Unmanned aerial vehicle task scheduling method and device for intelligent road network patrol

Abstract

The embodiment of the invention provides an unmanned aerial vehicle task scheduling method and device for intelligent road network patrol, and precise planning of an unmanned aerial vehicle patrol route is realized by constructing a three-dimensional road network topology model and an obstacle distribution map. And designing an unmanned aerial vehicle scheduling strategy based on task fitness, and extracting patrol rules from the task template library to perform intelligent scheduling in combination with the real-time position, the endurance mileage and the load state. A grading early warning mechanism is introduced, and the event influence range is analyzed through target detection and scene recognition, so that differentiated early warning of the roadside equipment and the vehicle-mounted terminal is realized. According to the method, the defects of the traditional technology in the aspects of task planning, unmanned aerial vehicle scheduling, early warning linkage and the like are effectively overcome, and the intelligent level and the emergency disposal effect of road network patrol are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of data processing, and specifically to a method and device for scheduling drone tasks for intelligent road network inspections. Background Art

[0002] Existing methods for dispatching drones for road network inspections have significant shortcomings. Traditional systems lack systematicity in mission planning and struggle to effectively integrate three-dimensional road network topology and obstacle distribution information, hindering the rationality of inspection routes.

[0003] Furthermore, existing technologies face bottlenecks in drone scheduling. Most systems fail to comprehensively consider the drone's location, range, and payload status, and lack a scheduling mechanism based on mission adaptability, resulting in suboptimal resource allocation.

[0004] The existing system has technical shortcomings in early warning linkage. It lacks the ability to dynamically analyze the impact of an event, making it difficult to implement tiered early warnings for roadside equipment and on-board terminals, hindering emergency response effectiveness. Addressing these issues is crucial for improving road network inspections. Summary of the Invention

[0005] In response to the problems in the existing technology, this application provides a drone task scheduling method and device for intelligent road network inspection, which can effectively solve the shortcomings of traditional technology in task planning, drone scheduling and early warning linkage, and significantly improve the intelligence level of road network inspection and emergency response effect.

[0006] In order to solve at least one of the above problems, the present application provides the following technical solutions: In a first aspect, the present application provides a method for scheduling drone tasks for intelligent road network inspection, comprising: Receive traffic accident, natural disaster, and road anomaly event information collected by the road network monitoring system, extract the event type, location, and event level attributes from the event information, construct a three-dimensional road network topology model based on the road network electronic map, mark the locations of buildings, overpasses, and tunnels in the three-dimensional road network topology model to construct an obstacle distribution map, calculate the emergency return points available for drones around the event location, and plan drone inspection routes and obstacle avoidance paths based on the obstacle distribution map; The real-time location information, remaining range, and payload status information of each drone are obtained from the drone management platform, and the mission fitness score of each drone is calculated to reach the event location along the patrol route. The drone with the highest mission fitness score is selected as the target drone. Based on the event type, patrol rules are extracted from the mission template library, and the patrol rules and the obstacle avoidance path are sent to the target drone to perform the patrol mission. Collect image data obtained by the target UAV during the patrol process, perform target detection and scene recognition on the image data, extract event impact range information, determine the distribution area of roadside equipment that needs to be linked based on the event impact range information, send graded warning information to electronic information boards and LED display screens within the distribution area, push different levels of warning information to the on-board terminal based on the distance between the vehicle and the location of the event, and update the content of the warning information in real time according to the progress of event handling.

[0007] Furthermore, the method further includes: reading an event information data stream from a road network monitoring system, establishing an event information cache queue to store the data stream, parsing a data packet header of the data stream to extract an event code and a timestamp, matching the event code to a preset event type mapping table to obtain an event type identifier, extracting the latitude and longitude coordinates of the event occurrence location and an event level value from the data stream message body, and writing the event type identifier, the latitude and longitude coordinates, and the event level value into an event attribute table; Vector data of an electronic road network map is read, a road network skeleton line matrix is constructed based on the vector data, three-dimensional spatial coordinates of road network nodes are calculated to generate a road network node set, a three-dimensional road network topology model is constructed using the road network skeleton line matrix and the road network node set, building outline data, overpass elevation data, and tunnel boundary data are retrieved from a geographic information database, the outline data, the elevation data, and the boundary data are converted into an obstacle boundary point set, and the obstacle boundary point set is marked in the three-dimensional road network topology model to generate an obstacle distribution map.

[0008] Furthermore, the method further includes: obtaining the center coordinates of the location where the event occurred, constructing a circular area of the search space with the center coordinates as the center, dividing the circular area into grid units, calculating the coordinates of the center point of each grid unit, judging whether the center point of each grid unit overlaps with the obstacle boundary point based on the obstacle distribution map, screening the center points of the grid units that do not overlap with the obstacle as a set of candidate return points, calculating the distance and height difference from each return point in the set of candidate return points to the location where the event occurred, sorting the set of candidate return points from near to far according to the distance, and selecting the return point with the highest ranking as the emergency return point; Construct a three-dimensional coordinate system of the road network topology map, project the coordinates of the emergency return point and the location where the incident occurred into the three-dimensional coordinate system, calculate the initial route from the emergency return point to the location where the incident occurred based on the A-star search algorithm, read the boundary point data of the obstacle distribution map, calculate the collision detection distance between the initial route and the obstacle, adjust the route node to generate an obstacle avoidance segment when a collision risk is detected, combine the obstacle avoidance segment with the route segment where no collision occurred to generate a patrol route, and smooth the patrol route according to the turning radius and climb angle constraints of the UAV.

[0009] Furthermore, the method further includes: receiving telemetry data packets of each drone from the drone management platform, parsing the telemetry data packets to obtain a drone identification code, reading the position coordinates, remaining battery power, flight speed, and mission payload type of the corresponding drone according to the identification code, calculating the remaining range based on the remaining battery power, determining the camera specifications and image transmission device performance of the drone according to the mission payload type, and writing the position coordinates, the remaining range, the flight speed, the camera specifications, and the image transmission device performance into a drone status information table; A task fitness score calculation model is established, and the straight-line distance between the drone's position coordinates and the location of the event, the ratio of the drone's remaining range to the patrol route length, the matching degree between the drone's flight speed and the expected patrol time, and the adaptability of the camera specifications to the patrol task requirements are used as scoring indicators. The weight coefficient of each scoring indicator is determined based on the hierarchical analysis method, and the task fitness score is obtained by weighted calculation of the scoring indicators and the weight coefficients. The drone with the highest task fitness score is selected as the target drone.

[0010] Furthermore, the method further includes: reading a mapping relationship between event types and patrol rules preset in a task template library, matching a corresponding patrol rule identifier based on the event type, extracting patrol start point coordinates, patrol end point coordinates, route sampling interval, flight altitude parameters, flight speed parameters, camera pitch angle parameters, and image acquisition frequency parameters according to the patrol rule identifier, and combining the patrol rule parameters with event occurrence location information and target drone identification code to generate a task configuration file; Construct a UAV mission control instruction set, encode the parameters in the mission configuration file according to the communication protocol specification, generate mission initialization instructions, route binding instructions, obstacle avoidance path instructions, and image acquisition instructions, establish a data communication link with the target UAV, send the control instruction set to the target UAV through the data communication link, receive the instruction verification code returned by the target UAV, and verify the reliability of the verification code to complete the mission instruction confirmation.

[0011] Furthermore, the method further includes: receiving an image data stream transmitted back by a target drone, parsing image frames and shooting timestamps from the image data stream, storing the image frames and shooting timestamps in an image cache queue, reading target drone position data to record shooting position coordinates of the image frames, performing geometric correction and image enhancement processing on the image frames, using a target detection model to identify vehicle distribution density, road traffic status, and traffic control facilities in the image, extracting the scope of the accident scene, vehicle backlog length, and road blockage location based on a scene recognition model, and generating a target detection result table and a scene recognition result table; The spatial position information in the target detection result table and the scene recognition result table are integrated to construct the boundary outline of the event impact range, calculate the geometric center and coverage radius of the boundary outline, establish a local coordinate system with the geometric center as the origin, mark the positions of information boards, display screens, and broadcasting equipment along the road in the local coordinate system, calculate the distance from the position of each device to the geometric center, and filter out devices whose distance is less than the coverage radius to generate the roadside equipment distribution area.

[0012] Furthermore, the system also includes: constructing a warning information classification template, dividing the warning level threshold based on the straight-line distance between the vehicle and the location of the incident, reading the communication address and device type identification of each device in the roadside device distribution area, matching the corresponding information release format according to the device type identification, generating device control instructions based on the warning information content according to the information release format, establishing a communication connection with each device, sending the device control instructions to the corresponding electronic information board and LED display screen, and receiving the instruction response confirmation information returned by the device; Acquire real-time location data of moving vehicles from the Internet of Vehicles platform, calculate the straight-line distance between each vehicle and the location where the event occurred, compare the straight-line distance with the warning level threshold to determine the warning information level, select the corresponding information push template based on the warning information level, write the event handling status information into the information push template to generate a vehicle-mounted terminal prompt message, push the prompt message to the vehicle-mounted terminal of the target vehicle through the Internet of Vehicles communication gateway, monitor the progress status of the event handling, and regenerate the warning information content when receiving the progress update.

