A method and apparatus for determining an illumination scheme

Abstract

The present application provides a method and apparatus for determining a lighting scheme. The method includes: obtaining at least one piece of spatial element information affecting lighting design in a lighting space based on a building information model of the lighting space; obtaining multiple groups of lighting variable groups set for the lighting space, where a lighting variable group includes initial values of multiple variable parameters; for each group of lighting variable groups, determining the index values of at least one lighting evaluation index based on at least one piece of spatial element information and the initial values of the variable parameters in the lighting variable group; based on the index values of at least one lighting evaluation index corresponding to the lighting variable group, the constraint conditions and optimization objectives of at least one lighting evaluation index, using a multi-objective optimization algorithm to optimize the initial values of the variable parameters in the lighting variable group, and determining multiple lighting schemes corresponding to multiple optimized lighting variable groups. The solution of the present application can combine the building information model to determine a relatively optimal lighting scheme in the lighting space of a building.

Description

Technical Field

[0001] This application relates to the technical field of lighting scheme design, and particularly to a method and device for determining a lighting scheme. Background Art

[0002] Lighting scheme design refers to determining a suitable lighting scheme for the lighting space of a building.

[0003] However, in current lighting scheme design, with the continuous development of lighting design, in order to achieve a better lighting design, the demand for integrating lighting with building design is increasing. Therefore, how to combine building information to determine a better lighting scheme is a technical problem that needs to be solved by those skilled in the art. Summary of the Invention

[0004] This application provides a method and device for determining a lighting scheme to determine a better lighting scheme in the lighting space of a building by combining a building information model.

[0005] On the one hand, this application provides a method for determining a lighting scheme, including:

[0006] Based on the building information model of the lighting space, obtaining at least one piece of spatial element information that affects lighting design in the lighting space;

[0007] Obtaining multiple groups of lighting variable groups set for the lighting space, where each lighting variable group includes initial values of multiple variable parameters related to the light source characteristics, lamp layout, and spatial interface material of the lighting space;

[0008] For each group of lighting variable groups, based on the at least one piece of spatial element information and the initial values of the variable parameters in the lighting variable group, determining the index values of at least one lighting evaluation index;

[0009] Based on the index values of at least one lighting evaluation index corresponding to the lighting variable group, as well as the constraint conditions and optimization objectives of the at least one lighting evaluation index, using a multi-objective optimization algorithm to optimize the initial values of the variable parameters in the lighting variable group, determining multiple groups of optimized lighting variable groups, and obtaining multiple lighting schemes corresponding to the multiple groups of optimized lighting variable groups. Each group of optimized variable groups includes: optimized values of the multiple variable parameters.

[0010] On the other hand, this application also provides a device for determining a lighting scheme, including:

[0011] An element determination unit for obtaining at least one piece of spatial element information that affects lighting design in the lighting space based on the building information model of the lighting space;

[0012] A variable acquisition unit for acquiring multiple groups of lighting variable groups set for the lighting space, where each lighting variable group includes initial values of multiple variable parameters related to the light source characteristics, lamp layout, and spatial interface material of the lighting space;

[0013] An index determination unit for determining the index values of at least one lighting evaluation index for each group of lighting variable groups based on the at least one spatial element information and the initial values of the variable parameters in the lighting variable group;

[0014] A scheme optimization unit for optimizing the initial values of the variable parameters in the lighting variable group by using a multi-objective optimization algorithm based on the index values of at least one lighting evaluation index corresponding to the lighting variable group, the constraint conditions, and the optimization objectives of the at least one lighting evaluation index, and determining multiple groups of optimized lighting variable groups, obtaining multiple lighting schemes corresponding to the multiple groups of optimized lighting variable groups. Each group of optimized variable groups includes: the optimized values of the multiple variable parameters.

[0015] As can be seen from the above, in the embodiments of the present application, based on the building information model of the lighting space, the information of each spatial element affecting the lighting design in the lighting space can be obtained more accurately. At the same time, for each lighting variable group set for the lighting space, the index values of at least one lighting evaluation index are determined by combining the information of each spatial element and the initial values of the variable parameters in the lighting space group. On this basis, by combining the index values of at least one lighting evaluation index corresponding to each lighting variable group, the constraint conditions, and the optimization objectives of the at least one lighting evaluation index, the initial values of the variable parameters in each lighting variable group can be continuously optimized by using a multi-objective optimization algorithm, so that finally multiple lighting schemes corresponding to multiple optimized lighting variable groups that are optimized and more suitable for the lighting space can be obtained, thereby realizing the determination of a better lighting scheme for the lighting space by combining the building information model. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained according to the provided drawings without creative efforts.

[0017] Figure 1 FIG. shows a schematic flowchart of a method for determining a lighting scheme provided by an embodiment of the present application;

[0018] Figure 2 FIG. shows another schematic flowchart of a method for determining a lighting scheme provided by an embodiment of the present application;

[0019] Figure 3It shows a schematic diagram of the respective value ranges of two indicators, namely the work surface illuminance and the space brightness coefficient, under different atmosphere requirements in the meeting room space in the embodiments of the present application;

[0020] Figure 4 It shows a schematic flowchart of the method for determining a lighting scheme provided by the embodiments of the present application in an application scenario;

[0021] Figures 5 - 8 It respectively shows schematic diagrams of the distribution correspondence relationships between lighting schemes and indicators under different light distributions and lighting methods;

[0022] Figure 9 It shows a schematic diagram of a composition structure of the device for determining a lighting scheme provided by the embodiments of the present application;

[0023] Figure 10 It shows another schematic diagram of a composition structure of the device for determining a lighting scheme provided by the embodiments of the present application. Detailed implementation manners

[0024] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0025] As Figure 1 shown, it shows a schematic flowchart of a method for determining a lighting scheme provided by the embodiments of the present application. The method of this embodiment can be applied to a computer device or a platform composed of multiple computer devices, etc., without limitation thereto.

[0026] The method of this embodiment may include:

[0027] S101, based on the building information model of the lighting space, obtain at least one piece of spatial element information affecting lighting design in the lighting space.

[0028] Among them, the Building Information Modeling (BIM) is to use the relevant information data of a building engineering project as the basis of the model to establish a building model, and simulate the real information of the building through digital information simulation.

[0029] In this application, according to different specific application scenarios, a building may involve multiple building spaces that require lighting design, and the building spaces that need lighting design can be regarded as a lighting space (also referred to as a lighting building space). For example, a lighting space can be a room or a place within a building. For example, a meeting room that requires lighting fixture selection and layout and other related lighting designs.

[0030] In this application, for any lighting space, spatial element information related to lighting design can be obtained based on the BIM model corresponding to the lighting space. Among them, the spatial element information may include some factors that can affect the lighting scheme design and have been determined within the lighting space.

[0031] For example, at least one piece of spatial element information in the lighting space may include, but is not limited to, one or more of the following elements: the function of the lighting space, morphological information, spatial dimensions, lighting fixture layout range information, and lighting work surface.

