Intelligent tunnel lighting control method and system based on artificial intelligence

Abstract

The invention provides an intelligent tunnel lighting control method and system based on artificial intelligence, and relates to the technical field of tunnel lighting control. The method comprises the steps that a plurality of vehicle detectors are arranged, and vehicle flow data and real-time vehicle speed data within preset time are acquired; judging whether the lighting power needs to be adjusted to the minimum power according to the traffic flow data; if the illumination power does not need to be adjusted to the minimum power, environment data of the multiple tunnel areas are collected in real time; determining an illumination demand index according to the environment data and the real-time vehicle speed data; determining adjusted lighting power according to the lighting demand index and the minimum power; acquiring illumination data; and determining an illumination response delay compensation amount according to the illumination data. According to the tunnel illumination control system and method, tunnel illumination can be accurately controlled according to multiple factors such as the traffic flow, the real-time vehicle speed, the multi-tunnel area environment data and the illumination data, the tunnel driving safety is improved, and the energy consumption of tunnel illumination is reduced.

Description

Technical Field

[0001] This invention relates to the field of tunnel lighting control technology, and in particular to an intelligent tunnel lighting control method and system based on artificial intelligence. Background Technology

[0002] Current technologies control tunnel lighting by taking into account factors such as ambient light outside the tunnel, measured illuminance in different sections of the tunnel, and driver and vehicle status information. However, they do not take into account factors such as traffic flow, real-time vehicle speed, environmental data from multiple tunnel areas, and lighting data to accurately control tunnel lighting. In other words, they cannot accurately control tunnel lighting based on factors such as traffic flow, real-time vehicle speed, environmental data from multiple tunnel areas, and lighting data.

[0003] The information disclosed in the background section of this application is intended only to enhance the understanding of the general background of this application and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0004] This invention provides an intelligent tunnel lighting control method and system based on artificial intelligence, which can solve the technical problem that related technologies cannot accurately control tunnel lighting based on factors such as traffic flow, real-time vehicle speed, environmental data of multiple tunnel areas, and lighting data.

[0005] According to a first aspect of the present invention, an artificial intelligence-based intelligent tunnel lighting control method is provided, comprising: setting up multiple vehicle detectors at a preset distance in front of the tunnel entrance and exit and in multiple tunnel areas to acquire traffic flow data and real-time vehicle speed data within a preset time period; determining, based on the traffic flow data, whether it is necessary to adjust the lighting power to the minimum power, wherein the minimum power is the minimum value of the lighting equipment power; if it is not necessary to adjust the lighting power to the minimum power, then acquiring environmental data of multiple tunnel areas in real time through multispectral image sensors and lidar set up in multiple tunnel areas, wherein the environmental data includes light intensity data and visibility data; determining a lighting demand index based on the environmental data and the real-time vehicle speed data; determining the adjusted lighting power based on the lighting demand index and the minimum power; acquiring lighting data, wherein the lighting data includes lighting delay time, spacing between lighting equipment, and illumination radius of the lighting equipment; and determining a lighting response delay compensation amount based on the lighting data.

[0006] Furthermore, based on the traffic flow data, determining whether it is necessary to adjust the lighting power to the minimum power includes: if the traffic flow data obtained at a preset distance position in front of the tunnel entrance and exit and at multiple tunnel areas are all 0, then it is determined that the lighting power needs to be adjusted to the minimum power; if the traffic flow data obtained at a preset distance position in front of the tunnel entrance and exit and at multiple tunnel areas is not 0, then it is determined that it is not necessary to adjust the lighting power to the minimum power.

[0007] Further, based on the environmental data and the real-time vehicle speed data, the lighting demand index is determined, including: obtaining the real-time vehicle count and tunnel length of multiple tunnel areas; obtaining the vehicle density based on the real-time vehicle count and tunnel length; averaging the real-time vehicle speed data obtained for each tunnel area to obtain the average vehicle speed; and determining the lighting demand index based on the vehicle density, the average vehicle speed, and the environmental data.

[0008] Further, based on the vehicle density, the average vehicle speed, and the environmental data, a lighting demand index is determined, including: according to the formula: Determine the lighting demand index for the i-th tunnel area. ,in, Let be the average vehicle speed in the i-th tunnel region. To preset the vehicle speed, Let be the vehicle density of the i-th tunnel region. To preset vehicle density, This represents the light intensity data for the i-th tunnel region. To preset light intensity data, Here is the visibility data for the i-th tunnel region. To preset visibility data, and The preset weights are k, the adjustment coefficient is k, and i is a positive integer.

