Unmanned aerial vehicle scheduling method and system based on multi-level collaborative decision

Abstract

The invention discloses an unmanned aerial vehicle scheduling method and system based on multi-level collaborative decision, and belongs to the technical field of unmanned aerial vehicle intelligent scheduling. Comprising the steps of obtaining a flight task of an unmanned aerial vehicle cluster, decomposing the flight task into a plurality of sub-tasks, and determining a sub-task allocation strategy in combination with single unmanned aerial vehicle performance and sub-task requirements; according to the sub-task allocation strategy, in combination with the feature importance of the flight influence factors, an objective function is constructed, iterative optimization is performed through a particle swarm optimization algorithm, and an optimal flight task execution scheme of the single unmanned aerial vehicle is determined; and according to the optimal flight task execution scheme, multi-unmanned aerial vehicle cooperative path planning is carried out through a path planning algorithm, and an optimal task execution path is generated. The task execution efficiency of the whole unmanned aerial vehicle system can be improved, the coping capacity of the unmanned aerial vehicle system in a dynamic complex environment is enhanced, and the problems that an existing unmanned aerial vehicle is low in response speed, low in accuracy, poor in environmental adaptability and the like in task allocation and resource scheduling are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) intelligent scheduling, and in particular to a UAV scheduling method and system based on multi-level collaborative decision-making. Background Art

[0002] The statements in this section merely mention background art related to the present invention and do not necessarily constitute prior art.

[0003] With the rapid development of drone technology, drones have been widely used in scenarios including power transmission line inspection, environmental monitoring, disaster relief, etc. In a complex dynamic environment, the mission execution capability and resource scheduling efficiency of drones have an important impact on the overall performance of the drone system.

[0004] Faced with complex and changeable environmental conditions, such as bad weather, communication interruptions, and dense obstacles, UAVs still have problems such as slow response speed, low accuracy, and poor environmental adaptability in task allocation and resource scheduling. These problems seriously restrict the widespread promotion and effectiveness of UAV technology in practical applications.

[0005] In addition, with the increase in mission complexity and the expansion of the size of drone groups, traditional single optimization methods are difficult to meet the task allocation and scheduling needs under multi-objective and multi-constraint conditions, affecting the efficiency of drone system mission execution. Summary of the invention

[0006] In order to address the deficiencies of the prior art, the present invention provides a drone scheduling method, system, electronic device, computer-readable storage medium and computer program product based on multi-level collaborative decision-making. By constructing a multi-level collaborative decision-making framework, the organic linkage between individual drone optimization and group collaborative management is achieved.

[0007] In a first aspect, the present invention provides a method for dispatching unmanned aerial vehicles based on multi-level collaborative decision-making; A UAV scheduling method based on multi-level collaborative decision-making, comprising: Obtain the flight mission of the drone cluster and decompose it into multiple subtasks. Combine the performance of individual drones and subtask requirements to determine the subtask allocation strategy. According to the subtask allocation strategy and the characteristic importance of flight influencing factors, the objective function is constructed, and iterative optimization is performed through the particle swarm optimization algorithm to determine the optimal flight mission execution plan for the single UAV; According to the optimal flight mission execution plan, multi-UAV collaborative path planning is carried out through the path planning algorithm to generate the optimal mission execution path.

[0008] In a second aspect, the present invention provides a drone scheduling system based on multi-level collaborative decision-making; A drone dispatching system based on multi-level collaborative decision-making, comprising: The task allocation module is configured to: obtain the flight mission of the drone cluster and decompose it into multiple subtasks, determine the subtask allocation strategy based on the performance of the individual drone and the subtask requirements; construct the objective function based on the subtask allocation strategy and the importance of the characteristics of the flight influencing factors, and perform iterative optimization through the particle swarm optimization algorithm to determine the optimal flight mission execution plan for the individual drone; The path planning module is configured to: perform multi-UAV collaborative path planning through a path planning algorithm according to the optimal flight mission execution plan to generate an optimal mission execution path.

[0009] In a third aspect, the present invention provides an electronic device; An electronic device includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the above-mentioned drone scheduling method based on multi-level collaborative decision-making.

[0010] In a fourth aspect, the present invention provides a computer-readable storage medium; A computer-readable storage medium having a computer program / instruction stored thereon, which, when executed by a processor, implements the steps of the above-mentioned drone scheduling method based on multi-level collaborative decision-making.

