Illumination light control method and equipment for traffic tunnel and medium

Abstract

The invention discloses an illumination light control method and equipment for a traffic tunnel and a medium, belongs to the technical field of tunnel illumination control, and aims to solve the problems that the illumination light control in the existing tunnel is relatively simple, the illumination light is difficult to adjust in time according to external light or traffic conditions in the tunnel, the visual adaptability of a driver is easy to be poor, and the illumination effect is poor. And safe driving of tunnel vehicles is not facilitated. The method comprises the following steps: collecting regional visibility data in each segmented scene; performing tunnel illumination feature analysis on the regional visibility data, and determining first control data for adjusting illumination light; performing compensation control on the illumination brightness of the illumination light to obtain second control data; performing brightness partial correlation prediction on the external brightness data and the regional traffic flow data under related human vision optimal experience, and determining optimal experience brightness data; and performing adaptive adjustment of control parameters on the first control data and the second control data to obtain optimal brightness control data.

Description

Technical Field

[0001] The present application relates to the field of tunnel lighting control, and in particular to a method, device and medium for controlling lighting in a traffic tunnel. Background Art

[0002] my country's transportation industry is developing continuously, and the scale of urban rail transit and highways is getting larger and larger. Driving safety, maintenance work safety and energy saving have received more and more attention. Tunnels are areas where safety accidents frequently occur. When cars drive in and out of tunnels, the light changes drastically, causing the driver's visual environment to change significantly, thus affecting the driver's driving state. At the same time, the traffic flow in traffic tunnels is relatively complex, and a good lighting environment is also an important factor affecting the driving safety of vehicles in tunnels. At the same time, attention should be paid to the high energy consumption and low intelligence level of tunnel lighting, so as to improve the intelligent control of lighting in tunnels.

[0003] The existing tunnel lighting safety has high energy consumption and the intelligent control integration is relatively simple. The brightness of the lighting designed in the tunnel is generally fixed. It is difficult to complete the intelligent control of the tunnel lights in advance according to the vehicles in the tunnel and the external light conditions. It is easy to have an adverse effect on the driver's visual environment, thereby affecting the safe driving of vehicles in the tunnel and increasing the safety risks of traffic tunnels. Summary of the invention

[0004] The embodiments of the present application provide a method, device and medium for controlling lighting in a traffic tunnel, which are used to solve the following technical problems: the lighting control in existing tunnels is relatively simple, and it is difficult to adjust the lighting in time according to external light or traffic conditions in the tunnel, which easily leads to poor visual adaptability of the driver, which is not conducive to safe driving of tunnel vehicles.

[0005] The present application embodiment adopts the following technical solutions: On the one hand, an embodiment of the present application provides a method for controlling lighting in a traffic tunnel, comprising: collecting regional visibility data in each segmented scene according to a preset traffic tunnel; performing tunnel lighting feature analysis on the regional visibility data based on external light data of the traffic tunnel, and determining first control data for adjusting the lighting; performing membership analysis on the regional vehicle flow data in each segmented scene, and determining lighting brightness optimization parameters based on fuzzy rules; performing compensation control on the lighting brightness of the lighting through the lighting brightness optimization parameters, and obtaining second control data for compensating the lighting; performing partial correlation prediction on the brightness under the optimal human visual experience for the external light data and the regional vehicle flow data through a preset brightness dynamic model, and determining optimal experience brightness data; and adaptively adjusting control parameters of the first control data and the second control data according to the optimal experience brightness data, and obtaining optimal brightness control data in each segmented scene.

[0006] The embodiment of the present application can improve the visual effect in the tunnel, reduce the driver's blind spots and visual fatigue, and thus improve traffic safety by controlling the lighting according to the regional visibility data. And by optimizing the lighting brightness according to the traffic flow data and fuzzy rules, the brightness of the lighting can be adjusted according to actual needs, avoiding energy waste and reducing energy costs. By predicting the best experience brightness data and adjusting the control parameters according to the data, the lighting brightness that is most suitable for the human eye can be provided, improving the driver's comfort and visual experience. At the same time, based on data analysis and algorithm models, the method realizes the automatic control of lighting, reduces the need for human intervention, and improves the intelligence level and operation efficiency of the system.

