Real-time road condition data acquisition method and device, storage medium and processor

Abstract

The invention discloses a real-time road condition data acquisition method and device, a storage medium and a processor. According to the scheme, a real-time road condition request of a user is received; determining a corresponding task based on the real-time road condition request, and setting a corresponding priority for the task; and determining a target unmanned aerial vehicle according to the priority of the task and the state information of the candidate unmanned aerial vehicles, and controlling the target unmanned aerial vehicle to execute the task to obtain real-time road condition data. Compared with many technical challenges that the unmanned aerial vehicle is applied to traffic management in the prior art, the robustness and adaptability of the system are enhanced from management and scheduling levels, solution of the challenges is supported, and the method has obvious advantages.

Description

Technical Field

[0001] The present application relates to the field of intelligent traffic management technology, and in particular to a method, device, storage medium and processor for acquiring real-time road condition data. Background Art

[0002] Traditional traffic management systems have obvious deficiencies in real-time road condition monitoring and emergency response. For example, traditional traffic management systems usually rely on fixed sensors (such as ring coil vehicle detectors) to collect traffic flow information. The data update frequency of these devices is low and cannot reflect changes in road conditions in real time, resulting in the traffic information provided often lagging behind and unable to meet rapidly changing traffic needs. For drivers, the way to obtain real-time road condition information is relatively single, and the information display is not intuitive and clear enough, which not only affects the driver's judgment, but may also cause new traffic congestion due to misleading information.

[0003] The rapid development of drone technology has provided new possibilities for traffic management. However, the application of drones in traffic management still faces many technical challenges: (1) In low-altitude environments, drones are easily blocked by buildings and electromagnetic interference, resulting in unstable signals; (2) Weather changes and airflow interference in low-altitude environments place higher demands on the flight stability and mission execution efficiency of drones; (3) Real-time interaction between drones and ground vehicles requires low latency and high reliability. If these technical challenges are not properly addressed, they will directly affect the actual effect of drone-based traffic management solutions, and thus affect user experience.

[0004] Faced with the many technical challenges of applying drones to traffic management, how to improve user experience is a technical problem that needs to be solved urgently. Summary of the invention

[0005] Based on the above problems, the present application provides a method, device, storage medium and processor for acquiring real-time traffic data, with the aim of applying drones to traffic management and improving the user experience.

[0006] The embodiments of the present application disclose the following technical solutions:

[0007] The first aspect of the present application provides a method for acquiring real-time traffic data, the method comprising:

[0008] Receive real-time traffic condition requests from users;

[0009] Based on the real-time traffic condition request, determine a corresponding task and set a corresponding priority for the task;

[0010] According to the priority of the task and the status information of the candidate drones, a target drone is determined, and the target drone is controlled to execute the task to obtain real-time road condition data.

[0011] Optionally, determining a target drone according to the priority of the task and the status information of candidate drones, and controlling the target drone to execute the task to obtain real-time road condition data, including:

[0012] Calculating matching values of multiple candidate drones using a deep learning model according to the priority and the status information of the candidate drones; the status information includes battery power, load, and flight distance;

[0013] Determining a target drone based on the matching values of the multiple drones;

[0014] When the start condition is met, controlling the target drone to execute the task to obtain real-time road condition data.

[0015] Optionally, based on the real-time road condition request, determining a corresponding task and setting a corresponding priority for the task, including:

[0016] Determining a corresponding task based on the request;

[0017] Setting a corresponding priority for the task according to the urgency and geographical location of the task.

[0018] Optionally, after determining a corresponding task based on the real-time road condition request and setting a corresponding priority for the task, further including:

[0019] Dynamically adjusting the priority of the task according to the real-time traffic condition.

[0020] Optionally, controlling the target drone to execute the task to obtain real-time road condition data, including:

[0021] Controlling the target drone to execute the task;

[0022] Obtaining feedback data of the task at preset time intervals;

[0023] Processing and analyzing the image in the feedback data based on the feedback data of the task to obtain real-time road condition data, and updating the status information of the task and the status information of the target drone.

[0024] Optionally, after obtaining the feedback data of the task at preset time intervals, further including:

[0025] If the feedback data of the task indicates that the status information of the task is abnormal, then within a preset time range, rematching a drone for the task according to the priority and the status information of the candidate drones.

