A large-depth space light environment optimization design method and system based on dynamic daylighting simulation and multi-objective optimization

Abstract

This invention provides a method for optimizing the lighting environment of deep spaces based on dynamic daylighting simulation and multi-objective optimization. The method includes: constructing a parameterized basic model of a typical deep space based on design variables; performing dynamic daylighting simulation analysis on the parameterized basic model to obtain analysis results; determining the optimization objective of the parameterized basic model based on the analysis results; iteratively optimizing the design variables using a multi-objective genetic algorithm based on the optimization objective to obtain a Pareto optimal solution set; and constraining and screening the Pareto optimal solution set to obtain the final design scheme and architectural design parameters. This method, by constructing a parameterized basic model of a deep space and conducting dynamic daylighting simulation analysis, combined with iterative optimization using a multi-objective genetic algorithm and constrained screening of the Pareto optimal solution set, improves the balance between effective daylighting and glare protection in deep spaces.

Description

Technical Field

[0001] This invention relates to the field of architectural design technology, and in particular to a method and system for optimizing the light environment of a deep space based on dynamic daylighting simulation and multi-objective optimization. Background Technology

[0002] Contemporary architecture tends to adopt deep, open floor plans to accommodate flexible functional needs and high-density pedestrian traffic. Office and reading spaces, as areas with extremely high visual demands, are directly affected by the quality of their natural lighting, impacting users' visual comfort and mental well-being, and are also a key aspect of building energy conservation.

[0003] However, existing technologies have the following shortcomings in lighting design for deep spaces: 1. Difficulty in ensuring uniform daylighting: Deep buildings generally face the contradiction of "insufficient daylighting in the core area due to excessive depth, while glare occurs in the window area due to excessive solar radiation." Traditional static daylighting indicators (such as the daylight factor DF) are mainly based on all-cloudy day models, which cannot reflect the dynamic climate change throughout the year, resulting in a large deviation between simulation results and actual usage.

[0004] 2. Complex coupling of design parameters: The lighting performance of a deep space is affected by multiple parameters such as the window-to-wall ratio, the size of the shading components, the light transmittance of the glass, and the interior depth. These parameters are mutually restrictive (for example, increasing the window-to-wall ratio can increase the illuminance at depth, but it will significantly increase the risk of glare at the window edge).

[0005] 3. Low optimization efficiency and lack of specificity: Existing design processes rely heavily on designers' experience or manual adjustments through trial and error, lacking automated multi-objective optimization methods. Although some studies have introduced genetic algorithms, there is a lack of a complete and systematic optimization design method that balances "useful daylight" and "glare protection" for the specific space type of "deep reading rooms". Summary of the Invention

[0006] The purpose of this invention is to provide a method and system for optimizing the light environment of a large-depth space based on dynamic lighting simulation and multi-objective optimization. By constructing a parameterized basic model of the large-depth space and conducting dynamic lighting simulation analysis, combined with iterative optimization using a multi-objective genetic algorithm and Pareto optimal solution set constraint screening, the balance between effective lighting and glare protection in a large-depth space can be improved.

[0007] To achieve the above objectives, the present invention provides the following solution: A method for optimizing the lighting environment of a deep space based on dynamic lighting simulation and multi-objective optimization includes the following steps: Construct a basic parameterized model of a typical space with large depth based on design variables; Dynamic lighting simulation analysis was conducted on a typical spatial parameterized basic model with large depth, and the analysis results were obtained. Based on the analysis results, the optimization objective of the typical spatial parameterization basic model with large depth is determined; Based on the optimization objective, a multi-objective genetic algorithm is used to iteratively optimize the design variables to obtain the Pareto optimal solution set; Constraint screening is performed on the Pareto optimal solution set to obtain the final design scheme and architectural design parameters.

[0008] Optionally, design variables include: window width factor, floor height, window height factor, and visible light transmittance of the glass; the formula for calculating the window height factor is: The formula for calculating the window width factor is: ;in, and These are the window height and the room height, respectively. and These refer to the window width and the room width, respectively.

[0009] Optionally, dynamic daylighting simulation analysis is performed on a typical deep spatial parameterized basic model to obtain analysis results, including: The standard meteorological files of the target location are imported into a large-depth typical spatial parameterization basic model to obtain a sky brightness distribution model; A lighting analysis grid for a typical spatial parameterized basic model with large depth is constructed at the first altitude above the ground; the first altitude is the typical desktop height. The material optical properties of the parameterized basic model of a large-depth typical space are determined based on the physical behavior of light when it comes into contact with the interior interface. The material optical properties include: wall reflectivity, ceiling reflectivity, floor reflectivity and glass material properties.

