A parameter correction method and system for tunnel dynamic lighting environment simulation

Abstract

The application provides a parameter correction method and system for tunnel dynamic lighting environment simulation, and relates to the technical field of tunnel lighting detection.The method comprises the following steps: collecting tunnel road surface optical data, lamp operation state data, external daylight brightness data, traffic parameter data and environment parameter data; estimating an initial parameter set based on the data and a preset scattering / reflection model; obtaining and normalizing a measured illumination field according to measurement geometry and spectral response; constructing a three-dimensional tunnel lighting simulation model to generate a simulated illumination field; comparing the corrected illumination field and the simulated illumination field, calculating a difference index and iteratively optimizing parameters until the difference converges, and obtaining a final corrected parameter set.The application improves the accuracy, adaptability and energy efficiency level of tunnel lighting simulation and regulation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tunnel lighting detection, in particular to a parameter correction method and system for tunnel dynamic lighting environment simulation. BACKGROUND

[0002] Highway and urban rail transit tunnels need to consider the lighting adaptation of multiple factors such as day and night, sunny and rainy, dry and wet road surface, and traffic flow changes under all-weather operating conditions. Traditional tunnel lighting design usually establishes a photometric calculation model based on static design values such as typical road surface reflectance, fixed vehicle speed, standard external brightness, etc., and completes the initial debugging during the construction phase. During the operation period, it relies on periodic manual inspection or laying illuminance meters on the road surface for sampling verification. However, fixed contact measurement can interfere with traffic operation and is easily damaged by vehicle rolling, making it difficult to cover the light distribution changes of each section such as entrance, transition, basic section, and exit under dynamic working conditions.

[0003] With the development of high-resolution imaging luminance meters, remote photoelectric sensors, laser scattering measurements, video image processing, and Internet of Things remote control technologies, non-contact array measurement devices can be installed on the tunnel vault or wall to continuously collect the road surface brightness distribution within a certain distance facing the driving direction. Combined with the bidirectional reflectance distribution function (BRDF) model measured in the laboratory or on site, the brightness is inversely calculated to the road surface illuminance and visual adaptation related indicators to provide real-time data basis for adaptive lighting. Based on the cloud simulation engine, the measurement data can be closed-loop fed back to gradually correct the lighting simulation model parameters, making the simulation results consistent with the real operating conditions, and providing support for energy efficiency optimization, safety threshold control, and multi-scenario lighting strategy generation.

[0004] However, the present technology does not consider the BRDF parameter drift caused by factors such as asphalt aging, humidity, pollution, wear, and maintenance, and the traditional design uses a single fixed reflectance, resulting in large simulation errors. Traditional technology requires laying illuminance meters on the road surface or manual measurement, which has insufficient data spatiotemporal resolution and has the risk of disturbing traffic.

[0005] Therefore, there is an urgent need for an automatic correction method that can use remote, non-contact, continuous brightness measurement and scattering parameter measurement data to automatically correct the key photometric parameters of the tunnel dynamic lighting simulation model in a partitioned, time-varying, and spectral manner. SUMMARY

[0006] To overcome the shortcomings of the prior art, the purpose of the present application is to provide a parameter correction method and system for tunnel dynamic lighting environment simulation, which significantly improves the accuracy, adaptability, and energy efficiency level of tunnel lighting simulation and control by fusing multi-source data acquisition, physical modeling, and iterative correction.

[0007] To achieve the above-mentioned purpose, the present application provides the following solutions:

[0008] A parameter correction method for tunnel dynamic lighting environment simulation, comprising:

[0009] a) synchronously collecting optical data of a tunnel road surface, lamp operating state data, external daylight brightness data, traffic parameter data, and environmental parameter data;

[0010] b) estimating an initial parameter set representing the light output characteristics of the tunnel road surface, wall surface, and lamps based on the optical data, environmental parameter data, and a preset scattering / reflection model;

[0011] c) converting the optical data into a measured illuminance field according to the measurement geometry and spectral response, and normalizing the measured illuminance field using the lamp operating state data, the external daylight brightness data, the traffic parameter data, and the environmental parameter data to obtain a corrected illuminance field;

[0012] d) inputting the data collected in step a) and the initial parameter set into a tunnel three-dimensional lighting simulation model to generate a simulated illuminance field;

[0013] e) comparing the corrected illuminance field with the simulated illuminance field, calculating a difference index, and iteratively optimizing the initial parameter set and the effective light output coefficient of the lamps to obtain a corrected parameter set, with the difference index as the target;

[0014] f) updating the tunnel three-dimensional lighting simulation model with the corrected parameter set, and repeating steps d) to e) until the difference index meets a preset convergence threshold, to obtain a final corrected parameter set.

