Building carbon emission monitoring system based on BIM

Abstract

The invention relates to the technical field of intelligent carbon management, in particular to a building carbon emission monitoring system based on BIM (Building Information Modeling), which comprises a model construction module, a thermal inertia analysis module, a carbon list generation module, a carbon emission monitoring module and an energy consumption parameter optimization module. According to the method, the carbon emission parameter set is generated in combination with the IFC data structure through standardized analysis of the material type, the size and the engineering quantity of the component, so that the carbon emission factor has the targeted adjustment capability. An actual measurement environment temperature change sequence is introduced into thermal inertia calculation, response difference correction is carried out on the thermal capacity and the heat conduction behavior of the component, and the dynamic thermal performance of the material in different environments is accurately reflected. In stage carbon emission measurement and calculation, component thermal inertia response and functional time periods are fused for refined staging, and functional frequency and time occupancy ratio are superposed in carbon emission calculation, so that dual sensitive adjustment of carbon emission on time and component function dimensions is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent carbon management, and in particular to a building carbon emission monitoring system based on BIM. Background Art

[0002] The field of intelligent carbon management technology encompasses the sensing, recording, calculation, and analysis of the entire carbon emissions process. It leverages information technology to accurately acquire, dynamically monitor, and centrally manage carbon emissions data, enabling refined tracking and quantitative control of carbon emissions. The core of this technology is data-driven monitoring and management of carbon emissions throughout the entire construction and operation process of buildings through the integration of building information models, IoT devices, and information systems.

[0003] The BIM-based building carbon emissions monitoring system refers to a system constructed using Building Information Modeling (BIM) technology and integrating the needs of building energy consumption and carbon emissions monitoring. By connecting to BIM data from the building design, construction, and operation and maintenance phases, the system monitors and analyzes energy consumption and carbon emissions data during the construction process in real time, achieving effective management and control of building carbon emissions. This includes data collection and processing methods based on BIM models, the construction of computational models for energy consumption and carbon emissions monitoring, and the integration of energy efficiency data throughout the building lifecycle. By combining the BIM platform with an energy efficiency data management system, data tracking can be achieved throughout the entire construction project process, from design to operation and maintenance. IoT technology is used to obtain real-time energy consumption data for each device within the building, and carbon emissions statistics are calculated using a big data analysis platform.

[0004] In the process of monitoring building carbon emissions, existing technologies often rely on static BIM model data to match carbon factors, lacking time-dependent adjustments to material thermal response behavior. This leads to the problem of applying fixed parameters to carbon emission assessments. Factor selection is primarily based on direct assignment of values ​​based on material type, ignoring the effects of thermal conduction delays and ambient temperature fluctuations during component use. This results in deviations between carbon emission values ​​and measured energy consumption. Carbon emission estimates are typically divided by phase, without further granularity, such as component activation frequency or duration, failing to reflect the dynamic contribution of different components at different stages. In lifecycle forecasting, carbon emission trajectories are often set based on empirical curves, lacking data-driven trend evolution logic. This results in a lack of continuity and sensitivity in the forecast path. Furthermore, existing solutions primarily rely on manual correction or static regression to update carbon factors, lacking global optimization capabilities and limiting the accuracy of carbon emission control. These shortcomings not only affect the accuracy of carbon emission assessments but also hinder the closed-loop dynamic monitoring and parameter optimization, making it difficult to meet the refined requirements of carbon management throughout the building lifecycle. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose a building carbon emission monitoring system based on BIM.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a BIM-based building carbon emission monitoring system, the system comprising: The model building module is used to obtain component composition data, including material type, size, and engineering quantity information. It parses BIM data based on the IFC standard as component engineering quantity parameters, calculates preliminary carbon emission factors, integrates them into a carbon standard factor set, and transmits them to the thermal inertia analysis module. The thermal inertia analysis module is used to call the component engineering quantity parameters and the carbon standard factor set, combine the statistical ambient temperature change sequence, perform correction operations on the material heat capacity coefficient and thermal conduction delay time, and output the component thermal inertia characteristic parameters to pass to the carbon list generation module; The carbon list generation module is used to calculate the carbon emissions in each stage by referring to the thermal inertia characteristic parameters of the component and the carbon standard factor set, adjust the carbon emission factors in the construction and operation stages based on the carbon emissions, and generate a phased carbon emission list to be transmitted to the carbon emission monitoring module; The carbon emission monitoring module is used to input the staged carbon emission list into the LEAP model, construct the carbon emission intensity change path, obtain the carbon emission forecast sequence for each stage of the life cycle, analyze the carbon emission peak time, component type and component contribution rate, and output the carbon emission change trend monitoring results.

[0007] The improvements of the present invention are that the component engineering quantity parameters include component quality indicators, dimension combination forms, and engineering quantity structure levels; the component thermal inertia characteristic parameters are specifically heat capacity adjustment coefficient, thermal conductivity delay factor, and temperature change response coefficient; the staged carbon emission list includes an initial construction period carbon emission list, an operation and maintenance period carbon emission list, and a carbon emission factor adjustment record; the carbon emission change trend monitoring results are specifically a carbon intensity evolution curve, a carbon peak time prediction value, and a component contribution ranking list.

[0008] The present invention is improved in that the model building module includes: The component data extraction submodule obtains the material type, size and engineering quantity information in the component composition data, classifies and numbers the material type, constructs a component size structure table based on the size and engineering quantity information, and combines the component size structure table with the classification number results to generate a component code list; The parameter calculation and aggregation submodule, based on the component code list, calls the geometric parameters and physical information of the corresponding component in the IFC standard data structure, parses and obtains the volume, quantity and material density of the component, combines the three parameters to form the engineering quantity parameters of the single component, aggregates the engineering quantity data of all components, and generates a total component engineering quantity value set; The carbon factor generation submodule selects basic carbon emission factor data that matches the component material properties based on the total component engineering quantity value set, compares the component material density with the standard factor density benchmark, adjusts the carbon factor according to the deviation ratio, maps the adjusted carbon factor to the component engineering quantity parameter, and performs integration processing to generate a carbon standard factor set; The standard factor density benchmark is obtained by referring to the component material classification items in the industry carbon emission database.

