Small-clear-distance tunnel-intercommunication safety warning device

Abstract

The invention relates to the technical field of tunnel management, in particular to a small-clear-distance'tunnel-intercommunication 'safety warning device which comprises a plurality of indicating boards, a plurality of indicating lamps and a plurality of illuminating lamps which are installed in a tunnel and further comprises a safety warning system. The safety warning system comprises a data acquisition module, a data analysis module, a vehicle guiding module, a tunnel supervision module and an early warning feedback module. The data acquisition module is used for acquiring vehicle data and environment data in small-clear-distance tunnel-intercommunication and transmitting the vehicle data and the environment data to the data analysis module; the vehicle data comprises vehicle speed, vehicle flow and vehicle real-time position, and the environment data comprises water level height, air temperature and lumen inside and outside the tunnel; the method is clear in logic and complete in design, and can effectively improve the traffic safety of a small-clear-distance tunnel-intercommunication project.

Description

Technical Field

[0001] This invention relates to the field of tunnel management technology, specifically to a safety warning device for "tunnel-interchange" with small clearance. Background Technology

[0002] Due to factors such as topography, geological conditions, and construction costs, the available locations for tunnel exits and interchanges on mountainous expressways are greatly limited. This inevitably leads to a series of engineering examples where the clearance between the tunnel exit and the preceding interchange (mainline exit) is too small. Tunnel-interchange sections where the distance from the tunnel exit to the starting point of the transition section of the preceding interchange is less than the recommended value (1000m) in the "Highway Route Design Specifications" and "Highway Grade Separation Design Rules" are called "small clearance tunnel-interchange" sections.

[0003] In existing technologies, safety warnings for drivers in such tunnels mainly rely on signs and indicator lights to remind them of speed limits, tunnel length, and driving direction, thereby reducing the risk of traffic accidents in tunnels and improving driving safety. However, this method relies solely on drivers observing signs and indicator lights, lacking active control over vehicles, resulting in a higher risk of traffic accidents and a low safety factor in "tunnel-interchange" sections with small clearances.

[0004] In summary, addressing the issue that existing safety warning technologies rely solely on drivers observing signs and indicator lights, lacking proactive vehicle control and resulting in low safety levels in tunnels, has become a pressing problem in this field. Therefore, it is necessary to propose a more proactive vehicle control system for "tunnel-interchange" safety warning devices with shorter clearances. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a safety warning device for tunnel-interchange connections with small clearances. Through the design of the safety warning system, it can provide diverse safety alerts to vehicles in the tunnel based on their speed, traffic flow, real-time location, water level, air temperature, and luminous flux inside and outside the tunnel. This proactively controls vehicle movement and improves the safety of vehicles traveling within the tunnel.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows: a safety warning device for a small clearance tunnel-interchange, comprising several signs, indicator lights and lighting installed in the tunnel, and a safety warning system, which includes a data acquisition module, a data analysis module, a vehicle guidance module, a tunnel monitoring module and an early warning feedback module.

[0007] The data acquisition module is used to collect vehicle data and environmental data inside and outside the tunnel and transmit them to the data analysis module. The vehicle data includes vehicle speed, traffic flow and real-time vehicle location, and the environmental data includes water level, air temperature and luminous flux inside and outside the tunnel.

[0008] The data analysis module is used to assess the tunnel's traffic flow based on the traffic volume, adjust the tunnel's speed limit accordingly, generate speed limit prompts, and transmit them to the vehicle guidance module. Traffic flow is directly proportional to the tunnel's speed limit. It also analyzes the real-time location of vehicles within the tunnel to determine their distribution, generates driving suggestions based on this distribution, and transmits them to the vehicle guidance module. Finally, it compares vehicle speeds with the current tunnel speed limit; if a vehicle's speed exceeds the tunnel's speed limit, the data analysis module generates speed adjustment suggestions and transmits them to the vehicle guidance module.

[0009] The vehicle guidance module is used to receive speed limit reminders, vehicle driving suggestions, and speed adjustment suggestions transmitted by the data analysis module. The vehicle guidance module is also used to interface with the vehicle's voice system and use the vehicle's voice system to broadcast the speed limit reminders, vehicle driving suggestions, and speed adjustment suggestions by voice.

