Carbon emission calculation method and system based on building main material equivalent weight

Abstract

The invention discloses a carbon emission calculation method and system based on building main material equivalent, belongs to the technical field of carbon emission calculation, and solves the problems that an existing carbon emission calculation method is high in data requirement, low in calculation speed and insufficient in applicability. By analyzing historical project data, screening out key principal materials and constructing an algorithm model between consumption and total carbon emission, 'less data and high precision 'rapid calculation is realized, indirect carbon emission generated by building material production, transportation and construction is mapped to the key principal materials through weights, a whole-process carbon emission equivalent evaluation system is established, and the evaluation accuracy is improved. And the whole-process carbon emission can be output in a one-stop manner by inputting the consumption. The model output is an interpretable regression equation with a clear coefficient weight, a transparent decision basis is provided, and in addition, the key main materials are pre-determined through an algorithm, so that a new project can directly utilize the use amount of the existing key main materials in the project cost process to carry out carbon emission quick calculation.

Description

Technical Field

[0001] This invention relates to the field of carbon emission calculation technology, specifically to a method and system for calculating carbon emissions based on the equivalent of building materials. Background Technology

[0002] With the increasing severity of global climate change, the construction industry, as one of the major sources of carbon emissions, has made its low-carbon transformation crucial for achieving the "dual carbon" goals. Carbon emissions throughout the building lifecycle mainly include stages such as building material production and transportation, construction, building operation, and demolition and recycling. While energy consumption and carbon emissions during the operation phase have been controlled to some extent with continuously improving building energy efficiency standards, the "physical phase," including building material production, transportation, and construction processes, has become the focus and challenge for building emission reduction. Currently, existing technologies for calculating and predicting building-related carbon emissions mainly achieve this through the following pathways: The first category is inventory analysis based on Life Cycle Assessment (LCA) theory. For example, there's a community carbon emission calculation model based on LCA theory disclosed in announcement CN116756468A. This type of method typically relies on a detailed bill of materials, calculating the total by summarizing activity level data such as all building materials, construction machinery shifts, and transportation distances, and multiplying them by the corresponding carbon emission factors. However, the drawback of this type of method is its extreme dependence on complete data; the calculation process is cumbersome and time-consuming. This makes it unusable in "low-data environments" where data is scarce, thus limiting its guiding role.

[0003] The second category is refined calculation methods based on Building Information Modeling (BIM). For example, the low-carbon building material selection method based on BIM 5D technology, disclosed in public account CN120338404A, extracts component information from the BIM model for calculation, achieving high accuracy. However, this type of method has the drawback of requiring extremely high completeness and detail (Level of Detail) of the BIM model. Building a high-precision BIM model requires significant investment of manpower and time, and if the material properties defined in the model are not standardized, it can easily lead to deviations in the calculation results, making it difficult to meet the needs of rapid estimation.

[0004] The third category is prediction methods based on artificial intelligence, such as the building carbon emission prediction method based on the FE-BO-HGBoost combined model disclosed in announcement number CN121119370A. These methods mainly use historical data to train the model to predict carbon emissions. However, they have obvious shortcomings: on the one hand, the model exhibits "black box" characteristics, making it difficult to reveal a clear quantitative relationship between input variables (such as the amount of specific main materials used) and carbon emission results, and lacking physical interpretability; on the other hand, existing models often isolate the calculation boundaries, focusing only on operating energy consumption or treating building materials, transportation, and construction in isolation, failing to establish an integrated mapping relationship between key main materials and total physical carbon emissions including transportation and construction, and ignoring their inherent strong correlation.

[0005] In summary, existing technologies for building-based carbon emission accounting either rely on extremely detailed project lifecycle data, leading to high costs and computational delays, or employ complex black-box models that lose their guiding significance in engineering applications, resulting in problems such as high data requirements, slow computation speed, and insufficient applicability. Summary of the Invention

[0006] To address the aforementioned shortcomings of existing technologies, this invention provides a carbon emission calculation method and system based on the equivalent of building materials, which solves the problems of high data requirements, slow calculation speed, and insufficient applicability of existing carbon emission calculation methods.

