Building full-life-cycle carbon emission management system and method based on calculation, management and reduction system

Abstract

The invention relates to the technical field of building carbon emission management, in particular to a building full-life-cycle carbon emission management system and method based on a calculation management system. The system comprises a data management module for acquiring and processing carbon emission data in a full life cycle stage of a building, mapping the processed carbon emission data with a factor database through a uniform interface and then storing the processed carbon emission data in the database; the carbon emission accounting module is used for executing carbon emission accounting and simulation and uncertainty analysis; the intelligent management module is used for carrying out real-time monitoring, carbon emission trend analysis, abnormity early warning and visual display on the carbon emission data; and the optimization emission reduction module operates an optimization algorithm, generates an emission reduction scheme through iterative processing and performs carbon asset management. According to the building full-life-cycle carbon emission management method based on the calculation, management and reduction system, closed-loop cooperation of accurate accounting, intelligent management and emission reduction optimization is achieved.

Description

Technical Field

[0001] This invention relates to the field of building carbon emission management technology, and in particular to a building life-cycle carbon emission management system and method based on a calculation, management and reduction system. Background Technology

[0002] Under the "dual carbon" strategy, the construction industry, as a significant sector of carbon emissions, urgently needs to establish a systematic and intelligent full life-cycle carbon emission management system. Current building carbon emission management technologies mainly focus on the operation phase, employing fixed carbon emission factors and static accounting methods, lacking the ability to dynamically monitor and intelligently optimize carbon emissions across all stages of the building's life cycle (material production, construction, operation and maintenance, demolition and recycling).

[0003] Existing building carbon emission management systems generally suffer from the following defects and shortcomings: (1) Insufficient coverage: Most systems only focus on the operation phase and ignore the carbon emission contributions of the construction, demolition and building materials phases, thus failing to form a full life cycle management.

[0004] (2) Data isolation and lack of standards: The data collection methods are not uniform at different stages, and there is a lack of unified data standards and interface specifications, which makes it difficult to integrate and transfer data across stages.

[0005] (3) Insufficient intelligence: Most existing systems are static accounting tools and lack dynamic optimization capabilities based on real-time feedback.

[0006] (4) Lack of coordination mechanism: accounting, management and emission reduction are separated, and there is a lack of a closed-loop working mechanism that integrates accounting, management and emission reduction.

[0007] (5) Lack of carbon asset management: The lack of carbon credit account management, quota management and trading capabilities limits the economic value of carbon management. Summary of the Invention

[0008] The present invention aims to solve at least one of the technical problems existing in the prior art.

[0009] Therefore, this invention provides a building lifecycle carbon emission management system based on a calculation, management, and reduction system, which realizes a closed-loop collaboration of accurate accounting, intelligent management, and optimized emission reduction.

[0010] According to an embodiment of the present invention, a building lifecycle carbon emission management system based on a computational, management, and reduction system is provided, the system comprising: The data management module acquires and processes carbon emission data throughout the entire life cycle of a building, and saves the processed carbon emission data to the database after mapping it to the factor database through a unified interface. The full life cycle stages include: material production stage, construction stage, operation stage, and demolition stage; The carbon emission accounting module performs carbon emission accounting, simulation, and uncertainty analysis. The intelligent management module monitors carbon emission data in real time, analyzes carbon emission trends, provides early warnings of anomalies, and displays the data visually. The emission reduction module is optimized, the optimization algorithm is run, and emission reduction plans are generated through iterative processing and carbon asset management is carried out.

[0011] The beneficial effects of this invention are that the building life cycle carbon emission management system based on the calculation, management and reduction system is a full life cycle carbon emission intelligent management system composed of a data management module, a carbon emission accounting module, an intelligent management module and an optimization and emission reduction module. It covers all stages of the entire life cycle, fills the existing management gaps, and realizes a dynamic cycle of accounting, management and optimization.

[0012] According to one embodiment of the present invention, the carbon emission data includes: Carbon emission factors and production data during the material production stage; Equipment energy consumption data during the construction phase; Energy consumption data of each system during the operation phase; And data on waste treatment and recycling rates during the demolition phase; Processing carbon emission data throughout the entire life cycle of a building includes the following steps: S11, The carbon emission data is stored in the database through a standardized field dictionary and unique object encoding to establish a one-to-one mapping relationship between the carbon emission data and the factor database; S12, the carbon emission data is managed using versioning, traceable logs, data quality verification and permission leveling mechanisms to ensure unified management of the carbon emission data, so that the carbon emission data can be directly accessed by the carbon emission accounting module, the intelligent management module and the emission reduction optimization module.

