Highway tunnel vehicle-mounted lighting system for measuring and calculating lamp lightening time based on interval intelligence

Abstract

The invention discloses a highway tunnel vehicle-mounted lighting system for intelligently measuring and calculating lamp lightening time based on an interval, and relates to the technical field of highway tunnel lighting energy saving. The invention discloses a highway tunnel vehicle-mounted lighting system for intelligently measuring and calculating lamp lightening time based on an interval. The highway tunnel vehicle-mounted lighting system comprises an interval division module, a lighting analysis module and a lighting control module. According to the method, the highway tunnel is subjected to refined interval division, vehicle shielding features, identification information and timestamps are combined, vehicle passing key features are dynamically obtained, the illumination path of each vehicle can be accurately predicted, and vehicle-mounted response and on-demand illumination are achieved; and front and back external environment illumination change information is fused, sunlight reflection illumination features are extracted, interference of strong sunlight reflection, entrance and exit light difference and the like on illumination control is eliminated from the source, and the environment adaptability and recognition accuracy of an illumination system are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy-saving lighting in highway tunnels, and in particular to a highway tunnel vehicle-mounted lighting system based on interval intelligent calculation of lamp brightening time. Background Art

[0002] Existing highway tunnel lighting systems often use fixed-duration or sensor-triggered control to control lamps. These systems are unable to precisely adjust to actual vehicle traffic conditions and ambient light fluctuations, resulting in excessive lighting energy consumption or insufficient localized illumination, impacting traffic safety. Traditional systems struggle to respond promptly in challenging scenarios, particularly those involving strong sunlight reflection, misjudgment of following vehicles, and abnormal deceleration. This leads to visual blind spots and control delays. Furthermore, existing systems often lack the ability to dynamically integrate predicted vehicle paths with natural light interference, resulting in delayed updates to lighting control strategies and a difficulty meeting the needs of high-speed, densely packed, or specialized traffic conditions.

[0003] Therefore, it is necessary to consider an intelligent lighting control method based on the linkage between vehicle traffic characteristics and external lighting changes to achieve precise lighting and dynamic strategy updates within the interval. Summary of the Invention

[0004] The present invention aims to provide a highway tunnel vehicle-mounted lighting system based on intelligent interval calculation of lamp dimming time, thereby achieving precise interval-mounted vehicle-mounted lighting and dynamic strategy updates.

[0005] The highway tunnel vehicle-mounted lighting system based on interval intelligent calculation of lamp dimming time includes:

[0006] The interval division module is used to set the current highway tunnel to include several tunnel lighting intervals; divide the several tunnel lighting intervals into 5 tunnel sections according to the interval position. n , n=1,2,…,5;

[0007] The lighting analysis module includes an information acquisition unit and a lighting analysis unit; the information acquisition unit is used to obtain real-time vehicle identification information, vehicle passing timestamps, and vehicle occlusion characteristics in the tunnel section D1; based on the vehicle identification information, vehicle passing timestamps, and vehicle occlusion characteristics, the module performs analysis to obtain key vehicle passage characteristics and predict vehicle lighting paths; based on the predicted vehicle lighting paths, the module generates a predicted passage lighting demand Q n ; At the same time, obtain external environment lighting data, and output sunlight reflection lighting characteristics based on the external environment lighting data;

[0008] The lighting analysis unit is used for tunnel section D n , establish lighting demand queue Z n ; Lighting demand queue Z n Contains all the currently received predicted traffic lighting needs; based on the lighting demand queue Z nand daylight reflection lighting characteristics to output tunnel lighting control strategy P n ;

[0009] Lighting control module, used to continuously update the lighting demand queue Z n and tunnel lighting control strategy P n , control the lighting of the current highway tunnel according to the tunnel lighting range.

[0010] As a preferred technical solution of the present invention, the specific steps of analyzing the vehicle identification information, the vehicle passing timestamp and the vehicle occlusion characteristics include:

[0011] Perform occlusion waveform analysis based on vehicle occlusion characteristics to extract occlusion duration, occlusion intensity change curve, and occlusion integrity index; use a pre-trained vehicle feature recognition model for analysis and output vehicle pass labels;

[0012] Perform special label recognition based on vehicle identification information to obtain vehicle category labels;

[0013] The vehicle speed is calculated based on the vehicle passing timestamp to obtain the initial vehicle speed; the path is calculated based on the initial vehicle speed combined with the current highway tunnel to obtain the predicted vehicle lighting path;

[0014] The vehicle traffic labels and vehicle category labels are combined to obtain the key features of vehicle traffic.

[0015] As a preferred technical solution of the present invention, the specific steps of calculating the path of a vehicle lighting path based on the initial vehicle speed and the current highway tunnel are as follows:

[0016] Based on historical vehicle traffic data, for tunnel section D n Establish tunnel traffic impact factor Y n ; Assume that the initial speed of the vehicle is V1; in tunnel section D n In the process, the predicted average vehicle speed V is corrected. n , where V n =V n-1 *Y n ;

[0017] Based on the predicted average vehicle speed V n and tunnel section D n The length of the tunnel section L n , predicted in tunnel section D n Vehicle entry time T n1 and vehicle departure time T n2 ; Traverse all tunnel segments D n , and the combination is used to obtain the predicted vehicle lighting path.

