Carbon emission accounting and tracking method and system based on process construction method and BIM

Abstract

The invention provides a process construction method and BIM-based carbon emission accounting and tracking method and system, and the method comprises the steps: constructing an extended BIM model fusing geology, process and carbon emission attributes, and building a construction carbon emission model through employing a mixed life cycle method; and mechanical energy consumption is simulated and predicted through discrete events, and model components are associated, so that dynamic tracking and visualization of the carbon footprint during the construction period are realized. According to the invention, accurate accounting, dynamic tracking and visual display of carbon emission in the construction period can be realized, a scientific basis is provided for carbon emission management in the construction process, and optimization of construction organization, reduction of carbon emission and improvement of environmental benefits and sustainability of construction projects are facilitated.

Description

Technical Field

[0001] This invention relates to the field of construction carbon emission management and sustainable development technology, and more specifically, to a carbon emission accounting and tracking method and system based on construction process and BIM. Background Technology

[0002] In the field of construction engineering, with increasing emphasis on environmental protection and sustainable development, carbon emission management during construction has become a crucial issue. Traditional methods for calculating construction carbon emissions often rely on empirical estimations or simple life cycle assessments, making it difficult to accurately reflect the dynamic carbon emissions during construction. These methods typically fail to comprehensively consider multiple factors such as geological conditions, construction techniques, and energy consumption of machinery and equipment, resulting in inaccurate calculations and hindering effective guidance for carbon reduction measures during construction. Furthermore, existing technologies are insufficient in the real-time tracking and visualization of construction carbon emissions, failing to provide construction managers with intuitive dynamic information on carbon emissions and hindering timely adjustments to construction strategies to optimize carbon emission performance.

[0003] In implementing the embodiments of the present invention, the prior art has at least the following problems or defects: it cannot comprehensively consider the impact of factors such as geological conditions, construction technology and mechanical equipment energy consumption on construction carbon emissions, resulting in inaccurate accounting results; it lacks effective dynamic tracking and visualization means, which is not conducive to construction managers monitoring and optimizing carbon emission performance in real time. Summary of the Invention

[0004] This invention provides a method and system for carbon emission accounting and tracking based on process methods and BIM.

[0005] In a first aspect of the present invention, a method for carbon emission accounting and tracking based on process methods and BIM is provided, comprising: S1. Based on geological survey data and engineering design drawings, construct a building information model of the pumped storage power station hub area, denoted as BIM model; S2. Extend the BIM model with industrial basic standards to integrate geological attributes, construction process attributes and carbon emission attributes to generate an extended BIM model; S3. Based on the extended BIM model, establish a carbon emission accounting model for the construction period; the carbon emission accounting model for the construction period adopts a hybrid life cycle assessment method, and the formula for calculating the total carbon emissions E is as follows:

[0006] Where E represents the total carbon emissions during the construction period. This represents the carbon emissions of the i-th unit project, where i is the unit project number, ranging from 1 to 9. It represents the cumulative calculation of the carbon emissions of unit projects 1 to 9. S4. Based on the discrete event simulation method, the energy consumption data of construction machinery and equipment are simulated and predicted, and the data are input into the carbon emission accounting model during the construction period to perform quantitative calculation of carbon emissions during the construction process. S5. Associate the carbon emission data obtained from the quantitative calculation with the components in the extended BIM model, and realize the dynamic tracking and visualization of the carbon footprint during the construction period based on the integration of the extended BIM model with the progress management software.

[0007] Furthermore, the specific steps for extending the BIM model to industrial basic standards in step S2 include: S21. Define specific component entities related to the underground cavern of the pumped storage power station through entity extension, including the IfcTunnel entity for representing the main structure of the tunnel and the IfcSupport entity for representing the support system. S22. Add attribute sets related to construction technology, material consumption, and equipment energy consumption through attribute set expansion, including the PSet_GeologicalFeatures attribute set for describing geological layer characteristics, the PSet_CarbonEmissionFactor attribute set for recording material carbon emission factors, the PSet_ConstructionMethods attribute set for recording construction technology parameters, and the PSet_OperationalResourceUsage attribute set for recording equipment resource usage. S23. Establish semantic association rules to dynamically associate geological information, construction process parameters and carbon emission factors.

[0008] Furthermore, the PSet_GeologicalFeatures attribute set includes rock type, layer thickness, fracture density, permeability, and shear strength attributes; the PSet_CarbonEmissionFactor attribute set includes material carbon emission factor and cumulative material usage attributes; the PSet_ConstructionMethods attribute set includes process name, applicable geological conditions, explosive type, and usage amount attributes; and the PSet_OperationalResourceUsage attribute set includes equipment energy consumption parameters and usage time attributes.

[0009] Furthermore, the specific steps in step S4 for predicting the energy consumption data of construction machinery and equipment based on the discrete event simulation method include: S41. Based on the construction organization design, determine the construction sequence and separate the construction machinery actions involved in each sequence into discrete events; S42. Collect activity time sample data for each discrete event through on-site monitoring or video analysis, and determine its time probability density function F(t) using distribution fitting software; where F(t) represents the time probability density function, and t represents the activity time of the discrete event; S43. In the simulation platform, input the discrete event, the time probability density function F(t), and the quantity of construction resources to construct a discrete event model; S44. Run the discrete event model and output statistical results through the simulator to obtain the actual operating time of each construction machinery and equipment. The actual operation time Calculated using the formula:

[0010] in, This indicates the total actual operating time of the construction machinery and equipment. This represents the number of times the j-th discrete event occurs. This represents the average duration of the j-th discrete event, where j is the discrete event number, ranging from 1 to n, and n is the total number of discrete events. It represents the cumulative calculation of (number of occurrences × average duration) for discrete events from 1 to n. S45. Based on the actual operation time Based on the energy consumption quota per hour of construction machinery, the energy consumption Q of the machinery is calculated using the following formula:

[0011] Where Q represents the total mechanical energy consumption, This represents the actual operating time of the i-th mechanical device. This represents the hourly energy consumption quota of the i-th mechanical equipment, k is the energy consumption correction coefficient, and i is the mechanical equipment number. It means that the (actual operating time × hourly energy consumption quota) of all mechanical equipment is accumulated and then multiplied by the energy consumption correction coefficient.

[0012] Further, in step S42, the time probability density function F(t) is one of a uniform distribution, a normal distribution, a triangular distribution, or an exponential distribution; the number of times the sample data for each discrete event activity time is recorded is not less than 30; the goodness of fit is calculated using the Kolmogorov-Smirnov test, and the probability distribution model with the smallest KS value is selected as the time probability density function for the discrete event; wherein, the KS value is the Kolmogorov-Smirnov test statistic, used to measure the degree of fit between the sample data and the theoretical probability distribution, and the smaller the KS value, the higher the degree of fit.

[0013] Furthermore, in step S3, the carbon emissions from the main construction project... The calculations cover the earthwork excavation, concrete construction, and underground powerhouse construction processes; among them, the carbon emissions generated by blasting demolition are included. The calculation formula is:

[0014] in, This indicates the carbon emissions from blasting demolition operations. This represents the amount of the i-th type of explosive consumed. Let represent the carbon emission factor of the i-th type of explosive. This indicates the amount of carbon emissions generated during the production of explosives. This represents the carbon emissions generated by the transportation of explosives. i is the type number of the explosive, ranging from 1 to n, and n is the total number of types of explosives. It represents the cumulative calculation of (consumption × carbon emission factor) of 1 to n types of explosives, plus the carbon emissions generated by the production and transportation of explosives.

[0015] Furthermore, the specific steps for achieving dynamic tracking and visualization in step S5 include: S51. The schedule data is bidirectionally associated with the components in the extended BIM model through the application programming interface; S52. Load the lightweight extended BIM model based on the 3D engine; S53. Based on the schedule data, use a time dynamic data format file to define the display attributes of model components in different time intervals, and generate a planned carbon footprint prediction curve. S54. Based on the actual progress data collected on-site, dynamically update the time dynamic data format file to generate the actual carbon footprint curve, and compare and analyze it with the planned carbon footprint prediction curve.