[0015] Preferably, step a) comprises:

[0016] collecting the optical data of the tunnel road surface through imaging brightness measurement devices arranged at different sections inside the tunnel; the optical data includes tunnel road surface brightness, reflection characteristics, and light spot distribution information;

[0017] real-time acquiring the lamp operating state data through a data interface of the tunnel lighting system; the lamp operating state data includes on / off state, dimming level, and output power information;

[0018] real-time acquiring the external daylight brightness data through an external light environment sensor; the external daylight brightness data includes external sky brightness and ground surface reflection brightness information at the tunnel entrance area;

[0019] collecting the traffic parameter data through a traffic detection device; the traffic parameter data includes traffic volume, vehicle speed, and vehicle type information;

[0020] The environmental parameter data is collected by an environmental monitoring device; the environmental parameter data includes temperature, humidity, and air pollutant concentration information.

[0021] Preferably, step b) comprises:

[0022] According to the optical data, the luminance distribution corresponding to the road surface and wall surface area is extracted, and the reflection and scattering behavior is modeled in combination with the light source layout and the incident angle distribution;

[0023] Based on the environmental parameter data, the scattering / reflection response model of the material surface is corrected, and the scattering factor in different states is established;

[0024] The initial parameter set is estimated using the following formula:

[0025]

[0026] Where R is the comprehensive reflection coefficient to be solved, which represents the light output characteristics of the tunnel road surface, wall surface, and lamp, L m is the measured area average luminance, which reflects the actual luminance level of the target area, L b is the background stray light luminance, which represents the influence of non-target reflection or environmental stray light on the measurement result, E is the incident illuminance of the corresponding area, which represents the intensity of incident light flux per unit area, θ is the incident angle between the incident light and the surface normal, which reflects the directionality of the light, ρ e is the environmental coupling reflectivity, which represents the overall coupling effect of the tunnel interior surface to the outside daylight and multiple reflections;

[0027] According to the comprehensive reflection coefficient, an initial parameter set representing the light output characteristics of the tunnel road surface, wall surface, and lamp is formed for subsequent simulation.

[0028] Preferably, step c) comprises:

[0029] According to the measurement geometry parameters, the optical data is converted into ground equivalent luminance distribution according to the field-of-view-to-ground projection relationship;

[0030] According to the measured spectral response characteristics, the response difference of different wavebands to luminance perception is corrected to obtain a measured illuminance field conforming to the visual standard;

[0031] The measured illuminance field is normalized and corrected using the lamp operating state data, the outside daylight luminance data, the traffic parameter data, and the environmental parameter data, using the formula

[0032]

[0033] The corrected illuminance field is obtained, where E c (x,y) is the corrected illuminance value; E m(x, y) is the measured illuminance value; L d is the ambient daylight luminance; Φ l is the instantaneous luminous flux of the lamp; τ a is the ambient transmittance; D v is the vehicle passing density; λ is the traffic obstruction attenuation coefficient.

[0034] Preferably, step d) comprises:

[0035] Constructing a three-dimensional geometric model of the tunnel, mapping the tunnel road surface, wall surface and lamp coordinate data collected in step a) to a spatial grid;

[0036] Loading the initial parameter set obtained in step b) and the lamp operating state data to the three-dimensional geometric model of the tunnel to generate a light source library and a surface optical property library;

[0037] Based on global illumination solving, the simulated illuminance is calculated at coordinate (x, y) using the following formula and the simulated illuminance field matrix is output:

[0038]

[0039] wherein E s is the simulated illuminance value; Φ i is the effective luminous flux of the i-th lamp; g i (x, y) is the geometric attenuation coefficient from the i-th lamp to coordinate (x, y); ρ i is the comprehensive reflection coefficient of the surface corresponding to the i-th lamp; μ is the tunnel aerosol attenuation coefficient; d i (x, y) is the optical path distance from the i-th lamp to coordinate (x, y); N is the number of lamps.

[0040] Preferably, step e) comprises:

[0041] Aligning the corrected illuminance field and the simulated illuminance field on a unified spatial grid to obtain a difference matrix;

[0042] Constructing an objective function J(q) with the difference matrix and updating the parameter vector q using an adaptive gradient step;

[0043] Iteratively optimizing according to the following formula, and outputting the corrected parameter set when the objective function converges to a threshold value:

[0044]

[0045] wherein q k+1 is the updated parameter vector after the k+1th round; q k is the parameter vector after the kth round; α is the adaptive learning rate; M is the number of grid points; is the corrected illuminance value of the mth grid point; is the corrected illuminance value of the mth grid point;k The calculated simulated illuminance value; δ is a stability constant to prevent the denominator from being zero; g k Let be the cumulative vector of the squared gradient in the k-th round; ε is the numerical stability factor.