[0007] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows: On the one hand, a method for calculating carbon emissions based on building material equivalents is provided, including the following steps: S100. Collect full data from multiple historical building projects. The full data should include data from at least three dimensions: building material consumption, building material construction, and building material transportation. S200. Preprocess all data for each historical building project and convert the carbon emission data of all dimensions in the preprocessed data of each historical building project into total physical carbon emissions. S300. Calculate the Pearson correlation coefficient between each building material and the total physical carbon emissions of each historical building project in the building material consumption, and take the building materials with the Pearson correlation coefficient greater than the preset coefficient threshold as the preliminary screening main materials; calculate the variance expansion factor between each preliminary screening main material and other preliminary screening main materials, and take the corresponding preliminary screening main materials with the variance expansion factor less than the preset factor threshold as the undetermined main materials. S400. Calculate the construction energy consumption coupling influence index between each type of undetermined main material and the corresponding building material construction data in each historical building project, and obtain the average value of the construction energy consumption coupling influence index of each type of undetermined main material. The corresponding undetermined main material whose average value is greater than the preset coupling threshold is taken as the key main material. S500. Construct a linear regression model by taking the consumption of each key material and the total physical carbon emissions of each historical building project as independent and dependent variables, respectively, and introduce a regularization penalty term to solve the coefficient vector of the linear regression model. S600: Obtain the consumption of each key main material in the new construction project, and input it into the solved linear regression model to obtain the total physical carbon emissions of the new construction project.

[0008] Furthermore, the expression for calculating the total physical carbon emissions of each historical building project is as follows: ; ; ; in, The total physical carbon emissions for each historical building project; , and These are the physical carbon emissions from building material production, building material transportation, and building material construction, respectively. and The first Consumption and carbon emission factors of various building materials; This represents the total number of building material categories. and The first Carbon emission factors for the average transportation distance and unit weight transportation distance of various building materials; and These are the first stages of the building materials construction process. Total energy consumption and carbon emission factors of the species; This refers to the total amount of energy.

[0009] Furthermore, the expression for the Pearson correlation coefficient between each building material and the total physical carbon emissions of each historical building project in the building material consumption dimension is as follows: in, For the first Pearson correlation coefficients for various building materials; For the first The first historical building project The consumption of various building materials; For the first Total material carbon emissions of each historical building project; This represents the total number of historical building projects. The first among all historical building projects Average consumption of various building materials; This represents the average total physical carbon emissions across all historical building projects.

[0010] Furthermore, the expression for calculating the variance inflation factor between each pre-selected main material and other pre-selected main materials is as follows: in, For the first A preliminary screening factor for the variance expansion of the main materials; For the first Goodness of fit between the initial screening material and other initial screening materials.

[0011] Furthermore, the expression for calculating the construction energy consumption coupling influence index between each undetermined main material and the corresponding construction dimension data of each historical building project is as follows: in, For the first The coupling effect index of construction energy consumption of undetermined main materials; For the first Pearson correlation coefficient for undetermined main materials; It is a correlation-based nonlinear activation function; and All are weighting coefficients. ; This refers to the total construction period of current historical building projects. This represents the total number of types of building materials and construction equipment. In order to be in At any given time, the unit consumed is number one. The first type of undetermined main material to be called The shift quota for this type of machinery; For the first The unit energy consumption intensity of various building material construction equipment; For the first Carbon emission factor conversion coefficient of various building materials and construction equipment; For the first Standard deviation of the dispersion of consumption of certain main materials and corresponding construction equipment for building materials; The Gaussian time-domain decay weighting factor is... For the first The central moment when the consumption rate of the undetermined main material reaches its peak; The first among all historical building projects The arithmetic mean of the standard deviations of the consumption of certain main materials over time; To correct for deviation values.

[0012] Furthermore, step S500 includes: S510. Construct a linear regression model, the expression of which is: in, The dependent variable vector; , , and They are the 1st, 2nd, and 3rd respectively. The and the first Total material carbon emissions of each historical building project; The vector of independent variables; , and The first and second historical building projects, respectively. The and the first Consumption of key main materials; , and They are the first and second of the second historical building projects, respectively. The and the first Consumption of key main materials; , and The first The first of the historical building projects, the first The and the first Consumption of key main materials; , and The first The first of the historical building projects, the first The and the first Consumption of key main materials; For coefficient vectors; The intercept; , and The first and the second, respectively The and the first The weight of each key main material; This is the error vector; S520. Construct the objective function of the linear regression model, the expression of which is: in, The objective function is... For regularization parameters; S530, Regarding the objective function Differentiate and set to zero, then solve. : in, It is an identity matrix.

[0013] Furthermore, step S500 also includes: S540. Obtain the validation set. Each sample in the validation set includes the consumption of key main materials and the actual total carbon emissions of the building project. S550. Input the consumption of key main materials for each sample in the validation set into the solved linear regression model to obtain the predicted total carbon emissions for each sample. S560. Calculate the goodness of fit of the validation set. and mean absolute percentage error : ; in, and The first The actual and predicted total carbon emissions for each sample; The total number of samples in the validation set; To verify the mean of the true total carbon emissions of all samples in the validation set; S570, Judgment and Are all values ​​less than their respective set thresholds? If so, the current linear regression model meets the requirements; otherwise, proceed to step S580. S580, Modify regularization parameters For the objective function Differentiate and set to zero, then solve. For the objective function Differentiate and set to zero, then solve. Then return to step S540.

