by missing statistical or monitoring data. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A system flow diagram of a building carbon emission accounting method based on emission factor method and drone mapping technology provided by an embodiment of the present invention;

[0050] Figure 2 A schematic diagram of the workflow of a calculation procedure in a building carbon emission calculation method based on an emission factor method and drone mapping technology provided by an embodiment of the present invention;

[0051] Figure 3 for Figure 1 Schematic diagram of the process of calculating building area;

[0052] Figure 4 for Figure 1 Schematic diagram of the process of predicting building types. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0054] like Figures 1 to 4 As shown, a building carbon emission accounting method based on emission factor method and drone mapping technology disclosed in the present invention is used to calculate carbon emissions of completed buildings or blocks; it includes the following steps:

[0055] Step S1: Establishment of carbon emission factor database

[0056] Step S11: Filter carbon emission factor data related to the entire life cycle of the building from authoritative carbon emission databases at home and abroad; the carbon emission factor database filters high-quality data from authoritative carbon emission factor databases at home and abroad such as CPCD China Product Life Cycle Greenhouse Gas Emission Coefficient Set, IPCC Carbon Emission Factor Library, and "Building Carbon Emission Calculation Standard" (GB / T 51366-2019).

[0057] Step S12: sort out and summarize the carbon emission factor data, unify the data units, and form a database; the carbon emission factor data is unified in data units according to the "Building Carbon Emission Calculation Standard" (GB / T 51366-2019).

[0058] In this embodiment, by tracing the data, identifying factors such as region, time and accounting boundaries, the established carbon emission factor database contains the following information of the carbon emission factor: category, name, unit, production link, source type, source, applicable area and time.

[0059] Step S2: UAV mapping modeling to estimate building area

[0060] Step S21: Use a drone to photograph and collect data on completed buildings or blocks; the drone uses a multi-beam equidistant photography method to take two-dimensional photos of the building at equal intervals; the shooting angle and interval are determined by pre-set parameters to ensure that a high-overlapping image covering the entire building is obtained; obtain the two-dimensional photo information of the target building, including images at different perspectives and heights, to ensure full coverage of the building's external structure.

[0061] In this embodiment, a DJI drone is used to obtain pictures of a designated building or a designated area from various angles through equidistant cruise shooting, and then the pictures are imported into the DJI Intelligent Map software. The software can build a 3D model of the building or the area based on this set of photos, and obtain some key parameters such as length, width, height, etc. through model point selection for machine learning.

[0062] Step S22: Perform image processing and construct a three-dimensional model based on the above data; use image processing software to process the images collected by the drone, eliminate distortion and chromatic aberration, and perform splicing; use computer vision algorithms to extract feature points from the two-dimensional images, generate point cloud data, and construct the three-dimensional model.

[0063] In this embodiment, we use the DJI Zhitu software that is compatible with the DJI drone to process the collected graphics. After preprocessing, the three-dimensional model and contour map generated by DJI Zhitu can be used for further data processing.

[0064] Step S23: Obtain a contour distribution map based on the three-dimensional model to estimate the building area of ​​the completed building or block; extract the elevation data of the building from the three-dimensional model generated by DJI Zhitu, generate a digital elevation model, and draw a contour distribution map of the building; calculate the floor area of ​​the building by projecting the bottom surface of the three-dimensional model on a two-dimensional plane; calculate the total surface area of ​​the building based on the three-dimensional model, including walls, roofs and other parts.

[0065] Step S3: Machine learning to determine building type

[0066] Step S31: extracting shape parameters of the building in the three-dimensional model; using a data set of known building types for annotation, associating each set of building shape parameters with its corresponding building type; this embodiment extracts the shape parameters of the building from the three-dimensional model, including height, width, length, surface area, volume and contour distribution, and the selection principle is based on associating the building shape parameters with its corresponding building type.

[0067] Step S32: Use the model to classify and learn the building types; convert the original data into features suitable for use by the machine learning model.

[0068] (1) Feature engineering: This is to convert raw data into features suitable for use by machine learning models. The length, width, height, volume, and proportion of a building are considered as features and used to describe the shape and structure of the building.

[0069] (2) Similarity measurement: By comparing the feature differences between buildings, we can quantify the degree of similarity between them.

[0070] (3) Optimization Algorithm: We use a simple but effective optimization algorithm to calculate the similarity score, namely the negative exponential function. By converting the feature difference into a score and mapping the score range through the negative exponential function, we are able to better capture the differences between buildings.

[0071] (4) Structured data processing: Organize building type information by using structures and manage multiple building types through arrays.

[0072] Step S33: taking the extracted shape parameters of the building as independent variables and the building type as dependent variable, and using the labeled data set to train the model; the model includes vector machine, decision tree, random forest, and neural network.

[0073] Step S34: input the newly collected building shape parameters into the trained model, calculate the similarity score to determine the most suitable building type, and output the predicted building type.

[0074] Step S35: Compare the predicted building type with the actual building type to verify the judgment result of the model, and make adjustments and improvements; evaluate the performance of the model through a cross-validation method, and adjust the model parameters or improve the feature extraction method based on the results to ensure the accuracy and robustness of the model.