[0016] Furthermore, the specific steps for generating the planned carbon footprint prediction curve in step S53 and the actual carbon footprint curve in step S54 include: S531. Extract the planned schedule information associated with the extended BIM model components from the database of the schedule management software. The planned schedule information includes the planned start time, planned completion time and corresponding quantity data of each construction operation. S532. Based on the aforementioned carbon emission accounting model for the construction period, calculate the carbon emissions of the planned completed work within each time interval. The calculation formula is as follows:

[0017] in, This represents the planned carbon emissions within the time interval t. This represents the planned quantity of the i-th type of project to be completed within the time interval t. The carbon emission factor represents the amount of work completed in the i-th category, t represents the set time interval, and i is the category number of the work. This means that the (planned completion amount × carbon emission factor) of all categories of work is accumulated within the time interval t. S533. Construct a CzmI format file, which consists of multiple data packets. Each data packet corresponds to an extended BIM model component and includes the following attributes: a unique identifier id, a time interval, a display attribute show, and a custom carbon emission attribute carbonProperty. Here, id is the component's unique identifier, used to distinguish different extended BIM model components; interval is the time interval, used to define the construction time period corresponding to the data; show is the display attribute, used to define the component's display status in the 3D engine; and carbonProperty is the custom carbon emission attribute, used to store the component's corresponding carbon emission-related data. S534. Based on the CzmI format file, dynamically render the extended BIM model in the 3D engine, visualize the carbon emission data in a time series manner, and generate a planned carbon footprint prediction curve. S535. During the construction process, real-time progress data is collected through IoT devices to update the interval attribute and carbonProperty attribute in the CzmI format file and generate the actual carbon footprint curve. S536. By comparing and analyzing the planned carbon footprint prediction curve and the actual carbon footprint curve, carbon emission deviations are identified. When the actual carbon emissions exceed the preset threshold of the planned value, an early warning mechanism is automatically triggered. The preset threshold is the maximum allowable deviation between the actual carbon emissions and the planned carbon emissions, which is used to determine whether carbon emissions are within a reasonable control range.

[0018] Furthermore, the method further includes step S6: S6. Construct a dynamic optimization model for construction progress based on the integration of BIM and progress management software; using total carbon emissions and total construction period as dual objective functions, generate multiple construction organization and management schemes based on the carbon emission accounting model during the construction period using optimization algorithms, and compare and optimize them. The specific steps for constructing the dynamic optimization model of construction progress in step S6 include: S61. Establish a bi-objective optimization function with the objectives of minimizing the total construction period T and minimizing the total carbon emissions E during construction:

[0019] Where T represents the total time from the start of construction to completion, E represents the total carbon emissions during the entire construction process, F is the bi-objective optimization function, and min represents finding the minimum value. It is in column vector form, representing the total construction period and total carbon emissions as two dimensions for dual-objective optimization; S62. A Monte Carlo tree search algorithm is used to perform a multi-solution optimization search. The algorithm includes the following steps: S621. Selection: Starting from the root node, select the most promising child node. The selection strategy is based on the UCT formula:

[0020] in, Let represent the average reward value of node j, and n represent the number of visits to the parent node. This indicates the number of times child node j has been visited. The exploration constant is used to balance the exploration (selecting under-visited nodes) and exploitation (selecting nodes with high average reward values) of the algorithm. UCT is the Upper Confidence Bound for Trees value, which is used to measure the selection priority of child nodes. The larger the UCT value, the higher the probability that the child node is selected. ln is the symbol for the natural logarithm. S622, Extension: When encountering a node that is not fully expanded, create one or more child nodes, each child node representing a possible construction process option or resource allocation scheme; S623. Simulation: Starting from the extended node, use a stochastic strategy to simulate until the project is completed, and record the project duration and carbon emission data during the simulation process; S624, Backtracking: Backtrack the simulation results to update the statistics of all nodes on the path, including the number of visits and the reward value; S63. In the state transition process, an environmental node is introduced, which includes random variables such as geological condition changes, equipment failure rate, and weather impact; wherein, the random variables are variables with uncertain factors such as the degree of geological condition changes, the probability of equipment failure, and the level of weather impact, and their values ​​follow a specific probability distribution. S64. Define the reward function R, taking into account both construction period and carbon emission performance:

[0021] Where R is the overall reward value, representing the time-limited bonus. The baseline construction period is the pre-set standard total construction period. The simulated construction period refers to the total construction period obtained through algorithm simulation. This indicates a carbon emission reward. The baseline carbon emissions, i.e., the pre-set standard total carbon emissions for construction, The simulated carbon emissions are the total carbon emissions from construction obtained through algorithm simulation; α and β are weighting coefficients, used to measure the importance of the construction period bonus and carbon emission bonus in the overall bonus value, respectively, and α+β=1; S65. Through iterative search, output the Pareto optimal solution set to provide a trade-off between construction period and carbon emissions for each construction scheme; where the Pareto optimal solution set is the set of schemes in multi-objective optimization where no other scheme can improve one objective without harming another, that is, each scheme in the set is non-dominated; S66. Based on the Pareto optimal solution set, the decision-maker can select the optimal construction organization scheme according to the project priority and feed the scheme back to the schedule management software and the extended BIM model.

[0022] In a second aspect of the invention, a carbon emission accounting and tracking system based on process methods and BIM is provided, comprising: The BIM model building module is used to build a building information model of the pumped storage power station hub area based on geological survey data and engineering design drawings. The IFC standard extension module is used to extend the building information model with industrial basic standards to integrate geological attributes, construction technology attributes and carbon emission attributes to generate an extended BIM model. The carbon emission accounting model establishment module is used to establish a construction period carbon emission accounting model based on the extended BIM model and using a hybrid life cycle assessment method. The discrete event simulation module is used to simulate and predict the energy consumption data of construction machinery and equipment based on the discrete event simulation method. The carbon quantification calculation module is used to input the output data of the discrete event simulation module into the construction period carbon emission accounting model to perform carbon emission quantification calculation during the construction process. The carbon tracking and display module is used to associate the carbon emission data obtained from quantitative calculations with the components in the extended BIM model, and to realize the dynamic tracking and visualization of the carbon footprint during the construction period based on the integration of the extended BIM model with the progress management software.

[0023] The embodiments of the present invention have at least the following beneficial effects: 1. By constructing an extended BIM model that integrates geological attributes, construction technology attributes, and carbon emission attributes, integrated management of multi-dimensional information during the construction process was achieved. This not only provides comprehensive and accurate basic data for carbon emission accounting during the construction period, but also enables various data during the construction process to be interconnected and work collaboratively, thereby improving the accuracy and reliability of carbon emission accounting and solving the problem of inaccurate accounting caused by scattered data and incomplete information in traditional methods.

[0024] 2. Based on discrete event simulation methods, energy consumption data of construction machinery and equipment is simulated and predicted, and combined with a carbon emission accounting model to achieve dynamic quantitative calculation of carbon emissions during construction. This method can reflect the actual energy consumption of construction machinery and equipment and its impact on carbon emissions in real time, providing construction managers with real-time and accurate carbon emission data support. This helps to identify high-energy-consuming and high-carbon-emission construction links in a timely manner, thereby enabling targeted optimization measures to be taken. This solves the problem that existing technologies cannot perform real-time dynamic monitoring and quantitative calculation of carbon emissions during construction.

[0025] 3. By linking the quantitatively calculated carbon emission data with the components in the extended BIM model, and integrating the extended BIM model with the schedule management software, dynamic tracking and visualization of the carbon footprint during the construction period were achieved. This allows construction managers to intuitively understand the carbon emission status of each component and each construction stage, facilitating collaborative management of construction progress and carbon emissions. Simultaneously, by comparing the planned carbon footprint prediction curve with the actual carbon footprint curve, carbon emission deviations can be detected in a timely manner and an early warning mechanism can be triggered. This provides a scientific basis for optimizing and adjusting the construction schedule, solving the problem of the lack of effective dynamic carbon emission tracking and visualization methods in existing technologies, which hinders construction managers from real-time monitoring and optimization of carbon emission performance. Attached Figure Description

[0026] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated in the drawings by way of example and not limitation, wherein: Figure 1 This is a flowchart illustrating a carbon emission accounting and tracking method based on process technology and BIM, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of a carbon emission accounting and tracking system based on process method and BIM provided in an embodiment of the present invention. Detailed Implementation

[0027] The principles and spirit of the invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement the invention, and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided to make the invention more thorough and complete, and to fully convey the scope of the invention to those skilled in the art.

