BIM-based dynamic assessment and prediction system for carbon emissions

Abstract

This invention relates to the field of carbon emission assessment technology, specifically a BIM-based dynamic carbon emission assessment and prediction system. The BIM-based dynamic carbon emission assessment and prediction system includes a data extraction and matching module, a carbon emission calculation module, a life-cycle analysis module, and a results visualization module. In this invention, by automatically extracting data from building materials and connecting it to a carbon emission database, the system achieves the ability to accurately match material properties and quantities. This makes the calculation of carbon emissions not only more detailed but also possesses personalized analysis capabilities. Each material and construction activity is calculated independently, providing more detailed data support. It enables real-time monitoring and prediction of carbon emissions throughout the entire life cycle, enhancing the practicality of the data, enabling faster response to environmental changes, and implementing effective carbon reduction measures. The application of data visualization technology makes carbon emission information more intuitive and understandable, enhancing information dissemination and application efficiency, and increasing public and professional awareness and participation in environmental issues.

Description

Technical Field

[0001] This invention relates to the field of carbon emission assessment technology, and in particular to a BIM-based dynamic carbon emission assessment and prediction system. Background Technology

[0002] The field of carbon emission assessment technology primarily focuses on quantifying, monitoring, and analyzing carbon emissions to achieve environmental protection goals. It utilizes various methods and tools to assess the overall carbon footprint of individuals, organizations, or systems over a specific period, with a focus on high-emission sectors such as construction, transportation, and industry. Carbon assessment technologies include life cycle assessment (LCA), cloud-based monitoring systems, AI, and big data analytics to provide accurate and dynamic emission data. This not only helps achieve carbon reduction targets but also facilitates policy development and the optimization of sustainable development strategies.

[0003] Among them, the BIM-based building carbon emission dynamic assessment and prediction system combines detailed building data from BIM technology with a methodology for carbon emission assessment. This system allows for real-time monitoring and prediction of building project carbon emissions, helping project managers and decision-makers make more environmentally friendly choices during design, construction, and operation. Its main uses include optimizing building design to reduce environmental impact, monitoring carbon emissions during construction, and assessing energy efficiency and carbon footprint during building operation, thereby supporting the goals of green building and sustainable urban development.

[0004] Existing technologies typically provide only static or one-off carbon footprint data, lacking the ability to respond quickly to real-time changes. This limitation leads to insufficient real-time performance and accuracy in carbon emission monitoring, especially during the building and construction phases. For example, when monitoring carbon emissions during construction using traditional methods, data update delays often result in discrepancies between estimated and actual emissions, affecting the timeliness and effectiveness of carbon reduction measures. This makes it difficult to accurately assess the specific contribution of each stage to total carbon emissions, potentially leading to inefficiency and resource waste in strategy development and implementation. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a BIM-based dynamic assessment and prediction system for carbon emissions.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a BIM-based dynamic carbon emission assessment and prediction system comprising:

[0007] The data extraction and matching module automatically extracts the type and quantity of building materials based on BIM, connects the BIM data with the database associated with carbon emissions, verifies and matches the corresponding material properties and quantities, and generates project-level carbon emission metadata.

[0008] The carbon emission calculation module uses the project-level carbon emission metadata to classify and process the data, divides it according to the different material types and construction activities, calculates the carbon emission standard factor independently for each data item based on the classification results, and summarizes and organizes the calculation results to form sub-item carbon emission information.

[0009] The full life cycle analysis module receives the sub-item carbon emission information, summarizes relevant data according to building stage, integrates and analyzes stage data through time series analysis, calculates the contribution rate of each stage to the total carbon emissions based on the integration results, performs trend prediction, comprehensively evaluates the building's full life cycle carbon emissions, and outputs an overview of the building's full life cycle carbon emissions.

[0010] The results visualization module calculates the carbon emission ratio for each building stage based on the overall carbon emission overview of the building's entire lifecycle. It then transforms the data into charts using data visualization technology, determines the carbon emission change trend for each building stage based on the transformation results, and generates visual analysis results of carbon emissions.

[0011] As a further aspect of the present invention, the steps for obtaining the project-level carbon emission metadata are as follows:

[0012] Scan the BIM model, identify and record the type and quantity of each building material, and use formulas...

