A metro station partitioned lighting control method and system

Abstract

The application provides a subway station partition lighting control method and system, and relates to the technical field of subway lighting, and comprises the following steps: obtaining illumination demand data according to three-dimensional building model data, functional area division data and passenger flow data; obtaining initial illumination intensity distribution data by simulating original lamp arrangement scheme, three-dimensional building model data and the illumination demand data; obtaining illumination intensity superposition matrix by processing the initial illumination intensity distribution data; adjusting the illumination adjustment data of the reflection area according to the optical characteristic data and the passenger flow data; obtaining the area optical interaction matrix by processing the illumination adjustment data, the illumination intensity superposition matrix and the illumination demand data; and obtaining the real-time control strategy according to the area optical interaction matrix. The application solves the problems of uneven light, local over-brightness or over-darkness caused by complex building structures by dynamically analyzing the illumination demand and the actual illumination distribution of each area.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of subway lighting, in particular to a subway station partition lighting control method and system. BACKGROUND

[0002] Currently, with the rapid development of subway systems, the lighting design of subway stations not only needs to meet the basic functional requirements, but also is increasingly influenced by architectural design styles. Modern subway stations often incorporate complex curves, broken lines and other unique architectural elements in their interior design, combined with glass, metal, wall and other decorative materials of different materials, which provide a unique stylized appearance for the station. However, this complex architectural structure brings new challenges to the lighting system. The existing lighting system usually relies on fixed lamp arrangement and simple lighting control method, which cannot adapt to the propagation, reflection and absorption characteristics of light in these complex geometrical structures. In addition, the lighting needs of different functional areas (such as waiting area, corridor, entrance and exit, etc.) are significantly different, and the existing technology often lacks a processing mechanism for light interference between areas, resulting in uneven lighting effect or local over-brightness or over-darkness. The existing technology mainly tries to solve these problems by increasing the number of lamps or adjusting the angle of the lamps, but this method not only makes it difficult to achieve fine control, but also may increase energy consumption, and cannot effectively solve the lighting problems brought 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 partition lighting control method and system to meet the lighting needs under the stylized design of subway stations. SUMMARY

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

[0005] On the one hand, the present application provides a subway station partition lighting control method, comprising:

[0006] obtaining three-dimensional architectural model data of a subway station, optical property data of various building materials in the station, an original lamp arrangement scheme, functional area division data and passenger flow data at different time periods;

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

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

[0009] process the initial lighting intensity distribution data based on a preset weighted light superposition model, calculate the superposition effect of multiple light sources in the same region to obtain a lighting intensity superposition matrix;

[0010] simulate the line-of-sight angle of passengers at different times and in different positions according to the optical property data and the passenger flow data to obtain line-of-sight tracking data, and quantitatively adjust the brightness of the lamps in the reflection region based on the line-of-sight tracking data to obtain lighting adjustment data for the reflection region;

[0011] perform finite element analysis processing according to the lighting adjustment data, the lighting intensity superposition matrix and the lighting demand data, evaluate the mutual interference and conduction path of light between functional regions of the station to obtain a regional optical mutual influence matrix;

[0012] dynamically optimize the brightness and lighting timing of the lamps in each region according to the regional optical mutual influence matrix and in combination with real-time feedback data of sensors to obtain a real-time control strategy.

[0013] In another aspect, the application also provides a subway station partition lighting control system, comprising:

[0014] An acquisition module is configured to acquire three-dimensional building model data of a subway station, optical property data of various building materials in the station, an original lamp arrangement scheme, functional region division data and passenger flow data at different times;

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

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

[0017] A processing module is configured to process the initial lighting intensity distribution data based on a preset weighted light superposition model, calculate the superposition effect of multiple light sources in the same region to obtain a lighting intensity superposition matrix;

[0018] An adjusting module is configured to simulate a line-of-sight angle of a passenger at different time periods and different positions to obtain line-of-sight tracking data according to the optical characteristic data and the passenger flow data, and quantitatively adjust a brightness of a lamp of a reflection area to obtain illumination adjustment data of the reflection area based on the line-of-sight tracking data;

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

[0020] A control module is configured to dynamically optimize the brightness of the lamp and an illumination timing of each area to obtain a real-time control strategy according to the regional optical mutual influence matrix and sensor real-time feedback data.

[0021] The present application has the following beneficial effects:

[0022] The present application solves the problem of uneven light, local over-brightness or over-darkness caused by a complex building structure by dynamically analyzing the illumination demand and actual illumination distribution of each area; the present application accurately calculates and optimizes the light mutual interference and the conduction path between different functional areas by a weighted light superposition model and a finite element analysis method, avoids the superposition effect of the illumination influence between areas, and thus guarantees the independent illumination demand of each functional area.

[0023] Other features and advantages of the present application will be illustrated in the following description, and some will become apparent from the description, or will be understood through implementation of the embodiments of the present application. BRIEF DESCRIPTION OF DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments, and it should be understood that the following drawings only show some of the embodiments of the present application, and thus should not be regarded as a limitation to the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of the drawings.

[0025] Figure 1 A flow chart of a subway station partition illumination control method described in the embodiments of the present application;

[0026] Figure 2 A flow chart of a Monte Carlo ray tracing algorithm described in the embodiments of the present application. DETAILED DESCRIPTION

[0027] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some but not all of the embodiments of the present application. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of protection of the present application.

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

[0029] Embodiment 1

[0030] The embodiment provides a subway station partition lighting control method.

[0031] Referring to Figure 1 , the method includes steps S100, S200, S300, S400, S500, S600 and S700.

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

[0033] It can be understood that the three-dimensional architectural model data of the subway station can accurately express the complex geometry inside the station, including curve and broken line elements. The three-dimensional model provides a necessary spatial framework for the propagation path and reflection characteristics of light. The optical property data of various building materials in the station relates to the reflectivity, absorptivity and transmissivity of the materials, which can affect the interaction between light and materials. The acquisition of the original lamp arrangement scheme provides benchmark information for the current lighting system, including the type, position, power and lighting angle of the lamps and other parameters. The functional area division data provides the basis for the refinement of lighting control, which can formulate corresponding lighting requirements according to the use functions of different areas (such as waiting areas, passages and entrances, etc.). The passenger flow data in different time periods provides the basis for dynamic adjustment of the lighting system.

