Unmanned aerial vehicle and ground inspection vehicle collaborative operation method and system based on dynamic task allocation

Abstract

The invention discloses an unmanned aerial vehicle and ground inspection vehicle collaborative operation system and method based on dynamic task allocation. The system comprises a task scheduling server, an unmanned aerial vehicle and an inspection vehicle operation unit. The server maintains a global road section state table and calculates a dynamic environment risk quantized value; after receiving a terminal task request, querying a target road section state and a risk value; an arbitration mode is selected according to the threshold interval to which the risk value belongs: standard collaborative decision is carried out in low risk, dynamic priority addition is given in medium risk, so that the inspection vehicle requests priority authorization, and only the inspection vehicle is authorized in high risk; if the road section is occupied, conflict coordination or safety task migration is executed. According to the invention, the self-adaptive cooperation of the unmanned aerial vehicle and the inspection vehicle in a dynamic environment is realized, through risk-driven intelligent arbitration, the operation safety is effectively guaranteed, the equipment conflict is avoided, and the overall inspection efficiency and the system reliability are improved.

Description

Technical Field

[0001] This invention belongs to the field of intelligent traffic management and automated inspection technology, specifically relating to a method and system for collaborative operation of unmanned aerial vehicles and ground inspection vehicles based on dynamic task allocation. Background Technology

[0002] With the increasing sophistication and intelligence of urban management, the collaborative operation of drones and ground inspection vehicles has become an important technological means in areas such as traffic patrol, facility inspection, and parking management. A common implementation involves equipping both drones and inspection vehicles with independent control systems and task planning modules, with each performing patrol and evidence collection tasks according to preset or manually designated areas. However, this approach has significant drawbacks: due to the lack of a unified task scheduling and real-time status synchronization mechanism, drones and inspection vehicles are prone to repeatedly photographing the same road segment or target vehicle, resulting in unnecessary consumption of computing, storage, and communication resources. More importantly, when their task areas overlap, potential physical conflicts or "order-grabbing" may occur, affecting operational efficiency and posing safety risks.

[0003] Furthermore, existing collaborative scheduling logic is often based on fixed equipment capabilities or preset static priorities, failing to fully consider external environmental factors, especially the dynamic impact of weather conditions on equipment suitability. Drone operations are highly dependent on weather conditions; their flight safety and operational efficiency significantly decrease or are completely limited in severe weather conditions such as strong winds, low visibility, rain, or thunderstorms, while inspection vehicles are relatively less affected. When weather conditions change abruptly, existing systems cannot intelligently perceive environmental risks and dynamically adjust task allocation strategies, potentially leading to safety hazards such as drones operating under hazardous weather conditions, or efficiency issues such as task response interruptions. The system's adaptability and robustness are insufficient.

[0004] Therefore, there is an urgent need for a collaborative system that can achieve dynamic task allocation and environmental adaptive scheduling to solve resource conflicts and operational safety issues, and improve overall operational efficiency. Summary of the Invention

[0005] To address the resource conflicts and inability to adapt to dynamic environmental changes caused by independent operations of drones and inspection vehicles in existing technologies, this invention proposes a collaborative operation method and system for drones and ground inspection vehicles based on dynamic task allocation. At the system level, this method ensures that only one operating device is authorized to perform a task on any given inspection segment within any given time period. Furthermore, it dynamically adjusts the equipment scheduling strategy based on real-time environmental risks, thereby fundamentally avoiding task conflicts and duplication of work, ensuring operational safety under adverse weather conditions, and achieving optimized allocation and adaptive management of computing, communication, and inspection resources. This significantly improves the overall operational efficiency, safety, and intelligence level of the air-ground collaborative inspection system.