[0010] Optionally, optimization objectives include maximizing effective daylighting and minimizing glare risk.

[0011] Optionally, based on the optimization objective, a multi-objective genetic algorithm is used to iteratively optimize the design variables to obtain a Pareto optimal solution set, including: Geometric and material optical properties are determined by designing variables; Using design variables as optimization inputs and spatial daylighting rate and natural daylighting glare index as objective functions, Pareto optimal solution sets are obtained through non-dominated sorting and genetic operations.

[0012] Optionally, constraints are applied to the Pareto optimal solution set to obtain the final design scheme and architectural design parameters, including: The constraints on the space daylighting rate and natural daylighting glare index are determined based on daylighting, glare control, and the actual needs of the project. The Pareto optimal solution set is constrained and filtered according to the constraints to obtain the feasible solution set. The points in the feasible solution set are labeled and sorted to obtain the final design scheme and architectural design parameters.

[0013] A system for optimizing the lighting environment of a deep space based on dynamic lighting simulation and multi-objective optimization includes: The parametric modeling module is used to construct a parametric basic model of a typical space with large depth based on design variables. The dynamic lighting simulation module is used to perform dynamic lighting simulation analysis on the parameterized basic model of a typical space with large depth and obtain the analysis results. The optimization target selection module is used to determine the optimization target of the typical spatial parameterized basic model with large depth based on the analysis results; The multi-objective optimization module is used to iteratively optimize the design variables based on the optimization objective using a multi-objective genetic algorithm to obtain the Pareto optimal solution set. The decision module is used to perform constraint screening on the Pareto optimal solution set to obtain the final design scheme and architectural design parameters.

[0014] According to specific embodiments provided by the present invention, the following technical effects are disclosed: The present invention provides a method and system for optimizing the light environment of deep spaces based on dynamic daylighting simulation and multi-objective optimization. The method includes: constructing a parameterized basic model of a typical deep space based on design variables; performing dynamic daylighting simulation analysis on the parameterized basic model of the typical deep space to obtain analysis results; determining the optimization objective of the parameterized basic model of the typical deep space based on the analysis results; iteratively optimizing the design variables using a multi-objective genetic algorithm based on the optimization objective to obtain a Pareto optimal solution set; and constraining and screening the Pareto optimal solution set to obtain the final design scheme and architectural design parameters. This method, by constructing a parameterized basic model of a deep space and conducting dynamic daylighting simulation analysis, combined with iterative optimization using a multi-objective genetic algorithm and constrained screening of the Pareto optimal solution set, improves the balance between effective daylighting and glare protection in deep spaces. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1This is a flowchart of the light environment optimization design method for large-depth spaces based on dynamic lighting simulation and multi-objective optimization according to the present invention. Figure 2 This is a schematic diagram illustrating the working principle of the large-depth spatial light environment optimization process according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the system structure for optimizing the light environment in a deep space based on dynamic lighting simulation and multi-objective optimization, as per the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] like Figure 1 and Figure 2 As shown, this invention provides a method for optimizing the light environment of a large-depth space based on dynamic lighting simulation and multi-objective optimization, comprising the following steps: Step 100: Construct a parametric basic model of a typical space with large depth based on the design variables; Step 200: Perform dynamic lighting simulation analysis on the parameterized basic model of a large-depth space and obtain the analysis results; Step 300: Determine the optimization objective of the parameterized basic model of a typical space with large depth based on the analysis results; Step 400: Based on the optimization objective, the design variables are iteratively optimized using a multi-objective genetic algorithm to obtain the Pareto optimal solution set; Step 500: Perform constraint screening on the Pareto optimal solution set to obtain the final design scheme and architectural design parameters.

[0020] In the specific implementation process, step 100, based on the Rhino or Grasshopper platform and BIM or CAD drawings, establishes a three-dimensional geometric model of the target deep-depth typical space, dividing the deep-depth space into a window-adjacent area and an interior area. Simultaneously, parameters that significantly affect the lighting environment are selected as design variables, including: window width coefficient. (Variation range 0.6-0.9), Floor height H (Variation range 3-3.9m), Window height coefficient (Variation range 0.4-0.7), visible light transmittance (VLT) of glass (Variation range 0.5-0.8), and the calculation formulas for window height coefficient and window width coefficient are as follows: ; ; in, and These are the window height and the room height, respectively. and These refer to the window width and the room width, respectively.