[0009] Further, determining the adjusted lighting power based on the lighting demand index and the minimum power includes: obtaining the maximum power of the tunnel lighting, wherein the maximum power is the maximum value of the lighting equipment power; and determining the adjusted lighting power based on the maximum power, the minimum power, and the lighting demand index.

[0010] Further, determining the adjusted lighting power based on the maximum power, the minimum power, and the lighting demand index includes: according to the formula: , Determine the adjusted lighting power for the i-th tunnel region. ,in, Let i be the comprehensive lighting demand index for the i-th tunnel area. , and These are the lighting demand indices for the (i-1), i, and i+1th tunnel regions, respectively. For the preset coupling coefficient, For minimum power, Let M be the maximum power, M be the number of tunnel regions, i ≤ M, and i be a positive integer.

[0011] Further, based on the lighting data, determining the lighting response delay compensation amount includes: obtaining the driver's preset visual adaptation time; obtaining the maximum speed limit of the tunnel; and determining the lighting response delay compensation amount based on the preset visual adaptation time, the maximum speed limit, and the lighting data.

[0012] Further, based on the preset visual adaptation time, the maximum speed limit, and the lighting data, the lighting response delay compensation amount is determined, including: according to the formula: Determine the lighting response delay compensation amount L, where, For lighting delay time, To preset the visual adaptation time, To limit the maximum speed, For the spacing of lighting equipment, The illumination radius of the lighting equipment.

[0013] According to a second aspect of the present invention, an artificial intelligence-based intelligent tunnel lighting control system is provided, comprising: a vehicle detection module for setting multiple vehicle detection devices at a preset distance position in front of the tunnel entrance and exit and in multiple tunnel areas to acquire traffic flow data and real-time vehicle speed data within a preset time period; a judgment module for determining whether it is necessary to adjust the lighting power to the minimum power based on the traffic flow data, wherein the minimum power is the minimum value of the lighting equipment power; an environmental data module for collecting environmental data of multiple tunnel areas in real time through multispectral image sensors and lidar set in multiple tunnel areas if it is not necessary to adjust the lighting power to the minimum power, wherein the environmental data includes light intensity data and visibility data; a lighting demand index module for determining a lighting demand index based on the environmental data and the real-time vehicle speed data; an adjusted lighting power module for determining an adjusted lighting power based on the lighting demand index and the minimum power; a lighting data module for acquiring lighting data, wherein the lighting data includes lighting delay time, lighting equipment spacing, and lighting equipment illumination radius; and a lighting response delay compensation module for determining a lighting response delay compensation amount based on the lighting data.

[0014] Technical Effects: According to this invention, by setting vehicle detectors at preset distances in front of tunnel entrances and exits and in multiple tunnel areas, traffic flow data within a preset time period is acquired. This data is used to determine whether the lighting power needs to be adjusted to the minimum power, thereby reducing lighting power and effectively saving electricity. When it is not necessary to adjust the lighting power to the minimum power, accurate environmental data collection and determination of the lighting demand index allow the lighting in the tunnel to be dynamically adjusted according to the actual situation, contributing to the safe driving of vehicles in the tunnel. Tunnel lighting can be precisely controlled based on factors such as traffic flow, real-time vehicle speed, environmental data from multiple tunnel areas, and lighting data, improving tunnel driving safety and reducing tunnel lighting energy consumption. When determining the lighting demand index, vehicle density, average vehicle speed, and environmental data can be used to determine the lighting demand index. Through the dynamic coupling of multiple factors such as traffic conditions and environmental conditions, the actual lighting intensity required by the tunnel can be assessed more accurately and comprehensively, reducing the problem of insufficient or excessive lighting caused by inadequate consideration of a single factor. When determining the adjusted lighting power, the maximum power, minimum power, and lighting demand index can be used to determine the adjusted lighting power. The lighting power can be smoothly adjusted based on the comprehensive lighting demand index to provide drivers with suitable lighting conditions, improve driving safety and comfort, reduce over-lighting or under-lighting, help optimize the allocation of lighting resources, improve energy efficiency, and reduce electricity costs. When determining the lighting response delay compensation amount, the amount can be determined by preset visual adaptation time, maximum speed limit, and lighting data. Combining the driver's visual characteristics, vehicle driving characteristics, and the layout and performance of the lighting system makes the lighting response delay compensation amount closer to the actual response, reducing the risk of traffic accidents caused by visual maladaptation, improving lighting control performance and efficiency, and saving electricity.

