3D printing building carbon emission visual simulation system based on virtual reality

Abstract

The invention relates to the technical field of virtual reality, in particular to a 3D printing building carbon emission visual simulation system based on virtual reality, and the system comprises a building component carbon value analysis module which retrieves the material type and the volume of each building component based on BIM data, and calls a unit carbon emission factor and a design life according to the material type and a material extrusion process. According to the method, building component material types and volumes are retrieved based on BIM data in a three-dimensional virtual environment, carbon emission factors and design lives are retrieved in combination with a material extrusion process, and the total carbon emission amount and life values of the components are calculated, so that whole-process correlation analysis of carbon emission data from material attributes to life cycles is realized; the adjacent member connection pair is established by traversing the member adjacency relation and identifying the physical contact boundary, and the interface carbon coupling degree is comprehensively generated by combining the total carbon emission, the service life value and the disassembly difficulty, so that the dynamic expression of the carbon emission between the members and the structural relation is enhanced.

Description

Technical Field

[0001] This invention relates to the field of virtual reality technology, and in particular to a virtual reality-based visualization and simulation system for carbon emissions from 3D-printed buildings. Background Technology

[0002] Virtual reality technology refers to a technological system that uses computer graphics generation, sensor interaction, real-time rendering and other technologies to construct highly realistic three-dimensional digital environments, providing users with an immersive, multi-sensory interactive experience.

[0003] Existing technologies for visualizing building carbon emissions suffer from limitations, including a single data dimension, a coarse representation of carbon emission distribution, and a lack of interactive depth. Because carbon emission data is often presented as static indicators or simple color overlays, it fails to reflect the coupling relationships between carbon emissions and structural complexity between components. This makes it difficult to support dynamic perception and control of high-risk carbon emission areas or construction phases, hindering the timely identification and optimization of high-carbon emission nodes during building design and construction. Therefore, improvements are needed. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and propose a virtual reality-based visualization simulation system for carbon emissions from 3D printed buildings.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a virtual reality-based 3D printing building carbon emission visualization simulation system includes:

[0006] The building component carbon value analysis module, based on BIM data, retrieves the material type and volume of each building component, and then, according to the material type and material extrusion process, retrieves the unit carbon emission factor and design life, calculates the total carbon emissions and life value of each component, and establishes a carbon value set for the entire life cycle of the component.

[0007] The component connection carbon coupling module, based on the carbon value set of the entire life cycle of the component, traverses the adjacency relationship of the component, identifies all contacting components and establishes adjacent component connection pairs, calls the carbon emissions and lifespan of each component in the adjacent component connection pairs, and calculates and generates the interface carbon coupling degree sequence in combination with the disassembly difficulty of the support structure at the connection interface.

[0008] The interface geometry generation module is connected. Based on the interface carbon coupling degree sequence, component pairs are screened and contact surfaces are identified to generate an interface adhesion geometry mesh. Then, the vertex displacement and mesh damage rules of the interface adhesion geometry mesh are defined when the user drags the component in virtual reality to establish an interactive tearable dynamic form.

[0009] The scene carbon perception atmosphere rendering module, based on the carbon value set of the entire life cycle of the components, summarizes the total carbon emissions of all visible components within the virtual reality view frustum, calculates the viewpoint carbon perception evaluation value, maps the viewpoint carbon perception evaluation value to the color saturation, brightness and vignette intensity values ​​of the scene, and generates a global illumination and post-processing parameter set.

[0010] Preferably, the steps for obtaining the carbon value set of the component throughout its entire life cycle are as follows:

[0011] Based on BIM data, the material type, component volume and component printing layer parameters corresponding to each building component are retrieved one by one, and the material type is used as the key retrieval index to generate a set of material type, component volume and component printing layer parameters;

[0012] Based on the material type, component volume, and component printing layer parameter set, the corresponding material extrusion process parameters are called for different material types. Based on the process parameters, the unit carbon emission factor and design life value corresponding to the material type and material extrusion process are matched one by one to generate a set of unit carbon emission factor and design life value.

[0013] Based on the aforementioned set of unit carbon emission factors and design life values, the component volume of each building component is called one by one to calculate the total carbon emission value of each component. At the same time, the design life value is associated with the component, and the total carbon emission and life value pairs corresponding to each component are summarized to obtain the carbon value set of the entire life cycle of the component.

[0014] Preferably, the step of obtaining the adjacent component connection pair is as follows:

[0015] Based on the carbon value set of the entire life cycle of the components, the adjacency relationship data of the components is retrieved one by one, it is determined whether there is a physical contact boundary in the adjacency relationship data of the components, all component combinations with physical contact boundaries are screened and the total carbon emissions and design life of the corresponding components are recorded, and an adjacency component connection pair dataset is generated.

[0016] Preferably, the step of obtaining the interface carbon coupling degree sequence is as follows:

[0017] Based on the adjacent component connection pair dataset, calculate the interface carbon locking index;

[0018] Based on the interface carbon coupling degree, all adjacent component connection pairs are traversed, and the interface carbon coupling degrees are sorted from smallest to largest to form an interface carbon coupling degree sequence.

[0019] Preferably, the step of obtaining the interface adhesion geometry mesh is as follows:

[0020] Based on the interface carbon coupling degree sequence, using interface carbon coupling degree as the screening criterion, a threshold for interface carbon coupling degree is set and the interface carbon coupling degree values ​​are compared item by item. Adjacent component connection pairs with interface carbon coupling degree exceeding the threshold are screened to generate high carbon locked adjacent component connection pairs.

[0021] Based on the high-carbon locked adjacent component connection pairs, retrieve and extract the component geometric data of each high-carbon locked adjacent component connection pair, extract the spatial contact surface data of each pair of adjacent components from the component geometric data, and generate a contact surface data set.

[0022] Based on the aforementioned contact surface data set, the interfacial carbon coupling degree of each contact surface and the interlayer adhesion strength of the component are called one by one. The particle distribution density is set according to the ratio of interfacial carbon coupling degree to interlayer adhesion strength, and the particle influence radius is set according to the interlayer adhesion strength to generate an interfacial adhesion geometric mesh.

[0023] Preferably, the step of obtaining the interactive tearing dynamic form is as follows:

[0024] Based on the interface-adhesive geometric mesh, the position coordinate data of each grid vertex of the interface-adhesive geometric mesh is extracted one by one. Combined with the displacement vector generated by the user's real-time dragging operation in virtual reality, the real-time displacement value of each grid vertex position coordinate data is calculated one by one to generate a set of real-time displacement data of grid vertices.

[0025] Based on the real-time displacement data set of the grid vertices, the deformation of each grid unit in the interface adhesion geometry grid is calculated one by one. The difference between the original geometric length of the grid unit and the geometric length after real-time displacement is compared and judged. A grid damage threshold is set, and it is determined whether the grid damage threshold is exceeded, and a grid damage judgment result set is generated.

[0026] Based on the set of mesh damage determination results, the damage determination result of each mesh unit is judged one by one. If the difference in geometric length of the mesh unit exceeds the damage threshold, the damage status of the mesh unit is updated in real time in the virtual reality scene, the tearing effect is displayed, and an interactive tearing dynamic form is formed.

[0027] Preferably, the step of obtaining the viewpoint carbon perception evaluation value is as follows:

[0028] Based on the carbon value set of the entire life cycle of the components, all visible components within the virtual reality view frustum are traversed, and the total carbon emissions of each visible component, the distance from the viewpoint to the centroid of the component, and the proportion of the component's projected area on the retina are obtained one by one. All data are then combined to generate a carbon perception data set of visible components within the view frustum.

[0029] Based on the carbon perception data set of visible components within the view cone, the viewpoint carbon perception evaluation value is calculated.

[0030] Preferably, the step of obtaining the global illumination and post-processing parameter set is as follows:

[0031] Based on the viewpoint carbon perception evaluation value, the viewpoint carbon perception evaluation value is mapped to a preset range of color saturation, brightness, and vignette intensity to generate a global illumination and post-processing parameter set as the output result of virtual reality scene rendering.

