Intelligent control system and method for highway tunnel illumination

Abstract

The embodiment of the invention discloses an intelligent control system and method for highway tunnel lighting. The system comprises a vehicle detection unit used for detecting whether vehicles exist inside and outside a tunnel; the data acquisition unit is used for acquiring traffic flow, vehicle speed and outside-tunnel brightness information in real time; 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 intelligent illumination control module for processing and analysis; meanwhile, fault information of the vehicle detection unit, the data acquisition unit and the lighting lamp is sent to the emergency control unit; and the tunnel lamp control unit is used for regulating and controlling the brightness of the illumination lamps in each section of the tunnel according to the tunnel lamp brightness regulation and control command. The embodiment aims at reducing energy consumption of the expressway tunnel while ensuring safety.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel lighting, and in particular to an intelligent control system and method for highway tunnel lighting. Background Art

[0002] With the rapid development of my country's expressways, the scale and number of expressway tunnel construction are also expanding. Due to the special characteristics of expressway tunnels, which are in a closed environment and require continuous lighting, how to ensure driving safety in tunnels and reduce energy consumption in tunnels has become an important research issue.

[0003] On the one hand, when vehicles on highways enter and exit tunnels, drivers will experience short-term visual adaptation problems due to sudden changes in light, greatly increasing the probability of traffic accidents; inside the tunnel, exhaust emissions from vehicles passing through gather to form smog, which reduces the air quality in the tunnel and affects visibility, also threatening the driver's driving safety. On the other hand, keeping the lighting on for a long time during the use of the tunnel ensures the driver's driving safety, but causes the tunnel to consume a lot of energy when there is sufficient light or low traffic, increasing energy consumption costs.

[0004] In view of this, the present invention is proposed. Summary of the invention

[0005] In view of the above-mentioned problems, the purpose of the present invention is to provide an intelligent control system and method for highway tunnel lighting, aiming to ensure the driving safety of drivers while minimizing the energy consumption of highway tunnels.

[0006] In a first aspect, an embodiment of the present invention provides an intelligent control system for highway tunnel lighting, comprising:

[0007] The vehicle detection unit includes a vehicle detection module installed outside the tunnel at a position several hundred meters in front of the tunnel, which is used to detect whether there is a vehicle about to enter the tunnel; and also includes 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 installed in each section of the tunnel, which are used to detect whether there is a vehicle in each section of the tunnel;

[0008] The data acquisition unit includes a vehicle flow detection module, a vehicle speed detection module and an external brightness detection module installed near the tunnel entrance, which is used to collect vehicle flow, vehicle speed and external brightness information in real time. The distance of the installation position from the tunnel entrance is determined by the driving speed.

[0009] The main control module is used to receive the information detected by the vehicle detection unit and the data acquisition unit, and input it to the lighting intelligent control module for processing and analysis; at the same time, the fault information of the vehicle detection unit, the data acquisition unit and the lighting fixture is sent to the emergency control unit;

[0010] The tunnel lamp control unit is used to adjust the brightness of each segment of the tunnel lighting fixture according to the tunnel lamp brightness control command issued by the lighting intelligent control module;

[0011] An emergency control unit, comprising a first emergency module, a second emergency module and a third emergency module;

[0012] Wherein, the first emergency module is used to use the traffic volume, vehicle speed and brightness outside the tunnel collected by the data acquisition unit as input sources when any vehicle detection module in the vehicle detection unit cannot be used, and use the built-in intelligent lighting brightness adjustment model of the lighting intelligent control module to analyze and obtain the appropriate lighting brightness value of each section of the tunnel, and dynamically adjust the brightness of the lighting fixtures of each section of the tunnel according to a preset period;

[0013] The second emergency module is used to divide a day into four stages: early morning, daytime, dusk and night according to 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 four stages according to the preset control commands;

[0014] The third emergency module is used to enable the backup lighting equipment in time when the lighting fixture fails and cannot be used;

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

[0016] In a second aspect, an embodiment of the present invention provides a method for intelligent control of highway tunnel lighting, which is applied to the above-mentioned intelligent control system for highway tunnel lighting. The method comprises the following steps:

[0017] Step 1: Use the vehicle detection unit to detect the vehicle information inside and outside the tunnel; use the vehicle flow detection module, the vehicle speed detection module and the tunnel brightness detection module to detect the vehicle flow, vehicle speed and tunnel brightness information;

[0018] Step 2: Determine whether the vehicle detection module outside the tunnel detects that a vehicle is about to enter the tunnel. If not, execute step 3; if yes, execute step 5;

[0019] Step 3: Determine whether a vehicle is detected in the tunnel. If not, execute step 4; if yes, execute step 5.

