Subway station partition lighting control method and system

Abstract

The invention provides a subarea illumination control method and system for a subway station, and relates to the technical field of subway illumination, and the method comprises the steps: carrying out the calculation according to three-dimensional building model data, functional area division data and passenger flow data, and obtaining illumination demand data; simulating the original lamp arrangement scheme, the three-dimensional building model data and the illumination demand data to obtain initial illumination intensity distribution data; processing the initial illumination intensity distribution data to obtain an illumination intensity superposition matrix; adjusting according to the optical characteristic data and the passenger flow data to obtain illumination adjustment data of the reflection area; processing according to the illumination adjustment data, the illumination intensity superposition matrix and the illumination demand data to obtain a regional optical interaction matrix; and obtaining a real-time control strategy according to the regional optical interaction matrix. According to the method, the illumination requirements and actual illumination distribution of all the areas are dynamically analyzed, and the problems that due to a complex building structure, light is not uniform, and the local part is too bright or too dark are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of subway lighting, and in particular to a method and system for controlling zone lighting in a subway station. Background Art

[0002] At present, with the rapid development of subway systems, the lighting design of subway stations must not only meet basic functional requirements, but is also increasingly influenced by architectural design styles. Modern subway stations often incorporate unique architectural elements such as complex curves and broken lines in their interior design, and combine decorative materials of different materials such as glass, metal, and wall surfaces. These designs provide the station with a unique stylized appearance. However, this complex architectural structure brings new challenges to the lighting system. Existing lighting systems usually rely on fixed lamp arrangements and simple lighting control methods, and cannot adapt to the propagation, reflection, and absorption characteristics of light under these complex geometric structures. In addition, there are significant differences in the lighting requirements of different functional areas (such as waiting areas, passages, entrances and exits, etc.), and the existing technology often lacks a processing mechanism for inter-regional light interference, resulting in uneven lighting effects or local overbrightness or overdarkness. The existing technology mainly attempts to solve these problems by increasing the number of lamps or adjusting the angle of the lamps, but this method is not only difficult to achieve refined control, but may also increase energy consumption, and cannot effectively solve the lighting problems caused by the complex architectural design in modern subway stations.

[0003] Based on the above-mentioned defects of the prior art, there is an urgent need for a subway station zone lighting control method and system to meet the lighting needs under the stylized design of subway stations. Summary of the invention

[0004] The purpose of the present invention is to provide a subway station zone lighting control method and system to improve the above problems. In order to achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0005] On the one hand, the present application provides a method for controlling lighting in a subway station area, comprising:

[0006] Obtain the 3D architectural model data of the subway station, the optical properties data of various building materials in the station, the original lighting layout plan, the functional area division data and the passenger flow data at different time periods;

[0007] Perform demand analysis and processing according to the three-dimensional building model data, the functional area division data and the passenger flow data, and calculate and obtain the lighting demand data of each area;

[0008] Using a Monte Carlo ray tracing algorithm to simulate the original lamp arrangement plan, the three-dimensional building model data and the lighting demand data, and obtaining initial lighting intensity distribution data by simulating the light propagation, reflection and absorption of the lamps;

[0009] The initial light intensity distribution data is processed based on a preset weighted light superposition model, and the superposition effect of multiple light sources in the same area is calculated to obtain a light intensity superposition matrix;

[0010] According to the optical characteristic data and the passenger flow data, the sight angles of passengers at different time periods and different positions are simulated to obtain sight tracking data, and the brightness of lamps in the reflection area is quantitatively adjusted based on the sight tracking data to obtain the illumination adjustment data of the reflection area;

[0011] Perform finite element analysis based on the illumination adjustment data, the illumination intensity superposition matrix and the illumination demand data to evaluate the mutual interference and conduction paths of light between the functional areas of the station to obtain a regional optical interaction matrix;

[0012] According to the regional optical interaction matrix, the brightness and lighting timing of the lamps in each area are dynamically optimized in combination with the real-time feedback data from the sensor to obtain a real-time control strategy.

[0013] On the other hand, the present application also provides a subway station zone lighting control system, comprising:

[0014] The acquisition module is used to obtain the three-dimensional architectural model data of the subway station, the optical characteristics data of various building materials in the station, the original lighting layout plan, the functional area division data and the passenger flow data at different time periods;

[0015] An analysis module, used to perform demand analysis and processing according to the three-dimensional building model data, the functional area division data and the passenger flow data, and calculate the lighting demand data of each area;

[0016] A simulation module, which uses a Monte Carlo ray tracing algorithm to simulate the original lamp arrangement scheme, the three-dimensional building model data and the lighting demand data, and obtains initial light intensity distribution data by simulating the light propagation, reflection and absorption of the lamps;

[0017] A processing module processes the initial light intensity distribution data based on a preset weighted light superposition model, and calculates the superposition effect of multiple light sources in the same area to obtain a light intensity superposition matrix;

[0018] an adjustment module, for simulating the sight angles of passengers at different time periods and different positions to obtain sight tracking data according to the optical characteristic data and the passenger flow data, and quantitatively adjusting the brightness of lamps in the reflection area based on the sight tracking data to obtain illumination adjustment data of the reflection area;

[0019] A construction module is used to perform finite element analysis processing according to the illumination adjustment data, the illumination intensity superposition matrix and the illumination demand data, and evaluate the mutual interference and conduction paths of light between the functional areas of the station to obtain a regional optical interaction matrix;

[0020] The control module is used to dynamically optimize the brightness and lighting timing of lamps in each area according to the regional optical interaction matrix and in combination with real-time feedback data from sensors to obtain a real-time control strategy.

[0021] The beneficial effects of the present invention are:

[0022] The present invention solves the problems of uneven lighting and local overbrightness or overdarkness caused by complex building structures by dynamically analyzing the lighting requirements and actual lighting distribution of each area. The present invention accurately calculates and optimizes the mutual interference and conduction paths of light between different functional areas through a weighted light superposition model and finite element analysis method, thereby avoiding the superposition effect of lighting influences between areas and ensuring the independent lighting requirements of each functional area.

[0023] Other features and advantages of the present invention will be set forth in the following description, and in part will become apparent from the description, or may be understood by practicing embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0025] Figure 1 It is a schematic diagram of the flow of the subway station zone lighting control method described in an embodiment of the present invention;

[0026] Figure 2 Flow chart of the Monte Carlo ray tracing algorithm described in an embodiment of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0028] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0029] Embodiment 1:

[0030] This embodiment provides a method for controlling zone lighting in a subway station.

[0031] See also Figure 1 , the figure shows that the method includes step S100, step S200, step S300, step S400, step S500, step S600 and step S700.

[0032] Step S100, obtaining the three-dimensional building model data of the subway station, the optical property data of various building materials in the station, the original lamp layout plan, the functional area division data and the passenger flow data at different time periods;

[0033] It is understandable that the 3D architectural model data of the subway station can accurately express the complex geometric shapes inside the station, including elements such as curves and broken lines. The 3D model provides the necessary spatial framework for the propagation path and reflection characteristics of light. The optical property data of various building materials in the station, involving the reflectivity, absorptivity and transmittance of the materials, can affect the interaction between light and materials. The acquisition of the original lamp layout plan provides benchmark information for the current lighting system, including parameters such as lamp type, location, power and lighting angle. The functional area division data provides a basis for the refinement of lighting control, and can formulate corresponding lighting requirements according to the use functions of different areas (such as waiting areas, passages and entrances and exits). Passenger flow data at different times provides a basis for dynamic adjustment of the lighting system.

