Public building carbon effect level evaluation method and system based on big data analysis

Abstract

The invention relates to the technical field of carbon emission, and relates to a public building carbon effect level evaluation method and system based on big data analysis. The method comprises the following steps: acquiring annual actual carbon dioxide emissions of a plurality of buildings and carbon emission influence factor data of each building in real time; according to the carbon emission influence factor coefficient, the carbon emission influence factor data of each building and the annual actual carbon dioxide emission of each building, calculating the carbon effect index and the carbon effect index cumulative percentage of each building to obtain a fitted curve equation; assigning the carbon effect index scores of the curve equation one by one, and inversely calculating the corresponding carbon effect index through the curve equation; according to the carbon effect index score and the carbon effect index score obtained through back calculation, constructing a carbon effect index score table; and calculating the carbon effect index of the target building, and querying the carbon effect index score table to determine the carbon effect score of the target building. According to the method, accurate grading of the carbon effect level of the building is achieved, and the carbon effect level of the building is accurately reflected.

Description

Technical Field

[0001] This application relates to the technical field of carbon emissions, and further relates to a method and system for evaluating the carbon efficiency level of public buildings based on big data analysis. Background Art

[0002] Currently, the grading methods for the carbon efficiency level of buildings on the market do not fully consider the objective influencing factors during the use of different building types, such as the number of energy users, operation duration, climate conditions, etc. These factors have a significant impact on the building's carbon emissions, but the existing grading methods fail to take these factors into account, resulting in a lack of discrimination in the grading results and being unable to accurately reflect the carbon efficiency level of the building. Summary of the Invention

[0003] To solve the above technical problems, this application provides a method and system for evaluating the carbon efficiency level of public buildings based on big data analysis, which realizes the precise grading of the carbon efficiency level of buildings and accurately reflects the carbon efficiency level of buildings.

[0004] In the first aspect, this application provides a method for evaluating the carbon efficiency level of public buildings based on big data analysis, including: obtaining the actual annual carbon dioxide emissions of multiple buildings and the carbon emission influencing factor data of each building in real time; calculating the carbon efficiency index and the cumulative percentage of the carbon efficiency index of each building according to the carbon emission influencing factor coefficient, the carbon emission influencing factor data of each building, and the actual annual carbon dioxide emissions of each building, and fitting a curve equation for the carbon efficiency index of each building and the cumulative percentage of the carbon efficiency index of each building; assigning scores to the carbon efficiency index scores in the curve equation one by one from a preset numerical range, and inversely calculating the corresponding carbon efficiency index through the curve equation; constructing a carbon efficiency index scoring table according to the carbon efficiency index scores and the carbon efficiency index inversely calculated through the curve equation, where each carbon efficiency index score has a corresponding carbon efficiency index range.

[0005] The above method for evaluating the carbon efficiency level of public buildings based on big data analysis can ensure the accuracy and timeliness of the evaluation by obtaining the actual annual carbon dioxide emissions and carbon emission influencing factor data of buildings in real time. Using the carbon emission influencing factor coefficient to calculate the cumulative percentage of the carbon efficiency index makes the evaluation of the building's carbon efficiency level more scientific and comprehensive. By assigning scores and inversely calculating the carbon efficiency index through the curve equation, and constructing the final carbon efficiency index scoring table, the precise grading of the carbon efficiency level of buildings is realized, and the carbon efficiency level of buildings is accurately reflected.

[0006] In one implementation, calculating the cumulative percentage of the carbon efficiency index for each building based on the carbon emission impact factor coefficient, the carbon emission impact factor data of each building, and the actual annual carbon dioxide emissions of each building specifically includes: calculating the annual carbon dioxide baseline emissions for each building according to the carbon emission impact factor coefficient and the carbon emission impact factor data of each building; calculating the carbon efficiency index for each building according to the annual carbon dioxide baseline emissions and the actual annual carbon dioxide emissions of each building; and calculating the cumulative percentage of the carbon efficiency index for each building according to the carbon efficiency index of each building.