[0034] Step S200: Based on the 3D building model data, functional area division data and passenger flow data, perform demand analysis and processing to calculate the lighting demand data for each area;

[0035] It should be noted that this step enables precise calculation of the lighting requirements for each functional area of ​​the subway station, allowing the lighting system to flexibly adapt to different usage scenarios and pedestrian flow dynamics in practical applications. This refined demand analysis ensures the rationality and effectiveness of the lighting scheme, thereby optimizing the use of lighting resources, improving energy efficiency, and enhancing the passenger travel experience.

[0036] Step S300: Use the Monte Carlo ray tracing algorithm to simulate the original lighting fixture layout scheme, 3D building model data and lighting requirement data. By simulating the light propagation, reflection and absorption of the lighting fixtures, the initial light intensity distribution data is obtained.

[0037] It's important to note that Monte Carlo ray tracing is a technique based on random sampling, effectively handling complex lighting calculations. In this process, by modeling the light emission characteristics of luminaires, a large number of random light paths are generated. These rays propagate through a 3D building model and interact with different building material surfaces. Each ray undergoes reflection, refraction, and absorption during propagation; these complex physical processes are accurately incorporated into the algorithm. Secondly, by combining 3D building model data, the algorithm can consider the geometric complexity of the building's interior, such as the impact of curved surfaces and edges on light propagation. This modeling capability makes the lighting simulation more realistic, reflecting the true behavior of light in space. Furthermore, the optical properties of building materials, such as reflectivity and absorptivity, significantly affect the intensity and direction of light as it passes through. These properties are dynamically calculated in the Monte Carlo algorithm, ensuring the accuracy of the simulation results. Lighting demand data provides the target basis for calculating the light intensity distribution. By comparing with the demand data, insufficient or excessive lighting in various areas can be identified, laying the foundation for subsequent lighting adjustments and optimizations. The final initial illumination intensity distribution data characterizes the actual illumination intensity status of each area under the current lighting fixture arrangement scheme.

[0038] Step S400: Based on the preset weighted light superposition model, process the initial light intensity distribution data and calculate the superposition effect of multiple light sources in the same area to obtain the light intensity superposition matrix;

[0039] It can be understood 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 where multiple lamps illuminate the area at the same time, it is necessary to calculate the contribution of each lamp to determine the final illumination intensity. This method can effectively eliminate the non-uniformity of illumination and ensure that the illumination level of each area meets the design standards and usage requirements.

[0040] Step S500, according to the optical characteristic data and the passenger flow data, simulating the line of sight angle of the passenger in different time periods and different positions to obtain the line of sight tracking data, and quantitatively adjusting the brightness of the lamps in the reflection area based on the line of sight tracking data to obtain the illumination adjustment data of the reflection area;

[0041] It can be understood that the process of line of sight tracking uses passenger flow data to analyze the distribution and flow characteristics of passengers in different time periods. By modeling the dynamic behavior of passengers in the station, including the movement path and stay time of different passengers in a certain area. This process involves fluid dynamics or multi-agent system simulation, treating passengers as fluid particles to better reflect their behavior patterns and space occupation. Further, the brightness of the lamps in the reflection area is quantitatively adjusted to ensure that the lighting effect meets the actual visual needs of passengers. This process involves evaluating the intensity of light interference to ensure that the brightness settings of the lamps not only meet the overall lighting needs in the area, but also take into account the line of sight angle of passengers in a particular location to avoid glare or shadows. Through analysis and quantitative adjustment of line of sight tracking data, dynamic and personalized lighting control can be achieved to improve passenger comfort in the station.

[0042] Step S600, according to the illumination adjustment data, the superposition matrix of the illumination intensity and the illumination demand data, performing finite element analysis processing to evaluate the mutual interference and conduction path of light between different functional areas of the station to obtain a regional optical interaction matrix;

[0043] In this step, finite element analysis is used to simulate the propagation behavior and mutual interference effect 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 adjusting the lighting, the illumination adjustment data provides the brightness settings of the lamps in each area, while the superposition matrix of the illumination intensity contains the illumination intensity of each light source in a particular area and their interaction information. Combining these two with the illumination demand data, the lighting state of each functional area can be accurately evaluated, and potential light interference areas can be identified. When adjusting the lighting, the illumination adjustment data provides the brightness settings of the lamps in each area, while the superposition matrix of the illumination intensity contains the illumination intensity of each light source in a particular area and their interaction information.

[0044] Step S700, dynamically optimizing the luminance and lighting timing of each area according to the regional optical interaction matrix and combining the real-time feedback data of the sensor to obtain a real-time control strategy.

[0045] It can be understood that the regional optical interaction matrix records the illumination intensity, lamp layout and mutual interference effect of each area, and lays a data foundation for subsequent real-time adjustment. When the lighting conditions or passenger flow in the station change, the matrix is used to quickly identify the affected areas and make targeted adjustments. In combination with the real-time feedback data of the sensor, the lighting state and passenger activity of each area can be monitored in real time. For example, a light sensor can detect changes in lighting, and a motion sensor can identify the flow and stay area of passengers. These data provide an important basis for optimizing the luminance and lighting timing of the lamps, so that the lighting system can quickly respond to environmental changes and adjust the luminance of the corresponding area. For example, during the peak period when the passenger flow is large, the lighting intensity of the waiting area can be increased to provide better visual comfort and safety; and during the period when the passenger flow is small, 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 through the space partitioning algorithm processing;

[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, position and distribution of building materials of each area. These information provides an intuitive reference for space partitioning. The functional area division data defines the functional characteristics of different areas inside the station, such as the waiting area, ticketing area, corridor, etc. In combination with the two kinds of data, the space partitioning algorithm can identify and define the three-dimensional space boundary of each functional area. Preferably, in the present embodiment, voxel technology is used to discretize the complex three-dimensional space into a series of small cubic units, so as to facilitate subsequent calculation.