[0013]

[0014] Calculate the total amount of material N i This yields a list of total material quantities, where x ij Let k represent the number of materials i included in the j-th component in the model, and k represent the total number of components.

[0015] Based on the aforementioned total material list, it is matched against the database using a formula.

[0016] C i =N i ×e i ×(1+δ i )

[0017] Calculate the carbon emission estimate C for the i-th material. i The carbon emission estimates of the generated materials, where e i δ is the carbon emission factor per unit. i This is an adjustment factor based on the material's usage environment;

[0018] Using the carbon emission estimates of the aforementioned materials, combined with the project scale and material usage frequency, a formula is employed.

[0019]

[0020] Calculate the total carbon emissions M for the entire project to obtain project-level carbon emission metadata, where w i The weights are adjusted according to the characteristics of the project, where n represents the type of material.

[0021] As a further aspect of the present invention, the steps for classifying the differentiated material types and construction activities are as follows:

[0022] Data is extracted from the project-level carbon emission metadata to identify material types and construction activity characteristics, perform preliminary classification operations, and generate preliminary classification information.

[0023] Using the preliminary classification information, complexity analysis is applied to further refine the data classification, using formulas.

[0024]

[0025] Calculate the weighted classification value D, and output the refined classification result, where S j The classification data points obtained for preliminary classification information, d j , where is the weighting coefficient, emphasizing the accuracy of data item classification, and m represents the number of categories in the preliminary classification information;

[0026] Based on the refined classification results, normalization is performed using the formula...

[0027]

[0028] Calculate the normalized value F, construct and output the classification framework, where D q It is the output of the refined classification results. This represents the sum of all categorical data, used for normalization processing, where p represents the number of data points.

[0029] As a further aspect of the present invention, the steps for obtaining the sub-item carbon emission information are as follows:

[0030] Based on the aforementioned classification framework, carbon emission standard factors are applied independently for each classification, using formulas.

[0031]

[0032] Calculate the categorized carbon emissions E, where F i It is a data item that has undergone normalization in the classification framework, f i It is a carbon emission standard factor used to enhance the accuracy of factor application; U represents data item F. i Quantity;

[0033] Based on the aforementioned carbon emission classifications, adjustments are made in accordance with national standards, using a formula.

[0034]

[0035] Calculate and generate adjusted carbon emission data A, where E j Carbon emissions representing differential classification, ξ j As an adjustment factor used to meet national standards, r represents E. j Classification categories;

[0036] The adjusted carbon emission data are summarized and organized to form sub-item carbon emission information.

[0037] As a further aspect of the present invention, the execution steps of integrating and analyzing data through time series analysis are as follows:

[0038] Receive the sub-item carbon emission information, collect carbon emission data for each stage of construction, perform preliminary data processing and store the data in the database to generate a preliminary dataset;

[0039] Based on the preliminary dataset, it is categorized and summarized according to construction stage, using a formula.

[0040]

[0041] The generation stage categorizes and summarizes the data S, where K j For the data point in stage j, u j The classification coefficient represents the degree of importance attached to the differentiating stages;

[0042] Using the categorized and summarized data from the aforementioned stages, time series analysis is performed to integrate and analyze data trends, employing formulas.

[0043]

[0044] Calculate the integrated carbon emission data T to generate time series analysis results, where S t a represents the summary data of category t. t These are weighting coefficients in time series analysis, used to ensure accurate representation of time dependencies, where L represents the number of categories.

[0045] As a further aspect of the present invention, the steps for obtaining the building lifecycle carbon emission overview are as follows:

[0046] Based on the time series analysis results, the formula is used.

[0047]

[0048] Calculate the weighted contribution rate G of the building phase to total carbon emissions, and generate phase carbon emission contribution rate data, where T t For the time series results at stage t, b t This is the contribution evaluation coefficient;

[0049] Using the carbon emission contribution rate data for the aforementioned period, future carbon emission trends are predicted using the formula.

[0050]

[0051] Calculate the predicted value P for the future total carbon emission trend, and generate the trend prediction result, where G t For the carbon emission contribution rate of the stage, γ m α and α are prediction parameters used to ensure the accuracy and adaptability of the prediction, and exp(·) represents the natural exponential function;

[0052] By integrating the aforementioned trend forecasts, we assess carbon emissions throughout the entire life cycle and compile and generate a full-cycle carbon emissions overview.