[0013] In a second aspect, the present application provides a drone task scheduling device for intelligent road network inspection, comprising: A path planning module is used to receive information on traffic accidents, natural disasters, and road anomalies collected by the road network monitoring system, extract the event type, location, and level attributes from the event information, construct a three-dimensional road network topology model based on the road network electronic map, mark the locations of buildings, overpasses, and tunnels in the three-dimensional road network topology model to construct an obstacle distribution map, calculate the emergency return points available to drones around the event location, and plan drone inspection routes and obstacle avoidance paths based on the obstacle distribution map; The mission inspection module is used to obtain the real-time location information, remaining range, and payload status information of each drone from the drone management platform, calculate the mission fitness score of each drone to reach the event location along the inspection route, select the drone with the highest mission fitness score as the target drone, extract inspection rules from the mission template library based on the event type, and send the inspection rules and the obstacle avoidance path to the target drone to perform the inspection mission; The abnormal alarm module is used to collect image data obtained by the target UAV during the patrol process, perform target detection and scene recognition on the image data, extract event impact range information, determine the distribution area of roadside equipment that needs to be linked based on the event impact range information, send graded warning information to the electronic information board and LED display screen within the distribution area, push different levels of warning information to the on-board terminal based on the distance between the vehicle and the location of the event, and update the content of the warning information in real time according to the progress of event handling.

[0014] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for scheduling drone tasks for intelligent road network inspection are implemented.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the drone task scheduling method for intelligent road network inspection.

[0016] In a fifth aspect, the present application provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the drone task scheduling method for intelligent road network inspection.

[0017] As can be seen from the above technical solutions, the present application provides a method and device for scheduling drone tasks for intelligent road network inspections, which realizes accurate planning of drone inspection routes by constructing a three-dimensional road network topology model and an obstacle distribution map. A drone scheduling strategy based on task adaptability is designed, and inspection rules are extracted from the task template library for intelligent scheduling in combination with real-time location, cruising range, and load status. A hierarchical early warning mechanism is introduced, and the scope of event impact is analyzed through target detection and scene recognition to realize differentiated early warnings for roadside equipment and vehicle-mounted terminals. This method effectively solves the shortcomings of traditional technologies in task planning, drone scheduling, and early warning linkage, and significantly improves the intelligence level of road network inspections and the effectiveness of emergency response. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0019] Figure 1 Schematic diagram of the flow of the method for scheduling UAV tasks for intelligent road network inspection in an embodiment of the present application; Figure 2 This is a structural diagram of a UAV task scheduling device for intelligent road network inspection in an embodiment of the present application; Figure 3 Schematic diagram of the structure of the electronic device in the embodiment of the present application.

[0020] Reference numerals: Electronic device 9600, central processing unit 9100, memory 9140, communication module 9110, input unit 9120, audio processor 9130, display 9160, power supply 9170, buffer memory 9141, application / function storage unit 9142, data storage unit 9143, driver program storage unit 9144, antenna 9111, speaker 9131, microphone 9132. DETAILED DESCRIPTION

[0021] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0022] The acquisition, storage, use, and processing of data in this application's technical solution comply with relevant national laws and regulations.

[0023] Taking into account the problems existing in the prior art, the present application provides a method and device for scheduling drone tasks for intelligent road network inspections. By constructing a three-dimensional road network topology model and an obstacle distribution map, accurate planning of drone inspection routes is achieved. A drone scheduling strategy based on task adaptability is designed, and inspection rules are extracted from the task template library for intelligent scheduling in combination with real-time location, cruising range, and load status. A hierarchical early warning mechanism is introduced, and the scope of event impact is analyzed through target detection and scene recognition to achieve differentiated early warnings for roadside equipment and vehicle-mounted terminals. This method effectively solves the shortcomings of traditional technologies in task planning, drone scheduling, and early warning linkage, and significantly improves the intelligence level of road network inspections and the effectiveness of emergency response.

[0024] In order to effectively solve the shortcomings of traditional technologies in task planning, drone scheduling and early warning linkage, and significantly improve the intelligent level of road network inspection and emergency response effect, this application provides an embodiment of a drone task scheduling method for intelligent road network inspection, see Figure 1 The intelligent road network inspection UAV task scheduling method specifically includes the following contents: Step S101: Receive traffic accident, natural disaster, and road anomaly event information collected by the road network monitoring system, extract the event type, occurrence location, and event level attributes from the event information, construct a three-dimensional road network topology model based on the road network electronic map, mark the locations of buildings, overpasses, and tunnels in the three-dimensional road network topology model to construct an obstacle distribution map, calculate the emergency return points available to drones around the event location, and plan drone inspection routes and obstacle avoidance paths based on the obstacle distribution map; Optionally, this embodiment addresses issues such as delayed event information processing, inaccurate network modeling, and irrational drone route planning in traditional road network inspections by innovatively designing an intelligent inspection solution based on a three-dimensional road network model. This embodiment first establishes a data interface with the road network monitoring system and uses distributed message queue technology to receive event information in real time. The message parsing engine extracts key attributes: Event = Parse(Type, Location, Level), where Type is the event type code, Location is the latitude and longitude coordinates, and Level is the event level.

[0025] This embodiment deeply optimizes the road network modeling mechanism. A three-dimensional road network topology model is constructed based on high-precision electronic map data, employing a multi-level gridding strategy to enhance modeling accuracy. The system maps road network nodes to a three-dimensional coordinate system: Node(x, y, z) = Transform(Lat, Lon, Alt), where Lat and Lon represent latitude and longitude coordinates, and Alt represents elevation. The road network skeleton is constructed through node connectivity, taking into account geometric characteristics such as road slope and curvature.

[0026] This embodiment innovatively implements an obstacle modeling strategy. Geometric data for buildings, overpasses, tunnels, and other facilities is retrieved from a geographic information database, and obstacle contour models are constructed using a boundary extraction algorithm. The system uses an octree structure to partition and manage space, improving collision detection efficiency. For large buildings, convex hull decomposition simplifies the geometric model, maintaining computational efficiency while ensuring safety margins.

[0027] This embodiment optimizes the emergency return point calculation mechanism. A search space is constructed centered on the event location, and candidate return points are identified through gridding and accessibility analysis. The system considers terrain restrictions and obstacle avoidance requirements, and evaluates the availability of return points based on drone performance parameters. Special attention is paid to elevated sections and complex interchanges, ensuring the safety of return points through a multi-level search.

[0028] This embodiment innovatively designs a route planning scheme. It uses an improved A-star algorithm for path search, incorporating obstacle safety distance as a key factor in the cost function. The system optimizes route smoothness using Bezier curves to ensure the drone's motion constraints are met. For flight segments requiring obstacle avoidance, a dynamic programming algorithm generates the optimal obstacle avoidance path.

[0029] This embodiment further optimizes the route verification mechanism. A virtual flight environment is constructed to simulate and verify the planned route. The system uses Monte Carlo methods to assess route safety, accounting for uncertainties such as wind field effects and position errors. Especially for routes crossing densely built-up areas, probabilistic collision detection ensures the reliability of the planned path.

[0030] This embodiment innovatively implements a task scheduling strategy. It dynamically adjusts the optimization objectives for route planning based on the type and urgency of the incident. Using a multi-objective optimization approach, the system balances flight time, energy consumption, and safety to generate inspection plans that meet actual needs. For complex road conditions, segmented planning reduces computational complexity.

[0031] This embodiment achieves precise representation of the road network environment through 3D modeling technology. It demonstrates strong modeling capabilities and planning efficiency, particularly when dealing with complex urban scenarios. Through precise environmental perception and intelligent path planning, the system can rapidly respond to various emergencies and provide reliable mission support for drone inspections.

[0032] The innovative design of this embodiment not only solves the modeling accuracy and planning efficiency problems in traditional methods, but also establishes a sustainable optimization task planning framework. Through the continuous updating of environmental models and the dynamic adjustment of planning strategies, the system can continuously improve its adaptability to various scenarios and provide strong guarantees for intelligent road network inspections. This intelligent planning mechanism ensures that the system always maintains efficient processing capabilities and reliable planning results when faced with complex and changing road conditions. In particular, through multi-dimensional safety assessments, the reliability of drone inspections has been significantly improved, and rapid response to emergencies has been achieved.

[0033] Step S102: Obtaining the real-time location information, remaining range, and payload status information of each drone from the drone management platform, calculating the mission fitness score of each drone to reach the event location along the patrol route, selecting the drone with the highest mission fitness score as the target drone, extracting patrol rules from the mission template library based on the event type, and sending the patrol rules and the obstacle avoidance path to the target drone to perform the patrol mission; Optionally, this embodiment addresses the challenges of irrational drone scheduling, inefficient task allocation, and unclear inspection rules in traditional road network inspections by innovatively designing a drone scheduling solution based on task adaptability. This embodiment first utilizes a distributed data collection architecture to acquire real-time drone status information through the management platform's data interface. Each drone is equipped with a high-precision positioning module and an intelligent power management system to ensure the accuracy of its location and endurance data.

[0034] This embodiment deeply optimizes the drone status evaluation mechanism. The task fitness is calculated through the data fusion algorithm: Score = w1 Distance+w2 Battery + w3 Speed+w4 Payload, where Distance is the distance score between the drone and the target location, Battery is the endurance score, Speed is the speed matching score, Payload is the payload compatibility score, and w1 to w4 are the corresponding weight coefficients. The system uses an adaptive weight adjustment strategy to dynamically optimize weight configuration based on mission urgency.

[0035] This embodiment innovatively implements a range assessment strategy. The battery management system acquires real-time power data and calculates the remaining range based on weather conditions and flight load. The system considers the impact of environmental factors such as wind speed and temperature on range, and uses an energy consumption model to predict the actual available range. Especially for long-distance inspection missions, multi-point range planning ensures reliable execution.

[0036] This embodiment optimizes the payload status analysis mechanism. The system evaluates the compatibility of drone-mounted equipment for different inspection missions. By analyzing the compatibility between sensor performance parameters and mission requirements, the optimal equipment combination is selected. For example, for traffic accident inspections, the resolution and zoom capabilities of high-definition cameras are prioritized; for natural disaster inspections, the detection performance of thermal imaging equipment is prioritized.

[0037] This embodiment innovatively designs a task rule extraction solution. It establishes a structured task template library and pre-sets standardized inspection rules for different types of events. The system uses a rule mapping engine to quickly match applicable inspection templates, extracting key control parameters and execution processes. For complex scenarios, a complete task execution plan is generated through rule combination.

