BIM-based green building energy-saving design optimization method and system

Abstract

The application relates to the technical field of data processing, and discloses a green building energy-saving design optimization method and system based on BIM. The method comprises the following steps: identifying the thermal parameter of a BIM component to obtain a layered thermal resistance value data table; performing thermal bridge positioning calculation to form a correction coefficient set; performing building heat transfer coefficient calculation to generate heat transfer load data; projecting the data to a VR space for visualization; adjusting the thermal insulation parameter verification to determine an optimization scheme. The application improves the precision and efficiency of building energy-saving design. The problems of inaccurate thermal bridge calculation, difficult intuitive understanding of thermal environment distribution and lack of real-time feedback in parameter optimization verification in traditional energy-saving design are solved.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a BIM-based method and system for optimizing energy-saving design of green buildings. Background Technology

[0002] In existing technologies, building energy-saving design mainly relies on traditional thermal calculation software and two-dimensional drawings for analysis. Designers calculate the building's heat transfer coefficient and energy consumption indicators by inputting the material parameters and geometric dimensions of the building envelope. BIM technology is becoming increasingly mature in building design, capable of creating three-dimensional building models containing rich information, providing a geometric and attribute data foundation for building performance analysis. Meanwhile, virtual reality technology is widely used in the field of building visualization, providing designers with an immersive three-dimensional spatial experience. These technologies offer new technical means and development directions for optimizing building energy-saving design.

[0003] However, traditional thermal calculation methods cannot accurately handle thermal bridging effects in complex building structures, especially at critical locations such as the connections between beams / columns and walls, where the impact of thermal bridging is often simplified or ignored, leading to significant errors in building heat transfer performance calculations. Existing energy-saving design processes lack intuitive visualization tools, making it difficult for designers to visually understand the thermal environment distribution within buildings. Energy-saving optimization processes rely heavily on experience and lack scientific verification methods. Furthermore, traditional design methods cannot provide real-time feedback for parameter adjustments and effect verification, requiring designers to repeatedly switch between different software programs, resulting in inefficient and error-prone optimization processes.

[0004] Based on the in-depth analysis of the aforementioned technological status quo, the problem lies in how to accurately extract the thermal parameters of components from the BIM model and establish a complete layered thermal resistance data system. If there is a deviation in this step, all subsequent calculations will lose accuracy. However, because the widespread thermal bridging phenomenon in actual buildings can significantly alter the heat transfer performance of components, accurately identifying and calculating the heat transfer impact of complex thermal bridge locations in buildings requires considering the interaction relationships between components based on the extracted thermal parameters. Once the impact of thermal bridges is accurately quantified, the traditional numerical calculation results are too abstract for designers, making it difficult to intuitively understand the distribution patterns of the spatial thermal environment. Therefore, it is urgent to solve the technical problem of how to transform abstract heat transfer calculation results into intuitive three-dimensional visualization. Summary of the Invention

[0005] This application provides a BIM-based method and system for optimizing energy-saving design in green buildings, which improves the accuracy and efficiency of building energy-saving design. It solves the problems of inaccurate thermal bridge calculations, difficulty in intuitively understanding thermal environment distribution, and lack of real-time feedback for parameter optimization verification in traditional energy-saving design.

[0006] Firstly, this application provides a BIM-based method for optimizing energy-saving design of green buildings. This method includes: identifying thermal parameters of the material layers of BIM components to obtain a layered thermal resistance data table; calculating the location of thermal bridges at component connection interfaces based on the layered thermal resistance data table to form a set of thermal bridge correction coefficients; calculating the overall building heat transfer coefficient based on the set of thermal bridge correction coefficients to generate heat transfer load data; projecting the heat transfer load data into a VR space for temperature field visualization to generate a heat distribution display result; and verifying the thermal response by adjusting the insulation parameters in the heat distribution display result to determine the optimal configuration scheme for the components.

[0007] Secondly, this application provides a BIM-based green building energy-saving design optimization system, which includes:

[0008] The identification module is used to identify the thermal parameters of the material layers of BIM components and obtain a layered thermal resistance value data table.

[0009] The positioning module is used to perform thermal bridge positioning calculations at the component connection interface based on the layered thermal resistance data table, and to form a set of thermal bridge correction coefficients.

[0010] The calculation module is used to perform the calculation of the overall heat transfer coefficient of the building based on the set of thermal bridge correction coefficients, and generate heat transfer load data;

[0011] The projection module is used to project the heat transfer load data into the VR space for temperature field visualization processing, and generate heat distribution display results;

[0012] The verification module is used to verify the thermal response by adjusting the insulation parameters in the heat distribution display results, and to determine the optimal configuration scheme of the components.

[0013] Thirdly, a BIM-based green building energy-saving design optimization device is provided, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the BIM-based green building energy-saving design optimization device to execute the aforementioned BIM-based green building energy-saving design optimization method.

[0014] Fourthly, a computer-readable storage medium is provided, wherein instructions are stored therein, which, when executed on a computer, cause the computer to perform the aforementioned BIM-based green building energy-saving design optimization method.

[0015] The technical solution provided in this application utilizes a feature to identify and process thermal parameters at the material layers of BIM components, ensuring the accuracy and reliability of the data foundation for thermal calculations. Through structural analysis, database queries, and thermal resistance accumulation, the traditional process of obtaining thermal parameters relying on manual input is transformed into an automated data extraction process, avoiding human error and data omissions, thus laying a solid foundation for subsequent thermal bridge calculations and heat transfer analysis. The technical feature of calculating thermal bridge locations at component connection interfaces based on layered thermal resistance value data tables solves the key problem of neglecting or simplifying the impact of thermal bridges in traditional energy-saving calculations. Through spatial relationship analysis, thermal resistance difference calculation, and geometric shape analysis, it achieves accurate identification and quantitative calculation of thermal bridge locations in complex building structures, making building heat transfer performance assessments more closely aligned with actual engineering conditions. The technical feature of calculating the overall building heat transfer coefficient based on a set of thermal bridge correction coefficients organically integrates the impact of thermal bridges into the overall building thermal performance evaluation system. Through comprehensive calculations such as correction of the building envelope heat transfer coefficient, weighted calculation of building orientations, and superposition of solar radiation heat gain, it generates building heat transfer load data that considers multiple factors, significantly improving the accuracy and reliability of energy-saving analysis. The technology of projecting heat transfer load data into VR space for temperature field visualization overcomes the limitations of traditional energy-saving design, which relies on abstract numerical analysis. Through visualization techniques such as spatial coordinate mapping, color coding, and 3D shading rendering, complex heat transfer calculations are transformed into intuitive 3D heat distribution displays. This allows designers to observe and understand the spatial distribution patterns of the building's thermal environment in an immersive way, greatly enhancing the intuitiveness and comprehensibility of energy-saving design. The technology of verifying thermal response by adjusting insulation parameters in the heat distribution display results achieves closed-loop feedback for energy-saving optimization design. Through interactive optimization processes such as hotspot identification, material replacement selection, real-time rendering updates, visual difference recognition, and optimal selection, the traditional offline design mode is transformed into a real-time responsive intelligent design process, significantly improving the efficiency and quality of energy-saving optimization.

