Modular design method for environment-friendly fabricated building

Abstract

The invention belongs to the technical field of building design, and discloses an environment-friendly fabricated building modular design method. The method comprises the steps that customer demand data is acquired and analyzed, building components are determined, and adaptive components are screened out from a pre-constructed modular component library; building components which do not exist in the modular component library are marked as missing components, and all the missing components are subjected to personalized design; combining all the adaptive components and the missing components to form a fabricated building model, comprehensively evaluating the environmental protection performance, and judging whether a component optimization instruction is generated or not; if the component optimization instruction is generated, evaluating the environmental protection performance of each adaptive component and the missing component, and identifying a defective component; parameter optimization is carried out on each defective component, and a design scheme is generated; according to the method, diversified building requirements can be effectively met, in-depth analysis and customized design of building components are achieved, and the environmental influence is reduced to the maximum extent.

Description

Technical Field

[0001] This invention relates to the field of architectural design technology, and more specifically, to an environmentally friendly modular design method for prefabricated buildings. Background Technology

[0002] With the intensification of global climate change and the continuous improvement of environmental awareness, the construction industry, as a major consumer of resources and a major source of carbon emissions, is facing unprecedented pressure to transform. Traditional construction methods suffer from problems such as long construction cycles, serious resource waste, and significant environmental pollution. Prefabricated buildings, as a new construction method, are gradually becoming an important direction for the green development of the construction industry due to their standardized design, factory production, and assembly construction. However, prefabricated buildings still face many challenges in practical applications: on the one hand, there is a contradiction between standardization and personalized needs, making it difficult to meet the diverse requirements of different customers; on the other hand, the environmental performance evaluation system for prefabricated components is imperfect, making it difficult to achieve green design throughout the entire life cycle. Therefore, there is an urgent need for an intelligent prefabricated building design method to promote the minimization of environmental impact while meeting diverse needs, and to drive the construction industry towards a greener, lower-carbon, and more sustainable development direction.

[0003] Patent CN119026225A discloses a modular method for rapidly generating preliminary architectural design schemes. The method includes: a user inputting basic information about the architectural scheme; selecting the area to be generated within an orthogonal grid based on the input information, and determining the number of floors using a push-pull control axis; generating a preliminary 3D architectural model based on the required area and number of floors; the user making detailed adjustments to the preliminary 3D architectural model to obtain the final 3D architectural model; real-time calculation and updating of the preliminary architectural scheme data based on the generated 3D architectural model, and displaying the data on the user interface; and outputting the generated architectural scheme and data. This invention reduces manpower and time costs in the design process, thereby lowering the overall design cost.

[0004] However, while the aforementioned technologies can achieve architectural design, they mainly focus on the rapid construction of building forms and cannot conduct in-depth analysis and customized design of building components. At the same time, the aforementioned technologies have not yet established a complete environmental performance assessment and optimization mechanism, and cannot achieve component optimization based on environmental standards. As a result, the design scheme fails to meet the optimal environmental requirements, making it difficult to meet the comprehensive needs for environmental performance and personalization in complex building projects, thus limiting the scalability of its application in complex building engineering.

[0005] In view of this, the present invention proposes an environmentally friendly modular design method for prefabricated buildings to solve the above problems. Summary of the Invention

[0006] To overcome the aforementioned deficiencies of the prior art and achieve the above objectives, the present invention provides the following technical solution: an environmentally friendly modular design method for prefabricated buildings, comprising:

[0007] S1: Obtain customer demand data;

[0008] S2: Analyze customer demand data using a component identification algorithm to determine building components and select suitable components from a pre-built modular component library;

[0009] S3: Mark building components that do not exist in the modular component library as missing components, and perform personalized design on all missing components;

[0010] S4: Combine all compatible and missing components to form a prefabricated building model, and comprehensively evaluate the environmental performance of the prefabricated building model through a multi-dimensional index system to determine whether to generate a component optimization instruction. If no component optimization instruction is generated, a design scheme is generated.

[0011] S5: If a component optimization instruction is generated, a quantitative analysis method is used to evaluate the environmental performance of each compatible and missing component, and to identify defective components among the compatible and missing components.

[0012] S6: For each defective component, apply an iterative optimization algorithm to optimize the parameters and generate a design scheme.

[0013] Furthermore, the customer demand data includes architectural drawing information and customer cost information; the architectural drawing information includes architectural floor plans, architectural elevations, architectural sections, and architectural structural drawings.

[0014] The method for determining building components includes:

[0015] Based on the architectural drawings, a building information model is constructed using BIM software. Using the built-in tools of the BIM software, the component information corresponding to each component in the building information model is extracted. The component information includes the component type and component size, and all extracted components are marked as building components.

[0016] Methods for selecting compatible components include:

[0017] From the modular component library, all components with the same component type as building components are selected and marked as selected components. All selected components are classified according to their corresponding component types, and selected components with the same component type are grouped into component sets, with each component set corresponding to a component type. Dimensional and connection analyses are performed on the components in each component set, and one component is selected from each component set and marked as an adaptable component. The modular component library is a component database containing various standardized components, and each component has component information, connection information, cost information, and component combination materials. The connection information includes the connection method and connection point location.

[0018] Furthermore, the step of selecting one component from each set of components includes:

[0019] Step S101: Identify the load-bearing components in the building components, and mark the component set corresponding to each load-bearing component as a load-bearing set;

[0020] Step S102: Mark each component in the load-bearing set as a load-bearing screening component, and obtain the component size and connection information corresponding to each load-bearing screening component from the modular component library;

[0021] Step S103: Compare the component size of each load-bearing component with the component size of the corresponding load-bearing screening component, select one component from each load-bearing set, and mark it as the current component;

[0022] Step S104: Mark all building components connected to each current component as successor components, mark all component sets corresponding to successor components as successor sets, mark each component in the successor set as a successor filter component, and obtain the component size and connection information corresponding to each successor filter component from the modular component library.

[0023] Step S105: Based on the component size and connection information of each successor component and the corresponding successor filtered component, select one component from each successor set and mark it as the update component, and update the current component as the update component;

[0024] Step S106: Repeat steps S104 to S105 until a component is selected from each set of components, at which point the loop ends.

