Highway maintenance supervision method and system

Abstract

The invention discloses a road maintenance supervision method and system, and relates to the technical field of road maintenance, and the method comprises the steps: obtaining a driving record and a driving track uploaded by a mobile vehicle end; driving records are analyzed through a computer vision technology, and damage information appearing on the road surface is recognized and marked in a video; determining a damaged road section corresponding to each piece of damage information in the driving track according to the driving track and the time node corresponding to each piece of damage information in the video data; and generating road maintenance instruction information according to the damaged road section and the corresponding damaged road section picture. By implementing the method, the defects of limited coverage, untimely discovery and the like of traditional fixed monitoring and manual inspection are overcome, numerous running vehicles are used as data sources, the monitoring coverage is expanded, the timeliness of damage discovery is improved, and efficient and accurate information support is provided for road maintenance.

Description

Technical Field

[0001] The present application relates to the technical field of highway maintenance, and in particular to a highway maintenance supervision method and system. Background Art

[0002] After a highway is built and opened to traffic, the quality of the highway will gradually decline due to the wear and impact of wheels, erosion and weathering by natural forces such as rainstorms, floods, sandstorms, ice and snow, sunlight, ice melting, as well as human damage and certain defects left over from construction. Therefore, after the highway is built and opened to traffic, maintenance and repair measures must be taken, and it must be continuously updated and improved. Highway maintenance must repair damaged parts in a timely manner, otherwise it will lead to increased investment in repair projects, shorten the service life of the highway, and cause losses to road users. Highway maintenance must also pay attention to emergency services and repairs to keep the highway unobstructed. Highway maintenance also requires partial improvement, renewal and construction of sections, structures and facilities along the route that have too low original technical standards to improve the highway's traffic capacity and service level.

[0003] In the related technology, surveillance cameras are usually installed at specific locations on the highway to monitor whether the highway is damaged. In addition, there are also technicians driving inspection vehicles to collect damage information of the target road section, which is convenient for the subsequent generation of corresponding maintenance strategies.

[0004] However, highways cover a wide area and have a complex geographical environment. Especially in remote or hard-to-reach areas, traditional supervision methods make it difficult to detect highway damage in a timely manner, which is not conducive to highway maintenance and supervision. Summary of the invention

[0005] The present application provides a highway maintenance supervision method and system for solving the problem of how to timely discover damage to the highway, thereby assisting relevant personnel in performing maintenance supervision on the highway.

[0006] In a first aspect, the present application provides a highway maintenance supervision method, which is applied to a highway maintenance supervision system, and the method comprises: Obtain driving records and driving trajectories uploaded by the mobile car. The driving records are video data captured by the vehicle-mounted monitoring equipment. Analyze driving records through computer vision technology to identify and mark road damage in the video; According to the driving trajectory and the time node corresponding to each damage information in the video data, determine the damaged road section corresponding to each damage information in the driving trajectory; Highway maintenance instruction information is generated based on the damaged road section and the corresponding damaged road section picture, and the damaged road section picture is obtained by intercepting the video picture frame of the driving record data.

[0007] Through the above embodiments, the highway maintenance supervision system obtains the driving record video data and driving trajectory uploaded by the mobile car, uses computer vision technology to analyze the video to identify the road damage information, and then determines the road section location corresponding to each damage according to the driving trajectory and the time node of the damage, and then generates maintenance instructions based on the damaged road section information and damaged pictures. This method overcomes the defects of traditional fixed monitoring and manual inspections, such as limited coverage and untimely discovery. It uses many driving vehicles as data sources, expands the monitoring coverage, improves the timeliness of damage discovery, and provides efficient and accurate information support for highway maintenance.

[0008] In some embodiments, after the step of generating highway maintenance instruction information according to the damaged road section and the corresponding damaged road section image, the method further includes: Determine other moving vehicles on the same driving trajectory; All damaged road sections and corresponding voice prompt information on the same driving trajectory are sent to other mobile car terminals.