[0018] As a preferred technical solution of the present invention, the predicted traffic lighting demand Q is generated based on the predicted vehicle lighting path. n Specific steps include:

[0019] For tunnel section D n , construct the standard lighting response template R n ;

[0020] Match vehicle traffic lighting demand factors based on key vehicle traffic characteristics;

[0021] Based on the vehicle traffic lighting demand factor to the standard lighting response template R n Perform lighting correction to obtain the predicted traffic lighting demand Q n .

[0022] As a preferred technical solution of the present invention, the specific steps of outputting the daylight reflected lighting characteristics based on the external environment lighting data include:

[0023] The external environment lighting data includes front external environment lighting data and rear external environment lighting data;

[0024] The front external environment lighting data and the rear external environment lighting data both include ambient lighting data, reflected light data, and lighting intensity data; a front external environment lighting change sequence is constructed based on the front external environment lighting data; and a rear external environment lighting change sequence is constructed based on the rear external environment lighting data;

[0025] The illumination gradient change rate is calculated based on the front external environment illumination change sequence; the ambient illumination data, reflected light data, and illumination intensity data are synchronized in the front external environment illumination data to obtain illumination synchronization characteristics; the spectral time difference is extracted based on the front external environment illumination change sequence; and the front daylight reflected illumination characteristics are obtained based on the illumination synchronization characteristics and the spectral time difference;

[0026] Similarly, the rear daylight reflection lighting characteristics are obtained based on the analysis of the rear external environment illumination change sequence;

[0027] The front daylight reflected lighting feature and the rear daylight reflected lighting feature are combined to obtain the daylight reflected lighting feature.

[0028] As a preferred technical solution of the present invention, based on the lighting demand queue Z n and daylight reflection lighting characteristics to output tunnel lighting control strategy P n The specific steps include:

[0029] Based on lighting demand queue Z n All the predicted traffic lighting demands received in the tunnel are pooled to obtain the initial control strategy C for the tunnel section. n ;

[0030] The initial control strategy C of the tunnel section is based on the front daylight reflection lighting characteristics, the rear daylight reflection lighting characteristics and the swarm optimization algorithm. n Optimize and adjust to obtain the tunnel lighting control strategy P n .

[0031] As a preferred technical solution of the present invention, the swarm optimization algorithm is a tuna swarm optimization algorithm.

[0032] The present invention has the following advantages:

[0033] 1. The present invention divides highway tunnels into fine intervals and dynamically obtains key vehicle traffic characteristics by combining vehicle occlusion characteristics, identification information and passing timestamps. It can accurately predict the lighting path of each vehicle and achieve vehicle-responsive and on-demand lighting. It also integrates the front and rear external environment lighting change information, extracts the characteristics of daylight reflection lighting, and eliminates the interference of strong sunlight reflection, entrance and exit light differences, etc. on lighting control at the source, significantly improving the environmental adaptability and recognition accuracy of the lighting system.

[0034] 2. By constructing a lighting demand queue and performing pooling fusion, the present invention can uniformly dispatch lighting demands in multi-vehicle traffic scenarios, avoiding lighting gaps and resource waste; with the help of a swarm optimization algorithm, it can further intelligently optimize the lighting control strategy while ensuring traffic safety and visual continuity, and achieve multi-dimensional optimal configuration of lighting areas, brightness levels and durations; overall, it has the combined advantages of high responsiveness, high energy saving and high adaptability, effectively improving tunnel traffic safety and energy efficiency management. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a structural diagram of a highway tunnel vehicle-mounted lighting system based on interval intelligent calculation of lamp dimming time adopted in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0037] Highway tunnel vehicle lighting system based on interval intelligent calculation of lamp dimming time, see Figure 1 Shown, including:

[0038] The interval division module is used to set the current highway tunnel to include several tunnel lighting intervals; divide the several tunnel lighting intervals into 5 tunnel sections according to the interval position. n , n=1, 2, ..., 5; a plurality of tunnel lighting sections all include LED tunnel light groups for lighting;

[0039] Among them, tunnel section D1 is the front area of the tunnel entrance, used for pre-entry preparation and pre-judgment of vehicles. It may appear in the area with strong sunlight, forming a light-dark contrast with the tunnel entrance. The driver's vision has not yet adapted to the darkness of the tunnel and needs to perceive it in advance. The entrance should not be suddenly bright, and natural light reflections should be avoided to interfere with sensor triggering. Tunnel section D2 is the entrance adaptation zone, which is the process of vehicle driver's vision adapting from light to dark. High-brightness lighting should be used when lighting to simulate the bright environment outside the tunnel to alleviate visual gap. Tunnel section D3 is the middle area of the tunnel, which is the main traffic area during the driving process. Adequate sight distance and visual clarity should be maintained. Tunnel section D4 is the exit adaptation zone, which is a visual transition from dark to bright. The vision in the tunnel has adapted to the dark environment, and sudden entry into strong sunlight will cause glare. A "gradual darkening zone" should be set to transition outward to avoid sudden changes in vision. Tunnel section D5 is the rear area of the tunnel exit, which is a short-term adjustment and light release area after leaving the tunnel. The light should be maintained at medium to low brightness for several seconds to avoid sudden loss of light in the dark.