[0014] On the other hand, a system for calculating carbon emissions based on building material equivalents is provided, comprising: The data acquisition module is used to collect full data from multiple historical building projects. The full data includes data from at least three dimensions: building material consumption, building material construction, and building material transportation. The data processing module is used to preprocess all the data of each historical building project and convert the carbon emission data of all dimensions of the preprocessed data into the total physical carbon emissions. The pending main material screening module is used to calculate the Pearson correlation coefficient between each building material in the building material consumption and the total physical carbon emissions of each historical building project, and to select the corresponding building materials whose Pearson correlation coefficient is greater than the preset coefficient threshold as the preliminary screening main materials; calculate the variance expansion factor between each preliminary screening main material and other preliminary screening main materials, and select the corresponding preliminary screening main materials whose variance expansion factor is less than the preset factor threshold as the pending main materials. The key main material screening module is used to calculate the construction energy consumption coupling influence index between each type of pending main material and the corresponding building material construction data in each historical building project, and to obtain the average value of the construction energy consumption coupling influence index of each type of pending main material. The corresponding pending main material whose average value is greater than the preset coupling threshold is regarded as the key main material. The linear regression model building module is used to construct a linear regression model by taking the consumption of each key main material and the total physical carbon emissions of each historical building project as independent and dependent variables, respectively, and introduces a regularization penalty term to solve the coefficient vector of the linear regression model. The decision-making module is used to predict the total physical carbon emissions of new construction projects based on the consumption of each key material in the project and using a linear regression model.

[0015] Furthermore, systems based on carbon emission calculation methods using building material equivalents also include: The management module is used to set multiple management cycles in new construction projects, decompose the carbon emission benchmark value of the new construction project into carbon emission limits corresponding to multiple management cycles, and predict the total physical carbon emissions of the corresponding management cycle based on the consumption of key main materials in each management cycle; determine whether the total physical carbon emissions of the current management cycle exceed the carbon emission limit, and if so, issue an early warning; otherwise, archive the total physical carbon emissions of the current management cycle.

[0016] Compared with the prior art, the present invention has the following significant advantages: 1. Establish a rapid calculation mechanism with "less data, higher accuracy". By analyzing the full data of multiple historical building projects, a few key main materials (such as concrete and steel) that contribute the most to building carbon emissions are selected, and an algorithmic model is constructed to link the consumption of these key main materials with the total building carbon emissions. Thus, without the need to comprehensively monitor all auxiliary materials, construction facilities, and transportation data, the total emissions can be accurately calculated based solely on the existing data of main material usage, greatly reducing the dimensionality and difficulty of data collection (the existing engineering cost workflow requires an estimate of the project quantity during the design phase, verification of the project quantity in stages during construction, and settlement of the actual project quantity upon project completion, while this invention requires no additional data collection in new building projects, and can directly use the existing data of the engineering cost workflow for rapid calculation).

[0017] 2. Construct a "Full-Process Carbon Emission Equivalent" Evaluation System. This system reveals the quantitative coupling relationship between key building materials and carbon emissions from building material transportation and construction, proposing the concept of "key building materials." Indirect carbon emissions generated during building material production, transportation, and construction are distributed to each key building material through algorithmic weighting, achieving a one-stop calculation of "input key building material consumption, output full-process carbon emissions," thus solving the problem of fragmented calculation boundaries in traditional methods.

[0018] 3. Provides interpretable scientific decision-making basis. Unlike traditional black-box models, this invention outputs regression equations with clear mathematical meaning (including the coefficient weights of key building materials on building carbon emissions). This not only provides small and medium-sized construction enterprises with a low-cost, easy-to-use self-assessment tool, but also provides government departments with transparent and scientific quantitative basis for formulating carbon emission quota standards based on building material consumption and verifying carbon indicators. This helps promote the standardization and intelligentization of carbon emission management in the construction industry and improves its applicability.

[0019] 4. The data required for the calculation of key main materials in this invention comes from historical archives of historical building projects (such as construction logs, monthly progress payment settlement statements, etc.). Since it is based on retrospective analysis of historical static data, rather than real-time streaming calculation of projects under construction, there is no time lag or implementation difficulty associated with on-site operations. Once the set of key main materials is determined, this complex integral calculation (construction energy consumption coupling impact index) does not need to be performed again in subsequent rapid calculation applications for new projects, improving calculation speed and applicability.

