Tunnel safety management and hidden danger analysis system

Abstract

The invention relates to the technical field of tunnel hidden danger management, in particular to a tunnel safety management and hidden danger analysis system. Comprising a brightness identification module which is used for obtaining the brightness of a tunnel entrance section, a tunnel exit section and the outside of a tunnel collected by sensing equipment, and respectively calculating the brightness difference of the entrance section and the brightness difference of the exit section; the identification road section module is used for generating identification road sections according to the tunnel length, the identification road sections comprise a first identification road section and a second identification road section, and the first identification road section is a road section close to a tunnel entrance outside the tunnel, the second identification road section and a road section close to a tunnel exit inside the tunnel; the vehicle acquisition module is used for acquiring vehicle data on the identified road section; and the early warning analysis module is used for judging an accident potential risk according to the vehicle data on the identified road section, the tunnel length and the brightness difference of the entrance section or the brightness difference of the exit section, and carrying out early warning on the vehicle on the identified road section when the accident potential risk is higher than a preset threshold value.

Description

Technical Field

[0001] The present invention relates to the technical field of tunnel hidden danger management, and specifically to a tunnel safety management and hidden danger analysis system. Background Art

[0002] Due to the special traffic environment, tunnels are frequent sections of road traffic accidents, and rear-end collisions and single-vehicle collisions with tunnel walls are prone to occur. Through statistical analysis, from the perspective of the spatial distribution of highway tunnel traffic accidents, accident forms, vehicle types and influencing factors, my country's highway tunnel traffic data has the following characteristics: 1. The probability of traffic accidents increases with the length of the highway tunnel. 2. Affected by the "white hole effect" and "black hole effect", the probability of traffic accidents near the entrance of highway tunnels is the highest. 3. In terms of traffic accident forms, motor vehicle rear-end collisions account for more than 60%, and single-vehicle collisions account for more than 20%.

[0003] The "black hole effect" refers to the situation when a car enters a long tunnel entrance. The brightness of the sky, open road surface, nearby buildings, etc. in the driver's field of vision is higher than the brightness of the tunnel entrance. Under the effect of the induction phenomenon, although the tunnel entrance is actually quite bright, the driver still feels that the tunnel entrance is darker, so he cannot recognize the situation near the tunnel entrance and find obstacles. At night, when the driver exits the bright tunnel and before the road is unlit, the outside of the tunnel will also become a kind of black hole. On the contrary, when a car passes through a long tunnel and approaches the exit, the exit looks like a white hole due to the extremely high external brightness seen through the exit, showing extremely strong glare, which makes the driver feel uncomfortable. Therefore, the main spatial range of tunnel traffic accidents is at the entrance and exit of the tunnel. The white hole effect and the black hole effect are the main reasons for the obstruction of the driver's vision and the occurrence of rear-end collisions.

[0004] At present, the main way to avoid traffic accidents caused by white hole effect and black hole effect is to strengthen the design of tunnel lighting system, especially for the tunnel lighting of the entrance and exit sections, and strive to reduce the difference in illumination inside and outside the tunnel. However, according to the current data statistics, the probability of traffic accidents near the tunnel entrance is still the highest, and it is obviously impossible to curb the occurrence of traffic accidents by lighting alone. Most highway tunnels are built on highways, with high vehicle speeds and heavy traffic, and the consequences of traffic accidents are serious. Therefore, how to reduce the impact of white hole effect and black hole effect on drivers and reduce the occurrence of traffic accidents is an urgent problem to be solved. Summary of the invention

[0005] The technical problem solved by the present invention is to provide a tunnel safety management and hidden danger analysis system, which can reduce the impact of black hole effect and white hole effect on drivers.

[0006] The basic solution provided by the present invention is: a tunnel safety management and hidden danger analysis system, including a server and a sensor device, wherein the server includes a brightness recognition module, a road section recognition module, a vehicle acquisition module and an early warning analysis module; The brightness recognition module is used to obtain the brightness of the tunnel entrance section, tunnel exit section and the outside of the tunnel collected by the sensor equipment, and calculate the brightness difference of the entrance section and the brightness difference of the exit section respectively; An identification section module, used for generating an identification section according to the tunnel length, wherein the identification section includes a first identification section and a second identification section, wherein the first identification section is a section outside the tunnel close to the tunnel entrance, and the second identification section is a section inside the tunnel close to the tunnel exit; A vehicle acquisition module is used to acquire vehicle data on the identified road section; The early warning analysis module determines the risk of potential accidents based on the vehicle data on the identified road section, the tunnel length, and the brightness difference of the entrance section or the brightness difference of the exit section. When the risk of potential accidents is higher than the preset threshold, an early warning is issued to the vehicles on the identified road section.