[0009] The present invention is improved in that the thermal inertia analysis module includes: The component data linkage submodule calls the component engineering quantity parameters and the component number, material name and total component engineering quantity value in the carbon standard factor set, performs matching processing based on the component number, selects component items with complete material and size information, and performs data joint processing on the matching results and the statistical ambient temperature change sequence to generate a component thermal response input set; The thermal parameter correction submodule, based on the component thermal response input set, calls the initial value of the heat capacity coefficient and the initial time of the thermal conduction delay corresponding to the material, combines the hourly temperature gradient in the ambient temperature change sequence, compares the deviation difference between the measured temperature difference of the component thermal response and the theoretical temperature difference, adjusts the original heat capacity coefficient and the thermal conduction delay time according to the deviation difference, and obtains a thermal parameter correction coefficient pair; The inertia parameter output submodule calculates the heat flux response amplitude, temperature transfer time interval and reaction rate interval of each component in parallel based on the heat capacity and thermal conductivity correction coefficient of each component in the thermal parameter correction coefficient, combined with the component material type and size and structure parameters, to obtain the component thermal inertia characteristic parameters.

[0010] The present invention is improved in that the carbon list generation module includes: The stage carbon measurement submodule divides each component into environmental response cycles within the construction, construction, and operation phases based on the thermal inertia characteristic parameters of the component and the carbon standard factor, including the heat capacity, heat conduction time, and unit carbon emissions. The carbon emissions of each component within a specified time period are calculated by pairing the thermal inertia and carbon factors within the cycle, and the total carbon emissions for the stage are summarized. The factor value adjustment submodule detects the usage frequency and functional status of the components in each stage based on the difference between the carbon emission values ​​and carbon factor values ​​of the components in the construction stage and the operation stage in the total carbon emissions of the stage, takes the thermal inertia response cycle of the components as a reference, performs coefficient supplementation processing on the adjusted carbon emission factor value, and obtains a stage factor adjustment coefficient group; The emission list construction submodule calls the correction value of each component in the stage factor adjustment coefficient group and the initial carbon factor value to perform numerical combination, reallocates the carbon emission factor according to the stage in which the component is located, and combines the number of components in each stage with the corresponding carbon emission value, and outputs the adjusted data by stage to establish a staged carbon emission list.

[0011] The present invention is improved in that a coefficient supplement process is performed on the adjusted carbon emission factor value, using the formula: ; Computing components In the cycle Factor offset adjustment value under ; in, It is a cycle The actual observed carbon emission factor is is the reference benchmark factor for the same period, It is a cycle Thermal inertia response time, It is a component In all The sum of the thermal response durations under the cycles, It is a cycle Function running time, It is a component The total duration of the function over all cycles.

[0012] The present invention is improved in that the carbon emission monitoring module includes: The emission path construction submodule inputs the carbon emission values ​​of each stage into the LEAP model based on the carbon emissions of the components and the stage information in the staged carbon emission list, arranges and integrates the corresponding time nodes, normalizes the total carbon emission values ​​of multiple stages according to the building area, extracts the change slope and fluctuation density in the time series, and generates a carbon emission intensity change path; The stage sequence prediction submodule calls the change trend and stage boundary information in the carbon emission intensity change path, and based on the life cycle segmentation setting in the LEAP model, performs time series filling and trend deduction for multiple stages of construction, delivery, and operation. It continuously calculates the change nodes and superimposes the stage effect factors to obtain the life cycle carbon emission sequence; The peak contribution analysis submodule extracts the total carbon emission value and component number of the component in the peak period according to the peak node of carbon emissions in each stage of the life cycle carbon emission sequence, calculates the proportion of component carbon emissions, divides the components into categories, summarizes the emission contribution of the components in the peak interval, and outputs the carbon emission change trend monitoring results.

[0013] The present invention has the following improvements: for the stage effect factor, the formula is adopted: ; Computing components In the stage Phase effect factor ; in, It is a component In the stage The actual average carbon emissions value, It is a component In the stage Industry reference carbon emission value, Is the component function utilization coefficient, indicating the component in the stage The activation frequency in is the component time occupancy coefficient, which indicates the component’s The ratio of the usage time to the standard time.

[0014] The present invention is improved in that it further includes an energy consumption parameter optimization module, which uses a particle swarm optimization algorithm to iteratively adjust the parameters of the carbon emission factor based on the carbon emission change trend monitoring results, outputs a carbon factor optimization list, and updates the carbon emission parameters to obtain energy consumption parameter optimization results; The carbon factor optimization list includes a correction coefficient set, an optimization adaptation parameter set, and an update iteration record. The energy consumption parameter optimization result specifically refers to a carbon factor correction table, a life cycle energy consumption index, and an optimized carbon emission benchmark.