[0025] Furthermore, in step S103, the method for selecting one component from each load-bearing set includes:

[0026] For each load-bearing component, subtract the corresponding value from the size of the corresponding load-bearing screening component to obtain the dimensional tolerance. Analyze each dimensional tolerance sequentially, marking all load-bearing screening components with non-negative dimensional tolerances as primary candidate components, and leaving no component with negative dimensional tolerances. Set a tolerance threshold, and compare the dimensional tolerance of each primary candidate component with the threshold. Mark all primary candidate components with dimensional tolerances less than the threshold as secondary candidate components, and leave no component with dimensional tolerances greater than or equal to the threshold. Add the dimensional tolerances of each secondary candidate component sequentially to obtain the total tolerance. Compare the total tolerances of components with the same total tolerance for each load-bearing set, and select the secondary candidate component with the smallest total tolerance from each load-bearing set.

[0027] In step S105, the method for selecting a component from each successor set includes:

[0028] Retrieve the connection information of each current component from the modular component library; compare the connection information of each subsequent component with the connection information of each corresponding subsequent filter component; delete the filter components whose connection information is different from that of the subsequent components from the corresponding successor set, and retain the filter components whose connection information is the same as that of the subsequent components in the corresponding successor set; mark the successor set after deleting components as a candidate set, and select one component from each candidate set; the method for selecting one component from each candidate set is the same as the method for selecting one component from each load-bearing set.

[0029] Furthermore, the method for personalized design of all missing components includes:

[0030] Based on the building information model, all building components connected to the missing component are marked as adjacent components; the connection information of each adjacent component is obtained and used as the connection information of the corresponding missing component; the cost information of each adaptable component is obtained from the modular component library; the cost information of each adaptable component is subtracted from the customer cost information in turn to obtain the cost margin; different numerical labels are set for different component types, connection methods and connection point locations to obtain type labels, method labels and location labels.

[0031] The cost margin, along with the type label, method label, location label, and component size corresponding to each missing component, are sequentially input into a trained cost allocation model to predict cost allocation data, which includes cost information for each missing component. The type label, method label, location label, and cost information for each missing component are then used as a set of analysis data, with each set corresponding to a specific missing component. Each set of analysis data is then input into a trained material determination model to predict the corresponding material label. The material label is a numerical label corresponding to the material combination of the component; different material combinations of components have different material labels. Based on the component information, connection information, and material combination of each missing component, a personalized design is performed for each missing component. Both the cost allocation model and the material determination model are deep neural network models.

[0032] Furthermore, the method for combining all adapting components and missing components includes:

[0033] Obtain the component combination material corresponding to each adaptable component from the modular component library, and map the component information, connection information and component combination material corresponding to each adaptable component and missing component to the corresponding building component in the building information model in sequence to complete the combination of all adaptable components and missing components.

[0034] Methods for comprehensively evaluating the environmental performance of prefabricated building models include:

[0035] The material properties of each building component are obtained, and each set of material properties is sequentially mapped to the corresponding building component in the building information model to form a building physical model. The building physical model is analyzed in multiple dimensions using comprehensive building performance simulation software to calculate the carbon emissions of the building physical model. The reciprocal of the carbon emissions of the building physical model is used as the environmental performance of the prefabricated building model.

[0036] The method for determining whether to generate a component optimization instruction is as follows:

[0037] Based on the architectural drawings, obtain the building type and scale. The building scale includes the total building area and building height. The total building area is the sum of the horizontal projected areas of all floors, and the building height is the vertical distance from the ground to the highest point of the building. Different numerical labels are assigned to different building types and marked as building tags. The building tags and building scale of the prefabricated building model are input into the environmental analysis model to predict the corresponding environmental thresholds. The environmental analysis model is a deep neural network model. The environmental performance of the prefabricated building model is compared with the environmental thresholds. If the environmental performance is greater than the environmental threshold, no component optimization instructions are generated; if the environmental performance is less than or equal to the environmental threshold, component optimization instructions are generated.

[0038] Furthermore, the method for evaluating the environmental performance of each compatible and missing component includes:

[0039] The material thermal parameters corresponding to each compatible and missing component are obtained, and these parameters are mapped to the corresponding building components in the building information model to form a thermal performance model for each building component. For each building component's thermal performance model, heat transfer characteristics are analyzed using heat transfer simulation software to calculate the energy loss value corresponding to each building component. A carbon emission factor is preset, and the energy loss value corresponding to each building component is multiplied by the carbon emission factor to obtain the carbon emission of each building component. The reciprocal of the carbon emission of each building component is used as the environmental performance of the corresponding compatible or missing component.

[0040] The method for identifying defective components in both adapted and missing components includes:

[0041] The environmental performance of each building component is divided by the environmental performance of the prefabricated building model to obtain the component weight; the weight of each component is multiplied by the environmental threshold to obtain the sub-environmental threshold corresponding to each building component; the environmental performance of each building component is compared with the corresponding sub-environmental threshold, and building components with environmental performance less than or equal to the corresponding sub-environmental threshold are marked as defective components, while building components with environmental performance greater than the corresponding sub-environmental threshold are not marked.

[0042] Furthermore, methods for optimizing parameters for each defective component include:

[0043] Obtain a material set, which includes all materials applicable to various building components; randomly select 'a' materials from the material set to construct a set of material combinations, resulting in b sets of material combinations, all of which are distinct, where 'a' and 'b' are both integers greater than 0; randomly select 'c' material combinations to construct a set of parameters, resulting in d sets of parameter sets, all of which are distinct, where 'c' represents the number of defective components, and each material combination corresponds one-to-one with a defective component, and 'd' is an integer greater than 0; assign sequentially increasing numerical labels to the d sets of parameter sets and mark them as set labels, with the set label range being [1, d]; randomly select a set label as the initial iteration center, subtract one from 'd' and divide by two to obtain the initial iteration radius, and set the iteration count to 0;

[0044] Define the iterative process as follows: generate m candidate solutions within the range of the set labels, and calculate the environmental optimization degree corresponding to each candidate solution, 1 < m < d, and the candidate solutions correspond one-to-one with the set labels; mark the candidate solution with the highest environmental optimization degree as the temporary optimal solution, and determine whether to move the iteration center to the temporary optimal solution, and adjust the iteration radius according to the iteration center;

[0045] The process involves executing an iterative process, incrementing the iteration count by one with each iteration. An iterative threshold is preset; when the iteration count is greater than or equal to the threshold, the iterative process is stopped, the set label corresponding to the iteration center is marked as the optimal label, and the parameters of each defective component are optimized based on the parameter set corresponding to the optimal label.