[0040] The lighting analysis module includes an information acquisition unit and a lighting analysis unit; the information acquisition unit is used to obtain real-time vehicle identification information, vehicle passing timestamps, and vehicle occlusion characteristics in the tunnel section D1; based on the vehicle identification information, vehicle passing timestamps, and vehicle occlusion characteristics, the module performs analysis to obtain key vehicle passage characteristics and predict vehicle lighting paths; based on the predicted vehicle lighting paths, the module generates a predicted passage lighting demand Q n ; At the same time, obtain external environment lighting data, and output sunlight reflection lighting characteristics based on the external environment lighting data;

[0041] An obstruction-type infrared vehicle detector and camera are installed at the tunnel entrance to obtain vehicle identification information, vehicle passage timestamps, and vehicle obstruction characteristics. Vehicle identification information is used to determine whether it is a special vehicle and the vehicle model. The vehicle passage timestamp is recorded at the moment of vehicle obstruction and is used to calculate the vehicle's initial speed. This serves as the zero moment for all lighting prediction paths and lighting time windows, and is used to determine the timing difference between the front and rear vehicles and identify the following vehicle or convoy structure. The vehicle obstruction characteristics are used to identify the obstruction duration, waveform integrity, jumps or multiple peaks, and can also distinguish whether it is a false trigger caused by other interfering objects such as billboards.

[0042] The specific steps for analyzing vehicle identification information, vehicle passing timestamps, and vehicle occlusion features include:

[0043] Perform occlusion waveform analysis based on vehicle occlusion characteristics to extract occlusion duration, occlusion intensity change curve, and occlusion integrity index; use a pre-trained vehicle feature recognition model for analysis and output vehicle pass labels;

[0044] In occlusion waveform analysis, the occlusion duration indicates the total length of time a vehicle blocks the infrared signal within the sensor's field of view, reflecting the vehicle's length or speed. The occlusion intensity curve describes the temporal trend of infrared signal intensity during the occlusion process, and can be used to determine the vehicle's profile, passing posture, and the presence of abnormal interference. The occlusion integrity index comprehensively measures the continuity, smoothness, and morphological integrity of the waveform to determine whether the occlusion event represents a truly stable vehicle passage or whether there are abnormalities such as jumps, intermittent motion, or artifacts.

[0045] The training process of the vehicle feature recognition model includes: first, collecting infrared sensor data covering a variety of vehicle types, traffic conditions, and occlusion waveforms, and combining it with image data to establish a fully annotated training set; then, selecting a basic model suitable for time series or image processing (such as 1D-CNN or ResNet), setting classification and validity recognition as training objectives, and using cross-entropy or focal loss functions for optimization; during the training process, the generalization ability is improved by adjusting the learning rate, introducing data augmentation, and introducing early stopping strategies; when the validation set accuracy and occlusion validity recognition accuracy meet the set standards, the model training is completed and used for subsequent vehicle traffic label determination and false trigger identification; in the vehicle feature recognition model, the vehicle traffic label identification process is based on the occlusion waveform characteristics. For example, when the occlusion duration exceeds the set threshold and the waveform profile is continuous and stable, it is determined to be a valid vehicle; if the occlusion time is too short, the waveform has jump interference, or it cannot match a valid vehicle ID, it is determined to be a false trigger and subsequent analysis is terminated; if there are two obvious peaks in the waveform that match the vehicle length and time interval characteristics, it is recorded as a suspected following vehicle structure for subsequent lighting compensation judgment;

[0046] Special tags are identified based on vehicle identification information to obtain vehicle category tags. Vehicle identification information is matched with a preset vehicle feature database to extract the vehicle's registration type, appearance features, and access rights. Based on the matching results, it is determined whether the vehicle belongs to a special vehicle category (such as an ambulance, fire truck, hazardous chemical transport vehicle, etc.) or a large or medium-sized vehicle (such as a truck or bus), and the corresponding vehicle category tag is generated accordingly, which serves as an important basis for subsequent lighting scheduling priorities and response strategy adjustments.

[0047] The preset vehicle feature database is constructed through multi-source data collection and classification, mainly including vehicle registration information in the traffic management system, vehicle type archives in the highway toll system, historical traffic records and special vehicle registration data. The above data are obtained from publicly available data; a unique identifier is established for each vehicle in the database, and its vehicle category (such as passenger car, truck, special vehicle), appearance parameters (such as length, height), traffic priority and special traffic rules are associated. In order to improve real-time matching efficiency, the database is updated and cleaned regularly, and supports regional configuration and dynamic loading to ensure the accuracy and timeliness of tag recognition.