[0020] 5. The management module of this invention decomposes the carbon emission benchmark value into periodic limits (such as monthly limits) and combines them with real-time data prediction to achieve dynamic control of the construction process. Once the limit is exceeded, an immediate warning is issued, which facilitates timely intervention and promotes the transformation of carbon emissions from "static assessment" to "process control", thus helping the construction industry to manage low-carbon emissions. Attached Figure Description

[0021] Figure 1 This is a flowchart of a method for calculating carbon emissions based on the equivalent of building materials.

[0022] Figure 2 This is a flowchart illustrating the system application method for calculating carbon emissions based on the equivalent of building materials. Detailed Implementation

[0023] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0024] Considering the problems of high data requirements, slow calculation speed, and insufficient applicability of existing carbon emission calculation methods, this embodiment provides a carbon emission calculation method based on building material equivalents, referring to... Figure 1 The steps include: S100. Collect full data from multiple historical building projects. The full data should include data from at least three dimensions: building material consumption, building material construction, and building material transportation.

[0025] S200. Preprocess all data for each historical building project and convert all carbon emission data in all dimensions of the preprocessed data for each historical building project into total physical carbon emissions.

[0026] The expression for calculating the total physical carbon emissions of each historical building project is as follows: ; ; ; in, The total physical carbon emissions for each historical building project; , and These are the physical carbon emissions from building material production, building material transportation, and building material construction, respectively. and The first Consumption and carbon emission factors of various building materials; This represents the total number of building material categories. and The first Carbon emission factors for the average transportation distance and unit weight transportation distance of various building materials; and These are the first stages of the building materials construction process. Total energy consumption and carbon emission factors of the species; This refers to the total amount of energy.

[0027] S300. Calculate the Pearson correlation coefficient between each building material and the total physical carbon emissions of each historical building project in the building material consumption dimension, and take the building materials with the Pearson correlation coefficient greater than the preset coefficient threshold as the initial screening main materials; calculate the variance expansion factor between each initial screening main material and other initial screening main materials, and take the corresponding initial screening main materials with the variance expansion factor less than the preset factor threshold as the undetermined main materials.

[0028] The expression for calculating the Pearson correlation coefficient between each building material and the total physical carbon emissions of each historical building project in the building material consumption dimension is as follows: in, For the first Pearson correlation coefficients for various building materials; For the first The first historical building project The consumption of various building materials; For the first Total material carbon emissions of each historical building project; This represents the total number of historical building projects. The first among all historical building projects Average consumption of various building materials; This represents the average total physical carbon emissions across all historical building projects.

[0029] The expression for calculating the variance inflation factor between each preliminarily selected main material and other preliminarily selected main materials is as follows: in, For the first A preliminary screening factor for the variance expansion of the main materials; For the first Goodness of fit between the initial screening material and other initial screening materials.

[0030] In this implementation, the preset coefficient threshold is preferably 0.6, meaning that a building material with a Pearson correlation coefficient greater than 0.6 is considered as a preliminary main material for screening. The preset factor threshold is preferably 10. If the variance inflation factor of a preliminary main material is greater than or equal to 10, it indicates that there is severe collinearity in the data dimensions of the preliminary main material, and it will be removed. All preliminary main materials with variance inflation factors less than 10 will be included in the set of undetermined main materials.

[0031] S400. Calculate the construction energy consumption coupling influence index between each type of undetermined main material and the corresponding building material construction data in each historical building project, and obtain the average value of the construction energy consumption coupling influence index for each type of undetermined main material. Ensure that this average value is greater than a preset coupling threshold. The corresponding undetermined main material is taken as the key main material.

[0032] S500. Construct a linear regression model by taking the consumption of each key material and the total physical carbon emissions of each historical building project as independent and dependent variables, respectively, and introduce a regularization penalty term to solve the coefficient vector of the linear regression model.

[0033] S600: Obtain the consumption of each key main material in the new construction project, and input it into the solved linear regression model to obtain the total physical carbon emissions of the new construction project.