[0013] According to one embodiment of the present invention, the carbon emission accounting module performs carbon emission accounting, simulation, and uncertainty analysis, specifically including the following steps: S21, Input assembly, retrieve carbon emission data for each stage from the database; S22, accounting calculation, is based on the LCA accounting and energy consumption statistics fusion algorithm to calculate carbon emissions at each stage of the life cycle, intensity per unit area at each stage of the life cycle, total carbon emissions and total intensity of the life cycle; S23, Simulation Analysis: Simulate scenarios for the baseline, generate an influence matrix, and obtain scenario output; S24, Uncertainty Analysis: Key inputs are set, Monte Carlo sampling is performed, and uncertainty indicators are obtained. S25, Output results are stored in the database. The carbon emissions at each stage of the life cycle, the intensity per unit area at each stage of the life cycle, the total carbon emissions, total intensity, scenario output, and uncertainty indicators of the life cycle are written into the database so that the intelligent management module and the optimized emission reduction module can call them.

[0014] According to one embodiment of the present invention, the intelligent management module performs real-time monitoring of carbon emission data, carbon emission trend analysis, anomaly warning, and visualization, specifically including the following steps: S31 acquires the output results of the carbon emission accounting module and the real-time energy consumption data of each system during the operation phase in real time, and performs data standardization processing and unified interface management. S32 provides anomaly warnings for standardized data based on threshold method, rate of change method, trend deviation, environmental / operating condition correction, and preset rule templates; S33. Based on the data in the database, a dataset is constructed and input into the carbon peak prediction model to analyze the carbon emission trend.

[0015] According to one embodiment of the present invention, constructing a dataset based on the data in the database and inputting it into a carbon peak prediction model to analyze carbon emission trends specifically includes the following steps: S331, Data preparation: Acquire time-series data of energy consumption by item, time-series data of carbon emissions at each stage, meteorological and operating condition factors and retrofit event markers, and construct a dataset; S332, feature engineering, generates dummy variables related to seasons, holidays, and working days based on the date, then performs first-order differencing and detrending on the carbon emission time series and energy consumption time series, and finally uses ADF to test stationarity to obtain a set of feature data that meets the requirements of the time series model; S333, Construct a family of models, establish a carbon peak prediction model based on feature data, and set exogenous variables; S334, Training and Validation: Input the data from the dataset into the carbon peak prediction model, evaluate MAPE and RMSE through rolling window cross-validation, and obtain the prediction results and key driving factors. S335, Peak determination, based on the first derivative of the predicted curve and the peak stability, yields the peak year and confidence interval; S336, Write the results: Save the prediction results, peak year, confidence interval and key driving factors to the database for use by the optimized emission reduction module.

[0016] According to one embodiment of the present invention, running an optimization algorithm to generate emission reduction plans through iterative processing and managing carbon assets specifically includes the following steps: S41, Data Acquisition: Acquire the output results of the carbon emission accounting module and the processing results of the intelligent management module; S42, Candidate measures are generated by filtering building material replacement, equipment upgrades, operation and maintenance strategies and photovoltaic grid connection related measures from the database, and filtering them according to feasibility, lifespan and investment to obtain a set of measures; S43, Parameter and Constraint Assembly: Assemble emission reduction coefficients, energy consumption change rate, initial investment, operating costs, lifespan and discount rate for each measure, and establish a constraint set; S44, Data Alignment, maps the output of the carbon emission accounting module and the processing results of the intelligent management module to the influence matrix to form the model input tensor; S45, Solve the configuration, set the population size, crossover probability, mutation probability and termination criterion, input the model input tensor, measure set and constraint set into the intelligent optimization algorithm for optimization calculation, and obtain the emission reduction scheme set through feasibility screening. The emission reduction scheme set includes scheme name, emission reduction amount tCO2e, ROI, payback period, budget occupation and impact on comfort or operating conditions. S46, Carbon Asset Linkage and Writeback, maps emission reductions (tCO2e) to allowances / offsets, generates revenue forecasts and account operation instructions, and saves them to the intelligent management module via API.

[0017] According to one embodiment of the present invention, the formula for calculating carbon emissions at each stage is as follows: ; The formulas for calculating the intensity per unit area at each stage are as follows: ; The formula for calculating total lifecycle carbon emissions is: ; The formula for calculating the strength per unit area is: ; in, t Indicates the stages of the material's lifecycle, including production, construction, operation, or demolition. s Indicates a sub-item, system, or device. For the stage t middle section s The amount of activity or energy consumption, For the corresponding emission factors, This indicates the carbon emissions at each stage. This represents total carbon emissions. For the intensity per unit area at each stage, Intensity per unit area This refers to the total building area or the measured area.