[0009] Through the above embodiments, the highway maintenance supervision system determines other vehicles on the same road section and pushes damage information and voice prompts to these vehicles, so that drivers can know the road conditions ahead in advance, reducing the risk of traffic accidents caused by road conditions and improving driving safety.

[0010] In some embodiments, the step of obtaining the driving record and driving trajectory uploaded by the mobile vehicle terminal specifically includes: When the number of data collections for the same road section reaches a preset threshold, data collection is performed again after a preset time period.

[0011] Through the above embodiments, the highway maintenance supervision system sets the threshold value and collection interval for the number of data collections on the same road section to avoid too frequent repeated data collection, reduce the burden of data transmission and processing, and improve data processing efficiency.

[0012] In some embodiments, before analyzing the driving record by computer vision technology to identify and mark the damaged information on the road surface in the video, the method further includes: Receive temporary driving record data uploaded by the mobile terminal of the car during the non-upload period; Priority should be given to identifying and marking road damage information in temporary driving record data.

[0013] Through the above embodiments, the highway maintenance supervision system receives driving records temporarily uploaded during the non-upload period and processes them with priority, which on the one hand makes up for the time interval of regular uploads, and on the other hand can timely discover sudden damage, thereby improving the real-time and flexibility of supervision.

[0014] In some embodiments, the step of analyzing the driving record by computer vision technology and identifying and marking the damaged information on the road surface in the video specifically includes: Obtain driving record data of multiple vehicles on the same road section; If there are a preset number of driving record data corresponding to the same damaged position, the damaged position is marked.

[0015] Through the above embodiments, the highway maintenance supervision system compares and analyzes the driving record data of multiple vehicles on the same road section, and only marks the locations where the data of multiple vehicles show damage, thereby improving the reliability of damage identification and reducing false alarms.

[0016] In some embodiments, the step of determining the damaged road section corresponding to each damage information in the driving trajectory according to the driving trajectory and the time node corresponding to each damage information in the video data specifically includes: Obtain the curve of vehicle speed changing over time in the driving record; The position coordinates of the damage information in the driving trajectory are determined based on the change curve.

[0017] Through the above embodiments, the highway maintenance supervision system introduces the vehicle speed change curve to more accurately determine the position coordinates of the damage on the driving trajectory, providing a reliable basis for subsequent damage positioning and generating maintenance instructions.

[0018] In some embodiments, after the step of generating highway maintenance instruction information according to the damaged road section and the corresponding damaged road section image, the method further includes: Mark the damaged road sections and the corresponding damaged road section pictures on the map; Display the risk level corresponding to each damaged road section; Highway maintenance instruction information corresponding to damaged road sections exceeding the preset risk level will be pushed first.

[0019] Through the above embodiments, the highway maintenance supervision system visually marks the location of damaged road sections, damaged pictures and risk levels on the map, and gives priority to pushing maintenance instructions to high-risk damage. Visual presentation makes it easier for managers to quickly understand road conditions, and hierarchical processing improves maintenance efficiency.

[0020] In a second aspect, the present application provides a highway maintenance supervision system, the highway maintenance supervision system comprising: one or more processors and a memory; The memory is coupled to the one or more processors, and the memory is used to store computer program code, which includes computer instructions. The one or more processors call the computer instructions so that the highway maintenance supervision system can implement a highway maintenance supervision method provided in the above embodiment, which will not be repeated here.

[0021] In a third aspect, the present application provides a computer-readable storage medium, including instructions. When the instructions are executed on a highway maintenance supervision system, the highway maintenance supervision system can implement a highway maintenance supervision method provided in the above embodiment, which will not be repeated here.

[0022] In a fourth aspect, the present application provides a computer program product. When the computer program product runs on a highway maintenance supervision system, the highway maintenance supervision system can implement a highway maintenance supervision method provided in the above embodiment, which will not be repeated here.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Using the driving record videos and trajectory data uploaded by many vehicles to replace traditional fixed monitoring equipment and manual inspections, it greatly expands the temporal and spatial coverage of highway damage monitoring and realizes full-time, full-domain, flexible data collection. It effectively solves the current problem of insufficient highway supervision coverage and untimely discovery.

