Large-scale carbon footprint intelligent analysis platform based on carbon consensus mechanism

Abstract

The present invention provides a large-model carbon footprint intelligent analysis platform based on a carbon consensus mechanism, which relates to the technical field of carbon footprint analysis. The intelligent analysis platform is used to analyze whether the carbon footprint of a target building complex exceeds the standard. The intelligent analysis platform can obtain a preset feature label list set, obtain the total set of original carbon footprint list sets corresponding to the target building complex based on the preset feature label list set, obtain the weighted average carbon footprint corresponding to the preset feature label based on the total set of original carbon footprint list sets, obtain the total carbon footprint corresponding to the target building complex based on the weighted average carbon footprint corresponding to the preset feature label, and determine whether the carbon footprint of the target building complex exceeds the standard based on the total carbon footprint and the preset total carbon footprint threshold. It can be seen that the present invention only needs to collect the original carbon footprint and determine whether the carbon footprint of the target building complex exceeds the standard based on the original carbon footprint, which is conducive to improving the accuracy of the result of determining whether the carbon footprint of the target building complex exceeds the standard.

Description

Technical Field

[0001] The present invention relates to the field of carbon footprint analysis technology, and in particular to a large-model carbon footprint intelligent analysis platform based on a carbon consensus mechanism. Background Art

[0002] The construction industry spans multiple high-carbon industries such as steel, cement, and coal-fired power. The total carbon emissions of the entire industry are very high. Carbon footprint accounting for a building complex can fully understand the greenhouse gas emissions generated throughout its life cycle. Furthermore, it can determine whether the carbon footprint of a building complex exceeds the standard. Determining whether the carbon footprint of a building complex exceeds the standard is crucial for assessing the environmental impact of the building complex. Therefore, it is necessary to calculate the carbon footprint of the building complex and determine whether the carbon footprint of the building complex exceeds the standard to provide a basis for assessing the environmental impact of the building complex.

[0003] The existing method for calculating the carbon footprint of a building complex and determining whether the carbon footprint of the building complex exceeds the standard is: collecting relevant data on the building complex during the entire life cycle stages such as design, construction, operation and demolition, using methods such as life cycle assessment (LCA) to calculate the total carbon footprint value, and comparing the total carbon footprint value with the threshold specified by industry standards or policies to determine whether the carbon footprint of the building complex exceeds the standard.

[0004] However, the above method also has the following technical problems:

[0005] The above method requires a large amount of detailed data and precise information to complete the carbon footprint accounting at each stage to obtain the total carbon footprint, which increases the workload and cost of data collection. It may also lead to inaccurate estimation of the final total carbon footprint due to incomplete or uncertain data, and thus lead to inaccurate results in determining whether the carbon footprint of the building complex exceeds the standard. Summary of the Invention

[0006] In view of the above technical problems, the technical solution adopted by the present invention is:

[0007] The present invention provides a large-scale carbon footprint intelligent analysis platform based on a carbon consensus mechanism. The intelligent analysis platform is used to analyze whether the carbon footprint of a target building complex exceeds the standard. The intelligent analysis platform includes a processor and a memory storing a computer program. When the computer program is executed by the processor, the following steps are implemented:

[0008] S1. Obtain the preset feature label list set A={A1, A2, ..., A i ,……,A m}, A i ={A i1 , A i2 ,……,A ij ,……,A in(i)}, where Ai is a list of preset feature labels corresponding to the i-th stage of the building life cycle, i ranges from 1 to m, m is the number of stages in the building life cycle, A ij is the jth preset feature label corresponding to the i-th stage of the building's life cycle, the value of j ranges from 1 to n(i), and n(i) is the number of preset feature labels corresponding to the i-th stage of the building's life cycle.

[0009] S2, according to A, obtain the total set of original carbon footprint lists corresponding to the target building complex B = {B 1 , B 2 ,……,B r ,……,B s}, B r ={B r 1, B r 2, ..., B r i , ... B r m}, B r i ={B r i1 , B r i2 ,……,B r ij ,……,B r in(i)}, where B r is the original carbon footprint list set corresponding to the rth target building in the target building complex, r ranges from 1 to s, s is the number of target buildings in the target building complex, B r i B r China A i The corresponding raw carbon footprint list, B r ij B r i China A ij The corresponding original carbon footprint is the carbon footprint generated by the features corresponding to the preset feature tags during the entire life cycle of the target building.

[0010] S3, based on B r ij Get A ij The corresponding weighted average carbon footprint E ij .