[0007] In a feasible implementation manner, according to the segmented scenes of the preset traffic tunnel, the regional visibility data in each of the segmented scenes is collected, specifically including: according to the tunnel length of the traffic tunnel, the light and dark change law data inside the traffic tunnel is represented by a curve to determine the light and dark adaptation curve; wherein the horizontal axis of the light and dark adaptation curve is the tunnel length, and the vertical axis of the light and dark adaptation curve is the light and dark change law data; the light and dark adaptation curve is converted into a gradient curve under a slope range change to obtain a step change curve; based on a preset step change range, the step change curve is divided into regional segments to determine the segmented scenes; wherein the segmented scenes include: an entrance area scene, an entrance transition area scene, an intermediate area scene, an exit transition area scene and an exit scene; through a number of visibility sensors in each of the segmented scenes, and based on a preset time interval, the regional visibility data in each of the segmented scenes is collected.

[0008] In a feasible implementation manner, based on the external light data of the traffic tunnel, the regional visibility data is analyzed for tunnel lighting characteristics to determine the first control data for adjusting the lighting, specifically including: based on a light sensor installed outside the tunnel, collecting the external light data; and determining the current meteorological data according to the meteorological area where the traffic tunnel is located; fusing the external light data and the current meteorological data to analyze and obtain external visibility data; performing feature matching processing between the external visibility data and the regional visibility data in the first regional scene to obtain a first compensation lighting parameter based on the first regional scene; wherein the first regional scene includes: an entrance regional scene and an exit regional scene; and adjusting the lighting according to the first compensation lighting parameter. The lighting is controlled and adjusted to obtain the first control data in the first area scene; the first compensation area visibility data in the first area scene under the control of the first compensation lighting parameter is obtained; and the first compensation area visibility data is feature matched with the area visibility data in the second area scene to determine the second compensation lighting parameter in the second area scene; wherein the second area scene includes an entrance transition area scene and an exit transition area scene; based on the second compensation lighting parameter, the first control data in the second area scene is determined; according to the second compensation area visibility data under the second compensation lighting parameter, the area visibility data in the middle area scene is feature matched, and based on the third compensation lighting parameter, the first control data in the middle area scene is determined.

[0009] In a feasible implementation manner, a membership analysis is performed on the regional traffic flow data in each of the segmented scenes to determine the lighting brightness optimization parameters based on fuzzy rules, specifically including: collecting the regional traffic flow data in each of the segmented scenes; wherein the regional traffic flow data include: vehicle speed data and traffic volume data; using a preset Gaussian membership function, calculating the variable value change trend between the regional traffic flow data and the regional visibility data in each of the segmented scenes, and determining the mapping membership of the regional traffic flow element in the regional visibility element; according to the mapping membership, fuzzy processing is performed on the required lighting brightness parameters in the current segmented scene to determine the ideal lighting brightness parameters; the ideal lighting brightness parameters are compared with the actual lighting brightness parameters in the current segmented scene to determine the lighting brightness optimization parameters in the current segmented scene; wherein the lighting brightness optimization parameters are used to optimize and adjust the lighting lights.

[0010] In a feasible implementation manner, the lighting brightness of the lighting light is compensated and controlled by the lighting brightness optimization parameters to obtain second control data for compensating the lighting light, specifically including: performing curve fitting analysis on the actual road surface brightness and the actual visibility in the current segmented scene to obtain the road surface brightness fitting value in the current segmented scene; performing difference calculation on the road surface brightness fitting value according to the lighting brightness optimization parameters to obtain the lighting brightness loss rate in the current segmented scene; performing power increase and decrease compensation processing on the lighting light according to the lighting brightness loss rate, and determining the power compensation data; converting the power compensation data into control instructions to obtain the second control data.

[0011] In a feasible implementation, the external light data and the regional traffic flow data are predicted for partial correlation of brightness under optimal human visual experience through a preset dynamic brightness model to determine the optimal experience brightness data, specifically including: training the historical light data set and the historical human visual data set for a correlation model under multi-factor coupling through a preset neural network model to obtain the brightness dynamic model; wherein the multi-factors include at least: light intensity, light source color, human eye light perception and human eye light comfort; the external light data are input into the brightness dynamic model to determine the visual optimal brightness data corresponding to the external light data; the ideal tunnel light data corresponding to the current regional traffic flow data is predicted for human visual brightness through the brightness dynamic model to obtain theoretical optimal brightness data; the visual optimal brightness data and the theoretical optimal brightness data are analyzed and calculated for partial correlation of brightness to obtain a partial correlation coefficient; based on the partial correlation coefficient, the optimal experience brightness data between the visual optimal brightness data and the theoretical optimal brightness data is determined.

