Method and device for evaluating light field in passage site

Abstract

The invention discloses a method and a device for evaluating a light field in a passage site, and the method comprises the steps: building a reliability evaluation system for the light field information flow of channels such as a tunnel in a warehouse passage site and the visual response characteristics of a driver according to a driving change model of the driver and an eye stimulation model of the driver; therefore, the driving change model of the driver and the eye stimulation model of the driver are subjected to multi-dimensional control, so that the accuracy of the reliability evaluation system is ensured, and a dynamic illumination test is carried out for a tunnel scene and the driver. Besides, triggering a smoke test based on the tunnel scene, collecting the smoke concentration, defining a smoke penetration coefficient based on the smoke concentration and the brightness of each light source in tunnel illumination, defining a smoke penetration effect grade based on the smoke penetration coefficient, and if the smoke penetration effect grade is lower than a preset smoke penetration effect grade, determining that the smoke penetration effect grade is higher than the preset smoke penetration effect grade. And if so, triggering the adjustment of the brightness of each light source in the tunnel illumination, thereby realizing the autonomous regulation and control of each light source in the tunnel illumination in the warehouse passing site.

Description

Technical Field

[0001] The present invention relates to the technical field of light field evaluation, and in particular to a light field evaluation method and device in a traffic area. Background Art

[0002] With the development of production demand, the areas occupied by various storage sites are getting larger and larger. In order to ensure the effective operation of transfer vehicles in storage sites, tunnels and other channels have been set up in large storage sites to connect the passage areas blocked by different storage areas. Due to the complex lighting conditions in the storage areas, the light sources in the tunnels illuminate the vehicles, and the transfer vehicles have driving changes and irritation to the driver's eyes during driving. In the existing technology, when evaluating the light field settings of such tunnel areas, there is no control over driving changes and eye irritation, which affects the accuracy of the reliability evaluation system and cannot perform dynamic lighting tests on tunnel scenes and drivers. Summary of the invention

[0003] The purpose of the present invention is to overcome the shortcomings of the prior art. The present invention provides a method and device for evaluating the light field in a passage. In a tunnel scene, a passage in the tunnel scene is defined, and lighting data in the passage is dynamically collected; effective lighting data is determined based on the screening of lighting data in the passage, and a tunnel lighting system is constructed according to the effective lighting data; a three-dimensional evaluation system is constructed according to the tunnel lighting system; in the three-dimensional evaluation system, various light intensity parameters in the tunnel lighting are collected, and multiple first influencing factors are defined according to the various light intensity parameters and the driver, and a driving change model of the driver is constructed based on the multiple first influencing factors; at the same time, a second influencing factor is defined according to the light color parameters of the light source in the tunnel lighting and the driving state of the driver, and an eye stimulation model of the driver is constructed based on the multiple second influencing factors; the driving change model of the driver and the eye stimulation model of the driver are collected, and a reliability evaluation system of the tunnel light field information flow and the visual response characteristics of the driver is constructed according to the driving change model of the driver and the eye stimulation model of the driver, so as to control the driving change model of the driver and the eye stimulation model of the driver in multiple dimensions, so as to ensure the accuracy of the reliability evaluation system, and perform dynamic lighting tests on the tunnel scene and the driver.

[0004] In addition, a smoke test is triggered based on the tunnel scene, and the smoke concentration is collected. The smoke penetration coefficient is defined based on the smoke concentration and the brightness of each light source in the tunnel lighting. The smoke penetration effect level is defined based on the smoke penetration coefficient. If the smoke penetration effect level is lower than the preset smoke penetration effect level, the brightness of each light source in the tunnel lighting is adjusted, thereby realizing autonomous regulation of each light source in the tunnel lighting.

[0005] The embodiment of the present invention provides a method for evaluating a light field in a traffic venue, which is applied to a light field evaluation scenario in a traffic venue;

[0006] The light field evaluation method in the passage venue comprises:

[0007] In the tunnel scene, define the passage area in the tunnel scene and dynamically collect the lighting data in the passage area;

[0008] Determine effective lighting data based on the screening of lighting data in the traffic area, and build a tunnel lighting system based on the effective lighting data;

[0009] Construct a three-dimensional evaluation system based on the tunnel lighting system;

[0010] In the three-dimensional evaluation system, various light intensity parameters in the tunnel lighting are collected, and multiple first influencing factors are defined according to the various light intensity parameters and the driver, and the driver's driving change model is constructed based on the multiple first influencing factors; at the same time, the second influencing factors are defined according to the light color parameters of the light source in the tunnel lighting and the driver's driving state, and the driver's eye stimulation model is constructed based on the multiple second influencing factors;

[0011] Collect the driver's driving change model and the driver's eye stimulation model, and build a reliability evaluation system for tunnel light field information flow and driver's visual response characteristics based on the driver's driving change model and the driver's eye stimulation model;

[0012] A smoke test is triggered based on a tunnel scene, and smoke concentration is collected. The smoke penetration coefficient is defined based on the smoke concentration and the brightness of each light source in the tunnel lighting. The smoke penetration effect level is defined based on the smoke penetration coefficient. If the smoke penetration effect level is lower than the preset smoke penetration effect level, the brightness of each light source in the tunnel lighting is adjusted.

[0013] Optionally, in the tunnel scene, defining a passage area in the tunnel scene and dynamically collecting lighting data in the passage area includes:

[0014] Collect the location of the tunnel and build the corresponding tunnel scene according to the location of the tunnel;

[0015] In the tunnel scene, traverse the tunnel scene;

[0016] Defining a passage area within the tunnel scene based on traversal of the tunnel scene;

[0017] Define lighting collection paths based on traffic areas;

[0018] Dynamic collection is performed along the lighting collection path to dynamically collect lighting data in the passage site.

[0019] Optionally, the determining effective lighting data based on the screening of lighting data in the traffic area, and constructing a tunnel lighting system according to the effective lighting data, includes:

[0020] Freeze the lighting data in the traffic area;

[0021] Trigger anomaly detection based on lighting data in the traffic area, and filter the lighting data in the traffic area;

[0022] Determine the valid lighting data based on the screening of lighting data in the traffic venue;

[0023] According to the division of effective lighting data, corresponding dimensions are matched, and multiple lighting data of the same dimension are used to construct corresponding lighting data sets;

[0024] Construct a tunnel lighting system based on lighting data sets of multiple different dimensions.

[0025] Optionally, constructing a three-dimensional evaluation system based on the tunnel lighting system includes:

[0026] Fixed frame tunnel lighting system;

[0027] According to the division of the tunnel lighting system, multiple lighting subsystems are formed;

[0028] Define corresponding system types according to multiple lighting subsystems;

[0029] Multiple lighting subsystems are associated, and a three-dimensional evaluation system is constructed based on the multiple lighting subsystems and corresponding types. In this case, the three-dimensional evaluation system is a multi-dimensional evaluation system for drivers under tunnel lighting.