[0048] The vehicle speed is calculated based on the vehicle passing timestamp to obtain the initial vehicle speed; the path is calculated based on the initial vehicle speed combined with the current highway tunnel to obtain the predicted vehicle lighting path;

[0049] When a vehicle enters tunnel section D1, the initial occlusion timestamp is recorded. The speed is measured by combining the occlusion duration and vehicle length to obtain the initial vehicle speed. The vehicle passage label and vehicle category label are combined to obtain the key characteristics of vehicle passage.

[0050] Based on the initial vehicle speed and the current highway tunnel, the path is calculated to obtain the specific steps for predicting the vehicle lighting path, including:

[0051] Based on historical vehicle traffic data, for tunnel section D n Establish tunnel traffic impact factor Y n ; Assume that the initial speed of the vehicle is V1; in tunnel section D n In the process, the predicted average vehicle speed V is corrected. n , where V n =V n-1 *Y n ;

[0052] Based on the predicted average vehicle speed V n and tunnel section D n The length of the tunnel section L n , predicted in tunnel section D n Vehicle entry time T n1 and vehicle departure time T n2 ; Traverse all tunnel segments D n , combined to obtain the predicted vehicle lighting path;

[0053] Tunnel Section D n Tunnel traffic influencing factor Y nIt is a dynamic correction parameter constructed based on historical vehicle traffic data statistics, used to reflect the typical impact of a specific tunnel section on vehicle speed. Its construction process includes: collecting a large number of vehicles' actual traffic speeds and initial entry speeds in each tunnel section, and calculating the speed attenuation or acceleration ratio through regression analysis or sliding mean; at the same time, combining the physical characteristics of the tunnel section (such as slope, curve, lighting conditions), traffic environment (such as traffic density, accident frequency), and control factors (such as speed limit signs, monitoring coverage) for weighted correction. Finally, Y n As a factor reflecting the changing trend of the average speed of the section, it is used to correct the speed of the previous section to estimate the speed of the current section, thereby improving the accuracy and adaptability of the lighting path prediction. For example, analysis of historical vehicle traffic data shows that vehicles exhibit typical speed change trends in different tunnel sections: in the front area of the tunnel entrance (D1), due to the strong light and the black hole effect, drivers often experience slight deceleration or speed fluctuations; after entering the entrance adaptation zone (D2), due to the visual transition from bright to dark, the vehicle speed drops significantly, reaching the lowest speed point in the entire tunnel; after entering the middle area of the tunnel (D3), as visual adaptation is completed, the vehicle speed gradually recovers and stabilizes; in the exit adaptation zone (D4), due to the white hole effect, some vehicles will again experience slight deceleration or speed fluctuations; finally, in the rear area of the tunnel exit (D5), the driver's vision is wide and the environment is clear, and the speed generally returns to normal or accelerates, showing an accelerated passage trend.

[0054] The predicted vehicle lighting path is a continuous lighting interval formed by traversing each tunnel section in the tunnel, calculating the vehicle's entry and exit times in each section based on the predicted average speed of the vehicle in each section and the tunnel section length, and combining the lighting time windows of each section. Whether each tunnel section is included in the predicted lighting path depends on the vehicle's expected coverage period in the section. Based on this, it is determined whether the lighting should be turned on during this period, and the path range is corrected in real time according to the actual traffic status of the vehicle, so as to ensure that the lighting is synchronized with the vehicle during the entire passage process, which not only meets the safety visual requirements but also realizes dynamic energy-saving control.

[0055] Generate predicted traffic lighting demand Q based on predicted vehicle lighting path n Specific steps include:

[0056] For tunnel section D n , construct the standard lighting response template R n ;

[0057] Match vehicle traffic lighting demand factors based on key vehicle traffic characteristics;

[0058] Based on the vehicle traffic lighting demand factor to the standard lighting response template R n Perform lighting correction to obtain the predicted traffic lighting demand Q n ;

[0059] The standard lighting response template is a set of basic lighting control parameters preset for each tunnel section, which is used to represent the default lighting strategy under typical vehicle traffic conditions. It is constructed based on the physical function of the tunnel section, visual adaptation requirements, and traffic safety strategy. For example, the front area of the tunnel entrance (D1) is mainly for guidance, and low-intensity lighting is used for visual pre-adaptation. The entrance adaptation area (D2) needs to provide high-brightness, gradually brightening lighting to alleviate the visual gap caused by the black hole effect. The middle area of the tunnel (D3) uses a baseline brightness and dynamically illuminates the corresponding section based on the predicted vehicle travel time to ensure sight distance and energy saving. The exit adaptation area (D4) is set with a gradual darkening transition to prevent sudden glare caused by the white hole effect, while strengthening exit guidance lighting. The rear area of the tunnel exit (D5) provides a short-term low-brightness lighting buffer to help drivers smoothly transition from a dark environment to the external environment, ensuring visual safety and continuity. The specific lighting parameters are set manually.

