Fabricated building carbon emission control method, system, equipment and medium

Abstract

The invention relates to a fabricated building carbon emission management and control method, system and device and a medium, and belongs to the technical field of constructions.The management and control method comprises the steps that a basic data set of a fabricated building is obtained; on the basis of the carbon emission factor database, calculating carbon emission values of all stages of the full life cycle of the fabricated building according to the basic data set; comparing the carbon emission value with a preset carbon emission control threshold value; if the carbon emission value exceeds a preset carbon emission control threshold value, generating an adjustment scheme based on a multi-objective optimization algorithm; and updating the basic data set according to the adjustment scheme, and generating a carbon emission control strategy. According to the invention, the carbon emission value of the whole life cycle can be accurately evaluated, optimized and dynamically adjusted.

Description

Technical Field

[0001] The present application relates to the field of building technology, and in particular to a method, system, equipment and medium for controlling carbon emissions from prefabricated buildings. Background Art

[0002] As global climate change becomes increasingly severe, international attention to carbon emissions is growing. As a significant source of global carbon emissions, the construction industry has become a key area for reducing greenhouse gas emissions and promoting sustainable development. Buildings emit significant amounts of carbon dioxide throughout their lifecycles, particularly during the production and transportation of building materials, construction, use, and demolition phases. These emissions arise not only from the production and transportation of building materials but also from energy consumption during construction, machinery usage, and energy consumption during the building's operation. Therefore, reducing carbon emissions from the construction industry and improving building energy efficiency have become key priorities in global climate policy.

[0003] Currently, carbon emission assessment methods in the construction industry are often simplified, often focusing only on a single phase of a building, such as energy efficiency assessment during the design phase or emission control during the construction phase. However, these methods overlook the interactions and impacts of carbon emissions across various phases. For example, emissions at one stage may influence design or construction choices at other stages. Existing methods are often unable to effectively integrate and integrate carbon emission information from different phases, lacking a comprehensive consideration of the entire life cycle. This results in an inability to accurately assess and control carbon emissions during the building's operational phase. In the field of prefabricated buildings, in particular, how to accurately assess, optimize, and dynamically adjust carbon emissions throughout the entire life cycle is a pressing issue for the construction industry. Summary of the Invention

[0004] In order to accurately evaluate, optimize and dynamically adjust the carbon emission values ​​throughout the entire life cycle, this application provides a method, system, equipment and medium for carbon emission control of prefabricated buildings.

[0005] In the first aspect, the present application provides a method for controlling carbon emissions from prefabricated buildings, which adopts the following technical solutions: A method for controlling carbon emissions from prefabricated buildings, comprising: Obtaining a basic data set for prefabricated buildings; wherein the basic data set includes building design parameters, component types and quantities, transportation distance parameters, building material types and usage data, and energy consumption data during the building's use phase; Based on the carbon emission factor database, the carbon emission values ​​of each stage of the prefabricated building's entire life cycle are calculated according to the basic data set; wherein the stages include the building materials production stage, the component transportation stage, the on-site assembly stage, and the use stage; Comparing the carbon emission value with a preset carbon emission control threshold; If the carbon emission value exceeds the preset carbon emission control threshold, an adjustment plan is generated based on a multi-objective optimization algorithm; the adjustment plan includes an optimized combination of the building design parameters, component types, and building material types; The basic data set is updated according to the adjustment plan, and a carbon emission control strategy is generated.

[0006] By adopting the above technical solutions, from the accurate collection of basic data sets to the calculation of the carbon emission factor database, and then to the adjustment plan generated by the multi-objective optimization algorithm, the entire process ensures that the carbon emissions of construction projects during the design, construction and use stages can be effectively controlled. This not only helps to reduce the burden of the construction industry on the environment, but also improves the flexibility and adaptability of carbon emission management, thereby providing strong support for the carbon neutrality goals of the construction industry.

[0007] Optionally, the steps of constructing the carbon emission factor database include: Access the preset carbon emission database through the API interface to obtain the original data of carbon emission factors for building materials production, transportation tools, assembly processes and regional energy structure; Standardizing the raw data of the carbon emission factor and cleaning the abnormal data based on an anomaly detection algorithm to obtain standardized data of the carbon emission factor; The cleaned carbon emission factor standardized data is multi-dimensionally classified according to building material type, transportation mode and regional characteristics, and stored in a hierarchical database; Calculate the regional transportation distance correction coefficient based on GIS map data, and dynamically update the transportation and production carbon emission factors in the database in combination with the regional power grid carbon emission factors; Implement logical rule verification on the data in the database, mark abnormal entries that violate preset constraints, and fill in missing data entries through fault-tolerant matching strategies; The carbon emission factor database is constructed by integrating the classified storage, dynamic update and logical verification data.

[0008] By implementing this technical solution, carbon emission factor data can be effectively managed and maintained, ensuring more accurate carbon emission calculations throughout the life cycle of a building project. Furthermore, based on the updated and integrated database, architectural designers can more accurately calculate and optimize the carbon emissions of construction projects, driving the construction industry towards a low-carbon, sustainable future.

[0009] Optionally, the step of calculating the carbon emission values ​​of the prefabricated building at each stage of its life cycle based on the basic data set based on the carbon emission factor database includes: Calling the carbon emission factor database to match the carbon emission factors corresponding to the basic data set, including building material production factors, transportation factors, assembly factors, and energy factors in the use phase; Calculate the carbon emissions during the building materials production phase based on the building materials type and usage data, combined with the building materials production factors; Calculate the carbon emissions during the component transportation phase based on component type and quantity data, combined with the transportation distance parameter and the transportation factor; Based on the component type and quantity data, combined with the assembly factors, calculate the carbon emissions value of the on-site assembly stage; Calculate the carbon emissions during the use phase based on the building's energy consumption data during the use phase and in combination with the energy factors during the use phase; The carbon emission values ​​of each stage in the entire life cycle of the prefabricated building are obtained by integrating the carbon emission values ​​of the building materials production stage, component transportation stage, on-site assembly stage and use stage.

[0010] By adopting this technical solution, and based on a phased carbon emissions calculation method for the entire life cycle, carbon emissions at every stage, from design to use, are accurately calculated. Through detailed data analysis and factor matching, this approach provides scientific decision-making support for architectural designers. By gradually calculating and comprehensively evaluating carbon emissions from the production, transportation, assembly, and use of building materials, this approach provides a concrete basis for optimizing carbon emissions in the construction industry.