[0030] Optionally, in the three-dimensional evaluation system, various light intensity parameters in the tunnel lighting are collected, and multiple first influencing factors are defined according to the various light intensity parameters and the driver, and the driver's driving change model is constructed based on the multiple first influencing factors; at the same time, second influencing factors are defined according to the light color parameters of the light source in the tunnel lighting and the driver's driving state, and the driver's eye stimulation model is constructed based on the multiple second influencing factors, including:

[0031] In the three-dimensional evaluation system, various light intensity parameters in tunnel lighting are collected;

[0032] Constructing a light intensity parameter set based on each light intensity parameter;

[0033] Associating a set of light intensity parameters with a driving state of the driver;

[0034] Defining a plurality of first influencing factors according to the light intensity parameter set and the driving state of the driver;

[0035] constructing a driving change model of the driver based on a plurality of first influencing factors;

[0036] defining a second influencing factor according to the light color parameters of the light source in the tunnel lighting and the driving state of the driver, and constructing a driver's eye stimulation model based on multiple second influencing factors;

[0037] Optionally, the collecting of the driver's driving change model and the driver's eye stimulation model, and constructing a reliability evaluation system of the tunnel light field information flow and the driver's visual response characteristics according to the driver's driving change model and the driver's eye stimulation model, include:

[0038] Collecting the driving change model of the driver and the eye stimulation model of the driver;

[0039] associating a driver's eye irritation model with a driver's eye irritation model;

[0040] According to the driver's driving change model and the driver's eye stimulation model, a reliability evaluation system of tunnel light field information flow and driver's visual response characteristics is established.

[0041] Optionally, the collecting of the driver's driving change model and the driver's eye stimulation model, and constructing a reliability evaluation system of the tunnel light field information flow and the driver's visual response characteristics according to the driver's driving change model and the driver's eye stimulation model, further includes:

[0042] In the reliability evaluation system of tunnel light field information flow and driver visual response characteristics, the reliability evaluation system introduces the reliability theory, the structural bearing capacity is S, the load is D, and the corresponding probability density function is f s (S) and f d (D), S and D are independent of each other, then the performance function Z is expressed as:

[0043] Z=g(S,D)=SD

[0044] When Z = 0, the system is in the best state, indicating that the light field environment and visual requirements are consistent, and its failure probability is:

[0045]

[0046] The random variables S and D obey the normal distribution, and their means and standard deviations are μs, μd and σs respectively. Then the performance function Z = g(S, D) = SD also obeys the normal distribution, and its means and standard deviations are

[0047] μz=μs-μd and

[0048] Failure probability Perform a standard normal transformation on it, have:

[0049]

[0050] In the formula, Φ is the standard normal distribution function; let have:

[0051] P f =Φ(-β)

[0052] In the formula, β is called the reliability index;

[0053]

[0054] Optionally, triggering a smoke test based on a tunnel scene and collecting smoke concentration, defining a smoke penetration coefficient based on the smoke concentration and the brightness of each light source in the tunnel lighting, defining a smoke penetration effect level based on the smoke penetration coefficient, and triggering adjustment of the brightness of each light source in the tunnel lighting if the smoke penetration effect level is lower than a preset smoke penetration effect level, includes:

[0055] Trigger smoke test based on tunnel scenario;

[0056] In the tunnel scene, collect smoke concentration;

[0057] Correlate smoke concentration and the brightness of each light source in tunnel lighting;

[0058] The smoke penetration factor is defined based on the smoke concentration and the brightness of each light source in the tunnel lighting;

[0059] Defines the level of smoke penetration effect based on the smoke penetration coefficient.

[0060] Optionally, the smoke test is triggered based on the tunnel scene, and the smoke concentration is collected, a smoke penetration coefficient is defined based on the smoke concentration and the brightness of each light source in the tunnel lighting, and a smoke penetration effect level is defined based on the smoke penetration coefficient. If the smoke penetration effect level is lower than the preset smoke penetration effect level, the adjustment of the brightness of each light source in the tunnel lighting is triggered, and further includes:

[0061] If the smoke penetration effect level is lower than the preset smoke penetration effect level, then the level difference between the smoke penetration effect level and the preset smoke penetration effect level is defined;

[0062] triggering corresponding brightness optimization logic based on the level difference, and triggering adjustment of the brightness of each light source in the tunnel lighting based on the brightness optimization logic;

[0063] At this time, adjust the brightness of each light source to conduct smoke penetration tests at different brightness levels; adjust the light source to be tested and the variable light source group to conduct smoke penetration tests under lighting conditions of LED lights with different color temperatures.

[0064] The smoke penetration rate η is used to evaluate the smoke penetration performance of the light source, and its expression is as follows:

[0065]

[0066] Where I(λ) is the light intensity value under a certain smoke concentration; I0(λ) is the light intensity value when the smoke concentration = 0.

[0067] In addition, an embodiment of the present invention further provides a light field evaluation device in a traffic area, and the light field evaluation device in the traffic area includes:

[0068] The acquisition module is used to define the passage area in the tunnel scene and dynamically acquire the lighting data in the passage area;

[0069] A system module is used to determine effective lighting data based on the screening of lighting data in the passage area, and to construct a tunnel lighting system based on the effective lighting data;

[0070] A three-dimensional evaluation module is used to construct a three-dimensional evaluation system based on the tunnel lighting system;

[0071] An eye stimulation module is used to collect various light intensity parameters in the tunnel lighting in the three-dimensional evaluation system, and define multiple first influencing factors according to the various light intensity parameters and the driver, and build a driving change model of the driver based on the multiple first influencing factors; at the same time, define a second influencing factor according to the light color parameters of the light source in the tunnel lighting and the driving state of the driver, and build an eye stimulation model of the driver based on the multiple second influencing factors;

[0072] A reliability module is used to collect the driver's driving change model and the driver's eye stimulation model, and to build a reliability evaluation system for the tunnel light field information flow and the driver's visual response characteristics based on the driver's driving change model and the driver's eye stimulation model;

[0073] The smoke penetration effect module is used to trigger the smoke test based on the tunnel scene and collect the smoke concentration. The smoke penetration coefficient is defined based on the smoke concentration and the brightness of each light source in the tunnel lighting. The smoke penetration effect level is defined based on the smoke penetration coefficient. If the smoke penetration effect level is lower than the preset smoke penetration effect level, the brightness of each light source in the tunnel lighting is adjusted.

[0074] In an embodiment of the present invention, through the method in the embodiment of the present invention, a passage area in a tunnel scene is defined in the tunnel scene, and lighting data in the passage area is dynamically collected; effective lighting data is determined based on the screening of lighting data in the passage area, and a tunnel lighting system is constructed according to the effective lighting data; a three-dimensional evaluation system is constructed according to the tunnel lighting system; in the three-dimensional evaluation system, various light intensity parameters in the tunnel lighting are collected, and multiple first influencing factors are defined according to the various light intensity parameters and the driver, and a driver's driving change model is constructed based on the multiple first influencing factors; at the same time, a second influencing factor is defined according to the light color parameters of the light source in the tunnel lighting and the driving state of the driver, and a driver's eye stimulation model is constructed based on the multiple second influencing factors; the driver's driving change model and the driver's eye stimulation model are collected, and a reliability evaluation system of the tunnel light field information flow and the driver's visual response characteristics is constructed according to the driver's driving change model and the driver's eye stimulation model, so as to control the driver's driving change model and the driver's eye stimulation model in multiple dimensions, so as to ensure the accuracy of the reliability evaluation system, and perform dynamic lighting tests on tunnel scenes and drivers.

[0075] In addition, a smoke test is triggered based on the tunnel scene, and the smoke concentration is collected. The smoke penetration coefficient is defined based on the smoke concentration and the brightness of each light source in the tunnel lighting. The smoke penetration effect level is defined based on the smoke penetration coefficient. If the smoke penetration effect level is lower than the preset smoke penetration effect level, the brightness of each light source in the tunnel lighting is adjusted, thereby realizing autonomous regulation of each light source in the tunnel lighting.