[0015] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Other features and aspects of the invention will become clearer from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other embodiments can be obtained based on these drawings without creative effort.

[0017] Figure 1 An exemplary flowchart of an artificial intelligence-based smart tunnel lighting control method according to an embodiment of the present invention is shown.

[0018] Figure 2 An exemplary flowchart illustrating the determination and adjustment of lighting power according to an embodiment of the present invention is shown;

[0019] Figure 3 A flowchart for calculating the lighting demand index according to an embodiment of the present invention is shown as an example;

[0020] Figure 4 An exemplary flowchart illustrating the calculation of adjusted lighting power according to an embodiment of the present invention is shown;

[0021] Figure 5 An exemplary flowchart illustrating the calculation of lighting response delay compensation amount according to an embodiment of the present invention is shown;

[0022] Figure 6 A block diagram of an artificial intelligence-based smart tunnel lighting control system according to an embodiment of the present invention is shown as an example. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] The technical solution of the present invention will be described in detail below with reference to specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0025] Figure 1An exemplary flowchart of an AI-based smart tunnel lighting control method according to an embodiment of the present invention is shown. The method includes: Step S1, setting up multiple vehicle detectors at a preset distance in front of the tunnel entrance and exit and in multiple tunnel areas to acquire traffic flow data and real-time vehicle speed data within a preset time period; Step S2, determining whether the lighting power needs to be adjusted to the minimum power based on the traffic flow data, wherein the minimum power is the minimum value of the lighting equipment power; Step S3, if the lighting power does not need to be adjusted to the minimum power, then collecting environmental data of multiple tunnel areas in real time using multispectral image sensors and lidar set up in multiple tunnel areas, wherein the environmental data includes light intensity data and visibility data; Step S4, determining a lighting demand index based on the environmental data and the real-time vehicle speed data; Step S5, determining the adjusted lighting power based on the lighting demand index and the minimum power; Step S6, acquiring lighting data, wherein the lighting data includes lighting delay time, lighting equipment spacing, and lighting equipment illumination radius; Step S7, determining a lighting response delay compensation amount based on the lighting data.

[0026] According to an embodiment of the present invention, an artificial intelligence-based intelligent tunnel lighting control method acquires traffic flow data over a preset time period by setting vehicle detectors at preset distances in front of the tunnel entrance and exit and in multiple tunnel areas. This data is used to determine whether the lighting power needs to be adjusted to the minimum power, thereby reducing lighting power and effectively saving electricity. When it is not necessary to adjust the lighting power to the minimum, accurate environmental data acquisition and lighting demand index determination allow the tunnel lighting to be dynamically adjusted according to actual conditions, contributing to safe vehicle operation within the tunnel. The method can precisely control tunnel lighting based on factors such as traffic flow, real-time vehicle speed, environmental data from multiple tunnel areas, and lighting data, improving tunnel driving safety and reducing tunnel lighting energy consumption.

[0027] According to one embodiment of the present invention, in step S1, a vehicle detector is installed at a predetermined distance (e.g., 800m) in front of the tunnel entrance and exit. The tunnel interior is evenly divided into multiple different tunnel areas according to its length, and multiple vehicle detectors are installed in multiple tunnel areas, with the multiple vehicle detectors in each tunnel area being evenly distributed. The vehicle detectors acquire traffic flow data and real-time vehicle speed data within a predetermined time (e.g., 10 minutes).

[0028] According to an embodiment of the present invention, in step S2, it is determined whether the lighting power needs to be adjusted to the minimum power based on the traffic flow data, wherein the minimum power is the minimum value of the lighting equipment power, the lighting equipment supports power adjustment, and the power adjustment range of all lighting equipment in the tunnel is consistent.

[0029] Figure 2 An exemplary flowchart illustrating the determination and adjustment of lighting power according to an embodiment of the present invention is shown.

