Unmanned aerial vehicle task allocation method and device, equipment and storage medium

Abstract

The invention relates to an unmanned aerial vehicle task allocation method, device and equipment and a storage medium, and belongs to the technical field of unmanned aerial vehicles, and the method comprises the steps: obtaining a to-be-allocated task and a current state model of all unmanned aerial vehicles; performing structured processing on the to-be-allocated task to obtain a task data structure of the to-be-allocated task; determining a plurality of first unmanned aerial vehicles meeting the task type and the observation requirement in all the unmanned aerial vehicles based on the current state models of all the unmanned aerial vehicles; determining at least one second unmanned aerial vehicle executing the to-be-allocated task from the first unmanned aerial vehicles based on the task data structure of the to-be-allocated task, the current state models of all the first unmanned aerial vehicles and a preset task allocation rule; and performing task allocation on all the second unmanned aerial vehicles based on the task data structure of the to-be-allocated tasks. The method has the advantages that scientific and reasonable unmanned aerial vehicle task allocation is achieved, and the overall task execution efficiency and success rate are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a method, device, equipment and storage medium for allocating tasks to unmanned aerial vehicles. Background Art

[0002] With the rapid development of UAV technology, UAVs are increasingly used in military reconnaissance, environmental monitoring, agricultural plant protection, logistics and distribution, etc. However, in the current UAV task scheduling system, there is a relatively primitive task processing method, that is, there is a lack of a real "scheduling" mechanism.

[0003] Traditionally, when a drone faces multiple flight missions, these missions are often considered as a whole and assigned to a specific drone. Once the drone receives these missions, it will execute them in sequence according to the internal task queue. As a result, the drone is less efficient in executing flight missions, which affects the overall mission completion efficiency. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method, device, equipment and storage medium for allocating tasks of unmanned aerial vehicles, aiming to solve at least one of the above-mentioned technical problems.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] In the first aspect, the present application provides a method for allocating tasks to a drone, using the following technical solution:

[0007] A method for allocating tasks to unmanned aerial vehicles, comprising:

[0008] Obtaining the current state models of the tasks to be assigned and all drones, wherein the current state model of each drone includes the current position, remaining power, sensor type, observation capability, and information of the tasks to be performed;

[0009] Performing structured processing on the tasks to be assigned to obtain a task data structure of the tasks to be assigned, wherein the task data structure includes a task type, a task name, a task priority, target area information, and observation requirements, wherein the task type is point continuous monitoring, line continuous observation, single polygonal area imaging, discretely distributed multiple polygonal area imaging, or annular area imaging;

[0010] Based on the current state models of all the drones, determining a plurality of first drones that meet the mission type and the observation requirement from among all the drones;

[0011] Determine at least one second drone that executes the task to be assigned from each of the first drones based on the task data structure of the task to be assigned, the current state models of all the first drones, and a preset task assignment rule;

[0012] Based on the task data structure of the tasks to be assigned, tasks are assigned to all the second drones.

[0013] The beneficial effect of the present invention is that by obtaining the current state model of the drone and performing structured processing on the tasks to be assigned, the method can quickly understand the task requirements and the capabilities of the drone, thereby efficiently performing task assignment. Based on the task type and observation requirements, combined with factors such as the current position and remaining power of the drone, multiple first drones are determined, and then at least one suitable second drone is selected to perform the task according to the preset task assignment rules, thereby achieving scientific and reasonable drone task assignment and improving the efficiency and success rate of overall task execution.

[0014] Based on the above technical solution, the present invention can also be improved as follows.

[0015] Further, the determining of a plurality of first drones based on the task type, the observation requirement and the current state models of all drones includes:

[0016] Determine the sensor type, observation capability and power requirement of the UAV that performs the task to be assigned according to the task type and observation requirements of the task to be assigned;

[0017] Determine a first set of drones based on the sensor types, observation capabilities, and power requirements of the required drones;

[0018] Determine, according to the to-be-executed task information of each drone in the first drone set, a second drone set that meets the task priority of the to-be-assigned task;

[0019] Obtaining historical completion efficiency of each drone in the second drone set performing tasks of the task type;

[0020] Determine, according to the historical completion efficiency of each drone in the second drone set, a third drone set that meets the efficiency threshold corresponding to the task priority of the task to be assigned;

[0021] All drones in the third drone set are used as first drones.