[0007] The principle and advantage of the present invention are: the brightness of the tunnel entrance section, tunnel exit section and the outside of the tunnel is collected by the sensing equipment, the brightness difference of the tunnel entrance section and the brightness difference of the tunnel exit section are calculated, and the identification section is generated according to the tunnel length, which is used to identify and warn the vehicles in the identification section. The vehicle data on the identification section is collected, and according to the vehicle data on the identification section and the brightness difference of the tunnel entrance section or the tunnel exit section, it is judged whether there is a hidden danger of the "black hole effect" or "white hole effect" causing the risk of an accident when the current vehicle enters or exits the tunnel. If the hidden danger risk is high, the vehicle on the identification section is warned.

[0008] Compared with the existing technology, an identified road section is generated based on tunnel information, and hidden dangers are identified for vehicles in the identified road section. When the hidden danger risk is high, the vehicles on the identified road section are warned, and when the hidden danger risk is low, the vehicles on the identified road section are not warned. Since there is still a certain brightness difference inside and outside the tunnel after the lighting control of the tunnel entrance and exit, the accident hidden danger risk of the vehicle is identified and judged on the road section about to enter or exit the tunnel, and intelligent warning is given according to the size of the hidden danger risk, so that the driver can make a pre-judgment in advance and make corresponding adjustments, thereby reducing the occurrence of accidents.

[0009] Further, the road segment identification module includes a road condition analysis module, an environment analysis module and a road segment generation module; A road condition analysis module is used to obtain road condition data, wherein the road condition data includes tunnel length and road environment, and generate a road condition coefficient according to the road condition data. The longer the tunnel length, the greater the road condition coefficient; the more complex the road environment, the greater the road condition coefficient; An environmental analysis module is used to generate an environmental coefficient according to the brightness difference of the entrance section and the brightness difference of the exit section. The larger the brightness difference of the entrance section or the brightness difference of the exit section, the larger the environmental coefficient; The road segment generation module is used to generate the length of the first identified road segment or the second identified road segment according to the road condition system and the environmental coefficient:

[0010] Wherein, L is the length of the first identified section or the second identified section, is the preset basic length, is the road condition coefficient, is the environmental factor.

[0011] The length of the identified section depends on the tunnel length and tunnel environment. The longer the tunnel itself is, the longer the length of the identified section is. The more complex the road environment of the tunnel entrance and tunnel exit is, the longer the length of the identified section is. The length of the identified section is adjusted by identifying the tunnel length and tunnel environment.

[0012] Further, the early warning analysis module includes a first identification module, a threshold management module and an early warning management module; The first recognition module is used to obtain the vehicle speed on the identified road section, and determine the existence of accident hazard risk when the vehicle speed on the identified road section is higher than a preset vehicle speed threshold; A threshold management module, used to adjust the vehicle speed threshold according to the brightness difference of the entrance section. The greater the brightness difference of the entrance section, the smaller the vehicle speed threshold; The warning management module is used to control the warning device on the first identified road section to issue a warning when it is determined that the vehicle speed is higher than a preset vehicle speed threshold.

[0013] The speed of the vehicle on the identified road section is obtained. The higher the vehicle speed, the greater the hidden danger of accident risk caused by the "black hole effect" or "white hole effect" when entering or exiting the tunnel. When the vehicle speed exceeds the preset speed threshold, the vehicle is warned. At the same time, the speed threshold is adjusted according to the brightness difference of the tunnel entrance section or the brightness difference of the exit section. The greater the brightness difference, the greater the impact of the "black hole effect" or "white hole effect". Therefore, the greater the brightness difference, the greater the speed threshold.

[0014] Further, the early warning analysis module includes a second recognition module and an image recognition module; An image recognition module is used to obtain vehicle images in the first recognition section and the second recognition section, and when a vehicle enters the tunnel entrance section from the first recognition section, obtain the vehicle identification and record the vehicle entry time; A second identification module is used to obtain the entry time of the vehicle according to the vehicle identification when the vehicle enters the second identification section, and obtain the driving time of the vehicle in the tunnel according to the entry time; The threshold management module is also used to adjust the vehicle speed threshold according to the driving time of the vehicle in the tunnel. The lower the driving time, the higher the vehicle speed threshold.