[0030] According to an embodiment of the present invention, step S2 includes: step S21, if the traffic flow data obtained at a preset distance position in front of the tunnel entrance and exit and at multiple tunnel areas are all 0, then it is determined that the lighting power needs to be adjusted to the minimum power; step S22, if one of the traffic flow data obtained at the preset distance position in front of the tunnel entrance and exit and at multiple tunnel areas is not 0, then it is determined that the lighting power does not need to be adjusted to the minimum power.

[0031] According to one embodiment of the present invention, if the traffic flow data obtained at a preset distance position in front of the tunnel entrance and exit and at multiple tunnel areas are all 0, that is, no vehicles pass through in front of the tunnel entrance and exit and in various areas inside the tunnel during the current preset time period, and the traffic flow is zero, it is determined that the lighting power needs to be adjusted to the minimum power, that is, the brightness of the lighting equipment is at the lowest state, which can save power resources. If the traffic flow data obtained at a preset distance position in front of the tunnel entrance and exit and at multiple tunnel areas are not 0, that is, vehicles are about to or are currently driving normally in the tunnel during the current preset time period, and the traffic flow is not zero, it is determined that the lighting power does not need to be adjusted to the minimum power. For driving safety, optimization and adjustment are made according to the actual situation to provide sufficient lighting brightness.

[0032] According to one embodiment of the present invention, in step S3, if it is not necessary to adjust the lighting power to the minimum power, the current traffic flow in the tunnel is not extremely low, and vehicles are passing normally. To ensure driving safety, the current lighting conditions need to be optimized. A multispectral image sensor collects image information within the tunnel area in real time. By analyzing and processing images of different spectral bands, the average light intensity within the tunnel, i.e., light intensity data, is obtained. A lidar can collect visibility data in real time. Multiple multispectral image sensors and multiple lidars can be set up in each tunnel area, and the average value of the obtained data is taken as the light intensity data and visibility data.

[0033] According to one embodiment of the present invention, in step S4, a lighting demand index is determined based on the environmental data and the real-time vehicle speed data.

[0034] Figure 3 A flowchart for calculating the lighting demand index according to an embodiment of the present invention is shown as an example.

[0035] According to an embodiment of the present invention, step S4 includes: step S41, obtaining the real-time vehicle count and tunnel length of multiple tunnel areas; step S42, obtaining the vehicle density based on the real-time vehicle count and the tunnel length; step S43, averaging the real-time vehicle speed data obtained for each tunnel area to obtain the average vehicle speed; and step S44, determining the lighting demand index based on the vehicle density, the average vehicle speed, and the environmental data.

[0036] According to one embodiment of the present invention, the real-time vehicle count can be obtained through a video detector, the total tunnel length can be obtained from tunnel construction data, and the tunnel area length can be obtained by dividing the tunnel into sections. Vehicle density is the number of vehicles per unit length of tunnel, i.e., the ratio between the real-time vehicle count and the tunnel area length. For each tunnel area, the average vehicle speed is obtained by averaging the real-time vehicle speed data acquired within that tunnel area. The lighting demand index reflects the current required lighting intensity within the tunnel.

[0037] According to one embodiment of the present invention, determining the lighting demand index based on the vehicle density, the average vehicle speed, and the environmental data includes: determining the lighting demand index of the i-th tunnel area according to formula (1). ,

[0038] (1),

[0039] in, Let be the average vehicle speed in the i-th tunnel region. To preset the vehicle speed, Let be the vehicle density of the i-th tunnel region. To preset vehicle density, This represents the light intensity data for the i-th tunnel region. To preset light intensity data, Here is the visibility data for the i-th tunnel region. To preset visibility data, and The preset weights are k, the adjustment coefficient is k, and i is a positive integer.