[0016] The structural analysis algorithm in thermal parameter identification and processing automatically extracts material name, thickness, density, and other attribute information by traversing the material hierarchy objects of BIM components. This avoids the inefficiency and high error rate of traditional manual input methods. The automation feature of the algorithm reduces the workload of thermal parameter processing in large-scale building projects by several times, while ensuring data consistency and accuracy. The geometric intersection algorithm in thermal bridge location calculation achieves intelligent identification of thermal bridge locations in complex building structures by accurately calculating the overlapping areas of component boundary boxes. The spatial analysis capability of this algorithm makes thermal bridge problems, which traditionally required experienced engineers to identify, automatically detectable, significantly reducing the reliance on the professional skills of designers. The weighted average algorithm in heat transfer coefficient calculation considers the different degrees of influence of the exterior wall area of ​​each orientation on the overall heat transfer performance. Through area weight allocation, it achieves a more scientific and reasonable assessment of the overall building heat transfer coefficient. The weighted processing feature of this algorithm makes the building heat transfer performance evaluation results more consistent with the actual physical laws of heat transfer. In VR visualization processing, spatial coordinate mapping algorithms establish a precise correspondence between heat load data and three-dimensional spatial positions. Combined with color coding algorithms, this enables an intuitive conversion of temperature values ​​into visual colors. The synergistic effect of these algorithms transforms abstract numerical calculation results into easily understandable visual information, providing designers with an unprecedented thermal environment perception experience. Real-time response algorithms in insulation parameter optimization rapidly recalculate component thermal resistance and heat load, achieving synchronous updates between parameter adjustments and effect display. The real-time calculation characteristic of this algorithm reduces optimization verification work, which traditionally required hours or even days, to a response time of seconds, significantly improving the iterative optimization efficiency of energy-saving designs. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a schematic diagram of one embodiment of the BIM-based green building energy-saving design optimization method in this application.

[0019] Figure 2 This is a schematic diagram of one embodiment of the BIM-based green building energy-saving design optimization system in this application.

[0020] Figure 3 This is a schematic block diagram of the structure of the BIM-based green building energy-saving design optimization equipment in this embodiment of the invention. Detailed Implementation

[0021] This application provides a BIM-based method and system for optimizing energy-saving design in green buildings. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0022] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the BIM-based green building energy-saving design optimization method in this application includes:

[0023] Step S101: Perform thermal parameter identification processing on the material layers of BIM components to obtain a layered thermal resistance value data table;

[0024] Step S102: Calculate the location of thermal bridges at the component connection interface based on the layered thermal resistance data table, and form a set of thermal bridge correction coefficients;

[0025] Step S103: Calculate the overall heat transfer coefficient of the building based on the set of thermal bridge correction coefficients to generate heat transfer load data;

[0026] Step S104: Project the heat transfer load data into the VR space for temperature field visualization processing to generate heat distribution display results;

[0027] Step S105: Verify the thermal response by adjusting the insulation parameters in the heat distribution display results, and determine the optimal configuration scheme for the components.

[0028] It is understood that the executing entity of this application can be a BIM-based green building energy-saving design optimization system, or it can be a terminal or a server; the specific implementation is not limited here. This application's embodiment uses a server as an example for illustration.

[0029] Specifically, the system analyzes the material hierarchy of each component in the BIM model. Wall components in a Building Information Model typically contain multiple material layers, such as interior wall layers, insulation layers, structural layers, and exterior wall layers. The system reads the material attribute data of the BIM components, extracting basic information such as the name, thickness, and density of each material layer. Then, based on the material name, it queries the thermal database for corresponding thermal parameters such as thermal conductivity, specific heat capacity, and thermal resistance. Thermal resistance is calculated by dividing the thickness by the thermal conductivity, and the total thermal resistance of the component is obtained by summing the thermal resistance values ​​of each layer. Layered thermal resistance data tables are stored according to component types such as walls, roofs, and floors, with each component corresponding to a unique thermal resistance identifier. Thermal bridge location calculations identify the connection interfaces of materials with different thermal resistance values ​​by analyzing the contact relationships of components in three-dimensional space. Thermal bridges refer to areas in the building envelope with relatively low thermal resistance, typically appearing at the connections between beams / columns and walls, and at the junctions between window frames and walls. The system calculates the difference in thermal resistance between adjacent components. When the difference in thermal resistance exceeds a set threshold, it marks the location of a thermal bridge. Then, it extracts the geometric dimensions, material composition, and other characteristic parameters of the thermal bridge location. The system then uses the thermal bridge line heat transfer coefficient calculation formula to solve the correction coefficient for each thermal bridge point. All thermal bridge correction coefficients are summarized to form a set of correction coefficients.

[0030] The calculation of the overall building heat transfer coefficient applies a thermal bridge correction factor to the correction process of the building envelope heat transfer coefficient. The corrected heat transfer coefficient equals the original heat transfer coefficient plus the thermal bridge correction value. The system performs a weighted calculation based on the exterior wall area of ​​each orientation of the building to obtain the overall building heat transfer coefficient. This is then combined with factors such as indoor and outdoor temperature difference and solar radiation heat gain to calculate the building's heat load. The heat load data includes hourly heat transfer values, reflecting the building's thermal environment at different times. Step S104, the VR spatial temperature field visualization process, maps the heat load data to a three-dimensional coordinate system in the virtual reality environment, with each spatial location corresponding to a temperature value. The system sets color coding rules based on the temperature value range: high-temperature areas are displayed in red, low-temperature areas in blue, and moderate-temperature areas in yellow or green. Three-dimensional shading rendering technology applies color information to the surface of the VR building model, forming a three-dimensional thermal cloud map effect, allowing users to observe the temperature distribution inside the building through VR devices.

