Intelligent tunnel dimming method based on solar energy

Abstract

The invention is suitable for the technical field of road tunnel dimming control, provides a tunnel intelligent dimming method based on solar energy, and is applied to a tunnel intelligent dimming control system based on solar energy. Comprising a target acquisition module, a target identification module, a brightness calculation module, a light regulation and control module, a wireless communication module, a memory, an alarm, a processing center, an intelligent control terminal and a power management system. The target acquisition module, the target identification module, the brightness calculation module, the light regulation and control module, the wireless communication module, the memory and the alarm are respectively connected with the processing center; the smart phone, the tablet personal computer or the intelligent remote controller is in wireless network connection with the wireless communication module within the range of the wireless network or the Internet. According to the invention, the light is turned on when the vehicle comes and turned off when the vehicle leaves in the tunnel, the electric energy consumption and maintenance cost are reduced, traffic accidents are avoided, and safe passage in the tunnel is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of highway tunnel dimming control, and in particular to a tunnel intelligent dimming method based on solar energy in the field of artificial intelligence. Background Art

[0002] In the remote and uninhabited areas of the northwest, due to the high cost of power supply and wiring, many road tunnels have no lighting. When vehicles enter or exit, the vehicle lights illuminate the tunnel and it is bright white. When there are no vehicles entering or exiting, the tunnel is dark. Every year, traffic accidents caused by poor tunnel lighting account for more than half of tunnel accidents. Therefore, the quality of tunnel lighting directly determines the safety of driving. Scientific and reasonable adjustment of tunnel lighting can not only create a good tunnel driving environment, reduce accidents, and reduce tunnel costs. Therefore, in order to set up wind power generation or solar photovoltaic power generation nearby, there is no need to lay cables over long distances. However, most tunnels only dim the lights according to the time period. For example, the lighting brightness is uniformly turned on to the maximum at night, and basic lighting is maintained during the day. If it is a rainy day, a stronger light brightness is required, and the light brightness cannot be the same as that on a sunny day. Otherwise, this not only wastes energy, but also easily causes traffic accidents. The lights must be brighter on rainy days, and smaller on sunny days, and can even be turned off in some areas.

[0003] The existing "tunnel intelligent dimming method, system and computer storage medium (publication number: CN115968088)" provides a tunnel intelligent dimming method based on the isolated forest and 3 Sigma principles. It extracts historical light intensity data, seasonal data, climate data, time point data and other features from tunnel data resources to determine whether the historical and real-time light intensity data are abnormal and give the correct dimming level. However, when the real-time weather conditions are completely different from those in the historical data, the system cannot control the lights, so it has great limitations. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the purpose of the present invention is to provide a solar-based intelligent tunnel dimming method. By setting a target acquisition module, a target recognition module, a brightness calculation module, and a light control module, when a vehicle is about to enter the tunnel, the lights are automatically turned on and the light brightness in the tunnel is adjusted to ensure the normal travel of vehicles or pedestrians; when a vehicle or pedestrian leaves the tunnel or there are no vehicles or pedestrians within 50 meters behind, the street lights behind the lights are automatically turned off, so that the lights are turned on when the car comes and turned off when the car leaves, reducing energy consumption and maintenance costs, avoiding traffic accidents, and ensuring safe passage in the tunnel.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is: A solar-based intelligent tunnel dimming method is applied to a solar-based intelligent tunnel dimming control system, comprising a target acquisition module, a target recognition module, a brightness calculation module, a light control module, a wireless communication module, a memory, an alarm, a processing center, an intelligent control terminal, and a power management system; the target acquisition module, the target recognition module, the brightness calculation module, the light control module, the wireless communication module, the memory, and the alarm are respectively connected to the processing center; the smart phone, the tablet computer or the smart remote control is respectively connected to the wireless network of the wireless communication module within the range of a wireless network or the Internet; the power management system comprises a solar power supply group, a wind power supply group, and a state grid power supply group, and is connected to the light control module; The wireless communication module is provided with a wireless network unit, which is responsible for sending and receiving wireless signals and automatically networking with the intelligent control terminal within an effective network range; The alarm device compares the actual light brightness in front of the target object with the tunnel light brightness standard stored in the memory. If the standard is not met, the alarm device automatically sounds an audible alarm and notifies the user to continue adjusting the brightness until the standard is met. The memory is responsible for storing information of the target acquisition module, the target recognition module, the brightness calculation module, the light control module, the wireless communication module, the alarm, and the tunnel light brightness standard; The processing center is responsible for the information transmission of the target acquisition module, target recognition module, brightness calculation module, light control module, wireless communication module, alarm, and memory. It is the hub of the system and compares the actual light brightness in front of the target with the tunnel light brightness standard stored in the memory: if it meets the standard, it continues to supply power to maintain the brightness; if it does not meet the standard, it transmits it to the alarm and notifies to continue adjusting the brightness until it meets the standard; The target acquisition module includes a model building unit, an image processing unit, a filtering and denoising unit, and a feature extraction unit. The image of the vehicle, person, or animal entering the tunnel entrance is acquired through a high-definition camera and converted into identifiable target information, and then transmitted to the target recognition module; The target recognition module includes a model training unit, a loss function unit, a training verification unit, and a target identification unit. It obtains images of vehicles or people entering the tunnel entrance through a high-definition camera and converts them into identifiable target information, and transmits them to the brightness calculation module; The brightness calculation module includes a brightness acquisition unit, a brightness calculation unit, a brightness comparison unit, a brightness difference unit, and a tunnel brightness unit, which is responsible for obtaining the actual brightness of the tunnel pavement according to the target object entering the tunnel entrance and calculating the surrounding comprehensive brightness, and passing it to the lighting control module; The light control module includes a current adjustment unit, a brightness acquisition unit, and a time control unit, which are responsible for adjusting the light brightness according to the required light brightness until the target object leaves the tunnel and automatically turns off to ensure that "the lights are on when the car comes and off when the car leaves".

[0006] The present invention provides a solar-based tunnel intelligent dimming method, comprising the following steps: S10, before dimming, the target acquisition module acquires the image of the vehicle, person or animal entering the tunnel entrance through the high-definition camera and converts it into identifiable target information, and transmits it to the target recognition module; S20, the target recognition module obtains the image of the vehicle or person entering the tunnel entrance through the high-definition camera and converts it into identifiable target information, and transmits it to the brightness calculation module; S30, the brightness calculation module obtains the actual brightness of the tunnel road surface according to the target object entering the tunnel entrance and calculates the surrounding comprehensive brightness so as to accurately adjust the light brightness, and transmits it to the light control module; S40, the light control module adjusts the light brightness according to the required light brightness until the target object leaves the tunnel and turns off the light automatically to ensure that "the light is on when the car comes and off when the car leaves" to save energy.