[0011] S4. According to E ij Obtain the total carbon footprint R corresponding to the target building complex, where R meets the following conditions:

[0012] R=Σ mi=1 Σ n(i) j=1 E ij .

[0013] S5. When R>R 0 When R≤R 0 When the carbon footprint of the target building complex is determined to be within the standard, R 0 is a preset total carbon footprint threshold.

[0014] The present invention has at least the following beneficial effects:

[0015] The present invention provides a large-model carbon footprint intelligent analysis platform based on a carbon consensus mechanism, which is used to analyze whether the carbon footprint of a target building complex exceeds the standard. The intelligent analysis platform can obtain a preset feature label list set corresponding to the entire life cycle of the building, obtain the total set of original carbon footprint list sets corresponding to the target building complex based on the preset feature label list set, obtain the weighted average carbon footprint corresponding to the preset feature label based on the total set of original carbon footprint list sets, and add the weighted average carbon footprints corresponding to all preset feature labels to obtain the total carbon footprint corresponding to the target building complex. When the total carbon footprint is greater than the preset total carbon footprint threshold, it is determined that the carbon footprint of the target building complex exceeds the standard; otherwise, it is determined that the carbon footprint of the target building complex does not exceed the standard. It can be seen that the present invention only needs to collect the carbon footprint data generated by the features corresponding to the preset feature labels in the stages of the entire life cycle of the target buildings in the target building complex, that is, the original carbon footprint, and determine whether the carbon footprint of the target building complex exceeds the standard based on the original carbon footprint, which is conducive to improving the accuracy of the result of determining whether the carbon footprint of the target building complex exceeds the standard. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A flowchart of a large-scale carbon footprint intelligent analysis platform based on a carbon consensus mechanism provided in an embodiment of the present invention executing a computer program to analyze whether the carbon footprint of a target building complex exceeds the standard. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar tasks and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0020] An embodiment of the present invention provides a large-scale carbon footprint intelligent analysis platform based on a carbon consensus mechanism. The intelligent analysis platform is used to analyze whether the carbon footprint of a target building complex exceeds the standard. The intelligent analysis platform includes a processor and a memory storing a computer program. When the computer program is executed by the processor, the following steps are implemented: Figure 1 As shown:

[0021] S1. Obtain the preset feature label list set A={A1, A2, ..., A i ,……,A m}, A i ={A i1 , A i2 ,……,A ij ,……,A in(i)}, where A i is a list of preset feature labels corresponding to the i-th stage of the building life cycle, i ranges from 1 to m, m is the number of stages in the building life cycle, A ij is the jth preset feature label corresponding to the i-th stage of the building's life cycle, and the value of j ranges from 1 to n(i), and n(i) is the number of preset feature labels corresponding to the i-th stage of the building's life cycle. The preset feature label is the name of the feature that can generate carbon footprints during the stage of the building's life cycle. For example, if the i-th stage of the building's life cycle is the design stage, the selection of building materials and design of energy efficiency in the design stage can all generate carbon footprints. Therefore, A ijIt can be for building material selection, energy efficiency design, etc.

[0022] Optionally, m=4 and when m=4, the first stage of the building's life cycle is the design stage, the second stage of the building's life cycle is the construction stage, the third stage of the building's life cycle is the operation and maintenance stage, and the fourth stage of the building's life cycle is the demolition stage.

[0023] S2, according to A, obtain the total set of original carbon footprint lists corresponding to the target building complex B = {B 1 , B 2 ,……,B r ,……,B s}, B r ={B r 1, B r 2, ..., B r i , ... B r m}, B r i ={B r i1 , B r i2 ,……,B r ij ,……,B r in(i)}, where B r is the original carbon footprint list set corresponding to the rth target building in the target building complex, r ranges from 1 to s, s is the number of target buildings in the target building complex, B r i B r China A i The corresponding raw carbon footprint list, B r ij B r i China A ij The corresponding original carbon footprint is the carbon footprint generated by the features corresponding to the preset feature tags during the entire life cycle of the target building.

[0024] S3, based on B r ij Get A ij The corresponding weighted average carbon footprint E ij .

[0025] Specifically, step S3 includes the following sub-steps S31-S34:

[0026] S31, B r i As C ri To obtain A i The corresponding initial carbon footprint list set C i ={C 1 i , C 2 i ,……,C r i ,……,C s i}, C r i ={C r i1 , C r i2 ,……,C r ij ,……,C r in(i)}, where C r i A i The corresponding r-th initial carbon footprint list, C r ij C r i China A ij The corresponding initial carbon footprint.