[0024] 2. Apply computer vision technology to analyze driving video, intelligently identify road damage information, and then use the driving trajectory, damage occurrence time and speed change curve to accurately determine the specific location coordinates of the damage on the road section. This method realizes the automation of damage identification and high-precision damage positioning, providing a reliable basis for subsequent maintenance decisions.

[0025] 3. Based on the location of the damaged road section, damage level pictures and risk level identified, the road condition information is intuitively presented on the map, and graded maintenance instructions are automatically generated, with high-risk damage being pushed and handled first. This realizes the visualization of maintenance management and the intelligence of decision-making, enabling managers to quickly grasp the overall situation and carry out targeted maintenance, thus improving the accuracy and timeliness of highway maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of a highway maintenance supervision method in an embodiment of the present application; Figure 2 This is another flow chart of a highway maintenance supervision method in an embodiment of the present application; Figure 3 It is a schematic diagram of the structure of a physical device of a highway maintenance and supervision system in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0028] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0029] For ease of understanding, the application scenarios of the relevant technical embodiments are introduced below.

[0030] With the continuous expansion of the highway network and the continuous growth of traffic volume, the problem of highway damage is becoming increasingly serious, posing hidden dangers to traffic safety and traffic efficiency. Taking a certain province as an example, by the end of 2023, the mileage of highways in the province has exceeded 5,000 kilometers, and the mileage of ordinary national and provincial roads has exceeded 100,000 kilometers. According to statistics, last year alone, there were more than 2,000 traffic accidents caused by road damage in the province, with direct economic losses of more than 100 million yuan. Traditional highway maintenance mainly relies on regular manual inspections and fixed monitoring equipment installed along the line to detect damage. However, due to limited manpower and material resources, it is difficult to conduct high-frequency and full-coverage inspections on tens of thousands of kilometers of roads. Especially in remote and inconvenient sections of road, the inspection frequency is low, and the damage problem is more likely to be ignored. Although fixed monitoring equipment can work continuously, its deployment is limited, and it can only monitor local sections, making it difficult to achieve full road network coverage. In general, the current means of discovering highway damage have problems such as insufficient coverage and untimely discovery, resulting in many damages not being handled in a timely manner, which buries hidden dangers for the safe operation of highways. Highway management departments urgently need a road condition monitoring method that can achieve full-time and space and dynamic perception, so as to carry out maintenance work more proactively and efficiently.

[0031] At present, facing the above problems, the Highway Management Office of Province A mainly uses two methods to monitor the road conditions within its jurisdiction. One is to arrange patrol personnel to drive vehicles to conduct routine inspections on the road sections, and record the damage by observation and taking pictures; the other is to use the surveillance cameras deployed along the road to capture road images, and then have a dedicated person check the images to find damage. Take the section of the Beijing-Shanghai Expressway in Province A as an example. The total length is 500 kilometers, with a monitoring point every 2 kilometers, a total of 250 monitoring points. The patrol personnel need to drive 500 kilometers back and forth every day, with high work intensity but limited coverage. In order to save manpower, on average, only 1-2 inspections can be conducted per week. Once new damage occurs, it is likely that it will not be discovered before the next inspection. Although surveillance images can be collected on a regular basis, they require dedicated personnel to monitor them continuously. Due to the large number of images, complex damage conditions, low efficiency and easy fatigue, there are often omissions. In addition, many damages are small and difficult to detect, which also increases the difficulty of human eye recognition. More importantly, both of the above methods lack effective positioning means, making it difficult to accurately record the damaged kilometer mark information. The combination of various factors resulted in the damage being discovered untimely and the location being unclear, and subsequent maintenance work could not be carried out in a targeted manner, so the problem remained unsolved for a long time.

