A highway traffic protection facility control method, system, server and medium

Abstract

A method, system, server, and medium for controlling highway traffic protection facilities relate to the technical field of data processing systems. The method includes: when an abnormal vehicle exists on a target highway, controlling a first variable signboard to display a warning message; when the abnormal cause of the abnormal vehicle belongs to a preset cause set, and the abnormal vehicle has not stopped within a preset stop time, simulating the predicted vehicle movement route and vehicle and personnel safety value of each control scheme through a digital twin model; when there is a vehicle and personnel safety value exceeding a preset threshold, obtaining a target control scheme and a target predicted vehicle movement route; when the target protection facility executes the target control scheme, when the actual vehicle movement route does not match the target predicted vehicle movement route, determining a new control scheme; after the new control scheme is executed, determining a replacement scheme for the target movable protection facility. Implementation of the above technical scheme improves the overall safety of the highway traffic system.

Description

Technical Field

[0001] The present application relates to the technical field of data processing systems, and in particular to a method, system, server and medium for controlling highway traffic protection facilities. Background Art

[0002] Traffic protection facilities play a vital role in the modern highway transportation system. They not only affect the safety of road users but also have a profound impact on ensuring the smooth operation of the transportation system. With the continuous growth of traffic volume and the continuous increase in vehicle speeds, more stringent requirements are being placed on the effectiveness and reliability of highway traffic protection facilities.

[0003] Currently, when a vehicle's braking system fails and cannot rely on its own braking ability to stop the vehicle, in order to stop the vehicle quickly, the vehicle driver generally scrapes or collides with surrounding protective facilities to slow the vehicle down until it stops.

[0004] However, when the vehicle breaks down and cannot be stopped, the driver will be in a state of extreme tension and panic, and will not be able to accurately control the angle, strength and position of the scrape or collision with the protective facilities. As a result, when the vehicle contacts the protective facilities, it is prone to unpredictable rollovers, uncontrolled rotations and other dangerous situations. This not only poses a great threat to the driver's own life safety, but is also very likely to cause harm to surrounding vehicles and pedestrians traveling normally, causing serious traffic accidents and affecting the safety and operation efficiency of road traffic. Summary of the Invention

[0005] The present application provides a highway traffic protection facility control method, system, server and medium, which can improve the overall safety of the highway traffic system.

[0006] In the first aspect, the present application provides a method for controlling highway traffic protection facilities, which include intelligent guardrails, automatic roadblocks and variable signboards. The method includes: when an abnormal vehicle is detected on a target highway, controlling the first variable signboard of the target section where the abnormal vehicle is located to display warning information, the warning information is the position of the abnormal vehicle, and the target section is one of multiple sections in the target highway; when the abnormal cause of the abnormal vehicle belongs to a preset cause set, and the abnormal vehicle has not stopped within a preset stop time, the current position, driving speed and driving direction of the abnormal vehicle are input into a pre-established digital twin model of highway traffic to obtain the predicted vehicle movement route and vehicle and personnel safety value corresponding to each control scheme, and the control scheme is used The target protection facility on the target highway is controlled to generate friction with the abnormal vehicle, so that the abnormal vehicle stops; when the vehicle occupant safety value exceeds a preset threshold, the target control scheme and the target predicted vehicle movement route corresponding to the highest vehicle occupant safety value are obtained; when the target protection facility is controlled to execute the target control scheme, when it is detected that the actual movement route of the abnormal vehicle does not match the target predicted vehicle movement route, a new control scheme and a new predicted vehicle movement route are re-determined; after it is detected that the target protection facility has executed the new control scheme, a replacement scheme for the target movable protection facility is determined based on the real-time facility images of each target movable protection facility on the target highway and the historical usage records of the movable protection facilities on all highways.

[0007] Using this technical solution, when an abnormal vehicle is detected, a timely warning message is displayed via the first variable sign, rapidly transmitting the abnormal vehicle's location to surrounding vehicles, enabling drivers of surrounding vehicles to react in advance. This effectively avoids rear-end collisions and other accidents caused by untimely information, thus mitigating safety risks at the source. If an abnormal vehicle is detected as unable to stop on its own, a digital twin model is used to simulate the predicted vehicle movement path and vehicle and occupant safety values based on different control schemes. This allows for accurate simulation of various protective device intervention scenarios, and then selects the target control scheme with the highest vehicle and occupant safety value to maximize the safety of occupants of the abnormal vehicle. Simultaneously, the protective device is controlled to execute the target control scheme, without driver intervention. The protective device automatically approaches the abnormal vehicle and causes a collision or collision with it to bring it to a timely stop. This prevents more serious accidents caused by improper driver operation, reduces disruption to normal traffic flow, and improves the overall safety and operational efficiency of the highway transportation system. After the vehicle stops, damage to the protective device that caused the collision or collision with the abnormal vehicle is promptly detected, allowing for appropriate maintenance and replacement arrangements to ensure that the protective device is in optimal operating condition at all times, providing continuous and reliable protection for highway traffic safety.

[0008] In combination with some embodiments of the first aspect, in some embodiments, when the abnormal cause of the abnormal vehicle belongs to a preset cause set and the abnormal vehicle has not stopped within the preset stop time, the current position, driving speed and driving direction of the abnormal vehicle are input into a pre-established digital twin model of highway traffic to obtain the predicted vehicle movement route and vehicle and personnel safety values corresponding to each control scheme, specifically including: when the abnormal cause of the abnormal vehicle belongs to a preset cause set and the abnormal vehicle has not stopped within the preset stop time, obtaining the driving speed, driving direction and current position of the abnormal vehicle; determining one or more control schemes based on the target protection facilities on the target highway and the relative position information of the target protection facilities and the abnormal vehicle; inputting the driving speed, the driving direction, the current position and the control scheme into a pre-established digital twin model of highway traffic to obtain the predicted vehicle movement route and vehicle and personnel safety values corresponding to each control scheme.

[0009] Using the above technical solution, vehicle information and control solutions are input into the digital twin model, and the predicted vehicle movement routes and vehicle and personnel safety values corresponding to each solution are simulated, just like conducting multiple rehearsals in a virtual environment. This allows the system to evaluate the effectiveness of the solution in advance and select the best solution that can both ensure the safety of vehicles and personnel and effectively guide abnormal vehicles to stop, thereby improving the safety and reliability of the abnormal vehicle handling process, speeding up the handling of abnormal vehicles, reducing interference with normal traffic flow, and improving overall traffic operation efficiency.

[0010] In combination with some embodiments of the first aspect, in some embodiments, after detecting that the target protection facility has completed the execution of the new control scheme, a replacement scheme for the target movable protection facility is determined based on the real-time facility images of each target movable protection facility on the target highway and the historical usage records of the movable protection facilities on all highways, specifically including: after detecting that the target protection facility has completed the execution of the new control scheme, determining the first current health status value of the target movable protection facility based on the real-time facility images of each target movable protection facility on the target highway; counting the total number of repairs and replacements and the second current health status value of other movable protection facilities corresponding to the target movable protection facility among the movable protection facilities on all highways except the target movable protection facility based on the historical usage records of the movable protection facilities on all highways; determining a replacement scheme for each protection facility in the target movable protection facility based on the first current health status value, the total number of times, and the second current health status value, the replacement scheme including not replacing the movable protection facility, replacing the other movable protection facilities with the target movable protection facility, and replacing with a new movable protection facility.

[0011] The above technical solution determines the first current health status value of the target movable protective facility through real-time facility images, which can intuitively reflect the actual current condition of the protective facility and provide a basis for determining whether the protective facility needs to be replaced. The replacement plan is determined by combining the first current health status value, the total number of times, and the second current health status value. This fully considers multiple factors such as the target facility's own health status and the historical performance of similar facilities. If the target facility is in good health, it can be chosen not to replace it to avoid unnecessary waste of resources. If the target facility is seriously damaged and there are no new facilities to replace it, the movable protective facilities on other roads that are relatively good, less used, and corresponding to the target movable protective facility will be deployed and replaced. This maximizes the use of existing resources, ensures that the protective facilities are always in the best operating state, and continuously provide solid and reliable protection for highway traffic safety. At the same time, it reduces maintenance costs, improves the utilization efficiency of traffic management resources, and enhances the overall stability and reliability of the highway traffic system.