[0060] In generating the predicted traffic lighting demand Q n In the process, the corresponding vehicle traffic lighting demand factors are matched based on the key characteristics of vehicle traffic, and the standard lighting templates of each tunnel section are dynamically adjusted; for example: for small vehicles, the standard template is usually used, and the lighting advance time can be appropriately shortened in the entrance and exit areas; medium or large vehicles need to expand the lighting range and light up in advance to eliminate potential blind spots; special vehicles such as hazardous chemical transport vehicles or fire trucks will trigger synchronized lighting of the entire tunnel section to ensure continuous visibility; if a suspected following vehicle structure is identified, the lighting paths of the front and rear vehicles will be merged, and the lighting time window will be uniformly expanded to prevent lighting faults; if the vehicle speed is abnormally low, the duration of each lighting section will be extended to avoid the light being extinguished before the vehicle leaves; when the vehicle has an emergency label, priority will be given to turning on the full tunnel pre-lighting, and synchronizing the front and rear lighting to avoid lighting blind spots. In the case of light interference, such as strong forward reflections, the lighting brightness threshold of the entrance area is increased to prevent the sensor from mistakenly judging not to light up; and in the scenario of a white hole effect that may occur at the exit, the dimming time of the exit area lighting is increased, and the tunnel exit buffer lighting time is extended to ensure traffic safety and visual continuity.

[0061] The lighting analysis unit is used for tunnel section D n , establish lighting demand queue Z n ; Lighting demand queue Z n Contains all the currently received predicted traffic lighting needs; based on the lighting demand queue Z n and daylight reflection lighting characteristics to output tunnel lighting control strategy P n ;

[0062] The specific steps of outputting the daylight reflection lighting characteristics based on the external environment lighting data include:

[0063] The external environment lighting data includes front external environment lighting data and rear external environment lighting data;

[0064] Acquire front external environment lighting data between tunnel sections D1 and D2, and acquire rear external environment lighting data between tunnel sections D4 and D5;

[0065] Data collection points for front-end external ambient lighting are set up between tunnel sections D1 and D2, and for rear-end external ambient lighting between sections D4 and D5. These points aim to capture changes in ambient lighting before and after a vehicle enters and exits the tunnel, reflecting the actual intensity of sunlight interference and reflections. Regions D1 and D2 represent the transition zone from bright sunlight into a relatively dim tunnel, where black hole effects or strong sunlight reflections can occur, impacting driving safety and sensor judgment. Therefore, precise collection of ambient and reflected light characteristics before tunnel entry is crucial. Regions D4 and D5 represent the transition zone from tunnel exit into a brightly lit environment, often accompanied by a white hole effect, which can temporarily disrupt the driver's vision. Therefore, data collection of external lighting characteristics before and after tunnel exit is crucial to developing a reasonable exit lighting dimming strategy and delayed lighting buffer mechanism, enabling the overall lighting system to dynamically perceive and intelligently adapt to the natural light environment.

[0066] The front external environment lighting data and the rear external environment lighting data both include ambient lighting data, reflected light data and lighting intensity data;

[0067] Taking the vehicle passing timestamp as the starting point, the front light sampling period is calculated based on the vehicle's initial speed; the front external environment light data is obtained within the front light sampling period, and the front external environment light change sequence is constructed based on the front external environment light data;

[0068] After identifying the timestamp of the vehicle passing through tunnel section D1, the estimated time of the vehicle in the area before entering the tunnel is calculated based on the vehicle's initial speed, starting from this time point, thereby determining the forward lighting sampling period. Subsequently, the forward lighting acquisition operation is synchronously started within this time window to obtain real-time ambient lighting data, reflected light data, and lighting intensity data in the area before the vehicle enters. The collected data is organized into a forward external ambient lighting change sequence in chronological order, reflecting the dynamic trend of light intensity changes over time, providing continuous and accurate raw lighting information support for subsequent lighting synchronization analysis, light gradient calculation, and daylight reflection feature extraction.

[0069] Based on the predicted average vehicle speeds V3 and V4, an advance light perception window is obtained; a rear light sampling period is set based on the advance light perception window; rear external environment light data is obtained within the rear light sampling period, and a rear external environment light change sequence is constructed based on the rear external environment light data;

[0070] After predicting that a vehicle is about to enter tunnel section D3, the estimated time it will reach the exit adaptation zone is calculated based on its average speed in areas D3 and D4. This allows for a pre-perception window to be established for predicting changes in the lighting environment before and after exiting the tunnel. Within this pre-perception window, a post-light sampling period is set during which ambient light data, reflected light data, and lighting intensity data from the exit area are continuously collected. The collected data is organized chronologically into a post-external ambient light change sequence, which dynamically reflects the changing trends in natural light before and after the vehicle exits the tunnel, providing data support for dimming control and buffer strategy optimization of the exit section lighting.