[0049] Step S220, according to the three-dimensional space division data and the passenger flow data, the passenger behavior modeling processing is performed, the passenger behavior modeling algorithm based on fluid dynamics is used, the passengers are regarded as fluid particles, and the flow and distribution of passengers in different time periods are simulated in combination with the functional characteristics of the area, to obtain the passenger density distribution data of each functional area;

[0050] During this process, passengers are treated as fluid particles, and the principles of fluid dynamics are used to simulate their dynamic behavior over different time periods more effectively. This approach takes into account the interactions, movement speed, and direction of passengers within the station, allowing for a more detailed modeling of passenger flow. By setting initial conditions (such as passenger entry and exit flow) and boundary conditions (such as the impact of internal station structures on flow), a dynamic simulation environment is established, reflecting the aggregation and dispersion of passengers in various functional areas. In combination with the functional characteristics of the areas, the algorithm not only considers the number of passengers but also takes into account the purpose of use in different areas. For example, during peak hours, the passenger density in the waiting area may significantly increase, while the density in the passageway or ticketing area may be relatively low. Through this modeling process, accurate passenger density distribution data can be generated, 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 function attribute in the functional area division data, through the lighting demand model processing, the best lighting intensity of different functional areas is calculated based on the perception of human eye to 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 according to the passenger density distribution data. The higher the passenger density, the more frequent the activities in the area, and the demand of human eye to brightness will increase. Therefore, in high-density areas such as waiting areas, entrances and exits, the lighting demand model will tend to recommend higher lighting 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 function attribute in the functional area division data further refines the lighting demand. For example, the light in the passageway area needs to help guide the passenger flow, 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 problem, but is closely related to the actual use of the area. Through the lighting demand model, this step considers the adaptability of human eye to light under various complex conditions. Different lighting environments will affect the brightness perception of human eye, for example, strong light may cause glare, and too dark environment may cause visual fatigue. The model integrates the basic principles of visual science when calculating the best lighting intensity, considering factors such as brightness, contrast, uniformity, etc., to ensure that passengers can obtain the best visual experience in different areas.

[0053] Step S240, according to the target lighting intensity, perform lighting uniformity calculation processing to obtain the lighting demand data of each area.

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

[0055]

[0056] where I req,k represents the lighting demand intensity of region k; n represents the number of light sources in the region; i represents the serial number of the light source; w i represents the lighting weight coefficient of each light source i on region k; I target,k represents the target lighting intensity of region k; a represents the regularization coefficient, which controls the weight of the mutual interference of light between regions; m represents the number of other regions interacting with region k; j represents the region serial number; R kj represents the reflection coefficient between region k and other region j; I actual,i represents the actual lighting intensity of light source i in region k; θ kj represents the angle-dependent function of light from region k to region j; d kj represents the distance from region k to region j, describing the effect of lighting intensity decay 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 scheme and the three-dimensional building model data, the position, direction, type and power, beam angle of each lamp in the three-dimensional space are determined through the light source positioning processing, and the lighting distribution parameters of the lamps are obtained;

[0059] This process begins with determining the installation position and orientation of each luminaire based on the three-dimensional architectural model. Specifically, the three-dimensional coordinates of the luminaire determine its physical location in the building, while its orientation determines the angle of projection and coverage of the light rays, which is crucial for complex spatial structures. On this basis, the physical characteristics of different types of luminaires also need to be considered. For example, LED, halogen, or fluorescent lamps differ in power, luminous efficiency, and light decay characteristics, and detailed calculations of their output power are needed 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. Beam angle is another key parameter that determines the range of light emission angles of the luminaire. Different beam angles will produce different lighting coverage and intensity decay rates, and wide-angle luminaires will produce more uniform lighting distribution, while narrow-angle luminaires may concentrate light in local areas. Through the combination of these data and precise modeling, the lighting distribution parameters of the luminaire can be obtained, which are further used to calculate the propagation path, reflection, and absorption process of light in three-dimensional space, ensuring the accuracy of the subsequent lighting simulation stage.

[0060] Step S320, according to the lighting distribution parameters and three-dimensional space division data, applying the Monte Carlo ray tracing algorithm to simulate the propagation, reflection, and refraction of light in space for each luminaire, as well as the multiple reflection and attenuation behavior when the light encounters curved and broken line architectural structures, obtaining the light propagation path of each region;

[0061] As shown in Figure 2 , Figure 2A flowchart of the Monte Carlo ray tracing algorithm is shown. It should be noted that the lighting distribution parameters provide the algorithm with the luminous intensity, beam angle, and position and direction of each luminaire, which are the basis for accurately simulating the behavior of light rays. By setting the light source model, it is possible to simulate the emission of a large number of light rays and simulate the propagation path of the light rays through random sampling techniques. During the movement of the light rays in space, combined with the three-dimensional space division data, the propagation of the 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 a real environment, light rays will reflect and refract when encountering surfaces of different materials, especially in building structures with curves and folds, the behavior of light rays is more complex. 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 ray passes and its effect on the light intensity are considered. This way of random sampling and statistical analysis makes the final light propagation path not only consider direct lighting, but also cover indirect lighting caused by reflection and refraction. Through this process, the final light propagation path data of each region not only reflects the lighting coverage of the luminaire, but also reveals the propagation characteristics of light rays inside the building, such as the intensity change and distribution of light rays in different areas.

[0062] In step S330, according to the light propagation path and optical characteristic data, after light reflection and absorption processing, the dynamic reflection of light rays under the geometric structure is calculated using the light reflection model and the Fresnel reflection model to obtain the lighting intensity distribution of each region;

[0063] It can be understood that the light reflection model is used to describe the process of light reflection on surfaces of different materials, while the Fresnel reflection model further refines the reflection characteristics of light at the interface of different media, especially according to the incident angle of the light and the refractive index of the medium, to calculate the change in reflection intensity of the light. Through these models, complex dynamic reflection behavior can be simulated, such as the degree of reflection of light on glass, metal, or surfaces of different materials, which can be significantly different. The calculation formula is:

[0064]

[0065] where I total,k represents the final lighting intensity distribution of region 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 region k; F(θ i,k , n1, n2) represents the Fresnel reflection coefficient, which is used to describe the influence of the incident angle of the light θ i,k and the refractive index of the medium n1, n2 on the reflection intensity; d i,kdenotes the distance from the light source i to region k; P k denotes the number of reflections within region k; p denotes the sequence number of the reflection path; L i,k denotes the initial path length from the light source i to region k; L p,k denotes the length of the p-th path after multiple reflections within region k; γ denotes the path attenuation exponent.

