A Dynamic Lighting Control Method for Highway Tunnels Based on Multi-Source Perception of Pedestrians, Vehicles, and Environment

Abstract

This invention belongs to the field of tunnel control technology, specifically a dynamic lighting control method for highway tunnels based on multi-source perception of people, vehicles, and the environment. It calculates the visual lighting benchmark for each zone based on external natural light intensity and lighting specifications, corrects the illuminance based on traffic flow density, vehicle speed, and the proportion of large vehicles, and further corrects for ambient light loss based on air humidity, road surface conditions, and the reflectivity of the inner walls. Simultaneously, it considers the working time and operating temperature of the lighting devices, quantifies energy efficiency decay, and calibrates the target illuminance for each lamp. Ultimately, it achieves stepless smooth dimming of the lighting devices and automatically compensates for local lighting gaps when a lighting device malfunctions. This overcomes the shortcomings of traditional fixed-level and experience-based dimming, taking into account human visual adaptation, dynamic changes in the human and vehicle environment, and compensation for aging and light decay of the lighting devices, thereby improving driving safety in highway tunnels and saving energy.

Description

Technical Field

[0001] This invention relates to the field of tunnel control technology, specifically a dynamic lighting control method for highway tunnels based on multi-source perception of people, vehicles, and the environment. Background Technology

[0002] Highway tunnels are key structures in road traffic networks. The transition between light and dark and the uniformity of lighting in tunnels not only directly affect drivers' visual adaptation and driving safety, but also account for a large proportion of the daily operating costs of highways. As a result, the industry continues to demand more intelligent, refined and energy-efficient management and control of tunnel lighting.

[0003] Currently, highway tunnel lighting generally adopts a control mode of fixed setting or manual experience-based dimming, which lacks differentiated control logic for tunnel zones. It cannot integrate multiple factors such as changes in natural light, traffic flow, and environmental conditions for quantitative matching and adjustment. It also does not take into account the aging and light decay of the light-emitting devices over long-term operation and the energy efficiency degradation caused by temperature. It is difficult to accurately adapt to real-time driving scenarios and the actual operating conditions of the light-emitting devices, and meet the comprehensive control needs of driving safety, visual comfort, and energy saving.

[0004] Therefore, developing a dynamic lighting control method for highway tunnels that features independent zoned control, multi-source sensing linkage between people, vehicles, and the environment, and adaptive calibration of the energy efficiency of the lighting device has become an urgent technical problem to be solved in the field of tunnel management technology. Summary of the Invention

[0005] The purpose of this invention is to provide a method for dynamic lighting control in highway tunnels based on multi-source perception of human, vehicle, and environmental factors, in order to address the technical deficiencies mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a dynamic lighting control method for highway tunnels based on multi-source perception of people, vehicles, and environment, which establishes a dedicated progressive functional unit and executes the following steps in sequence:

[0007] Step 1: Calculate the visual lighting baseline for each zone: Based on the intensity of external natural light and the inherent parameters of each zone preset in the system, calculate and output the visual lighting baseline value for each zone.

[0008] Step 2, Traffic Flow Situation Correction Illumination Reference: Through three-level progressive quantization correction, the corrected lighting reference values ​​for each zone are obtained;

[0009] Step 3: Ambient lighting correction and illuminance prediction: The corrected lighting reference value is corrected using the ambient lighting loss coefficient to obtain the ambient-adaptive lighting prediction value for each zone.

[0010] Step 4: Energy efficiency calibration target illuminance: Decompose the overall illuminance estimate for each zone and calibrate it using the energy efficiency attenuation coefficient;

[0011] Step 5: Dynamic Adjustment Execution: Adjust the light-emitting device according to the final target illuminance and monitor the working status of the light-emitting device in real time;

[0012] This method abandons the traditional fixed-gear, experience-based dimming control mode, integrates multi-source perception of people, vehicles, and environment with the hardware status characteristics of light-emitting devices, and realizes refined, precise, and fault-self-healing dynamic lighting control of tunnel zones, comprehensively improving the adaptability of tunnel lighting and long-term operational stability.