[0015] When the vehicle enters the second identified road section and is about to exit the tunnel, the driving time of the vehicle in the tunnel is obtained according to the vehicle identification, and the vehicle identification may be a license plate number.

[0016] When the vehicle appears in the second identification section, it is about to exit the tunnel. After the ambient brightness changes, the human eye needs a certain amount of time to adapt to the brightness change. The shorter the time the vehicle travels in the tunnel, the shorter the time to adapt to the brightness in the tunnel, and the closer the brightness range to which the human eye adapts is to the brightness outside the tunnel. Therefore, when the vehicle appears in the second identification section, the speed threshold can be adjusted according to the driving time of the vehicle in the tunnel. If the driving time of the vehicle is short, the speed threshold can be adjusted higher.

[0017] Furthermore, the image recognition module is further used to obtain the vehicle behavior of the vehicle in the first recognized road section and the tunnel entrance section according to the vehicle image, wherein the vehicle behavior includes acceleration and deceleration; The second recognition module is further used to determine the environmental adaptation level of the vehicle driver according to the vehicle behavior in the first recognition section and the tunnel entrance section: When the first identification section is decelerated, and the tunnel entrance section is decelerated, the environmental adaptation level is 1; When the first identification section is accelerated and the tunnel entrance section is decelerated, the environmental adaptation level is 2; When the first identification section is decelerated and the tunnel entrance section is accelerated, the environmental adaptation level is 3; The first identified road section is accelerated, the tunnel entrance section is accelerated, and the environmental adaptation level is 4; The threshold management module has a preset judgment time. When the vehicle's driving time in the tunnel is less than the judgment time, the speed threshold is increased. When the vehicle's driving time in the tunnel is longer than the judgment time, the speed threshold is lowered. The judgment time is adjusted according to the environmental adaptation level. The higher the environmental adaptation level, the longer the judgment time.

[0018] By obtaining the driver's vehicle behavior in the first identification section and the tunnel entrance section, if the driver slows down in the tunnel entrance section and also slows down in the first identification section, it means that the driver has a very poor ability to adapt to light changes. If the driver accelerates in the first identification section and accelerates in the tunnel entrance section, the driver's ability to adapt to light changes is also poor. If the driver slows down in the first identification section and accelerates in the tunnel entrance section, the driver's ability to adapt to the environment is good. If the driver accelerates in the first identification section and accelerates in the tunnel entrance section, the driver's ability to adapt to the environment is excellent. Then, when adjusting the vehicle speed threshold according to the driving time, the judgment is made according to the judgment time. If the driving time is lower than the judgment time, it is more adapted to the ambient brightness outside the tunnel. If the driving time is higher than the judgment time, it is more adapted to the ambient brightness inside the tunnel. At the same time, the judgment time can be adjusted according to the environmental adaptation level. The higher the environmental adaptation level, the stronger the adaptability when changes occur, so the judgment time can be increased.

[0019] Furthermore, the early warning analysis module also includes a behavior recognition module; The behavior recognition module is used to recognize vehicle behavior based on vehicle images and determine whether there is risky behavior based on the vehicle behavior; The early warning management module is also used to issue early warnings when risky behaviors occur.

[0020] Furthermore, the warning method for warning vehicles on the identified road section includes one or more of voice warning, image warning and flash light warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 The present invention is a logic block diagram of a tunnel safety management and hidden danger analysis system embodiment of the present invention. DETAILED DESCRIPTION

[0022] The following is further described in detail through specific implementation methods: The embodiment is basically as shown in the attached Figure 1 As shown: A tunnel safety management and hidden danger analysis system can reduce the impact of black hole effect and white hole effect on drivers.

[0023] The basic solution provided by the present invention is: a tunnel safety management and hidden danger analysis system, including a server and a sensor device, wherein the server includes a brightness recognition module, a road section recognition module, a vehicle acquisition module and an early warning analysis module; The brightness recognition module is used to obtain the brightness of the tunnel entrance section, tunnel exit section and the outside of the tunnel collected by the sensor equipment, and calculate the brightness difference of the entrance section and the brightness difference of the exit section respectively; An identification section module, used for generating an identification section according to the tunnel length, wherein the identification section includes a first identification section and a second identification section, wherein the first identification section is a section outside the tunnel close to the tunnel entrance, and the second identification section is a section inside the tunnel close to the tunnel exit; A vehicle acquisition module is used to acquire vehicle data on the identified road section; The early warning analysis module determines the risk of potential accidents based on the vehicle data on the identified road section, the tunnel length, and the brightness difference of the entrance section or the brightness difference of the exit section. When the risk of potential accidents is higher than the preset threshold, an early warning is issued to the vehicles on the identified road section.