[0066] In the formula, the incident light intensity represents the initial light intensity of the light source when illuminating the region, while the absorption coefficient describes the degree of absorption of light by the region material. The reflection coefficient is the ability of each region to reflect light according to the material properties. For example, different materials such as walls, floors, etc. have different reflection and absorption characteristics, so R i,k and the value of A k directly affect the light intensity received by each region. The introduction of the Fresnel reflection coefficient makes the influence of the light incidence angle on the reflectivity more accurate, especially in the case of light entering building materials from air or reflecting from glass. The reflected light intensity is closely related to the angle and the refractive index of the material. In addition, the distance attenuation factor is included in the formula, which considers the natural attenuation of light intensity with distance. This process conforms to the physical law that light intensity decreases inversely proportional to the square of the distance. The calculation of path length and the number of reflection paths takes into account the contribution of multiple reflections of light within the region to the final light intensity. The cumulative effect of these reflection paths is adjusted by the path attenuation exponent, which reflects the gradual weakening of light energy with the increase of reflection times. Finally, the light intensity distribution obtained by this calculation process can accurately describe the actual lighting conditions of each region under the influence of reflection and absorption, providing accurate input data for subsequent lighting optimization.

[0067] Step S340, according to the light intensity distribution and lighting demand data, error analysis processing is carried out, by comparing the lighting demand and the actual intensity of each region, the error value of each region is quantified to obtain the initial light intensity distribution data.

[0068] This comparison process is done through an error calculation method, where the lighting requirement of each zone represents the ideal lighting intensity needed for the passenger and functional requirements, while the actual intensity comes from the calculation of the light propagation, reflection, absorption, etc. Through this error analysis, it is possible to determine whether the current luminaire arrangement and light propagation can meet the design requirements, especially for areas with non-uniform, too bright or insufficient lighting. The calculation of error values is not just a simple difference comparison, but also uses more complex error models, such as absolute error, relative error or root mean square error, etc. This way it is possible to 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 requirement due to high reflectance or being too close to the light source, while other areas may have insufficient lighting due to absorption characteristics or obstructions. These error values provide a clear direction for subsequent lighting system optimization, allowing the system to adjust the layout, brightness or lighting strategy of the luminaires in the next step. The final initial lighting 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, the step S400 comprises a step S410, a step S420, a step S430 and a step S440.

[0070] The step S410, according to the initial lighting intensity distribution data, carries out a light source contribution decomposition processing, determines the independent contribution value of each luminaire to the light of each zone by decomposing the light intensity in each zone, and obtains the light contribution value of each light source in each zone;

[0071] In particular, modern subway stations usually have complex geometrical structures, such as multi-level platforms, interlaced corridors, and decorative elements with artistic sense. Such designs increase the complexity of the light propagation path, causing multiple reflection, absorption, and refraction phenomena of the light from the luminaires in different areas, thereby affecting the distribution of the actual illumination. In addition, different types of luminaires are usually used in the station, such as downlights, linear lights, LED light strips, chandeliers, etc., which have different beam angles, light diffusion properties, and power characteristics, and have a direct impact on the decomposition of the light source contribution. First, for most modern subway stations, areas such as platforms, waiting halls, and corridors often use linear lights or LED light strips for uniform basic lighting. Such luminaires are usually installed on the ceiling or side wall, and the light provides illumination for a large area through direct illumination and side reflection. At this time, the decomposition of the light source contribution needs to not only focus on the direct illumination area of the luminaire, but also consider the multiple reflections caused by the high reflectivity of the wall, ceiling, or even the ground (such as metal or polished stone). By decomposing the illumination contribution of each luminaire in the complex environment of the station, the effective illumination intensity and reflectivity of each luminaire on the platform or waiting area can be calculated, which can better understand the multi-path propagation of light, especially the attenuation and scattering behavior when the light is blocked or passes through structures. Second, decorative lighting in the station, such as chandeliers and artistic luminaires, is usually used at the entrance or atrium of the station, aiming to highlight the area features and enhance the spatial hierarchy. Unlike functional lighting, these luminaires have a more concentrated beam angle, and the light may be more used to emphasize certain architectural elements or artistic decorations. When performing light source contribution decomposition, the local lighting effect of these luminaires on the surrounding environment needs to be considered. Since these luminaires are mostly point sources, their lighting contribution decays significantly at a distance, but may cause local over-brightness or uneven illumination in close proximity. By independently extracting the illumination contribution of each decorative luminaire, the actual lighting effect on a specific area and how to coordinate with other functional luminaires to avoid excessive overlap or interference of light can be clearly understood. In addition, the optical properties of different luminaires also affect the decomposition process of their illumination contribution. For example, LED luminaires have high light 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 not only needs to consider the initial power and beam angle of the luminaire, but also needs to combine the architectural structure and material properties of the location where the luminaire is located to calculate the multiple reflections and absorptions of light, thereby accurately quantifying the contribution value of each luminaire to the area. This accurate light source contribution decomposition based on existing station structures and luminaire types not only improves the intelligent control capability of the lighting system, but also realizes energy efficiency optimization, ensuring reasonable lighting layout in different functional areas.

[0072] Step S420, according to the light contribution value and the corresponding light propagation path, applying a light interaction model to process the light superposition effect of each light source, based on the light intensity linear superposition principle, analyzing the light overlap of multiple light sources at the same position and the multiple superposition effect of light after reflection into the same area, to obtain light superposition effect data;

[0073] It can be understood that the light interaction model adopts the light intensity linear superposition principle, which is based on the superposition property of light, that is, multiple light rays at the same spatial position can be added to form the combined illumination intensity. In this process, considering the illumination intensity of each light source in the region and their relative positions, the system will calculate the light overlap when the light rays are superimposed. This is very important to ensure the coordination between lamps and avoid uneven brightness distribution caused by light overlap. For example, in a junction area of a subway station, there may be multiple lamps for concentrated lighting. Through this model, it can be identified which light sources have the most significant light overlap in this area, and the influence of this overlap on the final illumination intensity. In addition, the model also analyzes the multiple superposition effect of light after reflection into the same area, by comprehensively considering the attenuation caused by these reflections and the transmission path of the light rays, the system can accurately calculate the actual contribution of each light ray to the illumination intensity in the region. In public places such as subway stations, multiple reflections of light may not only cause brightness enhancement, but also cause light interference, especially in narrow or complex spaces. Therefore, by applying the light interaction model to analyze the light superposition effect of the light source, the influence of each light source and its interaction can be accurately captured in a complex environment.