[0028] The following is for reference. Figure 1 , Figure 1This is a flowchart illustrating a carbon emission accounting and tracking method based on process technology and BIM, provided as an embodiment of the present invention. Figure 1 As shown, a carbon emission accounting and tracking method based on process technology and BIM includes: S1. Based on geological survey data and engineering design drawings, construct a building information model of the pumped storage power station hub area, denoted as BIM model; S2. Extend the BIM model with industrial basic standards to integrate geological attributes, construction process attributes and carbon emission attributes to generate an extended BIM model; S3. Based on the extended BIM model, establish a carbon emission accounting model for the construction period; the carbon emission accounting model for the construction period adopts a hybrid life cycle assessment method, and the formula for calculating the total carbon emissions E is as follows:

[0029] Where E represents the total carbon emissions during the construction period. This represents the carbon emissions of the i-th unit project, where i is the unit project number, ranging from 1 to 9. It represents the cumulative calculation of the carbon emissions of unit projects 1 to 9. S4. Based on the discrete event simulation method, the energy consumption data of construction machinery and equipment are simulated and predicted, and the data are input into the carbon emission accounting model during the construction period to perform quantitative calculation of carbon emissions during the construction process. S5. Associate the carbon emission data obtained from the quantitative calculation with the components in the extended BIM model, and realize the dynamic tracking and visualization of the carbon footprint during the construction period based on the integration of the extended BIM model with the progress management software.

[0030] By constructing an extended BIM model, geological attributes, construction technology attributes, and carbon emission attributes are integrated, thereby enabling accurate calculation and dynamic tracking of carbon emissions during the construction period.

[0031] A BIM model is a digital building information model that integrates geometric information, spatial relationships, geographic information, and attribute information of building components. In this invention, by extending the BIM model with industrial-standard extensions, various attribute data from the construction process can be integrated, providing fundamental data support for subsequent carbon emission accounting.

[0032] The carbon emission accounting model during the construction period adopts a hybrid life cycle assessment approach. This approach comprehensively considers the impact of various factors during the construction process on carbon emissions, enabling a more accurate calculation of the total carbon emissions during the construction period. The discrete event simulation method, on the other hand, simulates and predicts the energy consumption data of construction machinery and equipment, providing more accurate energy consumption data input for carbon emission accounting.

[0033] Construction progress management software is used to manage construction progress. By integrating with extended BIM models, it can achieve dynamic tracking and visualization of carbon footprint during construction, providing construction managers with intuitive dynamic information on carbon emissions.

[0034] Specifically, the BIM model in this invention is constructed based on geological survey data and engineering design drawings, and it can accurately reflect the building information of the pumped storage power station hub area. Geological survey data includes the characteristics of geological layers, such as rock type, layer thickness, and fracture density, which are crucial for understanding the geological conditions that may be encountered during construction. Engineering design drawings provide detailed design information for the buildings, including structural layout and component dimensions.

[0035] When extending the BIM model to industrial-grade standards, specific component entities related to the underground caverns of pumped-storage power stations were defined. For example, the IfcTunnel entity represents the main tunnel structure, and the IfcSupport entity represents the support system. Simultaneously, attribute sets related to construction technology, material consumption, and equipment energy consumption were added through attribute set extension. For instance, the PSet_GeologicalFeatures attribute set describes geological layer characteristics, and the PSet_CarbonEmissionFactor attribute set records material carbon emission factors. These attribute sets cover various factors that may affect carbon emissions during construction, such as material carbon emission factors, construction process parameters, and equipment energy consumption parameters.

[0036] In discrete event simulation methods, mechanical actions in construction processes are separated into discrete events. By collecting activity time sample data and determining their time probability density functions, the actual operating time of construction machinery and equipment can be predicted more accurately, thereby calculating the energy consumption of machinery. In the carbon emission accounting model during the construction period, the total carbon emission E is calculated by summing the carbon emissions Ei of each unit project. The unit project number i ranges from 1 to 9, covering the main engineering stages in the construction process.

[0037] Preferably, when constructing a BIM model, detailed geological survey data and engineering design drawings are first required. These data will serve as the basic inputs for BIM model construction. Geological survey data can be obtained using professional geological survey equipment and software, while engineering design drawings are provided by a professional design team. When expanding the BIM model, the defined IfcTunnel and IfcSupport entities have clear geometric and attribute characteristics, which can accurately represent the structure and support system of underground caverns. Adding attribute sets requires setting specific parameters based on the construction technology and material characteristics. For example, parameters such as rock type and layer thickness included in the PSet_GeologicalFeatures attribute set can be directly obtained from the geological survey report. In the discrete event simulation method, when determining the time probability density function of discrete events, activity time sample data can be collected through on-site monitoring or video analysis. The sample data should be recorded at least 30 times to ensure data reliability. The time probability density function is determined by distribution fitting software. One of the following distributions can be selected: uniform distribution, normal distribution, triangular distribution, or exponential distribution. The specific distribution to choose depends on the characteristics of the sample data. When calculating mechanical energy consumption, the energy consumption correction factor k is an important parameter. It can be adjusted according to actual construction conditions and equipment performance to ensure the accuracy of the calculation results. Through these detailed steps and parameter settings, this invention can achieve accurate accounting and dynamic tracking of carbon emissions during the construction period, providing scientific decision support for construction managers.

[0038] In some embodiments, the specific steps of extending the BIM model to industrial basic standards in step S2 include: S21. Define specific component entities related to the underground cavern of the pumped storage power station through entity extension, including the IfcTunnel entity for representing the main structure of the tunnel and the IfcSupport entity for representing the support system. S22. Add attribute sets related to construction technology, material consumption, and equipment energy consumption through attribute set expansion, including the PSet_GeologicalFeatures attribute set for describing geological layer characteristics, the PSet_CarbonEmissionFactor attribute set for recording material carbon emission factors, the PSet_ConstructionMethods attribute set for recording construction technology parameters, and the PSet_OperationalResourceUsage attribute set for recording equipment resource usage. S23. Establish semantic association rules to dynamically associate geological information, construction process parameters and carbon emission factors.

[0039] It should be noted that this invention employs a series of specific steps to ensure the integration of geological attributes, construction process attributes, and carbon emission attributes when extending the BIM model to industrial-grade standards. This extension process is crucial for achieving accurate carbon emission accounting during the construction phase. The BIM model extension is accomplished through two methods: entity extension and attribute set extension. Entity extension defines specific component entities related to the underground caverns of pumped storage power stations, while attribute set extension adds attribute sets related to construction processes, material consumption, and equipment energy consumption. By establishing semantic association rules, geological information, construction process parameters, and carbon emission factors can be dynamically correlated, thereby providing accurate data support for subsequent carbon emission accounting.

[0040] Specifically, in the entity extension method, the IfcTunnel entity represents the main tunnel structure, containing information such as the tunnel's geometry, dimensions, and location; the IfcSupport entity represents the support system, containing information such as the support structure's type, material, and dimensions. In the attribute set extension method, the PSet_GeologicalFeatures attribute set describes geological layer characteristics, including parameters such as rock type, layer thickness, fracture density, permeability, and shear strength; the PSet_CarbonEmissionFactor attribute set records the material carbon emission factor and cumulative material usage, where the material carbon emission factor refers to the carbon dioxide emissions generated per unit mass of material during production, transportation, and use, and the cumulative material usage refers to the total usage of various materials during construction; the PSet_ConstructionMethods attribute set records construction process parameters, including process name, applicable geological conditions, explosive type, and usage amount; and the PSet_OperationalResourceUsage attribute set records equipment resource usage, including equipment energy consumption parameters and usage time. These attribute sets cover various factors that may affect carbon emissions during the construction process, providing comprehensive data support for carbon emission accounting.

[0041] Preferably, when constructing an extended BIM model, the geometric and attribute characteristics of the IfcTunnel and IfcSupport entities need to be precisely defined based on engineering design drawings and geological survey data. For example, the geometry of the IfcTunnel entity can be accurately drawn using 3D modeling software based on the design drawings, and its attribute characteristics, such as material type and dimensions, can be obtained from the design parameters.

[0042] For the PSet_GeologicalFeatures attribute set, rock type can be obtained from lithological analysis in the geological survey report, layer thickness can be determined from borehole data, and parameters such as fracture density, permeability, and shear strength can be obtained through laboratory or field tests. In the PSet_CarbonEmissionFactor attribute set, the material carbon emission factor can be obtained from data provided by material suppliers or relevant standards, while the cumulative material usage can be statistically analyzed through material procurement and usage records during construction.