[0012] In combination with some embodiments of the first aspect, in some embodiments, after the step of determining a replacement scheme for the target movable protective facility based on the real-time facility images of each target movable protective facility on the target highway and the historical usage records of the movable protective facilities on all highways after detecting that the target protective facility has completed the execution of the new control scheme, the method further includes: when the abnormal vehicle stops, obtaining vehicle image data and vehicle real-time operation data of the abnormal vehicle; determining an explosion risk value of the abnormal vehicle based on the vehicle image data and the vehicle real-time operation data; when the explosion risk value is greater than or equal to a preset explosion risk threshold, controlling a second variable corresponding to a section of the target highway within a first preset range from the abnormal vehicle The signboard displays a warning message, which is used to warn other vehicles on the target highway except the abnormal vehicle to maintain an explosion-safe distance from the abnormal vehicle; when the explosion risk value is less than a preset explosion risk threshold, the predicted processing time for moving the abnormal vehicle to the target trailer is calculated based on the driving time of the target trailer to reach the current position of the abnormal vehicle and the preset vehicle movement time; based on the predicted processing time and the obstacle conditions of each lane in the target section, the display content of the third variable signboard corresponding to each section of the target highway is determined, and the display content is the first content or the second content, the first content is the passable lanes and impassable lanes of the target section, and the second content is that the target section is impassable.

[0013] Using this technical solution, when the explosion risk exceeds a preset threshold, the second variable sign within a specific range is immediately controlled to display a warning message, promptly reminding other vehicles to maintain a safe distance from the abnormal vehicle, effectively preventing possible explosion accidents and protecting the lives and property of surrounding vehicles and personnel. When the explosion risk is lower, the predicted handling time is calculated based on the target tow truck arrival time and the preset vehicle movement time. The third variable sign displays the content based on the obstacle conditions in each lane of the target road section. This display informs drivers of other vehicles in advance of the required handling time for the abnormal vehicle and the traffic conditions of each lane, helping them plan their routes in advance, avoiding congestion or delays caused by lack of knowledge, and minimizing the impact on normal traffic order.

[0014] In combination with some embodiments of the first aspect, in some embodiments, after the step of controlling the second variable signboard corresponding to the section of the target highway within the first preset range from the abnormal vehicle to display a warning message when the explosion risk value is greater than or equal to the preset explosion risk threshold, the method also includes: obtaining fire extinguisher information within the second preset range from the abnormal vehicle, the fire extinguisher information including the location information of the fire extinguisher; controlling the sound playing device closest to the fire extinguisher to play the fire extinguisher information and the location information of the abnormal vehicle.

[0015] By adopting the above technical solution, when it is detected that a vehicle is highly likely to explode, the information of fire extinguishers around the vehicle is obtained in a timely manner, and the fire extinguisher information and the location information of the abnormal vehicle are played through the sound playback device, so that surrounding users can quickly find the fire extinguisher and bring the fire extinguisher to the abnormal vehicle as soon as they hear the information. In the early stage of the explosion or fire of the abnormal vehicle, fire-fighting measures can be quickly taken to effectively curb the spread of the fire and reduce the possibility and degree of harm of the explosion.

[0016] In combination with some embodiments of the first aspect, in some embodiments, the display content of the third variable signboard corresponding to each section of the target highway is determined based on the predicted processing time and the obstacle conditions of each lane in the target section, specifically including: determining the predicted time for the vehicle to travel from each section to the target section based on the average vehicle speed corresponding to each section of the target highway and the section length of each section; if there is a target predicted time in the predicted time that is less than the predicted processing time, determining the traffic status of each target lane in the target section based on the obstacle conditions of each lane in the target section, and the traffic status includes passable and impassable; if there is a passable target lane, then determining the display content of the third variable signboard of the section corresponding to the target predicted time to be the first content; if there is no passable target lane, then determining the display content of the third variable signboard of the section corresponding to the target predicted time to be the second content.

[0017] The above technical solution accurately estimates the time it takes for vehicles to reach the target road section by calculating the average speed and length of each target highway section, laying a solid time foundation for traffic guidance. When a vehicle is expected to reach the target road section within a short period of time and the road section is handling an abnormal vehicle, the lane's traffic status is determined based on the presence of lane obstacles. If a lane is available, signs display information about permitted and prohibited lanes, assisting drivers in planning lane selection and avoiding congestion. If a lane is unavailable, signs inform drivers that the road section is impassable, prompting them to take a detour in advance. This improves traffic safety and convenience, optimizes resource allocation, and ensures the efficient and orderly operation of the highway transportation system.

[0018] In combination with some embodiments of the first aspect, in some embodiments, after the step of obtaining the target control scheme and target predicted vehicle movement route corresponding to the highest vehicle occupant safety value when the vehicle occupant safety value exceeds a preset threshold, the method also includes: when there is a traffic light intersection in the target predicted vehicle movement route, obtaining the real-time position of the abnormal vehicle; when the distance between the real-time position and the stop line of the traffic light intersection is within a preset distance range, controlling the traffic light corresponding to the traffic light intersection and the driving direction of the abnormal vehicle to be green.

[0019] By adopting the above technical solution, the position of abnormal vehicles can be obtained in real time. When it is detected that an abnormal vehicle is about to approach the stop line of a traffic light intersection, the traffic light in the corresponding direction will be immediately switched to green, allowing the abnormal vehicle to pass unimpeded. This avoids the situation where the abnormal vehicle cannot stop and encounters a red traffic light, and collides with vehicles and pedestrians traveling normally in other directions, causing traffic accidents, thereby improving the overall safety of the highway traffic system.

[0020] On the second aspect, the embodiment of the present application provides a protective facility control system, characterized in that it includes: a sign control module, which is used to control the first variable sign of the target road section where the abnormal vehicle is located to display warning information when an abnormal vehicle is detected on the target road; a model input module, which is used to input the current position, speed and direction of the abnormal vehicle into a pre-established digital twin model of highway traffic when the abnormal cause of the abnormal vehicle belongs to a preset cause set and the abnormal vehicle has not stopped within the preset stop time, so as to obtain the predicted vehicle movement route and vehicle and personnel safety value corresponding to each control scheme; a target control scheme determination module, which is used to determine the vehicle movement route and vehicle and personnel safety value corresponding to each control scheme when the vehicle and personnel are detected on the target road; When the safety value exceeds a preset threshold, the target control scheme and target predicted vehicle movement route corresponding to the highest vehicle and personnel safety value are obtained; a new control scheme determination module is used to redetermine a new control scheme and a new predicted vehicle movement route when it is detected that the actual movement route of the abnormal vehicle does not match the target predicted vehicle movement route while controlling the target protection facility to execute the target control scheme; a replacement scheme determination module is used to determine a replacement scheme for the target movable protection facility based on the real-time facility images of each target movable protection facility on the target highway and the historical usage records of the movable protection facilities on all highways after it is detected that the target protection facility has completed the execution of the new control scheme.

[0021] In the third aspect, an embodiment of the present application provides a protective facility control server, comprising: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the protective facility control server to execute the method described in the first aspect and any possible implementation method of the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium comprising instructions. When the instructions are executed on a protection facility control server, the protection facility control server executes the method described in the first aspect and any possible implementation of the first aspect.

[0023] It is understandable that the protective facility control system provided in the second aspect, the protective facility control server provided in the third aspect, and the storage medium provided in the fourth aspect are all used to execute the method provided in this application. Therefore, the beneficial effects that can be achieved can be referenced to the beneficial effects of the corresponding methods and will not be repeated here.

[0024] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0025] 1. This application uses digital twin model simulation to obtain predicted vehicle movement routes and vehicle and occupant safety values based on different control schemes. It can accurately simulate the intervention of various protective facilities and select the target control scheme with the highest vehicle and occupant safety value to maximize the safety of occupants in abnormal vehicles. At the same time, the protective facilities are controlled to execute the target control scheme without the need for driver operation. The protective facilities automatically approach the abnormal vehicle and cause scratches or collisions with the abnormal vehicle to stop the vehicle in time, avoiding more serious accidents caused by improper operation of the vehicle driver, reducing the interference of traffic accidents on normal traffic flow, and improving the overall safety and operational efficiency of the highway transportation system.

[0026] 2. This application uses a variable sign to display a warning message. When a vehicle is detected as highly likely to explode, it promptly reminds other vehicles to maintain a safe distance from the abnormal vehicle, effectively preventing possible explosions and protecting the lives and property of surrounding vehicles and personnel. Simultaneously, a sound playback device plays the fire extinguisher information and the abnormal vehicle's location information. This allows nearby users to quickly locate the fire extinguisher and bring it to the vicinity of the abnormal vehicle upon hearing the message. This allows rapid firefighting measures to be taken in the early stages of an explosion or fire in the abnormal vehicle, effectively curbing the spread of the fire and reducing the likelihood and severity of the explosion.

