A tunnel lighting control system and method based on variable cut-off factor

Abstract

The application provides a tunnel lighting control system and method based on a variable reduction coefficient, and the method comprises the following steps: system starting, collecting data through a sensing module; predicting the time of vehicle arrival at each lighting section of the tunnel, the traffic density, the vehicle speed and the vehicle type distribution based on the collected forward-looking information outside the tunnel; receiving the sensing data and the prediction results by a control module, calculating the traffic dynamic reduction factor and the maintenance attenuation reduction factor through a double-factor coupling calculation unit respectively, coupling to obtain the total variable reduction coefficient, and generating a dimming instruction; receiving the dimming instruction by an execution module, adjusting the brightness of the corresponding lane LED lamp group through a dimming driving module; and collecting the actual brightness of each lane in the tunnel in real time by a feedback module, judging whether the brightness meets the standard and continuously monitoring the running state of the lamp. The application can realize precise, forward-looking and differentiated control of tunnel lighting, and takes into account driving safety and energy saving.

Description

Technical Field

[0001] This invention relates to the field of tunnel lighting control technology, and more specifically, to a tunnel lighting control system and method based on a variable reduction coefficient. Background Technology

[0002] The common "black hole / white hole" effect in tunnels necessitates a reduction coefficient control method to mitigate its visual discomfort and improve driving safety. However, this method uses a fixed reduction coefficient, typically between 1.4 and 1.6, which cannot be dynamically adjusted based on actual tunnel conditions. This leads to energy waste due to over-illumination and insufficient brightness due to aging and dust accumulation, failing to meet driving needs. Furthermore, existing reduction coefficient control only considers traffic factors like flow rate and speed, neglecting the effects of light decay and dirt on the lamps themselves. This results in low precision and poor brightness compliance. Additionally, the current method employs a uniform dimming across the entire tunnel, failing to differentiate between lane traffic density and vehicle type, thus hindering lane-specific dimming.

[0003] Therefore, none of the existing technologies have simultaneously achieved the organic integration of AI predictive dimming, two-factor coupled calculation, and lane-differentiated control, which cannot fundamentally solve the contradiction between energy consumption and safety, and are difficult to meet the intelligent and refined needs of modern tunnel lighting. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the purpose of this invention is to provide a tunnel lighting control system and method based on a variable reduction coefficient, which solves the technical problems of existing systems such as lag in reduction coefficient adjustment, low accuracy, homogeneous control, high energy consumption, and poor safety, and realizes precise, forward-looking, and differentiated control of tunnel lighting, taking into account both driving safety and energy conservation.

[0005] To solve the above problems, the technical solution of the present invention is as follows:

[0006] A tunnel lighting control system based on a variable reduction factor includes:

[0007] The sensing module is used to collect environmental parameters, vehicle parameters, and lighting operation parameters inside and outside the tunnel, including an external brightness detector, an external forward-looking vehicle detection unit, an internal lane-level vehicle detection unit, an internal brightness sensor, a lighting status sensor, and an environmental sensor.

[0008] The prediction module, electrically connected to the perception module, is used to predict the time, traffic density, vehicle speed, and vehicle type distribution of vehicles arriving at each lighting section of the tunnel based on data collected by the tunnel-outside forward-looking vehicle detection unit and through an AI short-time prediction model, and transmits the prediction results to the control module.

[0009] The control module is connected to the prediction module and the feedback module respectively. The control module has a built-in two-factor coupled calculation unit and a lane differentiation allocation unit. The two-factor coupled calculation unit is used to calculate the traffic dynamic reduction factor and the maintenance attenuation reduction factor, and coupled them to obtain the total variable reduction coefficient. The lane differentiation allocation unit is used to allocate differentiated variable reduction coefficient values ​​to each lane according to the predicted traffic state and actual traffic flow information of each lane. At the same time, the control module generates dimming instructions in advance according to the vehicle arrival time output by the prediction module to realize forward dimming.

[0010] The execution module, connected to the control module, includes LED tunnel light groups and a dimming drive module; the LED tunnel light groups are arranged in lane groups, with each lane corresponding to an independent light group; the dimming drive module receives dimming commands from the control module and adjusts the output brightness of the corresponding lane light group to achieve independent dimming at the lane level.