[0011] In a fifth aspect, the present invention provides a computer program product; A computer program product includes a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned drone scheduling method based on multi-level collaborative decision-making.

[0012] Compared with the prior art, the present invention has the following beneficial effects: 1. The technical solution provided by the present invention designs a method for optimizing the performance and executing tasks of individual UAVs. By studying the performance optimization and task execution methods of individual UAVs, the adaptive capability and efficient execution of UAVs in complex task environments are realized; the mission success rate of UAVs under different environmental conditions is improved, and the service life of UAVs is extended, laying a solid foundation for the application of UAVs in various task scenarios.

[0013] 2. The technical solution provided by the present invention designs and provides a collaborative optimization and task allocation strategy at the drone group level, which improves the overall execution efficiency and task completion of the drone cluster, effectively solves problems such as uneven task allocation and waste of resources, and promotes the development of drone cluster technology in complex application scenarios.

[0014] 3. The technical solution provided by the present invention, by building a multi-level collaborative decision-making framework, studies and realizes the organic linkage between individual drone optimization and group collaborative management. This multi-level collaborative design not only improves the task execution efficiency of the entire drone system, but also enhances the system's ability to respond in dynamic and complex environments, providing new ideas for the intelligent and clustered development of drone systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0016] Figure 1 A flowchart of a drone scheduling method based on multi-level collaborative decision-making provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0017] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0018] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0019] Embodiment 1 During the execution of flight missions, existing drones have problems such as slow response speed, low accuracy and difficulty in adapting to complex dynamic environments. Therefore, the present invention provides a drone scheduling method based on multi-level collaborative decision-making, which improves the mission execution efficiency of the drone system and its coping capabilities in dynamic and complex environments by constructing a multi-level collaborative decision-making framework.

[0020] Next, combine Figure 1 , a drone scheduling method based on multi-level collaborative decision-making disclosed in this embodiment is described in detail. The drone scheduling method based on multi-level collaborative decision-making includes the following steps: S1. Obtain the flight mission of the drone cluster and decompose it into multiple subtasks. Combine the performance of individual drones and subtask requirements to determine the subtask allocation strategy.

[0021] During the task execution process of existing drone clusters, there are problems such as uneven task allocation and waste of resources. Therefore, in this embodiment, task allocation and collaborative optimization of multiple individual drones are first performed at the drone group level, thereby improving the overall execution efficiency and task completion rate of the drone cluster in a multi-task, multi-target environment.

[0022] As an implementation mode, S1 specifically includes: S101. Initialize the allocation of flight missions for the drone cluster, decompose the flight missions into multiple subtasks, consider the subtask requirements and the advantages of individual drones, and preliminarily divide the subtasks into different individual drones.

[0023] In this embodiment, when performing the decomposition and preliminary division of subtasks, the operating personnel manually match them according to the most important requirements of the flight mission and the greatest advantages of the single UAV.

[0024] S102. Based on the preliminary division of subtasks, combined with the performance of the single UAV and the requirements of the subtasks, determine the subtask allocation strategy.

[0025] If the flight mission is a monitoring mission, for example, an auction mechanism is used to evaluate each subtask. Based on the load of the single UAV, the mission execution cost of the single UAV and the mission distance of the single UAV, the bid price corresponding to each single UAV is obtained, and the subtask is assigned to the single UAV with the lowest bid price.

[0026] The bidding price is as follows: ; in, It is a single drone Subtask The bid price; It is a single drone Complete subtasks the implementation costs; It is a single drone To subtask The distance It's a drone The load, α and β It is the coefficient that adjusts the influence of distance and load on the bidding price.

[0027] If all bids for a subtask are too high, you may need to adjust your bidding parameters or bid again.

[0028] In this embodiment, an auction mechanism is used to evaluate each subtask, which can ensure that the most suitable single drone for executing this subtask is selected according to the priority of the subtask.

[0029] If the flight mission is a rescue mission, in some embodiments, for example, based on the urgency of the subtasks and the mission distance of the single UAV, a greedy algorithm is used to determine the subtask allocation strategy, and the mission execution of the single UAV is gradually optimized; the specific process is as follows: (1) Based on the urgency of the subtask and the mission distance of the single UAV, determine the task score of each single UAV corresponding to each subtask, and then evaluate which single UAV is most suitable for performing that subtask. The task score is expressed as follows: ; in, Indicates a single drone Subtask Rating, Indicates a single drone To subtask distance; Represents a subtask The urgency of The coefficient that regulates the impact of the urgency of the subtask.