[0034] Step S200, performing demand analysis and processing according to the three-dimensional building model data, the functional area division data and the passenger flow data, and calculating the lighting demand data of each area;

[0035] It should be noted that this step achieves accurate calculation of the lighting requirements of each functional area of ​​the subway station, so that the lighting system can flexibly respond to different usage scenarios and passenger flow dynamics in actual applications. This refined demand analysis ensures the rationality and effectiveness of the lighting solution, thereby optimizing the use of lighting resources, improving energy efficiency, and improving passengers' travel experience.

[0036] Step S300: using a Monte Carlo ray tracing algorithm to simulate the original lamp arrangement plan, the three-dimensional building model data and the lighting demand data, and obtaining initial lighting intensity distribution data by simulating the light propagation, reflection and absorption of the lamps;

[0037] It should be noted that the Monte Carlo ray tracing algorithm is a random sampling-based technology that can effectively handle complex lighting calculation problems. In this process, a large number of random paths of light are generated by modeling the light emission characteristics of lamps. These light rays propagate in the three-dimensional building model and interact with different building material surfaces. Each light ray undergoes reflection, refraction and absorption during propagation, and these complex physical processes are accurately incorporated into the algorithm. Secondly, combined with the three-dimensional building model data, the algorithm can take into account the geometric complexity inside the building, such as the impact of surfaces and edges on light propagation. This modeling capability makes the lighting simulation closer to the actual situation and can reflect the real behavior of light in space. In addition, the optical properties of building materials, such as reflectivity and absorptivity, will also have a significant impact on the intensity and direction of light when it passes through. These properties are dynamically calculated in the Monte Carlo algorithm to ensure the accuracy of the simulation results. The lighting demand data provides a target basis for the calculation of the light intensity distribution. By comparing with the demand data, it is possible to identify the insufficient or excessive lighting in each area, laying the foundation for subsequent lighting adjustment and optimization. The final initial light intensity distribution data represents the actual light intensity status of each area under the current lighting arrangement scheme.

[0038] Step S400: processing the initial light intensity distribution data based on a preset weighted light superposition model, calculating the superposition effect of multiple light sources in the same area to obtain a light intensity superposition matrix;

[0039] It is understandable that in actual scenarios, the superposition matrix not only reflects the overall contribution of each light source, but also reveals the interference and synergy between light sources. For example, in a waiting area, there are multiple lamps illuminating the area at the same time. At this time, it is necessary to perform a weighted calculation on the contribution of each lamp to determine the final light intensity. This method can effectively eliminate the unevenness of light and ensure that the light level in each area meets the design standards and usage requirements.

[0040] Step S500: According to the optical characteristic data and the passenger flow data, the sight angles of passengers at different time periods and different positions are simulated to obtain sight tracking data, and the brightness of the lamps in the reflection area is quantitatively adjusted based on the sight tracking data to obtain the illumination adjustment data of the reflection area;

[0041] It is understandable that the process of sight tracking uses passenger flow data to analyze the distribution and flow characteristics of passengers in different time periods. The dynamic behavior of passengers in the station is captured through modeling, including the movement paths and residence times of different passengers in specific areas. This process involves the simulation of fluid dynamics or multi-agent systems, treating passengers as fluid particles to better reflect their behavior patterns and space occupancy. Further, the brightness of the lamps in the reflective area is quantitatively adjusted to ensure that the lighting effect meets the actual visual needs of the passengers. This process involves the evaluation of the intensity of light interference to ensure that the brightness setting of the lamps not only meets the overall lighting needs in the area, but also takes into account the sight angle of passengers at specific locations to avoid glare or shadows. Through the analysis and quantitative adjustment of sight tracking data, dynamic and personalized lighting control can be achieved to improve the comfort of passengers in the station.

[0042] Step S600: Perform finite element analysis based on the illumination adjustment data, the illumination intensity superposition matrix and the illumination demand data to evaluate the mutual interference and conduction paths of light between the functional areas of the station and obtain a regional optical interaction matrix;

[0043] In this step, finite element analysis is used to simulate the propagation behavior and mutual interference effects of light between different areas. By dividing the station space into multiple small units, the reflection, refraction and absorption of light on different material surfaces can be analyzed in detail. When making lighting adjustments, the lighting adjustment data provides the brightness settings of the lamps in each area, and the lighting intensity superposition matrix contains the light intensity of each light source in a specific area and its interaction information. Combining these two with the lighting demand data, the lighting status of each functional area can be accurately evaluated and potential light interference areas can be identified. When making lighting adjustments, the lighting adjustment data provides the brightness settings of the lamps in each area, and the lighting intensity superposition matrix contains the light intensity of each light source in a specific area and its interaction information.

[0044] Step S700: According to the regional optical interaction matrix, the brightness and lighting timing of the lamps in each area are dynamically optimized in combination with the real-time feedback data from the sensor to obtain a real-time control strategy.

[0045] It is understandable that the regional optical interaction matrix records the light intensity, lamp layout and mutual interference effect of each area, laying a data foundation for subsequent real-time adjustments. When the lighting conditions or passenger flow in the station change, this matrix can be used to quickly identify the affected areas and make targeted adjustments. Combined with the real-time feedback data of the sensor, the lighting status and passenger activities of each area can be monitored in real time. For example, the light sensor can detect the change of light, and the motion sensor can identify the flow and stay area of ​​passengers. These data provide an important basis for optimizing the brightness and timing of lamps, so that the lighting system can respond quickly to environmental changes and adjust the brightness of lamps in the corresponding areas. For example, during peak hours with large passenger flow, the lighting intensity of the waiting area can be increased to provide better visual comfort and security; while during periods with less passenger flow, the lighting intensity can be reduced to save energy.

[0046] Further, step S200 includes step S210, step S220, step S230 and step S240.

[0047] Step S210, according to the three-dimensional building model data and the functional area division data, the three-dimensional space division data of each functional area is obtained by processing with a space partitioning algorithm;

[0048] It should be noted that the three-dimensional building model data contains the geometric information of the subway station, such as the shape, size, location of each area and the distribution of building materials. This information provides an intuitive reference for space division. The functional area division data defines the functional characteristics of different areas inside the station, such as waiting areas, ticket areas, passages, etc. Combining these two types of data, the space partitioning algorithm can identify and define the three-dimensional space boundaries of each functional area. Preferably, in this embodiment, voxelization technology is used to discretize the complex three-dimensional space into a series of small cubic units to facilitate subsequent calculations.