[0027] 3. This application obtains the position of abnormal vehicles in real time. When it detects that an abnormal vehicle is about to approach the stop line of a traffic light intersection, it immediately switches the traffic light in the corresponding direction to green, allowing the abnormal vehicle to pass unimpeded. This avoids the situation where the abnormal vehicle cannot stop and encounters a red traffic light, and collides with vehicles and pedestrians traveling normally in other directions, causing traffic accidents, thereby improving the overall safety of the highway traffic system. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a schematic diagram of a system architecture to which the highway traffic protection facility control method according to an embodiment of the present application can be applied;

[0029] Figure 2 This is a flow chart of a method for controlling highway traffic protection facilities in an embodiment of the present application;

[0030] Figure 3 This is another flowchart of the highway traffic protection facility control method according to an embodiment of the present application;

[0031] Figure 4 This is a module architecture diagram of the protective facility control system in an embodiment of the present application;

[0032] Figure 5 This is an exemplary hardware structure diagram of the protective facility control server in an embodiment of the present application. DETAILED DESCRIPTION

[0033] The terms used in the following examples of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of this application, the singular expressions "a," "an," "said," "above," "the," and "this" are intended to include plural expressions as well, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in this application refers to any or all possible combinations comprising one or more of the listed items.

[0034] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of the technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, unless otherwise specified, "plurality" means two or more.

[0035] Figure 1 It is a structural diagram of a system architecture to which the highway traffic protection facility control method in the embodiment of the present application can be applied.

[0036] See also Figure 1 ,The highway traffic protection facility control system includes protection facilities, ,traffic lights and protection facility control servers.

[0037] The protective facility control server, the system's core component, analyzes and processes vehicle driving video image data and real-time operational data acquired through communications with traffic systems, onboard systems, and other systems. It then generates control plans for the protective facilities and sends control commands to the protective equipment and traffic control system. Traffic lights receive control commands forwarded by the server through the traffic control system and change their display status accordingly. Protective facilities receive control commands from the server and execute the corresponding control plans accordingly.

[0038] Protective measures include intelligent guardrails, automatic roadblocks, collision mitigation systems, and variable signage. When an unauthorized vehicle approaches, intelligent guardrails extend scraping arms with cushioning material but a certain degree of rigidity from either side of the guardrail. As the unauthorized vehicle passes, the scraping arms contact the side of the vehicle, gradually slowing it down through friction and bringing it to a stop. Automatic roadblocks rapidly rise from the ground when a vehicle approaches. Their surface features a special anti-slip texture, resembling densely packed speed bumps. This creates a jarring sensation when vehicles pass over them, forcing them to slow down. The collision mitigation system consists of an intelligent sensing unit, a hydraulic buffer, and a flexible protective net. Upon detecting an unauthorized vehicle approaching, a warning signal is transmitted to the hydraulic buffer, which then activates a hydraulic pump to automatically deploy the net at a specific angle based on the vehicle's speed. The net's high-damping rubber coating and memory foam, combined with the hydraulic damping valve, convert the kinetic energy of the collision into heat, bringing the vehicle to a smooth stop. The variable signage displays various warning messages to alert passing vehicles to the unauthorized vehicle.

[0039] Through the above system architecture, the highway traffic protection facility control system can control the scraping or collision between the protection facilities and abnormal vehicles according to the generated control plan, so that the abnormal vehicles can be stopped in time to avoid serious traffic accidents.

[0040] In related technologies, when a vehicle's braking system fails and it is unable to stop by its own braking ability, in order to quickly stop the vehicle, the driver will generally scrape or collide with surrounding protective facilities to slow the vehicle down until it stops. However, when the vehicle fails and cannot be stopped, the driver will be in a state of extreme tension and panic, and will not be able to accurately control the angle, force, and position of the scrape or collision with the protective facilities. As a result, when the vehicle contacts the protective facilities, it is prone to unpredictable rollovers, uncontrolled rotations, and other dangerous situations. This not only poses a great threat to the driver's own life safety, but is also very likely to cause harm to nearby vehicles and pedestrians traveling normally, causing serious traffic accidents and affecting the safety and operational efficiency of highway traffic.

[0041] By adopting the highway traffic protection facility control method in the embodiment of the present application, when it is detected that an abnormal vehicle has malfunctioned and cannot rely on its own braking ability to stop the vehicle, a control scheme for the protection facility is obtained by simulating a pre-established digital twin model of highway traffic, and the protection facility is controlled to execute the control scheme. Without the need for the vehicle driver to operate, the protection facility automatically approaches the abnormal vehicle and causes a scratch or collision with the abnormal vehicle to stop the vehicle in time, thereby avoiding the occurrence of more serious accidents caused by improper operation of the vehicle driver, thereby improving the overall safety and operation efficiency of the highway traffic system.

[0042] The following combination Figure 2To illustrate the method of the embodiment of the present application.

[0043] See also Figure 2 , which is a flow chart of the highway traffic protection facility control method in an embodiment of the present application.

[0044] S201. When an abnormal vehicle is detected on a target road, a first variable signboard on the target road section where the abnormal vehicle is located is controlled to display a warning message.

[0045] The warning information is the location of the abnormal vehicle, and the target road section is one of multiple road sections in the target highway.

[0046] Specifically, the real-time data interaction interface of the traffic management department is used to communicate with the traffic monitoring system to obtain the vehicle driving video data on the target highway.

[0047] Next, based on the vehicle driving video data, an image recognition algorithm is used to analyze the video data frame by frame. First, the video data is split into frames in chronological order. Each frame is then processed using an image recognition algorithm (such as a deep learning-based object detection algorithm). During the algorithm training phase, the algorithm is trained using a large amount of image data containing both normal vehicles and abnormal vehicles (such as those parked illegally, driving the wrong way, speeding, or with hazard lights on), enabling it to accurately identify the characteristics of various abnormal vehicles. During the frame-by-frame analysis process, the algorithm extracts the features of each vehicle in the image and compares these features with those of abnormal vehicles to determine whether the vehicle is an abnormal vehicle. When an abnormal vehicle is detected, the algorithm tracks and analyzes the abnormal vehicle in multiple consecutive frames to ensure the accuracy of the detection results. A vehicle is confirmed as an abnormal vehicle when it meets the abnormal vehicle characteristics in multiple consecutive frames. After determining the presence of an abnormal vehicle, the algorithm will further determine the specific location coordinates of the abnormal vehicle on the target highway through information such as road signs, markings, and the relative position of the vehicle in the image, combined with pre-stored electronic map data of the target highway.

[0048] Next, based on the specific location coordinates of the abnormal vehicle on the target highway and the section division information of the target highway (this information is pre-stored in the storage device and includes information such as the starting point and end point coordinates of each section and the section number), the target section where the abnormal vehicle is located is located.

[0049] Finally, a control instruction is determined based on the specific location coordinates of the abnormal vehicle. This instruction is then transmitted to the variable sign via a wireless communication module. Upon receiving the instruction, the variable sign displays the specific location coordinates of the abnormal vehicle according to a preset display template and font style, prompting passing vehicles to avoid the vehicle and take appropriate safety measures.

[0050] S202. When the abnormal cause of the abnormal vehicle belongs to a preset cause set and the abnormal vehicle has not stopped within the preset stop time, the current position, driving speed and driving direction of the abnormal vehicle are input into the pre-established digital twin model of highway traffic to obtain the predicted vehicle movement route and vehicle and personnel safety value corresponding to each control scheme.

[0051] The control scheme is used to generate friction between the target protective equipment on the target road and the abnormal vehicle, causing the abnormal vehicle to stop. The preset cause set includes abnormal causes such as brake system failure that prevent the vehicle from stopping normally. The vehicle and occupant safety value refers to the safety value of the users in the abnormal vehicle.

[0052] Specifically, the wireless communication module establishes a communication connection with the vehicle's onboard equipment, acquiring real-time vehicle operating data, including but not limited to vehicle speed, braking status, and driver-issued vehicle control commands. Vehicle control commands are generated by the vehicle's control system. For example, when the driver turns the steering wheel, the steering wheel angle sensor transmits a signal to the control system. The system then generates the corresponding steering control command based on this signal and other information, including the vehicle's current driving status.

[0053] Determine the cause of the abnormality based on the real-time operating data provided by the on-board equipment. For real-time operating data related to the braking system, compare the pressure value in the braking system in the real-time operating data with the pressure value within the normal working range. If the real-time brake pressure value is lower than the normal range and the vehicle speed is not reduced due to the deceleration command issued by the driver, it is determined that the cause of the abnormality may be a brake system failure. For the vehicle's driving direction data, extract the vehicle's driving direction data from the real-time operating data. If the vehicle's driving direction is abnormally offset and does not match the steering operation command issued by the driver, it is determined that the vehicle has a steering system failure. For the vehicle's driving speed, extract the vehicle's real-time speed data from the real-time operating data. If the vehicle's speed rises or falls abnormally quickly without the driver actively issuing an acceleration command or deceleration command, it is determined that the vehicle may have a power system failure.