[0015] The present invention is improved in that the energy consumption parameter optimization module includes: The factor variable initialization submodule extracts the initial value and variation range of the carbon emission factor of the component in the corresponding stage based on the component carbon emission intensity variation path, peak interval and component category contribution in the carbon emission change trend monitoring results, divides the variation range into intervals, sets the initial position of the optimized particle and assigns a stage label to generate a carbon emission factor variable group; The emission factor iteration submodule calls the carbon emission factor parameter value and variation range of each stage component in the carbon emission factor variable group, compares the difference between the carbon emission factor value corresponding to the current position of each round of particles and the target carbon emission value in the life cycle carbon emission sequence, adjusts the particle position proportionally according to the difference, records the optimal position, and obtains the carbon factor optimization list; The energy consumption parameter updating submodule compares the adjusted value of the component carbon emission factor parameter in the carbon factor optimization list with the original carbon emission factor parameter, replaces the optimized carbon emission factor parameter in the parameter table according to the category and stage sequence of the component, and establishes the energy consumption parameter optimization result.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, the carbon emission parameter set is generated by standardizing the analysis of component material type, size and engineering quantity, and combining the IFC data structure to enable the carbon emission factor to have the ability to be adjusted in a targeted manner. The measured ambient temperature change sequence is introduced in the thermal inertia calculation, and the response difference correction is performed on the heat capacity and thermal conductivity behavior of the component to accurately reflect the dynamic thermal performance of the material under different environments. In the stage carbon emission calculation, the thermal inertia response of the component and the functional period are integrated for detailed periodization, and the functional frequency and time occupancy ratio are superimposed in the carbon emission calculation to achieve dual sensitive adjustment of carbon emissions to time and component functional dimensions. In the process of establishing the life cycle prediction path, the carbon emission evolution path covering the entire process is generated by jointly fitting the time series change trend and the stage boundary conditions, and the peak nodes are extracted to rank the component contributions, effectively identifying the core sources of carbon emissions. At the parameter optimization level, the particle swarm algorithm is used to dynamically optimize the carbon factor, and factors such as the peak interval and contributing components are incorporated into the factor adjustment logic to ensure the continuity and control accuracy of the carbon emission parameters. The construction of the above-mentioned multi-dimensional input and feedback links enables carbon emission monitoring to have adjustable structural levels, sensitive response to time rhythm, dynamic optimization of prediction accuracy and system linkage of factor adjustment, which promotes carbon emission management from static indicator verification to dynamic process regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 This is a system module diagram proposed by the present invention; Figure 2 This is the system framework diagram proposed by the present invention; Figure 3 A schematic diagram of the building blocks of the model of the present invention; Figure 4 is a schematic diagram of the thermal inertia analysis module of the present invention; Figure 5 A schematic diagram of a carbon list generation module of the present invention; Figure 6 is a schematic diagram of a carbon emission monitoring module of the present invention; Figure 7 Schematic diagram of the energy consumption parameter optimization module of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate and simplify the description of the present invention. They do not indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation, and therefore should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly specified. See also Figure 1 The present invention provides a technical solution: a building carbon emission monitoring system based on BIM, the system includes: The model building module is used to obtain component composition data, including material type, size, and engineering quantity information. It parses BIM data based on the IFC standard as component engineering quantity parameters, calculates preliminary carbon emission factors, integrates them into a carbon standard factor set, and transmits them to the thermal inertia analysis module. The thermal inertia analysis module is used to call the component engineering quantity parameters and carbon standard factor set, combine the statistical ambient temperature change sequence, perform correction operations on the material heat capacity coefficient and thermal conductivity delay time, and output the component thermal inertia characteristic parameters to pass to the carbon list generation module; The carbon list generation module is used to calculate the carbon emissions at each stage by referring to the thermal inertia characteristic parameters of the components and the carbon standard factor set. The carbon emission factors of the construction and operation stages are adjusted based on the carbon emissions, and a phased carbon emission list is generated and transmitted to the carbon emission monitoring module. The carbon emission monitoring module is used to input the phased carbon emission list into the LEAP model, construct the carbon emission intensity change path, obtain the carbon emission forecast sequence for each stage of the life cycle, analyze the carbon emission peak time, component type and component contribution rate, and output the carbon emission change trend monitoring results; The component engineering quantity parameters include component quality indicators, size combination forms, and engineering quantity structure levels. The component thermal inertia characteristic parameters are specifically the heat capacity adjustment coefficient, thermal conductivity delay factor, and temperature change response coefficient. The phased carbon emission list includes the initial construction period carbon emission list, the operation and maintenance period carbon emission list, and the carbon emission factor adjustment record. The carbon emission trend monitoring results are specifically the carbon intensity evolution curve, the carbon peak time prediction value, and the component contribution ranking list. The carbon factor optimization list includes the correction coefficient set, the optimization adaptation parameter set, and the update iteration record. The energy consumption parameter optimization results specifically refer to the carbon factor correction table, the life cycle energy consumption index, and the optimized carbon emission benchmark.

[0020] See also Figure 2 and Figure 3 , the model building modules include: The component data extraction submodule obtains the material type, size and engineering quantity information in the component composition data, classifies and numbers the material type, constructs a component size structure table based on the size and engineering quantity information, and combines the component size structure table with the classification number results to generate a component code list; Obtain the material type, size and quantity information in the component composition data. This process requires calling the standard component list database in the building information modeling system, retrieving the component unique identifier and the corresponding material field, and then classifying it into a first-level classification according to the material type, such as concrete, steel, bricks, insulation materials, etc. Each type of material is then subdivided into a second-level classification according to the material type. For example, steel is further subdivided into HRB400 steel bars, Q235 steel plates, etc. Each type of material is uniformly coded according to system rules. The coding structure is such as "MT-01" and "MT-02", where "MT" represents MaterialType and "01" and "02" are classification serial numbers. A material type number table is generated based on the classification number processing results; then the geometric information associated with the component is extracted, including the length of the component. ,width ,high , the component volume is obtained by the following formula: ,in: : Component volume (unit: ), : Component length (unit: m), : Width of component (unit: m), : Component height (unit: m). For example, a component is ,Width ,high , then its volume is , call the monomer quantity of the component in the component list data , build engineering quantity information: ,in: :Total engineering quantity of components (unit: ), : Component quantity (unit: piece). If the component quantity is , then , a dimension structure table is constructed in the system according to the component ID and geometric dimensions. The structure table lists fields such as component ID, dimension combination, volume, quantity, material number, etc., and combines the component dimension structure table with the aforementioned material number table. The field merging method is used to combine the data. In the data table, the dimension combination and material number of the same component ID are matched and arranged to form a full list of components, and finally a component code list is generated.

[0021] The parameter calculation and aggregation submodule uses the geometric parameters and physical information of the corresponding components in the IFC standard data structure based on the component code list, parses and obtains the volume, quantity, and material density of the components, combines the three parameters to form the engineering quantity parameters of the single component, and aggregates the engineering quantity data of all components to generate a total component engineering quantity value set; Based on the component code list, call the component geometric parameters and physical information in the IFC standard data structure, search the fields under the entity in the IFC file structure, and extract the component volume. ,quantity and density , such as volume ,quantity If the material is HRB400 steel bar, the density , then the quality parameters are constructed for: ,in: : Component mass (unit: kg), : Component volume (unit: ), : Material density (unit: ). Substituting the data into , we get , forming a set of single engineering quantity parameters of the component , and record it in the structural list; summarize all components, perform component dimension aggregation operations, and sum up all component engineering quantity data: ,in: :Total engineering quantity of all components (Unit: ), : No. The engineering quantity of each component, : Total number of components. For example, the total quantity of HRB400 steel components is , group and summarize by component category, generate a statistical table of total component engineering quantities, and form a total component engineering quantity value set.