[0021] Preferably, dynamic lighting simulation analysis is performed on a typical spatial parameterized basic model with large depth to obtain analysis results, including: The standard meteorological files of the target location are imported into a large-depth typical spatial parameterization basic model to obtain a sky brightness distribution model; A lighting analysis grid for a typical spatial parameterized basic model with large depth is constructed at the first altitude above the ground; the first altitude is the typical desktop height. The material optical properties of the parameterized basic model of a large-depth typical space are determined based on the physical behavior of light when it comes into contact with the interior interface. The material optical properties include: wall reflectivity, ceiling reflectivity, floor reflectivity and glass material properties.

[0022] In the specific implementation process, step 200 imports the standard meteorological file (EPW) of the target location based on the Honeybee or Ladybug plugin, and reads the solar radiation data in the EPW file through a simulation engine (such as Radiance or Daysim) to construct a sky brightness distribution model. This file contains detailed hourly climate data (8760 hours per year) for the target location in a typical meteorological year (TMY). Importing this file provides realistic external boundary conditions for daylighting modeling, ensuring that the light environment simulation is not based on a static analysis at a certain moment, but on a comprehensive assessment of dynamic climate change throughout the year. Next, a daylighting analysis grid is established at 0.75m above the ground (typical tabletop height), with a grid size of 0.6m × 0.6m to ensure the accuracy of large-space simulation. At the same time, material optical properties, including wall reflectivity, ceiling reflectivity, ground reflectivity, and glass material properties, are set according to the physical behavior (reflection, transmission, absorption) of light when it comes into contact with indoor interfaces.

[0023] In the specific implementation process, step 300, in order to resolve the contradiction of "dark interior and bright exterior" in a deep space, sets two mutually exclusive optimization objectives. Objective 1 is to maximize effective daylighting, that is, to maximize spatial daylight autonomy (sDA). Specifically, it is the percentage of area on the test surface that reaches or exceeds a certain DA threshold during the specified usage period throughout the year. In some embodiments, the DA threshold is DA. 450,50% Objective 1 aims to ensure sufficient natural light in the core area of ​​the deep space. Objective 2 aims to minimize glare risk, specifically by minimizing the Daylight Glare Probability (DGP). This is determined by measuring the percentage of satisfaction with a specific view based on vertical eye illuminance. A satisfaction score < 0.35 indicates imperceptible glare, 0.35–0.4 indicates perceptible glare, 0.4–0.45 indicates disturbing glare, and > 0.45 indicates intolerable glare. Objective 2 is used to control glare in the window area.

[0024] Preferably, based on the optimization objective, a multi-objective genetic algorithm is used to iteratively optimize the design variables to obtain a Pareto optimal solution set, including: Geometric and material optical properties are determined by designing variables; Using design variables as optimization inputs and spatial daylighting rate and natural daylighting glare index as objective functions, Pareto optimal solution sets are obtained through non-dominated sorting and genetic operations.

[0025] In the specific implementation process, step 400 connects the design variables and optimization objectives into the optimization engine. In step 100, the window width coefficient, floor height, window height coefficient, and visible light transmittance of the glass have been determined as design variables. In step 300, a dual-objective optimization model with the objectives of maximizing spatial daylighting efficiency (sDA) and minimizing solar glare probability (DGP) has been established. Therefore, step 400 uses a multi-objective genetic algorithm to iteratively optimize the design variables in order to obtain a Pareto optimal solution set that meets the comprehensive performance requirements of the light environment in a large-depth space.

[0026] In some embodiments, the multi-objective genetic algorithm is automatically implemented using the Grasshopper parametric modeling environment and the Octopus multi-objective optimization plugin in Rhino software to simplify the manual coding and data transfer process. The specific steps are as follows: First, a basic geometric model of a large depth space is established in Rhino, and then the C++ model is implemented in Grasshopper. w H, C hVLT is set as the input parameter, driving the geometric and physical properties such as window geometry, floor height, and glass material. Next, Grasshopper interfaces with dynamic daylighting simulation engines like Radiance or Daysim, inputting the geometric properties and materials determined by the design variables into the simulation engine, automatically calculating the sDA and DGP for each scheme in batches. Then, in Octopus, the four design variable nodes are used as optimization inputs, and the output nodes of sDA and DGP are used as two objective functions. The population size is set to 50, the maximum number of iterations is 20-50, and the crossover and mutation probabilities are set according to algorithm recommendations. Octopus automatically performs non-dominated sorting and genetic operations, automatically generating different combinations of design parameters, calling the Radiance kernel for simulation, and selecting the Pareto front solution set based on the simulation results. The Pareto solution set represents a series of non-dominated solutions that achieve the optimal balance between sDA and DGP.