[0046] Preferably, the adaptive gradient step size for the parameter vector q includes the initial parameter set and the effective light output coefficient of the lamp.

[0047] Preferably, step f) includes:

[0048] The correction parameter set q obtained in step e) k+1 Write the data into the tunnel's 3D lighting simulation model and generate an updated version of the model;

[0049] Call the updated model and recalculate the simulated illuminance field.

[0050] The new round of difference index ∈ is calculated using the following formula. k+1 :

[0051]

[0052] If ∈ k+1 ≤∈ th Then output q k+1 As the final set of correction parameters; otherwise, q k+1 Set this as the new iteration starting point and return to execute steps d) and e); where, ∈ k+1 The difference index is the (k+1)th iteration; M is the total number of grid points; This is the corrected illuminance value for the m-th grid point; For parameter set q k+1 The calculated simulated illuminance value; δ is a stability constant to prevent the denominator from being zero; ∈ th This is the preset convergence threshold.

[0053] A parameter correction system for simulating dynamic lighting environment in tunnels, comprising:

[0054] The data acquisition unit is used to simultaneously collect optical data of the tunnel surface, lighting operation status data, external daytime light intensity data, traffic parameter data, and environmental parameter data.

[0055] The parameter modeling unit is used to estimate the initial parameter set characterizing the light output characteristics of the tunnel pavement, walls and lamps based on the optical data, environmental parameter data and preset scattering / reflection model;

[0056] An illuminance reconstruction and correction unit is configured to convert the optical data into a measured illuminance field according to the measurement geometry and spectral response, and normalize and correct the measured illuminance field using the luminaire operation state data, the ambient daylight intensity data, the traffic parameter data, and the environmental parameter data to obtain a corrected illuminance field;

[0057] A lighting simulation unit is configured to input the data collected by the data collection unit and the initial parameter set into a three-dimensional tunnel lighting simulation model to generate a simulated illuminance field;

[0058] A difference analysis and optimization unit is configured to compare the corrected illuminance field with the simulated illuminance field, calculate a difference index, and iteratively optimize the initial parameter set and the effective light output coefficient of the luminaire based on the difference index to obtain a corrected parameter set;

[0059] A model updating and convergence judgment unit is configured to update the three-dimensional tunnel lighting simulation model with the corrected parameter set, and repeat the steps of the lighting simulation unit and the difference analysis and optimization unit until the difference index meets a preset convergence threshold to obtain a final corrected parameter set.

[0060] According to the specific embodiments of the present application, the following technical effects are achieved:

[0061] (1) The present application synchronously collects the optical data of the tunnel road surface, the luminaire operation state data, the ambient daylight intensity data, the traffic parameter data, and the environmental parameter data, combines the three-dimensional simulation model and the optical and physical characteristics, establishes a closed-loop adaptive correction mechanism, overcomes the problems of sparse measurement points, single data, and inability to reflect dynamic changes in real time in the prior art, realizes accurate modeling and correction of the illuminance of the tunnel road surface under different road sections and different environmental conditions, and improves the matching and applicability of the simulation model to the actual working conditions.

[0062] (2) The present application constructs a measured illuminance field and a normalization correction method based on the measurement geometry and the spectral response, combines multi-dimensional parameters such as luminaires, daylight, traffic, and environmental factors, significantly improves the corresponding accuracy between the results of remote non-contact brightness measurement and the actual equivalent illuminance of human eye vision, avoids the measurement errors and traffic interference problems caused by traditional reliance on single-point illuminometers or manual inspection, and has higher engineering practicality and safety.

[0063] (3) The present application realizes dynamic inversion and updating of core optical parameters such as the scattering and reflection parameters of the tunnel road surface and the effective light output coefficient of the luminaire through an iterative optimization strategy driven by a difference index, solves the technical problems that the existing design model is detached from the field and cannot be adaptively adjusted with the aging and environmental changes of the road surface, makes the simulation results consistent with the actual operation state, and enhances the robustness and credibility of the model.

[0064] (4) The final output correction parameter set of the present application can be directly used to guide the dynamic light control of the tunnel, ensuring the visual continuity and safety of key sections such as the entrance black hole effect and the exit white hole effect, while realizing energy saving optimization under different flow, day and night, and climate conditions. Compared with the existing scheme relying on fixed light curve, it has better adaptability and energy efficiency performance, significantly improving the intelligent level of tunnel operation management. BRIEF DESCRIPTION OF DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0066] Figure 1 The method flowchart provided for the embodiments of the present application is as shown in

[0067] Figure 2 The system structure schematic diagram provided for the embodiments of the present application is as shown in DETAILED DESCRIPTION

[0068] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute some embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0069] The purpose of the present application is to provide a parameter correction method and system for tunnel dynamic lighting environment simulation, which significantly improves the accuracy, adaptability and energy efficiency level of tunnel lighting simulation and regulation by fusing multi-source data acquisition, physical modeling and iterative correction.