[0032] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0033] In this invention, by retrieving the material type and volume of building components from BIM data in a three-dimensional virtual environment, and combining the carbon emission factor and design life with the material extrusion process, the total carbon emissions and lifespan of the components are calculated. This achieves a full-process correlation analysis of carbon emission data from material properties to lifespan. By traversing the adjacency relationships of components and identifying physical contact boundaries to establish adjacent component connection pairs, the interface carbon coupling degree is generated by combining the total carbon emissions, lifespan value, and dismantling difficulty. This enhances the dynamic expression of the relationship between carbon emissions and structure between components. By selecting component pairs with high interface carbon coupling degree and generating interface adhesion geometric mesh, and then combining user interaction to define vertex displacement and damage rules, interactive tearing dynamic forms are presented, greatly improving the interactive realism of the carbon emission visualization process. By combining the total carbon emissions of components within the view frustum with the combined calculation of spatial perception parameters, the viewpoint carbon perception evaluation value is calculated and mapped to scene lighting parameters, promoting the transformation of carbon emission data into visual perception and bringing intuitive perception capabilities for the spatial distribution and risk identification of carbon emissions. Attached Figure Description

[0034] Figure 1 This is a system flowchart of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention.

[0036] Please see Figure 1 The present invention provides a technical solution: a virtual reality-based 3D printing building carbon emission visualization simulation system comprising:

[0037] The building component carbon value analysis module, based on BIM data, retrieves the material type and volume of each building component, and then, according to the material type and material extrusion process, retrieves the unit carbon emission factor and design life, calculates the total carbon emissions and life value of each component, and establishes a carbon value set for the entire life cycle of the component.

[0038] The component connection carbon coupling module, based on the carbon value set of the entire life cycle of the component, traverses the adjacency relationship of the component, identifies all contacting components and establishes adjacent component connection pairs, calls the carbon emissions and lifespan of each component in the adjacent component connection pair, and calculates and generates the interface carbon coupling degree sequence by combining the disassembly difficulty of the supporting structure at the connection interface.

[0039] The interface geometry generation module is connected. Based on the interface carbon coupling degree sequence, component pairs are screened and contact surfaces are identified to generate interface adhesion geometry mesh. Then, the vertex displacement and mesh breaking rules of the interface adhesion geometry mesh are defined when the user drags the component in virtual reality to establish an interactive tearable dynamic form.

[0040] The scene carbon perception atmosphere rendering module, based on the carbon value set of the entire life cycle of components, summarizes the total carbon emissions of all visible components within the virtual reality view frustum, calculates the viewpoint carbon perception evaluation value, maps the viewpoint carbon perception evaluation value to the color saturation, brightness and vignette intensity values ​​of the scene, and generates a global illumination and post-processing parameter set.

[0041] The steps for obtaining the carbon value set of a component throughout its entire life cycle are as follows:

[0042] Based on BIM data, the material type, component volume and component printing layer parameters corresponding to each building component are retrieved one by one, and the material type is used as the key retrieval index to generate a set of material type, component volume and component printing layer parameters;

[0043] Based on the material type, component volume, and component printing layer parameter set, the corresponding material extrusion process parameters are called for different material types. Based on the process parameters, the unit carbon emission factor and design life value corresponding to the material type and material extrusion process are matched one by one to generate a set of unit carbon emission factor and design life value.

[0044] Based on the set of unit carbon emission factors and design life values, the component volume of each building component is called one by one to calculate the total carbon emission value of each component. At the same time, the design life value is associated with the component and the total carbon emission and life value of each component are summarized to obtain the carbon value set of the entire life cycle of the component.

[0045] Specifically, based on BIM data, the system first accesses the BIM model file through an interface. This model file adopts an industrial basic class standard format, such as IFC4. The system's internal parser traverses the spatial structure hierarchy of the BIM model, starting from the top-level IfcProject and parsing downwards to IfcBuilding, IfcBuildingStorey, and finally locating all independent building component entities, such as IfcWall, IfcColumn, IfcBeam, and IfcSlab. For each identified component entity, the system calls its attribute set and extracts the material name defined in the material level, i.e., the "material type," by querying the component's IfcRelAssociatesMaterial relationship, such as "recycled polymer modified concrete" or "basalt fiber reinforced PETG." Simultaneously, the system queries the component's IfcRelDefinesByProperties relationship to locate the associated quantity attribute set and extract the component's net volume value, i.e., the "component volume," in cubic meters. Then, the system proceeds... The system first searches for a predefined custom attribute set named "3DPrintingParameters" in the component's attribute set and extracts the specific process parameters of the component during the 3D printing production process, namely "component printing layer parameters". These parameters include layer height (e.g., 5 mm), nozzle diameter (e.g., 30 mm), printing speed (e.g., 150 mm / s), nozzle movement path fill rate (e.g., 25%), and material extrusion temperature (e.g., 220 degrees Celsius). The system temporarily stores the extracted material type, component volume, and component printing layer parameters containing multiple specific values ​​for each component as a structured data unit. After traversing all building components, the system reorganizes and classifies the data units of all components using "material type" as the primary key, constructing a hash table or dictionary structure indexed by material type. Each key (material type) corresponds to a list containing the volume and printing layer parameters of all components using the same material. Finally, a structured set of material types, component volumes, and component printing layer parameters organized around the material is generated.

[0046] Based on the material type, component volume, and component printing layer parameter set, the system first accesses an internally pre-built "Material Process Carbon Emission and Lifetime Benchmark Database." This database is established by integrating data from publicly available life cycle assessment databases (such as Ecoinvent and GaBi), Environmental Product Declarations (EPDs) provided by 3D printing material suppliers, and experimental data on energy consumption and material durability of specific printing processes from relevant academic research. Each record in the database includes the material name, benchmark extrusion process parameters (such as reference temperature and reference speed), benchmark carbon emission factor per unit mass of material (unit: kgCO2e / kg, covering A1-A3, i.e., raw material supply, transportation, and manufacturing stages), benchmark design life (unit: years), and a set of process adjustment coefficients. Subsequently, the system iterates through each material type in the set generated in the previous step, using that material type to query the database and retrieve its corresponding benchmark data and process adjustment coefficients. Next, it dynamically corrects the parameters based on the specific "component printing layer parameters" extracted from the BIM model. For example, the calculation of the unit carbon emission factor takes into account the effects of printing speed and temperature. The correction process can be expressed as: E adj =E base ×(1+k v ·(V actual -V base ))×(1+k T ·(T actual -T base )), where E adj It is the corrected unit carbon emission factor, E base The baseline carbon emission factor, V, was obtained from a database. actual and T actual It refers to the actual printing speed and printing temperature of the component, V base and T base These are the baseline speeds and temperatures defined in the database (e.g., 150 mm / s and 220 degrees Celsius), while k v and k T It is a sensitivity coefficient for speed and temperature (e.g., k is set based on experimental data). v =0.001, k T =0.002), these coefficients reflect the additional energy consumption and carbon emissions caused by deviating from the baseline process. Similarly, the design life is also adjusted according to the printing process parameters, mainly based on the negative correlation model between interlayer adhesion strength and printing speed. The faster the printing speed, the worse the interlayer adhesion may be, thus shortening the effective life. The system performs the above matching and dynamic correction calculation once for each material type and all its corresponding components, and finally generates an accurate unit carbon emission factor and design life value for each component. These values ​​are then summarized to generate a set of unit carbon emission factor and design life values.