[0074] Step S430, based on the preset weighted light superposition model, the light superposition effect data is weighted processed, based on the contribution value of the light source in each region and the length of the light propagation path, the attenuation of the light on the material surface, the weighting coefficient of the light in the space is adjusted, the light intensity weighted value of each light source in the region is calculated;

[0075] It should be noted that the propagation path length and attenuation of light are important factors in determining the intensity of illumination, and the reflection and absorption ability of light on different material surfaces can 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 the light when passing 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 located close to each other and with high contribution values will be given greater weight, while those located far away or with small contributions will be subject to corresponding attenuation processing. Through this weighting processing, the model can effectively integrate the light intensity information from different light sources to obtain a more accurate light intensity weighting value. This not only reflects the influence of a single light source, but also reveals the comprehensive effect of multiple light sources interacting in a complex space.

[0076] Step S440, according to the light intensity weighting value, combining the spatial position relationship of each light source and the distribution of lamps and lanterns, applying the light intensity superposition algorithm, using linear superposition method to weight and sum the illumination contribution of each light source in the region, and analyzing the occlusion and light attenuation between light sources to obtain the light intensity superposition matrix.

[0077] Specifically, the linear superposition method adds the light intensity weighting value of each light source to generate a preliminary light intensity superposition matrix. When performing weighted summation, the distance, direction and relative position of each light source with other light sources in the region need to be considered to ensure that the calculated light intensity truly reflects the mutual influence of each light source. Secondly, in terms of occlusion and light attenuation analysis between light sources, a light attenuation model needs to be applied to quantitatively evaluate the obstacles and unfavorable conditions encountered by light during propagation. For example, if a light source is blocked by surrounding building structures and cannot directly illuminate a certain area, the illumination 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 to further adjust the illumination contribution value of each light source to the region. Through the comprehensive consideration of these factors, the generated light intensity superposition matrix can be more accurate and reasonable.

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

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

[0080] It can be understood that in different areas of the station, the optical properties such as reflectivity, refractivity, and absorptivity, etc. will be different, so for each area, the corresponding optical model needs to be used to calculate the behavior of the reflected light. For example, the characteristics of light reflection on smooth glass surfaces are quite different from those on rough concrete walls. By analyzing these reflection characteristics in detail, the system can predict the propagation path of light in space and identify the key angles that may affect the passenger's field of view. Secondly, the determination of the key angles involves calculating the intersection between the reflected light and the passenger's line of sight. Specifically, the path of the general tracking light is identified, and the points of intersection between the passenger's line of sight are identified, which indicates the possibility of reflected light entering the passenger's field of view. Through these calculations, the resulting line of sight angle distribution data not only helps to assess the impact of light sources on passengers, but also provides an important basis for optimizing lighting design.

[0081] Step S520, according to the line of sight angle distribution data and the passenger flow data, the line of sight tracking process is carried out, and the line of sight trajectory data is obtained by simulating the moving path of the passenger's line of sight and the interaction with the reflection area at different time periods;

[0082] Step S530, according to the line of sight trajectory data, the evaluation process is carried out, and the light interference intensity data is obtained by quantitatively evaluating the interference intensity of the reflection area to the passenger's line of sight through multiple reflection path calculations and light intensity attenuation;

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

[0084] Step S540, according to the light interference intensity data, the luminance of the lamps in the reflection area is quantitatively adjusted to obtain the light adjustment data.

[0085] Specifically, a quantitative adjustment model is first constructed, which not only considers the optical characteristics of the lamps, but also takes into account the visual comfort of passengers, calculating the target brightness to which each reflection area should be adjusted. At the same time, a detailed brightness adjustment strategy is developed, including the adjustment amplitude, speed and time, to ensure that the adjustment process not only meets the comfort requirements of passengers, but also does not cause discomfort due to rapid changes in brightness. Before implementing the adjustment, 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 perform actual brightness adjustment. This method not only improves 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, the step S600 comprises a step S610, a step S620, a step S630 and a step S640.

[0087] The step S610 comprises a step S611 and a step S612.

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

[0089] The step S620 comprises a step S621 and a step S622.

[0090] It can be understood that this step simulates the superposition effect of light using light conduction path data and calculates the light interference intensity, thereby identifying the most severely interfered areas and visually presenting these areas through the generated interference heat map.

[0091] Further, the step S620 comprises a step S621 to S624.

[0092] The step S621 comprises a step S6211 and a step S6212.

[0093] Specifically, first, the collected light path data is cleaned and formatted to ensure consistency and accuracy. Then, the starting point, propagation path, and termination point of each light ray are mapped to the three-dimensional model of the subway station to achieve precise correspondence in space. Finally, light propagation data from different sources is integrated to build a comprehensive light propagation network, providing detailed information for subsequent analysis.

[0094] Step S622, according to the detailed description data of the light propagation in the station, construct a light interference analysis model, through the interaction analysis of light sources, get the interference characteristic data of the superposition and reflection interference between different light sources;

[0095] It should be noted that this step is based on the principle of physical optics to construct a mathematical model that can simulate the behavior of light, including direct, reflection and refraction, etc. Then parameterize the characteristics of all lamps in the station, including brightness, color temperature, beam angle, etc. to calculate in the model. Finally, analyze the interference factors by combining the mutual influence between different light sources, such as the area of excessive brightness caused by light superposition and the glare problem caused by reflection.

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

[0097] Step S624, according to the simulation result data of the simulation of light superposition effect, through the light interference intensity calculation processing, get the interference intensity data of each light path after superposition with the existing light source in the area;

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

[0099] Step S630, according to the light mutual interference data, perform finite element analysis processing, by discretizing the light conduction path in each area, gradually optimize the propagation and attenuation of cross-area light, get the optimization data of inter-regional light conduction path;

[0100] Specifically, first, discretize the continuous light conduction path, decompose it into a finite number of elements, each element 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 cross-area light distribution can be achieved. In this process, by constantly adjusting the intensity and propagation direction of light and other parameters, the optimal lighting effect is ensured.

[0101] Step S640, according to the light conduction path optimization data, the regional optical interaction analysis processing is carried out, the propagation, reflection and transmission behavior of light between different functional areas are analyzed, and the light interaction intensity between different areas is quantified, and the regional optical interaction matrix is generated.