[0007] In one implementation, the carbon emission impact factor data of each building includes multiple impact factor quantification indicators, the carbon emission impact factor coefficient includes multiple impact factor quantification indicator coefficients, and each impact factor quantification indicator corresponds one-to-one with each impact factor quantification indicator coefficient; the formula for calculating the annual carbon dioxide baseline emissions for each building is:

[0008] C P = A + Bx1 + Cx2 + Dx3 + …,

[0009] where x1, x2, and x3 are all the impact factor quantification indicators, B, C, and D are all the impact factor quantification indicator coefficients, A is a constant, and C P is the annual carbon dioxide baseline emissions for each building.

[0010] In one implementation, the formula for calculating the carbon efficiency index for each building is:

[0011] CER = C / C P ,

[0012] where CER is the carbon efficiency index for each building, and C is the actual annual carbon dioxide emissions of each building.

[0013] In one implementation, the formula for calculating the cumulative percentage of the carbon efficiency index for each building is:

[0014]

[0015] where P is the cumulative percentage of the i-th building, CER i is the carbon efficiency index of the i-th building, and n is the number of buildings.

[0016] The above evaluation method for the carbon efficiency level of public buildings based on big data analysis provides a reference benchmark for building carbon emissions by calculating the annual baseline carbon dioxide emissions of each building, ensuring fair comparison among different buildings. By comparing the baseline emissions with the actual emissions, the calculation of the carbon efficiency index intuitively reflects the carbon efficiency level of the building. The fitting curve equation is obtained by calculating the carbon efficiency index and the cumulative percentage of the carbon efficiency index, and the carbon efficiency index scoring table is constructed based on the carbon efficiency index score and the carbon efficiency index obtained by back-calculation through the curve equation after scoring, realizing the accurate grading of the carbon efficiency level of the building and accurately reflecting the carbon efficiency level of the building. Moreover, by obtaining the annual actual carbon dioxide emissions and carbon emission influencing factor data of multiple buildings in real time, and calculating the carbon efficiency index, the cumulative percentage of the carbon efficiency index, etc. in real time, the system can realize the real-time automatic update of the carbon efficiency index scoring table, further improving the user experience.

[0017] In one implementation, the annual actual carbon dioxide emissions include direct carbon emissions and indirect carbon emissions.

[0018] In a second aspect, the present application also provides an evaluation system for the carbon efficiency level of public buildings based on big data analysis, including: a collection module configured to obtain the annual actual carbon dioxide emissions of multiple buildings and the carbon emission influencing factor data of each building in real time; a calculation module configured to calculate the cumulative percentage of the carbon efficiency index of each building according to the carbon emission influencing factor coefficient, the carbon emission influencing factor data of each building, and the annual actual carbon dioxide emissions of each building, and fit the carbon efficiency index of each building and the cumulative percentage of the carbon efficiency index of each building to obtain a curve equation; the calculation module is further configured to score the carbon efficiency index score in the curve equation one by one from a preset numerical range, and back-calculate the carbon efficiency index corresponding to each score through the curve equation; a construction module configured to construct a carbon efficiency index scoring table according to the carbon efficiency index score and the carbon efficiency index obtained by back-calculation through the curve equation after scoring, where there is a corresponding carbon efficiency index range for each carbon efficiency index score.

[0019] In one implementation, the calculation module is further configured to calculate the annual baseline carbon dioxide emissions of each building according to the carbon emission influencing factor coefficient and the carbon emission influencing factor data of each building; the calculation module is further configured to calculate the carbon efficiency index of each building according to the annual baseline carbon dioxide emissions and the annual actual carbon dioxide emissions of each building; the calculation module is further configured to calculate the cumulative percentage of the carbon efficiency index of each building according to the carbon efficiency index of each building.

[0020] In a third aspect, the present application further provides a computer device, including a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the steps of any one of the above-mentioned methods for evaluating the carbon efficiency level of public buildings based on big data analysis.

[0021] In a fourth aspect, the present application further provides a computer storage medium, on which a computer program or instruction is stored. When the computer program or instruction is executed by a processor, the steps of any one of the above-mentioned methods for evaluating the carbon efficiency level of public buildings based on big data analysis are implemented.