[0007] Further, the step S10 comprises the following steps: S11, the model building unit classifies and labels the collected six types of target images entering the tunnel, and establishes a training set and a test set required for training the target type prediction model, and transmits them to the image processing unit; S12, the image processing unit performs pre-processing of the target image by cropping, adjusting the resolution, and graying the image, so as to extract the features of the subsequent image information, and transmit the information to the filtering and denoising unit; S13, the filtering and denoising unit obtains the denoised image according to the median filtering method calculation formula "g(I,j)=Med{g(x,y)" for subsequent feature extraction, and transmits it to the feature extraction unit; S14. The feature extraction unit extracts the color, shape, and dynamic features of the target object in the denoised target image through a series of convolutional layers of a convolutional neural network to facilitate subsequent target recognition.

[0008] Further, the step S20 comprises the following steps: S21, the model training unit trains the labeled category target image data set through the convolutional neural network to obtain the probability of the corresponding category, calculates the minimum average value of the loss function, and passes it to the loss function unit; S22, the loss function unit calculates the loss function according to the formula "l = -[y i logai +(1-y i )log(1-a i )] "Get the minimum average value of the loss function and pass it to the training and verification unit; S23, the training verification unit trains the designed neural network using the training set through the back propagation algorithm, so that it gradually learns the target object features in the image and passes them to the target identification unit; S24. The target identification unit inputs the trained image into the convolutional neural network model, and uses the trained model to predict the presence of a target object entering the tunnel and its category in the new monitoring image.

[0009] Further, the step S30 comprises the following steps: S31, the brightness acquisition unit obtains the brightness of the target object entering the tunnel entrance and the brightness of the surrounding sky, road, and environment through the brightness sensors arranged at the tunnel entrance and outside, and transmits them to the brightness calculation unit; S32. The brightness calculation unit obtains the average brightness of the tunnel entrance according to the average brightness calculation formula "La=γ*LC+ρ*LR+ε*LE+τ*Lt, La is the average brightness at the tunnel entrance, LC is the sky brightness outside the tunnel entrance, LR is the road brightness outside the tunnel, LE is the environment brightness outside the tunnel, Lt is the brightness of the critical zone of the tunnel, γ, ρ, ε are the proportions of the sky, road, and environment outside the tunnel respectively, and τ is the proportion of the critical zone of the tunnel", and transmits it to the brightness contrast unit; S33, the brightness contrast unit obtains the brightness difference of the tunnel entrance according to the brightness contrast calculation formula "LC=(L0-La) / La, LC is the brightness contrast of the tunnel entrance, L0 is the target brightness of the tunnel entrance, and Lb is the average brightness of the tunnel entrance", and transmits it to the brightness difference unit; S34, the brightness difference unit obtains the brightness difference recognizable to the human eye in the critical area of ​​the tunnel entrance according to the brightness difference calculation formula "Dt=Lt*DL / LC, Dt is the brightness difference recognizable to the human eye in the critical area of ​​the tunnel entrance, Lt is the brightness contrast of the critical area of ​​the tunnel entrance, DL is the actual brightness difference in the critical area of ​​the tunnel entrance, LC is the brightness contrast of the critical area of ​​the tunnel entrance", and transmits it to the tunnel brightness unit; S35. The tunnel brightness unit obtains the actual brightness of the light in the tunnel according to the actual brightness calculation formula of the light in the tunnel: "LE=φ*U*K*N / (S*W)+Dt, LE is the actual brightness in the tunnel, φ is the total luminous flux of the light source, U is the light source utilization coefficient, K is the light source maintenance coefficient, N is the lighting coefficient in the tunnel, S is the installation spacing of street lights in the tunnel, W is the road width in the tunnel, and Dt is the brightness difference that can be recognized by the human eye in the critical area of ​​the tunnel entrance."

[0010] Further, the step S40 comprises the following steps: S41, the current regulating unit increases the lamp current through the power management system according to the actual brightness of the light in the tunnel to improve the light brightness, so that the light brightness reaches a predetermined brightness, and transmits it to the brightness acquisition unit; S42, the brightness acquisition unit detects the actual light brightness in front of the target object through the intelligent sensor set in the tunnel to ensure the accuracy of the light brightness and transmits it to the processing center; S43. The processing center compares the actual light brightness in front of the target object with the tunnel light brightness standard stored in the memory: if it meets the standard, the power supply continues to maintain the brightness; if it does not meet the standard, the alarm is transmitted to the alarm and notified to continue adjusting the brightness until the standard brightness is reached.

[0011] S44. The time control unit obtains information through the intelligent sensors installed on both sides of the tunnel. If the vehicle is within 50 meters behind or leaves the tunnel exit, the lights will automatically dim or turn off to save energy.

[0012] A solar-based intelligent tunnel dimming control system provided by the present invention also includes a computer-readable storage medium containing a memory; the memory stores a computer program, and when the functional modules execute the computer program, the steps of any one of the above-mentioned solar-based intelligent tunnel dimming methods are implemented; the computer-readable storage medium stores a computer program, and when the computer program is executed by the functional modules, the steps of any one of the above-mentioned solar-based intelligent tunnel dimming methods are implemented.

[0013] The present invention provides a solar-based intelligent tunnel dimming control system, which also includes a solar-based intelligent tunnel dimming control device, which is implemented by the solar-based intelligent tunnel dimming method described above.

[0014] The beneficial effects of the present invention compared with the prior art are as follows: 1) Brightness adjustment is performed in sections in the tunnel: the entrance interval sensing line, a street light sensing unit is set every 50 meters inside the tunnel, and the exit sensing line is set in sections, and the lighting control is automatically performed in sections. The brightness of the street lights can be automatically adjusted according to the time period, and the switch and brightness of one or more street lights can be controlled by grouping them every 50 meters; the wireless communication module can support remote setting of lighting parameters, and the lighting time and brightness of the street lights can be set remotely, so as to remotely control the street lights; the current, voltage, power and other data of the lamps and equipment in the tunnel are detected by intelligent sensor data, and the terminal is online, offline, and fault status monitoring is carried out to realize intelligent analysis of system faults; wireless grid communication street lights are selected, and the street lights in the tunnel are separately formed into a local network using the street light wireless grid networking method, and then the intelligent networking of street lights is realized through the 4G network cloud method; 2) The gateway is a 4G wireless gateway with the following features: based on a wireless mesh network, it can automatically form a network and complete communication without power-on, supports automatic networking and automatic recovery of networking, provides power through solar power supply groups and wind power supply groups, automatically forms a wireless network with each street light node, implements ultra-long-distance wireless multi-level relay communication, and has the advantages of relay coverage of up to 10KM.

[0015] 3) Each street lamp has the following characteristics: ultra-large capacity, a single gateway supports up to 500 street lamp nodes; its controller supports dimming drive 5-100% brightness dimming, supports a maximum load power of 1000W, supports multiple dimming drives in parallel, and has 5-segment time-controlled dimming. It executes the switch time according to the dark time set by the parameters, supports morning light time, and automatically turns on the lights before dawn according to the set dawn time. It automatically adjusts the lighting time according to the GIS geographical location and season. It can monitor current, voltage, power, power factor, and electric energy in real time, automatically count daily power consumption and total power consumption, support single-lamp and group batch switch control and parameter setting, support IoT remote communication, real-time viewing, automatic fault alarm, support PC web pages, official accounts, and mobile APP to view the status of street lamps and control street lamps, and display information on the GIS map on the control terminal to make the status of the lamps clear at a glance.