[0032] Suppose that a highway maintenance supervision method and system provided by the present application is used. The highway maintenance supervision system first establishes a connection with the on-board recorder of the user's vehicle through software. The on-board recorder can automatically take uninterrupted photos of the road surface during driving, record the driving trajectory at the same time, and then upload the video and trajectory data to the highway maintenance supervision system at preset time intervals. The system uses computer vision technology to intelligently analyze the video content, identify and mark the damaged points on the road surface. By comprehensively analyzing the trajectory, video timestamp, vehicle speed and other data, the exact kilometer mark position of each damaged point can be calculated. With complete information such as the type, degree, and location of the damage, the system can automatically generate maintenance recommendations, and mark the corresponding road sections in red on the map of the jurisdiction area to intuitively inform the management personnel.

[0033] For ease of understanding, the following is a description of the process of the method provided in this implementation. Figure 1 The figure is a flow chart of a highway maintenance supervision method in an embodiment of the present application.

[0034] S101. Obtain driving records and driving trajectories uploaded by a mobile vehicle.

[0035] Vehicle-mounted monitoring equipment commonly equipped on vehicles, such as driving recorders, front and rear view cameras, etc., can continuously capture road video images while the vehicle is driving. At the same time, vehicle-mounted GPS devices can also record the vehicle's driving trajectory in real time, including latitude and longitude coordinates, timestamps and other information.

[0036] On this basis, the driver can install specific software on the vehicle's central control screen or his own mobile phone. This specific software can upload the video footage captured by the on-board monitoring equipment and the driving trajectory information recorded by the on-board GPS device to the highway maintenance and supervision system. Specifically, the above-mentioned specific software can upload data to the cloud server of the highway maintenance and supervision system through a wireless communication module (such as 4G / 5G network, Internet of Vehicles, etc.). At the same time, considering the large amount of data, a certain upload time interval can be set, such as uploading every 10 minutes. The system can also selectively increase the upload frequency for key sections and special periods (such as at night or in bad weather) according to the needs of road network monitoring, which is not limited here.

[0037] It should be noted that any vehicle that has the above-mentioned specific software installed and has vehicle-mounted monitoring equipment and vehicle-mounted GPS equipment can collect and upload video data and driving trajectories. It is no longer limited to specific inspection vehicles or inspection personnel. In addition, the vehicle-mounted GPS device can also be replaced by the driver's mobile phone or navigation system, which is not limited here.

[0038] S102. Analyze driving records using computer vision technology to identify and mark road damage information in the video.

[0039] Specifically, the highway maintenance and supervision system first takes screenshots of the acquired video frame by frame to obtain a series of road surface images. Then, a pre-trained convolutional neural network model, such as YOLO, SSD, etc., is used to detect and locate the damaged road surface area in the image and output its bounding box coordinates. Next, an image segmentation model (such as U-Net, Mask R-CNN) is used to annotate the damaged area at the pixel level to outline the precise outline of the damage. At the same time, the classification model can determine the type of damage, such as common rutting, potholes, cracks, looseness, and gnawing on asphalt pavement, which are not limited here.

[0040] For example, a 30-second driving video was uploaded from a private car traveling at 1,823 kilometers on the G5 Beijing-Kunming Expressway. The system first captured it into 300 road images at equal intervals. After the image was processed by the target detection model, an abnormal area was selected on the 72nd image and it was initially determined to be a damaged road surface. Subsequently, the segmentation model annotated the area at the pixel level, marking an irregular polygonal outline. After the classification network judgment, the system identified that this was a surface crack about 30 cm long and 5 cm wide.

[0041] S103: Determine the damaged road section corresponding to each damage information in the driving trajectory according to the driving trajectory and the time node corresponding to each damage information in the video data.

[0042] After detecting the road damage information in the video, the next step is to determine the exact location of the damage, match it with the road network map, and find the corresponding highway section and mileage number.

[0043] Specifically, the highway maintenance and supervision system records the timestamp of each frame while analyzing the video. When damage is found in the image at a certain point in time, the system immediately queries the driving GPS coordinates corresponding to that point in time. Since the vehicle's speed and direction change dynamically over time, the front and rear displacements of the damaged image relative to the GPS coordinates can be calculated through the relationship between speed, direction and time. This allows the system to select road sections within a certain range before and after the GPS point and mark them as damaged sections. In addition, the system can also combine the road reference lines in the high-precision map to correct GPS errors and further improve the accuracy of positioning.

