A highway tunnel lighting intelligent control system and method

Abstract

The embodiment of the application discloses a highway tunnel lighting intelligent control system and method. The system comprises: a vehicle detection unit for detecting whether there is a vehicle inside and outside the tunnel; a data acquisition unit for collecting traffic flow, vehicle speed and outside brightness information in real time; a main control module for receiving information detected by the vehicle detection unit and the data acquisition unit and inputting the information to a lighting intelligent control module for processing and analysis; at the same time, sending fault information of the vehicle detection unit, the data acquisition unit and the lighting lamp to an emergency control unit; and a tunnel lamp control unit for regulating the brightness of each segmented lighting lamp in the tunnel according to a tunnel lamp brightness regulation command. The embodiment aims to ensure safety while reducing energy consumption of the highway tunnel.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of tunnel lighting, in particular to a highway tunnel lighting intelligent control system and method. BACKGROUND

[0002] With the rapid development of highways in China, the scale and quantity of highway tunnel construction are also expanding. Due to the particularity of the closed environment and the need for continuous lighting of highway tunnels, how to ensure the driving safety in the tunnel and reduce the energy consumption in the tunnel has become an important problem.

[0003] On the one hand, when the highway vehicle enters and exits the tunnel, the driver will have a short-term visual adaptation problem due to the sudden change of light, and the probability of traffic accidents will greatly increase. Inside the tunnel, the exhaust emitted by the vehicle as it drives gathers to form smoke, which reduces the air quality in the tunnel and thus affects the visibility, also threatening the driving safety of the driver. On the other hand, during the use of the tunnel, the lighting is kept on for a long time, which ensures the driving safety of the driver, but still consumes a large amount of energy when the tunnel is in sufficient light or the traffic volume is low, thereby increasing the energy consumption cost.

[0004] In view of this, the present application is proposed. SUMMARY

[0005] In view of the above problems, the present application aims to provide a highway tunnel lighting intelligent control system and method, which aims to ensure the driving safety of the driver while minimizing the energy consumption of the highway tunnel.

[0006] In a first aspect, the present application provides a highway tunnel lighting intelligent control system, comprising:

[0007] The vehicle detection unit comprises a tunnel outside vehicle detection module installed hundreds of meters in front of the tunnel, which is used to detect whether a vehicle is about to enter the tunnel; and further comprises a tunnel entrance section vehicle detection module, a tunnel transition section vehicle detection module, a tunnel middle section vehicle detection module and a tunnel exit section vehicle detection module, which are respectively installed in each section of the tunnel, and are used to detect whether there is a vehicle in each section of the tunnel.

[0008] The data acquisition unit comprises a vehicle flow detection module, a vehicle speed detection module and an outside hole brightness detection module installed near the tunnel entrance, which are used to acquire the vehicle flow, vehicle speed and outside hole brightness information in real time, and the installation position is determined by the driving speed.

[0009] The main control module is used for receiving information detected by the vehicle detection unit and the data acquisition unit, and inputting the information to the lighting intelligent control module for processing and analysis; meanwhile, fault information of the vehicle detection unit, the data acquisition unit and the lighting lamps is sent to the emergency control unit;

[0010] The tunnel lamp control unit is used for regulating and controlling the brightness of each segmented lighting lamp in the tunnel according to the tunnel lamp brightness regulation and control command issued by the lighting intelligent control module.

[0011] The emergency control unit comprises a first emergency module, a second emergency module and a third emergency module.

[0012] The first emergency module is used for, when any vehicle detection module in the vehicle detection unit cannot be used, taking the traffic flow, the vehicle speed and the outside brightness information collected by the data acquisition unit as an input source, analyzing the suitable lighting brightness value of each segmented tunnel by using the built-in lighting brightness intelligent regulation model of the lighting intelligent control module, and dynamically regulating and controlling the brightness of each segmented tunnel lighting lamp according to a pre-set period.

