A method for optimizing underground space lighting by coupling spatial elements and composite window opening system

Abstract

The invention discloses an underground space lighting optimization method by coupling space elements and a composite window opening system, comprising: creating an underground space lighting prototype parameterized model based on the design data of the underground space; creating a composite window opening system parameterized model; assigning the composite window opening system parameterized model to a specified position of the underground space lighting prototype parameterized model to obtain an overall parameterized model; by re-adjusting the underground space lighting prototype parameterized model or the composite window opening system parameterized model, making the light path of natural light incident on the overall parameterized model meet expectations; determining optimization performance indicators and threshold ranges; taking the optimization performance indicators of the overall parameterized model being within the corresponding threshold ranges as optimization targets, performing optimization search using a genetic algorithm to obtain an optimal solution set that meets the optimization targets; and screening out a final optimization solution from the optimal solution set that meets the optimization targets.

Description

Technical Field

[0001] The present invention belongs to the technical field of building lighting, and in particular relates to an underground space lighting optimization method of a coupling space element and a composite window opening system. Background Art

[0002] The development and utilization of underground space has gradually become an important part of urban construction. Compared with above-ground buildings, underground space has poor lighting performance, which has a certain negative impact on human comfort and building energy conservation.

[0003] At present, there are two main methods for regulating the use of natural light in underground spaces. One is based on spatial element regulation, which aims to improve the lighting performance of underground spaces by optimizing the form and material of common spatial elements such as skylights, atriums, and sunken squares. The second is based on system element regulation, which aims to enhance the ability of underground spaces to actively obtain and control natural light by applying advanced lighting systems. Lighting systems are mainly divided into two categories, one is an independent lighting system, and the other is a composite window system combined with windows. Independent lighting systems mainly include light pipes, optical fibers, etc. They are suitable for windowless spaces in buildings, have strong equipment characteristics, are relatively independent in the composition of building spaces, and form a similar look and feel to lamps. The composite window system is mainly composed of redirected optical components and ordinary substrate glass, and the redirected optical components include but are not limited to prism glass, light-guiding blinds, etc. They can form a more organic integration with the building space, and while participating in the form of transparent enclosure structures, redirect the propagation route of natural light in the room, thereby increasing the building's ability to utilize natural light.

[0004] Since the composite window system involves expertise in optical physics and advanced building simulation, architects usually regulate underground space lighting based on spatial elements, and rarely focus on the application of composite window systems in architectural design, which is not conducive to maximizing the passive lighting rights of underground spaces. In addition, during the lighting regulation process, it is easy to ignore the potential impact of lighting on thermal comfort and energy consumption. Therefore, it is necessary to explore the coordinated regulation method of coupling spatial elements and composite window systems to fully enhance the underground space's ability to utilize natural light in a composite manner. Summary of the invention

[0005] Purpose of the invention: In order to fill the technical gap in the application of composite window systems in architectural design, and to solve the problem that the potential impact of lighting on thermal comfort and energy consumption is easily ignored in the process of lighting regulation, the present invention proposes an underground space lighting optimization method that couples spatial elements and composite window systems. By carrying out parametric collaborative modeling and visualization analysis of spatial elements and composite window systems, genetic algorithms are used to perform multi-objective optimization of performance indicators in three dimensions of visual comfort, thermal comfort, and building energy consumption, thereby effectively improving the comprehensive lighting performance of underground space in the early design stage, which has a driving effect on the construction of comfortable, healthy, and energy-saving underground space.

[0006] Technical solution: A method for optimizing underground space lighting by coupling spatial elements and a composite window opening system, comprising the following steps:

[0007] Step 1: Based on the design data of the underground space, a parametric model of the underground space lighting prototype is created, where the parametric model of the underground space lighting prototype is a parametric model including the optical properties of the underground space lighting prototype and the thermal properties of the underground space lighting prototype; the design data of the underground space includes space element variables, the range of space element variables, the optical properties of the materials involved, the thermal properties of the materials involved, operation schedule parameters and local meteorological data;