[0034] Specifically, this embodiment considers that the definition of a key building material depends not only on its own physical consumption but also on the degree of "passive use" of high-energy-consuming machinery and equipment during construction. Therefore, a construction energy consumption coupling influence index is introduced. This serves as the final selection criterion. By analyzing the entire historical data set, a [system / mechanism] is established. The main material to be determined and the first The time-dimensional correlation mapping of various construction machines is expressed as follows: in, For the first The coupling effect index of construction energy consumption of undetermined main materials; For the first Pearson correlation coefficient for undetermined main materials; It is a correlation nonlinear activation function; used to amplify the weight of highly correlated materials. The more the main material to be determined is consumed, the higher the total physicochemical carbon emissions will be. and These are the direct material carbon emission weights and the indirect mechanical energy consumption weights, respectively. During the training phase, a grid search algorithm can be used, with the objective function being to maximize the goodness of fit of the model on the validation set. The algorithm iterates within the interval [0,1] with a step size of 0.01 to finally determine the optimal weight combination. This refers to the total construction period of the current historical building project, encompassing the entire construction cycle. Within the time domain, the instantaneous energy consumption impact index generated by the coupling between materials and machinery is continuously accumulated and integrated to obtain the total cumulative impact of the material throughout its entire life cycle. This represents the total number of types of building materials and construction equipment. Let be the material-mechanical dependence function, representing that in At any given time, the unit consumed is number one. The first type of undetermined main material to be called The fixed number of pump truck shifts required for each type of machinery, for example: how many pump truck shifts are needed per cubic meter of concrete. For the first The unit energy consumption intensity of various building material construction equipment; For the first Carbon emission factor conversion coefficient of various building materials and construction equipment; For the first The standard deviation of the dispersion of the consumption of certain main materials and the corresponding construction equipment is used to penalize abnormal matches with excessive data fluctuations. This is a Gaussian time-domain decay weighting factor used to characterize the time sensitivity of material consumption. For time; For the first The central moment when the consumption rate of the undetermined main material reaches its peak; The first among all historical building projects The arithmetic mean of the standard deviations of the consumption of certain main materials over time; when The smaller the value, the more concentrated the use of construction machinery for the selected building material is. Near this location, the model has a stronger ability to suppress data noise during off-peak periods. To correct for bias values, the indirect carbon emission effect of materials "passively calling on machinery" (such as the number of pump truck shifts required per cubic meter of concrete) was quantified by using parameters such as nonlinear activation functions and Gaussian time-domain decay weights. This addresses the shortcomings of existing methods that ignore mechanical coupling. The construction energy consumption coupling effect index amplifies the weight of key main materials, making the selection more accurate.

[0035] In this embodiment, the calculation and feature selection process of the construction energy consumption coupling impact index can be implemented using the Python programming language. Numerical integration is performed using the `integrate.quad` function in the SciPy library, and further processed using the NumPy library. The calculation of the construction energy consumption coupling impact index in step S400 is only used for feature extraction during the algorithm development phase. The required time-series data comes from historical archives of completed historical building projects (such as construction logs, monthly progress payment settlement sheets, etc.). Since this is a retrospective analysis based on historical static data, rather than a real-time streaming calculation of projects under construction, there is no time lag or implementation difficulty associated with on-site operations. Once the set of key main materials is determined, this complex integration calculation does not need to be performed again in subsequent rapid calculation applications for new building projects.

[0036] In this embodiment, concrete, steel reinforcement, and masonry were ultimately selected as the main materials for the model's independent variables. The consumption of these three key materials accounts for more than 85% of the total carbon emissions from building construction.

[0037] Specifically, step S500 includes: S510. Construct a linear regression model, the expression of which is: in, The dependent variable vector; , , and They are the 1st, 2nd, and 3rd respectively. The and the first Total material carbon emissions of each historical building project; The vector of independent variables; , and The first and second historical building projects, respectively. The and the first Consumption of key main materials; , and They are the first and second of the second historical building projects, respectively. The and the first Consumption of key main materials; , and The first The first of the historical building projects, the first The and the first Consumption of key main materials; , and The first The first of the historical building projects, the first The and the first Consumption of key main materials; For coefficient vectors; The intercept; , and The first and the second, respectively The and the first The weight of each key main material; This is the error vector.

[0038] S520. Construct the objective function of the linear regression model, the expression of which is: in, The objective function is... This is a regularization parameter that can effectively suppress overfitting; S530, Regarding the objective function Differentiate and set to zero: Solve , ; in, It is an identity matrix.

[0039] As a further embodiment, step S500 also includes: S540. Obtain the validation set. Each sample in the validation set includes the consumption of key main materials and the actual total carbon emissions of the building project. S550. Input the consumption of key main materials for each sample in the validation set into the solved linear regression model to obtain the predicted total carbon emissions for each sample. S560. Calculate the goodness of fit of the validation set. and mean absolute percentage error : ; in, and The first The actual and predicted total carbon emissions for each sample; The total number of samples in the validation set; To verify the mean of the true total carbon emissions of all samples in the validation set; S570, Judgment and Are all values ​​less than their respective set thresholds? If so, the current linear regression model meets the requirements; otherwise, proceed to step S580. S580, Modify regularization parameters For the objective function Differentiate and set to zero, then solve. For the objective function Differentiate and set to zero, then solve. Then return to step S540.