[0013] The second emergency module is used for, when any detection module in the data acquisition unit cannot be used, dividing a day into four stages of morning, day, dusk and night according to the traffic volume and the illumination intensity, and regulating and controlling the brightness of the tunnel lighting lamps according to the pre-set regulation and control command in the four stages.

[0014] The third emergency module is used for starting the standby lighting equipment in time when the lighting lamps cannot be used due to faults.

[0015] When two or more emergency modules respond at the same time, the priority is: the third emergency module is greater than the second emergency module, and the second emergency module is greater than the first emergency module.

[0016] In a second aspect, the embodiment of the present application provides a highway tunnel lighting intelligent control method applied to the highway tunnel lighting intelligent control system, and the method comprises the following steps:

[0017] Step 1: detecting vehicle information inside and outside the tunnel by using the vehicle detection unit; detecting traffic flow, vehicle speed and outside brightness information by using a traffic flow detection module, a vehicle speed detection module and an outside brightness detection module.

[0018] Step 2: judging whether the vehicle detection module outside the tunnel detects a vehicle about to enter the tunnel, if not, executing step 3; if yes, executing step 5.

[0019] Step 3: judging whether there is a vehicle detected inside the tunnel, if not, executing step 4; if yes, executing step 5.

[0020] Step 4: judging whether a vehicle is detected in the tunnel in a second preset time T2, if not, issuing a control command to make the tunnel lighting lamps start a low energy consumption operation state, if yes, executing step 5.

[0021] Step 5: judging whether the brightness of each segmented lighting lamp in the tunnel is regulated in a first preset time T1, if not, collecting the traffic flow, speed and brightness outside the tunnel, and obtaining the lighting brightness value suitable for each segment of the tunnel through the built-in lighting brightness intelligent adjustment model of the intelligent lighting control module, and executing step 6, if yes, keeping the current tunnel lighting lamp brightness regulation state unchanged.

[0022] Step 6: judging whether the lighting brightness value of each segment of the tunnel obtained through the built-in lighting brightness intelligent adjustment model of the intelligent lighting control module in step 5 is the same as the current lighting lamp brightness value, if not, regulating the brightness of each segmented lighting lamp in the tunnel by using the tunnel lamp control unit, if yes, keeping the current lighting lamp brightness unchanged.

[0023] In a third aspect, an electronic device is provided, including:

[0024] one or more processors;

[0025] a memory for storing one or more programs,

[0026] When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned expressway tunnel lighting intelligent control method.

[0027] In a fourth aspect, a computer readable storage medium is provided, characterized in that a computer program is stored thereon, and the program is executed by a processor to implement the above-mentioned expressway tunnel lighting intelligent control method.

[0028] Compared with the prior art, the present application has the following beneficial effects:

[0029] (1) The expressway tunnel lighting intelligent control system and method can reduce the regulation frequency of the lighting lamps in the tunnel through the preset time interval, and prolong the service life of the lighting lamps.

[0030] (2) The expressway tunnel lighting intelligent control system and method can comprehensively consider various situations of faults of hardware devices such as detection devices and lighting lamps, and take corresponding emergency measures in time to ensure the traffic safety of drivers.

[0031] (3) The highway tunnel lighting intelligent control system and method provided by the application can realize on-demand lighting, ensure the driving safety of drivers, and reduce energy consumption to the maximum extent. BRIEF DESCRIPTION OF DRAWINGS

[0032] The accompanying drawings, which are part of the present application, serve to further understand the present application, and the illustrative embodiments of the present application and the description thereof serve to explain the present application, but do not constitute improper limitations on the present application. Obviously, the drawings described below are only some embodiments, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the drawings.

[0033] Figure 1 The figure is a schematic diagram of the overall structure of the highway tunnel lighting intelligent control system.

[0034] Figure 2 The figure is a flow chart of the highway tunnel lighting intelligent control method.

[0035] Figure 3 The figure is a flow chart of the lighting brightness intelligent adjustment model built in the lighting intelligent control module.