[0076] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0078] Figure 1 is a flow chart of a light field evaluation method in a traffic venue in an embodiment of the present invention;

[0079] Figure 2 is a flow chart of S11 in the light field evaluation method in a traffic venue in an embodiment of the present invention;

[0080] Figure 3 is a flow chart of S12 in the light field evaluation method in a traffic venue in an embodiment of the present invention;

[0081] Figure 4is a flow chart of S13 in the light field evaluation method in a traffic venue in an embodiment of the present invention;

[0082] Figure 5 is a flow chart of S14 in the light field evaluation method in a traffic venue in an embodiment of the present invention;

[0083] Figure 6 is a flow chart of S15 in the light field evaluation method in a traffic venue in an embodiment of the present invention;

[0084] Figure 7 is a flow chart of S16 in the light field evaluation method in a traffic venue in an embodiment of the present invention;

[0085] Figure 8 Schematic diagram of the structure of a light field evaluation device in a traffic area according to an embodiment of the present invention;

[0086] Fig. 9 The figure is a hardware diagram of an electronic device according to an exemplary embodiment. DETAILED DESCRIPTION

[0087] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0088] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0089] See also Figures 1 to 9 A light field evaluation method in a traffic venue is applied to a light field evaluation scenario in a traffic venue. The light field evaluation method in a traffic venue includes:

[0090] Step S11: In the tunnel scene, define the passage area in the tunnel scene, and dynamically collect lighting data in the passage area.

[0091] Step S12: Determine valid lighting data based on the screening of lighting data in the passage, and construct a tunnel lighting system according to the valid lighting data.

[0092] Step S13: constructing a three-dimensional evaluation system according to the tunnel lighting system.

[0093] Step S14: In the three-dimensional evaluation system, various light intensity parameters in the tunnel lighting are collected, and multiple first influencing factors are defined according to the various light intensity parameters and the driver, and the driver's driving change model is constructed based on the multiple first influencing factors; at the same time, second influencing factors are defined according to the light color parameters of the light source in the tunnel lighting and the driver's driving state, and the driver's eye stimulation model is constructed based on the multiple second influencing factors.

[0094] Step S15: collecting the driver's driving change model and the driver's eye stimulation model, and constructing a reliability evaluation system of the tunnel light field information flow and the driver's visual response characteristics according to the driver's driving change model and the driver's eye stimulation model.

[0095] Step S16: triggering a smoke test based on a tunnel scene, collecting smoke concentration, defining a smoke penetration coefficient based on the smoke concentration and the brightness of each light source in the tunnel lighting, defining a smoke penetration effect level based on the smoke penetration coefficient, and triggering adjustment of the brightness of each light source in the tunnel lighting if the smoke penetration effect level is lower than a preset smoke penetration effect level.

[0096] In an embodiment of the present invention, through the method in the embodiment of the present invention, in a tunnel scene, a passage area in the tunnel scene is defined, and lighting data in the passage area is dynamically collected; effective lighting data is determined based on the screening of lighting data in the passage area, and a tunnel lighting system is constructed according to the effective lighting data; a three-dimensional evaluation system is constructed according to the tunnel lighting system; in the three-dimensional evaluation system, various light intensity parameters in the tunnel lighting are collected, and multiple first influencing factors are defined according to the various light intensity parameters and the driver, and a driver's driving change model is constructed based on the multiple first influencing factors; at the same time, a second influencing factor is defined according to the light color parameters of the light source in the tunnel lighting and the driving state of the driver, and a driver's eye stimulation model is constructed based on the multiple second influencing factors; the driver's driving change model and the driver's eye stimulation model are collected, and a reliability evaluation system of the tunnel light field information flow and the driver's visual response characteristics is constructed according to the driver's driving change model and the driver's eye stimulation model, so as to control the driver's driving change model and the driver's eye stimulation model in multiple dimensions, so as to ensure the accuracy of the reliability evaluation system, and perform dynamic lighting tests on tunnel scenes and drivers.

[0097] In addition, a smoke test is triggered based on the tunnel scene, and the smoke concentration is collected. The smoke penetration coefficient is defined based on the smoke concentration and the brightness of each light source in the tunnel lighting. The smoke penetration effect level is defined based on the smoke penetration coefficient. If the smoke penetration effect level is lower than the preset smoke penetration effect level, the brightness of each light source in the tunnel lighting is adjusted, thereby realizing autonomous regulation of each light source in the tunnel lighting.

[0098] refer to Figure 2 , in step S11, in the tunnel scene, a passage area in the tunnel scene is defined, and lighting data in the passage area is dynamically collected;

[0099] In the specific implementation process of the present invention, the specific steps may be:

[0100] S111: collecting the location of the tunnel, and constructing a corresponding tunnel scene according to the location of the tunnel;

[0101] S112: in the tunnel scene, traverse the tunnel scene;

[0102] S113: defining a passage area in the tunnel scene based on the traversal of the tunnel scene;

[0103] S114: defining a lighting collection path based on the passage site;

[0104] S115: Perform dynamic collection along the lighting collection path to dynamically collect lighting data in the passage.

[0105] In an embodiment of the present application, the location of the tunnel is collected, and a corresponding tunnel scene is constructed according to the location of the tunnel, so as to facilitate the control of the tunnel scene. At this time, in the tunnel scene, the tunnel scene is traversed; based on the traversal of the tunnel scene, the passage area in the tunnel scene is defined, so as to introduce the passage area in the tunnel scene and further control the passage area in the tunnel scene.

[0106] At this time, a lighting collection path is defined based on the passage site to facilitate dynamic collection along the lighting collection path, so as to dynamically collect the lighting data in the passage site, thereby introducing the lighting data in the passage site, further controlling the lighting data in the passage site, and ensuring the real-time collection of the lighting data in the passage site.

[0107] refer to Figure 3 In step S12, valid lighting data is determined based on the screening of lighting data in the passage area, and a tunnel lighting system is constructed according to the valid lighting data;

[0108] In the specific implementation process of the present invention, the specific steps may be:

[0109] S121: Freeze the lighting data in the traffic area.

[0110] S122: triggering anomaly detection based on the lighting data in the passage area, and screening the lighting data in the passage area.

[0111] S123: Determine whether there is valid lighting data based on screening of lighting data in the traffic area.

[0112] S124: Dimensions corresponding to the division of effective lighting data.

[0113] S125: Construct a corresponding lighting data set from multiple lighting data of the same dimension.

[0114] S126: Construct a tunnel lighting system based on lighting data sets of multiple different dimensions.

[0115] In an embodiment of the present application, the lighting data in the passage is frozen, and the lighting data in the passage is further controlled, so as to trigger anomaly detection based on the lighting data in the passage, and the lighting data in the passage is screened; in an embodiment, dynamic imaging, noise elimination and other technologies are used to eliminate invalid data of tunnel LED lighting light field detection, accurately obtain the continuous brightness distribution of the tunnel pavement, and improve the stability and detection accuracy of dynamic light field detection equipment.

[0116] At the same time, based on the principle of direct light beam and secondary reflection, the reflection characteristics of tunnel pavement and wall materials are tested, a brightness level conversion model between the driver's field of view angle plane and other spatial planes is established, the hardware composition and parameter design of the tunnel LED light field dynamic detection equipment are optimized, and a set of technical standards for the light field dynamic detection hardware system suitable for tunnel LED lighting are established.