[0049] Step S220: Perform passenger behavior modeling based on the three-dimensional space division data and the passenger flow data, use a passenger behavior modeling algorithm based on fluid dynamics, regard passengers as fluid particles, and simulate the flow and distribution of passengers in different time periods in combination with regional functional characteristics to obtain passenger density distribution data for each functional area;

[0050] In this process, passengers are regarded as fluid particles, and with the help of the principles of fluid dynamics, the dynamic behavior of passengers in different time periods can be more effectively simulated. This method takes into account the interaction, movement speed and direction of passengers inside the station, allowing for more detailed modeling of passenger flow. By setting initial conditions (such as the entry and exit flow of passengers) and boundary conditions (such as the impact of the internal structure of the station on the flow), a dynamic simulation environment is established to reflect the gathering and dispersion process of passengers in various functional areas. Combined with the functional characteristics of the area, the algorithm takes into account not only the changes in the number of passengers, but also the purpose of use of different areas. For example, during peak hours, the passenger density in the waiting area may increase significantly, while the density in the passage or ticket area may be relatively low. This modeling process can generate accurate passenger density distribution data, providing detailed information on the intensity of passenger activity in each area.

[0051] Step S230: According to the passenger density distribution data and the regional functional attributes in the functional area division data, after being processed by the lighting demand model, the optimal lighting intensity of different functional areas is calculated based on the human eye's perception of brightness under different lighting conditions, and the target lighting intensity of each functional area is obtained;

[0052] It should be noted that the lighting demand model determines the actual lighting demand of each area based on the passenger density distribution data. The higher the passenger density, the more frequent the activities in the area, and the higher the demand for brightness of the human eye. Therefore, in high-density areas such as waiting areas, entrances and exits, the lighting demand model tends to recommend higher light intensity. In areas with low passenger flow, such as office areas or equipment maintenance areas, the lighting demand is relatively low. This dynamic adjustment avoids unnecessary energy consumption and improves the energy-saving effect of the system. Secondly, the regional functional attributes in the functional area division data further refine the lighting demand. For example, the light in the channel area needs to help guide the flow of passengers, while the lighting in the waiting area needs to provide sufficient comfort and clarity. Therefore, the lighting demand of different areas is not just a simple brightness issue, but is also closely related to the actual use of the area. Through the lighting demand model, this step takes into account the adaptability of the human eye to light under various complex conditions. Different lighting environments affect the brightness perception of the human eye. For example, strong light may cause glare, and too dark an environment may cause visual fatigue. The model incorporates the basic principles of visual science when calculating the optimal light intensity, taking into account factors such as brightness, contrast, and uniformity to ensure that passengers can get the best visual experience in different areas.

[0053] Step S240: perform illumination uniformity calculation processing according to the target illumination intensity to obtain illumination requirement data of each area.

[0054] Specifically, the target light intensity for each area is first identified, which is the ideal lighting level based on the evaluation of the functional area characteristics and passenger needs. Next, in order to ensure the uniformity of the lighting, the actual contribution of all light sources in the area needs to be considered. By evaluating the number of light sources and their relative impact on the lighting of a specific area, a more comprehensive reflection of the distribution of lighting can be achieved. In the calculation process, the optical interaction between areas also needs to be considered. Specifically, the light emitted by the light source not only directly illuminates the area, but also reflects and scatters with the surrounding environment. Therefore, by introducing the reflection characteristics between areas, the mutual influence of light between different areas is quantified. In this way, the propagation and attenuation of light between different areas can be effectively evaluated, so as to obtain more accurate lighting needs. In addition, the factors of light source distance and incident angle are also taken into account. The light intensity of areas with a long distance from the light source will be weakened due to distance attenuation, and the incident angle of the light will affect the reflection and diffusion characteristics of the light. The combined influence of these factors will help ensure that the lighting between each area can be evenly covered. The final calculation formula is:

[0055]

[0056] Among them, I req,k represents the required illumination intensity of area k; n represents the number of light sources in the area; i represents the sequence number of the light source; w i I represents the illumination weight of each light source i on area k; target,k represents the target light intensity of region k; α represents the regularization coefficient, which is used to control the weight of light interference between regions; m represents the number of other regions that interact with region k; j represents the region number; R kj represents the reflection coefficient between region k and other regions j; I actual,i represents the actual illumination intensity of light source i in area k; θ kj represents the angular dependence function of light from region k to region j; d kj Represents the distance from area k to area j, describing the effect of light intensity attenuation with distance.

[0057] Further, step S300 includes step S310, step S320, step S330 and step S340.

[0058] Step S310: According to the original lamp arrangement plan and the three-dimensional building model data, the position, direction, type, power and beam angle of each lamp in the three-dimensional space are determined through light source positioning processing to obtain the illumination distribution parameters of the lamp;

[0059] This process first determines the installation position and direction of each lamp based on the three-dimensional building model. Specifically, the three-dimensional coordinates of the lamp determine its physical position in the building, while its direction determines the projection angle and coverage of the light, which is crucial for complex spatial structures. On this basis, the physical characteristics of different types of lamps must also be considered. For example, LEDs, halogen lamps or fluorescent lamps vary in power, luminous efficiency, and light decay characteristics. Their output power needs to be carefully calculated to ensure that the light source can produce the expected lighting effect. The choice of power directly affects the intensity distribution of light, which not only affects the brightness of the target area, but also affects the reflection, absorption and attenuation of light in space. The beam angle is another key parameter that determines the luminous angle range of the lamp. Beams of different angles will produce different illumination coverage and intensity decay rates. Wide-angle lamps will produce a more uniform illumination distribution, while narrow-angle lamps may concentrate the light in a local area. Through the combination and precise modeling of these data, the illumination distribution parameters of the lamp can be obtained, which are further used to calculate the propagation path, reflection and absorption process of light in three-dimensional space to ensure the accuracy of the subsequent illumination simulation stage.

[0060] Step S320: Based on the illumination distribution parameters and the three-dimensional space division data, a Monte Carlo ray tracing algorithm is applied to simulate the propagation, reflection and refraction of the light of each lamp in space, as well as the multiple reflections and attenuation behaviors of the light when encountering curved and broken line architectural structures, to obtain the light propagation path of each area;

[0061] like Figure 2 As shown, Figure 2The flowchart of the Monte Carlo ray tracing algorithm is shown. It should be noted that the illumination distribution parameters provide the algorithm with the luminous intensity, beam angle, position and direction of each lamp, which is the basis for accurately simulating the behavior of light. By setting the light source model, a large number of light rays can be simulated, and the propagation path of light rays can be simulated by random sampling technology. In the process of light rays moving in space, combined with the three-dimensional space division data, the propagation of light rays in the building structure can be accurately tracked. The advantage of the Monte Carlo algorithm lies in its ability to handle complex optical phenomena. In the actual environment, light rays will reflect and refract when encountering surfaces of different materials, especially in architectural structures with curves and broken lines, the behavior of light rays is more complicated. This step simulates the process of multiple reflections and attenuation by calculating the reflection coefficient and refractive index of light rays when encountering surfaces of different angles and curvatures, ensuring that each surface through which the light rays pass and their impact on the light intensity are considered. This random sampling and statistical analysis method allows the final light propagation path to take into account not only direct illumination, but also indirect illumination caused by reflection and refraction. Through this process, the data on light propagation paths in each area are finally obtained, which not only reflects the lighting coverage of the lamps, but also reveals the propagation characteristics of light inside the building, such as the intensity changes and distribution of light in different areas.