[0020] 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 in a low-energy operation state; if yes, execute step 5.

[0021] 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, the collected traffic flow, vehicle speed, and brightness outside the tunnel are analyzed through the built-in lighting brightness intelligent adjustment model of the lighting intelligent control module to obtain the lighting brightness value suitable for each section of the tunnel, and execute step 6; if so, keep the current tunnel lighting fixture brightness control state unchanged.

[0022] Step 6: Determine whether the lighting brightness values ​​of each section of the tunnel obtained by analyzing the lighting brightness intelligent adjustment model built into the lighting intelligent control module in step 5 are the same as the brightness value of the current lighting fixtures. If not, use the tunnel lighting fixture control unit to adjust the brightness of the lighting fixtures in each section of the tunnel; if so, keep the current lighting fixture brightness unchanged.

[0023] In a third aspect, an embodiment of the present invention provides an electronic device, 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 highway tunnel lighting intelligent control method.

[0027] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, characterized in that a computer program is stored thereon, and when the program is executed by a processor, the above-mentioned intelligent control method for highway tunnel lighting is implemented.

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

[0029] (1) The intelligent control system and method for highway tunnel lighting described in the present invention reduces the number of times the lighting fixtures in the tunnel are adjusted by presetting the time interval, thereby extending the service life of the lighting fixtures;

[0030] (2) The intelligent control system and method for highway tunnel lighting described in the present invention comprehensively considers various situations in which hardware equipment such as detection devices and lighting fixtures fail, and ensures the safety of drivers by taking corresponding emergency measures in a timely manner;

[0031] (3) The intelligent control system and method for highway tunnel lighting described in the present invention is that when there are no vehicles passing through the tunnel, the lighting fixtures adopt a low-energy consumption operating state; when there are vehicles passing through the tunnel, the lighting fixtures adjust the brightness of the lighting fixtures according to a proposed lighting brightness adjustment model to achieve on-demand lighting, thereby minimizing energy consumption while ensuring the driver's driving safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The accompanying drawings are part of this application and are used to provide a further understanding of the present invention. The illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but do not constitute an improper limitation of the present invention. Obviously, the drawings described below are only some embodiments. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

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

[0034] Figure 2 The present invention is a flow chart of an intelligent control method for highway tunnel lighting.

[0035] Figure 3 The present invention relates to a flow chart of a lighting brightness intelligent adjustment model built into a lighting intelligent control module.

[0036] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Specific implementation methods

[0038] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments.

[0039] In the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. "Including" and "comprising" mentioned throughout the specification and claims are open-ended terms and should be interpreted as "including but not limited to".

[0040] See also Figure 1-Figure 2 The present invention provides an intelligent control system for highway tunnel lighting, characterized in that the intelligent control system for highway tunnel lighting includes 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.

[0041] The vehicle detection unit includes a tunnel outside vehicle detection module installed at a position several hundred meters in front of the tunnel, and the tunnel outside vehicle detection module is used to detect whether there is a vehicle about to enter the tunnel. The vehicle detection unit also includes 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 installed in each section of the tunnel, respectively, and these modules are used to detect whether there is a vehicle in each section of the tunnel.

[0042] Optionally, each detection module in the vehicle detection unit is a vehicle detection device such as a surveillance camera, and is evenly distributed near the outside of the tunnel and in each section inside the tunnel, and can accurately and in real time detect whether there are vehicles near and inside the tunnel.

[0043] The data acquisition unit includes a vehicle flow detection module, a vehicle speed detection module and an outside-tunnel brightness detection module installed near the tunnel entrance. These modules are used to collect vehicle flow, vehicle speed and outside-tunnel brightness information in real time. The distance of the installation position from the tunnel entrance is determined by the driving speed.

[0044] The main control module is used to receive the information detected by the vehicle detection unit and the data acquisition unit, and input it into the lighting intelligent control module connected thereto for processing and analysis, and then issue control commands to the tunnel lighting control unit; at the same time, the fault information of the vehicle detection unit, the data acquisition unit and the lighting fixture is sent to the emergency control unit connected thereto.