[0038] This embodiment deeply optimizes the command delivery mechanism. It utilizes a reliable communication protocol to establish a command transmission channel, and an encryption algorithm ensures the security of command transmission. The system monitors communication quality in real time and automatically switches to a backup channel when signal degradation is detected. Especially for critical commands, a confirmation mechanism ensures reliable delivery.

[0039] This embodiment innovatively implements a task execution monitoring strategy. A real-time feedback mechanism is established to monitor drone execution status and task progress. The system uses a state prediction algorithm to assess task execution risk and promptly adjusts inspection strategies when anomalies are detected. In multi-drone collaborative scenarios, a task coordination mechanism ensures the continuity and integrity of inspections.

[0040] This embodiment achieves precise allocation of drone inspection tasks through intelligent scheduling technology. It demonstrates strong decision-making and efficient response, particularly when handling emergencies. Through a scientific evaluation mechanism and reliable execution plan, the system can quickly select the most suitable drone for inspection tasks, providing strong support for road network monitoring.

[0041] The innovative design of this embodiment not only solves the scheduling efficiency and task execution issues in traditional methods, but also establishes a task management framework that can be continuously optimized. Through the continuous optimization of the evaluation model and the improvement of the execution strategy, the system can continuously improve its adaptability to various inspection scenarios and provide reliable guarantees for intelligent road network inspections. This intelligent scheduling mechanism ensures that the system always maintains efficient decision-making capabilities and reliable execution results when faced with complex and changing road conditions. In particular, through standardized task templates and flexible rule management, the standardization and execution efficiency of inspection tasks have been significantly improved.

[0042] Step S103: Collect image data obtained by the target UAV during the inspection process, perform target detection and scene recognition on the image data, extract event impact range information, determine the distribution area of roadside equipment that needs to be linked based on the event impact range information, send graded warning information to the electronic information board and LED display screen within the distribution area, push different levels of warning information to the on-board terminal based on the distance between the vehicle and the location of the event, and update the content of the warning information in real time according to the progress of event handling.

[0043] Optionally, this embodiment addresses the challenges of inaccurate image analysis, imprecise impact assessment, and delayed warning information release in traditional road network inspections by innovatively designing a deep learning-based intelligent warning solution. This embodiment first receives the image data stream captured by the drone in real time via a high-speed image transmission link and employs hardware acceleration technology to ensure real-time image processing. The system then preprocesses the received images, including geometric correction, illumination balancing, and noise suppression, to improve the accuracy of subsequent analysis.

[0044] This example deeply optimizes the target detection mechanism. It uses an improved YOLOv5 model for multi-target detection: Detection = Model(Image, Confidence), where Image is the input image and Confidence is the detection confidence threshold. The model is optimized for traffic scenarios using transfer learning methods, enabling accurate identification of key targets such as vehicles, pedestrians, and road infrastructure. The attention mechanism improves detection robustness, particularly for images in complex weather conditions.

[0045] This embodiment innovatively implements a scene understanding strategy. A scene recognition model is constructed based on a semantic segmentation network, extracting scene semantic information through multi-scale feature fusion. The system accurately segments key areas such as accident scenes and disaster zones, and calculates the impact range based on spatial location information. For example, for traffic accident scenarios, the impact is assessed by the length of vehicle backlogs and the degree of road blockage; for natural disasters, the threat level is determined by the size of the damaged area and its spread.

[0046] This embodiment optimizes the impact range analysis mechanism. It constructs a spatial analysis model based on geographic information and maps detection results to the actual road network coordinate system. The system uses a spatial clustering algorithm to determine the boundaries of the impact area and calculates the impact spread range based on road topology. For complex road networks, graph theory algorithms are used to analyze the impact diffusion path, enabling precise scoping.

[0047] This embodiment innovatively designs a device linkage solution. It builds a device selection strategy based on impact range information and uses spatial indexing to quickly locate roadside devices requiring linkage. The system optimizes device combinations based on device coverage and information display quality. Especially for critical road sections, multi-device collaboration ensures comprehensive coverage of warning information.

[0048] This embodiment further optimizes the warning grading mechanism. Based on the distance between the vehicle and the incident location, a multi-level warning strategy is established: Warning_Level = f(Distance, Severity), where Distance is the distance between the vehicle and Severity is the severity of the incident. The system adjusts the warning level using dynamic thresholds to ensure the relevance and effectiveness of warning information.

[0049] This embodiment innovatively implements an information push strategy. Differentiated information release schemes are employed for different types of terminal devices. The system ensures timely delivery of critical information through message priority management. For in-vehicle terminals, the system dynamically adjusts the push strategy by predicting the time of potential impact based on the vehicle's direction and speed.

[0050] This embodiment uses deep learning technology to achieve intelligent analysis and early warning of road network events. It demonstrates strong perception and decision-making efficiency, particularly when handling emergencies. Through precise scene understanding and rapid information dissemination, the system can effectively mitigate the impact of incidents and provide strong support for road safety management.

[0051] The innovative design of this embodiment not only solves the analysis accuracy and response speed issues of traditional methods, but also establishes a sustainably optimized early warning framework. Through continuous optimization of the analysis model and improvement of the early warning strategy, the system can continuously improve its ability to handle various events, providing reliable support for intelligent road network management. This intelligent early warning mechanism ensures that the system maintains efficient analysis capabilities and reliable early warning results when faced with complex and changing road conditions. In particular, the multi-level information release strategy significantly improves the coverage and timeliness of early warning information, achieving proactive prevention and control of road safety.

[0052] From the above description, it can be seen that the drone task scheduling method for intelligent road network inspection provided by the embodiment of the present application can realize the accurate planning of drone inspection routes by constructing a three-dimensional road network topology model and an obstacle distribution map. A drone scheduling strategy based on task adaptability is designed, and inspection rules are extracted from the task template library for intelligent scheduling in combination with real-time position, cruising range and load status. A hierarchical early warning mechanism is introduced, and the scope of event impact is analyzed through target detection and scene recognition to realize differentiated early warning of roadside equipment and vehicle-mounted terminals. This method effectively solves the shortcomings of traditional technologies in task planning, drone scheduling and early warning linkage, and significantly improves the intelligence level of road network inspection and the effect of emergency response.

[0053] In one embodiment of the method for scheduling drone tasks for intelligent road network inspection of the present application, the following contents may also be specifically included: Step S201: reading an event information data stream from a road network monitoring system, establishing an event information cache queue to store the data stream, parsing the data packet header of the data stream to extract the event code and timestamp, matching the event code to a preset event type mapping table to obtain an event type identifier, extracting the latitude and longitude coordinates of the event location and the event level value from the data stream message body, and writing the event type identifier, the latitude and longitude coordinates, and the event level value into an event attribute table; Step S202: Read vector data of the electronic road network map, construct a road network skeleton line matrix based on the vector data, calculate the three-dimensional spatial coordinates of the road network nodes to generate a road network node set, use the road network skeleton line matrix and the road network node set to construct a three-dimensional road network topology model, retrieve building outline data, overpass elevation data, and tunnel boundary data from a geographic information database, convert the outline data, the elevation data, and the boundary data into an obstacle boundary point set, mark the obstacle boundary point set in the three-dimensional road network topology model to generate an obstacle distribution map.

[0054] Optionally, this embodiment addresses the issues of delayed event information processing, insufficient road network modeling accuracy, and inaccurate obstacle marking in traditional road network monitoring. It innovatively designs a road network modeling solution based on real-time data streams. This embodiment first establishes a high-speed data channel to access the road network monitoring system and uses zero-copy technology to achieve efficient data stream reading. An event information cache queue is constructed using memory mapping technology: Queue_Size = Buffer_Length Event_Size, where Buffer_Length is the buffer length and Event_Size is the size of a single event data.

[0055] This embodiment deeply optimizes the data parsing mechanism. A streaming parser is used to process data packets, and packet header information is parsed using a custom protocol. The system has designed an efficient event encoding system that includes event categories, subcategories, and attribute identifiers, and quickly extracts encoding information through bitwise operations. For timestamp processing, a unified time base is used to ensure the timing accuracy of event sequences. Especially for high-concurrency scenarios, multi-level queue management ensures real-time data processing.

[0056] This embodiment innovatively implements an event mapping strategy. It constructs a structured event type mapping table, enabling fast lookups through hash indexing. The system establishes standardized description templates for different types of traffic events, including scenarios such as accidents, disasters, and road damage. For example, traffic accidents are subdivided into specific types such as rear-end collisions, rollovers, and multi-vehicle collisions; natural disasters are further subdivided into subtypes such as flooding, landslides, and icing.

[0057] This embodiment optimizes the location information processing mechanism. It extracts longitude and latitude coordinates from the data stream message body and maps them to a unified spatial reference system using a coordinate conversion algorithm. The system uses a high-precision positioning model to correct coordinate errors, specifically accounting for factors such as GPS signal drift and multipath effects. For event level assessment, a multi-dimensional classification standard is established, comprehensively considering the scope, duration, and severity of the event.

[0058] This embodiment innovatively designs a road network modeling solution. It constructs a road network skeleton based on vector data: Network = Matrix(Nodes, Edges), where Nodes represents the network node set and Edges represents the road segment connections. The system calculates node spatial coordinates through 3D projection transformation, taking into account geometric features such as road longitudinal slope and superelevation to ensure the model's geometric accuracy.

[0059] This embodiment deeply optimizes the topology construction mechanism. It generates a road network topology model using a skeleton line matrix and node sets, and analyzes network connectivity through graph theory algorithms. The system considers special structures such as complex intersections and overpasses, ensuring the accuracy of topological relationships through multi-level modeling. In particular, for complex road sections such as roundabouts and interchanges, a hierarchical modeling strategy is employed to ensure clear representation of spatial relationships.