[0071] The illumination gradient change rate is calculated based on the front external environment illumination change sequence; the ambient illumination data, reflected light data, and illumination intensity data are synchronized in the front external environment illumination data to obtain illumination synchronization characteristics; the spectral time difference is extracted based on the front external environment illumination change sequence; and the front daylight reflected illumination characteristics are obtained based on the illumination synchronization characteristics and the spectral time difference;

[0072] The sequence of changes in the front external environmental illumination is analyzed, and the amplitude of the change in illumination intensity per unit time is calculated to obtain the illumination gradient change rate, which is used to judge the severity of illumination changes in the tunnel entry area. A temporal synchronization judgment is performed between the collected ambient illumination data, reflected light data, and illumination intensity data to evaluate whether the change trends of the three are consistent, thereby extracting illumination synchronization features and identifying the degree of coupling between natural light and reflected interference. At the same time, based on the response differences of different spectral components in the time dimension, the spectral time difference index is extracted to identify the presence of highly reflective materials or interfering light sources. Combining the illumination synchronization features with the spectral time difference, a comprehensive judgment is made as to whether there is significant sunlight reflection interference in the current tunnel entry environment, and then accurate front sunlight reflection lighting features are extracted to optimize the triggering strategy and brightness adjustment logic of the tunnel entry lighting.

[0073] Similarly, based on the rear external environment illumination change sequence, the rear daylight reflected lighting characteristics are obtained; based on the rear external environment illumination change sequence, the illumination intensity change rate per unit time is first calculated to obtain the illumination gradient change rate, which is used to evaluate the severity of the change in natural light in the tunnel exit area; then, in the rear external environment illumination data, the ambient illumination data, reflected light data, and illumination intensity data are time-axis aligned and correlated to determine the consistency of the three types of data in the change trend, thereby extracting the illumination synchronization characteristics; the response delay of different spectral channels during the change process is analyzed, and the spectral time difference index is extracted to identify whether there is strong reflection or sudden light source interference in the exit area; the illumination synchronization characteristics and spectral time difference are combined to generate the rear daylight reflected lighting characteristics, which are used to assist in the dynamic adjustment of the exit adaptation zone lighting strategy and optimize the dimming control, delay duration, and buffer brightness setting of the tunnel exit lighting.

[0074] The front daylight reflected lighting feature and the rear daylight reflected lighting feature are combined to obtain the daylight reflected lighting feature.

[0075] Based on lighting demand queue Z n and daylight reflection lighting characteristics to output tunnel lighting control strategy P n The specific steps include:

[0076] Based on lighting demand queue Z n All the predicted traffic lighting demands received in the tunnel are pooled to obtain the initial control strategy C for the tunnel section. n After receiving all the predicted traffic lighting demands in the tunnel section lighting demand queue, the lighting control parameters contained in each demand are first parsed and time-axis aligned; then the overlapping or adjacent lighting demands are merged using a pooling operation to extract the common characteristics of the lighting demands and form a unified time interval and brightness configuration strategy; on this basis, according to the demand density, vehicle type distribution and the degree of interval overlap, the light group opening sequence, lighting duration and spatial distribution are reasonably arranged, thereby generating an initial tunnel section control strategy covering all current valid predicted lighting demands, providing a structured lighting baseline for subsequent optimized scheduling.

[0077] The initial control strategy C of the tunnel section is based on the front daylight reflection lighting characteristics, the rear daylight reflection lighting characteristics and the swarm optimization algorithm. n Optimize and adjust to obtain the tunnel lighting control strategy P n ; Among them, the swarm optimization algorithm is the tuna swarm optimization algorithm;

[0078] First, the initial lighting control strategy C of the tunnel section n The lighting control parameters in the algorithm are encoded as an optimizable solution vector, which serves as the basic structure in the search space. A tuna population consisting of a set of candidate solutions is then generated, with each individual representing a possible lighting strategy. Key parameters of the tuna population optimization algorithm, such as population size, maximum number of iterations, migration probability, and adaptive weighting factor, are set to provide a balance between global convergence and local jump capabilities for subsequent searches. These key parameters are manually set.

[0079] A fitness function is constructed for each candidate lighting strategy, comprehensively considering energy efficiency, safety, and lighting adaptability. Energy efficiency is estimated by multiplying the lighting time and brightness, while safety is measured by the overlap with the target time window in the lighting demand queue. Light adaptability incorporates the characteristics of front and rear daylight reflection lighting, acting as brightness correction factors at the tunnel entrance and exit sections, respectively, to evaluate the strategy's robustness in complex lighting interference scenarios. The fitness function minimizes energy consumption as its primary objective, while also imposing constraints to ensure that the safe lighting range is met.

[0080] In each iteration, individual tuna update their strategies based on their fitness values, the current optimal individual position, and their migration behavior model. Some individuals swim in groups close to the current optimal strategy to accelerate global convergence, while others simulate random swimming to explore boundary solutions and escape local optima. The update process uses an adaptive perturbation factor to guide the adjustment of parameters such as the lighting time window and brightness level of the light group within a reasonable range. In the updated candidate strategy, the brightness threshold and lighting advance time of the tunnel entrance section (D1 and D2) are further adjusted by integrating the previous sunlight reflection characteristics to combat the black hole effect. At the same time, the dimming intensity and delay time of the exit section (D4 and D5) are optimized by integrating the subsequent sunlight reflection characteristics to ensure that the white hole effect is mitigated. This stage plays a role in local illumination adjustment, target-specific compensation for key transition intervals that were not refined in the global optimization, and improve the lighting system's responsiveness to extreme environments. The system terminates when the preset number of iterations is reached or the fitness function converges. It selects the lighting strategy represented by the current optimal individual as the final tunnel section lighting control strategy. This strategy takes into account lighting demand coverage, adaptation to ambient light interference, and overall energy consumption optimization. It supports on-vehicle segment triggering and dynamic adjustment, providing an efficient, safe, and intelligent lighting control solution for practical applications.

