An indoor daylighting rate simulation system for interior design

Abstract

The present application relates to an indoor daylighting simulation system for interior design. The system includes: a dynamic meteorological simulation module that generates a dynamically changing sky model based on real-time meteorological data; a building property analysis module that analyzes building optical property parameters using building information model data and hyperspectral imaging data; a ray tracing processing module that performs ray tracing based on the dynamic sky model and building optical property parameters to obtain indoor glare distribution data; a reverse parameter optimization module that generates window morphology parameters and sunshade layout parameters based on the target illumination range and indoor glare distribution data; and a real-time visualization verification module that visualizes the window morphology parameters and sunshade layout parameters to obtain updated daylighting simulation results, which are used to verify the daylighting effect of the design solution. Through dynamic meteorological fusion, non-uniform material modeling, and real-time optimization feedback, the system can improve the accuracy and adaptability of indoor daylighting simulation.

Description

Technical Field

[0001] The invention belongs to the field of computer technology, and in particular relates to an indoor daylighting rate simulation system for indoor design. Background Art

[0002] With the development of computer-aided design and building information modeling (BIM) technologies, indoor daylighting simulation has gradually become an important tool for architectural design and green building evaluation. However, current indoor daylighting simulation strategies primarily rely on static meteorological parameters, such as a fixed sun angle and preset sky brightness, and simplified architectural optical properties. On the one hand, the use of simplified architectural optical properties often assumes uniform reflectivity and transmittance of materials, ignoring the scattering properties of non-uniform materials. On the other hand, static meteorological parameters cannot accurately simulate the impact of dynamic meteorological changes on indoor daylighting. Summary of the Invention

[0003] Based on this, it is necessary to provide an indoor daylighting rate simulation system for interior design to address the above technical problems, so as to improve the accuracy of indoor daylighting simulation.

[0004] In a first aspect, the present application provides an indoor daylighting rate simulation system for interior design, the system comprising:

[0005] The dynamic weather simulation module is used to generate a dynamic sky model based on the real-time weather data of the target area to obtain a dynamically changing sky model;

[0006] A building property analysis module is used to analyze and process building optical properties based on building information model data and hyperspectral imaging data to obtain building optical property parameters. The building optical property parameters include window transmittance parameters, wall material reflectance parameters, and scattering characteristic parameters of non-uniform materials. The hyperspectral imaging data includes reflectance data and transmittance data of non-uniform materials, and the building information model data includes window structure parameters and wall material parameters.

[0007] A ray tracing processing module is used to perform dynamic ray tracing processing based on the dynamically changing sky model and building optical property parameters to obtain indoor glare distribution data;

[0008] The reverse parameter optimization module is used to perform reverse parameter optimization processing based on the target illumination range and indoor glare distribution data set by the user, and generate window shape parameters and shading device layout parameters;

[0009] The real-time visualization verification module is used to perform real-time visualization rendering processing based on window shape parameters and shading device layout parameters to obtain updated daylighting simulation results. The updated daylighting simulation results are used to verify the daylighting effects of interior design schemes under different conditions.

[0010] In one embodiment, the dynamic weather simulation module includes:

[0011] Data acquisition and correction subunit, used to:

[0012] Obtain real-time meteorological data, including cloud optical thickness data, solar altitude angle data, and ground reflectivity data;

[0013] According to the real-time cloud optical thickness data, outliers are eliminated to obtain the corrected cloud optical thickness data;

[0014] Dynamically correct the ground reflectivity data according to the satellite remote sensing data of the target area to obtain the corrected ground reflectivity data;

[0015] Model building subunit, used to:

[0016] The corrected cloud optical thickness data and the corrected ground reflectivity data are input into the cloud motion model based on deep learning to obtain the predicted cloud motion trajectory;

[0017] By combining the predicted cloud motion trajectory and solar altitude angle data, a dynamically changing sky model is obtained.

[0018] In one embodiment, the building attribute parsing module includes:

[0019] Non-uniform material analysis subunit, used for:

[0020] Perform hyperspectral imaging scanning on non-uniform materials to obtain reflectance and transmittance data of the surface of non-uniform materials at different incident angles;

[0021] Classify and store the reflectivity data and transmittance data to generate a discrete scattering characteristic data set;

[0022] The cubic spline interpolation algorithm is used to smoothly interpolate the discrete scattering characteristic data set to generate a continuous material parameter curve;

[0023] The building property analysis and integration subunit is used to associate and bind the continuous material parameter curve with the window transmittance parameters and wall material reflectance parameters in the building information model to obtain the building optical property parameters.

[0024] In one embodiment, the ray tracing processing module includes:

[0025] Ray tracing subunit, used to:

[0026] Calculate the solar azimuth and altitude of direct light at the target time based on the dynamically changing sky model;

[0027] Based on the sun's azimuth and altitude, the main ray projection process is performed to generate a direct indoor light distribution map. Based on the window transmittance parameters and the wall material reflectance parameters, the Monte Carlo reverse path tracing algorithm is used to calculate the indoor glass curtain wall area and highly reflective material surfaces to obtain the reflected light intensity data and the scattered light intensity data.

[0028] The light data fusion subunit is used to combine the indoor direct light distribution map, reflected light intensity data and scattered light intensity data to generate indoor glare distribution data.