[0026] In the second aspect of the present application, a real-time traffic condition data acquisition device is provided. The device includes:

[0027] A request receiving module, configured to receive a user's real-time traffic condition request;

[0028] A priority setting module, configured to determine a corresponding task based on the real-time traffic condition request and set a corresponding priority for the task;

[0029] A data acquisition module, configured to determine a target drone according to the priority of the task and the status information of candidate drones, control the target drone to execute the task, and obtain real-time traffic condition data.

[0030] Optionally, the data acquisition module is specifically configured to:

[0031] Perform calculations using a deep learning model according to the priority and the status information of candidate drones to obtain matching values of multiple candidate drones; the status information includes power, load, and flight distance;

[0032] Determine the target drone based on the matching values of the multiple drones;

[0033] Control the target drone to execute the task to obtain real-time traffic condition data when the start condition is satisfied.

[0034] In the third aspect of the present application, a computer-readable storage medium is provided. A computer program is stored in the computer-readable storage medium. When the program is run by a processor, the real-time traffic condition data acquisition method provided in any implementation manner of the first aspect is implemented.

[0035] In the fourth aspect of the present application, a processor is provided. The processor is used to run a computer program, and when the program runs, the real-time traffic condition data acquisition method provided in any implementation manner of the first aspect is executed.

[0036] Compared with the prior art, the present application has the following beneficial effects:

[0037] The present application provides a method for acquiring real-time traffic data, which receives a user's real-time traffic request; based on the real-time traffic request, determines a corresponding task and sets a corresponding priority for the task. By instantly receiving and processing the user's request, the system can quickly start the subsequent process to ensure the timeliness and accuracy of the information, which not only improves the response speed of the service, but also enhances the user's dependence on the system, greatly improving the user's experience. According to the priority of the task and the status information of the candidate drones, the target drone is determined, and the target drone is controlled to perform the task to obtain real-time traffic data. Combining the task priority with the drone status information to select the most suitable drone for performing the task can maximize the use of existing resources and ensure the smooth completion of the task. By integrating user needs, task management and drone scheduling, this method realizes the application of drones in traffic management, quickly and accurately obtains real-time traffic data, and greatly improves the user's experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0039] Figure 1 A flowchart of a method for acquiring real-time traffic data provided by an embodiment of the present application;

[0040] Figure 2 A flowchart of another method for acquiring real-time traffic data provided in an embodiment of the present application;

[0041] Figure 3 A schematic diagram of the structure of a real-time traffic data acquisition device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] As described above, the current application of drones in traffic management still faces many technical challenges: (1) In low-altitude environments, drones are easily blocked by buildings and electromagnetic interference, resulting in unstable signals; (2) Weather changes and airflow interference in low-altitude environments place higher demands on the flight stability and mission execution efficiency of drones; (3) Real-time interaction between drones and ground vehicles requires low latency and high reliability. If these technical challenges are not properly addressed, they will directly affect the actual effect of drone-based traffic management solutions, and thus affect user experience. Faced with the many technical challenges of applying drones to traffic management, how to improve user experience is a technical problem that needs to be solved urgently.

[0043] In view of the above problems, through research, the inventor has proposed a method, apparatus, storage medium and processor for obtaining real-time traffic condition data, which receives a user's real-time traffic condition request; determines a corresponding task based on the real-time traffic condition request, and sets a corresponding priority for the task; determines a target drone according to the priority of the task and the status information of candidate drones, and controls the target drone to execute the task to obtain real-time traffic condition data.

[0044] In order to enable those skilled in the art to better understand the solutions of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0045] See Figure 1 , which is a flowchart of a method for obtaining real-time traffic condition data provided by an embodiment of this application. As Figure 1 shown, the method includes the following steps:

[0046] S101. Receive a user's real-time traffic condition request.

[0047] The user can submit a request through a mobile application, a web page or other terminal devices. The user's request serves as an input signal to initiate subsequent operations such as task allocation, drone scheduling, and data collection. By responding to the user's request in real time, the user's satisfaction and trust can be significantly improved, which helps to enhance the user's usage experience.

[0048] S102. Based on the real-time traffic condition request, determine a corresponding task and set a corresponding priority for the task.