[0117] In addition, effective lighting data is determined based on the screening of lighting data in the passage; corresponding dimensions are determined according to the division of effective lighting data; multiple lighting data of the same dimension are used to construct corresponding lighting data sets; a tunnel lighting system is constructed based on lighting data sets of multiple different dimensions, so as to facilitate multi-dimensional control according to lighting data sets of multiple different dimensions, thereby ensuring accurate control of the tunnel lighting system.

[0118] refer to Figure 4 , in step S13, a three-dimensional evaluation system is constructed according to the tunnel lighting system;

[0119] In the specific implementation process of the present invention, the specific steps may be:

[0120] S131: Fixed frame tunnel lighting system.

[0121] S132: forming a plurality of lighting subsystems according to the division of the tunnel lighting system.

[0122] S133: Define corresponding system types according to the multiple lighting subsystems.

[0123] S134: Associating multiple lighting subsystems, and constructing a three-dimensional evaluation system based on the multiple lighting subsystems and corresponding types. At this time, the three-dimensional evaluation system is a multi-dimensional evaluation system for drivers under tunnel lighting.

[0124] In the embodiments of the present application, the tunnel lighting system is frozen, the tunnel lighting system is further controlled, and multiple lighting subsystems are formed according to the division of the tunnel lighting system, so as to introduce multiple lighting subsystems and realize further processing of multiple lighting subsystems.

[0125] Therefore, corresponding system types are defined according to multiple lighting subsystems; multiple lighting subsystems are associated, and a three-dimensional evaluation system is constructed based on the multiple lighting subsystems and the corresponding types. At this time, the three-dimensional evaluation system is a multi-dimensional evaluation system for drivers under tunnel lighting.

[0126] In the three-dimensional evaluation system, the visual efficacy method is used to study the influence of tunnel lighting brightness level, uniformity and other light intensity parameters on driver reaction time, pupil area, blinking frequency and other parameters, and to construct a brightness level-uniformity relationship curve that meets the driver's comprehensive visual needs.

[0127] At the same time, based on the theory of mesopic vision photometry and chromaticity, combined with visual performance experiments, the influence of light color parameters such as color temperature and spectral distribution of tunnel lighting sources on the driver's pupil area change rate, blinking frequency and other biopsychological reactions is studied, and a quantitative relationship model of light source color temperature-visual performance under the human eye three-stimulus value chromaticity coordinate system is established.

[0128] Furthermore, based on the Rayleigh scattering and Mie scattering theories, combined with the light source smoke penetration test, the influence of spectral distribution on the smoke penetration of tunnel LED light sources under haze systems with different particle size distributions is studied, and the quantitative evaluation index and threshold of light source smoke penetration based on spectral distribution are established; at the same time, the CIE sky brightness model is used, combined with the on-site measurement and investigation of the tunnel dimming control benchmark parameters, to obtain the brightness change curves outside the tunnel at different longitudes and latitudes and tunnel entrance directions, and propose an energy consumption evaluation method for tunnel LED lighting systems that matches lighting needs; therefore, based on the reliability theory, the weight influence relationship between the various comprehensive visual efficacy indicators of the driver is determined, and a driving safety and comfort evaluation system based on the tunnel light field information flow is constructed.

[0129] refer to Figure 5, S14: In the three-dimensional evaluation system, various light intensity parameters in the tunnel lighting are collected, and multiple first influencing factors are defined according to the various light intensity parameters and the driver, and the driver's driving change model is constructed based on the multiple first influencing factors; at the same time, a second influencing factor is defined according to the light color parameters of the light source in the tunnel lighting and the driver's driving state, and the driver's eye stimulation model is constructed based on the multiple second influencing factors;

[0130] In the specific implementation process of the present invention, the specific steps may be:

[0131] S141: In the three-dimensional evaluation system, various light intensity parameters in the tunnel lighting are collected.

[0132] S142: Constructing a light intensity parameter set based on each light intensity parameter.

[0133] S143: Associating the light intensity parameter set with the driver's driving status.

[0134] S144: Define a plurality of first influencing factors according to the light intensity parameter set and the driving status of the driver.

[0135] S145: Constructing a driving change model of the driver based on the plurality of first influencing factors.

[0136] S146: defining a second influencing factor according to the light color parameters of the light source in the tunnel lighting and the driving state of the driver, and constructing an eye stimulation model for the driver based on the plurality of second influencing factors.

[0137] In an embodiment of the present application, in a three-dimensional evaluation system, various light intensity parameters in tunnel lighting are collected, and each light intensity parameter is controlled, so that a light intensity parameter set is constructed based on each light intensity parameter, thereby achieving further processing of the light intensity parameter set.

[0138] At this time, the light intensity parameter set and the driver's driving status are associated; multiple first influencing factors are defined according to the light intensity parameter set and the driver's driving status; and the driver's driving change model is constructed based on the multiple first influencing factors, so as to introduce the driver's driving change model and realize multi-dimensional control of the light intensity parameter set and the driver's driving status.

[0139] At the same time, the second influencing factor is defined according to the light color parameters of the light source in the tunnel lighting and the driving status of the driver, and the driver's eye stimulation model is constructed based on multiple second influencing factors, so as to introduce the driver's eye stimulation model and realize the multi-dimensional control of the light color parameters of the light source in the tunnel lighting and the driving status of the driver.

[0140] refer to Figure 6, S15: collecting the driver's driving change model and the driver's eye stimulation model, and constructing a reliability evaluation system of the tunnel light field information flow and the driver's visual response characteristics according to the driver's driving change model and the driver's eye stimulation model;

[0141] In the specific implementation process of the present invention, the specific steps may be:

[0142] S151: Collecting the driver's driving change model and the driver's eye stimulation model.

[0143] S152: Associating the driver's eye irritation model with the driver's eye irritation model.

[0144] S153: Constructing a reliability evaluation system of tunnel light field information flow and driver's visual response characteristics according to the driver's driving change model and the driver's eye stimulation model.

[0145] In an embodiment of the present application, in a tunnel scene, a passage area within the tunnel scene is defined, and lighting data in the passage area is dynamically collected; effective lighting data is determined based on the screening of lighting data in the passage area, and a tunnel lighting system is constructed based on the effective lighting data; a three-dimensional evaluation system is constructed based on the tunnel lighting system; in the three-dimensional evaluation system, various light intensity parameters in the tunnel lighting are collected, and multiple first influencing factors are defined based on the various light intensity parameters and the driver, and a driver's driving change model is constructed based on the multiple first influencing factors; at the same time, a second influencing factor is defined based on the light color parameters of the light source in the tunnel lighting and the driving state of the driver, and a driver's eye stimulation model is constructed based on the multiple second influencing factors; the driver's driving change model and the driver's eye stimulation model are collected, and a reliability evaluation system of the tunnel light field information flow and the driver's visual response characteristics is constructed based on the driver's driving change model and the driver's eye stimulation model, so as to control the driver's driving change model and the driver's eye stimulation model in multiple dimensions, so as to ensure the accuracy of the reliability evaluation system, and perform dynamic lighting tests on tunnel scenes and drivers.

[0146] At this time, the driver's driving change model and the driver's eye stimulation model are collected to associate the driver's eye stimulation model and the driver's eye stimulation model, so as to construct a reliability evaluation system of the tunnel light field information flow and the driver's visual response characteristics according to the driver's driving change model and the driver's eye stimulation model. The reliability evaluation system is introduced to ensure the accuracy of the reliability evaluation system.