[0043] In the PSet_ConstructionMethods attribute set, the process name and applicable geological conditions can be determined based on the construction plan and geological report, while the explosive type and usage amount can be set according to the blasting design.

[0044] In the PSet_OperationalResourceUsage attribute set, equipment energy consumption parameters can be obtained from the equipment's technical manual, and usage time can be determined through construction plans and actual operation records. Through these detailed steps and parameter settings, this invention ensures the accuracy and completeness of the extended BIM model, providing a solid foundation for accurate calculation and dynamic tracking of carbon emissions during the construction period.

[0045] In some embodiments, the PSet_GeologicalFeatures attribute set includes rock type, layer thickness, fracture density, permeability, and shear strength attributes; the PSet_CarbonEmissionFactor attribute set includes material carbon emission factor and cumulative material usage attributes; the PSet_ConstructionMethods attribute set includes process name, applicable geological conditions, explosive type, and usage amount attributes; and the PSet_OperationalResourceUsage attribute set includes equipment energy consumption parameters and usage time attributes.

[0046] It should be noted that when extending the BIM model to industrial-grade standards, this invention defines the specific content of each attribute set in detail. These attribute sets are used to record key information during the construction process to enable more accurate carbon emission accounting. The PSet_GeologicalFeatures attribute set describes the characteristics of geological layers, the PSet_CarbonEmissionFactor attribute set records the carbon emission factor and cumulative usage of materials, the PSet_ConstructionMethods attribute set records construction process parameters, and the PSet_OperationalResourceUsage attribute set records equipment resource usage. The definition of these attribute sets ensures that the extended BIM model can comprehensively reflect various factors during the construction process, providing detailed data support for subsequent carbon emission accounting and tracking.

[0047] Specifically, the PSet_GeologicalFeatures attribute set includes parameters such as rock type, layer thickness, fracture density, permeability, and shear strength. Rock type refers to the specific type of rock in the geological layer, such as granite or limestone; layer thickness refers to the thickness of the geological layer, usually measured in meters; fracture density refers to the number of fractures per unit volume of rock, typically used to assess rock integrity; permeability refers to the rock's ability to allow fluids to pass through, usually measured in Darcy; and shear strength refers to the rock's ability to resist shear failure, usually measured in Pascals. The PSet_CarbonEmissionFactor attribute set includes the material carbon emission factor and cumulative material usage. The material carbon emission factor refers to the amount of carbon dioxide emitted per unit mass of material during production, transportation, and use, while the cumulative material usage refers to the total amount of various materials used during construction. The `PSet_ConstructionMethods` attribute set includes parameters such as process name, applicable geological conditions, explosive type, and usage amount. The process name refers to the specific construction method, such as drill-and-blast method or tunnel boring machine method; applicable geological conditions refer to the geological type to which the process is applicable; explosive type refers to the type of explosive used, such as TNT or emulsion explosives; and usage amount refers to the total amount of explosives used. The `PSet_OperationalResourceUsage` attribute set includes equipment energy consumption parameters and usage time. Equipment energy consumption parameters refer to the energy consumed by the equipment per unit time, usually in kilowatt-hours; usage time refers to the actual operating time of the equipment during construction, usually in hours.

[0048] Preferably, when constructing the PSet_GeologicalFeatures attribute set, the rock type can be obtained from lithological analysis in the geological survey report, the layer thickness can be determined from borehole data, and parameters such as fracture density, permeability, and shear strength can be obtained through laboratory or field tests. In the PSet_CarbonEmissionFactor attribute set, the material carbon emission factor can be obtained from data provided by material suppliers or relevant standards, and the cumulative material usage can be statistically analyzed through material procurement and usage records during construction. In the PSet_ConstructionMethods attribute set, the process name and applicable geological conditions can be determined based on the construction plan and geological report, while the explosive type and usage can be set according to the blasting design. In the PSet_OperationalResourceUsage attribute set, equipment energy consumption parameters can be obtained from the equipment's technical manual, and the usage time can be determined through the construction plan and actual operation records. Through these detailed steps and parameter settings, this invention ensures the accuracy and completeness of the extended BIM model, providing a solid foundation for accurate calculation and dynamic tracking of carbon emissions during the construction period.

[0049] In some embodiments, the specific steps in step S4 for predicting the energy consumption data of construction machinery and equipment based on the discrete event simulation method include: S41. Based on the construction organization design, determine the construction sequence and separate the construction machinery actions involved in each sequence into discrete events; S42. Collect activity time sample data for each discrete event through on-site monitoring or video analysis, and determine its time probability density function F(t) using distribution fitting software; where F(t) represents the time probability density function, and t represents the activity time of the discrete event; S43. In the simulation platform, input the discrete event, the time probability density function F(t), and the quantity of construction resources to construct a discrete event model; S44. Run the discrete event model and output statistical results through the simulator to obtain the actual operating time of each construction machinery and equipment. The actual operation time Calculated using the formula:

[0050] in, This indicates the total actual operating time of the construction machinery and equipment. This represents the number of times the j-th discrete event occurs. This represents the average duration of the j-th discrete event, where j is the discrete event number, ranging from 1 to n, and n is the total number of discrete events. It represents the cumulative calculation of (number of occurrences × average duration) for discrete events from 1 to n. S45. Based on the actual operation time Based on the energy consumption quota per hour of construction machinery, the energy consumption Q of the machinery is calculated using the following formula:

[0051] Where Q represents the total mechanical energy consumption, This represents the actual operating time of the i-th mechanical device. This represents the hourly energy consumption quota of the i-th mechanical equipment, k is the energy consumption correction coefficient, and i is the mechanical equipment number. It means that the (actual operating time × hourly energy consumption quota) of all mechanical equipment is accumulated and then multiplied by the energy consumption correction coefficient.

[0052] It should be noted that this invention employs a discrete event simulation-based method when predicting the energy consumption data of construction machinery and equipment. This method discretizes the mechanical actions in the construction process, treating each action as a discrete event, and predicts the actual energy consumption of the machinery and equipment by simulating the frequency and duration of these events. This method can more accurately reflect the actual operating conditions of machinery and equipment during construction, providing reliable data support for the quantitative calculation of carbon emissions during the construction period. The key to the discrete event simulation method lies in determining the time probability density function of each discrete event. This requires collecting activity time sample data through on-site monitoring or video analysis, and using distribution fitting software to determine the most suitable distribution model.

[0053] Specifically, the discrete event simulation method includes the following key steps: First, based on the construction organization design, the construction sequence is determined, and the actions of construction machinery involved in each sequence are separated into discrete events. For example, a construction sequence may include multiple discrete events, such as the start-up, operation, and shutdown of machinery. The activity time sample data for each discrete event needs to be collected through on-site monitoring or video analysis. This data will be used to determine the time probability density function. The time probability density function can be one of a uniform distribution, normal distribution, triangular distribution, or exponential distribution; the specific choice depends on the characteristics of the sample data. The goodness of fit is calculated using the Kolmogorov-Smirnov KS test, and the probability distribution model with the smallest KS value is selected as the time probability density function for that discrete event. The smaller the KS value, the higher the degree of fit between the sample data and the theoretical probability distribution. In the simulation platform, the discrete events, the time probability density function, and the quantity of construction resources are input to construct the discrete event model. After running the model, the simulator outputs statistical results to obtain the actual operating time of each piece of construction machinery. Finally, based on the actual operating time and the energy consumption quota per hour of construction machinery, the energy consumption of the machinery is calculated.

[0054] Preferably, when implementing the discrete event simulation method, it is first necessary to plan the construction organization design in detail, clarifying each construction procedure and its corresponding construction machinery actions. For example, in the earthwork excavation procedure, it may involve the digging action of excavators, the loading action of loaders, and the transportation action of transport vehicles; these actions can all be considered discrete events. For each discrete event, at least 30 activity time sample data points need to be collected to ensure the statistical reliability of the data. These sample data can be analyzed using distribution fitting software to determine the most suitable time probability density function. When constructing the discrete event model, the time probability density function of each discrete event, the quantity of construction resources (such as the number of machinery and equipment), and the logical relationship of the construction procedures need to be input. After running the model, the simulator will output the actual operating time of each construction machinery and equipment; this time data will be used to calculate the mechanical energy consumption. In specific calculations, the energy consumption correction coefficient k can be adjusted according to the actual construction conditions and equipment performance to ensure the accuracy of the calculation results. Through these detailed steps and parameter settings, this invention can more accurately predict the energy consumption data of construction machinery and equipment, providing reliable data support for the quantitative calculation of carbon emissions during the construction period.