[0031] Thermal response verification involves adjusting insulation parameters via a VR interactive interface. When a user clicks on a high-temperature area on the building surface, the system displays component information and the current insulation configuration at that location. By selecting different insulation material types or adjusting the insulation layer thickness, the system recalculates the component's thermal resistance and heat transfer coefficient, updates the overall building's heat load data, and the heat distribution display in the VR environment changes accordingly. The system records temperature changes before and after each parameter adjustment, determines the insulation improvement effect through comparative analysis, and ultimately generates an optimized component configuration scheme containing information such as recommended insulation materials, thickness configuration, and expected energy-saving effects.

[0032] For example, the system identified four material layers in the exterior wall components: a gypsum board interior wall layer, a rock wool insulation layer, a concrete structural layer, and a painted exterior wall layer. By querying a thermal database, the thermal conductivity of each layer was found to be 0.25, 0.045, 1.74, and 0.87 Kelvin per meter, respectively. Combining this with the thickness of each layer, the total thermal resistance of the exterior wall was calculated to be 2.86 Kelvin per square meter. Thermal bridges were detected at the connections between beams / columns and the exterior wall. The thermal conductivity of the concrete beams was much higher than that of the insulation layer, forming a rapid heat transfer channel. The system calculated the heat transfer coefficient of the thermal bridge line and applied a correction factor, adjusting the exterior wall heat transfer coefficient from 0.35 to 0.42 Kelvin per square meter. The VR environment showed the thermal bridge as a distinct red high-temperature area. After the user adjusted the insulation layer thickness from 80 mm to 120 mm, the thermal resistance increased to 3.75 square meters Kelvin per watt, and the heat transfer coefficient decreased to 0.32 watts per square meter Kelvin. The red area displayed in the VR environment shrank significantly and turned orange, and the temperature distribution became more uniform.

[0033] In one specific embodiment, the process of performing step S101 may specifically include the following steps:

[0034] Structural analysis is performed on the material hierarchy objects of BIM components to obtain the component's hierarchical organizational structure data;

[0035] Based on the component layered organizational structure data, the material name field is queried from the thermal database to obtain a set of thermal parameters for a single layer material.

[0036] Based on the set of thermal parameters of a single-layer material, the thermal resistance values ​​of each material layer are accumulated and calculated to obtain the equivalent thermal resistance coefficient of the component.

[0037] The equivalent thermal resistance coefficients of components are classified and organized according to the type of walls and windows to obtain a component thermal resistance value index table.

[0038] Based on the component thermal resistance index table, thermal resistance values ​​of all building components are matched to obtain a layered thermal resistance data table.

[0039] Specifically, when performing structural analysis on BIM component material hierarchy objects, it is necessary to extract the layered material information of the components from the Building Information Model (BIM). The BIM model stores building components according to the actual construction layers, and each component object contains an array of material hierarchy objects. Structural analysis extracts the basic information of each material layer by traversing the material attribute nodes of the component objects. This information includes attribute data such as material name, thickness, layer number, and material density. It also records the arrangement order and relative position of each material layer within the component. The component layered organizational structure data is stored in a tree structure, with the root node representing the entire component and child nodes representing each material layer. Each child node contains complete attribute information for that layer.

[0040] When performing thermal database queries on the material name field based on the component layering structure data, it is necessary to establish a mapping relationship between material names and thermal parameters. The thermal database stores standard thermal performance parameters for various building materials, including key data such as thermal conductivity, specific heat capacity, thermal diffusivity, and vapor permeability. The query process employs a string matching algorithm, using the material name field from the component layering structure data as the query key to retrieve the corresponding thermal parameter record from the thermal database. When the material name is a perfect match, the corresponding parameter is returned directly; when multiple similar materials exist, precise matching is performed based on auxiliary attributes such as density and thickness. The set of thermal parameters for a single layer contains all the data required for calculating the thermal conductivity, thickness, and thermal resistance of each material layer.

[0041] When calculating the thermal resistance of each material layer based on its thermal parameters, the equivalent thermal resistance coefficient of the component is calculated using the principle of thermal resistance superposition. The thermal resistance value is calculated by dividing the thickness of each material layer by its thermal conductivity to obtain the single-layer thermal resistance value, and then summing the thermal resistance values ​​of all material layers within the component. This summation is performed sequentially according to the actual arrangement of the material layers to ensure the accuracy of the thermal resistance calculation. The equivalent thermal resistance coefficient of the component represents its total ability to resist heat transfer; a higher value indicates better thermal insulation performance.

[0042] When classifying and organizing the equivalent thermal resistance coefficients of building components according to wall and window types, it is necessary to group and manage them based on the functional attributes and geometric characteristics of the components. The classification process first identifies the component type, including building envelope types such as exterior walls, interior walls, roofs, floors, doors, and windows. Then, the equivalent thermal resistance coefficients are stored in the corresponding classification data tables according to the component type. Each classification data table contains information such as the thermal resistance value, component identifier, and geometric dimensions of that type of component. A component thermal resistance value index table establishes a fast lookup relationship between component identifiers and thermal resistance values, and the index table uses a hash table data structure to achieve fast retrieval.

[0043] When performing thermal resistance value matching for all building components based on the component thermal resistance index table, it is necessary to traverse each component object in the building model and search for the corresponding thermal resistance value data in the index table using the component identifier. The matching process uses a key-value pair query method, using the component's unique identifier as the query key to return the complete thermal resistance information of that component. When a corresponding record exists in the index table for the component identifier, the thermal resistance value data is directly extracted and associated with that component; when no matching record is found for the component identifier, the thermal resistance value is recalculated based on the component's material composition, and the index table is updated. The layered thermal resistance value data table integrates the thermal resistance information of all components in the building, including complete data such as component location coordinates, orientation angle, thermal resistance value, and component type.

[0044] In one specific embodiment, the process of performing step S102 may specifically include the following steps:

[0045] The spatial positional relationship of the components in the layered thermal resistance data table is analyzed to obtain the set of coordinates of the component contact interface.