[0029] In one embodiment, the reverse parameter optimization module includes:

[0030] The parameter setting subunit is used to determine the lighting uniformity index and glare safety threshold according to the target illumination range;

[0031] Parameter optimization subunit, used to:

[0032] Construct a multi-objective optimization function whose constraints include building structure strength, cost threshold, and adjustable range of shading devices;

[0033] Based on the non-dominated sorting genetic algorithm, the window opening and closing angles and the sunshade spacing are combined and optimized to generate the initial solution set.

[0034] A set of feasible solutions that meet the constraints is extracted from the initial solution set through the Pareto front screening algorithm;

[0035] The optimal solution in the set of feasible solutions is mapped to the parametric modeling tool to generate window shape parameters and shading device layout parameters.

[0036] In one embodiment, the real-time visual verification module includes:

[0037] 3D simulation subunit for:

[0038] Based on the window shape parameters and the sunshade device layout parameters, a dynamic indoor lighting scene is constructed in a 3D rendering engine. The dynamic indoor lighting scene includes the window devices and the sunshade devices.

[0039] Based on a dynamically changing sky model, a dynamic particle system is used to simulate the dynamic effects of natural light in the room. Dynamic effects include light intensity fluctuations caused by cloud cover and real-time changes in reflected light.

[0040] Dynamically adjust analog subunits for:

[0041] Obtaining user adjustment instructions, and adjusting the window device and the sunshade device according to the user adjustment instructions, generating updated sunshade device layout parameters and updated window shape parameters;

[0042] Recalculating indoor daylight coefficient distribution data according to the updated sunshade device layout parameters and the updated window shape parameters to generate updated daylight coefficient distribution data;

[0043] The updated daylight factor distribution data is mapped to the 3D rendering engine, the light intensity distribution and glare risk area annotations in the indoor dynamic lighting scene are updated, and the updated daylight factor simulation results are output.

[0044] In a second aspect, the present application further provides a method for simulating indoor daylighting rate for interior design, the method comprising:

[0045] Based on the real-time meteorological data of the target area, a dynamic sky model is generated and processed to obtain a dynamically changing sky model;

[0046] Based on the building information model data and hyperspectral imaging data, the building optical properties are analyzed and processed to obtain the building optical property parameters, which include window transmittance parameters, wall material reflectance parameters, and scattering characteristic parameters of non-uniform materials. The hyperspectral imaging data includes reflectance data and transmittance data of non-uniform materials, and the building information model data includes window structure parameters and wall material parameters.

[0047] Based on the dynamically changing sky model and building optical property parameters, dynamic ray tracing is performed to obtain indoor glare distribution data;

[0048] Based on the target illumination range and indoor glare distribution data set by the user, reverse parameter optimization is performed to generate window shape parameters and shading device layout parameters;

[0049] According to the window shape parameters and shading device layout parameters, real-time visual rendering processing is performed to obtain the updated daylighting rate simulation results.

[0050] In a third aspect, the present application further provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the first aspect when executing the computer program.

[0051] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the first aspect when executed by a processor.

[0052] The aforementioned indoor daylighting simulation system for interior design uses a dynamic meteorological simulation module to generate a dynamically changing sky model based on real-time meteorological data. This model accurately reflects the dynamic changes in sky lighting under different time periods and weather conditions, providing accurate light source input for subsequent ray tracing processing. The building property analysis module, by combining building information model data and hyperspectral imaging data, comprehensively considers window transmittance, wall reflectivity, and the scattering characteristics of non-uniform materials to comprehensively analyze the optical properties of the building, thereby effectively compensating for the shortcomings of traditional strategies in simplifying the processing of building optical properties. The ray tracing processing module uses the dynamic sky model and building optical property parameters to perform dynamic ray tracing, accurately calculating indoor glare distribution data and providing data support for subsequent parameter optimization. The reverse parameter optimization module can generate optimized window shape parameters and shading device layout parameters through an inverse optimization algorithm based on the target illumination range and indoor glare distribution data, thereby achieving a reverse mapping from target illumination to design parameters. The real-time visual verification module can render the optimized parameters in real time to obtain updated daylighting simulation results, and then intuitively present the lighting effects of the design scheme under different conditions, providing designers with strong decision-making support.

[0053] Through the coordinated operation of the above-mentioned multiple modules, the system can not only effectively improve the accuracy and adaptability of indoor lighting simulation, but also intuitively present the lighting effects of interior design schemes under different conditions, providing designers with more scientific and intuitive design tools, and promoting the development of the interior design field towards a more refined and intelligent direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 A schematic structural diagram of an indoor daylighting rate simulation system for interior design provided by an exemplary embodiment of the present invention;

[0056] Figure 2 A flow chart of a method for simulating indoor daylighting rate for interior design is provided as an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0058] In one embodiment, Figure 1 As shown, a system 100 for simulating indoor daylighting rate for interior design is provided. This embodiment uses the system applied to a terminal as an example for illustration. It is understood that the system can also be applied to a server, or to a system including a terminal and a server, and implemented through the interaction between the terminal and the server. In this embodiment, the system includes:

[0059] The dynamic weather simulation module 101 is used to generate a dynamic sky model based on the real-time weather data of the target area to obtain a dynamically changing sky model.

[0060] Specifically, real-time meteorological data can include meteorological parameters such as sun position, cloud cover, atmospheric transparency, and solar radiation intensity. Based on this data, the direction and intensity of direct sunlight can be calculated based on parameters such as the sun's altitude and azimuth. The distribution and intensity of scattered light can be simulated by combining cloud distribution and atmospheric conditions, thereby constructing a sky model that reflects current lighting conditions. This model not only accurately simulates real-world lighting changes but also responds to dynamic changes in meteorological conditions in real time, providing realistic light source input for subsequent ray tracing.