[0049] According to the specific content of the user's request (such as the target area, traffic condition type, urgency, etc.), the user's needs are converted into executable tasks, and priorities are set for the tasks. For example, priorities are set for the tasks according to the urgency of the tasks. Tasks related to traffic accidents and road construction are given high priorities, while conventional tasks such as ordinary traffic condition queries are given low priorities.

[0050] By determining tasks and setting priorities based on the user's request, resources can be managed more efficiently, the task processing process can be optimized, and timely responses to tasks can be ensured. This link not only improves the operating efficiency and service quality of the system, but also provides a clear direction and basis for subsequent drone scheduling and obtaining real-time data.

[0051] S103. Determine the target drone according to the priority of the task and the status information of the candidate drones, and control the target drone to execute the task to obtain real-time traffic condition data.

[0052] Among them, the candidate drones include the drones provided by the system and the third-party drones registered through the system platform; the status information of the candidate drones includes but is not limited to battery power, flight distance limit, current location, and whether other tasks are being carried out, etc.

[0053] In an implementable embodiment, for an urgent task, quickly determine the drone with the shortest distance and the best status to execute the task. Once the target drone is determined, the system will send specific task instructions to it, including the destination, monitoring range, data collection parameters, etc. After receiving the task instructions, the drone immediately executes the task, collects real-time traffic condition data according to the set parameters, and transmits these data back to the system.

[0054] By precisely matching the task requirements with the status of the drones, the success rate and completion quality of the tasks can be significantly improved, ensuring that each task can be efficiently executed. This process not only realizes the effective management of limited resources, but also greatly improves the flexibility and response efficiency of the system, providing a strong technical guarantee for real-time traffic condition monitoring.

[0055] A method for obtaining real-time traffic condition data provided by an embodiment of the present application receives a user's real-time traffic condition request; based on the real-time traffic condition request, determines the corresponding task and sets the corresponding priority for the task. By immediately receiving and processing the user's request, the system can quickly start the subsequent process, ensuring the timeliness and accuracy of the information. This not only improves the response speed of the service, but also enhances the user's dependence on the system, greatly improving the user's experience. Determine the target drone according to the priority of the task and the status information of the candidate drones, and control the target drone to execute the task to obtain real-time traffic condition data. By combining the task priority with the drone status information to select the most suitable drone to execute the task, the existing resources can be maximally utilized to ensure the smooth completion of the task. This method realizes the application of drones in traffic management by integrating user requirements, task management, and drone scheduling, quickly and accurately obtaining real-time traffic condition data, and greatly improving the user's experience.

[0056] On the basis of the above embodiment, in order to further improve the method for obtaining real-time traffic condition data, a step of dynamically adjusting the priority of the task according to the real-time traffic condition is added, and the system involved in the following embodiment relies on the deep integration ability of the OnStar enterprise application platform EAPP.

[0057] See Figure 2, this figure is a flowchart of another real-time traffic condition data acquisition method provided by an embodiment of this application. As Figure 2 shown, this method includes the following steps:

[0058] S201. Receive the user's real-time traffic condition request.

[0059] The user can submit a request through a mobile application, a web page or other terminal devices. The user's request serves as an input signal to initiate subsequent operations such as task allocation, drone scheduling, and data collection.

[0060] By responding to the user's request in real time, the user's satisfaction and trust can be significantly improved, which helps to enhance the user's experience.

[0061] S202. Based on the real-time traffic condition request, determine the corresponding task and set the corresponding priority for the task.

[0062] By comprehensively evaluating the urgency and geographical location of the task, it is ensured that high-priority tasks are processed first, avoiding wasting resources on low-priority tasks. High-priority tasks can be responded to faster, meeting the user's demand for real-time traffic condition data, especially providing timely support in emergency situations (such as traffic accidents or road closures). This way greatly improves the user's experience.

[0063] In an implementable embodiment:

[0064] Based on the request, determine the corresponding task;

[0065] According to the urgency and geographical location of the task, set the corresponding priority for the task.