[0047] Based on the set of unit carbon emission factor and design life values, the system first needs to convert the unit of unit carbon emission factor from unit mass (kgCO2e / kg) to unit volume (kgCO2e / m³). 3 This step involves querying the density value (unit: kg / m³) of each material in the "Materials, Processes, Carbon Emissions and Lifetime Benchmark Database" to calculate the volume of the components extracted from the BIM. 3 This can be accomplished by, for example, the density of a certain recycled polymer concrete is 2100 kg / m³. 3 Subsequently, the system iterates through the data records of each building component, which already contain the component volume, the corrected unit carbon emission factor, and the adjusted design life. For each component, the system calculates the total carbon emissions using the formula: Total carbon emissions of a component = Corrected unit carbon emission factor × Material density × Component volume. Taking a wall component with ID "W-101" as an example, if its volume is 8.2m³... 3 The material is "recycled polymer concrete," and its density is found to be 2100 kg / m³. 3 Furthermore, after the correction calculated in the previous step, the unit carbon emission factor is 0.12 kgCO2e / kg. Therefore, the total carbon emission of this component is 0.12 kgCO2e / kg × 2100 kg / m³. 3 ×8.2m 3 The result is 2066.4 kg CO2e. After calculating the total carbon emission value, the system pairs this value with the corresponding adjusted design life value of the component (e.g., 65 years after adjustment based on the printing process), forming a structured data pair containing the component's unique identifier (e.g., "W-101"), the total carbon emission (2066.4 kg CO2e), and the design life (65 years). The system repeats this calculation and association process for all building components in the BIM model, summarizing the total carbon emission and life value pairs generated for all components, and finally forming a complete and detailed set of carbon values ​​for the entire life cycle of the components, recording the carbon emission of each independent component in the manufacturing stage and its expected service life.

[0048] The steps for obtaining adjacent component connection pairs are as follows:

[0049] Based on the carbon value set of the entire life cycle of the components, the adjacency relationship data of the components is retrieved one by one to determine whether there is a physical contact boundary in the adjacency relationship data. All component combinations with physical contact boundaries are selected and the total carbon emissions and design life of the corresponding components are recorded to generate a dataset of adjacent component connection pairs.

[0050] Specifically, based on the carbon data set of a component's entire lifecycle, the system first initiates a geometric adjacency analysis program. The goal of this program is to traverse the geometric representations of all components in the BIM model to identify physically contacting component pairs. Specifically, the program reads the unique identifier and three-dimensional geometric data of each component from the carbon data set. This geometric data is typically described using boundary representation (B-rep). For a building model containing N components, the program constructs a list containing N×(N-1) / 2 possible component pairs for iterative checking. In each iteration, the program selects a pair of components, for example... The program first checks component A (a beam with ID "B-201") and component B (a column with ID "C-201"). Then, a coarse bounding box intersection test is performed. If the bounding boxes of components A and B do not intersect, they are considered to have no physical contact, and the program proceeds to check the next pair of components. If the bounding boxes intersect, the program enters a precise geometric surface intersection test stage. This stage calculates the intersection of all polygons in the two component B-rep models. To handle minor gaps or floating-point precision errors in modeling, the program sets a contact tolerance threshold. This threshold is set according to... According to the BIM modeling accuracy standard adopted by the project, for example, for a LOD350 (Level of Development 350) model, the tolerance threshold can be set to 1 mm. If the closest distance between the geometric surfaces of two components is less than this 1 mm threshold, or if their geometry overlaps in volume, the system determines that there is a physical contact boundary between them. Once physical contact is determined, the system records the identifiers of the pair of components ("B-201" and "C-201") and immediately uses these two identifiers as indexes to query and extract the carbon values ​​of the components throughout their entire life cycle. The system assigns corresponding total carbon emissions and design life values ​​to each component. For example, the query shows that B-201 has a total carbon emissions of 850 kg CO2e and a design life of 75 years; C-201 has a total carbon emissions of 1500 kg CO2e and a design life of 75 years. Finally, the system combines the identifier of component A, the identifier of component B, the total carbon emissions and design life of component A, and the total carbon emissions and design life of component B into a new record. After traversing all possible component pairs and completing the above judgment and recording process, all component combination records identified as having physical contact boundaries together constitute the adjacent component connection pair dataset.

[0051] The steps for obtaining the interface carbon coupling degree sequence are as follows:

[0052] Based on the dataset of adjacent component connection pairs, the interface carbon locking index is calculated using the following formula:

[0053]

[0054] Among them, C IC,k C is the interfacial carbon locking index for the k-th adjacent component connection pair. op,k The carbon emission value of the connection and disassembly operation of the kth adjacent component is expressed in kgCO2e and C. waste,k The carbon emission value of the dismantling waste from the connection of the kth adjacent component is expressed in kgCO2e and E. A,k The total life-cycle carbon emissions of component A connected to the kth adjacent component are expressed in kgCO2e and E. B,k The carbon emission value of component B, which is connected to the kth adjacent component, is the total life cycle carbon emission value, in kgCO2e. T represents the maximum value of the material recycling potential score for the connection between the k-th adjacent components, and is dimensionless. proj The total design life of the project is expressed in years (T). conn,k Let be the design life of the connection interface of the k-th adjacent component connection, in years, β be the cycle value sensitivity coefficient, and α be the life impact factor.

[0055] Based on the interface carbon coupling degree, all adjacent component connection pairs are traversed and sorted from smallest to largest to form an interface carbon coupling degree sequence.

[0056] Specifically, the formula: The advantage of this formula lies in its normalized comparison of direct carbon emissions (operational carbon, waste carbon) generated during dismantling with the total life-cycle carbon emissions of the connected components themselves. Furthermore, it introduces two dimensions: material recycling potential and design life, adjusted through a recycling value sensitivity coefficient β and a lifespan impact factor α. This allows the assessment results to dynamically respond to different project emphases on circular economy and durability. For example… The award recognizes connection designs that utilize high-recycling-value materials, while The project penalizes weak design flaws where the connection lifespan is far shorter than the overall project lifespan. This multi-factor coupled calculation method ensures that the evaluation results reflect not only the physical difficulty of disassembly.

[0057] C op,kThe carbon emission value for the dismantling operation of the k-th adjacent component connection is obtained based on the analysis of the dismantling process. First, the system accesses a pre-set "dismantling process database." This database defines in detail the tools, personnel, and working hours required for dismantling based on the connection type (e.g., 3D-printed integrated connection, bolted connection, welded connection, adhesive connection) and component material (e.g., steel, concrete, polymer). For example, for an integrated connection between a 3D-printed recycled concrete wall and a ground beam, the database might define the dismantling method as "small hydraulic breaker breaking," requiring a 3kW hydraulic breaker, with an estimated operation time of 2 hours and 1 person-hour of labor. Based on these parameters, combined with the carbon emission factor of the local power grid and the carbon emission equivalent of the construction industry workforce, the system calculates the operational carbon emission. Specific example: C op,k = (Equipment power × operating time × grid factor) + (Required manpower × labor equivalent) = (3kW × 2h × 0.5703kgCO2e / kWh) + (1 person-hour × 0.5kgCO2e / person-hour) = 3.4218 + 0.5 = 3.9218kgCO2e.

[0058] C waste,k This value represents the carbon emissions from the dismantling of the k-th adjacent component connection. It is derived from the amount of waste generated at the connection interface during dismantling and the carbon emissions generated by its disposal method. The system first estimates the waste generation rate from the "Dismantling Process Database" based on the connection type. For destructive dismantling, such as concrete breaking, the waste generation rate is higher; for mechanical connections, the rate is lower. Taking the previously mentioned integrated concrete connection as an example, approximately 0.02 cubic meters of concrete fragments will be generated at the interface during dismantling. The system then queries the "Construction Waste Treatment Carbon Factor Database," which contains carbon emission factors corresponding to different waste material treatment paths (such as landfill, recycling, and incineration). For concrete fragments, if the default treatment path is recycled aggregate, the carbon factor is -0.5 kgCO2e / kg (a negative value indicates carbon reduction benefits from recycling); if it is landfill, it is +0.2 kgCO2e / kg. The system selects the corresponding factor based on the project's waste management strategy. For example, if the concrete density is 2400 kg / m³... 3 If the waste management strategy is recycling, then C waste,k =Waste volume × density × treatment factor = 0.02m³ 3 ×2400kg / m 3 ×(-0.5kgCO2e / kg)=-24kgCO2e, this negative value indicates that the dismantling and disposal process has the potential for carbon emission reduction.