[0040] As a further aspect of this embodiment, this embodiment also provides a system for calculating carbon emissions based on the equivalent of building materials, including: The data acquisition module is used to collect full data from multiple historical building projects. The full data includes data from at least three dimensions: building material consumption, building material construction, and building material transportation.

[0041] The data processing module is used to preprocess all data for each historical building project and convert the carbon emission data of all dimensions of the preprocessed data into total physical carbon emissions.

[0042] The pending main material screening module is used to calculate the Pearson correlation coefficient between each building material in the building material consumption and the total physical carbon emissions of each historical building project, and to select the corresponding building materials whose Pearson correlation coefficient is greater than the preset coefficient threshold as the preliminary screening main materials; calculate the variance expansion factor between each preliminary screening main material and other preliminary screening main materials, and select the corresponding preliminary screening main materials whose variance expansion factor is less than the set preset factor threshold as the pending main materials.

[0043] The key main material screening module is used to calculate the construction energy consumption coupling influence index between each type of pending main material and the corresponding building material construction data in each historical building project, and to obtain the average value of the construction energy consumption coupling influence index of each type of pending main material. The corresponding pending main material whose average value is greater than the preset coupling threshold is regarded as the key main material.

[0044] The linear regression model building module is used to construct a linear regression model by taking the consumption of each key material and the total physical carbon emissions of each historical building project as independent and dependent variables, respectively, and introduces a regularization penalty term to solve the coefficient vector of the linear regression model.

[0045] The decision-making module is used to predict the total physical carbon emissions of new construction projects based on the consumption of each key material in the project and using a linear regression model.

[0046] The management module is used to set multiple management cycles in new construction projects, decompose the carbon emission benchmark value of the new construction project into carbon emission limits corresponding to multiple management cycles, and predict the total physical carbon emissions of the corresponding management cycle based on the consumption of key main materials in each management cycle; determine whether the total physical carbon emissions of the current management cycle exceed the carbon emission limit, and if so, issue an early warning; otherwise, archive the total physical carbon emissions of the current management cycle.

[0047] As a further embodiment, this embodiment also provides a system application method for calculating carbon emissions based on building material equivalents, including the project decision-making stage, design stage, transportation and construction management stage, and completion stage, referencing... Figure 2 .

[0048] Specifically, in the K1 project decision-making phase: based on the detailed bill of quantities for the new construction project, input the usage data of key main materials. Materials requiring input include the usage of concrete (in cubic meters), steel reinforcement (in tons), and masonry materials (in cubic meters). The information required here is the total usage of the three main material grades. For example, in a project that actually used C20, C30, and C40 concrete, the total usage of these three grades needs to be entered. The system successfully receives and saves the usage data of all main materials, displaying the entered values ​​and units in real time on the interface. The system automatically verifies the data format and combines it with the entered project information to ensure the input data is within a reasonable range. After confirming that all material usage data is correct, the user clicks the "Start Calculation" button to directly initiate the carbon emission calculation process. The system will immediately call the built-in calculation engine to process and analyze the input material usage data, without requiring additional confirmation from the user. The calculation process runs automatically in the background, with the user interface remaining in its current state while awaiting the results. Due to the efficiency of the calculation model, carbon emission calculations are completed almost instantly, requiring virtually no waiting time. Once the calculation is complete, users can view detailed carbon emission calculation results in the "Actual Carbon Emissions" section. This interface displays the project's actual carbon emissions, along with the calculation timestamp and other supplementary data such as basic project information.

[0049] This stage mainly utilizes the engineering quantities estimated from the investment estimate. Therefore, the calculation results can be set as the carbon emission estimate for this project and used for preparing the design brief and scheme design and other related work.

[0050] K2, Design Phase This phase covers the entire process from design budget, construction drawing design, bidding, to contract formation. Following the method described in K1, the consumption of key main materials is extracted from the contract quantity data and entered into the system for calculation. The core objective of this phase is to revise the carbon emission estimates from the decision-making phase, thereby setting a baseline carbon emission value for the project. This baseline value will serve as the control line for subsequent carbon emission management work.

[0051] K3. Transportation and Construction Management Phase: Management personnel need to preset management cycles in the system. The system will automatically decompose the carbon emission baseline value into carbon emission limits for each cycle. Within each management cycle, the consumption of key main materials is extracted from the engineering measurement data according to the K1 method and entered into the system for calculation. If the total physical carbon emissions within the cycle exceed the set limit, the system will automatically issue an alert and push it to the relevant responsible persons for intervention; if the limit is not exceeded, the data will be automatically archived. Through the cyclical management of multiple management cycles, timely detection and dynamic control of carbon emission anomalies during construction can be achieved.