[0054] If communication with the abnormal vehicle's onboard systems is unavailable, traffic cameras, radar, or other sensors on the road monitor the abnormal vehicle's location, speed, direction, and other information in real time. These devices can capture the vehicle's trajectory, speed changes, and directional deviations. Data fusion algorithms (such as Kalman filtering and Bayesian estimation) are then used to integrate the various types of data acquired from multiple sensors. Next, historical data is combined with machine learning models (such as decision trees and neural networks) to construct a library of abnormal behavior patterns. This historical data contains various past vehicle abnormalities and their corresponding operating data. The machine learning model learns and analyzes this historical data to identify the characteristics and patterns of vehicle operating data associated with different abnormal causes. Finally, the operating data of the currently monitored abnormal vehicle is compared with the established library of abnormal behavior patterns to determine the cause of the abnormal vehicle's anomaly. For example, if a vehicle's speed decreases abnormally, radar detects a significantly shortened braking distance, and road sensors indicate uniform wheel pressure distribution, the historical data can be used to infer that the vehicle may have a mechanical brake system failure.

[0055] When the abnormal reason of the abnormal vehicle belongs to the preset reason set, the timer with the preset stop time is started, and when the timer stops timing, it is determined whether the abnormal vehicle has stopped driving. If so, the following step S205 is executed to determine the replacement plan of the movable protective facilities. If not, the current position, driving speed and driving direction of the abnormal vehicle are obtained. It can be obtained by establishing a connection with the vehicle-mounted system of the abnormal vehicle; it can also be obtained by identifying the driving video data of the abnormal vehicle through an image recognition algorithm, obtaining the video data of the road section where the abnormal vehicle is located from the traffic monitoring system, and using advanced image recognition algorithms (such as target detection and tracking algorithms based on deep learning) to analyze the abnormal vehicle in the video frame by frame, calculate the driving direction through the position change of the vehicle in the image, estimate the driving speed based on the change in vehicle size and time interval in the image, and determine the current position of the vehicle in combination with traffic map data.

[0056] Determine one or more control schemes based on the current position and driving direction of the abnormal vehicle and the relative positional relationship between the abnormal vehicle and the various protective facilities on the highway. Determine the driving lane of the abnormal vehicle based on the current position and driving direction of the abnormal vehicle. Obtain a corresponding table of protective facilities corresponding to the target road. Search the corresponding table for the first set of protective facilities corresponding to the driving lane. Based on the current position of the abnormal vehicle and the position coordinates of each protective facility in the first set of protective facilities, extract the target protective facilities in the first set of protective facilities that are directly in front of or to the side of the abnormal vehicle. Search for one or more control schemes that match the target protective facilities in the preset set of control schemes.

[0057] The abnormal vehicle's model, current location, speed, direction, and control strategy are input into a pre-established digital twin model of highway traffic. This digital twin model is constructed based on a variety of data, including the actual geographic information of the target highway, the specific parameters (such as location, size, material, and age) and real-time status of protective facilities, and the vehicle dynamics model. Based on a large amount of pre-collected data, the model utilizes Geographic Information System (GIS) technology to construct a three-dimensional virtual model of the target highway, accurately recreating the actual geographic environment of the highway. Incorporating the parameter data of the protective facilities, the location and configuration of the protective facilities are accurately arranged within the virtual model. A vehicle dynamics model is introduced, which can describe the motion of vehicles under various driving conditions, such as acceleration, deceleration, and cornering. The vehicle dynamics model is combined with the highway virtual model to form a basic model framework capable of simulating vehicle movement on the highway. Once the basic model framework is constructed, the model is trained using a large amount of pre-collected training data. By continuously adjusting the model parameters, the model accurately predicts vehicle dynamics and the response of protective facilities based on the input vehicle information and highway environmental parameters. During training, supervised learning methods are used to compare the model's predictions with actual training data, calculate the error, and update the model's parameters through a back-propagation algorithm to minimize the prediction error. Simultaneously, reinforcement learning techniques are employed to allow the model to autonomously learn and make decisions in a simulated traffic environment. By continuously trying different strategies and receiving rewards based on environmental feedback, the model's decision-making capabilities are optimized, enabling it to simulate actual highway traffic conditions, including vehicle dynamics on the road and the real-time status of protective facilities.

[0058] Based on the input vehicle model, the model retrieves and calls detailed parameters corresponding to that model from the built-in vehicle dynamics parameter database. These parameters include, but are not limited to, vehicle mass, center of gravity, moment of inertia, tire-ground friction coefficient, and suspension system characteristics. These parameters are essential for accurately simulating the vehicle's motion under various conditions.

[0059] Based on the input of the abnormal vehicle's current position, the model accurately locates the vehicle within the virtual target road scenario. The model references the target road's actual geographic information, including its direction, slope, and curvature, to place the vehicle on the appropriate road section and lane.

[0060] Based on the input speed and direction, the model sets the vehicle's initial motion state. It calculates the vehicle's velocity vector at that initial moment, including its magnitude and direction. Furthermore, the model combines the vehicle's dynamic parameters to calculate the forces acting on the vehicle in this initial state, such as air resistance and rolling friction.

[0061] Based on the input control scheme, the model will analyze it in detail. The control scheme may include various operational instructions for the target protective equipment, such as deformation, opening or closing the protective equipment. Based on these instructions, the model will simulate the corresponding operations of the target protective equipment in the virtual scene.

[0062] After the model sets the vehicle's initial state and the control plan required for the target protective equipment, it begins simulating the vehicle's motion. Based on the vehicle's dynamic equations, the model calculates parameters such as the vehicle's position, velocity, and acceleration in real time at each time step. Simultaneously, the model continuously performs collision detection to determine whether the vehicle collides with or scrapes against the target protective equipment or other objects. When a collision or scrape event is detected, the model calculates physical quantities such as the impact force and energy transfer generated during the collision or scrape based on parameters such as the material, shape, and velocity of the vehicle and protective equipment.

[0063] Based on the vehicle's motion simulation and collision detection results, the model generates a predicted vehicle trajectory. The model records the vehicle's position changes throughout the simulation, forming a continuous trajectory, known as the predicted vehicle trajectory. This trajectory accounts for the vehicle's interaction with protective equipment and the vehicle's changes in motion after a collision or scrape.

[0064] To assess the safety of vehicle occupants under different control schemes, the model calculates a vehicle occupant safety value based on the physical quantities experienced during a collision or scrape. The model references vehicle safety design standards and ergonomic principles to convert parameters such as impact force and acceleration during a collision into injury indicators for vehicle occupants. For example, the model calculates the peak acceleration experienced by the driver's head during a collision and, based on relevant safety standards, determines whether this acceleration exceeds a safety threshold, thereby assessing the driver's risk of head injury. By comprehensively considering multiple injury indicators, the model calculates a comprehensive vehicle occupant safety value, which is used to measure the safety of vehicle occupants under the control scheme.

[0065] For multiple control schemes input, the model will repeat the above steps, simulate and evaluate each control scheme in turn, and obtain the predicted vehicle movement route and vehicle and personnel safety value corresponding to each control scheme.

[0066] S203. When a vehicle and occupant safety value exceeds a preset threshold, obtain a target control scheme and a target predicted vehicle movement route corresponding to the highest vehicle and occupant safety value.

[0067] Specifically, when a vehicle's occupant safety value exceeds a preset threshold, all vehicles' occupant safety values are sorted from highest to lowest. Once sorted, the target control solution and target predicted vehicle movement route corresponding to the highest-ranked vehicle's occupant safety value are retrieved. The target predicted vehicle movement route's endpoint is then sent to the nearest traffic officer and the tow truck driver of the available target tow truck.

[0068] S204. When the target protection facility is controlled to execute the target control plan, if it is detected that the actual movement route of the abnormal vehicle does not match the target predicted vehicle movement route, a new control plan and a new predicted vehicle movement route are re-determined.

[0069] Specifically, according to the target control plan, the control instructions of each facility in the target protection facility are determined, and the control instructions are sent to the corresponding target protection facility. After receiving the control instructions, the target protection facility performs corresponding operations according to the control instructions to implement the target control plan.