[0032] Among them, the function of the lighting space is used to characterize the lighting demand type or lighting use of the lighting space, etc. The morphological information of the lighting space can characterize the spatial form of the lighting space. For example, the lighting space is a cuboid space or a conical space, etc. The spatial dimensions of the lighting space can include the dimensions of each spatial dimension of the lighting space. For example, the specific values of length, width, and height. The lighting fixture layout range information is used to describe the range within the target lighting space where lighting fixtures can be arranged. The lighting work surface is the horizontal plane required for visual work within the lighting space or other surfaces involving lighting requirements.

[0033] S102, obtain multiple groups of lighting variable groups set for the lighting space.

[0034] Among them, the lighting variable group includes: the initial values of multiple variable parameters. The variable parameters in the lighting variable group refer to the variable parameters for which specific numerical values or information need to be determined in the lighting scheme. A set of lighting schemes necessarily corresponds to a group of lighting variable groups.

[0035] In this application, the lighting variable group may include: the initial values of multiple variable parameters related to the light source characteristics, lighting fixture layout, and spatial interface materials of the lighting space.

[0036] Among them, the light source characteristics refer to the parameters related to the light-emitting characteristics of the light source. For example, the light source characteristics may include: the luminous flux of the light source and the light distribution method. Among them, the luminous flux represents the amount of light emitted by the light source, and the light distribution method refers to the spatial distribution of the configured luminous intensity. There is a corresponding relationship between the optional ranges of the light distribution and the luminous flux and the types of lighting fixtures to be set within the lighting space.

[0037] The variable parameters related to the luminaire layout refer to the parameters that may characterize the luminaire layout scheme obtained after determining the luminaire model and installation method information based on the light source characteristics. For example, the variable parameters related to the luminaire layout may include: the lighting method and layout form of the luminaire, where the initial values of the lighting method and layout form are optional values determined based on the light source characteristics. The lighting method indicates the way the luminaire illuminates the lighting space, which can be divided into two types: direct lighting and indirect lighting. The initial value of the layout form of the luminaire can be the parameter value of a set of parameters extracted from the luminaire layout rules to achieve the control of the specific luminaire layout under the layout rules.

[0038] The variable parameters related to the spatial interface material of the lighting space refer to the reflectivity parameters of at least one plane involved in the lighting space. For example, it can include the reflectivities of the ceiling, walls, and floor.

[0039] It can be understood that the initial values of the variable parameters in each lighting variable group can be set manually according to experience, but the lighting variable group is not the optimal lighting scheme. Therefore, in this application, based on multiple sets of lighting variable groups set, the optimized lighting variable group needs to be determined through subsequent steps to obtain the lighting scheme corresponding to the optimized lighting variable group.

[0040] S103. For each lighting variable group, based on at least one piece of spatial element information and the initial values of the variable parameters within the lighting variable group, determine the index values of at least one lighting evaluation index.

[0041] In this application, the lighting evaluation index can be set according to the lighting design concept and specific design requirements. The lighting evaluation index in this application can be part or all of multiple lighting design indexes from the three integrated design objectives of "light environment improvement", "space function enhancement", and "energy efficiency improvement".

[0042] Among them, the "light environment improvement" objective means shaping the overall atmosphere of the lighting space on the basis of meeting visual functions and visual comfort, and achieving good spatial perception and interaction.

[0043] The "space function enhancement" objective is the organization of the spatial sequence by light and shadow, which requires giving play to the role of light and shadow in aspects such as connection and transition, penetration and hierarchy, guidance and suggestion in the space, and meeting the optimization requirements of the sequence organization of multi-space combinations.

[0044] The "energy efficiency improvement" objective means reducing the energy consumption of lighting and improving the energy efficiency.

[0045] In a possible implementation, the two goals of "light environment improvement" and "space function enhancement" can be quantified by four lighting evaluation indicators: illuminance uniformity, working plane illuminance in the lighting space, Unified Glare Rating (UGR), and spatial luminance coefficient Feu.

[0046] The goal of "energy efficiency improvement" can be quantified by two lighting evaluation indicators: lighting power density LPD and light transmission efficiency.

[0047] Correspondingly, at least one lighting evaluation indicator in this application may include one or more of the above six lighting evaluation indicators.

[0048] Among them, the meanings and limits of the four indicators of working plane illuminance, illuminance uniformity, UGR, and LPD can be determined in combination with the building lighting design code. For example, sufficient illuminance is a prerequisite for maintaining visual work. Improving uniformity can improve the working environment and visual comfort. Reducing UGR can avoid visual discomfort caused by glare. Reducing LPD is beneficial to energy conservation and emission reduction.

[0049] Among them, the spatial luminance coefficient Feu is an indicator for evaluating the subjective luminance perception of users. The indicator value of Feu can be obtained through the following formula (1):

[0050] Feu = 1.5 × Lg 0.7 (Formula 1);

[0051] In the above definition formula, Lg is the geometric mean luminance, which is numerically equal to the geometric mean of the luminance within the induced field of view. Specifically, it can be determined based on the BIM information of the lighting space. The specific determination method is not limited. The unit of the geometric mean luminance is candela per square meter (cd / m 2 ). In specific calculations, the part with a luminance greater than 1000 cd / m 2 in the lighting space is considered a light source, which has little impact on the luminance perception and is not included in the geometric mean luminance. The induced field of view is the horizontal field of view from -50° to 50° and the vertical field of view from -50° to 35°.

[0052] It can be understood that Feu can not only evaluate the luminance perception alone, but also shape the atmosphere of the space together with the working plane illuminance, providing guidance for lighting design based on the usage scenario. For example, high illuminance + high Feu can create an open atmosphere, low illuminance + high Feu can create a soft atmosphere, high illuminance + low Feu can create a serene atmosphere, and low illuminance + low Feu can create a relaxed atmosphere. For spaces with different functions, different Feu and illuminance value ranges should be selected to define the space atmosphere.

[0053] The basic definition of light transmission efficiency refers to the transmission efficiency of light in various lighting "operations". In the present invention, the ratio of the luminous flux obtained on the lighting working surface to the total luminous flux is used as the light transmission efficiency. Optimizing the light transmission efficiency is beneficial to improving the accuracy of light distribution in the design.

[0054] It can be understood that considering that currently, when evaluating the quantity and quality of lighting in the design of lighting schemes, the illuminance is still the core, and the illuminance can only reflect the light incident on the indoor building surface and cannot reflect the light emission on the indoor building surface, so it is impossible to determine an optimal lighting scheme. Based on this, in order to improve the effect of the lighting scheme, the present application can optimize the lighting scheme from the perspective of luminance. Luminance can be understood as the light emission on the surface of the lighting space, and since luminance reflects the intuitive feeling of the human eye, the lighting design scheme can pay more attention to the user experience.

[0055] On this basis, the present application can use lighting evaluation indicators related to two objectives of light environment improvement and space function enhancement with luminance as the core to evaluate the lighting scheme. Therefore, at least one lighting evaluation indicator of the present application includes at least part or all of four lighting evaluation indicators related to light environment improvement and space function enhancement.