[0045] The tunnel lamp control unit includes 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. According to the tunnel lamp brightness control command issued by the lighting intelligent control module, the brightness of each segment lighting fixture of the tunnel is controlled respectively.

[0046] The emergency control unit includes a first emergency module, a second emergency module and a third emergency module. Among them, when any vehicle detection module in the vehicle detection unit fails and cannot be used, the first emergency module uses the vehicle flow, vehicle speed and brightness outside the tunnel collected by the data acquisition unit as input sources, uses the built-in intelligent adjustment model of lighting brightness in the lighting intelligent control module to analyze and obtain the appropriate lighting brightness value of each section of the tunnel, and dynamically adjusts the brightness of the lighting fixtures in each section of the tunnel according to a preset cycle. When any detection module in the data acquisition unit fails and cannot be used, the second emergency module divides a day into four stages of early morning, daytime, dusk and night according to the traffic volume and light intensity, and adjusts the brightness of the tunnel lighting fixtures according to the four stages according to the preset control command. The third emergency module promptly activates the backup lighting equipment when the lighting fixture fails and cannot be used. When there are two or more emergency modules responding at the same time, the execution priority is: the third emergency module is greater than the second emergency module and greater than the first emergency module.

[0047] The present invention also provides a method for intelligently controlling highway tunnel lighting, comprising the following steps:

[0048] Step 1: Use the vehicle detection unit to detect the vehicle information inside and outside the tunnel; use the vehicle flow detection module, the vehicle speed detection module and the brightness detection module outside the tunnel to detect the vehicle flow, vehicle speed and brightness information outside the tunnel; based on the above information, execute step 2.

[0049] Step 2: Determine whether the vehicle detection module outside the tunnel detects that a vehicle is about to enter the tunnel. If not, execute step 3; if yes, execute step 5.

[0050] Step 3: Determine whether a vehicle is detected in the tunnel. If not, execute step 4; if yes, execute step 5.

[0051] 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 in a low-energy operation state; if yes, execute step 5.

[0052] 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, the collected traffic flow, vehicle speed, and brightness outside the tunnel are analyzed through the lighting brightness intelligent adjustment model built into the lighting intelligent control module to obtain the appropriate lighting brightness value for each section of the tunnel, and execute step 6; if so, keep the current tunnel lighting fixture brightness adjustment state unchanged.

[0053] Step 6: Determine whether the tunnel lighting fixture brightness control state obtained by analyzing the built-in lighting brightness intelligent adjustment model of the lighting intelligent control module in step 5 is the same as the current lighting fixture brightness control state. If not, use the tunnel lighting fixture control unit to adjust the brightness of each segmented lighting fixture in the tunnel; if so, keep the current lighting fixture brightness control state unchanged.

[0054] In a specific implementation, the first preset time T1 and the second preset time T2 may be in the range of 10 minutes to 15 minutes, so as to avoid frequent adjustment of the brightness of the lighting fixture and extend the service life of the lighting fixture.

[0055] In another specific implementation, the values ​​of the first preset time T1 and the second preset time T2 may also be determined by the following method:

[0056] The first preset time T1 can be determined according to the change intensity of the traffic volume, vehicle speed and brightness outside the tunnel. If the change intensity of these three factors is greater, it means that the brightness of the tunnel lighting fixtures needs to be adjusted more frequently to adapt to such changes, and the first preset time T1 is shorter. Conversely, the first preset time T1 is longer. Therefore, the calculation formula of the first preset time T1 is as follows:

[0057] T1=T 10 +f(θ1)*α1

[0058] Among them, T 10 is the first preset time as the basis, α1 is the variable range, θ1 is the comprehensive change intensity; for reference, T 10 The possible value is 10min, and the possible value of α1 is 5min.

[0059] In order to convert the comprehensive change intensity of each factor into the range of 0-1, a variant of the hyperbolic tangent function, namely f(θ), is introduced. As θ increases, f(θ) gradually decreases. The formula is as follows:

[0060]

[0061] The comprehensive change intensity θ1 is to integrate the change intensity of the three factors, and the calculation formula is as follows:

[0062] θ1=w 11 *θ 11 +w 12 *θ 12 +w 13 *θ 13

[0063] Among them, θ 11 ,θ 12 and θ 13 are the change intensity of traffic volume, vehicle speed and brightness outside the tunnel respectively; w11 、w 12 and w 13 are the weight coefficients of traffic volume, vehicle speed and brightness outside the cave respectively.