[0046] The thermal resistance difference of different material connection parts is calculated based on the coordinate set of the component contact interface to obtain the thermal resistance jump position data.

[0047] Geometric shape analysis of beam-column-wall connection nodes is performed based on thermal resistance jump location data to obtain thermal bridge geometric characteristic parameters.

[0048] The geometric characteristic parameters of the thermal bridge are input into the thermal bridge calculation formula to solve for the correction coefficient, and the correction value of the single-point thermal bridge is obtained.

[0049] The thermal bridge correction coefficient set is obtained by summarizing the values ​​of single-point thermal bridge corrections for all thermal bridge locations in the building.

[0050] In one specific embodiment, the process of inputting the thermal bridge geometric feature parameters into the thermal bridge calculation formula for correction coefficient calculation can specifically include the following steps:

[0051] The thermal bridge length in the geometric feature parameters of the thermal bridge is extracted linearly to obtain the effective calculated length of the thermal bridge.

[0052] The thermal conductivity of the material at the thermal bridge is calculated by weighted average based on the effective calculated length of the thermal bridge, and the equivalent thermal conductivity of the thermal bridge is obtained.

[0053] The thermal resistance of the thermal bridge section thickness is calculated based on the equivalent thermal conductivity of the thermal bridge to obtain the thermal bridge line heat transfer coefficient.

[0054] The heat transfer coefficient of the thermal bridge line is multiplied by the indoor and outdoor temperature difference to obtain the heat transfer per unit length of the thermal bridge.

[0055] The total heat transfer of the component is further calculated based on the heat transfer per unit length of the thermal bridge to obtain the correction value for the single-point thermal bridge.

[0056] Specifically, when performing spatial position relationship analysis on components in the layered thermal resistance data table, it is necessary to extract the three-dimensional coordinate information and geometric boundary data of each component from the BIM model. Spatial position relationship analysis involves reading the geometric positioning parameters of the components, including their start coordinates, end coordinates, normal vectors, thickness dimensions, and other spatial attributes. Then, a geometric intersection algorithm is used to determine the contact relationship between different components. The intersection algorithm calculates the overlapping area of ​​the boundary boxes of two components; when the overlapping area is greater than a set threshold, a contact relationship is confirmed. The component contact interface coordinate set records the geometric information of the interface for each pair of contacting components, including detailed data such as the start coordinates, end coordinates, interface area, and contact angle of the boundary line, while also marking the component identifiers and material properties on both sides of the contact interface.

[0057] When calculating the thermal resistance difference at the connection points of different materials based on the coordinate set of the component contact interface, it is necessary to extract the thermal resistance values ​​of the components on both sides of the contact interface and calculate the degree of difference. The thermal resistance difference calculation process involves querying the thermal resistance coefficients of the components on both sides of the contact interface from the layered thermal resistance value data table and obtaining the thermal resistance difference by subtracting the values. When the thermal resistance difference exceeds a preset threshold, the location is marked as a thermal resistance jump location, indicating a point in the building envelope where the heat conduction performance changes drastically. The thermal resistance jump location data includes the coordinates of the jump point, the magnitude of the thermal resistance difference, the identification of adjacent components, and the jump type. The jump type is divided into two cases based on the direction of thermal resistance change: thermal resistance increase and thermal resistance decrease. When performing geometric shape analysis on beam-column-wall connection nodes based on the thermal resistance jump location data, it is necessary to identify the intersection of structural components and envelope components and extract geometric features. The geometric shape analysis process first filters out different types of structural components such as beams, columns, and walls based on component type attributes, and then analyzes the spatial arrangement and connection methods of these components at the thermal resistance jump locations.

[0058] When performing linear dimension extraction on the thermal bridge length, a key geometric characteristic parameter, the effective length portion actually involved in heat conduction is identified and extracted from the overall geometric dimensions of the thermal bridge. This linear dimension extraction employs a geometric analysis method, identifying the material distribution and continuity within the thermal bridge region. It excludes non-conducting components such as decorative layers, air gaps, and discontinuous materials, retaining only the length of solid material forming a continuous heat transfer path. The formula for calculating the effective length of the thermal bridge is:

[0059]

[0060] in Indicates the effective calculated length of the thermal bridge. Indicates the total geometric length of the thermal bridge. This indicates the length of the non-heat-transfer portion that needs to be excluded, in meters.

[0061] When performing a weighted average calculation of the thermal conductivity of materials in the thermal bridge region based on the effective calculated length of the thermal bridge, it is necessary to identify the various material types involved in the thermal bridge region and their distribution proportions within the effective length. The weighted average calculation divides the effective calculated length of the thermal bridge into segments according to material type, measures the actual length occupied by each material in the total effective length, and then uses the proportion of each material length as a weighting coefficient to perform a weighted summation of the thermal conductivity of the corresponding materials. The formula for calculating the equivalent thermal conductivity of a thermal bridge is:

[0062]

[0063] in Indicates the equivalent thermal conductivity of the thermal bridge. This represents the thermal conductivity of the i-th material. The length of the i-th material in the effective calculated length of the thermal bridge is represented by , and n represents the number of material types involved in the thermal bridge. The units are watts per meter Kelvin and meters, respectively.

[0064] When calculating the thermal resistance of a thermal bridge based on its equivalent thermal conductivity, the basic principle of thermal resistance calculation is to perform a ratio calculation between the thickness and the thermal conductivity. The calculation uses the thermal bridge cross-sectional thickness as the numerator and the equivalent thermal conductivity as the denominator for division to obtain the thermal resistance of the thermal bridge. Then, the reciprocal of this thermal resistance is taken to obtain the heat transfer coefficient of the thermal bridge line. The formula for calculating the heat transfer coefficient of the thermal bridge line is:

[0065]

[0066] in Indicates the heat transfer coefficient of the thermal bridge line. The values ​​indicate the thickness of the thermal bridge section, in watts per meter (Kelvin) and meters, respectively.