[0013] Furthermore, step one is implemented based on the visual adaptation and judgment unit, dividing the tunnel into three control zones: the entrance section, the middle section, and the exit section.

[0014] Furthermore, step two is implemented based on the driving situation simulation unit. It uses millimeter-wave radar to collect data on traffic density, average vehicle speed, and the proportion of large vehicles in different zones, and then performs three-level superposition coefficient correction calculations to accurately compensate for light shading loss caused by different traffic conditions.

[0015] Furthermore, step three is implemented using an ambient light and shadow coupling unit. It collects the air humidity of the zone, the road surface condition, and the reflectivity parameters of the tunnel inner wall to solve for the light and shadow loss coefficient. Based on the loss coefficient, it corrects the lighting reference value to adapt to the real-time tunnel environment and reduce the deviation between the theoretical illuminance and the actual on-site illuminance.

[0016] Furthermore, step four is completed by the energy efficiency adaptive calibration unit. Based on the location and number of light-emitting devices in the tunnel, the energy efficiency adaptive calibration unit breaks down the overall lighting forecast of the zone into initial illuminance instructions for each lamp, thereby refining the zone lighting index to individual lighting devices.

[0017] Furthermore, the energy efficiency adaptive calibration unit calculates the energy efficiency attenuation coefficient by combining the cumulative working time of the LED lamps and the real-time operating temperature. Based on the energy efficiency attenuation coefficient, it performs reverse calibration to obtain the final target illuminance of a single lamp, thus compensating for the problem of luminous power attenuation caused by lamp aging and temperature rise.

[0018] Furthermore, in step five, the dimming operation is initiated by the intelligent drive execution unit through power line carrier communication, which sends a stepless dimming command. The brightness of the lamp changes smoothly and continuously, without any level-based brightness jumps, thus preventing sudden changes in light from interfering with the driver's visual adaptation process.

[0019] Furthermore, the intelligent drive execution unit collects the voltage, current and communication information of the lamps in real time, and adjusts the illuminance of the surrounding intact lamps to make up for the missing light after identifying equipment failures.

[0020] Compared with the prior art, the beneficial effects of the present invention are:

[0021] 1. In this invention, by abandoning the traditional fixed-gear and experience-based dimming mode, the target traffic area is divided into independent light control zones. The illuminance value is calculated by integrating multiple factors such as external natural light, traffic density, driving speed, proportion of large vehicles, air humidity, road surface condition, and internal wall reflection. This accurately adapts to the human eye's light and dark adaptation characteristics, compensates for traffic obstruction and ambient light loss, avoids sudden changes in light and dark and glare interference, and achieves on-demand dimming while ensuring driving safety, thus significantly saving energy.

[0022] 2. In this invention, by fully considering the aging and light decay problems caused by the working time and operating temperature of the light-emitting device, energy efficiency calibration and illuminance compensation are performed on each lamp to solve the drawback of the actual illumination not meeting the standard during long-term operation. In addition, the stepless smooth dimming method eliminates light flicker. It can also monitor the fault of the light-emitting device in real time and automatically increase the illuminance of the surrounding light-emitting devices to supplement the light, maintain uniform illumination throughout the area, greatly improve the operational stability, fault tolerance and long-term dimming accuracy of the lighting system, and effectively upgrade the level of intelligent lighting management. Attached Figure Description

[0023] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0024] Figure 1 This is a schematic diagram of the operation method of the present invention;

[0025] Figure 2 This is a schematic diagram of the overall system structure of the present invention. Detailed Implementation

[0026] 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.