[0030] (2) According to the task score, the single UAV with the highest score is selected to perform the subtask, and the subtask is removed from the task list. At the same time, the status of the single UAV (such as load, energy, etc.) is updated to ensure that the UAV does not repeatedly undertake tasks that exceed its capabilities during task allocation.

[0031] Here, the single drone with the highest mission score is the one that has the best overall performance after taking into account the mission distance and urgency.

[0032] S2. According to the subtask allocation strategy and the characteristic importance of flight influencing factors, the objective function is constructed, and iterative optimization is performed through the particle swarm optimization algorithm to determine the optimal flight mission execution plan for the single UAV.

[0033] At the individual level of a single UAV, in this embodiment, the performance and task execution method of the single UAV are optimized to improve the adaptability and efficient execution of the single UAV in a complex mission environment.

[0034] As an implementation mode, S2 specifically includes: S201. Obtain historical feature data of the current single UAV and the corresponding mission success rate, perform feature importance evaluation on the flight influencing factors in the historical feature data according to the mission success rate, and obtain the relative importance weight of each flight influencing factor; construct an objective function according to the flight influencing factors and the corresponding relative importance weights.

[0035] Specifically, historical feature data includes the location, endurance, performance parameters and environmental conditions of a single drone. The location of a single drone includes the current location and target location of the single drone and the impact of the location on mission execution; the endurance includes the remaining battery power of the drone, the estimated flight time and the limitation of the endurance on mission execution; the performance parameters include the performance indicators such as the flight speed, load capacity and sensor accuracy of the drone; and the environmental conditions include weather conditions (wind speed, precipitation, etc.), obstacle distribution and airspace restrictions. For example, linear regression is used to analyze the impact of the characteristics of a single drone on the success rate of the mission, and the regression coefficient is used to reflect the impact of the characteristics of a single drone on the success of the subtask. The obtained objective function is expressed as follows: ; in, is the objective function, i.e., the task success rate; is the independent variable, which represents the location, endurance and other characteristics of the single UAV; is the regression coefficient, which indicates the contribution of each single UAV feature to the mission success rate, that is, the relative importance weight of each flight influencing factor; is the intercept term, is the error term.

[0036] In some embodiments, a machine learning model (such as a random forest, a decision tree) may also be used to automatically learn the relationship between features and target variables through training data, and output the relative importance weights of individual drone features.

[0037] S202: Based on the above objective function, with the goal of maximizing the mission success rate, and with the flight time of a single drone, the remaining battery power of a single drone, and the endurance distance of a single drone as constraints, the particle swarm optimization algorithm is used for iterative optimization to determine the optimal mission execution plan. The specific process is as follows: S2031, particle swarm initialization. The task execution plan of each single UAV is represented as a particle. The position of each particle represents the parameter set of the single UAV to perform the task (such as flight path, speed, task sequence, etc.), and the speed of each particle represents the speed of change of the parameter set.

[0038] S2032, initializing particle position and velocity. Specifically including: (1) Position initialization: Generate an initial position for each particle based on historical data or heuristic rules. These positions are the preliminary mission plans of the single UAV, that is, the subtask allocation strategy obtained in S1 to cover a larger search space.

[0039] (2) Speed ​​initialization: Assign an initial speed to each particle. The speed determines the direction and amplitude of the particle's movement in the search space. The initial speed is usually also a random value.

[0040] (3) Iterative optimization. Calculate the fitness value: Use the objective function determined previously to calculate the fitness value of each particle. The higher the fitness value, the better the current particle position (i.e., the drone mission plan).

[0041] (4) Update the individual best position: For each particle, if the current fitness value is better than its historical best position, the current position is updated to the individual best position of the particle; Update the global best position: In the entire particle group, find the particle position with the highest fitness value and update it to the global best position.

[0042] The formula of the update process is as follows: ; in, It is a particle Speed ​​in the next step; Represents the inertia weight, which is used to control the particle's dependence on the current velocity; and represents the learning factor, which controls the degree to which the particle follows the individual best position and the global best position respectively; and Represents a random number between [0,1], used to increase the randomness of particles.