[0010] The tunnel monitoring module is used to monitor real-time images, crown settlement, horizontal convergence, and mid-wall displacement data of the tunnel. It is used to construct a tunnel prediction model using deep learning algorithms. The tunnel prediction model is trained based on crown settlement, horizontal convergence, and mid-wall displacement data from several tunnels, as well as tunnel collapse images. The model divides the tunnel into several 1x1m areas and calculates the stress concentration coefficient of each area based on the current crown settlement, horizontal convergence, and mid-wall displacement data. It also constructs a 3D image of the tunnel based on real-time images, rendering each area in the 3D image with different color depths based on the stress concentration coefficient; the color depth is proportional to the stress concentration coefficient. Furthermore, the tunnel prediction model generates a tunnel collapse simulation image using the 3D image of the tunnel based on the stress concentration coefficient of each area and transmits it to the early warning feedback module.

[0011] The formula for calculating the stress concentration factor is as follows: K t =σ max / σ nom (1).

[0012] Among them, K t The stress concentration factor is... s max This represents the maximum stress within the current region. s nom This represents the average stress of the entire tunnel.

[0013] The formula for calculating the stress value of the arch is as follows: σv=Δh / H (2).

[0014] in, sv This represents the stress value at the crown of the arch. H For tunnel burial depth, D h represents the settlement of the arch.

[0015] The formula for calculating the stress value generated by horizontal convergence is as follows: σh=Δd / R (3).

[0016] in, s h The stress value generated by horizontal convergence. R The radius of the tunnel excavation. Δd This is the horizontal convergence value.

[0017] The formula for calculating the stress value caused by the displacement of the rock wall is as follows: σγ =Δw / L (4).

[0018] in, s c This represents the stress value caused by the displacement of the middle rock wall. L This represents the longitudinal length of the middle dike. Δw This refers to the displacement of the middle rock wall.

[0019] The early warning feedback module is used to set the average stress threshold and stress concentration factor threshold for the tunnel. When the average stress of the tunnel exceeds the average stress threshold, the early warning feedback module provides overall inspection prompts and tunnel passage prompts for the tunnel, transmitting the overall inspection prompts to the remote control terminal of the tunnel management personnel and providing feedback on the tunnel passage prompts to the vehicle guidance module. When the stress concentration factor in any area within the tunnel exceeds the stress concentration factor threshold, the early warning feedback module generates area inspection prompts and area passage prompts for that area, transmitting the area inspection prompts to the remote control terminal of the tunnel management personnel and providing feedback on the area passage prompts to the vehicle guidance module. The vehicle guidance module is used to broadcast the tunnel passage prompts and area passage prompts via the vehicle's voice system.

[0020] Furthermore, when the maximum stress in a certain area of ​​the tunnel exceeds the regional stress threshold, the tunnel prediction model will determine that there is a risk of collapse in that area. The tunnel prediction model records the difference between the maximum stress value in the area and the regional stress threshold as the collapse coefficient, and the collapse risk is directly proportional to the collapse coefficient.

[0021] Furthermore, several light strips are installed inside the tunnel. The data analysis module is also used to set traffic flow thresholds. When the traffic flow is below the threshold, the light strips are green; when the traffic flow exceeds the threshold, the light strips are red. The data analysis module is used to adjust the speed limit based on the ratio of traffic flow to the traffic flow threshold.

[0022] Furthermore, the data analysis module is also used to adjust the lumen output of the lighting fixtures based on the lumen output inside and outside the tunnel; the lumen output of the lighting fixtures increases sequentially from the tunnel entrance to the tunnel exit, and the lumen output of the lighting fixtures at the tunnel exit is equal to the lumen output outside the tunnel.

[0023] Furthermore, the data analysis module is also used to calculate the distance between the vehicle and the next sign based on the vehicle's real-time location, and generate the indicator distance; calculate the distance between the vehicle and the tunnel exit and ramp exit, and generate the exit distance; and transmit the indicator distance and exit distance to the vehicle guidance module, which uses the vehicle's voice system to announce the indicator distance and exit distance.