[0075] Step S4: Estimation of carbon emission activity level data for the entire life cycle of the building

[0076] Step S41: estimating the amount of building materials used in the building construction phase based on the building area and building type; the estimated building materials include metal building materials such as steel, aluminum alloy, non-metal building materials such as cement, glass, wood, and other building materials such as composite panels.

[0077] Step S42: Estimate the energy consumption during the building construction phase based on the building area and building type, including electricity consumption and fossil energy consumption; the method for estimating energy consumption in this embodiment refers to the provisions of Section 3.1 of the "Guidelines for Calculating Building Carbon Emissions (Trial)" of the Guangdong Provincial Department of Housing and Urban-Rural Development.

[0078] Step S43: Estimate the energy consumption during the building operation phase based on the building area and building type, including electricity consumption and fossil energy consumption; the method for estimating energy consumption in this embodiment refers to the electricity consumption intensity of public buildings in the "2021 Shanghai State Organ Office Buildings and Large Public Buildings Energy Consumption Monitoring and Analysis Report".

[0079] Step S44: Estimate the energy consumption in the embodiment of the building demolition stage based on the building area and building type, including electricity consumption and fossil energy consumption; the method of estimating energy consumption in this embodiment refers to the provisions of Section 3.1 of the "Guidelines for Calculating Building Carbon Emissions (Trial)" of the Guangdong Provincial Department of Housing and Urban-Rural Development.

[0080] The amount of building materials used during the building construction phase and the energy consumption during the building construction, operation and demolition phases constitute the activity level data of carbon emissions over the entire life cycle of the building.

[0081] Step S5: Data import and carbon emission accounting

[0082] Develop accounting programs based on SQL-ServerManagement Studio database and C# language.

[0083] Summarize the three-dimensional model, the building area, the contour distribution map, and the building type judgment result; and ensure that all data are imported into the accounting program in a consistent format, including shape parameters, area and type.

[0084] The following formula is used to calculate carbon emissions:

[0085] Carbon emissions = ∑(activity level × carbon emission factor);

[0086] In the formula, the carbon emission factors are provided by the database, including the carbon emission factors of main energy sources, electricity, building materials, and building materials transportation, etc.;

[0087] The activity level data is estimated based on the building area and building type, and includes the amount of building materials used during the building construction phase and the amount of energy consumed during the building construction, operation, and demolition phases.

[0088] The calculation procedure of the present invention mainly refers to the "Building Carbon Emission Calculation Standard" (GB / T51366-2019), converts the carbon emission factors in some domestic and foreign authoritative databases into the units in the "Building Carbon Emission Calculation Standard" (GB / T 51366-2019) and stores them in the database. The data structure combines the structures of multiple databases and simplifies them as much as possible while ensuring the integrity of important information. The data structure includes: category, name, unit, generation link, source type, source, applicable area and time.

[0089] Finally, the accounting program generates a detailed carbon emissions accounting report.

[0090] The database in this embodiment is based on the data in the "Building Carbon Emission Calculation Standard" (GB / T51366-2019). According to the same classification method, carbon emission factor data that meets the accounting requirements are selected from authoritative open source databases at home and abroad such as the IPCC Carbon Emission Factor Library and the CPCD China Product Life Cycle Greenhouse Gas Emission Coefficient Set. The data in the database in this embodiment has been converted, classified, and unified in data structure with reference to the "Standard".

[0091] Most of the data in the database are extracted from authoritative statistical yearbooks, and some are extracted from papers or research reports, which are highly credible. The units of carbon emission factors in different databases are different, and the data structures are inconsistent. Therefore, they need to be sorted and classified. In this embodiment, the main objects of carbon emissions are Chinese buildings, so the selected data mainly belong to Chinese or global data.

[0092] In this embodiment, the classification basis of carbon emission factors in IPCC and "Building Carbon Emission Calculation Standard" (GB / T51366-2019) is referred to, and a database classification method is formulated to meet the needs of building carbon emission accounting. There is a logical connection between similar carbon emission factors, which is scientific.

[0093] In order to improve the credibility of the data and make it more suitable for monitoring China's actual carbon emission levels, we used the data in the "Building Carbon Emission Calculation Standard" (GB / T 51366-2019) as a benchmark to analyze and summarize all the data. For data with a large amount of data, we mainly focus on its extreme points and quartiles. We found that the data in the "Building Carbon Emission Calculation Standard" (GB / T51366-2019) is often located in the middle of the data, which shows that the data in the "Building Carbon Emission Calculation Standard" (GB / T51366-2019) is representative, and to some extent verifies the scientific reliability of the data we selected.