[0046] Furthermore, methods for generating m candidate solutions include:

[0047] From the interval [-1,1], generate m random coefficients. Multiply each of the m random coefficients by the iteration radius and add the iteration center to obtain m iteration coefficients. Select m set labels with the same value as the iteration coefficients from the range of set labels and use them as m candidate solutions.

[0048] Methods for calculating the environmental optimization degree corresponding to candidate solutions include:

[0049] Based on the parameter set corresponding to the set label of the candidate solution, the component combination material of each defective component is optimized, and the environmental performance of each defective component is re-evaluated; the environmental performance of each missing component is multiplied by the corresponding component weight, and then added together to obtain the missing environmental degree.

[0050] Each defective component's type label, method label, location label, and material label are used as a set of evaluation data, with each set of evaluation data corresponding to a defective component. Each set of evaluation data is input into a pre-trained cost evaluation model (a deep neural network model) to evaluate the corresponding cost information. The cost information of each defective component is summed sequentially to obtain the defect cost information. All building components not marked as defective are marked as normal components, and the cost information of each normal component is summed sequentially to obtain the normal cost information. The defect cost information and normal cost information are summed to obtain the total cost information. The total cost information is compared with the customer cost information. If the total cost information is greater than the customer cost information, the environmental optimization degree corresponding to the candidate solution is 0. If the total cost information is less than or equal to the customer cost information, the missing environmental optimization degree is multiplied by the corresponding weight coefficient in a preset weight set to obtain the environmental optimization weight, and the total cost information is multiplied by the corresponding weight coefficient in the preset weight set to obtain the cost weight. The environmental optimization degree is obtained by subtracting the cost weight from the environmental optimization weight; the environmental optimization degree is a non-negative number.

[0051] Furthermore, methods for determining whether to move the iteration center to the provisional optimal solution include:

[0052] Calculate the environmental optimization degree of the set label corresponding to the iteration center and mark it as the center optimization degree; compare the center optimization degree with the environmental optimization degree of the temporary optimal solution; if the center optimization degree is less than the environmental optimization degree of the temporary optimal solution, move the iteration center to the temporary optimal solution; if the center optimization degree is greater than or equal to the environmental optimization degree of the temporary optimal solution, do not move the iteration center to the temporary optimal solution.

[0053] Methods for adjusting the iteration radius based on the iteration center include:

[0054] If the iteration center is moved to the temporary optimal solution, a first adjustment coefficient is randomly selected from the interval [0.5,1], and the iteration radius is multiplied by the first adjustment coefficient to complete the adjustment of the iteration radius; if the iteration center is not moved to the temporary optimal solution, a second adjustment coefficient is randomly selected from the interval [0,0.5], and the iteration radius is multiplied by the second adjustment coefficient to complete the adjustment of the iteration radius.

[0055] The technical effects and advantages of the modular design method for environmentally friendly prefabricated buildings of this invention are as follows:

[0056] By identifying building components and screening modular components, suitable components that meet building requirements can be effectively selected from a pre-built component library, balancing personalized needs with standardized production. For building components missing from the modular component library, deep learning models are used for personalized design, which not only matches customer needs but also allocates cost budgets reasonably, improving the design's relevance and economy. During the prefabricated building model construction and environmental performance assessment stages, factors such as building type and scale are fully considered to set environmental target thresholds that meet actual needs, ensuring that the design scheme meets the project's environmental requirements. Environmental performance is assessed for each building component, and for defective components that do not meet environmental performance standards, iterative optimization algorithms are used to optimize parameters, maximizing the overall building's environmental performance while meeting budget costs, achieving green design throughout the entire life cycle. This effectively meets diverse building needs, enabling in-depth analysis and customized design of building components while minimizing environmental impact. It not only improves design efficiency but also promotes the transformation of the construction industry towards green, low-carbon, and sustainable development through comprehensive environmental performance assessment, meeting the comprehensive needs for environmental performance and personalization in complex building projects, and providing an innovative path for achieving intelligent building design. Attached Figure Description

[0057] Figure 1 This is a flowchart of an environmentally friendly prefabricated building modular design method according to Embodiment 1 of the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Example 1

[0060] Please see Figure 1 As shown in this embodiment, an environmentally friendly prefabricated building modular design method includes:

[0061] S1: Obtain customer demand data.

[0062] Customer demand data is a comprehensive collection of information reflecting customers' expectations for the functionality, performance, and economic aspects of a building project. This data includes architectural drawings and customer cost information. Architectural drawings include floor plans (horizontal cross-sections of the building viewed from above, showing room layouts, wall locations, and window and door distribution), elevations (vertical views of the building's exterior, showing its appearance, height, and window locations), and sections (internal structural diagrams of the building cut vertically along a specific direction, showing interior space height, floor relationships, staircase locations, and walls). The building structure includes structural drawings (showing the load-bearing system and structural components, including the dimensions, locations, and connections of supporting components such as load-bearing columns, beams, floor slabs, and walls), reflecting the client's specific requirements for spatial layout (corresponding to wall components, partition components, floor slab components, etc.), appearance (corresponding to exterior wall panel components, window components, etc.), internal structure (corresponding to stair components, interior wall components, ceiling components, etc.), and load-bearing system (corresponding to column components, beam components, wall panel components, etc.); the client cost information is the total budget for the entire construction project, reflecting the client's expectations for the construction project budget and cost control.

[0063] S2: Use component recognition algorithms to analyze customer demand data, identify building components, and select suitable components from a pre-built modular component library.