[0011] Optionally, the steps of generating an adjustment plan based on a multi-objective optimization algorithm include: Defining decision variables and optimization objectives for a multi-objective optimization algorithm; wherein the decision variables include building material types, component types, and building design parameters, and the optimization objectives include carbon emissions, cost increments, and building function scores; Construct an initial solution set that includes optimization schemes for building material types, components, and architectural design parameters; Calculating the optimization target score of each solution based on the initial solution set; Screen the candidate solutions with the best optimization goal score through non-dominated sorting; The initial solution set is iteratively updated until the predicted carbon emission value of the candidate solution is lower than the preset carbon emission control threshold, thereby obtaining a final adjustment solution.

[0012] By adopting these technical solutions, a balance is achieved between reducing building carbon emissions, controlling costs, and meeting functional requirements. By clearly defining decision variables and optimization objectives, and utilizing non-dominated sorting and iterative update algorithms, the system can select the best solution from multiple optimization options, providing building designers with a comprehensive, flexible, and efficient decision support tool to ensure that buildings achieve the optimal balance between carbon emission control, cost management, and functionality.

[0013] Optionally, the steps of constructing an initial solution set including building material type optimization solutions, component optimization solutions, and building design parameter optimization solutions include: Select alternative building materials from the pre-set low-carbon building material library to generate an optimized plan for building material types; Adjust rules based on component type to generate component optimization solutions that reduce transportation distance or assembly energy consumption; Based on the parameter perturbation rules, the building design parameters are adjusted to generate the building design parameter optimization scheme.

[0014] By adopting the above-mentioned technical solutions, replacing low-carbon building materials, optimizing component design, and adjusting building design parameters, a significant reduction in carbon emissions has been achieved. The use of low-carbon building materials not only reduces carbon emissions during the production phase but also promotes the application of green building materials. By optimizing component design, energy consumption and carbon emissions during transportation and assembly were reduced, improving construction efficiency. Fine-tuning building design parameters further reduced energy consumption during the building's use phase, thereby reducing overall carbon emissions. Through scientific optimization strategies, this overall solution not only reduces carbon emissions but also ensures the functionality and affordability of construction projects, providing a sustainable development solution for the construction industry.

[0015] Optionally, after the step of generating the carbon emission control strategy, the following steps are further included: Receiving actual carbon emission monitoring data collected after executing the carbon emission control strategy; Comparing the actual carbon emission monitoring data with the predicted carbon emission value of the carbon emission control strategy to obtain a deviation rate; When the deviation rate exceeds a preset deviation threshold, the carbon emission factor is corrected according to the deviation rate; The revised carbon emission factor is written into the carbon emission factor database and marked with a version number and an update timestamp.

[0016] By implementing this technical solution, deviations can be detected and carbon emission factors corrected promptly, ensuring that the carbon emission factor database remains up-to-date and up-to-date, providing more accurate carbon emission calculation and management support for future construction projects. This technical solution enhances the flexibility and precision of carbon emission management, ensuring that construction projects can achieve continuous carbon emission optimization.

[0017] In a second aspect, the present application provides a prefabricated building carbon emission control system, which adopts the following technical solutions: A carbon emission control system for assembled buildings, comprising: An acquisition module is used to acquire a basic data set of prefabricated buildings; wherein the basic data set includes building design parameters, component types and quantities, transportation distance parameters, building material types and usage data, and energy consumption data during the building use phase; A carbon emission value calculation module, configured to calculate the carbon emission values ​​of each stage of the prefabricated building's life cycle based on the carbon emission factor database and the basic data set; wherein the stages include the building material production stage, the component transportation stage, the on-site assembly stage, and the use stage; A comparison module, configured to compare the carbon emission value with a preset carbon emission control threshold; an adjustment module, configured to generate an adjustment plan based on a multi-objective optimization algorithm when the carbon emission value exceeds the preset carbon emission control threshold; the adjustment plan includes an optimized combination of the building design parameters, component types, and building material types; The carbon emission control module is used to update the basic data set according to the adjustment plan and generate a carbon emission control strategy.

[0018] Optionally, the management and control system further includes: An actual monitoring data receiving module, configured to receive actual carbon emission monitoring data collected after executing the carbon emission control strategy; a deviation comparison module, configured to compare the actual carbon emission monitoring data with the predicted carbon emission value of the carbon emission control strategy to obtain a deviation rate; a correction module, configured to correct the carbon emission factor according to the deviation rate when the deviation rate exceeds a preset deviation threshold; The database update module is used to write the revised carbon emission factor into the carbon emission factor database and mark the version number and update timestamp.

[0019] In a third aspect, the present application provides a computer device that adopts the following technical solution: A computer device comprises a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method according to the first aspect.

[0020] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium stores a computer program capable of being loaded by a processor and executing any one of the methods in the first aspect.

[0021] In summary, this application includes at least one of the following beneficial technical effects: By accurately calculating carbon emissions at each stage of a building's lifecycle and comparing them with preset carbon emission control thresholds, and using a multi-objective optimization algorithm to generate adjustment plans when carbon emissions exceed the preset thresholds, the system can optimize building design parameters, component types, and building material types, thereby achieving effective carbon emission control. Ultimately, by updating the basic data set and generating carbon emission control strategies, the system can ensure that construction projects meet predetermined carbon emission targets while meeting functional and economic requirements, thereby achieving sustainable development in the construction industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a first flow chart of a method for controlling carbon emissions from prefabricated buildings according to one of the embodiments of the present application.

[0023] Figure 2 This is a second flow chart of a method for controlling carbon emissions from prefabricated buildings according to one of the embodiments of the present application.

[0024] Figure 3 This is a third flow chart of a method for controlling carbon emissions from prefabricated buildings according to one of the embodiments of the present application.

[0025] Figure 4 This is a fourth flow chart of a method for controlling carbon emissions from prefabricated buildings according to one of the embodiments of the present application.

[0026] Figure 5 This is the fifth flow chart of a method for controlling carbon emissions from prefabricated buildings according to one of the embodiments of the present application.

[0027] Figure 6 This is the sixth flow chart of a method for controlling carbon emissions from prefabricated buildings according to one of the embodiments of the present application. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of this application more clear, the following Figure 1-6 It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0029] The embodiments of the present application disclose a method for controlling carbon emissions from prefabricated buildings.