[0052] To ensure data authenticity and auditability, the system assigns a unique traceability ID to each piece of key material data entered, supporting reverse tracing of data sources. Users can view historical carbon emission results for each period in the calculation interface, compare planned and actual values, and understand carbon emission trends.

[0053] K4. Completion Phase: Based on the K1 method, extract the consumption of key main materials from the final settlement data, input them into the system for calculation, and obtain the actual carbon emissions of the project. The system will automatically integrate the carbon emission benchmark value set in K2 with the process management data archived in K3, perform final accounting, and output a comprehensive evaluation report. This report covers basic project information, consumption of key main materials, calculation results of total physical and chemical carbon emissions throughout the process, comparative analysis of planned and actual values ​​for each management cycle, etc., and evaluates the project's low-carbon management performance from multiple dimensions through visualization.

[0054] In summary, this solution simplifies daily monitoring and data collection during project implementation. Construction companies can achieve dynamic control of carbon emissions, resolving the issues of high workload, long cycles, and high investment associated with traditional carbon monitoring. Furthermore, the system can aggregate real-time main material data from different construction sites to enterprise-level or government-level cloud platforms through a unified data interface standard. This not only enables refined management of individual projects but also creates an industry-wide big data pool, providing continuous data support for the iterative optimization and calibration of core algorithm models, ultimately achieving standardization, digitalization, and intelligentization of carbon emission management in the construction industry.

Claims

1. A method for calculating carbon emissions based on the equivalent of a building main material, characterized by, Including the following steps: S100. Collect full data from multiple historical building projects. The full data includes data from at least three dimensions: building material consumption, building material construction, and building material transportation. S200. Preprocess all data for each historical building project and convert the carbon emission data of all dimensions of the preprocessed data into total physical carbon emissions. S300: Calculate the Pearson correlation coefficient between each building material and the total physical carbon emissions of each historical building project, and use the building materials whose Pearson correlation coefficient is greater than the preset coefficient threshold as the initial screening main materials. Calculate the variance inflation factor between each pre-selected main material and other pre-selected main materials, and designate the corresponding pre-selected main materials whose variance inflation factor is less than the preset factor threshold as the main materials to be determined. S400. Calculate the construction energy consumption coupling influence index between each type of undetermined main material and the corresponding building material construction data in each historical building project, and obtain the average value of the construction energy consumption coupling influence index of each type of undetermined main material. The corresponding undetermined main material whose average value is greater than the preset coupling threshold is taken as the key main material. S500. Construct a linear regression model by taking the consumption of each key material and the total physical carbon emissions of each historical building project as independent and dependent variables, respectively, and introduce a regularization penalty term to solve the coefficient vector of the linear regression model. S600: Obtain the consumption of each key main material in the new construction project, and input it into the solved linear regression model to obtain the total physical carbon emissions of the new construction project.

2. The carbon emission calculation method based on building material equivalents according to claim 1, characterized in that, The expression for calculating the total physical carbon emissions of each historical building project is as follows: ； ； ； ； in, The total physical carbon emissions for each historical building project; , and These are the physical carbon emissions from building material production, building material transportation, and building material construction, respectively. and The first Consumption and carbon emission factors of various building materials; This represents the total number of building material categories. and The first Carbon emission factors for the average transportation distance and unit weight transportation distance of various building materials; and The first stage of building materials construction Total energy consumption and carbon emission factors of the species; This refers to the total amount of energy.

3. The carbon emission calculation method based on building material equivalents according to claim 1, characterized in that, The expression for calculating the Pearson correlation coefficient between each building material and the total physical carbon emissions of each historical building project in the building material consumption dimension is as follows: in, For the first Pearson correlation coefficients for various building materials; For the first The first historical building project The consumption of various building materials; For the first Total material carbon emissions of each historical building project; The total number of historical building projects; The first among all historical building projects Average consumption of various building materials; This represents the average total physical carbon emissions across all historical building projects.

4. The carbon emission calculation method based on building material equivalents according to claim 1, characterized in that, The expression for calculating the variance inflation factor between each preliminarily selected main material and other preliminarily selected main materials is as follows: in, For the first A preliminary screening factor for the variance expansion of the main materials; For the first Goodness of fit between the initial screening material and other initial screening materials.