[0053] As a further aspect of the present invention, the step of converting data into a chart is as follows:

[0054] Based on the aforementioned overview of carbon emissions throughout the building's lifecycle, carbon emissions z1, z2, ..., z2 at each building stage are collected and recorded. n The carbon emission data set for the formation stage is Z = {z1, z2, z...} i , ..., z H}, where z i This represents the carbon emissions in stage i.

[0055] Based on the carbon emission data set for the aforementioned stage, the formula is used.

[0056]

[0057] Calculate the carbon emission ratio B for stage i. i Output a set of carbon emission ratios, where Y i is the carbon emission coefficient of the building type or materials used in stage i, and H represents the number of data points in the stage carbon emission data set.

[0058] Based on the aforementioned set of carbon emission ratios, data visualization technology is applied to transform the data into charts and generate data visualization results.

[0059] As a further aspect of the present invention, the step of obtaining the carbon emission visual analysis results is as follows:

[0060] Based on the data visualization results, trend information is extracted, and the carbon emission trend at each stage is analyzed using formulas.

[0061]

[0062] Calculate the phase trend deviation value V to generate preliminary trend analysis results, where... It is the average of the set of carbon emission proportions; using the preliminary trend analysis results, a deeper analysis of carbon emission trends is conducted, employing the formula,

[0063]

[0064] Calculate the standardized coefficient of variation R to obtain detailed trend confirmation results, where σ represents the standard deviation, enhancing the flexibility and sensitivity of trend analysis;

[0065] Based on the detailed trend confirmation results, significant trend changes are highlighted to reflect the carbon emission trends at each stage, generating visual analysis results of carbon emissions.

[0066] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0067] This invention achieves the ability to accurately match material properties and quantities by automatically extracting data from building materials and connecting it with a carbon emission database. This makes the calculation of carbon emissions not only more detailed but also provides personalized analysis capabilities. Each material and construction activity is calculated independently, providing more detailed data support. It enables real-time monitoring and prediction of carbon emissions throughout the entire life cycle, enhancing the practicality of the data and enabling faster response to environmental changes and the implementation of effective carbon reduction measures. Through the application of data visualization technology, carbon emission information becomes more intuitive and easier to understand, enhancing the efficiency of information dissemination and application, and thereby increasing public and professional awareness and participation in environmental issues. Attached Figure Description

[0068] Figure 1 This is a system flowchart of the present invention;

[0069] Figure 2 This is a flowchart illustrating the process of acquiring project-level carbon emission metadata for this invention.

[0070] Figure 3 This is a flowchart illustrating the classification of differentiated material types and construction activities according to the present invention.

[0071] Figure 4 This is a flowchart illustrating the process of obtaining carbon emission information for each component of the present invention.

[0072] Figure 5 This is a flowchart illustrating the execution process of integrating data during the time series analysis phase in this invention.

[0073] Figure 6 This is a flowchart illustrating the process of obtaining an overview of carbon emissions throughout the entire building lifecycle in this invention.

[0074] Figure 7 This is a flowchart illustrating the process of converting data into charts in this invention.

[0075] Figure 8This is a flowchart illustrating the process of obtaining the visual analysis results of carbon emissions in this invention. Detailed Implementation

[0076] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0077] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0078] Please see Figure 1 The BIM-based dynamic carbon emission assessment and prediction system includes:

[0079] The data extraction and matching module automatically extracts the type and quantity of building materials based on BIM, connects the BIM data with the database associated with carbon emissions, verifies and matches the corresponding material properties and quantities, and generates project-level carbon emission metadata.

[0080] The carbon emission calculation module uses project-level carbon emission metadata to classify and process the data, dividing it according to different material types and construction activities. Based on the classification results, the carbon emission standard factor is calculated independently for each data item, and the calculation results are summarized and organized to form sub-item carbon emission information.

[0081] The full life cycle analysis module receives carbon emission information by category, summarizes relevant data according to building stage, integrates and analyzes stage data through time series analysis, calculates the contribution rate of each stage to total carbon emissions based on the integration results, makes trend predictions, comprehensively evaluates the carbon emissions of the building throughout its life cycle, and outputs an overview of the building's full life cycle carbon emissions.