[0067] When multiplying the heat transfer coefficient of a thermal bridge line by the indoor-outdoor temperature difference, the building's indoor and outdoor design temperature parameters need to be determined. The multiplication operation involves multiplying the heat transfer coefficient of the thermal bridge line by the indoor-outdoor temperature difference to obtain the heat transfer per unit length of the thermal bridge. The formula for calculating the heat transfer per unit length of a thermal bridge is:

[0068]

[0069] in This indicates the amount of heat transferred per unit length of a thermal bridge. Indicates the temperature difference between indoors and outdoors. Indicates the indoor design temperature. The outdoor design temperature is indicated by watts per meter and Kelvin, respectively.

[0070] When performing additional calculations on the total heat transfer of a component based on the heat transfer per unit length of thermal bridges, the extra heat loss caused by the thermal bridges needs to be added to the original heat transfer of the component. The additional calculations first multiply the heat transfer per unit length of the thermal bridge by the effective calculated length of the thermal bridge to obtain the total heat transfer of the thermal bridge. Then, the total heat transfer of the thermal bridge is added to the original heat transfer of the component to obtain the corrected total heat transfer of the component. Finally, the correction value for a single-point thermal bridge is obtained by comparing the corrected heat transfer with the original heat transfer. The formula for calculating the correction value for a single-point thermal bridge is:

[0071]

[0072] in This indicates the single-point thermal bridge correction value. This represents the total heat transfer of the component after considering thermal bridges. This indicates the original heat transfer of the component, and the unit is watts.

[0073] When summarizing the numerical values ​​of all thermal bridge locations in a building based on single-point thermal bridge correction values, it is necessary to collect and statistically analyze the correction values ​​of all thermal bridge points in the building. The numerical summarization process iterates through each thermal bridge location in the thermal resistance jump location data, extracts the corresponding single-point thermal bridge correction value, and then groups and summarizes them according to attributes such as component type, floor location, and orientation. The summarization calculation uses a weighted average method, assigning weights to the correction values ​​based on the thermal bridge length; longer thermal bridges have a greater weight in the summarization. The thermal bridge correction coefficient set contains complete information such as the correction coefficient, thermal bridge coordinates, influence range, and correction intensity for each thermal bridge location in the building.

[0074] In one specific embodiment, the process of executing step S103 may specifically include the following steps:

[0075] The values ​​in the thermal bridge correction coefficient set are processed by the heat transfer coefficient correction calculation of the building envelope to obtain the corrected heat transfer coefficient of the building envelope.

[0076] The overall heat transfer coefficient of the building is obtained by weighting the exterior wall area of ​​each orientation of the building based on the corrected heat transfer coefficient of the building envelope.

[0077] The heat transfer load of the building foundation is obtained by calculating the heat transfer of the indoor and outdoor temperature difference based on the overall heat transfer coefficient of the building.

[0078] The total building heat load is obtained by superimposing the heat transfer load of the building foundation with the heat gain from solar radiation.

[0079] The heat transfer load data is obtained by dynamically calculating and processing hourly meteorological parameters based on the total building heat load.

[0080] Specifically, when calculating the heat transfer coefficient correction for building envelopes using values ​​from the set of thermal bridge correction factors, these correction factors need to be applied to the original heat transfer coefficient of the building envelope. The calculation employs a numerical multiplication method, multiplying the original heat transfer coefficient of the building envelope by the corresponding thermal bridge correction factor to obtain the corrected heat transfer coefficient considering the influence of thermal bridges. The correction calculation is performed according to the type of building envelope; different types of building envelopes, such as exterior walls, roofs, and floors, undergo corresponding numerical corrections based on the location and correction factors of the thermal bridges they contain. The corrected heat transfer coefficient reflects the degree of influence of thermal bridges on the overall thermal performance of the building envelope; a higher value indicates poorer insulation performance.

[0081] When performing weighted calculations on the exterior wall areas of a building in different orientations based on the corrected heat transfer coefficient of the building envelope, it is necessary to consider the distribution and area proportion of the exterior walls in different orientations. The weighted calculation process first calculates the exterior wall areas in the four main orientations (east, south, west, and north). Then, it multiplies the corrected heat transfer coefficient of the building envelope for each orientation by the corresponding exterior wall area to obtain the weighted heat transfer coefficient for each orientation. Finally, it sums the weighted heat transfer coefficients for all orientations and divides them by the total exterior wall area to obtain the overall building heat transfer coefficient. The core principle of the weighted calculation process is that larger exterior walls have a greater impact on the overall heat transfer performance of the building, and therefore need to be given a larger weight in the calculation. The overall building heat transfer coefficient is an important indicator for evaluating the comprehensive thermal performance of the building envelope and is used in subsequent calculations of building heat load.

[0082] When calculating the heat transfer based on the overall building heat transfer coefficient and the indoor-outdoor temperature difference, the basic calculation method of heat transfer is used to multiply the three parameters: heat transfer coefficient, area, and temperature difference. The heat transfer calculation process multiplies the overall building heat transfer coefficient by the total area of ​​the building envelope and the indoor-outdoor temperature difference to obtain the building's foundation heat transfer under steady-state conditions. The building foundation heat load represents the rate of heat transfer within the building envelope under a given temperature difference and is a crucial foundational data for building energy consumption calculations. The heat transfer calculation process employs steady-state heat transfer theory, assuming that the indoor and outdoor temperatures remain constant and that the internal temperature of the building envelope exhibits a linear distribution.

[0083] When calculating the combined heat load of a building foundation and the heat gain from solar radiation, the additional impact of solar radiation on the building's thermal environment must be considered. The combined calculation first calculates the solar radiation intensity received by the building's exterior surfaces on each side. Then, it calculates the solar radiation gain based on the solar radiation absorption coefficient of the building envelope. Finally, it adds the solar radiation gain to the building foundation heat load to obtain the total building heat load. The calculation of solar radiation gain requires consideration of multiple factors, including the solar radiation data for the building's location, the building's orientation, and the properties of the building envelope materials. The total building heat load comprehensively considers both the heat transfer from the building envelope and the solar radiation gain, providing a more accurate reflection of the building's actual thermal environment.

[0084] When dynamically calculating hourly meteorological parameters based on the building's total heat load, the static heat load calculation needs to be extended to a dynamic calculation considering time variations. The dynamic calculation process employs an hourly calculation method, dividing the 24 hours of a day into 24 time periods, with each time period using corresponding meteorological parameters for heat load calculation. Hourly meteorological parameters include environmental parameters that change over time, such as outdoor dry-bulb temperature, solar radiation intensity, wind speed, and relative humidity. The dynamic calculation process adjusts the calculated total building heat load based on the hourly meteorological parameters, obtaining heat transfer load data that reflects the changing patterns of the building's thermal environment throughout the day. This heat transfer load data is stored in time-series format, containing hourly heat load values ​​for each of the 24 hours, for subsequent VR visualization processing and energy-saving optimization analysis.