[0070] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail in combination with the drawings and specific embodiments.

[0071] Figure 1 The method flowchart provided for the embodiments of the present application is as shown in Figure 1 As shown in the method flowchart provided for the embodiments of the present application, the present application provides a parameter correction method for tunnel dynamic lighting environment simulation, which comprises:

[0072] a) synchronously collecting optical data of a tunnel road surface, lamp operation state data, external daylight brightness data, traffic parameter data and environmental parameter data;

[0073] b) estimating an initial parameter set representing the tunnel pavement, wall and light fixture light output characteristics based on the optical data, environmental parameter data and preset scattering / reflection model;

[0074] c) converting the optical data into measured illuminance field according to the measurement geometry and spectral response, and normalizing the measured illuminance field using the light fixture operating state data, external daylight brightness data, traffic parameter data and environmental parameter data to obtain a corrected illuminance field;

[0075] d) inputting the data collected in step a) and the initial parameter set into the tunnel three-dimensional lighting simulation model to generate a simulated illuminance field;

[0076] e) comparing the corrected illuminance field and the simulated illuminance field, calculating a difference index, and iteratively optimizing the initial parameter set and the effective light output coefficient of the light fixture to obtain a corrected parameter set, with the difference index as the target;

[0077] f) updating the tunnel three-dimensional lighting simulation model with the corrected parameter set, and repeating steps d) to e) until the difference index meets a preset convergence threshold, to obtain the final corrected parameter set.

[0078] In the embodiment of the present application, the tunnel pavement optical data in step a) is collected by preset imaging brightness measurement devices in different sections of the tunnel, including the entrance section, transition section, basic section and exit section. The devices have high dynamic range imaging and standard brightness meter calibration functions, and can collect real-time brightness distribution, reflection characteristics and light spot distribution data of the tunnel pavement and walls. The imaging brightness data is uploaded to the data processing platform at a preset period (e.g. every 5 seconds), combined with the on-site deployed three-dimensional geometric model, to accurately restore the optical characteristic information under different fields of view for subsequent calculation.

[0079] Preferably, in the embodiment of the present application, the light fixture operating state data, external daylight brightness data, traffic parameter data and environmental parameter data in step a) are collected by the following means: the light fixture operating state data is obtained in real time through the interface of the original or newly deployed lighting monitoring system of the tunnel, including the on / off state, dimming level and output power parameters of each light fixture; the external daylight brightness data is collected in real time by external light environment sensors deployed at the entrance area of the tunnel, covering the sky brightness and ground reflection brightness; the traffic parameter data is obtained by traffic detection devices such as radar speedometers, video recognition units, etc., to obtain vehicle flow, speed and vehicle type information; the environmental parameter data is obtained by air quality monitoring sensors to obtain temperature, humidity and air pollutant concentration, and all data is uploaded after being associated with time stamp and spatial identifier for subsequent simulation modeling and normalization correction.

[0080] In the embodiment of the present application, the estimation of the initial parameter set in step b) is first performed by processing the optical data collected in step a) to extract the luminance distribution information of the tunnel pavement and wall surface regions. The luminance distribution information is combined with the specific spatial layout of the tunnel interior lamps, the light distribution characteristics, and the incident angle distribution of the light radiation direction to establish a reflection and scattering characteristic modeling library for describing the reflection and scattering behavior of different surface materials to light. In this modeling process, the optical response differences of the pavement and wall surface materials are considered, and the effective light receiving conditions of each surface are back calculated by the known light output parameters and arrangement relationship of the lamps to form the initial values of the reflection and scattering parameters.

[0081] Further preferably, on the basis of the above embodiment, the surface scattering and reflection responses in the established model are corrected in combination with the environmental parameter data obtained in step a), including temperature, humidity, and air pollutant concentration information, to form scattering factors under different pavement states (such as dry, wet, or polluted). According to the measured actual luminance levels of the tunnel pavement and wall surface, as well as the influence of background stray light, in combination with the measured incident illuminance values and incident angle information, the equivalent reflection factor reflecting the influence of the tunnel surface on external light and multiple reflections is comprehensively analyzed to finally form the initial parameter set characterizing the tunnel pavement, wall surface, and lamp light output characteristics for subsequent simulation model calling and calculation.