[0082] The results visualization module is based on an overview of carbon emissions throughout the building lifecycle. It calculates the carbon emission ratio for each building stage, transforms the data into charts using data visualization technology, determines the carbon emission change trend for each building stage based on the transformation results, and generates visual analysis results of carbon emissions.

[0083] Project-level carbon emission metadata includes material type and quantity; sub-item carbon emission information includes classification processing results, independent calculation results, and summary data; the building life cycle carbon emission overview includes stage classification summary, total contribution rate calculation results, and trend prediction results; and carbon emission visual analysis results include carbon emission ratio charts and change trend charts.

[0084] Please see Figure 2 The steps for obtaining project-level carbon emission metadata are as follows:

[0085] Scan the BIM model, identify and record the type and quantity of each building material, and use formulas...

[0086]

[0087] Calculate the total amount of material N i This yields a list of total material quantities, where x ij Let k represent the number of materials i included in the j-th component in the model, and k represent the total number of components.

[0088] Based on the total material quantity list, it is matched with the database using a formula.

[0089] C i =N i ×e i ×(1+δ i )

[0090] Calculate the carbon emission estimate C for the i-th material. i The carbon emission estimates of the generated materials, where e i δ is the carbon emission factor per unit. i This is an adjustment factor based on the material's usage environment;

[0091] Using the carbon emission estimates of materials, combined with the project scale and the frequency of material use, a formula is employed.

[0092]

[0093] Calculate the total carbon emissions M for the entire project to obtain project-level carbon emission metadata, where w i The weights are adjusted according to the characteristics of the project, where n represents the type of material.

[0094] Suppose a BIM model includes 3 components, where the quantity of material i in these 3 components is 5, 3 and 2 respectively.

[0095] but:

[0096] N i =x i1 +x i2 +x i3=5+3+2=10

[0097] This indicates that the element of material i was used 10 times in the BIM model of the entire building project, providing a quantitative basis for assessing the contribution of using the material to the overall building structure. For example, if material i is concrete, this number can be used to assess the structural strength and stability requirements, and also serves as the basis for subsequent carbon emission calculations.

[0098] Assume that the unit carbon emission factor e of material i i The concentration is 0.5 kg (per unit of carbon dioxide), and the environmental adjustment factor is δ. i The value is 0.1, combined with the aforementioned calculation results N i =10, then:

[0099] C i =10×0.5×(1+0.1)=10×0.5×1.1=5.5

[0100] This value represents the total carbon emissions generated from the production, transportation and use of material i as 5.5 kg (per unit of carbon dioxide). It takes into account the quantity of material and the carbon emission factor per unit of material, and uses an environmental adjustment coefficient to reflect the carbon emission impact under specific environmental conditions.

[0101] Assume there are only two materials (n=2), and the calculated carbon emissions are 5.5 kg and 3 kg.

[0102] Assuming weight factors w1 = 1 and w2 = 1, substituting them into the formula, we get:

[0103] M=(5.5×1)+(3×1)=5.5+3=8.5

[0104] The result, M = 8.5 kg, represents the total carbon emissions for the entire project. This result takes into account the carbon emission contributions of all materials used, and the weighted average reflects the frequency and importance of different materials in the project.

[0105] Please see Figure 3 The steps for classifying differentiated material types and construction activities are as follows:

[0106] Data is extracted from project-level carbon emission metadata to identify material types and construction activity characteristics, perform preliminary classification operations, and generate preliminary classification information.

[0107] Using preliminary classification information, complexity analysis is applied to refine the data classification, employing formulas.

[0108]

[0109] Calculate the weighted classification value D, and output the refined classification result, where Sj The classification data points obtained for preliminary classification information, d j , where is the weighting coefficient, emphasizing the accuracy of data item classification, and m represents the number of categories in the preliminary classification information;

[0110] Based on the refined classification results, normalization is performed using a formula.

[0111]

[0112] Calculate the normalized value F, construct and output the classification framework, where D q It is the output of the refined classification results. This represents the sum of all categorical data, used for normalization processing, where p represents the number of data points.