[0022] The carbon factor generation submodule selects basic carbon emission factor data that matches the component material properties based on the total component engineering quantity set, compares the component material density with the standard factor density benchmark, adjusts the carbon factor according to the deviation ratio, maps the adjusted carbon factor to the component engineering quantity parameters, and integrates them to generate a carbon standard factor set; The standard factor density benchmark is obtained by consulting the component material classification items in the industry carbon emission database; According to the total value set of component engineering quantities, the basic carbon emission factor data matching the component material properties is selected, and the comparison table of material types and standard carbon emission factors in the construction industry carbon database is called to match the component material classification results with the codes in the carbon database. For example, the carbon emission factor corresponding to HRB400 is , the density benchmark of HRB400 in the carbon database is , if the actual density of a component is , the density deviation ratio is calculated as follows: ,in: : density deviation rate (dimensionless), : Actual material density of the component (unit: ), :Carbon factor standard density benchmark (unit: ). Substituting the numerical values ​​into: , and then the original carbon emission factor Make corrections: ,in: : Adjusted carbon emission factor (unit: ), :Basic carbon emission factor (unit: ). Substitute into the calculation and we get , and then the adjusted carbon emission factor and component quality Combined, calculate the total carbon emissions of components: ,in: :Total carbon emissions of components (Unit: ), : Component mass (unit: kg), : Adjusted carbon factor (unit: ). Substituting into the calculation, we get: , record the carbon emission value of the component under the material number, and correspond it with the component code list, integrate all component carbon emission data to form a carbon standard factor set.

[0023] See also Figure 2 and Figure 4 , thermal inertia analysis module includes: The component data linkage submodule calls the component engineering quantity parameters and the component number, material name and total engineering quantity value in the carbon standard factor set, performs matching processing based on the component number, selects component items with complete material and size information, and performs data joint processing on the matching results and the statistical ambient temperature change sequence to generate the component thermal response input set; Call the component quantity parameters and the component number, material name and total value of the component quantity in the carbon standard factor set. First, extract the number field corresponding to each component from the parsed component parameter library, compare the component number with the component code in the carbon standard factor set item by item, perform matching registration on the entries that exist in both databases and have the same code, retain these data as valid items, and verify the material name field in the matching items. Exclude components with empty material fields or marked as "unknown". Check whether the size parameters of the remaining items are complete, that is, whether they include indicators such as volume, size combination, and quantity. Only components that meet the three conditions of component code, material name and size integrity are retained for subsequent calculations. On this basis, obtain the same component code. The time series data corresponding to the number is used to import the hourly temperature change data of the area where the building is located. This data is the daily distribution sequence of the annual average temperature, and the record format is a 24-hour × 365-day matrix. The meteorological data of the building's location is used as the input source. For example, if a project is located in Guangzhou, the daily average temperature value of the corresponding year in the local meteorological database is read, one group for each hour, to form a temperature change sequence for the whole year, with a total length of 8760 groups of data. This temperature change data is combined with the component engineering quantity information according to the component number. Each component record corresponds to an hourly temperature sequence fragment, and a unified data structure table is constructed. The columns include component number, material type, volume, quantity, total engineering quantity and 8760 rows of hourly temperature record entries, which are finally integrated to form the component thermal response input set.

[0024] The thermal parameter correction submodule, based on the component thermal response input set, calls the initial value of the heat capacity coefficient and the initial thermal conduction delay time corresponding to the material. Combined with the hourly temperature gradient in the ambient temperature change sequence, it compares the deviation between the measured temperature difference and the theoretical temperature difference of the component thermal response. Based on the deviation difference, the original heat capacity coefficient and thermal conduction delay time are adjusted to obtain the thermal parameter correction coefficient pair. Based on the component thermal response input set, the initial heat capacity coefficient and heat conduction delay time value corresponding to the material name identified by the component are read. The initial value refers to the value table provided in the thermal performance specification of building materials. For example, the initial value of concrete heat capacity is set to , the initial setting of thermal delay time is , then extract the hourly temperature record of each component from the component thermal response input set, calculate the temperature difference between any two consecutive time points in the temperature sequence as the temperature gradient, for example, if the temperature of a component at the 101st hour and the 100th hour is 29.6°C and 28.9°C respectively, the gradient is 0.7°C, and the temperature gradient value is used together with the total engineering quantity and unit density value of the component recorded in the early stage to simulate the thermal response. The system records the measured temperature difference between the outer surface and inner core temperature sensors of the component in the corresponding time period as 1.8°C, while the theoretical temperature difference estimated based on the initial thermal parameters is 2.3°C, and the deviation between the two is 0.5°C. Correction is made based on the deviation. If the deviation exceeds the set difference threshold of 0.3°C, the coefficient adjustment operation is triggered. At this time, the corrected heat capacity value is reduced by 6% based on the original value, that is, the heat capacity correction value is adjusted to , the thermal delay time is increased by 8% at the same time, that is, adjusted to , record the correction value into the component thermal parameter structure table, and archive the difference between the original value and the adjusted value to generate a thermal parameter correction coefficient pair.