[0008] Selecting design data of a redirected optical component in a composite window system and characteristic data of a substrate glass in the composite window system, and creating a composite window system parameterized model, wherein the composite window system parameterized model is a parameterized model including optical properties of the composite window system and thermal properties of the composite window system; the design data of the redirected optical component includes morphological variables, ranges of morphological variables and optical properties; the characteristic data of the substrate glass includes thickness, solar heat gain coefficient and visible light transmittance;

[0009] Step 2: assign the composite window system parametric model to the specified position of the underground space lighting prototype parametric model to obtain the overall parametric model;

[0010] Step 3: Tracing the light path of the natural light incident on the overall parametric model. If the light path of the natural light incident on the overall parametric model meets expectations, proceed to step 4. If the light path of the natural light incident on the overall parametric model does not meet expectations, readjust the underground space lighting prototype parametric model or readjust the composite window system parametric model.

[0011] Step 4: Determine one or more optimization performance indicators and a threshold range for each optimization performance indicator;

[0012] Step 5: Taking one or more optimization performance indicators of the overall parameterized model as being within the corresponding threshold range, the spatial element variables of the underground space as being within the range of its spatial element variables, and the morphological variables of the redirected optical components as being within the range of its morphological variables as the optimization targets, a genetic algorithm is used for optimization to obtain an optimal solution set that meets the optimization targets;

[0013] Step 6: Filter out a final optimization solution from the optimal solution set that meets the optimization goal, wherein the final optimization solution includes: final values ​​of the spatial element variables of the underground space and final values ​​of the morphological variables of the redirected optical component.

[0014] Furthermore, the optical properties of the underground space lighting prototype include the reflectivity and transmittance of each interface of the underground space lighting prototype.

[0015] Furthermore, the thermal properties of the underground space lighting prototype include the thermal conductivity and thermal resistance of each interface of the underground space lighting prototype.

[0016] Furthermore, the operation schedule parameters include: operation parameters of the lighting system inside the underground space, operation parameters of the fresh air system inside the underground space, operation parameters of the air conditioning inside the underground space, operation parameters of the heating system inside the underground space and operation parameters of the electrical equipment inside the underground space.

[0017] Further, the optical properties of the redirected optical component are obtained according to the following steps:

[0018] Obtaining the optical properties of the materials involved in the redirected optical component in the visible light band, solar energy band and infrared band; based on this, generating a bidirectional scattering distribution function dataset of the redirected optical component in the visible light band, solar energy band and infrared band;

[0019] Based on the bidirectional scattering distribution function data set of the redirected optical component in the infrared band, the transmittance of the redirected optical component in the infrared band, the front reflectivity of the redirected optical component in the infrared band, and the rear reflectivity of the redirected optical component in the infrared band are calculated;

[0020] According to the following formula, the radiance of the front side of the optical component redirected in the infrared band and the radiance of the rear side of the optical component redirected in the infrared band are calculated:

[0021] ;

[0022] ;

[0023] In the formula, represents the radiance of the front side of the optical component redirected in the infrared band, represents the transmittance of the redirected optical component in the infrared band, represents the reflectivity of the front side of the optical component redirected in the infrared band, represents the radiance of the rear side of the optical component redirected in the infrared band, Represents the reflectivity of the rear side of the redirected optical component in the infrared band;

[0024] The optical properties of the redirected optical component are jointly constituted by the bidirectional scattering distribution function dataset of the redirected optical component in the visible light band, the bidirectional scattering distribution function dataset of the redirected optical component in the solar band, the front side radiance of the redirected optical component in the infrared band, and the back side radiance of the redirected optical component in the infrared band.

[0025] Furthermore, the creation of the composite window system parameterized model specifically includes:

[0026] Combining the design data of the redirected optical component in the composite window system and the characteristic data of the substrate glass, the optical properties and thermal properties of the composite window system are calculated;

[0027] Based on the optical and thermal properties of the composite window system, a parametric model of the composite window system is created.