[0012] In a feasible implementation, according to the optimal experience brightness data, the control parameters of the first control data and the second control data are adaptively adjusted to obtain the optimal brightness control data in each of the segmented scenes, specifically including: determining the optimal control data based on the optimal experience brightness data; according to the optimal control data, decentralizing the control authority of the first control data and the second control data to obtain a first control authority and a second control authority; according to the first control authority, performing linkage follow-up control on the second control authority to determine a second follow-up control authority; through the first control authority and the second follow-up control authority, adaptively adjusting the control strategy of the lighting in each of the segmented scenes to obtain the optimal brightness control data.

[0013] In a feasible implementation, in the middle area scene of the segmented scene, the control strategy corresponding to the second control data is determined as the optimal brightness control strategy in the middle area scene; based on the optimal brightness control strategy, the optimal brightness control data in the middle area scene is obtained.

[0014] In a second aspect, an embodiment of the present application further provides a lighting control device for a traffic tunnel, the device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor so that the at least one processor can execute a lighting control method for a traffic tunnel as described in any of the above-mentioned embodiments.

[0015] In a third aspect, an embodiment of the present application further provides a non-volatile computer storage medium, characterized in that the storage medium is a non-volatile computer-readable storage medium, and the non-volatile computer-readable storage medium stores at least one program, each of which includes instructions, and when the instructions are executed by a terminal, the terminal executes a method for controlling lighting in a traffic tunnel as described in any of the above-mentioned embodiments.

[0016] The present application provides a method, device and medium for controlling lighting in a traffic tunnel. Compared with the prior art, the embodiments of the present application have the following beneficial technical effects: 1. Improve visibility in traffic tunnels: By controlling lighting according to regional visibility data, the visual effect in the tunnel can be improved, reducing the driver's blind spots and visual fatigue, thereby improving traffic safety.

[0017] 2. Reduce energy consumption: By optimizing lighting brightness based on traffic flow data and fuzzy rules, the brightness of lighting can be adjusted according to actual needs, avoiding energy waste and reducing energy costs.

[0018] 3. Provide the best human visual experience: By predicting the best experience brightness data and adjusting the control parameters based on the data, the lighting brightness that is most suitable for the human eye can be provided, improving the driver's comfort and visual experience.

[0019] 4. Automated lighting control: This method is based on data analysis and algorithm models to achieve automated control of lighting, reduce the need for human intervention, and improve the intelligence level and operational efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 A flow chart of a method for controlling lighting in a traffic tunnel provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a lighting control device for a traffic tunnel provided in an embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0022] The present application embodiment provides a method for controlling lighting in a traffic tunnel. Figure 1 As shown, the lighting control method for a traffic tunnel specifically includes steps S101-S106: S101. According to the segmented scenes of a preset traffic tunnel, regional visibility data in each segmented scene is collected.

[0023] Specifically, firstly, it is necessary to represent the light-dark variation law data inside the traffic tunnel according to the tunnel length of the traffic tunnel, and determine the light-dark adaptation curve, wherein the horizontal axis of the light-dark adaptation curve is the tunnel length, and the vertical axis of the light-dark adaptation curve is the light-dark variation law data.

[0024] Furthermore, the light-dark adaptation curve is converted into a gradient curve under a relevant slope range change to obtain a step change curve.

[0025] In one embodiment, based on the step-changing curve under the change of light and dark, the segmented scenes can be better divided, that is, the curve segment with a certain range of step-changing is defined as a segmented scene, and then the overall division of all traffic tunnels is completed.

[0026] Furthermore, based on the preset step change range, the step change curve is segmented into regions to determine segmented scenes, wherein the segmented scenes include: an entrance region scene, an entrance transition region scene, an intermediate region scene, an exit transition region scene, and an exit scene.

[0027] Furthermore, it is also necessary to utilize a number of visibility sensors in each segmented scene and collect regional visibility data in each segmented scene based on a preset time interval.