[0044] Taking the G5 Beijing-Kunming Expressway as an example, assuming that the crack captured by the private car at 1823 kilometers appears at the 24th second of the video. Querying the GPS records, it is found that at the 24th second, the vehicle was located at 24°39'18.23" north latitude and 102°51'40.18" east longitude. Combined with the vehicle speed of 90km / h and the driving direction of due north, the system calculated that the crack was about 20 meters backward from the GPS point. Superimposing high-precision map data, the crack was finally located on the lane at 1823 kilometers + 20 meters on the G5 Beijing-Kunming Expressway.

[0045] S104: Generate highway maintenance instruction information based on the damaged road section and the corresponding damaged road section image.

[0046] Specifically, for each damage identified, the highway maintenance and supervision system records the location information such as the highway number, driving direction, lane, pile number and mileage. Next, a close-up image of the damaged area is captured from the video to intuitively display the appearance characteristics of the damage. In addition, the system can also judge the severity of the damage based on the length, width, depth and other dimensions of the damage, as well as the morphological features such as edges and textures, such as mild, moderate and severe levels, which are not limited here. Since there are often multiple damages on the same road section, the system can also comprehensively consider the number, density and continuous distribution of damages within the road section to score the overall road condition of the road section and identify key repair sections.

[0047] The system automatically generates corresponding maintenance suggestions for different types and levels of damage. For example, mild cracks only need to be grouted, moderate potholes require local repairs, and severe damage is recommended to be milled and re-paved. The maintenance suggestions also include the reference engineering volume and cost budget for each plan, providing a reference for the final maintenance decision. The above information is eventually pushed to the highway management department in the form of maintenance instructions, presented through the system's visual interface, and a recommended disposal time limit is given based on the damage risk level, such as severe damage should be repaired within 24 hours, etc., which is not limited here.

[0048] Taking the crack at 1823 km + 20 m on the G5 Beijing-Kunming Expressway in the above example, the system determines that it is slightly damaged based on the width, depth, direction, etc. of the crack, and recommends filling the crack within 7 days, with a reference cost of 500 yuan. The other 5 adjacent cracks are comprehensively assessed as D-level (the worst on a 5-level scale) for the 100-meter section, and it is recommended that the entire section be repaired within a week with a budget of 5,000 yuan. After the relevant information is generated, it is immediately pushed to the G5 Beijing-Kunming Expressway maintenance station and visually marked on the road network maintenance map of the system terminal in the station. The maintenance station managers can clearly understand the damage of the sections under their jurisdiction, and formulate the optimal maintenance plan based on factors such as road condition scores, maintenance cycles, and budgets to eliminate safety hazards in a timely manner.

[0049] In the above embodiment, the highway maintenance supervision system obtains the driving record video data and driving trajectory uploaded by the mobile car, uses computer vision technology to analyze the video to identify the road damage information, and then determines the road section location corresponding to each damage according to the driving trajectory and the time node of the damage, and then generates maintenance instructions based on the damaged road section information and damaged pictures. This method overcomes the defects of traditional fixed monitoring and manual inspections, such as limited coverage and untimely discovery. It uses many driving vehicles as data sources, expands the monitoring coverage, improves the timeliness of damage discovery, and provides efficient and accurate information support for highway maintenance.

[0050] The following is a more detailed description of the process of the method provided by this embodiment. Figure 2 Shown is another flow chart of a highway maintenance supervision method in an embodiment of the present application.

[0051] S201: After detecting that the number of data collection times for the same road section reaches a preset threshold, data collection is performed again after a preset time period.

[0052] The highway maintenance supervision system first sets a data collection threshold and collection interval. When it is detected that the number of driving record data uploads on a certain road section has reached the threshold, the system will suspend data collection on that road section and resume collection after a preset time interval.

[0053] For example, on a section of a highway, the highway maintenance and supervision system sets the collection threshold to 10 times / hour, with a collection interval of 2 hours. Within a certain hour, when it is detected that 10 vehicles have uploaded driving records for this section, the system stops receiving new data for this section until the next hour after 2 hours. In this way, the timeliness of the data can be guaranteed while reducing the burden of redundant data on the system.