[0043] The formula for position update is as follows: ; in, It is a particle In the next step the new location.

[0044] S2033: Determine whether the set maximum number of iterations has been reached, or whether the change in the global optimal position has been very small. If the convergence condition is met, stop the iteration; otherwise, return to S2033 to continue the optimization.

[0045] Once the algorithm converges, determine the global optimal position in the particle swarm g best , as the optimal mission execution plan for UAVs.

[0046] In this embodiment, the constraint condition is expressed as: ; ; ; In the formula, Indicates the flight time of a single drone. Indicates the maximum flight time of a single drone. Indicates the remaining battery power of a single drone. Indicates the minimum residual current of a single drone. Indicates the endurance distance of a single drone. Indicates the maximum endurance distance of a single drone.

[0047] S3. Based on the optimal UAV mission execution plan, multi-UAV collaborative path planning is performed through the path planning algorithm to generate the optimal path. Specifically, it includes: S301. According to the optimal mission execution plan of the UAV, with no obstacles and meeting flight conditions as constraints, use the A* path planning algorithm to generate a path for each single UAV, ensuring that the path of each UAV is optimal while meeting the constraints.

[0048] As an implementation mode, S301 specifically includes: S3011. The current position of the single UAV is used as the starting point node and added to the open list (OpenList) to store the path nodes to be evaluated. The target point is set as the task execution point that the single UAV needs to reach. Initialize the closed list (Closed List) to store the nodes that have been evaluated to avoid repeated evaluation and path backtracking.

[0049] S3012, select the current node: select the node with the lowest cost estimate f(n) from the open list as the current node. The cost estimate f(n) represents: ; in, It represents the actual cost of a single UAV flying from the starting point to the current node, which is usually calculated based on the flight distance of the path or other cost functions; Represents the heuristic estimated cost from the current node to the target point, which can be estimated by Euclidean distance or Manhattan distance.

[0050] S3013, expand the current node: evaluate all the adjacent nodes of the current node, that is, the possible flight directions of the single UAV near the current flight path. For each adjacent node: If an adjacent node is already in the closed list, skip the node. If an adjacent node is not in the open list, add it to the open list, set the current node as the parent node of the adjacent node, calculate and record the adjacent node's If an adjacent node is already in the open list, but the cost of reaching the adjacent node through the current node is If it is lower, the parent node of the adjacent node is updated to be the current node, and its value.

[0051] S3014. Remove the current node from the open list and add it to the closed list, indicating that the node has been evaluated and will not be considered again.

[0052] S3015, check the end point: if the current node is the target point, stop searching, the path planning is completed, and the drone can fly according to the planned path. If the open list is empty, it means that no path from the starting point to the target point is found, and the path planning fails.

[0053] S3016, path backtracking and generation: Once the target point is selected as the current node, the path planning process ends. At this point, the system will start from the target point and backtrack along the parent node pointer of each node until it returns to the starting point. This backtracking process generates the optimal path for the single UAV, ensuring that the single UAV can fly efficiently from the starting point to the target point. The final generated path will be used as the flight route for the single UAV to perform the mission.

[0054] Furthermore, it also includes: S4. Obtain the real-time flight status data and real-time path data of each single UAV, and determine whether a sudden environmental change occurs based on the real-time flight status data and real-time path data. If a sudden environmental change occurs, use the adaptive ant colony algorithm to check whether the remaining power of the single UAV can complete each task in the current task sequence. If so, no adjustment is required. If not, an early warning is fed back to the control center.

[0055] In this embodiment, the real-time flight status data includes weather conditions. If the weather conditions are strong winds, rain, etc., it is considered that a sudden environmental change has occurred.

[0056] As an implementation method, the adaptive ant colony algorithm is used to check whether the remaining power of a single drone can complete each task in the current task sequence, specifically including: S401, based on the distances between the unfinished mission points of the single UAV, determine the mission execution order that only considers the distance factor through the adaptive ant colony algorithm. The specific process is as follows: S4011. Initialize pheromone distribution, expressed as: ; In the formula, is the initial pheromone concentration of the i, j road section, is the basic unit for pheromone initialization, M is the number of iterations; A is the path obtained by the nearest neighbor algorithm, that is, finding the task point closest to the current task point of the single UAV, and then finding the task point closest to the task point, and so on, to obtain the initial path.