[0016] 4) To ensure normal communication, a gateway is set up at each tunnel entrance at both ends of the tunnel, and wireless networking is established with each street lamp in the tunnel. A split controller with wireless networking is selected, and a one-to-many solution is used, that is, one controller with multiple drive solutions. A single controller can support 1000W power, and adjacent street lamps can be driven in parallel in the tunnel, and one controller can control the switch and adjust the brightness.

[0017] Therefore, when a vehicle is about to enter a tunnel, the lights will automatically turn on and adjust the light brightness in the tunnel to ensure the normal operation of vehicles or pedestrians; when a vehicle or pedestrian leaves the tunnel or there are no vehicles or pedestrians within 50 meters behind, the street lights behind the lights will automatically turn off, so that the lights will turn on when the vehicle approaches and turn off when the vehicle leaves, reducing energy consumption and maintenance costs, avoiding traffic accidents, and ensuring safe passage in the tunnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or exemplary technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 It is a schematic diagram of the system application of the present invention; Figure 2 It is a schematic diagram of the system module of the present invention; Figure 3 It is a schematic diagram of a target acquisition module of the present invention; Figure 4 It is a schematic diagram of a target recognition module of the present invention; Figure 5 is a schematic diagram of a brightness calculation module of the present invention; Figure 6 This is a schematic diagram of a lighting control module of the present invention; Figure 7 It is a schematic diagram of the method flow control program of the present invention; Figure 8 This is a schematic diagram of step S10 in the method flow of the present invention; Fig. 9 This is a schematic diagram of step S20 in the method flow of the present invention; Fig.10 This is a schematic diagram of step S30 in the method flow of the present invention; Fig.11 It is a program schematic diagram of step S40 in the method flow of the present invention. DETAILED DESCRIPTION

[0020] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0021] The specific implementation of the present invention is described in detail below in conjunction with specific embodiments: In order to make the technical problems, technical solutions and beneficial effects to be solved by the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0022] It should be noted that when a module is referred to as being "disposed on" another module, it can be directly on the other module or indirectly on the other module. When a module is referred to as being "connected to" another module, it can be directly connected to the other module or indirectly connected to the other module.

[0023] In the description of this application, "multiple" means two or more, unless otherwise clearly and specifically defined. "Several" means one or more, unless otherwise clearly and specifically defined.

[0024] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "connected" and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0025] See also Figure 1 , 2 As shown, the present invention provides a tunnel intelligent dimming method based on solar energy, which is applied to a tunnel intelligent dimming control system based on solar energy, including a target acquisition module, a target recognition module, a brightness calculation module, a light control module, a wireless communication module, a memory, an alarm, a processing center, an intelligent control terminal, and a power management system; the target acquisition module, the target recognition module, the brightness calculation module, the light control module, the wireless communication module, the memory, and the alarm are respectively connected to the processing center; the smart phone, the tablet computer or the smart remote control are respectively connected to the wireless network of the wireless communication module within the range of a wireless network or the Internet; the power management system includes a solar power supply group, a wind power supply group, and a state grid power supply group, and is connected to the light control module; the wireless communication module is provided with a wireless network unit, which is responsible for sending and receiving wireless signals, and is automatically networked with the intelligent control terminal within the effective network range.

[0026] To further illustrate, the intelligent control terminal includes a desktop computer 11 and a mobile terminal 12, the mobile terminal 12 includes a smart phone, a tablet computer, and a smart remote control, the wireless communication module includes a gateway 2 and a wireless network unit 3 of each node, and the wireless network unit 3 is wirelessly connected to each street lamp 4 in the tunnel, thereby controlling the switch and brightness of each street lamp in the tunnel.

[0027] The alarm compares the actual light brightness in front of the target object with the tunnel light brightness standard stored in the memory. If the standard is not met, an audible alarm is automatically issued and a notification is given to continue adjusting the brightness until the standard is met.

[0028] The memory is responsible for storing information of the target acquisition module, the target recognition module, the brightness calculation module, the light control module, the wireless communication module, the alarm, and the tunnel light brightness standard.

[0029] The processing center is responsible for information transmission among the target acquisition module, target recognition module, brightness calculation module, lighting control module, wireless communication module, alarm, and memory. It is the hub of the system and compares the actual light brightness in front of the target object with the tunnel light brightness standard stored in the memory: if the standard is met, power is continued to be supplied to maintain the brightness; if the standard is not met, the alarm is transmitted and notified to continue adjusting the brightness until the standard brightness is reached.

[0030] See also Figure 3 As shown, the target acquisition module includes a model building unit, an image processing unit, a filtering and denoising unit, and a feature extraction unit. The image of the vehicle, person or animal entering the tunnel entrance is acquired through a high-definition camera and converted into identifiable target object information, and then transmitted to the target recognition module.

[0031] Furthermore, the model building unit classifies and labels the six types of target images entering the tunnel, establishes a training set and a test set required for training the target type prediction model, and transmits them to the image processing unit; the image processing unit performs pre-processing of the target image by cropping, adjusting the resolution, and graying the target image for subsequent feature extraction of image information, and transmits it to the filtering and denoising unit; the filtering and denoising unit obtains the denoised image according to the median filtering formula "g(i, j) = Med{g(x, y)" for subsequent feature extraction, and transmits it to the feature extraction unit; the feature extraction unit extracts the color, shape, and dynamic features of the target in the image through a series of convolutional layers of a convolutional neural network from the denoised target image for subsequent target recognition.

[0032] See also Figure 4 As shown, the target recognition module includes a model training unit, a loss function unit, a training verification unit, and a target identification unit. It obtains images of vehicles or people entering the tunnel entrance through a high-definition camera and converts them into identifiable target information, and transmits them to the brightness calculation module.

[0033] Furthermore, the model training unit trains the labeled category target image data set through a convolutional neural network to obtain the probability of the corresponding category, and calculates the minimum average value of the loss function, and passes it to the loss function unit; the loss function unit calculates the loss function according to the loss function calculation formula "l = -[y i loga i +(1-y i )log(1-a i)]” obtain the minimum average value of the loss function and pass it to the training verification unit; the training verification unit trains the designed neural network using the training set through the back propagation algorithm, so that it gradually learns the target features in the image, and passes it to the target identification unit; the target identification unit inputs the trained image into the convolutional neural network model, and uses the trained model to predict the new monitoring image and whether there is a target entering the tunnel and its category.

[0034] See also Figure 5 As shown, the brightness calculation module includes a brightness acquisition unit, a brightness calculation unit, a brightness comparison unit, a brightness difference unit, and a tunnel brightness unit, which is responsible for obtaining the actual brightness of the tunnel pavement according to the target object entering the tunnel entrance and calculating the surrounding comprehensive brightness, and passing it to the lighting control module.