[0064] The calculation formula for the change intensity of each factor is as follows:

[0065]

[0066] Among them, θ x is θ 11 ,θ 12 or θ 13 ,θ new is the latest detected value of the corresponding factor, θ old is the last detected value of the corresponding factor x, and Δt is the detection time interval between two detections.

[0067] The second preset time T2 can be determined based on factors such as weather and time period. The weather can be divided into sunny, rainy, cloudy, snowy and foggy days, and the time period can be divided into peak hours and non-peak hours. When the weather is sunny and the time period is peak hours, the traffic volume is relatively large, and the vehicle detection time range can be lengthened, that is, the longer the second preset time T2 is, the less unnecessary frequent adjustment of the tunnel lighting fixtures is, and vice versa, the shorter the second preset time T2 is. Therefore, the calculation formula of the second preset time T2 is as follows:

[0068] T2=T 20 *w 21 *w 22

[0069] Among them, T 20 The second preset time is the basic time, w 21 and w 22 are the weight coefficients of weather and time period respectively; for reference, T 20 The possible values ​​are 10min, w 21 and w 22 The value can be determined based on actual conditions, with higher weight coefficients being assigned to weather and time periods with relatively heavy traffic, and all of them should be greater than or equal to 1.

[0070] In summary, some existing tunnel lighting control systems use a control method that turns on the lights when the vehicle enters and turns off the lights when the vehicle leaves. In the case of high traffic volume, this will lead to frequent control of lighting fixtures, affecting the service life and stability of lighting fixtures. In this embodiment, when it is necessary to adjust the brightness of the lighting fixture, it is first determined whether the adjustment has been made within the preset time T1. If it has been made, the current brightness is kept unchanged; if it has not been made, a new adjustment is made, which is different from the "light up when the vehicle comes" in the prior art; at the same time, when the vehicle leaves the tunnel, it has to wait for a period of time T2 before entering the low-energy operation state, which is different from the "turn off when the vehicle leaves" in the prior art, thereby reducing the number of adjustments to the lighting fixtures in the tunnel and extending the service life of the lighting fixtures.

[0071] In addition, the prior art also uses time control and hierarchical control, which cannot be flexibly adjusted according to the actual traffic conditions and changes in ambient light, resulting in energy waste and poor lighting effects. However, this application uses traffic-based adjustment as a normalized adjustment method, and provides two traffic condition detection methods: a vehicle detection unit and a data acquisition unit, in conjunction with an out-of-tunnel brightness detection module and an emergency module, which fully ensures the adaptability of the lamp brightness control to traffic conditions and changes in ambient light, and is more suitable for actual road conditions.

[0072] Furthermore, the lighting brightness intelligent adjustment model built into the lighting intelligent control module is a pre-trained convolutional neural network-long short-term memory network and attention mechanism combined model (CNN-LSTM-Attention), such as Figure 3 As shown, including:

[0073] Input layer, including time series of traffic flow data, vehicle speed data and brightness data outside the cave;

[0074] CNN layer, extracts feature components by performing layer-by-layer convolution and pooling operations on the data sequence;

[0075] LSTM layer, which captures temporal correlation and long-term dependencies in the data sequence, and sets a Dropout layer to prevent overfitting;

[0076] Attention layer, which introduces the attention mechanism to assign different attention weights to the output of the LSTM layer for weighted summation;

[0077] The output layer outputs the lighting brightness of each segment in the tunnel through a fully connected layer.

[0078] 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 and prevent overfitting.

[0079] It should be noted that the LSTM layer receives the output from the CNN layer, including a cell state, forget gate, input gate and output gate, and the processing process is as follows:

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

[0081] i t =σ(W i ·[h t-1 ,x t ]+b i )

[0082]

[0083] o t =σ(W o ·[h t-1 ,x t ]+b o )

[0084] h t =o t tanh(C t )

[0085] Among them, 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, W c and b c are temporary unit weights and bias coefficients, respectively, C t and are the cell states and their candidate values ​​at time t, o t represents the output gate, h t is the output at time t, x t is the input at time t, σ is the sigmoid activation function, and tanh is the hyperbolic tangent activation function

[0086] It should be noted that the attention weight calculation process in the Attention layer is as follows:

[0087] u t =tanh(W e *h t +b e )

[0088]

[0089] s t =∑α t *h t

[0090] Where t is the length of the time series, u t is the key-value vector of the attention layer, W e and b e h t The weight parameter matrix and bias vector, 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, s t is the result of weighted summation.