[0011] The feedback module, connected to the sensing module and the control module, is used to collect actual brightness data inside the tunnel, compare it with the brightness required by the standard, generate a correction signal and feed it back to the control module. The control module adjusts the variable reduction coefficient value of the corresponding lane in real time according to the correction signal to realize closed-loop control.

[0012] The communication module is used to enable data transmission between the various modules.

[0013] Preferably, the external vehicle detection unit is deployed 300-800m upstream of the tunnel entrance, using a combination of millimeter-wave radar and high-definition video cameras to detect the vehicle type, speed, distance, and traffic density of approaching vehicles; the internal lane-level vehicle detection unit is deployed according to lanes to collect vehicle information for each lane in real time; the lighting status sensor is used to collect the operating time, current, voltage, and temperature of the LED tunnel lights to calculate the light decay coefficient; the environmental sensor is used to collect information on temperature, humidity, visibility, and rain, snow, and fog conditions inside the tunnel.

[0014] Preferably, the AI ​​short-term prediction model uses an LSTM neural network, with input parameters including vehicle type, speed, distance, traffic density, and weather information of vehicles approaching from outside the tunnel, and output parameters including the predicted traffic conditions of each lighting section.

[0015] Preferably, the feedback module also monitors the operating status of the lamps in real time, collects abnormal operating signals of the lamps, outputs lamp maintenance warnings, and reminds staff to replace or clean them.

[0016] Furthermore, the present invention also provides a tunnel lighting control method based on a variable reduction coefficient, comprising the following steps:

[0017] The system starts up and collects data through the sensing module;

[0018] The prediction module predicts the time, traffic density, vehicle speed, and vehicle type distribution of vehicles arriving at each lighting section of the tunnel based on the collected information on oncoming vehicles outside the tunnel.

[0019] The control module receives sensing data and prediction results, calculates the traffic dynamic reduction factor and maintenance attenuation reduction factor respectively through the dual-factor coupling calculation unit, and couples them to obtain the total variable reduction coefficient, and generates a dimming command.

[0020] The execution module receives a dimming command and adjusts the brightness of the corresponding lane's LED light group through the dimming driver module;

[0021] The feedback module collects the actual brightness of each lane in the tunnel in real time, determines whether the brightness meets the standard, and continuously monitors the operating status of the lights.

[0022] Preferably, the sensing module collects real-time information on external tunnel brightness, oncoming vehicles outside the tunnel, vehicles in each lane inside the tunnel, tunnel brightness, lighting operating parameters, and environmental information, and transmits the collected data to the prediction module and the control module.

[0023] Preferably, the prediction module, based on the collected information on oncoming vehicles outside the tunnel, uses an LSTM neural network model to predict the time, traffic density, vehicle speed, and vehicle type distribution of vehicles arriving at each lighting section of the tunnel within the next 10-60 seconds, and transmits the prediction results to the control module.

[0024] Preferably, the control module calculates the traffic dynamic reduction factor K through a two-factor coupled calculation unit. t With maintenance attenuation reduction factor K m The total variable reduction coefficient K is obtained through coupling. 总 =K t ×K m ;

[0025] The traffic dynamic reduction factor K t The calculation formula is:

[0026] K t =f(Q, V, L 20 , T 天气 ),

[0027] Where Q is the traffic density, V is the vehicle speed, and L is the vehicle speed. 20 For the brightness outside the cave, T 天气 Weather coefficient;

[0028] The maintenance attenuation reduction factor K m The calculation formula is:

[0029] K m =K1×K2×K3,

[0030] Where K1 is the light decay coefficient of the light source, K2 is the pollution coefficient of the lamp, and K3 is the pollution coefficient of the tunnel wall;

[0031] The control module, through the lane differentiation allocation unit, assigns differentiated K lanes to each lane based on the predicted traffic conditions and actual traffic flow information. 总 value.

[0032] Preferably, the step of the execution module receiving a dimming command and adjusting the brightness of the corresponding lane LED light group through the dimming drive module specifically includes: the control module sending a dimming command to the execution module 5-10 seconds in advance based on the arrival time of vehicles in each lighting section output by the prediction module; the execution module receiving the dimming command and adjusting the output brightness of the corresponding lane light group through the dimming drive module.

