Skylight design method, device and equipment and storage medium
By combining parametric building models and optimization tools, the skylight design process is automated, solving the problems of time-consuming and inefficient traditional manual adjustments, and achieving efficient and accurate optimization of skylight design for large-space buildings.
Patent Information
- Application Number
- CN202511020173.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-07-23
- Publication Date
- 2025-11-07
AI Technical Summary
Traditional skylight design for large-space buildings relies on manual iterative adjustments, making it difficult to find a balanced solution that meets all design requirements. This process is time-consuming, inefficient, and lacks accuracy.
By combining parametric building models with optimization tools, the building is broken down into skylights and components. Design and material parameters are set, and performance simulations are performed using geographical environment simulation data. Finally, a genetic algorithm is used to find the optimal design and material parameters.
It improves the efficiency and accuracy of skylight design, and the automated modeling and simulation process reduces human intervention, significantly improving design efficiency and making it suitable for complex, large-space building projects.
Smart Images
Figure CN120910955A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the field of architectural design, and in particular to a skylight design method, device, equipment and storage medium. BACKGROUND
[0002] In the design of a skylight in a traditional large-space building (such as a railway station, a high-speed railway station, etc.), the optimization process of the related parameters of the skylight design mainly relies on manual iterative adjustment. A designer usually simulates an existing scheme, checks various indexes affecting the design according to design specifications and target requirements, and then adjusts the design parameters and material parameters of the skylight manually to improve performance and repeatedly simulates and adjusts. However, the subjectivity of manual adjustment is strong, and it is often difficult to find an ideal scheme that meets all design requirements or balances various performance indexes. Moreover, the manual adjustment is time-consuming and inefficient, and the accuracy of the final design result is difficult to guarantee. SUMMARY
[0003] Embodiments of the present application provide a skylight design method, device, equipment and storage medium to solve at least one problem in the related art. The technical solutions are as follows:
[0004] In a first aspect, the embodiments of the present application provide a method for designing a skylight, comprising:
[0005] splitting a spatial building into a skylight and a plurality of building components, setting a first design parameter for the skylight and a second design parameter for each building component, and generating a parameterized building model of the spatial building;
[0006] setting a first material parameter for the skylight and a second material parameter for each building component, and obtaining a simulation model of the spatial building;
[0007] obtaining geographical environment simulation data corresponding to the spatial building, performing building performance simulation according to the geographical environment simulation data, the parameterized building model, the simulation model and an optimization target, and obtaining a performance simulation result;
[0008] determining optimal target design parameters and optimal target material parameters according to an optimization tool and the performance simulation result.
[0009] In an embodiment, the splitting of the spatial building into the skylight and the plurality of building components, the setting of the first design parameter for the skylight and the second design parameter for each building component, and the generation of the parameterized building model of the spatial building comprise:
[0010] splitting the spatial building into the skylight and the plurality of building components, the building components including a roof, a ceiling, a wall, a floor, a window and a sunshade component;
[0011] setting the first design parameters for the skylights and the second design parameters for the roof, the ceiling, the wall, the floor, the window and the shading device to generate a parametric building model of the space building;
[0012] The first design parameters include a type, a number and an area ratio of the skylights, and the area ratio is limited in a preset constraint range.
[0013] In an embodiment, the first material parameters are set for the skylights and the second material parameters are set for the building components to obtain a simulation model of the space building includes:
[0014] The first material parameters are set for the skylights and the second material parameters are set for the building components.
[0015] The simulation model of the space building is constructed according to the parametric building model, the first material parameters and the second material parameters of the building components.
[0016] The first material parameters include a visible light transmittance and a skylight transmittance when the optimization target includes a light environment simulation, and the first material parameters include a heat transfer coefficient and a thermal radiation shielding coefficient when the optimization target includes an energy consumption simulation.
[0017] In an embodiment, the geographical environment simulation data corresponding to the space building is obtained includes:
[0018] An epw file of the climate data is downloaded from a specified website through an Open EPW And STAT Weather Files module of Ladybug Tools.
[0019] The geographical environment parameters corresponding to the space building are obtained, and the geographical environment parameters include a geographical location, a building orientation and building surrounding environment data.