[0022] Compared with the prior art, the present invention has at least one of the following beneficial effects:

[0023] 1. By obtaining the actual annual carbon dioxide emissions of a building and the data of carbon emission influencing factors in real time, the accuracy and timeliness of the evaluation can be ensured. Calculating the cumulative percentage of the carbon efficiency index using the carbon emission influencing factor coefficient makes the evaluation of the building's carbon efficiency level more scientific and comprehensive. By assigning values and back-calculating the carbon efficiency index through a curve equation, and constructing the final carbon efficiency index scoring table, accurate grading of the building's carbon efficiency level is achieved, accurately reflecting the carbon efficiency level of the building.

[0024] 2. By calculating the annual carbon dioxide baseline emissions of each building, a reference baseline for building carbon emissions is provided, ensuring fair comparison between different buildings. Calculating the carbon efficiency index by comparing the baseline emissions with the actual emissions intuitively reflects the carbon efficiency level of the building. By calculating the carbon efficiency index and the cumulative percentage of the carbon efficiency index to obtain the fitting curve equation, and back-calculating the carbon efficiency index through the curve equation after scoring and assigning points to the carbon efficiency index to construct the carbon efficiency index scoring table, accurate grading of the building's carbon efficiency level is achieved, accurately reflecting the carbon efficiency level of the building. Moreover, the system can realize real-time automatic update of the carbon efficiency index scoring table by obtaining the actual annual carbon dioxide emissions of multiple buildings and the data of carbon emission influencing factors in real time, and calculating the carbon efficiency index, the cumulative percentage of the carbon efficiency index, etc. in real time, further improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above characteristics, technical features, advantages and their implementation manners of the present invention will be further described below in a clear and understandable manner in combination with the drawings in the preferred embodiments.

[0026] Figure 1 The flowchart of a method for evaluating the carbon efficiency level of public buildings based on big data analysis provided by an embodiment of the present application is shown;

[0027] Figure 2 The flowchart of calculating the cumulative percentage of the carbon efficiency index provided by an embodiment of the present application is shown. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will describe the specific embodiments of the present invention with reference to the accompanying drawings. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings, and other embodiments can also be obtained.

[0029] To make the drawings concise, only the parts related to the invention are schematically shown in each drawing, and they do not represent the actual structure of the product. In addition, to make the drawings concise and easy to understand, in some drawings, components with the same structure or function are only schematically shown as one of them, or only one of them is marked. In this document, "one" not only means "only this one", but also can mean "more than one" situation.

[0030] It should be further understood that the term "and / or" used in the description of the present application and the appended claims refers to any combination and all possible combinations of one or more of the related listed items, and includes these combinations.

[0031] In this document, it should be noted that unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0032] In addition, in the description of the present application, the terms "first", "second", etc. are only used for distinguishing descriptions, and cannot be understood as indicating or implying relative importance.

[0033] It should be noted that the above embodiments can be freely combined according to needs. The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the technical field, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

[0034] Carbon emission influencing factors (or objective influencing factors of building carbon emissions) refer to the relevant factors of carbon dioxide emissions due to energy, resource, and material consumption during the entire life cycle of a building.

[0035] The cumulative percentage of the carbon efficiency index is a statistical indicator used to measure the proportion of the number of buildings that reach a certain specific percentage in a series of building samples when sorted from smallest to largest according to the carbon efficiency index (CER) among the total number of samples. This indicator reflects the distribution of carbon emission efficiency in the building samples by calculating the ratio of the sum of the cumulative carbon efficiency indices to the total sum of the carbon efficiency indices of all samples and then multiplying by 100 to obtain the percentage.

[0036] The carbon efficiency index scoring table (or the carbon efficiency index grade percentage scoring table) is an evaluation tool that arranges the carbon efficiency indices of buildings in ascending order and gives a percentage score according to their relative positions among all buildings. This scoring table divides the carbon efficiency levels of buildings into different grades, such as excellent, good, qualified, and unqualified, by comparing the carbon efficiency levels of buildings with the industry average level or other benchmarks. In the embodiments of the present application, by combining the data on the influencing factors of building carbon emissions and the actual annual carbon dioxide emissions of buildings, the cumulative percentage of the carbon efficiency index and the carbon efficiency index of buildings are calculated, and then a carbon efficiency index scoring table is constructed based on the carbon efficiency index and the cumulative percentage of the carbon efficiency index, which can achieve at least one of the following beneficial effects: accurately grading the carbon efficiency levels of buildings and accurately reflecting the carbon efficiency levels of buildings; or considering the influence of carbon emission influencing factors, making the evaluation of the carbon efficiency levels of buildings more scientific and comprehensive.