[0085] In one specific embodiment, the process of executing step S104 may specifically include the following steps:

[0086] The heat transfer load data is processed by spatial coordinate mapping to obtain the VR spatial temperature numerical distribution matrix;

[0087] The temperature range is color-coded and assigned according to the VR space temperature numerical distribution matrix to obtain a heat distribution color mapping table.

[0088] The VR architectural space is rendered in three dimensions using a thermal distribution color mapping table to obtain a stereoscopic thermal cloud map display effect.

[0089] The 3D thermal cloud map display effect is integrated with VR interactive controls to obtain an operable thermal distribution interface.

[0090] The user's viewpoint movement is responded to in real time by the operable heat distribution interface to obtain the heat distribution display result.

[0091] Specifically, when performing spatial coordinate mapping on heat transfer load data, it is necessary to establish a correspondence between the heat transfer load data and the VR three-dimensional spatial coordinate system. The spatial coordinate mapping process first extracts the three-dimensional geometric information of the building space from the BIM model, including dimensional parameters such as room length, width, and height. Then, a three-dimensional coordinate system is established with the building's geometric center as the origin. The mapping process spatially locates the heat transfer load data according to its actual distribution within the building space, with each heat transfer load value corresponding to a specific coordinate point in the VR space. The VR space temperature value distribution matrix is ​​stored using a three-dimensional array data structure. The three dimensions of the array correspond to the X-axis, Y-axis, and Z-axis coordinates of the space, respectively, and each array element stores the temperature value at that coordinate location. The temperature value is calculated by the ratio of the heat transfer load data to the building's heat capacity, reflecting the thermal environment conditions at various locations within the building space.

[0092] When color-coding and assigning temperature ranges based on the VR spatial temperature distribution matrix, it is necessary to establish a mapping relationship between temperature values ​​and visual colors. The color coding and assignment process first analyzes the maximum and minimum values ​​in the temperature distribution matrix to determine the range of temperature variation, and then divides the temperature range into several equal temperature intervals. Color coding uses the color temperature principle, mapping low-temperature areas to cool tones, high-temperature areas to warm tones, and medium-temperature areas to neutral tones. The thermal distribution color mapping table uses the RGB color model, assigning each temperature interval a set of RGB values, forming a complete correspondence between temperature and color. The color transitions in the color mapping table use a linear interpolation algorithm to ensure smooth and continuous color changes between adjacent temperature intervals, avoiding abrupt color jumps.

[0093] When performing 3D shading and rendering of VR architectural spaces based on a thermal distribution color map, color information needs to be applied to the 3D surface of the architectural model. The 3D shading and rendering process employs a surface shading algorithm. Based on the temperature value at each coordinate point in the VR space temperature distribution matrix, the corresponding RGB color value is looked up in the thermal distribution color map, and then assigned to the corresponding position on the architectural model's surface. The rendering process considers the surface normals and lighting conditions, adjusting the brightness and saturation of the colors through lighting calculations to create a three-dimensional shading effect. The 3D thermal cloud map display is a visual representation of the color distribution on the 3D architectural model's surface; different color areas represent different temperature levels, allowing users to intuitively understand the thermal environment inside the building by observing the color distribution.

[0094] When integrating a 3D thermal cloud map display with VR interactive controls, user interface elements need to be created within the VR environment. Interface integration involves adding interactive controls such as a temperature value display panel, color legends, and view control buttons to the VR space. These controls, combined with the 3D thermal cloud map display, form a complete visual interface. The interactive controls employ a 3D UI design, with their position and size adaptively adjusting based on the user's viewing distance to ensure clear visibility from different viewing angles. The operable thermal distribution interface includes interactive functions such as click selection, value query, and area zoom. Users operate these functions using the VR device's controller to obtain detailed temperature information.

[0095] When responding to user viewpoint movements in real-time using an operable heat distribution interface, a dynamic feedback mechanism between user actions and interface display needs to be established. Real-time response processing monitors changes in the position of the user's head and hand controllers. When the user moves their viewpoint or changes their viewing distance, the VR rendering engine recalculates the heat distribution display content within the visible area. The response processing employs a frame rate synchronization mechanism to ensure that the interface display remains synchronized with user actions, avoiding delays or stuttering. The heat distribution display results dynamically adjust the displayed content based on the user's current viewpoint and focus of observation, including features such as enhanced color contrast, magnified details in local areas, and real-time temperature updates.

[0096] In one specific embodiment, the process of executing step S105 may specifically include the following steps:

[0097] Hotspot identification is performed on temperature anomaly areas in the thermal distribution display results to obtain a list of key components for improvement.

[0098] Based on the list of key components to be improved, the types of insulation materials are replaced and selected to obtain a set of candidate insulation solutions;

[0099] Based on the set of candidate thermal insulation solutions, the heat distribution of the VR environment is updated and rendered in real time to obtain a solution comparison display interface.

[0100] Visual difference recognition processing was performed on the temperature changes in the scheme comparison display interface to obtain the insulation effect verification results.

[0101] Based on the results of the insulation effect verification, the insulation configuration of the components is selected and optimized to obtain the optimal configuration scheme of the components.

[0102] Specifically, when identifying hotspots in the thermal distribution display results, it's necessary to identify areas with significantly higher or lower temperatures from the thermal distribution results displayed in the VR environment. Hotspot identification uses a threshold detection algorithm. First, the average and standard deviation of all temperature data in the thermal distribution display results are calculated. Then, a criterion for judging temperature anomalies is set; typically, areas exceeding the average plus or minus twice the standard deviation are marked as temperature anomaly areas. The identification process traverses each temperature data point in the VR space, specially marking data points that meet the anomaly criteria, while simultaneously recording the spatial coordinates of the anomaly areas and the corresponding BIM component identifiers. Temperature anomaly areas are highlighted in the VR interface using different visual markers; for example, a red flashing box indicates a high-temperature anomaly area, and a blue flashing box indicates a low-temperature anomaly area. A key improvement is to the component list, which uses the spatial location of temperature anomaly areas to find the corresponding BIM components, including the building envelope components such as walls, doors, windows, and roofs involved in the area. The list records detailed information such as the component type, location, and current insulation configuration.