[0062] Step S330: According to the light propagation path and optical characteristic data, after light reflection and absorption processing, the dynamic reflection of light under the geometric structure is calculated using the light reflection model and the Fresnel reflection model to obtain the light intensity distribution of each area;

[0063] It is understandable that the illumination reflection model is used to describe the process of light reflecting on the surfaces of different materials, while the Fresnel reflection model further refines the reflection characteristics of light at the junction of different media, especially calculating the change in light reflection intensity based on the incident angle of light and the refractive index of the medium. Through these models, complex dynamic reflection behaviors can be simulated. For example, the degree of light reflection on glass, metal or different material surfaces will be significantly different. The calculation formula is:

[0064]

[0065] Among them, I total,k represents the final illumination intensity distribution of area k; I incident,i represents the incident light intensity from light source i; A k represents the absorption coefficient of region k; R i,k represents the reflection coefficient of light source i in area k; F(θ i,k ,n1,n2) represents the Fresnel reflection coefficient, which is used to describe the incident angle θ of the light i,k And the influence of medium refractive index n1 and n2 on reflection intensity; d i,krepresents the distance from light source i to region k; P k represents the number of reflection paths in area k; p represents the sequence number of the reflection path; L i,k represents the initial path length from light source i to region k; L p,k It represents the length of path p after multiple reflections in area k; γ represents the path attenuation exponent.

[0066] In the formula, the incident light intensity represents the initial light intensity of the light source when illuminating the area, and the absorption coefficient describes the degree of absorption of light by the area material. The reflection coefficient is the ability of each area to reflect light according to the material characteristics. For example, different materials such as walls and floors reflect and absorb light differently, so R i,k With A k The value of directly affects the light intensity received by each area. The introduction of the Fresnel reflection coefficient makes the effect of the incident angle of light on the reflectivity more accurate, especially when the light enters the building material from the air or reflects from the glass. The reflected light intensity is closely related to the angle and the refractive index of the material. In addition, the factor of distance attenuation is incorporated into the formula by considering the natural attenuation of light intensity with the distance of the light source; this process conforms to the law of physics that light intensity decreases with the inverse square of the distance. The calculation of path length and the number of reflection paths takes into account the contribution of light to the final light intensity after multiple reflections in the area. The cumulative effect of these reflection paths is adjusted by the path attenuation exponent, reflecting that the energy of the light gradually decreases with the increase in the number of reflections. Ultimately, the light intensity distribution obtained by this calculation process can accurately describe the actual lighting conditions of each area under the influence of reflection and absorption, providing accurate input data for subsequent lighting optimization.

[0067] Step S340: Perform error analysis processing according to the light intensity distribution and the light demand data, and quantify the error value of each area to obtain initial light intensity distribution data by comparing the light demand of each area with the actual intensity.

[0068] This comparison process is completed through an error calculation method, in which the lighting demand of each area represents the lighting intensity required for passengers and functional needs under ideal conditions, while the actual intensity comes from the calculation results of light propagation, reflection, absorption and other processes. Through this error analysis, it is possible to determine whether the current lamp arrangement and light propagation can meet the design requirements, especially to accurately quantify the areas with uneven, overly bright or insufficient lighting. The calculation of the error value is not only a simple difference comparison, but also uses more complex error models such as absolute error, relative error or root mean square error. This can more clearly describe the degree to which the lighting distribution deviates from the expected. For example, some areas may have actual lighting intensity exceeding the requirements due to excessively high reflectance or too close to the light source, while other areas may have insufficient lighting due to absorption characteristics or occlusion. These error values ​​provide a clear direction for the subsequent optimization of the lighting system, enabling the system to make adjustments to the layout, brightness or lighting strategy of the lamps in the next step. The final initial light intensity distribution data not only describes the actual effect of the existing lighting scheme, but also provides a basis for optimization and adjustment.

[0069] Further, step S400 includes step S410, step S420, step S430 and step S440.

[0070] Step S410: performing light source contribution decomposition processing according to the initial light intensity distribution data, determining the independent contribution value of each lamp to the light in each area by decomposing the light intensity in each area, and obtaining the light contribution value of each light source in each area;

[0071] Specifically, modern subway stations usually have complex geometric structures, such as multi-level platforms, staggered passages, and artistic decorative elements. This design increases the complexity of the light propagation path, causing the light of the lamps to produce multiple reflections, absorption and refraction phenomena in different areas, thus affecting the actual distribution of light. In addition, different types of lamps are usually used in stations, such as downlights, linear lamps, LED light strips, chandeliers, etc. The beam angles, light diffusion and power characteristics of these lamps are different, which have a direct impact on the light source contribution decomposition. First of all, for most modern subway stations, areas such as platforms, waiting halls and passages often use linear lamps or LED light strips for uniform basic lighting. These lamps are mostly installed on the ceiling or side walls, and the light provides illumination for a large area through direct illumination and side reflection. At this time, the light source contribution decomposition needs to pay attention not only to the direct illumination area of ​​the lamp, but also to consider the multiple reflections caused by high reflectivity materials (such as metal or polished stone) on the walls, ceilings and even the ground. By decomposing the lighting contribution of each lamp in the complex environment of the station and calculating the effective light intensity and reflectivity of each lamp on the platform or waiting area, we can better understand the multipath propagation of light, especially the attenuation and scattering behavior of light when it is blocked by obstacles or passes through structures. Secondly, decorative lighting in the station, such as chandeliers, artistic lamps, etc., are usually used at the entrance or atrium of the station to highlight regional characteristics and enhance the sense of spatial hierarchy. Unlike functional lighting, the beam angle of these lamps is more concentrated, and the light may be used more to emphasize certain architectural elements or artistic decorations. When decomposing the light source contribution, it is necessary to consider the local lighting effect of these lamps on the surrounding environment. Since these lamps are mostly point light sources, their lighting contribution is significantly attenuated in areas farther away, but may cause local overbrightness or uneven lighting at close range. By extracting the lighting contribution of each decorative lamp independently, we can clearly understand its actual lighting effect on a specific area and how to cooperate with other functional lamps to avoid excessive overlap or interference of lighting. In addition, the optical properties of different lamps also affect the decomposition process of their lighting contribution. For example, LED lamps have high luminous efficiency and strong light diffusion, but in some station structures, obstacles such as columns and arched ceilings may cause light reflection or obstruction. Therefore, the decomposition analysis must not only consider the initial power and beam angle of the lamp, but also combine the building structure and material properties of the lamp location to calculate the multiple reflections and absorption of light, so as to accurately quantify the contribution of each lamp to the area. This precise light source contribution decomposition based on the existing station structure and lamp type not only improves the intelligent control capability of the lighting system, but also optimizes energy efficiency and ensures a reasonable lighting layout in different functional areas.

[0072] Step S420: According to the illumination contribution value and the corresponding light propagation path, a light interaction model is applied to process the light superposition effect of each light source. Based on the principle of linear superposition of light intensity, the illumination overlap of multiple light sources at the same position and the multiple superposition effects of light entering the same area after reflection are simultaneously analyzed to obtain light superposition effect data.

