A method of visualizing carbon emissions calculations for building construction processes

Abstract

The present application relates to carbon emission processing technical field, specifically to a kind of visual building construction process carbon emission calculation method, comprising: each building component is endowed with component identification and binds the carbon footprint data of building material corresponding to building component in construction stage, after generating a plurality of target carbon units, carbon genome data model is generated by combining a plurality of target carbon units;According to preset carbon emission factor, the resource consumption data and construction activity data generated in current building construction process are converted into carbon emission data stream;Carbon emission data stream is fused with carbon genome data model, updates the carbon emission state of each target carbon unit in building construction process, generates the carbon emission distribution information mapped with building three-dimensional space position and building construction progress, and carbon emission distribution information is visualized rendering, solve the technical problems that there is significant deviation between the calculation result and the carbon emission amount actually generated in construction process.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of carbon emission treatment, and in particular to a visual building construction process carbon emission calculation method. BACKGROUND

[0002] Building construction process carbon emission calculation is of great significance to promoting the low-carbon transformation of the construction industry and is the basis for realizing accurate carbon footprint control, optimizing construction schemes and achieving emission reduction targets.

[0003] Existing carbon emission calculation methods mainly rely on two technical paths: one is static calculation based on design stage budget data or historical experience coefficients, and the other is post-hoc collection and statistical summary of energy tickets and material lists in the construction process. However, due to the serious dependence of the accounting process on static data, the dynamic changes in resource consumption and mechanical activities in the construction site cannot be captured in real time, resulting in a significant deviation between the calculation results and the actual carbon emissions generated in the construction process, which cannot effectively support real-time carbon efficiency diagnosis and dynamic optimization and control in the construction process. SUMMARY

[0004] In order to solve the technical problem of significant deviation between the calculation results and the actual carbon emissions generated in the construction process, the application provides a visual building construction process carbon emission calculation method.

[0005] The visual building construction process carbon emission calculation method provided by the application adopts the following technical scheme:

[0006] A visual building construction process carbon emission calculation method comprises:

[0007] Each building component is assigned a component identifier and bound to the carbon footprint data of the building material corresponding to the building component in the construction stage. After generating a plurality of target carbon units, the plurality of target carbon units are combined to generate a carbon genome data model;

[0008] According to the preset carbon emission factor, the resource consumption data and construction activity data generated in the current building construction process are converted into carbon emission data flow;

[0009] The carbon emission data flow and the carbon genome data model are fused, the carbon emission state of each target carbon unit in the building construction process is updated, the carbon emission distribution information mapped to the three-dimensional spatial position of the building and the construction progress of the building is generated, and the carbon emission distribution information is visualized and rendered.

[0010] Further, the step of assigning each building component a component identifier and binding the carbon footprint data of the building material corresponding to the building component in the construction stage to generate a plurality of target carbon units, and then combining the plurality of target carbon units to generate a carbon genome data model comprises:

[0011] After each building component is given a corresponding component identifier through the blockchain network, each component identifier and the carbon footprint data of the building component to which each component identifier respectively belongs are written into a corresponding blockchain node to generate an initial carbon unit;

[0012] When it is verified that the carbon footprint data contained in the initial carbon unit is valid, the carbon account in the initial carbon unit is activated, and the carbon value contained in the carbon footprint data is injected into the carbon account to form a target carbon unit;

[0013] According to the three-dimensional spatial positions of each building component in the building information, each target carbon unit is coupled according to the three-dimensional spatial positions to form a carbon genome data model.

[0014] Further, the step of generating an initial carbon unit includes:

[0015] When it is verified that the carbon footprint data contained in the initial carbon unit is not valid, a data anomaly marker is generated, and the data anomaly marker is associated with the component identifier corresponding to the carbon footprint data to form a data anomaly group;

[0016] After obtaining the new carbon footprint data of the building component corresponding to the component identifier in the data anomaly group, the new carbon footprint data is written into the blockchain node to which the component identifier belongs, and the initial carbon unit is updated to obtain a new initial carbon unit;

[0017] If it is verified that the new carbon footprint data contained in the new initial carbon unit is valid, the carbon account in the new initial carbon unit is activated;

[0018] If it is verified that the new carbon footprint data contained in the new initial carbon unit is not valid, a new data anomaly marker is generated.

[0019] Further, the step of converting the resource consumption data and construction activity data generated in the current building construction process into carbon emission data flow according to the preset carbon emission factor includes:

[0020] The building material consumption is multiplied by the first preset carbon emission factor to generate a first direct carbon emission component, and the mechanical fuel consumption is weighted by the second preset carbon emission factor to generate a second direct carbon emission component;

[0021] The transportation distance data is multiplied by the third preset carbon emission factor to generate a first indirect carbon emission component, and the mechanical use time is weighted by the fourth preset carbon emission factor to generate a second indirect carbon emission component;

[0022] The first direct carbon emission component, the second direct carbon emission component, the first indirect carbon emission component and the second indirect carbon emission component are aligned and aggregated according to the construction time sequence to generate a carbon emission data stream.