[0056] S104, based on the indicator values of at least one lighting evaluation indicator corresponding to the lighting variable group, as well as the constraint conditions and optimization objectives of at least one lighting evaluation indicator, use a multi-objective optimization algorithm to optimize the initial values of the variable parameters in the lighting variable group, determine multiple optimized lighting variable groups, and obtain multiple lighting schemes corresponding to the multiple optimized lighting variable groups.

[0057] Among them, the constraint conditions of the lighting evaluation indicator can be the value range that the indicator value of the lighting evaluation indicator needs to satisfy or the constraint relationship between different lighting evaluation indicators, etc.

[0058] In the present application, the constraint conditions of at least one lighting evaluation indicator include at least one of the following:

[0059] The indicator limit value in the lighting design standard that the lighting evaluation indicator needs to satisfy;

[0060] The user-set conditions that a single lighting evaluation indicator needs to satisfy;

[0061] The user-set conditions that at least one lighting evaluation indicator needs to satisfy.

[0062] Among them, the lighting design standard can be the currently commonly used lighting design standard, and these lighting design standards can stipulate the indicator limit values that different lighting indicators need to satisfy.

[0063] In practical applications, for different lighting spaces and their actual application scenarios, it is also necessary to set the conditions that the lighting evaluation indicators need to meet in combination with the actual design requirements by lighting designers, that is, the user-set conditions.

[0064] For example, in an optional way, it is possible to set the constraint on whether the combination of Feu and illuminance value falls within the atmosphere suitable for the space function. The atmosphere limit value is determined by the Feu-illuminance-atmosphere relationship diagram of each type of space given by the SmartArchi2020 plan.

[0065] Among them, the optimization goal refers to the goal that the lighting evaluation indicators are expected to achieve when optimizing the lighting variable group. Among them, the calculation formulas of the lighting evaluation indicators and this optimization goal actually construct the objective function of the optimization process.

[0066] For example, the optimization goal can be the maximum or minimum of the lighting evaluation indicators. For example, if the lighting evaluation indicator is the workplane illuminance, then the optimization goal is the maximum workplane illuminance; if the lighting evaluation indicator is the light transmission efficiency, then the optimization goal can be the maximum light transmission efficiency; and if the lighting evaluation indicator is the unified glare rating UGR, then the optimization goal is the minimum UGR.

[0067] It can be understood that for the convenience of distinction, the lighting variable group finally optimized by using the multi-objective optimization algorithm is called the optimized lighting variable group. Correspondingly, each optimized lighting variable group includes the optimized values of multiple variable parameters respectively.

[0068] Among them, the multi-objective optimization algorithm can be a heuristic algorithm with random optimization characteristics, which can find the Pareto solution of the optimization problem with multiple conflicting objective functions (optimization goals). Through the multi-objective optimization algorithm, the values of the variable parameters in each lighting variable group can be continuously iteratively optimized.

[0069] For example, the multi-objective optimization algorithm can be the third-generation Generalized Differential Evolution algorithm (GDE-3).

[0070] Of course, this multi-objective optimization algorithm can also be other differential evolution algorithms or other heuristic algorithms, and there is no restriction on this.

[0071] As can be seen from the above, in the embodiments of the present application, based on the building information model of the lighting space, it is possible to more accurately obtain the information of each spatial element affecting the lighting design in the lighting space. At the same time, for each lighting variable group set for the lighting space, in combination with the information of each spatial element and the initial values of the variable parameters in the lighting space group, the index values of at least one lighting evaluation index are determined. On this basis, by combining the index values of at least one lighting evaluation index corresponding to each lighting variable group, the constraint conditions and optimization objectives of the at least one lighting evaluation index, and using a multi-objective optimization algorithm, the initial values of the variable parameters in each lighting variable group can be continuously optimized, so that finally a lighting scheme corresponding to multiple optimized lighting variable groups that are optimized and more suitable for the lighting space can be obtained, thereby realizing the determination of a better lighting scheme for the lighting space in combination with the building information model.

[0072] It can be understood that the optimized values of the multiple variable parameters in the multiple optimized lighting variable groups after optimization may not be very intuitive for designers or architects to understand and may not be suitable for direct reference and utilization as a lighting scheme by designers and architects. To facilitate architects and other users to understand the lighting schemes corresponding to each optimized lighting variable group, after determining multiple groups of optimized lighting variable groups, the present application can also: based on the optimized values of the variable parameters in the optimized lighting variable group, according to the conversion formula of the target description feature corresponding to the variable parameter, determine the feature value of the target description feature. Among them, the target description feature corresponding to the variable parameter is used to more intuitively describe the function or meaning of the variable parameter. For example, the target description feature can be energy consumption, light distribution, etc., which can be specifically set according to needs and is not limited thereto.

[0073] On the other hand, after obtaining multiple groups of optimized lighting variable groups, in order to enable architects and other users to more intuitively understand the influence relationship of each group of optimized lighting variable groups on each lighting evaluation index, or to understand the lighting scenarios suitable for different optimized lighting variable groups, etc., the present application can also determine at least one of the following four types of evaluation information:

[0074] (1) For each group of optimized lighting variable groups, based on at least one piece of spatial element information and the optimized values of each variable parameter in the optimized lighting variable group, determine the correlation between at least one lighting evaluation index.

[0075] (2) For each type of variable parameter, based on at least one piece of spatial element information and the optimized values of other variable parameters except the variable parameter in the optimized lighting variable group, determine the correlation between the variable parameter and different lighting evaluation indexes.

[0076] (3) Combine at least one piece of spatial element information and the optimized lighting variable group to determine the linear regression analysis result between different variable parameters and lighting evaluation indexes.

[0077] (4) Based on the index values of at least one lighting evaluation index corresponding to each optimized lighting variable group, and combined with the clustering algorithm, determine multiple clustering categories corresponding to multiple optimized lighting variable groups and the clustering centers of each clustering category.

[0078] Among them, in (1), for each group of optimized lighting variable groups, the correlation degree between different lighting evaluation indexes can be determined by combining the Spearman rank correlation coefficient. Through (1), the situation of other lighting evaluation indexes changing after one lighting evaluation index changes can be reflected.

[0079] In (2), it can be divided into two parts: qualitative analysis and quantitative analysis. Qualitative analysis uses the Spearman correlation coefficient to evaluate the relationship between the "rate" characteristics in the variable parameters, the layout of the lamps in the space and each lighting evaluation index respectively, and gives suggestions for improving a single lighting evaluation index through positive and negative correlations. Quantitative analysis uses the Pearson correlation coefficient to evaluate whether there is a linear relationship between the "rate" factor, the "quantity" factor in the variable parameters and each lighting evaluation index, and screens out linearly related factors for the next step of regression analysis.

[0080] Among them, "quantity" refers to the absolute quantity of light energy distribution in space; "rate" refers to the relative proportion of light energy distribution in space.

[0081] Among them, the standard for evaluating the strength of correlation can be set according to needs. For example: the value of correlation is: 0 - 0.2 is uncorrelated, 0.2 - 0.4 is weakly correlated, 0.4 - 0.6 is moderately correlated, 0.6 - 0.8 is strongly correlated, 0.8 - 1.0 is extremely strongly correlated.