[0057] S4012, calculate the transfer probability, and use the roulette method to randomly select the next segment, where the transfer probability is expressed as: ; In the formula, for time The transition probability of the road segment, for time Pheromone concentration of road section; for The path length between each point of the road segment according to A; is the set of paths that are not included in the taboo table and are associated with the current node; It is a pheromone-inspiring factor; is the heuristic value heuristic factor; s is the current path to be calculated.

[0058] S4013, pheromone update, the updated pheromone concentration is expressed as: ; ; In the formula, is the current iteration number, is the set of all the road sections that all ants have walked in the previous iteration. is the set of optimal paths walked by all ants in the previous iteration; It is the basic unit of pheromone.

[0059] The pheromone concentration on the path will be updated. The update method of the classic ant colony algorithm will cause the pheromone concentration on the optimal path selected in each iteration to increase significantly, which will cause the selection preference of the ant colony to be too concentrated in the next iteration, increasing the possibility of the algorithm falling into the local optimum. In this embodiment, a phased pheromone update is adopted to dynamically adjust the pheromone update. In the early stage of the algorithm operation, the pheromone concentration on the optimal path is weakened to ensure that the ant colony has a wider search range.

[0060] After the iteration is completed, the task ranking that only considers the cruising distance factor is obtained.

[0061] S402, according to the task sorting considering only the cruising distance factor, the distances between tasks are summed up to obtain the total task distance, and it is determined whether the remaining cruising power can meet the total task distance requirement. If so, the task is executed accordingly, otherwise, an early warning is fed back to the control center.

[0062] Here, the distance between each unfinished mission point of a single UAV is determined based on the executed mission points in the real-time path data and the UAV path obtained by the A* path planning algorithm in S3.

[0063] In some embodiments, it also includes: analyzing the results of task execution, identifying potential problems, and upgrading and adjusting the system through a complete task feedback mechanism to improve the execution capability of future tasks. Specifically including: (1) After the mission is completed, each individual drone uploads the execution data (such as cruising time, path information, remaining battery power, etc.) to the group control center. The group layer analyzes this data to evaluate the effectiveness of the mission execution and summarize potential problems and improvement points in the mission execution.

[0064] (2) Based on the feedback data, the system can adjust the optimization method at the individual level and the coordination strategy at the group level to adapt to future mission requirements. For example, by analyzing past mission data, the system can better balance the load of drones in future mission allocation and avoid performance degradation of some drones due to overwork.

[0065] For example, first, after the mission is completed, each individual drone uploads the remaining power and cruising time to the group control center; then, the group control center calculates the variance of the remaining power of the individual drone and the variance of the cruising time, and compares the variance of the remaining power with the variance of the cruising time. If it is closer to 1, the task distribution is more even and less adjustment is needed; the further away from 1, the more adjustment is needed, and the single drone tasks with less remaining power and longer cruising time need to be recorded, and their impact coefficients need to be increased during the next planning.

[0066] Embodiment 2 This embodiment discloses a drone scheduling system based on multi-level collaborative decision-making, including: The task allocation module is configured to: obtain the flight mission of the drone cluster and decompose it into multiple subtasks, and determine the subtask allocation strategy based on the performance of the individual drones and the subtask requirements; According to the subtask allocation strategy and the characteristic importance of flight influencing factors, the objective function is constructed, and iterative optimization is performed through the particle swarm optimization algorithm to determine the optimal flight mission execution plan for the single UAV; The path planning module is configured to: perform multi-UAV collaborative path planning through a path planning algorithm according to the optimal flight mission execution plan to generate an optimal mission execution path.

[0067] It should be noted that the above-mentioned task assignment module and path planning module correspond to the steps in Embodiment 1, and the examples and application scenarios implemented by the above-mentioned modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above-mentioned Embodiment 1. It should be noted that the above-mentioned modules as part of the system can be executed in a computer system such as a set of computer executable instructions.

[0068] Embodiment 3 Embodiment 3 of the present invention provides an electronic device, including a memory and a processor, and computer instructions stored in the memory and executed on the processor. When the computer instructions are executed by the processor, the steps of the above-mentioned drone scheduling method based on multi-level collaborative decision-making are completed.