[0023] Further, the step of fusing the carbon emission data stream with the carbon genome data model comprises:

[0024] According to the timestamps and the component identifiers in the carbon emission data stream, after matching the corresponding target building component in the carbon genome data model, the carbon emission data stream is associated to the corresponding building three-dimensional spatial position of the target building component to obtain a carbon emission-space mapping relationship;

[0025] According to the carbon emission-space mapping relationship, a carbon emission network is generated, taking the target building component as a node and the carbon emission flow path as an edge;

[0026] Based on the carbon emission flow path in the carbon emission network, the carbon emission amount is accumulated in the target carbon account of the carbon genome data model according to the construction time sequence.

[0027] Further, the step of generating carbon emission distribution information mapped with the building three-dimensional spatial position and the building construction progress comprises:

[0028] According to the carbon account of each target carbon unit and the building three-dimensional spatial position of each target carbon unit in the carbon genome data model, a carbon density distribution field is generated;

[0029] Based on the feature isosurfaces corresponding to the respective carbon emission concentrations extracted from the carbon density distribution field;

[0030] The carbon emission concentration corresponding to each feature isosurface is bound with the building three-dimensional spatial position covered by the carbon emission concentration, and the construction progress timestamp is associated with each building three-dimensional spatial position to generate carbon emission distribution information.

[0031] Further, the step of visualizing and rendering the carbon emission distribution information comprises:

[0032] According to the building three-dimensional spatial position, the carbon emission concentration and the construction progress timestamp contained in the carbon emission distribution information, an initial carbon emission rendering effect is generated;

[0033] Based on the initial carbon emission rendering effect, the flow and diffusion behavior of carbon emission is simulated to obtain a target carbon emission rendering effect;

[0034] The target carbon emission rendering effect is fused and rendered with the building information to obtain a carbon emission visual picture.

[0035] The beneficial effects achieved are:

[0036] The application provides a visual building construction process carbon emission calculation method, comprising: assigning each building component a component identifier and binding the carbon footprint data of the building component corresponding building material in the construction stage, generating a plurality of target carbon units, and then combining the plurality of target carbon units to generate a carbon genome data model; converting the resource consumption data and construction activity data generated in the current building construction process into carbon emission data flow according to a preset carbon emission factor; fusing the carbon emission data flow and the carbon genome data model, updating the carbon emission state of each target carbon unit in the building construction process, generating carbon emission distribution information mapped with the three-dimensional spatial position of the building and the construction progress, and visualizing the carbon emission distribution information.

[0037] That is, in the present application, by assigning each building component a component identifier and binding the carbon footprint data to form a target carbon unit, a refined carbon genome data model is constructed, providing a static benchmark for accurate attribution of carbon emissions. Then, by converting the real-time collected resource consumption data and construction activity data into dynamic carbon emission data flow, the synchronization of input data and construction process is ensured. Then, the carbon emission data flow and the carbon genome data model are fused, the carbon emission state of each target carbon unit in the building construction process is updated, the macro project emission data is accurately distributed and accumulated to the component level carbon emission state. Finally, by generating carbon emission distribution information mapped with the three-dimensional spatial position of the building and the construction progress, it is ensured that the calculation result not only conforms to the actual total amount, but also truly reproduces the dynamic generation process of carbon emission in space and time distribution. The carbon emission distribution information is visualized, which not only presents the calculation result intuitively, but more importantly, a closed-loop verification mechanism is constructed, which enables management personnel to compare the visualized result with the actual construction activities on site in real time, intuitively judge whether the calculation is accurate, and fundamentally eliminate the measurement deviation caused by static lag of data. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 is a step flowchart of the visual building construction process carbon emission calculation method of the present application;

[0039] Figure 2 is a step flowchart of generating a carbon genome data model for the present application;

[0040] Figure 3 is a step flowchart of generating a carbon emission data flow for the present application;

[0041] Figure 4 is a step flowchart of generating carbon emission distribution information and visualizing the carbon emission distribution information for the present application;

[0042] Figure 5 is an example schematic diagram of a carbon emission network of the present application. Detailed Implementation

[0043] The following is in conjunction with the appendix Figures 1-5 This application will be described in further detail.

[0044] In the description of this application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the present 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 present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0045] In the description of this application, it should be noted that, unless otherwise expressly 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 between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0046] This application discloses a method for calculating carbon emissions during the building construction process using visualization.

[0047] Please refer to Figure 1 The visualized carbon emission calculation method for building construction process proposed in this embodiment includes steps S10~S30:

[0048] Step S10: Assign component identifiers to each building component and bind the carbon footprint data of the building materials corresponding to the building component during the construction phase. After generating multiple target carbon units, combine the multiple target carbon units to generate a carbon genome data model.

[0049] In this step, by assigning component identifiers to each building component and binding the carbon footprint data of the building materials corresponding to the building component during the construction phase, the core purpose is to establish a unique identity and carbon footprint data association for each building component, thereby achieving accurate traceability and management of carbon emissions.