[0022] The beneficial effect of adopting the above further scheme is that by analyzing the task type, observation requirements, sensor type, observation capability and power requirement of the required drone of the task to be assigned, the method can more accurately screen out the first drone set that meets the task requirements. It ensures a higher matching degree between the task and the drone, and improves the success rate and quality of task execution. Further, according to the priority of the task to be assigned and the task information to be executed of each drone in the first drone set, the second drone set is determined to ensure that tasks with higher priority can be supported by drones with less task information to be executed. Combined with the historical completion efficiency, a more suitable third drone set is selected, thereby improving the efficiency and quality of overall task execution.

[0023] Further, the determining at least one second drone to execute the task to be assigned based on the task data structure of the task to be assigned, the state models of all the first drones and the preset task assignment rule includes:

[0024] Step S41, based on the target area information, determining whether one of the first drones can complete the task to be assigned;

[0025] Step S42, if one of the first drones can complete the task to be assigned, then based on the task data structure of the task to be assigned, the current state models of all the first drones and the preset particle swarm algorithm, determine a second drone that meets the set requirements, wherein the set requirements are that the flight path of each first drone meets the set path requirements and the energy consumption meets the set energy consumption requirements;

[0026] Step S43, if a drone cannot complete the task to be assigned, the target area information of the task to be assigned is divided based on a preset area division rule to obtain a plurality of block areas, and a corresponding block task is determined based on each of the block areas, each of which includes a block task data structure;

[0027] Step S44: for each of the block tasks, based on the block task data structure corresponding to the block task, the current state models of all the first drones and a preset particle swarm algorithm, determine a second drone that meets the set requirements.

[0028] The beneficial effect of adopting the above further scheme is that the UAV task allocation method can determine whether a single UAV can complete the task according to the target area information of the task to be assigned. If a single UAV can complete the task, the particle swarm algorithm is further used to determine the second UAV with the optimal flight path and energy consumption that meets the set requirements, thereby ensuring that the task is completed efficiently and energy-saving. If a single UAV cannot complete the task, the target area is divided into multiple block tasks, and a second UAV that meets the path and energy consumption requirements is determined for each block task, thereby achieving effective decomposition and efficient allocation of complex tasks.

[0029] Further, the determining of the second drone that meets the set requirements based on the task data structure of the task to be assigned, the current state models of all the first drones and a preset particle swarm algorithm includes:

[0030] Initializing a particle swarm based on the task data structure of the task to be assigned and the current state models of all the first UAVs, the particle swarm comprising a plurality of particles, each of the particles representing a task assignment scheme, and the task assignment scheme representing a UAV flight path;

[0031] Based on the preset objective function, calculate the fitness value of each particle;

[0032] Based on the fitness value, current position and current speed of each particle, updating the speed and position of each particle;

[0033] The updated speed and position of each particle are used as the current speed and current position of each particle in the particle swarm of the next iteration, and the cycle is repeated until the fitness value is less than the set threshold. The task allocation scheme corresponding to the particle that meets the set path requirements and energy consumption requirements is selected from the particle swarm of the current iteration cycle as the target task allocation scheme;

[0034] Based on the target task allocation plan, a first drone that executes the target task allocation plan is selected as the second drone.

[0035] The beneficial effects of adopting the above further scheme are: the particle swarm algorithm has the characteristics of global search capability and parallel computing, and can find a better task allocation and flight path solution in a shorter time. By adjusting the weight coefficient in the objective function, multiple performance indicators such as path length, energy consumption, etc. can be balanced to adapt to different application scenarios.

[0036] Further, the assigning of tasks to all the second drones based on the task data structure of the tasks to be assigned includes:

[0037] For any of the second drones, determining a task execution order of the second drone based on the task priority of the task to be assigned and the priority of the task information to be executed of the second drone;

[0038] For any of the second drones, tasks are assigned to the second drone based on the task execution order.

[0039] The beneficial effect of adopting the above further scheme is: based on the priority of the task to be assigned and the priority of the task information to be executed of the second UAV, the task execution order of the second UAV can be reasonably determined, thereby achieving efficient and orderly task allocation, ensuring that high-priority tasks are executed first, and improving the overall efficiency of task execution and resource utilization.