[0060] This embodiment innovatively implements an obstacle modeling strategy. Data on buildings, overpasses, tunnels, and other facilities is retrieved from a geographic information database, and obstacle outlines are constructed using a boundary extraction algorithm. The system employs 3D spatial indexing technology to manage obstacle data and improve spatial query efficiency. For tall buildings, 3D scanning generates precise outline models; for overpasses, the locations of piers and clearances are specifically marked.

[0061] This embodiment achieves precise modeling of the road network environment through data stream processing technology. It demonstrates strong modeling capabilities and processing efficiency, particularly when dealing with complex urban road networks. Through precise event parsing and efficient spatial modeling, the system can quickly construct realistic road network environment models, providing a reliable environmental perception foundation for drone inspections.

[0062] The innovative design of this embodiment not only solves the data processing and modeling accuracy issues of traditional methods, but also establishes a sustainably optimized environmental modeling framework. By improving data processing strategies and optimizing modeling methods, the system can continuously enhance its ability to represent complex road network environments, providing strong support for intelligent road network monitoring. This intelligent modeling mechanism ensures that the system maintains efficient processing capabilities and reliable modeling results when faced with complex and ever-changing urban road networks. In particular, the fusion analysis of multi-source data significantly improves the accuracy and completeness of environmental perception.

[0063] In one embodiment of the method for scheduling drone tasks for intelligent road network inspection of the present application, the following contents may also be specifically included: Step S301: Obtain the center coordinates of the location where the event occurred, construct a circular area of the search space with the center coordinates as the center, divide the circular area into grid cells, calculate the coordinates of the center point of each grid cell, determine whether the center point of each grid cell overlaps with the obstacle boundary point based on the obstacle distribution map, select the center points of the grid cells that do not overlap with the obstacle as a set of candidate return points, calculate the distance and height difference between each return point in the set of candidate return points and the location where the event occurred, sort the set of candidate return points from near to far according to distance, and select the return point with the highest ranking as the emergency return point; Step S302: Construct a three-dimensional coordinate system of the road network topology map, project the coordinates of the emergency return point and the location where the incident occurred into the three-dimensional coordinate system, calculate the initial route from the emergency return point to the location where the incident occurred based on the A-star search algorithm, read the boundary point data of the obstacle distribution map, calculate the collision detection distance between the initial route and the obstacle, adjust the route node to generate an obstacle avoidance segment when a collision risk is detected, combine the obstacle avoidance segment with the route segment where no collision occurred to generate a patrol route, and smooth the patrol route according to the turning radius and climb angle constraints of the UAV.

[0064] Optionally, this embodiment addresses issues such as irrational return point selection, unsafe route planning, and insufficient obstacle avoidance capabilities in traditional road network inspections by innovatively designing a route planning solution based on spatial analysis. This embodiment first constructs a search space centered around the event location and uses a polar coordinate system to calculate the search range: Search_Radius = f(Event_Type, Area_Size), where Event_Type is the event type and Area_Size is the affected area. This ensures that the search range covers all possible return locations.

[0065] This embodiment deeply optimizes the grid division mechanism. The circular search area is discretized using an adaptive grid division algorithm, and the grid size is dynamically adjusted based on the complexity of the area. The system uses a quadtree structure to manage grid cells and improves query efficiency through spatial indexing technology. For each grid cell, the center point coordinates are determined through centroid calculation, and a grid coding system is established to facilitate spatial retrieval. Especially in densely built-up areas, fine-grained division improves the accuracy of return point selection.

[0066] This embodiment innovatively implements an obstacle detection strategy. Based on a pre-built obstacle distribution map, a ray projection method is used to detect the spatial relationship between the grid center point and obstacles. The system ensures the safety of point selection through multi-level collision detection, taking into account the impact of overhead obstacles such as building heights and power lines. For example, in areas with high-rise buildings, the system calculates the shadow range of the building to avoid selecting obstructed locations.

[0067] This embodiment optimizes the return point evaluation mechanism. Selected candidate return points undergo a multi-dimensional evaluation, calculating the Euclidean distance and elevation difference to the target location. The system considers terrain undulations and building distribution, prioritizing return points through a weighted score. For inspections of critical road sections, special consideration is given to factors such as field of view and communication signal strength.

[0068] This embodiment innovatively designs a route planning scheme. A path search space is constructed in a three-dimensional coordinate system, and an improved A-star algorithm is used for initial route planning: Path_Cost = g(n) + h(n), where g(n) is the actual cost from the starting point to the current node, and h(n) is the estimated cost from the current node to the destination. The system optimizes search efficiency using a heuristic function, taking into account altitude changes and steering costs.

[0069] This embodiment deeply optimizes the collision detection mechanism. Dynamic collision detection is performed on planned routes, using bounding box technology to quickly determine potential collision risks. The system employs a hierarchical detection strategy, initially performing a rough check followed by a more refined check of potentially risky flight segments. Especially for complex intersections, three-dimensional spatial analysis ensures flight safety margins.

[0070] This embodiment innovatively implements an obstacle avoidance path generation strategy. When a collision risk is detected, the system adjusts the route nodes using an artificial potential field method to generate a safe obstacle avoidance path. A virtual force field simulates the repulsive effect of obstacles while also considering the attractive effect of the target point, enabling dynamic route planning. For multi-obstacle scenarios, a path optimization algorithm is used to find the optimal obstacle avoidance solution.

[0071] This embodiment utilizes intelligent planning technology to achieve safe drone route design. It demonstrates robust planning capabilities and obstacle avoidance, particularly in complex urban environments. Through scientific home point selection and precise route planning, the system ensures the safe execution of drone inspection missions, providing reliable support for road network monitoring.

[0072] The innovative design of this embodiment not only addresses the safety and reliability issues of traditional methods but also establishes a sustainable and optimized route planning framework. Through continuous improvement of planning strategies and optimization of obstacle avoidance methods, the system can continuously enhance its adaptability to complex scenarios, providing strong support for intelligent road network inspections. This intelligent planning mechanism ensures that the system maintains efficient planning capabilities and reliable obstacle avoidance in complex and changing urban environments. In particular, through multi-level safety assessments, the reliability and efficiency of drone inspections are significantly improved.

[0073] In one embodiment of the method for scheduling drone tasks for intelligent road network inspection of the present application, the following contents may also be specifically included: Step S401: Receive telemetry data packets from each drone from the drone management platform, parse the telemetry data packets to obtain a drone identification code, read the corresponding drone's location coordinates, remaining battery life, flight speed, and mission payload type based on the identification code, calculate the remaining range based on the remaining battery life, determine the camera specifications and image transmission device performance of the drone based on the mission payload type, and write the location coordinates, remaining range, flight speed, camera specifications, and image transmission device performance into a drone status information table; Step S402: Establish a task fitness score calculation model, use the straight-line distance between the drone's position coordinates and the event location, the ratio of the drone's remaining range to the patrol route length, the matching degree between the drone's flight speed and the expected patrol time, and the adaptability of the camera specifications to the patrol task requirements as scoring indicators, determine the weight coefficient of each scoring indicator based on the hierarchical analysis method, perform weighted calculation on the scoring indicators and the weight coefficients to obtain the task fitness score, and select the drone with the highest task fitness score as the target drone.

[0074] Optionally, this embodiment addresses the problems of inaccurate state perception, irrational task matching, and inefficient decision-making in traditional drone task scheduling by innovatively designing an intelligent scheduling solution based on multi-dimensional assessment. This embodiment first establishes a real-time communication link with the drone management platform and employs an efficient telemetry data transmission protocol to ensure real-time and reliable data collection. The telemetry data packet is processed by the data parsing engine: Telemetry = Parse(ID, Position, Battery, Speed, Payload), where the parameters represent the drone's identification, location information, battery status, flight parameters, and payload information, respectively.

[0075] This embodiment deeply optimizes the state information processing mechanism. Based on the real-time data of the battery management system, a mapping model between power and range is established: Range = f(Battery, Weight, Wind), where Battery is the current battery percentage, Weight is the takeoff weight, and Wind is the wind speed factor. The system establishes an energy consumption prediction model through historical data analysis, considering the impact of factors such as weather conditions and flight attitude on range. Different energy consumption parameters are used for different models to ensure the accuracy of range prediction.

[0076] This embodiment innovatively implements a payload assessment strategy. A device performance evaluation system is constructed for different types of mission payloads. The system quantitatively evaluates parameters such as camera resolution, focal length, and aperture, while also considering performance indicators such as bandwidth, latency, and anti-interference capabilities of the image transmission device. For example, for traffic accident inspections, the focus is on zoom capability and image stability; for disaster monitoring, thermal imaging and wide-angle coverage are more important.

[0077] This embodiment optimizes the status information management mechanism. It uses a distributed database to store drone status information, maintaining data currency through a real-time update mechanism. The system establishes a complete status tracking chain, recording drone performance and maintenance records, providing a reliable basis for decision-making in task allocation. Data verification is used to ensure the accuracy of key status parameters.

[0078] This embodiment innovatively designs a fitness scoring model. The scoring system is constructed based on the hierarchical analysis method: Score = w1 D+w2 R + w3 V+w4 C, where D is the distance score, R is the endurance score, V is the speed score, C is the payload score, and w1 to w4 are the corresponding weight coefficients. The system determines the initial weights through expert experience and historical data analysis, and dynamically optimizes the weight configuration through machine learning methods.

[0079] This implementation deeply optimizes the indicator calculation mechanism. For distance scoring, actual flight distance is calculated based on road topology; for endurance scoring, remaining battery capacity is assessed based on mission duration; for speed scoring, mission urgency is analyzed to determine the optimal flight speed; and for payload scoring, equipment suitability is assessed based on mission requirements. This multi-dimensional, comprehensive assessment ensures the selection of the most suitable execution unit.