[0024] Furthermore, the data analysis module is also used to calculate the distance between adjacent vehicles based on the real-time location of the vehicles, and to determine whether there is a risk of collision between the two vehicles based on the distance between the adjacent vehicles and their speeds. If the distance between adjacent vehicles decreases and the speed of the following vehicle does not decrease, the data analysis module determines that there is a risk of collision and immediately sends a deceleration warning to the following vehicle through the vehicle guidance module.

[0025] Furthermore, a display screen is installed at the tunnel entrance to show the current speed limit, traffic flow, air temperature, water level, and total tunnel length.

[0026] Furthermore, the data analysis module is also used to set water level thresholds. When the water level in the tunnel exceeds the threshold, the data analysis module generates a no-passage warning and displays the warning on the screen.

[0027] Furthermore, the data analysis module is also used to determine, based on real-time images inside the tunnel, whether the tunnel is under maintenance or whether a car accident has occurred inside the tunnel. If the tunnel is under maintenance or a car accident has occurred inside the tunnel, the data analysis module will also generate a no-passage warning.

[0028] Furthermore, the tunnel prediction model is also used to generate tunnel collapse simulation images using the tunnel's 3D images based on the collapse risk of each area of ​​the tunnel, and transmit them to the early warning feedback module; the early warning feedback module is used to transmit the tunnel collapse simulation images to the remote control terminal of the tunnel management personnel.

[0029] The above approach has the following beneficial effects: 1. The environment of "tunnel-interchange" with small clearance is complex and traffic flow varies greatly. In the existing technology, the speed limit of "tunnel-interchange" with small clearance is fixed, which is difficult to adapt to different traffic flow and is prone to congestion and even traffic accidents. Unlike the fixed speed limit in the tunnel in the existing technology, the present invention automatically adjusts the speed limit in the tunnel according to the traffic flow. When the traffic flow is large, the data analysis module will judge that the tunnel is not smooth, and the speed limit in the tunnel will be smaller. Conversely, the smoother the tunnel is, the larger the speed limit will be. This avoids congestion or accidents caused by excessive traffic flow and improves the tunnel's traffic efficiency.

[0030] 2. This invention can calculate vehicle distance and speed in real time. Based on changes in the distance between adjacent vehicles and changes in vehicle speed, it proactively sends deceleration warnings to vehicles with a higher risk of collision, reducing the occurrence of rear-end collisions and effectively improving vehicle driving safety. By interfacing with the vehicle's voice system, it automatically broadcasts the current speed limit, driving suggestions, indicated distances, and exit distance information in the tunnel, preventing drivers from making misjudgments due to distraction or lack of information, thus avoiding violations of traffic regulations or incorrect driving routes, and improving the legality and correctness of vehicle driving.

[0031] 3. This invention, through the design of a tunnel prediction module, can monitor real-time images of the tunnel, crown settlement, horizontal convergence, and mid-wall displacement data, thereby constructing a three-dimensional image of the tunnel, calculating the stress concentration factor of the tunnel, and rendering various regions in the three-dimensional image using colors of different depths. This allows tunnel managers to intuitively understand the stress distribution of the tunnel. At the same time, the tunnel prediction model also generates tunnel collapse simulation images, enabling tunnel managers to maintain the tunnel based on the tunnel collapse simulation images, significantly reducing the risk of tunnel collapse and improving the stability and safety of the tunnel.

[0032] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the safety warning system in the small clearance "tunnel-interchange" safety warning device of the present invention.

[0034] Figure 2 This is a functional diagram of the data acquisition module in the small clearance "tunnel-interchange" safety warning device of the present invention.

[0035] Figure 3 This is a functional diagram of the data analysis module in the small clearance "tunnel-interchange" safety warning device of the present invention.

[0036] Figure 4This is a functional diagram of the tunnel monitoring module in the small clearance "tunnel-interchange" safety warning device of the present invention. Detailed Implementation

[0037] The following detailed description illustrates the specific implementation method: Implementation, for example, attached Figure 1 , Figure 2 , Figure 3 and Figure 4 As shown: A safety warning device for a small clearance tunnel-interchange includes several signs, indicator lights and lighting installed in the tunnel, as well as a safety warning system.