[0073] It is understandable that the light interaction model adopts the principle of linear superposition of light intensity, which is based on the superposition property of light, that is, multiple light rays at the same spatial position can be added to form a composite light intensity. In this process, the system will calculate the light overlap when different light sources are superimposed, taking into account the light intensity of each light source in the area and their relative positions. This is very important to ensure the coordination between lamps and avoid uneven brightness distribution caused by light overlap. For example, in an intersection area of ​​a subway station, there may be multiple lamps intensively illuminated. Through this model, it is possible to identify which light sources have the most significant overlap in this area, and the impact of this overlap on the final light intensity. In addition, the model also analyzes the multiple superposition effects of light entering the same area after reflection. By comprehensively considering the attenuation caused by these reflections and the transmission path of light, the system can accurately calculate the actual contribution of each light ray to the light intensity in the area. In public places such as subway stations, multiple reflections of light may not only lead to an increase in brightness, but also cause interference with light, especially in areas with narrow or complex spaces. Therefore, by applying the light interaction model to analyze the light superposition effect of light sources, the impact of each light source and its interaction can be accurately captured in a complex environment.

[0074] Step S430: weighting the light superposition effect data based on a preset weighted light superposition model, adjusting the weighting coefficient of the light in space based on the contribution value of the light source in each area, the length of the light propagation path, and the attenuation of the light on the surface of the material, and calculating the light intensity weighted value of each light source in the area;

[0075] It should be noted that the propagation path length and attenuation of light are important factors in determining the intensity of light. The reflection and absorption ability of light on the surface of different materials will significantly affect its propagation effect in space. Therefore, it is necessary to evaluate the attenuation of light based on the reflectivity and absorptivity of light on the surface of the material. That is, the reflection coefficient and absorption coefficient of the material will be used as weight factors to adjust the effective light intensity of light when it passes through these surfaces. Secondly, the contribution value of the light source will be combined with the actual length of the light propagation path to form a comprehensive weighting coefficient. This means that light sources that are closer and have higher contribution values ​​will be given greater weights, while those that are farther away or have smaller contributions will be subject to corresponding attenuation treatment. Through this weighted treatment, the model can effectively integrate the light intensity information from different light sources to obtain a more accurate light intensity weighted value. This can not only reflect the impact of a single light source, but also reveal the comprehensive effect of the interaction of multiple light sources in a complex space.

[0076] Step S440: According to the light intensity weighted value, a light intensity superposition algorithm is applied in combination with the spatial position relationship of each light source and the distribution of lamps, and the light contribution of each light source to the area is weighted summed using a linear superposition method, and the occlusion and light attenuation between the light sources are analyzed to obtain a light intensity superposition matrix.

[0077] Specifically, the linear superposition method adds the weighted values ​​of the light intensity of each light source to generate a preliminary light intensity superposition matrix. When performing weighted summation, it is necessary to take into account the distance, direction and relative position of each light source to other light sources in the area, so as to ensure that the calculated light intensity can truly reflect the mutual influence of each light source. Secondly, in terms of occlusion and light attenuation analysis between light sources, it is necessary to apply a light attenuation model to quantitatively evaluate the obstacles and unfavorable conditions encountered during the propagation of light from the light source. For example, if a light source cannot directly illuminate a certain area due to the obstruction of surrounding building structures, the light contribution of the light source will need to be appropriately weakened. In addition, for objects that light may encounter during propagation, such as walls, columns or other decorative elements, the model will calculate their reflection and absorption characteristics, thereby further adjusting the light contribution value of each light source to the area. By comprehensively considering these factors, it can be ensured that the generated light intensity superposition matrix is ​​more accurate and reasonable.

[0078] Further, step S500 includes step S510, step S520, step S530 and step S540.

[0079] Step S510: simulating the optical reflection characteristics of different areas inside the station according to the optical characteristic data, determining the key angles that affect the passenger's field of vision by calculating the intersection points of the reflected light and the passenger's field of vision, and obtaining the field of vision angle distribution data;

[0080] It is understandable that in different areas of the station, optical properties such as reflectivity, refractive index, and absorptivity will be different, so for each area, a corresponding optical model is needed to calculate the behavior of reflected light. For example, the characteristics of light when reflected from a smooth glass surface are completely different from those when reflected from a rough concrete wall. By analyzing these reflection characteristics in detail, the system can predict the propagation path of light in space and then identify those key angles that may affect the passenger's field of view. Secondly, the process of determining the key angle involves calculating the intersection between the reflected light and the passenger's line of sight. Specifically, the path of the light is generally traced and the points where it intersects with the passenger's line of sight are identified. These intersections indicate the possibility of the reflected light entering the passenger's field of view. Through these calculations, the line of sight angle distribution data obtained not only helps to evaluate the impact of the light source on passengers, but also provides an important basis for optimizing lighting design.

[0081] Step S520: performing sight tracking processing according to the sight angle distribution data and the passenger flow data, and obtaining sight trajectory data by simulating the movement path of the passenger's sight at different time periods and the interaction relationship with the reflection area;

[0082] Step S530: Performing evaluation processing according to the sight trajectory data, through multiple reflection path calculations and light intensity attenuation, quantitatively evaluating the interference intensity of the reflection area on the passenger's sight line to obtain light interference intensity data;

[0083] First, the sight angle distribution data provides information about how light reflects in the station and where it intersects with the passenger's sight. This data can help understand the visual experience that passengers may experience in different locations and under different lighting conditions. At the same time, the passenger flow data reflects the number and flow direction of passengers in the station during a specific time period. Combining these two data sets can more comprehensively simulate the passenger's sight movement trajectory. In the specific sight tracking process, a dynamic model is used to predict the movement behavior of passengers during a specific time period. For example, during peak hours, the flow of passengers may be faster and more concentrated, so the sight tracking model will take into account the passenger's sight shift and focusing habits when moving quickly. During off-peak hours, passengers may stay longer in certain areas, resulting in a smoother and more diverse sight movement trajectory. This consideration of the time dimension makes sight tracking more accurate and can truly reflect the behavior of passengers. By simulating the movement path of the passenger's sight and its interaction with the reflection area, the system can obtain sight trajectory data. These data reveal how the passenger's sight is affected by light reflection, lamp position and light intensity during different time periods.

[0084] Step S540: quantitatively adjust the brightness of the lamps in the reflection area according to the light interference intensity data to obtain light adjustment data.

[0085] Specifically, a quantitative adjustment model is first constructed, which takes into account not only the optical properties of the lamps, but also the visual comfort of the passengers, and calculates the target brightness to which the lamps in each reflective area should be adjusted. At the same time, a detailed brightness adjustment strategy is formulated, including the amplitude, speed and time of the adjustment, to ensure that the adjustment process not only meets the comfort requirements of the passengers, but also does not cause discomfort due to too fast brightness changes. Before implementing the adjustment, a simulation verification is carried out through a ray tracing algorithm to ensure the effectiveness of the adjustment strategy. Finally, specific adjustment instructions are generated, which will guide each lamp to make actual brightness adjustments. This approach not only enhances the lighting experience of passengers, but also improves energy efficiency and demonstrates the potential of lighting systems in intelligently responding to different lighting conditions and passenger needs.

[0086] Further, step S600 includes step S610, step S620, step S630 and step S640.