[0036] Figure 4 The figure is a schematic diagram of the structure of an electronic device provided in an embodiment of the application. CONCRETE IMPLEMENTATION METHOD

[0037] The application will be further described below in combination with the drawings and specific implementation manners.

[0038] In the application, the terms "first" and "second" are only used for description purposes, and cannot be understood as indicating or implying relative importance. "Include" and "comprise" mentioned in the whole specification and claims are open terms, and should be interpreted as "include but not limited to".

[0039] Please refer to Figures 1-2 The application provides a highway tunnel lighting intelligent control system, characterized in that the highway tunnel lighting intelligent control system comprises a vehicle detection unit, a data acquisition unit, a main control module, a lighting intelligent control module, a tunnel lamp control unit, lighting lamps and an emergency control unit.

[0040] The vehicle detection unit comprises a tunnel outside vehicle detection module installed hundreds of meters in front of the tunnel, which is used to detect whether a vehicle is about to enter the tunnel. The vehicle detection unit also comprises a tunnel entrance section vehicle detection module, a tunnel transition section vehicle detection module, a tunnel middle section vehicle detection module and a tunnel exit section vehicle detection module, which are respectively installed in each section of the tunnel, and are respectively used to detect whether a vehicle exists in each section of the tunnel.

[0041] Optionally, each detection module in the vehicle detection unit is a vehicle detection device such as a monitoring camera, and is uniformly arranged near the tunnel outside and in each section of the tunnel, so as to accurately and timely detect whether a vehicle exists near the tunnel and in the tunnel.

[0042] The data collection unit comprises a vehicle flow detection module, a vehicle speed detection module and an outside brightness detection module installed near the tunnel entrance, which are respectively used to collect vehicle flow, vehicle speed and outside brightness information in real time. The distance from the installation position to the tunnel entrance is determined by the driving speed.

[0043] The main control module is used to receive the information detected by the vehicle detection unit and the data collection unit, and input the information to the lighting intelligent control module connected thereto for processing and analysis, and then issue a control command to the tunnel lamp control unit. At the same time, the fault information of the vehicle detection unit, the data collection unit and the lighting lamp is sent to the emergency control unit connected thereto.

[0044] The tunnel lamp control unit comprises a tunnel entrance section lamp brightness control module, a tunnel transition section lamp brightness control module, a tunnel middle section lamp brightness control module and a tunnel exit section lamp brightness control module, which are respectively used to control the brightness of the lighting lamps in each section of the tunnel according to the tunnel lamp brightness control command issued by the lighting intelligent control module.

[0045] The emergency control unit comprises a first emergency module, a second emergency module and a third emergency module. The first emergency module uses the traffic flow, vehicle speed and outside tunnel brightness information collected by the data acquisition unit as input sources when any vehicle detection module in the vehicle detection unit fails to use, analyzes the suitable lighting brightness values of each section of the tunnel by using the built-in lighting brightness intelligent adjustment model of the lighting intelligent control module, and dynamically controls the brightness of the tunnel lighting lamps and lanterns according to the pre-set period. The second emergency module divides a day into four stages of morning, day, dusk and night according to the traffic volume and light intensity when any detection module in the data acquisition unit fails to use, and controls the brightness of the tunnel lighting lamps and lanterns according to the pre-set control command in the four stages. The third emergency module uses the standby lighting equipment in time when the lighting lamps and lanterns fail to use. When two or more emergency modules respond at the same time, the priority is: the third emergency module is greater than the second emergency module, which is greater than the first emergency module.

[0046] The application also provides a highway tunnel lighting intelligent control method, which comprises the following steps:

[0047] Step 1: detecting the vehicle information inside and outside the tunnel by using the vehicle detection unit; detecting the traffic flow, vehicle speed and outside tunnel brightness information by using the traffic flow detection module, vehicle speed detection module and outside tunnel brightness detection module; and executing step 2 based on the above information.