[0059] E A,k With E B,kThese are the life-cycle carbon emissions of components A and B in the k-th adjacent component connection pair, respectively. When processing the k-th connection pair, these two pre-calculated values ​​are directly read from the "Adjacent Component Connection Pair Dataset." This dataset was generated in the previous steps by parsing BIM data and material and process data, and it contains a unique identifier for each component and its corresponding life-cycle carbon emission value. For example, for a connection pair consisting of wall A and floor slab B, the system directly extracts the life-cycle carbon emission value E of wall A from the dataset. A,k =2066.4kgCO2e, the life-cycle carbon emission value E of floor slab B B,k = 3500.0 kg CO2e.

[0060] This represents the maximum score of the material recycling potential score for the connection between the kth adjacent components. This score is a dimensionless value between 0 and 1, used to quantify the recycling value of materials. Its calculation relies on a multi-criteria evaluation model. First, the system establishes a "Material Recycling Attribute Table," which scores each building material (such as recycled PETG and basalt fiber concrete) based on three core indicators (0-10 points): recyclability (technology maturity, recycling efficiency), reusability (ability to maintain original form and performance after disassembly), and component purity (whether it is a composite material, difficulty of separation). For example, the score for recycled PETG is {recyclability: 9, reusability: 6, component purity: 8}. The system assigns weights to these three indicators according to the project's emphasis on the circular economy. The calculation formula is: S i =w rec ·P rec,i +w reu ·P reu,i +w pur ·P pur,i S i Here, w is the score for material i, w is the weight, and P is the rating. The weight is set based on the following criteria: for projects that emphasize material recycling and regeneration, w can be set accordingly. rec =0.6,w reu =0.2,w pur =0.2, these weights are determined by the project design team at the beginning of the project based on the cyclical design objectives, and the score is finally normalized by dividing by 10 to obtain M between 0 and 1. i For a connection pair, the system calculates the score M of the materials of the two components. A and M B And take its maximum value, that is Specific example: Component A is made of recycled PETG, M A= (0.6×9+0.2×6+0.2×8) / 10=0.82; Component B is made of ordinary concrete, and its score is {2, 1, 3}, M B =(0.6×2+0.2×1+0.2×3) / 10=0.2, then

[0061] T proj The total design life of the project is a macro-level parameter determined during the project planning phase. It is typically recorded in the core design documents or top-level information of the BIM model (e.g., the attribute set of IfcProject). The system reads this value directly from the BIM model via API. Its setting is based on the building's functional positioning, structural type, and relevant regulatory requirements. In this example, a 3D-printed demonstration office building is defined, with a total design life T... proj = 50 years.

[0062] T conn,k The design life of the connection interface for the k-th adjacent component is obtained by querying a "Connection Technology Durability Database." This database is built based on materials science experimental data, product technical manuals, and industry standards (such as ASTM standards). It matches the connection type (e.g., epoxy adhesive, high-strength bolt connection, polymer mortar masonry) with the expected service environment (e.g., indoor, outdoor, corrosive environment) to give a design life value. For example, for a connection using a specific type of structural adhesive, if the manufacturer's technical data shows that its design life in an indoor dry environment is 30 years, the system will use this value as T. conn,k The value of is given in the following example: For the 3D printed integrated connection in this example, since its material is the same as the component body, its durability is mainly affected by the development of microcracks at the interface. Based on accelerated aging test data, its design life is assessed as 40 years. Therefore, T conn,k = 40 years.

[0063] β is the cycle value sensitivity coefficient, a dimensionless adjustment factor used to adjust the material's cycle potential score. The influence of this coefficient in the final index calculation reflects the project's emphasis on circular economy principles. The setting process is as follows: The project design team first rates the project's commitment to circular economy principles based on its sustainable development goals. The rating is divided into three levels: Level 1 (Standard Practice), Level 2 (Enhanced Practice), and Level 3 (Leading Practice). Then, based on the rating, the corresponding β value is found in a pre-defined mapping table. This mapping table was developed by industry experts based on extensive case studies. For example, Level 1 corresponds to β = 0.25, Level 2 to β = 0.5, and Level 3 to β = 1.0. For instance, this exemplary office building project is positioned as an enhanced practice of circular economy, therefore its rating is Level 2, and the table shows β = 0.5.

[0064] α is the lifespan impact factor, a dimensionless exponent used to amplify or reduce the impact of a mismatch between the lifespan of a connection and the total lifespan of the project. Its value is greater than or equal to 1. When the lifespan of a connection is much shorter than the overall lifespan of the building, it means that it needs to be replaced during the building's life cycle, which will generate additional material consumption and carbon emissions. The larger the value of α, the stronger the penalty for such "short-lived" connections. Its setting can refer to the following guidelines: For general buildings, α can be 1.0-1.5; for monumental buildings or important infrastructure requiring high durability and low maintenance, α can be 1.5-2.0. This value is determined by the chief designer at the beginning of the project based on the importance of the building and the maintenance strategy. Specific example: For this demonstration office building, considering its demonstration nature and the requirements for long-term performance, α is set to 1.5.

[0065] Calculation process:

[0066] Based on the values ​​obtained from the above parameters, the interfacial carbon locking index C for the k-th adjacent component connection pair is calculated. IC,k The calculation is performed, and the specific parameter values ​​are substituted in as follows:

[0067] C op,k = 3.9218 kg CO2e;

[0068] C waste,k = -24kgCO2e;

[0069] E A,k =2066.4 kg CO2e;

[0070] E B,k =3500.0 kg CO2e;

[0071]

[0072] T proj = 50 years;

[0073] T conn,k =40 years;

[0074] β = 0.5;

[0075] α = 1.5;

[0076] The calculation process is as follows:

[0077]

[0078] C IC,k = (-0.003607)·(1.41)·(1.3975);

[0079] C IC,k ≈-0.00711;

[0080] The results indicate that the calculated interfacial carbon locking index for the k-th adjacent component connection pair is -0.00711. This value represents the "interfacial carbon coupling degree" of the connection pair, and its negative result mainly stems from the carbon emission value C of the dismantling waste. waste,k The value is negative, and its absolute value exceeds the carbon emission value C of the dismantling operation. op,k This analysis reveals a phenomenon: although the disassembly process of this connection requires energy consumption, the carbon emission reduction benefits brought about by the efficient recycling of the waste generated exceed the carbon emissions generated by the operation itself. Therefore, from a carbon perspective, disassembling this connection and recycling its materials is a net carbon emission reduction behavior. The index is very low (negative), which means that the "carbon lock-in" effect of this connection is extremely weak, indicating that this is an excellent connection design that is very conducive to the circular economy and low-carbon disassembly.

[0081] Based on the interface carbon coupling degree, a global sorting operation is then performed to quantify and rank the "disassemblyability" and "carbon lock-in" of all connections in the building. At this point, the system already possesses a "dataset of adjacent component connection pairs," and each record in the dataset has been augmented with a key field—the "interface carbon coupling degree"—through the previous calculation. The system first loads this complete dataset into memory, forming a data structure containing information on all connection pairs, such as a list of objects or a DataFrame. Each item in this structure includes the identifiers of the two components in the connection pair, their respective carbon emissions and lifetimes, and the interface carbon coupling degree just calculated. Subsequently, the sorting primary key is explicitly specified as "interface carbon coupling degree." This column of values ​​is sorted in ascending order, meaning it is arranged from smallest to largest. This means that the connection pair with the lowest interfacial carbon coupling degree (which may be negative, indicating net carbon reduction) will be placed at the beginning of the sequence, while the connection pair with the highest value (representing high carbon dismantling costs, low material recycling potential, and severe lifespan mismatch) will be placed at the end of the sequence. For example, if there are three connection pairs in the dataset with interfacial carbon coupling degrees of -0.00711, 0.152, and 0.893, after sorting, their order will strictly follow -0.00711, 0.152, and 0.893. After sorting, the system will output this ordered dataset as a whole, and this output result is a structured, global sequence of interfacial carbon coupling degrees.