5. The carbon emission calculation method based on building material equivalents according to claim 1, characterized in that, The expression for calculating the construction energy consumption coupling impact index between each undetermined main material and the corresponding construction dimension data of each building material in each historical building project is as follows: in, For the first The coupling effect index of construction energy consumption of undetermined main materials; For the first Pearson correlation coefficient for undetermined main materials; It is a correlation-based nonlinear activation function; and All are weighting coefficients. ; This refers to the total construction period of current historical building projects. This represents the total number of types of building materials and construction equipment. In order to be in At what time, the unit consumed is number one. The first type of undetermined main material to be called The shift quota for this type of machinery; For the first The unit energy consumption intensity of various building material construction equipment; For the first Carbon emission factor conversion coefficient of various building materials and construction equipment; For the first Standard deviation of the dispersion of consumption of certain undetermined main materials and corresponding construction equipment for building materials; The Gaussian time-domain decay weighting factor is... For the first The central moment when the consumption rate of the undetermined main material reaches its peak; The first among all historical building projects The arithmetic mean of the standard deviations of the consumption of certain main materials over time; To correct for deviation values.

6. The carbon emission calculation method based on building material equivalents according to claim 1, characterized in that, Step S500 includes: S510. Construct a linear regression model, the expression of which is: in, The dependent variable vector; , , and They are the 1st, 2nd, and 3rd respectively. The and the first Total material carbon emissions of each historical building project; The vector of independent variables; , and The first and second historical building projects, respectively. The and the first Consumption of key main materials; , and They are the first and second of the second historical building projects, respectively. The and the first Consumption of key main materials; , and The first The first of the historical building projects, the first The and the first Consumption of key main materials; , and The first The first of the historical building projects, the first The and the first Consumption of key main materials; For coefficient vectors; The intercept; , and The first and the second, respectively The and the first The weight of each key main material; This is the error vector; S520. Construct the objective function of the linear regression model, the expression of which is: in, The objective function is... For regularization parameters; S530, Regarding the objective function Differentiate and set to zero, then solve. : in, It is an identity matrix.

7. The carbon emission calculation method based on building material equivalents according to claim 6, characterized in that, Step S500 also includes: S540. Obtain a validation set, wherein each sample in the validation set includes the consumption of key main materials and the actual total carbon emissions of the building project. S550. Input the consumption of key main materials for each sample in the validation set into the solved linear regression model to obtain the predicted total carbon emissions for each sample. S560. Calculate the goodness of fit of the validation set. and mean absolute percentage error : ； in, and The first The actual and predicted total carbon emissions for each sample; The total number of samples in the validation set; To verify the mean of the true total carbon emissions of all samples in the validation set; S570, Judgment and Are all values ​​less than their respective set thresholds? If so, the current linear regression model meets the requirements; otherwise, proceed to step S580. S580, Modify regularization parameters For the objective function Differentiate and set to zero, then solve. For the objective function Differentiate and set to zero, then solve. Then return to step S540.

8. A system for calculating carbon emissions based on building material equivalents as described in any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to collect full data from multiple historical building projects. The full data includes data from at least three dimensions: building material consumption, building material construction, and building material transportation. The data processing module is used to preprocess all the data of each historical building project and convert the carbon emission data of all dimensions of the preprocessed data into the total physical carbon emissions. The pending main material screening module is used to calculate the Pearson correlation coefficient between each building material consumption and the total physical carbon emissions of each historical building project, and to use the corresponding building materials whose Pearson correlation coefficient is greater than the preset coefficient threshold as the initial screening main materials. Calculate the variance inflation factor between each pre-selected main material and other pre-selected main materials, and designate the corresponding pre-selected main materials whose variance inflation factor is less than the preset factor threshold as the main materials to be determined. The key main material screening module is used to calculate the construction energy consumption coupling influence index between each type of pending main material and the corresponding building material construction data in each historical building project, and to obtain the average value of the construction energy consumption coupling influence index of each type of pending main material. The corresponding pending main material whose average value is greater than the preset coupling threshold is regarded as the key main material. The linear regression model building module is used to construct a linear regression model by taking the consumption of each key main material and the total physical carbon emissions of each historical building project as independent and dependent variables, respectively, and introduces a regularization penalty term to solve the coefficient vector of the linear regression model. The decision-making module is used to predict the total physical carbon emissions of new construction projects based on the consumption of each key material in the project and using a linear regression model.

9. The system for calculating carbon emissions based on building material equivalents according to claim 8, characterized in that, Also includes: The management module is used to set multiple management cycles in new construction projects, decompose the carbon emission benchmark value of the new construction project into carbon emission limits corresponding to multiple management cycles, and predict the total physical carbon emissions of the corresponding management cycle based on the consumption of key main materials in each management cycle; determine whether the total physical carbon emissions of the current management cycle exceed the carbon emission limit, and if so, issue an early warning; otherwise, archive the total physical carbon emissions of the current management cycle.

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