[0028] Furthermore, the light path tracing of the natural light incident on the overall parameterized model specifically includes:

[0029] Obtain typical meteorological data of the underground space location, and based on this, generate a real-time three-dimensional sun trajectory at the underground space location;

[0030] Combined with the real-time three-dimensional sun trajectory of the underground space, and under the premise of complying with Snell's law and Fresnel's law, the light path of the natural light incident on the overall parametric model is simulated to achieve light path tracing of the natural light incident on the overall parametric model.

[0031] Furthermore, the optimization performance index is one or more of a lighting optimization index, a thermal comfort optimization index, and an energy consumption index per unit building area.

[0032] Furthermore, the one or more optimization performance indicators of the overall parameterized model are all within the corresponding threshold range, the spatial element variables of the underground space are within the range of its spatial element variables, and the morphological variables of the redirected optical components are within the range of its morphological variables as optimization targets, and the genetic algorithm is used for optimization to obtain the optimal solution set that meets the optimization target. The specific operations include:

[0033] Based on the optimization performance index, the simulation results of the overall parameterized model corresponding to each optimization performance index in the current iteration round are obtained;

[0034] Based on the simulation results of the overall parameterized model corresponding to each optimization performance index in the current iteration round, the spatial element variables of the underground space in the current iteration round, and the morphological variables of the redirected optical components in the current iteration round, a genetic algorithm is used to search for the optimal solution until the maximum number of iteration rounds is reached, and the optimal solution set that meets the optimization objectives is obtained.

[0035] Furthermore, based on the optimization performance index, the simulation results of the overall parameterized model corresponding to each optimization performance index in the current iteration round are obtained, specifically including:

[0036] If the optimization performance index includes a daylighting optimization index, a daylighting simulation is performed using the overall parameterized model under the current iteration round, the operation schedule parameters, the local meteorological data and the optical properties of the overall parameterized model under the current iteration round to obtain a daylighting simulation result;

[0037] If the optimization performance index includes the thermal comfort optimization index, the thermal comfort simulation is performed using the overall parameterized model under the current iteration round, the operation schedule parameters, the local meteorological data and the thermal properties of the overall parameterized model under the current iteration round to obtain the thermal comfort simulation results;

[0038] If the optimization performance index includes the energy consumption per unit building area, the energy consumption per unit building area is simulated using the overall parameterized model under the current iteration round, the operation schedule parameters, the local meteorological data and the thermal properties of the overall parameterized model under the current iteration round to obtain the energy consumption simulation result per unit building area.

[0039] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0040] (1) The method of the present invention integrates the collaborative modeling of typical underground space lighting space form and composite window opening system, providing an opportunity to further optimize the building lighting performance;

[0041] (2) While improving the quality of the natural light environment in underground spaces, the method of the present invention attempts to balance and analyze the potential impact of changes in lighting performance on human thermal comfort and energy consumption per unit building area, which is more conducive to creating indoor spaces that are comfortable for the human body and energy-saving and emission-reducing;

[0042] (3) The method of the present invention not only implants complex optical calculation principles, but also takes into account the architect-friendly features. Based on the Python programming language, the use of multiple simulation software is presented on the Grasshopper platform, thus broadening the design boundaries of architects. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flowchart of an underground space lighting optimization method for coupling spatial elements and a composite window opening system;

[0044] Figure 2 Schematic diagram of two types of underground space lighting prototypes;

[0045] Figure 3 Schematic diagram of spatial element variables of underground space in the embodiment;

[0046] Figure 4 Schematic diagram of prism glass in the embodiment;

[0047] Figure 5 It is a visual schematic diagram of light redirection by the composite window opening system in the embodiment. DETAILED DESCRIPTION

[0048] The technical solution of the present invention is now further described in conjunction with the accompanying drawings and embodiments of the specification.

[0049] To facilitate understanding of the technical solution of the present invention, the professional terms involved are now explained.

[0050] Grasshopper platform: A parametric platform that integrates a visual programming language and environment, running within the Rhinoceros3D computer-aided design software.

[0051] Radiance software: daylighting and lighting simulation software.

[0052] GenBSDF program: A program that generates BSDF files using the ray tracing function of the Radiance software.

[0053] BSDF (Bidirectional Scattering Distribution Function): A function that describes the interaction between light and materials, mainly including reflection and transmission behaviors.