[0006] The technical solution of the present invention is implemented as follows: a collaborative operation system of UAV and ground inspection vehicle based on dynamic task allocation, characterized in that it includes a task scheduling server and at least one UAV operation unit and at least one inspection vehicle operation unit. The drone operation unit includes a drone and its control terminal, and the inspection vehicle operation unit includes an inspection vehicle and its control terminal. The UAV control terminal and the inspection vehicle control terminal are respectively connected to the task scheduling server for sending task requests and receiving scheduling instructions. The task scheduling server maintains a globally shared road segment status table, which records the real-time occupancy status of each inspection road segment. The real-time occupancy status includes idle status, drone-occupied status, and inspection vehicle-occupied status. The task scheduling server is also used to calculate dynamic environmental risk quantification values ​​for each inspection segment. The task scheduling server is configured to receive task requests from the UAV control terminal or the inspection vehicle control terminal, wherein the task request specifies the target road segment and the type of the requested operating equipment. The task scheduling server is configured to execute the following processing flow in response to the task request: Query the current real-time occupancy status and environmental risk quantification value of the target road segment; If the target road segment is currently idle, the corresponding arbitration mode is selected for authorization decision based on the preset threshold range to which the environmental risk quantification value belongs; If the target road segment is currently occupied, then according to the environmental risk quantification value and the type of currently occupied equipment, conflict coordination processing is performed, and a task rejection, waiting or migration instruction is returned to the control terminal that sent the request. The task scheduling server is also configured to push the status change information to all online control terminals in real time after the road segment status table is updated.

[0007] This invention, through task scheduling and dynamic environmental response mechanisms, enables the task scheduling server to intelligently arbitrate and uniformly authorize task requests from drones and inspection vehicles based on a global road segment status table and real-time environmental risks. This ensures that any inspection segment is exclusively occupied by only one piece of equipment at a time, thereby completely avoiding task conflicts and duplicate shooting between drones and inspection vehicles at the system level, achieving effective complementarity and collaboration between air and ground inspection resources. Simultaneously, the introduction of a tiered arbitration strategy based on environmental risk quantification allows the system to dynamically adjust scheduling weights during weather deterioration, prioritizing or forcibly allocating tasks to inspection vehicles less affected by environmental conditions, significantly improving operational safety and task continuity under adverse weather conditions. Furthermore, a real-time status synchronization mechanism ensures consistent perception of the global task status across all operating terminals, improving the overall system response speed and collaborative efficiency.

[0008] As a further improvement to the above solution, the UAV control terminal is configured as follows: Query the task scheduling server for the list of currently available aerial inspection routes and their corresponding environmental risk quantification values; Based on the list and the environmental risk quantification value, a path optimization algorithm is used to generate a flight inspection route for the UAV. Before the drone performs the task, a task request for a specific road segment to be performed by the drone is sent to the task scheduling server.

[0009] By enabling drone terminals to autonomously acquire available road segments and risk information, and plan optimized routes that incorporate risk avoidance, the intelligence level and flight safety of single-drone operations are improved, ensuring the feasibility and conflict-free nature of their planned routes at the system level, and achieving a unity of autonomous decision-making and global collaboration.

[0010] As a further improvement to the above solution, the arbitration mode configured in the task scheduling server includes: The standard collaborative arbitration mode is activated when the environmental risk quantification value of the target road segment is lower than the first risk threshold. The task scheduling server makes a decision based solely on request priority or time order. The patrol vehicle priority arbitration mode is activated when the environmental risk quantification value of the target road segment is between the first risk threshold and the second risk threshold. The task scheduling server will authorize the patrol vehicle task request before the drone task request. The patrol vehicle-exclusive arbitration mode is activated when the environmental risk quantification value of the target road segment is higher than the second risk threshold. The task scheduling server rejects new task requests from the UAV control terminal and only accepts and arbitrates requests from the patrol vehicle control terminal.

[0011] By defining dynamic arbitration rules based on environmental risks, the system ensures that inspection vehicles are automatically and unconditionally given priority in high-risk road sections, eliminating coordination conflicts and significantly improving the safety and decision-making efficiency of operations in complex environments, while also ensuring the reliable execution of critical ground inspection tasks.

[0012] As a further improvement to the above scheme, the environmental risk quantification value R is calculated using the following model: R = w1 * f(W) + w2 * g(V) + w3 * h(P); Where f(W) is the risk contribution function based on wind speed and wind direction information, g(V) is the risk contribution function based on visibility and rainfall intensity information, h(P) is the risk contribution function based on lightning probability and road surface adhesion coefficient, and w1, w2, and w3 are the dynamic configuration weight coefficients corresponding to each function.