[0048] Step 2: judging whether the vehicle detection module outside the tunnel detects a vehicle about to enter the tunnel, and executing step 3 if not, and executing step 5 if yes.

[0049] Step 3: judging whether the vehicle detection module inside the tunnel detects a vehicle, and executing step 4 if not, and executing step 5 if yes.

[0050] Step 4: judging whether the vehicle detection module inside and outside the tunnel detects a vehicle within the second pre-set time T2, and issuing a control command to make the tunnel lighting lamps and lanterns start the low-energy-consumption operation state if not, and executing step 5 if yes.

[0051] Step 5: judging whether the brightness of the tunnel lighting lamps and lanterns in each section has been controlled within the first pre-set time T1, and executing step 6 by analyzing the suitable lighting brightness values of each section of the tunnel by using the built-in lighting brightness intelligent adjustment model of the lighting intelligent control module and using the collected traffic flow, vehicle speed and outside tunnel brightness if not, and keeping the current tunnel lighting lamps and lanterns brightness control state unchanged if yes.

[0052] Step 6: judging whether the tunnel lighting lamp brightness regulation state obtained by analyzing the built-in lighting brightness intelligent regulation model of the lighting intelligent control module in step 5 is the same as the current lighting lamp brightness regulation state, if not, regulating the brightness of the tunnel lighting lamps by the tunnel lamp control unit; if yes, keeping the current lighting lamp brightness regulation state unchanged.

[0053] In a specific embodiment, the value range of the first preset time T1 and the second preset time T2 can be 10min-15min, so as to avoid frequent regulation of the lighting lamp brightness and prolong the service life of the lighting lamp.

[0054] In another specific embodiment, the values of the first preset time T1 and the second preset time T2 can also be determined by the following method:

[0055] The first preset time T1 can be determined according to the traffic volume, vehicle speed and variation intensity of the outside brightness, if the variation intensity of the three factors is greater, it means that the tunnel lighting lamp brightness needs to be regulated more frequently to adapt to such changes, then the first preset time T1 is shorter, on the contrary, the first preset time T1 is longer. Therefore, the calculation formula of the first preset time T1 is as follows:

[0056] T1=T 10 +f(θ1)*α1

[0057] Wherein, T 10 is the basic first preset time, α1 is the variable range, θ1 is the comprehensive variation intensity; for reference, T 10 can be 10min, and α1 can be 5min.

[0058] In order to make the comprehensive variation intensity of each factor within 0-1, the variant of hyperbolic tangent function, i.e. f(θ), is introduced, and f(θ) gradually decreases with the increase of θ, the formula is as follows:

[0059]

[0060] The comprehensive variation intensity θ1 is to comprehensively consider the variation intensity of the three factors, and the calculation formula is as follows:

[0061] θ1=w 11 *θ 11 +w 12 *θ 12 +w 13 *θ 13

[0062] Wherein, θ 11 , θ 12 and θ 13 are the variation intensity of the traffic volume, vehicle speed and outside brightness respectively; w11 , w 12 and w 13 are weight coefficients of traffic flow, vehicle speed and outside brightness respectively.

[0063] The change intensity calculation formula of each factor is shown as follows:

[0064]

[0065] wherein, θ x is θ 11 , θ 12 or θ 13 , θ new is the latest detection value of the corresponding factor, θ old is the last detection value of the corresponding factor x, and Δt is the detection time interval between two detections.

[0066] The second preset time T2 can be determined according to factors such as weather and time period. The weather can be divided into sunny day, rainy day, cloudy day, snowy day and foggy day, and the time period can be divided into peak time period and non-peak time period. When the weather is sunny day and the time period is peak time period, the traffic flow is relatively more, and the vehicle detection time range can be lengthened, that is, the longer the second preset time T2, the less the unnecessary frequent regulation of the tunnel lighting lamps, and vice versa. Therefore, the calculation formula of the second preset time T2 is shown as follows:

[0067] T2 = T 20 *w 21 *w 22

[0068] wherein, T 20 is the basic second preset time, w 21 and w 22 are weight coefficients of weather and time period respectively; it can be referred that T 20 can be taken as 10 min, w 21 and w 22 can be taken according to the actual situation, the weather and time period with relatively more traffic flow are given higher weight coefficients, and both are greater than or equal to 1.