[0018] According to one embodiment of the present invention, the intelligent optimization algorithm adopts a multi-objective optimization framework, and the calculation formula is as follows:

[0019]

[0020] in: Input tensors to the measure set and model; Represents a constraint set; Represents a set of emission reduction schemes The total life cycle cost.

[0021] According to an embodiment of the present invention, step S24 specifically includes the following steps: S241, Input distribution modeling, sets the distribution type or parameter range for key inputs; S242 uses parallel computation for machine sampling, performs Monte Carlo sampling based on input distribution modeling, and obtains the probability distribution of life cycle carbon emissions; S243, statistical aggregation, calculates the mean, median, and quantiles by statistically summarizing the life cycle carbon emission sample set obtained from Monte Carlo simulation, and extracts the 95% confidence interval; S244, Sensitivity and Robustness Assessment, based on data from the Monte Carlo sample set, ranks the degree of influence of each input on the outcome by calculating standardized regression coefficients, Spearman correlation coefficients, or Sobol indices.

[0022] According to an embodiment of the present invention, a building life cycle carbon emission management method based on a calculation, management, and reduction system is provided, wherein the method employs the building life cycle carbon emission management system based on the calculation, management, and reduction system as described above.

[0023] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0024] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0025] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0026] Figure 1 This is a system schematic diagram of the preferred embodiment of the present invention.

[0027] Figure 2 This is a schematic diagram of carbon emission prediction in the preferred embodiment of the present invention.

[0028] Figure 3This is a flowchart of carbon emission accounting in the preferred embodiment of the present invention.

[0029] Figure 4 This is a flowchart of the intelligent management process of the preferred embodiment of the present invention.

[0030] Figure 5 This is a flowchart of the carbon emission prediction process in the preferred embodiment of the present invention.

[0031] Figure 6 This is an optimized emission reduction flowchart of the preferred embodiment of the present invention. Detailed Implementation

[0032] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0033] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, features defined with "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0034] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0035] Example 1 This application provides a building lifecycle carbon emission management system based on a computational, management, and reduction system, such as... Figure 1 As shown, the system includes: The data management module is configured to acquire and process carbon emission data throughout the entire life cycle of a building, and save the processed carbon emission data to the database after mapping it to the factor database through a unified interface. The carbon emission accounting module is configured to perform carbon emission accounting as well as simulation and uncertainty analysis.

[0036] The intelligent management module monitors carbon emission data in real time, analyzes carbon emission trends, provides early warnings of anomalies, and displays the data visually. The emission reduction optimization module is configured to run optimization algorithms, generate emission reduction plans through iterative processing, and manage carbon assets.

[0037] In this embodiment, the entire lifecycle stages include: material production, construction, operation, and demolition. The data management module acquires multi-source carbon emission data according to the entire lifecycle stages through IoT sensors, BIM interfaces, or manual input. The carbon emission data includes: Carbon emission factors and production data during the material production stage, such as material batches and output, supply chain traceability codes, and their corresponding emission factors; Energy consumption data of equipment during the construction phase, such as machine shifts, fuel consumption, active power of temporary power, reactive power, power factor, and operation logs; During the operation phase, energy consumption data for each system, such as sub-meters and operating status of sub-systems like air conditioning, lighting, power, and elevators, as well as meteorological and operational factors, are collected. The operating status includes start-up / shutdown, load, and power consumption. In addition, data on waste treatment and recycling rates during the demolition phase, such as waste sorting output, waste disposal destination, recycling rate and its deduction factor; Furthermore, processing carbon emission data throughout the entire life cycle of a building specifically includes the following steps: S11, carbon emission data is stored in the database through a standardized field dictionary and unique object codes to establish a one-to-one mapping relationship between carbon emission data and factor database; the factor database in this embodiment covers emission factors in each stage of material production, transportation, construction, operation and demolition, including material and process factors, energy consumption factors, operating condition factors and disposal and recycling factors. S12 employs versioned management, traceable logs, data quality verification, and hierarchical access control mechanisms for carbon emission data to ensure unified management of carbon emission data and enable the carbon emission data to be directly accessed by the carbon emission accounting module, intelligent management module, and emission reduction optimization module.