[0027] Reference Figure 1-2 As shown, the dynamic lighting control method for highway tunnels based on multi-source perception of people, vehicles, and environment proposed in this invention abandons the outdated mode of fixed settings, experience-based dimming, single-vehicle analysis, and ignoring hardware losses. It marks the target tunnel as the target traffic area, divides the entire target traffic area into three independent control zones: the entrance section, the middle section, and the exit section, and builds a dedicated progressive functional unit. The specific control method flow is as follows:

[0028] Step 1: Calculate the visual lighting baseline by zone:

[0029] This step is achieved through a visual adaptation and judgment unit, which serves as the system's initial quantitative benchmark unit. This unit precisely calculates the specific visual lighting benchmark values ​​for the entrance, middle section, and exit areas. This benchmark represents a standardized, safe lighting intensity under conditions of no traffic interference and no ambient light loss, strictly adhering to the physiological characteristics of human eye adaptation to light and dark and national standards. It provides a unique and accurate underlying quantitative benchmark for subsequent traffic flow correction and environmental adjustment, helping to eliminate lighting control benchmark deviations from the outset. The workflow for this step is as follows:

[0030] The visual adaptation and judgment unit directly retrieves two types of core standardized data output by the edge computing unit: one is the natural light intensity parameter G outside the target passage area collected in real time by the external light sensor, in cd / m²; the other is the inherent parameters of the target passage area partition preset in the system, including the visual transition coefficient K1 of the entrance section, the stable visual coefficient K2 of the middle section, and the glare suppression coefficient K3 of the exit section. All three types of coefficients are fixed in value according to the "Detailed Rules for Lighting Design of Highway Tunnels" and do not change with the scene.

[0031] Furthermore, the values ​​of the three fixed coefficients are clearly defined. Preferably, K1 is set in the range of 0.25 to 0.35 to meet the requirements of the transition from light to dark at the entrance; K2 is fixed at 1.0 to ensure stable visual safety in the middle section; and K3 is set in the range of 0.15 to 0.25 to meet the requirements of anti-glare at the exit.

[0032] The visual adaptation and judgment unit uses independent quantization calculation logic for the three major zones of the target passage area to complete the calculation of the visual lighting baseline value step by step, as shown in the example below:

[0033] For the entrance section of the target passage area, the external natural light intensity and the entrance visual transition coefficient are coupled and calculated. The stronger the external natural light, the more significant the visual lag effect of the driver from the bright environment to the dark environment, and the higher the required basic lighting illuminance. The visual lighting reference value X1 of the entrance section is accurately calculated by the formula X1=G×K1.

[0034] For the middle section of the target passage area, where there is no natural light and the driver's vision is in a stable adaptation state, there is no need for large fluctuations with external strong light. The mid-section visual lighting reference value X2 is calculated using the formula X2=Go×K2, where Go is the basic safe lighting value for the target passage area at night, preferably constant at 30cd / m².

[0035] For the exit section of the target passage area, the focus is on suppressing the glare effect when the light transitions from dark to bright. The buffer illuminance is matched according to the external natural light intensity, and the visual lighting reference value X3 of the exit section is calculated using the formula X3=G×K3.

[0036] It should be noted that the visual adaptation judgment unit dynamically updates the baseline values ​​of the three zones based on the real-time natural light intensity throughout the process. The generated visual lighting baseline values ​​of the entrance, middle section and exit zones are completely transmitted to the driving situation inference unit. Moreover, the generated zone visual lighting baseline values ​​(X1, X2, X3) are standardized and reproducible quantitative results, avoiding the fuzzy defects of traditional experience-based values. They are not associated with any vehicle dynamic data throughout the process and only represent the inherent visual safety requirements of the target passage area zones.

[0037] Step 2: Traffic Flow Situation Correction Illumination Reference:

[0038] This step is implemented through the driving situation simulation unit, which is a strongly correlated and progressive core unit. It receives the zoned visual lighting baseline values ​​output by the visual adaptation and judgment unit, and based on the overall statistical data of clustered traffic flow in each zone of the target traffic area, it completes the dynamic traffic flow adaptation correction of the static baseline through a three-level quantization correction logic. It accurately calculates the corrected lighting reference values ​​for each zone, achieving a layer-by-layer in-depth analysis from static visual safety standards to dynamic traffic flow scenarios. This accurately offsets lighting deviations caused by clustered traffic light and shadow occlusion, high-speed visual attenuation, and large vehicle shading. It helps solve the problems of ambiguity and logical errors in traditional traffic flow correction, significantly improving the accuracy of dynamic scene adaptation. The workflow of this step is as follows:

[0039] The driving situation simulation unit simultaneously receives the three-group zonal visual lighting reference values ​​transmitted by the visual adaptation and judgment unit, as well as the pre-processed zonal cluster traffic flow data. The data is generated by the full-domain millimeter-wave radar zonal statistics and includes: zonal traffic flow density M (unit: vehicles / 100m), zonal average vehicle speed V (unit: km / h), and the proportion of large vehicle clusters P (unitless, value 0 to 1).