[0054] Energyplus software: building energy simulation software.

[0055] Window software: A software developed by the Berkeley Laboratory in California, USA, for calculating the thermal properties of windows.

[0056] Embodiment: This embodiment proposes an underground space lighting optimization method based on coupled spatial elements and a composite window opening system. Taking the underground space lighting optimization based on coupled spatial elements and a composite window opening system of a sunken square in Nanjing as an example, the main steps of this optimization method are described:

[0057] Step 1: Obtain the design data of the underground space and create a parametric model of the underground space lighting prototype. The parametric model of the underground space lighting prototype in this embodiment is a parametric model that includes the optical properties of the underground space lighting prototype and the thermal properties of the underground space lighting prototype. The optical properties of the underground space lighting prototype include but are not limited to the reflectivity and transmittance of each interface of the underground space lighting prototype, and the thermal properties of the underground space lighting prototype include but are not limited to the thermal conductivity (U value) and thermal resistance (R) of each interface of the underground space lighting prototype. In this embodiment, the design data of the underground space include but are not limited to: design scope ( Figure 3 The larger square shown in bold is the design range), restricted opening range, spatial element variables, range of spatial element variables, optical properties of the materials involved, thermal properties of the materials involved, operating schedule parameters and local meteorological data.

[0058] The underground space lighting prototypes involved in this step mainly include two types, such as Figure 2 As shown, one is to use the skylight on the top for lighting, which is called the rising type; the other is to use the sunken space for lighting, which is called the sinking type.

[0059] like Figure 3 As shown, the spatial element variables of the underground space include but are not limited to: the net height Hi of the indoor space, the thickness of the cover soil Hd, the position of the sunken space (Px, Py), the length Lx in the x-axis direction, the length Ly in the y-axis direction, the dividing height Hs of the ordinary glass and the composite window system on the lighting surface, and the thickness Hg of the structural layer. Among them, the thickness Hg of the structural layer is a fixed value of 2.0 m. Figure 3 In the figure, dark grey represents the composite fenestration system and light grey represents the substrate glass.

[0060] The operating schedule parameters involved in this step include, but are not limited to: operating parameters of the lighting system inside the underground space, operating parameters of the fresh air system inside the underground space, operating parameters of the air conditioning inside the underground space, operating parameters of the heating system inside the underground space, and operating parameters of the electrical equipment inside the underground space.

[0061] Step 2: Select the design data of the redirected optical component in the composite window system, and select the characteristic data of the substrate glass in the composite window system, perform parametric modeling on the composite window system in the Grasshopper platform, and obtain a parametric model of the composite window system, which is a parametric model that includes the optical properties of the composite window system and the thermal properties of the composite window system; the design data of the redirected optical component includes morphological variables, the range of morphological variables and optical properties. The specific operations include: first, select prism glass as the redirected optical component; and perform parametric modeling on the prism glass in the Grasshopper platform. Figure 4 As shown. The prism glass is mainly composed of a first plane L1, a second plane L2, a third plane L3 and a fourth plane L4. In order to define the morphological variables in this embodiment, a first reference plane Lh, a second reference plane L5 and a third reference plane L6 are introduced. The second reference plane L5 and the third reference plane L6 are respectively the extensions of the first plane L1 and the second plane L2. The first reference plane Lh passes through the intersection of the second reference plane L5 and the third reference plane L6, and is perpendicular to the fourth plane L4. It is stipulated that the angles between the second reference plane L5, the third reference plane L6 and the first reference plane Lh are α1 and α2 respectively; the angle between the first plane L1 and the third plane L3 is α3. The area ratio of the first plane L1 in the second reference plane L5 is set to T.

[0062] The prism glass of this embodiment is made of PET material, and the optical properties of the PET material in the visible light band, solar light band and infrared band are obtained, and the optical properties include transmittance and refractive index, and the PET material is defined in Radiance.

[0063] Using the GenBSDF program in Radiance, BSDF data sets in visible light band, solar band and infrared band are generated for the prismatic glass that has been material defined in Radiance, including: BSDF data set in visible light band (Visible BSDF), BSDF data set in solar band (Solar BSDF) and BSDF data set in infrared band (IR BSDF).