[0050] By concretizing the abstract carbon emission information to each entity building component, by generating a plurality of target carbon units, by closely combining the physical properties of the building components with the carbon footprint data, by ensuring that each building component has its independent carbon footprint record, and by combining the generated target carbon units to generate a carbon genome data model, the carbon genome data model integrates the carbon footprint information of all building components, and can realize the specific effect of constructing a structured and scalable carbon data basic framework, i.e., the carbon genome data model, which not only covers the carbon footprint data of each building component, but also provides accurate and consistent data support for subsequent dynamic calculation and visualization analysis of carbon emissions by providing a global perspective.

[0051] In step S20, the resource consumption data and construction activity data generated in the current building construction process are converted into carbon emission data streams according to the preset carbon emission factor.

[0052] In this step, the resource consumption data and construction activity data generated in the current building construction process are converted into carbon emission data streams by the preset carbon emission factor, and the fundamental purpose is to establish a quantitative conversion relationship between real-time construction activities and carbon emissions.

[0053] By converting the dispersed, different dimensional on-site construction data, such as fuel consumption, mechanical operation time, material transportation distance, etc., into a unified carbon emission quantity, a continuous and structured carbon emission data stream is formed, which realizes the quantification of dynamically changing real-time construction activities into traceable carbon emission data streams, and provides uniform and standardized data input for subsequent accurate attribution of carbon emissions.

[0054] In step S30, the carbon emission data stream is fused with the carbon genome data model, the carbon emission state of each target carbon unit in the building construction process is updated, the carbon emission distribution information mapped with the three-dimensional spatial position of the building and the construction progress is generated, and the carbon emission distribution information is visualized and rendered.

[0055] By fusing the carbon emission data stream with the carbon genome data model, the association between dynamic carbon emission information and static building component carbon properties is established, and the accurate attribution and dynamic update of carbon emissions are realized.

[0056] In this step, according to the component identifier in the carbon emission data stream generated in the previous step, the corresponding target carbon unit in the carbon genome data model is matched, so as to update the carbon emission state of each target carbon unit in the building construction process in real time, and to change the static carbon footprint data to the dynamic carbon emission state reflecting the actual construction progress.

[0057] By mapping the updated carbon emission status with the three-dimensional spatial position of the building and the construction progress, carbon emission distribution information with space-time characteristics is generated. This process makes the abstract carbon emission status obtain specific attributes of spatial coordinates and time stamps.

[0058] Finally, by visualizing the carbon emission distribution information, the data is converted into intuitive graphical images, building a cognitive bridge for human-computer interaction, enabling management personnel to directly perceive the distribution rules and change trends of carbon emissions in the building space, thereby providing real-time, intuitive, and accurate decision-making basis for construction carbon emission management, and ultimately achieving the specific effect of precise monitoring and optimization of the whole process of carbon emissions.

[0059] In a feasible implementation, referring to Figure 2 The specific implementation steps of generating the carbon genome data model include steps S11-S17:

[0060] Step S11, after giving each building component a corresponding component identifier through the blockchain network, write each component identifier and the carbon footprint data of the building component to which each component identifier belongs to the corresponding blockchain node, and generate an initial carbon unit.

[0061] A distributed identifier generation algorithm based on a cryptographic hash function, such as SHA-256, is used to generate a globally unique digital identity code, i.e., a component identifier, for each building component in the blockchain network. This algorithm generates a non-repetitive component identifier by inputting building component manufacturer information, production timestamp, material batch number, etc., through hash operation and encoding processing.

[0062] Subsequently, the component identifier and its corresponding carbon footprint data, including the carbon emission quantification results of raw material collection, production and processing, transportation logistics, etc., are encapsulated into a structured data block through a smart contract, which contains multiple verification information such as the hash value of the component identifier, the carbon footprint data fingerprint, the timestamp, and the digital signature.

[0063] When writing to the blockchain node, the data block is cross-verified by multiple verification nodes, and after ensuring the authenticity of the data, the verified data block is packaged into a new block in the form of a transaction and is broadcasted through the entire network of the blockchain network for distributed storage, generating an initial carbon unit and building a carbon data trust cornerstone with complete audit traceability capability. Each initial carbon unit not only ensures the uniqueness of the component identifier and the integrity of the carbon footprint data through cryptographic means, but also makes all carbon data operation records publicly available and tamper-proof by using the distributed ledger feature of the blockchain, providing a reliable technical guarantee for the accurate tracing and verification of subsequent carbon footprint data. For example, when a third-party needs to verify the carbon footprint data of a certain building steel component, the verifier only needs to retrieve the initial carbon unit data stored on the blockchain, verify the digital signature and timestamp, and confirm the authenticity and validity of the data source without relying on traditional manual document verification processes.

[0064] Step S12, when the carbon footprint data contained in the initial carbon unit is verified to be valid, the carbon account in the initial carbon unit is activated, and the carbon value contained in the carbon footprint data is injected into the carbon account to form a target carbon unit.

[0065] The smart contract on the blockchain network automatically extracts the digital signature, timestamp, and hash value of the carbon footprint data in the initial carbon unit. First, it verifies whether the digital signature matches the public key of the authentication agency on record to confirm the legality of the data source. Then, it verifies the timeliness of the data by comparing the current blockchain timestamp with the data generation timestamp. Finally, it recalculates the hash value of the carbon footprint data and compares it with the original hash stored on the chain to ensure data integrity.