[0040] The driving situation simulation unit is equipped with a three-level progressive quantitative correction mechanism, which sequentially completes corrections for traffic density, vehicle speed, and vehicle type ratio. The entire process involves continuous calculation and progressively layered corrections, ultimately outputting accurate zone-corrected lighting reference values. An example of the correction logic is as follows:

[0041] Step 1: Carry out traffic density correction. Determine the degree of light and shadow occlusion based on the vehicle density of the zone. The higher the traffic density, the more serious the light occlusion of the vehicle cluster, and the higher the loss of road surface illumination uniformity. Set the density correction coefficient F1=1+M / 100. The larger M is, the higher the F1 gain, to achieve dense traffic illumination compensation.

[0042] Step 2: Implement vehicle speed correction. The faster the vehicle travels, the shorter the driver's dynamic visual recognition time, and the higher the requirements for lighting continuity and illuminance stability. Set the vehicle speed correction coefficient F2=1+V / 200 to adapt to the visual needs of high-speed traffic.

[0043] Step 3: Conduct vehicle cluster correction. Large vehicles have a large shading area, and cluster traffic will significantly aggravate the light and shadow of the entire target passage area. Set the vehicle correction coefficient F3=1+0.3P. The higher the proportion of large vehicles, the greater the illuminance compensation.

[0044] Finally, the driving situation simulation unit completes the calculation using the unified quantitative formula Y=X×F1×F2×F3, where X is the visual lighting baseline value of the corresponding zone, and Y is the corrected lighting reference value of the corresponding zone. Substituting X1, X2, and X3 respectively, the corrected lighting reference values ​​of the three groups of zones Y1 (entrance section), Y2 (middle section), and Y3 (exit section) are calculated simultaneously.

[0045] All correction parameters and calculation logic are fixed and standardized throughout the process. The output corrected lighting reference values ​​can be accurately adapted to the real-time cluster traffic situation of each zone. Furthermore, the generated zone corrected lighting reference values ​​(Y1, Y2, Y3) and the original correction parameters are all transmitted to the ambient light and shadow coupling unit.

[0046] Step 3: Ambient Light and Shadow Correction and Illuminance Prediction:

[0047] This step is achieved through an ambient light and shadow coupling unit. The core function of this unit is to quantitatively calculate the ambient light and shadow loss coefficients of each zone within the target traffic area. It conducts a comprehensive quantitative analysis of three types of environmental interference factors: air humidity, road surface conditions, and wall reflections within the target traffic area. It then performs precise environmental loss correction on the traffic flow-corrected lighting reference values, outputting standardized, environment-adaptive lighting estimates. This addresses the technical shortcoming of lacking quantitative calculations of environmental parameters and helps solve the problems of crude environmental adaptation and large deviations between theoretical and actual illuminance in traditional technologies. It ensures that the preset lighting values ​​match the real-time physical environment of the target traffic area. The workflow for this step is as follows:

[0048] The ambient light and shadow coupling unit receives the three-zone corrected lighting reference values ​​transmitted by the driving situation simulation unit, and at the same time retrieves the collected zone-standardized environmental data, including: zone air humidity S (unit: %RH), road surface condition coefficient W (e.g., dry state W=1.0, wet state W=1.15, water accumulation state W=1.3), inner wall reflectivity R (unitless, preferably 0.6 to 0.9), etc.