[0082] In (3), the prediction equations between the variable parameters and each lighting evaluation index are obtained through linear regression, so as to quickly predict the target values of the lighting evaluation indexes in further deepening. According to the consistency of physical units in fitting, for the working surface illuminance and Feu, only linear regression is performed with the "quantity" factor; for the uniformity and light transmission efficiency, since they are ratios between 0 and 1, only linear regression is performed with the "rate" factor.

[0083] In (4), first, m + 1 representative optimized lighting variable groups can be extracted from multiple groups of optimized lighting variable groups by the Subtract Clustering Method (SCM) algorithm, where m is the number of objective functions, and the specific value can be set according to needs. Then, combined with the scatter plot constructed between the variable parameters and the lighting evaluation indexes, determine the value range of the variable parameters whose values of each lighting evaluation index exceed the median level from multiple groups of optimized lighting variable groups, and verify it with the m + 1 representative optimized lighting variable groups obtained by the SCM algorithm.

[0084] Among them, in the representative optimized lighting variable groups, the optimized lighting variable group in which the variable parameters of each dimension are all within the value range of the variable parameters corresponding to the level exceeding the median is the preferred optimization scheme, and the other m representative optimized lighting variable groups are the sub-preferred optimization schemes.

[0085] It can be understood that in this application, there can be multiple possibilities for the multi-objective optimization algorithm. For the sake of easy understanding, the multi-objective optimization algorithm is briefly described below as the GDE-3 algorithm.

[0086] As Figure 2 shown, it shows another schematic flowchart of determining the lighting scheme provided by the embodiment of this application.

[0087] The method of this embodiment may include:

[0088] S201, based on the building information model of the lighting space, obtain at least one piece of spatial element information that affects the lighting design in the lighting space.

[0089] This step can refer to the relevant introduction in the previous embodiment and will not be elaborated here.

[0090] S202, obtain multiple groups of lighting variable groups set for the lighting space.

[0091] Among them, the lighting variable group includes: the initial values of multiple variable parameters related to the light source characteristics, lamp layout, and spatial interface material.

[0092] For example, each group of lighting variable groups may include: the initial values of luminous flux, light distribution mode, lighting mode of the lamp, layout form of the lamp, and reflectivity of at least one plane in the lighting space. Specifically, it can refer to the introduction in the previous embodiment and will not be elaborated here.

[0093] S203, based on each lighting variable group, determine the population to be optimized in the GDE-3 algorithm.

[0094] Among them, the population includes multiple individuals, and each individual corresponds to a group of lighting variable groups.

[0095] In a possible implementation manner, simultaneously with this step S203, it also involves initializing the initial parameters in the GDE-3 algorithm. The initial parameters may include: the number of generations Ttotal, population size N, number of objectives, scaling factor F, and crossover rate cr, etc., and there is no limitation thereto.

[0096] It can be understood that after initializing the GDE-3 algorithm, the initial population of the algorithm can be generated by combining the initial values of the variable parameters in each lighting variable group and parameters such as the initialized population size. Subsequently, the initial population will be updated and the populations determined in each iteration will be continuously iteratively updated.

[0097] S204. For each lighting variable group, based on at least one piece of spatial element information and the initial values of the variable parameters within the lighting variable group, determine the index values of at least one lighting evaluation index.

[0098] For example, a lighting calculation program can be pre-generated. The calculation functions of various lighting evaluation indexes can be configured in the lighting calculation program. Therefore, based on the lighting calculation program, the index values of each lighting evaluation index can be calculated.

[0099] Among them, the calculation methods of various lighting evaluation indexes can refer to the relevant introductions in the previous embodiments and will not be elaborated here.

[0100] S205. Based on the index values of at least one lighting evaluation index corresponding to the lighting variable group, as well as the constraint conditions and optimization objectives of at least one lighting evaluation index, determine the fitness of each individual in the population.

[0101] Among them, since each individual represents a lighting variable group, the fitness of the individuals in the population reflects whether the lighting variable group can make each lighting evaluation index meet the constraint conditions and achieve the optimization objective.

[0102] Among them, the specific implementation of determining the fitness of the individuals in the population can adopt any conventional calculation method and is not limited thereto.

[0103] S206. Based on the fitness of each individual in the population, determine whether the optimization end condition of the GDE-3 algorithm is satisfied. If not, execute step S207; if so, determine the lighting variable groups corresponding to each individual in the current population as the optimized lighting variable groups after optimization.

[0104] S207. Based on the GDE-3 algorithm, optimize the initial values of the variable parameters in the lighting variable groups corresponding to each individual in the population. For the lighting variable groups corresponding to each individual in the optimized population, return to execute S204.

[0105] If the current optimization end condition is not satisfied, it means that the values of the variable parameters in each lighting variable group do not enable the lighting evaluation index to meet the constraint conditions and achieve the optimization objective. Therefore, the values of the variable parameters in the lighting variable group are not the optimal values. On this basis, it is necessary to optimize each individual in the population by combining the population optimization method of the GDE-3 algorithm to optimize the parameter values of the variable parameters in the lighting variable group.

[0106] After optimizing each individual in the population, for the group of lighting variables corresponding to the optimized individual, the operations from S204 to S206 above need to be repeated to determine whether the end condition of the GDE-3 algorithm is satisfied.

[0107] Conversely, if the optimization end condition has been satisfied, it means that the values of the variable parameters in the group of lighting variables corresponding to each extraction in the current population are the optimal values. Then, the lighting scheme corresponding to the finally optimized group of lighting variables can be obtained, and thus the optimized group of lighting variables corresponding to each finally optimized lighting scheme can be output.

[0108] To more intuitively understand the implementation of the solution of the present application, the method for determining the lighting scheme of the present application will be introduced below in combination with a specific application scenario.

[0109] Taking the lighting space as the meeting room space as an example for illustration.

[0110] The meeting room space is more important for the production activities of urban workers. Users spend more time engaged in visual work in the meeting room than in other spaces. Therefore, higher lighting quality, a spatial atmosphere corresponding to the activities, and energy conservation as much as possible are more needed. The requirements for lighting quality in the meeting room space are: 1. Provide sufficient lighting to maintain visual work; 2. Ensure the comfort of visual work and avoid eye fatigue.

[0111] The meeting room has diverse requirements for the lighting spatial atmosphere: for design work, the meeting room is often used for communication and discussion, which requires a more active spatial atmosphere; for administrative work, the meeting room is used to decide major collective matters, which requires a more solemn spatial atmosphere.

[0112] Taking the serene atmosphere as an example, perform the lighting design of the meeting room space. In the meeting room space, the serene atmosphere refers to a space with high illuminance and low Feu. As Figure 3 , it shows the schematic diagram of the respective value ranges of the two indicators of the working surface illuminance and the spatial luminance coefficient Feu under different atmosphere requirements.