[0082] Specifically, in the embodiment of the present application, the reflection and scattering characteristic modeling library is established in a manner combining the partition bidirectional reflectance distribution function model (BRDF) and the scattering factor, and the surface optical responses of the tunnel pavement and wall surface are constructed respectively. Specifically, the pavement region presets the luminance response curve under the corresponding incident angle and reflection angle relationship according to different material types (such as asphalt, cement, etc.) and states (such as dry, wet, and polluted), and forms a discrete matrix containing the incident direction, exit direction, and reflection intensity relationship in combination with the measured scattering data, for describing the reflection and scattering characteristics under each angle condition; the wall surface region establishes a polynomial fitting model to describe its diffuse reflection characteristics according to the material surface roughness and reflectivity. The above model dynamically switches or interpolates according to the field environmental monitoring data, and outputs the effective reflection coefficient and scattering coefficient for simulation calculation as the optical property basis of each region surface in the tunnel three-dimensional lighting simulation model. The modeling library can cover the reflection and scattering changes under different materials and different working conditions, and ensure that the simulation results are highly consistent with the actual tunnel light field.

[0083] Further, the present application preferably adopts a modeling method based on the combination of the partition bidirectional reflectance distribution function (BRDF) and the scattering factor, and the core mathematical expression is as follows:

[0084]

[0085] wherein R(θ i ,θr φ) represents the surface reflection intensity under the condition of incident angle θ i , exit angle θ r , and azimuth angle difference φ; ρ d represents the diffuse reflection coefficient of the surface, which depends on the material and pollution state; θ r represents the included angle between the exit direction and the normal line; ρ s represents the specular reflection coefficient of the surface, which depends on the material smoothness; n represents the surface roughness coefficient, and the larger the value, the smoother the surface; α represents the included angle between the incident light and the specular reflection direction; and φ represents the azimuth angle difference between the incident and exit directions.

[0086] In the formula, the first term is a Lambert diffuse reflection term, and the second term is a Phong specular reflection term, and the two are weighted to describe the comprehensive reflection and scattering characteristics of the tunnel surface and wall surface under different surface states, and finally form a scattering / reflection model library for simulation.

[0087] Preferably, in the embodiments of the present application, the conversion of the optical data to the ground equivalent luminance distribution in step c) first performs geometric inverse calculation on the original image data according to the geometric parameters of the imaging luminance measuring device, including its field of view angle, installation height, pitch angle, and corresponding ground projection relationship, to obtain the actual corresponding position on the ground and the equivalent luminance distribution per unit area of each sampling point in the target view. At the same time, combined with the spectral response curve of the device, the difference between the collected data at different wave bands and the human eye visual response curve is corrected and unified into equivalent illuminance conforming to the visual standard to form the measured illuminance field data, providing basic data support for subsequent normalization processing.

[0088] Further preferably, on the basis of the above-mentioned measured illuminance field, the lamp operation state data, external daylight luminance data, traffic parameter data, and environmental parameter data in step a) are combined for normalization correction. Specifically, according to the real-time light output flux information of the lamp, the illuminance deviation caused by the change of light source output is corrected; according to the external daylight luminance, the influence of natural light penetration on the internal brightness of the tunnel is eliminated; according to the traffic detection data, the local shading effect of the light field caused by the change of vehicle density is evaluated and corrected; and according to the environmental parameter data, the influence of air pollutants and humidity changes on light transmittance is corrected. Finally, the corrected illuminance field after unified normalization processing is output to ensure that it reflects the real and stable light environment state for subsequent simulation model comparison and optimization use.

[0089] Preferably, in the embodiments of the present application, the construction of the tunnel three-dimensional lighting simulation model in step d) first establishes a three-dimensional geometric model corresponding to the actual structure of the tunnel according to the spatial coordinate data of the tunnel road surface, wall surface and lamps and lanterns collected in step a), and discretizes the road surface, wall surface, ceiling and other regions into grid cells to form a spatial grid cell library. Further, the initial parameter set obtained in step b) is loaded into the above grid cells, including the reflection characteristics, scattering characteristics, pollution state and the like of the surface material of each region, while the operating state data of each lamp is imported to record the luminous flux, light distribution curve, light source coordinate and direction information, forming a light source library and a surface optical property library for lighting calculation.

[0090] Further preferably, on the basis of the above geometric model and attribute library, the global lighting solving algorithm is used to simulate and calculate each coordinate point in the tunnel, the spatial attenuation coefficient of the light source to the specified coordinate point is calculated in combination with the real-time effective luminous flux data of each lamp, the simulated illuminance value of each coordinate point in the tunnel is solved point by point by considering the comprehensive reflection characteristics of the surface material corresponding to the coordinate, and finally a complete simulated illuminance field matrix is output for subsequent comparison and analysis with the measured data and parameter iterative optimization.