[0070] When the real-time driving status of the abnormal vehicle is detected as being in motion (i.e., when the target protective facility is executing the target control plan), the real-time movement route of the abnormal vehicle is determined based on its real-time location. Real-time video data of vehicles traveling on the target highway is acquired through communication with the traffic monitoring system. Image recognition algorithms (such as deep learning-based object detection and tracking algorithms) are used to identify the position of the abnormal vehicle in each frame. Then, by continuously tracking multiple frames of images and combining the changes in the vehicle's pixel coordinates in the images with the image scale information, the vehicle's actual position coordinates on the road are calculated. Based on the series of real-time position coordinates of the abnormal vehicle, these coordinate points are connected in chronological order to generate the real-time movement route of the abnormal vehicle.

[0071] A similarity calculation algorithm (e.g., a similarity calculation method based on Euclidean distance) is used to calculate the similarity between the real-time movement route and the target predicted movement route of the vehicle. If the similarity between the real-time movement route and the target predicted movement route falls below a preset threshold, the actual movement route of the abnormal vehicle is determined to be mismatched with the target predicted movement route.

[0072] If the actual moving route does not match the target predicted vehicle moving route, the above steps S202-S203 are executed to re-determine the new control scheme and the new predicted vehicle moving route based on the real-time position, real-time driving direction, and real-time driving speed of the abnormal vehicle.

[0073] S205. After detecting that the target protective facility has completed executing the new control plan, a replacement plan for the target movable protective facility is determined based on the real-time facility images of each target movable protective facility on the target highway and the historical usage records of the movable protective facilities on all highways.

[0074] Historical usage records include the number of replacements for movable protective devices at each installation location on each highway, and usage data for currently in use movable protective devices. Usage data includes detailed information such as the cumulative usage time of the protective device since its installation, the number of collisions or other damage it has sustained, the number of repairs, the severity and type of each damage, the average frequency of regular maintenance, real-time health status, installation location, and device model.

[0075] Specifically, when the abnormal vehicle's driving status is detected as stopped (i.e., after the target protective facility has completed the new control plan), the protective facility monitoring system obtains real-time facility images of each target movable protective facility on the target highway. These images are obtained from cameras or other image acquisition devices installed near the protective facility.

[0076] Based on real-time facility images, deep learning-based image recognition technology (such as convolutional neural network (CNN) models) is used to identify the extent and type of damage to protective equipment. This model, trained with extensive image data of protective equipment, can accurately identify various features of these facilities. Taking a CNN model as an example, after an image is input into the trained model, the model's convolutional layer performs convolution operations on the image, generating a series of feature maps that capture various details of the protective equipment, such as damage characteristics such as cracks, deformation, and discoloration. The pooling layer downsamples the feature maps, reducing data dimensionality while retaining key features and improving computational efficiency. The fully connected layer integrates the feature maps after convolution and pooling, outputting a feature vector that contains comprehensive feature information of the protective equipment image. After feature extraction, the model compares the extracted feature vector with a pre-established feature library of normal protective equipment conditions. This feature library stores a large number of feature vectors of protective facilities in normal use. By calculating the similarity between the current feature vector and the feature vector in the library (for example, using the cosine similarity algorithm to calculate the similarity), the current damage level (including no damage, slight, general, and severe, etc.) and the current damage type of the target movable protective facility are determined.

[0077] When the current damage level of the target movable protective facility is not no damage, the current health status value of the target movable protective facility is calculated based on the usage data, current damage level, and current damage type of the target movable protective facility, and the real-time health status value in the usage data of the protective facility is updated. The usage data of the movable protective facility currently in use corresponding to the target highway is searched in the historical usage records. Based on the cumulative usage time in the usage data, the cumulative usage time score corresponding to the cumulative usage time in the preset cumulative usage time score table is obtained. Based on the damage level and damage type of each damage in the usage data, the score corresponding to the damage type and damage level in the preset damage score table is obtained, and the full score of the preset damage score is subtracted from the sum of all scores to obtain the past damage score value of the target movable protective facility. Based on the average frequency of regular maintenance in the usage data, the regular maintenance score corresponding to the average frequency in the preset maintenance score table is obtained. Based on the cumulative usage time score, past damage score, regular maintenance score and preset weights, the current health status value of the target movable protective facility is obtained through weighted calculation, and the real-time health status value in the usage data of the protective facility is updated to the current health status value.

[0078] If the current health value of the target mobile protective facility is less than the preset health threshold, the preset importance level of the target mobile protective facility to be replaced is obtained. This importance level includes level 1, level 2, and level 3, with higher levels indicating the importance of the mobile protective facility to highway traffic safety and its criticality within the traffic protection system.

[0079] Based on the warehouse inventory information, determine whether there are protective facilities in the warehouse that are consistent with the target movable protective facilities. If so, retrieve the protective facilities in the warehouse for replacement. If not, and the importance level of the protective facility is level one, do not replace it temporarily until the purchasing staff purchases it. If not, and the importance level of the protective facility is level two or three, then based on the historical usage of all movable protective facilities on the highway, find a first movable protective facility with a protective facility model that is consistent with the protective facility, a health status score that is higher than the preset health status threshold, and an importance level of level one, and replace the first protective facility with the protective facility.

[0080] In an embodiment of the present application, when it is detected that an abnormal vehicle is unable to stop on its own, the predicted vehicle movement route and vehicle and personnel safety value based on different control schemes are simulated through the digital twin model, which can accurately simulate the intervention of various protective facilities, and then select the target control scheme with the highest vehicle and personnel safety value to maximize the safety of people in the abnormal vehicle. At the same time, the protective facilities are controlled to execute the target control scheme without the need for the vehicle driver to operate. The protective facilities automatically approach the abnormal vehicle and cause scratches or collisions with the abnormal vehicle to stop the vehicle in time, avoiding the occurrence of more serious accidents caused by improper operation of the vehicle driver, reducing the interference of traffic accidents on normal traffic flow, and improving the overall safety and operation efficiency of the highway traffic system.

[0081] The following combination Figure 3 To further illustrate the method of the embodiment of the present application.

[0082] See also Figure 3 , is another flow chart of the highway traffic protection facility control method in an embodiment of the present application.

[0083] S301. When an abnormal vehicle is detected on a target road, the first variable signboard of the target road section where the abnormal vehicle is located is controlled to display a warning message.

[0084] S302: When the abnormal cause of the abnormal vehicle belongs to a preset cause set and the abnormal vehicle has not stopped within the preset stop time, obtain the driving speed, driving direction and current position of the abnormal vehicle.

[0085] S303: Determine one or more control schemes based on target protective facilities on the target highway and relative position information between the target protective facilities and the abnormal vehicle.

[0086] S304: Input the driving speed, driving direction, current position, and control scheme into a pre-established digital twin model of highway traffic to obtain the predicted vehicle movement route and vehicle and personnel safety value corresponding to each control scheme.

[0087] S305. When there is a vehicle and occupant safety value exceeding a preset threshold, obtain a target control scheme and a target predicted vehicle movement route corresponding to the highest vehicle and occupant safety value.

[0088] Steps S301-S305 and Figure 2 Steps S201 to S203 in the illustrated embodiment are similar, and reference may be made to the description of steps S201 to S203 , which will not be repeated here.

[0089] S306. When there is a traffic light intersection in the target predicted vehicle movement route, obtain the real-time position of the abnormal vehicle.

[0090] Specifically, the locations of each traffic light intersection on the preset target highway are obtained. The locations of each moving point in the target predicted vehicle's movement route are traversed and matched with the locations of each traffic light intersection. If the location of a moving point matches the location of a traffic light intersection, it is determined that a traffic light intersection exists in the target predicted vehicle's movement route, and the preset stop line location corresponding to the matched traffic light intersection is obtained.

[0091] When a traffic light intersection is located within the predicted target vehicle's route, the system establishes a connection with the vehicle's onboard system to obtain the vehicle's real-time location. If a connection with the vehicle's onboard system is unavailable, the system uses an image recognition algorithm to identify the vehicle's driving video data and obtain its real-time location. Video data of the road section where the vehicle is located is obtained from the traffic monitoring system. Advanced image recognition algorithms (such as deep learning-based object detection and tracking algorithms) are then used to identify the vehicle in the video data. The system then determines the vehicle's real-time location on the target highway based on information such as road signs, road markings, and the vehicle's relative position in the image, combined with pre-stored electronic map data of the target highway.

[0092] S307: When the distance between the real-time position and the stop line at the traffic light intersection is within a preset distance range, the traffic light at the traffic light intersection corresponding to the driving direction of the abnormal vehicle is controlled to be green.