[0028] S102: Based on the external light data of the traffic tunnel, perform tunnel lighting feature analysis on the regional visibility data to determine first control data for adjusting the lighting.

[0029] Specifically, firstly, external light data is collected based on the light sensor installed outside the tunnel. And the current meteorological data is determined according to the meteorological area where the traffic tunnel is located. The external light data and the current meteorological data are data-fused, analyzed and the external visibility data is obtained. Then, the external visibility data and the regional visibility data in the first regional scene are subjected to feature matching processing between the data to obtain the first compensation lighting parameter based on the first regional scene. Among them, the first regional scene includes: the entrance regional scene and the exit regional scene. And according to the first compensation lighting parameter, the lighting is controlled and adjusted to obtain the first control data in the first regional scene.

[0030] Furthermore, the first compensation area visibility data in the first area scene under the control of the first compensation lighting parameter is obtained, and the first compensation area visibility data is subjected to feature matching processing with the area visibility data in the second area scene to determine the second compensation lighting parameter in the second area scene. The second area scene includes an entrance transition area scene and an exit transition area scene.

[0031] Further, based on the second compensation lighting parameter, first control data in the second area scene is determined.

[0032] Furthermore, according to the second compensation area visibility data under the second compensation lighting parameter, feature matching processing is performed on the area visibility data in the middle area scene, and based on the third compensation lighting parameter, the first control data in the middle area scene is determined.

[0033] In one embodiment, by calculating the correlation of visibility data of different regional scenes, the lighting in each regional scene can be controlled and adjusted for the first time under the environmental data, and based on the gradual decrease of visibility or brightness data of different regional scenes, and the correlation between different regional scenes, the first control data in different regional scenes are linked together. Based on the compensation lighting parameters, the control and adjustment of the lighting in different regional scenes is also realized, and finally the preliminary control data of the lighting after the analysis of the tunnel lighting characteristics, that is, the first control data, can be obtained. It can better utilize external light or traffic conditions in the tunnel to adjust the lighting in time, lay a data foundation for improving the visual adaptability of drivers, and is conducive to further improving the safe driving of tunnel vehicles in the future.

[0034] S103: Perform membership analysis on the regional traffic flow data in each segmented scene to determine lighting brightness optimization parameters based on fuzzy rules.

[0035] Specifically, it is necessary to first collect regional traffic flow data in each segmented scene, wherein the regional traffic flow data includes: vehicle speed data and traffic volume data.

[0036] Furthermore, combined with the preset Gaussian membership function, the variable value change trend between the regional traffic flow data and the regional visibility data in each segmented scene is calculated to determine the mapping membership of the regional traffic flow element in the regional visibility element.

[0037] Furthermore, according to the mapping membership, the required lighting brightness parameters in the current segmented scene are fuzzy processed to determine the ideal lighting brightness parameters.

[0038] Furthermore, the ideal lighting brightness parameter is compared with the actual lighting brightness parameter in the current segmented scene to determine the lighting brightness optimization parameter in the current segmented scene, wherein the lighting brightness optimization parameter is used to optimize and adjust the lighting light.

[0039] As a feasible implementation method, Gaussian function is a commonly used membership function, which is widely used in fuzzy logic and fuzzy set theory. The function describes the trend of membership changing with the increase of variable value in the form of Gaussian distribution curve. In fuzzy set theory, the membership function describes the degree of membership of an element in a fuzzy set. For a given element, the membership function measures its similarity with the fuzzy set by mapping the element to a real number between 0 and 1. Gaussian function is usually used to describe the membership of the center of a fuzzy set.

[0040] S104: Compensate and control the lighting brightness of the lighting light by optimizing the lighting brightness parameters to obtain second control data for compensating the lighting light.

[0041] Specifically, a curve fitting analysis is performed on the actual road brightness and the actual visibility in the current segmented scene to obtain the road brightness fitting value in the current segmented scene. Then, according to the lighting brightness optimization parameter, the road brightness fitting value is difference calculated to obtain the lighting brightness loss rate in the current segmented scene.

[0042] Furthermore, finally, the lighting light is subjected to power increase and decrease compensation processing through the lighting brightness loss rate, and power compensation data is determined. The power compensation data is subjected to control instruction conversion to obtain second control data.