[0035] Furthermore, the brightness acquisition unit obtains the brightness of the target object entering the tunnel entrance and the brightness of the surrounding sky, road, and environment through brightness sensors arranged at the tunnel entrance and outside, and transmits it to the brightness calculation unit; the brightness calculation unit obtains the average brightness of the tunnel entrance according to the average brightness calculation formula "La=γ*LC+ρ*LR+ε*LE+τ*Lt", and transmits it to the brightness contrast unit; the brightness contrast unit obtains the brightness difference at the tunnel entrance according to the brightness contrast calculation formula "LC=(L0-La) / La", and transmits it to the brightness difference unit; the brightness difference unit obtains the human eye-recognizable brightness difference in the critical area of ​​the tunnel entrance according to the brightness difference calculation formula "Dt=Lt*DL / LC", and transmits it to the tunnel brightness unit; the tunnel brightness unit obtains the actual brightness of the light in the tunnel according to the actual brightness calculation formula "LE=φ*U*K*N / (S*W)+Dt".

[0036] See also Figure 6 As shown, the light control module includes a current regulating unit, a brightness collection unit, and a time control unit, which are responsible for adjusting the light brightness according to the required light brightness until the target object leaves the tunnel and automatically turns off to ensure that "the light is on when the car comes and off when the car leaves".

[0037] Furthermore, the current regulating unit increases the lamp current through the power management system according to the actual brightness of the light in the tunnel to improve the light brightness, so that the light brightness reaches a predetermined brightness, and transmits it to the brightness acquisition unit; the brightness acquisition unit detects the actual light brightness in front of the target object through the intelligent sensor set in the tunnel to ensure the accuracy of the light brightness, and transmits it to the processing center; the time control unit obtains the light within 50 meters behind the vehicle or leaves the tunnel exit through the intelligent sensors set on both sides of the tunnel, and the light is automatically dimmed or turned off to save energy.

[0038] System operation working principle: Before dimming, the target acquisition module obtains the image of the vehicle, person or animal entering the tunnel entrance through the high-definition camera and converts it into identifiable target information, and passes it to the target recognition module; then the target recognition module obtains the image of the vehicle or person entering the tunnel entrance through the high-definition camera and converts it into identifiable target information, and passes it to the brightness calculation module; the brightness calculation module obtains the actual brightness of the tunnel road surface according to the target entering the tunnel entrance and calculates the surrounding comprehensive brightness, so as to accurately adjust the light brightness, and passes it to the light control module; the light control module is controlled to adjust the light brightness according to the light brightness that needs to be adjusted until the target leaves the tunnel and turns off automatically, so as to ensure that "the lights are on when the car comes and off when the car leaves" to save energy.

[0039] When administrators or highway managers are away from home, they can use smart phones or tablets to automatically connect to the wireless network or the Internet through the wireless communication module, and realize the integration of smart terminals, the Internet and artificial intelligence technology to achieve remote monitoring, automatic identification and intelligent scheduling functions. Through the intelligent system, managers can remotely monitor the lighting control status in the tunnel in real time through mobile phone APP or smart terminals, improve lighting control efficiency and reduce lighting power consumption costs.

[0040] See also Figure 7 As shown, the solar energy-based tunnel intelligent dimming method provided by the present invention comprises the following steps: S10, before dimming, the target acquisition module acquires the image of the vehicle, person or animal entering the tunnel entrance through the high-definition camera and converts it into identifiable target information, and transmits it to the target recognition module; See also Figure 8 As shown, the step S10 comprises the following steps: S11, the model building unit classifies and labels the collected six types of target images entering the tunnel, and establishes a training set and a test set required for training the target type prediction model, and transmits them to the image processing unit; It is further explained that by classifying and labeling the collected standard images of six colors of target objects entering the tunnel, namely trucks, buses, cars, motorcycles (including non-motor vehicles), people, and animals, the data set is used in a 9:1 relationship through the convolutional neural network deep learning method to establish the training set and test set required for the training of the tunnel entry target prediction model, thereby establishing a deep convolutional neural network structure with a depth of 10, including 3 convolutional layers, 3 maximum pooling layers, 2 fully connected layers, 1 softmax layer, and 1 output layer. 3 different convolution kernels are used in the three convolutional layers: the first layer is to extract edge features, the second layer is to extract main contour features, and the third layer is to extract texture features. The pooling layers are all calculated using the maximum pooling algorithm with overlapping 2*2 windows, and a unit activation function is added after each convolutional layer; the maximum pooling algorithm and unit activation function are well known technologies and will not be described here.

[0041] S12, the image processing unit performs pre-processing of the target image by cropping, adjusting the resolution, and graying the image, so as to extract the features of the subsequent image information, and transmit the information to the filtering and denoising unit; Further explanation: by cutting out the redundant parts, only the main part of the target object and the smoke entering the tunnel is retained; the original image is adjusted to a resolution of 800*600 pixels while ensuring that the image ratio and DPI of the target object entering the tunnel remain unchanged; and the pixel average method is used to calculate the formula "GRAY=(R+G+ B) / 3” converts the color RGB color image into a grayscale image, that is, the R, G, and B pixel points at the corresponding position are averaged to obtain the pixel point at that position; and the pixels are arranged from small to large according to the grayscale value and the median is taken as the new grayscale value.

[0042] S13, the filtering and denoising unit calculates the denoised image according to the median filtering method formula "g(i, j) = Med{g(x, y)" for subsequent feature extraction, and transmits it to the feature extraction unit; To further illustrate, the median filtering method calculation formula "g(i, j) = Med{g(x, y), (x, y)∈S" where g is the original image, g(i, j) is the pixel with coordinates (i, j) in the original image g, g(x, y) is the pixel with coordinates (x, y) in the denoised image, S is the pixel, and a domain space of K pixels is obtained using a given template to perform nonlinear processing on the image, and check the sample values ​​in the input signal to determine whether they represent the signal itself. By using an observation window composed of an odd number of samples, the values ​​in the window are sorted, the middle value is taken as the output, the earliest value is discarded, a new sample is obtained, and the above process is repeated until the noise in the image is completely removed.

[0043] S14, the feature extraction unit extracts the color, shape, and dynamic features of the target object in the denoised target image through a series of convolutional layers of a convolutional neural network to facilitate subsequent target recognition.

[0044] To further explain, the six types of targets entering the tunnel are classified: "truck" is category 1, "passenger bus" is category 2, "car" is category 3, "motorcycle" is category 4, "person" is category 5, and "animal" is category 6; when category 1 is a positive sample, the other categories are negative samples, that is, the labeled data is (1, 0, 0, 0, 0, 0), when category 2 is a positive sample, the other categories are negative samples, that is, the labeled data is (0, 1, 0, 0, 0, 0), when category 3 is a positive sample, the other categories are negative samples, that is, the labeled data is (0, 0, 1, 0, 0, 0), when category 4 is a positive sample, the other categories are negative samples, that is, the labeled data is (0, 0, 0, 1, 0, 0), when category 5 is a positive sample, the other categories are negative samples, that is, the labeled data is (0, 0, 0, 0, 1, 0), and when category 6 is a positive sample, the other categories are negative samples, that is, the labeled data is (0, 0, 0, 0, 0, 1).