[0082] The steps for obtaining the interface adhesion geometry mesh are as follows:

[0083] Based on the interface carbon coupling degree sequence, using interface carbon coupling degree as the screening criterion, a threshold for interface carbon coupling degree is set and the interface carbon coupling degree values ​​are compared item by item. Adjacent component connection pairs with interface carbon coupling degree exceeding the threshold are screened to generate high carbon locked adjacent component connection pairs.

[0084] Based on the high-carbon locked adjacent component connection pairs, the component geometry data of each high-carbon locked adjacent component connection pair is retrieved and extracted, and the spatial contact surface data of each pair of adjacent components is extracted from the component geometry data to generate a contact surface data set.

[0085] Based on the data set of the contact surfaces, the interfacial carbon coupling degree and interlayer adhesion strength of each contact surface are called one by one. The particle distribution density is set according to the ratio of interfacial carbon coupling degree to interlayer adhesion strength, and the particle influence radius is set according to the interlayer adhesion strength to generate the interfacial adhesion geometric mesh.

[0086] Specifically, based on the interface carbon coupling degree sequence, the system first performs statistical analysis on the interface carbon coupling degree values ​​throughout the sequence to set a dynamic, data-driven screening threshold. Specifically, the system calculates the mean and standard deviation of all interface carbon coupling degree values ​​in the sequence. The threshold is not a fixed empirical value, but rather defined using statistical methods as the mean plus one standard deviation. For example, if the calculated mean of the interface carbon coupling degree sequence is 0.18 and the standard deviation is 0.12, then the screening threshold will be set to 0.18 + 1.0 × 0.12 = 0.30. This threshold represents a significant boundary indicating that the interface carbon locking effect deviates from the average level in current building designs. The system then initiates the screening procedure, iterating through each adjacent component connection pair in the interface carbon coupling degree sequence. For the k-th connection pair in the sequence, the system reads its interface carbon coupling degree C. IC,k And compare it with the calculated threshold of 0.30. If C IC,k If the value is less than or equal to 0.30, for example, a coupling degree of 0.25 for a connection pair, then the connection pair is considered low or medium carbon locking, and the system will skip this entry and continue processing the next connection pair in the sequence. IC,k If the value is greater than 0.30, for example, if the coupling degree of a connection pair is 0.45, the system determines that the connection pair has a significant carbon lock-in effect and belongs to the objects that need to be focused on and visualized. The system will copy the complete data record of the adjacent component connection pair, including its component identifier, carbon emission value, lifetime and interface carbon coupling degree value, from the original sequence to a new set. By performing this iterative comparison and filtering operation on the entire interface carbon coupling degree sequence, the system finally generates a set that only contains those connection pairs whose interface carbon coupling degree values ​​exceed the dynamically set threshold, that is, high carbon lock-in adjacent component connection pairs.

[0087] Based on the high-carbon locking adjacent component connection pairs, the system performs a detailed geometric data retrieval and processing flow for each connection pair in the set. First, the system traverses the set of high-carbon locking adjacent component connection pairs. For any connection pair, such as the connection pair between wall "W-305" and floor slab "F-301", the system extracts the unique identifiers of these two components. Then, the system uses these identifiers as query indexes to search in the original BIM data repository, retrieving the complete 3D geometric data of "W-305" and "F-301" respectively. This data exists in the form of boundary representation (B-rep), describing every vertex, edge, and face of the component. After obtaining the geometric models of the two components, the system performs an intersection operation in 3D solid Boolean operations. The purpose of this operation is to accurately calculate whether the two geometric entities overlap or not in space. The result of the operation on the common contact area is a new, independent geometric entity that precisely represents the physical contact area between the wall and the floor slab. This result can be one or more three-dimensional entities (representing overlapping areas with volume) or two-dimensional surfaces (representing ideal surface contact). The system then analyzes this geometric entity generated by the intersection operation and extracts all its outer surfaces. These outer surfaces together constitute the spatial contact surface between two adjacent components. The system records the geometric information of these surfaces, including the list of polygon vertex coordinates that make up each surface, and the associated original component identifiers ("W-305" and "F-301"), as a data unit. The system repeats the above retrieval, Boolean operation, and surface extraction process for all connection pairs in the set of adjacent component connection pairs locked in the high carbon fiber, and summarizes all the extracted spatial contact surface data units to generate a contact surface data set.

[0088] Based on the contact surface dataset, the system generates a dynamic mesh for each contact surface to simulate tearing effects. This process begins by traversing the contact surface dataset. For each surface, the system first retrieves its associated interfacial carbon coupling degree value, which is directly obtained from the set of high-carbon locked adjacent component connections. Simultaneously, the system queries a pre-set "3D printing material physical property database" based on the material types of the two components constituting the contact surface, extracting the interlaminar bond strength of the material. This is a physical quantity measured in megapascals (MPa). For example, the interlaminar bond strength of a certain recycled polymer material is 2.8 MPa. Next, the system sets the number of particles distributed on the contact surface, i.e., the particle density, based on the ratio of interfacial carbon coupling degree to interlaminar bond strength. The specific calculation method is: Particle number = Surface area × (Base density + Scaling factor × Interfacial carbon coupling degree / Interlaminar bond strength), where the base density is a baseline value to ensure a minimum number of particles. If set to 50 particles / square meter, the scaling factor is used to amplify the effect of the ratio. For example, if set to 800, and the contact surface area is 1.5 square meters, its interfacial carbon coupling degree is 0.45, and the interlayer adhesion strength is 2.8 MPa, then the number of particles is 1.5 × (50 + 800 × 0.45 / 2.8) ≈ 268. The system uses the Poisson disk sampling method to uniformly and randomly distribute these 268 particles on the two-dimensional geometry of the contact surface. Subsequently, the system sets the influence radius of each particle according to the interlayer adhesion strength. This radius determines the "toughness" of the tearing effect, and the relationship is inversely proportional, that is, the higher the adhesion strength, the smaller the influence radius, and the more "brittle" the tearing effect. The calculation method is: influence radius = maximum radius – (interlayer adhesion strength / maximum strength) × (maximum radius – minimum radius). Setting the maximum adhesion strength to 5.0 MPa, the maximum radius to 0.15 meters, and the minimum radius to 0.03 meters, then for an adhesion strength of 2.8 MPa, its influence radius is 0.15 –

[0089] (2.8 / 5.0)×(0.15–0.03)≈0.083 meters. Finally, the system uses the positions of all generated particles on the surface as vertices, performs two-dimensional Delaunay triangulation on these vertices, and generates a mesh composed of a large number of small triangular facets. The topology of this mesh and the properties (influence radius) of each vertex together constitute the interface adhesion geometry mesh that can be used for subsequent interactions.

[0090] The steps to obtain the interactive tearing dynamic form are as follows:

[0091] Based on the interface-adhesive geometric mesh, the position coordinate data of each grid vertex of the interface-adhesive geometric mesh is extracted one by one. Combined with the displacement vector generated by the user's real-time drag operation in virtual reality, the real-time displacement value of each grid vertex position coordinate data is calculated one by one to generate a set of real-time displacement data of grid vertices.

[0092] Based on the real-time displacement data set of the mesh vertices, the deformation of each mesh element in the interface adhesion geometry mesh is calculated one by one. The difference between the original geometric length of the mesh element and the geometric length after real-time displacement is compared and judged. A mesh damage threshold is set, and it is determined whether the mesh damage threshold is exceeded, and a set of mesh damage judgment results is generated.

[0093] Based on the set of mesh damage determination results, the damage determination result of each mesh unit is judged one by one. If the difference in geometric length of the mesh unit exceeds the damage threshold, the damage status of the mesh unit is updated in real time in the virtual reality scene, the tearing effect is displayed, and an interactive tearing dynamic form is formed.