[0038] The safety warning system comprises a data acquisition module, a data analysis module, a vehicle guidance module, a tunnel monitoring module, and an early warning feedback module, all of which are interconnected by signals. The data acquisition module primarily collects vehicle and environmental data; the data analysis module performs analysis and calculations based on this data; the vehicle guidance module provides voice prompts to drivers; the tunnel monitoring module monitors various tunnel characteristics and assesses tunnel safety; and the early warning feedback module provides different feedback and warnings to drivers and tunnel management personnel based on tunnel safety requirements.

[0039] The specific functions of each module are as follows: The data acquisition module collects vehicle and environmental data inside and outside the tunnel and transmits it to the data analysis module. Vehicle data includes vehicle speed, traffic flow, and real-time vehicle location, while environmental data includes water level, air temperature, and luminous intensity inside and outside the tunnel. The data acquisition module primarily uses speedometers and cameras to monitor and collect vehicle speed, traffic flow, and real-time vehicle location. It also utilizes level gauges, temperature sensors, and light sensors to monitor and collect water level, air temperature, and luminous intensity inside and outside the tunnel.

[0040] The data analysis module is used to assess the tunnel's traffic flow level based on the traffic volume (the fewer vehicles in the tunnel, the higher the traffic flow level). It adjusts the tunnel's speed limit based on the traffic flow level, generates speed limit prompts, and transmits them to the vehicle guidance module. Traffic flow level is directly proportional to the tunnel's speed limit. It also analyzes the distribution of vehicles within the tunnel based on their real-time locations, generates driving suggestions based on this distribution, and transmits them to the vehicle guidance module. Finally, it compares vehicle speeds with the current tunnel speed limit. If a vehicle's speed exceeds the tunnel speed limit, the data analysis module generates speed adjustment suggestions and transmits them to the vehicle guidance module.

[0041] Several light strips are installed inside the tunnel. The data analysis module is also used to set traffic flow thresholds. When the traffic flow is below the threshold, the light strips are green; when the traffic flow exceeds the threshold, the light strips are red.

[0042] Specifically, taking speed limit prompts as an example, if the traffic flow threshold for tunnel A is set to 1000 vehicles / hour, the speed limit range is 70-80 km / h when the traffic flow is 1000 vehicles / hour. Currently, the traffic flow is only 492 vehicles / hour, at which point the speed limit indicator will turn green, indicating that tunnel A is currently experiencing high traffic congestion. Simultaneously, the data analysis module will adjust the speed limit based on the ratio of traffic flow to the traffic flow threshold. If the ratio is approximately 0.5, the speed limit range for tunnel A can be increased by 50%, resulting in a range of 105-120 km / h. The data analysis module will then generate a speed limit prompt: "Current road traffic congestion is high; speed limit range is 105-120 km / h." The data analysis module contains the minimum and maximum speed limits stipulated by traffic regulations, and will not exceed these limits when adjusting the speed limit range.

[0043] Taking vehicle driving suggestions as an example, if Tunnel B is a two-lane tunnel, and currently there are 80 vehicles traveling in the left lane of Tunnel B, with vehicles concentrated in the middle of the left lane; and only 50 vehicles traveling in the right lane, with vehicles evenly distributed; the data analysis module will compare the left and right lanes of Tunnel B and analyze that there are more vehicles in the left lane and congestion in the middle, while there are fewer vehicles in the right lane and it is smooth throughout; the data analysis module will generate vehicle driving suggestions based on the analysis results: it is recommended to travel in the right lane, which has fewer vehicles and is smooth throughout.

[0044] Taking speed adjustment suggestions as an example, if the current speed limit in tunnel C is 80-100 km / h, and a vehicle's current speed is 120 km / h, the data analysis module will determine whether the vehicle's current speed exceeds the speed limit. If the judgment is that the vehicle's current speed exceeds the maximum speed limit by 20%, the data analysis module will generate a speed adjustment suggestion: Current speed 120 km / h, speeding by 20%, please reduce to 80-100 km / h.

[0045] The vehicle guidance module receives speed limit reminders, driving suggestions, and speed adjustment suggestions from the data analysis module. The vehicle guidance module also interfaces with the vehicle's voice system to broadcast the speed limit reminders, driving suggestions, and speed adjustment suggestions via voice.