[0113] The implementation process of determining the lighting scheme of the meeting room will be introduced below in combination with the flowchart. As Figure 4 , it shows the schematic flowchart of the solution of the present application applied to the lighting design of the meeting room. The method of this embodiment may include:

[0114] S401, based on the BIM model of the meeting room, determine at least one piece of spatial element information of the meeting room.

[0115] In this embodiment, taking the spatial element information determined from the BIM model of the meeting room including the following content as an example:

[0116] The spatial form and dimensions of the meeting room are: length 14.4 m, width 7.2 m, height 3 m;

[0117] The reflectivities of the floor, walls, and ceiling of the meeting room are 0.15, 0.75, and 0.75 respectively;

[0118] The lighting range in the meeting room is the ceiling and its surroundings, without lighting on the walls;

[0119] The working plane of the meeting room is taken as the 0.75 m horizontal plane.

[0120] S402, obtaining multiple groups of lighting variable sets for this lighting space of the meeting room.

[0121] In this embodiment, since the surface materials of the meeting room have been determined, the surface reflectivity can not participate in the optimization. Based on this, the lighting variable sets include: luminous flux of the light source, light distribution mode, lighting mode of the luminaire, and layout form of the luminaires.

[0122] Among them, the light distribution mode and lighting mode are non-numerical variables. In this embodiment, 4 lighting and light distribution conditions generated by (direct lighting with wide light distribution, direct lighting with narrow light distribution) × (indirect lighting projected on the wall, indirect lighting projected on the ceiling) are set and optimized respectively.

[0123] Light distribution mode setting: For wide light distribution, it is a Lambertian panel light; for narrow light distribution, it is a circular spotlight.

[0124] Lighting mode condition setting: For lighting projected on the ceiling, 32 linear spotlights are arranged around the ceiling; for lighting projected on the wall, 36 linear spotlights are evenly arranged in the grooves around the ceiling.

[0125] As shown in Table 1, the information of the luminaires used in this scenario is shown.

[0126] Table 1

[0127]

[0128] Among them, the luminous flux of the light source and the luminaire layout scheme can be encoded as 8-dimensional variable parameters, as shown in Table 2:

[0129] Table 2

[0130]

[0131]

[0132] In Table 2 above, the first 5 parameter encodings in the "Variable Name" column represent the lighting positions, and the last 3 parameters represent the encoded luminous flux distribution.

[0133] Luminous Flux Distribution: dir_lum is the total luminous flux of the 24 directly illuminated lights, and lum_ratio is the proportion of the luminous flux of the 8 inner-ring lamps in dir_lum. The luminous flux of each inner-ring lamp is (dir_lum × lum_ratio / 8); the luminous flux of each of the 16 outer-ring lamps is [dir_lum × (1 - lum_ratio) / 16]. indir_lum is the total luminous flux of the indirect illumination.

[0134] The basic principle of lamp layout is to use lamps with the same light distribution for direct illumination, and arrange them in a 4×6 rectangular grid pattern on the ceiling, without arranging lamps within a range of 1200mm around the perimeter; the layout positions of the indirect illumination lamps are fixed.

[0135] In a possible implementation, for the layout of the lamp form, this embodiment gives a parametric description method for describing a 2a-row × 2b-column rectangular grid with central symmetry lamp layout on the ceiling, specifically as follows:

[0136] Taking the room width direction as the x-axis, the depth direction as the y-axis, and the upper left corner as the origin to establish a plane rectangular coordinate system. Taking the layout in the depth direction as an example, assume the width of the rectangular room is 2w, the depth is 2l, and no lamps are arranged within a range of d around the perimeter. The lamp itself occupies a rectangular space with an area of φ×φ. Therefore, the range where lamps can be arranged in the depth direction is [d, 2l - d]. The steps for arranging lamps in the depth direction are as follows:

[0137] First, determine the lamp layout coordinates closest to the center of the room. Since the lamp layout is centrally symmetric and b - 1 more lamps need to be arranged on the right side, the feasible range for the first lamp is [l + φ / 2, 2l - d - (b - 1)φ]. Uniformly map this interval on [0, 1] so that each value on [0, 1] linearly corresponds to a lamp layout position within the feasible range. Denote the ordinate of the first lamp as d1.

[0138] Second, determine the lamp layout coordinates of the next closest to the center. Since there is already 1 lamp on the left side and b - 2 more lamps need to be arranged on the right side, the feasible range for the second lamp layout is [d1 + φ, 2l - d - (b - 2)φ]. Uniformly map this interval on [0, 1], and denote the ordinate of the second lamp as d2.

[0139] Third, by analogy with the above two steps, find the coordinates of the first b lamps in the depth direction.

[0140] Finally, perform a central symmetry flip on the coordinates obtained in the previous 3 steps, that is, obtain all 2b ordinates in the depth direction.

[0141] It can be understood that the calculation method for the 2a abscissas in the width direction is the same. In this way, the lamp layout scheme is transformed into an a + b-dimensional variable parameter. The lamp layout grid can be generated according to the 2a×2b coordinates.

[0142] Above, a, b, and d can all be set according to actual needs. For example, in this embodiment, the value of a can be 2, the value of b can be 3, and the value of d can be 500 mm.

[0143] For the direct lighting fixture layout positions, they can be: taking the center point of the fixture as the layout position coordinates, the range where fixtures can be laid out is as follows: in the x direction (width of the surface), it is [1200, 6000], in the y direction (depth), it is [1200, 13200]. The area occupied by wide-beam fixtures is 560×560 mm, and the area occupied by narrow-beam fixtures is 100×100 mm.

[0144] S403, obtain at least one lighting evaluation index for the conference room configuration, as well as the constraint conditions and optimization objectives of at least one lighting evaluation index.

[0145] In this embodiment, for the two major objectives of "light environment improvement" and "space function enhancement", the optimized lighting evaluation indexes are set as Feu, working surface illuminance, illuminance uniformity, and unified glare rating UGR; for the objective of "energy efficiency improvement", due to the high luminous efficiency characteristics of LEDs, the lighting evaluation index to be optimized is set as the light transmission efficiency (hereinafter referred to as energy efficiency).

[0146] Considering that the GDE-3 algorithm optimization requires that there are contradictions among the objective functions and the number does not exceed 3. Therefore, for the characteristics of the serene atmosphere lighting, the optimization objective (or the objective function) is set as high illuminance, high uniformity, and high energy efficiency, that is, specifically as formula two below:

[0147]

[0148] Among them, E(x) is the value of the working surface illuminance of the scheme, U0(x) is the value of the illuminance uniformity of the scheme, and Eff(x) is the value of the light transmission efficiency of the scheme.

[0149] Considering both the industry standard regulations and the user design requirements, for Feu and illuminance of the lighting scheme to fall within the serene atmosphere, the constraint conditions can include the following formula three:

[0150]

[0151] For the industry standard rules, if the lighting indexes in the space meet the limit requirements of the "Architectural Lighting Design Standard", the constraint conditions can also include the following formula four:

[0152]

[0153] In the subsequent combination with the GDE-3 optimization process, for each violation of one constraint condition, 100000 can be added to the values of all optimization objectives (objective functions).