[0024] The brightness of the tunnel entrance section, tunnel exit section and the outside of the tunnel is collected through the sensing equipment, and the brightness difference of the tunnel entrance section and the brightness difference of the tunnel exit section are calculated. The identification section is generated according to the tunnel length, which is used to identify and warn vehicles in the identification section. The vehicle data on the identification section is collected, and based on the vehicle data on the identification section and the brightness difference of the tunnel entrance section or tunnel exit section, it is judged whether the current vehicle has the risk of accidents caused by the "black hole effect" or "white hole effect" when entering or exiting the tunnel. If the risk of hidden danger is high, the vehicle on the identification section will be warned.

[0025] Compared with the existing technology, an identified road section is generated based on tunnel information, and hidden dangers are identified for vehicles in the identified road section. When the hidden danger risk is high, the vehicles on the identified road section are warned, and when the hidden danger risk is low, the vehicles on the identified road section are not warned. Since there is still a certain brightness difference inside and outside the tunnel after the lighting control of the tunnel entrance and exit, the accident hidden danger risk of the vehicle is identified and judged on the road section about to enter or exit the tunnel, and intelligent warning is given according to the size of the hidden danger risk, so that the driver can make a pre-judgment in advance and make corresponding adjustments, thereby reducing the occurrence of accidents.

[0026] The road segment identification module includes a road condition analysis module, an environment analysis module and a road segment generation module; A road condition analysis module is used to obtain road condition data, wherein the road condition data includes tunnel length and road environment, and generate a road condition coefficient according to the road condition data. The longer the tunnel length, the greater the road condition coefficient; the more complex the road environment, the greater the road condition coefficient; An environmental analysis module is used to generate an environmental coefficient according to the brightness difference of the entrance section and the brightness difference of the exit section. The larger the brightness difference of the entrance section or the brightness difference of the exit section, the larger the environmental coefficient; The road segment generation module is used to generate the length of the first identified road segment or the second identified road segment according to the road condition system and the environmental coefficient:

[0027] Wherein, L is the length of the first identified section or the second identified section, is the preset basic length, is the road condition coefficient, is the environmental factor.

[0028] The length of the identified section depends on the tunnel length and tunnel environment. The longer the tunnel itself is, the longer the length of the identified section is. The more complex the road environment of the tunnel entrance and tunnel exit is, the longer the length of the identified section is. The length of the identified section is adjusted by identifying the tunnel length and tunnel environment.

[0029] The early warning analysis module includes a first identification module, a threshold management module and an early warning management module; The first recognition module is used to obtain the vehicle speed on the identified road section, and determine the existence of accident hazard risk when the vehicle speed on the identified road section is higher than a preset vehicle speed threshold; A threshold management module, used to adjust the vehicle speed threshold according to the brightness difference of the entrance section. The greater the brightness difference of the entrance section, the smaller the vehicle speed threshold; The warning management module is used to control the warning device on the first identified road section to issue a warning when it is determined that the vehicle speed is higher than a preset vehicle speed threshold.

[0030] The speed of the vehicle on the identified road section is obtained. The higher the vehicle speed, the greater the hidden danger of accident risk caused by the "black hole effect" or "white hole effect" when entering or exiting the tunnel. When the vehicle speed exceeds the preset speed threshold, the vehicle is warned. At the same time, the speed threshold is adjusted according to the brightness difference of the tunnel entrance section or the brightness difference of the exit section. The greater the brightness difference, the greater the impact of the "black hole effect" or "white hole effect". Therefore, the greater the brightness difference, the greater the speed threshold.

[0031] The early warning analysis module includes a second recognition module and an image recognition module; An image recognition module is used to obtain vehicle images in the first recognition section and the second recognition section, and when a vehicle enters the tunnel entrance section from the first recognition section, obtain the vehicle identification and record the vehicle entry time; A second identification module is used to obtain the entry time of the vehicle according to the vehicle identification when the vehicle enters the second identification section, and obtain the driving time of the vehicle in the tunnel according to the entry time; The threshold management module is also used to adjust the vehicle speed threshold according to the driving time of the vehicle in the tunnel. The lower the driving time, the higher the vehicle speed threshold.