[0054] In the above embodiment, the highway maintenance supervision system sets the threshold value and collection interval for the number of data collections on the same road section to avoid too frequent repeated data collection, reduce the burden of data transmission and processing, and improve data processing efficiency.

[0055] S202: receiving temporary driving record data uploaded by the mobile terminal of the vehicle during a non-upload time period.

[0056] This step is a supplement to conventional data collection, and its purpose is to capture important road condition information that occurs outside the preset time period and improve the real-time nature of supervision. Specifically, in addition to collecting data regularly according to the strategy in S201, the highway maintenance supervision system also allows the mobile terminal of the car to actively report some temporary driving record data during the non-upload period. These data are usually small in size but large in information, representing abnormal conditions witnessed by vehicle drivers, which may include sudden events such as road collapse, foreign objects, and traffic accidents that seriously affect driving safety.

[0057] For example, on a certain section of highway, due to heavy rains for several days, landslides occurred, and a large area of ​​mud and water appeared in the middle of the road. A truck equipped with on-board monitoring happened to pass by. After the driver noticed the abnormality, he immediately uploaded the driving record video of the last minute to the highway maintenance supervision system through the specific software on the mobile terminal, and attached a brief text description. Although it is not the regular data upload period for this section of road, the system still received this emergency information and gave it high priority.

[0058] S203: Prioritize the identification and marking of road damage information on temporary driving record data.

[0059] This step is the follow-up processing of the emergency reporting data in S202. Specifically, it uses computer vision algorithms to quickly identify possible road damage information from these data and mark them as key points, providing strong support for emergency response. Among them, since the temporarily uploaded driving record data usually represents an abnormally urgent road situation, it is very easy to cause an accident if it is not handled properly. Therefore, the highway maintenance and supervision system needs to give extraordinary priority to this part of the data to ensure that damage identification and information extraction are completed in the shortest time.

[0060] In the above embodiment, the highway maintenance supervision system receives the driving records temporarily uploaded during the non-upload period and processes them with priority, which on the one hand makes up for the time interval of regular uploads, and on the other hand can timely discover sudden damage, thereby improving the real-time and flexibility of supervision.

[0061] S204: Acquire driving record data of multiple vehicles on the same road section.

[0062] Specifically, the highway maintenance and supervision system first determines the same road section that multiple vehicles pass through. Specifically, it can be determined by matching the GPS trajectory data of the vehicles to determine the road section where the trajectories overlap. After obtaining the road section information, the system then searches the database to retrieve the driving record video data uploaded by all vehicles in the road section within a specific time range.

[0063] S205: If there are a preset number of driving record data corresponding to the same damaged position, mark the damaged position.

[0064] After acquiring driving video data from multiple vehicle perspectives, the highway maintenance and supervision system compares these heterogeneous data to find the damaged locations that are verified by the majority of vehicle data. Specifically, the system pre-sets a judgment threshold, that is, how many vehicles' data show damage at the location before it can be finally confirmed. Only when the damage reaches the preset number of vehicles will it be marked as a reliable damage point.

[0065] For example, take three cars A, B, and C on a section of a highway from K100+500 meters to K102+1000 meters. It is known that through trajectory matching, three cars A, B, and C have passed through the highway section one after another. Assume that the preset threshold for the number of damaged vehicles is 2. Through computer vision algorithm analysis, it was found that the driving records of cars A and C both identified a pothole at K101+300 meters, while the data of car B was not presented. After comparison, the damage identification results of two cars at this location are consistent and reach the preset threshold, so the system marks K101+300 meters as a suspected damage point. In contrast, if only car A is identified as damaged at a certain location, this is likely to be a misjudgment caused by factors such as vehicle bumps and light changes, and the system will not mark it.

[0066] In the above embodiment, the highway maintenance supervision system compares and analyzes the driving record data of multiple vehicles on the same road section, and only marks the locations where the data of multiple vehicles show damage, thereby improving the reliability of damage identification and reducing false alarms.