[0154] S404. For each group of lighting variables, calculate the index values of the at least one lighting evaluation index respectively.

[0155] S405. Based on the constraint conditions and optimization objectives of the at least one lighting evaluation index, adopt the GDE-3 algorithm to optimize the initial values of the variable parameters in the lighting variable group, determine multiple groups of optimized lighting variable groups, and obtain multiple lighting schemes corresponding to the multiple groups of optimized lighting variables.

[0156] It can be understood that during the process of running the GDE algorithm, for the scenario of this embodiment, the parameters set for the GDE algorithm and their values can be as shown in Table 3 below:

[0157] Table 3:

[0158] Parameter Value Algebra Ttotal 50 Population size N 100 Number of objectives m 3 Scaling factor F 0.5 Crossover rate cr 0.3

[0159] In this application, under the four working conditions of the above light distribution and lighting methods, the distribution relationships of the finally determined lighting schemes can be respectively as Figures 5 - 8 shown. Among them, Figure 5 is the distribution corresponding to the lighting scheme under the working condition of wide light distribution + ceiling projection, Figure 6 is the distribution corresponding to the lighting scheme under the working condition of narrow light distribution + ceiling projection, Figure 7 is the distribution corresponding to the lighting scheme under the working condition of wide light distribution + wall projection, Figure 8 is the distribution corresponding to the lighting scheme under the working condition of narrow light distribution + wall projection.

[0160] As shown in Table 4 below, this table shows the convergence of the solution sets of the lighting schemes combined with different variable parameter combinations in the serene atmosphere.

[0161] Table 4

[0162]

[0163] Combined with the comparison results obtained from the C-metric in Table 4, only 10% of the solutions in the lighting scheme of narrow light distribution + ceiling projection are dominated by narrow light distribution + wall projection, which is significantly better than the other three lighting schemes; among the other lighting schemes, 54% of the solutions of narrow light distribution + wall projection are dominated by narrow light distribution + ceiling projection, but not by the variable parameter of wide light distribution. Therefore, the overall ranking of advantages and disadvantages is: narrow light distribution + ceiling projection > narrow light distribution + wall projection > wide light distribution + wall projection ≈ wide light distribution + ceiling projection, and the output result of the optimization algorithm is the groups of lighting schemes corresponding to the narrow light distribution + ceiling projection working condition.

[0164] It can be understood that in the example of this application scenario, for the convenience of user understanding, this application can also transform the previously set variable parameters into lighting design schemes that can be understood by users.

[0165] For example, for lighting design and the material information of the spatial interface, the meaning is clear, and no transformation is required as described above.

[0166] For the light source selection and luminaire layout, taking a set of variables [wid_ratio1, wid_ratio2, len_ratio1, len_ratio2, len_ratio3, dir_lum, lum_ratio, indir_lum] mentioned in Table 2 as an example, the following is executed for the two lighting methods of direct and indirect lighting respectively:

[0167] The light distribution and luminaire layout scheme for indirect lighting are as specified in the lighting conditions introduced in the previous step S402. The luminous flux is evenly distributed on each indirect lighting luminaire with indir_lum;

[0168] The light distribution of direct lighting and the luminous flux have been described in the previous step S402.

[0169] The luminaire layout scheme involves four horizontal grid coordinates in sequence:

[0170]

[0171] Among them, the six vertical grid coordinates involved can be expressed in sequence as follows:

[0172]

[0173] In this application, the solution set analysis process and results are as follows:

[0174] For example, taking the determination of the eigenvalue of the target description feature according to the conversion formula of the target description feature corresponding to the variable parameter as an example, as shown in Table 5:

[0175] Table 5

[0176]

[0177]

[0178] As can be seen from Table 5, for different variable parameters in the lighting variable group corresponding to the optimized lighting schemes, the index values of the target description features can be calculated based on the calculation methods corresponding to the target description features in the third column of Table 5, and the meanings of the index values of the target description features are shown in the last column of Table 5.

[0179] Further, the present application also needs to determine at least one evaluation information such as the correlation between some indicators, so as to provide a basis for users to understand which lighting schemes are more applicable to which indicators, etc. In practical applications, considering that the target description features corresponding to variable parameters can more intuitively reflect the role or meaning of variable parameters, therefore, for the correlation between variable parameters and lighting evaluation indicators, as well as other evaluation information involving variable parameters, it is also possible to directly determine the correlation between the target description features corresponding to variable parameters and lighting evaluation indicators, or other evaluation information related to the target description features corresponding to variable parameters.

[0180] Based on this, the evaluation information determined by the present application can be as follows:

[0181] Correlation between lighting evaluation indicators:

[0182] Spearman correlation analysis shows that there is a negative correlation between illuminance and energy efficiency (r = -0.702, p < 0.01), and it belongs to a strong correlation; there is a negative correlation between uniformity and energy efficiency (r = -0.464, p < 0.01), belonging to a medium degree of correlation. Further, partial correlation analysis shows that after excluding the influence of some variables, the correlation between illuminance and energy efficiency (r = -0.597, p < 0.01) becomes a medium degree of correlation, and the correlation between uniformity and energy efficiency (r = -0.693, p < 0.01) becomes a strong correlation.

[0183] Among them, r is the coefficient value, and p represents significance.

[0184] Correlation between the target description features corresponding to variable parameters and lighting evaluation indicators:

[0185] In the qualitative analysis part, among the features affecting illuminance, those with strong correlation are the percentage of direct luminous flux (r = -0.817, p < 0.01), the percentage of indirect luminous flux (r = 0.817, p < 0.01), and the percentage of inner circle luminous flux (r = -0.602, p < 0.01). Therefore, to increase the illuminance of the working surface, first, the light energy should be distributed to indirect lighting. Those with non-strong correlation are the ratio of inner circle luminous flux to direct (r = -0.318, p < 0.01) and lateral irregularity (r = -0.474, p < 0.01). Therefore, to increase the illuminance of the working surface, the lateral lighting layout should be uniform.

[0186] Among the target description features corresponding to the variable parameters participating in the correlation analysis, no target description features that significantly affect uniformity are found.

[0187] The target description features affecting energy efficiency, which are strongly correlated, include the percentage of direct luminous flux (r = 0.848, p < 0.01) and the percentage of indirect luminous flux (r = -0.848, p < 0.01). Therefore, to improve energy efficiency, light energy should first be distributed to direct lighting. The features that are not strongly correlated are the percentage of outer-ring luminous flux (r = 0.553, p < 0.01), the percentage of inner-ring luminous flux (r = 0.457, p < 0.01), the ratio of direct lighting layout area (r = 0.248, p < 0.05), and the longitudinal irregularity (r = -0.254, p < 0.05). Therefore, to improve energy efficiency, the direct lighting luminous flux should also be distributed more to the outer ring, the direct lighting layout should be dispersed, and the longitudinal layout should be uniform.