[0013] By establishing a multi-factor risk quantification model and introducing dynamic weight coefficients, an adaptive assessment of environmental risks in the inspected road sections was achieved, providing more accurate data for the system to intelligently select arbitration modes and significantly improving the accuracy of safety decisions in collaborative operations.

[0014] As a further improvement to the above solution, in the patrol vehicle priority arbitration mode, the task scheduling server is configured as follows: Assign a dynamic priority bonus value to the task request from the inspection vehicle control terminal; Based on the dynamic priority bonus value, conflicting task requests are arbitrated to ensure that patrol vehicle task requests are given priority in the arbitration.

[0015] By introducing a dynamic priority-addition mechanism, the priority of inspection vehicle task requests can be accurately matched with the real-time risk level, thereby achieving intelligent decision-making in conflict arbitration that both ensures safety and avoids resource rigidity.

[0016] As a further improvement to the above solution, in the patrol vehicle-only arbitration mode, if the target road segment is already occupied by a drone, the task scheduling server executes a safe task migration process, specifically as follows: Send a mission abort and data return command to the UAV control terminal; Receive and verify the data transmitted back by the UAV control terminal; Once the verification is successful, the inspection task for the target road section will be authorized to the inspection vehicle control terminal that made the request.

[0017] By implementing a safe task migration process, the system ensures that operational authority can be automatically transferred from drones to inspection vehicles under high-risk conditions, thus avoiding interruptions in inspection work and data loss, and achieving orderly collaboration in emergency scenarios.

[0018] As a further improvement to the above scheme, the dynamic priority bonus value ΔP is calculated using the following formula: ΔP = k * (R - R_t1); Where R is the environmental risk quantification value, R_t1 is the first risk threshold, and k is a preset gain coefficient greater than zero.

[0019] By defining the formula for calculating the priority bonus value, the system scheduling has an objective and consistent decision-making basis, which significantly improves the accuracy and reliability of the risk response mechanism.

[0020] As a further improvement to the above scheme, the task scheduling server is also configured to dynamically adjust the calculation weight coefficient of the environmental risk quantification value based on the trend comparison between historical environmental data and current real-time data, so as to optimize the response accuracy of the arbitration mode.

[0021] By establishing an adaptive weight correction mechanism, the accuracy of the arbitration model's response to environmental changes and the reliability of its decisions have been improved.

[0022] As a further improvement to the above solution, the task scheduling server is configured to send an environmental risk warning to the control terminal of the relevant work unit that is currently on or planning to go to the section of road when the environmental risk quantification value of a specific inspection section changes drastically, and to suggest or trigger a reassessment of the task plan.

[0023] By monitoring sudden changes in risks in real time and proactively sending out early warnings, the system drives dynamic reassessment of task plans, improving the predictability and overall safety of operations in complex environments.

[0024] A method for collaborative operation between unmanned aerial vehicles (UAVs) and ground inspection vehicles based on dynamic task allocation, applied to the system described above, the method comprising: S1. The task scheduling server maintains a globally shared road segment status table and calculates dynamic environmental risk quantification values ​​for each road segment. S2. The UAV control terminal or the inspection vehicle control terminal sends a task request to the task scheduling server, the request specifying the target road section and the type of operating equipment. S3. The task scheduling server queries the real-time occupancy status of the target road segment and its environmental risk quantification value; S4. Select the corresponding arbitration mode based on the environmental risk quantification value, and make a judgment based on the real-time occupancy status: If the status is idle, the authorized object is determined according to the selected arbitration mode; If the status is occupied, conflict coordination will be carried out based on the selected arbitration mode and environmental risks. S5. The task scheduling server sends the judgment result and status update information to the relevant control terminal. S6. After receiving the task permission, the control terminal controls the corresponding drone or inspection vehicle to drive to the target road section to perform the task. S7. After the task is completed, the control terminal sends a task completion notification to the task scheduling server, and the server updates the real-time occupancy status of the corresponding road segment to an idle status.

[0025] By embedding dynamic risk assessment and multi-level intelligent arbitration rules, the system enables adaptive collaborative operation between drones and inspection vehicles in complex environments, thereby significantly improving the overall efficiency, safety, and intelligence level of inspection tasks.