[0064] Methods for determining building components include:

[0065] Based on the architectural drawings, a Building Information Model (BIM model) is constructed using BIM software (such as Revit, ArchiCAD, Bentley, etc.). This involves integrating information from architectural floor plans, elevations, sections, and structural drawings into a 3D model containing various components. Using built-in tools in the BIM software (such as family parameters and project parameters in Revit), the component information corresponding to each component in the BIM model is extracted. The component information includes component type (such as wall components, stair components, column components, etc.) and component dimensions (such as length, width, height, thickness, etc.). All extracted components are then labeled as architectural components.

[0066] Methods for selecting compatible components include:

[0067] From the modular component library, all components with the same component type as building components are selected and marked as selected components. All selected components are categorized according to their corresponding component types, and selected components with the same component type are grouped into component sets, with each component set corresponding to a specific component type. Dimensional and connection analyses are performed on the components in each component set, and one component is selected from each set and marked as a compatible component. The modular component library is a component database containing various standardized components, and each component has component information, connection information, cost information, and component assembly materials; connection information... The information includes connection methods and connection point locations; connection methods refer to the ways in which components are connected, describing how components are physically connected and fixed to each other, such as welding, bolting, riveting, and masonry connections; connection point locations refer to the specific locations where components are connected, i.e., the contact points or connection points between components, such as end connections, mid-section connections, and corner connections; cost information is the total economic expenditure incurred by the component in the procurement, processing, transportation, and installation stages, covering the economic expenditure throughout the entire life cycle of the component; component composite materials are composite materials formed by combining multiple different materials, such as reinforced concrete, composite wood, and aluminum alloy glass curtain walls.

[0068] The steps to select one component from each set of components include:

[0069] Step S101: Identify the load-bearing components in the building structure and mark the component set corresponding to each load-bearing component as the load-bearing set. Load-bearing components are the components in the building structure that bear the main load and play a decisive role in the stability and safety of the entire building. Load-bearing components include beam components, column components, wall components, etc.

[0070] Step S102: Mark each component in the load-bearing set as a load-bearing screening component, and obtain the component size and connection information corresponding to each load-bearing screening component from the modular component library;

[0071] Step S103: Compare the component size of each load-bearing component with the component size of the corresponding load-bearing screening component, select one component from each load-bearing set, and mark it as the current component;

[0072] Step S104: Mark all building components connected to each current component as successor components, mark all component sets corresponding to successor components as successor sets, mark each component in the successor set as a successor filter component, and obtain the component size and connection information corresponding to each successor filter component from the modular component library.

[0073] Step S105: Based on the component size and connection information of each successor component and the corresponding successor filtered component, select one component from each successor set and mark it as the update component, and update the current component as the update component;

[0074] Step S106: Repeat steps S104 to S105 until a component is selected from each set of components, at which point the loop ends.

[0075] In step S103 above, the method for selecting one component from each load-bearing set includes:

[0076] For each load-bearing component, subtract the corresponding value from the size of the corresponding load-bearing screening component to obtain the dimensional tolerance. Analyze each dimensional tolerance sequentially, marking all load-bearing screening components with non-negative dimensional tolerances as primary candidate components, and leaving load-bearing screening components with negative dimensional tolerances unmarked. Preset a tolerance threshold, which is pre-set by those skilled in the art based on actual conditions. Compare the dimensional tolerance of each primary candidate component with the tolerance threshold, marking all primary candidate components with dimensional tolerances less than the tolerance threshold as secondary candidate components, and leaving primary candidate components with dimensional tolerances greater than or equal to the tolerance threshold unmarked. Add the dimensional tolerances of each secondary candidate component sequentially to obtain the total tolerance. Compare the total tolerances of the same load-bearing sets, and select the secondary candidate component with the smallest total tolerance from each load-bearing set.

[0077] In step S105 above, the method for selecting a component from each successor set includes:

[0078] Retrieve the connection information of each current component from the modular component library; compare the connection information of each subsequent component with the connection information of each corresponding subsequent filter component; delete the filter components whose connection information is different from that of the subsequent components from the corresponding successor set, and retain the filter components whose connection information is the same as that of the subsequent components in the corresponding successor set; mark the successor set after deleting components as a candidate set, and select one component from each candidate set; the method for selecting one component from each candidate set is the same as the method for selecting one component from each load-bearing set.

[0079] S3: Mark building components that do not exist in the modular component library as missing components, and perform personalized design on all missing components.

[0080] Methods for customizing all missing components include:

[0081] Based on the Building Information Model (BIM), all building components connected to the missing component are marked as adjacent components. The connection information of each adjacent component is obtained and used as the connection information for the corresponding missing component. The cost information of each adaptable component is obtained from the modular component library. The cost margin is obtained by subtracting the cost information of each adaptable component from the customer cost information. Different numerical labels are set for different component types, connection methods, and connection point locations, resulting in type labels, method labels, and location labels. The type label is the numerical label corresponding to the component type, the method label is the numerical label corresponding to the connection method, and the location label is the numerical label corresponding to the connection point location.

[0082] The cost margin, along with the type label, method label, location label, and component size corresponding to each missing component, are sequentially input into the trained cost allocation model to predict cost allocation data. This cost allocation data includes cost information for each missing component. The type label, method label, location label, and cost information for each missing component are then used as a set of analysis data, with each set corresponding to a specific missing component. Each set of analysis data is then input into the trained material determination model to predict the corresponding material label. The material label is a numerical label corresponding to the material combination of the component; different material combinations of components have different material labels. Based on the component information, connection information, and material combination of each missing component, a personalized design is performed for each missing component. Both the cost allocation model and the material determination model are deep neural network models. Deep neural network models are existing technology, and the specific training process will not be elaborated upon here.

[0083] S4: Combine all compatible and missing components to form a prefabricated building model, and comprehensively evaluate the environmental performance of the prefabricated building model through a multi-dimensional index system to determine whether to generate a component optimization instruction. If no component optimization instruction is generated, a design scheme is generated.