[0147] In the reliability evaluation system of tunnel light field information flow and driver visual response characteristics, the reliability evaluation system introduces the reliability theory, the structural bearing capacity is S, the load is D, and the corresponding probability density function is fs (S) and f d (D), S and D are independent of each other, then the performance function Z is expressed as:

[0148] Z=g(S,D)=SD

[0149] When Z = 0, the system is in the best state, indicating that the light field environment and visual requirements are consistent, and its failure probability is:

[0150]

[0151] The random variables S and D obey the normal distribution, and their means and standard deviations are μs, μd and σs respectively. Then the performance function Z = g(S, D) = SD also obeys the normal distribution, and its means and standard deviations are

[0152] μz=μs-μd and

[0153] Failure probability Perform a standard normal transformation on it, have:

[0154]

[0155] In the formula, Φ is the standard normal distribution function; let have:

[0156] P f =Φ(-β)

[0157] In the formula, β is called the reliability index;

[0158]

[0159] At this time, the failure probability can be indirectly characterized by the reliability index, which is essentially the same. The mean value of the performance function is μz, the standard deviation is σz, and the probability density function is f z (Z). On the horizontal axis Z, the distance from the origin (Z = 0, failure point) to the average value μz of the density function curve is βσz. If βσz is large, the area of ​​the shaded part is small, the failure probability is small, and the reliability of the structure is large. On the contrary, if βσz is small, the area of ​​the shaded part is large, the failure probability is large, and the reliability of the structure is small. Therefore, β and P f — can be used as an indicator to measure the reliability of the structure. In this way, the geometric meaning of the reliability index is to measure the distance from the mean to the origin in units of standard deviation. The larger βσz is, the smaller the failure probability is, and the smaller βσz is, the greater the failure probability is.

[0160] In addition, an expert evaluation feedback system was introduced. Through on-site measurements and surveys, a large amount of feedback data from tunnel LED dimming control and detection equipment was obtained. Installation procedures for tunnel LED dimming control and detection equipment, as well as a correction model for tunnel illumination and brightness detection data, were proposed, and an expert feedback subsystem for tunnel LED lighting efficiency was established.

[0161] At the same time, based on the characteristics of light field parameter changes that affect lighting comfort and safety during the dimming process of tunnel LED lighting, the influence mechanism and law of brightness change amplitude and frequency on visual efficacy are studied, and the tunnel LED lighting dimming safety control threshold is proposed, and an expert feedback subsystem for tunnel LED dimming control is established. In addition, through on-site testing, the actual lighting needs of tunnels with different longitudes and latitudes and opening directions are analyzed, and the tunnel LED lighting light field energy consumption detection data is compared to establish an expert feedback subsystem for tunnel LED lighting energy consumption.

[0162] refer to Figure 7 , S16: triggering a smoke test based on a tunnel scene, collecting smoke concentration, defining a smoke penetration coefficient based on the smoke concentration and the brightness of each light source in the tunnel lighting, defining a smoke penetration effect level based on the smoke penetration coefficient, and triggering adjustment of the brightness of each light source in the tunnel lighting if the smoke penetration effect level is lower than a preset smoke penetration effect level;

[0163] In the specific implementation process of the present invention, the specific steps may be:

[0164] S161: Trigger smoke test based on tunnel scenario.

[0165] S162: In the tunnel scene, collect smoke concentration.

[0166] S163: Correlate the smoke density and the brightness of each light source in the tunnel lighting.

[0167] S164: Define a smoke penetration coefficient based on smoke concentration and brightness of each light source in the tunnel lighting.

[0168] S165: Defines the smoke penetration effect level based on the smoke penetration coefficient.

[0169] S166: If the smoke penetration effect level is lower than the preset smoke penetration effect level, define the level difference between the smoke penetration effect level and the preset smoke penetration effect level; trigger the corresponding brightness optimization logic based on the level difference, and trigger the adjustment of the brightness of each light source in the tunnel lighting based on the brightness optimization logic.

[0170] In the specific implementation process of the present invention, a smoke test is triggered based on a tunnel scene, and the smoke concentration is collected. The smoke penetration coefficient is defined based on the smoke concentration and the brightness of each light source in the tunnel lighting. The smoke penetration effect level is defined based on the smoke penetration coefficient. If the smoke penetration effect level is lower than the preset smoke penetration effect level, the brightness of each light source in the tunnel lighting is triggered to adjust, thereby realizing autonomous regulation of each light source in the tunnel lighting.

[0171] At this time, a smoke test is triggered based on a tunnel scene; in the tunnel scene, the smoke concentration is collected; the smoke concentration and the brightness of each light source in the tunnel lighting are associated, so as to further control the smoke concentration and the brightness of each light source in the tunnel lighting, so as to define the smoke penetration coefficient based on the smoke concentration and the brightness of each light source in the tunnel lighting.

[0172] Therefore, the smoke penetration effect level is defined based on the smoke penetration coefficient; if the smoke penetration effect level is lower than the preset smoke penetration effect level, the level difference between the smoke penetration effect level and the preset smoke penetration effect level is defined; based on the level difference, the corresponding brightness optimization logic is triggered, and based on the brightness optimization logic, the brightness adjustment of each light source in the tunnel lighting is triggered.

[0173] At this time, adjust the brightness of each light source to conduct smoke penetration tests at different brightness levels; adjust the light source to be tested and the variable light source group to conduct smoke penetration tests under lighting conditions of LED lights with different color temperatures.

[0174] The smoke penetration rate η is used to evaluate the smoke penetration performance of the light source, and its expression is as follows:

[0175]

[0176] Where I(λ) is the light intensity value under a certain smoke concentration; I0(λ) is the light intensity value when the smoke concentration = 0.

[0177] For the smoke test in the tunnel scene, in the test preparation stage, the equipment and instruments are debugged and calibrated, and the test light source is turned on. The test begins after the light source is stable. The spectral distribution of the first light source to be tested is measured by a spectrometer to obtain the spectral radiation energy of each wavelength under smoke-free conditions.

[0178] Turn on the smoke generating system and make it work according to the specified working conditions, so that the smoke diffuses evenly in the dark box and the smoke concentration in the tunnel model reaches the test set value. Use a spectrometer to measure the spectral distribution of the light source. Thus, the spectral distribution and radiation energy of the light source to be tested under the smoke concentration are obtained.

[0179] After the measurement is completed, wait for the smoke to dissipate and clean the fog and wall residues in the tunnel model. Adjust the smoke concentration and conduct smoke penetration tests at different concentrations. After the concentration is adjusted, adjust the light source lighting brightness and conduct smoke penetration tests at different brightness levels. Finally, adjust the light source to be tested and the variable light source group, and conduct smoke penetration tests under different color temperature LED lighting conditions.

[0180] The smoke penetration rate η is used to evaluate the smoke penetration performance of the light source, and its expression is as follows:

[0181]

[0182] Where, I(λ) is the light intensity value under a certain smoke concentration, cd; I0(λ) is the light intensity value when the smoke concentration = 0, cd.

[0183] In summary, through the smoke penetration test, a quantitative relationship curve between the color temperature, spectral distribution and smoke penetration rate of tunnel LED light sources is established, and technical control indicators and thresholds suitable for evaluating the safety of tunnel light fields in haze environments are proposed.