[0067] S206: Determine the position coordinates of the damage information in the driving trajectory according to the curve of the change in the speed of the vehicle over time in the driving record.

[0068] After determining the location of the suspected damaged point, in order to further clarify its kilometer mark and specific coordinates, the highway maintenance supervision system obtains the curve of vehicle speed change over time during driving.

[0069] Based on the change curve, the system can determine the actual position of the vehicle at different times, and then determine the actual position coordinates of the vehicle corresponding to different picture frames.

[0070] In the above embodiment, the highway maintenance supervision system introduces the vehicle speed change curve to more accurately determine the position coordinates of the damage on the driving trajectory, providing a reliable basis for subsequent damage positioning and generation of maintenance instructions.

[0071] S207: Determine other mobile vehicle terminals on the same driving trajectory.

[0072] After generating the highway maintenance instruction information, the highway maintenance supervision system further analyzes the driving track of the damaged road section to determine other vehicles that will pass through the road section in a short time. Specifically, the system can determine other mobile vehicles traveling on the same driving track through the vehicle location information uploaded by specific software.

[0073] S208: Send all damaged road sections on the same driving trajectory and corresponding voice prompt information to other mobile vehicle terminals.

[0074] After determining the vehicle that needs to be warned, the highway maintenance and supervision system automatically generates voice prompt information and pushes the location of the damaged section and its voice prompt to the target vehicle. The voice prompt includes the start and end mileage of the damaged section, location description, damage type, risk level, etc., using a concise and clear expression method to avoid using too many professional terms. At the same time, the voice should dynamically adjust the broadcast timing according to the distance between the damaged location and the target vehicle to ensure that the driver can hear the content clearly and reserve enough reaction time.

[0075] In the above embodiment, the highway maintenance supervision system identifies other vehicles on the same road section and pushes damage information and voice prompts to these vehicles, so that drivers can know the road conditions ahead in advance, reducing the risk of traffic accidents caused by road conditions and improving driving safety.

[0076] S209: Mark the damaged road section on the map, as well as the corresponding damaged road section picture and risk level.

[0077] Specifically, the highway maintenance supervision system marks the location range of damaged sections on the map, adds schematic diagrams or real-life photos of damaged sections, and assesses the risk level based on the degree of damage. For example, the system marks K100-K110 as damaged on a highway map, and a pop-up window displays a 50-meter-long road crack image of the section, which is marked as yellow (representing medium risk) in the legend. Clicking the damage icon can also jump to a more detailed information page to learn about parameters such as the damaged area and depth.

[0078] S210. Prioritize the push of highway maintenance instruction information corresponding to damaged road sections exceeding a preset risk level.

[0079] Specifically, after determining the damage risk level, the highway maintenance supervision system will highlight and prioritize damaged road sections that exceed the preset threshold (such as high risk), prompting the highway maintenance department to arrange maintenance as soon as possible.

[0080] In the above embodiment, the highway maintenance supervision system visually marks the location of the damaged road section, damaged pictures and risk level on the map, and gives priority to pushing maintenance instructions to high-risk damage. Visual presentation makes it easier for managers to quickly understand road conditions, and hierarchical processing improves maintenance efficiency.

[0081] The highway maintenance monitoring system of the embodiment of the present invention is applied to electronic equipment. Figure 3 A schematic diagram of the architecture of an electronic device suitable for implementing an embodiment of the present invention is shown.

[0082] It should be noted that Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0083] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructions (computer programs), or by controlling related hardware through instructions (computer programs), and the instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. The electronic device of this embodiment includes a storage medium and a processor, wherein a plurality of instructions are stored in the storage medium, and the instructions can be loaded by the processor to execute any step of the method provided in the embodiment of the present invention.