[0113] Assume the initial classification information can be further divided into two categories, with the data for each category as follows:

[0114] S1 = 15

[0115] S2 = 11

[0116] The weighting coefficients are set as follows:

[0117] d1 = 0.7

[0118] d2 = 0.3

[0119] Substituting into the formula, we get:

[0120] D = 0.7 × 15 2 +0.3×11 2

[0121] =0.7×225+0.3×121=157.5+36.3=193.8

[0122] A D value of 193.8 indicates that, under more granular classification criteria, the data was reweighted and squared to emphasize differences in importance. This value is used for further data processing or decision analysis, such as determining which materials or construction activities should be subject to focused monitoring.

[0123] Assume the output of the above calculation consists of two data points:

[0124] D1 = 193.8

[0125] D2 = 106.2

[0126] Substituting into the formula, we get:

[0127]

[0128] An F = 1.0 indicates that the normalized data reflects the contribution of each category equally, providing unbiased support for further decision-making or report preparation. The results demonstrate the relative importance of each component during the classification process and ensure the consistency and comparability of the final analysis.

[0129] Please see Figure 4 The steps for obtaining detailed carbon emission information are as follows:

[0130] Based on the classification framework, carbon emission standard factors are applied independently for each classification, using formulas.

[0131]

[0132] Calculate the categorized carbon emissions E, where F i It is a data item that has undergone normalization in the classification framework, f i It is a carbon emission standard factor used to enhance the accuracy of factor application; U represents data item F. i Quantity;

[0133] Adjustments are made based on categorized carbon emissions and national standards, using a formula.

[0134]

[0135] Calculate and generate adjusted carbon emission data A, where E j Carbon emissions representing differential classification, ξ j As an adjustment factor used to meet national standards, r represents E. j Classification categories;

[0136] The adjusted carbon emission data were compiled and organized to form detailed carbon emission information.

[0137] Assuming there are two types of material data, based on the aforementioned classification framework, the following is obtained:

[0138] F1 = 0.646 (for more environmentally sensitive materials).

[0139] F2 = 0.354 (materials with less environmental impact).

[0140] The carbon emission standard factor is set as follows:

[0141] f1 = 1.2

[0142] f2 = 0.8

[0143] Substituting into the formula, we get:

[0144] E = 1.2 × 0.75 3 +0.8×0.25 3

[0145] = 1.2 × 0.421875 + 0.8 × 0.015625

[0146] =0.50625 + 0.0125 = 0.51875

[0147] E = 0.51875 represents the total carbon emissions after considering the environmental impact weights of materials. This value reflects the total carbon emissions of different materials or construction activities weighted according to their environmental sensitivity, and helps in developing more effective environmental policies.

[0148] Assume that the carbon emissions calculated above are adjusted in two parts:

[0149] E1 = 0.4

[0150] E2 = 0.11875

[0151] The adjustment factor is set as follows:

[0152] ξ1=1.05

[0153] ξ2=0.95

[0154] Substituting into the formula, we get:

[0155]

[0156] A = 0.566 indicates the comprehensive carbon emission data calculated under the adjustment factor of national standards, used for reporting or further environmental action planning to ensure that the project complies with regulatory requirements.

[0157] Please see Figure 5 The steps for integrating and analyzing data through time series analysis are as follows:

[0158] Receive detailed carbon emission information, collect carbon emission data for each stage of construction, perform preliminary data processing and store the data in the database, and generate a preliminary dataset.

[0159] Based on the initial dataset organization, the data was categorized and summarized according to construction stages, using formulas.

[0160]

[0161] The generation stage categorizes and summarizes the data S, where K j For the data point in stage j, u j The classification coefficient represents the degree of importance attached to the differentiating stages;

[0162] By utilizing the phased categorized and summarized data, time series analysis is performed to integrate and analyze data change trends, using formulas...

[0163]

[0164] Calculate the integrated carbon emission data T to generate time series analysis results, where S t a represents the summary data of category t. t These are weighting coefficients in time series analysis, used to ensure accurate representation of time dependencies, where L represents the number of categories.