[0061] The building property analysis module 102 is used to perform building optical property analysis based on the building information model data and the hyperspectral imaging data to obtain building optical property parameters. The building optical property parameters include window transmittance parameters, wall material reflectance parameters, and scattering characteristic parameters of non-uniform materials. The hyperspectral imaging data includes reflectance data and transmittance data of non-uniform materials, and the building information model data includes window structure parameters and wall material parameters.

[0062] Specifically, the building attribute analysis module 102 can first receive structural data from the building information model, such as structural parameters such as the size, shape, and position of windows, and information such as the type and distribution of wall materials. In addition, the module can also scan the surface of indoor building materials using hyperspectral imaging equipment to obtain hyperspectral imaging data. This hyperspectral imaging data contains reflectivity and transmittance information of indoor non-uniform materials at different wavelengths. By combining building information model data with hyperspectral imaging data, the module can accurately analyze the optical properties of various parts of the building. Schematically, for the window part, the transmittance parameters of light of different wavelengths can be obtained; for the wall material, its reflectivity parameters in the visible light range and the scattering characteristic parameters of the non-uniform material can be obtained.

[0063] The ray tracing processing module 103 is used to perform dynamic ray tracing processing based on the dynamically changing sky model and building optical property parameters to obtain indoor glare distribution data.

[0064] Specifically, the ray tracing processing module 103 can determine the position, intensity and spectral distribution of the outdoor light source based on the dynamic sky model, and set the physical rules for the interaction between light and various parts of the building in combination with the optical property parameters of the building. Subsequently, the module can simulate and trace the physical processes of light propagation path, reflection, refraction, etc. in the room through ray tracing algorithms such as the Monte Carlo ray tracing method. During the tracing process, the module can record the number of reflections, refractions and energy attenuation of the light on the propagation path, so as to calculate the light intensity and distribution at each point in the room. Among them, the module can analyze the light intensity and distribution by paying attention to the situation where the light directly enters the observer's field of view, generate indoor glare distribution data, and display the severity and distribution area of ​​glare in an intuitive visual form, thereby providing key data support for subsequent lighting simulation and design scheme verification, and helping designers understand the indoor lighting conditions and visual comfort under different conditions.

[0065] The reverse parameter optimization module 104 is used to perform reverse parameter optimization processing according to the target illumination range and indoor glare distribution data set by the user, and generate window shape parameters and sunshade device layout parameters.

[0066] Specifically, the reverse parameter optimization module 104 can take as input the user-defined target illumination range and indoor glare distribution data, construct a parameter space containing window morphology parameters and shading device layout parameters, and define an objective function to measure the degree to which the indoor lighting effect matches the target illumination range and the glare control under the current parameter combination. Illustratively, this module can use optimization algorithms such as genetic algorithms and particle swarm optimization to search and iteratively optimize within the parameter space, gradually adjusting the parameters of the windows and shading devices so that the indoor lighting effect meets the user-defined target illumination range while reducing the impact of glare. Illustratively, during the optimization process, this module can also consider the structural constraints and design specifications of the actual building to ensure the feasibility and implementability of the generated parameter solution.

[0067] The real-time visualization verification module 105 is used to perform real-time visualization rendering processing based on the window shape parameters and the sunshade device layout parameters to obtain an updated daylighting simulation result. The updated daylighting simulation result is used to verify the daylighting effect of the interior design scheme under different conditions.

[0068] Specifically, the real-time visual verification module can receive the window shape parameters and sunshade layout parameters optimized by the reverse parameter optimization module 104, and use the real-time rendering engine to perform efficient visual rendering in combination with the three-dimensional model of the building and the dynamic sky model. Indicatively, the module can support multiple rendering modes such as real-time preview mode and high-quality rendering mode to meet the design requirements at different stages. In real-time preview mode, the module can quickly generate preliminary visualization results of the lighting effect, so that designers can quickly evaluate the approximate effect of the design scheme; while in high-quality rendering mode, the module generates highly realistic lighting effect images or animations for design display and evaluation through sophisticated light and shadow calculations and material performance. In addition, the module can also provide a variety of visual analysis tools, such as illuminance distribution maps, glare level displays, daylighting rate statistics, etc., so as to be able to gain an in-depth understanding of the lighting situation from different angles.

[0069] In the aforementioned indoor daylighting simulation system 100 for interior design, the dynamic meteorological simulation module 101 generates a dynamically changing sky model based on real-time meteorological data for the target area. This model accurately reflects changes in illumination during different time periods and under different weather conditions, providing more realistic light source input for subsequent ray tracing processing. The building property analysis module 102 can combine building information model data and hyperspectral imaging data to comprehensively analyze the building's optical properties, including parameters such as window transmittance, wall reflectivity, and the scattering characteristics of non-uniform materials. This effectively compensates for the shortcomings of traditional strategies in simplifying building optical properties, making subsequent daylighting simulation results more realistic. The ray tracing processing module 103 uses the dynamic sky model and building optical property parameters to perform dynamic ray tracing, accurately calculating indoor glare distribution data and providing a scientific basis for subsequent parameter optimization. The inverse parameter optimization module 104 can use an inverse optimization algorithm to rapidly generate optimized window shape parameters and shading device layout parameters based on the user-defined target illumination range and indoor glare distribution data, achieving a reverse mapping from target illumination to design parameters. The real-time visual verification module 105 can visually present the lighting effects of the design scheme under different conditions by visually rendering the optimized parameters, providing powerful decision-making support for designers.