[0033] Preferably, the feedback module collects the actual brightness of each lane in the tunnel in real time, determines whether the brightness meets the standard, and continuously monitors the operating status of the lights. Specifically, the feedback module collects the actual brightness of each lane in the tunnel in real time and compares it with the brightness required by the standard. If the actual brightness deviates from the target brightness by more than ±5%, a correction signal is generated and fed back to the control module. The control module adjusts the variable reduction coefficient value of the corresponding lane until the brightness meets the standard. At the same time, the feedback module continuously monitors the operating status of the lights and outputs a light maintenance warning to remind staff to replace or clean them in a timely manner.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. This invention uses forward prediction technology to predict the arrival time and status of vehicles in advance, realize forward dimming, completely eliminate the lag of traditional dimming, avoid the black hole effect and white hole effect, shorten the driver's visual adaptation time, and significantly improve driving safety.

[0036] 2. This invention proposes a dual-factor coupled calculation of the variable reduction coefficient, which organically combines traffic dynamics factors with maintenance attenuation factors to achieve total dynamic adaptive adjustment of the reduction coefficient. Compared with the existing single-factor adjustment scheme, the adjustment accuracy is higher, ensuring that the brightness in the tunnel always meets the standard accurately, while avoiding excessive lighting and reducing energy consumption.

[0037] 3. This invention achieves lane-differentiated control by allocating different reduction coefficients according to the differences in traffic flow and vehicle type in each lane, taking into account both the safety of large vehicle lanes and the energy efficiency of small vehicle lanes, thus solving the defects of homogeneous control in existing systems.

[0038] 4. This invention adopts closed-loop feedback control to correct brightness deviation in real time, which is not affected by lamp aging, dirt and environmental changes, thus improving system stability. At the same time, it outputs maintenance warnings to reduce operation and maintenance costs.

[0039] 5. The system of this invention has strong compatibility and can be adapted to various highway and railway tunnels. It does not require large-scale modification of existing lighting facilities, has low construction difficulty, and is highly scalable. Attached Figure Description

[0040] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0041] Figure 1 This is a block diagram of the tunnel lighting control system based on the variable reduction coefficient of the present invention;

[0042] Figure 2 This is a flowchart of the tunnel lighting control method based on a variable reduction coefficient according to the present invention. Detailed Implementation

[0043] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0044] Specifically, the present invention provides a tunnel lighting control system based on a variable reduction coefficient, such as... Figure 1 As shown, the system includes a sensing module 1, a prediction module 2, a control module 3, an execution module 4, a feedback module 5, and a communication module 6. The modules interact with each other through the communication module 6.

[0045] The sensing module is used to collect environmental parameters, vehicle parameters, and lighting operation parameters inside and outside the tunnel. It includes an external brightness detector, an external forward-looking vehicle detection unit, an internal lane-level vehicle detection unit, an internal brightness sensor, a lighting status sensor, and an environmental sensor. The external forward-looking vehicle detection unit is deployed 300-800m upstream of the tunnel entrance and uses a combination of millimeter-wave radar and a high-definition video camera to detect the type, speed, distance, and traffic density of approaching vehicles. The internal lane-level vehicle detection unit is deployed along each lane to collect vehicle information in real time. The lighting status sensor collects the operating time, current, voltage, and temperature of the LED tunnel lights to calculate the light decay coefficient. The environmental sensor collects information on temperature, humidity, visibility, and weather conditions such as rain, snow, and fog inside the tunnel.

[0046] The prediction module is electrically connected to the perception module and is used to predict, based on data collected by the tunnel's external forward-looking vehicle detection unit, the arrival time, traffic density, vehicle speed, and vehicle type distribution of vehicles in each illuminated section of the tunnel within the next 10-60 seconds using an AI short-term prediction model. The prediction results are then transmitted to the control module. In this embodiment, the AI ​​short-term prediction model uses an LSTM neural network. The input parameters are the vehicle type, speed, distance, traffic density, and weather information of vehicles approaching from outside the tunnel. The output parameter is the predicted traffic status of each illuminated section.

[0047] The control module, as the core of the system, is connected to the prediction module and the feedback module, respectively, and is used to calculate the variable reduction coefficient and output dimming commands. The control module has a built-in two-factor coupled calculation unit and a lane differentiation allocation unit. The two-factor coupled calculation unit is used to calculate the traffic dynamic reduction factor K. t With maintenance attenuation reduction factor K m The total variable reduction coefficient K is obtained through coupling. 总 =K t ×K m The lane differentiation allocation unit is used to allocate differentiated K values ​​to each lane based on the predicted traffic conditions and actual traffic flow information of each lane. 总 The control module generates dimming commands in advance based on the vehicle arrival time output by the prediction module, thereby achieving forward dimming.