[0066] After the above three verifications are passed, the smart contract automatically performs the carbon account activation operation, i.e., generates a new data structure bound to the component identifier on the blockchain, i.e., a carbon account, and writes the carbon value contained in the verified carbon footprint data to the initial balance field of the carbon account, thereby activating the carbon account and converting the initial carbon unit to a target carbon unit, establishing a state conversion mechanism for carbon footprint data. Only valid data that has passed strict verification can enter the dynamic accounting link, ensuring the accuracy and reliability of subsequent carbon emission calculations. At the same time, the automatic verification and activation process implemented by the blockchain smart contract effectively avoids errors and fraud risks that may be caused by human intervention.

[0067] Step S13, according to the three-dimensional spatial position of each building component in the building information, the target carbon units are coupled according to the three-dimensional spatial position of the building to form a carbon genome data model.

[0068] Firstly, the three-dimensional spatial position of each building component is extracted from the BIM (Building Information Modeling), and the component identifier corresponding to each target carbon unit is obtained. Then, the spatial indexing algorithm based on octree is used to divide the building space into three-dimensional grid, and the mapping relationship between the three-dimensional spatial position of the building component and the three-dimensional grid unit is established. Then, through the hash table structure, the component identifier of each target carbon unit is associated with the corresponding three-dimensional grid unit, and the spatial adjacency relationship between building components is recorded. Finally, according to the spatial topological relationship of building components, a carbon genome data model is constructed in the form of graph data structure, in which the node is the target carbon unit and the edge is the spatial connection relationship between building components.

[0069] This step establishes the spatial organization framework of carbon footprint data, so that each target carbon unit not only contains its own carbon footprint information, but also has a clear spatial position attribute and topological relationship with adjacent building components, providing a complete spatial data basis for subsequent real-time spatial mapping and visualization analysis of carbon emissions.

[0070] It should be noted that the three-dimensional spatial position of the building in the embodiment includes the geometric center point coordinates and the spatial boundary information.

[0071] Step S14, when the verification obtains that the carbon footprint data contained in the initial carbon unit does not have validity, a data exception marker is generated, and the data exception marker is associated with the component identifier corresponding to the carbon footprint data to form a data exception group.

[0072] When the verification step of step S12 is passed, it is verified that the carbon footprint data contained in the initial carbon unit does not have validity, and a data exception marker containing invalid time, invalid type and invalid reason code is automatically generated.

[0073] Subsequently, by constructing the key-value pair mapping table of component identifier and data exception marker, the distributed hash table technology is used to bidirectionally bind each data exception marker with the corresponding component identifier to form an exception data group with component identifier as index and data exception marker as value. The strong association of exception marker and component identifier ensures that each exception carbon unit can be accurately identified and isolated, preventing invalid data from polluting the entire carbon genome data model.

[0074] Step S15, after obtaining the new carbon footprint data of the building component corresponding to the component identifier in the data exception group, the new carbon footprint data is written into the blockchain node to which the component identifier belongs, and the initial carbon unit is updated to obtain a new initial carbon unit.

[0075] When the data anomaly group is detected, a data request containing the component identification and data anomaly label is automatically sent to the carbon management platform of the corresponding building material supplier. After receiving the data request, the carbon management platform extracts the latest new carbon footprint data from the certified carbon accounting database through its internal data pipeline, and returns the encrypted new carbon footprint data to the requesting system through the application programming interface.

[0076] The system repackages the received new carbon footprint data with the component identification to generate a new data storage transaction containing the digital signature, timestamp, and version number of the new data. Subsequently, the new data storage transaction is broadcast to the validation nodes in the blockchain network. After consensus confirmation by the validation nodes, the transaction is packaged and added to the blockchain node to which the component identification belongs, resulting in a new initial carbon unit. However, the record of the original invalid data block is retained for audit and traceability.

[0077] Step S16, if the new carbon footprint data contained in the new initial carbon unit is verified to be valid, the carbon account in the new initial carbon unit is activated.

[0078] When the new carbon footprint data contained in the new initial carbon unit is verified to be valid through the verification step of step S12, the carbon account in the new initial carbon unit is activated according to the activation step described in step S12.

[0079] Step S17, if the new carbon footprint data contained in the new initial carbon unit is verified to be invalid, a new data anomaly label is generated.

[0080] If the new carbon footprint data contained in the new initial carbon unit is still verified to be invalid through the verification step of step S12, a new data anomaly label is generated after the new carbon footprint data is obtained again for the generation of a new initial carbon unit. The generation of the target carbon unit corresponding to the component identification is ended and an exception is reported when the verification is passed or the number of verification failures reaches the expected number, for example, five times.

[0081] In a feasible implementation, referring to FIG. 1, the specific implementation steps for generating the carbon emission data stream include steps S21-S23: Figure 3

[0082] Before describing the specific implementation steps, it should be noted that the resource consumption data in this embodiment includes building material consumption and mechanical fuel consumption, and the construction activity data includes transportation distance data and mechanical usage time.