[0020] The geographical environment simulation data corresponding to the space building is obtained according to the epw file and the geographical environment parameters.
[0021] In an embodiment, the building performance simulation is performed according to the geographical environment simulation data, the parametric building model, the simulation model and the optimization target to obtain a performance simulation result includes:
[0022] The target simulation engine is determined and the precision parameters of the target simulation engine are set based on the optimization target.
[0023] The simulation area is determined from the simulation model, and the simulation model is converted into a format recognizable by Ladybug Tools.
[0024] The building performance simulation is performed on the simulation model in the converted format in the simulation area according to the target simulation engine to obtain the performance simulation result.
[0025] The performance simulation result includes a daylight factor, an effective daylight illuminance, a radiation amount, and an annual solar exposure.
[0026] In an embodiment, the determining the optimal target design parameters and the optimal target material parameters according to the optimization tool and the performance simulation result comprises:
[0027] The performance simulation result is optimized by a genetic algorithm of the optimization tool Octopus.
[0028] In the optimization process, whether a convergence state is reached is determined based on a convergence graph of the optimization tool Octopus.
[0029] When the convergence state is reached, the optimal first target design parameter is determined from the first design parameters, the optimal first target material parameter is determined from the first material parameters, the optimal second target design parameter is determined from the second design parameters, and the optimal second target material parameter is determined from the second material parameters.
[0030] In an embodiment, the determining whether the convergence state is reached in the optimization process based on the convergence graph of the optimization tool Octopus comprises:
[0031] In the optimization process, the overlap of elite solutions and Pareto non-dominated solutions in the convergence graph of the optimization tool Octopus is determined.
[0032] If the elite solutions and the Pareto non-dominated solutions overlap and remain overlapped after several optimization iterations, it is determined that the convergence state is reached.
[0033] In a second aspect, an embodiment of the present application provides a sunroof design device, comprising:
[0034] A generation module is configured to split a spatial building into a sunroof and a plurality of building components, set first design parameters for the sunroof, and set second design parameters for each building component to generate a parameterized building model of the spatial building.
[0035] A setting module is configured to set first material parameters for the sunroof and second material parameters for each building component to obtain a simulation model of the spatial building.
[0036] A simulation module is configured to obtain simulation data of a geographical environment corresponding to the spatial building, perform building performance simulation according to the simulation data of the geographical environment, the parameterized building model, the simulation model, and an optimization target, and obtain a performance simulation result.
[0037] The determining module is configured to determine the optimal target design parameter and the optimal target material parameter according to the optimization tool and the performance simulation result.
[0038] In a third aspect, an electronic device is provided, which includes a processor and a memory storing instructions, the instructions being loaded and executed by the processor to implement the method in any of the embodiments of the above aspects.
[0039] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, the computer program being executed to implement the method in any of the embodiments of the above aspects.
[0040] The beneficial effects of the above technical solutions at least include:
[0041] By splitting the spatial building into the skylight and the plurality of building components, setting the first design parameter for the skylight and the second design parameter for each building component, generating the parameterized building model of the spatial building, setting the first material parameter for the skylight and the second material parameter for each building component, obtaining the simulation model of the spatial building, obtaining the geographical environment simulation data corresponding to the spatial building, performing the building performance simulation according to the geographical environment simulation data, the parameterized building model, the simulation model and the optimization target, and obtaining the performance simulation result, the optimal target design parameter and the optimal target material parameter are determined according to the optimization tool and the performance simulation result, and the intelligent modeling and simulation improve the efficiency and the accuracy compared with the manual adjustment.
[0042] The above summary is merely intended to illustrate the present description and is not intended to limit in any way. In addition to the illustrative aspects, embodiments and features described above, those aspects, embodiments and features will be readily apparent to those skilled in the art by reference to the drawings and the following detailed description. BRIEF DESCRIPTION OF DRAWINGS
[0043] In the drawings, like reference numerals refer to same or similar functionalities throughout the several views. The drawings are not necessarily to scale. It is to be understood that the drawings only depict several embodiments of the disclosure and are not to be considered as limiting the scope of the disclosure.