[0094] Specifically, based on the interface-adhesive geometric mesh, the system performs vertex displacement calculations in a real-time loop synchronized with the virtual reality display refresh rate, at a frequency of 90 times per second. At the start of each frame's calculation, the system first acquires the user's real-time 3D position and pose of their hand through the virtual reality controller's tracking system. When the user presses the designated grab button on the controller, the system emits a virtual ray from the controller's position and calculates the intersection points of this ray with all interface-adhesive geometric meshes in the scene. The mesh containing the intersection point closest to the controller is identified as the user's interaction target, and the position of this intersection point on the mesh surface is recorded. The system then uses a search algorithm, such as kd-tree nearest neighbor search, to find the mesh vertex closest to this intersection point in the vertex data of that mesh and marks it as the "drag master vertex." Next, the system calculates the position change of the user's controller from the previous frame to the current frame, obtaining a 3D displacement vector. This displacement vector is directly applied to the "drag master vertex" to calculate its new position in the world coordinate system. For all other non-master vertices on the interface-adhesive geometric mesh, the system uses a distance-based weighted attenuation model to calculate their displacements. Specifically, for any vertex v i Its displacement vector The calculation method is as follows: in It is the displacement vector of "drag the main vertex", dist(v i ,v p ) is vertex v i Drag the main vertex (v) p The Euclidean distance, and R pThe particle influence radius is set for "drag the main vertex" when generating the mesh. This formula ensures that vertices closer to the drag point move more, while vertices outside the influence radius are unaffected. The displacement effect exhibits a smooth quadratic decay. The system performs this calculation for every vertex on the mesh and records its updated 3D coordinates. Finally, at the end of each frame, the new coordinates of all vertices are summarized to generate a set of real-time displacement data of the mesh vertices.

[0095] Based on the real-time displacement data set of mesh vertices, the system immediately evaluates the structural integrity of the interface-adhesive geometric mesh after updating the vertex positions in each frame. The core of this process is to traverse every edge that constitutes all triangular units in the mesh. For any edge in the mesh, the system first reads the identifiers of the two vertices it connects from its definition, and uses these identifiers to query the initial position coordinates of these two vertices when the mesh was created and their new position coordinates after the real-time displacement calculated in the previous step. The system calculates the original geometric length of the edge, i.e., the Euclidean distance between the initial positions of its two vertices, and stores it as a baseline value. Subsequently, the system recalculates the Euclidean distance between these two vertices at the new positions to obtain the geometric length after the real-time displacement. Then, the system calculates the geometric length difference of this edge, i.e., the geometric length after the real-time displacement minus the original geometric length. This difference represents the stretching amount of the edge. Next, the system sets a unique mesh failure threshold for this edge. The setting of this threshold is directly related to the physical properties of the component material. The specific setting method is: Mesh failure threshold = Original geometric length × (base fracture strain rate + (maximum interlaminar bond strength – current interlaminar bond strength) × strain adjustment factor), where "base fracture strain rate" is a minimum tensile tolerance set for the weakest material, for example, 0.15 (i.e., stretching more than 15% of the original length), "maximum interlaminar bond strength" is the highest strength value defined in the material database, for example, 5.0 MPa, "current interlaminar bond strength" is a specific value called from the component properties, for example, 2.8 MPa, and "strain adjustment factor" is a factor used to adjust the strength effect. The coefficient, for example, is 0.08. According to this method, the critical value of the edge damage will be calculated as the original geometric length × (0.15 + (5.0 – 2.8) × 0.08) = the original geometric length × 0.326. Finally, the system compares the calculated difference in geometric length with this critical value of mesh damage. If the difference exceeds the critical value, the edge is determined to be damaged and its damage status is recorded as "true". Otherwise, it is recorded as "false". After traversing all edges in the mesh, the set of all these determination results constitutes the set of mesh damage determination results.

[0096] Based on the set of mesh damage assessment results, the system updates the visual appearance and physical structure of the interface's adhered geometric mesh in real time before the rendering phase of each frame. The system reads the damage assessment result of each edge in the set one by one. If the assessment result of an edge is "true," meaning its geometric length difference exceeds its corresponding mesh damage threshold, the system will perform a topology modification operation. Specifically, the system will remove the index pair of the two vertices constituting this edge from the rendering data structure. This means that in subsequent rendering calls, the graphics processor will no longer draw the line segment connecting these two vertices, thus visually creating a crack or gap. Simultaneously, at the physical simulation level, the system updates the mesh's adjacency information, removing this edge from the shared edge list of the two triangle units it connects to, physically separating these two originally adjacent triangles. When the user holds... As dragging continues, more and more edges are identified as broken and removed. A previously connected single mesh may break into multiple unconnected sub-mesh. To handle this situation, the system periodically performs a connectivity analysis on the graph structure formed by the vertices and remaining edges of the entire interface-attached geometric mesh, for example, whenever more than 10 edges are removed. This analysis uses breadth-first search or depth-first search algorithms to identify all sets of independent vertices and edges. Each identified connected component is encapsulated by the system into a brand-new, independent interface-attached geometric mesh object, with its own vertex set, edge set, and face set, which can be independently grabbed and dragged by the user. This series of continuous processing steps, from identifying breakage, updating rendering, modifying topology, to finally splitting into independent objects, together constitute the interactive tearing dynamic form perceived by the user in virtual reality.

[0097] The steps for obtaining the viewpoint carbon perception evaluation value are as follows:

[0098] Based on the carbon value set of the entire life cycle of the components, all visible components within the virtual reality view frustum are traversed, and the total carbon emissions of each visible component, the distance from the viewpoint to the centroid of the component, and the proportion of the component's projected area on the retina are obtained one by one. All data are combined to generate a carbon perception data set of visible components within the view frustum.

[0099] Based on the carbon perception data set of visible components within the view frustum, the viewpoint carbon perception evaluation value is calculated using the following formula:

[0100]

[0101] Among them, P CI F is the viewpoint carbon perception evaluation value. j The total carbon emissions of the j-th visible component, expressed in kgCO2e, r j B is the distance from the viewpoint to the centroid of the j-th visible component, in meters. jLet C be the percentage of the projected area of ​​the j-th visible component on the retina, which is dimensionless. ref For reference carbon flux baseline, η is the distribution perception adjustment factor, H is the carbon emission distribution Gini coefficient, and m is the total number of visible components within the view cone.

[0102] Specifically, based on the carbon value set of the entire life cycle of the components, the system first performs frustum culling and occlusion culling algorithms in each frame's rendering cycle to determine the building components visible from the current user's virtual reality viewpoint (i.e., the VR camera). Specifically, the system obtains the view frustum defined by the VR camera, which consists of six planes (near, far, up, down, left, and right). Then, it traverses the axis-aligned bounding boxes (AABBs) of all building components in the scene, using the separating axis theorem to quickly determine whether each bounding box is completely outside the six planes of the view frustum. If a component's bounding box is completely outside the view frustum, the component is determined to be invisible and removed from subsequent calculations. After frustum culling, large components that obstruct the view (such as walls and floors) are rendered first, and the number of pixels they cover in the depth buffer is queried. Subsequently, for other components located behind these large components, the system checks whether their bounding boxes are completely occluded. If they are completely occluded... If an element is occluded, it is also considered invisible. After these two steps of elimination, the system obtains a list of "all visible components within the view frustum". Subsequently, the system traverses each visible component in this list, uses its unique identifier to query the component's life-cycle carbon value set, and directly extracts its "total carbon emissions". At the same time, the system obtains the world coordinates of the VR camera as the viewpoint position and reads the pre-stored centroid coordinates of the component. By calculating the Euclidean distance between the two points, the system obtains the "distance from the viewpoint to the centroid of the component". Next, the system transforms the AABB vertices of the component to screen space through the view-projection matrix, calculates the pixel area of ​​the two-dimensional rectangular bounding box formed on the screen, and divides this area by the total number of pixels in the single-eye resolution of the VR display (e.g., 2048x2048) to obtain the "projection area ratio of the component on the retina". Finally, the system combines the three values ​​of total carbon emissions, distance, and projection area ratio of each visible component as a data unit, and summarizes the data units of all visible components to generate a carbon perception data set of visible components within the view frustum.