[0091] Specifically, the tunnel three-dimensional geometric model can be described by using a parametric surface equation, and the mathematical expression is:

[0092] P(u,v,w) = [X(u,v,w), Y(u,v,w), Z(u,v,w)]

[0093] wherein P(u,v,w) is the three-dimensional coordinates of any point in the spatial grid, X(u,v,w), Y(u,v,w), Z(u,v,w) represent the coordinate values of the point in the length, width and height directions in the three-dimensional space respectively, and u, v, w are normalized surface parameters corresponding to the normalized scale variables in the length direction, transverse direction and height direction of the tunnel. The above parameters are all derived from the tunnel structure data collected in step a), ensuring that the geometric model is highly consistent with the actual working condition.

[0094] Preferably, in the embodiments of the present application, the calculation of the difference index in step e) first performs point-to-point registration on the corrected illuminance field obtained in step c) and the simulated illuminance field obtained in step d) under the unified spatial grid corresponding to the tunnel three-dimensional simulation model, calculates the difference matrix of the two in the global range by comparing the illuminance values at the corresponding positions of each grid cell. The difference matrix reflects the spatial distribution difference between the actual measurement result and the simulation result, and is used to quantify the consistency of the model and the actual working condition, providing basic data support for subsequent parameter optimization.

[0095] Further preferably, on the basis of the above difference matrix, an optimization function with the square sum of illumination difference as the target is constructed, and an adaptive gradient step algorithm is used to iteratively update the parameter vector, which includes the tunnel road surface and wall surface scattering reflection parameters and the effective light output coefficient of the lamp. In each iteration process, the learning rate is dynamically adjusted by accumulating the historical gradient change trend to improve the stability and convergence efficiency of the optimization process and avoid local optimal trap. The specific update rule considers the number of grid points, the difference between the measured illumination and the simulated illumination, and a numerical stability constant set to avoid division by zero error. At the same time, a cumulative term for gradient smoothing and a convergence condition judgment term are introduced, and until the target function converges to a preset threshold, the final correction parameter set is output to update the simulation model and support subsequent light environment regulation.

[0096] Preferably, in the embodiments of the present application, the adaptive gradient step is used to iteratively optimize the parameter vector, which includes the initial parameter set of the tunnel road surface and wall surface scattering reflection determined in step b), and the effective light output coefficient of each lamp. In each iteration, the above parameter vector is updated by the dynamically adjusted gradient step according to the target function result formed by the difference matrix of the previous round, and the updated parameters are used to redefine the optical properties of each surface unit in the tunnel simulation model and the output level of each lamp to further improve the fitting accuracy of the simulated illumination field to the measured illumination field.

[0097] Further preferably, on the basis of the updated parameters, the tunnel three-dimensional lighting simulation model is called again to calculate a new round of simulated illumination field. According to the comparison results of the measured illumination and the latest simulated illumination under the unified space grid, the root mean square difference index in the global range is calculated, which is used to evaluate the consistency degree of the simulation result and the actual working condition. In the specific calculation, the total number of grid points, the difference between the measured illumination value and the simulated illumination value of each grid point are considered, and a stability constant is introduced to avoid calculation abnormality caused by denominator tending to zero. If the obtained difference index is lower than the preset convergence threshold, the current parameter vector is determined as the final correction parameter set; otherwise, the current parameters are taken as the starting point of the new round of iteration, and the tunnel three-dimensional simulation and optimization iteration steps are continued to be executed until the convergence condition is met.

[0098] Corresponding to the above method, as shown in Figure 2 The present embodiment also provides a parameter correction system for tunnel dynamic lighting environment simulation, which comprises:

[0099] A data acquisition unit is configured to synchronously acquire optical data of a tunnel road surface, lamp operation state data, external daylight brightness data, traffic parameter data and environmental parameter data.

[0100] a parameter modeling unit configured to estimate an initial parameter set representing the characteristics of the tunnel pavement, the wall surface and the light output of the lamp based on the optical data, the environmental parameter data and a preset scattering / reflection model;

[0101] an illumination reconstruction and correction unit configured to convert the optical data into a measured illumination field according to the measurement geometry and the spectral response, and to normalize and correct the measured illumination field to obtain a corrected illumination field by using the lamp operation state data, the ambient daylight intensity data, the traffic parameter data and the environmental parameter data;

[0102] a lighting simulation unit configured to input the data collected by the data collection unit and the initial parameter set into a three-dimensional tunnel lighting simulation model to generate a simulated illumination field;

[0103] a difference analysis and optimization unit configured to compare the corrected illumination field with the simulated illumination field, calculate a difference index, and iteratively optimize the initial parameter set and the effective light output coefficient of the lamp based on the difference index to obtain a corrected parameter set;

[0104] a model updating and convergence judgment unit configured to update the three-dimensional tunnel lighting simulation model by using the corrected parameter set, and repeat the steps of the lighting simulation unit and the difference analysis and optimization unit until the difference index meets a preset convergence threshold to obtain a final corrected parameter set.