[0037] The following is elaborated with reference to the accompanying drawings:

[0038] Refer to the attached Figure 1 , which shows a flowchart of a method for evaluating the carbon efficiency level of public buildings based on big data analysis provided by the embodiments of the present application. As Figure 1 shown, it includes:

[0039] S100, obtaining the actual annual carbon dioxide emissions of multiple buildings and the data on the influencing factors of carbon emissions for each building in real time.

[0040] S110, calculating the carbon efficiency index and the cumulative percentage of the carbon efficiency index for each building according to the carbon emission influencing factor coefficient, the data on the influencing factors of carbon emissions for each building, and the actual annual carbon dioxide emissions of each building, and fitting the carbon efficiency index and the cumulative percentage of the carbon efficiency index for each building to obtain a curve equation.

[0041] S120, assigning scores to the carbon efficiency index scores in the curve equation one by one from a preset numerical range and inversely calculating the corresponding carbon efficiency index through the curve equation.

[0042] S130, constructing a carbon efficiency index scoring table based on the carbon efficiency index scores of buildings and the carbon efficiency indices inversely calculated through the curve equation, where each carbon efficiency index score has a corresponding carbon efficiency index range.

[0043] S140, calculate the carbon efficiency index of the target building, and query the carbon efficiency index score table based on the carbon efficiency index of the target building to determine the carbon efficiency index score of the target building.

[0044] Establish a real-time collected building carbon emission database to obtain the actual annual carbon dioxide emissions of multiple buildings and the carbon emission influencing factor data of each building. The building types include but are not limited to office buildings, hotel and restaurant buildings, shopping mall buildings, educational buildings, cultural and tourism buildings, scientific research buildings, stadium buildings, etc. The corresponding carbon emission influencing factor data for different types of buildings are different.

[0045] Furthermore, construct a corresponding carbon emission multiple regression equation for each building according to the actual annual carbon dioxide emissions of each building and the carbon emission influencing factor data of each building, and substitute the carbon emission influencing factor data of each building into its corresponding carbon emission multiple regression equation to obtain the annual carbon dioxide baseline emissions (or predicted carbon emissions) of each building. Further, according to the annual carbon dioxide baseline emissions and the actual annual carbon dioxide emissions of each building, obtain the carbon efficiency index of each building. Sort the carbon efficiency indexes of each building from small to large, and calculate the carbon efficiency index of each building in turn. At the same time, arrange the carbon efficiency indexes of each building from small to large, calculate the cumulative percentage of the carbon efficiency index, and perform fitting to obtain the curve equation. Further, the dependent variable carbon efficiency index score of the curve equation is scored one by one from a preset numerical interval (the preset numerical interval can be 1-100, or can be set according to the actual situation), and the independent variable corresponding to each score, that is, the carbon efficiency index, is calculated by back-calculating through the curve equation. Finally, construct a carbon efficiency index score table based on the building carbon efficiency index score and the carbon efficiency index obtained by back-calculating through the curve equation, where each carbon efficiency index score corresponds to a certain carbon efficiency index interval. Users can query the carbon efficiency index score of the target building according to the constructed carbon efficiency index score table, or obtain the ranking of the carbon efficiency index of the target building among all buildings according to the ranking of the carbon efficiency indexes of each building.

[0046] By obtaining the actual annual carbon dioxide emissions and carbon emission influencing factor data of the building in real time in the embodiments of the present application, the accuracy and timeliness of the evaluation can be ensured. Using the carbon emission influencing factor coefficient to calculate the cumulative percentage of the carbon efficiency index makes the evaluation of the building carbon efficiency level more scientific and comprehensive. By scoring and back-calculating the carbon efficiency index through the curve equation and constructing the final carbon efficiency index score table, the accurate grading of the building carbon efficiency level is realized, and the carbon efficiency level of the building is accurately reflected.

[0047] Reference appendix Figure 2 , which shows a flowchart for calculating the cumulative percentage of the carbon efficiency index provided by the embodiments of the present application. As Figure 2 shown, it includes:

[0048] S200. Calculate the annual baseline carbon dioxide emissions of each building based on the carbon emission influencing factor coefficients and the carbon emission influencing factor data of each building.