[0025] The inertia parameter output submodule calculates the heat capacity and thermal conductivity correction coefficients of each component based on the thermal parameter correction coefficient, combined with the component material type and size and structure parameters, and calculates the heat flux response amplitude, temperature transfer time interval and reaction rate interval of each component in parallel to obtain the component thermal inertia characteristic parameters; According to the thermal parameter correction coefficient, the heat capacity and thermal conductivity correction coefficient of each component are first matched to the material type of the component, and the reference range of the unit volume heat flux is called according to the material standard heat flux table. For example, the reference range of steel components is set to , and then combine the geometric parameters of the component such as volume and thickness to estimate its heated surface area and heat transfer path length. According to the adjusted values ​​of heat capacity and heat conduction time, re-estimate the temperature rise time and temperature rise amplitude of the component center after unit heat input, and thus calculate the temperature transfer time interval. Taking the concrete wall as an example, the thickness is 30cm and the area is , quality is , the corrected heat capacity is , the unit heat input is , input per hour Heat, the temperature rise of the component center is about , combined with the change in heat transfer rate, the time is adjusted to The surface temperature rise delay is measured to be 1 hour, so the temperature transfer time interval is constructed to be 4.3–4.8 hours. The temperature response rates of different materials in this interval are further recorded and classified and summarized. Finally, a component list containing indicators such as heat capacity correction value, thermal conduction delay, heat transfer rate, and response time is constructed to obtain the thermal inertia characteristic parameters of the components.

[0026] See also Figure 2and Figure 5 , the carbon list generation module includes: The stage carbon measurement submodule collects the heat capacity, heat conduction time and unit carbon emissions of components based on the thermal inertia characteristic parameters of the components and the carbon standard factor. It divides the environmental response period of each component into the construction, construction and operation phases. After matching the thermal inertia and carbon factor data within the period, it calculates the carbon emissions of each component within the specified time and summarizes the total carbon emissions of the phase. Based on the thermal inertia characteristic parameters of the component and the carbon standard factor, the heat capacity parameters of the component are first extracted. , thermal response time and unit carbon emission factor , in order to build a thermal response mapping system, the daily heat exchange fluctuation behavior of building components (such as concrete walls) is obtained through the material properties and operation data of the building components. The construction, construction and operation stages are divided into response cycles of different time periods. For example, the operation stage can be set as 4 cycles per day based on the change of summer sunshine intensity, each cycle is 6 hours, and the corresponding cycles are marked as 、 、 、 In each cycle, the ratio of the heat capacity of the component participating in the heat exchange is collected , and calculate it together with the basic carbon factor, specifically: if a component is a reinforced concrete wall, the mass , unit specific heat , unit carbon emission factor , then its total heat capacity is , if the activation heat capacity ratio per cycle is , the effective heat capacity during the cycle is , further assuming that the heat energy is equivalent to carbon energy consumption, the carbon emissions of this cycle are , the daily carbon emissions for the four cycles are 、 、 、 , then the daily carbon emission of the component is , extended to 180 days of operation, with a total of , this process can be extended to the construction (such as the formwork fixing stage) and construction (such as the spraying stage) sub-processes, forming a stage-by-stage carbon emission distribution map.

[0027] The factor value adjustment submodule detects the usage frequency and functional status of components in each stage based on the difference between the carbon emission values ​​and carbon factor values ​​of components in the construction stage and the operation stage in the total carbon emission amount of the stage, takes the thermal inertia response cycle of the component as a reference, performs coefficient supplementation processing on the adjusted carbon emission factor value, and obtains the stage factor adjustment coefficient group; Based on the difference in carbon emission values ​​and carbon factor values ​​between components in the construction and operation phases in the total carbon emission volume during each phase, it is necessary to first obtain the actual frequency of component use. For example, the frequency of use of the exterior wall components of an office building is 10 hours per day during the operation phase and 3 hours per day during the construction phase. Combined with the functional status of the components, if a highly enclosed exterior wall is set with a material with high thermal inertia (such as thick concrete), its thermal stability within the response cycle is significant. The thermal inertia parameters within the cycle are further used as a benchmark to form a factor adjustment reference set with the carbon emission values. A component-factor mapping table is constructed within each cycle. Then, for cycles with large factor differences, an adjustment coefficient is set. The expression for the adjustment coefficient is defined as follows: , in: : Represents a component In the cycle The factor offset adjustment value under ; :cycle The actual observed carbon emission factor is in ; : Reference benchmark factor for the same period, with consistent units; :cycle Thermal inertia response time (unit: hours); :member In all The sum of the thermal response durations under each cycle; :cycle Function running time (unit: hours); :member The total duration of the function in all cycles. Assume that the operation phase of a component is divided into 3 cycles, and the observation factors are 、 、 , the unified reference value is , the corresponding cycle thermal response time is , the function time is , calculate the parameters and get: , brought into the calculation: , , These three adjustment values ​​constitute the carbon factor correction sequence of the component in each cycle during the operation phase and serve as important input data for the subsequent calculation of the total carbon factor of the component.

[0028] The emission list construction submodule calls the phase factor adjustment coefficient group to combine the correction value of each component with the initial carbon factor value, reallocates the carbon emission factor according to the phase of the component, and combines the number of components in each phase with the corresponding carbon emission value. The adjusted data is output by phase to establish a phased carbon emission list; The correction value of each component in the stage factor adjustment coefficient group is combined with the initial carbon factor value. First, the components are classified according to the construction, construction or operation stage. For example, the foundation structure of a building belongs to the construction stage, the exterior wall structure belongs to the construction stage, and the air conditioning system belongs to the operation stage. Then, according to the component list, the initial carbon emission factor value and adjustment coefficient are extracted respectively to construct the total value of the revised carbon emission factor. , which is used for subsequent calculation of total carbon emissions, and the formula is as follows: ,in: :member The modified carbon emission factor in its life cycle stage is ; : basic carbon factor of the component (uncorrected value); :member In the Correction coefficient in a cycle; : The number of phase cycles. , the sum of the adjusted values ​​is: ,but: , calculate the total carbon emissions of the component using the following formula: ,in: :member Total carbon emissions, in units of ; : Component mass, in units of , in this case ; : Corrected carbon emission factor, take ; Substitute into the calculation: The total carbon emissions are used to classify and summarize the total emissions in each stage, and combined with the component number and material type to achieve phased summary output. The details can be output according to the component dimension in the component-level output, and finally a complete sub-item data structure containing the carbon factors and carbon emissions in each stage is established.