[0044] Figure 1 A step flowchart of a skylight design method according to an embodiment of the present disclosure;
[0045] Figure 2 A schematic diagram of a parameterized building model of a spatial building according to an embodiment of the present disclosure;
[0046] Figure 3Fig. 1 is a schematic diagram of a setting interface of material parameters according to an embodiment of the present application;
[0047] Figure 4 Fig. 2 is a schematic diagram of a setting interface of a test plane of a simulation area according to an embodiment of the present application;
[0048] Figure 5 Fig. 3 is a schematic diagram of a setting interface of simulation parameters according to an embodiment of the present application;
[0049] Figure 6 Fig. 4 is a structural block diagram of a sunroof design device according to an embodiment of the present application;
[0050] Figure 7 Fig. 5 is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION
[0051] In the following, only certain exemplary embodiments are simply described. As those skilled in the art can recognize, the described embodiments can be modified in various different ways without departing from the spirit or scope of the present application. Therefore, the drawings and the description are considered to be exemplary in nature rather than limiting.
[0052] Reference Figure 1 Fig. 6 is a flow chart of a sunroof design method according to an embodiment of the present application, which can at least include steps S100-S400:
[0053] S100, split a space building into a sunroof and a plurality of building components, set a first design parameter for the sunroof and a second design parameter for each building component, and generate a parameterized building model of the space building.
[0054] S200, set a first material parameter for the sunroof and a second material parameter for each building component, and obtain a simulation model of the space building.
[0055] S300, obtain geographical environment simulation data corresponding to the space building, perform building performance simulation according to the geographical environment simulation data, the parameterized building model, the simulation model, and an optimization target, and obtain a performance simulation result.
[0056] S400, determine optimal target design parameters and optimal target material parameters according to an optimization tool and the performance simulation result.
[0057] The technical scheme of the embodiment of the application splits the space building into a skylight and a plurality of building components, sets a first design parameter for the skylight and a second design parameter for each building component, generates a parameterized building model of the space building, sets a first material parameter for the skylight and a second material parameter for each building component, obtains a simulation model of the space building, obtains geographical environment simulation data corresponding to the space building, performs building performance simulation according to the geographical environment simulation data, the parameterized building model, the simulation model, and an optimization target, obtains a performance simulation result, determines optimal target design parameters and optimal target material parameters according to an optimization tool and the performance simulation result, and intelligently models and simulates, thereby improving efficiency and accuracy compared with manual adjustment.
[0058] In an embodiment, modeling is performed by using Rhino & Grasshopper, and step S100 includes steps S110-S120.
[0059] S110, the space building is split into a skylight and a plurality of building components, and the building components include a roof, a ceiling, a wall, a floor, a window, and a sunshade component.
[0060] Optionally, since the space building is composed of a plurality of different types of building components in addition to the skylight, the building components have an influence on the design of the skylight and need to be considered. In the embodiment of the application, the space building is split into a skylight and a plurality of building components, and the building components include but are not limited to a roof, a ceiling, a wall, a floor, a window, and a sunshade component.
[0061] S120, a first design parameter is set for the skylight, and a second design parameter is set for the roof, the ceiling, the wall (such as a curtain wall or a wall surface), the floor (ground), the window, and the sunshade component (or other light-blocking component), and a parameterized building model of the space building is generated.
[0062] Optionally, after splitting, a first design parameter is set for the skylight, including but not limited to the type of the skylight (such as a flat skylight, a strip skylight, or a point skylight), the number, and the area ratio, the area ratio is limited in a preset constraint range, for example, the constraint range of the flat skylight is an interval of 0% to 20%, and after setting the first design parameter, the corresponding skylight layout is automatically generated based on an algorithm; the number of the strip skylight and the point skylight can be adjusted, and the layout of the point skylight can be adjusted to ensure that the layout meets the design requirements. It should be noted that during parameterized modeling, the design parameters of the skylight and each building component are ensured to meet the limitation range of the software as variables, thereby ensuring the effectiveness of the design parameters, and different parameter combinations can be selected within the limitation range for simulation in subsequent simulation, a new design sample is automatically generated, and a parameterized building model of the space building is obtained, as shown in Figure 2 , to ensure that the parameterized building model can be used for simulation and optimization subsequently.