[0049] S210. Calculate the carbon efficiency index of each building based on the annual baseline carbon dioxide emissions and the actual annual carbon dioxide emissions of each building.

[0050] S220. Calculate the cumulative percentage of the carbon efficiency index of each building based on the carbon efficiency index of each building.

[0051] The carbon emission influencing factor data of each building includes multiple influencing factor quantification indicators (or related influencing factor quantification indicators). The multiple influencing factor quantification indicators include, but are not limited to, the building area of different business areas, the number of building energy users, the building operation duration, the building access volume, the air conditioning degree days / heating degree days, etc. Each influencing factor quantification indicator has a corresponding influencing factor quantification indicator coefficient (or the coefficient of the related influencing factor quantification indicator).

[0052] After obtaining the actual annual carbon dioxide emissions of multiple buildings and the carbon emission influencing factor data of each building, construct a corresponding carbon emission multiple regression equation for each building based on the actual annual carbon dioxide emissions of each building and the carbon emission influencing factor data of each building. Substitute the carbon emission influencing factor data of each building into its corresponding carbon emission multiple regression equation to obtain the annual baseline carbon dioxide emissions of each building. The specific formula is:

[0053] C P = A + Bx1 + Cx2 + Dx3 +…,

[0054] where x1, x2, and x3 are all influencing factor quantification indicators, B, C, and D are all influencing factor quantification indicator coefficients, A is a constant, and C P is the annual baseline carbon dioxide emissions of each building, with the unit of tons of carbon dioxide per year (tCO2 / a).

[0055] Furthermore, calculate the carbon efficiency index of each building based on the annual baseline carbon dioxide emissions and the actual annual carbon dioxide emissions of each building. The specific formula is:

[0056] CER = C / C P ,

[0057] where CER is the carbon efficiency index of each building, and C is the actual annual carbon dioxide emissions of each building.

[0058] Furthermore, sort the carbon efficiency indices of each building from smallest to largest, and calculate the cumulative percentage of the carbon efficiency index of each building in turn. The specific formula is as follows:

[0059]

[0060] Among them, P is the cumulative percentage of the i-th building, CER i is the carbon efficiency index of the i-th building, and n is the number of buildings. Fit the CER and P values to obtain a curve equation, then assign values to the dependent variable carbon efficiency index score in the curve equation one by one from 1 to 100, and inversely calculate the corresponding independent variable carbon efficiency index according to the curve equation. Finally, construct a carbon efficiency index scoring table based on the carbon efficiency index score of the building and the carbon efficiency index inversely calculated through the curve equation.

[0061] In the embodiment of the present application, by calculating the annual carbon dioxide baseline emissions of each building, a reference baseline for building carbon emissions is provided, ensuring fair comparison between different buildings. Using the comparison between the baseline emissions and the actual emissions to calculate the carbon efficiency index intuitively reflects the carbon efficiency level of the building. By calculating the cumulative percentage of the carbon efficiency index, establishing a mathematical model to obtain the curve equation between the building carbon efficiency index and the cumulative percentage of the carbon efficiency index, and constructing a carbon efficiency index scoring table based on the carbon efficiency index score of the building and the carbon efficiency index inversely calculated through the curve equation, accurate grading of the carbon efficiency level of the building is realized, accurately reflecting the carbon efficiency level of the building. Moreover, the system can realize real-time automatic update of the carbon efficiency index scoring table by obtaining the annual carbon dioxide actual emissions and carbon emission influencing factor data of multiple buildings in real time, and calculating the carbon efficiency index, cumulative percentage of the carbon efficiency index, etc. in real time, further improving the user experience.

[0062] In an embodiment of the present application, the annual carbon dioxide actual emissions include direct carbon emissions and indirect carbon emissions.