[0038] It should be noted that the unique object code consists of a timestamp, spatial location information, and object ID, in order to link and integrate the data with the factor database.

[0039] In this embodiment, carbon emission accounting, simulation, and uncertainty analysis are performed, such as... Figure 3 As shown, the specific steps include: S21, Input assembly, retrieve carbon emission data for each stage from the database; S22, accounting calculation, based on the LCA accounting and energy consumption statistics fusion algorithm, performs hierarchical calculation and summary of carbon emissions at each stage to obtain calculation results. The calculation results include carbon emissions at each stage, intensity per unit area at each stage of the life cycle, total carbon emissions in the life cycle, total intensity, contribution rate of key carbon sources, and time series curves. The LCA calculation formula for carbon emissions at each stage is as follows: ; The formulas for calculating the intensity per unit area at each stage are as follows: ; The formula for calculating Total Life Cycle Carbon Emissions (LCA) is as follows: ; The formula for calculating the strength per unit area is: ; in, t Indicates the stages of the material's lifecycle, including production, construction, operation, or demolition. s Indicates a sub-item, system, or device. For the stage t middle section s The amount of activity or energy consumption, For the corresponding emission factors, This indicates the carbon emissions at each stage. This represents total carbon emissions (tCO2e). The intensity per unit area at each stage (tCO2e / m²) 3 ), Intensity per unit area Total building area or measurable area (m²) 3 ).

[0040] Furthermore, the contribution rate of key carbon sources is the ratio of carbon emissions at each stage to total carbon emissions over the life cycle; the baseline time series curve is a time series obtained by mapping energy consumption data or activity data to a unified time axis and calculating carbon emissions over time.

[0041] S23, Simulation Analysis: Perform scenario simulation on the baseline time series to generate an impact matrix and obtain scenario output; Scenario simulation includes, but is not limited to, building material replacement, operating condition adjustment, or photovoltaic access, used to compare the emission and cost differences between the 'baseline scheme' and the 'scenario scheme'.

[0042] S24, Uncertainty Analysis: Key inputs are set, Monte Carlo sampling is performed, and uncertainty indicators are obtained. These indicators include the probability distribution of life-cycle carbon emissions, 95% confidence intervals, and sensitivity ranking. In this embodiment, the uncertainty indicators are also displayed using histograms and kernel density curves. Step S24 specifically includes the following steps: S241, Input distribution modeling, sets the distribution type and parameter range for key inputs (such as emission factors, equipment efficiency, or usage intensity). The distribution type includes normal, log-normal, or triangular distribution; the parameter range includes the upper and lower bounds and distribution shape of the key inputs (emission factors, equipment efficiency, usage intensity, etc.); the set parameter range can reflect the uncertainty of the data, and Monte Carlo sampling is based on random values ​​within the parameter range to generate a large number of samples, thereby evaluating the confidence interval and sensitivity of the results.

[0043] S242, parallel computation of random sampling, performs Monte Carlo sampling based on input distribution modeling, such as when N≈10 4 The probability distribution of life cycle carbon emissions is obtained through secondary sampling; S243, statistical aggregation, statistically summarizes the life cycle carbon emission sample set obtained from Monte Carlo simulation, calculates the mean, median, and quantiles, extracts the 95% confidence interval, and generates histograms or kernel density curves for display.

[0044] S244, Sensitivity and Robustness Assessment, based on data from the Monte Carlo sample set, calculates standardized regression coefficients, Spearman correlation coefficients, or Sobol indices to rank the degree of influence of each input on the results, thereby improving the accuracy of the calculation results.

[0045] S25, output results are stored in the database, including carbon emissions at each stage, intensity per unit area at each stage of the life cycle, total carbon emissions throughout the life cycle, total intensity, scenario outputs, and uncertainty indicators, which are then used by the intelligent management module and the emission reduction optimization module.

[0046] It should be noted that the confidence intervals obtained from uncertainty analysis and the ranking of the degree of influence of each input on the results are mainly used to calibrate input parameters such as emission factors and activity levels. The LCA calculation formula itself remains unchanged. Therefore, it is decoupled from LCA at the calculation level and only links subsequent calculations by updating the parameter table. For example, the factor and usage intensity are adjusted at the "input parameter level" (e.g., the empirical value is adjusted from 0.5 to 0.6), and then the carbon emissions of each stage are recalculated.

[0047] In this embodiment, the carbon emission accounting module will also generate a carbon emission report based on the carbon emissions at each stage, the intensity per unit area at each stage of the life cycle, the total carbon emissions of the life cycle, the total intensity, and uncertainty indicators, which will facilitate subsequent queries.