[0026] The beneficial effects of this invention are: (1) By sharing the road segment status table and dynamic environmental risk assessment, the system realizes real-time synchronization and conflict prediction of the operation status of UAVs and inspection vehicles, so that the task allocation can be dynamically adjusted according to real-time risks, which not only avoids equipment conflicts and repetitive operations, but also significantly improves the efficiency and resource utilization of collaborative inspection.

[0027] (2) Based on the environmental risk quantification value, the system automatically matches the multi-level arbitration mode. In high-risk road sections, the system can prioritize or only authorize the inspection vehicle to operate, ensuring the safety and reliability of operation under harsh conditions. At the same time, through the dynamic weight and priority addition mechanism, the scheduling decision is more accurate and adaptive.

[0028] (3) By designing a safety task migration process, the UAV mission can be automatically terminated and transferred to the inspection vehicle when the risk increases, ensuring data integrity and operational continuity, and enhancing the system's emergency response and collaborative stability under sudden environmental changes.

[0029] (4) The entire system is based on a unified scheduling strategy and real-time data push, eliminating the need to customize independent scheduling schemes for different scenarios. This reduces the complexity of deployment and maintenance of multi-device collaboration, and while improving inspection coverage and security, it also has good economy and scalability. Attached Figure Description

[0030] Figure 1 This is a flowchart of a collaborative operation method between UAVs and ground inspection vehicles based on dynamic task allocation. Detailed Implementation

[0031] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0032] Example: This embodiment discloses a collaborative operation system for unmanned aerial vehicles (UAVs) and ground inspection vehicles based on dynamic task allocation. The system includes a cloud-based task scheduling server, at least one UAV operation unit, and at least one inspection vehicle operation unit. The UAV operation unit consists of a UAV and its dedicated control terminal, while the inspection vehicle operation unit consists of an inspection vehicle and its onboard control terminal. The control terminals of each operation unit maintain a long-term connection with the task scheduling server via a wireless communication network for reporting status, sending task requests, and receiving scheduling instructions.

[0033] The task scheduling server maintains a globally shared road segment status table, stored in a database. Each record corresponds to a physical inspection road segment and records the occupancy status of that segment in real time, including "idle," "occupied by drone," and "occupied by inspection vehicle." Simultaneously, the server has a built-in dynamic risk assessment module. This module continuously receives environmental data from external data sources, such as wind speed, wind direction, visibility, rainfall intensity, lightning probability, and road surface adhesion coefficient, and calculates an environmental risk quantification value R for each road segment using a preset quantification model. The model is R = w1 * f(W) + w2 * g(V) + w3 * h(P), where f(W) is a risk contribution function based on wind speed and wind direction information. f(W) = (v_w / 10)^2 + 0.5 * sin(θ); Where v_w is the wind speed, and the angle between the wind direction and the road section direction is θ; g(V) is the risk contribution function based on visibility and rainfall intensity information: g(V) = exp(-0.5 * vis) + 0.2 * rain; Where vis represents visibility and rain represents rainfall intensity; h(P) is the risk contribution function based on lightning probability and road surface adhesion coefficient: h(P) = 5 * lightning + (0.8 - μ) * 2; Where lightning is the probability of lightning strikes, and μ is the road surface adhesion coefficient. w1, w2, and w3 are the dynamic configuration weight coefficients corresponding to each function.

[0034] The server can automatically adjust these weighting coefficients based on a comparison of trends in historical and real-time data to optimize the accuracy of risk assessment.

[0035] When the UAV control terminal needs to perform a task, it first queries the server for a list of currently available road segments suitable for aerial operations and their risk values. Then, based on this list, it uses a path optimization algorithm to plan a flight path for the UAV. Finally, before takeoff, it sends a task request to the server specifying the target road segment and the UAV as the operating equipment type. The inspection vehicle control terminal, on the other hand, directly sends a task request to the server specifying the target road segment and the inspection vehicle as the operating equipment type.