[0048] The execution module is connected to the control module and includes an LED tunnel light group and a dimming drive module. The LED tunnel light group is arranged in lane groups, with each lane corresponding to an independent light group. The dimming drive module receives dimming commands from the control module and adjusts the output brightness of the corresponding lane light group to achieve independent dimming at the lane level.

[0049] The feedback module is connected to the sensing module and the control module. It is used to collect actual brightness data inside the tunnel and compare it with the brightness required by the standard. If the actual brightness deviates from the target brightness by more than ±5%, a correction signal is generated and fed back to the control module. The control module adjusts the lane accordingly in real time based on the correction signal. 总 The value is adjusted until the brightness reaches the standard, thus achieving closed-loop control. At the same time, the feedback module monitors the operating status of the lamps in real time, collects abnormal operating signals of the lamps, and outputs lamp maintenance warnings to remind staff to replace or clean them.

[0050] The communication module adopts a combination of industrial Ethernet and wireless communication to realize data transmission between modules and ensure the real-time performance and stability of data transmission.

[0051] Furthermore, the present invention also provides a tunnel lighting control method based on a variable reduction coefficient, such as... Figure 2 As shown, the method includes the following steps:

[0052] S1: The system starts up and collects data through the sensing module;

[0053] Specifically, the system starts up and initializes each module as the starting point of the process. The sensing module collects real-time information on the brightness outside the tunnel, the information on oncoming vehicles outside the tunnel (vehicle type, speed, distance, traffic density), the vehicle information of each lane inside the tunnel, the brightness inside the tunnel, the operating parameters of the lamps (running time, current, voltage, temperature), and environmental information (temperature, humidity, visibility, weather), and transmits the collected data to the prediction module and the control module.

[0054] S2: The prediction module predicts the time, traffic density, vehicle speed, and vehicle type distribution of vehicles arriving at each lighting section of the tunnel based on the collected information on oncoming vehicles outside the tunnel.

[0055] Specifically, the prediction module uses the collected information on oncoming vehicles outside the tunnel and an LSTM neural network model to predict the time, traffic density, vehicle speed, and vehicle type distribution of vehicles arriving at each lighting section of the tunnel within the next 10 to 60 seconds, and then transmits the prediction results to the control module.

[0056] S3: The control module receives sensing data and prediction results, calculates the traffic dynamic reduction factor and maintenance attenuation reduction factor respectively through the dual-factor coupling calculation unit, couples them to obtain the total variable reduction coefficient, and generates a dimming command;

[0057] Specifically, the control module receives the perceived data and prediction results, and calculates the traffic dynamic reduction factor K through the two-factor coupled calculation unit. t With maintenance attenuation reduction factor K m The total variable reduction coefficient K is obtained through coupling. 总 =K t ×K m ;

[0058] The traffic dynamic reduction factor K t Calculation:

[0059] K t =f(Q, V, L 20 , T 天气 ),

[0060] Where Q is the traffic density, V is the vehicle speed, and L is the vehicle speed. 20 For the brightness outside the cave, T 天气 The weather coefficient is set at 1.0 for sunny days, 1.1 for cloudy days, 1.3 for rainy days, and 1.5 for foggy days; the specific calculation formula is as follows:

[0061] K t =1.0 + 0.001×Q - 0.0005×V + 0.0001×L 20 + (T天气 -1.0),

[0062] K t The value range is 1.0 to 2.0;

[0063] The maintenance attenuation reduction factor K m Calculation:

[0064] K m =K1×K2×K3,

[0065] Where K1 is the light decay coefficient of the light source, K2 is the pollution coefficient of the lamp, and K3 is the pollution coefficient of the tunnel wall;

[0066] K1 is calculated based on the lamp's operating time and temperature: K1 = 1.0 + 0.0001 × operating time (h) + 0.005 × (operating temperature - 25℃);

[0067] K2 is calculated based on the dust concentration inside the cave and the cleaning cycle of the lamps. K2 = 1.0 + 0.01 × dust concentration (mg / m³) + 0.005 × (cleaning cycle - 30d);

[0068] K3 is calculated based on the reflectivity of the tunnel wall, K3 = 1.0 / reflectivity, and the reflectivity is calibrated using the brightness sensor inside the tunnel and the test data of the wall surface;

[0069] K m The value range is 1.0 to 1.8.