[0048] In this embodiment, carbon emission data is monitored in real time, carbon emission trend analysis is performed, anomaly warnings are issued, and visualization is provided, such as... Figure 4 As shown, the specific steps include: S31, acquires the output results of the carbon emission accounting module and the real-time energy consumption data of each system during the operation phase in real time, and performs data standardization processing and unified interface management, specifically including the following steps: S311, establish a unified field dictionary for computing, management and reduction (such as ts, obj_id, stage, metric, unit, value, quality_flag, etc.) and a versioned JSON Schema (such as Schema vX.Y) to cover both output results and real-time energy consumption data; S312 uses a unique object code as the unique key; the unique object code is linked to BIM and asset ledger (components, equipment, circuits, areas). S313 performs ETL standardization on data through orchestration and cleaning, unit unification, caliber conversion, dimension mapping, and verification. S314 uses a REST / GraphQL API and message bus to transmit data, and supports idempotent keys and batch pagination. S315, the data saved to the database will include its source, definition, version, and timestamp, and will be written to the change audit log; S316 integrates the output results with real-time energy consumption data on the same time axis and object dimension by aligning the time window and object mapping table on the read / write side, so that each module can read them.

[0049] S32 provides anomaly warnings for standardized data based on threshold methods, rate of change methods, trend deviations, environmental / operating condition corrections, and preset rule templates; more specifically, for example: (1) Threshold method: When the emission intensity of the equipment or system is greater than the baseline mean ±2σ; or when the power of the equipment or system is greater than the P95 of the past 7 days, an abnormal alarm is triggered. (2) Rate of change method: When the cycle jump of 5–15 minutes is >20%, an abnormal alarm is triggered.

[0050] (3) Trend deviation status: When EWMA (exponential weighted moving average) / CUSUM (cumulative sum) deviates continuously from the threshold, an abnormal alarm will be triggered.

[0051] (4) Environmental and operating condition correction: Abnormalities are determined based on outdoor temperature, irradiance, and operating load regression residuals, and an abnormality alarm is triggered.

[0052] (5) Rule template: Configure equipment rules for high-emission equipment (such as chillers, boilers or elevators) to trigger abnormal alarms. Specific equipment rules include abnormal start-up and shutdown, excessive standby energy consumption or high energy consumption at low load, etc.

[0053] In this embodiment, abnormal events are uniformly written to / alarm / events as JSON events, with fields including ts, obj_id, metric, base, obs, method, severity, or ticket_id, and pushed to the management interface and the work order system.

[0054] S33, a dataset is constructed based on data from the database and input into the carbon peak prediction model to analyze carbon emission trends. Further, such as... Figure 2 , Figure 5 As shown, it specifically includes: S331, Data Preparation: Acquire time-series data of energy consumption by item, time-series data of carbon emissions at each stage, meteorological and operating condition factors and retrofit event markers, and construct a dataset. Meteorological and operating condition factors include degree-days, occupancy rate and load factor.

[0055] Emissions and energy consumption are continuously driven by factors such as operating conditions, weather, and occupancy over time. Using time-series data with a unified time axis as a foundation improves the accuracy of carbon emission prediction. Weather and operating condition factors are used to characterize the driving effect of operating conditions on energy consumption and emission time series, and are not treated as separate life cycle stages, but only as explanatory variables in the trend prediction model. Retrofit event markers are derived from project retrofit ledgers and operation and maintenance records. In this embodiment, retrofit event markers are written in with timestamp alignment when constructing the dataset, used to identify structural changes before and after retrofits in the carbon peak prediction model, and to provide a basis for subsequent analysis of key driving factors.

[0056] S332, feature engineering, generates dummy variables such as seasons, holidays, and working days based on the date, then performs first-order differencing and detrending on the carbon emission / energy consumption time series, and finally uses ADF to test stationarity to obtain a set of feature data that meets the requirements of the time series model. The feature data includes stationary feature series and operating condition dummy variables.