[0069] In summary, in the existing tunnel lighting control system, some use the control mode of turning on the light when a vehicle enters and turning off the light when the vehicle leaves. In the case of high traffic volume, this will lead to frequent control of the lighting lamps, affecting the service life and stability of the lighting lamps. In the embodiment, when the brightness of the lighting lamps needs to be adjusted, it is first determined whether the adjustment has been made within the preset time T1. If the adjustment has been made, the current brightness is maintained unchanged; if the adjustment has not been made, new adjustment is made, which is different from the existing technology of "turning on the light when a vehicle enters". At the same time, after the vehicle leaves the tunnel, a period of time T2 is waited before entering the low-energy-consumption running state, which is different from the existing technology of "turning off the light when a vehicle leaves", thereby reducing the number of adjustments of the lighting lamps in the tunnel and prolonging the service life of the lighting lamps.

[0070] In addition, in the prior art, there is also a control mode of time control and hierarchical control. This mode cannot be flexibly adjusted according to the actual traffic conditions and changes in ambient light, leading to problems of waste of energy consumption and poor lighting effect. The application provides two traffic condition detection modes of a vehicle detection unit and a data acquisition unit, cooperates with a tunnel outside brightness detection module and an emergency module, and fully guarantees the adaptability of the brightness adjustment of the lamps to the traffic conditions and changes in ambient light, which is more suitable for the actual road conditions.

[0071] Further, the lighting brightness intelligent adjustment model built in the lighting intelligent control module is a model combining a convolutional neural network-long short-term memory network and an attention mechanism (CNN-LSTM-Attention), which is pre-trained, as shown in Figure 3 The model includes:

[0072] An input layer, which inputs data including time series vehicle flow data, vehicle speed data and tunnel outside brightness data;

[0073] A CNN layer, which extracts feature components by layer-by-layer convolution and pooling operations on the data sequence;

[0074] An LSTM layer, which captures time correlation and long-term dependence in the data sequence and sets a Dropout layer to prevent overfitting;

[0075] An Attention layer, which introduces an attention mechanism to assign different attention weights to the output of the LSTM layer for weighted summation;

[0076] An output layer, which outputs the lighting brightness of each segment in the tunnel through a fully connected layer.

[0077] It should be noted that the convolution operation in the CNN layer is to extract features, and the pooling operation is to reduce the feature dimension to prevent overfitting.

[0078] It should be noted that the LSTM layer receives the output from the CNN layer, including a cell state, a forget gate, an input gate and an output gate, and the processing process is as follows:

[0079] f t = σ(W f ·[h t-1 , x t ]+b f )

[0080] i t = σ(W i ·[h t-1 , x t ]+b i )

[0081]

[0082] o t = σ(W o ·[h t-1 , x t ]+b o )

[0083] h t = o t · tanh(C t )

[0084] wherein f t represents the forget gate, W f and b f are the weight and bias coefficient of the forget gate, respectively, i t represents the input gate, W i and b i are the weight and bias coefficient of the input gate, respectively, W c and b c are the temporary cell weight and bias coefficient, respectively, C t and are the cell state and its candidate value at t time, respectively, o t represents the output gate, h t is the output at t time, x t is the input at t time, σ is the sigmoid activation function, and tanh is the hyperbolic tangent activation function

[0085] It should be noted that the attention weight calculation process in the Attention layer is as follows:

[0086] u t = tanh(W e *h t +b e )

[0087]

[0088] s t =∑α t *h t

[0089] where t is the length of the time series, u t is the key vector of the attention layer, W e and b e are the weight parameter matrix and bias vector of h t , respectively, h t is the feature vector output by the LSTM layer, b e is the bias vector, α t is the generated attention weight, tanh is the activation function, and s t is the result of weighted summation.