[0063] In addition, the second design parameters can include orientation, number, size, layout, etc., and different combinations of parameters can be selected within the limited range for subsequent simulation.
[0064] In an embodiment, step S200 includes steps S210-S220:
[0065] S210, set the first material parameters for the skylight and the second material parameters for each building component.
[0066] Optionally, when the optimization target includes light environment simulation, the first material parameters include visible light transmittance and skylight transmittance, and when the optimization target includes energy consumption simulation, the first material parameters include heat transfer coefficient and thermal radiation shielding coefficient, the values of the first material parameters can be selected based on the limited range of the first material parameters to form multiple different sets of first material parameters during subsequent simulation, for example, the range of visible light transmittance is (a, b), and the values are selected from this range for simulation during subsequent simulation. When adjusting the first material parameters, the total skylight transmittance can be adjusted based on the building structure form (such as truss or grid) to ensure that the material parameters match the actual structure. In addition, the second material parameters of each building component can include reduction coefficient, light transmittance, reflectivity, ETFE, glass, etc., as shown in FIG. 2B; similarly, different combinations of second material parameters can be selected within the limited range for simulation during subsequent simulation. Figure 3
[0067] S220, constructing a simulation model of the spatial building according to the parameterized building model, the first material parameters, and the second material parameters of each building component.
[0068] Optionally, after determining the first material parameters and the second material parameters of each building component, the parameterized building model including the skylight and the plurality of building components is combined to construct the simulation model of the spatial building.
[0069] In an embodiment, the step S300 of obtaining the geographical environment simulation data corresponding to the spatial building includes steps S310-S330:
[0070] S310, downloading the epw file of the climate data from a specified website through the Open EPW And STAT Weather Files module of Ladybug Tools.
[0071] Optionally, the epw file of the climate data is downloaded from a specified website through the Open EPW And STAT Weather Files module of Ladybug Tools and stored in a local computer for calling.
[0072] S320, obtaining geographical environment parameters corresponding to the spatial building.
[0073] Optionally, the geographical environment parameters corresponding to the spatial building are obtained, such as macro and meso environment parameters of the building, including but not limited to geographical position, building orientation, surrounding environment data of the building (for example, surrounding buildings, roads, rivers, etc.).
[0074] S330, obtaining geographical environment simulation data corresponding to the spatial building according to the epw file and the geographical environment parameters.
[0075] Optionally, the geographical environment simulation data corresponding to the spatial building is formed based on the epw file containing climate data and the geographical environment parameters, as a data set for subsequent simulation.
[0076] In an embodiment, the building performance simulation is performed according to the geographical environment simulation data, the parameterized building model, the simulation model and the optimization target in step S300, to obtain a performance simulation result, including steps S340-S360:
[0077] S340, determining a target simulation engine and setting an accuracy parameter of the target simulation engine based on the optimization target.
[0078] Optionally, when the optimization target includes light environment simulation, the target simulation engine adopts a random Monte Carlo sampling and back light tracking algorithm, and when the optimization target includes energy consumption simulation, the target simulation engine adopts a CFD turbulent flow model algorithm. It should be noted that since the accuracy of the target simulation engine has a nonlinear relationship with the calculation time, for example, doubling the accuracy may result in a three to five times increase in calculation time, therefore, the accuracy parameter of the target simulation engine is set based on actual needs, to balance the accuracy and time.
[0079] S350, determining a simulation region from the simulation model and converting the simulation model into a format recognizable by Ladybug Tools.
[0080] For example, the simulation region is determined by setting a simulation boundary in the simulation model, and the simulation model is converted into a format recognizable by Ladybug Tools. Figure 4As shown, optionally, the simulation area can be set using the HoneybeePlus test surface setting module or the LadybugTools 1.2.0 test surface setting module. For example, if the building is a high-speed rail station, the waiting area on the elevated station hall level can be determined as the simulation area from the simulation model, because the waiting area on the elevated station hall level is the main space used by passengers and also the main service space of the skylight. It should be noted that when setting the simulation area, a new test plane can be created (created through the HoneybeePlus test surface setting module), the height of the test plane is set to 0.75 meters, and a test mesh is generated on the test plane to ensure that the mesh covers the simulation area. Then, the simulation model is converted from a Rhino model to a format recognizable by LadybugTools to ensure that no data is lost, facilitating subsequent calculations. During the conversion process, it is necessary to check whether the model's geometric information and material parameters are correctly mapped to avoid simulation errors; at the same time, during subsequent simulations, the calculation progress needs to be monitored to ensure no abnormal interruptions.