[0070] Compared with traditional indoor lighting simulation strategies, this system can not only reflect the dynamic changes in the weather in real time, but also accurately model the optical properties of buildings through dynamic weather fusion, non-uniform material modeling and real-time optimization feedback. It also significantly improves the accuracy and adaptability of indoor lighting simulation through reverse optimization and real-time feedback mechanisms, thereby providing designers with scientific decision-making support and promoting the development of the interior design field towards refinement and intelligence.

[0071] In one embodiment, the dynamic weather simulation module 101 includes:

[0072] Data acquisition and correction subunit, used to:

[0073] Obtain real-time meteorological data, including cloud optical thickness data, solar altitude angle data, and ground reflectivity data;

[0074] According to the real-time cloud optical thickness data, outliers are eliminated to obtain the corrected cloud optical thickness data;

[0075] Dynamically correct the ground reflectivity data according to the satellite remote sensing data of the target area to obtain the corrected ground reflectivity data;

[0076] Model building subunit, used to:

[0077] The corrected cloud optical thickness data and the corrected ground reflectivity data are input into the cloud motion model based on deep learning to obtain the predicted cloud motion trajectory;

[0078] By combining the predicted cloud motion trajectory and solar altitude angle data, a dynamically changing sky model is obtained.

[0079] Specifically, cloud optical depth (COD) is a significant factor influencing outdoor lighting conditions, reflecting the clouds' ability to absorb and scatter light. Cloud optical depth data can then be preprocessed using a data cleaning algorithm to identify and mark outliers. These outliers can then be corrected using methods such as interpolation or neighbor substitution, effectively removing noise and erroneous information from the data to produce corrected COD data. Furthermore, the sun's position in the sky can be calculated by combining the current time and the geographic coordinates of the target area to obtain solar altitude data. Schematically, the solar altitude determines the angle of incidence of sunlight on the ground, affecting the intensity and distribution of direct sunlight. Ground reflectivity (GRD) data characterizes the surface's reflectivity of light, influencing the brightness of outdoor environments and the multiple reflections of light. Furthermore, dynamic changes in surface conditions, such as vegetation growth, soil moisture changes, and snowmelt, can affect ground reflectivity. The data acquisition and correction subunit can combine satellite remote sensing data with original ground reflectivity data through data fusion algorithm, update ground reflectivity parameters in real time, and obtain corrected ground reflectivity data, so that the ground reflectivity data can more realistically reflect the actual surface conditions and improve the accuracy of illumination simulation.

[0080] The model-building subunit can then use the corrected cloud optical thickness data and corrected ground reflectivity data as input into a deep learning-based cloud motion model. Schematically, this cloud motion model is a neural network model trained with a large amount of historical meteorological data, capable of learning the movement patterns and changing patterns of clouds under different meteorological conditions. By analyzing the spatiotemporal characteristics of the input data, the model can predict the cloud motion trajectory over a period of time, such as the direction of movement, speed, and morphological changes of the clouds. By combining the predicted cloud motion trajectory with real-time solar altitude data, considering the impact of ground reflectivity on ambient light, and incorporating light reflected from the ground into the sky model's illumination calculations, a dynamically changing sky model can be obtained. This model can accurately reflect the lighting conditions of the target area at different times and the distribution of direct light, scattered light, and reflected light, providing realistic light source input for subsequent ray tracing processing.

[0081] In one embodiment, the building attribute parsing module 102 includes:

[0082] Non-uniform material analysis subunit, used for:

[0083] Perform hyperspectral imaging scanning on non-uniform materials to obtain reflectance and transmittance data of the surface of non-uniform materials at different incident angles;

[0084] Classify and store the reflectivity data and transmittance data to generate a discrete scattering characteristic data set;

[0085] The cubic spline interpolation algorithm is used to smoothly interpolate the discrete scattering characteristic data set to generate a continuous material parameter curve;

[0086] The building property analysis and integration subunit is used to associate and bind the continuous material parameter curve with the window transmittance parameters and wall material reflectance parameters in the building information model to obtain the building optical property parameters.

[0087] Specifically, hyperspectral imaging of heterogeneous materials can be performed with high spectral resolution within the visible and near-infrared bands, accurately capturing the optical properties of heterogeneous materials at different wavelengths and obtaining reflectance and transmittance data at different incident angles. The acquired reflectance and transmittance data can then be categorized and stored according to parameters such as material type, incident angle, and wavelength, forming a structured discrete scattering characteristic dataset. This facilitates subsequent data processing and analysis, enabling rapid location and extraction of optical data under specific conditions. This discrete scattering characteristic dataset contains discrete sampling points of the reflectance and transmittance of the heterogeneous material at different incident angles and wavelengths, which reflect how the material's optical properties vary with angle and wavelength. For example, cubic spline interpolation is a commonly used interpolation method that generates smooth curves and maintains continuity of first- and second-order derivatives at interpolation points, ensuring smoothness and authenticity. Using the cubic spline interpolation algorithm to smoothly interpolate the discrete scattering characteristic dataset generates continuous reflectance and transmittance curves for the heterogeneous material, i.e., continuous material parameter curves. This continuous material parameter curve not only accurately reflects the material's optical properties but also provides reflectance and transmittance values ​​at any angle of incidence and wavelength, providing the necessary data foundation for subsequent ray tracing. By associating the continuous material parameter curve generated by the non-uniform material analysis subunit with the window transmittance parameters and wall material reflectance parameters in the building information model, a complete set of building optical property parameters can be obtained.