[0030] Reference Figure 1 A method for controlling carbon emissions from prefabricated buildings, comprising: Step S101, obtaining a basic dataset of prefabricated buildings; The basic data set includes building design parameters, component types and quantities, transportation distance parameters, building material types and usage data, and energy consumption data during the building use phase; Specifically, building design parameters include the building's scale, number of floors, functional use, and spatial layout; these factors directly affect the building's energy efficiency and carbon emissions. For example, a large commercial building will consume significantly more energy than a residential building, resulting in more carbon emissions. Component type and quantity, as well as transportation distance parameters, also have a significant impact on carbon emissions. Carbon emissions vary greatly during the production, transportation, and installation of different materials and components. Data on the types and quantities of building materials provide the basis for subsequent calculations, as different building materials (such as reinforced concrete, wood, and glass) have different carbon emission factors. In addition, energy consumption data for the building's use phase also needs to be collected. This includes data on the building's electricity consumption, the energy efficiency of its heating and air-conditioning systems, and other factors, all of which will affect the building's carbon emissions during its use phase.

[0031] It is understandable that if the basic data set contains errors or omissions, it will directly lead to inaccurate subsequent calculation results, thus affecting the effectiveness of carbon emission control strategies. Therefore, ensuring the accurate collection of basic data sets is a prerequisite for the successful implementation of carbon emission control methods.

[0032] Step S102, based on the carbon emission factor database, calculate the carbon emission values ​​of each stage of the prefabricated building's entire life cycle according to the basic data set; Among them, the various stages include the building materials production stage, component transportation stage, on-site assembly stage and use stage; Specifically, the carbon emissions of prefabricated buildings over their entire life cycle are calculated based on a carbon emission factor database. The carbon emission factor refers to the amount of carbon dioxide emitted per unit of activity or product during production, transportation, assembly, and use (usually expressed in kg / kg or kg / km). Carbon emission factors for the construction industry typically include carbon emission factors for energy consumption during the production, transportation, assembly, and use phases of building materials production.

[0033] It's important to note that the carbon emission factors of different building materials vary significantly. For example, the carbon emission factor for producing one cubic meter of concrete is much higher than that for producing one cubic meter of wood. Based on the building design parameters and the number of components, the carbon emission factors of these materials can be multiplied by the corresponding quantities used to determine the total carbon emissions for each stage of construction. During the component transportation phase, the distance, method of transportation (e.g., road, rail, shipping), and energy efficiency of the transportation vehicle (e.g., trucks, trains, ships, etc.) all affect carbon emissions. Carbon emissions during the on-site assembly phase are primarily related to factors such as on-site machinery energy consumption, personnel transportation, and equipment usage.

[0034] For example, when calculating carbon emissions from building material production, if a building design uses a large amount of high-carbon materials like concrete and steel, it's necessary to consult a relevant carbon emission factor database to understand the carbon emission factors for these materials (for example, the carbon emission factor for producing 1 ton of steel is 2 tons of CO2). Then, based on the building's actual design requirements (e.g., 50 tons of steel used), calculate the carbon emissions from the steel production stage. Similarly, carbon emissions from transportation, assembly, and other stages must also be calculated based on specific carbon emission factors.

[0035] As you can see, through this detailed carbon emissions calculation, the carbon emissions at each stage of the building's lifecycle are gradually quantified. This not only provides a scientific basis for subsequent carbon emissions control, but also provides data support for optimization and adjustment. Technically, this step helps designers fully understand the carbon emissions of a building project, identify potential high-emission areas, and lay the foundation for further optimization and adjustment.

[0036] Step S103, comparing the carbon emission value with a preset carbon emission control threshold; Carbon emissions control isn't an unlimited optimization process; rather, it's based on compliance with relevant laws, regulations, and environmental goals. At this stage, the system compares the calculated building carbon emissions against pre-set carbon emission control thresholds. These thresholds typically originate from national or local government building carbon emission standards, industry best practices, or a company's own carbon emission targets. These thresholds not only help determine whether optimization adjustments are necessary, but also help designers clarify a building project's carbon emissions compliance.

[0037] For example, suppose a building project is designed to emit no more than 2,000 tons of carbon annually during its operational phase. However, the system calculates the project's total carbon emissions at 2,500 tons, exceeding the pre-set control target. This excess indicates potential excessive carbon emissions in the building design or material selection, requiring optimization and adjustment.

[0038] Step S104: determine whether the carbon emission value exceeds the preset carbon emission control threshold; if so, jump to step S105; if not, do not perform any operation; Step S105, generating an adjustment plan based on a multi-objective optimization algorithm; Among them, the adjustment plan includes the optimized combination of building design parameters, component types and building material types; Specifically, when carbon emissions exceed a preset threshold, the system must implement optimization adjustments to reduce them. This step utilizes a multi-objective optimization algorithm, typically a highly efficient one like NSGA-II (Non-Dominated Sorting Genetic Algorithm II), to generate a set of possible adjustment scenarios to meet the carbon emission target. A multi-objective optimization algorithm is characterized by its ability to simultaneously consider multiple optimization objectives, such as reducing carbon emissions, improving building functionality, and controlling costs, and to find the optimal balance among these objectives.

[0039] For example, during the optimization process, building design parameters (such as the building's structural layout and material usage), component types (such as the types of wall and roof components), and building material types (such as using more low-carbon materials instead of high-carbon materials) will be adjusted as decision variables. For example, the optimization algorithm may recommend reducing the use of concrete and replacing it with low-carbon wood, or optimizing the building design to reduce the use of high-energy-consuming equipment and reduce the demand for the building's air conditioning system, thereby reducing energy consumption and carbon emissions.

[0040] It's no surprise that multi-objective optimization algorithms can efficiently find optimal solutions within multiple constraints. These solutions not only effectively control carbon emissions but also ensure the functionality and affordability of buildings. This approach provides architectural designers with a scientific and precise decision-making support tool, enabling building projects to meet carbon emission control targets while also balancing cost control and functional requirements.

[0041] Step S106: Update the basic data set according to the adjustment plan and generate a carbon emission control strategy.

[0042] After the multi-objective optimization process yields an adjusted solution, the system updates the underlying data set based on the optimization results and applies the new design parameters, component types, and material combinations to the actual design of the building project. This step focuses on translating the optimization results into specific carbon emission control strategies to ensure that the entire project meets the predetermined carbon emission targets.

[0043] Specifically, the updated basic data set includes the adjusted architectural design, material selection, and construction schedule. This data will be input into the project's BIM (Building Information Modeling) system to ensure strict adherence to the optimized design during construction. Furthermore, the carbon emission management strategy will involve monitoring and adjusting carbon emissions during the construction and occupancy phases to ensure that carbon emissions throughout the building's lifecycle remain in line with expected targets.