[0043] In one embodiment, the visibility level in a highway tunnel has a great influence on the conversion efficiency of the lighting brightness of the lamps. For different visibility environments, if the same lighting conditions are adopted, the road surface brightness in the tunnel will not reach the design value, which will bury a safety hazard. Therefore, a curve fitting analysis is performed on the measured actual road surface brightness and actual visibility in the tunnel to obtain the road surface brightness fitting value in the current segmented scene, which can meet the lighting optimization requirements of safety and energy saving. At the same time, adaptive control can be achieved for the regional traffic flow data in each segmented scene. The corresponding lighting control strategy can be adjusted according to the different traffic flows to achieve further control of the tunnel lighting fixtures.

[0044] S105. Using a preset dynamic brightness model, the external light data and the regional traffic flow data are used to predict the partial correlation of brightness under the optimal human visual experience, and the optimal experience brightness data is determined.

[0045] Specifically, a preset neural network model is used to train the historical light data set and the historical human vision data set under the multi-factor coupling correlation model to obtain a brightness dynamic model. The multi-factors include at least: light intensity, light source color, human eye light perception, and human eye light comfort.

[0046] Furthermore, the external light data is input into the brightness dynamic model to determine the visual optimal brightness data corresponding to the external light data.

[0047] Furthermore, the ideal tunnel light data corresponding to the current area traffic flow data is used to predict the human visual brightness through the brightness dynamic model to obtain the theoretical optimal brightness data.

[0048] Furthermore, it is also necessary to perform partial correlation analysis and calculation on the visual optimal brightness data and the theoretical optimal brightness data in relation to brightness to obtain a partial correlation coefficient. Based on the partial correlation coefficient, the optimal experience brightness data between the visual optimal brightness data and the theoretical optimal brightness data is determined.

[0049] As a feasible implementation method, partial correlation analysis is used to calculate the correlation between two variables under the influence of other variables. It can identify and quantify the direct relationship between two variables and eliminate the interference of other variables on their relationship. In correlation analysis, the correlation coefficient is usually used to analyze or measure the degree of linear correlation between these variables. However, the simple correlation coefficient is affected by other factors and often reflects non-essential connections. To accurately reflect the intrinsic connection between two economic variables, it is necessary to calculate the partial correlation coefficient. By comparing the partial correlation coefficient with the correlation coefficient, it will be more real and reliable to determine the intrinsic linear connection between the two variables.

[0050] S106: Adaptively adjust the control parameters of the first control data and the second control data according to the best experience brightness data to obtain optimal brightness control data in each segmented scene.

[0051] Specifically, firstly, the best control data is determined based on the best experience brightness data. According to the best control data, the first control data and the second control data are decentralized to obtain the first control authority and the second control authority. Then, according to the first control authority, the second control authority is linked to follow control to determine the second follow control authority. Then, through the first control authority and the second follow control authority, the control strategy of the lighting in each segmented scene is adaptively adjusted to obtain the optimal brightness control data.

[0052] As a feasible implementation, in the middle area scene of the segmented scene, the control strategy corresponding to the second control data is determined as the optimal brightness control strategy in the middle area scene. Based on the optimal brightness control strategy, the optimal brightness control data in the middle area scene is obtained.

[0053] In addition, the present application also provides a lighting control device for a traffic tunnel, such as Figure 2 As shown, the lighting control device 200 of the traffic tunnel specifically includes: At least one processor 201. And a memory 202 in communication with the at least one processor 201. The memory 202 stores instructions that can be executed by the at least one processor 201, so that the at least one processor 201 can execute: According to the segmented scenes of the preset traffic tunnel, the regional visibility data in each segmented scene is collected; Based on the external light data of the traffic tunnel, the tunnel lighting characteristics are analyzed on the regional visibility data to determine the first control data for adjusting the lighting; Perform membership analysis on the regional traffic flow data in each segmented scene to determine the lighting brightness optimization parameters based on fuzzy rules; By optimizing the lighting brightness parameters, the lighting brightness of the lighting light is compensated and controlled to obtain second control data for compensating the lighting light; Through the preset dynamic brightness model, the external light data and the regional traffic flow data are used to predict the partial correlation of brightness under the optimal human visual experience, and the optimal experience brightness data is determined; According to the best experience brightness data, the control parameters of the first control data and the second control data are adaptively adjusted to obtain the optimal brightness control data in each segmented scene.