[0090] It should be noted that the formula for calculating the output value in the output layer is as follows:

[0091] y t =σ(W j s t +b j )

[0092] where y t is the output at time t, W j and b j are the weight coefficient and bias coefficient, respectively.

[0093] Further, the training steps of the built-in lighting brightness intelligent adjustment model of the lighting intelligent control module include:

[0094] Step 1: Collect time series of traffic data, speed data and outside brightness data for standardization processing, and divide them into training set and validation set.

[0095] Step 2: Initialize model parameters, use Adam algorithm to iteratively train the CNN-LSTM-Attention model, and use cross-entropy as the loss function;

[0096] Step 3: When the loss function value reaches the preset threshold or the number of iterations reaches the preset maximum training number, terminate the training, obtain the optimal parameters of the model, and save the training results;

[0097] Step 4: Input the validation set into the trained CNN-LSTM-Attention model for verification to obtain the final lighting brightness intelligent adjustment model.

[0098] Further, the step 1 comprises: calculating the required illumination brightness value of each segment according to the "Highway Tunnel Lighting Design Details JTG / TD70 / 2-01-2014", and labeling each sample with the required illumination brightness of the sample in each segment of the tunnel.

[0099] To sum up, the embodiment provides a highway tunnel lighting intelligent control system and method, which has the following beneficial effects:

[0100] (1) The highway tunnel lighting intelligent control system and method reduces the number of control of the lighting lamps in the tunnel by presetting the time interval, and prolongs the service life of the lighting lamps.

[0101] (2) The highway tunnel lighting intelligent control system and method comprehensively considers various situations of faults of the hardware devices such as the detection device and the lighting lamps, and ensures the traffic safety of the driver by taking corresponding emergency measures in time.

[0102] (3) The highway tunnel lighting intelligent control system and method adopts a low-energy-consumption operation state of the lighting lamps when there is no vehicle in the tunnel, and adjusts the brightness of the lighting lamps according to the proposed illumination brightness adjustment model when there is a vehicle in the tunnel, so as to realize on-demand lighting, ensure the driving safety of the driver, and minimize the energy consumption.

[0103] Figure 4 A structural schematic diagram of an electronic device provided by the embodiment is shown in FIG. 1. Figure 4 As shown in FIG. 1, the device includes a processor 60, a memory 61, an input device 62, and an output device 63; the number of processors 60 in the device can be one or more, Figure 4 and the processor 60 in the device is taken as an example; the processor 60, the memory 61, the input device 62, and the output device 63 in the device can be connected through a bus or other means, Figure 4 and the connection through the bus is taken as an example.

[0104] The memory 61 is a computer-readable storage medium, which can be used to store software programs, computer-executable programs, and modules, such as the program instructions / modules of the dynamic updating method of the node routing in the wireless short-distance communication network in the embodiment.

[0105] The memory 61 can include a program storage area and a data storage area, where the program storage area can store an operating system, application programs required by at least one function, and the data storage area can store data created according to the use of the terminal, etc. In addition, the memory 61 can include a high-speed random access memory, and can also include a nonvolatile memory such as at least one magnetic disk storage device, flash memory device, or other nonvolatile solid-state storage device. In some examples, the memory 61 can further include a memory disposed remotely with respect to the processor 60, which can be connected to the device through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0106] The input device 62 can be used to receive input digital or character information, and to generate key signal inputs related to user settings of the device and function control. The output device 63 can include a display device such as a display screen.

[0107] The embodiment of the present application also provides a computer readable storage medium, which has stored thereon a computer program, and the computer program is executed by a processor to implement the expressway tunnel lighting intelligent control method of any embodiment.

[0108] The computer storage medium of the embodiment of the present application can adopt any combination of one or more computer readable media. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. More specific examples (non-exhaustive list) of the computer readable storage medium include: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, device or apparatus.