[0045] S20, the target recognition module obtains the image of the vehicle or person entering the tunnel entrance through the high-definition camera and converts it into identifiable target information, and transmits it to the brightness calculation module; See also Fig. 9 As shown, the step S20 comprises the following steps: S21, the model training unit trains the labeled category target image data set through a convolutional neural network to obtain the probability of the corresponding category, calculates the minimum average value of the loss function, and passes it to the loss function unit; It is further explained that the target image dataset that has been labeled with categories is trained through the constructed 10-layer deep convolutional neural network structure to obtain the probability of the corresponding category; a labeled dataset containing target samples is required, including the bounding box coordinates and category labels of the target area, and these data are used to train the YOLOv5 model, and the hyperparameters are adjusted to optimize the detection performance; in the model predicts multiple bounding boxes in the image, each box is attached with a confidence score and category probability. For the detection of the target, the system will screen out boxes with high confidence and belonging to the target category, including non-maximum suppression (NMS) and other technologies to remove overlapping prediction boxes to ensure that each target is only labeled once; and the probability of the corresponding category is obtained by training the labeled category target image dataset through a convolutional neural network, and the data is continuously trained using the back propagation principle.

[0046] S22, the loss function unit calculates the loss function according to the formula "l = -[y i loga i +(1-yi )log(1-a i )] "Get the minimum average value of the loss function and pass it to the training and verification unit; Further explanation: y in the loss function calculation formula i is the category label data of the i-th sample, a i is the output probability of the i-th sample until the total loss function of all samples is minimized, so that the probability of the predicted sample is close to 0 in the remaining categories and close to 1 in this category, such as Fig.10 As shown; taking category 1 as an example, when the i-th input is white smoke, that is, category 1 is a positive sample, and the other categories are negative samples, the labeled category data yi is (1, 0, 0, 0, 0, 0), and the data of the output layer is substituted into the loss function, and the back-propagation gradient descent is used to find the weight threshold, which is substituted for the second training, and repeated until the final curve of l tends to be flat.

[0047] S23, the training verification unit trains the designed neural network using the training set through the back propagation algorithm, so that it gradually learns the target object features in the image and passes them to the target identification unit; It is further explained that during the training process, the weights and biases in the network are updated through the back-propagation algorithm, and the designed neural network is trained so that it gradually learns the features of the target object entering the tunnel in the image; the test set is brought into the trained model to determine its accuracy, that is, the test set is used to evaluate whether the trained model can be put into use; the validation set is used to evaluate the performance of the trained model on new data, and indicators such as accuracy, precision, and recall can be used to evaluate the performance of the model, and adjustments and improvements can be made as needed.

[0048] S24, the target identification unit inputs the trained image into the convolutional neural network model, and uses the trained model to predict the new monitoring image and whether there is a target object entering the tunnel and its category.

[0049] To further explain, the image is input into the model, and the trained model is used to predict the new monitoring image. The output result is used to determine whether there is a target object entering the tunnel and its type. If the model's judgment on the target object entering the tunnel reaches a certain threshold, the early warning system is triggered and corresponding dimming measures are taken in time; the types of targets entering the tunnel include trucks (large trucks, small trucks, special-purpose vehicles, special vehicles, trailers, container trucks, four-wheeled agricultural transport vehicles, three-wheeled agricultural transport vehicles), buses (large buses, medium buses, commercial vehicles with more than 7 seats, vans), cars (sedans, commercial vehicles with less than 7 seats, off-road vehicles), motorcycles (three-wheeled motorcycles, two-wheeled motorcycles, light motorcycles, electric bicycles, bicycles, human-powered tricycles, animal-powered vehicles), people, and animals (large animals such as cattle, sheep, and horses). If it is a tunnel on a highway, the targets are only the first three types.

[0050] S30, the brightness calculation module obtains the actual brightness of the tunnel road surface according to the target object entering the tunnel entrance and calculates the surrounding comprehensive brightness, so as to accurately adjust the light brightness, and transmits it to the light control module; See also Fig.10 As shown, the step S30 comprises the following steps: S31, the brightness acquisition unit obtains the brightness of the target object entering the tunnel entrance and the brightness of the surrounding sky, road, and environment through the brightness sensors arranged at the tunnel entrance and outside, and transmits them to the brightness calculation unit; To further explain, a brightness sensor is set up in the tunnel to obtain the actual brightness of the tunnel entrance when the target object enters the critical point of the tunnel entrance, and brightness sensors are set at different positions outside the tunnel entrance to facilitate the acquisition of the brightness of the sky, road, and environment respectively, and the position setting of the brightness sensor of the expressway tunnel and the low-speed highway tunnel is related to the speed limit of the tunnel entrance. If the speed limit is high, the position where the brightness sensor is set is far away from the tunnel entrance. If the speed limit is low, the position where the brightness sensor is set is close to the tunnel entrance and is affected by the speed limit. However, by setting a number of brightness sensors at different distances outside the low-speed tunnel entrance, the brightness around the targets of different speeds can be obtained respectively.

[0051] S32. The brightness calculation unit obtains the average brightness of the tunnel entrance according to the average brightness calculation formula "La=γ*LC+ρ*LR+ε*LE+τ*Lt, La is the average brightness at the tunnel entrance, LC is the sky brightness outside the tunnel entrance, LR is the road brightness outside the tunnel, LE is the environment brightness outside the tunnel, Lt is the brightness of the critical zone of the tunnel, γ, ρ, ε are the proportions of the sky, road, and environment outside the tunnel respectively, and τ is the proportion of the critical zone of the tunnel", and transmits it to the brightness contrast unit; It is further explained that the sky brightness LC outside the tunnel entrance, the road brightness LR outside the tunnel, the environment brightness LE outside the tunnel, and the brightness Lt of the critical zone of the tunnel are respectively obtained by detecting brightness sensors set at different positions outside the tunnel, which can be one or more sensors, set according to the specific environmental conditions outside the tunnel entrance, where γ+ρ+ε+τ=1; due to the rainy season in the south, there are several kinds of weather on a mountain, and two tunnel entrances that are not far apart, one of which is sunny and the other is rainstorming, have completely different sky brightness and environment brightness; if the outside of the tunnel entrance is all mountains, or sea / lakes, or plains / Gobi, the environment brightness is also different, and the environment brightness in different directions and orientations is also different, for example, the environment brightness in the east-west direction is much brighter than that in the north-south direction; the road brightness outside the tunnel is different due to the influence of the road surface color and vehicle lights, for example: the road brightness of cement roads is brighter than that of asphalt roads, and the road brightness of vehicles with lights on is brighter than that of vehicles without lights on.

[0052] S33, the brightness contrast unit obtains the brightness difference of the tunnel entrance according to the brightness contrast calculation formula "LC=(L0-La) / La, LC is the brightness contrast of the tunnel entrance, L0 is the target brightness of the tunnel entrance, and Lb is the average brightness of the tunnel entrance", and transmits it to the brightness difference unit; To further illustrate, the average brightness Lb of the tunnel entrance is the background brightness of the tunnel entrance, which is calculated in the above step S32 and is related to the surrounding environment, direction, road brightness, whether the vehicle turns on the lights and the brightness of the lights, etc. If people walk into the tunnel without driving or drive, it depends on whether there are one person or multiple people, whether they carry lamps and turn on the lights, and the brightness of the lights. If animals such as cattle, sheep, and horses enter the tunnel alone, it depends on whether the animals carry luminous lamps and the brightness of the lights. The tunnel entrance target brightness L0 is obtained according to the provisions of the "Highway Tunnel Lighting Design Rules" (JTG / T D70 / 2-01-2014).