[0103] formula: The advantage of the formula is that, firstly, it reduces the total carbon emissions F of the components. j It is converted into a "carbon flux", which increases with distance r. j The intensity decreases with increasing intensity, and depends on its "presence" in the field of vision. jWeighting is applied, which makes the evaluation results more in line with human visual intuition—that is, nearby, large high-carbon objects have a stronger sensory impact than distant, small high-carbon objects. Secondly, a reference carbon flux benchmark value C is introduced. ref Normalization is performed so that the evaluation value P CI To make it a relative, dimensionless index, which facilitates horizontal comparison and standardized mapping, a correction term (1-η·H) consisting of a distribution perception adjustment factor η and a carbon emission distribution Gini coefficient H is introduced. This term quantifies and adjusts the uniformity of carbon emission distribution within the field of view. When carbon emissions are highly concentrated on a few components (high Gini coefficient H), the total evaluation value will be appropriately lowered. Conversely, when carbon emissions are evenly distributed among many components within the field of view (low Gini coefficient H), the total evaluation value is less affected.

[0104] F j This represents the total carbon emissions of the j-th visible component, expressed in kgCO2e. This parameter is directly derived from the "Carbon Sensing Data Set of Visible Components within the View Frustum" generated in the previous step. Each item in this set contains the total carbon emissions precisely retrieved for each visible component from the "Component Life Cycle Carbon Value Set." This value was calculated at the beginning of the project based on BIM data, material type, 3D printing process parameters, and the life cycle assessment database. It represents the implicit carbon emissions of the component throughout its entire life cycle, from raw material extraction to manufacturing completion. When calculating the viewpoint carbon sensing evaluation value, this pre-stored value is directly read for the j-th visible component within the view frustum. For example, for a column component with ID "C-402" in the field of view, the system reads its corresponding total carbon emissions F from the Carbon Sensing Data Set of Visible Components within the View Frustum. j = 1850 kg CO2e.

[0105] r j This is the distance from the viewpoint to the centroid of the j-th visible component, in meters. This parameter is also directly obtained from the "Carbon Sensing Data Set of Visible Components within the View Frustum". In the previous step, the Euclidean distance between the 3D world coordinates of the VR camera (viewpoint) and the pre-stored 3D world coordinates of the centroid of each visible component was calculated in real time. The centroid coordinates are calculated and stored once during the BIM model import and represent the geometric center of the component. This distance value reflects the physical proximity between the observer and the carbon emission source and is a key factor affecting the perception intensity. When calculating the evaluation value, the system directly calls this pre-calculated distance value. For example, for the column component "C-402" in the field of view, the system reads its distance r from the current viewpoint. j =8.5m.

[0106] B jLet B be the proportion of the projected area of ​​the j-th visible component on the retina. This is a dimensionless parameter, directly obtained from the "Carbon Perception Data Set of Visible Components within the Visual Frustum." In the previous step, the system calculated the pixel area occupied by the component on the screen by projecting its 3D bounding box onto the 2D screen space, and divided it by the total number of pixels per eye in the VR headset, thus obtaining a proportion value between 0 and 1. This value quantifies the component's proportion in the user's field of vision. A component occupying most of the field of vision, even if its unit carbon emission is low, can have a significant overall perceptual impact. For example, for the column component "C-402," the system reads its projected area proportion B in the current viewing angle. j =0.08.

[0107] C ref The carbon flux benchmark is set to provide a comparative anchor for viewpoint carbon perception assessments. This benchmark is determined based on green building design standards and the functional positioning of the building type. The specific setting process is as follows: For example, the recommended carbon emission intensity for a Grade A office building is 350 kg CO2e / m³. 2 Then, considering the typical human-computer interaction scale of architectural spaces, an average viewing distance is set. For example, for office spaces, the average viewing distance is set to 5 meters. Finally, the reference carbon flux benchmark value is calculated as follows: This value represents the "standard" carbon flux level that should be perceived at a typical distance in a space that meets green building standards.

[0108] η is the distribution perception moderating factor, a dimensionless coefficient used to adjust the degree of influence of uneven carbon emission distribution on the final perceived evaluation. Its value is based on the results of a psychophysical experiment. In this experiment, multiple groups of participants were shown virtual building scenarios with the same total carbon emissions but different distribution patterns (concentrated vs. dispersed), and their subjective ratings of the scenario's "carbon load perception" (e.g., a 1-7 scale) were recorded. Then, the value of η was adjusted through curve fitting to ensure that the calculated P... CI The value has the highest Pearson correlation coefficient with the subjects' subjective average rating. When the negative information sources are more evenly distributed, people's overall negative perception will be enhanced. Through such experimental calibration, a moderating factor that can reflect this psychological effect can be obtained. For example, after calibration, η = 0.6 was determined.

[0109] H is the Gini coefficient for carbon emission distribution. This parameter quantifies the inequality in the "carbon flux" contributed by each visible component within the field of view. It is dynamically calculated in each frame. First, the carbon flux contribution value of each visible component is calculated. Then, x of all visible components (m in total) j Sort the values ​​in ascending order to obtain an ordered sequence x.(1) ,x (2) ,…,x (m) The formula for calculating the Gini coefficient is:

[0110] The result of this formula ranges from 0 (all components contribute equally to carbon flux) to 1 (all carbon flux contributions are concentrated on one component). For example, if there are three visible components in the field of view with carbon flux contributions of x1 = 5, x2 = 15, and x3 = 8, and these are sorted as {5, 8, 15}, then...

[0111] m is the total number of visible components within the view frustum. This is an integer that is dynamically determined in each frame. It is equal to the total number of components that are finally determined to be visible after view frustum culling and occlusion culling in the previous step. This value is obtained directly from the size or number of elements of the "carbon sensing data set of visible components within the view frustum". For example, in a certain frame, if the system determines that there are 15 components visible in the user's field of view, then m = 15.

[0112] Calculation process:

[0113] From a specific viewpoint, for example, if there are three visible components (m=3) within the field of view, the parameters are as follows:

[0114] Component 1 (wall): F1 = 2500, r1 = 6, B1 = 0.2;

[0115] Component 2 (column): F2 = 1850, r2 = 8.5, B2 = 0.08;

[0116] Component 3 (beam): F3 = 1200, r3 = 7, B3 = 0.05;

[0117] The global parameter used is: C ref =14, η=0.6.

[0118] Step 1: Calculate the carbon flux contribution of each component.

[0119]

[0120] Step 2: Calculate the total carbon flux ∑x j :

[0121] ∑x j =13.89 + 2.05 + 1.22 = 17.16;

[0122] Step 3: Calculate the Gini coefficient H for carbon emission distribution:

[0123] First, the carbon flux contribution values ​​are sorted as follows: {1.22, 2.05, 13.89}:

[0124]

[0125] Step 4: Calculate the final viewpoint carbon perception evaluation value P. CI :

[0126]

[0127] P CI =1.2257·(1-0.2952);

[0128] P CI =1.2257·0.7048=0.864;

[0129] The results show that the carbon perception evaluation value of the current viewpoint is 0.864. This value is a dimensionless index. Since it is less than 1.0, it means that the carbon load perceived by the current user is slightly lower than the reference level under the preset "green building standard". Although the total carbon flux (17.16) is slightly higher than the reference benchmark value (14), due to the extremely uneven distribution of carbon emissions in the field of view (the Gini coefficient is as high as 0.492), most of the carbon flux is concentrated on component 1 (wall). This concentrated distribution reduces the overall perception evaluation value.

[0130] The steps for obtaining the global illumination and post-processing parameter set are as follows:

[0131] Based on the viewpoint carbon perception evaluation value, the viewpoint carbon perception evaluation value is mapped to a preset range of color saturation, brightness and vignette intensity to generate a global illumination and post-processing parameter set as the output result of virtual reality scene rendering.