[0027] Specifically, C r ij =B r ij .

[0028] S32, C r i Input to A i The corresponding random forest model and the output of the random forest model are used to obtain C r i Corresponding classification identification list D r i ={D r i1 , D r i2 ,……,D r ij ,……,D r in(i)}, where D r ij C r ij The corresponding classification identifier, where C r ij The corresponding classification is C r i Input to A iThe corresponding random forest model is described later in the random forest model with A ij The identifier of the corresponding decision tree output.

[0029] Specifically, the output of the decision tree is either a "1" or a "0".

[0030] Furthermore, the mark "1" indicates that the carbon footprint exceeds the standard.

[0031] Furthermore, the mark "0" indicates that the carbon footprint does not exceed the standard.

[0032] Specifically, A i The corresponding random forest model includes n(i) decision trees.

[0033] Furthermore, A i The n(i) decision trees in the corresponding random forest model correspond one-to-one to the n(i) preset feature labels corresponding to the i-th stage of the building life cycle, which can be understood as: A i Each decision tree in the corresponding random forest is i The preset feature labels in A and the preset feature labels corresponding to any two decision trees in the random forest are different, i The decision trees in the corresponding random forest model make decisions based on the preset feature labels corresponding to them.

[0034] S33, when D 1 ij , D 2 ij ,……,D r ij ,……,D s ij When both are marked as "1" or "0", A ij As the stable feature label corresponding to the i-th stage of the building life cycle, E ij Meet the following conditions:

[0035] E ij =W i1 ×Σ s r=1 (C r ij -F ij ) / s, where W i1 is the preset feature weight corresponding to the stable feature label corresponding to the i-th stage of the building life cycle, F ij A i The corresponding random forest model is ijThe preset characteristic carbon footprint threshold corresponding to the corresponding decision tree can be understood as the threshold used by the decision tree to determine whether the initial carbon footprint corresponding to the preset characteristic label thereof exceeds the standard when making a decision. When the initial carbon footprint corresponding to the preset characteristic label corresponding to the decision tree exceeds the preset characteristic carbon footprint threshold, it is considered that the initial carbon footprint exceeds the standard and an identification "1" is output; otherwise, it is considered that the initial carbon footprint does not exceed the standard and an identification "0" is output. Those skilled in the art know that the preset characteristic weights corresponding to the stable characteristic labels corresponding to the i-th stage of the building's life cycle are set by those skilled in the art according to actual needs and will not be elaborated here.

[0036] S34, when D 1 ij , D 2 ij ,……,D r ij ,……,D s ij When neither is marked as "1" nor is marked as "0", A ij As the unstable feature label corresponding to the i-th stage of the building life cycle, E ij Meet the following conditions:

[0037] E ij =W i2 ×Σ s r=1 ((C r ij -F ij )×G r ij ) / s,W i2 is the preset feature weight corresponding to the unstable feature label corresponding to the i-th stage of the building life cycle, G r ij D r ij The corresponding quantity ratio, where D r ij The corresponding quantity ratio is based on D 1 ij , D 2 ij ,……,D r ij ,……,D s ij Middle and D r ij The number of identical classification identities and s are obtained.

[0038] Specifically, when D r ij When the symbol is “1”, Gr ij =H 1 ij / s,H 1 ij D 1 ij , D 2 ij ,……,D r ij ,……,D s ij is the number of classification marks marked with "1"; when D r ij When the mark is "0", G r ij =H 0 ij / s,H 0 ij D 1 ij , D 2 ij ,……,D r ij ,……,D s ij The number of classification identifiers marked with "0" is known to those skilled in the art. The preset feature weights corresponding to the unstable feature labels corresponding to the i-th stage of the building's entire life cycle are set by those skilled in the art according to actual needs and will not be elaborated here.

[0039] Through the above steps, an initial carbon footprint list set corresponding to the preset feature label list is obtained according to the total set of the original carbon footprint list set, the initial carbon footprint list corresponding to the preset feature label list is input into the random forest model corresponding to the preset feature label list to obtain a classification identifier list corresponding to the initial carbon footprint list, and the weighted average carbon footprint corresponding to the preset feature label is obtained based on the classification identifier in the classification identifier list, which is conducive to improving the accuracy of obtaining the weighted average carbon footprint. Furthermore, the sum of the weighted average carbon footprints corresponding to all preset feature labels is used as the total carbon footprint corresponding to the target building complex. When the total carbon footprint is greater than the preset total carbon footprint threshold, it is determined that the carbon footprint of the target building complex exceeds the standard. Otherwise, it is determined that the carbon footprint of the target building complex does not exceed the standard, which is conducive to improving the accuracy of the result of determining whether the carbon footprint of the target building complex exceeds the standard.