[0084] Methods for combining all compatible and missing components include:

[0085] Obtain the component combination material corresponding to each adaptable component from the modular component library, and map the component information, connection information and component combination material corresponding to each adaptable component and missing component to the corresponding building component in the building information model in sequence to complete the combination of all adaptable components and missing components.

[0086] Methods for comprehensively evaluating the environmental performance of prefabricated building models include:

[0087] Those skilled in the art obtain the material properties (such as thermal conductivity, density, specific heat capacity, and other physical properties) corresponding to each building component through material handbooks, academic papers, industry standards, etc.; map each set of material properties to the corresponding building components in the building information model in sequence to form a building physical model; use comprehensive building performance simulation software (such as IES VirtualEnvironment, TRNSYS, OpenStudio, etc.) to conduct multi-dimensional comprehensive analysis of the building physical model, such as thermal bridge effect analysis, thermal resistance analysis, airtightness analysis, etc., calculate the carbon emissions of the building physical model, and use the reciprocal of the carbon emissions of the building physical model as the environmental performance of the prefabricated building model.

[0088] The method for determining whether to generate a component optimization instruction is as follows:

[0089] Based on the architectural drawings, obtain the building type and scale. Building type refers to the building's purpose, such as residential buildings (e.g., multi-story residential buildings, high-rise apartments), office buildings (e.g., office buildings, government office buildings), commercial buildings (e.g., shopping malls, supermarkets), educational buildings (e.g., schools, training centers), etc. Building scale includes total floor area and building height. Total floor area is the sum of the horizontal projected areas of all floors, and building height is the vertical distance from the ground to the highest point of the building. Different numerical labels are assigned to different building types and marked as building labels. The building labels and building scale of the prefabricated building model are input into the environmental analysis model to predict the corresponding environmental thresholds. The environmental analysis model is a deep neural network model. The environmental performance of the prefabricated building model is compared with the environmental thresholds. If the environmental performance is greater than the environmental threshold, no component optimization instruction is generated, indicating that the current prefabricated building model meets environmental requirements and no component optimization is needed. If the environmental performance is less than or equal to the environmental threshold, a component optimization instruction is generated, indicating that the current prefabricated building model does not meet environmental requirements and component optimization is needed.

[0090] It should be noted that different types and sizes of buildings have significantly different needs and performance in terms of energy consumption, carbon emissions, and resource use; building type determines the building's functional requirements, thereby affecting energy consumption patterns and environmental protection goals; while building size is directly related to the building's overall resource consumption and carbon emissions; therefore, by considering these factors, environmental standards for buildings can be set more accurately to ensure that they meet reasonable environmental requirements during the design and construction process.

[0091] The design scheme is a prefabricated building model.

[0092] S5: If a component optimization instruction is generated, a quantitative analysis method is used to evaluate the environmental performance of each compatible and missing component, and to identify defective components among the compatible and missing components.

[0093] Methods for evaluating the environmental performance of each compatible and missing component include:

[0094] Those skilled in the art obtain the material thermal parameters (such as thermal resistance, thermal conductivity, airtightness, etc.) corresponding to each compatible and missing component through material handbooks, product specifications, environmental certification documents, etc.; map the material thermal parameters of each compatible and missing component to the corresponding building components in the building information model to form a thermal performance model for each building component; analyze the heat transfer characteristics of each building component's thermal performance model using heat transfer simulation software (such as THERM, HEAT, etc.) to calculate the energy loss value corresponding to each building component; preset a carbon emission factor, which is pre-set by those skilled in the art according to actual conditions; multiply the energy loss value corresponding to each building component by the carbon emission factor to obtain the carbon emission of each building component, and use the reciprocal of the carbon emission of each building component as the environmental performance of the corresponding compatible or missing component.

[0095] Methods for identifying defective components in adaptable and missing components include:

[0096] The environmental performance of each building component is divided by the environmental performance of the prefabricated building model to obtain the component weight; the weight of each component is multiplied by the environmental threshold to obtain the sub-environmental threshold corresponding to each building component; the environmental performance of each building component is compared with the corresponding sub-environmental threshold, and building components with environmental performance less than or equal to the corresponding sub-environmental threshold are marked as defective components, while building components with environmental performance greater than the corresponding sub-environmental threshold are not marked.

[0097] S6: For each defective component, apply an iterative optimization algorithm to optimize the parameters and generate a design scheme.

[0098] Methods for optimizing parameters for each defective component include:

[0099] Obtain a material set, which includes all materials applicable to various building components; randomly select 'a' types of materials from the material set to construct a set of material combinations, and construct a total of 'b' sets of material combinations, where each of the 'b' sets of material combinations is different, and both 'a' and 'b' are integers greater than 0. In this embodiment, 2 ≤ a ≤ 5 is preferred; randomly select 'c' sets of material combinations to construct a set of parameter sets, and construct a total of 'd' sets of parameter sets, where each of the 'd' sets of parameter sets is different, where 'c' is the number of defective components, and each material combination corresponds one-to-one with a defective component, and 'd' is an integer greater than 0; assign sequentially increasing numerical labels to the 'd' sets of parameter sets and mark them as set labels, with the set label range being [1, 'd']; randomly select a set label as the initial iteration center, subtract one from 'd' and divide by two to obtain the initial iteration radius, and set the iteration count to 0;

[0100] Define the iterative process as follows: generate m candidate solutions within the range of the set labels, and calculate the environmental optimization degree corresponding to each candidate solution, 1 < m < d, and the candidate solutions correspond one-to-one with the set labels; mark the candidate solution with the highest environmental optimization degree as the temporary optimal solution, and determine whether to move the iteration center to the temporary optimal solution, and adjust the iteration radius according to the iteration center;

[0101] The process is iterated, and the iteration count is incremented by one for each iteration. An iteration threshold is preset, which is set by those skilled in the art based on the actual situation. When the iteration count is greater than or equal to the iteration threshold, the iteration process is stopped, the set label corresponding to the iteration center is marked as the best label, and the parameters of each defective component are optimized based on the parameter set corresponding to the best label.

[0102] Methods for generating m candidate solutions include:

[0103] Generate m random coefficients from the interval [-1,1]. Multiply each of the m random coefficients by the iteration radius and add the iteration center to obtain m iteration coefficients. Select m set labels with the same values ​​as the iteration coefficients from the range of set labels and use them as m candidate solutions.