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

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

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

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

[0106] Step S720, according to the light interference weight coefficient, combining the real-time feedback data of the sensor, after the light demand adjustment processing, the adaptive control model is used to optimize the lighting demand of each area, and the real-time lighting demand of each area is obtained by dynamically adapting to the change of environmental light and the fluctuation of passenger flow density;

[0107] It can be understood that first, the light interference weight coefficient is applied to ensure the suitability and uniformity of the lighting of each area. Then, the environmental light data and passenger flow 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, according to the real-time lighting demand, the brightness optimization processing is carried out, and the lamp brightness adjustment parameter of each area is obtained;

[0109] Specifically, this step first analyzes real-time lighting requirements, considering environmental light, passenger flow density, and other related factors. Then a brightness adjustment algorithm is designed, which can calculate the optimal brightness adjustment parameters according to these requirements. Mathematical optimization methods are used to determine the brightness adjustment parameters, taking into account the physical characteristics and performance limitations of the lamps.

[0110] Step S740, according to the lamp brightness adjustment parameters, through the lighting timing optimization processing, the real-time control strategy of each area is obtained.

[0111] Specifically, the influence of time variables such as different times of the day and different days of the week is considered 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 based on the timing optimization results and monitor the adjustment effect through a feedback system. This ensures 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 partitioned lighting control system, the system comprises:

[0114] The acquisition module is configured to acquire three-dimensional architectural model data of the subway station, optical property data of various types of building materials in the station, an original lamp arrangement scheme, functional area division data, and passenger flow data for different time periods.

[0115] The analysis module is configured to perform demand analysis processing based on the three-dimensional architectural model data, the functional area division data, and the passenger flow data, and calculate lighting demand data for each area.

[0116] The simulation module is configured to use a Monte Carlo ray tracing algorithm to simulate the original lamp arrangement scheme, the three-dimensional architectural model data, and the lighting demand data. By simulating the light propagation, reflection, and absorption of the lamps, initial light intensity distribution data is obtained.

[0117] The processing module is configured to process the initial light intensity distribution data based on a preset weighted light superposition model, calculate the superposition effect of multiple light sources in the same area, and obtain a light intensity superposition matrix.

[0118] The adjustment module is configured to simulate the line of sight angles of passengers at different times and in different positions based on the optical property data and the passenger flow data to obtain line of sight tracking data, and quantitatively adjust the brightness of the lamps in the reflection area based on the line of sight tracking data to obtain lighting adjustment data for the reflection area.

[0119] The construction module is configured to perform finite element analysis processing according to the illumination adjustment data, the illumination intensity superposition matrix and the illumination demand data, to evaluate the light mutual interference and the conduction path between the functional areas of the station to obtain a regional optical mutual influence matrix.

[0120] The control module is configured to dynamically optimize the luminous intensity and the illumination timing of the lamps in each area according to the regional optical mutual influence matrix and the real-time feedback data of the sensor to obtain a real-time control strategy.

[0121] In one specific embodiment of the present application, the analysis module comprises:

[0122] The first division unit is configured to obtain three-dimensional space division data of each functional area by performing space division algorithm processing according to the three-dimensional building model data and the functional area division data.

[0123] The first simulation unit is configured to perform passenger behavior modeling processing according to the three-dimensional space division data and the passenger flow data, to simulate the flow and distribution of passengers in different time periods by using a passenger behavior modeling algorithm based on fluid dynamics, considering the passengers as fluid particles and combining the regional functional characteristics, to obtain passenger density distribution data of each functional area.

[0124] The first calculation unit is configured to calculate the optimal illumination intensity of different functional areas based on the perception of human eyes to brightness under different illumination conditions, to obtain target illumination intensity of each functional area, by performing illumination demand model processing according to the passenger density distribution data and the regional functional attributes in the functional area division data.

[0125] The second calculation unit is configured to obtain illumination demand data of each area by performing illumination uniformity calculation processing according to the target illumination intensity.

[0126] In one specific embodiment of the present application, the simulation module comprises:

[0127] The first processing unit is configured to determine the position, direction, type of lamps in the three-dimensional space and the power and beam angle of each lamp by performing light source positioning processing according to the original lamp arrangement scheme and the three-dimensional building model data, to obtain illumination distribution parameters of the lamps.

[0128] The second simulation unit is configured to simulate the propagation, reflection and refraction of light of each lamp in space and the multiple reflection and attenuation behavior of light when encountering curved and broken line building structures by applying a Monte Carlo ray tracing algorithm according to the illumination distribution parameters and the three-dimensional space division data, to obtain light propagation paths of each area.

[0129] The third calculation unit is configured to calculate dynamic reflection of the light rays under the geometric structure by using the illumination reflection model and the Fresnel reflection model through illumination reflection and absorption processing according to the light ray propagation path and the optical characteristic data, and obtain the illumination intensity distribution of each region.

[0130] The first analysis unit is configured to perform error analysis processing according to the illumination intensity distribution and the illumination demand data, compare the illumination demand and the actual intensity of each region, and quantize the error value of each region to obtain the initial illumination intensity distribution data.

[0131] It should be noted that, as for the system in the above-mentioned embodiments, the specific manner in which each module performs operations has been described in detail in the embodiments related to the method, and will not be described in detail here.