[0066] The system receives the user's real-time traffic condition request, including the user's geographical location and the specific content of the request. The system analyzes the real-time traffic condition request and generates a corresponding task description, such as task type, task scope, etc., evaluates the urgency of the task, for example, evaluates based on time sensitivity, user requirements, and impact scope, etc. Time sensitivity means that emergencies such as traffic accidents and road closures usually have high priority. If the user clearly marks "urgent" or "expedited", the priority is increased. The impact scope means that tasks involving main roads, highways or large transportation hubs have higher priorities. Based on the geographical location of the task, consider the distance from the drone site, the complexity of the geographical environment and weather conditions, etc. Different weights can be assigned to the urgency and geographical location information of the task, and finally the corresponding priority for the task is set.

[0067] S203. Dynamically adjust the priority of the task according to the real-time traffic condition.

[0068] In practical applications, real-time traffic conditions may change at any time (such as sudden traffic accidents, road closures, sharp increases in traffic flow, etc.). Therefore, it is necessary to dynamically adjust the priority of tasks to ensure the timeliness and rationality of resource allocation.

[0069] The system obtains real-time traffic condition data through sensor data, data transmitted back by drones, and third-party data sources, etc. Based on the real-time traffic data, it identifies events that may affect the task priority, such as traffic accidents, road closures, and traffic jams, etc. Based on the nature and impact degree of the events, the system dynamically adjusts the priority of the tasks. If the event is highly relevant and urgent to the task (such as a serious traffic accident occurring within the task target area), the priority of this task will be increased. If the event has no direct impact on the task (such as a road closure outside the task target area), the priority of this task will be maintained or decreased.

[0070] Dynamically adjusting the task priority based on real-time traffic conditions is an important means to achieve efficient and intelligent task scheduling. This method not only improves the system's response ability but also optimizes resource allocation, providing better services for users.

[0071] S204. Determine the target drone according to the adjusted priority of the task and the status information of the candidate drones, and control the target drone to execute the task to obtain real-time road condition data.

[0072] Among them, the candidate drones include the drones provided by the system and the third-party drones registered through the system platform.

[0073] In an implementable embodiment:

[0074] According to the priority and the status information of the candidate drones, use a deep learning model for calculation to obtain the matching values of multiple candidate drones; the status information includes battery power, load, and flight distance;

[0075] Based on the matching values of the multiple drones, determine the target drone;

[0076] When the start conditions are met, control the target drone to execute the task to obtain real-time road condition data.

[0077] Among them, the start conditions include the confirmation of the task by the drone operator (pilot).

[0078] After determining the target UAV for task execution, the system notifies the drone operator about the new task through message notifications or a dedicated application interface, such as the flight route of the target UAV, the expected environmental conditions, and the current status information of the target UAV. The drone operator reviews the task based on the provided information, and the factors considered during the review include the safety of the flight environment, the urgency of the task, and the rationality of resource allocation. If everything is normal and the drone operator agrees to execute the task, click the "Confirm" button in the task confirmation window that pops up on the application interface; otherwise, the drone operator can choose to reject the task or suggest adjusting the task parameters. For example, request to replace the UAV or modify the flight path. After obtaining the confirmation from the drone operator, the system officially sends a start command to the target UAV to start task execution.

[0079] This approach combines the advantages of automated intelligent scheduling with the professionalism and flexibility of human judgment, ensuring both efficient task execution and maximum safety and accuracy of operations.

[0080] In an implementable embodiment, the system evaluates candidate drone operators based on their historical task data and skill levels, and matches a suitable drone operator for each task.

[0081] Among them, the historical task data of the drone operator includes task completion rate, task success rate, task duration, and user evaluations, etc.; the skill levels include flight experience, technical certifications, and special skills, etc.

[0082] Through the above method, the system can not only intelligently match drone operators with tasks, but also ensure the safety and reliability of tasks through manual confirmation. This method not only improves the task execution efficiency but also enhances the flexibility and adaptability of the system.

[0083] In an implementable embodiment:

[0084] Control the target UAV to execute the task;

[0085] At preset time intervals, obtain feedback data of the task;

[0086] Based on the feedback data of the task, process and analyze the images in the feedback data to obtain real-time road condition data, and update the status information of the task and the status information of the target UAV.