[0046] Existing technology displays speed limits via signs, but drivers may fail to recognize these signs due to excessive speed or lack of focus, leading to information gaps and speeding. Vehicle guidance modules, by utilizing the vehicle's voice system to provide clearer voice prompts, can better remind drivers and prevent them from missing speed limits in tunnels due to excessive speed or lack of focus, thus improving vehicle safety.

[0047] The tunnel monitoring module is used to monitor real-time images, crown settlement, horizontal convergence, and mid-wall displacement data of the tunnel. The tunnel monitoring module mainly uses fiber optic strain gauges to monitor and collect crown settlement and horizontal convergence of the tunnel; it uses array-type displacement gauges and borehole inclinometers to monitor and collect mid-wall displacement; and it collects real-time images of the tunnel through cameras.

[0048] Specifically, taking Tunnel A as an example, operators can install fiber optic strain gauges (accuracy ±1με) circumferentially along Tunnel A at 5m intervals, focusing on monitoring the junction of the arch crown, arch waist, and invert. The impact echo method is used to periodically check the concrete thickness and voids. At the same time, an array of displacement gauges is installed vertically on the middle rock wall at 15m intervals, combined with borehole inclinometers to capture deep rock mass displacement.

[0049] The tunnel monitoring module is used to construct a tunnel prediction model using deep learning algorithms. This model is trained based on data from several tunnels, including crown settlement, horizontal convergence, and mid-wall displacement, as well as tunnel collapse images. The model divides the tunnel into several 1x1m areas (adjustable according to actual needs) and calculates the stress concentration coefficient for each area based on current crown settlement, horizontal convergence, and mid-wall displacement data. It also constructs a 3D image of the tunnel based on real-time images and renders each area using different depths of color based on the stress concentration coefficient; color depth is proportional to the stress concentration coefficient. Furthermore, the model generates a tunnel collapse simulation image from the 3D image based on the stress concentration coefficients of each area and transmits it to the early warning feedback module.

[0050] The formula for calculating the stress concentration factor is as follows: K t =σ max / σ nom (1).

[0051] Among them, K t σ is the stress concentration factor. max σ is the maximum stress in the current region. nom This represents the average stress of the entire tunnel.

[0052] σv=Δh / H (2).

[0053] in, sv This represents the stress value at the crown of the arch. H For tunnel burial depth, D h represents the settlement of the arch.

[0054] The formula for calculating the stress value generated by horizontal convergence is as follows: σh=Δd / R (3).

[0055] in, s h The stress value generated by horizontal convergence. R The radius of the tunnel excavation. Δd This is the horizontal convergence value.

[0056] The formula for calculating the stress value caused by the displacement of the rock wall is as follows: σγ =Δw / L (4).

[0057] in, s c This represents the stress value caused by the displacement of the middle rock wall. L This represents the longitudinal length of the middle dike. Δw This refers to the displacement of the middle rock wall.

[0058] Specifically, when the stress concentration factor of a certain area is greater than 1, that area will appear red in the 3D graphic, and the larger the stress concentration factor, the darker the color. When the stress concentration factor of a certain area is equal to 1, that area will appear yellow in the 3D graphic. When the stress concentration factor of a certain area is less than 1, that area will appear blue in the 3D graphic, and the smaller the stress concentration factor, the lighter the color. Tunnel managers only need to judge the stress distribution of each area of ​​the tunnel based on the color of each area in the 3D graphic, and carry out inspection and maintenance on the red areas.

[0059] The early warning feedback module is used to set the average stress threshold and stress concentration factor threshold for the tunnel. When the average stress of the tunnel exceeds the average stress threshold, the early warning feedback module provides overall inspection prompts and tunnel passage prompts for the tunnel, and transmits the overall inspection prompts to the remote control terminal of the tunnel management personnel, and feeds back the tunnel passage prompts to the vehicle guidance module. When the stress concentration factor in any area within the tunnel exceeds the stress concentration factor threshold, the early warning feedback module generates area inspection prompts and area passage prompts for that area, transmits the area inspection prompts to the remote control terminal of the tunnel management personnel, and feeds back the area passage prompts to the vehicle guidance module. The vehicle guidance module is used to broadcast the tunnel passage prompts and area passage prompts via the vehicle's voice system.