[0057] S333, construct a family of models, establish a carbon peak prediction model based on feature data, and set exogenous variables, including variables such as temperature, occupancy rate, load factor, and irradiance; Carbon peak prediction models include, but are not limited to, one or two of ARIMA, ARIMAX, SARIMA, Prophet, LSTM, or Seq2Seq. In this embodiment, the carbon peak prediction models include two types of models: ARIMA / SARIMA and LSTM / Seq2Seq. The ARIMA / SARIMA model is used to handle linear seasonal trends, while the LSTM / Seq2Seq model can capture nonlinear disturbances such as equipment start-up and shutdown and occupancy fluctuations. The two types of models are respectively suitable for linear seasonal characteristics and nonlinear operating condition disturbances. Model integration can improve the robustness of trend prediction. S334, Training and Validation: Input the data from the dataset into the carbon peak prediction model, evaluate MAPE and RMSE through rolling window cross-validation, and obtain the prediction results and key driving factors.

[0058] It should be noted that the key driving factors are obtained based on the feature importance analysis of the carbon peak prediction model. For example, standardized regression coefficients are used for linear models, and SHAP values ​​or permutation importance measures are used for nonlinear models to measure the influence of each input feature on the peak year and emission level. The items with the highest contribution are selected as key driving factors.

[0059] S335, Peak determination, gives the peak year and confidence interval based on the first derivative of the predicted curve and the peak stability (e.g., the peak value decreases continuously for ≥K cycles); S336, Processing results are written, saving the prediction results, peak year, confidence interval and key driving factors to the database for use by the emission reduction optimization module.

[0060] The intelligent management module aggregates data and visualizes decision-making, enabling carbon emission monitoring, energy consumption trend analysis, anomaly identification, and multi-dimensional comparison within a unified data view. It also supports managers in intuitively understanding the system status through charts, reports, and a digital twin interface. The intelligent management module also sets up multi-level thresholds and early warning mechanisms, automatically triggering alarms for excessive energy consumption, abnormal emissions, and deviations from targets, and generating optimization instructions or parameter requests that are transmitted to the optimization and emission reduction module. The intelligent management module also records user operations and strategy feedback, serving as input for the optimization and emission reduction module.

[0061] In this embodiment, an optimization algorithm is run to generate emission reduction plans through iterative processing and to manage carbon assets. For example... Figure 6 As shown, the specific steps include: S41, Data Acquisition: Acquire the output results of the carbon emission accounting module and the processing results of the intelligent management module. The output results of the carbon emission accounting module include: carbon emissions at each stage, intensity per unit area at each stage of the life cycle, total carbon emissions in the life cycle, total intensity, scenario output, and uncertainty indicators. The processing results of the intelligent management module include: prediction results, peak year, confidence interval, and key driving factors.

[0062] S42, Candidate measures generation: This involves filtering building material replacement, equipment upgrades, operation and maintenance strategies, and photovoltaic grid connection-related measures from the database, and then filtering by feasibility, lifespan, and investment to obtain a set of measures. S43, Parameter and Constraint Assembly: Assemble emission reduction coefficients, energy consumption change rate, initial investment, operating cost, lifetime and discount rate for each measure, and establish a constraint set, which includes budget, comfort or operating conditions, schedule, concurrency dependency and quota boundary. S44, Data Alignment, maps the output of the carbon emission accounting module and the processing results of the intelligent management module to the influence matrix to form the model input tensor; S45, Solve the configuration, set the population size, crossover probability, mutation probability and termination criterion, input the model input tensor, measure set and constraint set into the intelligent optimization algorithm for optimization calculation, and obtain the emission reduction scheme set through feasibility screening. The emission reduction scheme set includes scheme name, emission reduction amount tCO2e, ROI, payback period, budget occupation and impact on comfort or operating conditions. S46, Carbon Asset Linkage and Writeback, maps emission reductions (tCO2e) to allowances / offsets, generates revenue forecasts and account operation instructions, and saves them to the intelligent management module via API.

[0063] In this embodiment, the intelligent optimization algorithm uses a set of measures, a model input tensor, and a set of constraints as input variables. Through iterative calculation, it generates a set of optimal emission reduction schemes that meet both emission reduction targets and economic requirements, thereby improving the accuracy of emission reduction decisions. Furthermore, the intelligent optimization algorithm employs a multi-objective optimization framework, and the calculation formula is as follows:

[0064]

[0065] in: Input tensors to the measure set and model; Represents a constraint set; Represents a set of emission reduction schemes The total life cycle cost includes cost items such as CAPEX (initial investment) and OPEX (operating costs).

[0066] Furthermore, the objective function is adjusted using a weighted method. and The solution is obtained by combining weights to generate a single optimal emission reduction scheme.

[0067] The building lifecycle carbon emission management system based on the calculation, management and reduction system in this application embodiment is a full lifecycle carbon emission intelligent management system composed of a data management module, a carbon emission accounting module, an intelligent management module and an optimization and emission reduction module. It covers all stages of the full lifecycle, fills the existing management gaps, and realizes a dynamic cycle of accounting, management and optimization.