[0027] Preferably, the Pareto optimal solution set is constrained and screened to obtain the final design scheme and architectural design parameters, including: The constraints on the space daylighting rate and natural daylighting glare index are determined based on daylighting, glare control, and the actual needs of the project. The Pareto optimal solution set is constrained and filtered according to the constraints to obtain the feasible solution set. The points in the feasible solution set are labeled and sorted to obtain the final design scheme and architectural design parameters.

[0028] In the specific implementation process, step 500 selects the final solution from the Pareto solution set based on the specific functional preferences of the reading room (such as a greater emphasis on anti-glare or a greater emphasis on deep lighting). Specifically, based on relevant lighting and glare control standards and the actual needs of the project, constraints on the space's daylight rate and glare control are set. All solutions in the Pareto solution set are then constrained and screened according to the constraints to obtain a feasible solution set that meets the basic specifications and project requirements. At the same time, the solution set is automatically filtered according to the constraints in the Octopus interface or post-processing script, retaining only feasible solutions for subsequent decision-making. Then, the parameters of the optimal solution are exported and the building model is updated. Specifically, the points that meet the constraints are first filtered or marked in the Octopus frontier plot. Then, the multi-objective sorting or export function of Octopus is used to export and sort the sDA, DGP and comprehensive evaluation values. Then, by selecting a Pareto point in the Octopus interface, Grasshopper automatically displays the corresponding building model and light environment distribution (such as daylight factor distribution map and glare risk distribution map), thereby assisting designers in making intuitive comparisons. Finally, for the final determined solution, the corresponding window opening size, floor height parameters and glass material parameters are exported in Grasshopper for subsequent construction drawing design and engineering implementation.

[0029] like Figure 3 As shown, the present invention also provides a large-depth space light environment optimization design system based on dynamic lighting simulation and multi-objective optimization, comprising: The parametric modeling module is used to construct a parametric basic model of a typical space with large depth based on design variables. The dynamic lighting simulation module is used to perform dynamic lighting simulation analysis on the parameterized basic model of a typical space with large depth and obtain the analysis results. The optimization target selection module is used to determine the optimization target of the typical spatial parameterized basic model with large depth based on the analysis results; The multi-objective optimization module is used to iteratively optimize the design variables based on the optimization objective using a multi-objective genetic algorithm to obtain the Pareto optimal solution set. The decision module is used to perform constraint screening on the Pareto optimal solution set to obtain the final design scheme and architectural design parameters.

[0030] In one specific embodiment, a standard reading space in a university library was selected as the optimization target. This space has a depth of 9.0 meters, a floor height of 4.5 meters, and faces due south, making it a typical deep open space. The design goal is to maximize natural lighting in the core area of ​​the reading room (increasing sDA) while minimizing the risk of direct sunlight glare in the window areas (reducing DGP) throughout the year.

[0031] This embodiment utilizes Rhino or Grasshopper to build the basic geometric model of the reading room. The spatial grid size is set to 0.6m × 0.6m, the working surface height is set to 0.75m, and four key design parameters with the greatest impact on the lighting environment are selected as variables: window width coefficient, floor height, window height coefficient, and visible light transmittance of the glass. Then, the constructed multi-objective optimization module is integrated, with a population size of 50 and an iteration cycle of 30, initiating the calculation with the dual optimization objectives of "maximizing sDA (>450 lux)" and "minimizing DGP (<0.4)". Simultaneously, to balance the conflict between effective lighting depth and the probability of glare near the windows, relatively high window height coefficients (0.6~0.7), window width coefficients (around 0.7), glass transmittance (around 0.7), and suitable spatial clearance (3.3~3.6m) are adopted. Finally, the optimal balance point is found between "sufficient lighting" and "anti-glare" in the deep space, and specific facade design parameters that meet visual comfort requirements are output.

[0032] The beneficial effects of this invention are as follows: 1) By introducing spatial daylighting (sDA) as an optimization target, the illuminance distribution of the window area and the core area can be accurately balanced. Through simulation verification, the optimized scheme can significantly improve the effective daylighting area ratio of the deep reading room, while controlling the unacceptable glare area to the lowest level. 2) Based on 8760 hours of meteorological data throughout the year, dynamic simulation was conducted. The spatial daylighting rate (sDA) and natural daylighting glare index (DGP) were selected as evaluation indicators to reflect the real impact of solar altitude angle and climate change on the light environment of deep spaces. It has good adaptability in different seasons throughout the year. 3) It integrates parametric modeling and genetic algorithms, replacing the inefficient cycle of traditional manual "modeling-simulation-modification-re-simulation". Through automatic calculation of hundreds or thousands of parameter combinations by computer, it can find the "optimal solution" that is difficult for human designers to find by experience in a short time, which greatly improves the scientificity and efficiency of the design of tall and complex spatial buildings. 4) Pareto solutions provide a visual basis for trade-offs, allowing designers to clearly see "how much shading depth can be sacrificed in exchange for how much interior lighting," thus making more refined decisions that meet the actual needs of the project.