[0104] Methods for calculating the environmental optimization degree corresponding to candidate solutions include:

[0105] Based on the parameter set corresponding to the set label of the candidate solution, the component combination material of each defective component is optimized, and the environmental performance of each defective component is re-evaluated. The method for re-evaluating the environmental performance of each defective component is consistent with the method for evaluating the environmental performance of each missing component in S5. The environmental performance of each missing component is multiplied by the corresponding component weight, and then added together to obtain the missing environmental degree.

[0106] Each defective component's type label, method label, location label, and material label are used as a set of evaluation data, with each set of data corresponding to a defective component. Each set of evaluation data is then input into a pre-trained cost evaluation model (a deep neural network model) to calculate the corresponding cost information. The cost information for each defective component is then summed sequentially to obtain the defect cost information. All building components not marked as defective are marked as normal components, and the cost information for each normal component is summed sequentially to obtain the normal cost information. The defect cost information is then summed with the normal cost information to obtain the total cost information. Finally, the total cost information is compared with the customer cost. The information is compared; if the total cost information is greater than the customer cost information, the environmental optimization degree corresponding to the candidate solution is 0; if the total cost information is less than or equal to the customer cost information, the missing environmental optimization degree is multiplied by the corresponding weight coefficient in the preset weight set to obtain the environmental optimization degree weight, and the total cost information is multiplied by the corresponding weight coefficient in the preset weight set to obtain the cost weight; the environmental optimization degree is obtained by subtracting the cost weight from the environmental optimization degree weight, and the environmental optimization degree is a non-negative number; the weight set includes the weight coefficient corresponding to the missing environmental optimization degree and the weight coefficient corresponding to the total cost information, and the weight set is preset by those skilled in the art according to the actual situation.

[0107] Methods for determining whether to move the iteration center to the provisional solution include:

[0108] Calculate the environmental optimization degree of the set label corresponding to the iteration center and mark it as the center optimization degree; compare the center optimization degree with the environmental optimization degree of the temporary optimal solution; if the center optimization degree is less than the environmental optimization degree of the temporary optimal solution, move the iteration center to the temporary optimal solution; if the center optimization degree is greater than or equal to the environmental optimization degree of the temporary optimal solution, do not move the iteration center to the temporary optimal solution.

[0109] Methods for adjusting the iteration radius based on the iteration center include:

[0110] If the iteration center is moved to the temporary optimal solution, a first adjustment coefficient is randomly selected from the interval [0.5,1], and the iteration radius is multiplied by the first adjustment coefficient to complete the adjustment of the iteration radius; if the iteration center is not moved to the temporary optimal solution, a second adjustment coefficient is randomly selected from the interval [0,0.5], and the iteration radius is multiplied by the second adjustment coefficient to complete the adjustment of the iteration radius.

[0111] After parameter optimization, the component combination materials corresponding to each defective component are sequentially mapped to the corresponding building components in the prefabricated building model, and the prefabricated building model is used as the design scheme.

[0112] This embodiment effectively selects suitable components that meet building requirements from a pre-built component library through building component identification and modular component screening, achieving a balance between personalized needs and standardized production. For building components missing from the modular component library, a deep learning model is used for personalized design, which not only matches customer needs but also rationally allocates cost budgets, improving the design's relevance and economy. In the prefabricated building model construction and environmental performance assessment stages, factors such as building type and scale are fully considered, and environmental target thresholds that meet actual needs are set to ensure that the design scheme meets the project's environmental requirements. Environmental performance is assessed for each building component, and for defective components that do not meet environmental performance standards, iterative optimization algorithms are used to optimize parameters, maximizing the overall building's environmental performance while meeting budget costs, achieving green design throughout the entire life cycle. This effectively meets diverse building needs, enabling in-depth analysis and customized design of building components while minimizing environmental impact. It not only improves design efficiency but also promotes the transformation of the construction industry towards green, low-carbon, and sustainable development through comprehensive environmental performance assessment, meeting the comprehensive needs for environmental performance and personalization in complex building projects, and providing an innovative path for achieving intelligent building design.

[0113] Example 2

[0114] This application also provides an electronic device. The electronic device may include one or more processors and one or more memories. The memories store computer-readable code that, when executed by the one or more processors, can perform an environmentally friendly prefabricated building modular design method as described above.

[0115] The method or system according to the embodiments of this application can also be implemented using the architecture of the electronic device shown in this application. The electronic device may include a bus, one or more CPUs, ROM, RAM, a communication port connected to a network, input / output, a hard disk, etc. The storage device in the electronic device, such as a ROM or hard disk, may store an environmentally friendly prefabricated building modular design method provided in this application. Furthermore, the electronic device may also include a user interface. Of course, the architecture shown in this application is merely exemplary; when implementing different devices, one or more components of the electronic device shown in this application may be omitted according to actual needs.

[0116] Example 3

[0117] Please refer to the accompanying drawings. One embodiment of this application discloses a computer-readable storage medium. The computer-readable storage medium stores computer-readable instructions. When executed by a processor, the computer-readable instructions can perform an environmentally friendly modular design method for prefabricated buildings according to an embodiment of this application, as described above. The storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc.

[0118] Furthermore, according to embodiments of this application, the processes described in the above-referenced flowcharts can be implemented as computer software programs. For example, this application provides a non-transitory machine-readable storage medium storing machine-readable instructions that can be executed by a processor to perform instructions corresponding to the method steps provided in this application, such as an environmentally friendly prefabricated building modular design method. When this computer program is executed by a central processing unit (CPU), it performs the functions defined in the method of this application.

[0119] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0120] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0121] In the description of this invention, it should be understood that the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0122] In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0123] In the description of this invention, "several" means one or more, and "a large number" means two or more.