[0184] At the same time, the material reflection characteristic test was introduced; compared with the road lighting environment, due to the special tubular structure of the tunnel, affected by the wall, road surface and top space, the lighting environment in the tunnel is more complex. Accurately grasping the reflection characteristics of the tunnel road surface and wall materials is particularly critical to optimizing the structure and parameters of the light field detection equipment and improving the detection accuracy. Therefore, this project intends to build a material reflection characteristic test platform to measure the reflection characteristics of common tunnel road and wall materials.

[0185] a) Before testing, turn on the light source for preheating. The preheating time is about 20 minutes.

[0186] b) Turn on the computer, imaging luminance meter, mechanical turntable and other testing instruments in sequence, and place the material sample horizontally with the center of the sample located at the intersection of the two rotating axes of the mechanical arm.

[0187] c) Adjust the optical axis angle and focal length of the imaging luminance meter so that the material to be tested is clearly imaged on the luminance meter display screen, and ensure that the center of the field of view of the luminance meter coincides with the center of the material sample to be tested; the error is ≤±0.06.

[0188] d) Adjust and calibrate the mechanical turntable through computer system software.

[0189] e) After all equipment is debugged, start the measurement test and record the brightness data through the system software.

[0190] f) After the brightness test is completed, use an illuminance meter to measure the illuminance of the horizontal plane where the material to be tested is located.

[0191] The measurement of brightness outside the tunnel and the evaluation of lighting energy consumption were introduced;

[0192] The calculation formula for the brightness outside the tunnel is as follows:

[0193] L 20 (S) = γ·L C +ρ·L R +ε·L E +τ·L th

[0194] Where, L C , L R , L E , L th are the brightness of the sky, road surface, environment and tunnel entrance, cd·m -2 ; γ, ρ, ε, τ are the percentages of sky, road surface, environment and tunnel entrance respectively. C , L R , L E , L th They are measured by a brightness meter, and γ, ρ, ε, and τ are determined based on the 20° cone angle field photograph taken at the tunnel entrance.

[0195] In addition, this project is based on the sky brightness model proposed by CIE and combined with the field measured parameters of some tunnels to calculate the tunnel external brightness L in other regions of my country (not Zhejiang Province). 20 (S). The calculation process is as follows.

[0196] 1) Determine the sun position parameters

[0197] a) Sun altitude angle a t

[0198]

[0199] Where l is the longitude of the calculation point, rad; δ is the solar declination angle, rad; t is the solar time, decimal hours.

[0200] b) Solar azimuth a s

[0201]

[0202] 2) Calculate the brightness of direct sunlight

[0203] a) Extraterrestrial illumination E xt

[0204]

[0205] In the formula, E sc is the solar constant, klx; j is the Julian Day.

[0206] b) Sea level illumination E dn

[0207] E dn =E xt e -cm

[0208] Where c is the atmospheric extinction coefficient and m is the optical air mass.

[0209] c) Horizontal illuminance E dh , vertical surface illumination E dv

[0210] E dh =E dn sina t

[0211] E dv =E dn cos a i

[0212] In the formula, a t is the solar altitude angle, rad; a i is the incident angle of direct sunlight relative to the vertical plane, rad.

[0213] d) Scene brightness L' produced by direct sunlight d

[0214]

[0215] Where BRDF (θ, φ; θ', φ') is the bidirectional reflectance distribution function of the scene surface, θ is the incident zenith angle, φ is the incident azimuth angle, θ' is the reflection zenith angle, and φ' is the reflection azimuth angle.

[0216] 3) Calculate the brightness of sky scattered light

[0217] a) According to the sky brightness model recommended by CIE, select various parameters according to the actual situation in each region;

[0218] b) Calculate the brightness L of a certain sky element i and the zenith brightness L z Ratio

[0219]

[0220] in

[0221]

[0222] Where a and b are the hue parameters of brightness; c, d, and e are the parameters of the scattering characteristic curve; x is the shortest angular distance between the sky element and the sun, rad; Z is the angular distance between a certain sky element and the zenith, rad; Z s is the angular distance between the sun and the zenith, rad.

[0223] c) Calculate the zenith brightness L Z

[0224] L Z =(A+B sin C a t )ZL

[0225] Where A is the sunrise / sunset illumination, klx; B is the solar altitude illumination coefficient, klx; C is the solar altitude illumination index; a t is the solar altitude angle, rad; ZL is the zenith brightness factor, kcd / (m 2 ·klx).

[0226] d) Using the solid angle projection theorem, calculate the illumination E generated by the sky element on any plane ki

[0227]

[0228] Where Ω is the solid angle corresponding to the sky element, sr.

[0229] e) Calculate the scattered brightness L' generated by the sky element ki

[0230]

[0231] f) Calculate L' generated by all sky cells k

[0232]

[0233] 4) Calculate the scene brightness value L'

[0234] L′=L d ′+L k '

[0235] 5) According to the proportion and reflection coefficient of each scene at the tunnel entrance, the brightness L outside the tunnel is calculated by weighting 20 (S).

[0236] In summary, according to the brightness L outside the tunnel 20(S) The actual measurement (or theoretical calculation) results are combined with the tunnel design speed and traffic volume to calculate the actual tunnel lighting brightness requirements. When conducting tunnel light field detection, the actual tunnel road brightness is measured using brightness detection equipment. By comparing it with the tunnel lighting requirements, the tunnel lighting energy consumption in different regions (differences in longitude and latitude) and different orientations (sun entry azimuth) is evaluated.

[0237] Light field detection parameter noise elimination technology is introduced. Noise parameter elimination technology is an important step in traffic flow video detection. It processes the set detection area of ​​each frame image to determine whether there is invalid noise data.

[0238] 1) Determine the virtual detection area:

[0239] First, the tunnel lighting video is obtained based on the light field detection device, and multiple sets of continuous brightness images are preprocessed and collected as background images to the cache. A virtual detection line is set at an appropriate position, and the virtual detection area is determined according to its starting and ending coordinates. The virtual detection area generally has two forms: rectangle and detection line. In order to reduce the amount of program calculation, the detection line form with the pixel points on the detection line as the detection object is selected;

[0240] In actual operation, the detection line is not necessarily a continuous straight line segment, but a dot matrix composed of pixels. Therefore, the detection point selects the pixel point that is close to the point on the detection line to form a virtual detection area. Taking the detection line as an example, let P(i,j) be the point on the virtual detection area, and the detection algorithm can be expressed as:

[0241]

[0243] 2) Determine the background template value:

[0244] The background template value refers to the grayscale value of each pixel in the detection area when no brightness noise data is generated. The steps for determining the background template are:

[0245] a) collecting background images;

[0246] b) drawing a virtual detection line;

[0247] c) performing image enhancement processing on the background image;

[0248] d) Reading out the image data of the background image and separating the pixel values ​​of the points in the virtual detection area as the background template values ​​when the noise occurs.

[0249] 3) Noise data analysis and statistics:

[0250] Enhance the real-time collected video image, convert the image into a binary image, subtract the grayscale value of the pixel points in the virtual detection area of ​​the current image from the corresponding points in the background template, and count the points with a subtraction result of 255 as noise data points, and set their grayscale value to 255.

[0251] After judging all points in the virtual detection area, filter the mutation points again. Take the upper left corner of the screen as the coordinate origin, sort all points in the virtual detection line area from top to bottom and from left to right. Assuming that the sequence number of the current processing point is i and the pixel gray value is V[i], if the condition is met

[0252] V[i]=255

[0253] V[i-2]=V[i-1]=V[i]=V[i+1]=V[i+2]

[0254] Then it is considered that this point is indeed a noise data point caused by reasons not related to light field parameters.