[0055] In some embodiments, in step S42, the time probability density function F(t) is one of a uniform distribution, a normal distribution, a triangular distribution, or an exponential distribution; the number of times the sample data for each discrete event activity time is recorded is not less than 30; the goodness of fit is calculated using the Kolmogorov-Smirnov test, and the probability distribution model with the smallest KS value is selected as the time probability density function for the discrete event; wherein, the KS value is the Kolmogorov-Smirnov test statistic, used to measure the degree of fit between the sample data and the theoretical probability distribution, and the smaller the KS value, the higher the degree of fit.

[0056] It should be noted that this invention employs a statistical test-based method to ensure the accuracy of the selected distribution model when determining the time probability density function of discrete events. This method uses the Kolmogorov-Smirnov (K-S) test to measure the goodness of fit between the sample data and the theoretical probability distribution, and selects the probability distribution model with the smallest K-S value as the time probability density function of the discrete event. The K-S test is a non-parametric test used to compare the differences between the sample data and the theoretical distribution. In this way, it can be ensured that the selected time probability density function accurately reflects the temporal characteristics of the discrete event, thereby improving the accuracy and reliability of discrete event simulation.

[0057] Specifically, the time probability density function can be one of the following: uniform distribution, normal distribution, triangular distribution, or exponential distribution. Each distribution has its specific mathematical characteristics and applicable scenarios. A uniform distribution indicates that the activity time of an event is equally likely within a certain range, suitable for situations where the activity time is relatively uniform; a normal distribution is suitable for situations where the activity time is symmetrically distributed around a central value; a triangular distribution is suitable for situations where the activity time varies between the minimum, most likely, and maximum values; and an exponential distribution is suitable for situations where the activity time exhibits a long-tailed distribution. The sample data for each discrete event's activity time should be recorded at least 30 times to ensure that the sample data has sufficient statistical significance, thereby improving the reliability of the fitting results. The K-S test assesses the goodness of fit by calculating the maximum difference between the sample data and the theoretical distribution; the smaller the K-S value, the better the fit between the sample data and the theoretical distribution.

[0058] Preferably, during implementation, detailed activity time monitoring of each discrete event is first required to ensure that at least 30 sample data points are collected. This data can be acquired through on-site monitoring equipment or video analysis software. Then, distribution fitting software is used to analyze the sample data, trying different distribution models and calculating the K-S value for each model. For example, for a specific discrete event, if the sample data shows that the activity time is relatively concentrated and symmetrical, a normal distribution might be chosen for fitting; if the activity time is relatively uniform within a certain range, a uniform distribution might be chosen. By comparing the K-S values ​​of different distribution models, the distribution with the smallest K-S value is selected as the time probability density function for the discrete event. This ensures that the selected distribution model most accurately reflects the activity time characteristics of the discrete event, thereby improving the accuracy and reliability of discrete event simulation.

[0059] In some embodiments, in step S3, the carbon emissions from the main construction project are... The calculations cover the earthwork excavation, concrete construction, and underground powerhouse construction processes; among them, the carbon emissions generated by blasting demolition are included. The calculation formula is:

[0060] in, This indicates the carbon emissions from blasting demolition operations. This represents the amount of the i-th type of explosive consumed. Let represent the carbon emission factor of the i-th type of explosive. This indicates the amount of carbon emissions generated during the production of explosives. This represents the carbon emissions generated by the transportation of explosives. i is the type number of the explosive, ranging from 1 to n, and n is the total number of types of explosives. It represents the cumulative calculation of (consumption × carbon emission factor) of 1 to n types of explosives, plus the carbon emissions generated by the production and transportation of explosives.

[0061] It should be noted that this invention pays particular attention to the carbon emissions generated by blasting demolition when calculating the carbon emissions of the main construction project. Blasting demolition is one of the carbon-emitting stages of the main project, and its accurate calculation is crucial for the overall construction carbon emissions accounting. The calculation of carbon emissions from blasting demolition considers not only the consumption of explosives and their carbon emission factors, but also the carbon emissions generated during the production and transportation of explosives. This comprehensive calculation method can more accurately reflect the environmental impact of blasting demolition and provides an important basis for the accurate accounting of carbon emissions during the construction period.

[0062] Specifically, in the formula for calculating carbon emissions from blasting demolition construction, This indicates the carbon emissions from blasting demolition operations. Indicates the first The consumption of this type of explosive is usually measured in kilograms; Indicates the first The carbon emission factor of explosives is usually expressed in kilograms of carbon dioxide per kilogram of explosives. This indicates the carbon emissions during the explosives production process. These figures represent the carbon emissions during the transportation of explosives, typically expressed in kilograms of carbon dioxide. The calculation first requires determining the consumption of each type of explosive, which can be obtained from construction and material procurement records. The carbon emission factor for explosives can be obtained from relevant standards or data provided by suppliers. Carbon emissions during the production and transportation of explosives can be estimated through supply chain analysis or industry standard data.

[0063] Preferably, when calculating the carbon emissions of blasting demolition construction, it is first necessary to record the consumption of each type of explosive in detail, which can be done through material usage records during the construction process. For example, in a specific blasting construction project, multiple explosives may be used, such as TNT and emulsion explosives. For each explosive, its actual usage during the construction process needs to be recorded. The carbon emission factor of the explosive can be obtained from the supplier's Environmental Product Declaration (EPD) or by referring to industry standard data. The carbon emissions during the production and transportation of explosives can be estimated through supply chain analysis, for example, by calculating the transportation distance and mode of transportation from the production site to the construction site to estimate the carbon emissions during transportation. In the calculation process, the consumption of each explosive is multiplied by its corresponding carbon emission factor, and then the carbon emissions during the production and transportation of explosives are added to obtain the total carbon emissions of blasting demolition construction. This detailed calculation method can ensure that the calculation results of carbon emissions for blasting demolition construction are accurate and reliable, providing important support for the accurate accounting of carbon emissions during the construction period.

[0064] In some embodiments, the specific steps for implementing dynamic tracking and visualization in step S5 include: S51. The schedule data is bidirectionally associated with the components in the extended BIM model through the application programming interface; S52. Load the lightweight extended BIM model based on the 3D engine; S53. Based on the schedule data, use a time dynamic data format file to define the display attributes of model components in different time intervals, and generate a planned carbon footprint prediction curve. S54. Based on the actual progress data collected on-site, dynamically update the time dynamic data format file to generate the actual carbon footprint curve, and compare and analyze it with the planned carbon footprint prediction curve.

[0065] It should be noted that this invention employs a method of bidirectionally linking schedule data with an extended BIM model to achieve dynamic tracking and visualization of the carbon footprint during the construction phase. This method loads a lightweight extended BIM model using a 3D engine and generates a planned carbon footprint prediction curve based on the schedule data. Simultaneously, it dynamically updates the actual carbon footprint curve based on actual progress data collected on-site. By comparing and analyzing the planned and actual carbon footprint curves, carbon emission deviations can be intuitively identified, thereby enabling effective management and optimization of carbon emissions during the construction phase. This method not only improves the transparency of carbon emission management but also provides real-time decision support for construction managers.

[0066] Specifically, the schedule data is bidirectionally associated with the components in the extended BIM model via an Application Programming Interface (API). An API is an interface that allows interaction between different software systems, enabling real-time synchronization of schedule data and the BIM model. The 3D engine is a software tool for loading and rendering 3D models, capable of handling complex 3D data and performing efficient rendering. The lightweight extended BIM model refers to an optimized version of the original BIM model to reduce data volume and improve loading speed while retaining key information. The time-dynamic data format file is a file format used to define the display attributes of model components within different time intervals. It includes the component's unique identifier (id), the time interval (interval), the display attribute (show), and a custom carbon emission attribute (carbonProperty). These attributes are used to store and display the carbon emission data of the component at different construction stages.