[0124] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0125] All formulas in this manual are dimensionless and calculated numerically. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0126] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A modular design method for environmentally friendly prefabricated buildings, characterized in that, include: S1: Obtain customer demand data; S2: Analyze customer demand data using a component identification algorithm to determine building components and select suitable components from a pre-built modular component library; S3: Mark building components that do not exist in the modular component library as missing components, and perform personalized design on all missing components; S4: Combine all compatible and missing components to form a prefabricated building model, and comprehensively evaluate the environmental performance of the prefabricated building model through a multi-dimensional index system to determine whether to generate a component optimization instruction. If no component optimization instruction is generated, a design scheme is generated. S5: If a component optimization instruction is generated, a quantitative analysis method is used to evaluate the environmental performance of each compatible and missing component, and to identify defective components among the compatible and missing components. S6: For each defective component, apply an iterative optimization algorithm to optimize the parameters and generate a design scheme.

2. The modular design method for environmentally friendly prefabricated buildings according to claim 1, characterized in that, The customer demand data includes architectural drawings and customer cost information; The architectural drawing information includes architectural floor plans, architectural elevations, architectural sections, and architectural structural drawings; The method for determining building components includes: Based on the architectural drawings, a building information model is constructed using BIM software. Using the built-in tools of the BIM software, the component information corresponding to each component in the building information model is extracted. The component information includes the component type and component size, and all extracted components are marked as building components. Methods for selecting compatible components include: From the modular component library, all components with the same component type as building components are selected and marked as selected components. All selected components are classified according to their corresponding component types, and selected components with the same component type are grouped into component sets, with each component set corresponding to a component type. Dimensional and connection analyses are performed on the components in each component set, and one component is selected from each component set and marked as an adaptable component. The modular component library is a component database containing various standardized components, and each component has component information, connection information, cost information, and component combination materials. The connection information includes the connection method and connection point location.

3. The modular design method for environmentally friendly prefabricated buildings according to claim 2, characterized in that, The step of selecting one component from each set of components includes: Step S101: Identify the load-bearing components in the building components, and mark the component set corresponding to each load-bearing component as a load-bearing set; Step S102: Mark each component in the load-bearing set as a load-bearing screening component, and obtain the component size and connection information corresponding to each load-bearing screening component from the modular component library; Step S103: Compare the component size of each load-bearing component with the component size of the corresponding load-bearing screening component, select one component from each load-bearing set, and mark it as the current component; Step S104: Mark all building components connected to each current component as successor components, mark all component sets corresponding to successor components as successor sets, mark each component in the successor set as a successor filter component, and obtain the component size and connection information corresponding to each successor filter component from the modular component library. Step S105: Based on the component size and connection information of each successor component and the corresponding successor filtered component, select one component from each successor set and mark it as the update component, and update the current component as the update component; Step S106: Repeat steps S104 to S105 until a component is selected from each set of components, at which point the loop ends.

4. The modular design method for environmentally friendly prefabricated buildings according to claim 3, characterized in that, In step S103, the method for selecting one component from each load-bearing set includes: For each load-bearing component, subtract the corresponding value from the size of the corresponding load-bearing screening component to obtain the dimensional tolerance. Analyze each dimensional tolerance sequentially, marking all load-bearing screening components with non-negative dimensional tolerances as primary candidate components, and leaving no component with negative dimensional tolerances. Set a tolerance threshold, and compare the dimensional tolerance of each primary candidate component with the threshold. Mark all primary candidate components with dimensional tolerances less than the threshold as secondary candidate components, and leave no component with dimensional tolerances greater than or equal to the threshold. Add the dimensional tolerances of each secondary candidate component sequentially to obtain the total tolerance. Compare the total tolerances of components with the same total tolerance for each load-bearing set, and select the secondary candidate component with the smallest total tolerance from each load-bearing set. In step S105, the method for selecting a component from each successor set includes: Retrieve the connection information of each current component from the modular component library; compare the connection information of each subsequent component with the connection information of each corresponding subsequent filter component; delete the filter components whose connection information is different from that of the subsequent components from the corresponding successor set, and retain the filter components whose connection information is the same as that of the subsequent components in the corresponding successor set; mark the successor set after deleting components as a candidate set, and select one component from each candidate set; the method for selecting one component from each candidate set is the same as the method for selecting one component from each load-bearing set.

5. The modular design method for environmentally friendly prefabricated buildings according to claim 4, characterized in that, The method for personalized design of all missing components includes: Based on the building information model, all building components connected to the missing component are marked as adjacent components; the connection information of each adjacent component is obtained and used as the connection information of the corresponding missing component; the cost information of each adaptable component is obtained from the modular component library; the cost information of each adaptable component is subtracted from the customer cost information in turn to obtain the cost margin; different numerical labels are set for different component types, connection methods and connection point locations to obtain type labels, method labels and location labels. The cost margin, along with the type label, method label, location label, and component size corresponding to each missing component, are sequentially input into a trained cost allocation model to predict cost allocation data, which includes cost information for each missing component. The type label, method label, location label, and cost information for each missing component are then used as a set of analysis data, with each set corresponding to a specific missing component. Each set of analysis data is then input into a trained material determination model to predict the corresponding material label. The material label is a numerical label corresponding to the material combination of the component; different material combinations of components have different material labels. Based on the component information, connection information, and material combination of each missing component, a personalized design is performed for each missing component. Both the cost allocation model and the material determination model are deep neural network models.

6. The modular design method for environmentally friendly prefabricated buildings according to claim 5, characterized in that, The method for combining all compatible and missing components includes: Obtain the component combination material corresponding to each adaptable component from the modular component library, and map the component information, connection information and component combination material corresponding to each adaptable component and missing component to the corresponding building component in the building information model in sequence to complete the combination of all adaptable components and missing components. Methods for comprehensively evaluating the environmental performance of prefabricated building models include: The material properties of each building component are obtained, and each set of material properties is sequentially mapped to the corresponding building component in the building information model to form a building physical model. The building physical model is analyzed in multiple dimensions using comprehensive building performance simulation software to calculate the carbon emissions of the building physical model. The reciprocal of the carbon emissions of the building physical model is used as the environmental performance of the prefabricated building model. The method for determining whether to generate a component optimization instruction is as follows: Based on the architectural drawings, obtain the building type and scale. The building scale includes the total building area and building height. The total building area is the sum of the horizontal projected areas of all floors, and the building height is the vertical distance from the ground to the highest point of the building. Different numerical labels are assigned to different building types and marked as building tags. The building tags and building scale of the prefabricated building model are input into the environmental analysis model to predict the corresponding environmental thresholds. The environmental analysis model is a deep neural network model. The environmental performance of the prefabricated building model is compared with the environmental thresholds. If the environmental performance is greater than the environmental threshold, no component optimization instructions are generated; if the environmental performance is less than or equal to the environmental threshold, component optimization instructions are generated.