[0093] Specifically, based on the real-time location and the preset stop line position at the traffic light intersection, the distance between the real-time location and the stop line at the traffic light intersection is calculated. If the distance is within the preset distance range, the target traffic light device corresponding to the abnormal vehicle's direction of travel is determined based on the abnormal vehicle's direction of travel. A communication connection is established with the traffic signal control system, and a control command is sent to the system. Upon receiving the command, the traffic signal control system sends a corresponding command to the target traffic light device. Upon receiving the command, the target traffic light device switches its display to green, allowing the abnormal vehicle to pass directly through the traffic light intersection.

[0094] S308. When the target protection facility is controlled to execute the target control plan, if it is detected that the actual movement route of the abnormal vehicle does not match the target predicted vehicle movement route, a new control plan and a new predicted vehicle movement route are re-determined.

[0095] Step S308 and Figure 2 Step S204 in the illustrated embodiment is similar, and reference may be made to the description of step S204 , which will not be repeated here.

[0096] S309: After detecting that the target protective facility has completed executing the new control scheme, determine a first current health status value of the target movable protective facility.

[0097] After detecting that the target protective facility has completed executing the new control scheme, a first current health status value of the target movable protective facility is determined based on the real-time facility image of each target movable protective facility on the target highway.

[0098] Among them, the real-time facility status includes the cumulative usage time, the current damage level (including no damage, minor, general and severe, etc.), the current damage type and the current health status value.

[0099] Step S309 and Figure 2 In the illustrated embodiment, the identification of the current damage degree and current damage type of the target movable protective facility and the calculation of the current health status value in step S205 are similar. Please refer to the description in step S205 and will not be repeated here.

[0100] S310: Count the total number of repairs and replacements of all movable protective facilities on the highway, excluding the target movable protective facility and corresponding to the target movable protective facility, and the second current health status value.

[0101] According to the historical usage records of all movable protective facilities on all roads, the total number of repairs and replacements of other movable protective facilities corresponding to the target movable protective facilities on all roads, except the target movable protective facilities, and the second current health status value are counted.

[0102] Specifically, historical usage records are obtained. Usage data for currently-in-use mobile protective facilities that match the model of the target mobile protective facility are extracted from the historical usage records to obtain a first set of mobile protective facilities and their corresponding usage data. Based on the usage data, the number of maintenance visits for each protective facility in the first set of mobile protective facilities is obtained.

[0103] Then, according to the historical usage records, the number of facility replacements of the installation location corresponding to each protective facility in the first movable protective facility set is obtained.

[0104] Finally, the sum of the number of repairs and the number of facility replacements is calculated to obtain the total number of repairs and replacements for each protective facility in the first set of movable protective facilities. Simultaneously, the real-time health status value from the usage data corresponding to each protective facility in the first set of movable protective facilities is extracted as the second current health status value for each protective facility.

[0105] S311. Determine a replacement plan for each protective facility in the target movable protective facility.

[0106] A replacement scheme for each protective facility in the target movable protective facility is determined according to the first current health status value, the total number of times, and the second current health status value.

[0107] Among them, the replacement options include not replacing the movable protective facilities, replacing other movable protective facilities with the target movable protective facilities, and replacing new movable protective facilities.

[0108] Specifically, if the first current health value of the target movable protective facility is within a preset normal health value range, the replacement solution is determined to be not replacing the movable protective facility. If the first current health value of the target movable protective facility is not within the preset normal health value range, it is determined whether there is a protective facility of the same model in inventory. If so, the replacement solution is to replace the new movable protective facility in inventory. If not, the target total number of repairs and replacements for the target movable protective facility is calculated. If the target total number is less than a preset threshold, the movable protective facility is not replaced and is awaiting procurement by procurement personnel. If the target total number is greater than or equal to the preset threshold, a first movable protective facility in the first movable protective facility set is searched for whose second current health value is within the normal health value range corresponding to the target movable protective facility and whose total number of repairs and replacements is less than the preset threshold, thereby obtaining a second movable protective facility set. Based on the installation location of each second movable protective facility in the second movable protective facility set and the installation location of the target movable protective facility, the distance between each second movable protective facility and the target movable protective facility is calculated. The second movable protective facility with the closest distance is selected for replacement with the target movable protective facility.

[0109] S312. When the abnormal vehicle stops, obtain vehicle image data and real-time vehicle operation data of the abnormal vehicle.

[0110] Specifically, when the abnormal vehicle's driving state is detected as stopped in real time, the system communicates with the traffic monitoring system to obtain vehicle image data of the abnormal vehicle. At the same time, the system establishes a communication connection with the vehicle-mounted equipment of the abnormal vehicle through the wireless communication module to obtain the vehicle's real-time operating data.

[0111] S313. Determine the explosion risk value of the abnormal vehicle.

[0112] Determine the explosion risk value of abnormal vehicles based on vehicle image data and real-time vehicle operation data.

[0113] Specifically, based on vehicle image data, a pre-trained deep learning model (such as a convolutional neural network (CNN)) is used to identify abnormal features in vehicle images, such as damage, deformation, fluid leakage, or smoke. This deep learning model is trained on a large amount of image data containing various vehicles in normal conditions and in varying degrees of abnormal conditions. During the training process, each image is carefully annotated to identify the specific abnormal features and corresponding categories of the vehicles in the image. By continuously adjusting the model parameters, the model can accurately identify various abnormal features. After the vehicle image data is input into this pre-trained deep learning model, the model extracts and analyzes the image features, comparing each part of the image with the learned feature patterns. When a pattern matching abnormal features such as damage, deformation, fluid leakage, or smoke is detected, the model outputs the corresponding recognition result.

[0114] The vehicle image data is fed into this pre-trained deep learning model to generate the model's recognition output. Based on the various abnormal features in the recognition results, the corresponding scores are searched in a pre-set abnormal feature score table. All scores are then added together to generate a risk score for the vehicle image data.

[0115] At the same time, key parameters related to vehicle explosion risk are extracted from the vehicle's real-time operating data, such as engine temperature, fuel pressure, battery voltage, and coolant temperature. Based on the preset normal operating range of the parameters, a determination is made as to whether any key parameters exceed the corresponding normal operating range. If so, the abnormal parameters and their corresponding deviation values are obtained. The scores corresponding to the abnormal parameters and their deviation values are searched in the preset abnormal parameter score table. All scores are summed to obtain the risk score for the vehicle's real-time operating data.

[0116] Finally, the risk scores of the vehicle image data and the real-time vehicle operation data are comprehensively evaluated. Using pre-set weights, a final explosion risk value is calculated. This risk value is expressed as a numerical value, with higher values indicating a greater explosion risk.

[0117] S314: When the explosion risk value is greater than or equal to a preset explosion risk threshold, control the second variable sign to display a warning message.

[0118] When the explosion risk value is greater than or equal to a preset explosion risk threshold, the second variable signboard corresponding to the section of the target highway within a first preset range from the abnormal vehicle is controlled to display a warning message.

[0119] Among them, the warning information is used to warn other vehicles on the target road except the abnormal vehicle to maintain an explosion safety distance between themselves and the abnormal vehicle.

[0120] Specifically, when the explosion risk value is greater than or equal to a preset explosion risk threshold, a control instruction is determined based on the specific location coordinates of the abnormal vehicle and a preset warning message template. This control instruction is then sent to a second variable signboard corresponding to a section of the target highway within a first preset range of the abnormal vehicle. Upon receiving the control instruction, the second variable signboard clearly displays the specific location coordinates of the abnormal vehicle and the warning message according to the preset warning message template and font style, reminding passing vehicles to maintain an explosion-safe distance from the abnormal vehicle.

[0121] S315: Obtain information about fire extinguishers within a second preset range from the abnormal vehicle.

[0122] The fire extinguisher information includes the location information of the fire extinguisher.

[0123] Specifically, the target highway's facility layout information and the specific location coordinates of the abnormal vehicle are obtained. Based on the abnormal vehicle's location and a pre-set second range (e.g., a certain distance from the abnormal vehicle), the target highway's facility database is searched for fire extinguisher information within that range. This information is typically stored in a traffic facility management system, and the relevant data is obtained through communication with that system.

[0124] S316. Control the sound playing device closest to the fire extinguisher to play the fire extinguisher information and the location information of the abnormal vehicle.

[0125] Specifically, based on the location information of each fire extinguisher in the fire extinguisher information and the preset location information of each sound playback device, the distance between each sound playback device and each fire extinguisher is calculated. Based on the distance between each sound playback device and each fire extinguisher, the sound playback device closest to each fire extinguisher is determined.

[0126] Based on the location information of each fire extinguisher and the location of the abnormal vehicle, a control instruction for the sound playback device corresponding to each fire extinguisher is generated. The control instruction is sent to the corresponding sound playback device. After receiving the control instruction, the sound playback device automatically plays the location information of the fire extinguisher closest to the device and the location information of the abnormal vehicle.