[0040] S4. According to E ij Obtain the total carbon footprint R corresponding to the target building complex, where R meets the following conditions:

[0041] R=Σ m i=1 Σ n(i) j=1 Eij .

[0042] S5. When R>R 0 When R≤R 0 When the carbon footprint of the target building complex is determined to be within the standard, R 0 is a preset total carbon footprint threshold.

[0043] The present invention provides a large-model carbon footprint intelligent analysis platform based on a carbon consensus mechanism, which is used to analyze whether the carbon footprint of a target building complex exceeds the standard. The intelligent analysis platform can obtain a preset feature label list set corresponding to the entire life cycle of the building, obtain the total set of original carbon footprint list sets corresponding to the target building complex based on the preset feature label list set, obtain the weighted average carbon footprint corresponding to the preset feature label based on the total set of original carbon footprint list sets, and add the weighted average carbon footprints corresponding to all preset feature labels to obtain the total carbon footprint corresponding to the target building complex. When the total carbon footprint is greater than the preset total carbon footprint threshold, it is determined that the carbon footprint of the target building complex exceeds the standard; otherwise, it is determined that the carbon footprint of the target building complex does not exceed the standard. It can be seen that the present invention only needs to collect the carbon footprint data generated by the features corresponding to the preset feature labels in the stages of the entire life cycle of the target buildings in the target building complex, that is, the original carbon footprint, and determine whether the carbon footprint of the target building complex exceeds the standard based on the original carbon footprint, which is conducive to improving the accuracy of the result of determining whether the carbon footprint of the target building complex exceeds the standard.

[0044] An embodiment of the present invention also provides a non-transitory computer-readable storage medium, which can be set in an electronic device to store a computer program related to a method in the method embodiment. The computer program is loaded and executed by the processor to implement the method provided in the above embodiment.

[0045] An embodiment of the present invention further provides an electronic device, comprising: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method provided in the above embodiment when executing the computer program.

[0046] An embodiment of the present invention further provides a computer program product comprising program code. When the program product is run on an electronic device, the program code is used to enable the electronic device to execute the steps of the method according to various exemplary embodiments of the present invention described above in this specification.

[0047] Although some specific embodiments of the present invention have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present invention. It should also be understood by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the present invention.