[0165] Suppose we have 3 data points, and the classification coefficients are as follows:

[0166] u1 = 1.0, K1 = 100 kg CO2

[0167] u2 = 0.5, K2 = 150 kg CO2

[0168] u3 = 1.5, K3 = 200 kg CO2

[0169] Substitute into the formula and perform the calculation:

[0170]

[0171] S≈285kg shows the normalized carbon emission data, taking into account the importance of the stage and the amount of carbon emissions, thus preparing for time series analysis.

[0172] Based on the aforementioned calculation results, and assuming the weights of the three stages...

[0173] a1 = 0.3

[0174] a² = 0.4

[0175] a3 = 0.3

[0176] To simplify the process, assume that the three stages are the same:

[0177] S1=285, S2=285, S3=285

[0178] Substitute into the formula and calculate:

[0179]

[0180] T = 285 kg CO2 represents the integrated carbon emission data after time-weighted processing, reflecting the average carbon emission trend throughout the entire construction project phase, providing a basis for further prediction and assessment. This value is used to quantify the environmental impact of a construction project throughout its construction period.

[0181] Please see Figure 6 The steps to obtain an overview of a building's total carbon emissions throughout its lifecycle are as follows:

[0182] Based on the time series analysis results, the formula is used.

[0183]

[0184] Calculate the weighted contribution rate G of the building phase to total carbon emissions, and generate phase carbon emission contribution rate data, where T t For the time series results at stage t, b t This is the contribution evaluation coefficient;

[0185] Using data on carbon emission contribution rates during the current phase, we predict future carbon emission trends using a formula.

[0186]

[0187] Calculate the predicted value P for the future total carbon emission trend, and generate the trend prediction result, where G t For the carbon emission contribution rate of the stage, γ m α and α are prediction parameters used to ensure the accuracy and adaptability of the prediction, and exp(·) represents the natural exponential function;

[0188] By integrating trend forecasts, assessing carbon emissions throughout the entire life cycle, and compiling and generating a full-cycle carbon emissions overview.

[0189] Assume the time series analysis data (T-value) and contribution evaluation coefficients for the three stages are as follows:

[0190] T1=100, T2=150, T3=200

[0191] b1=0.2, b2=0.5, b3=0.3

[0192] Substituting into the formula, we get:

[0193]

[0194] The result G≈0.348 shows the weighted average contribution rate, reflecting the contribution of different building phases to total carbon emissions based on their importance and carbon emissions.

[0195] Assume γ m =1 and α=0.05, combined with the previous calculation result G≈0.348, then:

[0196]

[0197] P≈3.054 represents a numerical value for predicting future total carbon emission trends based on current data. This value indicates the growth of carbon emissions over time and can be used for further strategic planning and resource allocation.

[0198] Please see Figure 7 The steps to transform data into charts are as follows:

[0199] Based on a comprehensive overview of building lifecycle carbon emissions, carbon emissions z1, z2, ..., z are collected and recorded for each building phase. n The carbon emission data set for the formation stage is Z = {z1, z2, z...} i , ..., z H}, where z i This represents the carbon emissions in stage i.

[0200] Based on the phased carbon emission data set, a formula is used.

[0201]

[0202] Calculate the carbon emission ratio B for stage i. i Output a set of carbon emission ratios, where Y i is the carbon emission coefficient of the building type or materials used in stage i, and H represents the number of data points in the stage carbon emission data set.

[0203] Based on a set of carbon emission ratios, data visualization technology is applied to transform the data into charts and generate data visualization results.

[0204] Assuming numerical values:

[0205] z1 = 100 kg CO2, initial construction phase.

[0206] z2 = 300 kg CO2, in the usage stage.

[0207] z3 = 200kgCO2, demolition stage.

[0208] Assumption coefficients:

[0209] Y1 = 1.0, initial construction phase.

[0210] Y2 = 0.8, in the usage stage.

[0211] Y3 = 1.2, demolition stage.

[0212] Calculate weighted carbon emissions:

[0213]

[0214] Carbon emission percentages for each stage:

[0215]

[0216] Calculations show that carbon emissions during the initial construction phase account for 17.24% of total emissions, while those during the usage and demolition phases each account for 41.38%. This means that the usage and demolition phases contribute significantly to total carbon emissions.

[0217] Please see Figure 8 The steps for obtaining the visual analysis results of carbon emissions are as follows:

[0218] Based on the data visualization results, trend information is extracted, and the carbon emission trends at each stage are analyzed using formulas.