[0127] S317: When the explosion risk value is less than the preset explosion risk threshold, calculate the predicted processing time for moving the abnormal vehicle to the target trailer.

[0128] When the explosion risk value is less than the preset explosion risk threshold, the predicted processing time for moving the abnormal vehicle to the target trailer is calculated based on the driving time of the target trailer to the current position of the abnormal vehicle and the preset vehicle moving time.

[0129] Specifically, when the explosion risk value is less than a preset explosion risk threshold, the current position of the target trailer and the current position of the abnormal vehicle are obtained. Based on the current position of the target trailer and the current position of the abnormal vehicle, the distance between the target trailer and the abnormal vehicle is calculated. Then, the average driving speed of the target trailer is obtained, and based on the average driving speed and distance, the driving time required for the target trailer to reach the current position of the abnormal vehicle is calculated. Finally, the driving time is added to the preset vehicle movement time to obtain the predicted processing time for the abnormal vehicle to move to the target trailer. Among them, the preset vehicle movement time is set based on historical data or experience, and is used to estimate the average time it takes to move the abnormal vehicle from its current position to the target trailer.

[0130] S318: Determine the predicted time it takes for the vehicle to travel from each road section to the target road section.

[0131] The predicted time it takes for a vehicle to travel from each road section to the target road section is determined based on the average vehicle speed corresponding to each road section and the road section length of each road section.

[0132] Specifically, the road traffic database is retrieved to obtain the road segment information for all road segments between each road segment and the target road segment. The road traffic database stores detailed information on all road segments, including the starting point, end point, length, and connections to other road segments. The road segment information for each road segment includes one or more first road segments and their corresponding lengths. A vehicle must travel from this first road segment to reach the target road segment after passing through these first road segments.

[0133] The average vehicle speed for each preset road section is obtained. Based on the average vehicle speed and road section information for each road section, the first travel time for each first road section in the road section information corresponding to each road section is calculated in sequence. All first travel times corresponding to each road section are sequentially added together to obtain the predicted travel time for the vehicle to travel from each road section to the target road section.

[0134] S319: If the target prediction time in the prediction time is less than the prediction processing time, determine the traffic status of each target lane in the target road section.

[0135] If the target prediction time in the prediction time is less than the prediction processing time, the traffic status of each target lane in the target section is determined according to the obstacle conditions of each lane in the target section.

[0136] The traffic status includes passable and impassable.

[0137] Specifically, if the target prediction duration is less than the predicted processing duration, the system communicates with the traffic monitoring system to obtain road image data for the target road segment. It then uses image recognition algorithms, such as deep learning-based object detection algorithms (e.g., YOLO and Faster R-CNN), to identify obstacles in the road images. These algorithms, trained on extensive road image data containing various obstacles (e.g., accident vehicles and protective equipment), can accurately detect obstacles (vehicles and protective equipment) and their corresponding locations and ranges in the images.

[0138] After the obstacles are identified, the lane where each obstacle is located is determined based on the lane division information of the target road section (this information can be pre-stored in a database or obtained from the road information system of the traffic management department).

[0139] For each lane, if there is no obstacle in the lane, the lane's traffic status is determined to be passable; if there is an obstacle in the lane, the lane's traffic status is determined to be impassable.

[0140] S320: If there is a passable target lane, determine that the display content of the third variable sign is the first content.

[0141] If there is a passable target lane, the display content of the third variable signboard of the road section corresponding to the target predicted duration is determined to be the first content.

[0142] Among them, the first content is the accessible lanes and inaccessible lanes of the target road section.

[0143] Specifically, according to the traffic status of each target lane in the target road section, it is determined whether there is a target lane with a traffic status that is passable.

[0144] If so, first content is generated based on the traffic status of each target lane. A control instruction is determined based on the first content and a preset lane prompt information template corresponding to the first content. The control instruction is sent to the third variable signboard of the road section corresponding to the target predicted duration. After receiving the control instruction, the third variable signboard displays the passable lanes and impassable lanes in the target road section on the signboard according to the first content, the preset lane prompt information template, and the font style, reminding passing vehicles whether they can pass through the passable lanes in the target road section. If not, the following step S321 is executed.

[0145] S321: If there is no passable target lane, determine that the display content of the third variable sign is the second content.

[0146] If there is no passable target lane, the display content of the third variable signboard of the road section corresponding to the target predicted duration is determined to be the second content.

[0147] Among them, the second content is that the target road section is impassable.

[0148] Specifically, a control instruction is determined based on the second content and a preset no-traffic warning message template corresponding to the second content. This control instruction is then sent to a third variable signboard located on the road section corresponding to the target predicted duration. Upon receiving the control instruction, the third variable signboard displays a no-traffic warning message on the target road section according to the preset no-traffic warning message template and font style, alerting passing vehicles that the target road section is impassable and requiring them to change routes promptly.

[0149] In an embodiment of the present application, when the explosion risk value exceeds a preset threshold, information about fire extinguishers around the vehicle is obtained, and the fire extinguisher information and the location information of the abnormal vehicle are played through a sound playback device. This allows surrounding users to quickly find the fire extinguisher and bring it to the abnormal vehicle as soon as they hear the information. In the early stages of an explosion or fire in the abnormal vehicle, fire-fighting measures are quickly taken to effectively curb the spread of the fire and reduce the possibility and degree of damage caused by the explosion. At the same time, by obtaining the location of the abnormal vehicle in real time, when it is detected that the abnormal vehicle is about to approach the stop line of the traffic light intersection, the traffic light in the corresponding direction is immediately switched to green, allowing the abnormal vehicle to pass unimpeded. This avoids the situation where the abnormal vehicle, when unable to stop, encounters a red traffic light and collides with vehicles and pedestrians traveling normally in other directions, causing traffic accidents, thereby improving the overall safety of the highway traffic system.

[0150] The above describes the highway traffic protection facility control method in the embodiment of the present application. The following describes in detail the protection facility control system in the embodiment of the present application in combination with the above highway traffic protection facility control method.

[0151] See also Figure 4 , which is a module architecture diagram of the protective facility control system in an embodiment of the present application.

[0152] In some embodiments, the protective facility control system includes:

[0153] The sign control module 401 is used to control the first variable sign on the target road section where the abnormal vehicle is located to display a warning message when an abnormal vehicle is detected on the target road;

[0154] Model input module 402 is used to input the current position, speed, and direction of the abnormal vehicle into a pre-established digital twin model of highway traffic when the abnormal cause of the abnormal vehicle belongs to a preset cause set and the abnormal vehicle has not stopped within a preset stopping time, thereby obtaining the predicted vehicle movement route and vehicle and occupant safety value corresponding to each control scheme;

[0155] The target control scheme determination module 403 is used to obtain the target control scheme and target predicted vehicle movement route corresponding to the highest vehicle occupant safety value when there is a vehicle occupant safety value exceeding a preset threshold;

[0156] A new control scheme determining module 404 is configured to re-determine a new control scheme and a new predicted vehicle movement route when it is detected that the actual movement route of the abnormal vehicle does not match the target predicted vehicle movement route while the target protective facility is being controlled to execute the target control scheme;

[0157] The replacement scheme determination module 405 is used to determine the replacement scheme of the target movable protective facility based on the real-time facility images of each target movable protective facility on the target highway and the historical usage records of the movable protective facilities on all highways after detecting that the target protective facility has completed the execution of the new control scheme.

[0158] An embodiment of the present application also provides a protective facility control server. The protective facility control server in the embodiment of the present application is described below in conjunction with the hardware structure diagram of the protective facility control server provided in the present application.

[0159] See also Figure 5 , which is a schematic diagram of an exemplary hardware structure of a protective facility control server in an embodiment of the present application.

[0160] In some embodiments, the protective facility control server 500 includes a computer device, which can be a terminal device. The computer device includes a processor 501, a memory 502, a communication module 503, an input device 504, and an output device 505 connected via a system bus. The processor 501 of the computer device is used to provide computing and control capabilities. The memory 502 of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database is used to store data. The communication module 503 of the computer device is used to communicate with various systems to obtain corresponding image data, vehicle operation data, etc. The input device 504 of the computer device is used to receive image data and vehicle operation data transmitted by other systems. The output device 505 of the computer device is used to display data such as control schemes. When the computer program is executed by the processor 501, it implements the highway traffic protective facility control method in the embodiment of the present application.

[0161] Those skilled in the art will understand that Figure 5The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0162] In some embodiments of the present application, a computer-readable storage medium is provided, comprising instructions. When the instructions are executed on the protective facility control server 500, the protective facility control server 500 can execute the highway traffic protective facility control method in the embodiment of the present application.