[0070] The control module, through the lane differentiation allocation unit, assigns differentiated K lanes to each lane based on the predicted traffic conditions (traffic density, vehicle type distribution) and actual traffic information. 总 Value; among them, K of the large vehicle lane and the hazardous chemical vehicle concentration lane 总 The value is 0.2 to 0.3 higher than that of the car lane, and the traffic density is greater than 100 vehicles / h. 总 The value is 0.1 to 0.2 higher than that of lanes with a traffic density of less than 50 vehicles / h.

[0071] S4: The execution module receives the dimming command and adjusts the brightness of the corresponding lane LED light group through the dimming driver module;

[0072] Specifically, the control module sends a dimming command to the execution module 5 to 10 seconds in advance based on the arrival time of vehicles in each lighting section output by the prediction module. The execution module receives the dimming command and adjusts the output brightness of the corresponding lane light group through the dimming drive module, realizing "adjusting the light before the vehicle arrives".

[0073] S5: The feedback module collects the actual brightness of each lane in the tunnel in real time, determines whether the brightness meets the standard, and continuously monitors the operating status of the lights.

[0074] Specifically, the feedback module collects the actual brightness of each lane in the tunnel in real time and compares it with the brightness required by the standard. If the actual brightness deviates from the target brightness by more than ±5%, a correction signal is generated and fed back to the control module, which then adjusts the K value of the corresponding lane. 总 The value is adjusted until the brightness meets the standard; at the same time, the feedback module continuously monitors the operating status of the lamps. When K1≥1.6 or K2≥1.5, a lamp maintenance warning is output (light decay exceeds the standard or serious dirt), reminding staff to replace or clean the lamps in time.

[0075] After a vehicle leaves a certain lighting section, the control module delays for 3-5 seconds and lowers the K value of that section. 总 The value is reduced to a base value of 1.0~1.1 to avoid ineffective energy consumption, thereby achieving energy-saving control.

[0076] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A tunnel lighting control system based on a variable reduction coefficient, characterized in that, The system includes: The sensing module is used to collect environmental parameters, vehicle parameters, and lighting operation parameters inside and outside the tunnel, including an external brightness detector, an external forward-looking vehicle detection unit, an internal lane-level vehicle detection unit, an internal brightness sensor, a lighting status sensor, and an environmental sensor. The prediction module, electrically connected to the perception module, is used to predict the time, traffic density, vehicle speed, and vehicle type distribution of vehicles arriving at each lighting section of the tunnel based on data collected by the tunnel-outside forward-looking vehicle detection unit and through an AI short-time prediction model, and transmits the prediction results to the control module. The control module is connected to the prediction module and the feedback module respectively. The control module has a built-in two-factor coupled calculation unit and a lane differentiation allocation unit. The two-factor coupled calculation unit is used to calculate the traffic dynamic reduction factor and the maintenance attenuation reduction factor, and coupled them to obtain the total variable reduction coefficient. The lane differentiation allocation unit is used to allocate differentiated variable reduction coefficient values ​​to each lane according to the predicted traffic state and actual traffic flow information of each lane. At the same time, the control module generates dimming instructions in advance according to the vehicle arrival time output by the prediction module to realize forward dimming. The execution module, connected to the control module, includes LED tunnel light groups and a dimming drive module; the LED tunnel light groups are arranged in lane groups, with each lane corresponding to an independent light group; the dimming drive module receives dimming commands from the control module and adjusts the output brightness of the corresponding lane light group to achieve independent dimming at the lane level. The feedback module, connected to the sensing module and the control module, is used to collect actual brightness data inside the tunnel, compare it with the brightness required by the standard, generate a correction signal and feed it back to the control module. The control module adjusts the variable reduction coefficient value of the corresponding lane in real time according to the correction signal to realize closed-loop control. The communication module is used to enable data transmission between the various modules.

2. The tunnel lighting control system based on a variable reduction coefficient according to claim 1, characterized in that, The external vehicle detection unit is deployed 300-800m upstream of the tunnel entrance and uses a combination of millimeter-wave radar and high-definition video camera to detect the type, speed, distance, and traffic density of oncoming vehicles. The internal lane-level vehicle detection unit is deployed according to the lanes and is used to collect vehicle information of each lane in real time. The lighting status sensor is used to collect the running time, current, voltage and temperature of the LED tunnel lights, and to calculate the light decay coefficient; the environmental sensor is used to collect information on temperature and humidity, visibility and rain, snow and fog inside the tunnel.