[0087] The system collects feedback data from the target drone and relevant sensors at fixed time intervals (such as every second, every minute, or other periods set according to task requirements). The feedback data includes, but is not limited to, road images, task status, drone status, and environmental data. Among them, the task status includes whether the task is proceeding as planned, whether some goals have been completed, and whether there are abnormal situations, etc. The drone status includes the current battery level, flight speed, position, and load conditions, etc. The environmental data includes weather conditions, airspace changes, and traffic flow, etc. The system performs data processing and analysis operations on the received feedback data to generate real-time road condition data required by users and updates the status information of the task and the status information of the target drone. Among them, the real-time road condition data includes traffic flow information, congestion conditions, accident information, and road construction status, etc.

[0088] By obtaining feedback data at preset time intervals, getting real-time road condition data based on the feedback data, and updating the task status and drone status information, the system realizes real-time monitoring and dynamic adjustment of the task execution process. The real-time feedback data ensures the timeliness of the road condition data. The drone can continuously transmit the latest environmental data, enabling the system to quickly update the road condition data and provide it to users. This method not only improves the reliability and efficiency of the task but also optimizes resource utilization and enhances the security and adaptability of the system.

[0089] Among them, after obtaining the feedback data of the task according to the preset time interval, it further includes:

[0090] If the feedback data of the task indicates that the status information of the task is abnormal, then within a preset time range, according to the priority and the status information of the candidate drones, a drone is rematched for the task.

[0091] After detecting that the task status is abnormal, the system will automatically trigger the process of rematching the drone within a preset time range, such as 30 seconds, and select the drone with the highest matching value from the candidate drones as the new target drone to take over the original task.

[0092] Introducing an exception handling mechanism ensures that even when encountering sudden problems, alternative solutions can be quickly found, significantly improving the success rate and reliability of the task. Users will not be affected by drone failures or task interruptions. The system can ensure the continuity and timeliness of the task, enhancing user satisfaction and the usage experience.

[0093] Another real-time traffic condition data acquisition method provided by the embodiments of the present application receives a user's real-time traffic condition request; based on the real-time traffic condition request, determines a corresponding task, and sets a corresponding priority for the task. By immediately receiving and processing the user's request, the system can quickly initiate subsequent processes, ensuring the timeliness and accuracy of information. This not only improves the response speed of the service but also enhances the user's dependence on the system, greatly improving the user experience. According to the priority of the task and the status information of candidate drones, determines a target drone, controls the target drone to execute the task, and obtains real-time traffic condition data. Dynamically adjusting the task priority based on the real-time traffic condition is an important means to achieve efficient and intelligent task scheduling. This method not only improves the system's response ability but also optimizes resource allocation, providing better services for users. Combining the task priority with the drone status information to select the most suitable drone to execute the task can maximize the utilization of existing resources and ensure the smooth completion of the task. This method realizes the application of drones in traffic management by integrating user needs, task management, and drone scheduling, quickly and accurately obtains real-time traffic condition data, and greatly improves the user experience.

[0094] Based on the real-time traffic condition data acquisition method introduced in the foregoing embodiments, correspondingly, the present application further provides a real-time traffic condition data acquisition device. Figure 3 The following is a schematic structural diagram of the device. As Figure 3 shown, the real-time traffic condition data acquisition device includes:

[0095] A request receiving module 301, configured to receive a user's real-time traffic condition request.

[0096] A priority setting module 302, configured to determine a corresponding task based on the real-time traffic condition request, and set a corresponding priority for the task.

[0097] A data acquisition module 303, configured to determine a target drone according to the priority of the task and the status information of candidate drones, control the target drone to execute the task, and obtain real-time traffic condition data.

[0098] Optionally, the data acquisition module is specifically configured to:

[0099] Calculate, using a deep learning model, matching values of multiple candidate drones according to the priority and the status information of candidate drones; the status information includes power, load, and flight distance;

[0100] Determine a target drone based on the matching values of the multiple drones;

[0101] Control the target drone to execute the task to obtain real-time traffic condition data when the start condition is satisfied.

[0102] Optionally, determining a corresponding task based on the real-time traffic condition request and setting a corresponding priority for the task includes:

[0103] Determining a corresponding task based on the request;

[0104] Setting a corresponding priority for the task according to the urgency degree and geographical location of the task.

[0105] Optionally, after determining a corresponding task based on the real-time traffic condition request and setting a corresponding priority for the task, it further includes:

[0106] Dynamically adjusting the priority of the task according to the real-time traffic condition.