[0068] Example 2 Based on the same inventive concept as the building life cycle carbon emission management system based on the calculation, management and reduction system in the foregoing embodiments, this application provides a building life cycle carbon emission management method based on the calculation, management and reduction system, which adopts the building life cycle carbon emission management system based on the calculation, management and reduction system as described above.

[0069] The foregoing Figure 1 The various variations and specific examples of the building life cycle carbon emission management system based on the calculation, management, and reduction system in Example 1 are also applicable to the building life cycle carbon emission management method based on the calculation, management, and reduction system in this example. Through the foregoing detailed description of the building life cycle carbon emission management system based on the calculation, management, and reduction system, those skilled in the art can clearly understand the building life cycle carbon emission management method based on the calculation, management, and reduction system in this example. Therefore, for the sake of brevity, it will not be described in detail here.

[0070] Based on the above-described preferred embodiments of the present invention, and through the foregoing description, those skilled in the art can make various changes and modifications without departing from the inventive concept. The technical scope of this invention is not limited to the contents of the specification, but must be determined according to the scope of the claims.

Claims

1. A building lifecycle carbon emission management system based on computational management and reduction, characterized in that, The system includes: The data management module acquires and processes carbon emission data throughout the entire life cycle of a building, and saves the processed carbon emission data to the database after mapping it to the factor database through a unified interface. The full life cycle stages include: material production stage, construction stage, operation stage, and demolition stage; The carbon emission accounting module performs carbon emission accounting, simulation, and uncertainty analysis. The intelligent management module monitors carbon emission data in real time, analyzes carbon emission trends, provides early warnings of anomalies, and displays the data visually. The emission reduction module is optimized, the optimization algorithm is run, and emission reduction plans are generated through iterative processing and carbon asset management is carried out.

2. The building lifecycle carbon emission management system based on computational management and reduction system as described in claim 1, characterized in that, The carbon emission data includes: Carbon emission factors and production data during the material production stage; Equipment energy consumption data during the construction phase; Energy consumption data of each system during the operation phase; And data on waste treatment and recycling rates during the demolition phase; Processing carbon emission data throughout the entire life cycle of a building includes the following steps: S11, The carbon emission data is stored in the database through a standardized field dictionary and unique object encoding to establish a one-to-one mapping relationship between the carbon emission data and the factor database; S12, the carbon emission data is managed using versioning or traceable logs, data quality verification and permission leveling mechanisms to ensure unified management of the carbon emission data, so that the carbon emission data can be directly accessed by the carbon emission accounting module, the intelligent management module and the emission reduction optimization module.

3. The building lifecycle carbon emission management system based on computational management and reduction system as described in claim 1, characterized in that, The carbon emission accounting module performs carbon emission accounting, simulation, and uncertainty analysis, specifically including the following steps: S21, Input assembly, retrieve carbon emission data for each stage from the database; S22, accounting calculation, is based on the LCA accounting and energy consumption statistics fusion algorithm to calculate carbon emissions at each stage of the life cycle, intensity per unit area at each stage of the life cycle, total carbon emissions and total intensity of the life cycle; S23, Simulation Analysis: Simulate scenarios for the baseline, generate an influence matrix, and obtain scenario output; S24, Uncertainty Analysis: Key inputs are set, Monte Carlo sampling is performed, and uncertainty indicators are obtained. S25, Output results are stored in the database. The carbon emissions at each stage of the life cycle, the intensity per unit area at each stage of the life cycle, the total carbon emissions, total intensity, scenario output, and uncertainty indicators of the life cycle are written into the database so that the intelligent management module and the optimized emission reduction module can call them.

4. The building lifecycle carbon emission management system based on computational management and reduction system as described in claim 2, characterized in that, The intelligent management module performs real-time monitoring of carbon emission data, carbon emission trend analysis, anomaly warning, and visualization, specifically including the following steps: S31 acquires the output results of the carbon emission accounting module and the real-time energy consumption data of each system during the operation phase in real time, and performs data standardization processing and unified interface management. S32 provides anomaly warnings for standardized data based on threshold method, rate of change method, trend deviation, environment, working condition correction, and preset rule templates. S33. Based on the data in the database, a dataset is constructed and input into the carbon peak prediction model to analyze the carbon emission trend.