[0033] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0034] Specific examples have been used to illustrate the principles and implementation methods of this invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this invention. Furthermore, those skilled in the art will recognize that, based on the ideas of this invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for optimizing the light environment of a deep space based on dynamic lighting simulation and multi-objective optimization, characterized in that, Includes the following steps: Construct a basic parameterized model of a typical space with large depth based on design variables; Dynamic lighting simulation analysis was performed on the parameterized basic model of the large-depth typical space, and the analysis results were obtained. Based on the analysis results, the optimization objective of the typical spatial parameterization basic model with large depth is determined; Based on the optimization objective, the design variables are iteratively optimized using a multi-objective genetic algorithm to obtain a Pareto optimal solution set. Constraint screening is performed on the Pareto optimal solution set to obtain the final design scheme and architectural design parameters.

2. The method for optimizing the light environment of a deep space based on dynamic lighting simulation and multi-objective optimization according to claim 1, characterized in that, The design variables include: window width factor, floor height, window height factor, and visible light transmittance of the glass; the formula for calculating the window height factor is: The formula for calculating the window width coefficient is as follows: ;in, and These are the window height and the room height, respectively. and These refer to the window width and the room width, respectively.

3. The method for optimizing the light environment of a deep space based on dynamic lighting simulation and multi-objective optimization according to claim 1, characterized in that, Dynamic lighting simulation analysis was performed on the parameterized basic model of the large-depth typical space, and the analysis results are as follows: The standard meteorological file of the target location is imported into the large-depth typical spatial parameterization basic model to obtain the sky brightness distribution model; A lighting analysis grid for the parameterized basic model of the large-depth typical space is constructed at a first height above the ground; the first height is the typical desktop height. The material optical properties of the parameterized basic model of the large-depth typical space are determined based on the physical behavior of light when it comes into contact with the indoor interface; the material optical properties include: wall reflectivity, ceiling reflectivity, floor reflectivity and glass material properties.

4. The method for optimizing the light environment of a deep space based on dynamic lighting simulation and multi-objective optimization according to claim 1, characterized in that, The optimization objectives include maximizing effective light intake and minimizing glare risk.

5. The method for optimizing the light environment of a deep space based on dynamic lighting simulation and multi-objective optimization according to claim 3, characterized in that, Based on the aforementioned optimization objective, a multi-objective genetic algorithm is used to iteratively optimize the design variables to obtain a Pareto optimal solution set, including: The geometric properties and the optical properties of the material are determined by the design variables; Using the design variables as optimization inputs and the spatial daylighting rate and natural daylighting glare index as objective functions, the Pareto optimal solution set is obtained through non-dominated sorting and genetic operations.

6. The method for optimizing the light environment of a deep space based on dynamic lighting simulation and multi-objective optimization according to claim 5, characterized in that, The Pareto optimal solution set is constrained and filtered to obtain the final design scheme and architectural design parameters, including: The constraints on the space daylighting rate and the natural daylighting glare index are determined based on daylighting, glare control, and the actual needs of the project. The Pareto optimal solution set is constrained and filtered according to the constraints to obtain a feasible solution set. The points in the feasible solution set are labeled and sorted to obtain the final design scheme and the architectural design parameters.

7. A spatial lighting environment optimization design system for large-depth spaces based on dynamic lighting simulation and multi-objective optimization, characterized in that, include: The parametric modeling module is used to construct a parametric basic model of a typical space with large depth based on design variables. The dynamic lighting simulation module is used to perform dynamic lighting simulation analysis on the parameterized basic model of the large-depth typical space and obtain the analysis results. The optimization target selection module is used to determine the optimization target of the large depth typical spatial parameterization basic model based on the analysis results; A multi-objective optimization module is used to iteratively optimize the design variables based on the optimization objective using a multi-objective genetic algorithm to obtain a Pareto optimal solution set. The decision module is used to perform constraint screening on the Pareto optimal solution set to obtain the final design scheme and architectural design parameters.

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