[0053] S34, the brightness difference unit obtains the brightness difference recognizable to the human eye in the critical area of ​​the tunnel entrance according to the brightness difference calculation formula "Dt=Lt*DL / LC, Dt is the brightness difference recognizable to the human eye in the critical area of ​​the tunnel entrance, Lt is the brightness contrast of the critical area of ​​the tunnel entrance, DL is the actual brightness difference in the critical area of ​​the tunnel entrance, LC is the brightness contrast of the critical area of ​​the tunnel entrance", and transmits it to the tunnel brightness unit; Further explanation: The formula "Dt = Lt * DL / LC" is derived from "LV = LC / Lt = DL / Dt". In the formula, Dt is the luminance difference recognizable by the human eye in the critical area of the tunnel entrance, that is, the luminance difference between the target luminance and the background luminance when the human eye can recognize the target in the tunnel; Lt is the luminance contrast in the critical area of the tunnel entrance, which is obtained by accumulating historical experience data such as light intensity, time point, season, and climate; DL is the actual luminance difference in the critical area of the tunnel entrance, that is, the actual difference between the target luminance and the background luminance in the tunnel, which is obtained by detecting the background luminance with a sensor and calculating it with the known target luminance; LC is the luminance contrast in the critical area of the tunnel entrance, which is obtained by calculating in the above step S33; LV is the visible luminance level in the critical area of the tunnel entrance, that is, the ratio of the actual luminance contrast LC between the target luminance (known) and the background luminance (detected) in the critical area of the tunnel entrance to the critical luminance contrast Lt.

[0054] S35. The tunnel luminance unit obtains the actual luminance of the tunnel lights according to the actual luminance calculation formula of the tunnel lights "LE = φ * U * K * N / (S * W) + Dt", where LE is the actual luminance in the tunnel, φ is the total luminous flux of the light source, U is the light source utilization factor, K is the light source maintenance factor, N is the lighting layout coefficient in the tunnel, S is the installation spacing of the road lights in the tunnel, W is the road width in the tunnel, and Dt is the luminance difference recognizable by the human eye in the critical area of the tunnel entrance.

[0055] Further explanation: The formula "LE = φ * U * K * N / (S * W) + Dt" is obtained by substituting "L theory = φ * U * K * N / (S * W)" into "LE = L theory + Dt". Since the luminance of the lights obtained by the human eye is a little smaller than the actual luminance emitted by the lights, when calculating the luminance of the lights, the luminance difference obtained by the human eye must be added to achieve the most effective luminance of the lights, so that the human eye can see clearly without being blurred; the actual luminance LE of the tunnel lights must be greater than the luminance of the lights theoretically emitted by the light source, and the difference is different according to the visibility of the human eye. The lower the visibility of the human eye, the greater the difference, and vice versa; the light source utilization factor U is generally between 0.5 and 0.6, and the light source maintenance factor K generally takes 0.7; when arranged in a single row or staggered, the lighting layout coefficient N in the tunnel is 1, and when arranged symmetrically, the lighting layout coefficient N in the tunnel is 2; the units of each parameter are uniformly specified for statistics, and will not be listed one by one here with too much elaboration.

[0056] S40. The lighting control module adjusts the luminance of the lights according to the luminance of the lights to be adjusted until the target object leaves the tunnel and automatically turns off to ensure "the lights are on when the vehicle comes and the lights are off when the vehicle leaves".

[0057] Please refer to Fig.11 As shown, the step S40 includes the following steps: S41, the current regulating unit increases the lamp current through the power management system according to the actual brightness of the light in the tunnel to increase the brightness of the light, so that the light brightness reaches a predetermined brightness, and transmits it to the brightness acquisition unit; It is further explained that the power management system includes a solar power supply group, a wind power supply group, and a state-grid power supply group, which can automatically adjust the current to ensure the brightness of the light. For highway tunnels located in remote areas of the northwest and southwest or in deserts and Gobi deserts, the solar power supply group and wind power supply group located near the tunnel are responsible for power supply. If there is sunshine, power is generated and stored 24 hours a day. When there is wind, power is automatically generated and stored to ensure that the reserve power supply can be used to power the tunnel when there is neither solar energy nor wind energy. For other areas where there is a state grid near the tunnel, a state grid power supply group is additionally set up, and the solar power supply group and the wind power generation power supply group are used to supply power respectively. If both have no reserve power supply, the state grid power supply group automatically connects to the state grid for power supply to ensure that the tunnel can be powered at any time 24 hours a day, and to ensure the safety of vehicles, pedestrians, animals and other targets entering and exiting the tunnel.

[0058] S42, the brightness acquisition unit detects the actual light brightness in front of the target object through the intelligent sensor set in the tunnel to ensure the accuracy of the light brightness and transmits it to the processing center; To further illustrate, the image brightness information of the area in front of the moving target is collected by using a CMOS image sensing element through an intelligent sensor, and is processed by a digital image processing algorithm, and the average grayscale value of the image is calculated, and the relationship between the contrast coefficient and the exposure is used to calculate the target's light brightness value, and the brightness distribution on both sides of the road is detected, and the brightness sensor is activated by sensing the movement of vehicle lights and infrared sensing of pedestrians or animals to start the sensing program; for a one-way tunnel, a number of intelligent sensors are set at a designed distance from the entrance to obtain the light brightness at the moving position of the vehicle, pedestrian or animal; for a two-way tunnel, a number of intelligent sensors are set up from one side of the entrance at both ends along the forward direction to ensure that the light brightness can be fully sensed and detected when the vehicles, pedestrians or animals on both sides move, thereby ensuring the accuracy of the light brightness detection.

[0059] S43. The processing center compares the actual light brightness in front of the target object with the tunnel light brightness standard stored in the memory: if it meets the standard, the power supply continues to maintain the brightness; if it does not meet the standard, the alarm is transmitted to the alarm and notified to continue adjusting the brightness until the standard brightness is reached.

[0060] To further explain, the tunnel light brightness standard refers to the sum of the target brightness L0 in the tunnel and the brightness difference Dt that can be recognized by the human eye in the tunnel as stipulated in the "Highway Tunnel Lighting Design Rules" (JTG / TD70 / 2-01-2014). The brightness difference Dt that can be recognized by the human eye in the tunnel is related to factors such as the actual brightness in the tunnel, the visibility of the driver or pedestrian, and whether there are vehicle lights in the opposite direction. When the brightness in front of the tunnel reaches the brightness that can be recognized by the human eye, it meets the tunnel light brightness standard, so that the driver or pedestrian can see the brightness of the light in front clearly and can drive or walk with confidence. When the brightness in front is lower than the brightness that can be recognized by the human eye, it is impossible to drive or walk. When the brightness in front exceeds the brightness that can be recognized by the human eye, it is safe to drive or walk, avoiding traffic accidents.