Claims

1. A large-scale carbon footprint intelligent analysis platform based on a carbon consensus mechanism, which is used to analyze whether the carbon footprint of a target building complex exceeds the standard. It is characterized by: The intelligent analysis platform includes a processor and a memory storing a computer program. When the computer program is executed by the processor, the following steps are implemented: S1. Obtain the preset feature label list set A={A1, A2, ..., A i ,……,A m }, A i ={A i1 , A i2 ,……,A ij ,……,A in(i) }, where A i is a list of preset feature labels corresponding to the i-th stage of the building life cycle, i ranges from 1 to m, m is the number of stages in the building life cycle, A ij is the jth preset feature label corresponding to the i-th stage of the building's life cycle, where j ranges from 1 to n(i), and n(i) is the number of preset feature labels corresponding to the i-th stage of the building's life cycle; S2, according to A, obtain the total set of original carbon footprint lists corresponding to the target building complex B = {B 1 , B 2 ,……,B r ,……,B s }, B r ={B r 1, B r 2, ..., B r i , ... B r m }, B r i ={B r i1 , B r i2 ,……,B r ij ,……,B r in(i) }, where B r is the original carbon footprint list set corresponding to the rth target building in the target building complex, r ranges from 1 to s, s is the number of target buildings in the target building complex, B r i B r China A i The corresponding raw carbon footprint list, B r ij B r i China A ij The corresponding original carbon footprint is the carbon footprint generated by the features corresponding to the preset feature tags during the entire life cycle of the target building; S3, based on B r ij Get A ij The corresponding weighted average carbon footprint E ij ; Step S3 includes the following sub-steps S31-S34: S31, B r i As C r i To obtain A i The corresponding initial carbon footprint list set C i ={C 1 i , C 2 i ,……,C r i ,……,C s i }, C r i ={C r i1 , C r i2 ,……,C r ij ,……,C r in(i) }, where C r i A i The corresponding r-th initial carbon footprint list, C r ij C r i China A ij the corresponding initial carbon footprint; S32, C r i Input to A i The corresponding random forest model and the output of the random forest model are used to obtain C r i Corresponding classification identification list D r i ={D r i1 , D r i2 ,……,D r ij ,……,D r in(i) }, where D r ij C r ij The corresponding classification identifier, where C r ij The corresponding classification is C r i Input to A i The corresponding random forest model is described later in the random forest model with A ij The corresponding decision tree output identifier is "1" or "0". i Each decision tree in the corresponding random forest is i The predetermined feature labels in the random forest correspond to one of the predetermined feature labels, and the predetermined feature labels corresponding to any two decision trees in the random forest are different; S33, when D 1 ij , D 2 ij ,……,D r ij ,……,D s ij When both are marked as "1" or "0", A ij As the stable feature label corresponding to the i-th stage of the building life cycle, E ij Meet the following conditions: E ij =W i1 ×Σ s r=1 (C r ij -F ij ) / s, where W i1 is the preset feature weight corresponding to the stable feature label corresponding to the i-th stage of the building life cycle, F ij A i The corresponding random forest model is ij The preset characteristic carbon footprint threshold corresponding to the corresponding decision tree is the threshold used by the decision tree to determine whether the initial carbon footprint corresponding to the preset characteristic tag thereof exceeds the standard when making a decision; when the initial carbon footprint corresponding to the preset characteristic tag corresponding to the decision tree exceeds the preset characteristic carbon footprint threshold, it is considered that the initial carbon footprint exceeds the standard and an indicator "1" is output; otherwise, it is considered that the initial carbon footprint does not exceed the standard and an indicator "0" is output; S34, when D 1 ij , D 2 ij ,……,D r ij ,……,D s ij When neither is marked "1" nor is marked "0", A ij As the unstable feature label corresponding to the i-th stage of the building life cycle, E ij Meet the following conditions: E ij =W i2 ×Σ s r=1 ((C r ij -F ij )×G r ij ) / s,W i2 is the preset feature weight corresponding to the unstable feature label corresponding to the i-th stage of the building life cycle, G r ij D r ij The corresponding quantity ratio, where D r ij The corresponding quantity ratio is based on D 1 ij , D 2 ij ,……,D r ij ,……,D s ij Middle and D r ij The number of identical classification identifiers and s are obtained; S4. According to E ij Obtain the total carbon footprint R corresponding to the target building complex, where R meets the following conditions: R=Σ m i=1 S n(i) j=1 E ij ; S5. When R>R 0 When R≤R 0 When the carbon footprint of the target building complex is determined to be within the standard, R 0 is a preset total carbon footprint threshold.

2. The large-scale carbon footprint intelligent analysis platform based on the carbon consensus mechanism according to claim 1 is characterized in that: The preset feature labels are the names of features that can generate carbon footprints during the stages of the building's life cycle.

3. The large-scale carbon footprint intelligent analysis platform based on the carbon consensus mechanism according to claim 1 is characterized in that: C r ij =B r ij 。 4. The large-scale carbon footprint intelligent analysis platform based on the carbon consensus mechanism according to claim 2 is characterized in that: The mark "1" indicates that the carbon footprint exceeds the standard.

5. The large-scale carbon footprint intelligent analysis platform based on the carbon consensus mechanism according to claim 1 is characterized in that: The mark "0" indicates that the carbon footprint does not exceed the standard.

6. The large-scale carbon footprint intelligent analysis platform based on the carbon consensus mechanism according to claim 1 is characterized in that: A i The corresponding random forest model includes n(i) decision trees.

7. The large-scale carbon footprint intelligent analysis platform based on the carbon consensus mechanism according to claim 6 is characterized in that: A i The n(i) decision trees in the corresponding random forest model correspond one-to-one to the n(i) preset feature labels corresponding to the i-th stage of the building's life cycle.

8. The large-scale carbon footprint intelligent analysis platform based on the carbon consensus mechanism according to claim 1 is characterized in that: When D r ij When the mark is "1", G r ij =H 1 ij / s,H 1 ij D 1 ij , D 2 ij ,……,D r ij ,……,D s ij The number of category identifiers with the identifier "1".

9. The large-scale carbon footprint intelligent analysis platform based on the carbon consensus mechanism according to claim 1 is characterized in that: When D r ij When the mark is "0", G r ij =H 0 ij / s,H 0 ij D 1 ij , D 2 ij ,……,D r ij ,……,D s ij The number of category identifiers with the identifier "0".

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