[0080] This embodiment innovatively implements a decision-making optimization strategy. It uses a dynamic programming algorithm to optimize task allocation, taking into account the synergistic effects of multiple drones. The system assesses task execution risk through state prediction, maximizing resource utilization while ensuring mission quality. For urgent tasks, a rapid decision-making mechanism shortens response time.

[0081] This embodiment achieves intelligent scheduling of drone missions through multi-dimensional evaluation technology. It demonstrates strong decision-making capabilities and matching efficiency, particularly when handling complex mission scenarios. Through precise state perception and a scientific evaluation mechanism, the system can quickly select the most suitable drone for patrol missions, providing reliable technical support for road network monitoring.

[0082] The innovative design of this embodiment not only solves the state assessment and task matching problems in traditional methods, but also establishes a sustainable optimization scheduling framework. Through the continuous optimization of the evaluation model and the improvement of the decision-making strategy, the system can continuously improve its adaptability to various task scenarios, providing a strong guarantee for intelligent road network inspection. This intelligent scheduling mechanism ensures that the system always maintains efficient decision-making capabilities and reliable execution results when faced with complex and changing road conditions. In particular, through comprehensive multi-dimensional evaluation, the accuracy and execution efficiency of task allocation are significantly improved.

[0083] In one embodiment of the method for scheduling drone tasks for intelligent road network inspection of the present application, the following contents may also be specifically included: Step S501: Read the mapping relationship between event types and patrol rules preset in the task template library, match the corresponding patrol rule identifier based on the event type, extract the patrol start point coordinates, patrol end point coordinates, route sampling interval, flight altitude parameters, flight speed parameters, camera pitch angle parameters, and image acquisition frequency parameters according to the patrol rule identifier, and combine the patrol rule parameters with the event location information and the target drone identification code to generate a task configuration file; Step S502: Construct a UAV mission control instruction set, encode the parameters in the mission configuration file according to the communication protocol specification, generate mission initialization instructions, route binding instructions, obstacle avoidance path instructions, and image acquisition instructions, establish a data communication link with the target UAV, send the control instruction set to the target UAV through the data communication link, receive the instruction verification code returned by the target UAV, and verify the reliability of the verification code to complete the mission instruction confirmation.

[0084] Optionally, this embodiment addresses the challenges of incomplete inspection rules, unreliable command issuance, and inaccurate task configuration in traditional drone mission execution by innovatively designing a template-based task configuration solution. This embodiment first constructs a structured task template library, employing a layered design to ensure rule integrity. A template parsing engine implements rule mapping: Rule = Map(Event_Type, Rule_ID), where Event_Type is the event type code and Rule_ID is the corresponding rule identifier, enabling precise matching of event types with inspection rules.

[0085] This embodiment deeply optimizes the rule parameter extraction mechanism. Based on rule identifiers, the system uses a parameterized configuration method to extract the key parameters required for task execution. Different parameter combinations are set for different inspection tasks. For example, traffic accident inspections require a lower flight altitude and a higher image acquisition frequency to obtain clear details of the accident scene; while natural disaster monitoring requires a larger route sampling interval and a wider coverage area to comprehensively assess the scope of the disaster.

[0086] This embodiment innovatively implements a task configuration generation strategy. Extracted rule parameters are intelligently integrated with real-time event information to construct a standardized configuration file: Config = Combine(Rules, Event, UAV_ID), where Rules represents the rule parameter set, Event represents the event information, and UAV_ID represents the UAV identifier. The system ensures configuration integrity and rationality through parameter validation, paying particular attention to the logical relationships between parameters, such as the matching relationship between flight altitude and camera pitch angle.

[0087] This embodiment optimizes the instruction set construction mechanism. Based on standard communication protocols, task configurations are converted into executable control instructions. The system employs a hierarchical instruction structure to ensure orderly task execution. Initialization instructions include basic task parameters and execution conditions; route binding instructions define the flight path and control points in detail; obstacle avoidance instructions include safe routes and avoidance strategies; and image acquisition instructions specify acquisition timing and image parameters.

[0088] This embodiment innovatively designs a communication link management solution. It employs an encrypted communication protocol to ensure secure command transmission: Command = Encode(Config, Protocol), where Config represents the configuration information and Protocol represents the communication protocol specification. The system ensures communication reliability through multi-channel backup, automatically switching communication channels when signal interference occurs. A confirmation mechanism is employed to ensure reliable delivery of critical commands.

[0089] This embodiment deeply optimizes the instruction verification mechanism. A comprehensive verification process is established, ensuring instruction integrity through cyclic redundancy checking. The system incorporates a multi-level verification strategy, including instruction format verification, parameter range verification, and execution condition verification. For complex task instructions, segmented verification improves verification efficiency and ensures accuracy at every execution step.

[0090] This embodiment innovatively implements a task confirmation strategy. A two-way communication mechanism is established to monitor the execution status of instructions in real time. The system assesses task execution risk through status feedback and promptly adjusts the instruction sequence when anomalies are detected. For multi-stage tasks, state machine management ensures task continuity and integrity.

[0091] This example achieves precise drone mission execution through templated configuration technology. It demonstrates robust configuration capabilities and execution efficiency, particularly when handling complex inspection scenarios. Through standardized command management and reliable communication mechanisms, the system ensures accurate issuance and reliable execution of mission instructions, providing strong support for road network inspections.

[0092] The innovative design of this embodiment not only addresses the configuration accuracy and command reliability issues of traditional methods, but also establishes a continuously optimized task execution framework. Through the continuous improvement of rule templates and optimization of the command system, the system can continuously enhance its adaptability to various inspection scenarios, providing reliable support for intelligent road network monitoring. This intelligent configuration mechanism ensures that the system maintains efficient execution capabilities and reliable task results when faced with complex and ever-changing inspection tasks. In particular, the multi-level command verification significantly improves the accuracy and reliability of task execution.

[0093] In one embodiment of the method for scheduling drone tasks for intelligent road network inspection of the present application, the following contents may also be specifically included: Step S601: Receive the image data stream transmitted back by the target drone, parse the image frames and shooting timestamps from the image data stream, store the image frames and shooting timestamps in an image cache queue, read the target drone's position data to record the shooting position coordinates of the image frames, perform geometric correction and image enhancement on the image frames, use the target detection model to identify the vehicle distribution density, road traffic status, and traffic control facilities in the image, extract the accident scene range, vehicle backlog length, and road blockage location based on the scene recognition model, and generate a target detection result table and a scene recognition result table; Step S602: Fuse the spatial position information of the target detection result table and the scene recognition result table, construct the boundary outline of the event impact range, calculate the geometric center and coverage radius of the boundary outline, establish a local coordinate system with the geometric center as the origin, mark the positions of information boards, display screens, and broadcasting equipment along the road in the local coordinate system, calculate the distance from the position of each device to the geometric center, and filter out devices whose distance is less than the coverage radius to generate a roadside equipment distribution area.

[0094] Optionally, this embodiment addresses the issues of delayed image processing, inaccurate scene understanding, and inaccurate impact assessment in traditional road network inspections by innovatively designing a deep learning-based intelligent analysis solution. This embodiment first establishes a high-speed image transmission channel and uses real-time stream processing technology to receive image data. Key information is extracted through a data stream parsing engine: Frame = Parse(Image, Timestamp, Position), where Image is the image data, Timestamp is the capture time, and Position is the location information.

[0095] This embodiment deeply optimizes the image preprocessing mechanism. Geometric correction is performed based on drone attitude information to eliminate image deformation and distortion. The system uses an adaptive enhancement algorithm to improve image quality: Enhancement = f(Brightness, Contrast, Noise), where the parameters represent brightness, contrast, and noise levels, respectively. Specifically for images taken under varying weather conditions, a scene-adaptive method optimizes image features to ensure the accuracy of subsequent analysis.

[0096] This embodiment innovatively implements a target detection strategy. It uses an improved YOLOv5 model for multi-target detection, optimizing model performance through transfer learning. The system focuses on key targets such as vehicles, pedestrians, and facilities, tailored to the characteristics of traffic scenarios. The model input is a preprocessed image, and the output includes information such as target category, location, and confidence level. An attention mechanism is used to improve detection accuracy, particularly for small targets in densely populated scenes.

[0097] This embodiment optimizes the scene recognition mechanism. It builds a scene understanding model based on a deep learning network and improves recognition accuracy through multi-scale feature fusion. The system performs semantic segmentation on key scenes such as accident scenes, vehicle queues, and road blockages, and assesses impact by combining spatial location information. For example, it analyzes traffic congestion based on vehicle density distribution and assesses traffic capacity based on road occupancy.

[0098] This embodiment innovatively designs a data fusion solution. It spatially registers target detection and scene recognition results to create a unified analysis framework. Using multi-source information fusion technology, the system integrates target locations, scene features, and spatial relationships to generate a complete event description. Especially for complex scenarios, time series analysis is used to assess event trends.

[0099] This embodiment deeply optimizes the impact range analysis mechanism. An event impact model is constructed based on fused data, and the impact range is determined using a boundary extraction algorithm. The system uses a convex hull algorithm to generate the impact area outline, calculating the geometric center and coverage radius. For irregularly shaped impact areas, polygonal approximation is used to improve representation accuracy.

[0100] This embodiment innovatively implements a device distribution analysis strategy. It establishes a device location index in the local coordinate system and uses spatial queries to identify affected devices. The system considers road topology and evaluates device coverage and information transmission efficiency. For critical road sections, multi-level screening ensures the effective dissemination of warning information.

[0101] This embodiment uses intelligent analysis technology to accurately assess road network events. It demonstrates strong perception and analytical efficiency, particularly when dealing with complex traffic scenarios. By applying deep learning models, the system can quickly understand the state of the scene, accurately assess the impact of events, and provide a reliable basis for issuing warnings.