[0032] When the vehicle enters the second identified road section and is about to exit the tunnel, the driving time of the vehicle in the tunnel is obtained according to the vehicle identification, and the vehicle identification may be a license plate number.

[0033] When the vehicle appears in the second identification section, it is about to exit the tunnel. After the ambient brightness changes, the human eye needs a certain amount of time to adapt to the brightness change. The shorter the time the vehicle travels in the tunnel, the shorter the time to adapt to the brightness in the tunnel, and the closer the brightness range to which the human eye adapts is to the brightness outside the tunnel. Therefore, when the vehicle appears in the second identification section, the speed threshold can be adjusted according to the driving time of the vehicle in the tunnel. If the driving time of the vehicle is short, the speed threshold can be adjusted higher.

[0034] The image recognition module is further used to obtain the vehicle behavior of the vehicle in the first recognized road section and the tunnel entrance section according to the vehicle image, wherein the vehicle behavior includes acceleration and deceleration; The second recognition module is further used to determine the environmental adaptation level of the vehicle driver according to the vehicle behavior in the first recognition section and the tunnel entrance section: When the first identification section is decelerated, and the tunnel entrance section is decelerated, the environmental adaptation level is 1; When the first identification section is accelerated and the tunnel entrance section is decelerated, the environmental adaptation level is 2; When the first identification section is decelerated and the tunnel entrance section is accelerated, the environmental adaptation level is 3; The first identified road section is accelerated, the tunnel entrance section is accelerated, and the environmental adaptation level is 4; The threshold management module has a preset judgment time. When the vehicle's driving time in the tunnel is less than the judgment time, the speed threshold is increased. When the vehicle's driving time in the tunnel is longer than the judgment time, the speed threshold is lowered. The judgment time is adjusted according to the environmental adaptation level. The higher the environmental adaptation level, the longer the judgment time.

[0035] By obtaining the driver's vehicle behavior in the first identification section and the tunnel entrance section, if the driver slows down in the tunnel entrance section and also slows down in the first identification section, it means that the driver has a very poor ability to adapt to light changes. If the driver accelerates in the first identification section and accelerates in the tunnel entrance section, the driver's ability to adapt to light changes is also poor. If the driver slows down in the first identification section and accelerates in the tunnel entrance section, the driver's ability to adapt to the environment is good. If the driver accelerates in the first identification section and accelerates in the tunnel entrance section, the driver's ability to adapt to the environment is excellent. Then, when adjusting the vehicle speed threshold according to the driving time, the judgment is made according to the judgment time. If the driving time is lower than the judgment time, it is more adapted to the ambient brightness outside the tunnel. If the driving time is higher than the judgment time, it is more adapted to the ambient brightness inside the tunnel. At the same time, the judgment time can be adjusted according to the environmental adaptation level. The higher the environmental adaptation level, the stronger the adaptability when changes occur, so the judgment time can be increased.

[0036] The early warning analysis module also includes a behavior recognition module; The behavior recognition module is used to recognize vehicle behavior based on vehicle images and determine whether there is risky behavior based on the vehicle behavior; The early warning management module is also used to issue early warnings when risky behaviors occur.

[0037] The warning method for warning vehicles on the identified road section includes one or more of voice warning, image warning and flash light warning.

[0038] The above are only embodiments of the present invention. Common knowledge such as the known specific structures and characteristics in the scheme is not described in detail here. Ordinary technicians in the relevant field know all the common technical knowledge in the technical field to which the invention belongs before the application date or priority date, can obtain all the existing technologies in the field, and have the ability to apply conventional experimental means before that date. Ordinary technicians in the relevant field can improve and implement this scheme in combination with their own abilities under the enlightenment given by this application. Some typical known structures or known methods should not become obstacles for ordinary technicians in the relevant field to implement this application. It should be pointed out that for those skilled in the art, without departing from the structure of the present invention, several deformations and improvements can be made, which should also be regarded as the scope of protection of the present invention, and these will not affect the effect of the implementation of the present invention and the practicality of the patent. The scope of protection required by this application shall be based on the content of its claims, and the specific implementation methods and other records in the specification can be used to interpret the content of the claims.