5. The building lifecycle carbon emission management system based on computational management and reduction system as described in claim 1, characterized in that, A dataset is constructed based on the data in the database and input into the carbon peak prediction model to analyze carbon emission trends. The specific steps include: S331, Data preparation: Acquire time-series data of energy consumption by item, time-series data of carbon emissions at each stage, meteorological and operating condition factors and retrofit event markers, and construct a dataset; S332, feature engineering, generates dummy variables related to seasons, holidays, and working days based on the date, then performs first-order differencing and detrending on the carbon emission time series and energy consumption time series, and finally uses ADF to test stationarity to obtain a set of feature data that meets the requirements of the time series model; S333, Construct a family of models, establish a carbon peak prediction model based on feature data, and set exogenous variables; S334, Training and Validation: Input the data from the dataset into the carbon peak prediction model, evaluate MAPE and RMSE through rolling window cross-validation, and obtain the prediction results and key driving factors. S335, Peak determination, based on the first derivative of the predicted curve and the peak stability, yields the peak year and confidence interval; S336, Write the results: Save the prediction results, peak year, confidence interval and key driving factors to the database for use by the optimized emission reduction module.

6. The building lifecycle carbon emission management system based on computational management and reduction system as described in claim 4, characterized in that, Running optimization algorithms to generate emission reduction plans and manage carbon assets through iterative processing specifically includes the following steps: S41, Data Acquisition: Acquire the output results of the carbon emission accounting module and the processing results of the intelligent management module; S42, Candidate measures are generated by filtering building material replacement, equipment upgrades, operation and maintenance strategies and photovoltaic grid connection related measures from the database, and filtering them according to feasibility, lifespan and investment to obtain a set of measures; S43, Parameter and Constraint Assembly: Assemble emission reduction coefficients, energy consumption change rate, initial investment, operating cost, lifetime and discount rate for each measure, and establish a constraint set; S44, Data Alignment, maps the output of the carbon emission accounting module and the processing results of the intelligent management module to the influence matrix to form the model input tensor; S45, Solve the configuration, set the population size, crossover probability, mutation probability and termination criterion, input the model input tensor, measure set and constraint set into the intelligent optimization algorithm for optimization calculation, and obtain the emission reduction scheme set through feasibility screening. The emission reduction scheme set includes scheme name, emission reduction amount tCO2e, ROI, payback period, budget occupation and impact on comfort or operating conditions. S46, Carbon Asset Linkage and Writeback, maps emission reductions (tCO2e) to allowances / offsets, generates revenue forecasts and account operation instructions, and saves them to the intelligent management module via API.

7. The building lifecycle carbon emission management system based on calculation, management, and reduction system as described in claim 3, characterized in that, The formulas for calculating carbon emissions at each stage are as follows: ； The formulas for calculating the intensity per unit area at each stage are as follows: ； The formula for calculating total lifecycle carbon emissions is: ； The formula for calculating the strength per unit area is: ； in, t Indicates the stages of the material's lifecycle, including production, construction, operation, or demolition. s Indicates a sub-item, system, or device. For the stage t middle section s The amount of activity or energy consumption, For the corresponding emission factors, This indicates the carbon emissions at each stage. This represents total carbon emissions. For the intensity per unit area at each stage, Intensity per unit area This refers to the total building area or the measured area.

8. The building lifecycle carbon emission management system based on computational management and reduction system as described in claim 6, characterized in that, The intelligent optimization algorithm adopts a multi-objective optimization framework, and the calculation formula is as follows: in: Input tensors to the measure set and model; Represents a constraint set; Represents a set of emission reduction schemes The total life cycle cost.

9. The building lifecycle carbon emission management system based on computational management and reduction system as described in claim 3, characterized in that, Step S24 specifically includes the following steps: S241, Input distribution modeling, sets the distribution type or parameter range for key inputs; S242 uses parallel computation for machine sampling, performs Monte Carlo sampling based on input distribution modeling, and obtains the probability distribution of life cycle carbon emissions; S243, statistical aggregation, calculates the mean, median, and quantiles by statistically summarizing the life cycle carbon emission sample set obtained from Monte Carlo simulation, and extracts the 95% confidence interval; S244, Sensitivity and Robustness Assessment, based on data from the Monte Carlo sample set, ranks the degree of influence of each input on the outcome by calculating standardized regression coefficients, Spearman correlation coefficients, or Sobol indices.

10. A building lifecycle carbon emission management method based on a computational management system, characterized in that, The method employs a building lifecycle carbon emission management system based on the calculation, management, and reduction system as described in any one of claims 1 to 9.

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