[0067] Preferably, when implementing dynamic tracking and visualization, the first step is to associate the schedule data with the components in the extended BIM model via API. This can be achieved by writing specific scripts or using existing BIM management software. Then, a lightweight extended BIM model is loaded using a 3D engine, ensuring rapid loading and real-time updates. Next, a planned carbon footprint prediction curve is generated based on the schedule data. This requires extracting the planned schedule information associated with the extended BIM model components from the schedule management software's database, including the planned start and finish times of each construction operation and the corresponding quantities. Based on the construction period carbon emission accounting model, the carbon emissions of the planned completed quantities within each time interval are calculated and stored in a time-dynamic data format file. During construction, actual progress data is collected in real time via IoT devices, and the time intervals and carbon emission attributes in the time-dynamic data format file are updated to generate the actual carbon footprint curve. Finally, by comparing and analyzing the planned carbon footprint prediction curve and the actual carbon footprint curve, carbon emission deviations are identified. When the actual carbon emissions exceed a preset threshold of the planned value, an early warning mechanism is automatically triggered to remind construction managers to take appropriate measures. This method ensures accurate and timely dynamic tracking and visualization of carbon emissions during the construction period, providing strong decision support for construction managers.

[0068] In some embodiments, the specific steps for generating the planned carbon footprint prediction curve in step S53 and generating the actual carbon footprint curve in step S54 include: S531. Extract the planned schedule information associated with the extended BIM model components from the database of the schedule management software. The planned schedule information includes the planned start time, planned completion time and corresponding quantity data of each construction operation. S532. Based on the aforementioned carbon emission accounting model for the construction period, calculate the carbon emissions of the planned completed work within each time interval. The calculation formula is as follows:

[0069] in, This represents the planned carbon emissions within the time interval t. This represents the planned quantity of the i-th type of project to be completed within the time interval t. The carbon emission factor represents the amount of work completed in the i-th category, t represents the set time interval, and i is the category number of the work. This means that the (planned completion amount × carbon emission factor) of all categories of work is accumulated within the time interval t. S533. Construct a CzmI format file, which consists of multiple data packets. Each data packet corresponds to an extended BIM model component and includes the following attributes: a unique identifier id, a time interval, a display attribute show, and a custom carbon emission attribute carbonProperty. Here, id is the component's unique identifier, used to distinguish different extended BIM model components; interval is the time interval, used to define the construction time period corresponding to the data; show is the display attribute, used to define the component's display status in the 3D engine; and carbonProperty is the custom carbon emission attribute, used to store the component's corresponding carbon emission-related data. S534. Based on the CzmI format file, dynamically render the extended BIM model in the 3D engine, visualize the carbon emission data in a time series manner, and generate a planned carbon footprint prediction curve. S535. During the construction process, real-time progress data is collected through IoT devices to update the interval attribute and carbonProperty attribute in the CzmI format file and generate the actual carbon footprint curve. S536. By comparing and analyzing the planned carbon footprint prediction curve and the actual carbon footprint curve, carbon emission deviations are identified. When the actual carbon emissions exceed the preset threshold of the planned value, an early warning mechanism is automatically triggered. The preset threshold is the maximum allowable deviation between the actual carbon emissions and the planned carbon emissions, which is used to determine whether carbon emissions are within a reasonable control range.

[0070] It should be noted that this invention employs a method based on a time-dynamic data format file when generating the planned carbon footprint prediction curve and the actual carbon footprint curve. This method extracts planned progress information from the database of the progress management software and calculates the planned carbon emissions for each time interval based on a construction period carbon emission accounting model, thereby generating the planned carbon footprint prediction curve. Simultaneously, actual progress data is collected in real time through IoT devices, dynamically updating the time-dynamic data format file to generate the actual carbon footprint curve. By comparing and analyzing these two curves, carbon emission deviations can be identified, and an early warning mechanism is automatically triggered when the actual carbon emissions exceed a preset threshold of the planned value. This method provides construction managers with intuitive dynamic information on carbon emissions, helping to adjust construction strategies in a timely manner to optimize carbon emission performance.

[0071] Specifically, the planned schedule information includes the planned start time, planned completion time, and corresponding workload data for each construction task. This data is stored in the progress management software's database and can be retrieved via API. The construction period carbon emission accounting model is used to calculate the carbon emissions of the planned workload within each time interval. This represents the planned carbon emissions within the time interval \(t\). Indicates time interval The first phase of the internal plan to be completed Class of work quantity, Indicates the first Carbon emission factors for similar engineering quantities. The time-dynamic data format file (CzmI) consists of multiple data packets, each corresponding to an extended BIM model component. Each data packet contains the component's unique identifier (id), time interval (interval), display attribute (show), and a custom carbon emission attribute (carbonProperty). These attributes store the carbon emission-related data for the component and dynamically render the extended BIM model in the 3D engine, visualizing the carbon emission data in a time-series format. The preset threshold is a pre-defined maximum allowable deviation between actual and planned carbon emissions, used to determine whether carbon emissions are within a reasonable control range.

[0072] Preferably, during implementation, the planned progress information associated with the extended BIM model components needs to be extracted from the database of the progress management software. This can be achieved by writing specific scripts or using existing data extraction tools. Then, based on the construction period carbon emission accounting model, the carbon emissions of the planned completed work volume within each time interval are calculated. Specifically, the planned completed work volume for each type of work volume needs to be multiplied by its corresponding carbon emission factor, and the results are summed to obtain the planned carbon emissions for that time interval. Next, a CzmI format file is constructed to store the calculated carbon emission data in the corresponding data package. The lightweight extended BIM model is loaded into the 3D engine, and the model is dynamically rendered based on the data in the CzmI format file to generate a planned carbon footprint prediction curve. During construction, actual progress data is collected in real time through IoT devices, and the time intervals and carbon emission attributes in the CzmI format file are updated to generate the actual carbon footprint curve. Finally, by comparing and analyzing the planned carbon footprint prediction curve and the actual carbon footprint curve, carbon emission deviations are identified. When the actual carbon emissions exceed the preset threshold of the planned value, an early warning mechanism is automatically triggered to remind construction managers to take appropriate measures. This method ensures accurate and timely dynamic tracking and visualization of carbon emissions during the construction period, providing strong decision support for construction managers.

[0073] In some embodiments, the method further includes step S6: S6. Construct a dynamic optimization model for construction progress based on the integration of BIM and progress management software; using total carbon emissions and total construction period as dual objective functions, generate multiple construction organization and management schemes based on the carbon emission accounting model during the construction period using optimization algorithms, and compare and optimize them. The specific steps for constructing the dynamic optimization model of construction progress in step S6 include: S61. Establish a bi-objective optimization function with the objectives of minimizing the total construction period T and minimizing the total carbon emissions E during construction:

[0074] Where T represents the total time from the start of construction to completion, E represents the total carbon emissions during the entire construction process, F is the bi-objective optimization function, and min represents finding the minimum value. It is in column vector form, representing the total construction period and total carbon emissions as two dimensions for dual-objective optimization; S62. A Monte Carlo tree search algorithm is used to perform a multi-solution optimization search. The algorithm includes the following steps: S621. Selection: Starting from the root node, select the most promising child node. The selection strategy is based on the UCT formula:

[0075] in, Let represent the average reward value of node j, and n represent the number of visits to the parent node. This indicates the number of times child node j has been visited. The exploration constant is used to balance the exploration (selecting under-visited nodes) and exploitation (selecting nodes with high average reward values) of the algorithm. UCT is the Upper Confidence Bound for Trees value, which is used to measure the selection priority of child nodes. The larger the UCT value, the higher the probability that the child node is selected. ln is the symbol for the natural logarithm. S622, Extension: When encountering a node that is not fully expanded, create one or more child nodes, each child node representing a possible construction process option or resource allocation scheme; S623. Simulation: Starting from the extended node, use a stochastic strategy to simulate until the project is completed, and record the project duration and carbon emission data during the simulation process; S624, Backtracking: Backtrack the simulation results to update the statistics of all nodes on the path, including the number of visits and the reward value; S63. In the state transition process, an environmental node is introduced, which includes random variables such as geological condition changes, equipment failure rate, and weather impact; wherein, the random variables are variables with uncertain factors such as the degree of geological condition changes, the probability of equipment failure, and the level of weather impact, and their values ​​follow a specific probability distribution. S64. Define the reward function R, taking into account both construction period and carbon emission performance:

[0076] Where R is the overall reward value, representing the time-limited bonus. The baseline construction period is the pre-set standard total construction period. The simulated construction period refers to the total construction period obtained through algorithm simulation. This indicates a carbon emission reward. The baseline carbon emissions, i.e., the pre-set standard total carbon emissions for construction, The simulated carbon emissions are the total carbon emissions from construction obtained through algorithm simulation; α and β are weighting coefficients, used to measure the importance of the construction period bonus and carbon emission bonus in the overall bonus value, respectively, and α+β=1; S65. Through iterative search, output the Pareto optimal solution set to provide a trade-off between construction period and carbon emissions for each construction scheme; where the Pareto optimal solution set is the set of schemes in multi-objective optimization where no other scheme can improve one objective without harming another, that is, each scheme in the set is non-dominated; S66. Based on the Pareto optimal solution set, the decision-maker can select the optimal construction organization scheme according to the project priority and feed the scheme back to the schedule management software and the extended BIM model.