[0219]

[0220] Calculate the phase trend deviation value V to generate preliminary trend analysis results, where... It is the average of the set of carbon emission proportions; using the preliminary trend analysis results, a deeper analysis of carbon emission trends is conducted, employing formulas.

[0221]

[0222] Calculate the standardized coefficient of variation R to obtain detailed trend confirmation results, where σ represents the standard deviation, enhancing the flexibility and sensitivity of trend analysis;

[0223] Based on detailed trend confirmation results, key change points are identified, significant trend changes are highlighted, carbon emission trends at each stage are reflected, and visual analysis results of carbon emissions are generated.

[0224] Based on the aforementioned calculation results:

[0225] B1 = 17.24%

[0226] B2 = 41.38%

[0227] B3 = 41.38%

[0228] but:

[0229]

[0230] Variance calculation:

[0231]

[0232] The calculated result of 129.5 represents the average variance between the carbon emission proportion and the average value at each construction phase. A large variance indicates significant variability, suggesting that the distribution of carbon emissions fluctuates considerably across different phases, which is crucial for assessing the effectiveness of carbon emission policies at each phase.

[0233] Based on the aforementioned calculation results, the standard deviation is:

[0234]

[0235] Substituting into the formula, we get:

[0236]

[0237] The calculated result of 0.999 represents the standardized coefficient of variation between the carbon emission proportion of each stage and the average value, which is close to 1. This indicates that the fluctuation of carbon emissions in each stage is uniform with respect to the standard deviation and the differences are small, which helps to make precise adjustments and optimizations to carbon emission policies.

[0238] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A BIM-based dynamic assessment and prediction system for carbon emissions, characterized in that, The system includes: The data extraction and matching module automatically extracts the type and quantity of building materials based on BIM, connects the BIM data with the database associated with carbon emissions, verifies and matches the corresponding material properties and quantities, and generates project-level carbon emission metadata. The carbon emission calculation module uses the project-level carbon emission metadata to classify and process the data, divides it according to the different material types and construction activities, calculates the carbon emission standard factor independently for each data item based on the classification results, and summarizes and organizes the calculation results to form sub-item carbon emission information. The full life cycle analysis module receives the sub-item carbon emission information, summarizes relevant data according to building stage, integrates and analyzes stage data through time series analysis, calculates the contribution rate of each stage to the total carbon emissions based on the integration results, performs trend prediction, comprehensively evaluates the building's full life cycle carbon emissions, and outputs an overview of the building's full life cycle carbon emissions. The results visualization module calculates the carbon emission ratio for each building stage based on the overall carbon emission overview of the building's entire life cycle, transforms the data into charts using data visualization technology, determines the carbon emission change trend for each building stage based on the transformation results, and generates visual analysis results of carbon emissions. The execution steps for integrating and analyzing data through time series analysis are as follows: Receive the sub-item carbon emission information, collect carbon emission data for each stage of construction, perform preliminary data processing and store the data in the database to generate a preliminary dataset; Based on the preliminary dataset, it is categorized and summarized according to construction stage, using a formula. ； Data summary during generation phase ,in, For the first Data points for each stage The classification coefficient represents the degree of importance attached to the differentiating stages; Using the categorized and summarized data from the aforementioned stages, time series analysis is performed to integrate and analyze data trends, employing formulas. ； Computational integration of carbon emission data Generate time series analysis results, among which, Representing the Categorized summary data, These are weighting coefficients used in time series analysis to ensure an accurate representation of time dependencies. Indicates the number of categories; The steps for obtaining the building's full-lifecycle carbon emissions overview are as follows: Based on the time series analysis results, the formula is used. ； Calculate the weighted contribution rate of the construction phase to total carbon emissions. Data on carbon emission contribution rate during the generation phase, among which, For the first Time series results for the phase, This is the contribution evaluation coefficient; Using the carbon emission contribution rate data for the aforementioned period, future carbon emission trends are predicted using the formula. ； Calculate the predicted value of future total carbon emissions trends Generate trend prediction results, among which, The contribution rate of carbon emissions to the stage. and These are the prediction parameters used to ensure the accuracy and adaptability of the predictions. Represents the natural exponential function; By integrating the aforementioned trend forecasts, we assess carbon emissions throughout the entire life cycle and compile and generate a full-cycle carbon emissions overview.