[0063] The embodiment of the present application also provides a public building carbon efficiency level evaluation system based on big data analysis, including: a collection module configured to obtain the annual carbon dioxide actual emissions of multiple buildings and the carbon emission influencing factor data of each building in real time; a calculation module configured to calculate the cumulative percentage of the carbon efficiency index of each building according to the carbon emission influencing factor coefficient, the carbon emission influencing factor data of each building, and the annual carbon dioxide actual emissions of each building, and fit the carbon efficiency index of each building and the cumulative percentage of the carbon efficiency index of each building to obtain a curve equation; the calculation module is further configured to assign scores to the carbon efficiency index scores in the curve equation one by one from a preset numerical range, and inversely calculate the carbon efficiency index through the curve equation; a construction module configured to construct a carbon efficiency index scoring table according to the carbon efficiency index score of the building and the carbon efficiency index inversely calculated through the curve equation, where there is a corresponding carbon efficiency index range for each carbon efficiency index score; a query module configured to calculate the carbon efficiency index of the target building, and query the carbon efficiency index scoring table based on the carbon efficiency index of the target building to determine the carbon efficiency index score of the target building.

[0064] The relevant content of the embodiments of this application has been described in the foregoing embodiments, and will not be elaborated herein.

[0065] In an embodiment of this application, the calculation module is further configured to calculate the annual carbon dioxide baseline emissions of each building according to the carbon emission impact factor coefficient and the carbon emission impact factor data of each building; the calculation module is further configured to calculate the carbon efficiency index of each building according to the annual carbon dioxide baseline emissions and the actual annual carbon dioxide emissions of each building; the calculation module is further configured to calculate the cumulative percentage of the carbon efficiency index of each building according to the carbon efficiency index of each building.

[0066] The relevant content of the embodiments of this application has been described in the foregoing embodiments, and will not be elaborated herein.

[0067] The embodiments of this application also provide a computer device including a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the steps of the method for evaluating the carbon efficiency level of public buildings based on big data analysis described in any one of the foregoing embodiments.

[0068] The embodiments of this application also provide a computer storage medium having a computer program or instruction stored thereon. When the computer program or instruction is executed by a processor, the steps of the method for evaluating the carbon efficiency level of public buildings based on big data analysis described in any one of the foregoing embodiments are implemented.

[0069] It should be noted that the above embodiments can be freely combined according to needs. The above are only the preferred embodiments of the present invention. It should be pointed out that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for evaluating the carbon efficiency level of public buildings based on big data analysis, characterized in that, Including: Obtain the actual annual carbon dioxide emissions of multiple buildings in real time, as well as the carbon emission influencing factor data of each of the said buildings; Calculate the carbon efficiency index and the cumulative percentage of the carbon efficiency index of each of the said buildings according to the carbon emission influencing factor coefficient, the carbon emission influencing factor data of each of the said buildings, and the actual annual carbon dioxide emissions of each of the said buildings, and fit the carbon efficiency index of each of the said buildings and the cumulative percentage of the carbon efficiency index of each of the said buildings to obtain a curve equation; Assign scores to the carbon efficiency index scores in the curve equation one by one from a preset numerical range, and inversely calculate the corresponding carbon efficiency index through the curve equation; Construct a carbon efficiency index scoring table according to the carbon efficiency index score and the carbon efficiency index inversely calculated through the curve equation, where there is a corresponding carbon efficiency index range for each of the said carbon efficiency index scores; Calculate the carbon efficiency index of the target building, and query the carbon efficiency index scoring table based on the carbon efficiency index of the target building to determine the carbon efficiency index score of the target building.

2. The public building carbon efficiency level evaluation method based on big data analysis according to claim 1, wherein, The calculation of the carbon efficiency index and the cumulative percentage of the carbon efficiency index of each of the said buildings according to the carbon emission influencing factor coefficient, the carbon emission influencing factor data of each of the said buildings, and the actual annual carbon dioxide emissions of each of the said buildings specifically includes: Calculate the annual carbon dioxide baseline emissions of each of the said buildings according to the carbon emission influencing factor coefficient and the carbon emission influencing factor data of each of the said buildings; Calculate the carbon efficiency index of each of the said buildings according to the annual carbon dioxide baseline emissions and the actual annual carbon dioxide emissions of each of the said buildings; Calculate the cumulative percentage of the carbon efficiency index of each of the said buildings according to the carbon efficiency index of each of the said buildings.