[0061] S44. The time control unit obtains information through the intelligent sensors installed on both sides of the tunnel. If the vehicle is within 50 meters behind or leaves the tunnel exit, the lights will automatically dim or turn off to save electricity.

[0062] Further explanation: when the intelligent sensor obtains the moving distance of a vehicle, pedestrian or animal reaching 50 meters, then when the vehicle, pedestrian or animal enters the tunnel entrance, if there are no other vehicles, pedestrians or animals within 50 meters behind (if there is a curved tunnel behind, a lamp sensing unit is set within 10-40 meters according to its curvature, which can control the switch and brightness of the light to ensure the contrast brightness of the front field), the street lights 50 meters away will start to go out one after another until the next vehicle, pedestrian or animal enters the tunnel and starts to sense and start the street lights again. When a vehicle, pedestrian or animal leaves the tunnel exit, if there is no vehicle, person or animal within 50 meters behind, the lights in the tunnel will automatically turn off until the next vehicle, person or animal enters the 50-meter range or the tunnel entrance, then the lights will automatically turn on. A sensing line is set at the entrance and exit of the tunnel, so the lights in the tunnel can be started or turned off at any time, and the lights will automatically turn off 50 meters behind the vehicle, person or animal. If the distance between the vehicle, person or animal behind and the front is more than 50 meters, the lights in front will be automatically turned on to ensure the normal operation of vehicles, pedestrians or animals.

[0063] It is further explained that the above-mentioned steps are displayed in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise clearly stated in this document, there is no strict order restriction for the execution of these steps. These steps can also be executed according to other orders, and some steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed and completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0064] To further illustrate, the present invention is described according to the content implemented by the software program of the solar-based tunnel intelligent dimming control system. Each solar-based tunnel intelligent dimming method is divided into several modules or units to implement the software program instructions generated by each step. The software program instructions include the above-mentioned solar-based tunnel intelligent dimming method.

[0065] A solar-based intelligent tunnel dimming control system provided by the present invention also includes a computer-readable storage medium containing a memory; the memory stores a computer program, and when the functional modules execute the computer program, the steps of any one of the above-mentioned solar-based intelligent tunnel dimming methods are implemented; the computer-readable storage medium stores a computer program, and when the computer program is executed by the functional modules, the steps of any one of the above-mentioned solar-based intelligent tunnel dimming methods are implemented.

[0066] To further illustrate, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two, and may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus or device, including but not limited to: 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, wherein the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, carrying a computer-readable program code, and the propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above, and may also be any computer-readable medium other than a computer-readable storage medium, and the computer-readable signal medium may send, propagate or transmit a program for use by or in combination with an instruction execution system, apparatus or device, and the program code that may be contained may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0067] It is further explained that the computer program is divided into multiple modules / units, and the multiple modules / units are stored in the memory and executed by each module / unit to complete the present invention; the multiple modules / units can be a series of computer program instructions that can complete specific functions, and the instructions are used to describe the execution process of the computer program in each module / unit.

[0068] It is further explained that the contents described above in the present invention are written in accordance with the contents implemented by the software copyrights such as "Zero Carbon Smart Street Light Control Platform (Registration Number: 2024SR0854949)", "Zero Carbon Smart Street Light Data Acquisition Management System (Registration Number: 2024SR0868986)", and "Smart Street Light, Smart Pipeline Network Linkage Control System (Registration Number: 2024SR0885211)" applied for by our company on June 24, 2024.

[0069] The present invention provides a solar-based tunnel intelligent dimming control system, which also includes a solar-based tunnel intelligent dimming control device, which is implemented by the solar-based tunnel intelligent dimming method described above.

[0070] It is further explained that the control device is composed of the above-mentioned related equipment or devices, and can be made into a complete set of tunnel street lights together with lighting equipment according to the needs of the road management department; it can also be made into a unit device of each functional module belonging to each step or the entire control device, and each control device can be connected to the street light equipment through a wireless connection during installation; the existing tunnel street light equipment can also be upgraded and modified by adding the control device, thereby saving equipment costs. These equipment structures are not described in detail here.

[0071] For those skilled in the art, it is obvious that the present application is not limited to the details of the above-mentioned exemplary embodiments, and the present application can be implemented in other specific forms without departing from the spirit or basic characteristics of the present application. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present application is defined by the attached claims rather than the above description, and it is intended that all changes that fall within the meaning and scope of the equivalent elements of the claims are included in the present application. The present invention can also be applied to street lamp control systems in railway tunnels or high-speed railway tunnels, but the control of street lamps in groups of 50 meters behind the vehicle is cancelled, and only the automatic turning on of the lights when entering the tunnel entrance and the automatic turning off of the lights when leaving the tunnel exit are retained. Since it is rail transportation, there is no need to control the brightness, and it can be turned on and off.

[0072] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical solution of the present application can be modified or replaced by equivalents, and all should be included in the scope of protection of the present application.

Claims

1. A solar-based intelligent tunnel dimming method, characterized in that: The method is applied to a tunnel intelligent dimming control system based on solar energy, and the system includes a target acquisition module, a target recognition module, a brightness calculation module, a light control module, a wireless communication module, a memory, an alarm, a processing center, an intelligent control terminal, and a power management system; the target acquisition module, the target recognition module, the brightness calculation module, the light control module, the wireless communication module, the memory, and the alarm are respectively connected to the processing center; the smart phone, the tablet computer or the smart remote control is respectively connected to the wireless network of the wireless communication module within the range of a wireless network or the Internet; the power management system includes a solar power supply group, a wind power supply group, and a state grid power supply group, and is connected to the light control module; The method comprises the following steps: S10, before dimming, the target acquisition module acquires the image of the vehicle, person or animal entering the tunnel entrance through the high-definition camera and converts it into identifiable target information, and transmits it to the target recognition module; S20, the target recognition module obtains the image of the vehicle or person entering the tunnel entrance through the high-definition camera and converts it into identifiable target information, and transmits it to the brightness calculation module; S30, the brightness calculation module obtains the actual brightness of the tunnel road surface according to the target object entering the tunnel entrance and calculates the surrounding comprehensive brightness, so as to accurately adjust the light brightness, and transmits it to the light control module; S40, the light control module adjusts the light brightness according to the required light brightness until the target object leaves the tunnel and automatically turns off the light to ensure that "the light is on when the car comes and off when the car leaves" to save energy.