[0044] Understandably, generating and applying this carbon emission control strategy provides a concrete operational basis for the actual implementation of the project. This strategy not only helps the construction team control carbon emissions during the actual construction process, but also ensures that carbon emissions are continuously monitored and reduced during the operation of the building during the use phase.

[0045] In the above implementation, from the accurate collection of basic data sets to the calculation of the carbon emission factor database, and then to the adjustment plan generated by the multi-objective optimization algorithm, the entire process ensures that the carbon emissions of construction projects during the design, construction and use stages can be effectively controlled. It not only helps to reduce the burden of the construction industry on the environment, but also improves the flexibility and adaptability of carbon emission management, thereby providing strong support for the carbon neutrality goals of the construction industry.

[0046] Reference Figure 2 As an implementation method for constructing a carbon emission factor database, the specific construction steps include: Step S201: Access a preset carbon emission database through an API interface to obtain raw data on carbon emission factors for building material production, transportation tools, assembly processes, and regional energy structure; Specifically, during the database construction process, the first step is to connect to official carbon emission data sources through an API. The API is a standard application programming interface that automatically accesses carbon emission data from various sources, including the International Organization for Standardization, national ecological and environmental authorities, or industry certification bodies. These data sources provide information on carbon emission factors during the production of building materials (such as cement and steel), unit carbon emissions from transportation (such as trucks and trains), carbon emissions from assembly processes, and carbon emission factors for regional power grids.

[0047] For example, the API interface can obtain from an environmental protection organization that the carbon emission factor for producing 1 ton of steel is 2.5 tons of CO2, while the carbon emission factor for transporting 1 ton of steel by road is 0.2 tons of CO2 / km. The system automatically obtains this data to provide a basis for subsequent data processing.

[0048] Step S202: Standardize the original data of the carbon emission factor and clean the abnormal data based on the abnormality detection algorithm to obtain the standardized data of the carbon emission factor; Raw carbon emission factor data may come from different institutions, and their units and measurement methods may differ. Therefore, these data must first be standardized to ensure consistent units across all data (for example, kgCO₂ / kg building materials, kgCO₂ / km·ton of freight, or kgCO₂ / kWh). Subsequently, anomaly detection algorithms (such as the 3σ principle or the isolation forest algorithm) are applied to identify and remove abnormal data that deviate from the typical data range. This process helps eliminate inaccurate or extreme data and ensures the reliability of the final data.

[0049] For example, if one data source indicates that the carbon emission factor for producing 1 ton of concrete is 12 tons of CO2, while other sources show 8 tons of CO2, the anomaly detection algorithm will identify the abnormal data of 12 tons of CO2 and eliminate it.

[0050] Step S203: multi-dimensionally classify the cleaned carbon emission factor standardized data according to building material type, transportation mode, and regional characteristics, and store them in a hierarchical database; The cleaned data needs to be categorized according to various dimensions, including building material type (such as steel, concrete, and wood), transportation mode (such as road, rail, and shipping), and regional characteristics (such as the energy structure of different regions). This multi-dimensional data classification allows for efficient organization and storage of information, making queries and access more efficient.

[0051] For example, the database will be divided into a first-level classification based on building material type (such as steel, wood), and a second-level classification based on transportation type (such as truck, train). Within each type of transportation, it can be further divided according to different energy types (such as diesel, natural gas).

[0052] Step S204: Calculate the regional transportation distance correction coefficient based on the GIS map data, and dynamically update the transportation and production link carbon emission factors in the database in combination with the regional power grid carbon emission factors; GIS (Geographic Information System) technology is used to calculate regional transportation distance correction factors. This factor, combined with the regional power grid's carbon emission factors, dynamically updates the carbon emission factors for transportation and production. For example, differences in transportation distances and energy mixes between regions may affect the calculation of carbon emission factors. During this step, the system adjusts existing carbon emission factors based on real-time geographic information and power grid data to ensure they reflect the latest regional characteristics.

[0053] For example, if a region's power grid uses more renewable energy and has a lower carbon emission factor, the corresponding production process carbon emission factor will be reduced. In this case, the system will make corrections based on the region's power grid factor and update the carbon emission factor of the production process.

[0054] Step S205 , performing logic rule verification on the data in the database, marking abnormal entries that violate preset constraints, and filling in missing data entries through a fault-tolerant matching strategy; To ensure the validity of the data in the database, the system applies logical rule checks to the stored data. For example, for similar building materials, the difference in carbon emission factors should not exceed a certain threshold. If the data violates these logical rules, the system will flag the abnormal entry and make corrections. For missing data, the system uses a fault-tolerant matching strategy to infer and fill in the missing parts from related data.

[0055] For example, if the carbon emission factor for wood production is generally lower than that for steel, then if a data entry indicates that the carbon emission factor for wood is higher than that for steel, that data entry will be marked as an anomaly. Additionally, for missing data, the system may use the average carbon emission factor for wood to fill in the gaps.

[0056] Step S206: Integrate the classified stored, dynamically updated and logically verified data to construct a carbon emission factor database.

[0057] Among them, after completing data classification storage, dynamic update and verified data processing, the system integrates all processed data and finally builds a structured carbon emission factor database. The database supports fast query and dynamic update, and can be connected with other systems (such as BIM models, etc.) to provide the required carbon emission factor data.

[0058] For example, by integrating data from different dimensions, a database is constructed that can support the calculation of carbon emissions throughout the entire life cycle of a building, allowing designers to quickly call relevant carbon emission factors for calculations during the design, construction, and operation stages.

[0059] This implementation effectively manages and maintains carbon emission factor data, ensuring more accurate carbon emission calculations throughout the lifecycle of a building project. Furthermore, through the updated and integrated database, architectural designers can more accurately calculate and optimize carbon emissions for their projects, driving the construction industry towards a low-carbon, sustainable future.

[0060] Reference Figure 3 As an implementation of step S102, the step of calculating the carbon emission values ​​of each stage of the entire life cycle of the prefabricated building based on the carbon emission factor database and the basic data set includes: Step S301: calling the carbon emission factor database to match the carbon emission factor corresponding to the basic data set; Among them, carbon emission factors include building materials production factors, transportation factors, assembly factors and energy factors in the use stage; Specifically, carbon emission factors associated with the basic dataset are retrieved from the carbon emission factor database. Corresponding carbon emission factors are matched based on different building components (such as steel, concrete, etc.), transportation methods, assembly methods, and energy consumption during the building's use phase. These factors are used to calculate carbon emissions at each stage.