[0049] First, the ambient light and shadow loss coefficient H of the zone is accurately calculated using a multi-factor coupling formula. The formula is H=1+(S / 1000)+(W-1)+(0.9-R). It should be noted that the higher the air humidity, the more suspended water mist particles in the air, the stronger the light scattering loss, and the larger the value of S, the higher the value of H. Wet roads and water accumulation will cause diffuse reflection and glare, and W increases with the severity of road surface conditions, thus increasing the loss coefficient. The smaller the reflectivity R of the inner wall, the stronger the light absorption capacity of the wall, the more serious the loss of uniformity of illumination in the whole area, and H increases accordingly.

[0050] Then, after completing the calculation of the loss coefficient H, the estimated value Z of the zoned environment-adaptive lighting is calculated using the fixed correction formula Z=Y / H, where Y is the corrected lighting reference value for the corresponding zone, and H is the real-time light and shadow loss coefficient for the corresponding zone. The greater the light loss, the higher the corrected estimated value, thus accurately compensating for the light attenuation and glare interference caused by the environment.

[0051] Finally, the generated ambient light and shadow loss coefficients (H1, H2, H3) and ambient light adaptation predictions (Z1, Z2, Z3) for each zone are simultaneously transmitted to the energy efficiency adaptive calibration unit. It should be noted that the ambient light and shadow coupling unit independently calculates H1, H2, H3 and the corresponding Z1, Z2, Z3 for the three zones. The middle section of the target passage area is enclosed and humid with severe water mist accumulation, resulting in a generally high real-time light and shadow loss coefficient. The entrance and exit sections have good ventilation, resulting in a relatively low real-time light and shadow loss coefficient. The calculation logic is fixed throughout, the parameters are unique, and the results are reproducible, solving the problem of vague environmental parameter judgment and lack of quantitative basis in the original system.

[0052] Step 4: Energy efficiency calibration target illuminance:

[0053] This step relies on the energy efficiency adaptive calibration unit. In response to the technical defects of existing technologies that only consider the loss in human and vehicle environment scenarios and completely ignore the light decay caused by the aging of the light-emitting device hardware and the reduction of luminous efficiency due to operating temperature, the energy efficiency adaptive calibration unit follows the actual engineering control logic. First, it decomposes the overall illuminance estimate of the zone into the initial illuminance command of a single group of LED light-emitting devices. Then, it combines the hardware operating parameters of the light-emitting device to quantitatively calculate the energy efficiency decay coefficient, completes the illuminance adaptive calibration for each lamp, and outputs the final target illuminance of each light-emitting device.

[0054] This approach not only solves the matching problem between zone commands and control, but also compensates for the luminous efficiency degradation caused by aging of the light-emitting device and operating temperature. It achieves a complete conversion from zone scene parameters to precise commands and compensates for hardware losses, significantly improving the dimming accuracy and overall system stability under long-term operation. The workflow of this step is as follows:

[0055] The energy efficiency adaptive calibration unit receives three independent environmentally adaptable lighting estimates Z1, Z2, and Z3 from the ambient light and shadow coupling unit for the entrance, middle, and exit sections. First, it retrieves the pre-set zoning information of the light-emitting devices in the target passage area. According to the zoning range, the location and number of light-emitting devices, the overall illuminance estimate of each zone is decomposed into the initial illuminance command of each group of LED light-emitting devices in the corresponding area. This completes the layer-by-layer decomposition of zoning parameters into commands, ensuring that the overall lighting requirements of the zone are accurately implemented to a single device.

[0056] After the instruction is disassembled, the hardware status data uploaded by the dedicated acquisition terminal of the light-emitting device is retrieved synchronously, including the cumulative working time Ti (unit: h) and real-time working temperature Ui (unit: ℃) of the light-emitting device i (i is a positive integer with a value greater than zero) in the target passage area. Then, the energy efficiency attenuation quantification formula of the two-factor light-emitting device is used to calculate the energy efficiency attenuation coefficient K for each light-emitting device: Ki=1-(Ti / 100000)-[(Ui-25) / 2000].