Claims

1. A tunnel safety management and hidden danger analysis system, characterized in that: It includes a server and a sensor device, wherein the server includes a brightness recognition module, a road section recognition module, a vehicle acquisition module and a warning analysis module; The brightness recognition module is used to obtain the brightness of the tunnel entrance section, tunnel exit section and the outside of the tunnel collected by the sensor equipment, and calculate the brightness difference of the entrance section and the brightness difference of the exit section respectively; An identification section module, used for generating an identification section according to the tunnel length, wherein the identification section includes a first identification section and a second identification section, wherein the first identification section is a section outside the tunnel close to the tunnel entrance, and the second identification section is a section inside the tunnel close to the tunnel exit; A vehicle acquisition module is used to acquire vehicle data on the identified road section; The early warning analysis module determines the risk of potential accidents based on the vehicle data on the identified road section, the tunnel length, and the brightness difference of the entrance section or the brightness difference of the exit section. When the risk of potential accidents is higher than the preset threshold, the vehicle on the identified road section is warned; The early warning analysis module includes a first identification module, a threshold management module and an early warning management module; The first recognition module is used to obtain the vehicle speed on the identified road section, and determine the existence of accident hazard risk when the vehicle speed on the identified road section is higher than a preset vehicle speed threshold; A threshold management module, used to adjust the vehicle speed threshold according to the brightness difference of the entrance section. The greater the brightness difference of the entrance section, the smaller the vehicle speed threshold; The warning management module is used to control the warning device on the first identified road section to issue a warning when it is determined that the vehicle speed is higher than a preset vehicle speed threshold; The early warning analysis module includes a second recognition module and an image recognition module; An image recognition module is used to obtain vehicle images in the first recognition section and the second recognition section, and when a vehicle enters the tunnel entrance section from the first recognition section, obtain the vehicle identification and record the vehicle entry time; A second identification module is used to obtain the entry time of the vehicle according to the vehicle identification when the vehicle enters the second identification section, and obtain the driving time of the vehicle in the tunnel according to the entry time; The threshold management module is also used to adjust the vehicle speed threshold according to the driving time of the vehicle in the tunnel. The lower the driving time, the higher the vehicle speed threshold; The image recognition module is further used to obtain the vehicle behavior of the vehicle in the first recognized road section and the tunnel entrance section according to the vehicle image, wherein the vehicle behavior includes acceleration and deceleration; The second recognition module is further used to determine the environmental adaptation level of the vehicle driver according to the vehicle behavior in the first recognition section and the tunnel entrance section: When the first identification section is decelerated, and the tunnel entrance section is decelerated, the environmental adaptation level is 1; When the first identification section is accelerated and the tunnel entrance section is decelerated, the environmental adaptation level is 2; When the first identification section is decelerated and the tunnel entrance section is accelerated, the environmental adaptation level is 3; The first identified road section is accelerated, the tunnel entrance section is accelerated, and the environmental adaptation level is 4; The threshold management module has a preset judgment time. When the vehicle's driving time in the tunnel is less than the judgment time, the speed threshold is increased. When the vehicle's driving time in the tunnel is longer than the judgment time, the speed threshold is lowered. The judgment time is adjusted according to the environmental adaptation level. The higher the environmental adaptation level, the longer the judgment time.

2. A tunnel safety management and hidden danger analysis system according to claim 1, characterized in that: The road segment identification module includes a road condition analysis module, an environment analysis module and a road segment generation module; A road condition analysis module is used to obtain road condition data, wherein the road condition data includes tunnel length and road environment, and generate a road condition coefficient according to the road condition data. The longer the tunnel length, the greater the road condition coefficient; the more complex the road environment, the greater the road condition coefficient; An environmental analysis module is used to generate an environmental coefficient according to the brightness difference of the entrance section and the brightness difference of the exit section. The larger the brightness difference of the entrance section or the brightness difference of the exit section, the larger the environmental coefficient; The road segment generation module is used to generate the length of the first identified road segment or the second identified road segment according to the road condition system and the environmental coefficient: Wherein, L is the length of the first identified section or the second identified section, is the preset basic length, is the road condition coefficient, is the environmental factor.

3. A tunnel safety management and hidden danger analysis system according to claim 1, characterized in that: The early warning analysis module also includes a behavior recognition module; The behavior recognition module is used to recognize vehicle behavior based on vehicle images and determine whether there is risky behavior based on the vehicle behavior; The early warning management module is also used to issue early warnings when risky behaviors occur.

4. A tunnel safety management and hidden danger analysis system according to claim 1, characterized in that: The warning method for warning vehicles on the identified road section includes one or more of voice warning, image warning and flash light warning.

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