[0081] Lighting control module, used to continuously update the lighting demand queue Z n and tunnel lighting control strategy P n , the current highway tunnel controls the lamps according to the tunnel lighting interval; in this embodiment, in order to achieve dynamic and precise control of highway tunnel lighting, it is necessary to continuously receive and update the lighting demand queue and the tunnel section lighting control strategy, and perform real-time lamp scheduling according to the tunnel lighting interval; in order to avoid logical errors in lighting replacement (such as premature light turning off, repeated lighting or lighting faults), a time window queue mechanism is adopted to maintain an orderly task buffer queue for each lighting interval, and all lighting control instructions are judged, issued and executed in three stages based on the predicted travel time window; when new demand arrives, the lighting strategy is prioritized and the task is merged to ensure seamless connection between the old and new strategies; at the same time, a redundant lighting maintenance mechanism is set to reserve a minimum brightness buffer time at the lighting switching point to ensure a smooth transition of lighting changes and avoid visual mutations or control conflicts, thereby achieving a safe, continuous and logically flawless intelligent lighting replacement process.

[0082] For example, in the actual use of the onboard lighting system in a highway tunnel, the following situation may occur: a large truck A enters a highway tunnel at a normal speed. Its traffic characteristics have been identified by the sensor in tunnel section D1 and a predicted lighting path has been established. Based on this, the lighting demand Q for tunnel sections D2 to D5 is generated. n , lighting demand queue Z n Then the system outputs the initial lighting control strategy P n, controlling the lighting of related lamps along the path. About 5 seconds after truck A passed through, a small car B followed closely into the tunnel. Due to its high speed, it was predicted that it would approach or overtake the rear of vehicle A in section D3.

[0083] After car B is identified at D1, the system also collects its occlusion waveform, identification information, and passage timestamp, and calculates its initial speed and predicted lighting path. Due to the short distance between the vehicles, the system marks it as a following vehicle. The system finds that the predicted lighting path of car B overlaps with that of the preceding car A in a large number of sections, especially in the D3-D4 section. At this time, the lighting analysis module calculates the lighting requirements Q of the preceding and following vehicles. n Perform pooling fusion processing to form a unified lighting demand window; if the lighting control strategy P n If a certain section of lamps is already in the on state, only its lighting duration is extended and repeated on instructions are avoided to prevent waste of system resources or control logic conflicts. n On this basis, the system combines the current characteristics of daylight reflection lighting (such as strong reflection at the entrance and white hole effect at the exit) with the tuna school optimization algorithm to jointly optimize the lighting brightness, on-time and transition zone settings. In particular, if car B is predicted to quickly exit the D4-D5 area, the system extends the tail time of the exit lighting and adjusts the dimming strategy to avoid visual differences.

[0084] In the multi-vehicle traffic scenario, the system first uses sensors in tunnel section D1 to identify the vehicle information, occlusion characteristics and timestamps of each vehicle, and then generates their respective predicted lighting paths and traffic lighting requirements Q n ; The lighting analysis module converts the Q n Join the lighting demand queue Z of the corresponding tunnel section n , and perform time axis alignment and pooling processing to merge overlapping or adjacent lighting requirements into continuous lighting intervals to form the initial lighting control strategy C n ; Then, the system combines the current sunlight reflection lighting characteristics and uses the group optimization algorithm to optimize C n Perform dynamic optimization to generate the final control strategy P n , ensuring that all vehicle path lighting requirements are covered and conflict-free; the lighting control module updates Z in real time n With P n , accurately adjust the lighting and extinguishing timing of lamps according to the interval, and set a buffer time window and lighting redundancy mechanism to prevent lighting faults, premature lighting or repeated lighting caused by changes in vehicle speed, following vehicles or differences in vehicle types, ensuring smooth and intelligent lighting connection and energy-saving control in scenarios where multiple vehicles intersect or have dense traffic.

[0085] It should be understood that those skilled in the art may make improvements or modifications based on the above description, and all such improvements and modifications shall fall within the scope of protection of the appended claims. Any portion of this specification not described in detail is prior art known to those skilled in the art.