[0040] Further, after allocating tasks to all the second drones based on the task data structure to be assigned, the method further includes:

[0041] Obtaining flight status information and current mission information of each second UAV;

[0042] For any second UAV, determining a safe flight threshold of the second UAV based on the currently executed mission information;

[0043] For any of the second UAVs, comparing the flight status information of the second UAV with a corresponding safe flight threshold;

[0044] For any of the second UAVs, if the flight status information of the second UAV exceeds the corresponding safe flight threshold, an alarm message is sent to the command center.

[0045] The beneficial effect of adopting the above further scheme is that after completing the task allocation, the UAV task allocation method can obtain the flight status information of the second UAV and its current task execution information in real time, and determine the safe flight threshold based on the current task execution information, and timely discover anomalies by comparing the flight status information with the safe flight threshold, thereby effectively ensuring the safe operation of the UAV, and sending alarm information to the command center in an abnormal situation to ensure the reliability of task execution.

[0046] Further, the obtaining of the flight status information of each second UAV includes:

[0047] For any second drone, obtaining the location signal information, posture information, flight speed information, device operation information, and device configuration information of the drone through sensors on the second drone;

[0048] For any of the second UAVs, a flight status assessment is performed based on the position signal information, posture information, flight speed information, device operation information, and device configuration information to determine the flight status information of the UAV.

[0049] The beneficial effect of adopting the above further solution is that for any second drone, the position signal information, posture information, flight speed information, equipment operation information and equipment configuration information of the drone can be obtained in real time through the sensors on it. Based on this information, a comprehensive flight status evaluation is performed to accurately determine the flight status information of the drone, thereby ensuring the safety and reliability of the drone during the mission execution.

[0050] In the second aspect, the present application provides a drone task allocation device, which adopts the following technical solution:

[0051] A UAV task allocation device, comprising:

[0052] An acquisition module is used to acquire the current state model of the tasks to be assigned and all drones, wherein the current state model of each drone includes the current position, remaining power, sensor type, observation capability and information of the tasks to be performed;

[0053] A structured processing module is used to perform structured processing on the task to be assigned to obtain a task data structure of the task to be assigned, wherein the task data structure includes a task type, a task name, a task priority, target area information and observation requirements, and the task type is point continuous monitoring, line continuous observation, single polygonal area imaging, discretely distributed multiple polygonal area imaging or annular area imaging;

[0054] A first determination module is used to determine a plurality of first drones that meet the task type and the observation requirement from among all the drones based on current state models of all the drones;

[0055] A second determination module is used to determine at least one second drone that executes the task to be assigned from each of the first drones based on the task data structure of the task to be assigned, the current state models of all the first drones, and a preset task assignment rule;

[0056] A task allocation module is used to allocate tasks to all the second drones based on the task data structure of the tasks to be allocated.

[0057] In a third aspect, the present application provides an electronic device, which adopts the following technical solution:

[0058] An electronic device comprises a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executes the drone task allocation method described in any one of the first aspects.

[0059] In a fourth aspect, the present application provides a computer-readable storage medium, which adopts the following technical solution:

[0060] A computer-readable storage medium stores a computer program that can be loaded by a processor and executes the drone task allocation method described in any one of the first aspects.

[0061] Additional aspects and advantages of the present application will be partially given in the following description, which will become apparent from the following description, or will be understood through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A flowchart of a method for allocating tasks to a drone provided by an embodiment of the present invention;

[0063] Figure 2 A structural block diagram of a UAV task allocation device provided by one embodiment of the present invention;

[0064] Figure 3 A structural block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0065] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0066] In addition, the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article, unless otherwise specified, generally means that the associated objects before and after are in an "or" relationship.

[0067] An embodiment of the present application provides a method for allocating tasks for unmanned aerial vehicles, which can be executed by an electronic device, which can be a server or a mobile terminal device, wherein the server can be an independent physical server, a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services; the mobile terminal device can be a laptop computer, a desktop computer, etc., but is not limited to this.