[0083] ​Step S21, multiply the building material consumption by the first preset carbon emission factor to generate a first direct carbon emission component, and weight the mechanical fuel consumption by the second preset carbon emission factor to generate a second direct carbon emission component.

[0084] By traversing the building material consumption history data of each building material category, multiply each building material consumption by the corresponding first preset carbon emission factor (i.e. the carbon emission coefficient of unit building material in its production stage) to generate the first direct carbon emission component reflecting the implicit carbon emission in the building material production stage.

[0085] Then, determine the weight coefficient corresponding to each type of machinery according to the machinery type, operating condition and fuel heat value, and then weight the fuel consumption of each type of machinery by its corresponding weight coefficient and the second preset carbon emission factor (the carbon dioxide emission equivalent value generated in the complete combustion process of unit mechanical fuel such as diesel and gasoline) to generate the second direct carbon emission component reflecting the direct carbon emission generated by the fuel consumption of machinery in the operation process, wherein the weight coefficient is dynamically adjusted by the mechanical energy efficiency parameter and the load rate to accurately reflect the actual carbon emission intensity of different machinery.

[0086] This step distinguishes between building material consumption and mechanical fuel consumption, two different types of direct carbon emission sources, and uses differentiated calculation methods to ensure accurate measurement of carbon emissions contained in building materials and to achieve fine accounting of mechanical operation carbon emissions, providing a reliable direct emission data basis for building accurate carbon emission data flow.

[0087] Step S22, multiply the transportation distance data by the third preset carbon emission factor to generate a first indirect carbon emission component, and weight the mechanical usage time by the fourth preset carbon emission factor to generate a second indirect carbon emission component.

[0088] Multiply the total transportation mileage of each transportation tool by its corresponding third preset carbon emission factor (i.e. the transportation carbon emission coefficient of the corresponding transportation tool per unit distance) to generate the first indirect carbon emission component reflecting the carbon emission generated in the logistics transportation link.

[0089] And determine the weight coefficient according to the machinery type, depreciation rate and operating power, and then weight the actual usage time of each type of machinery, the corresponding weight coefficient and the fourth preset carbon emission factor (i.e. the carbon emission coefficient of machinery per unit time) to generate the second indirect carbon emission component reflecting the carbon emission generated by equipment depreciation in the use process of machinery, wherein the weight coefficient is dynamically adjusted by the mechanical energy efficiency parameter and the usage intensity.

[0090] This step ensures that all indirect carbon emission sources during the construction process are included in the calculation by separately quantifying indirect emissions from transportation activities and indirect emissions from machinery depreciation, thereby constructing a carbon emission data stream that comprehensively reflects the carbon footprint of construction activities.

[0091] Step S23: Align and aggregate the first direct carbon emission component, the second direct carbon emission component, the first indirect carbon emission component, and the second indirect carbon emission component according to the construction time sequence to generate a carbon emission data stream.

[0092] Using the construction time series as the baseline time axis, precise timestamps are added to the original data records of each carbon emission component. Then, a time window sliding matching mechanism is used to resample and align the data of different components at a uniform time granularity. After filling in missing data using linear interpolation, the first direct carbon emission component, the second direct carbon emission component, the first indirect carbon emission component, and the second indirect carbon emission component within the same time window are vector-summed to generate the total carbon emission value at each time point. Finally, these continuous total carbon emission values ​​are combined into a complete carbon emission data stream in chronological order, thus constructing a carbon emission data stream with temporal continuity. This integrates carbon emissions that were originally scattered from different sources and types into a unified time-series data sequence, providing a complete data foundation for subsequent dynamic tracking and time-series analysis of carbon emissions. This enables the system to accurately reflect the dynamic pattern of carbon emissions changing with the construction progress.

[0093] It should be noted that the construction time series is a chain of work activities with continuous construction timestamps, generated based on the construction schedule plan. It specifies the order of execution and duration of each construction activity on the timeline.

[0094] In one feasible implementation, refer to Figure 4 As shown, the specific implementation steps for generating carbon emission distribution information and visualizing and rendering the carbon emission distribution information include steps S31 to S39:

[0095] Step S31: Based on the timestamps and component identifiers in the carbon emission data stream, after matching the corresponding target building components in the carbon genome data model, the carbon emission data stream is associated with the three-dimensional spatial location of the building corresponding to the target building component to obtain the carbon emission-spatial mapping relationship.

[0096] Using component identifiers in the carbon emission data stream as key indexes, the system searches within the carbon genome data model component library to quickly locate target building components with the same identifiers. At the same time, timestamps are used to verify the timeliness of the data to ensure the accuracy of the matching.

[0097] Subsequently, the corresponding building three-dimensional spatial position of the target building component is extracted from the carbon genome data model, and the carbon emission amount, timestamp and other attributes in the carbon emission data stream are associated with the corresponding building three-dimensional spatial position to form a key-value pair mapping relationship with the spatial position as the key and the carbon emission attribute as the value, so as to obtain the carbon emission-space mapping relationship, so that the abstract carbon emission amount can be positioned to the specific building component position, and a spatial data basis is provided for subsequent spatial distribution analysis and visual rendering of carbon emissions.