[0102] The innovative design of this embodiment not only solves the scene understanding and scope assessment problems in traditional methods, but also establishes a sustainable optimization analysis framework. Through the continuous optimization of the analysis model and the improvement of the processing strategy, the system can continuously improve its adaptability to various scenarios and provide strong support for intelligent road network monitoring. This intelligent analysis mechanism ensures that the system always maintains efficient perception capabilities and reliable assessment results when facing complex and changing road conditions. In particular, through multi-dimensional data fusion, the accuracy of event impact assessment and the timeliness of warning issuance are significantly improved.

[0103] In one embodiment of the method for scheduling drone tasks for intelligent road network inspection of the present application, the following contents may also be specifically included: Step S701: Construct a warning information classification template, divide warning level thresholds based on the straight-line distance between the vehicle and the event location, read the communication address and device type identification of each device in the roadside device distribution area, match the corresponding information release format according to the device type identification, generate device control instructions based on the warning information content according to the information release format, establish a communication connection with each device, send the device control instructions to the corresponding electronic information board and LED display screen, and receive the instruction response confirmation information returned by the device; Step S702: Acquire the real-time location data of the moving vehicles from the Internet of Vehicles platform, calculate the straight-line distance between each vehicle and the location where the event occurred, compare the straight-line distance with the warning level threshold to determine the warning information level, select the corresponding information push template according to the warning information level, write the event handling status information into the information push template to generate the vehicle terminal prompt information, push the prompt information to the vehicle terminal of the target vehicle through the Internet of Vehicles communication gateway, monitor the event handling progress status, and regenerate the warning information content when receiving the progress update.

[0104] Optionally, this embodiment addresses the issues of untimely information distribution, inaccurate warning levels, and uncoordinated device linkage in traditional road network warnings by innovatively designing a multi-level intelligent warning release solution. This embodiment first constructs a hierarchical warning template library and uses adaptive threshold technology to classify warning levels: Level = f(Distance, Event_Type), where Distance is the distance from the vehicle to the event location and Event_Type is the event type, ensuring targeted warning information.

[0105] This embodiment deeply optimizes the device control mechanism. Based on device type identification, a unified device management framework is established. The system designs standardized information publishing formats for different display devices: Format = Template(Device_Type, Content_Type), where Device_Type represents the device type and Content_Type represents the content type. For example, for electronic information boards, a partitioned display strategy is adopted to rationally arrange the display positions of text and symbols; for LED displays, information scrolling and brightness adjustment are considered.

[0106] This embodiment innovatively implements a communication management strategy. It uses a multi-protocol adapter to connect various roadside devices, issuing commands through a unified communication interface. The system establishes a reliable communication link and monitors device status through a heartbeat detection mechanism. For key node devices, dual-channel backup ensures communication reliability. Adaptive power control improves communication quality, particularly in adverse weather conditions.

[0107] This embodiment optimizes the device response mechanism. A complete command confirmation process is established, and the command execution status is verified through a response mechanism. The system also incorporates an exception handling strategy that automatically switches to a backup device when a device failure is detected. Status feedback is used during the information release process to ensure the accuracy and completeness of displayed content.

[0108] This embodiment innovatively designs an IoV interaction solution. It obtains real-time vehicle location information from the IoV platform and uses a spatial computing engine to assess the relative relationship between vehicles and events. The system utilizes efficient spatial indexing technology to quickly screen target vehicles for alerts. For high-speed vehicles, predictive analysis allows for early warnings.

[0109] This embodiment deeply optimizes the information push mechanism. It dynamically adjusts the warning level based on vehicle distance and employs a differentiated push strategy: Warning = Select(Level, Template), where Level is the warning level and Template is the corresponding push template. The system analyzes the scenario to determine the urgency of the information, using a strong notification for urgent warnings and a standard push method for general warnings.

[0110] This embodiment innovatively implements a status update strategy. It establishes an event handling status monitoring mechanism to track handling progress in real time. The system uses a status analysis engine to assess event changes and promptly updates alerts when significant progress is detected. For ongoing events, regular updates ensure the timeliness of information.

[0111] This embodiment utilizes multi-level early warning technology to accurately report road network events. This demonstrates robust information dissemination and efficient coordination, particularly when handling emergencies. Through scientific early warning grading and reliable equipment control, the system can rapidly respond to various emergencies, providing strong support for road safety management.

[0112] The innovative design of this embodiment not only solves the information release and device linkage issues in traditional methods, but also establishes a sustainably optimized early warning framework. Through continuous improvement of early warning strategies and optimization of release mechanisms, the system can continuously enhance its responsiveness to various events, providing reliable support for intelligent road network management. This intelligent early warning mechanism ensures that the system maintains efficient release capabilities and reliable linkage effects when faced with complex and changing road conditions. In particular, the multi-channel information push significantly improves the coverage and timeliness of early warning information.

[0113] In order to effectively solve the shortcomings of traditional technologies in task planning, drone scheduling and early warning linkage, and significantly improve the intelligent level of road network inspection and emergency response effect, the present application provides an embodiment of a drone task scheduling device for intelligent road network inspection for realizing all or part of the contents of the drone task scheduling method for intelligent road network inspection, see Figure 2 The intelligent road network inspection UAV task scheduling device specifically includes the following contents: The path planning module 10 is configured to receive information on traffic accidents, natural disasters, and road anomalies collected by the road network monitoring system, extract the event type, location, and level attributes from the event information, construct a three-dimensional road network topology model based on an electronic road network map, mark the locations of buildings, overpasses, and tunnels in the three-dimensional road network topology model to construct an obstacle distribution map, calculate emergency return points available to drones around the event location, and plan drone inspection routes and obstacle avoidance paths based on the obstacle distribution map; The mission inspection module 20 is configured to obtain the real-time location information, remaining range, and payload status information of each drone from the drone management platform, calculate the mission fitness score of each drone to reach the event location along the inspection route, select the drone with the highest mission fitness score as the target drone, extract inspection rules from the mission template library based on the event type, and send the inspection rules and the obstacle avoidance path to the target drone to perform the inspection mission; The abnormal alarm module 30 is used to collect image data obtained by the target drone during the patrol process, perform target detection and scene recognition on the image data, extract event impact range information, determine the distribution area of roadside equipment that needs to be linked based on the event impact range information, send graded warning information to the electronic information board and LED display screen within the distribution area, push different levels of warning information to the vehicle terminal based on the distance between the vehicle and the location of the event, and update the content of the warning information in real time according to the progress of event handling.

[0114] From the above description, it can be seen that the drone task scheduling device for intelligent road network inspection provided by the embodiment of the present application can realize the precise planning of drone inspection routes by constructing a three-dimensional road network topology model and an obstacle distribution map. A drone scheduling strategy based on task adaptability is designed, and inspection rules are extracted from the task template library for intelligent scheduling in combination with real-time position, cruising range and load status. A hierarchical early warning mechanism is introduced, and the scope of event impact is analyzed through target detection and scene recognition to realize differentiated early warning of roadside equipment and vehicle-mounted terminals. This method effectively solves the shortcomings of traditional technologies in task planning, drone scheduling and early warning linkage, and significantly improves the intelligence level of road network inspection and the effect of emergency response.

[0115] From a hardware perspective, in order to effectively address the shortcomings of traditional technologies in task planning, drone scheduling, and early warning linkage, and significantly improve the intelligent level of road network inspection and the effectiveness of emergency response, this application provides an embodiment of an electronic device for implementing all or part of the content of the drone task scheduling method for intelligent road network inspection. The electronic device specifically includes the following content: A processor, a memory, a communications interface, and a bus; wherein the processor, the memory, and the communications interface communicate with each other via the bus; the communications interface is used to transmit information between the UAV task scheduling device for intelligent road network inspection and related devices such as the core business system, user terminals, and related databases; the logic controller can be a desktop computer, a tablet computer, a mobile terminal, etc., but this embodiment is not limited thereto. In this embodiment, the logic controller can be implemented with reference to the embodiment of the UAV task scheduling method for intelligent road network inspection and the embodiment of the UAV task scheduling device for intelligent road network inspection in the embodiment, the contents of which are incorporated herein and repeated parts are not repeated.

[0116] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0117] In practical applications, portions of the drone task scheduling method for intelligent road network inspections can be executed on the electronic device side as described above, or all operations can be performed on the client device. The specific selection can be based on the processing capabilities of the client device and the limitations of the user's usage scenario. This application does not impose any restrictions on this. If all operations are performed on the client device, the client device may also include a processor.

[0118] The aforementioned client device may include a communication module (i.e., a communication unit) capable of establishing a communication connection with a remote server to facilitate data transmission with the server. The server may include a server at the task scheduling center or, in other implementation scenarios, a server on an intermediate platform, such as a server on a third-party server platform that is communicatively linked to the task scheduling center server. The server may comprise a single computer device, a server cluster consisting of multiple servers, or a distributed server configuration.

[0119] Figure 3 Schematic block diagram of the system structure of the electronic device 9600 according to an embodiment of the present application. Figure 3 As shown, the electronic device 9600 may include a central processing unit 9100 and a memory 9140; the memory 9140 is coupled to the central processing unit 9100. It is worth noting that the Figure 3 is exemplary; other types of structures may also be used to supplement or replace this structure to implement telecommunication functions or other functions.