[0029] See also Figure 2 and Figure 6 , the carbon emission monitoring module includes: The emission path construction submodule inputs the carbon emission values ​​of each stage into the LEAP model based on the carbon emissions of the components and the stage information in the staged carbon emission list. The model then arranges and integrates the corresponding time nodes, normalizes the total carbon emission values ​​of various stages according to the building area, and extracts the change slope and fluctuation density in the time series to generate the carbon emission intensity change path. Based on the component carbon emissions and the stage information in the staged carbon emissions list, the carbon emission values ​​of each component in different stages (such as construction, construction, operation, etc.) are first extracted, and then the carbon emission data of each stage are input into the LEAP model. The LEAP model arranges and integrates the carbon emission information of each stage according to the time node of the construction project. In order to better analyze the relationship between carbon emissions in each stage and the building area, the carbon emission value of each stage is normalized according to the total area of ​​the building, and the unit is , in order to facilitate comparison and analysis between stages. The normalized calculation formula is as follows: ,in: Indicates the The carbon emission intensity per unit area in the stage is ; Indicates the The total carbon emissions in the stage, in units of ; is the total area of ​​the building in units of For example, suppose the total area of ​​a building is , the carbon emissions at a certain stage are , then the carbon emission intensity per unit area in this stage is: Next, the normalized carbon emission intensity series of each stage are arranged by time node to generate a continuous carbon emission intensity change path. In order to analyze the trend of carbon emission changes, the change slope and fluctuation density between each time node are further extracted. The change slope reflects the rate of change of carbon emission intensity over time. The slope is calculated using the central difference method: ,in: For time point The carbon emission intensity slope at and Time points and For example, if the carbon emission intensity series for a period is (unit: ), then at the middle time point The slope at is: ,In addition, the fluctuation density reflects the fluctuation frequency of carbon emission ,intensity in the time series. A threshold judgment condition is set. ,If the absolute value of the slope exceeds the set threshold (e.g. ), then the period is considered to be a high-volatility segment with significant carbon emission changes. For example, if the slope sequence of a stage is , it can be determined that the volatility density of this period is high.

[0030] The stage sequence prediction submodule uses the change trend and stage boundary information in the carbon emission intensity change path. Based on the life cycle segmentation setting in the LEAP model, it fills in the time series and deduces the trend of multiple stages such as construction, delivery, and operation. It continuously calculates the change nodes and superimposes the stage effect factors to obtain the life cycle carbon emission sequence. The change trend and stage boundary information in the carbon emission intensity change path are called, the known stage start and end points in the original path sequence are used as the index basis, and the life cycle stage settings in the LEAP model are used as the stage reference template. The data of the time segments with faults or discontinuities between the known stages are filled on the time axis. The filling method uses linear interpolation to fit the time gap. If the data from the 25th to the 30th year of the operation period is missing, the carbon intensity of the 24th year is used. 31st year As the starting and ending points, we can calculate 、 、 、 、 、 At the same time, the slope trend line of the change point at the stage transition boundary is extended, and the stage effect factor of each stage is superimposed to correct the fitting trend. The stage effect factor is used to adjust the increase or decrease in carbon emissions caused by stage characteristics. Its calculation formula is as follows: ,in: :member In the stage The stage effect factor of (dimensionless); :member In the stage The actual average carbon emissions value, in units of ; :member In the stage Industry reference carbon emission value, unit is ; : Component function utilization coefficient, indicating the component’s Frequency of activation (percentage) in ; : Component time occupancy coefficient, indicating the component's The ratio of the usage time to the standard time. In the operation stage For example, let’s assume that: the actual average carbon emission value ;Industry reference carbon emission value ; Functional utilization coefficient ; Time occupancy coefficient ; Substitute into the formula: , therefore, the effect factor of this component in the operation stage is , will be used as a gain coefficient when filling the carbon emission sequence above, superimposed on the corresponding interpolation point, and the corrected point value is , and finally generates a complete life cycle carbon emission sequence with stage characteristics and considering the impact of structural thermal inertia.

[0031] The peak contribution analysis submodule extracts the total carbon emission value and component number of the component during the peak period based on the peak node of carbon emissions in each stage of the life cycle carbon emission sequence, calculates the proportion of component carbon emissions, divides the components into categories, summarizes the emission contribution of the components during the peak period, and outputs the carbon emission trend monitoring results; According to the peak node of carbon emissions in each stage of the life cycle carbon emission sequence, the sliding window extraction method is used to identify the maximum local segment of carbon emissions. The judgment condition is set as the median of any five consecutive time nodes is greater than any point on both sides and exceeds the sliding average by more than 10%. It is included in the analysis sequence as the peak node. Then the total carbon emission data of the component in the peak interval and its component number are extracted. The category and stage to which it belongs are traced back through the component number, and the proportion of the total carbon emission of the component is calculated by stage and category. For example, the total carbon emission in the peak interval is , where the structural components are discharged as , the enclosure components are , equipment components are , then their contribution ratios are structural components , enclosure components , equipment components After classification, the components are summarized according to their categories to obtain the quantitative proportion of each component in the peak carbon emission range. The result is used as the input for component carbon emission distribution trend analysis to form a carbon peak contribution data set for reverse inference of high-intensity nodes in the carbon monitoring model.

[0032] See also Figure 2 and Figure 7 , also includes an energy consumption parameter optimization module. Based on the carbon emission trend monitoring results, the energy consumption parameter optimization module uses the particle swarm optimization algorithm to iteratively adjust the parameters of the carbon emission factor, outputs the carbon factor optimization list, and updates the carbon emission parameters to obtain the energy consumption parameter optimization results; The energy consumption parameter optimization module includes: The factor variable initialization submodule extracts the initial value and change range of the carbon emission factor of the component in the corresponding stage based on the change path, peak interval and component category contribution of the component carbon emission intensity in the carbon emission trend monitoring results, divides the change range into intervals, sets the initial position of the optimized particle and assigns a stage label to generate a carbon emission factor variable group; Based on the carbon emission intensity change path, peak interval and component category contribution of the components in the carbon emission change trend monitoring results, we first extract the initial value of the carbon emission factor of each component in different life cycle stages (such as construction, construction, operation, etc.) from the carbon emission intensity change path. Combined with the life cycle and stage characteristics of the components, the carbon emission factor and its change range of each stage are divided into intervals. According to the characteristics of these change ranges, the initial particle position suitable for each stage is determined, and a stage label is assigned to each particle. This label identifies the stage category of the carbon emission factor to which each particle belongs. For example, the initial values ​​of the carbon emission factors of a building in a certain project in different stages (construction, construction, operation) are , , , after calculation, the factor change ranges in each stage are Based on these change amplitudes, the starting positions of the particles are set for initialization and the phase labels (such as construction phase, construction phase, and operation phase) are marked. Subsequently, these initialized particle positions and labels are combined to form a carbon emission factor variable group for subsequent iterative calculation of emission factors.