2. The BIM-based dynamic carbon emission assessment and prediction system according to claim 1, characterized in that: The steps for obtaining the project-level carbon emission metadata are as follows: Scan the BIM model, identify and record the type and quantity of each building material, and use formulas... ； Calculate the total amount of materials This yields a list of total material quantities, including... For the model The quantity of material i included in each component, Indicates the total number of components; Based on the aforementioned total material list, it is matched against the database using a formula. ； Calculate the first Carbon emission estimates of the materials The carbon emission estimates of the generated materials, among which, As a unit carbon emission factor, This is an adjustment factor based on the material's usage environment; Using the carbon emission estimates of the aforementioned materials, combined with the project scale and material usage frequency, a formula is employed. ； Calculate the total carbon emissions of the entire project This yields project-level carbon emission metadata, including... Weights adjusted according to project characteristics. Indicates the type of material.

3. The BIM-based dynamic carbon emission assessment and prediction system according to claim 2, characterized in that: The steps for classifying the differentiated material types and construction activities are as follows: Data is extracted from the project-level carbon emission metadata to identify material types and construction activity characteristics, perform preliminary classification operations, and generate preliminary classification information. Using the preliminary classification information, complexity analysis is applied to further refine the data classification, using formulas. ； Calculate weighted classification values Output refined classification results, among which, The classification data points obtained for preliminary classification information, These are weighting coefficients, emphasizing the accuracy of data item classification. This indicates the number of categories in the preliminary classification information; Based on the refined classification results, normalization is performed using the formula... ； Calculate the normalized value Build and output a classification framework, in which, It is the output of the refined classification results. This represents the sum of all categorical data, used for normalization. Indicates the number of data points.

4. The BIM-based dynamic carbon emission assessment and prediction system according to claim 3, characterized in that: The steps for obtaining the sub-item carbon emission information are as follows: Based on the aforementioned classification framework, carbon emission standard factors are applied independently for each classification, using formulas. ； Calculate the carbon emissions by category ,in, These are data items that have undergone normalization within the classification framework. It is a carbon emission standard factor used to enhance the accuracy of factor application. Represents data items Quantity; Based on the aforementioned carbon emission classifications, adjustments are made in accordance with national standards, using a formula. ； Calculate and generate adjusted carbon emission data ,in, Carbon emissions representing the differences in classification. This is an adjustment factor used to meet national standards. express Classification categories; The adjusted carbon emission data are summarized and organized to form sub-item carbon emission information.

5. The BIM-based dynamic carbon emission assessment and prediction system according to claim 1, characterized in that: The steps for converting data into charts are as follows: Based on the aforementioned overview of carbon emissions throughout the building lifecycle, carbon emissions at each stage of the building process are collected and recorded. Formation stage carbon emission data set ,in, Indicates the first Carbon emissions at each stage; Based on the carbon emission data set for the aforementioned stage, the formula is used. ； Calculate the first Carbon emission ratio at each stage Output a set of carbon emission ratios, among which, It is the first The carbon emission coefficient of the building type or materials used at each stage. This indicates the number of data points in the carbon emission data set for a given period; Based on the aforementioned set of carbon emission ratios, data visualization technology is applied to transform the data into charts and generate data visualization results.

6. The BIM-based dynamic carbon emission assessment and prediction system according to claim 5, characterized in that: The steps for obtaining the visual analysis results of carbon emissions are as follows: Based on the data visualization results, trend information is extracted, and the carbon emission trend at each stage is analyzed using formulas. ； Calculate the trend deviation value during the calculation period This generates preliminary trend analysis results, among which... It is the average of the set of carbon emission proportions; Based on the preliminary trend analysis results, an in-depth analysis of carbon emission trends is conducted using formulas. ； Calculate the standardized coefficient of variation Detailed trend confirmation results were obtained, among which, It represents the standard deviation, enhancing the flexibility and sensitivity of trend analysis; Based on the detailed trend confirmation results, significant trend changes are highlighted to reflect the carbon emission trends at each stage, generating visual analysis results of carbon emissions.

Images