[0087] Step S610, calculating the light propagation path according to the illumination adjustment data, calculating the specific path of the light propagating from the source point to other areas by path tracing, and obtaining the light conduction path between the functional areas;

[0088] It should be noted that this step first integrates the lighting adjustment data and establishes a light propagation model that takes into account the three-dimensional structure of the station and the optical properties of the materials. Then, the ray tracing algorithm is used to simulate the propagation path of each light ray, including the reflection, refraction and attenuation of the light inside the station. During the simulation process, the interaction between light and the station structure is considered, such as the reflection and refraction of light on walls, floors, ceilings and other obstacles. By recording and analyzing these paths, hot spots and shadow areas of light propagation can be identified, and a detailed light conduction path map can be generated.

[0089] Step S620, performing light interference analysis processing according to the light transmission path, analyzing the superimposed interference effect of the light entering other areas with the existing light sources in the area based on the actual transmission path of the light, and obtaining light mutual interference data between functional areas;

[0090] It can be understood that this step uses the light transmission path data to simulate the superposition effect of light and calculates the light interference intensity, thereby identifying the areas with the most serious interference and visually displaying these areas through the generated interference heat map.

[0091] Further, step S620 includes steps S621 to S624.

[0092] Step S621: According to the light transmission path data, after path integration processing, detailed description data of the light propagation inside the station is obtained;

[0093] Specifically, the collected light path data is first cleaned and formatted to ensure data consistency and accuracy. Then the starting point, propagation path, and end point of each light are mapped to the 3D model of the subway station to achieve accurate spatial correspondence. Finally, the light path data from different sources are fused to construct a comprehensive light propagation network, providing detailed basic information for subsequent analysis.

[0094] Step S622: construct a light interference analysis model based on the detailed description of the light propagation inside the station, and obtain interference characteristic data of superposition and reflection interference between different light sources through light source interaction analysis and processing;

[0095] It should be noted that this step is based on the principles of physical optics to build a mathematical model that can simulate the behavior of light, including direct radiation, reflection, and refraction. Then the characteristics of all lamps in the station are parameterized, including brightness, color temperature, beam angle, etc., so that they can be calculated in the model. Finally, the interference factors are analyzed in combination with the mutual influence between different light sources, such as the over-brightness area caused by the superposition of light and the glare problem caused by reflection.

[0096] Step S623: According to the light interference analysis model, use ray tracing technology to simulate the light superposition effect processing to obtain simulation result data of light propagation and interaction between functional areas;

[0097] Step S624: According to the simulation result data of the simulated light superposition effect, the interference intensity data of each light path after superposition with the existing light sources in the area is obtained by performing light interference intensity calculation processing;

[0098] Specifically, we first define clear quantitative indicators to measure the intensity of light interference, such as brightness level, glare index, etc. Then we use statistical methods to analyze the simulation results to identify the main sources of light interference and the affected areas. Furthermore, we verify the accuracy of the simulation results through field measurements or comparison with historical data, and adjust the model parameters and re-simulate if necessary.

[0099] Step S630, performing finite element analysis processing according to the light mutual interference data, by discretizing the light conduction path in each area with finite elements, gradually optimizing the propagation and attenuation of light across regions, and obtaining optimized data of the light conduction path between regions;

[0100] Specifically, the continuous light conduction path is first discretized and decomposed into a finite number of elements, each of which represents the propagation characteristics of light in a specific area. By gradually optimizing the light propagation and attenuation of these elements, the uniformity and suitability of light distribution across regions can be achieved. In this process, parameters such as light intensity and propagation direction are continuously adjusted to ensure the optimal lighting effect.

[0101] Step S640: Perform regional optical interaction analysis processing based on the light conduction path optimization data, analyze the propagation, reflection and transmission behaviors of light between different functional areas, and quantify the light interaction intensity between different areas to generate a regional optical interaction matrix.

[0102] It can be understood that this step first analyzes the propagation, reflection and transmission behaviors of light between different functional areas of the station, which are affected by factors such as regional geometry, material properties and light wavelength. Next, these light interactions are quantified and the results are integrated into a matrix, where each element represents the intensity of light interaction between two areas.

[0103] Further, step S700 includes step S710, step S720, step S730 and step S740.

[0104] Step S710, performing light interference weight calculation processing according to the regional optical interaction matrix, by introducing a multi-dimensional weight matrix, combining the geometric characteristics of each region, and dynamically calculating the interference weight coefficient to obtain the light interference weight coefficient of each region;

[0105] It should be noted that the multidimensional weight matrix comprehensively considers factors such as light intensity, direction, and area, and combines the geometric characteristics of each area, such as shape, size, and relative position. Through dynamic calculation methods, the interference weight coefficient can be updated in real time to adapt to changes in light conditions inside the station.

[0106] Step S720: According to the light interference weight coefficient and in combination with the real-time feedback data from the sensor, after the light demand adjustment process, the lighting demand of each area is optimized by using the adaptive control model, and the real-time light demand of each area is obtained by dynamically adapting to the changes in ambient light and the fluctuations in crowd density;

[0107] It can be understood that the light interference weight coefficient is first applied to ensure the suitability and uniformity of lighting in each area. Then the ambient light data and crowd density data fed back by the sensor in real time are used to provide accurate input for the adaptive control model. The adaptive control model can dynamically adjust the lighting output according to the real-time data and weight coefficient to meet the actual needs of each area.

[0108] Step S730: Perform brightness optimization processing according to real-time lighting requirements to obtain brightness adjustment parameters of lamps in each area;

[0109] Specifically, this step first analyzes the real-time lighting requirements, taking into account ambient light, crowd density and other related factors. Then a brightness adjustment algorithm is designed, which can calculate the optimal brightness adjustment parameters based on these requirements. The brightness adjustment parameters are generally determined by mathematical optimization methods, while taking into account the physical characteristics and performance limitations of the lamps.

[0110] Step S740: adjusting parameters according to the brightness of the lamps and optimizing the lighting timing to obtain a real-time control strategy for each area.

[0111] Specifically, the influence of time variables, such as different times of the day and different days of the week, is taken into account when optimizing the timing to achieve more refined lighting control. In addition, coordination between different areas is considered to ensure the integrity and consistency of the lighting system. Finally, a real-time adjustment mechanism is implemented to dynamically adjust the lighting timing according to the timing optimization results, and the adjustment effect is monitored through a feedback system. Ensure that the lighting system can provide the best lighting effect according to actual needs while achieving efficient use of energy.

[0112] Embodiment 2:

[0113] This embodiment provides a subway station zone lighting control system, the system comprising:

[0114] The acquisition module is used to obtain the three-dimensional architectural model data of the subway station, the optical characteristics data of various building materials in the station, the original lighting layout plan, the functional area division data and the passenger flow data at different time periods;

[0115] The analysis module is used to perform demand analysis and processing based on the three-dimensional building model data, functional area division data and passenger flow data, and calculate the lighting demand data of each area;

[0116] The simulation module uses the Monte Carlo ray tracing algorithm to simulate the original lighting arrangement plan, 3D building model data and lighting demand data, and obtains the initial light intensity distribution data by simulating the light propagation, reflection and absorption of the lamps;

[0117] A processing module processes the initial light intensity distribution data based on a preset weighted light superposition model, and calculates the superposition effect of multiple light sources in the same area to obtain a light intensity superposition matrix;

[0118] An adjustment module, for simulating the sight angles of passengers at different time periods and different positions to obtain sight tracking data according to the optical characteristic data and the passenger flow data, and quantitatively adjusting the brightness of the lamps in the reflection area based on the sight tracking data to obtain the illumination adjustment data of the reflection area;

[0119] A construction module is used to perform finite element analysis based on the illumination adjustment data, illumination intensity superposition matrix and illumination demand data, evaluate the mutual interference and conduction paths of light between the functional areas of the station and obtain the regional optical interaction matrix;

[0120] The control module is used to dynamically optimize the brightness and lighting timing of lamps in each area according to the regional optical interaction matrix and the real-time feedback data from the sensor to obtain a real-time control strategy.