[0132] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited to this. Any person skilled in the art can easily think of changes or replacements within the technical range disclosed by the present application, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A subway station zoned lighting control method, characterized by, The method comprises the following steps: acquiring three-dimensional architectural model data of a subway station, optical characteristic data of various types of building materials in the station, an original lamp arrangement scheme, functional area division data, and passenger flow data at different time periods; performing demand analysis processing according to the three-dimensional architectural model data, the functional area division data, and the passenger flow data, and calculating light demand data of each area; performing simulation processing on the original lamp arrangement scheme, the three-dimensional architectural model data, and the light demand data using a Monte Carlo ray tracing algorithm, obtaining initial light intensity distribution data by simulating light propagation, reflection, and absorption of the lamps; processing the initial light intensity distribution data based on a preset weighted ray superposition model, calculating superposition effects of multiple light sources in the same area to obtain a light intensity superposition matrix; simulating line-of-sight angles of passengers at different time periods and in different positions based on the optical characteristic data and the passenger flow data to obtain line-of-sight tracking data, and quantitatively adjusting lamp brightness of a reflection area based on the line-of-sight tracking data to obtain light adjustment data of the reflection area; performing finite element analysis processing according to the light adjustment data, the light intensity superposition matrix, and the light demand data, evaluating light mutual interference and conduction paths between functional areas of the station to obtain a regional optical mutual influence matrix; dynamically optimizing lamp brightness and lighting timing of each area according to the regional optical mutual influence matrix and combining real-time feedback data of sensors to obtain a real-time control strategy; wherein the finite element analysis processing according to the light adjustment data, the light intensity superposition matrix, and the light demand data to evaluate light mutual interference and conduction paths between functional areas of the station to obtain a regional optical mutual influence matrix comprises the following steps: performing light propagation path calculation according to the light adjustment data, calculating specific paths of light rays propagating from a source point to other areas through path tracking to obtain light conduction paths between functional areas; performing light interference analysis processing according to the light conduction paths, analyzing superposition interference effects of light rays entering other areas with existing light sources in the areas based on actual conduction paths of the light rays to obtain light mutual interference data between functional areas; performing finite element analysis processing according to the light mutual interference data, gradually optimizing propagation and attenuation of cross-area light rays by discretizing light conduction paths in each area through finite elements to obtain light conduction path optimization data between areas; performing regional optical mutual influence analysis processing according to the light conduction path optimization data, analyzing propagation, reflection, and transmission behaviors of light rays between different functional areas, and quantifying light interaction intensity between different areas to generate a regional optical mutual influence matrix; wherein the light interference analysis processing according to the light conduction paths, analyzing superposition interference effects of light rays entering other areas with existing light sources in the areas based on actual conduction paths of the light rays to obtain light mutual interference data between functional areas comprises the following steps: performing path integration processing according to the light conduction path data to obtain detailed description data of light propagation inside the station. According to the detailed description data of the light propagation in the station interior, a light interference analysis model is constructed, and interference characteristic data of superposition and reflection interference between different light sources is obtained through light source interaction analysis and processing; According to the light interference analysis model, a simulation light superposition effect processing is performed using a ray tracing technology to obtain simulation result data of light propagation and interaction between functional areas; According to the simulation result data of the simulation light superposition effect, light interference intensity calculation processing is performed to obtain interference intensity data of each light path after superposition with existing light sources in the region.

2. The subway station zonal lighting control method according to claim 1, characterized by According to the three-dimensional building model data, the functional area division data and the passenger flow data, a demand analysis processing is performed to calculate light demand data of each region, including: According to the three-dimensional building model data and the functional area division data, three-dimensional space division data of each functional area is obtained through space partition algorithm processing; According to the three-dimensional space division data and the passenger flow data, a passenger behavior modeling processing is performed, a passenger behavior modeling algorithm based on fluid dynamics is used to regard passengers as fluid particles, and the flow and distribution of passengers in different time periods are simulated in combination with the functional characteristics of the region to obtain passenger density distribution data of each functional area; According to the passenger density distribution data and the functional attribute of the region in the functional area division data, a light demand model processing is performed to calculate the best light intensity of different functional areas based on the perception of human eyes to brightness under different light conditions, and target light intensity of each functional area is obtained. According to the target light intensity, a light uniformity calculation processing is performed to obtain light demand data of each region.

3. The subway station zonal lighting control method according to claim 1, characterized by A Monte Carlo ray tracing algorithm is used to perform simulation processing on the original lamp arrangement scheme, the three-dimensional building model data and the light demand data, and initial light intensity distribution data is obtained by simulating the light propagation, reflection and absorption of the lamps, including: According to the original lamp arrangement scheme and the three-dimensional building model data, light distribution parameters of the lamps are obtained through light source positioning processing to determine the position, direction, type of the lamps in the three-dimensional space, and the power and beam angle of each lamp. According to the light 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 light of each lamp in the space, and the multiple reflection and attenuation behavior of light when encountering curved and broken line building structures, to obtain light propagation paths of each region; According to the light propagation paths and the optical characteristic data, a light reflection and absorption processing is performed to calculate the dynamic reflection of light under geometric structures using a light reflection model and a Fresnel reflection model to obtain light intensity distribution of each region; According to the light intensity distribution and the light demand data, an error analysis processing is performed to compare the light demand and the actual intensity of each region, and error values of each region are quantified to obtain the initial light intensity distribution data.

4. The subway station zonal lighting control method according to claim 1, characterized by Based on a preset weighted light superposition model, the initial light intensity distribution data is processed to calculate the superposition effect of multiple light sources in the same region to obtain a light intensity superposition matrix, including: According to the initial light intensity distribution data, a light source contribution decomposition process is performed, the independent contribution value of each lamp to the light of each region is determined by decomposing the light intensity in each region, and the light contribution value of each light source in each region is obtained; According to the light contribution value and the corresponding light propagation path, a light ray interaction model is applied to process the light ray superposition effect of each light source, the light ray superposition effect data is obtained by simultaneously analyzing the light overlap of multiple light sources at the same position and the multiple superposition effects of light rays after reflection into the same region based on the light intensity linear superposition principle; Based on the preset weighted light ray superposition model, the light ray superposition effect data is weighted processed, the weighting coefficient of light rays in space is adjusted based on the contribution value of light sources in each region, the length of light propagation path, and the attenuation of light rays on the material surface, and the light intensity weighting value of each light source in the region is calculated; According to the light intensity weighting value, the light intensity superposition algorithm is applied combined with the spatial position relationship and lamp distribution of each light source, the light contribution of each light source to the region is weighted and summed using linear superposition, and the light intensity superposition matrix is obtained by analyzing the shielding and light attenuation between light sources.

5. The subway station zonal lighting control method according to claim 1, characterized by According to the optical characteristic data and the passenger flow data, the line of sight angle of passengers at different times and different positions is simulated to obtain line of sight tracking data, and the brightness of the lamps in the reflection area is quantitatively adjusted based on the line of sight tracking data to obtain light adjustment data of the reflection area, including: According to the optical characteristic data, the optical reflection characteristics of different regions inside the station are simulated, the key angles affecting the passenger's field of view are determined by calculating the intersection points of reflected light and passenger's line of sight, and the line of sight angle distribution data is obtained; According to the line of sight angle distribution data and the passenger flow data, line of sight tracking processing is performed, the moving path of the passenger's line of sight at different times and the interaction with the reflection area are simulated, and the line of sight trajectory data is obtained; According to the line of sight trajectory data, evaluation processing is performed, the interference intensity of the reflection area to the passenger's line of sight is quantitatively evaluated by multiple reflection path calculation and light intensity attenuation to obtain light interference intensity data; According to the light interference intensity data, the brightness of the lamps in the reflection area is quantitatively adjusted to obtain light adjustment data.