Claims

1. A road tunnel vehicle-mounted lighting system based on intelligent interval calculation of lamp dimming time, characterized in that: include: The interval division module is used to set the current highway tunnel to include several tunnel lighting intervals; Divide several tunnel lighting sections into 5 tunnel sections according to the section location n , n=1,2,…,5; The lighting analysis module includes an information acquisition unit and a lighting analysis unit; the information acquisition unit is used to obtain real-time vehicle identification information, vehicle passing timestamps, and vehicle occlusion characteristics in the tunnel section D1; based on the vehicle identification information, vehicle passing timestamps, and vehicle occlusion characteristics, the module performs analysis to obtain key vehicle passage characteristics and predict vehicle lighting paths; based on the predicted vehicle lighting paths, the module generates a predicted passage lighting demand Q n ; At the same time, obtain external environment lighting data, and output sunlight reflection lighting characteristics based on the external environment lighting data; The lighting analysis unit is used for tunnel section D n , establish lighting demand queue Z n ; Lighting demand queue Z n Contains all the currently received predicted traffic lighting needs; based on the lighting demand queue Z n and daylight reflection lighting characteristics to output tunnel lighting control strategy P n ; Lighting control module, used to continuously update the lighting demand queue Z n and tunnel lighting control strategy P n , control the lighting of the current highway tunnel according to the tunnel lighting range.

2. The highway tunnel vehicle-mounted lighting system based on interval intelligent calculation of lamp brightening time according to claim 1 is characterized in that: The specific steps for analyzing vehicle identification information, vehicle passing timestamps, and vehicle occlusion features include: Perform occlusion waveform analysis based on vehicle occlusion characteristics to extract occlusion duration, occlusion intensity change curve, and occlusion integrity index; use a pre-trained vehicle feature recognition model for analysis and output vehicle pass labels; Perform special label recognition based on vehicle identification information to obtain vehicle category labels; The vehicle speed is calculated based on the vehicle passing timestamp to obtain the initial vehicle speed; the path is calculated based on the initial vehicle speed combined with the current highway tunnel to obtain the predicted vehicle lighting path; The vehicle traffic labels and vehicle category labels are combined to obtain the key features of vehicle traffic.

3. The highway tunnel vehicle-mounted lighting system based on interval intelligent calculation of lamp brightening time according to claim 2 is characterized in that: Based on the initial vehicle speed and the current highway tunnel, the path is calculated to obtain the specific steps for predicting the vehicle lighting path, including: Based on historical vehicle traffic data, for tunnel section D n Establish tunnel traffic impact factor Y n ; Assume that the initial speed of the vehicle is V1; in tunnel section D n In the prediction, the average speed of vehicles passing through V is corrected. n , where V n =V n-1 *Y n ; Based on the predicted average vehicle speed V n and tunnel section D n The length of the tunnel section L n , predicted in tunnel section D n Vehicle entry time T n1 and vehicle departure time T n2 ; Traverse all tunnel segments D n , and the combination is used to obtain the predicted vehicle lighting path.

4. The highway tunnel vehicle-mounted lighting system based on interval intelligent calculation of lamp brightening time according to claim 3 is characterized in that: Generate predicted traffic lighting demand Q based on predicted vehicle lighting path n Specific steps include: For tunnel section D n , construct the standard lighting response template R n ; Match vehicle traffic lighting demand factors based on key vehicle traffic characteristics; Based on the vehicle traffic lighting demand factor to the standard lighting response template R n Perform lighting correction to obtain the predicted traffic lighting demand Q n .

5. The highway tunnel vehicle-mounted lighting system based on interval intelligent calculation of lamp brightening time according to claim 4 is characterized in that: The specific steps of outputting the daylight reflection lighting characteristics based on the external environment lighting data include: The external environment lighting data includes front external environment lighting data and rear external environment lighting data; The front external environment lighting data and the rear external environment lighting data both include ambient lighting data, reflected light data, and lighting intensity data; a front external environment lighting change sequence is constructed based on the front external environment lighting data; and a rear external environment lighting change sequence is constructed based on the rear external environment lighting data; The illumination gradient change rate is calculated based on the front external environment illumination change sequence; the ambient illumination data, reflected light data, and illumination intensity data are synchronized in the front external environment illumination data to obtain illumination synchronization characteristics; the spectral time difference is extracted based on the front external environment illumination change sequence; and the front daylight reflected illumination characteristics are obtained based on the illumination synchronization characteristics and the spectral time difference; Similarly, the rear daylight reflection lighting characteristics are obtained based on the analysis of the rear external environment illumination change sequence; The front daylight reflected lighting feature and the rear daylight reflected lighting feature are combined to obtain the daylight reflected lighting feature.

6. The highway tunnel vehicle-mounted lighting system based on interval intelligent calculation of lamp brightening time according to claim 5 is characterized in that: Based on lighting demand queue Z n and daylight reflection lighting characteristics to output tunnel lighting control strategy P n The specific steps include: Based on lighting demand queue Z n All the predicted traffic lighting demands received in the tunnel are pooled to obtain the initial control strategy C for the tunnel section. n ; The initial control strategy C of the tunnel section is based on the front daylight reflection lighting characteristics, the rear daylight reflection lighting characteristics and the swarm optimization algorithm. n Optimize and adjust to obtain the tunnel lighting control strategy P n .

7. The highway tunnel vehicle-mounted lighting system based on interval intelligent calculation of lamp brightening time according to claim 6 is characterized in that: in, The swarm optimization algorithm is the tuna swarm optimization algorithm.

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