[0088] In one embodiment, the ray tracing processing module 103 includes:

[0089] Ray tracing subunit, used to:

[0090] Calculate the solar azimuth and altitude of direct light at the target time based on the dynamically changing sky model;

[0091] Perform main ray projection based on the sun's azimuth and altitude angles to generate a direct indoor light distribution map;

[0092] Based on the window transmittance parameters and wall material reflectance parameters, the Monte Carlo reverse path tracing algorithm is used to calculate the indoor glass curtain wall area and high-reflective material surface to obtain the reflected light intensity data and scattered light intensity data;

[0093] The light data fusion subunit is used to combine the indoor direct light distribution map, reflected light intensity data and scattered light intensity data to generate indoor glare distribution data.

[0094] Specifically, based on the dynamically changing sky model generated by the dynamic weather simulation module, the solar azimuth and altitude of direct sunlight are calculated by comprehensively considering the sun's real-time position in the sky and the effects of clouds, the atmosphere, and other factors on light scattering and absorption. Based on the calculated solar azimuth and altitude, the ray tracing subunit simulates the path and distribution of direct sunlight entering the indoor space, taking into account the shading and transmission of light by the building structure. The ray tracing algorithm calculates the propagation of light within the room, ultimately generating an indoor direct light distribution map. This indoor direct light distribution map intuitively displays the illumination area and intensity distribution of direct light within the room. For indoor glass curtain wall areas and highly reflective surfaces, the Monte Carlo reverse path tracing algorithm can be used to simulate the reflection and scattering behavior of a large amount of light in these areas. Combined with the window transmittance parameters and the wall material reflectance parameters, the reflected and scattered light intensity data are calculated. The Monte Carlo reverse path tracing algorithm effectively handles complex optical reflection and scattering phenomena, providing highly accurate light intensity data. Finally, a data fusion algorithm can be used to match and superimpose the indoor direct light distribution map, reflected light intensity data, and scattered light intensity data, taking into account the superposition effect and mutual interference of light, to obtain indoor glare distribution data. This indoor glare distribution data not only includes the distribution of direct light but also integrates the effects of reflected and scattered light. It can accurately reflect the light intensity and glare level at each point in the room, providing comprehensive data support for subsequent lighting optimization simulations.

[0095] In one embodiment, the reverse parameter optimization module 104 includes:

[0096] The parameter setting subunit is used to determine the lighting uniformity index and glare safety threshold according to the target illumination range;

[0097] Parameter optimization subunit, used to:

[0098] Construct a multi-objective optimization function whose constraints include building structure strength, cost threshold, and adjustable range of shading devices;

[0099] Based on the non-dominated sorting genetic algorithm, the window opening and closing angles and the sunshade spacing are combined and optimized to generate the initial solution set.

[0100] A set of feasible solutions that meet the constraints is extracted from the initial solution set through the Pareto front screening algorithm;

[0101] The optimal solution in the set of feasible solutions is mapped to the parametric modeling tool to generate window shape parameters and shading device layout parameters.

[0102] Specifically, the lighting uniformity index is used to measure whether the lighting distribution in different areas of the room is uniform, avoiding local overbrightness or overdarkness; the glare safety threshold is used to ensure that indoor occupants do not experience visual discomfort due to excessive light reflection or direct exposure. Based on the light uniformity index and the glare safety threshold, a multi-objective optimization function can be constructed by comprehensively considering multiple factors such as lighting uniformity, glare control, building structure strength, cost, and the adjustable range of shading devices. Its constraints may include building structure strength, cost threshold, and the adjustable range of shading devices. Among them, building structure strength is used to ensure that adjustments to window opening and closing angles and visor spacing do not adversely affect the building structure. The cost threshold is used to control the additional costs caused by parameter adjustments within an acceptable range. The adjustable range of shading devices is used to ensure that the layout parameters of the shading devices are within the actual adjustable range.

[0103] Based on a multi-objective optimization function, a non-dominated sorting genetic algorithm can simulate natural selection and genetic mechanisms to search for an optimal solution set in parameter space. For example, a random set of window opening angle and shading slat spacing parameter combinations can be initialized as the initial population. The objective function value corresponding to each individual is then calculated and evaluated, and genetic operations such as selection, crossover, and mutation are performed to generate a new generation of populations. After repeated iterations, a set of non-dominated solutions that perform well in the multi-objective optimization problem is obtained, known as the initial solution set. The Pareto front screening algorithm then filters each solution in the initial solution set based on the constraints of the multi-objective optimization function, eliminating solutions that fail to meet constraints such as building structural strength, cost threshold, and shading device adjustable range. Solutions that meet all constraints and perform well on the objective function are retained, forming a set of feasible solutions. The optimal solutions in the set of feasible solutions are mapped to a parametric modeling tool, which quickly generates a corresponding building model. This intuitive display of detailed parameters such as window opening angle, size, and shape, and shading slat spacing and position provides a straightforward design reference and optimization basis.

[0104] In one embodiment, the real-time visual verification module 105 includes:

[0105] 3D simulation subunit for:

[0106] Based on the window shape parameters and the sunshade device layout parameters, a dynamic indoor lighting scene is constructed in a 3D rendering engine. The dynamic indoor lighting scene includes the window devices and the sunshade devices.