[0081] S360. Based on the target simulation engine, perform building performance simulation on the converted simulation model in the simulation area to obtain performance simulation results.
[0082] Optionally, after determining the target simulation engine, the target simulation engine is used to perform building performance simulation on the converted simulation model in the simulation area to obtain performance simulation results; wherein, the performance simulation results include, but are not limited to, DF (Daylight Factor), UDI (Useful Daylight Illuminance), RAD (Radiation), and ASE (Annual Sunlight Exposure).
[0083] like Figure 5 As shown in Figure 501 and Table 1 (Parameter Setting Table), it should be noted that when performing building performance simulation, step 501 allows inputting the address for obtaining climate files, step 502 sets simulation indicators, step 503 sets various simulation parameters and their corresponding values as shown in Table 1, step 504 inputs the number of CPU cores to be used, step 505 sets the simulation schedule, and step 506 utilizes the settings to perform the building performance simulation calculation module (target simulation engine). Simultaneously, hierarchical relationships and priorities can be set. For example, macroscopic parameters such as orientation, layout, and quantity have higher priority than detailed parameters such as skylight transmittance, visible light transmittance, heat transfer coefficient, and thermal radiation shading coefficient. Furthermore, during simulation optimization, optimization can be performed step-by-step according to the hierarchy, or all macroscopic and detailed parameters can be considered simultaneously for concurrent simulation optimization.
[0084] Table 1
[0085]
[0086]
[0087] In an embodiment, the step S400 comprises steps S410-S430:
[0088] S410, the performance simulation result is optimized by a genetic algorithm of an optimization tool Octopus.
[0089] Optionally, the specific algorithm parameters of the genetic algorithm are designed in advance: the mutation probability is 1 / (size+1), about 0.05, the crossover rate is 0.8, the mutation rate is 0.9, the population size is 20, the maximum number of generations is 50, and the HypeE algorithm is selected for optimization calculation. Specifically, by the genetic algorithm of the optimization tool Octopus, a parameterized setting battery is reserved, the first design parameter, the second design parameter, the first material parameter, and the second material parameter, and other variables such as the variable transmittance of the sunroof are connected to the input end of the Octopus, the output end points to the content of the performance simulation result, such as DF (Daylight Factor, daylight factor), UDI (Useful Daylight Illuminance, useful daylight illuminance), RAD (Radiation, radiation), and ASE (Annual Sunlight Exposure, annual sunlight exposure) for judging the evolution direction, forming a closed-loop process of modeling-simulation-data processing, and automatically iterating and optimizing.
[0090] S420, in the optimization process, whether the convergence state is reached is determined based on a convergence graph of the optimization tool Octopus.
[0091] Optionally, in the optimization process, the solution set information is expressed by a virtual space point, and manual intervention is supported to select priority genes, for example, according to the project requirements, solutions with higher daylight uniformity are given priority, and when operating, the optimization progress needs to be checked regularly to ensure that the parameter settings are correct and the iterative calculation is normally performed. In the embodiment of the present application, in the optimization process, the optimization tool Octopus generates a corresponding convergence graph, so that the coincidence of the elite solution (light gray part in the convergence graph) and the Pareto non-dominated solution (dark gray part in the convergence graph) in the convergence graph of the optimization tool Octopus can be determined. If the elite solution and the Pareto non-dominated solution appear to coincide, and remain coincident (i.e., remain stable) after several optimization iterations, it is determined that the convergence state is reached, that is, the optimization has converged.
[0092] It should be noted that in the optimization process, the solution set information can be expressed by virtual space points, supporting manual intervention to select priority genes, for example, according to the current project requirements, solutions with higher uniformity of lighting are given priority; During operation, the optimization progress needs to be checked regularly to ensure that the parameter settings are correct and the iterative calculation is carried out normally.