6. The subway station zonal lighting control method according to claim 1, characterized by According to the regional optical mutual influence matrix, the brightness and lighting timing of the lamps in each region are dynamically optimized combined with the real-time feedback data of the sensor to obtain a real-time control strategy, including: According to the regional optical mutual influence matrix, light interference weight calculation processing is performed, by introducing a multi-dimensional weight matrix, combined with the geometric characteristics of each region, the interference weight coefficient is dynamically calculated to obtain the light interference weight coefficient of each region; According to the light interference weight coefficient, combined with the real-time feedback data of the sensor, the lighting demand adjustment processing is performed, the adaptive control model is used to optimize the lighting demand of each region, and the real-time lighting demand of each region is dynamically adapted to the environmental light change and passenger flow density fluctuation; According to the real-time lighting demand, brightness optimization processing is performed to obtain the lamp brightness adjustment parameter of each region; According to the lamp brightness adjustment parameter, the lighting timing optimization processing is performed to obtain the real-time control strategy of each region.

7. A subway station zoned lighting control system, characterized by, The method comprises the following steps: An acquisition module is configured to acquire three-dimensional architectural model data of a subway station, optical characteristic data of various types of building materials in the station, an original lighting fixture arrangement scheme, functional area division data, and passenger flow data at different time periods; An analysis module is configured to perform demand analysis processing based on the three-dimensional architectural model data, the functional area division data, and the passenger flow data, and to calculate lighting demand data for each area; A simulation module is configured to perform simulation processing on the original lighting fixture arrangement scheme, the three-dimensional architectural model data, and the lighting demand data using a Monte Carlo ray tracing algorithm, to obtain initial lighting intensity distribution data by simulating the propagation, reflection, and absorption of lighting from the fixtures; A processing module is configured to process the initial lighting intensity distribution data based on a preset weighted ray superposition model, to calculate the superposition effect of multiple light sources in the same area, and to obtain a lighting intensity superposition matrix; An adjustment module is configured to simulate the line-of-sight angles of passengers at different time periods and in different positions based on the optical characteristic data and the passenger flow data, to obtain line-of-sight tracking data, and to quantitatively adjust the brightness of the fixtures in the reflection areas based on the line-of-sight tracking data, to obtain lighting adjustment data for the reflection areas; A construction module is configured to perform finite element analysis processing based on the lighting adjustment data, the lighting intensity superposition matrix, and the lighting demand data, to evaluate the mutual interference and conduction paths of the light between the functional areas of the station, and to obtain a regional optical mutual influence matrix; A control module is configured to dynamically optimize the brightness of the fixtures and the lighting timing of each area based on the regional optical mutual influence matrix and in combination with real-time feedback data from sensors, to obtain a real-time control strategy; The finite element analysis processing based on the lighting adjustment data, the lighting intensity superposition matrix, and the lighting demand data to evaluate the mutual interference and conduction paths of the light between the functional areas of the station to obtain a regional optical mutual influence matrix comprises the following steps: Performing ray propagation path calculation based on the lighting adjustment data, to calculate the specific paths of the rays from the source points to other areas by path tracking, to obtain the light conduction paths between the functional areas; Performing ray interference analysis processing based on the light conduction paths, to analyze the superposition interference effect of the rays entering other areas with the existing light sources in the areas based on the actual conduction paths of the rays, to obtain ray mutual interference data between the functional areas; Performing finite element analysis processing based on the ray mutual interference data, to gradually optimize the propagation and attenuation of the cross-area rays by performing finite element discretization on the light conduction paths in each area, to obtain light conduction path optimization data between the areas; Performing regional optical mutual influence analysis processing based on the light conduction path optimization data, to generate a regional optical mutual influence matrix by analyzing the propagation, reflection, and transmission behaviors of the rays between different functional areas and quantifying the mutual interaction intensity of the rays between different areas; The ray interference analysis processing based on the light conduction paths to analyze the superposition interference effect of the rays entering other areas with the existing light sources in the areas to obtain ray mutual interference data between the functional areas comprises the following steps: According to the light conduction path data, a detailed description data of the light propagation in the station is obtained through path integration processing; According to the detailed description data of the light propagation in the station, a light interference analysis model is constructed, and interference characteristic data of superposition and reflection interference between different light sources is obtained through light source interaction analysis processing; According to the light interference analysis model, a simulation light superposition effect processing is performed using a ray tracing technology to obtain simulation result data of light propagation and interaction between functional areas. According to the simulation result data of the simulation light superposition effect, interference intensity data of each light path after superposition with existing light sources in the region is obtained through light interference intensity calculation processing.

8. The subway station zonal lighting control system of claim 7, wherein The analysis module comprises: A first division unit is configured to obtain three-dimensional space division data of each functional area through space partition algorithm processing according to the three-dimensional building model data and functional area division data; A first simulation unit is configured to perform passenger behavior modeling processing according to the three-dimensional space division data and passenger flow data, use a passenger behavior modeling algorithm based on fluid dynamics to simulate the flow and distribution of passengers in different time periods by regarding passengers as fluid particles and combining with the functional characteristics of the region, and obtain passenger density distribution data of each functional area; A first calculation unit is configured to calculate the best illumination intensity of different functional areas based on the perception of human eyes to brightness under different illumination conditions according to the passenger density distribution data and the region function attributes in the functional area division data through an illumination demand model, and obtain target illumination intensity of each functional area; A second calculation unit is configured to perform illumination uniformity calculation processing according to the target illumination intensity to obtain illumination demand data of each region.

9. The subway station zonal lighting control system of claim 7, wherein The simulation module comprises: A first processing unit is configured to determine the position, direction, type and power and beam angle of each lamp in the three-dimensional space through light source positioning processing according to the original lamp arrangement scheme and the three-dimensional building model data, and obtain illumination distribution parameters of the lamp; A second simulation unit is configured to simulate the propagation, reflection and refraction of light of each lamp in the space and the multiple reflection and attenuation behavior of light when encountering curved and broken line building structures according to the illumination distribution parameters and three-dimensional space division data by applying a Monte Carlo ray tracing algorithm, and obtain light propagation paths of each region; A third calculation unit is configured to calculate the dynamic reflection of light under geometric structures using an illumination reflection model and a Fresnel reflection model through illumination reflection and absorption processing according to the light propagation paths and the optical characteristic data, and obtain illumination intensity distribution of each region; A first analysis unit is configured to perform error analysis processing according to the illumination intensity distribution and the illumination demand data by comparing the illumination demand and actual intensity of each region, and obtain initial illumination intensity distribution data by quantifying the error value of each region.

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