2. The solar-based intelligent tunnel dimming method according to claim 1, characterized in that: The system further comprises: The wireless communication module is provided with a wireless network unit, which is responsible for sending and receiving wireless signals and automatically networking with the intelligent control terminal within an effective network range; The alarm device compares the actual light brightness in front of the target object with the tunnel light brightness standard stored in the memory. If the standard is not met, the alarm device automatically sounds an audible alarm and notifies the user to continue adjusting the brightness until the standard brightness is reached. The memory is responsible for storing information of the target acquisition module, the target recognition module, the brightness calculation module, the light control module, the wireless communication module, the alarm, and the tunnel light brightness standard; The processing center is responsible for the information transmission of the target acquisition module, target recognition module, brightness calculation module, light control module, wireless communication module, alarm, and memory. It is the hub of the system and compares the actual light brightness in front of the target with the tunnel light brightness standard stored in the memory: if it meets the standard, it continues to supply power to maintain the brightness; if it does not meet the standard, it transmits it to the alarm and notifies to continue adjusting the brightness until it meets the standard; The target acquisition module includes a model building unit, an image processing unit, a filtering and denoising unit, and a feature extraction unit. The image of the vehicle, person, or animal entering the tunnel entrance is acquired through a high-definition camera and converted into identifiable target information, and then transmitted to the target recognition module; The target recognition module includes a model training unit, a loss function unit, a training verification unit, and a target identification unit. It obtains images of vehicles or people entering the tunnel entrance through a high-definition camera and converts them into identifiable target information, and transmits them to the brightness calculation module; The brightness calculation module includes a brightness acquisition unit, a brightness calculation unit, a brightness comparison unit, a brightness difference unit, and a tunnel brightness unit, which is responsible for obtaining the actual brightness of the tunnel pavement according to the target object entering the tunnel entrance and calculating the surrounding comprehensive brightness, and passing it to the lighting control module; The light control module includes a current adjustment unit, a brightness collection unit, and a time control unit, which is responsible for adjusting the light brightness according to the required light brightness until the target object leaves the tunnel and automatically turns off to ensure that "the lights are on when the car comes and off when the car leaves".

3. The solar-based tunnel intelligent dimming method according to claim 2, characterized in that: Described step S10, comprises the following steps: S11, the model building unit classifies and labels the collected six types of target images entering the tunnel, and establishes a training set and a test set required for training the target type prediction model, and transmits them to the image processing unit; S12, the image processing unit performs pre-processing of the target image by cropping, adjusting the resolution, and graying the image, so as to extract the features of the subsequent image information, and transmit the information to the filtering and denoising unit; S13, the filtering and denoising unit calculates the denoised image according to the median filtering method formula "g(i, j) = Med{g(x, y)" for subsequent feature extraction, and transmits it to the feature extraction unit; S14, the feature extraction unit extracts the color, shape, and dynamic features of the target object in the denoised target image through a series of convolutional layers of a convolutional neural network to facilitate subsequent target recognition.

4. The solar-based tunnel intelligent dimming method according to claim 2, characterized in that: Described step S20, comprises the following steps: S21, the model training unit trains the labeled category target image data set through a convolutional neural network to obtain the probability of the corresponding category, calculates the minimum average value of the loss function, and passes it to the loss function unit; S22, the loss function unit calculates the loss function according to the formula "l = -[y i loga i +(1-y i )log(1-a i )] "Get the minimum average value of the loss function and pass it to the training and verification unit; S23, the training verification unit trains the designed neural network using the training set through the back propagation algorithm, so that it gradually learns the target object features in the image and passes them to the target identification unit; S24, the target identification unit inputs the trained image into the convolutional neural network model, and uses the trained model to predict the new monitoring image and whether there is a target object entering the tunnel and its category.

5. The solar-based intelligent tunnel dimming method according to claim 2, characterized in that: Described step S30, comprises the following steps: S31, the brightness acquisition unit obtains the brightness of the target object entering the tunnel entrance and the brightness of the surrounding sky, road, and environment through the brightness sensors arranged at the tunnel entrance and outside, and transmits them to the brightness calculation unit; S32. The brightness calculation unit obtains the average brightness of the tunnel entrance according to the average brightness calculation formula "La=γ*LC+ρ*LR+ε*LE+τ*Lt, where La is the average brightness at the tunnel entrance, LC is the sky brightness outside the tunnel entrance, LR is the road brightness outside the tunnel, LE is the environment brightness outside the tunnel, Lt is the brightness of the critical zone of the tunnel, γ, ρ, ε are the proportions of the sky, road, and environment outside the tunnel, respectively, and τ is the proportion of the critical zone of the tunnel", and transmits it to the brightness contrast unit; S33, the brightness contrast unit obtains the tunnel entrance brightness difference according to the brightness contrast calculation formula "LC=(L0–La) / La, LC is the tunnel entrance brightness contrast, L0 is the tunnel entrance target brightness, and Lb is the tunnel entrance average brightness", and transmits it to the brightness difference unit; S34, the brightness difference unit obtains the brightness difference recognizable to the human eye in the critical area of ​​the tunnel entrance according to the brightness difference calculation formula "Dt=Lt*DL / LC, Dt is the brightness difference recognizable to the human eye in the critical area of ​​the tunnel entrance, Lt is the brightness contrast of the critical area of ​​the tunnel entrance, DL is the actual brightness difference in the critical area of ​​the tunnel entrance, LC is the brightness contrast of the critical area of ​​the tunnel entrance", and transmits it to the tunnel brightness unit; S35. The tunnel brightness unit obtains the actual brightness of the light in the tunnel according to the actual brightness calculation formula of the light in the tunnel: "LE=φ*U*K*N / (S*W)+Dt, where LE is the actual brightness in the tunnel, φ is the total luminous flux of the light source, U is the light source utilization coefficient, K is the light source maintenance coefficient, N is the lighting coefficient in the tunnel, S is the installation spacing of street lights in the tunnel, W is the road width in the tunnel, and Dt is the brightness difference that can be recognized by the human eye in the critical area of ​​the tunnel entrance." 6. The solar-based intelligent tunnel dimming method according to claim 2, characterized in that: The step S40, The following steps are involved: S41, the current regulating unit increases the lamp current through the power management system according to the actual brightness of the light in the tunnel to increase the light brightness, so that the light brightness reaches a predetermined brightness, and transmits it to the brightness acquisition unit; S42, the brightness collection unit detects the actual light brightness in front of the target object through the intelligent sensor set in the tunnel to ensure the accuracy of the light brightness and transmits it to the processing center; S43. The processing center compares the actual light brightness in front of the target object with the tunnel light brightness standard stored in the memory: if it meets the standard, the power supply continues to maintain the brightness; if it does not meet the standard, the alarm is transmitted to the alarm and notified to continue adjusting the brightness until the standard brightness is reached. S44. The time control unit obtains information through the intelligent sensors installed on both sides of the tunnel. If the vehicle is within 50 meters behind or leaves the tunnel exit, the lights will automatically dim or turn off to save energy.

7. The solar-based tunnel intelligent dimming method according to claims 1-6, characterized in that: The system also includes a computer-readable storage medium containing a memory; the memory stores a computer program, and when the functional modules execute the computer program, the steps of the solar-based tunnel intelligent dimming method described in any one of claims 1 to 6 are implemented; the computer-readable storage medium stores a computer program, and when the computer program is executed by the functional modules, the steps of the solar-based tunnel intelligent dimming method described in any one of claims 1 to 6 are implemented.

8. The solar-based intelligent tunnel dimming method according to claims 1-6, characterized in that: The system also includes a solar-powered intelligent tunnel dimming control device, which is implemented by the solar-powered intelligent tunnel dimming method described in any one of claims 1 to 6 above.

Images