3. The tunnel lighting control system based on a variable reduction coefficient according to claim 1, characterized in that, The AI ​​short-term prediction model uses an LSTM neural network. The input parameters are the vehicle type, speed, distance between vehicles, traffic density, and weather information of vehicles coming from outside the tunnel. The output parameters are the predicted traffic conditions of each lighting section.

4. The tunnel lighting control system based on a variable reduction coefficient according to claim 1, characterized in that, The feedback module also monitors the operating status of the lights in real time, collects abnormal operation signals, outputs maintenance warnings, and reminds staff to replace or clean the lights.

5. A tunnel lighting control method based on a variable reduction coefficient, characterized in that, The method includes the following steps: The system starts up and collects data through the sensing module; The prediction module predicts the time, traffic density, vehicle speed, and vehicle type distribution of vehicles arriving at each lighting section of the tunnel based on the collected information on oncoming vehicles outside the tunnel. The control module receives sensing data and prediction results, calculates the traffic dynamic reduction factor and maintenance attenuation reduction factor respectively through the dual-factor coupling calculation unit, and couples them to obtain the total variable reduction coefficient, and generates a dimming command. The execution module receives a dimming command and adjusts the brightness of the corresponding lane's LED light group through the dimming driver module; The feedback module collects the actual brightness of each lane in the tunnel in real time, determines whether the brightness meets the standard, and continuously monitors the operating status of the lights.

6. The tunnel lighting control method based on a variable reduction coefficient according to claim 5, characterized in that, The sensing module collects real-time data on external tunnel brightness, oncoming vehicle information outside the tunnel, vehicle information in each lane inside the tunnel, tunnel brightness, lighting operating parameters, and environmental information, and transmits the collected data to the prediction module and the control module.

7. The tunnel lighting control method based on a variable reduction coefficient according to claim 5, characterized in that, The prediction module, based on the collected information on oncoming vehicles outside the tunnel, uses an LSTM neural network model to predict the time, traffic density, vehicle speed, and vehicle type distribution of vehicles arriving at each lighting section of the tunnel within the next 10-60 seconds, and transmits the prediction results to the control module.

8. The tunnel lighting control method based on a variable reduction coefficient according to claim 5, characterized in that, The control module calculates the traffic dynamic reduction factor K through a two-factor coupling calculation unit. t With maintenance attenuation reduction factor K m The total variable reduction coefficient K is obtained through coupling. 总 =K t ×K m ; The traffic dynamic reduction factor K t The calculation formula is: K t =f(Q, V, L 20 , T 天气 ), Where Q is the traffic density, V is the vehicle speed, and L is the vehicle speed. 20 For the brightness outside the cave, T 天气 Weather coefficient; The maintenance attenuation reduction factor K m The calculation formula is: K m =K1×K2×K3, Where K1 is the light decay coefficient of the light source, K2 is the pollution coefficient of the luminaire, and K3 is the pollution coefficient of the tunnel wall; The control module, through the lane differentiation allocation unit, assigns differentiated K lanes to each lane based on the predicted traffic conditions and actual traffic flow information. 总 value.

9. The tunnel lighting control method based on a variable reduction coefficient according to claim 5, characterized in that, The step of the execution module receiving a dimming command and adjusting the brightness of the corresponding lane LED light group through the dimming drive module specifically includes: the control module sending a dimming command to the execution module 5-10 seconds in advance based on the arrival time of vehicles in each lighting section output by the prediction module; the execution module receiving the dimming command and adjusting the output brightness of the corresponding lane light group through the dimming drive module.

10. The tunnel lighting control method based on a variable reduction coefficient according to claim 5, characterized in that, The feedback module collects the actual brightness of each lane in the tunnel in real time, determines whether the brightness meets the standard, and continuously monitors the operating status of the lights. Specifically, the feedback module collects the actual brightness of each lane in the tunnel in real time and compares it with the brightness required by the standard. If the actual brightness deviates from the target brightness by more than ±5%, a correction signal is generated and fed back to the control module. The control module adjusts the variable reduction coefficient value of the corresponding lane until the brightness meets the standard. At the same time, the feedback module continuously monitors the operating status of the lights and outputs a light maintenance warning to remind staff to replace or clean them in a timely manner.

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