[0040] According to an embodiment of the present invention, in formula (1), The ratio of the average vehicle speed in the i-th tunnel area to the preset vehicle speed is the larger the ratio. The faster the vehicle travels, the clearer the driver's vision is required, and therefore the higher the lighting requirements. The preset vehicle speed can be 125% of the maximum speed limit of the tunnel. The ratio of the vehicle density in the i-th tunnel area to the preset vehicle density is the higher the ratio. The larger the ratio, the more vehicles are in the tunnel, the more congested it is, and the drivers need better lighting to identify the surrounding vehicles and road conditions. Therefore, the lighting requirements are higher. The preset vehicle density is 30 vehicles / km. The ratio of the preset light intensity data to the light intensity data of the i-th tunnel region is given. The larger the ratio, the more insufficient the current light intensity is, and the more lighting is needed. The preset light intensity data is the illuminance during a sunny day, for example, 1000 lux. This indicates that the more vehicles there are, the faster they travel, and the darker the environment, the higher the lighting requirements in the tunnel area. The preset weight is, for example, 0.6. The relative difference between the visibility data of the i-th tunnel area and the preset visibility data is the larger the relative difference, the higher the visibility of the tunnel area. The preset visibility data can be 100m. Let be a visibility-based function, representing the impact of visibility on lighting demand, when The larger the value, the closer the entire function value approaches 0, indicating higher visibility and a lower demand for additional lighting. The smaller the value, the closer the function value is to 1, indicating lower visibility and a higher demand for increased lighting. Here, k is an adjustment coefficient, for example, 5. This represents the ratio of the vehicle density in the i-th tunnel region to a preset vehicle density, multiplied by a visibility-based function. This function represents the impact of visibility on lighting requirements when vehicles are present; that is, the more vehicles and the lower the visibility, the higher the lighting requirement for that tunnel region. Set a preset weight, for example, 0.4. and By adding them together, we can obtain the lighting demand index for the i-th tunnel area. The larger the lighting demand index, the higher the lighting demand for the tunnel area.

[0041] In this way, the lighting demand index can be determined by vehicle density, average vehicle speed and environmental data. By dynamically coupling multiple factors such as traffic conditions and environmental conditions, the actual lighting intensity required by the tunnel can be assessed more accurately and comprehensively, reducing the problem of insufficient or excessive lighting caused by inadequate consideration of a single factor.

[0042] According to one embodiment of the present invention, in step S5, the adjusted lighting power is determined based on the lighting demand index and the minimum power.

[0043] Figure 4 An exemplary flowchart illustrating the calculation of adjusted lighting power according to an embodiment of the present invention is shown.

[0044] According to an embodiment of the present invention, step S5 includes: step S51, obtaining the maximum power of the tunnel lighting, wherein the maximum power is the maximum value of the lighting equipment power; step S52, determining the adjusted lighting power based on the maximum power, the minimum power and the lighting demand index.

[0045] According to one embodiment of the present invention, the maximum value of the lighting equipment power, i.e., the maximum power, is obtained, and the adjusted lighting power is determined based on the maximum power, the minimum power and the lighting demand index to provide suitable lighting conditions for the driver.

[0046] According to one embodiment of the present invention, determining the adjusted lighting power based on the maximum power, the minimum power, and the lighting demand index includes: determining the adjusted lighting power of the i-th tunnel region according to formulas (2) and (3). ,

[0047] (2),

[0048] (3),

[0049] in, Let i be the comprehensive lighting demand index for the i-th tunnel area. , and These are the lighting demand indices for the (i-1), i, and i+1th tunnel regions, respectively. For the preset coupling coefficient, For minimum power, Let M be the maximum power, M be the number of tunnel regions, i ≤ M, and i be a positive integer.

[0050] According to an embodiment of the present invention, in formula (2), when At that time, the comprehensive lighting demand index was ,when At that time, the comprehensive lighting demand index was That is, the combined lighting demand index of the two tunnel areas at the tunnel entrance and exit is its own lighting demand index. When At that time, the comprehensive lighting demand index was This represents a weighted summation of the lighting demand indices for the (i-1), i, and i+1th tunnel regions, resulting in a smooth brightness transition. Let the preset coupling coefficient be, for example, 0.2. In formula (3), when When it is larger, The closer That is, the higher the lighting demand in the tunnel area, the closer the adjusted lighting power will be to the maximum power, providing sufficient brightness to ensure driving safety. The closer to 0, The closer That is, the lower the lighting demand in the tunnel area, the closer the adjusted lighting power is to the average of the minimum and maximum power. When there are vehicles traveling and the lighting demand is low, the power of the lighting equipment is adjusted to the average of the minimum and maximum power, which is different from the case where the minimum power is maintained when there are no vehicles traveling.

[0051] In this way, the adjusted lighting power can be determined by the maximum power, minimum power, and lighting demand index. The lighting power can be smoothly adjusted according to the comprehensive lighting demand index to provide drivers with suitable lighting conditions, improve driving safety and comfort, reduce the occurrence of over-lighting or under-lighting, help to optimize the allocation of lighting resources, improve energy utilization efficiency, and reduce electricity costs.