[0255] 4) Eliminate noise data and recalculate light field detection parameters.

[0256] In an embodiment of the present invention, through the method in the embodiment of the present invention, in a tunnel scene, a passage area in the tunnel scene is defined, and lighting data in the passage area is dynamically collected; effective lighting data is determined based on the screening of lighting data in the passage area, and a tunnel lighting system is constructed according to the effective lighting data; a three-dimensional evaluation system is constructed according to the tunnel lighting system; in the three-dimensional evaluation system, various light intensity parameters in the tunnel lighting are collected, and multiple first influencing factors are defined according to the various light intensity parameters and the driver, and a driver's driving change model is constructed based on the multiple first influencing factors; at the same time, a second influencing factor is defined according to the light color parameters of the light source in the tunnel lighting and the driving state of the driver, and a driver's eye stimulation model is constructed based on the multiple second influencing factors; the driver's driving change model and the driver's eye stimulation model are collected, and a reliability evaluation system of the tunnel light field information flow and the driver's visual response characteristics is constructed according to the driver's driving change model and the driver's eye stimulation model, so as to control the driver's driving change model and the driver's eye stimulation model in multiple dimensions, so as to ensure the accuracy of the reliability evaluation system, and perform dynamic lighting tests on tunnel scenes and drivers.

[0257] In addition, a smoke test is triggered based on the tunnel scene, and the smoke concentration is collected. The smoke penetration coefficient is defined based on the smoke concentration and the brightness of each light source in the tunnel lighting. The smoke penetration effect level is defined based on the smoke penetration coefficient. If the smoke penetration effect level is lower than the preset smoke penetration effect level, the brightness of each light source in the tunnel lighting is adjusted, thereby realizing autonomous regulation of each light source in the tunnel lighting.

[0258] See also Figure 8 , Figure 8 Schematic diagram of the structure of the light field evaluation device in the traffic area in the embodiment of the present invention.

[0259] like Figure 8 As shown, a light field evaluation device in a traffic area, the light field evaluation device in the traffic area includes:

[0260] The acquisition module 21 is used to define the passage area in the tunnel scene and dynamically acquire the lighting data in the passage area in the tunnel scene;

[0261] The system module 22 is used to determine effective lighting data based on the screening of lighting data in the passage area, and to construct a tunnel lighting system according to the effective lighting data;

[0262] A three-dimensional evaluation module 23, used to construct a three-dimensional evaluation system according to the tunnel lighting system;

[0263] The eye stimulation module 24 is used to collect various light intensity parameters in the tunnel lighting in the three-dimensional evaluation system, and define a plurality of first influencing factors according to the various light intensity parameters and the driver, and construct a driving change model of the driver based on the plurality of first influencing factors; at the same time, define a second influencing factor according to the light color parameters of the light source in the tunnel lighting and the driving state of the driver, and construct an eye stimulation model of the driver based on the plurality of second influencing factors;

[0264] The reliability module 25 is used to collect the driving change model of the driver and the eye stimulation model of the driver, and to construct a reliability evaluation system of the tunnel light field information flow and the driver's visual response characteristics according to the driving change model of the driver and the eye stimulation model of the driver;

[0265] The smoke penetration effect module 26 is used to trigger a smoke test based on a tunnel scene and collect smoke concentration, define a smoke penetration coefficient based on the smoke concentration and the brightness of each light source in the tunnel lighting, and define a smoke penetration effect level based on the smoke penetration coefficient. If the smoke penetration effect level is lower than the preset smoke penetration effect level, the brightness of each light source in the tunnel lighting is adjusted.

[0266] See also Fig. 9 , refer to the following Fig. 9 The electronic device 40 according to this embodiment of the present invention will be described. Fig. 9 The electronic device 40 shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0267] like Fig. 9As shown, the electronic device 40 is in the form of a general computing device. The components of the electronic device 40 may include but are not limited to: at least one processing unit 41, at least one storage unit 42, and a bus 43 connecting different device components (including the storage unit 42 and the processing unit 41).

[0268] The storage unit stores program codes, which can be executed by the processing unit 41, so that the processing unit 41 executes the steps according to various exemplary embodiments of the present invention described in the above “Embodiment Method” section of this specification.

[0269] The storage unit 42 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 421 and / or a cache memory unit 422 , and may further include a read-only memory unit (ROM) 423 .

[0270] The storage unit 42 may also include a program / utility 424 having a set (at least one) of program modules 425, such program modules 425 including but not limited to: operating means, one or more application programs, other program modules and program data, each of which or some combination may include the implementation of a network environment.

[0271] Bus 43 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0272] The electronic device 40 may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device 40, and / or any device that enables the electronic device 40 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 44. Furthermore, the electronic device 40 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 45. Fig. 9 As shown, the network adapter 45 communicates with other modules of the electronic device 40 via the bus 43. It should be understood that although Fig. 9 Not shown, other hardware and / or software modules may be used in conjunction with the electronic device 40, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID devices, tape drives, and data backup planning devices.

[0273] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0274] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium can include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc. In addition, it stores computer program instructions, and when the computer program instructions are executed by a computer, the computer executes the above method.

[0275] In addition, the above is a detailed introduction to the light field evaluation method and device in the passage provided by the embodiment of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for evaluating a light field in a traffic area, characterized in that: Applicable to light field evaluation scenarios in traffic venues; The light field evaluation method in the passage venue comprises: In the tunnel scene, define the passage area in the tunnel scene and dynamically collect the lighting data in the passage area; Determine effective lighting data based on the screening of lighting data in the traffic area, and build a tunnel lighting system based on the effective lighting data; Construct a three-dimensional evaluation system based on the tunnel lighting system; In the three-dimensional evaluation system, various light intensity parameters in the tunnel lighting are collected, and multiple first influencing factors are defined according to the various light intensity parameters and the driver, and the driver's driving change model is constructed based on the multiple first influencing factors; at the same time, the second influencing factors are defined according to the light color parameters of the light source in the tunnel lighting and the driver's driving state, and the driver's eye stimulation model is constructed based on the multiple second influencing factors; Collect the driver's driving change model and the driver's eye stimulation model, and build a reliability evaluation system for tunnel light field information flow and driver's visual response characteristics based on the driver's driving change model and the driver's eye stimulation model; A smoke test is triggered based on a tunnel scene, and smoke concentration is collected. The smoke penetration coefficient is defined based on the smoke concentration and the brightness of each light source in the tunnel lighting. The smoke penetration effect level is defined based on the smoke penetration coefficient. If the smoke penetration effect level is lower than the preset smoke penetration effect level, the brightness of each light source in the tunnel lighting is adjusted.

2. The method for evaluating the light field in a traffic area according to claim 1, characterized in that: In the tunnel scene, a passage area in the tunnel scene is defined, and lighting data in the passage area is dynamically collected, including: Collect the location of the tunnel and build the corresponding tunnel scene according to the location of the tunnel; In the tunnel scene, traverse the tunnel scene; Defining a passage area within the tunnel scene based on traversal of the tunnel scene; Define lighting collection paths based on traffic areas; Dynamic collection is performed along the lighting collection path to dynamically collect lighting data in the passage site.