[0120] In one embodiment, the function of the drone task scheduling method for intelligent road network inspection can be integrated into the central processing unit 9100. The central processing unit 9100 can be configured to perform the following control: Step S101: Receive traffic accident, natural disaster, and road anomaly event information collected by the road network monitoring system, extract the event type, occurrence location, and event level attributes from the event information, construct a three-dimensional road network topology model based on the road network electronic map, mark the locations of buildings, overpasses, and tunnels in the three-dimensional road network topology model to construct an obstacle distribution map, calculate the emergency return points available to drones around the event location, and plan drone inspection routes and obstacle avoidance paths based on the obstacle distribution map; Step S102: Obtaining the real-time location information, remaining range, and payload status information of each drone from the drone management platform, calculating the mission fitness score of each drone to reach the event location along the patrol route, selecting the drone with the highest mission fitness score as the target drone, extracting patrol rules from the mission template library based on the event type, and sending the patrol rules and the obstacle avoidance path to the target drone to perform the patrol mission; Step S103: Collect image data obtained by the target UAV during the inspection process, perform target detection and scene recognition on the image data, extract event impact range information, determine the distribution area of roadside equipment that needs to be linked based on the event impact range information, send graded warning information to the electronic information board and LED display screen within the distribution area, push different levels of warning information to the on-board terminal based on the distance between the vehicle and the location of the event, and update the content of the warning information in real time according to the progress of event handling.

[0121] As can be seen from the above description, the electronic device provided in the embodiment of the present application realizes the precise planning of drone inspection routes by constructing a three-dimensional road network topology model and an obstacle distribution map. A drone scheduling strategy based on task adaptability is designed, and inspection rules are extracted from the task template library for intelligent scheduling in combination with real-time position, cruising range and load status. A hierarchical early warning mechanism is introduced, and the scope of event impact is analyzed through target detection and scene recognition to realize differentiated early warnings for roadside equipment and vehicle-mounted terminals. This method effectively solves the shortcomings of traditional technologies in task planning, drone scheduling and early warning linkage, and significantly improves the intelligence level of road network inspections and the effectiveness of emergency response.

[0122] In another embodiment, the drone task scheduling device for intelligent road network inspection can be configured separately from the central processor 9100. For example, the drone task scheduling device for intelligent road network inspection can be configured as a chip connected to the central processor 9100, and the drone task scheduling method function for intelligent road network inspection can be realized through the control of the central processor.

[0123] like Figure 3 As shown, the electronic device 9600 may further include: a communication module 9110, an input unit 9120, an audio processor 9130, a display 9160, and a power supply 9170. It is worth noting that the electronic device 9600 does not necessarily have to include Figure 3 In addition, the electronic device 9600 may also include all components shown in Figure 3 For components not shown, reference may be made to the prior art.

[0124] like Figure 3 As shown, the central processing unit 9100 is sometimes also referred to as a controller or operation control, and may include a microprocessor or other processor device and / or logic device. The central processing unit 9100 receives input and controls the operation of various components of the electronic device 9600.

[0125] Memory 9140 can be, for example, one or more of a cache, flash memory, hard drive, removable media, volatile memory, non-volatile memory, or other suitable devices. It can store the aforementioned failure-related information and also store programs that execute the relevant information. The CPU 9100 can execute the programs stored in memory 9140 to implement information storage or processing.

[0126] The input unit 9120 provides input to the central processing unit 9100. The input unit 9120 may be, for example, a keypad or touch input device. The power supply 9170 is used to provide power to the electronic device 9600. The display 9160 is used to display objects such as images and text. The display may be, for example, an LCD display, but is not limited thereto.

[0127] The memory 9140 may be a solid-state memory, such as a read-only memory (ROM), random access memory (RAM), or SIM card. Alternatively, it may be a memory that retains information even when power is off, can be selectively erased, and is capable of storing additional data. Examples of such memory are sometimes referred to as EPROMs. The memory 9140 may also be some other type of device. The memory 9140 includes a buffer memory 9141 (sometimes referred to as a buffer). The memory 9140 may include an application / function storage unit 9142 for storing application programs and function programs, or processes used by the central processing unit 9100 to execute operations of the electronic device 9600.

[0128] The memory 9140 may also include a data storage unit 9143 for storing data, such as contacts, digital data, images, sounds, and / or any other data used by the electronic device. The driver storage unit 9144 of the memory 9140 may include various driver programs for communication functions of the electronic device and / or for executing other functions of the electronic device (such as messaging applications, address book applications, etc.).

[0129] The communication module 9110 is a transmitter / receiver that transmits and receives signals via the antenna 9111. The communication module 9110 (transmitter / receiver) is coupled to the central processor 9100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.

[0130] Based on different communication technologies, multiple communication modules 9110 may be provided in the same electronic device, such as cellular network modules, Bluetooth modules, and / or wireless local area network modules. The communication module 9110 (transmitter / receiver) is also coupled to a speaker 9131 and a microphone 9132 via an audio processor 9130, providing audio output via the speaker 9131 and receiving audio input from the microphone 9132, thereby implementing common telecommunication functions. The audio processor 9130 may include any suitable buffer, decoder, amplifier, etc. Furthermore, the audio processor 9130 is coupled to the central processing unit 9100, enabling local recording via the microphone 9132 and playback of stored audio via the speaker 9131.

[0131] Embodiments of the present application also provide a computer-readable storage medium capable of implementing all steps of the method for scheduling drone tasks for intelligent road network inspections in the above-mentioned embodiment, where the execution subject is a server or a client. The computer-readable storage medium stores a computer program. When the computer program is executed by a processor, all steps of the method for scheduling drone tasks for intelligent road network inspections in the above-mentioned embodiment are implemented. For example, when the processor executes the computer program, the following steps are implemented: Step S101: Receive traffic accident, natural disaster, and road anomaly event information collected by the road network monitoring system, extract the event type, occurrence location, and event level attributes from the event information, construct a three-dimensional road network topology model based on the road network electronic map, mark the locations of buildings, overpasses, and tunnels in the three-dimensional road network topology model to construct an obstacle distribution map, calculate the emergency return points available to drones around the event location, and plan drone inspection routes and obstacle avoidance paths based on the obstacle distribution map; Step S102: Obtaining the real-time location information, remaining range, and payload status information of each drone from the drone management platform, calculating the mission fitness score of each drone to reach the event location along the patrol route, selecting the drone with the highest mission fitness score as the target drone, extracting patrol rules from the mission template library based on the event type, and sending the patrol rules and the obstacle avoidance path to the target drone to perform the patrol mission; Step S103: Collect image data obtained by the target UAV during the inspection process, perform target detection and scene recognition on the image data, extract event impact range information, determine the distribution area of roadside equipment that needs to be linked based on the event impact range information, send graded warning information to the electronic information board and LED display screen within the distribution area, push different levels of warning information to the on-board terminal based on the distance between the vehicle and the location of the event, and update the content of the warning information in real time according to the progress of event handling.

[0132] As can be seen from the above description, the computer-readable storage medium provided in the embodiment of the present application realizes the precise planning of drone inspection routes by constructing a three-dimensional road network topology model and an obstacle distribution map. A drone scheduling strategy based on task adaptability is designed, and inspection rules are extracted from the task template library for intelligent scheduling in combination with real-time location, cruising range and load status. A hierarchical early warning mechanism is introduced, and the scope of event impact is analyzed through target detection and scene recognition to realize differentiated early warnings for roadside equipment and vehicle-mounted terminals. This method effectively solves the shortcomings of traditional technologies in task planning, drone scheduling and early warning linkage, and significantly improves the intelligence level of road network inspections and the effectiveness of emergency response.

[0133] The embodiments of the present application also provide a computer program product capable of implementing all steps of the method for scheduling drone tasks for intelligent road network inspections in the above-mentioned embodiment, where the execution subject is a server or a client. When the computer program / instructions are executed by a processor, the steps of the method for scheduling drone tasks for intelligent road network inspections are implemented. For example, the computer program / instructions implement the following steps: Step S101: Receive traffic accident, natural disaster, and road anomaly event information collected by the road network monitoring system, extract the event type, occurrence location, and event level attributes from the event information, construct a three-dimensional road network topology model based on the road network electronic map, mark the locations of buildings, overpasses, and tunnels in the three-dimensional road network topology model to construct an obstacle distribution map, calculate the emergency return points available to drones around the event location, and plan drone inspection routes and obstacle avoidance paths based on the obstacle distribution map; Step S102: Obtaining the real-time location information, remaining range, and payload status information of each drone from the drone management platform, calculating the mission fitness score of each drone to reach the event location along the patrol route, selecting the drone with the highest mission fitness score as the target drone, extracting patrol rules from the mission template library based on the event type, and sending the patrol rules and the obstacle avoidance path to the target drone to perform the patrol mission; Step S103: Collect image data obtained by the target UAV during the inspection process, perform target detection and scene recognition on the image data, extract event impact range information, determine the distribution area of roadside equipment that needs to be linked based on the event impact range information, send graded warning information to the electronic information board and LED display screen within the distribution area, push different levels of warning information to the on-board terminal based on the distance between the vehicle and the location of the event, and update the content of the warning information in real time according to the progress of event handling.

[0134] As can be seen from the above description, the computer program product provided in the embodiment of the present application realizes the precise planning of drone inspection routes by constructing a three-dimensional road network topology model and an obstacle distribution map. A drone scheduling strategy based on task adaptability is designed, and inspection rules are extracted from the task template library for intelligent scheduling in combination with real-time location, cruising range and load status. A hierarchical early warning mechanism is introduced, and the scope of event impact is analyzed through target detection and scene recognition to realize differentiated early warnings for roadside equipment and vehicle-mounted terminals. This method effectively solves the shortcomings of traditional technologies in task planning, drone scheduling and early warning linkage, and significantly improves the intelligence level of road network inspections and the effectiveness of emergency response.

[0135] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, apparatuses, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0136] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (apparatus), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0137] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0139] Specific embodiments are used in the present invention to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core ideas. At the same time, for those skilled in the art, according to the ideas of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the contents of this specification should not be understood as limiting the present invention.