[0057] Where Ti represents the continuous cumulative working time of the light-emitting device. Long-term operation will cause LED chip aging and continuous decline in luminous efficiency. The larger the Ti value, the more serious the energy efficiency degradation. Ui represents the real-time housing operating temperature of the light-emitting device. 25℃ is the rated optimal operating temperature of the light-emitting device. The higher the temperature, the more significant the chip light decay and the lower the luminous energy efficiency. 100000 and 2000 are fixed constants for energy efficiency calibration of LED light-emitting devices in industry target traffic areas, which are adapted to the rated life and temperature control characteristics of light-emitting devices in high-speed target traffic areas. Ki is always less than 1, representing the decay ratio of the real-time luminous efficiency of the light-emitting device relative to the rated value.

[0058] Subsequently, illuminance calibration was performed lamp by lamp using the calibration formula Li=Zi / Ki, where Zi is the initial illuminance and Li is the final target illuminance. Since the illuminance output at the same power will decrease after the energy efficiency of the light-emitting device decays, the loss compensation is completed by increasing the illuminance value in reverse. It should be noted that the light-emitting device in the middle of the target passage area operates under high load for a long time, with poor ventilation and high temperature, resulting in a greater decrease in the energy efficiency decay coefficient and a higher illuminance compensation range. The light-emitting devices at the entrance and exit sections operate under better conditions, and the compensation range is correspondingly lower.

[0059] Meanwhile, the problem of overcompensation is avoided throughout the process. During the calculation, the illuminance threshold is set in accordance with the national target traffic area lighting specifications, and abnormal values ​​outside the range are eliminated. Finally, the final target illuminance of all light-emitting devices is summarized and uniformly transmitted to the intelligent drive execution unit. This solves the stubborn deviation problem of theoretically accurate dimming but insufficient actual output illuminance of light-emitting devices, and greatly improves the accuracy of lighting control and system stability under long-term operation.

[0060] Step 5: Dynamically adjust execution:

[0061] This step relies on an intelligent drive execution unit, which is the closed-loop execution unit at the end of the system. Based on the received final target illuminance, it issues dimming commands to achieve stepless and smooth dimming of the light-emitting devices. Simultaneously, it monitors the real-time operating status of all light-emitting devices, performs localized illuminance compensation for fault locations, and transmits back equipment operating data. This ensures precise implementation of control commands, guarantees visual comfort for drivers and passengers, and improves the system's fault tolerance and stability. The workflow for this step is as follows:

[0062] The intelligent drive execution unit receives the final target illuminance of each LED light-emitting device output by the energy efficiency adaptive calibration unit, and directly sends a stepless continuous dimming signal to the corresponding light-emitting device driver through the power line carrier communication protocol. The illuminance is smoothly and gradually adjusted throughout the process, without any gear jumps or light flicker, which helps to eliminate the problem of sudden changes in brightness caused by traditional gear dimming.

[0063] Furthermore, during dimming operation, the intelligent drive execution unit collects information such as the working voltage, working current, and communication status of the light-emitting devices in real time, automatically identifies fault conditions such as damage to the light-emitting devices and communication interruption, and immediately uploads information such as the fault location and fault type to the management and control platform once a local fault point is detected. At the same time, based on the overall lighting requirements of the zone to which the fault point belongs, it adaptively increases the output illuminance of the surrounding normal light-emitting devices, accurately compensates for the local lighting gap, and ensures that the uniformity of the lighting in the entire zone meets the standards.

[0064] Furthermore, the intelligent drive execution unit continuously synchronizes all vehicle and human environmental dynamic data and lighting device hardware status data at the front end, and coordinates with the front end unit to dynamically update dimming commands to keep the lighting status of the target passage area dynamically synchronized with the real-time scene and equipment operating conditions.

[0065] The working principle of this invention is as follows: When in use, the target traffic area of ​​the highway is divided into three major control zones: entrance, middle section and exit. First, the visual lighting benchmark of the zone is calculated by combining the external natural light intensity and lighting specifications. Then, the illuminance of the traffic flow is corrected according to the traffic flow density, driving speed and the proportion of large vehicles. The ambient light loss is corrected by comprehensively considering air humidity, road surface condition and inner wall reflectivity. At the same time, the energy efficiency decay is quantified by combining the working time of the light-emitting device and the operating temperature. The target illuminance is calibrated for each lamp to achieve stepless smooth dimming of the light-emitting device. The device is also monitored for equipment failure in real time and adaptively compensated for local lighting gaps.