[0107] Based on a dynamically changing sky model, a dynamic particle system is used to simulate the dynamic effects of natural light in the room. Dynamic effects include light intensity fluctuations caused by cloud cover and real-time changes in reflected light.

[0108] Dynamically adjust analog subunits for:

[0109] Obtaining user adjustment instructions, and adjusting the window device and the sunshade device according to the user adjustment instructions, generating updated sunshade device layout parameters and updated window shape parameters;

[0110] Recalculating indoor daylight coefficient distribution data according to the updated sunshade device layout parameters and the updated window shape parameters to generate updated daylight coefficient distribution data;

[0111] The updated daylight factor distribution data is mapped to the 3D rendering engine, the light intensity distribution and glare risk area annotations in the indoor dynamic lighting scene are updated, and the updated daylight factor simulation results are output.

[0112] Specifically, based on the window morphology parameters and sunshade layout parameters generated by the reverse parameter optimization module 104, a highly realistic indoor dynamic lighting scene is constructed in the 3D rendering engine. This scene includes key elements such as window devices and sunshade devices, and ensures that the parameters of each element, such as the position, size, and shape, accurately match the window morphology parameters and sunshade layout parameters. Furthermore, by importing a 3D building model, the parameters of the window devices and sunshade devices can be applied to the corresponding positions to construct a virtual indoor environment consistent with the actual building structure. Based on a dynamically changing sky model, the dynamic particle system can simulate the propagation, scattering, and intensity changes of light, presenting complex lighting phenomena such as light intensity fluctuations caused by cloud obstruction and real-time changes in reflected light. For example, when the position and thickness of clouds in the sky model or the position of the sun changes, the dynamic particle system can adjust the light parameters accordingly, so that the indoor lighting effect truly reflects the changes in external meteorological conditions, providing an immersive visual experience.

[0113] The dynamic adjustment simulation subunit is responsible for receiving user adjustment commands and making corresponding adjustments to the window and shading devices based on the commands. For example, the window opening angle, size, and shading slat spacing and position parameters can be directly manipulated through the interactive interface to generate corresponding user adjustment commands. After receiving the user adjustment commands, the subunit parses the specific adjustment parameters and generates updated shading device layout parameters and window shape parameters, thereby ensuring the system's interactivity and real-time performance. Based on the updated shading device layout parameters and window shape parameters, new daylight factor distribution data is calculated using ray tracing algorithms and optical models. Daylight factor distribution data reflects the intensity and distribution of light at different locations in the room and is an important basis for evaluating daylighting effects. Finally, the 3D rendering engine adjusts the brightness and color of light at each point in the scene based on the new daylight factor data, identifies and annotates areas likely to generate glare, and outputs updated daylight factor simulation results. These results can be presented to the user through intuitive graphics or animations, allowing them to evaluate the daylighting effects of the adjusted design under different conditions.

[0114] Based on the same inventive concept, Figure 2 As shown, a method for simulating indoor daylighting rate for interior design, the method comprising:

[0115] S201: performing dynamic sky model generation processing based on real-time meteorological data of the target area to obtain a dynamically changing sky model;

[0116] S202: Performing building optical property analysis based on the building information model data and the hyperspectral imaging data to obtain building optical property parameters, where the building optical property parameters include window transmittance parameters, wall material reflectance parameters, and scattering characteristic parameters of non-uniform materials. The hyperspectral imaging data includes reflectance data and transmittance data of non-uniform materials, and the building information model data includes window structure parameters and wall material parameters.

[0117] S203: Performing dynamic ray tracing based on the dynamically changing sky model and building optical property parameters to obtain indoor glare distribution data;

[0118] S204: Perform reverse parameter optimization processing based on the target illumination range set by the user and the indoor glare distribution data to generate window shape parameters and shading device layout parameters;

[0119] S205: Perform real-time visual rendering processing based on the window shape parameters and the sunshade device layout parameters to obtain an updated daylighting rate simulation result.

[0120] The aforementioned indoor daylighting simulation method for interior design generates a dynamically changing sky model based on real-time meteorological data for the target area. This process provides data support for subsequent ray tracing and daylighting simulation. By integrating real-time meteorological data, it accurately reflects changes in illumination under varying meteorological conditions. Secondly, the method analyzes building optical properties based on building information model data and hyperspectral imaging data, comprehensively considering the building's optical characteristics and ensuring accurate simulation of light-building interactions. Furthermore, by combining the dynamic sky model with building optical properties for dynamic ray tracing, it accurately assesses indoor lighting quality and visual comfort, and generates indoor glare distribution data, providing a quantitative basis for subsequent design optimization. Furthermore, the method performs inverse parameter optimization based on the user-defined target illumination range and indoor glare distribution data to generate window morphology parameters and shading device layout parameters. This achieves a reverse mapping from daylighting objectives to building parameters, ensuring that the design meets both daylighting performance and engineering feasibility. Finally, real-time visual rendering is performed based on the window shape parameters and shading device layout parameters to obtain the updated daylighting rate simulation results. This can intuitively demonstrate the lighting effects of the design scheme under different conditions, that is, the light intensity distribution and glare risk of different schemes can be intuitively verified.