[0103] When selecting replacement insulation materials based on the list of key improvement components, suitable insulation material options need to be screened from a material database. The replacement selection process first analyzes the current insulation configuration of each component in the list, including parameters such as insulation material type, thickness, and thermal conductivity. Then, it searches the material database for alternative materials with superior thermal performance. The material database contains complete data on the thermal parameters, physical properties, applicable scope, and cost information of various insulation materials. The replacement selection process screens suitable candidate materials based on component type and usage environment. For example, for exterior wall components, rock wool or polystyrene board with good weather resistance is preferred, while for roof components, extruded polystyrene board or polyurethane with strong waterproof performance is preferred. The candidate insulation scheme set contains multiple insulation material replacement schemes for each key improvement component. Each scheme records information such as the type, thickness, expected thermal resistance value, and construction requirements of the new material, forming a complete material replacement option library.

[0104] When performing real-time updates and rendering of the thermal distribution in the VR environment based on a set of candidate insulation schemes, it is necessary to recalculate the building's thermal performance after adopting the new insulation material and update the VR display. The real-time update rendering process replaces the original component parameters with the new material parameters from the candidate insulation schemes, re-executes the thermal calculation process, including steps such as component thermal resistance calculation, thermal bridge correction, and heat transfer load calculation, to obtain updated heat transfer load data. The rendering process maps the new heat transfer load data to the VR spatial coordinate system, regenerates the temperature distribution matrix, and updates the thermal distribution display effect in the VR environment through color coding and 3D shading algorithms. The scheme comparison display interface uses a split-screen or switching method to simultaneously display the thermal distribution effects of the original and improved schemes. Users can intuitively observe the changes in the building's thermal environment under different insulation schemes through the VR interface, which includes functional modules such as temperature comparison charts, improved area markers, and numerical change displays.

[0105] When performing visual difference recognition processing on the temperature changes displayed in the solution comparison interface, it is necessary to quantitatively analyze the degree of temperature difference between different insulation solutions. Visual difference recognition processing employs an image difference algorithm, comparing the heat distribution color images of the original and improved solutions at the pixel level, calculating the color difference value for each pixel. A larger difference value indicates a more significant temperature improvement at that location. The recognition process converts the color difference values ​​into temperature improvement magnitudes, and through statistical analysis, obtains quantitative indicators such as the overall improved area, average temperature reduction, and maximum temperature improvement value. The insulation effect verification results include a spatial distribution map of temperature improvement, statistical data on improvement effects, and an assessment of energy-saving potential. The verification results are displayed in the VR interface in the form of visual charts and numerical reports, allowing users to understand the actual improvement effects of different insulation solutions.

[0106] When selecting the optimal insulation configuration for components based on the insulation effect verification results, multiple factors such as insulation effect, economic cost, and construction feasibility need to be comprehensively considered. The optimal selection process establishes a multi-objective evaluation system, using temperature improvement effect as the main evaluation indicator, and material cost and construction difficulty as constraints. Candidate insulation schemes are comprehensively ranked using a weighted scoring method. The evaluation process calculates the comprehensive score for each insulation scheme, which includes the weighted sum of the insulation effect score multiplied by a weighted coefficient, the cost control score multiplied by a weighted coefficient, and the construction feasibility score multiplied by a weighted coefficient. The optimized component configuration scheme selects the insulation scheme with the highest comprehensive score as the recommended configuration. The scheme details the complete information for each key improved component, including the type of insulation material, thickness specifications, construction method, and expected improvement effect.

[0107] The BIM-based green building energy-saving design optimization method in the embodiments of this application has been described above. The BIM-based green building energy-saving design optimization system in the embodiments of this application is described below. Please refer to [link / reference]. Figure 2 One embodiment of the BIM-based green building energy-saving design optimization system in this application includes:

[0108] The identification module is used to identify the thermal parameters of the material layers of BIM components and obtain a layered thermal resistance value data table.

[0109] The positioning module is used to perform thermal bridge positioning calculations at the component connection interface based on the layered thermal resistance data table, and to form a set of thermal bridge correction coefficients.

[0110] The calculation module is used to perform the calculation of the overall heat transfer coefficient of the building based on the set of thermal bridge correction coefficients, and generate heat transfer load data;

[0111] The projection module is used to project the heat transfer load data into the VR space for temperature field visualization processing, and generate heat distribution display results;

[0112] The verification module is used to verify the thermal response by adjusting the insulation parameters in the heat distribution display results, and to determine the optimal configuration scheme of the components.

[0113] above Figure 2 The BIM-based green building energy-saving design optimization system in this embodiment of the invention will be described in detail from the perspective of modular functional entities. The BIM-based green building energy-saving design optimization equipment in this embodiment of the invention will be described in detail from the perspective of hardware processing.

[0114] Reference Figure 3 This invention also provides a BIM-based green building energy-saving design optimization device, which can be a server, and its internal structure can be as follows: Figure 3 As shown, the BIM-based green building energy-saving design optimization device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor, designed for computing, provides computational and control capabilities. The memory of the BIM-based green building energy-saving design optimization device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the BIM-based green building energy-saving design optimization device stores the data corresponding to this embodiment. The network interface of the BIM-based green building energy-saving design optimization device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0115] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the BIM-based green building energy-saving design optimization equipment to which the present invention is applied.

[0116] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the BIM-based green building energy-saving design optimization method.