[0188] In the quantitative analysis part, the target description features for regression analysis are screened. The strongly correlated target description features of the working surface illuminance are: direct lighting power per unit area (r = 0.948, p < 0.01), indirect lighting power per unit area (r = 0.746, p < 0.01), and outer-ring power per unit area (r = 0.848, p < 0.01). The features that are not strongly correlated are the inner-ring power per unit area (r = 0.343, p < 0.01); no target description features related to uniformity are found; the strongly correlated target description features of energy efficiency are the percentage of direct luminous flux (r = 0.885, p < 0.01), the percentage of indirect luminous flux (r = -0.885, p < 0.01), and the percentage of outer-ring luminous flux (r = 0.668, p < 0.01). The features that are not strongly correlated are the percentage of inner-ring luminous flux (r = -0.437, p < 0.01) and the ratio of direct lighting layout area (r = 0.348, p < 0.05).

[0189] Regression analysis:

[0190] After excluding the target description features with multicollinearity, two sets of multiple linear regression equations are obtained:

[0191] E = 15.652 + 156.603 × inner-ring power per unit area + 194.984 × outer-ring power per unit area + 59.836 × indirect lighting power per unit area;

[0192] Eff = 0.317 + 0.613 × percentage of inner-ring luminous flux + 0.759 × percentage of outer-ring luminous flux + 0.029 × ratio of direct lighting layout area;

[0193] Among them, E represents the working surface illuminance, and Eff represents the light transmission efficiency.

[0194] Range analysis and clustering analysis of the target description features corresponding to the variable parameters:

[0195] The range of the target description features corresponding to the variable parameters with relatively balanced optimization of the three indicators is as follows: the outer ring power per unit area is 2 - 2.5, the percentage of indirect luminous flux is 5% - 15%, the direct lighting power per unit area is 3.1 - 3.8, and the indirect lighting power per unit area is 0.25 - 0.5.

[0196] The SCM algorithm selects 4 clustering centers as representative lighting variable groups. Analyzing the 4 clustering centers, among them, the solutions with an indirect lighting power per unit area of 0.25, a percentage of indirect luminous flux of 7%, an outer ring power per unit area of 2.1, and a direct lighting power per unit area of 3.27 are all within the relatively optimal range and are the preferred solutions for further improvement; the other 3 solutions are the sub - preferred solutions for further improvement.

[0197] Corresponding to the method for determining the lighting scheme of the present application, the present application also provides a device for determining the lighting scheme.

[0198] As Figure 9 shown, it shows a schematic structural diagram of a composition of the device for determining the lighting scheme provided by an embodiment of the present application. The device in this embodiment may include:

[0199] The element determination unit 901 is configured to obtain at least one piece of spatial element information affecting the lighting design in the lighting space based on the building information model of the lighting space;

[0200] The variable acquisition unit 902 is configured to acquire multiple groups of lighting variable groups set for the lighting space, where each lighting variable group includes initial values of multiple variable parameters related to the light source characteristics, lamp layout, and spatial interface material of the lighting space;

[0201] The index determination unit 903 is configured to, for each group of lighting variable groups, determine the index values of at least one lighting evaluation index based on the at least one piece of spatial element information and the initial values of the variable parameters in the lighting variable group;

[0202] The scheme optimization unit 904 is configured to, based on the index values of at least one lighting evaluation index corresponding to the lighting variable group, the constraint conditions and optimization objectives of the at least one lighting evaluation index, and using a multi - objective optimization algorithm, optimize the initial values of the variable parameters in the lighting variable group, determine multiple groups of optimized lighting variable groups, and obtain multiple lighting schemes corresponding to the multiple groups of optimized lighting variable groups. Each group of optimized variable groups includes: the optimized values of the multiple variable parameters.

[0203] In a possible implementation manner, the index values determined by the index determination unit include the index values of at least one of the following lighting evaluation indexes:

[0204] Illuminance uniformity;

[0205] Illuminance of the working surface in the lighting space;

[0206] Unified glare value;

[0207] Spatial luminance coefficient;

[0208] Light transmission efficiency;

[0209] Illumination power density.

[0210] In yet another possible implementation, the device further includes:

[0211] A feature conversion unit, configured to, after the scheme optimization unit determines multiple groups of optimized lighting variable groups, based on the optimized values of the variable parameters in the optimized lighting variable groups, and according to the conversion formula of the target description feature corresponding to the variable parameter, determine the feature value of the target description feature.

[0212] In yet another possible implementation, the device further includes:

[0213] An evaluation information determination unit, configured to, after the scheme optimization unit determines multiple groups of optimized lighting variable groups, determine at least one of the following evaluation information:

[0214] For each group of optimized lighting variable groups, based on the at least one spatial element information and the optimized values of the variable parameters in the optimized lighting variable groups, determine the correlation between the at least one lighting evaluation index;

[0215] For each type of variable parameter, based on the at least one spatial element information and the optimized values of the other variable parameters except the variable parameter in the optimized lighting variable groups, determine the correlation between the variable parameter and different lighting evaluation indexes;

[0216] Combined with the at least one spatial element information and the optimized lighting variable groups, determine the linear regression analysis result between different variable parameters and lighting evaluation indexes;

[0217] Based on the index values of the at least one lighting evaluation index corresponding to each optimized lighting variable group, combined with the clustering algorithm, determine multiple clustering categories corresponding to the multiple optimized lighting variable groups and the clustering centers of the clustering categories.

[0218] In yet another possible implementation, as Figure 10 shown, it shows yet another possible schematic structural diagram of the device for determining the lighting scheme of the present application. As Figure 10 shown, in addition to including Figure 9In addition to the above several devices mentioned in the embodiments, such as the element determination unit 901, the variable acquisition unit 902, the index determination unit 903, and the solution optimization unit 904, the device further includes at least:

[0219] An algorithm initialization unit 905, configured to determine a population to be optimized in the third-generation general differential evolution algorithm based on each lighting variable group before the solution optimization unit determines the index values of at least one lighting evaluation index, where the population includes multiple individuals, and each individual corresponds to a group of lighting variable groups;

[0220] The solution optimization unit 904 includes:

[0221] A fitness determination unit 9041, configured to determine the fitness of each individual in the population based on the index values of at least one lighting evaluation index corresponding to the lighting variable group, as well as the constraint conditions and optimization objectives of the at least one lighting evaluation index;

[0222] An iteration control unit 9042, configured to, if the fitness of each individual in the population does not meet the optimization end condition of the third-generation general differential evolution algorithm, optimize the initial values of the variable parameters in the lighting variable group corresponding to each individual in the population based on the third-generation general differential optimization algorithm, and return to execute the operation of the index determination unit based on the lighting variable group corresponding to each individual in the optimized population;

[0223] An optimization confirmation unit 9043, configured to, if the fitness of each individual in the population meets the optimization end condition of the third-generation general differential evolution algorithm, determine the lighting variable group corresponding to each individual in the current population as the optimized lighting variable group after optimization.

[0224] It can be understood that in this application, terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims, and the above-mentioned drawings are used to distinguish similar parts, and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application described here can be implemented in an order other than those illustrated here.