[0033] The emission factor iteration submodule calls the carbon emission factor parameter value and variation range of each stage component in the carbon emission factor variable group, compares the difference between the carbon emission factor value corresponding to the current position of each round of particles and the target carbon emission value in the life cycle carbon emission sequence, adjusts the particle position proportionally according to the difference, records the optimal position, and obtains the carbon factor optimization list; The carbon emission factor parameter value and variation range of each stage component in the carbon emission factor variable group are called, and the difference between the carbon emission factor value corresponding to the current position of each particle and the target carbon emission value in the life cycle carbon emission sequence is compared. By calculating the difference between the current position of each particle and the target carbon emission value, the position of the particle is adjusted proportionally according to the difference, and the optimal position of each round is recorded. To this end, the position change formula of each particle is as follows: ,in: : The new position of the particle (i.e. the new carbon emission factor value); : The position of the particle (i.e. the current carbon emission factor value); : The adjustment step of the particle, representing the offset of the particle's current position to the optimal position; : Target carbon emission factor value (e.g. target life cycle emission value); : The carbon emission factor of the particle at its current position. Assume that the initial carbon emission factor of a particle is , the target carbon emission value is , the new position of the particle can be calculated according to the above formula: ,in, , substituting into: ,In each round of iteration, the position of the particle will be gradually adjusted, and the carbon emission factor of the particle will be recorded and updated according to the adjusted optimal position, and finally a list containing the optimized values ​​of the carbon emission factors of all components will be formed.

[0034] The energy consumption parameter update submodule compares the adjusted carbon emission factor parameters of the components in the carbon factor optimization list with the original carbon emission factor parameters, replaces the optimized carbon emission factor parameters in the parameter table according to the component category and stage sequence, and establishes the energy consumption parameter optimization results; According to the comparison results of the carbon emission factor parameter adjustment value of the component in the carbon factor optimization list and the original carbon emission factor parameter, adjustments are made based on the optimized carbon emission factor. According to the component category and stage sequence, the optimized carbon emission factor parameter replaces the original factor parameter and writes it into the parameter table. According to the staged adjustment, the carbon emission factor parameter of each component is replaced according to its carbon emission factor optimization value at different stages. For example, the initial carbon emission factor of a component is After iterative optimization of the emission factor, the optimized factor is , then the parameters of the component will be updated and replaced with the new The results are then tabulated to create an energy consumption parameter optimization table, facilitating subsequent carbon emissions calculations and energy consumption monitoring. This process continuously optimizes the carbon emission factors of different components at each stage of their lifecycle, ensuring that the carbon emission level of the entire building project is more aligned with target requirements.

[0035] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. The BIM-based building carbon emission monitoring system is characterized by: The system comprises: The model building module is used to obtain component composition data, including material type, size, and engineering quantity information. It parses BIM data based on the IFC standard as component engineering quantity parameters, calculates preliminary carbon emission factors, integrates them into a carbon standard factor set, and transmits them to the thermal inertia analysis module. The thermal inertia analysis module is used to call the component engineering quantity parameters and the carbon standard factor set, combine the statistical ambient temperature change sequence, perform correction operations on the material heat capacity coefficient and thermal conduction delay time, and output the component thermal inertia characteristic parameters to pass to the carbon list generation module; The carbon list generation module is used to calculate the carbon emissions in each stage by referring to the thermal inertia characteristic parameters of the component and the carbon standard factor set, adjust the carbon emission factors in the construction and operation stages based on the carbon emissions, and generate a phased carbon emission list to be transmitted to the carbon emission monitoring module; The carbon emission monitoring module is used to input the staged carbon emission list into the LEAP model, construct the carbon emission intensity change path, obtain the carbon emission forecast sequence for each stage of the life cycle, analyze the carbon emission peak time, component type and component contribution rate, and output the carbon emission change trend monitoring results.

2. The BIM-based building carbon emission monitoring system according to claim 1 is characterized by: The component engineering quantity parameters include component quality indicators, dimension combination forms, and engineering quantity structure levels. The component thermal inertia characteristic parameters are specifically the heat capacity adjustment coefficient, thermal conductivity delay factor, and temperature change response coefficient. The phased carbon emission list includes the initial construction period carbon emission list, the operation and maintenance period carbon emission list, and the carbon emission factor adjustment record. The carbon emission change trend monitoring results are specifically the carbon intensity evolution curve, the carbon peak time prediction value, and the component contribution ranking list.

3. The BIM-based building carbon emission monitoring system according to claim 1 is characterized by: The model building module includes: The component data extraction submodule obtains the material type, size and engineering quantity information in the component composition data, classifies and numbers the material type, constructs a component size structure table based on the size and engineering quantity information, and combines the component size structure table with the classification number results to generate a component code list; The parameter calculation and aggregation submodule, based on the component code list, calls the geometric parameters and physical information of the corresponding component in the IFC standard data structure, parses and obtains the volume, quantity and material density of the component, combines the three parameters to form the engineering quantity parameters of the single component, aggregates the engineering quantity data of all components, and generates a total component engineering quantity value set; The carbon factor generation submodule selects basic carbon emission factor data that matches the component material properties based on the total component engineering quantity value set, compares the component material density with the standard factor density benchmark, adjusts the carbon factor according to the deviation ratio, maps the adjusted carbon factor to the component engineering quantity parameter, and performs integration processing to generate a carbon standard factor set; The standard factor density benchmark is obtained by referring to the component material classification items in the industry carbon emission database.