[0061] For example, for concrete, the carbon emission factor database might provide a carbon emission factor for producing one ton of concrete (e.g., 2.3 tons of CO₂ / ton) or a carbon emission factor for transporting one ton of concrete (e.g., 0.1 tons of CO₂ / km·ton). Different assembly processes may correspond to different carbon emission factors. The system matches the corresponding carbon emission factors based on the building design and material selection, providing an accurate calculation basis.

[0062] Step S302, based on the building material type and usage data, combined with the building material production factors, calculate the carbon emission value of the building material production stage; Specifically, the carbon emissions from the production phase of building materials used in a building are calculated based on the building material type and usage data in the basic dataset, combined with building material production factors extracted from the carbon emission factor database. The carbon emission factor for each building material varies depending on factors such as production process and material type, so precise factor matching is crucial for accurate carbon emission estimation.

[0063] For example, assuming that a building uses 100 tons of steel and the carbon emission factor of steel production is 2.5 tons of CO2 / ton, then the carbon emissions in the steel production stage are: 100 tons × 2.5 tons of CO2 / ton = 250 tons of CO2.

[0064] Step S303, based on the component type and quantity data, combined with the transportation distance parameter and transportation factor, calculate the carbon emission value of the component transportation stage; The carbon emissions generated during the transportation of components from the production site to the construction site are calculated based on the component type, quantity data, and transportation distance parameters in the basic dataset, combined with the corresponding transportation factors. This process relies on correctly matching the carbon emission factors of the transportation mode (such as road, rail, and ship) and the relevant transportation tools (such as trucks and trains).

[0065] For example, assuming that the transportation distance of 100 tons of steel is 500 kilometers, and the truck's transportation carbon emission factor is 0.15 tons of CO2 / km·ton, then the carbon emission value of the transportation stage is: 100 tons × 500 kilometers × 0.15 tons of CO2 / km·ton = 7,500 tons of CO2.

[0066] Step S304: Calculate the carbon emission value of the on-site assembly stage based on the component type and quantity data and the assembly factor; Carbon emissions during the assembly phase primarily come from factors such as the energy consumption of on-site construction equipment and personnel transportation. Carbon emissions during this phase were calculated based on the component types and quantities in the basic dataset, combined with the carbon emission factors of the assembly process (such as the energy consumed per unit component assembly).

[0067] For example, assuming that the carbon emission factor of the assembly process is 0.5 tons of CO2 per component assembly, and 500 components are assembled on site, the carbon emissions in the on-site assembly stage are 500 components × 0.5 tons of CO2 / component = 250 tons of CO2.

[0068] Step S305, based on the energy consumption data of the building during the use phase and combined with the energy factor during the use phase, calculate the carbon emission value during the use phase; Carbon emissions during a building's in-use phase primarily come from daily energy consumption, such as electricity and gas. By combining energy consumption data from the building's in-use phase (such as annual electricity and gas consumption) with corresponding energy carbon emission factors (such as the carbon emission factor of the regional power grid), carbon emissions during the in-use phase are calculated.

[0069] For example, assuming that the building consumes 50,000 kWh of electricity per year and the carbon emission factor of the power grid in the area is 0.8 kg CO2 / kWh, the carbon emission value of the use phase is: 50,000 kWh × 0.8 kg CO2 / kWh = 40,000 kg CO2 (40 tons of CO2).

[0070] Step S306: The carbon emission values ​​of the building materials production stage, component transportation stage, on-site assembly stage, and use stage are integrated to obtain the carbon emission values ​​of each stage in the entire life cycle of the prefabricated building.

[0071] Among them, the carbon emission values ​​of each stage are summarized to obtain the total carbon emission value of the entire life cycle of the building, including the carbon emission values ​​of building materials production, component transportation, on-site assembly and building use stages, and then summarized to form a comprehensive carbon emission report.

[0072] For example, assuming that the carbon emissions in the building materials production stage are 250 tons of CO2, the transportation stage is 7,500 tons of CO2, the assembly stage is 250 tons of CO2, and the use stage is 40 tons of CO2, the total carbon emissions over the entire life cycle are: 250+7,500+250+40=8,040 tons of CO2.

[0073] This implementation, based on a phased carbon emissions calculation method across the entire lifecycle, accurately calculates carbon emissions at every stage, from design to use. Through detailed data analysis and factor matching, it provides scientific decision-making support for building designers. By gradually calculating and comprehensively evaluating carbon emissions from the production, transportation, assembly, and use of building materials, it provides a concrete basis for optimizing carbon emissions in the construction industry.

[0074] Reference Figure 4 As an implementation of step S105, the step of generating an adjustment plan based on a multi-objective optimization algorithm includes: Step S401, defining the decision variables and optimization objectives of the multi-objective optimization algorithm; The decision variables include building material types, component types, and building design parameters, and the optimization objectives include carbon emissions, cost increments, and building function scores. Specifically, decision variables are the objects that the algorithm aims to optimize, such as the types of building materials, component types and quantities, and architectural design parameters (such as window-to-wall ratio and floor height). Adjustments to these variables can directly impact a project's carbon emissions, costs, and functional requirements. The optimization objective is the metric that the algorithm aims to minimize or maximize, including carbon emissions (reducing carbon emissions), incremental costs (controlling cost increases), and building functionality (ensuring that design functionality is not compromised).

[0075] For example, suppose a construction project uses steel as the primary building material, and some components are designed to be large, resulting in long transportation distances. Decision variables would include steel alternatives (such as low-carbon concrete), adjustments to component types (such as reducing certain large components), and design parameters (such as adjusting the window-to-wall ratio). The optimization objectives are to reduce carbon emissions (for example, by selecting low-carbon materials instead of steel), control costs (for example, by optimizing material usage and reducing unnecessary transportation costs), and ensure the functionality of the building (for example, adjusting the window-to-wall ratio without compromising daylighting or comfort).

[0076] Step S402: constructing an initial solution set including building material type optimization solutions, component optimization solutions, and architectural design parameter optimization solutions; The initial solution set encompasses various optimization scenarios in building design, including optimization of building materials, components, and architectural design parameters. This initial solution set serves as input to the optimization algorithm and encompasses multiple possible solutions, ranging from low-carbon building material substitution to component adjustments and design optimization. Each solution represents a possible building design decision that can be selected in subsequent multi-objective optimization.