[0060] Specifically, assuming the average stress threshold of tunnel A is 15 MPa and the stress concentration factor threshold is 1.38, the current average stress of the tunnel is 10 MPa, and the maximum stress in area X is 1.4 MPa; the early warning feedback module will analyze and calculate that the current average stress of tunnel A (10 MPa) is less than the average stress threshold of 15 MPa, and the stress concentration factor in area X is 1.4, exceeding the stress concentration factor threshold of tunnel A. Therefore, the early warning feedback module will generate a regional inspection prompt for this area: the stress concentration factor in area X is higher than the stress concentration factor threshold, and it is recommended to inspect area X. Simultaneously, the early warning feedback module will generate a regional passage prompt for this area: there is an abnormal situation in the area 100m ahead; please proceed with caution.

[0061] When the maximum stress in a certain area within the tunnel exceeds the area's stress threshold, the tunnel prediction model determines that the area has a risk of collapse. The model records the difference between the maximum stress value and the area's stress threshold as the collapse coefficient, and the collapse risk is directly proportional to the collapse coefficient. The tunnel prediction model is also used to generate tunnel collapse simulation images from the tunnel's 3D images based on the collapse risk of each area and transmit these images to the early warning feedback module. The early warning feedback module then transmits the tunnel collapse simulation images to the remote control terminal of the tunnel management personnel.

[0062] Specifically, assuming the stress threshold of region Y is 12.5 MPa and the maximum stress within region Y is 15 MPa, the collapse coefficient in this region is (15-12.5) / 12.5 = 0.2. When the collapse coefficient is greater than 0, the tunnel prediction model will generate a simulated image of tunnel collapse in this region. The higher the collapse coefficient, the higher the probability of collapse. By simulating collapse, tunnel managers can intuitively understand the situation during tunnel collapse and thus prepare emergency plans. At the same time, by calculating the collapse coefficient, tunnel managers can data-drivenly determine which locations have a high risk of collapse and maintain these areas in advance, significantly reducing the risk of collapse.

[0063] The data analysis module is also used to adjust the lumen output of the lighting fixtures based on the lumen output inside and outside the tunnel. The lumen output of the lighting fixtures increases sequentially from the tunnel entrance to the tunnel exit, and the lumen output of the lighting fixtures at the tunnel exit is equal to the lumen output outside the tunnel.

[0064] Specifically, assuming the minimum lumen output inside the tunnel is 500 lux and the lumen output outside the tunnel exit is 5000 lux, the lighting can be increased sequentially from 500 lux at the tunnel entrance to the tunnel exit, with the lumen output of the lighting at the tunnel exit adjusted to 5000 lux. In this way, the impact of the black hole effect on drivers is significantly reduced, the driver's light adaptation time is shortened, and driving safety is improved.

[0065] The data analysis module is also used to calculate the distance between the vehicle and the next sign based on the vehicle's real-time location, and generate the indicator distance; calculate the distance between the vehicle and the tunnel exit and ramp exit, and generate the exit distance; and transmit the indicator distance and exit distance to the vehicle guidance module, which uses the vehicle's voice system to announce the indicator distance and exit distance.

[0066] Specifically, by automatically broadcasting the distance between tunnel exits and ramp exits via voice prompts, drivers can avoid misjudging signs due to distraction, which could lead to traffic violations or incorrect routes, thus improving the legality and accuracy of vehicle driving.

[0067] The data analysis module is also used to calculate the distance between adjacent vehicles based on the real-time location of the vehicles, and to determine whether there is a risk of collision between the two vehicles based on the distance between the adjacent vehicles and their speeds. If the distance between adjacent vehicles decreases and the speed of the following vehicle does not decrease, the data analysis module determines that there is a risk of collision and immediately sends a deceleration warning to the following vehicle through the vehicle guidance module.

[0068] A display screen is installed at the tunnel entrance, showing the current speed limit, traffic flow, air temperature, water level, and total tunnel length. The data analysis module also sets a water level threshold; when the water level exceeds the threshold, the module generates a "no passage" warning and displays it on the screen. Furthermore, the module uses real-time images to determine if the tunnel is under maintenance or if a traffic accident has occurred. If either is present, the module also generates a "no passage" warning.