3. The public building carbon efficiency level evaluation method based on big data analysis according to claim 2, wherein, The carbon emission influencing factor data of each of the said buildings includes multiple influencing factor quantification indicators, the carbon emission influencing factor coefficient includes multiple influencing factor quantification indicator coefficients, and each of the said influencing factor quantification indicators corresponds one by one to each of the said influencing factor quantification indicator coefficients; the formula for calculating the annual carbon dioxide baseline emissions of each of the said buildings is: CP = A + Bx1 + Cx2 + Dx3 + …, Among them, x1, x2, and x3 are all the quantification indexes of the influencing factors, B, C, and D are all the coefficient of the quantification indexes of the influencing factors, A is a constant, and C P is the annual carbon dioxide baseline emission of each of the buildings.

4. The public building carbon efficiency level evaluation method based on big data analysis according to claim 3, characterized in that The formula for calculating the carbon efficiency index of each of the said buildings is: CER = C / C P , where CER is the carbon efficiency index of each of the said buildings, and C is the actual annual carbon dioxide emissions of each of the said buildings.

5. The public building carbon efficiency level evaluation method based on big data analysis according to claim 4, wherein The formula for calculating the cumulative percentage of the carbon efficiency index of each of the said buildings is: where P is the cumulative percentage of the i-th building, CER i is the carbon efficiency index of the i-th building, and n is the number of buildings.

6. The method for evaluating the carbon efficiency level of public buildings based on big data analysis according to any one of claims 1-5, characterized in that, The actual annual carbon dioxide emissions include direct carbon emissions and indirect carbon emissions.

7. A public building carbon efficiency level evaluation system based on big data analysis, characterized in that, Including: A collection module configured to obtain the actual annual carbon dioxide emissions of multiple buildings in real time, as well as the carbon emission influencing factor data of each of the said buildings; A calculation module configured to calculate the carbon efficiency index and the cumulative percentage of the carbon efficiency index of each of the said buildings according to the carbon emission influencing factor coefficient, the carbon emission influencing factor data of each of the said buildings, and the actual annual carbon dioxide emissions of each of the said buildings, and fit the carbon efficiency index of each of the said buildings and the cumulative percentage of the carbon efficiency index of each of the said buildings to obtain a curve equation; The calculation module is further configured to assign scores to the carbon efficiency index scores in the curve equation one by one from a preset numerical range, and inversely calculate the corresponding carbon efficiency index through the curve equation; The construction module is configured to construct a carbon efficiency index scoring table according to the carbon efficiency index scores and the carbon efficiency indices inversely calculated through the curve equation, wherein there is a corresponding carbon efficiency index range for each of the carbon efficiency index scores; The query module is configured to calculate the carbon efficiency index of the target building, and query the carbon efficiency index scoring table based on the carbon efficiency index of the target building to determine the carbon efficiency index score of the target building.

8. The public building carbon efficiency level evaluation system based on big data analysis according to claim 7, wherein The calculation module is further configured to calculate the annual carbon dioxide baseline emissions of each building according to the carbon emission influencing factor coefficients and the carbon emission influencing factor data of each building; The calculation module is further configured to calculate the carbon efficiency index of each building according to the annual carbon dioxide baseline emissions of each building and the actual annual carbon dioxide emissions; The calculation module is further configured to calculate the cumulative percentage of the carbon efficiency index of each building according to the carbon efficiency index of each building.

9. The public building carbon efficiency level evaluation system based on big data analysis according to claim 8, characterized in that, The carbon emission influencing factor data of each building includes multiple influencing factor quantification indicators, the carbon emission influencing factor coefficients include multiple influencing factor quantification indicator coefficients, and each of the influencing factor quantification indicators corresponds to each of the influencing factor quantification indicator coefficients one by one; the formula for calculating the annual carbon dioxide baseline emissions of each building is: C P = A + Bx1 + Cx2 + Dx3 + …, wherein, x1, x2, and x3 are all the quantification indexes of the influencing factors, B, C, and D are all the coefficient of the quantification indexes of the influencing factors, A is a constant, and C P is the annual carbon dioxide baseline emission of each of the buildings.

10. The public building carbon efficiency level evaluation system based on big data analysis according to claim 9, characterized in that, The formula for calculating the carbon efficiency index of each building is: CER = C / C P , wherein, CER is the carbon efficiency index of each building, and C is the actual annual carbon dioxide emissions of each building.

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