[0098] Step S32, according to the carbon emission-space mapping relationship, a carbon emission network is generated with the target building component as the node and the carbon emission flow path as the edge.

[0099] Each target building component in the carbon emission-space mapping relationship is abstracted as a node in the graph structure, and each node is given its corresponding building three-dimensional spatial position attribute, and then the carbon emission conduction relationship between the target building components is identified by analyzing the construction process logic and material transportation path in the construction schedule, and a directed edge is established between the nodes with carbon flow, the direction of the edge is from the source of carbon emission to the terminal, and the weight of the edge is dynamically determined by the carbon emission amount on the corresponding path, finally a carbon emission network is constructed with the target building component as the node and the carbon flow path as the edge, realizing the conversion of discrete carbon emission data into a network model with topological connection relationship.

[0100] Referring to FIG. 1, Figure 5 Figure 5 Node A is a base component, node B is a column component, node C is a beam component, and node D is a wall component. Among them, the building three-dimensional spatial position attribute of node A is (x1, y1, z1), the building three-dimensional spatial position attribute of node B is (x2, y2, z2), the building three-dimensional spatial position attribute of node C is (x3, y3, z3), and the building three-dimensional spatial position attribute of node D is (x4, y4, z4).

[0101] And from node A to node B, it means that carbon flows from the base component to the column component, and the corresponding carbon emission amount is 50 ; from node B to node C, it means that carbon flows from the column component to the beam component, and the corresponding carbon emission amount is 100 ; from node C to node D, it means that carbon flows from the beam component to the wall component, and the corresponding carbon emission amount is 150 .

[0102] Step S33, based on the carbon emission flow path in the carbon emission network, the carbon emission amount is accumulated in the target carbon account of the carbon genome data model according to the construction time sequence.

[0103] ​After identifying the actual flow direction and path relationship of carbon emissions by traversing the source node and target node connected by each directed edge in the carbon emission network, the carbon emission data recorded on each flow path is sequentially added to the target carbon account balance field associated with the target building component according to the corresponding component identifier, according to the time sequence of the construction time stamp, establishing a dynamic attribution relationship between carbon emissions and specific building components, so that the carbon account of each building component can accurately reflect the cumulative carbon emissions it bears in the entire construction process, providing an accurate data basis for component-level carbon footprint tracing and carbon emission responsibility definition.

[0104] Step S34, generating a carbon density distribution field according to the carbon account of each target carbon unit and the three-dimensional spatial position of each target carbon unit in the carbon genome data model.

[0105] By analyzing the spatial distribution pattern of the target carbon units corresponding to all nodes in the carbon emission network, the distance between any two nodes is calculated, and the difference in their carbon emission values is counted, thereby establishing the correlation between distance and carbon emission value similarity, which is usually manifested as the closer the distance between nodes, the more similar their carbon emissions.

[0106] Then, for the nodes that need to be estimated (such as the nodes corresponding to the target building components for which the target carbon units fail to generate), search for the known nodes within a certain range around the nodes, and assign weight coefficients to these neighboring nodes according to the established correlation, the closer the nodes to the estimation point, the higher the weight they obtain, and the greater the influence of their carbon emissions on the estimation result; at the same time, the variation degree of the carbon emissions of the node group itself is also considered, if the node value fluctuation in a certain direction is small, the nodes from that direction may be given higher reliability weight. Finally, the carbon emissions of each neighboring node are multiplied by their corresponding weight coefficients and summed up, thereby obtaining the carbon density estimation value of the node to be estimated.

[0107] For nodes that do not need to be estimated, the carbon emissions in the carbon account corresponding to the node are directly converted into carbon density, i.e. the carbon emissions are divided by the volume or area of the target building component.

[0108] By repeating the above process point by point on the entire carbon emission network, a smooth and continuous carbon density distribution field is finally generated, which quantitatively analyzes the carbon emission intensity of any three-dimensional spatial position of the building.

[0109] Step S35, based on the feature isosurfaces corresponding to the various carbon emission concentrations extracted from the carbon density distribution field.

[0110] In the carbon density distribution field, different carbon emission concentration thresholds are set, and then each basic three-dimensional grid unit composed of adjacent building three-dimensional spatial positions in the carbon density distribution field is traversed. The intersection position on the boundary of each three-dimensional grid unit equal to the target carbon emission concentration threshold is calculated, and finally these intersection positions are connected into continuous three-dimensional curved surfaces. Each three-dimensional curved surface represents a specific carbon emission concentration characteristic isosurface in space. The main effect of this process is to convert the continuous carbon density numerical field into a series of intuitive geometric curved surfaces with clear concentration boundaries, enabling management personnel to clearly identify the distribution range and spatial form characteristics of carbon emission areas of different concentration levels, providing a visual basis for spatial partition management and precise control of carbon emissions.