[0121] In a specific embodiment disclosed in the present invention, the analysis module includes:

[0122] The first partitioning unit is used to obtain three-dimensional spatial partitioning data of each functional area through processing by a spatial partitioning algorithm according to the three-dimensional building model data and the functional area partitioning data;

[0123] The first simulation unit is used to perform passenger behavior modeling processing according to the three-dimensional space division data and the passenger flow data, using a passenger behavior modeling algorithm based on fluid dynamics, treating passengers as fluid particles, and simulating the flow and distribution of passengers in different time periods in combination with regional functional characteristics, to obtain passenger density distribution data for each functional area;

[0124] The first calculation unit is used to calculate the optimal illumination intensity of different functional areas based on the passenger density distribution data and the regional functional attributes in the functional area division data after being processed by the illumination demand model, and based on the human eye's perception of brightness under different illumination conditions, to obtain the target illumination intensity of each functional area;

[0125] The second calculation unit is used to perform illumination uniformity calculation processing according to the target illumination intensity to obtain illumination requirement data of each area.

[0126] In a specific embodiment disclosed in the present invention, the simulation module includes:

[0127] The first processing unit is used to determine the position, direction, type, power and beam angle of each lamp in the three-dimensional space through light source positioning processing according to the original lamp arrangement plan and the three-dimensional building model data, and obtain the light distribution parameters of the lamp;

[0128] The second simulation unit is used to simulate the propagation, reflection and refraction of the light of each lamp in space and the multiple reflection and attenuation behavior of the light when encountering curved and broken line building structures according to the illumination distribution parameters and three-dimensional space division data, and obtain the light propagation path of each area;

[0129] The third calculation unit is used to calculate the dynamic reflection of light under the geometric structure using the illumination reflection model and the Fresnel reflection model according to the light propagation path and the optical characteristic data, after the illumination reflection and absorption processing, to obtain the illumination intensity distribution of each area;

[0130] The first analysis unit is used to perform error analysis processing according to the light intensity distribution and the light demand data, and quantify the error value of each area to obtain initial light intensity distribution data by comparing the light demand of each area with the actual intensity.

[0131] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0132] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for controlling lighting in a subway station, characterized in that: include: Obtain the 3D architectural model data of the subway station, the optical properties data of various building materials in the station, the original lighting layout plan, the functional area division data and the passenger flow data at different time periods; Perform demand analysis and processing according to the three-dimensional building model data, the functional area division data and the passenger flow data, and calculate and obtain the lighting demand data of each area; Using a Monte Carlo ray tracing algorithm to simulate the original lamp arrangement plan, the three-dimensional building model data and the lighting demand data, and obtaining initial lighting intensity distribution data by simulating the light propagation, reflection and absorption of the lamps; The initial light intensity distribution data is processed based on a preset weighted light superposition model, and the superposition effect of multiple light sources in the same area is calculated to obtain a light intensity superposition matrix; According to the optical characteristic data and the passenger flow data, the sight angles of passengers at different time periods and different positions are simulated to obtain sight tracking data, and the brightness of lamps in the reflection area is quantitatively adjusted based on the sight tracking data to obtain the illumination adjustment data of the reflection area; Perform finite element analysis based on the illumination adjustment data, the illumination intensity superposition matrix and the illumination demand data to evaluate the mutual interference and conduction paths of light between the functional areas of the station to obtain a regional optical interaction matrix; According to the regional optical interaction matrix, the brightness and lighting timing of the lamps in each area are dynamically optimized in combination with the real-time feedback data from the sensor to obtain a real-time control strategy.

2. The subway station zone lighting control method according to claim 1 is characterized in that , performing demand analysis and processing according to the three-dimensional building model data, the functional area division data and the passenger flow data, and calculating the lighting demand data of each area, including: According to the three-dimensional building model data and the functional area division data, the three-dimensional space division data of each functional area is obtained by processing with a space partitioning algorithm; Passenger behavior modeling is performed according to the three-dimensional space division data and the passenger flow data, and a passenger behavior modeling algorithm based on fluid dynamics is used to treat passengers as fluid particles, and the flow and distribution of passengers in different time periods are simulated in combination with regional functional characteristics to obtain passenger density distribution data for each functional area; According to the passenger density distribution data and the regional functional attributes in the functional area division data, after being processed by a lighting demand model, the optimal lighting intensity of different functional areas is calculated based on the human eye's perception of brightness under different lighting conditions, and the target lighting intensity of each functional area is obtained; The illumination uniformity is calculated and processed according to the target illumination intensity to obtain illumination requirement data of each area.

3. The subway station zone lighting control method according to claim 1 is characterized in that ,Use Monte Carlo ray tracing algorithm to simulate the original lamp arrangement plan, the three-dimensional building model data and the lighting demand data, and obtain initial light intensity distribution data by simulating the light propagation, reflection and absorption of the lamps, including: According to the original lamp arrangement plan and the three-dimensional building model data, the position, direction, type, power and beam angle of each lamp in the three-dimensional space are determined through light source positioning processing to obtain the illumination distribution parameters of the lamp; According to the illumination distribution parameters and the three-dimensional space division data, a Monte Carlo ray tracing algorithm is applied to simulate the propagation, reflection and refraction of the light of each lamp in the space, as well as the multiple reflections and attenuation behaviors of the light when encountering curved and broken line architectural structures, to obtain the light propagation path of each area; According to the light propagation path and the optical characteristic data, after light reflection and absorption processing, the dynamic reflection of light under the geometric structure is calculated using the light reflection model and the Fresnel reflection model to obtain the light intensity distribution of each area; Error analysis is performed based on the light intensity distribution and the light demand data, and initial light intensity distribution data is obtained by quantifying the error value of each area by comparing the light demand of each area with the actual intensity.

4. The subway station zone lighting control method according to claim 1 is characterized in that , based on the preset weighted light superposition model, the initial light intensity distribution data is processed, and the superposition effect of multiple light sources in the same area is calculated to obtain a light intensity superposition matrix, including: Performing light source contribution decomposition processing according to the initial light intensity distribution data, determining the independent contribution value of each lamp to the light in each area by decomposing the light intensity in each area, and obtaining the light contribution value of each light source in each area; According to the illumination contribution value and the corresponding light propagation path, a light interaction model is applied to process the light superposition effect of each light source. Based on the principle of linear superposition of light intensity, the illumination overlap of multiple light sources at the same position and the multiple superposition effects of light entering the same area after reflection are simultaneously analyzed to obtain light superposition effect data; The light superposition effect data is weighted based on a preset weighted light superposition model, and the weighting coefficient of the light in space is adjusted based on the contribution value of the light source in each area, the length of the light propagation path, and the attenuation of the light on the surface of the material, so as to calculate the light intensity weighted value of each light source in the area; According to the light intensity weighted value, the light intensity superposition algorithm is applied in combination with the spatial position relationship of each light source and the distribution of lamps, and the light contribution of each light source to the area is weighted summed using a linear superposition method, and the occlusion and light attenuation between the light sources are analyzed to obtain the light intensity superposition matrix.