[0077] For example, for a prefabricated building project, the initial solution set may include the following options: using low-carbon steel instead of ordinary steel and optimizing component types to reduce weight and volume; adopting efficient assembly processes to reduce energy consumption and reduce carbon emissions during on-site assembly; adjusting design parameters, such as changing the window-to-wall ratio or adjusting floor height, to optimize the building's energy efficiency and functionality.

[0078] Step S403, calculating the optimization target score of each solution based on the initial solution set; For each solution in the initial solution set, its score across various optimization objectives is calculated. Each solution involves the evaluation of multiple objectives, such as carbon emissions, incremental cost, and building function scores. The system then scores each solution based on these objectives to facilitate ranking in the subsequent selection process. Objective scores are typically calculated and combined with specific carbon emission factors, cost factors, and function scoring models.

[0079] For example, suppose Option 1 (using low-carbon steel) scores 90 for carbon emissions, 80 for incremental cost, and 85 for functionality; while Option 2 (optimizing component weight) scores 85 for carbon emissions, 90 for incremental cost, and 88 for functionality. The system then ranks the options based on these scores and selects the one that best meets the objectives.

[0080] It is understandable that by quantifying the optimization objectives of each solution, a clear quantitative basis is provided for multi-objective optimization, ensuring that the optimization decision-making process is operational and transparent. This scoring mechanism can help decision makers quickly identify which solutions perform better across multiple dimensions.

[0081] Step S404: Screening the candidate solution with the best optimization target score through non-dominated sorting; The non-dominated sorting method is used to select candidate solutions with the best optimization objective scores. Non-dominated sorting is a multi-objective optimization method that aims to select solutions from a set of solutions that are superior across all optimization objectives. During this process, each solution is evaluated to see whether it has an advantage across all objectives, or whether other solutions are superior in certain objectives. This ranking method avoids the problems associated with single-objective optimization and ensures a relatively optimal balance across multiple objectives.

[0082] For example, if Option 1 performs better in terms of carbon emissions but worse in incremental cost and functionality, while Option 2 performs more balanced in terms of carbon emissions, cost, and functionality, then Option 2 may be selected as a candidate by the non-dominated sorting algorithm because it is close to optimal in multiple dimensions.

[0083] Step S405 , iteratively updating the initial solution set until the predicted carbon emission value of the candidate solution is lower than the preset carbon emission control threshold, thereby obtaining the final adjustment solution.

[0084] Specifically, through an iterative process, the solutions in the initial solution set are continuously updated until an optimal solution is found with a carbon emission value below a preset threshold. Each iteration adjusts design parameters and optimizes building materials and component types to further reduce carbon emissions. Through continuous optimization, an adjusted solution is ultimately determined that meets the carbon emission control threshold.

[0085] For example, suppose the carbon emissions of a solution in the initial solution set are 3,000 tons of CO2, exceeding the preset threshold of 2,500 tons of CO2. In the first round of iteration, the system may reduce carbon emissions to 2,800 tons by replacing low-carbon building materials and optimizing transportation plans. After several rounds of iteration, the system finally finds a solution with carbon emissions of 2,400 tons of CO2, which meets carbon emission control requirements.

[0086] The above implementation achieves a balance between reducing building carbon emissions, controlling costs, and meeting functional requirements. By clearly defining decision variables and optimization objectives, and utilizing a non-dominated sorting and iterative update algorithm, the system can select the optimal solution from multiple optimization options. This provides building designers with a comprehensive, flexible, and efficient decision-making support tool, ensuring that buildings achieve the optimal balance between carbon emission control, cost management, and functionality.

[0087] Reference Figure 5 As an implementation method of step S402, the step of constructing an initial solution set including a building material type optimization solution, a component optimization solution, and a building design parameter optimization solution includes: Step S501, selecting alternative building materials from a preset low-carbon building material library to generate an optimization plan for building material types; During the building design phase, selecting low-carbon building materials is a key measure for reducing carbon emissions. The pre-built low-carbon building material library contains screened and verified low-carbon building material options. These building materials have low carbon emissions during production, effectively reducing the overall carbon emissions of a construction project. The system selects appropriate low-carbon building materials based on the specific requirements of the building project (such as structural strength and functional requirements) and generates an optimized plan for building material types.

[0088] For example, suppose a building design plans to use ordinary concrete as the primary building material. Based on a carbon emission factor library, the system recommends alternative building materials, such as low-carbon concrete or steel fiber reinforced concrete, which have lower production emissions. Substituting ordinary concrete for this material reduces calculated carbon emissions, generating an optimized building material selection plan.

[0089] Step S502: Generate a component optimization plan that reduces transportation distance or assembly energy consumption based on the component type adjustment rules; Based on the adjustment rules for component types, the system can optimize components, such as reducing weight, changing shape or size to reduce carbon emissions during transportation, or selecting components that can be transported via more efficient transportation methods. Furthermore, optimizing the assembly process, such as reducing the use of high-energy mechanical equipment, can also reduce carbon emissions during the assembly phase.

[0090] Specifically, if a construction project requires transporting a large number of concrete components, through design optimization, some of these large concrete components can be replaced with smaller, more easily transported ones. This not only reduces carbon emissions during transportation, but also reduces the use of heavy equipment during assembly, reducing energy consumption.

[0091] Step S503: Based on the parameter perturbation rule, the building design parameters are adjusted to generate a building design parameter optimization solution.

[0092] The parameter perturbation rule allows for small adjustments within the building's design parameters (such as window-to-wall ratio, floor height, and building orientation), thereby reducing energy demand and carbon emissions. By varying these parameters, the system can effectively optimize factors such as thermal performance and natural lighting, reducing energy consumption and improving building efficiency, thereby indirectly reducing carbon emissions.

[0093] Specifically, assuming a building design with a window-to-wall ratio of 50%, fine-tuning this parameter to reduce it to 45% can significantly reduce the building's air conditioning and heating burden during its occupancy phase, thereby lowering its carbon emissions during operation. Furthermore, changing the building's orientation to better utilize natural light can also reduce energy consumption.