[0077] It should be noted that, in terms of dynamic optimization of construction progress, this invention constructs a dynamic optimization model for construction progress based on the integration of BIM and progress management software. This model uses total carbon emissions and total construction period as dual objective functions. It utilizes optimization algorithms to generate multiple construction organization and management schemes based on the carbon emission accounting model during the construction period, and then compares and optimizes them. This method not only considers the optimization of construction progress but also takes into account the control of carbon emissions, achieving synergistic optimization of construction progress and carbon emissions. It provides scientific decision support for construction managers and helps to reduce carbon emissions while ensuring construction progress.

[0078] Specifically, the bi-objective optimization function aims to minimize the total construction period. and minimizing total carbon emissions from construction For the target, it is represented as .here, It is the total time from the start of construction to completion. It is the total carbon emissions throughout the entire construction process, while This is a bi-objective optimization function, meaning that the total construction period and total carbon emissions are considered as the two dimensions for optimization. The Monte Carlo Tree Search algorithm is a sampling-based search algorithm used to find optimal solutions in complex decision spaces. The algorithm consists of four steps: selection, expansion, simulation, and backtracking. In the selection step, the most promising child node is selected based on the UCT formula; in the expansion step, new child nodes are created to explore new construction schemes; in the simulation step, a stochastic strategy is used to simulate the construction process and record the construction period and carbon emission data; in the backtracking step, the statistical information of all nodes on the path is updated. The environmental node introduces random variables such as changes in geological conditions, equipment failure rates, and weather effects. These variables take values ​​according to a specific probability distribution to simulate uncertainties in actual construction. Reward function. Taking into account both construction period and carbon emission performance, among which This indicates a bonus for completing the project on time. This indicates a carbon emission reward, while and These are weighting coefficients used to balance the importance of project duration and carbon emission incentives in the overall reward value. The Pareto optimal set is the set of solutions in multi-objective optimization where no other solution can improve one objective without compromising another, providing decision-makers with a trade-off between project duration and carbon emissions.

[0079] Preferably, when constructing a dynamic optimization model for construction progress, a dual-objective optimization function needs to be established first, clearly defining the two objectives: total construction period and total carbon emissions. Then, a Monte Carlo tree search algorithm is used for multi-scheme optimization. In the selection step, the priority of each child node is calculated according to the UCT formula, and the child node with the highest priority is selected for expansion. In the expansion step, new child nodes are created based on the actual construction situation, with each child node representing a possible construction technology selection or resource allocation scheme. In the simulation step, starting from the expanded node, a stochastic strategy is used to simulate the construction process until project completion, recording the construction period and carbon emission data during the simulation.

[0080] In the backtracking step, the simulation results are used to update the statistics of all nodes along the path, including the number of visits and reward values. During the state transition process, an environmental node is introduced to consider random factors such as changes in geological conditions, equipment failure rates, and weather effects. The values ​​of these factors are determined based on the probability distribution of actual construction data and historical experience. When defining the reward function, the duration reward and carbon emission reward are calculated based on the baseline construction period and baseline carbon emissions, with weighting coefficients... and Adjustments should be made based on project priorities. For example, if the project prioritizes timeline completion, then increase the timeline. The value of is determined through iterative search, outputting a Pareto optimal solution set that provides a trade-off between construction period and carbon emissions for each construction scheme. Decision-makers can select the optimal construction organization scheme from the Pareto optimal solution set based on project priorities and feed this scheme back into the schedule management software and extended BIM model to achieve coordinated optimization of construction schedule and carbon emissions.

[0081] like Figure 2 As shown in some embodiments, a carbon emission accounting and tracking system based on process methods and BIM is provided. The system includes: BIM model building module 201 is used to build a building information model of the pumped storage power station hub area based on geological survey data and engineering design drawings. IFC Standard Extension Module 202 is used to extend the building information model with industrial basic standards to integrate geological attributes, construction technology attributes and carbon emission attributes to generate an extended BIM model. Carbon emission accounting model establishment module 203 is used to establish a construction period carbon emission accounting model based on the extended BIM model and using a hybrid life cycle assessment method. Discrete event simulation module 204 is used to simulate and predict the energy consumption data of construction machinery and equipment based on discrete event simulation methods; The carbon quantification calculation module 205 is used to input the output data of the discrete event simulation module into the construction period carbon emission accounting model to perform carbon emission quantification calculation during the construction process. The carbon tracking display module 206 is used to associate the carbon emission data obtained by quantitative calculation with the components in the extended BIM model, and to realize the dynamic tracking and visualization of the carbon footprint during the construction period based on the integration of the extended BIM model with the progress management software.

[0082] It is understandable that the modules and references recorded in this carbon emission accounting and tracking system based on process methods and BIM are... Figure 1 The steps described correspond to those in the carbon emission accounting and tracking method based on process methods and BIM. Therefore, the operations, features, and beneficial effects described above for the carbon emission accounting and tracking method based on process methods and BIM are also applicable to the carbon emission accounting and tracking system based on process methods and BIM and its included modules, and will not be repeated here.

[0083] The above description is merely an explanation of some preferred embodiments of the present invention and the technical principles employed. Those skilled in the art should understand that the scope of the invention as described in the embodiments of the present invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A carbon emission accounting and tracking method based on process technology and BIM, characterized in that, Includes the following steps: S1. Based on geological survey data and engineering design drawings, construct a building information model of the pumped storage power station hub area, denoted as BIM model; S2. Extend the BIM model with industrial basic standards to integrate geological attributes, construction process attributes and carbon emission attributes to generate an extended BIM model; S3. Based on the extended BIM model, establish a carbon emission accounting model for the construction period; S4. Based on the discrete event simulation method, the energy consumption data of construction machinery and equipment are simulated and predicted, and the data are input into the carbon emission accounting model during the construction period to perform quantitative calculation of carbon emissions during the construction process. S5. Associate the carbon emission data obtained from the quantitative calculation with the components in the extended BIM model, and realize the dynamic tracking and visualization of the carbon footprint during the construction period based on the integration of the extended BIM model with the progress management software.

2. The method according to claim 1, characterized in that, The specific steps for extending the BIM model to industrial basic standards in step S2 include: S21. Define specific component entities related to the underground cavern of the pumped storage power station through entity extension, including the IfcTunnel entity for representing the main structure of the tunnel and the IfcSupport entity for representing the support system. S22. Add attribute sets related to construction technology, material consumption, and equipment energy consumption through attribute set expansion, including the PSet_GeologicalFeatures attribute set for describing geological layer characteristics, the PSet_CarbonEmissionFactor attribute set for recording material carbon emission factors, the PSet_ConstructionMethods attribute set for recording construction technology parameters, and the PSet_OperationalResourceUsage attribute set for recording equipment resource usage. S23. Establish semantic association rules to dynamically associate geological information, construction process parameters and carbon emission factors.

3. The method according to claim 2, characterized in that, The PSet_GeologicalFeatures attribute set includes rock type, layer thickness, fracture density, permeability, and shear strength attributes; the PSet_CarbonEmissionFactor attribute set includes material carbon emission factor and cumulative material usage attributes; the PSet_ConstructionMethods attribute set includes process name, applicable geological conditions, explosive type, and usage amount attributes; and the PSet_OperationalResourceUsage attribute set includes equipment energy consumption parameters and usage time attributes.