[0111] In the process of extracting isosurfaces, these preset carbon emission concentration thresholds are applied globally and uniformly, and are not set individually for a specific three-dimensional grid unit. When processing each three-dimensional grid unit, the same set of preset carbon emission concentration thresholds is used to determine whether the isosurface corresponding to a carbon emission concentration threshold passes through the three-dimensional grid unit and the specific position of the passing through by judging whether the carbon density value at each vertex of the three-dimensional grid unit crosses a carbon emission concentration threshold. Therefore, the carbon emission concentration threshold itself is determined in advance according to the management target, independent of the three-dimensional grid unit, and the processing of each three-dimensional grid unit is to compare its vertex data with the carbon emission concentration threshold, ensuring that the different concentration isosurfaces generated can objectively and consistently reflect the carbon emission distribution pattern in the entire building space.

[0112] Step S36, the carbon emission concentration corresponding to each characteristic isosurface is bound to the building three-dimensional spatial position covered by the carbon emission concentration, and the construction progress timestamp is associated with each building three-dimensional spatial position to generate carbon emission distribution information.

[0113] Each characteristic isosurface is assigned a specific carbon emission concentration value, such as a high concentration isosurface corresponding to 500 , a medium concentration isosurface corresponding to 300 , and a low concentration isosurface corresponding to 100 The carbon emission concentration value is associated with the building three-dimensional spatial position of the characteristic isosurface as an attribute label and stored as a coordinate-concentration association.

[0114] Meanwhile, based on the construction time sequence, a corresponding time label is added to each building three-dimensional spatial position, such as assigning a construction progress timestamp of "2025-10-01 10:00:00" to the building three-dimensional spatial position by querying the activity time of the building three-dimensional spatial position in the construction progress plan, to form a coordinate-time mapping relationship.

[0115] Finally, the coordinate-concentration correlation and the coordinate-time mapping relationship are fused to generate unified carbon emission distribution information, which contains building three-dimensional spatial positions, corresponding carbon emission concentration values, and corresponding construction progress timestamps, and a dynamic carbon emission dataset integrating three-dimensional attributes of space, concentration, and time is constructed, enabling the system to accurately trace the spatial distribution state of carbon emission concentration at any time point.

[0116] Step S37, according to the building three-dimensional spatial position, carbon emission concentration, and construction progress timestamp contained in the carbon emission distribution information, an initial carbon emission rendering effect is generated.

[0117] The carbon emission concentration corresponding to each building three-dimensional spatial position is converted into a specific color value (such as low concentration mapped to green, medium concentration mapped to blue, and high concentration mapped to red), and the current rendering time node is determined according to the construction progress timestamp, the carbon emission concentration corresponding to the time node is filtered out and rendered to generate an initial visualization effect representing carbon emission concentration by color depth and position relationship by three-dimensional spatial distribution, realizing the conversion of abstract carbon emission data into intuitive three-dimensional color graphics.

[0118] Step S38, based on the initial carbon emission rendering effect, the flow diffusion behavior of carbon emission is simulated to obtain a target carbon emission rendering effect.

[0119] From the initial carbon emission rendering effect, the building three-dimensional spatial position and carbon emission concentration corresponding to each color value are extracted, a corresponding particle unit is created for each color value, and the particle unit is assigned with initial position attribute (building three-dimensional spatial position), concentration attribute (carbon emission concentration), and motion attribute (such as initial speed of zero, mass weighted by carbon emission concentration).

[0120] Then, based on the convection and diffusion model, the motion trajectory of each particle unit is calculated, where the motion direction of the particle unit is determined by the virtual wind field vector and the concentration gradient direction, the motion speed is dynamically adjusted by the concentration difference and the diffusion coefficient, and the interaction between the particle unit and the building component is handled through collision detection. In the simulation process, the concentration of the particle unit decays with the increase of the diffusion distance, and the color of the particle unit is updated in real time through color mapping to reflect the change of carbon emission concentration.

[0121] Finally, according to the inherent time interval of the construction time sequence, the state (position, concentration, color) of the particle unit is rendered into a continuous animation sequence to obtain the target carbon emission rendering effect.

[0122] In step S39, the target carbon emission rendering effect is fused and rendered with the building information to obtain a carbon emission visual picture.

[0123] The three-dimensional geometric data of the building information is loaded as a basic scene layer, and then the target carbon emission rendering effect is taken as an overlay layer. The three-dimensional space reference of the two layers is completely matched through spatial coordinate alignment. The target carbon emission rendering effect is superimposed on the surface of the basic scene layer in a semi-transparent manner. The spatial occlusion relationship between the target carbon emission rendering effect and the building is processed to ensure the spatial correctness of the visual effect.

[0124] Finally, the light and shade of the building surface and the carbon emission effect are uniformly processed to generate a carbon emission visual picture. The mixed reality scene with high unification of the building entity and the carbon emission data is realized. The management personnel can intuitively observe the flow path and spatial distribution of the carbon emission in the real building model, and the precise spatial correlation between the carbon emission data and the building components is realized.

[0125] The above are preferred embodiments of the present application, and are not intended to limit the protection scope of the present application. Therefore, any equivalent changes made on the basis of the structure, shape, principle of the present application shall be covered within the protection scope of the present application.