[0084] Specifically, the storage medium and the processor are electrically connected directly or indirectly to realize data transmission or interaction. For example, these elements can be electrically connected to each other through one or more signal lines. The storage medium stores computer execution instructions for implementing the data access control method, including at least one software function module that can be stored in the storage medium in the form of software or firmware. The processor executes various functional applications and data processing by running the software program and module stored in the storage medium. The storage medium can be, but is not limited to, random access storage medium (Random Access Memory, referred to as: RAM), read-only storage medium (Read Only Memory, referred to as: ROM), programmable read-only storage medium (Programmable Read-Only Memory, referred to as: PROM), erasable read-only storage medium (Erasable Programmable Read-Only Memory, referred to as: EPROM), electrically erasable read-only storage medium (Electric Erasable Programmable Read-Only Memory, referred to as: EEPROM), etc. Among them, the storage medium is used to store programs, and the processor executes the program after receiving the execution instruction.

[0085] Furthermore, the software programs and modules in the above-mentioned storage medium may also include an operating system, which may include various software components and / or drivers for managing system tasks (such as memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide an operating environment for other software components. The processor may be an integrated circuit chip having signal processing capabilities. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc., which may implement or execute the various methods, steps, and logic flow diagrams disclosed in this embodiment. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0086] Since the instructions stored in the storage medium can execute the steps in any method provided in the embodiments of the present invention, the beneficial effects of any method provided in the embodiments of the present invention can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0087] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A highway maintenance supervision method, applied to a highway maintenance supervision system, characterized in that: The method comprises: Obtain driving records and driving trajectories uploaded by the mobile car end, wherein the driving records are video data captured by the vehicle-mounted monitoring equipment; Analyzing the driving record by computer vision technology to identify and mark the damaged information on the road surface in the video; Determine the damaged road section corresponding to each damage information in the driving trajectory according to the driving trajectory and the time node corresponding to each damage information in the video data; Highway maintenance instruction information is generated based on the damaged road section and the corresponding damaged road section picture, and the damaged road section picture is obtained by intercepting the video picture frame of the driving record data.

2. The method according to claim 1, characterized in that After the step of generating highway maintenance instruction information according to the damaged road section and the corresponding damaged road section picture, the method further includes: Determine other moving vehicles on the same driving trajectory; All damaged road sections on the same driving track and corresponding voice prompt information are sent to the other mobile vehicle terminals.

3. The method according to claim 1, characterized in that The step of obtaining the driving record and driving trajectory uploaded by the mobile vehicle terminal specifically includes: When the number of data collections for the same road section reaches a preset threshold, data collection is performed again after a preset time period.

4. The method according to claim 1, characterized in that: Before the step of analyzing the driving record by computer vision technology to identify and mark the damaged information on the road surface in the video, the method further includes: Receive temporary driving record data uploaded by the mobile terminal of the car during the non-upload period; The temporary driving record data is prioritized for road damage information identification and marking.

5. The method according to claim 1, characterized in that The step of analyzing the driving record by computer vision technology to identify and mark the damaged information on the road surface in the video specifically includes: Obtain driving record data of multiple vehicles on the same road section; If there are a preset number of driving record data corresponding to the same damaged position, the damaged position is marked.

6. The method according to claim 1, characterized in that The step of determining the damaged road section corresponding to each damage information in the driving trajectory according to the driving trajectory and the time node corresponding to each damage information in the video data specifically includes: Obtain the curve of vehicle speed changing over time in the driving record; The position coordinates of the damage information in the driving trajectory are determined based on the change curve.

7. The method according to claim 1, characterized in that After the step of generating highway maintenance instruction information according to the damaged road section and the corresponding damaged road section picture, the method further includes: Marking the damaged road section and the corresponding damaged road section picture on the map; Display the risk level corresponding to each damaged road section; Highway maintenance instruction information corresponding to damaged road sections exceeding the preset risk level will be pushed first.

8. A highway maintenance supervision system, characterized in that: The highway maintenance supervision system includes: 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 codes, wherein the computer program codes include computer instructions, and the one or more processors call the computer instructions to enable the highway maintenance supervision system to execute the method according to any one of claims 1 to 7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on a highway maintenance supervision system, the highway maintenance supervision system is caused to execute the method according to any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is run on a highway maintenance supervision system, the highway maintenance supervision system is enabled to execute the method according to any one of claims 1 to 7.

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