[0121] Furthermore, a dynamic sky model generation process is performed based on the real-time meteorological data of the target area to obtain a dynamically changing sky model, including:

[0122] Obtain real-time meteorological data, including cloud optical thickness data, solar altitude angle data, and ground reflectivity data;

[0123] According to the real-time cloud optical thickness data, outliers are eliminated to obtain the corrected cloud optical thickness data;

[0124] Dynamically correct the ground reflectivity data according to the satellite remote sensing data of the target area to obtain the corrected ground reflectivity data;

[0125] The corrected cloud optical thickness data and the corrected ground reflectivity data are input into the cloud motion model based on deep learning to obtain the predicted cloud motion trajectory;

[0126] By combining the predicted cloud motion trajectory and solar altitude angle data, a dynamically changing sky model is obtained.

[0127] Furthermore, based on the building information model data and hyperspectral imaging data, the building optical properties are analyzed and processed to obtain the building optical property parameters. The building optical property parameters include window transmittance parameters, wall material reflectance parameters, and scattering characteristic parameters of non-uniform materials, including:

[0128] Perform hyperspectral imaging scanning on non-uniform materials to obtain reflectance and transmittance data of the surface of non-uniform materials at different incident angles;

[0129] Classify and store the reflectivity data and transmittance data to generate a discrete scattering characteristic data set;

[0130] The cubic spline interpolation algorithm is used to smoothly interpolate the discrete scattering characteristic data set to generate a continuous material parameter curve;

[0131] The continuous material parameter curve is associated and bound with the window transmittance parameters and wall material reflectance parameters in the building information model to obtain the building optical property parameters.

[0132] Furthermore, based on the dynamically changing sky model and building optical property parameters, dynamic ray tracing processing is performed to obtain indoor glare distribution data, including:

[0133] Calculate the solar azimuth and altitude of direct light at the target time based on the dynamically changing sky model;

[0134] Perform main ray projection based on the sun's azimuth and altitude angles to generate a direct indoor light distribution map;

[0135] Based on the window transmittance parameters and wall material reflectance parameters, the Monte Carlo reverse path tracing algorithm is used to calculate the indoor glass curtain wall area and high-reflective material surface to obtain the reflected light intensity data and scattered light intensity data;

[0136] Indoor glare distribution data is generated by combining the indoor direct light distribution map, reflected light intensity data and scattered light intensity data.

[0137] Furthermore, based on the target illumination range and indoor glare distribution data set by the user, reverse parameter optimization is performed to generate window shape parameters and shading device layout parameters, including:

[0138] Determine the lighting uniformity index and glare safety threshold based on the target illumination range;

[0139] Construct a multi-objective optimization function whose constraints include building structure strength, cost threshold, and adjustable range of shading devices;

[0140] Based on the non-dominated sorting genetic algorithm, the window opening and closing angles and the sunshade spacing are combined and optimized to generate the initial solution set.

[0141] A set of feasible solutions that meet the constraints is extracted from the initial solution set through the Pareto front screening algorithm;

[0142] The optimal solution in the set of feasible solutions is mapped to the parametric modeling tool to generate window shape parameters and shading device layout parameters.

[0143] Furthermore, real-time visual rendering is performed based on the window shape parameters and shading device layout parameters to obtain updated daylighting simulation results, including:

[0144] Based on the window shape parameters and the sunshade device layout parameters, a dynamic indoor lighting scene is constructed in a 3D rendering engine. The dynamic indoor lighting scene includes the window devices and the sunshade devices.

[0145] Based on a dynamically changing sky model, a dynamic particle system is used to simulate the dynamic effects of natural light in the room. Dynamic effects include light intensity fluctuations caused by cloud cover and real-time changes in reflected light.

[0146] Obtaining user adjustment instructions, and adjusting the window device and the sunshade device according to the user adjustment instructions, generating updated sunshade device layout parameters and updated window shape parameters;

[0147] Recalculating indoor daylight coefficient distribution data according to the updated sunshade device layout parameters and the updated window shape parameters to generate updated daylight coefficient distribution data;

[0148] The updated daylight factor distribution data is mapped to the 3D rendering engine, the light intensity distribution and glare risk area annotations in the indoor dynamic lighting scene are updated, and the updated daylight factor simulation results are output.

[0149] In an exemplary embodiment, the present invention further provides a computer device comprising a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the steps of the indoor daylighting simulation system for interior design described in the present application. A multi-core processor is preferred to improve the system's parallel processing capabilities. The memory provides sufficient temporary storage space to support program execution and data processing. The memory capacity should be large enough to accommodate large amounts of supply information and computing tasks.

[0150] In an exemplary embodiment, the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of an indoor daylighting rate simulation system for interior design of the present application. The computer-readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a solid-state drive (SSD), or an optical disc. Among them, the random access memory may include a resistive random access memory (ReRAM) and a dynamic random access memory (DRAM).

[0151] The above-described embodiments merely represent several implementation methods of the embodiments of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that a person skilled in the art may make various modifications and improvements without departing from the concept of the embodiments of the present application, and these modifications and improvements fall within the scope of protection of the embodiments of the present application.

Claims

1. An indoor daylighting rate simulation system for interior design, characterized in that: The system comprises: The dynamic weather simulation module is used to generate a dynamic sky model based on the real-time weather data of the target area to obtain a dynamically changing sky model; A building property analysis module is used to perform building optical property analysis based on building information model data and hyperspectral imaging data to obtain building optical property parameters, wherein the building optical property parameters include window transmittance parameters, wall material reflectance parameters, and scattering characteristic parameters of non-uniform materials. The hyperspectral imaging data includes reflectance data and transmittance data of non-uniform materials, and the building information model data includes window structure parameters and wall material parameters. a ray tracing processing module, configured to perform dynamic ray tracing processing based on the dynamically changing sky model and the building optical property parameters to obtain indoor glare distribution data; A reverse parameter optimization module is used to perform reverse parameter optimization processing based on the target illumination range set by the user and the indoor glare distribution data to generate window shape parameters and shading device layout parameters; A real-time visual verification module is used to perform real-time visual rendering processing based on the window shape parameters and the sunshade device layout parameters to obtain an updated daylighting simulation result. The updated daylighting simulation result is used to verify the daylighting effect of the interior design scheme under different conditions.