[0091] It should be noted that the formula for calculating the output value in the output layer is as follows:

[0092] y t =σ(W j s t +b j )

[0093] Among them, y t is the output at time t, W j and b j are weight coefficient and bias coefficient respectively.

[0094] Furthermore, the training steps of the lighting brightness intelligent adjustment model built into the lighting intelligent control module include:

[0095] Step 1: Collect time series of traffic flow data, vehicle speed data and brightness data outside the tunnel, standardize them, and divide them into training set and validation set.

[0096] Step 2: Initialize the model parameters and use the Adam algorithm to iteratively train the CNN-LSTM-Attention model. The loss function uses cross entropy.

[0097] Step 3: When the loss function value reaches the preset threshold or the number of iterations reaches the preset maximum number of training rounds, the training is terminated, the optimal model parameters are obtained, and the training results are saved;

[0098] Step 4: Input the verification set into the trained CNN-LSTM-Attention model for verification to obtain the final lighting brightness intelligent adjustment model.

[0099] Furthermore, the step 1 includes: calculating the required lighting brightness value for each segment according to the "Highway Tunnel Lighting Design Specifications JTG / TD70 / 2-01-2014", and marking the lighting brightness required for each sample in each segment of the tunnel.

[0100] In summary, this embodiment provides a highway tunnel lighting intelligent control system and method, which has the following beneficial effects:

[0101] (1) The intelligent control system and method for highway tunnel lighting described in the present invention reduces the number of times the lighting fixtures in the tunnel are adjusted by presetting the time interval, thereby extending the service life of the lighting fixtures;

[0102] (2) The intelligent control system and method for highway tunnel lighting described in the present invention comprehensively considers various situations in which hardware equipment such as detection devices and lighting fixtures fail, and ensures the safety of drivers by taking corresponding emergency measures in a timely manner;

[0103] (3) The intelligent control system and method for highway tunnel lighting described in the present invention is that when there are no vehicles passing through the tunnel, the lighting fixtures adopt a low-energy consumption operating state; when there are vehicles passing through the tunnel, the lighting fixtures adjust the brightness of the lighting fixtures according to a proposed lighting brightness adjustment model to achieve on-demand lighting, thereby minimizing energy consumption while ensuring the driver's driving safety.

[0104] Figure 4 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 4 As shown, 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 A processor 60 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 by a bus or other means. Figure 4 The example of connecting through bus is taken in the following.

[0105] The memory 61 is a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the method for dynamically updating node routing in a wireless short-range communication network in the embodiment of the present invention. The processor 60 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 61, that is, realizing the above-mentioned intelligent control method for highway tunnel lighting.

[0106] The memory 61 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 61 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 61 may further include a memory remotely arranged relative to the processor 60, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0107] The input device 62 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 63 may include a display device such as a display screen.

[0108] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method for intelligent control of highway tunnel lighting of any embodiment is implemented.

[0109] The computer storage medium of the embodiment of the present invention 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 can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, 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, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0110] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0111] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0112] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may 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 may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0113] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An intelligent control system for highway tunnel lighting, characterized in that: include: The vehicle detection unit includes an out-of-tunnel vehicle detection module installed several hundred meters in front of the tunnel to detect whether a vehicle is about to enter the tunnel; It also includes 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 respectively installed in each section of the tunnel, and is used to detect whether there is a vehicle in each section of the tunnel; The data acquisition unit includes a vehicle flow detection module, a vehicle speed detection module and an external brightness detection module installed near the tunnel entrance, which is used to collect vehicle flow, vehicle speed and external brightness information in real time. The distance of the installation position from the tunnel entrance is determined by the driving speed. The main control module is used to receive the information detected by the vehicle detection unit and the data acquisition unit, and input it to the lighting intelligent control module for processing and analysis; at the same time, the fault information of the vehicle detection unit, the data acquisition unit and the lighting fixture is sent to the emergency control unit; The tunnel lamp control unit is used to adjust the brightness of each segment of the tunnel lighting fixture according to the tunnel lamp brightness control command issued by the lighting intelligent control module; An emergency control unit, comprising a first emergency module, a second emergency module and a third emergency module; Wherein, the first emergency module is used to use the traffic volume, vehicle speed and brightness outside the tunnel collected by the data acquisition unit as input sources when any vehicle detection module in the vehicle detection unit cannot be used, and use the built-in intelligent lighting brightness adjustment model of the lighting intelligent control module to analyze and obtain the appropriate lighting brightness value of each section of the tunnel, and dynamically adjust the brightness of the lighting fixtures of each section of the tunnel according to a preset period; The second emergency module is used to divide a day into four stages: early morning, daytime, dusk and night according to 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 four stages according to the preset control commands; The third emergency module is used to enable the backup lighting equipment in time when the lighting fixture fails and cannot be used; When two or more emergency modules respond at the same time, the execution priority is: the third emergency module is higher than the second emergency module, which is higher than the first emergency module.