[0069] Specifically, assuming the water level threshold for tunnel A is 0.48m, and due to heavy rain, the water level inside tunnel A rises to 0.52m, exceeding the threshold, the data analysis module generates a "No Entry" warning and displays it on the screen: "Tunnel A, water level threshold 0.48m, current water level 0.52m, no entry." This design effectively reminds drivers to avoid accidentally entering the tunnel, thus preventing traffic accidents.

[0070] The environment of narrow-clearance tunnels and interchanges is complex, with large fluctuations in traffic flow. Current technologies rely solely on drivers observing signs and indicator lights, lacking proactive control over vehicles. This leads to a high risk of traffic accidents and a low safety factor within these tunnels and interchanges. This invention, through the design of a safety warning system, provides diverse safety alerts to vehicles within the tunnel based on their speed, traffic flow, and real-time location, enabling proactive vehicle control and effectively improving the safety of driving within tunnels.

[0071] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A safety warning device for tunnel-interchange connections with small clearance, comprising several signs, indicator lights, and illumination lights installed inside the tunnel, characterized in that, It also includes a safety warning system, which comprises a data acquisition module, a data analysis module, a vehicle guidance module, a tunnel monitoring module, and an early warning feedback module; The data acquisition module is used to collect vehicle and environmental data inside and outside the tunnel and transmit them to the data analysis module; Vehicle data includes vehicle speed, traffic flow, and real-time vehicle location; environmental data includes water level inside and outside the tunnel, air temperature, and luminous flux. The data analysis module is used to assess the tunnel's traffic flow based on the traffic volume inside the tunnel, adjust the tunnel's speed limit according to the traffic flow, generate speed limit prompts and transmit them to the vehicle guidance module. The traffic flow is directly proportional to the tunnel's speed limit. It is used to analyze the distribution of vehicles in the tunnel based on their real-time location, generate vehicle driving suggestions based on the distribution of vehicles in the tunnel, and transmit them to the vehicle guidance module. This module compares the vehicle's speed with the current tunnel speed limit. If the vehicle's speed exceeds the tunnel speed limit, the data analysis module generates a speed adjustment suggestion and transmits it to the vehicle guidance module. The vehicle guidance module is used to receive speed limit prompts, vehicle driving suggestions, and speed adjustment suggestions transmitted by the data analysis module; The vehicle guidance module is also used to interface with the vehicle's voice system, using the vehicle's voice system to broadcast speed limit reminders, vehicle driving suggestions, and speed adjustment suggestions via voice. The tunnel monitoring module is used to monitor real-time images, crown settlement, horizontal convergence, and mid-wall displacement data of the tunnel. It is used to construct a tunnel prediction model using deep learning algorithms. The tunnel prediction model is trained based on crown settlement, horizontal convergence, and mid-wall displacement data from several tunnels, as well as tunnel collapse images. The tunnel prediction model divides the tunnel into several 1x1m areas and calculates the stress concentration coefficient of each area based on the current crown settlement, horizontal convergence, and mid-wall displacement data. It also constructs a 3D image of the tunnel based on real-time images and renders each area in the 3D image using different depths of color based on the stress concentration coefficient; the color depth is proportional to the stress concentration coefficient. The formula for calculating the stress concentration factor is as follows: K t =s max / s nom (1); Among them, K t The stress concentration factor is... σ max This represents the maximum stress within the current region. σ nom The average stress of the entire tunnel; The formula for calculating the stress value of the arch is as follows: σv=Δh / H (2); in, σv This represents the stress value at the crown of the arch. H For tunnel burial depth, Δ h represents the settlement of the arch crown; The formula for calculating the stress value generated by horizontal convergence is as follows: σh=Δd / R (3); in, σh The stress value generated by horizontal convergence. R The radius of the tunnel excavation. Δd This is the horizontal convergence value; The formula for calculating the stress value caused by the displacement of the rock wall is as follows: σγ =Δw / L (4); in, σ γ This represents the stress value caused by the displacement of the middle rock wall. L This represents the longitudinal length of the middle dike. Δw For the displacement of the middle dike; The early warning feedback module is used to set the average stress threshold and stress concentration factor threshold of the tunnel. When the average stress of the tunnel exceeds the average stress threshold, the early warning feedback module provides overall inspection prompts and tunnel passage prompts for the tunnel. It also transmits the overall inspection prompts to the remote control terminal of the tunnel management personnel and feeds back the tunnel passage prompts to the vehicle guidance module. When the stress concentration coefficient in any area within the tunnel exceeds the stress concentration coefficient threshold, the early warning feedback module generates area inspection prompts and area passage prompts for that area, transmits the area inspection prompts to the remote control terminal of the tunnel management personnel, and feeds back the area passage prompts to the vehicle guidance module; the vehicle guidance module is used to broadcast the tunnel passage prompts and area passage prompts via the vehicle's voice system.