[0107] Optionally, determining a target drone according to the priority of the task and the status information of candidate drones, controlling the target drone to execute the task, and obtaining real-time traffic condition data includes:

[0108] Obtaining feedback data at a preset time interval;

[0109] Updating the status information of the task and the status information of the target drone based on the feedback data.

[0110] Optionally, after obtaining the feedback data of the task at a preset time interval, it further includes:

[0111] If the feedback data of the task indicates that the status information of the task is abnormal, then within a preset time range, re-match a drone for the task according to the priority and the status information of candidate drones.

[0112] In addition, an embodiment of the present application further provides a computer-readable storage medium, in which a computer program is stored, and when the program is run by a processor, it implements the real-time traffic condition data acquisition method introduced in any manner of the method embodiment.

[0113] In addition, an embodiment of the present application further provides a processor, which is used to run a computer program, and when the program runs, it executes the real-time traffic condition data acquisition method introduced in any implementation manner of the foregoing method embodiment.

[0114] It should be noted that the various embodiments in this specification are described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key points of each embodiment are the differences from other embodiments. In particular, for the apparatus embodiments, since they are basically similar to the method embodiments, they are described relatively simply, and for the relevant parts, reference can be made to the descriptions in the method embodiments. The apparatus embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components indicated as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution in this embodiment. Those of ordinary skill in the art can understand and implement it without creative efforts.

[0115] As described above, it is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by those skilled in the art within the technical scope disclosed in the present application should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for obtaining real-time traffic condition data, characterized in that, Including: Receiving a real-time traffic condition request from a user; Based on the real-time traffic condition request, determining a corresponding task and setting a corresponding priority for the task; According to the priority of the task and the status information of candidate drones, determining a target drone, controlling the target drone to execute the task, and obtaining real-time traffic condition data.

2. The method according to claim 1, characterized in that, The step of "According to the priority of the task and the status information of candidate drones, determining a target drone, controlling the target drone to execute the task, and obtaining real-time traffic condition data" includes: Calculating, using a deep learning model, matching values of multiple candidate drones according to the priority and the status information of the candidate drones; the status information includes battery power, load, and flight distance; Based on the matching values of the multiple drones, determining a target drone; When the startup condition is met, controlling the target drone to execute the task to obtain real-time traffic condition data.

3. The method according to claim 1, wherein The step of "Based on the real-time traffic condition request, determining a corresponding task and setting a corresponding priority for the task" includes: Based on the request, determining a corresponding task; According to the urgency and geographical location of the task, setting a corresponding priority for the task.

4. The method according to claim 1, wherein After "Based on the real-time traffic condition request, determining a corresponding task and setting a corresponding priority for the task", it further includes: Dynamically adjusting the priority of the task according to the real-time traffic condition.

5. The method according to claim 1, characterized in that, The step of "Controlling the target drone to execute the task to obtain real-time traffic condition data" includes: Controlling the target drone to execute the task; Obtaining feedback data of the task at a preset time interval; Based on the feedback data of the task, processing and analyzing the image in the feedback data to obtain real-time traffic condition data, and updating the status information of the task and the status information of the target drone.

6. The method according to claim 5, wherein After "Obtaining feedback data of the task at a preset time interval", it further includes: If the feedback data of the task indicates that the status information of the task is abnormal, then within a preset time range, re-matching a drone for the task according to the priority and the status information of the candidate drones.

7. A real-time traffic condition data acquisition device, characterized in that, Including: A request receiving module for receiving a real-time traffic condition request from a user; A priority setting module for determining a corresponding task based on the real-time traffic condition request and setting a corresponding priority for the task; A data acquisition module for determining a target drone according to the priority of the task and the status information of candidate drones, controlling the target drone to execute the task, and obtaining real-time traffic condition data.

8. The device according to claim 7, characterized in that, Specifically, the data acquisition module is used for: Calculating, using a deep learning model, matching values of multiple candidate drones according to the priority and the status information of the candidate drones; the status information includes battery power, load, and flight distance; Based on the matching values of the multiple drones, determining a target drone; When the startup condition is met, controlling the target drone to execute the task to obtain real-time traffic condition data.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when run by a processor, implements the real-time traffic condition data acquisition method according to any one of claims 1-6.

10. A processor, characterized in that, For running a computer program, which, when run, executes the real-time traffic condition data acquisition method according to any one of claims 1-6.

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