[0132] Specifically, based on the viewpoint carbon perception evaluation value, the system executes a mapping procedure to convert this single value into a set of specific post-processing effect parameters. First, the system defines three target parameter ranges for post-processing effects. These ranges are pre-set based on visual psychology and art design principles: color saturation range is set to [0.5, 1.1], where 0.5 represents an almost gray monochrome world, 1.0 is normal color, and 1.1 is slightly vibrant color; brightness range is set to [0.7, 1.05], where 0.7 represents a dim environment, 1.0 is normal brightness, and 1.05 is slightly overexposed brightness; vignetting intensity range is set to [0, 0.7], where 0 represents no vignetting, and 0.7 represents a strong darkening effect at the screen edges. Next, the system sets an effective input range for the viewpoint carbon perception evaluation value, for example, [0.4, 2.5]. The center point of this range is 1. .0 corresponds to a normal, unchanged rendering effect. When the viewpoint carbon perception evaluation value (e.g., 0.864) calculated in the previous step is input, the system first normalizes it within the interval [0.4, 2.5] to obtain a mapping factor between 0 and 1. The calculation method is: Mapping factor = (evaluation value - lower limit of interval) / (upper limit of interval - lower limit of interval) = (0.864 - 0.4) / (2.5 - 0.4) ≈ 0.221. Then, the system uses this mapping factor to calculate the specific parameter values ​​of the current frame through linear interpolation within the preset three parameter ranges: Color saturation = 1.1 - mapping factor × (1.1 - 0.5) = 1.1 - 0.221 × 0.6 ≈ 0.967, Brightness = 1.05 - mapping factor × (1.05 - 0.7) = 1.05 - 0.221 × 0.35 ≈ 0.973, Vignette intensity = 0 + mapping factor.

[0133] ×(0.7-0)=0.221×0.7≈0.155. Finally, the system packages the calculated color saturation (0.967), brightness (0.973), and vignette intensity (0.155) values ​​to generate a global illumination and post-processing parameter set as the output result of virtual reality scene rendering.

Claims

1. A virtual reality-based visualization simulation system for carbon emissions from 3D-printed buildings, characterized in that, The system includes: The building component carbon value analysis module, based on BIM data, retrieves the material type and volume of each building component, and then, according to the material type and material extrusion process, retrieves the unit carbon emission factor and design life, calculates the total carbon emissions and life value of each component, and establishes a carbon value set for the entire life cycle of the component. The component connection carbon coupling module, based on the carbon value set of the entire life cycle of the component, traverses the adjacency relationship of the component, identifies all contacting components and establishes adjacent component connection pairs, calls the carbon emissions and lifespan of each component in the adjacent component connection pairs, and calculates and generates the interface carbon coupling degree sequence in combination with the disassembly difficulty of the support structure at the connection interface. The interface geometry generation module is connected. Based on the interface carbon coupling degree sequence, component pairs are screened and contact surfaces are identified to generate an interface adhesion geometry mesh. Then, the vertex displacement and mesh damage rules of the interface adhesion geometry mesh are defined when the user drags the component in virtual reality to establish an interactive tearable dynamic form. The scene carbon perception atmosphere rendering module, based on the carbon value set of the entire life cycle of the components, summarizes the total carbon emissions of all visible components within the virtual reality view frustum, calculates the viewpoint carbon perception evaluation value, maps the viewpoint carbon perception evaluation value to the color saturation, brightness and vignette intensity values ​​of the scene, and generates a global illumination and post-processing parameter set.

2. The virtual reality-based 3D printing building carbon emission visualization simulation system according to claim 1, characterized in that, The steps for obtaining the carbon value set of the component throughout its entire life cycle are as follows: Based on BIM data, the material type, component volume and component printing layer parameters corresponding to each building component are retrieved one by one, and the material type is used as the key retrieval index to generate a set of material type, component volume and component printing layer parameters; Based on the material type, component volume, and component printing layer parameter set, the corresponding material extrusion process parameters are called for different material types. Based on the process parameters, the unit carbon emission factor and design life value corresponding to the material type and material extrusion process are matched one by one to generate a set of unit carbon emission factor and design life value. Based on the aforementioned set of unit carbon emission factors and design life values, the component volume of each building component is called one by one to calculate the total carbon emission value of each component. At the same time, the design life value is associated with the component, and the total carbon emission and life value pairs corresponding to each component are summarized to obtain the carbon value set of the entire life cycle of the component.

3. The virtual reality-based 3D printing building carbon emission visualization simulation system according to claim 1, characterized in that, The steps for obtaining the adjacent component connection pairs are as follows: Based on the carbon value set of the entire life cycle of the components, the adjacency relationship data of the components is retrieved one by one, it is determined whether there is a physical contact boundary in the adjacency relationship data of the components, all component combinations with physical contact boundaries are screened and the total carbon emissions and design life of the corresponding components are recorded, and an adjacency component connection pair dataset is generated.

4. The virtual reality-based 3D printing building carbon emission visualization simulation system according to claim 1, characterized in that, The steps for obtaining the interface carbon coupling degree sequence are as follows: Based on the adjacent component connection pair dataset, calculate the interface carbon locking index; Based on the interface carbon coupling degree, all adjacent component connection pairs are traversed, and the interface carbon coupling degrees are sorted from smallest to largest to form an interface carbon coupling degree sequence.

5. The virtual reality-based 3D printing building carbon emission visualization simulation system according to claim 1, characterized in that, The steps for obtaining the interface adhesion geometry mesh are as follows: Based on the interface carbon coupling degree sequence, using interface carbon coupling degree as the screening criterion, a threshold for interface carbon coupling degree is set and the interface carbon coupling degree values ​​are compared item by item. Adjacent component connection pairs with interface carbon coupling degree exceeding the threshold are screened to generate high carbon locked adjacent component connection pairs. Based on the high-carbon locked adjacent component connection pairs, retrieve and extract the component geometric data of each high-carbon locked adjacent component connection pair, extract the spatial contact surface data of each pair of adjacent components from the component geometric data, and generate a contact surface data set. Based on the aforementioned contact surface data set, the interfacial carbon coupling degree of each contact surface and the interlayer adhesion strength of the component are called one by one. The particle distribution density is set according to the ratio of interfacial carbon coupling degree to interlayer adhesion strength, and the particle influence radius is set according to the interlayer adhesion strength to generate an interfacial adhesion geometric mesh.

6. The virtual reality-based 3D printing building carbon emission visualization simulation system according to claim 1, characterized in that, The steps for obtaining the interactive tearing dynamic form are as follows: Based on the interface-adhesive geometric mesh, the position coordinate data of each grid vertex of the interface-adhesive geometric mesh is extracted one by one. Combined with the displacement vector generated by the user's real-time dragging operation in virtual reality, the real-time displacement value of each grid vertex position coordinate data is calculated one by one to generate a set of real-time displacement data of grid vertices. Based on the real-time displacement data set of the grid vertices, the deformation of each grid unit in the interface adhesion geometry grid is calculated one by one. The difference between the original geometric length of the grid unit and the geometric length after real-time displacement is compared and judged. A grid damage threshold is set, and it is determined whether the grid damage threshold is exceeded, and a grid damage judgment result set is generated. Based on the set of mesh damage determination results, the damage determination result of each mesh unit is judged one by one. If the difference in geometric length of the mesh unit exceeds the damage threshold, the damage status of the mesh unit is updated in real time in the virtual reality scene, the tearing effect is displayed, and an interactive tearing dynamic form is formed.

7. The virtual reality-based 3D printing building carbon emission visualization simulation system according to claim 1, characterized in that, The steps for obtaining the viewpoint carbon perception evaluation value are as follows: Based on the carbon value set of the entire life cycle of the components, all visible components within the virtual reality view frustum are traversed, and the total carbon emissions of each visible component, the distance from the viewpoint to the centroid of the component, and the proportion of the component's projected area on the retina are obtained one by one. All data are then combined to generate a carbon perception data set of visible components within the view frustum. Based on the carbon perception data set of visible components within the view cone, the viewpoint carbon perception evaluation value is calculated.

8. The virtual reality-based 3D printing building carbon emission visualization simulation system according to claim 1, characterized in that, The steps for obtaining the global illumination and post-processing parameter set are as follows: Based on the viewpoint carbon perception evaluation value, the viewpoint carbon perception evaluation value is mapped to a preset range of color saturation, brightness, and vignette intensity to generate a global illumination and post-processing parameter set as the output result of virtual reality scene rendering.

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