2. The safety warning device for "tunnel-interchange" with small clearance as described in claim 1, characterized in that, When the maximum stress in a certain area of ​​the tunnel exceeds the area stress threshold, the tunnel prediction model will determine that there is a risk of collapse in that area. The tunnel prediction model records the difference between the maximum stress value in the area and the area stress threshold as the collapse coefficient. The collapse risk is directly proportional to the collapse coefficient.

3. The safety warning device for "tunnel-interchange" with small clearance as described in claim 2, characterized in that, Several light strips are installed inside the tunnel. The data analysis module is also used to set traffic flow thresholds. When the traffic flow is below the threshold, the light strips are green; when the traffic flow exceeds the threshold, the light strips are red. The data analysis module is used to adjust the speed limit based on the ratio of traffic flow to the traffic flow threshold.

4. The safety warning device for "tunnel-interchange" with small clearance as described in claim 3, characterized in that, The data analysis module is also used to adjust the lumen output of the lighting fixtures based on the lumen output inside and outside the tunnel. The lumen output of the lighting fixtures increases sequentially from the tunnel entrance to the tunnel exit, and the lumen output of the lighting fixtures at the tunnel exit is equal to the lumen output outside the tunnel.

5. The safety warning device for "tunnel-interchange" with small clearance as described in claim 4, characterized in that, The data analysis module is also used to calculate the distance between the vehicle and the next sign based on the vehicle's real-time location, and to generate the indicator spacing. Calculate the distance between the vehicle and the tunnel exit and ramp exit, and generate the exit spacing; The indicated spacing and exit spacing are transmitted to the vehicle guidance module, which then uses the vehicle's voice system to announce the indicated spacing and exit spacing.

6. The safety warning device for "tunnel-interchange" with small clearance as described in claim 5, characterized in that, The data analysis module is also used to calculate the distance between adjacent vehicles based on the real-time location of the vehicles, and to determine whether there is a risk of collision between the two vehicles based on the distance between the adjacent vehicles and their speeds. If the distance between adjacent vehicles decreases and the speed of the following vehicle does not decrease, the data analysis module determines that there is a risk of collision and immediately sends a deceleration warning to the following vehicle through the vehicle guidance module.

7. The safety warning device for "tunnel-interchange" with small clearance as described in claim 6, characterized in that, A display screen is installed at the tunnel entrance to show the current speed limit, traffic flow, air temperature, water level, and total tunnel length.

8. The safety warning device for "tunnel-interchange" with small clearance as described in claim 7, characterized in that, The data analysis module is also used to set water level thresholds. When the water level in the tunnel exceeds the threshold, the data analysis module generates a no-passage warning and displays the warning on the screen.

9. The safety warning device for "tunnel-interchange" with small clearance as described in claim 8, characterized in that, The data analysis module is also used to determine, based on real-time images inside the tunnel, whether the tunnel is under maintenance or whether a car accident has occurred inside the tunnel. If the tunnel is under maintenance or a car accident has occurred inside the tunnel, the data analysis module will also generate a no-passage warning.

10. The safety warning device for "tunnel-interchange" with small clearance as described in claim 9, characterized in that, The tunnel prediction model is also used to generate tunnel collapse simulation images using three-dimensional images of the tunnel based on the collapse risk of each area of ​​the tunnel, and transmit them to the early warning feedback module. The early warning feedback module is used to transmit simulated images of tunnel collapse to the remote control terminal of tunnel management personnel.

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