[0093] S430, when the convergence state is reached, determining the first target design parameter corresponding to the optimal from the first design parameter, determining the first target material parameter corresponding to the optimal from the first material parameter, determining the second target design parameter corresponding to the optimal from the second design parameter, and determining the second target material parameter corresponding to the optimal from the second material parameter, the target design parameter includes the first target design parameter and the second target design parameter, and the target material parameter includes the first target material parameter and the second target material parameter.
[0094] In some embodiments, the results can be used to calculate the perspective view to further observe the position of the Pareto frontier solution in the overall calculation, the perspective view visualizes the calculation results in the form of a polyline, and the black line represents the Pareto frontier solution, supporting three-dimensional rotation observation, so as to analyze the frontier solution distribution from different angles.
[0095] Finally, after confirming that the convergence state is reached, the optimization results are read and screened, and when screening, the solution set that best meets the target can be further selected according to the project requirements, and finally the first target design parameter corresponding to the optimal is determined from the first design parameter, the first target material parameter corresponding to the optimal is determined from the first material parameter, the second target design parameter corresponding to the optimal is determined from the second design parameter, and the second target material parameter corresponding to the optimal is determined from the second material parameter, and the final target design parameter includes the first target design parameter and the second target design parameter, and the target material parameter includes the first target material parameter and the second target material parameter.
[0096] In addition, during the screening process, it is recommended to save the convergence graph and perspective view data of each optimization for subsequent analysis and verification.
[0097] The embodiment of the application innovatively integrates a parameterized design tool, simulation software and an optimization tool Octopus, and constructs a complete skylight design parameter intelligent calculation and iterative optimization tool. Compared with a traditional manual optimization mode, the embodiment of the application solves the conflict problem in multi-optimization target design, automatically optimizes and generates a design parameter and material parameter scheme that balances and meets different optimization targets by using a genetic algorithm and a Pareto frontier analysis, and ensures the optimality of the result. Secondly, through an automatic modeling, simulation and optimization process, the embodiment of the application greatly reduces manual intervention and repetitive work, significantly improves design efficiency, and is especially suitable for complex large-space building projects. Finally, the embodiment of the application supports visual convergence judgment and three-dimensional perspective analysis, the design process is transparent and intuitive, and is convenient for designers to intervene and adjust, and the embodiment of the application balances intelligence and flexibility. In summary, the embodiment of the application realizes a technical breakthrough in accuracy, efficiency and practicability, and provides an innovative solution for large-space building skylight design.
[0098] With reference to Figure 6 , a structural block diagram of a skylight design device according to an embodiment of the application is shown, and the device can include:
[0099] A generation module is configured to split a space building into a skylight and a plurality of building components, set a first design parameter for the skylight and a second design parameter for each building component, and generate a parameterized building model of the space building.
[0100] A setting module is configured to set a first material parameter for the skylight and a second material parameter for each building component, and obtain a simulation model of the space building.
[0101] A simulation module is configured to obtain geographical environment simulation data corresponding to the space building, perform building performance simulation according to the geographical environment simulation data, the parameterized building model, the simulation model and an optimization target, and obtain a performance simulation result.
[0102] A determination module is configured to determine an optimal target design parameter and an optimal target material parameter according to an optimization tool and the performance simulation result.
[0103] The functions of the modules in the device according to the embodiment of the application can be referred to the corresponding description in the above method, and will not be described here again.
[0104] With reference to Figure 7 , a structural block diagram of an electronic device according to an embodiment of the application is shown, and the electronic device includes a memory 310 and a processor 320. The memory 310 stores instructions executable on the processor 320, and the processor 320 loads and executes the instructions to implement the skylight design method in the above embodiment. The number of the memory 310 and the processor 320 can be one or more.
[0105] In an implementation, the electronic device further includes a communication interface 330 for communicating with external devices to transmit and receive data. If the memory 310, the processor 320 and the communication interface 330 are implemented independently, the memory 310, the processor 320 and the communication interface 330 can be connected to each other through a bus and complete communication with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used to represent the bus in the figure, but it does not mean that there is only one bus or only one type of bus.