[0225] It should be noted that each embodiment in this specification is described in a progressive manner. The key point of each embodiment is to illustrate the differences from other embodiments. The same or similar parts among the embodiments can be referred to each other. At the same time, the features described in each embodiment of this specification can be replaced or combined with each other, enabling those skilled in the art to implement or use this application. For device embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0226] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0227] The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0228] The above are only the preferred embodiments of the present application. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. A method for determining an illumination scheme, characterized in that, Including: Based on the building information model of the lighting space, obtaining at least one piece of spatial element information that affects lighting design in the lighting space; Obtaining multiple groups of lighting variable groups set for the lighting space, where each lighting variable group includes initial values of multiple variable parameters related to the light source characteristics, luminaire layout, and spatial interface material of the lighting space; For each group of lighting variable groups, based on the at least one piece of spatial element information and the initial values of the variable parameters in the lighting variable group, determining the index values of at least one lighting evaluation index; Based on the index values of the at least one lighting evaluation index corresponding to the lighting variable group, as well as the constraint conditions and optimization objectives of the at least one lighting evaluation index, using a multi-objective optimization algorithm to optimize the initial values of the variable parameters in the lighting variable group, determining multiple groups of optimized lighting variable groups, and obtaining multiple lighting schemes corresponding to the multiple groups of optimized lighting variable groups. Each group of optimized variable groups includes: the optimized values of the multiple variable parameters.

2. The method according to claim 1, characterized in that The at least one lighting evaluation index includes at least one of the following: Illuminance uniformity; Workplane illuminance in the lighting space; Unified glare value; Spatial brightness coefficient; Light transmission efficiency; Lighting power density.

3. The method according to claim 1, wherein After determining multiple groups of optimized lighting variable groups, it further includes: Based on the optimized values of the variable parameters in the optimized lighting variable group, according to the conversion formula of the target description characteristics corresponding to the variable parameters, determining the characteristic values of the target description characteristics.

4. The method according to claim 1, wherein After determining multiple groups of optimized lighting variable groups, it further includes: Determining at least one of the following evaluation information: For each group of optimized lighting variable groups, based on the at least one piece of spatial element information and the optimized values of each variable parameter in the optimized lighting variable group, determining the correlation between the at least one lighting evaluation index; For each type of variable parameter, based on the at least one piece of spatial element information and the optimized values of the other variable parameters except the variable parameter in the optimized lighting variable group, determining the correlation between the variable parameter and different lighting evaluation indexes; Combining the at least one piece of spatial element information and the optimized lighting variable group, determining the linear regression analysis results between different variable parameters and lighting evaluation indexes; Based on the index values of the at least one lighting evaluation index corresponding to each optimized lighting variable group, combining a clustering algorithm, determining multiple clustering categories corresponding to the multiple optimized lighting variable groups and the cluster centers of the clustering categories.

5. The method according to claim 1, wherein Before determining the index values of the at least one lighting evaluation index, it further includes: Based on each lighting variable group, determining the population to be optimized in the third-generation general differential evolution algorithm, where the population includes multiple individuals, and each individual corresponds to a group of lighting variable groups; The step of using a multi-objective optimization algorithm to optimize the initial values of the variable parameters in the lighting variable group based on the index values of the at least one lighting evaluation index corresponding to the lighting variable group, as well as the constraint conditions and optimization objectives of the at least one lighting evaluation index, and determining multiple groups of optimized lighting variable groups, includes: Determine the fitness of each individual in the population based on the index values of at least one lighting evaluation index corresponding to the lighting variable group, as well as the constraint conditions and optimization objectives of the at least one lighting evaluation index; If the fitness of each individual in the population does not meet the optimization termination condition of the third-generation general differential evolution algorithm, optimize the initial values of the variable parameters in the lighting variable group corresponding to each individual in the population based on the third-generation general differential optimization algorithm, and based on the lighting variable groups corresponding to each individual in the optimized population, return to execute the operation of determining the index values of the at least one lighting evaluation index; If the fitness of each individual in the population meets the optimization termination condition of the third-generation general differential evolution algorithm, determine the lighting variable groups corresponding to each individual in the current population as the optimized lighting variable groups after optimization.

6. The method according to claim 1, characterized in that, The constraint conditions of the at least one lighting evaluation index include at least one of the following: The index limit values in the lighting design standards that the lighting evaluation index needs to meet; The user-defined conditions that the lighting evaluation index needs to meet; The user-defined conditions that need to be met among the at least one lighting evaluation index.

7. The method according to claim 1, characterized in that, The variable parameters related to the light source characteristics include: luminous flux and light distribution mode; The variable parameters related to the luminaire layout include: the lighting mode and layout form of the luminaires, where the initial values of the lighting mode and layout form are optional values determined based on the light source characteristics; The variable parameters related to the spatial interface material include: the reflectivity of at least one plane in the lighting space.

8. A device for determining an illumination scheme, characterized in that, Include: An element determination unit, configured to obtain at least one piece of spatial element information affecting lighting design in the lighting space based on the building information model of the lighting space; A variable acquisition unit, configured to acquire multiple groups of lighting variable groups set for the lighting space, where the lighting variable group includes: the initial values of multiple variable parameters related to the light source characteristics, luminaire layout, and spatial interface material of the lighting space; An index determination unit, configured to, for each group of lighting variable groups, determine the index values of at least one lighting evaluation index based on the at least one piece of spatial element information and the initial values of the variable parameters in the lighting variable group; A scheme optimization unit, configured to, based on the index values of at least one lighting evaluation index corresponding to the lighting variable group, as well as the constraint conditions and optimization objectives of the at least one lighting evaluation index, adopt a multi-objective optimization algorithm to optimize the initial values of the variable parameters in the lighting variable group, determine multiple groups of optimized lighting variable groups after optimization, and obtain multiple lighting schemes corresponding to the multiple groups of optimized lighting variable groups, where each group of optimized variable groups includes: the optimized values of the multiple variable parameters.

9. The device according to claim 8, characterized in that, Further include: A feature conversion unit, configured to, after the scheme optimization unit determines multiple groups of optimized lighting variable groups, based on the optimized values of the variable parameters in the optimized lighting variable group, determine the feature values of the target description features according to the conversion formula of the target description features corresponding to the variable parameters.

10. The device according to claim 8, characterized in that, Further include: An evaluation information determination unit, configured to determine at least one item of evaluation information by at least one of the following methods after the solution optimization unit determines multiple groups of optimized lighting variable groups: For each group of optimized lighting variable groups, determine the correlation between the at least one lighting evaluation index based on the at least one item of spatial element information and the optimized values of the respective variable parameters in the optimized lighting variable group; For each type of variable parameter, determine the correlation between the variable parameter and different lighting evaluation indexes based on the at least one item of spatial element information and the optimized values of the other variable parameters except the variable parameter in the optimized lighting variable group; Combine the at least one item of spatial element information and the optimized lighting variable group to determine the linear regression analysis result between different variable parameters and lighting evaluation indexes; Based on the index values of the at least one lighting evaluation index corresponding to each optimized lighting variable group, and in combination with a clustering algorithm, determine multiple clustering categories corresponding to the multiple optimized lighting variable groups and the clustering centers of the clustering categories.

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