[0052] According to one embodiment of the present invention, in step S6, the lighting delay time is obtained by consulting the operation manual of the lighting control system, the spacing between lighting devices is obtained by consulting the construction drawings of the tunnel, wherein the lighting devices in the tunnel are uniformly distributed, and the illumination radius of the lighting devices is obtained by consulting the instruction manual of the lighting devices.

[0053] According to one embodiment of the present invention, in step S7, the lighting response delay compensation amount is determined based on the lighting data. Since the lighting control system has a reaction time (lighting delay time), when the vehicle detector at a preset distance position in front of the tunnel entrance detects the first vehicle and immediately issues a command to adjust the lighting power, the preset distance position in front of the tunnel entrance is far from the tunnel entrance, which may lead to premature adjustment of the lighting power and waste of power resources. Therefore, it is necessary to consider at what distance (lighting response delay compensation amount) the first vehicle is from the tunnel entrance before issuing the command to adjust the lighting power, so that the lighting equipment has just reached the required brightness when the first vehicle arrives. Specifically, detecting the first vehicle after adjusting the lighting power to the minimum power can be achieved by setting a monitoring device, such as a camera, at a distance from the tunnel entrance with the lighting response delay compensation amount. When the first vehicle is detected, the command to adjust the lighting power can be issued immediately.

[0054] Figure 5 A flowchart for calculating the lighting response delay compensation amount according to an embodiment of the present invention is shown as an example.

[0055] According to an embodiment of the present invention, step S7 includes: step S71, obtaining the driver's preset visual adaptation time; step S72, obtaining the maximum speed limit of the tunnel; step S73, determining the lighting response delay compensation amount based on the preset visual adaptation time, the maximum speed limit and the lighting data.

[0056] According to one embodiment of the present invention, the driver's preset visual adaptation time is the time required for the driver's eyes to achieve normal visual function when moving from one lighting environment to another (e.g., from a bright environment outside the tunnel to a darker environment inside the tunnel), for example, 10 seconds. The maximum speed limit of the tunnel is, for example, 80 km / h or 60 km / h. The lighting response delay compensation amount can meet the driver's visual adaptation needs inside the tunnel, improving driving safety.

[0057] According to one embodiment of the present invention, determining the lighting response delay compensation amount based on the preset visual adaptation time, the maximum speed limit, and the lighting data includes: determining the lighting response delay compensation amount L according to formula (4).

[0058] (4),

[0059] in, For lighting delay time, To preset the visual adaptation time, To limit the maximum speed, For the spacing of lighting equipment, The illumination radius of the lighting equipment.

[0060] According to one embodiment of the present invention, in formula (4), This is the ratio between the distance between lighting equipment and the illumination radius, added to 1. The larger this result is, the farther the distance between lighting equipment is relative to the illumination range, and the more discontinuous the lighting coverage. Appropriate compensation is needed to make up for the lack of lighting coverage. This is the ratio of the spacing between lighting fixtures to the illumination radius, reflecting the continuity of lighting coverage. Adding to 1 indicates the base compensation distance (i.e. Based on this, a correction factor related to the uniformity of lighting layout is superimposed, when When (ideal continuous illumination), the correction factor is 1, meaning no additional compensation is required. When the correction factor is greater than 1, it indicates that due to the discontinuous lighting, the vehicle needs to trigger the command to adjust the lighting power at a greater distance, so that the driver's visual adaptation needs in the tunnel can be met when the vehicle enters. The base compensation distance is the product of the sum of the lighting delay time and the preset visual adaptation time, multiplied by the maximum speed limit. and Multiplying these amounts yields the lighting response delay compensation. When the first vehicle is a distance equal to the lighting response delay compensation from the tunnel entrance / exit, a command to adjust the lighting power is issued.

[0061] In this way, the amount of lighting response delay compensation can be determined by preset visual adaptation time, maximum speed limit and lighting data. Combined with the driver's visual characteristics, vehicle driving characteristics and the layout and performance of the lighting system, the amount of lighting response delay compensation is closer to the actual response, reducing the risk of traffic accidents caused by visual maladaptation, improving the control performance and efficiency of lighting and saving power resources.