[0105] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts of each embodiment can be referred to each other. For the system disclosed in the embodiments, the description is relatively simple because it corresponds to the method disclosed in the embodiments. The relevant parts can be referred to the description of the method.

[0106] The principles and implementation manners of the present application are described by using specific examples in the present application. The above description of the embodiments is only used to help understand the method of the present application and its core idea. For those skilled in the art, the specific implementation manners and application ranges can be changed according to the idea of the present application. In conclusion, the content of the specification should not be understood as a limitation of the present application.

Claims

1. A parameter correction method for simulating dynamic lighting environment in tunnels, characterized in that, include: a) Synchronously collect optical data of the tunnel surface, lighting status data, external daytime light intensity data, traffic parameter data, and environmental parameter data; b) Based on the optical data, environmental parameter data, and the preset scattering / reflection model, estimate the initial parameter set characterizing the light output characteristics of the tunnel pavement, walls, and luminaires; c) Based on the measured geometry and spectral response, the optical data is converted into a measured illuminance field, and the measured illuminance field is normalized and corrected using the luminaire operating status data, the external daytime light intensity data, the traffic parameter data, and the environmental parameter data to obtain a corrected illuminance field; d) Input the data collected in step a) and the initial parameter set into the tunnel 3D lighting simulation model to generate a simulated illuminance field; e) Compare the corrected illuminance field with the simulated illuminance field, calculate the difference index, and use the difference index as the target to iteratively optimize the initial parameter set and the effective light output coefficient of the lamp to obtain the corrected parameter set; f) Update the tunnel 3D lighting simulation model with the correction parameter set, and repeat steps d) to e) until the difference index meets the preset convergence threshold to obtain the final correction parameter set; Step e) includes: Align the corrected illuminance field and the simulated illuminance field on a uniform spatial grid to obtain the difference matrix; Constructing the objective function using the difference matrix And an adaptive gradient step size is used to adjust the parameter vector. Update; Iterative optimization is performed using the following formula, and the set of corrected parameters is output when the objective function converges to the threshold: ； in, For the first The parameter vector updated in the round; For the first Wheel parameter vector; Adaptive learning rate; This represents the number of grid points; For the first Corrected illuminance value for each grid point; For The calculated simulated illuminance value; To prevent the stability constant from having a denominator of zero; For the first The cumulative vector of the squared gradient of the round; This is the numerical stability factor.

2. The parameter correction method for simulating dynamic lighting environment in tunnels according to claim 1, characterized in that, Step a) includes: Optical data of the tunnel surface is collected by imaging brightness measurement devices installed in different sections inside the tunnel; the optical data includes tunnel surface brightness, reflection characteristics, and light spot distribution information. The operating status data of the lamps is acquired in real time through the data interface of the tunnel lighting system; the operating status data of the lamps includes switch status, dimming level and output power information. The external daytime light intensity data is acquired in real time by an external light environment sensor; the external daytime light intensity data includes information on the brightness of the sky and the brightness of the ground reflection in the tunnel entrance area. The traffic parameter data is collected by traffic detection equipment; the traffic parameter data includes traffic flow, vehicle speed, and vehicle type information. The environmental parameter data is collected through environmental monitoring equipment; the environmental parameter data includes temperature, humidity and air pollutant concentration information.

3. The parameter correction method for simulating dynamic lighting environment in tunnels according to claim 1, characterized in that, Step b) includes: Based on the optical data, the brightness distribution of the corresponding road and wall areas is extracted, and the reflection and scattering behavior is modeled in combination with the light source layout and incident angle distribution; Based on the environmental parameter data, the scattering / reflection response model of the material surface is modified, and scattering factors under different states are established; The initial parameter set is estimated using the following formula: ； in, The desired comprehensive reflectance coefficient characterizes the light output properties of the tunnel surface, walls, and luminaires. The measured average luminance of the area reflects the actual luminance level of the target area. The background stray light intensity represents the influence of non-target reflections or ambient stray light on the measurement results. The incident illuminance is the illuminance of the corresponding region, representing the intensity of the incident luminous flux per unit area. The angle between the incident light and the surface normal reflects the directionality of the incident light. Environmental coupling reflectivity represents the overall coupling effect of the tunnel's internal surface on external daylight and multiple reflections. An initial parameter set characterizing the light output characteristics of the tunnel road surface, walls, and lighting fixtures is formed based on the comprehensive reflection coefficient for subsequent simulation.