[0094] In the above-mentioned implementation, the substitution of low-carbon building materials, optimization of component design, and adjustment of building design parameters have achieved a significant reduction in carbon emissions. The use of low-carbon building materials not only reduces carbon emissions during the building materials production stage, but also promotes the application of green building materials. By optimizing component design, energy consumption and carbon emissions during the transportation and assembly stages are reduced, and construction efficiency is improved. Fine-tuning of building design parameters further reduces energy consumption during the building's use phase, thereby reducing overall carbon emissions. Through scientific optimization strategies, the overall solution not only reduces carbon emissions but also ensures the functionality and economy of construction projects, providing a sustainable development solution for the construction industry.

[0095] Reference Figure 6 As a further implementation method of the carbon emission control method for prefabricated buildings, after the step of generating the carbon emission control strategy, the method further includes: Step S601, receiving actual carbon emission monitoring data collected after executing the carbon emission control strategy; After implementing carbon emission control strategies, building projects will conduct actual carbon emission monitoring to collect data on actual carbon emissions generated during operation. This data, typically acquired through sensors, energy monitoring systems, and other tools, can reflect the building's actual energy usage and carbon emissions during operation. Monitoring this actual data provides a basis for subsequent analysis, verifying the effectiveness of control strategies and supporting necessary adjustments.

[0096] For example, during the use phase of a building, the actual electricity consumption data of the building is collected through the intelligent energy efficiency monitoring system and the corresponding carbon emissions are calculated to obtain the actual carbon emission data.

[0097] Step S602: Compare the actual carbon emission monitoring data with the predicted carbon emission value of the carbon emission control strategy to obtain a deviation rate; The deviation rate is calculated by comparing actual carbon emission monitoring data with pre-forecasted carbon emission values. Predicted carbon emission values ​​are carbon emission values ​​predicted based on adjustment plans before the carbon emission control strategy is generated. By comparing actual data with the predicted values, the deviation rate can be calculated, reflecting the difference between actual carbon emissions and projected carbon emissions after the control strategy is implemented.

[0098] Step S603: determine whether the deviation rate exceeds a preset deviation threshold. If so, jump to step S604; if not, do not execute any steps; Step S604, correcting the carbon emission factor according to the deviation rate; Specifically, when the deviation rate between actual and predicted carbon emissions exceeds a preset threshold, the carbon emission factor needs to be revised. The deviation threshold is a pre-set tolerance range. Exceeding this range indicates that the original carbon emission factor contains errors and may not accurately reflect actual carbon emissions. Therefore, the system needs to revise the carbon emission factor based on the deviation rate. This revision process is usually based on actual monitoring data and changes in environmental variables to ensure that the carbon emission factor is more in line with reality.

[0099] For example, assuming that the preset deviation threshold is 5%, when the deviation rate is 10%, it exceeds the threshold, so the system needs to correct the carbon emission factor, which may be done by updating the production carbon emission factor of building materials or adjusting the energy consumption factor of the building use phase.

[0100] Step S605: Write the revised carbon emission factor into the carbon emission factor database and mark the version number and update timestamp.

[0101] Revised carbon emission factors must be updated in the carbon emission factor database to ensure that the data in the database is always up-to-date and accurate. Furthermore, revised factors must be versioned and timestamped for easy traceability and management. This not only helps ensure the timeliness of the data in the database but also facilitates comparison and review of different versions of data in future analyses.

[0102] For example, suppose the original carbon emission factor is 2.5 tons of CO2 per ton of a certain building material produced. After correction, the new carbon emission factor is 2.3 tons of CO2 per ton. The system updates this new correction factor to the carbon emission factor database, marks it with the version number v2.0, and updates the timestamp.

[0103] This implementation enables timely detection of deviations and carbon emission factor corrections, ensuring the carbon emission factor database remains up-to-date and accurate, providing more accurate carbon emission calculation and management support for future construction projects. This technical solution enhances the flexibility and precision of carbon emission management, ensuring continuous carbon emission optimization for construction projects.

[0104] The embodiments of the present application also disclose a carbon emission control system for prefabricated buildings.

[0105] A carbon emission control system for prefabricated buildings, the control system comprising: An acquisition module is used to obtain the basic data set of prefabricated buildings; the basic data set includes building design parameters, component types and quantities, transportation distance parameters, building material types and usage data, and energy consumption data during the building use phase; The carbon emission value calculation module is used to calculate the carbon emission values ​​of each stage of the prefabricated building's life cycle based on the carbon emission factor database and the basic data set. The stages include the building materials production stage, component transportation stage, on-site assembly stage, and use stage. A comparison module, used to compare the carbon emission value with the preset carbon emission control threshold; An adjustment module is used to generate an adjustment plan based on a multi-objective optimization algorithm when carbon emissions exceed a preset carbon emission control threshold. The adjustment plan includes an optimized combination of building design parameters, component types, and building material types. The carbon emission control module is used to update the basic data set according to the adjustment plan and generate the carbon emission control strategy.

[0106] As a further implementation method of the control system, it also includes: The actual monitoring data receiving module is used to receive the actual carbon emission monitoring data collected after the carbon emission control strategy is implemented; The deviation comparison module is used to compare the actual carbon emission monitoring data with the predicted carbon emission values ​​of the carbon emission control strategy to obtain the deviation rate; A correction module, used to correct the carbon emission factor according to the deviation rate when the deviation rate exceeds a preset deviation threshold; The database update module is used to write the revised carbon emission factor into the carbon emission factor database and mark the version number and update timestamp.

[0107] An assembled building carbon emission control system according to an embodiment of the present application can implement any of the above-mentioned control methods, and the specific working processes of each module in the control system can refer to the corresponding processes in the above-mentioned method embodiments.

[0108] In the several embodiments provided in this application, it should be understood that the provided methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for example, the division of a module is merely a logical functional division, and in actual implementation, other division methods may be used, such as combining or integrating multiple modules into another system, or ignoring or not implementing certain features.

[0109] The embodiment of the present application also discloses a computer device.

[0110] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, a method for controlling carbon emissions from prefabricated buildings as described above is implemented.

[0111] The embodiment of the present application also discloses a computer-readable storage medium.

[0112] A computer-readable storage medium stores a computer program that can be loaded by a processor and executed by any one of the above-mentioned methods for controlling carbon emissions of prefabricated buildings.

[0113] Among them, computer-readable storage media can be any tangible medium that contains or stores a program that can be used by or in combination with an instruction execution system, apparatus or device; the program code contained on the computer-readable medium can be transmitted using any appropriate medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0114] It should be noted that, in the above embodiments, the description of each embodiment has different emphases. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0115] The above are all preferred embodiments of the present application and are not intended to limit the scope of protection of this application. Unless otherwise specified, any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features. In other words, unless otherwise specified, each feature is merely an example of a series of equivalent or similar features.