5. The subway station zone lighting control method according to claim 1 is characterized in that ,According to the optical characteristic data and the passenger flow data, the sight angles of passengers at different time periods and different positions are simulated to obtain sight tracking data, and the brightness of the lamps in the reflection area is quantitatively adjusted based on the sight tracking data to obtain the illumination adjustment data of the reflection area, including: The optical reflection characteristics of different areas inside the station are simulated according to the optical characteristic data, and the key angles affecting the passenger's field of vision are determined by calculating the intersection points of the reflected light and the passenger's field of vision, thereby obtaining the field of vision angle distribution data; Performing sight tracking processing according to the sight angle distribution data and the passenger flow data, and obtaining sight trajectory data by simulating the movement path of the passenger's sight at different time periods and the interaction relationship with the reflection area; Performing evaluation processing according to the sight trajectory data, quantitatively evaluating the interference intensity of the reflection area on the passenger's sight line through multiple reflection path calculations and light intensity attenuation to obtain light interference intensity data; The brightness of the lamps in the reflection area is quantitatively adjusted according to the light interference intensity data to obtain light adjustment data.

6. The subway station zone lighting control method according to claim 1 is characterized in that , according to the illumination adjustment data, the illumination intensity superposition matrix and the illumination demand data, finite element analysis is performed to evaluate the mutual interference and conduction paths of light between the functional areas of the station to obtain the regional optical interaction matrix, including: Calculate the light propagation path according to the illumination adjustment data, calculate the specific path of the light propagating from the source point to other areas through path tracing, and obtain the light conduction path between the functional areas; Performing light interference analysis and processing according to the light transmission path, analyzing the superimposed interference effect of the light entering other areas with the existing light sources in the area based on the actual transmission path of the light, and obtaining light mutual interference data between functional areas; Performing finite element analysis processing on the light mutual interference data, discretizing the light conduction path in each area by finite element, gradually optimizing the propagation and attenuation of light across the areas, and obtaining optimized data on the light conduction path between areas; A regional optical interaction analysis process is performed based on the light transmission path optimization data, and a regional optical interaction matrix is ​​generated by analyzing the propagation, reflection and transmission behaviors of light between different functional areas and quantifying the light interaction intensity between different areas.

7. The subway station zone lighting control method according to claim 1 is characterized in that ,According to the regional optical interaction matrix, combined with the real-time feedback data from the sensor, the brightness and lighting timing of the lamps in each area are dynamically optimized to obtain a real-time control strategy, including: The light interference weight calculation process is performed according to the regional optical interaction matrix, and the interference weight coefficient is dynamically calculated by introducing a multi-dimensional weight matrix and combining the geometric characteristics of each region to obtain the light interference weight coefficient of each region; According to the light interference weight coefficient, combined with the real-time feedback data from the sensor, after the light demand adjustment processing, the lighting demand of each area is optimized by using the adaptive control model, and the real-time light demand of each area is obtained by dynamically adapting to the changes in ambient light and the fluctuations in crowd density; Perform brightness optimization processing according to the real-time lighting requirements to obtain the brightness adjustment parameters of the lamps in each area; According to the lamp brightness adjustment parameters, after lighting timing optimization processing, a real-time control strategy for each area is obtained.

8. A subway station zone lighting control system, characterized in that: include: The acquisition module is used to obtain the three-dimensional architectural model data of the subway station, the optical characteristics data of various building materials in the station, the original lighting layout plan, the functional area division data and the passenger flow data at different time periods; An analysis module, used to perform demand analysis and processing according to the three-dimensional building model data, the functional area division data and the passenger flow data, and calculate the lighting demand data of each area; A simulation module, which uses a Monte Carlo ray tracing algorithm to simulate the original lamp arrangement scheme, the three-dimensional building model data and the lighting demand data, and obtains initial light intensity distribution data by simulating the light propagation, reflection and absorption of the lamps; A processing module processes the initial light intensity distribution data based on a preset weighted light superposition model, and calculates the superposition effect of multiple light sources in the same area to obtain a light intensity superposition matrix; an adjustment module, for simulating the sight angles of passengers at different time periods and different positions to obtain sight tracking data according to the optical characteristic data and the passenger flow data, and quantitatively adjusting the brightness of lamps in the reflection area based on the sight tracking data to obtain illumination adjustment data of the reflection area; A construction module is used to perform finite element analysis processing according to the illumination adjustment data, the illumination intensity superposition matrix and the illumination demand data, and evaluate the mutual interference and conduction paths of light between the functional areas of the station to obtain a regional optical interaction matrix; The control module is used to dynamically optimize the brightness and lighting timing of lamps in each area according to the regional optical interaction matrix and in combination with real-time feedback data from sensors to obtain a real-time control strategy.

9. The subway station zone lighting control system according to claim 8 is characterized in that , the analysis module includes: A first partitioning unit is used to obtain three-dimensional spatial partitioning data of each functional area through processing by a spatial partitioning algorithm according to the three-dimensional building model data and the functional area partitioning data; A first simulation unit is used to perform passenger behavior modeling processing according to the three-dimensional space division data and the passenger flow data, using a passenger behavior modeling algorithm based on fluid dynamics, treating passengers as fluid particles, and simulating the flow and distribution of passengers in different time periods in combination with regional functional characteristics, so as to obtain passenger density distribution data of each functional area; A first calculation unit is used to calculate the optimal illumination intensity of different functional areas based on the passenger density distribution data and the regional functional attributes in the functional area division data after processing by the illumination demand model, and based on the human eye's perception of brightness under different illumination conditions, to obtain the target illumination intensity of each functional area; The second calculation unit is used to perform illumination uniformity calculation processing according to the target illumination intensity to obtain illumination requirement data of each area.

10. The subway station zone lighting control system according to claim 8, characterized in that , the simulation module includes: A first processing unit is used to determine the position, direction, type, power and beam angle of each lamp in the three-dimensional space through light source positioning processing according to the original lamp arrangement plan and the three-dimensional building model data, and obtain the lighting distribution parameters of the lamp; A second simulation unit is used to apply a Monte Carlo ray tracing algorithm to simulate the propagation, reflection and refraction of light from each lamp in space and the multiple reflections and attenuation behaviors of light when encountering curved and broken line building structures according to the illumination distribution parameters and the three-dimensional space division data, so as to obtain the light propagation path of each area; A third calculation unit is used to calculate the dynamic reflection of the light under the geometric structure using a light reflection model and a Fresnel reflection model according to the light propagation path and the optical characteristic data, after light reflection and absorption processing, to obtain the light intensity distribution of each area; The first analysis unit is used to perform error analysis processing according to the light intensity distribution and the light demand data, and quantify the error value of each area to obtain initial light intensity distribution data by comparing the light demand of each area with the actual intensity.

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