[0064] Based on the BSDF dataset in the infrared band (IR BSDF), the transmittance of the redirected optical component in the infrared band (IR transmittance, ), IR reflectance front, ) , IR reflectance back of the optical component redirected in the infrared band ):

[0065] ;

[0066] In the formula, Is a general symbol, indicating , , Any one of them; i represents the incident direction, j represents the outgoing direction, Indicates that in calculation , , When any one of the following is given, the optical property values ​​of the incident direction and the outgoing direction are given. , represent the angles between the energy flow direction and the incident and outgoing directions, respectively. , are the infinitesimal values ​​of the solid angle at a given position in the incident direction and the outgoing direction respectively.

[0067] The IR emissivity front of the optical component redirected in the infrared band is calculated according to the following formula: ), IR emissivity back of the optical component redirected in the infrared band, ):

[0068] ;

[0069] ;

[0070] The BSDF dataset in the visible light band (Visible BSDF), the BSDF dataset in the solar band (Solar BSDF), the transmittance of the redirected optical component in the infrared band, the front radiance of the redirected optical component in the infrared band, and the back radiance of the redirected optical component in the infrared band are integrated into a single BSDF dataset, which is called the BSDF dataset of the redirected optical component. The BSDF dataset of the redirected optical component is the optical properties of the redirected optical component.

[0071] According to the relevant requirements of the "Architectural Lighting Design Standard GB50033-2013" and the "Public Building Energy Saving Design Standard GB 50189-2015", as well as the actual situation of the specific project, the substrate glass with the required light and thermal property values ​​is selected from the international glass database, and the characteristic data of the substrate glass is obtained, which includes but is not limited to thickness, visible light transmittance (Tvis), solar heat gain coefficient (SHGC), etc. In this embodiment, the thickness of the selected substrate glass is 6 mm, the visible light transmittance (Tvis) is 0.98, and the solar heat gain coefficient (SHGC) is 0.83.

[0072] Based on Python programming language, the Window software is connected to the Grasshopper platform, and the optical properties and thermal properties of the composite window system are calculated by combining the BSDF data set of the redirected optical components and the characteristic data of the substrate glass. The thermal properties of the composite window system include but are not limited to the solar heat gain coefficient (SHGC), thermal conductivity (U value) and visible light transmittance (Tvis). The calculation of this step depends on the existing algorithm, so it will not be described in detail.

[0073] Step 3: Create a visualization module with forward ray tracing function to analyze whether the light redirection effect of the composite window system meets the design expectations; the specific operations include:

[0074] Obtain typical annual meteorological data for Nanjing, import it into the meteorological module built on the Grasshopper platform, and generate real-time three-dimensional sun trajectory;

[0075] The composite window system parametric model is assigned to the designated position of the underground space lighting prototype parametric model to obtain the overall parametric model;

[0076] like Figure 5 As shown, based on the Python programming language, Snell's law and Fresnel's law are implanted in the Grasshopper platform, and combined with the real-time three-dimensional solar trajectory in Nanjing, the path of natural light entering the overall parametric model is tracked, and the paths of reflected and refracted light are analyzed and displayed in real time; if the path of natural light entering the overall parametric model does not meet expectations, the underground space lighting prototype parametric model is readjusted and returns to step 1, or the composite window system parametric model is readjusted and returns to step 2; if it meets expectations, go to step 4.

[0077] Step 4: Extract the lighting optimization index, thermal comfort optimization index, energy consumption index per unit building area and their corresponding threshold ranges from the "Building Lighting Design Standard GB50033-2013", "Green Building Evaluation Standard GB / T 50378-2019", and "Civil Building Energy Consumption Standard GB / T 51161-2016". The extracted lighting optimization index, thermal comfort optimization index, and energy consumption index per unit building area are called optimization performance indicators. The specific operations include:

[0078] According to the "Architectural Lighting Design Standard GB50033-2013", the indoor floor is used as the reference plane and 200 lux is specified as the minimum illumination value. To prevent excessive lighting, 3000 lux is selected as the upper limit. The average proportion of time that all measurement points meet the illumination range of 200-3000 lux between 8:00 and 17:00 every day throughout the year is used as the lighting optimization index (AvgUDI 200lux-3000lux ). All measuring points are evenly arranged on the indoor floor at intervals of 1m.