[0036] Upon receiving any task request, the server immediately queries the target road segment's current occupancy status and its latest environmental risk quantification value in the global status table, and initiates an arbitration process. The arbitration process selects different modes based on the threshold range to which the risk value falls: if the risk value is below the first risk threshold, it enters the standard collaborative mode, adjudicating based on the inherent priority of the request or the order of arrival time; if the risk value is between the first and second risk thresholds, it enters the patrol vehicle priority mode. In this mode, the server calculates a dynamic priority bonus value ΔP for task requests from patrol vehicles, determined by the formula ΔP = k * (R - R_t1), where R is the environmental risk quantification value, R_t1 is the first risk threshold, and k is a preset gain coefficient, thus granting patrol vehicle requests absolute priority in arbitration; if the risk value is above the second risk threshold, it enters the patrol vehicle exclusive mode. In this mode, the server rejects all new drone task requests and only processes patrol vehicle requests.

[0037] For example, when the wind speed v_w is 8 m / s, the angle θ between the wind direction and the road section is 90°, the visibility vis is 1 km, the rainfall intensity rain is 10 mm / h, the lightning probability lightning is 0.3, and the road surface adhesion coefficient μ is 0.4: f(W) = (8 / 10)^2 + 0.5 * 1 = 0.64 + 0.5 = 1.14; g(V) = exp(-0.5*1) + 0.2*10 ≈ 0.6065 + 2 = 2.6065; h(P) = 5*0.3 + (0.8-0.4)*2 = 1.5 + 0.8 = 2.3; R = 0.3 * 1.14 + 0.4 * 2.6065 + 0.3 * 2.3 = 0.342 + 1.0426 + 0.69 =2.0746; When the preset first risk threshold R_t1=1.5 and the second risk threshold R_t2=3.0, the system will automatically activate the patrol vehicle priority arbitration mode.

[0038] In this mode, the system will initiate dynamic priority enhancement calculation. Assuming a preset gain coefficient k = 10, the dynamic priority enhancement value ΔP is calculated as follows: ΔP = k * (R - R_t1) = 10 * (2.0746 - 1.5) = 10 * 0.5746 = 5.746. In the subsequent arbitration logic, the priority score of all patrol vehicle task requests will be automatically increased by 5.746 points to ensure that they are given priority authorization.

[0039] If the target road segment is occupied by a drone in the patrol vehicle exclusive mode, the server will execute a security task migration process: First, it will send a command to the drone control terminal occupying the road segment to stop the task and immediately return the collected data; after receiving and verifying the integrity of the data, the server will officially authorize the patrol task of the road segment to the requesting patrol vehicle control terminal.

[0040] Following the arbitration ruling, the server issues authorization or denial instructions to the requesting control terminal and immediately updates the global road segment status table, proactively pushing any status change information to all online control terminals in real time. Authorized control terminals then direct their drones or inspection vehicles to the target road segment to perform operations. Upon completion of the task, they send a completion notification to the server, which updates the road segment's status to idle. Furthermore, when the server detects a rapid change in the environmental risk quantification value of a road segment within a short period, it proactively sends environmental risk warning information to the control terminals of all work units currently operating on or planning to travel to that road segment, recommending that they reassess their task plans.

[0041] like Figure 1 As shown, this embodiment discloses a method for collaborative operation between UAVs and ground inspection vehicles based on dynamic task allocation. This method is applied to the aforementioned system, and its specific steps include: S1. The task scheduling server initializes and maintains a globally shared road segment status table, and calculates a dynamic environmental risk quantification value in real time for each inspected road segment recorded in the table. The environmental risk quantification value is calculated by comprehensively considering wind speed, visibility, rainfall intensity, lightning probability, and road surface adhesion coefficient information, and using a mathematical model with preset weighting coefficients.

[0042] S2. When the UAV control terminal or the inspection vehicle control terminal needs to perform a task, it sends a task request to the task scheduling server. The request clearly specifies the target road segment and the type of work equipment represented by the terminal.

[0043] S3. After receiving the request, the task scheduling server immediately queries the real-time occupancy status of the target road segment and its current environmental risk quantification value.