2. The system according to claim 1, wherein: The dynamic weather simulation module includes: Data acquisition and correction subunit, used to: Acquiring the real-time meteorological data, wherein the real-time meteorological data includes real-time cloud optical thickness data, solar altitude angle data, and ground reflectivity data; According to the real-time cloud optical thickness data, outlier elimination processing is performed to obtain corrected cloud optical thickness data; Dynamically correcting the ground reflectivity data according to the satellite remote sensing data of the target area to obtain corrected ground reflectivity data; Model building subunit, used to: Inputting the corrected cloud optical thickness data and the corrected ground reflectivity data into a cloud motion model based on deep learning to obtain a predicted cloud motion trajectory; The dynamically changing sky model is obtained by combining the predicted cloud motion trajectory and the solar altitude angle data.

3. The system according to claim 1, wherein: The building attribute parsing module includes: Non-uniform material analysis subunit, used for: Performing hyperspectral imaging scanning on the non-uniform material to obtain the reflectivity data and the transmittance data of the surface of the non-uniform material at different incident angles; Classifying and storing the reflectivity data and the transmittance data to generate a discrete scattering characteristic data set; Using a cubic spline interpolation algorithm to smoothly interpolate the discrete scattering characteristic data set to generate a continuous material parameter curve; The building property analysis and integration subunit is used to associate and bind the continuous material parameter curve with the window transmittance parameter and the wall material reflectance parameter in the building information model to obtain the building optical property parameters.

4. The system according to claim 1, wherein: The ray tracing processing module includes: Ray tracing subunit, used to: Calculating the solar azimuth and altitude of direct light at a target time based on the dynamically changing sky model; Performing main ray projection processing based on the solar azimuth angle and the altitude angle to generate a direct light distribution map in the room; According to the window transmittance parameter and the wall material reflectance parameter, the Monte Carlo reverse path tracing algorithm is used to calculate the indoor glass curtain wall area and the high reflective material surface to obtain the reflected light intensity data and the scattered light intensity data; The light data fusion subunit is used to combine the indoor direct light distribution map, the reflected light intensity data and the scattered light intensity data to generate the indoor glare distribution data.

5. The system according to claim 1, wherein: The reverse parameter optimization module includes: A parameter setting subunit, configured to determine a lighting uniformity index and a glare safety threshold according to the target illumination range; Parameter optimization subunit, used to: Constructing a multi-objective optimization function, wherein the constraints of the multi-objective optimization function include building structure strength, cost threshold, and adjustable range of the shading device; Based on the non-dominated sorting genetic algorithm, the window opening and closing angles and the sunshade spacing are combined and optimized to generate the initial solution set. Extracting a set of feasible solutions that meet the constraints from the initial solution set by using a Pareto frontier screening algorithm; The optimal solution in the set of feasible solutions is mapped to a parametric modeling tool to generate the window shape parameters and the sunshade device layout parameters.

6. The system according to claim 1, wherein: The real-time visual verification module includes: 3D simulation subunit for: Constructing an indoor dynamic lighting scene in a three-dimensional rendering engine according to the window shape parameters and the sunshade device layout parameters, wherein the indoor dynamic lighting scene includes the window device and the sunshade device; Based on the dynamically changing sky model, a dynamic particle system is used to simulate the dynamic effects of natural light in the room, including light intensity fluctuations caused by cloud cover and real-time changes in reflected light; Dynamically adjust analog subunits for: Obtaining a user adjustment instruction, and adjusting the window device and the sunshade device according to the user adjustment instruction, generating updated sunshade device layout parameters and updated window shape parameters; Recalculating indoor daylight coefficient distribution data according to the updated sunshade device layout parameters and the updated window shape parameters to generate updated daylight coefficient distribution data; The updated daylight factor distribution data is mapped to the three-dimensional rendering engine, the light intensity distribution and glare risk area marking in the indoor dynamic lighting scene are updated, and the updated daylight factor simulation result is output.

7. A method for simulating indoor daylighting rate for interior design, characterized in that: The method comprises: Based on the real-time meteorological data of the target area, a dynamic sky model is generated and processed to obtain a dynamically changing sky model; Performing building optical property analysis processing based on the building information model data and the hyperspectral imaging data to obtain building optical property parameters, wherein the building optical property parameters include window transmittance parameters, wall material reflectance parameters, and scattering characteristic parameters of non-uniform materials, the hyperspectral imaging data includes reflectance data and transmittance data of non-uniform materials, and the building information model data includes window structure parameters and wall material parameters; Based on the dynamically changing sky model and the building optical property parameters, dynamic ray tracing processing is performed to obtain indoor glare distribution data; Perform reverse parameter optimization processing based on the target illumination range set by the user and the indoor glare distribution data to generate window shape parameters and shading device layout parameters; According to the window shape parameters and the sunshade device layout parameters, real-time visual rendering processing is performed to obtain an updated daylighting rate simulation result.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the system according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the system according to any one of claims 1 to 6 are implemented.

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