[0062] According to an embodiment of the present invention, an artificial intelligence-based intelligent tunnel lighting control method acquires traffic flow data within a preset time period by setting vehicle detectors at preset distances in front of the tunnel entrance and exit and in multiple tunnel areas. This data is used to determine whether the lighting power needs to be adjusted to the minimum power, thereby reducing lighting power and effectively saving electricity. When it is not necessary to adjust the lighting power to the minimum power, accurate environmental data acquisition and lighting demand index determination allow the tunnel lighting to be dynamically adjusted according to the actual situation, contributing to safe vehicle operation within the tunnel. Tunnel lighting can be precisely controlled based on multiple factors such as traffic flow, real-time vehicle speed, environmental data from multiple tunnel areas, and lighting data, improving tunnel driving safety and reducing tunnel lighting energy consumption. When determining the lighting demand index, vehicle density, average vehicle speed, and environmental data can be used. Through the dynamic coupling of multiple factors related to traffic conditions and environmental conditions, the actual lighting intensity required by the tunnel can be assessed more accurately and comprehensively, reducing problems of insufficient or excessive lighting caused by inadequate consideration of a single factor. When determining the adjusted lighting power, the maximum power, minimum power, and lighting demand index can be used to determine the adjusted lighting power. The lighting power can be smoothly adjusted based on the comprehensive lighting demand index to provide drivers with suitable lighting conditions, improve driving safety and comfort, reduce over-lighting or under-lighting, help optimize the allocation of lighting resources, improve energy efficiency, and reduce electricity costs. When determining the lighting response delay compensation amount, the amount can be determined by preset visual adaptation time, maximum speed limit, and lighting data. Combining the driver's visual characteristics, vehicle driving characteristics, and the layout and performance of the lighting system makes the lighting response delay compensation amount closer to the actual response, reducing the risk of traffic accidents caused by visual maladaptation, improving lighting control performance and efficiency, and saving electricity.

[0063] Figure 6An exemplary block diagram of an AI-based intelligent tunnel lighting control system according to an embodiment of the present invention is shown. The system includes: a vehicle detection module for setting multiple vehicle detection devices at a preset distance in front of the tunnel entrance and exit and in multiple tunnel areas to acquire traffic flow data and real-time vehicle speed data within a preset time period; a judgment module for determining whether the lighting power needs to be adjusted to the minimum power based on the traffic flow data, wherein the minimum power is the minimum value of the lighting equipment power; an environmental data module for collecting environmental data of multiple tunnel areas in real time through multispectral image sensors and lidar set in multiple tunnel areas if the lighting power does not need to be adjusted to the minimum power, wherein the environmental data includes light intensity data and visibility data; a lighting demand index module for determining a lighting demand index based on the environmental data and the real-time vehicle speed data; an adjusted lighting power module for determining the adjusted lighting power based on the lighting demand index and the minimum power; a lighting data module for acquiring lighting data, wherein the lighting data includes lighting delay time, lighting equipment spacing, and lighting equipment illumination radius; and a lighting response delay compensation module for determining a lighting response delay compensation amount based on the lighting data.

[0064] This invention can be a method, apparatus, system, and / or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for performing various aspects of the invention.

[0065] Those skilled in the art should understand that the embodiments of the present invention described above and shown in the accompanying drawings are merely examples and do not limit the present invention. The objectives of the present invention have been fully and effectively achieved. The functions and structural principles of the present invention have been demonstrated and explained in the embodiments, and any variations or modifications may be made to the implementation of the present invention without departing from the stated principles.