[0044] S4. Based on the preset threshold range to which the queried environmental risk quantification value belongs, automatically select the corresponding arbitration mode for judgment: If the target road segment is idle, the authorization target is determined according to the selected arbitration mode: when the risk value is lower than the first threshold, the decision is made according to the order or priority of the requests, i.e., the standard collaborative mode; when the risk value is between the first and second thresholds, the patrol vehicle task request is given priority authorization by calculating a dynamic priority bonus, i.e., the patrol vehicle priority mode; when the risk value is higher than the second threshold, only the patrol vehicle is authorized, i.e., the patrol vehicle exclusive mode.

[0045] If the target road segment is occupied, conflict resolution is carried out in accordance with the selected arbitration mode. For example, in a high-risk situation, if the road segment is occupied by a drone, it is instructed to safely terminate its mission and transmit data back, and then the access is transferred to the inspection vehicle.

[0046] S5. After the arbitration ruling, the task scheduling server sends the ruling to the requesting control terminal, updates the global road segment status, and pushes the status change information to all online terminals.

[0047] S6. The control terminal that has obtained mission authorization controls the corresponding drone or inspection vehicle to drive to the target road section to perform the inspection mission.

[0048] S7. After the task is completed, the control terminal sends a completion notification to the task scheduling server, and the server then updates the occupancy status of the road segment to idle, completing one work cycle.

[0049] In addition, the task scheduling server can dynamically optimize the parameters of the risk calculation model based on the changing trends of environmental data, and proactively send early warnings to relevant work terminals when the risk value rises sharply, so as to trigger dynamic adjustments to the task plan.

[0050] Based on the disclosure and teachings of the foregoing specification, those skilled in the art can make changes and modifications to the above embodiments. Therefore, the present invention is not limited to the specific embodiments disclosed and described above, and some modifications and changes to the invention should also fall within the protection scope of the claims of the present invention. Furthermore, although some specific terms are used in this specification, these terms are only for convenience of explanation and do not constitute any limitation on the present invention.

Claims

1. A collaborative operation system for unmanned aerial vehicles (UAVs) and ground inspection vehicles based on dynamic task allocation, characterized in that, It includes a task scheduling server, at least one drone operation unit, and at least one inspection vehicle operation unit; The drone operation unit includes a drone and its control terminal, and the inspection vehicle operation unit includes an inspection vehicle and its control terminal. The UAV control terminal and the inspection vehicle control terminal are respectively connected to the task scheduling server for sending task requests and receiving scheduling instructions. The task scheduling server maintains a globally shared road segment status table, which records the real-time occupancy status of each inspection road segment. The real-time occupancy status includes idle status, drone-occupied status, and inspection vehicle-occupied status. The task scheduling server is also used to calculate dynamic environmental risk quantification values ​​for each inspection segment. The task scheduling server is configured to receive task requests from the UAV control terminal or the inspection vehicle control terminal, wherein the task request specifies the target road segment and the type of the requested operating equipment. The task scheduling server is configured to execute the following processing flow in response to the task request: Query the current real-time occupancy status and environmental risk quantification value of the target road segment; If the target road segment is currently idle, the corresponding arbitration mode is selected for authorization decision based on the preset threshold range to which the environmental risk quantification value belongs; If the target road segment is currently occupied, then according to the environmental risk quantification value and the type of currently occupied equipment, conflict coordination processing is performed, and a task rejection, waiting or migration instruction is returned to the control terminal that sent the request. The task scheduling server is also configured to push the status change information to all online control terminals in real time after the road segment status table is updated.

2. The collaborative operation system of UAV and ground inspection vehicle based on dynamic task allocation as described in claim 1, characterized in that, The UAV control terminal is configured as follows: Query the task scheduling server for the list of currently available aerial inspection routes and their corresponding environmental risk quantification values; Based on the list and the environmental risk quantification value, a path optimization algorithm is used to generate a flight inspection route for the UAV. Before the drone performs the task, a task request for a specific road segment to be performed by the drone is sent to the task scheduling server.