Claims

1. A method for scheduling UAV tasks for intelligent road network inspection, characterized in that: The method comprises: Receive traffic accident, natural disaster, and road anomaly event information collected by the road network monitoring system, extract the event type, location, and event level attributes from the event information, construct a three-dimensional road network topology model based on the road network electronic map, mark the locations of buildings, overpasses, and tunnels in the three-dimensional road network topology model to construct an obstacle distribution map, calculate the emergency return points available for drones around the event location, and plan drone inspection routes and obstacle avoidance paths based on the obstacle distribution map; The real-time location information, remaining range, and payload status information of each drone are obtained from the drone management platform, and the mission fitness score of each drone is calculated to reach the event location along the patrol route. The drone with the highest mission fitness score is selected as the target drone. Based on the event type, patrol rules are extracted from the mission template library, and the patrol rules and the obstacle avoidance path are sent to the target drone to perform the patrol mission. Collect image data obtained by the target UAV during the patrol process, perform target detection and scene recognition on the image data, extract event impact range information, determine the distribution area of roadside equipment that needs to be linked based on the event impact range information, send graded warning information to electronic information boards and LED display screens within the distribution area, push different levels of warning information to the on-board terminal based on the distance between the vehicle and the location of the event, and update the content of the warning information in real time according to the progress of event handling.

2. The method for scheduling UAV tasks for intelligent road network inspection according to claim 1 is characterized in that: The method comprises: receiving traffic accident, natural disaster, and road abnormality event information collected by a road network monitoring system, extracting event type, occurrence location, and event level attributes from the event information, constructing a three-dimensional road network topology model based on a road network electronic map, and marking the locations of buildings, overpasses, and tunnels in the three-dimensional road network topology model to construct an obstacle distribution map, including: Reading an event information data stream from a road network monitoring system, establishing an event information cache queue to store the data stream, parsing a data packet header of the data stream to extract an event code and a timestamp, matching the event code to a preset event type mapping table to obtain an event type identifier, extracting the latitude and longitude coordinates of the event occurrence location and an event level value from the data stream message body, and writing the event type identifier, the latitude and longitude coordinates, and the event level value into an event attribute table; Vector data of an electronic road network map is read, a road network skeleton line matrix is constructed based on the vector data, three-dimensional spatial coordinates of road network nodes are calculated to generate a road network node set, a three-dimensional road network topology model is constructed using the road network skeleton line matrix and the road network node set, building outline data, overpass elevation data, and tunnel boundary data are retrieved from a geographic information database, the outline data, the elevation data, and the boundary data are converted into an obstacle boundary point set, and the obstacle boundary point set is marked in the three-dimensional road network topology model to generate an obstacle distribution map.

3. The method for scheduling UAV tasks for intelligent road network inspection according to claim 1 is characterized in that: The calculating of available emergency return points for drones around the location where the event occurred, and planning of drone inspection routes and obstacle avoidance paths based on the obstacle distribution map, include: Obtain the center coordinates of the location where the event occurred, construct a circular area of the search space with the center coordinates as the center, divide the circular area into grid cells, calculate the coordinates of the center point of each grid cell, determine whether the center point of each grid cell overlaps with the obstacle boundary point based on the obstacle distribution map, select the center points of the grid cells that do not overlap with the obstacle as a set of candidate return points, calculate the distance and height difference between each return point in the set of candidate return points and the location where the event occurred, sort the set of candidate return points from near to far according to distance, and select the return point with the highest ranking as the emergency return point; Construct a three-dimensional coordinate system of the road network topology map, project the coordinates of the emergency return point and the location where the incident occurred into the three-dimensional coordinate system, calculate the initial route from the emergency return point to the location where the incident occurred based on the A-star search algorithm, read the boundary point data of the obstacle distribution map, calculate the collision detection distance between the initial route and the obstacle, adjust the route node to generate an obstacle avoidance segment when a collision risk is detected, combine the obstacle avoidance segment with the route segment where no collision occurred to generate a patrol route, and smooth the patrol route according to the turning radius and climb angle constraints of the UAV.

4. The method for scheduling UAV tasks for intelligent road network inspection according to claim 1 is characterized in that: The method of obtaining the real-time location information, remaining range, and payload status information of each drone from the drone management platform, calculating the mission fitness score of each drone to reach the event location along the patrol route, and selecting the drone with the highest mission fitness score as the target drone includes: Receive telemetry data packets from each drone from the drone management platform, parse the telemetry data packets to obtain a drone identification code, read the corresponding drone's location coordinates, remaining battery life, flight speed, and mission payload type based on the identification code, calculate the remaining range based on the remaining battery life, determine the camera specifications and image transmission device performance of the drone based on the mission payload type, and write the location coordinates, remaining range, flight speed, camera specifications, and image transmission device performance into a drone status information table; A task fitness score calculation model is established, and the straight-line distance between the drone's position coordinates and the location of the event, the ratio of the drone's remaining range to the patrol route length, the matching degree between the drone's flight speed and the expected patrol time, and the adaptability of the camera specifications to the patrol task requirements are used as scoring indicators. The weight coefficient of each scoring indicator is determined based on the hierarchical analysis method, and the task fitness score is obtained by weighted calculation of the scoring indicators and the weight coefficients. The drone with the highest task fitness score is selected as the target drone.

5. The method for scheduling UAV tasks for intelligent road network inspection according to claim 1 is characterized in that: The extracting inspection rules from the task template library based on the event type, and sending the inspection rules and the obstacle avoidance path to the target UAV to perform the inspection task, includes: Read the mapping relationship between event types and patrol rules preset in the task template library, match the corresponding patrol rule identifier based on the event type, extract the patrol start point coordinates, patrol end point coordinates, route sampling interval, flight altitude parameters, flight speed parameters, camera pitch angle parameters, and image acquisition frequency parameters according to the patrol rule identifier, and combine the patrol rule parameters with the event location information and the target drone identification code to generate a task configuration file; Construct a UAV mission control instruction set, encode the parameters in the mission configuration file according to the communication protocol specification, generate mission initialization instructions, route binding instructions, obstacle avoidance path instructions, and image acquisition instructions, establish a data communication link with the target UAV, send the control instruction set to the target UAV through the data communication link, receive the instruction verification code returned by the target UAV, and verify the reliability of the verification code to complete the mission instruction confirmation.

6. The method for dispatching UAV tasks for intelligent road network inspection according to claim 1, characterized in that: The collecting of image data acquired by the target UAV during the inspection process, performing target detection and scene recognition on the image data, extracting event impact range information, and determining the distribution area of roadside equipment that needs to be linked based on the event impact range information, includes: Receive the image data stream sent back by the target drone, parse the image frames and shooting timestamps from the image data stream, store the image frames and shooting timestamps in an image cache queue, read the target drone's position data to record the shooting position coordinates of the image frames, perform geometric correction and image enhancement on the image frames, use the target detection model to identify the vehicle distribution density, road traffic status, and traffic control facilities in the image, extract the accident scene range, vehicle backlog length, and road blockage location based on the scene recognition model, and generate a target detection result table and a scene recognition result table; The spatial position information in the target detection result table and the scene recognition result table are integrated to construct the boundary outline of the event impact range, calculate the geometric center and coverage radius of the boundary outline, establish a local coordinate system with the geometric center as the origin, mark the positions of information boards, display screens, and broadcasting equipment along the road in the local coordinate system, calculate the distance from the position of each device to the geometric center, and filter out devices whose distance is less than the coverage radius to generate the roadside equipment distribution area.

7. The method for dispatching UAV tasks for intelligent road network inspection according to claim 1, characterized in that: The method includes sending graded warning information to the electronic information boards and LED display screens within the distribution area, pushing different levels of warning information to the vehicle terminal based on the distance between the vehicle and the location of the incident, and updating the content of the warning information in real time according to the progress of the incident handling, including: Construct a warning information classification template, divide the warning level threshold based on the straight-line distance between the vehicle and the incident location, read the communication address and device type identification of each device in the roadside device distribution area, match the corresponding information release format according to the device type identification, generate device control instructions based on the warning information content according to the information release format, establish a communication connection with each device, send the device control instructions to the corresponding electronic information board and LED display screen, and receive the instruction response confirmation information returned by the device; Acquire real-time location data of moving vehicles from the Internet of Vehicles platform, calculate the straight-line distance between each vehicle and the location where the event occurred, compare the straight-line distance with the warning level threshold to determine the warning information level, select the corresponding information push template based on the warning information level, write the event handling status information into the information push template to generate a vehicle-mounted terminal prompt message, push the prompt message to the vehicle-mounted terminal of the target vehicle through the Internet of Vehicles communication gateway, monitor the progress status of the event handling, and regenerate the warning information content when receiving the progress update.

8. An intelligent UAV task scheduling device for road network inspection, characterized in that: The device comprises: A path planning module is used to receive information on traffic accidents, natural disasters, and road anomalies collected by the road network monitoring system, extract the event type, location, and level attributes from the event information, construct a three-dimensional road network topology model based on the road network electronic map, mark the locations of buildings, overpasses, and tunnels in the three-dimensional road network topology model to construct an obstacle distribution map, calculate the emergency return points available to drones around the event location, and plan drone inspection routes and obstacle avoidance paths based on the obstacle distribution map; The mission inspection module is used to obtain the real-time location information, remaining range, and payload status information of each drone from the drone management platform, calculate the mission fitness score of each drone to reach the event location along the inspection route, select the drone with the highest mission fitness score as the target drone, extract inspection rules from the mission template library based on the event type, and send the inspection rules and the obstacle avoidance path to the target drone to perform the inspection mission; The abnormal alarm module is used to collect image data obtained by the target UAV during the patrol process, perform target detection and scene recognition on the image data, extract event impact range information, determine the distribution area of roadside equipment that needs to be linked based on the event impact range information, send graded warning information to the electronic information board and LED display screen within the distribution area, push different levels of warning information to the on-board terminal based on the distance between the vehicle and the location of the event, and update the content of the warning information in real time according to the progress of event handling.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the drone task scheduling method for intelligent road network inspection described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the drone task scheduling method for intelligent road network inspection described in any one of claims 1 to 7 are implemented.

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