7. The modular design method for environmentally friendly prefabricated buildings according to claim 6, characterized in that, The method for evaluating the environmental performance of each compatible and missing component includes: The material thermal parameters corresponding to each compatible and missing component are obtained, and these parameters are mapped to the corresponding building components in the building information model to form a thermal performance model for each building component. For each building component's thermal performance model, heat transfer characteristics are analyzed using heat transfer simulation software to calculate the energy loss value corresponding to each building component. A carbon emission factor is preset, and the energy loss value corresponding to each building component is multiplied by the carbon emission factor to obtain the carbon emission of each building component. The reciprocal of the carbon emission of each building component is used as the environmental performance of the corresponding compatible or missing component. The method for identifying defective components in both adapted and missing components includes: The environmental performance of each building component is divided by the environmental performance of the prefabricated building model to obtain the component weight; the weight of each component is multiplied by the environmental threshold to obtain the sub-environmental threshold corresponding to each building component; the environmental performance of each building component is compared with the corresponding sub-environmental threshold, and building components with environmental performance less than or equal to the corresponding sub-environmental threshold are marked as defective components, while building components with environmental performance greater than the corresponding sub-environmental threshold are not marked.

8. The modular design method for environmentally friendly prefabricated buildings according to claim 7, characterized in that, Methods for optimizing parameters for each defective component include: Obtain a material set, which includes all materials applicable to various building components; randomly select 'a' materials from the material set to construct a set of material combinations, resulting in b sets of material combinations, all of which are distinct, where 'a' and 'b' are both integers greater than 0; randomly select 'c' material combinations to construct a set of parameters, resulting in d sets of parameter sets, all of which are distinct, where 'c' represents the number of defective components, and each material combination corresponds one-to-one with a defective component, and 'd' is an integer greater than 0; assign sequentially increasing numerical labels to the d sets of parameter sets and mark them as set labels, with the set label range being [1, d]; randomly select a set label as the initial iteration center, subtract one from 'd' and divide by two to obtain the initial iteration radius, and set the iteration count to 0; Define the iterative process as follows: generate m candidate solutions within the range of the set labels, and calculate the environmental optimization degree corresponding to each candidate solution, 1 < m < d, and the candidate solutions correspond one-to-one with the set labels; mark the candidate solution with the highest environmental optimization degree as the temporary optimal solution, and determine whether to move the iteration center to the temporary optimal solution, and adjust the iteration radius according to the iteration center; The process is iterated, and the iteration count is incremented by one for each iteration. An iteration threshold is preset. When the iteration count is greater than or equal to the iteration threshold, the iteration process is stopped, the set label corresponding to the iteration center is marked as the optimal label, and the parameters of each defective component are optimized according to the parameter set corresponding to the optimal label.

9. The modular design method for environmentally friendly prefabricated buildings according to claim 8, characterized in that, Methods for generating m candidate solutions include: From the interval [-1,1], generate m random coefficients. Multiply each of the m random coefficients by the iteration radius and add the iteration center to obtain m iteration coefficients. Select m set labels with the same value as the iteration coefficients from the range of set labels and use them as m candidate solutions. Methods for calculating the environmental optimization degree corresponding to candidate solutions include: Based on the parameter set corresponding to the set label of the candidate solution, the component combination material of each defective component is optimized, and the environmental performance of each defective component is re-evaluated; the environmental performance of each missing component is multiplied by the corresponding component weight, and then added together to obtain the environmental degree of the missing component. Each defective component's type label, method label, location label, and material label are used as a set of evaluation data, with each set of evaluation data corresponding to a defective component. Each set of evaluation data is input into a pre-trained cost evaluation model (a deep neural network model) to evaluate the corresponding cost information. The cost information of each defective component is summed sequentially to obtain the defect cost information. All building components not marked as defective are marked as normal components, and the cost information of each normal component is summed sequentially to obtain the normal cost information. The defect cost information and normal cost information are summed to obtain the total cost information. The total cost information is compared with the customer cost information. If the total cost information is greater than the customer cost information, the environmental optimization degree corresponding to the candidate solution is 0. If the total cost information is less than or equal to the customer cost information, the missing environmental optimization degree is multiplied by the corresponding weight coefficient in a preset weight set to obtain the environmental optimization weight, and the total cost information is multiplied by the corresponding weight coefficient in the preset weight set to obtain the cost weight. The environmental optimization degree is obtained by subtracting the cost weight from the environmental optimization weight; the environmental optimization degree is a non-negative number.

10. The modular design method for environmentally friendly prefabricated buildings according to claim 9, characterized in that, Methods for determining whether to move the iteration center to the provisional solution include: Calculate the environmental optimization degree of the set label corresponding to the iteration center and mark it as the center optimization degree; compare the center optimization degree with the environmental optimization degree of the temporary optimal solution; if the center optimization degree is less than the environmental optimization degree of the temporary optimal solution, move the iteration center to the temporary optimal solution; if the center optimization degree is greater than or equal to the environmental optimization degree of the temporary optimal solution, do not move the iteration center to the temporary optimal solution. Methods for adjusting the iteration radius based on the iteration center include: If the iteration center is moved to the temporary optimal solution, a first adjustment coefficient is randomly selected from the interval [0.5,1], and the iteration radius is multiplied by the first adjustment coefficient to complete the adjustment of the iteration radius; if the iteration center is not moved to the temporary optimal solution, a second adjustment coefficient is randomly selected from the interval [0,0.5], and the iteration radius is multiplied by the second adjustment coefficient to complete the adjustment of the iteration radius.

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