[0106] Optionally, in a specific implementation, if the memory 310, the processor 320 and the communication interface 330 are integrated on a chip, the memory 310, the processor 320 and the communication interface 330 can complete communication with each other through an internal interface.
[0107] The embodiment of the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the sunroof design method provided in the above embodiment.
[0108] The embodiment of the present application further provides a chip, which includes a processor, and the processor is configured to call and run instructions stored in a memory, so that a communication device installed with the chip executes the method provided in the embodiment of the present application.
[0109] The embodiment of the present application further provides a chip, which includes an input interface, an output interface, a processor and a memory, the input interface, the output interface, the processor and the memory are connected through an internal connection path, and the processor is configured to execute code in the memory, and when the code is executed, the processor is configured to execute the method provided in the embodiment of the present application.
[0110] It is to be understood that the above-described processor can be a central processing unit (CPU), but can also be other general purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs) or other programmable logic devices, discrete gates or transistor logic components, discrete hardware components, etc. The general purpose processor can be a microprocessor or any conventional processor, etc. It is to be noted that the processor can be an advanced RISC machine (ARM) architecture processor.
[0111] Further, the memory described above can include a read-only memory and a random access memory, and can also include a non-volatile random access memory. The memory can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories. The non-volatile memory can include a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically EPROM (EEPROM) or a flash memory. The volatile memory can include a random access memory (RAM) used as an external cache. By way of example but not limitation, many forms of RAM are available. For example, static random access memory (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM) and direct rambus RAM (DR RAM) can be used.
[0112] In the above-described embodiments, all or part can be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded on a computer, all or part generates a flow or function according to the present application. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium.
[0113] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. Also, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.
[0114] In addition, the terms "first", "second", etc. are used only for the purpose of description and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly specified.
[0115] Any process or method descriptions or descriptions in flow charts or otherwise described herein can be understood as representing code modules, segments, or portions of code that include one or more executable instructions for implementing specific logic functions or steps in the process. And the scope of the preferred embodiments of the present application includes additional implementation in which the functions can be performed in different order, including substantially concurrently or in reverse order, according to the functions involved.
[0116] The logic and / or steps represented in the flow chart or otherwise described herein, for example, can be considered as a list of executable instructions for implementing the logic function, which can be specifically implemented in any computer-readable medium for instruction execution system, device or equipment, such as computer-based system, system including processor or other system that can take instructions from instruction execution system, device or equipment and execute instructions, or in conjunction with these instructions execution system, device or equipment.
[0117] It should be understood that each part of the present application can be realized by hardware, software, firmware or a combination thereof. In the above embodiments, a plurality of steps or methods can be realized by software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above-mentioned embodiment methods can be completed by a program instructing the relevant hardware, which can be stored in a computer readable storage medium and includes one or a combination of the steps of the embodiment methods when executed.
[0118] In addition, each functional unit in each embodiment of the present application can be integrated in one processing module, or each unit can be physically present separately, or two or more units can be integrated in one module. The above-mentioned integrated module can be realized in the form of hardware or in the form of a software functional module. The above-mentioned integrated module, if realized in the form of a software functional module and sold or used as an independent product, can also be stored in a computer readable storage medium. The storage medium can be a read-only memory, a magnetic disk or an optical disk, etc.
[0119] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of various changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.
Claims
1. A sunroof design method characterized by, The method comprises the following steps: splitting a space building into a skylight and a plurality of building components, setting a first design parameter for the skylight and a second design parameter for each building component, and generating a parameterized building model of the space building; setting a first material parameter for the skylight and a second material parameter for each building component to obtain a simulation model of the space building; obtaining geographical environment simulation data corresponding to the space building, and performing building performance simulation according to the geographical environment simulation data, the parameterized building model, the simulation model, and an optimization target to obtain a performance simulation result; determining optimal target design parameters and optimal target material parameters according to an optimization tool and the performance simulation result.