[0079] According to the Green Building Evaluation Standard GB / T 50378-2019, the proportion of time that the main functional rooms of the building are in the thermal comfort zone is selected as the thermal comfort optimization index (T cft ). The time in the thermal comfort zone mainly refers to the time period between 8:00 and 17:00, which satisfies -0.5≤PMV≤0.5.

[0080] According to the "Civil Building Energy Consumption Standard GB / T 51161-2016", the energy consumption index per unit building area is adopted, and the evaluation period is one year.

[0081] Step 5: Based on the Grasshopper platform, the overall parametric model and optimization objectives are integrated. The optimization objectives mentioned in this step refer to that the optimization performance indicators of the overall parametric model are all within the corresponding threshold range, the spatial element variables of the underground space are within the range of its spatial element variables, and the morphological variables of the redirected optical components are within the range of its morphological variables. The genetic algorithm is used to carry out multi-objective optimization, and the optimal solution set is judged and screened. The specific operations include: selecting the genetic algorithm NSGA-II, building a multi-objective optimization module, and setting the operating parameters. The operating parameters are shown in Table 1.

[0082] Table 1 Operating parameters of genetic algorithm NSGA-II

[0083]

[0084] According to the optimized performance indicators determined in step 4, based on Radiance, Energyplus software builds the lighting simulation module, thermal comfort simulation module, and unit building area energy consumption simulation module;

[0085] The overall parameterized model, Nanjing meteorological data, operation schedule parameters and their corresponding optical properties are connected to the lighting simulation module to obtain the lighting simulation results. It should be noted that the overall parameterized model obtained after each iteration needs to recalculate its optical properties before performing lighting simulation.

[0086] The overall parameterized model, Nanjing meteorological data, operation schedule parameters and corresponding thermal properties are connected to the thermal comfort simulation module to obtain the thermal comfort simulation results. It should be noted that the overall parameterized model obtained after each iteration needs to recalculate its thermal properties before thermal comfort simulation is performed.

[0087] The overall parameterized model, Nanjing meteorological data, operation schedule parameters and corresponding thermal properties are connected to the unit building area energy consumption simulation module to obtain the unit building area energy consumption simulation results; it should be noted that the overall parameterized model obtained after each iteration needs to recalculate its thermal properties before performing unit building area energy consumption simulation;

[0088] The lighting simulation results, thermal comfort simulation results and unit building area energy consumption simulation results are connected to the multi-objective optimization module;

[0089] The spatial element variables of the underground space and the morphological variables of the redirected optical components are connected to the multi-objective optimization module in sequence. Multi-objective optimization is performed to obtain the optimal solution set that meets the optimization objectives. According to personal preferences (spatial aesthetics, functional use, etc.) and auxiliary conditions (weights, economic indicators, etc.), the appropriate solution is selected from the optimal solution set that meets the optimization objectives.

[0090] The fitness function of the genetic algorithm NSGA-II used in this embodiment is expressed as:

[0091] ;

[0092] In the formula, Indicates the calculated value of energy consumption per unit building area in the current solution. It represents the minimum calculated value of energy consumption per unit building area among all optimal solutions. It represents the calculated value of the maximum energy consumption per unit building area among all the optimal solutions. Indicates the calculated value of the lighting optimization index in the current solution. It represents the minimum calculated value of the lighting optimization index among all the optimal solutions. It represents the maximum calculated value of the lighting optimization index among all the optimal solutions. represents the calculated value of thermal comfort optimization index in the current scheme, represents the minimum calculated value of thermal comfort optimization index among all optimal solutions. It represents the maximum calculated value of thermal comfort optimization index among all optimal solutions.

[0093] The fitness function further narrows the set of optimal solutions through a weighted method. The fitness function first normalizes each objective to the index value, then adds the index values ​​on the basis that the three optimization objectives are regarded as equal weights, and finally ranks all the optimal solutions, as shown in Table 2.