Claims

1.A method for intelligent tunnel lighting control based on artificial intelligence, characterized in that, The application relates to a tunnel lighting control method and device. According to the traffic flow data, it is judged whether the lighting power needs to be adjusted to the minimum power, wherein the minimum power is the minimum value of the lighting device power; if the lighting power does not need to be adjusted to the minimum power, the environment data of the multiple tunnel areas is collected in real time through the multispectral image sensor and the laser radar arranged in the multiple tunnel areas, wherein the environment data comprises light intensity data and visibility data; according to the environment data and the real-time vehicle speed data, the lighting demand index is determined; according to the lighting demand index and the minimum power, the adjusted lighting power is determined; the lighting data is obtained, wherein the lighting data comprises lighting delay time, lighting device spacing and lighting device irradiation radius; and according to the lighting data, the lighting response delay compensation amount is determined. According to the traffic flow data, it is judged whether the lighting power needs to be adjusted to the minimum power, comprising: if the traffic flow data obtained at the preset distance position in front of the tunnel entrance and exit and at the multiple tunnel areas are all 0, it is judged that the lighting power needs to be adjusted to the minimum power; if the traffic flow data obtained at the preset distance position in front of the tunnel entrance and exit and at the multiple tunnel areas are not all 0, it is judged that the lighting power does not need to be adjusted to the minimum power. 2.The AI-based intelligent tunnel lighting control method of claim 1, wherein, According to the environment data and the real-time vehicle speed data, the lighting demand index is determined, comprising: the real-time vehicle number of the multiple tunnel areas and the tunnel area length are obtained; according to the real-time vehicle number and the tunnel area length, the vehicle density is obtained; the real-time vehicle speed data obtained in each tunnel area is averaged to obtain the average vehicle speed; and according to the vehicle density, the average vehicle speed and the environment data, the lighting demand index is determined. 3.The AI-based intelligent tunnel lighting control method of claim 1, wherein, According to the lighting demand index and the minimum power, the adjusted lighting power is determined, comprising: the maximum power of the tunnel lighting is obtained, wherein the maximum power is the maximum value of the lighting device power; according to the maximum power, the minimum power and the lighting demand index, the adjusted lighting power is determined. 4.The AI-based intelligent tunnel lighting control method of claim 3, wherein, According to the vehicle density, the average vehicle speed and the environmental data, a lighting demand index is determined, comprising: according to the formula: determining the lighting demand index of the ith tunnel area , wherein, is the average vehicle speed of the ith tunnel area, is a preset vehicle speed, is the vehicle density of the ith tunnel area, is a preset vehicle density, is the light intensity data of the ith tunnel area, is a preset light intensity data, is the visibility data of the ith tunnel area, is a preset visibility data, and is a preset weight value, k is an adjustment coefficient, and i is a positive integer. 5.The AI-based intelligent tunnel lighting control method of claim 1, wherein, According to the lighting data, the lighting response delay compensation amount is determined, comprising: the preset visual adaptation time of the driver is obtained; the maximum speed limit of the tunnel is obtained; and according to the preset visual adaptation time, the maximum speed limit and the lighting data, the lighting response delay compensation amount is determined. 6.The AI-based intelligent tunnel lighting control method of claim 5, wherein, According to the maximum power, the minimum power and the lighting demand index, a post-adjustment lighting power is determined, including: according to the formula: , The post-adjustment lighting power of the i-th tunnel area is determined , is the comprehensive lighting demand index of the i-th tunnel area, , and are the lighting demand indexes of the i-1, i, i+1 tunnel areas respectively, is a preset coupling coefficient, is the minimum power, is the maximum power, M is the number of tunnel areas, i≤M, and i is a positive integer. 7.The AI-based intelligent tunnel lighting control method of claim 1, wherein, The application relates to a tunnel lighting control method and device. 8.The AI-based intelligent tunnel lighting control method of claim 7, wherein, According to the preset visual adaptation time, the maximum speed limit and the lighting data, determining a lighting response delay compensation amount, comprising: Determining a lighting response delay compensation amount L, wherein, is a lighting delay time, is a preset visual adaptation time, is a maximum speed limit, is a lighting device spacing, is a lighting device irradiation radius. 9.A smart tunnel lighting control system based on artificial intelligence, configured to perform the smart tunnel lighting control method based on artificial intelligence according to any one of claims 1-8. ​ The vehicle detector module is arranged at a preset distance position in front of a tunnel entrance and a plurality of tunnel areas, and a plurality of vehicle detectors are arranged to obtain traffic flow data and real-time vehicle speed data within a preset time; the judgment module is used for judging whether the lighting power needs to be adjusted to the minimum power according to the traffic flow data, wherein the minimum power is the minimum value of the lighting device power; the environment data module is used for collecting the environment data of the plurality of tunnel areas in real time through the multispectral image sensor and the laser radar arranged in the plurality of tunnel areas if the lighting power does not need to be adjusted to the minimum power, wherein the environment data includes light intensity data and visibility data; the lighting demand index module is used for determining the lighting demand index according to the environment data and the real-time vehicle speed data; the adjusted lighting power module is used for determining the adjusted lighting power according to the lighting demand index and the minimum power; the lighting data module is used for obtaining lighting data, wherein the lighting data includes lighting delay time, lighting device spacing and illumination radius of the lighting device; and the lighting response delay compensation amount module is used for determining the lighting response delay compensation amount according to the lighting data.

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