4. The parameter correction method for simulating dynamic lighting environment in tunnels according to claim 1, characterized in that, Step c) includes: Based on the measured geometric parameters, the optical data is converted into an equivalent ground brightness distribution according to the field-of-view projection relationship; Based on the measured spectral response characteristics, the differences in the response of different spectral bands to brightness perception are corrected to obtain a measured illuminance field that meets visual standards. Using the lamp operating status data, the ambient daytime light intensity data, the traffic parameter data, and the environmental parameter data, the measured illuminance field is normalized and corrected using the following formula: ； The corrected illuminance field is obtained, where, To correct the illuminance value; This is the measured illuminance value; The brightness of ambient daylight; For the instantaneous luminous flux of the luminaire; Environmental transmittance; Vehicle throughput density; This is the traffic obstruction attenuation coefficient.

5. The parameter correction method for simulating dynamic lighting environment in tunnels according to claim 1, characterized in that, Step d) includes: Construct a three-dimensional geometric model of the tunnel and map the coordinate data of the tunnel road surface, walls and lighting fixtures collected in step a) into a spatial grid. The initial parameter set and lamp operating status data obtained in step b) are loaded into the tunnel three-dimensional geometric model to generate a light source library and a surface optical property library. Based on global illumination solution, in coordinates The simulated illuminance is calculated using the following formula, and the simulated illuminance field matrix is ​​output: ； in, This is a simulated illuminance value; For the first The effective luminous flux of each lamp; For the first Each light fixture to coordinates The geometric attenuation coefficient; For the first The overall reflectance coefficient of the surface corresponding to each lamp; The aerosol attenuation coefficient in the tunnel; For the first Each light fixture to coordinates Optical path distance; This refers to the number of light fixtures.

6. The parameter correction method for simulating dynamic lighting environment in tunnels according to claim 1, characterized in that, Adaptive gradient step size on parameter vector This includes the initial parameter set and the effective light output coefficient of the lamp.

7. The parameter correction method for simulating dynamic lighting environment in tunnels according to claim 1, characterized in that, Step f) includes: The correction parameter set obtained in step e) Write the data into the tunnel's 3D lighting simulation model and generate an updated version of the model; Call the updated model and recalculate the simulated illuminance field. ; The new round of difference indicators will be calculated using the following formula. : ； like Then output As the final set of correction parameters; otherwise, Set this as the new iteration starting point and return to execute steps d) and e); where, For the first Round iteration difference index; This represents the total number of grid points; For the first Corrected illuminance value for each grid point; For parameter set The calculated simulated illuminance value; To prevent the stability constant from having a denominator of zero; This is the preset convergence threshold.

8. A parameter correction system for simulating dynamic lighting environment in tunnels, characterized in that, include: The data acquisition unit is used to simultaneously collect optical data of the tunnel surface, lighting operation status data, external daytime light intensity data, traffic parameter data, and environmental parameter data. The parameter modeling unit is used to estimate the initial parameter set characterizing the light output characteristics of the tunnel pavement, walls and lamps based on the optical data, environmental parameter data and preset scattering / reflection model; The illuminance reconstruction and correction unit is used to convert the optical data into a measured illuminance field based on the measurement geometry and spectral response, and to normalize and correct the measured illuminance field using the lamp operating status data, the external daytime light brightness data, the traffic parameter data and the environmental parameter data to obtain a corrected illuminance field. The lighting simulation unit is used to input the data collected by the data acquisition unit and the initial parameter set into the tunnel three-dimensional lighting simulation model to generate a simulated illuminance field; The difference analysis and optimization unit is used to compare the corrected illuminance field with the simulated illuminance field, calculate the difference index, and iteratively optimize the initial parameter set and the effective light output coefficient of the lamp with the difference index as the target to obtain the corrected parameter set. The model update and convergence judgment unit is used to update the tunnel three-dimensional lighting simulation model with the correction parameter set, and repeat the steps of the lighting simulation unit and the difference analysis and optimization unit until the difference index meets the preset convergence threshold to obtain the final correction parameter set. The difference analysis and optimization unit includes: Align the corrected illuminance field and the simulated illuminance field on a uniform spatial grid to obtain the difference matrix; Constructing the objective function using the difference matrix And an adaptive gradient step size is used to adjust the parameter vector. Update; Iterative optimization is performed using the following formula, and the set of corrected parameters is output when the objective function converges to the threshold: ； in, For the first The parameter vector updated in the round; For the first Wheel parameter vector; Adaptive learning rate; This represents the number of grid points; For the first Corrected illuminance value for each grid point; For The calculated simulated illuminance value; To prevent the stability constant from having a denominator of zero; For the first The cumulative vector of the squared gradient of the round; This is the numerical stability factor.

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