3. The collaborative operation system of UAV and ground inspection vehicle based on dynamic task allocation as described in claim 1, characterized in that, The arbitration modes configured in the task scheduling server include: The standard collaborative arbitration mode is activated when the environmental risk quantification value of the target road segment is lower than the first risk threshold. The task scheduling server makes a decision based solely on request priority or time order. The patrol vehicle priority arbitration mode is activated when the environmental risk quantification value of the target road segment is between the first risk threshold and the second risk threshold. The task scheduling server will authorize the patrol vehicle task request before the drone task request. The patrol vehicle-exclusive arbitration mode is activated when the environmental risk quantification value of the target road segment is higher than the second risk threshold. The task scheduling server rejects new task requests from the UAV control terminal and only accepts and arbitrates requests from the patrol vehicle control terminal.

4. The collaborative operation system of UAV and ground inspection vehicle based on dynamic task allocation as described in claim 3, characterized in that, The environmental risk quantification value R is calculated using the following model: R = w1 * f(W) + w2 * g(V) + w3 * h(P); Where f(W) is the risk contribution function based on wind speed and wind direction information, g(V) is the risk contribution function based on visibility and rainfall intensity information, h(P) is the risk contribution function based on lightning probability and road surface adhesion coefficient, and w1, w2, and w3 are the dynamic configuration weight coefficients corresponding to each function.

5. The collaborative operation system of UAV and ground inspection vehicle based on dynamic task allocation as described in claim 3, characterized in that, In the inspection vehicle priority arbitration mode, the task scheduling server is configured as follows: Assign a dynamic priority bonus value to the task request from the inspection vehicle control terminal; Based on the dynamic priority bonus value, conflicting task requests are arbitrated to ensure that patrol vehicle task requests are given priority in the arbitration.

6. The collaborative operation system of UAV and ground inspection vehicle based on dynamic task allocation as described in claim 3, characterized in that, In the patrol vehicle-only arbitration mode, if the target road segment is already occupied by a drone, the task scheduling server executes a safe task migration process, specifically as follows: Send a mission abort and data return command to the UAV control terminal; Receive and verify the data transmitted back by the UAV control terminal; Once the verification is successful, the inspection task for the target road section will be authorized to the inspection vehicle control terminal that made the request.

7. The collaborative operation system of UAV and ground inspection vehicle based on dynamic task allocation as described in claim 5, characterized in that, The dynamic priority bonus value ΔP is calculated using the following formula: ΔP = k * (R - R_t1); Where R is the environmental risk quantification value, R_t1 is the first risk threshold, and k is a preset gain coefficient greater than zero.

8. The collaborative operation system of UAV and ground inspection vehicle based on dynamic task allocation as described in claim 1, characterized in that, The task scheduling server is also configured to dynamically adjust the calculation weight coefficient of the environmental risk quantification value based on the trend comparison between historical environmental data and current real-time data, so as to optimize the response accuracy of the arbitration mode.

9. The collaborative operation system of UAV and ground inspection vehicle based on dynamic task allocation as described in claim 1, characterized in that, The task scheduling server is configured to send an environmental risk warning to the control terminal of the relevant work unit that is currently on or planning to go to the section of road when the environmental risk quantification value of a specific inspection section changes drastically, and to suggest or trigger a reassessment of the task plan.

10. A method for collaborative operation between unmanned aerial vehicles (UAVs) and ground inspection vehicles based on dynamic task allocation, applied to the system as described in any one of claims 1 to 9, characterized in that, The method includes: S1. The task scheduling server maintains a globally shared road segment status table and calculates dynamic environmental risk quantification values ​​for each road segment. S2. The UAV control terminal or the inspection vehicle control terminal sends a task request to the task scheduling server, the request specifying the target road section and the type of operating equipment. S3. The task scheduling server queries the real-time occupancy status of the target road segment and its environmental risk quantification value; S4. Select the corresponding arbitration mode based on the environmental risk quantification value, and make a judgment based on the real-time occupancy status: If the status is idle, the authorized object is determined according to the selected arbitration mode; If the status is occupied, conflict coordination will be carried out based on the selected arbitration mode and environmental risks. S5. The task scheduling server sends the judgment result and status update information to the relevant control terminal. S6. After receiving the task permission, the control terminal controls the corresponding drone or inspection vehicle to drive to the target road section to perform the task. S7. After the task is completed, the control terminal sends a task completion notification to the task scheduling server, and the server updates the real-time occupancy status of the corresponding road segment to an idle status.

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