[0094] Table 2 Top 10 optimal solutions in this embodiment

[0095]

[0096] This embodiment combines the Grasshopper platform and Python programming language, integrates Radiance lighting simulation software, Energyplus energy consumption simulation software, and Window software, carries out parametric collaborative modeling and visual analysis of spatial elements and composite window systems, and uses genetic algorithms to perform multi-objective optimization of performance indicators in three dimensions: lighting, thermal comfort, and building energy consumption. This effectively improves the comprehensive lighting performance of underground spaces in the early design stage, which has a driving effect on the construction of comfortable, healthy, and energy-saving underground spaces.

Claims

1. A method for optimizing underground space lighting by coupling spatial elements and a composite window system, characterized in that: The following steps are involved: Step 1: Based on the design data of the underground space, a parametric model of the underground space lighting prototype is created, where the parametric model of the underground space lighting prototype is a parametric model including the optical properties of the underground space lighting prototype and the thermal properties of the underground space lighting prototype; the design data of the underground space includes space element variables, the range of space element variables, the optical properties of the materials involved, the thermal properties of the materials involved, operation schedule parameters and local meteorological data; Selecting design data of a redirected optical component in a composite window system and characteristic data of a substrate glass in the composite window system, and creating a composite window system parameterized model, wherein the composite window system parameterized model is a parameterized model including optical properties of the composite window system and thermal properties of the composite window system; the design data of the redirected optical component includes morphological variables, ranges of morphological variables and optical properties; the characteristic data of the substrate glass includes thickness, solar heat gain coefficient and visible light transmittance; Step 2: assign the composite window system parametric model to the specified position of the underground space lighting prototype parametric model to obtain the overall parametric model; Step 3: Tracing the light path of the natural light incident on the overall parametric model. If the light path of the natural light incident on the overall parametric model meets expectations, proceed to step 4. If the light path of the natural light incident on the overall parametric model does not meet expectations, readjust the underground space lighting prototype parametric model or readjust the composite window system parametric model. Step 4: Determine one or more optimization performance indicators and a threshold range for each optimization performance indicator; Step 5: Taking one or more optimization performance indicators of the overall parameterized model as being within the corresponding threshold range, the spatial element variables of the underground space as being within the range of its spatial element variables, and the morphological variables of the redirected optical components as being within the range of its morphological variables as the optimization targets, a genetic algorithm is used for optimization to obtain an optimal solution set that meets the optimization targets; Step 6: Filter out a final optimization solution from the optimal solution set that meets the optimization goal, wherein the final optimization solution includes: final values ​​of the spatial element variables of the underground space and final values ​​of the morphological variables of the redirected optical component.

2. The underground space lighting optimization method of coupling spatial elements and composite window opening system according to claim 1 is characterized by: The optical properties of the underground space lighting prototype include the reflectivity and transmittance of each interface of the underground space lighting prototype.

3. The underground space lighting optimization method of coupling spatial elements and composite window opening system according to claim 1, characterized in that: The thermal properties of the underground space lighting prototype include the thermal conductivity and thermal resistance of each interface of the underground space lighting prototype.

4. The underground space lighting optimization method of coupling spatial elements and composite window opening system according to claim 1 is characterized by: The operation schedule parameters include: operation parameters of the lighting system inside the underground space, operation parameters of the fresh air system inside the underground space, operation parameters of the air conditioning inside the underground space, operation parameters of the heating system inside the underground space and operation parameters of the electrical equipment inside the underground space.