[0163] In some embodiments of the present application, a computer program product is also provided. When the computer program product runs on the protection facility control server 500, the protection facility control server 500 executes the highway traffic protection facility control method in the embodiment of the present application.

[0164] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

[0165] As used in the above embodiments, the term “when” may be interpreted to mean “if” or “after” or “in response to determining that” or “in response to detecting that”, depending on the context. Similarly, the phrases “upon determining that” or “if (stated condition or event) is detected” may be interpreted to mean “if determining that” or “in response to determining that” or “upon detecting (stated condition or event)” or “in response to detecting (stated condition or event)”, depending on the context.

[0166] In the above embodiments, all or part of the embodiments can be implemented using software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., a floppy disk, hard disk, tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive).

[0167] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A method for controlling highway traffic protection facilities, characterized in that: The protective facilities include intelligent guardrails, automatic roadblocks, and variable signboards, and the method includes: When an abnormal vehicle is detected on a target highway, controlling a first variable signboard of a target road section where the abnormal vehicle is located to display a warning message, wherein the warning message is the location of the abnormal vehicle, and the target road section is one of multiple road sections of the target highway; When the abnormal cause of the abnormal vehicle belongs to a preset cause set and the abnormal vehicle has not stopped within a preset stopping time, the current position, speed, and direction of the abnormal vehicle are input into a pre-established digital twin model of highway traffic to obtain the predicted vehicle movement route and vehicle and occupant safety value corresponding to each control scheme. The control scheme is used to control the target movable protective facility on the target highway to generate friction with the abnormal vehicle, so that the abnormal vehicle stops; When the vehicle and occupant safety value exceeds a preset threshold, obtaining a target control scheme and a target predicted vehicle movement route corresponding to the highest vehicle and occupant safety value; In the case of controlling the target movable protective facility to execute the target control scheme, when it is detected that the actual movement route of the abnormal vehicle does not match the target predicted vehicle movement route, re-determining a new control scheme and a new predicted vehicle movement route; After detecting that the target movable protective facility has completed executing the new control scheme, a replacement scheme for the target movable protective facility is determined based on the real-time facility images of each target movable protective facility on the target highway and the historical usage records of the movable protective facilities on all highways.

2. The method according to claim 1, characterized in that When the abnormal cause of the abnormal vehicle belongs to a preset cause set and the abnormal vehicle has not stopped within the preset stop time, the current position, speed and direction of the abnormal vehicle are input into a pre-established digital twin model of highway traffic to obtain the predicted vehicle movement route and vehicle and occupant safety value corresponding to each control scheme, specifically including: When the abnormal cause of the abnormal vehicle belongs to a preset cause set and the abnormal vehicle has not stopped within a preset stop time, obtaining the driving speed, driving direction and current position of the abnormal vehicle; determining one or more control schemes based on target movable protective facilities on the target highway and relative position information between the target movable protective facilities and the abnormal vehicle; The driving speed, the driving direction, the current position and the control scheme are input into a pre-established digital twin model of highway traffic to obtain the predicted vehicle movement route and vehicle and personnel safety value corresponding to each control scheme.

3. The method according to claim 1, characterized in that After detecting that the target movable protective facility has completed the execution of the new control scheme, determining a replacement scheme for the target movable protective facility based on the real-time facility image of each target movable protective facility on the target highway and the historical usage records of the movable protective facilities on all highways, specifically includes: after detecting that the target movable protective facility has completed the execution of the new control scheme, determining a first current health status value of the target movable protective facility based on the real-time facility image of each target movable protective facility on the target highway; According to the historical usage records of the movable protective facilities on all roads, the total number of repairs and replacements of the movable protective facilities on all roads, excluding the target movable protective facility and corresponding to the target movable protective facility, and the second current health status value are counted; Based on the first current health status value, the total number of times and the second current health status value, a replacement plan for each protective facility in the target movable protective facility is determined, and the replacement plan includes not replacing the movable protective facility, replacing the other movable protective facilities with the target movable protective facility and replacing with a new movable protective facility.

4. The method according to claim 1, wherein After the step of determining a replacement plan for the target movable protective facility based on the real-time facility image of each target movable protective facility on the target highway and the historical usage records of movable protective facilities on all highways after detecting that the target movable protective facility has completed the execution of the new control plan, the method further includes: When the abnormal vehicle stops, acquiring vehicle image data and vehicle real-time operation data of the abnormal vehicle; determining an explosion risk value of the abnormal vehicle based on the vehicle image data and the vehicle real-time operation data; When the explosion risk value is greater than or equal to a preset explosion risk threshold, a second variable sign corresponding to a section of the target highway within a first preset range from the abnormal vehicle is controlled to display a warning message, wherein the warning message is used to warn other vehicles on the target highway except the abnormal vehicle to maintain an explosion safety distance from the abnormal vehicle; When the explosion risk value is less than a preset explosion risk threshold, the predicted processing time for moving the abnormal vehicle to the target trailer is calculated based on the driving time for the target trailer to reach the current position of the abnormal vehicle and the preset vehicle movement time; based on the predicted processing time and the obstacle conditions of each lane in the target section, the display content of the third variable sign corresponding to each section of the target highway is determined, and the display content is the first content or the second content, the first content is the passable lanes and the impassable lanes of the target section, and the second content is that the target section is impassable.

5. The method according to claim 4, characterized in that After the step of controlling the second variable sign corresponding to a section of the target highway within a first preset range from the abnormal vehicle to display a warning message when the explosion risk value is greater than or equal to a preset explosion risk threshold, the method further includes: Acquiring fire extinguisher information within a second preset range from the abnormal vehicle, the fire extinguisher information including location information of the fire extinguisher; The sound playing device closest to the fire extinguisher is controlled to play the fire extinguisher information and the position information of the abnormal vehicle.

6. The method according to claim 4, characterized in that The step of determining the display content of the third variable sign corresponding to each road section of the target highway according to the predicted processing time and the obstacle conditions of each lane in the target road section specifically includes: Determining the predicted time it takes for a vehicle to travel from each road section to the target road section based on the average vehicle speed corresponding to each road section and the road section length of each road section; If a target predicted duration exists in the predicted durations and is shorter than the predicted processing duration, determining a passable state of each target lane in the target road section according to an obstacle condition of each lane in the target road section, wherein the passable state includes passable and impassable; If the target lane is passable, determining that the display content of the third variable signboard of the road section corresponding to the target predicted duration is the first content; If the target lane does not exist, the display content of the third variable signboard of the road section corresponding to the target predicted duration is determined to be the second content.

7. The method according to claim 1, characterized in that After the step of obtaining a target control scheme and a target predicted vehicle movement route corresponding to a maximum vehicle occupant safety value when the vehicle occupant safety value exceeds a preset threshold, the method further includes: When there is a traffic light intersection in the target predicted vehicle movement route, obtaining the real-time position of the abnormal vehicle; When the distance between the real-time position and the stop line of the traffic light intersection is within a preset distance range, the traffic light at the traffic light intersection corresponding to the driving direction of the abnormal vehicle is controlled to be green.

8. A protective facility control system, characterized in that: include: A sign control module is used to control a first variable sign on a target road section where an abnormal vehicle is located to display a warning message when an abnormal vehicle is detected on the target road; A model input module is configured to input the current position, speed, and direction of the abnormal vehicle into a pre-established digital twin model of highway traffic when the abnormal cause of the abnormal vehicle belongs to a preset cause set and the abnormal vehicle has not stopped within a preset stopping time, thereby obtaining a predicted vehicle movement route and vehicle and occupant safety value corresponding to each control scheme; a target control scheme determination module, configured to obtain a target control scheme and a target predicted vehicle movement route corresponding to a maximum vehicle occupant safety value when the vehicle occupant safety value exceeds a preset threshold; a new control scheme determining module for re-determining a new control scheme and a new predicted vehicle movement route when it is detected that the actual movement route of the abnormal vehicle does not match the target predicted vehicle movement route while controlling the target movable protective facility to execute the target control scheme; The replacement scheme determination module is used to determine a replacement scheme for the target movable protective facility based on the real-time facility images of each target movable protective facility on the target highway and the historical usage records of the movable protective facilities on all highways after detecting that the target movable protective facility has completed the execution of the new control scheme.

9. A protective facility control server, characterized in that: include: one or more processors and memory; The memory is coupled to the one or more processors, and the memory is used to store computer program code, where the computer program code includes computer instructions. The one or more processors call the computer instructions to enable the protection facility control server to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing computer instructions, characterized in that: When the computer instructions are executed on the protection facility control server, the protection facility control server is caused to execute the method according to any one of claims 1 to 7.

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