4. The BIM-based building carbon emission monitoring system according to claim 1, characterized in that: The thermal inertia analysis module includes: The component data linkage submodule calls the component engineering quantity parameters and the component number, material name and total component engineering quantity value in the carbon standard factor set, performs matching processing based on the component number, selects component items with complete material and size information, and performs data joint processing on the matching results and the statistical ambient temperature change sequence to generate a component thermal response input set; The thermal parameter correction submodule, based on the component thermal response input set, calls the initial value of the heat capacity coefficient and the initial time of the thermal conduction delay corresponding to the material, combines the hourly temperature gradient in the ambient temperature change sequence, compares the deviation difference between the measured temperature difference of the component thermal response and the theoretical temperature difference, adjusts the original heat capacity coefficient and the thermal conduction delay time according to the deviation difference, and obtains a thermal parameter correction coefficient pair; The inertia parameter output submodule calculates the heat flux response amplitude, temperature transfer time interval and reaction rate interval of each component in parallel based on the heat capacity and thermal conductivity correction coefficient of each component in the thermal parameter correction coefficient, combined with the component material type and size and structure parameters, to obtain the component thermal inertia characteristic parameters.

5. The BIM-based building carbon emission monitoring system according to claim 1 is characterized in that: The carbon list generation module includes: The stage carbon measurement submodule divides each component into environmental response cycles within the construction, construction, and operation phases based on the thermal inertia characteristic parameters of the component and the carbon standard factor, including the heat capacity, heat conduction time, and unit carbon emissions. The carbon emissions of each component within a specified time period are calculated by pairing the thermal inertia and carbon factors within the cycle, and the total carbon emissions for the stage are summarized. The factor value adjustment submodule detects the usage frequency and functional status of the components in each stage based on the difference between the carbon emission values ​​and carbon factor values ​​of the components in the construction stage and the operation stage in the total carbon emissions of the stage, takes the thermal inertia response cycle of the components as a reference, performs coefficient supplementation processing on the adjusted carbon emission factor value, and obtains a stage factor adjustment coefficient group; The emission list construction submodule calls the correction value of each component in the stage factor adjustment coefficient group and the initial carbon factor value to perform numerical combination, reallocates the carbon emission factor according to the stage in which the component is located, and combines the number of components in each stage with the corresponding carbon emission value, and outputs the adjusted data by stage to establish a staged carbon emission list.

6. The BIM-based building carbon emission monitoring system according to claim 5, characterized in that: The coefficient supplementation process is performed on the adjusted carbon emission factor value using the formula: ； Computing components In the cycle Factor offset adjustment value under ; in, It is a cycle The actual observed carbon emission factor is is the reference benchmark factor for the same period, It is a cycle Thermal inertia response time, It is a component In all The sum of the thermal response durations under the cycles, It is a cycle Function running time, It is a component The total duration of the function over all cycles.

7. The BIM-based building carbon emission monitoring system according to claim 1, characterized in that: The carbon emission monitoring module includes: The emission path construction submodule inputs the carbon emission values ​​of each stage into the LEAP model based on the carbon emissions of the components and the stage information in the staged carbon emission list, arranges and integrates the corresponding time nodes, normalizes the total carbon emission values ​​of multiple stages according to the building area, extracts the change slope and fluctuation density in the time series, and generates a carbon emission intensity change path; The stage sequence prediction submodule calls the change trend and stage boundary information in the carbon emission intensity change path, and based on the life cycle segmentation setting in the LEAP model, performs time series filling and trend deduction for multiple stages of construction, delivery, and operation. It continuously calculates the change nodes and superimposes the stage effect factors to obtain the life cycle carbon emission sequence; The peak contribution analysis submodule extracts the total carbon emission value and component number of the component in the peak period according to the peak node of carbon emissions in each stage of the life cycle carbon emission sequence, calculates the proportion of component carbon emissions, divides the components into categories, summarizes the emission contribution of the components in the peak interval, and outputs the carbon emission change trend monitoring results.

8. The BIM-based building carbon emission monitoring system according to claim 7, characterized in that: For the stage effect factor, the formula is used: ； Computing components In the stage Phase effect factor ; in, It is a component In the stage The actual average carbon emissions value, It is a component In the stage Industry reference carbon emission value, Is the component function utilization coefficient, indicating the component in the stage The activation frequency in is the component time occupancy coefficient, which indicates the component’s The ratio of the usage time to the standard time.

9. The BIM-based building carbon emission monitoring system according to claim 1, characterized in that: The system further includes an energy consumption parameter optimization module, which uses a particle swarm optimization algorithm to iteratively adjust the parameters of the carbon emission factor based on the carbon emission change trend monitoring results, outputs a carbon factor optimization list, and updates the carbon emission parameters to obtain energy consumption parameter optimization results; The carbon factor optimization list includes a correction coefficient set, an optimization adaptation parameter set, and an update iteration record. The energy consumption parameter optimization result specifically refers to a carbon factor correction table, a life cycle energy consumption index, and an optimized carbon emission benchmark.

10. The BIM-based building carbon emission monitoring system according to claim 9, characterized in that: The energy consumption parameter optimization module includes: The factor variable initialization submodule extracts the initial value and variation range of the carbon emission factor of the component in the corresponding stage based on the component carbon emission intensity variation path, peak interval and component category contribution in the carbon emission change trend monitoring results, divides the variation range into intervals, sets the initial position of the optimized particle and assigns a stage label to generate a carbon emission factor variable group; The emission factor iteration submodule calls the carbon emission factor parameter value and variation range of each stage component in the carbon emission factor variable group, compares the difference between the carbon emission factor value corresponding to the current position of each round of particles and the target carbon emission value in the life cycle carbon emission sequence, adjusts the particle position proportionally according to the difference, records the optimal position, and obtains the carbon factor optimization list; The energy consumption parameter updating submodule compares the adjusted value of the component carbon emission factor parameter in the carbon factor optimization list with the original carbon emission factor parameter, replaces the optimized carbon emission factor parameter in the parameter table according to the category and stage sequence of the component, and establishes the energy consumption parameter optimization result.

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