5. The underground space lighting optimization method of coupling spatial elements and composite window opening system according to claim 1 is characterized by: The optical properties of the redirected optical component are obtained according to the following steps: Obtaining the optical properties of the materials involved in the redirected optical component in the visible light band, solar energy band and infrared band; based on this, generating a bidirectional scattering distribution function dataset of the redirected optical component in the visible light band, solar energy band and infrared band; Based on the bidirectional scattering distribution function data set of the redirected optical component in the infrared band, the transmittance of the redirected optical component in the infrared band, the front reflectivity of the redirected optical component in the infrared band, and the rear reflectivity of the redirected optical component in the infrared band are calculated; According to the following formula, the radiance of the front side of the optical component redirected in the infrared band and the radiance of the rear side of the optical component redirected in the infrared band are calculated: ； ； In the formula, represents the radiance of the front side of the optical component redirected in the infrared band, represents the transmittance of the redirected optical component in the infrared band, represents the reflectivity of the front side of the optical component redirected in the infrared band, represents the radiance of the rear side of the optical component redirected in the infrared band, Represents the reflectivity of the rear side of the redirected optical component in the infrared band; The optical properties of the redirected optical component are jointly constituted by the bidirectional scattering distribution function dataset of the redirected optical component in the visible light band, the bidirectional scattering distribution function dataset of the redirected optical component in the solar band, the front side radiance of the redirected optical component in the infrared band, and the back side radiance of the redirected optical component in the infrared band.

6. The underground space lighting optimization method of coupling spatial elements and composite window opening system according to claim 1 is characterized by: The specific operations of creating a composite window system parameterized model include: Combining the design data of the redirected optical component in the composite window system and the characteristic data of the substrate glass, the optical properties and thermal properties of the composite window system are calculated; Based on the optical and thermal properties of the composite window system, a parametric model of the composite window system is created.

7. The underground space lighting optimization method of coupling spatial elements and composite window opening system according to claim 1 is characterized by: The light path tracing of the natural light incident on the overall parameterized model includes: Obtain typical meteorological data of the underground space location, and based on this, generate a real-time three-dimensional sun trajectory at the underground space location; Combined with the real-time three-dimensional sun trajectory of the underground space, and under the premise of complying with Snell's law and Fresnel's law, the light path of the natural light incident on the overall parametric model is simulated to achieve light path tracing of the natural light incident on the overall parametric model.

8. The underground space lighting optimization method of coupling spatial elements and composite window opening system according to claim 1 is characterized by: The optimization performance index is one or more of a lighting optimization index, a thermal comfort optimization index, and an energy consumption index per unit building area.

9. The underground space lighting optimization method of coupling spatial elements and composite window opening system according to claim 8, characterized in that: The one or more optimization performance indicators of the overall parameterized model are all within the corresponding threshold range, the spatial element variables of the underground space are within the range of its spatial element variables, and the morphological variables of the redirected optical components are within the range of its morphological variables as the optimization target, and the genetic algorithm is used for optimization to obtain the optimal solution set that meets the optimization target. The specific operations include: Based on the optimization performance index, the simulation results of the overall parameterized model corresponding to each optimization performance index in the current iteration round are obtained; Based on the simulation results of the overall parameterized model corresponding to each optimization performance index in the current iteration round, the spatial element variables of the underground space in the current iteration round, and the morphological variables of the redirected optical components in the current iteration round, a genetic algorithm is used to search for the optimal solution until the maximum number of iteration rounds is reached, and the optimal solution set that meets the optimization objectives is obtained.

10. The underground space lighting optimization method of coupling spatial elements and composite window opening system according to claim 9, characterized in that: The above-mentioned optimization performance index is based on which the simulation results of the overall parameterized model corresponding to each optimization performance index in the current iteration round are obtained, specifically including: If the optimization performance index includes a daylighting optimization index, a daylighting simulation is performed using the overall parameterized model under the current iteration round, the operation schedule parameters, the local meteorological data and the optical properties of the overall parameterized model under the current iteration round to obtain a daylighting simulation result; If the optimization performance index includes the thermal comfort optimization index, the thermal comfort simulation is performed using the overall parameterized model under the current iteration round, the operation schedule parameters, the local meteorological data and the thermal properties of the overall parameterized model under the current iteration round to obtain the thermal comfort simulation results; If the optimization performance index includes the energy consumption per unit building area, the energy consumption per unit building area is simulated using the overall parameterized model under the current iteration round, the operation schedule parameters, the local meteorological data and the thermal properties of the overall parameterized model under the current iteration round to obtain the energy consumption simulation result per unit building area.

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