2. The sunroof design method of claim 1, wherein: The method of splitting a space building into a skylight and a plurality of building components, setting a first design parameter for the skylight and a second design parameter for each building component, and generating a parameterized building model of the space building comprises the following steps: splitting the space building into a skylight and a plurality of building components, wherein the building components include a roof, a ceiling, a wall, a floor, a window, and a sunshade component; setting a first design parameter for the skylight and a second design parameter for the roof, the ceiling, the wall, the floor, the window, and the sunshade component to generate a parameterized building model of the space building; wherein the first design parameter includes the type, number, and area ratio of the skylight, and the area ratio is limited within a preset constraint range.
3. The sunroof design method of claim 2, wherein: The method of setting a first material parameter for the skylight and a second material parameter for each building component to obtain a simulation model of the space building comprises the following steps: setting a first material parameter for the skylight and a second material parameter for each building component; constructing a simulation model of the space building according to the parameterized building model, the first material parameter, and the second material parameter of each building component; wherein when the optimization target includes light environment simulation, the first material parameter includes visible light transmittance and skylight transmittance, and when the optimization target includes energy consumption simulation, the first material parameter includes heat transfer coefficient and thermal radiation shielding coefficient.
4. The sunroof design method of claim 3, wherein: The method of obtaining geographical environment simulation data corresponding to the space building comprises the following steps: downloading an epw file of climate data from a specified website through the Open EPW And STAT Weather Files module of Ladybug Tools; obtaining geographical environment parameters corresponding to the space building, wherein the geographical environment parameters include geographical location, building orientation, and surrounding environment data of the building; obtaining geographical environment simulation data corresponding to the space building according to the epw file and the geographical environment parameters.
5. The sunroof design method according to claim 3 or 4, characterized in that: The method of performing building performance simulation according to the geographical environment simulation data, the parameterized building model, the simulation model, and the optimization target to obtain a performance simulation result comprises the following steps: determining a target simulation engine and setting precision parameters of the target simulation engine based on the optimization target; determining a simulation area from the simulation model and converting the simulation model into a format recognizable by Ladybug Tools; performing building performance simulation on the simulation model in the converted format in the simulation area according to the target simulation engine to obtain a performance simulation result; wherein the performance simulation result includes daylight factor, effective daylight illuminance, radiation, and annual solar exposure.
6. The sunroof design method of claim 5, wherein: The determining the optimal target design parameters and the optimal target material parameters according to the optimization tool and the performance simulation result comprises: The performance simulation result is optimized by a genetic algorithm of the optimization tool Octopus; In the optimization process, whether a convergence state is reached is determined based on a convergence graph of the optimization tool Octopus; When the convergence state is reached, the optimal first target design parameter is determined from the first design parameters, the optimal first target material parameter is determined from the first material parameters, the optimal second target design parameter is determined from the second design parameters, and the optimal second target material parameter is determined from the second material parameters, the target design parameters comprise the first target design parameter and the second target design parameter, and the target material parameters comprise the first target material parameter and the second target material parameter.
7. The sunroof design method of claim 6, wherein: The determining whether the convergence state is reached in the optimization process based on the convergence graph of the optimization tool Octopus comprises: In the optimization process, whether the convergence state is reached is determined based on a convergence graph of the optimization tool Octopus; In the optimization process, whether the convergence state is reached is determined based on a convergence graph of the optimization tool Octopus; 8. A sunroof design apparatus characterized by, Comprise: The generation module is configured to split the spatial building into a skylight and a plurality of building components, set the first design parameters for the skylight, and set the second design parameters for each building component to generate a parameterized building model of the spatial building; The setting module is configured to set the first material parameters for the skylight and the second material parameters for each building component to obtain a simulation model of the spatial building; The simulation module is configured to obtain geographical environment simulation data corresponding to the spatial building, perform building performance simulation according to the geographical environment simulation data, the parameterized building model, the simulation model, and an optimization target, and obtain a performance simulation result; The determination module is configured to determine the optimal target design parameters and the optimal target material parameters according to the optimization tool and the performance simulation result.
9. An electronic device, comprising: Comprise: The processor and the memory, the memory stores instructions, the instructions are loaded and executed by the processor to realize the method of any one of claims 1-7.
10. A computer readable storage medium, the computer readable storage medium stores a computer program, the computer program is executed to realize the method of any one of claims 1-7.