[0066] Abandoning traditional fixed dimming levels and experience-based dimming modes, this system aligns with the human eye's ability to adapt to varying light levels, precisely adapts to dynamic changes in the environment, effectively compensates for aging and light decay of the lighting device, provides smooth dimming without flickering or sudden changes in brightness, significantly improves driving safety in highway target traffic areas, saves energy, and comprehensively enhances the level of intelligent management and control of tunnel lighting.

[0067] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, enabling those skilled in the art to better understand and utilize it. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for dynamic lighting control in highway tunnels based on multi-source perception of pedestrians, vehicles, and environment, characterized in that: Includes the following steps: Step 1: Calculate the visual lighting baseline for each zone: Based on the intensity of external natural light and the inherent parameters of each zone preset in the system, calculate and output the visual lighting baseline value for each zone. Step 2, Traffic Flow Situation Correction Illumination Reference: Through three-level progressive quantization correction, the corrected lighting reference values ​​for each zone are obtained; Step 3: Ambient lighting correction and illuminance prediction: The corrected lighting reference value is corrected using the ambient lighting loss coefficient to obtain the ambient-adaptive lighting prediction value for each zone. Step 4: Energy efficiency calibration target illuminance: Decompose the overall illuminance estimate for each zone and calibrate it using the energy efficiency attenuation coefficient; Step 5: Dynamic Adjustment Execution: Adjust the light-emitting device according to the final target illuminance and monitor the working status of the light-emitting device in real time.

2. The method for dynamic lighting control of highway tunnels based on multi-source perception of people, vehicles, and environment as described in claim 1, is characterized in that, Step one is implemented based on the visual adaptation and judgment unit, dividing the tunnel into three control zones: the entrance section, the middle section, and the exit section.

3. The method for dynamic lighting control of highway tunnels based on multi-source perception of people, vehicles, and environment as described in claim 1, is characterized in that... Step two is implemented based on the driving situation simulation unit, using millimeter-wave radar to collect data on traffic density, average vehicle speed and the proportion of large vehicles in different zones, and then performing three-level superposition coefficient correction calculations in sequence.

4. The method for dynamic lighting control of highway tunnels based on multi-source perception of people, vehicles, and environment as described in claim 1, characterized in that, Step 3 is implemented using the ambient light and shadow coupling unit. It collects the air humidity of the zone, the road surface condition, and the reflectivity of the tunnel inner wall to solve for the light and shadow loss coefficient. Based on the loss coefficient, it corrects the lighting reference value.

5. The method for dynamic lighting control of highway tunnels based on multi-source perception of people, vehicles, and environment as described in claim 1, characterized in that, Step four is completed by the energy efficiency adaptive calibration unit. Based on the location and number of light-emitting devices in the tunnel, the energy efficiency adaptive calibration unit breaks down the overall light distribution estimate of the zone into initial illuminance instructions for each lamp.

6. The method for dynamic lighting control of highway tunnels based on multi-source perception of people, vehicles, and environment as described in claim 5, is characterized in that, The energy efficiency adaptive calibration unit calculates the energy efficiency attenuation coefficient by combining the cumulative working time of the LED lamps and the real-time operating temperature, and obtains the final target illuminance of a single lamp by reverse calibration based on the energy efficiency attenuation coefficient.

7. The method for dynamic lighting control of highway tunnels based on multi-source perception of people, vehicles, and environment as described in claim 1, characterized in that, The dimming operation in step five is initiated by the intelligent drive execution unit sending a stepless dimming command via power line carrier communication.

8. The method for dynamic lighting control of highway tunnels based on multi-source perception of people, vehicles, and environment as described in claim 7, is characterized in that, The intelligent drive execution unit collects the voltage, current and communication information of the lamps in real time, and adjusts the illuminance of the surrounding intact lamps to make up for the lack of light after identifying equipment failure.

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