[0109] The computer readable signal medium can include a data signal propagated in a baseband or as a part of a carrier wave, in which a computer readable program code is borne. Such a propagated data signal can take on multiple forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination thereof. The computer readable signal medium can also be any computer readable medium that is not a computer readable storage medium and that can send, propagate or transmit a program for use by or in connection with an instruction execution system, device or apparatus.

[0110] The program code embodied on the computer readable media can be transmitted using any appropriate medium, including but not limited to wireless, wire line, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0111] Computer program code for carrying out operations of the present application can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider).

[0112] The specific embodiments described above are intended to be illustrative of the present application and should not be construed as limiting the scope of the present application. Those skilled in the art will be able to devise numerous alternative ways of practicing the present application without departing from the scope of the present application. Accordingly, the present application is not limited to the specific embodiments described above, but only by the scope of the appended claims.

Claims

1. A smart control system for highway tunnel lighting, characterized in that, include: The vehicle detection unit includes a vehicle detection module installed hundreds of meters in front of the tunnel to detect whether a vehicle is about to enter the tunnel. It also includes vehicle detection modules installed in the tunnel entrance section, tunnel transition section, tunnel middle section, and tunnel exit section, respectively, to detect whether there are vehicles in each section of the tunnel. The data acquisition unit includes a traffic flow detection module, a vehicle speed detection module, and an external brightness detection module installed near the tunnel entrance. These modules are used to collect real-time traffic flow, vehicle speed, and external brightness information. The distance between the installation location and the tunnel entrance is determined by the vehicle speed. The main control module receives information detected by the vehicle detection unit and data acquisition unit, and inputs it into the lighting intelligent control module for processing and analysis; at the same time, it sends fault information from the vehicle detection unit, data acquisition unit, and lighting fixtures to the emergency control unit. The tunnel lighting control unit is used to adjust the brightness of the lighting fixtures in each section of the tunnel according to the brightness adjustment command issued by the intelligent lighting control module. The emergency control unit includes a first emergency module, a second emergency module, and a third emergency module; The first emergency module is used to take the traffic flow, vehicle speed and tunnel brightness information collected by the data acquisition unit as input source when any vehicle detection module in the vehicle detection unit is unavailable, and use the intelligent lighting brightness adjustment model built into the lighting intelligent control module to analyze and obtain the appropriate lighting brightness value for each section of the tunnel, and dynamically adjust the brightness of the lighting fixtures in each section of the tunnel according to a preset cycle. The second emergency module is used to divide the day into four stages—morning, daytime, dusk, and night—based on traffic volume and light intensity when any detection module in the data acquisition unit is unavailable, and to adjust the brightness of the tunnel lighting fixtures according to the preset control commands in these four stages. The third emergency module is used to promptly activate backup lighting equipment when lighting fixtures malfunction and become unusable. When two or more emergency modules respond simultaneously, the execution priority is: the third emergency module > the second emergency module > the first emergency module.

2. The intelligent control system for highway tunnel lighting according to claim 1, characterized in that, The detection modules in the vehicle detection unit are a monitoring camera, a lidar, and a millimeter-wave radar. The tunnel lighting control unit includes a tunnel entrance section lighting brightness adjustment module, a tunnel transition section lighting brightness adjustment module, a tunnel middle section lighting brightness adjustment module, and a tunnel exit section lighting brightness adjustment module, which are used to adjust the brightness of the lighting fixtures in the tunnel entrance section, transition section, middle section, and exit section, respectively.