[0054] The embodiment of the present application can improve the visual effect in the tunnel, reduce the driver's blind spots and visual fatigue, and thus improve traffic safety by controlling the lighting according to the regional visibility data. And by optimizing the lighting brightness according to the traffic flow data and fuzzy rules, the brightness of the lighting can be adjusted according to actual needs, avoiding energy waste and reducing energy costs. By predicting the best experience brightness data and adjusting the control parameters according to the data, the lighting brightness that is most suitable for the human eye can be provided, improving the driver's comfort and visual experience. At the same time, based on data analysis and algorithm models, the method realizes the automatic control of lighting, reduces the need for human intervention, and improves the intelligence level and operation efficiency of the system.

[0055] Each embodiment in this application is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0056] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects as the corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0057] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0058] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0059] Memory may include non-permanent storage in a computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0060] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0061] The above describes specific embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0062] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the embodiments of the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present application should be included in the scope of the claims of the present application.

Claims

1. A method for controlling lighting in a traffic tunnel, characterized in that: The method comprises: According to the segmented scenes of the preset traffic tunnel, collecting regional visibility data in each segmented scene; Based on the external light data of the traffic tunnel, performing tunnel lighting characteristic analysis on the regional visibility data to determine first control data for adjusting the lighting; Performing membership analysis on the regional traffic flow data in each of the segmented scenes to determine the lighting brightness optimization parameters based on fuzzy rules; Compensation control is performed on the illumination brightness of the illumination light by using the illumination brightness optimization parameter to obtain second control data for compensating the illumination light; Through a preset dynamic brightness model, the external light data and the regional traffic flow data are used to predict the partial correlation of brightness under the optimal human visual experience, and the optimal experience brightness data is determined; According to the best experienced brightness data, the control parameters of the first control data and the second control data are adaptively adjusted to obtain the optimal brightness control data in each of the segmented scenes.

2. A method for controlling lighting in a traffic tunnel according to claim 1, characterized in that: According to the preset segmented scenes of the traffic tunnel, the regional visibility data in each segmented scene is collected, specifically including: According to the tunnel length of the traffic tunnel, the light-dark variation law data inside the traffic tunnel is represented by a curve to determine a light-dark adaptation curve; wherein the horizontal axis of the light-dark adaptation curve is the tunnel length, and the vertical axis of the light-dark adaptation curve is the light-dark variation law data; Performing gradient curve conversion on the light-dark adaptation curve under the change of the relevant slope range to obtain a step change curve; Based on the preset step change range, the step change curve is segmented into regions to determine the segmented scenes; wherein the segmented scenes include: an entrance area scene, an entrance transition area scene, an intermediate area scene, an exit transition area scene and an exit scene; The regional visibility data in each of the segmented scenes is collected through a plurality of visibility sensors in each of the segmented scenes and based on a preset time interval.

3. The method for controlling lighting in a traffic tunnel according to claim 1, characterized in that: Based on the external light data of the traffic tunnel, the tunnel lighting characteristics are analyzed on the regional visibility data to determine the first control data for adjusting the lighting, specifically including: Based on a light sensor installed outside the tunnel, the external light data is collected; and according to the meteorological area where the traffic tunnel is located, the current meteorological data is determined; Performing data fusion on the external light data and the current meteorological data, analyzing and obtaining external visibility data; Performing feature matching processing between the external visibility data and the regional visibility data in the first regional scene, and obtaining a first compensation lighting parameter based on the first regional scene; wherein the first regional scene includes: an entrance regional scene and an exit regional scene; and controlling and adjusting the lighting light according to the first compensation lighting parameter, and obtaining first control data in the first regional scene; Obtaining first compensation area visibility data in the first area scene under the control of the first compensation lighting parameter; and performing feature matching processing on the first compensation area visibility data and area visibility data in the second area scene to determine second compensation lighting parameters in the second area scene; wherein the second area scene includes an entrance transition area scene and an exit transition area scene; Determining first control data in the second area scene based on the second compensation lighting parameter; According to the second compensation area visibility data under the second compensation lighting parameter, feature matching processing is performed on the area visibility data in the middle area scene, and based on the third compensation lighting parameter, the first control data in the middle area scene is determined.