Claims

1. A method for controlling carbon emissions from prefabricated buildings, characterized in that: The control method includes: Obtaining a basic data set for prefabricated buildings; wherein the basic data set includes building design parameters, component types and quantities, transportation distance parameters, building material types and usage data, and energy consumption data during the building's use phase; Based on the carbon emission factor database, the carbon emission values ​​of each stage of the prefabricated building's entire life cycle are calculated according to the basic data set; wherein the stages include the building materials production stage, the component transportation stage, the on-site assembly stage, and the use stage; Comparing the carbon emission value with a preset carbon emission control threshold; If the carbon emission value exceeds the preset carbon emission control threshold, an adjustment plan is generated based on a multi-objective optimization algorithm; the adjustment plan includes an optimized combination of the building design parameters, component types, and building material types; The basic data set is updated according to the adjustment plan, and a carbon emission control strategy is generated.

2. A method for controlling carbon emissions from prefabricated buildings according to claim 1, characterized in that: The steps of constructing the carbon emission factor database include: Access the preset carbon emission database through the API interface to obtain the original data of carbon emission factors for building materials production, transportation tools, assembly processes and regional energy structure; Standardizing the raw data of the carbon emission factor and cleaning the abnormal data based on an anomaly detection algorithm to obtain standardized data of the carbon emission factor; The cleaned carbon emission factor standardized data is multi-dimensionally classified according to building material type, transportation mode and regional characteristics, and stored in a hierarchical database; Calculate the regional transportation distance correction coefficient based on GIS map data, and dynamically update the transportation and production carbon emission factors in the database in combination with the regional power grid carbon emission factors; Implement logical rule verification on the data in the database, mark abnormal entries that violate preset constraints, and fill in missing data entries through fault-tolerant matching strategies; The carbon emission factor database is constructed by integrating the classified storage, dynamic update and logical verification data.

3. A method for controlling carbon emissions from prefabricated buildings according to claim 2, characterized in that: Based on the carbon emission factor database, the step of calculating the carbon emission values ​​of each stage of the prefabricated building throughout its life cycle according to the basic data set includes: Calling the carbon emission factor database to match the carbon emission factors corresponding to the basic data set, including building material production factors, transportation factors, assembly factors, and energy factors in the use phase; Calculate the carbon emissions during the building materials production phase based on the building materials type and usage data, combined with the building materials production factors; Calculate the carbon emissions during the component transportation phase based on component type and quantity data, combined with the transportation distance parameter and the transportation factor; Based on the component type and quantity data, combined with the assembly factors, calculate the carbon emissions value of the on-site assembly stage; Calculate the carbon emissions during the use phase based on the building's energy consumption data during the use phase and the energy factors during the use phase; The carbon emission values ​​of each stage in the entire life cycle of the prefabricated building are obtained by integrating the carbon emission values ​​of the building materials production stage, component transportation stage, on-site assembly stage and use stage.

4. The method for controlling carbon emissions from prefabricated buildings according to claim 1, wherein: The steps for generating an adjustment plan based on a multi-objective optimization algorithm include: Defining decision variables and optimization objectives for a multi-objective optimization algorithm; wherein the decision variables include building material types, component types, and building design parameters, and the optimization objectives include carbon emissions, cost increments, and building function scores; Construct an initial solution set that includes optimization schemes for building material types, components, and architectural design parameters; Calculating the optimization target score of each solution based on the initial solution set; Screen the candidate solutions with the best optimization goal score through non-dominated sorting; The initial solution set is iteratively updated until the predicted carbon emission value of the candidate solution is lower than the preset carbon emission control threshold, thereby obtaining a final adjustment solution.

5. A method for controlling carbon emissions from prefabricated buildings according to claim 4, characterized in that: The steps for constructing an initial solution set including building material type optimization solutions, component optimization solutions, and architectural design parameter optimization solutions include: Select alternative building materials from the pre-set low-carbon building material library to generate an optimized plan for building material types; Adjust rules based on component type to generate component optimization solutions that reduce transportation distance or assembly energy consumption; Based on the parameter perturbation rules, the building design parameters are adjusted to generate the building design parameter optimization scheme.

6. A method for controlling carbon emissions from prefabricated buildings according to any one of claims 1 to 5, characterized in that: The steps of generating carbon emission control strategies also include: Receiving actual carbon emission monitoring data collected after executing the carbon emission control strategy; Comparing the actual carbon emission monitoring data with the predicted carbon emission value of the carbon emission control strategy to obtain a deviation rate; When the deviation rate exceeds a preset deviation threshold, the carbon emission factor is corrected according to the deviation rate; The revised carbon emission factor is written into the carbon emission factor database and marked with a version number and an update timestamp.

7. A carbon emission control system for assembled buildings, characterized in that: The control system includes: An acquisition module is used to acquire a basic data set of prefabricated buildings; wherein the basic data set includes building design parameters, component types and quantities, transportation distance parameters, building material types and usage data, and energy consumption data during the building use phase; A carbon emission value calculation module, configured to calculate the carbon emission values ​​of each stage of the prefabricated building's life cycle based on the carbon emission factor database and the basic data set; wherein the stages include the building material production stage, the component transportation stage, the on-site assembly stage, and the use stage; A comparison module, configured to compare the carbon emission value with a preset carbon emission control threshold; an adjustment module, configured to generate an adjustment plan based on a multi-objective optimization algorithm when the carbon emission value exceeds the preset carbon emission control threshold; the adjustment plan includes an optimized combination of the building design parameters, component types, and building material types; The carbon emission control module is used to update the basic data set according to the adjustment plan and generate a carbon emission control strategy.

8. The carbon emission control system for prefabricated buildings according to claim 7, characterized in that: The control system also includes: An actual monitoring data receiving module, configured to receive actual carbon emission monitoring data collected after executing the carbon emission control strategy; a deviation comparison module, configured to compare the actual carbon emission monitoring data with the predicted carbon emission value of the carbon emission control strategy to obtain a deviation rate; a correction module, configured to correct the carbon emission factor according to the deviation rate when the deviation rate exceeds a preset deviation threshold; The database update module is used to write the revised carbon emission factor into the carbon emission factor database and mark the version number and update timestamp.

9. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 6 when executing the program.

10. A computer-readable storage medium, characterized in that: A computer program is stored which can be loaded by a processor and execute the method according to any one of claims 1 to 6.

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