[0117] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0118] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a BIM-based green building energy-saving design optimization device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0119] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A BIM-based green building energy-saving design optimization method, characterized in that, The method includes: Thermal parameters of BIM component materials are identified and processed to obtain a layered thermal resistance data table. Based on this data table, thermal bridge location calculations are performed at component connection interfaces to form a set of thermal bridge correction coefficients. The overall building heat transfer coefficient is calculated based on this set, generating heat transfer load data. This heat transfer load data is then projected into a VR space for temperature field visualization, producing a heat distribution display result. Thermal response verification is performed by adjusting the insulation parameters in the heat distribution display result, determining the optimal component configuration scheme. The step of verifying thermal response by adjusting the insulation parameters in the thermal distribution display results and determining the optimal configuration scheme for components includes: identifying hotspots in the temperature anomaly areas of the thermal distribution display results to obtain a list of key components for improvement; replacing and selecting insulation material types based on the list of key components for improvement to obtain a set of candidate insulation schemes; performing real-time updating and rendering of the VR environment thermal distribution based on the set of candidate insulation schemes to obtain a scheme comparison display interface; performing visual difference recognition processing on the temperature changes in the scheme comparison display interface to obtain insulation effect verification results; and selecting the optimal insulation configuration for components based on the insulation effect verification results to obtain an optimized configuration scheme for components. The process of projecting the heat transfer load data into a VR space for temperature field visualization to generate a heat distribution display result includes: performing spatial coordinate mapping on the heat transfer load data to obtain a VR space temperature value distribution matrix; performing color coding allocation on temperature ranges according to the VR space temperature value distribution matrix to obtain a heat distribution color mapping table; performing three-dimensional color rendering on the VR building space based on the heat distribution color mapping table to obtain a stereoscopic thermal cloud map display effect; integrating the stereoscopic thermal cloud map display effect with VR interactive controls to obtain an operable heat distribution interface; and performing real-time response processing on user viewpoint movement according to the operable heat distribution interface to obtain a heat distribution display result; wherein, color coding rules are set according to the temperature value range.

2. The BIM-based green building energy-saving design optimization method according to claim 1, characterized in that, The process of identifying thermal parameters for the material layers of BIM components to obtain a layered thermal resistance data table includes: Structural analysis is performed on the material hierarchy objects of BIM components to obtain the component's hierarchical organizational structure data; Based on the component's layered organizational structure data, the material name field is queried from a thermal database to obtain a set of thermal parameters for a single-layer material. Based on the set of thermal parameters of the single-layer material, the thermal resistance values ​​of each material layer are accumulated and calculated to obtain the equivalent thermal resistance coefficient of the component. The equivalent thermal resistance coefficients of the components are classified and organized according to the type of walls, doors and windows to obtain a component thermal resistance value index table. Based on the component thermal resistance index table, thermal resistance values ​​of all building components are matched to obtain a layered thermal resistance data table.

3. The BIM-based green building energy-saving design optimization method according to claim 1, characterized in that, The step of calculating the location of thermal bridges at the component connection interface based on the layered thermal resistance data table, forming a set of thermal bridge correction coefficients, includes: The spatial position relationship of the components in the layered thermal resistance data table is analyzed to obtain the coordinate set of the component contact interface. Based on the coordinate set of the component contact interface, the thermal resistance difference of the connection parts of different materials is calculated to obtain the thermal resistance jump position data. Based on the thermal resistance jump location data, the geometric shape analysis of the beam-column-wall connection node is performed to obtain the thermal bridge geometric characteristic parameters. The thermal bridge geometric feature parameters are input into the thermal bridge calculation formula to solve for the correction coefficient, and the single-point thermal bridge correction value is obtained. The thermal bridge correction coefficient set is obtained by summarizing the values ​​of the single-point thermal bridge correction values ​​for all thermal bridge locations in the building.

4. The BIM-based green building energy-saving design optimization method according to claim 3, characterized in that, The step of inputting the geometric characteristic parameters of the thermal bridge into the thermal bridge calculation formula for correction coefficient calculation to obtain the single-point thermal bridge correction value includes: The thermal bridge length in the thermal bridge geometric feature parameters is extracted linearly to obtain the effective calculated length of the thermal bridge. The thermal conductivity of the material at the thermal bridge location is calculated by weighted average based on the effective calculated length of the thermal bridge to obtain the equivalent thermal conductivity of the thermal bridge. The thermal resistance of the thermal bridge section thickness is calculated based on the equivalent thermal conductivity of the thermal bridge to obtain the thermal bridge line heat transfer coefficient. The heat transfer coefficient of the thermal bridge line is multiplied by the indoor and outdoor temperature difference to obtain the heat transfer per unit length of the thermal bridge. The total heat transfer of the component is further calculated based on the heat transfer per unit length of the thermal bridge to obtain the correction value for the single-point thermal bridge.

5. The BIM-based green building energy-saving design optimization method according to claim 1, characterized in that, The calculation of the overall building heat transfer coefficient based on the set of thermal bridge correction coefficients, generating heat transfer load data, includes: The values ​​in the set of thermal bridge correction coefficients are processed by the heat transfer coefficient correction calculation of the building envelope to obtain the corrected heat transfer coefficient of the building envelope. Based on the modified heat transfer coefficient of the building envelope, the area of ​​the exterior wall in each direction of the building is weighted and calculated to obtain the overall heat transfer coefficient of the building. Based on the overall heat transfer coefficient of the building, the heat transfer of the indoor and outdoor temperature difference is calculated to obtain the heat transfer load of the building foundation. The total building heat load is obtained by superimposing the heat transfer load of the building foundation with the heat gain from solar radiation. The heat transfer load data is obtained by dynamically calculating and processing hourly meteorological parameters based on the total building heat load.

6. A BIM-based green building energy-saving design optimization system, characterized in that, For implementing the BIM-based green building energy-saving design optimization method as described in any one of claims 1-5, the BIM-based green building energy-saving design optimization system comprises: The identification module is used to identify the thermal parameters of the material layers of BIM components and obtain a layered thermal resistance value data table. The positioning module is used to perform thermal bridge positioning calculations at the component connection interface based on the layered thermal resistance data table, and to form a set of thermal bridge correction coefficients. The calculation module is used to perform the calculation of the overall heat transfer coefficient of the building based on the set of thermal bridge correction coefficients, and generate heat transfer load data; The projection module is used to project the heat transfer load data into the VR space for temperature field visualization processing, and generate heat distribution display results; The verification module is used to verify the thermal response by adjusting the insulation parameters in the heat distribution display results, and to determine the optimal configuration scheme of the components.

7. A BIM-based green building energy-saving design optimization device, characterized in that, It includes a memory and a processor, the memory storing a computer program that can run on the processor, and the processor executing the computer program to implement the BIM-based green building energy-saving design optimization method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it causes the processor to execute the BIM-based green building energy-saving design optimization method as described in any one of claims 1 to 5.

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