[0094] In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0095] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0096] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A building carbon emission accounting method based on emission factor method and drone mapping technology, which is used to calculate carbon emissions of completed buildings or blocks; characterized in that: The steps include: Step S1: Establishment of carbon emission factor database Step S11: Filter carbon emission factor data related to the entire life cycle of the building from authoritative carbon emission databases at home and abroad; Step S12: sort out and summarize the carbon emission factor data, unify the data units, and form a database. The database contains the following information of the carbon emission factor: category, name, unit, generation link, source type, source, applicable area and time. Step S2: UAV mapping modeling to estimate building area Step S21: using a drone to photograph and collect data on completed buildings or blocks; Step S22: performing image processing and constructing a three-dimensional model based on the above data; Step S23: obtaining a contour distribution map based on the three-dimensional model to estimate the building area of ​​the completed building or block; Step S3: Machine learning to determine building type Step S31: extracting shape parameters of the building in the three-dimensional model; using a data set of known building types for annotation, and associating each set of building shape parameters with its corresponding building type; Step S32: using the model to classify and learn the building types; Step S33: taking the extracted building shape parameters as independent variables, taking the building type as dependent variable, and using the labeled data set to train the model; Step S34: input the newly collected building shape parameters into the trained model, and output the predicted building type; Step S35: comparing the predicted building type with the actual building type to verify the judgment result of the model, and make adjustments and improvements; Step S4: Estimation of carbon emission activity level data for the entire life cycle of the building Step S41: estimating the amount of building materials used in the building construction phase based on the building area and building type; Step S42: estimating energy consumption during the building construction phase based on the building area and building type, including electricity consumption and fossil energy consumption; Step S43: estimating the energy consumption of the building during operation based on the building area and building type, including electricity consumption and fossil energy consumption; Step S44: estimating energy consumption during the building demolition phase based on the building area and building type, including electricity consumption and fossil energy consumption; The amount of building materials used during the building construction phase and the energy consumption during the building construction, operation and demolition phases constitute the activity level data of carbon emissions over the entire life cycle of the building. Step S5: Data import and carbon emission accounting Develop a calculation program to calculate carbon emissions using the following formula: Carbon emissions = ∑(activity level × carbon emission factor); In the formula, the carbon emission factors are provided by the database, including the carbon emission factors of main energy sources, electricity, building materials, and building materials transportation, etc.; The activity level data is estimated based on the building area and building type, and includes the amount of building materials used during the building construction phase and the amount of energy consumed during the building construction, operation, and demolition phases.

2. According to claim 1, a building carbon emission accounting method based on emission factor method and drone mapping technology is characterized in that: In step S11, the carbon emission factor database selects high-quality data from authoritative carbon emission factor databases at home and abroad, such as CPCD China Product Life Cycle Greenhouse Gas Emission Coefficient Set, IPCC Carbon Emission Factor Library, and "Building Carbon Emission Calculation Standard" GB / T 51366-2019.

3. According to claim 1, a building carbon emission accounting method based on emission factor method and drone mapping technology is characterized in that: In step S12, the carbon emission factor data is unified in data units according to the "Building Carbon Emission Calculation Standard" GB / T51366-2019, and through data traceability, elements such as region, time and accounting boundaries are identified. The carbon emission factor database contains the following information on the carbon emission factor: category, name, unit, generation link, source type, source, applicable area and time.

4. According to claim 1, a method for calculating building carbon emissions based on emission factor method and drone mapping technology is characterized in that: In the step S23, based on the elevation data of the building extracted from the three-dimensional model, a digital elevation model is generated, and a contour distribution map of the building is drawn; Calculating the floor area of ​​the building by projecting the bottom surface of the three-dimensional model onto a two-dimensional plane; Based on the three-dimensional model, the total surface area of ​​the building, including walls, roof and other parts, is calculated.

5. According to claim 1, a method for calculating building carbon emissions based on emission factor method and drone mapping technology is characterized in that: In step S31, the shape parameters of the building, including height, width, length, surface area, volume and contour distribution, are extracted from the three-dimensional model, and the corresponding building type is associated based on the building shape parameters.

6. The method for calculating building carbon emissions based on emission factor method and drone mapping technology according to claim 1 is characterized in that: In step S32, the model includes vector machine, decision tree, random forest, neural network.

7. The method for calculating building carbon emissions based on emission factor method and drone mapping technology according to claim 1 is characterized in that: In step S35, the performance of the model is evaluated by a cross-validation method, and the model parameters are adjusted or the feature extraction method is improved according to the results to ensure the accuracy and robustness of the model.

8. The method for calculating building carbon emissions based on emission factor method and drone mapping technology according to claim 1 is characterized in that: The activity level data in step S4 covers the carbon emission activity level data of the building throughout its life cycle, including the amount of building materials used during the building construction phase and the energy consumption during the building construction, operation, and demolition phases.

9. The method for calculating building carbon emissions based on emission factor method and drone mapping technology according to claim 1 is characterized in that: The accounting program in step S5 is developed based on the SQL-Server Management Studio database and C# language.

10. The method for calculating building carbon emissions based on emission factor method and drone mapping technology according to claim 1 is characterized in that: In the step S5, the amount of building materials used in the building construction phase and the amount of energy consumed in the building construction, operation, and demolition phases are summarized; The activity level data of carbon emissions throughout the life cycle of the building is imported into the accounting program for carbon emissions accounting, and finally a detailed carbon emissions accounting report is generated.

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