3. A method for intelligent control of lighting in highway tunnels, characterized in that, The method, applied to the intelligent control system for highway tunnel lighting as described in any one of claims 1-2, comprises the following steps: Step 1: Use the vehicle detection unit to detect vehicle information inside and outside the tunnel; use the traffic flow detection module, vehicle speed detection module, and tunnel exterior brightness detection module to detect traffic flow, vehicle speed, and tunnel exterior brightness information; Step 2: Determine whether the vehicle detection module outside the tunnel has detected a vehicle about to enter the tunnel. If not, proceed to step 3; if yes, proceed to step 5. Step 3: Determine if a vehicle is detected inside the tunnel. If not, proceed to Step 4; if yes, proceed to Step 5. Step 4: Determine whether a vehicle is detected inside or outside the tunnel within the second preset time T2. If not, issue a control command to turn on the tunnel lighting fixtures to low-energy operation. If so, proceed to step 5; Step 5: Determine whether the brightness of the lighting fixtures in each section of the tunnel has been adjusted within the first preset time T1. If not, analyze the collected traffic flow, vehicle speed, and external brightness through the intelligent lighting brightness adjustment model built into the intelligent lighting control module to obtain the lighting brightness values ​​applicable to each section of the tunnel, and execute Step 6. If yes, keep the current brightness adjustment state of the tunnel lighting fixtures unchanged. Step 6: Determine whether the lighting brightness values ​​of each tunnel segment obtained from the intelligent lighting brightness adjustment model built into the intelligent lighting control module in Step 5 are the same as the current lighting value. If not, adjust the brightness of the lighting fixtures in each tunnel segment using the tunnel lighting control unit; if so, keep the current lighting brightness unchanged.

4. The intelligent control method for highway tunnel lighting according to claim 3, characterized in that, The first preset time T1 is determined according to the following formula: Among them, T 10 The first preset time is based on α1, which is a variable range, and θ 11 θ 12 and θ 13 These represent the changes in traffic volume, vehicle speed, and external brightness, respectively; w 11 w 12 and w 13 These are the weighting coefficients for traffic flow, vehicle speed, and external brightness, respectively.

5. The intelligent control method for highway tunnel lighting according to claim 3, characterized in that, The second preset time T2 is determined according to the following formula: T2=T 20 *w 21 *w 22 Among them, T 20 Based on the second preset time, w 21 and w 22 These are the weighting coefficients for weather and time period, respectively; the greater the traffic volume, the greater the weight of weather and time period, and both are greater than or equal to 1.

6. The intelligent control method for highway tunnel lighting according to claim 3, characterized in that, The intelligent lighting control module's built-in intelligent lighting brightness adjustment model is a pre-trained model combining a convolutional neural network-long short-term memory network and an attention mechanism, including: The input layer includes time-series traffic flow data, vehicle speed data, and external brightness data. The CNN layer extracts feature components by performing layer-by-layer convolution and pooling operations on the data sequence; The LSTM layer captures the temporal correlations and long-term dependencies in the data sequence, and the Dropout layer is set to prevent overfitting. The Attention layer introduces an attention mechanism to assign different attention weights to the output of the LSTM layer for weighted summation. The output layer outputs the lighting brightness of each segment within the tunnel through a fully connected layer.

7. The intelligent control method for highway tunnel lighting according to claim 3, characterized in that, The training steps for the intelligent lighting brightness adjustment model built into the intelligent lighting control module include: Step 1: Collect time-series traffic flow data, vehicle speed data, and tunnel brightness data, perform standardization processing, and divide them into training set and validation set; Step 2: Initialize model parameters and iteratively train the CNN-LSTM-Attention model using the Adam algorithm, with cross-entropy as the loss function; Step 3: Terminate training when the loss function value reaches the preset threshold or the number of iterations reaches the preset maximum number of training rounds, obtain the optimal parameters of the model, and save the training results; Step 4: Input the validation set into the trained CNN-LSTM-Attention model for validation to obtain the final intelligent lighting brightness adjustment model.

8. The intelligent control method for highway tunnel lighting according to claim 7, characterized in that, Step 1 includes: calculating the required lighting brightness value for each segment according to relevant standards, and marking the required lighting brightness for each sample in each segment of the tunnel.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the intelligent control method for highway tunnel lighting as described in any one of claims 3-8.

10. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the intelligent control method for highway tunnel lighting as described in any one of claims 3-8.

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