3. The method for evaluating the light field in a traffic area according to claim 2, characterized in that: The effective lighting data is determined based on the screening of lighting data in the passage, and a tunnel lighting system is constructed according to the effective lighting data, including: Freeze the lighting data in the traffic area; Trigger anomaly detection based on lighting data in the traffic area, and filter the lighting data in the traffic area; Determine the valid lighting data based on the screening of lighting data in the traffic venue; The corresponding dimensions are determined according to the division of effective lighting data; Construct a corresponding lighting data set from multiple lighting data of the same dimension; Construct a tunnel lighting system based on lighting data sets of multiple different dimensions.

4. The method for evaluating the light field in a traffic area according to claim 3, characterized in that: The three-dimensional evaluation system is constructed according to the tunnel lighting system, including: Fixed frame tunnel lighting system; According to the division of the tunnel lighting system, multiple lighting subsystems are formed; Define corresponding system types according to multiple lighting subsystems; Multiple lighting subsystems are associated, and a three-dimensional evaluation system is constructed based on the multiple lighting subsystems and corresponding types. In this case, the three-dimensional evaluation system is a multi-dimensional evaluation system for drivers under tunnel lighting.

5. The method for evaluating the light field in a traffic area according to claim 4, characterized in that: In the three-dimensional evaluation system, various light intensity parameters in the tunnel lighting are collected, and multiple first influencing factors are defined according to the various light intensity parameters and the driver, and the driver's driving change model is constructed based on the multiple first influencing factors; at the same time, second influencing factors are defined according to the light color parameters of the light source in the tunnel lighting and the driver's driving state, and the driver's eye stimulation model is constructed based on the multiple second influencing factors, including: In the three-dimensional evaluation system, various light intensity parameters in tunnel lighting are collected; Constructing a light intensity parameter set based on each light intensity parameter; Associating a set of light intensity parameters with a driving state of the driver; Defining a plurality of first influencing factors according to the light intensity parameter set and the driving state of the driver; constructing a driving change model of the driver based on a plurality of first influencing factors; A second influencing factor is defined according to the light color parameters of the light source in the tunnel lighting and the driving state of the driver, and a driver's eye stimulation model is constructed based on a plurality of second influencing factors.

6. The method for evaluating the light field in a traffic area according to claim 5, characterized in that: The method collects the driving change model of the driver and the eye stimulation model of the driver, and constructs a reliability evaluation system of the tunnel light field information flow and the driver's visual response characteristics according to the driving change model of the driver and the eye stimulation model of the driver, including: Collecting the driving change model of the driver and the eye stimulation model of the driver; associating a driver's eye irritation model with a driver's eye irritation model; According to the driver's driving change model and the driver's eye stimulation model, a reliability evaluation system of tunnel light field information flow and driver's visual response characteristics is constructed.

7. The method for evaluating the light field in a traffic area according to claim 6, characterized in that: The collecting of the driving change model of the driver and the eye stimulation model of the driver, and building a reliability evaluation system of the tunnel light field information flow and the driver's visual response characteristics according to the driving change model of the driver and the eye stimulation model of the driver, further includes: In the reliability evaluation system of tunnel light field information flow and driver visual response characteristics, the reliability evaluation system introduces the reliability theory, the structural bearing capacity is S, the load is D, and the corresponding probability density function is f s (S) and f d (D), S and D are independent of each other, then the performance function Z is expressed as: Z=g(S,D)=SD When Z = 0, the system is in the best state, indicating that the light field environment and visual requirements are consistent, and its failure probability is: The random variables S and D obey the normal distribution, and their mean and standard deviation are μs, μd and σs respectively. Then the performance function Z = g(S, D) = SD also obeys the normal distribution, and its mean and standard deviation are: μz=μs-μd and Failure probability Perform a standard normal transformation on it, have: In the formula, Φ is the standard normal distribution function; let have: P f =Φ(-β) In the formula, β is called the reliability index; 8. The method for evaluating the light field in a traffic area according to claim 7, characterized in that: The smoke test is triggered based on the tunnel scene, and the smoke concentration is collected. The smoke penetration coefficient is defined based on the smoke concentration and the brightness of each light source in the tunnel lighting. The smoke penetration effect level is defined based on the smoke penetration coefficient. If the smoke penetration effect level is lower than the preset smoke penetration effect level, the brightness of each light source in the tunnel lighting is adjusted, including: Trigger smoke test based on tunnel scenario; In the tunnel scene, collect smoke concentration; Correlate smoke concentration and the brightness of each light source in tunnel lighting; The smoke penetration factor is defined based on the smoke concentration and the brightness of each light source in the tunnel lighting; Defines the level of smoke penetration effect based on the smoke penetration coefficient.

9. The method for evaluating light field in a traffic area according to claim 8, characterized in that: The smoke test is triggered based on the tunnel scene, and the smoke concentration is collected, the smoke penetration coefficient is defined based on the smoke concentration and the brightness of each light source in the tunnel lighting, and the smoke penetration effect level is defined based on the smoke penetration coefficient. If the smoke penetration effect level is lower than the preset smoke penetration effect level, the brightness of each light source in the tunnel lighting is triggered to adjust, and further includes: If the smoke penetration effect level is lower than the preset smoke penetration effect level, a level difference between the smoke penetration effect level and the preset smoke penetration effect level is defined; a corresponding brightness optimization logic is triggered based on the level difference, and adjustment of the brightness of each light source in the tunnel lighting is triggered based on the brightness optimization logic; At this time, adjust the brightness of each light source to conduct smoke penetration tests at different brightness levels; adjust the light source to be tested and the variable light source group to conduct smoke penetration tests under lighting conditions of LED lights with different color temperatures. The smoke penetration rate η is used to evaluate the smoke penetration performance of the light source, and its expression is as follows: Where I(λ) is the light intensity value under a certain smoke concentration; I0(λ) is the light intensity value when the smoke concentration is 0.

10. A light field evaluation device in a traffic area, characterized in that: The light field evaluation device in the passage is applied to the light field evaluation method in the passage as claimed in any one of claims 1 to 9, and the light field evaluation device in the passage comprises: The acquisition module is used to define the passage area in the tunnel scene and dynamically acquire the lighting data in the passage area; A system module is used to determine effective lighting data based on the screening of lighting data in the passage area, and to construct a tunnel lighting system based on the effective lighting data; A three-dimensional evaluation module is used to construct a three-dimensional evaluation system based on the tunnel lighting system; An eye stimulation module is used to collect various light intensity parameters in the tunnel lighting in the three-dimensional evaluation system, and define multiple first influencing factors according to the various light intensity parameters and the driver, and build a driving change model of the driver based on the multiple first influencing factors; at the same time, define a second influencing factor according to the light color parameters of the light source in the tunnel lighting and the driving state of the driver, and build an eye stimulation model of the driver based on the multiple second influencing factors; A reliability module is used to collect the driver's driving change model and the driver's eye stimulation model, and to build a reliability evaluation system for the tunnel light field information flow and the driver's visual response characteristics based on the driver's driving change model and the driver's eye stimulation model; The smoke penetration effect module is used to trigger the smoke test based on the tunnel scene and collect the smoke concentration. The smoke penetration coefficient is defined based on the smoke concentration and the brightness of each light source in the tunnel lighting. The smoke penetration effect level is defined based on the smoke penetration coefficient. If the smoke penetration effect level is lower than the preset smoke penetration effect level, the brightness of each light source in the tunnel lighting is adjusted.

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