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

3. A highway tunnel lighting intelligent control method, characterized in that: Applied to the highway tunnel lighting intelligent control system as claimed in any one of claims 1-2, the method comprises the following steps: Step 1: Use the vehicle detection unit to detect the vehicle information inside and outside the tunnel; use the vehicle flow detection module, the vehicle speed detection module and the tunnel brightness detection module to detect the vehicle flow, vehicle speed and tunnel brightness information; Step 2: Determine whether the vehicle detection module outside the tunnel detects that a vehicle is about to enter the tunnel. If not, execute step 3; if yes, execute step 5; Step 3: Determine whether a vehicle is detected in the tunnel. If not, execute step 4; if yes, execute 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 fixture in a low-energy operation state; If yes, go to step 5; Step 5: Determine whether the brightness of each segment lighting fixture in the tunnel has been adjusted within the first preset time T1. If not, analyze the collected traffic volume, vehicle speed, and brightness outside the tunnel through the built-in lighting brightness intelligent adjustment model of the lighting intelligent control module to obtain the lighting brightness value suitable for each segment of the tunnel, and execute step 6; if yes, keep the current tunnel lighting fixture brightness adjustment state unchanged; Step 6: Determine whether the lighting brightness values ​​of each section of the tunnel obtained by analyzing the lighting brightness intelligent adjustment model built into the lighting intelligent control module in step 5 are the same as the brightness value of the current lighting fixtures. If not, use the tunnel lighting fixture control unit to adjust the brightness of the lighting fixtures in each section of the tunnel; if so, keep the current lighting fixture brightness unchanged.

4. The intelligent control method for highway tunnel lighting according to claim 3 is characterized in that: The first preset time T1 is determined according to the following formula: Among them, T 10 is the first preset time as the basis, α1 is the variable range, θ 11 ,θ 12 and θ 13 are the change intensity of traffic volume, vehicle speed and brightness outside the tunnel respectively; w 11 、w 12 and w 13 are the weight coefficients of traffic volume, vehicle speed and brightness outside the cave respectively.

5. The intelligent control method for highway tunnel lighting according to claim 3 is 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 The second preset time is the basic time, w 21 and w 22 are the weight coefficients of weather and time period respectively; the greater the traffic volume, the greater the weights 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 is characterized in that: The lighting brightness intelligent adjustment model built into the lighting intelligent control module is a pre-trained model combining a convolutional neural network, a long short-term memory network and an attention mechanism, including: Input layer, including time series of traffic flow data, vehicle speed data and brightness data outside the cave; CNN layer, extracts feature components by performing layer-by-layer convolution and pooling operations on the data sequence; LSTM layer, which captures temporal correlation and long-term dependencies in the data sequence, and sets a Dropout layer to prevent overfitting; Attention layer, which introduces the 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 in the tunnel through a fully connected layer.

7. The intelligent control method for highway tunnel lighting according to claim 3 is characterized in that: The training steps of the lighting brightness intelligent adjustment model built into the lighting intelligent control module include: Step 1: Collect the time series of traffic flow data, vehicle speed data and brightness data outside the tunnel, standardize them, and divide them into training set and validation set; Step 2: Initialize the model parameters and use the Adam algorithm to iteratively train the CNN-LSTM-Attention model. The loss function uses cross entropy. Step 3: When the loss function value reaches the preset threshold or the number of iterations reaches the preset maximum number of training rounds, the training is terminated, the optimal model parameters are obtained, and the training results are saved; Step 4: Input the verification set into the trained CNN-LSTM-Attention model for verification to obtain the final lighting brightness intelligent adjustment model.

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

9. An electronic device, characterized in that: include: one or more processors; a memory for storing 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: A computer program is stored thereon, and when the program is executed by a processor, the method for intelligent control of highway tunnel lighting as described in any one of claims 3-8 is implemented.

Images