4. The method according to claim 1, characterized in that, The specific steps in step S4 for predicting the energy consumption data of construction machinery and equipment based on the discrete event simulation method include: S41. Based on the construction organization design, determine the construction sequence and separate the construction machinery actions involved in each sequence into discrete events; S42. Collect activity time sample data for each discrete event through on-site monitoring or video analysis, and determine its time probability density function F(t) using distribution fitting software; S43. In the simulation platform, input the discrete event, the time probability density function F(t), and the quantity of construction resources to construct a discrete event model; S44. Run the discrete event model and output statistical results through the simulator to obtain the actual operating time of each construction machinery and equipment. The actual operation time Calculated using the formula: in, This indicates the total actual operating time of the construction machinery and equipment. This represents the number of times the j-th discrete event occurs. This represents the average duration of the j-th discrete event, where j is the sequence number of the discrete event, ranging from 1 to n, and n is the total number of discrete events. S45. Based on the actual operation time Based on the energy consumption quota per hour of construction machinery, the energy consumption Q of the machinery is calculated using the following formula: Where Q represents the total mechanical energy consumption, This represents the actual operating time of the i-th mechanical device. Let k represent the hourly energy consumption quota of the i-th mechanical equipment, k be the energy consumption correction coefficient, and i be the serial number of the mechanical equipment.

5. The method according to claim 4, characterized in that, In step S42, the time probability density function F(t) is one of a uniform distribution, a normal distribution, a triangular distribution, or an exponential distribution; the number of times the sample data for each discrete event's activity time is recorded is no less than 30; the goodness of fit is calculated using the Kolmogorov-Smirnov test, and the probability distribution model with the smallest KS value is selected as the time probability density function for the discrete event; wherein, the KS value is the Kolmogorov-Smirnov test statistic, used to measure the degree of fit between the sample data and the theoretical probability distribution, and the smaller the KS value, the higher the degree of fit.

6. The method according to claim 1, characterized in that, In step S3, the carbon emissions from the main construction project The calculations cover the earthwork excavation, concrete construction, and underground powerhouse construction processes; among them, the carbon emissions generated by blasting demolition are included. The calculation formula is: in, This indicates the carbon emissions from blasting demolition operations. This represents the amount of the i-th type of explosive consumed. Let represent the carbon emission factor of the i-th type of explosive. This indicates the amount of carbon emissions generated during the production of explosives. This represents the carbon emissions generated by the transportation of explosives, where i is the type number of the explosive, ranging from 1 to n, and n is the total number of types of explosives.

7. The method according to claim 1, characterized in that, The specific steps for achieving dynamic tracking and visualization in step S5 include: S51. The schedule data is bidirectionally associated with the components in the extended BIM model through the application programming interface; S52. Load the lightweight extended BIM model based on the 3D engine; S53. Based on the schedule data, use a time dynamic data format file to define the display attributes of model components in different time intervals, and generate a planned carbon footprint prediction curve. S54. Based on the actual progress data collected on-site, dynamically update the time dynamic data format file to generate the actual carbon footprint curve, and compare and analyze it with the planned carbon footprint prediction curve.

8. The method according to claim 7, characterized in that, The specific steps for generating the planned carbon footprint prediction curve in step S53 and the actual carbon footprint curve in step S54 include: S531. Extract the planned schedule information associated with the extended BIM model components from the database of the schedule management software. The planned schedule information includes the planned start time, planned completion time and corresponding quantity data of each construction operation. S532. Based on the aforementioned carbon emission accounting model for the construction period, calculate the carbon emissions of the planned completed work within each time interval. The calculation formula is as follows: in, This represents the planned carbon emissions within the time interval t. This represents the planned quantity of the i-th type of project to be completed within the time interval t. The carbon emission factor represents the i-th type of project quantity, t represents the set time interval, and i is the project quantity category number; S533. Construct a CzmI format file, which consists of multiple data packets. Each data packet corresponds to an extended BIM model component and includes the following attributes: a unique identifier id, a time interval, a display attribute show, and a custom carbon emission attribute carbonProperty. Here, id is the component's unique identifier, used to distinguish different extended BIM model components; interval is the time interval, used to define the construction time period corresponding to the data; show is the display attribute, used to define the component's display status in the 3D engine; and carbonProperty is the custom carbon emission attribute, used to store the component's corresponding carbon emission-related data. S534. Based on the CzmI format file, dynamically render the extended BIM model in the 3D engine, visualize the carbon emission data in a time series manner, and generate a planned carbon footprint prediction curve. S535. During the construction process, real-time progress data is collected through IoT devices to update the interval attribute and carbonProperty attribute in the CzmI format file and generate the actual carbon footprint curve. S536. By comparing and analyzing the planned carbon footprint prediction curve and the actual carbon footprint curve, carbon emission deviations are identified. When the actual carbon emissions exceed the preset threshold of the planned value, an early warning mechanism is automatically triggered. The preset threshold is the maximum allowable deviation between the actual carbon emissions and the planned carbon emissions, which is used to determine whether carbon emissions are within a reasonable control range.

9. The method according to claim 1, characterized in that, The method further includes step S6: S6. Construct a dynamic optimization model for construction progress based on the integration of BIM and progress management software; using total carbon emissions and total construction period as dual objective functions, generate multiple construction organization and management schemes based on the carbon emission accounting model during the construction period using optimization algorithms, and compare and optimize them. The specific steps for constructing the dynamic optimization model of construction progress in step S6 include: S61. Establish a bi-objective optimization function with the objectives of minimizing the total construction period T and minimizing the total carbon emissions E during construction: Where T represents the total time from the start of construction to completion, E represents the total carbon emissions during the entire construction process, F is the bi-objective optimization function, and min represents finding the minimum value; S62. A Monte Carlo tree search algorithm is used to perform a multi-solution optimization search. The algorithm includes the following steps: S621. Selection: Starting from the root node, select the most promising child node. The selection strategy is based on the UCT formula: in, Let represent the average reward value of node j, and n represent the number of visits to the parent node. This indicates the number of times child node j has been visited. To explore constants, UCT stands for Upper Confidence Bound for Trees, which measures the selection priority of child nodes. The larger the UCT value, the higher the probability that the child node will be selected. ln is the natural logarithm symbol. S622, Extension: When encountering a node that is not fully expanded, create one or more child nodes, each child node representing a construction process option or resource allocation scheme; S623. Simulation: Starting from the extended node, use a stochastic strategy to simulate until the project is completed, and record the project duration and carbon emission data during the simulation process; S624, Backtracking: Backtrack the simulation results to update the statistics of all nodes on the path, including the number of visits and the reward value; S63. In the state transition process, an environmental node is introduced, which includes random variables such as geological condition changes, equipment failure rate, and weather impact; wherein, the random variables are variables with uncertain factors such as the degree of geological condition changes, the probability of equipment failure, and the level of weather impact, and their values ​​follow a specific probability distribution. S64. Define the reward function R, taking into account both construction period and carbon emission performance: Where R is the overall reward value, representing the time-limited bonus. As the baseline construction period, To simulate the construction period, This indicates a carbon emission reward. As a baseline carbon emission, To simulate carbon emissions, α and β are weighting coefficients used to measure the importance of the construction period incentive and carbon emission incentive in the overall incentive value, respectively, and α+β=1; S65. Through iterative search, output the Pareto optimal solution set to provide a trade-off between construction period and carbon emissions for each construction scheme; S66. Based on the Pareto optimal solution set, the decision-maker can select the optimal construction organization scheme according to the project priority and feed the scheme back to the schedule management software and the extended BIM model.

10. A carbon emission accounting and tracking system based on process methods and BIM, used to implement the method described in any one of claims 1 to 9, characterized in that, include: The BIM model building module is used to build a building information model of the pumped storage power station hub area based on geological survey data and engineering design drawings. The IFC standard extension module is used to extend the building information model with industrial basic standards to integrate geological attributes, construction technology attributes and carbon emission attributes to generate an extended BIM model. The carbon emission accounting model establishment module is used to establish a construction period carbon emission accounting model based on the extended BIM model and using a hybrid life cycle assessment method. The discrete event simulation module is used to simulate and predict the energy consumption data of construction machinery and equipment based on the discrete event simulation method. The carbon quantification calculation module is used to input the output data of the discrete event simulation module into the construction period carbon emission accounting model to perform carbon emission quantification calculation during the construction process. The carbon tracking and display module is used to associate the carbon emission data obtained from quantitative calculations with the components in the extended BIM model, and to realize the dynamic tracking and visualization of the carbon footprint during the construction period based on the integration of the extended BIM model with the progress management software.

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