4. The method for controlling lighting in a traffic tunnel according to claim 1, characterized in that: The regional traffic flow data in each segmented scene is subjected to membership analysis to determine the lighting brightness optimization parameters based on fuzzy rules, specifically including: Collecting regional traffic flow data in each of the segmented scenes; wherein the regional traffic flow data includes: vehicle speed data and traffic volume data; By using a preset Gaussian membership function, the variable value change trend between the regional traffic flow data and the regional visibility data in each segmented scene is calculated to determine the mapping membership of the regional traffic flow element in the regional visibility element; According to the mapping membership, fuzzy processing is performed on the required lighting brightness parameters in the current segmented scene to determine the ideal lighting brightness parameters; The ideal lighting brightness parameter is compared with the actual lighting brightness parameter in the current segmented scene to determine the lighting brightness optimization parameter in the current segmented scene; wherein the lighting brightness optimization parameter is used to optimize and adjust the lighting light.

5. The method for controlling lighting in a traffic tunnel according to claim 1, characterized in that: By using the lighting brightness optimization parameter, the lighting brightness of the lighting light is compensated and controlled to obtain second control data for compensating the lighting light, specifically including: Performing curve fitting analysis on the actual road surface brightness and the actual visibility in the current segmented scene to obtain a road surface brightness fitting value in the current segmented scene; According to the lighting brightness optimization parameter, the road surface brightness fitting value is subjected to difference calculation to obtain the lighting brightness loss rate in the current segmented scene; According to the lighting brightness loss rate, the lighting light is subjected to power increase and decrease compensation processing, and power compensation data is determined; The power compensation data is converted into a control instruction to obtain the second control data.

6. The method for controlling lighting in a traffic tunnel according to claim 1, characterized in that: Through a preset dynamic brightness model, the external light data and the regional traffic flow data are used to predict the partial correlation of brightness under the optimal human visual experience, and the optimal experience brightness data is determined, specifically including: Through a preset neural network model, the historical light data set and the historical human vision data set are trained with a correlation model under multi-factor coupling to obtain the brightness dynamic model; wherein the multi-factors include at least: light intensity, light source color, human eye light perception and human eye light comfort; Inputting the external light data into the brightness dynamic model to determine the visual optimal brightness data corresponding to the external light data; The ideal tunnel light data corresponding to the current regional traffic flow data is used to predict the human visual brightness through the brightness dynamic model to obtain theoretical optimal brightness data; Performing partial correlation analysis and calculation on the visual optimal brightness data and the theoretical optimal brightness data with respect to brightness to obtain a partial correlation coefficient; Based on the partial correlation coefficient, the optimal experienced brightness data between the visual optimal brightness data and the theoretical optimal brightness data is determined.

7. The method for controlling lighting in a traffic tunnel according to claim 1, characterized in that: According to the best experience brightness data, adaptively adjusting control parameters of the first control data and the second control data to obtain optimal brightness control data in each segmented scene specifically includes: Determining optimal control data based on the optimal experience brightness data; According to the optimal control data, the first control data and the second control data are decentralized in terms of control authority to obtain a first control authority and a second control authority; According to the first control authority, performing linkage follow-up control on the second control authority to determine a second follow-up control authority; Through the first control authority and the second follow-up control authority, the control strategy of the lighting light in each segmented scene is adaptively adjusted to obtain the optimal brightness control data.

8. The method for controlling lighting in a traffic tunnel according to claim 7, characterized in that: In the middle area scene of the segmented scene, determining the control strategy corresponding to the second control data as the optimal brightness control strategy in the middle area scene; Based on the optimal brightness control strategy, optimal brightness control data in the middle area scene is obtained.

9. A lighting control device for a traffic tunnel, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, so that the at least one processor can execute the lighting control method for a traffic tunnel according to any one of claims 1-8.

10. A non-volatile computer storage medium, characterized in that: The storage medium is a non-volatile computer-readable storage medium, and the non-volatile computer-readable storage medium stores at least one program, each of which includes instructions, and when the instructions are executed by the terminal, the terminal executes the lighting control method for a traffic tunnel according to any one of claims 1-8.

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