Claims

1. A method for calculating carbon emissions during a visualized building construction process, characterized in that, include: Each building component is assigned a component identifier and the carbon footprint data of the building materials corresponding to the building component during the construction phase is bound. After generating multiple target carbon units, the multiple target carbon units are combined to generate a carbon genome data model. Based on the preset carbon emission factor, the resource consumption data and construction activity data generated during the current building construction process are converted into a carbon emission data stream; The carbon emission data stream is fused with the carbon genome data model to update the carbon emission status of each target carbon unit during the building construction process, generate carbon emission distribution information that maps to the three-dimensional spatial location of the building and the construction progress, and visualize the carbon emission distribution information. The step of assigning component identifiers to each building component and binding the carbon footprint data of the building materials corresponding to the building component during the construction phase, generating multiple target carbon units, and then combining the multiple target carbon units to generate a carbon genome data model includes: After assigning corresponding component identifiers to each of the building components through the blockchain network, the carbon footprint data of each component identifier and the building component to which each component identifier belongs are written into the corresponding blockchain node to generate an initial carbon unit. When the carbon footprint data contained in the initial carbon unit is verified to be valid, the carbon account in the initial carbon unit is activated, and the carbon value contained in the carbon footprint data is injected into the carbon account to form the target carbon unit. Based on the three-dimensional spatial location of each building component in the building information, each target carbon unit is linked according to the three-dimensional spatial location of the building to form the carbon genome data model; The step of fusing the carbon emission data stream with the carbon genome data model includes: Based on the timestamps and component identifiers in the carbon emission data stream, after matching the corresponding target building components in the carbon genome data model, the carbon emission data stream is associated with the three-dimensional spatial location of the building corresponding to the target building component to obtain the carbon emission-spatial mapping relationship. Based on the carbon emission-spatial mapping relationship, a carbon emission network is generated with target building components as nodes and carbon emission flow paths as edges; Based on the carbon emission flow paths in the carbon emission network, carbon emissions are accumulated into the target carbon account of the carbon genome data model according to the construction time series.

2. The method for calculating carbon emissions from a visualized building construction process according to claim 1, characterized in that, The step of generating the initial carbon unit is followed by: When the verification shows that the carbon footprint data contained in the initial carbon unit is not valid, a data anomaly marker is generated, and the data anomaly marker is associated with the component identifier corresponding to the carbon footprint data to form a data anomaly group. After obtaining the new carbon footprint data of the building component corresponding to the component identifier in the data anomaly group, the new carbon footprint data is written into the blockchain node to which the component identifier belongs, and the initial carbon unit is updated to obtain a new initial carbon unit. If the new carbon footprint data contained in the new initial carbon unit is verified to be valid, then the carbon account in the new initial carbon unit is activated. If the new carbon footprint data contained in the new initial carbon unit is found to be invalid after verification, a new data anomaly marker is generated.

3. The method for calculating carbon emissions during the visualized building construction process according to claim 1, characterized in that, in, The resource consumption data includes building material consumption and machinery fuel consumption; the construction activity data includes transportation distance data and machinery usage time; the step of converting the resource consumption data and construction activity data generated during the current building construction process into a carbon emission data stream according to a preset carbon emission factor includes: The first direct carbon emission component is generated by multiplying the building material consumption with the first preset carbon emission factor, and the second direct carbon emission component is generated by weighting the mechanical fuel consumption with the second preset carbon emission factor. The transportation distance data is multiplied by a third preset carbon emission factor to generate a first indirect carbon emission component, and the machine usage time is weighted by a fourth preset carbon emission factor to generate a second indirect carbon emission component. The carbon emission data stream is generated by aligning and aggregating the first direct carbon emission component, the second direct carbon emission component, the first indirect carbon emission component, and the second indirect carbon emission component according to the construction time sequence.

4. The method for calculating carbon emissions from a visualized building construction process according to claim 1, characterized in that, The steps for generating carbon emission distribution information that maps to the building's three-dimensional spatial location and construction progress include: Based on the carbon account of each target carbon unit and the three-dimensional spatial location of each target carbon unit in the carbon genome data model, a carbon density distribution field is generated; Based on the characteristic isosurfaces corresponding to various carbon emission concentrations extracted from the carbon density distribution field; The carbon emission concentration corresponding to each of the aforementioned feature isosurfaces is bound to the three-dimensional spatial location of the building covered by the carbon emission concentration, and the construction progress timestamp is associated with each of the aforementioned three-dimensional spatial locations of the building to generate the carbon emission distribution information.

5. The method for calculating carbon emissions from a visualized building construction process according to claim 4, characterized in that, The step of visualizing and rendering the carbon emission distribution information includes: Based on the building's three-dimensional spatial location, carbon emission concentration, and construction progress timestamp contained in the carbon emission distribution information, an initial carbon emission rendering effect is generated; Based on the initial carbon emission rendering effect, the flow and diffusion behavior of carbon emissions is simulated to obtain the target carbon emission rendering effect; The target carbon emission rendering effect is fused with building information to obtain a visual image of carbon emissions.

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