[0069] Embodiment 4 Embodiment 4 of the present invention provides a computer-readable storage medium for storing computer instructions. When the computer instructions are executed by a processor, the steps of the above-mentioned drone scheduling method based on multi-level collaborative decision-making are completed.

[0070] Embodiment 5 Embodiment 5 of the present invention provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the above-mentioned drone scheduling method based on multi-level collaborative decision-making.

[0071] The description of each embodiment in the above embodiments has different emphases. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0072] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. UAV scheduling method based on multi-level collaborative decision-making, It is characterized in that include: Obtain the flight mission of the drone cluster and decompose it into multiple subtasks. Combine the performance of individual drones and subtask requirements to determine the subtask allocation strategy. According to the subtask allocation strategy and the characteristic importance of flight influencing factors, the objective function is constructed, and iterative optimization is performed through the particle swarm optimization algorithm to determine the optimal flight mission execution plan for the single UAV; According to the optimal flight mission execution plan, multi-UAV collaborative path planning is carried out through the path planning algorithm to generate the optimal mission execution path.

2. The drone scheduling method based on multi-level collaborative decision-making as claimed in claim 1, It is characterized in that The subtask allocation strategy is determined by combining the performance of the single UAV and the subtask requirements. Specifically, each subtask is evaluated by using an auction mechanism, and the bid price corresponding to each single UAV is obtained by combining the load, task distance and execution cost of each single UAV. The subtask is allocated to the single UAV with the lowest bid price.

3. The drone scheduling method based on multi-level collaborative decision-making according to claim 1, It is characterized in that The subtask allocation strategy determined by combining the performance of the single UAV and the subtask requirements specifically includes: Combining the urgency of the subtask and the task distance of the single UAV, a greedy algorithm is used to determine the task score of each single UAV corresponding to each subtask; The single drone with the highest score is selected to perform the subtask, and the subtask is removed from the task list.

4. The drone scheduling method based on multi-level collaborative decision-making as claimed in claim 1, It is characterized in that The method of performing multi-UAV collaborative path planning through a path planning algorithm according to the optimal flight mission execution plan is as follows: according to the optimal mission execution plan of the UAV, with the constraints of not touching obstacles and meeting flight conditions, an A* path planning algorithm is used to generate an optimal mission execution path for each single UAV.

5. The drone scheduling method based on multi-level collaborative decision-making as claimed in claim 1, It is characterized in that The objective function constructed according to the subtask allocation strategy and in combination with the characteristic importance of flight influencing factors specifically includes: Obtain the historical feature data of the current single UAV and the corresponding mission success rate, evaluate the feature importance of the flight influencing factors in the historical feature data according to the mission success rate, and obtain the relative importance weight of each flight influencing factor; The objective function is constructed based on the flight influencing factors and the corresponding relative importance weights.

6. The drone scheduling method based on multi-level collaborative decision-making as claimed in claim 1, It is characterized in that Also includes: The real-time flight status data and real-time path data of each single UAV are obtained to determine whether a sudden change in the environment occurs. If so, the adaptive ant colony algorithm is used to check whether the power of the single UAV is sufficient to complete the task.

7. UAV dispatching system based on multi-level collaborative decision-making, It is characterized in that include: The task allocation module is configured to: obtain the flight mission of the drone cluster and decompose it into multiple subtasks, and determine the subtask allocation strategy based on the performance of the individual drones and the subtask requirements; According to the subtask allocation strategy and the characteristic importance of flight influencing factors, the objective function is constructed, and iterative optimization is performed through the particle swarm optimization algorithm to determine the optimal flight mission execution plan for the single UAV; The path planning module is configured to: perform multi-UAV collaborative path planning through a path planning algorithm according to the optimal flight mission execution plan to generate an optimal mission execution path.

8. An electronic device comprising a memory, a processor and a computer program stored in the memory, It is characterized in that The processor executes the computer program to implement the steps of the drone scheduling method based on multi-level collaborative decision-making as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program / instructions stored thereon, It is characterized in that When the computer program / instruction is executed by a processor, the steps of the drone scheduling method based on multi-level collaborative decision-making described in any one of claims 1-6 are implemented.

10. A computer program product comprising a computer program / instructions, It is characterized in that When the computer program / instruction is executed by a processor, the steps of the drone scheduling method based on multi-level collaborative decision-making described in any one of claims 1-6 are implemented.

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