[0068] like Figure 1As shown, a method for allocating unmanned aerial vehicle tasks includes steps S11 to S15:

[0069] Step S11, obtaining the current state models of the tasks to be assigned and all the drones, wherein the current state model of each drone includes the current position, remaining power, sensor type, observation capability and information of the tasks to be performed;

[0070] In one embodiment, the tasks to be assigned include, but are not limited to, power inspection tasks, terrain surveying and mapping tasks, and environmental monitoring tasks.

[0071] The current location of the drone is obtained in real time through the GPS module or other positioning technology; different types of drones may be equipped with different sensors, such as cameras, infrared sensors, laser radars, etc.; the observation capability represents the type and range of information that the drone can obtain through its own sensors, such as the resolution of the camera, the detection distance of the infrared sensor, etc. The drone's pending mission information represents the number of tasks that the drone is about to or needs to perform and the task priority of each of the pending tasks.

[0072] Step S12, performing structured processing on the task to be assigned to obtain a task data structure of the task to be assigned, wherein the task data structure includes a task type, a task name, a task priority, target area information, and observation requirements, and the task type is point continuous monitoring, line continuous observation, single polygonal area imaging, discretely distributed multiple polygonal area imaging, or annular area imaging;

[0073] In one embodiment, a task instance is created based on the task to be assigned. Specifically, the task type is extracted from the task to be assigned, and it is verified whether the task type is valid. If the task type is valid, the task name is extracted from the task to be assigned, and the task name has a unique identifier. Thereafter, the task priority is extracted from the task to be assigned, and the task priority is converted into structured data. Thereafter, the target area description is extracted from the task to be assigned. For point continuous monitoring tasks, the target area description is parsed into a point object. For line continuous observation tasks, the target area description is parsed into a line object. For a single polygonal area imaging task, the target area description is parsed into a single polygonal object. For discretely distributed multiple polygonal area imaging tasks, the target area description is parsed into a collection of polygonal objects. For annular area imaging tasks, the target area description is parsed into annular area objects. Finally, the observation requirements are extracted from the input information from the task to be assigned and converted into a dictionary type.

[0074] The above-mentioned task type, task name, task priority, target area information and observation requirements are integrated into a structured task data structure to obtain the task data structure of the task to be assigned.

[0075] Afterwards, check whether the task priority is within the set range, check whether the target area information meets the geometric object requirements, and check whether the observation requirements meet the set observation requirements. If all are met, add the task data structure to the task list to assign tasks to be assigned.

[0076] Step S13, based on the current state models of all the drones, determining a plurality of first drones that meet the mission type and the observation requirement from among all the drones;

[0077] In one implementation, step S13 specifically includes the following sub-steps (steps S131 to S136):

[0078] Step S131, determining the sensor type, observation capability and power requirement of the drone that executes the task to be assigned according to the task type and observation requirement of the task to be assigned;

[0079] Step S132, determining a first set of drones according to the sensor types, observation capabilities, and power requirements of the required drones;

[0080] Step S133, determining a second set of drones that meets the task priority of the task to be assigned according to the task information to be executed of each drone in the first set of drones;

[0081] Step S134, obtaining the historical completion efficiency of each drone in the second drone set performing tasks of the task type;

[0082] Step S135, determining a third set of drones that meets the efficiency threshold corresponding to the task priority of the task to be assigned according to the historical completion efficiency of each drone in the second set of drones;

[0083] Step S136: All drones in the third drone set are used as first drones.

[0084] In the above implementation, first, according to the task type and observation requirements of the task to be assigned, the sensor type, observation capability and power requirement required for the task are analyzed. For example, the task requires the use of an infrared sensor, a resolution of a preset first resolution and a flight time of a preset first flight time. Then, among the currently available drones, drones that meet the sensor type, observation capability and power requirement are selected to form a first drone set. Then, according to the priority of the task to be assigned and the task information to be performed of each drone in the first drone set, such as the urgency of the task and the amount of remaining tasks, drones that can perform new tasks within a reasonable time are further selected to form a second drone set. Then, according to the historical completion efficiency of each drone in the second drone set in performing tasks of the task type and the task priority of the task to be assigned, a sorting selection method is used to select the drones that are most likely to complete the task efficiently to form a third drone set, thereby finally determining multiple first drones.

[0085] Step S14, determining at least one second drone that executes the task to be assigned from each of the first drones based on the task data structure of the task to be assigned, the current state models of all the first drones, and a preset task assignment rule;

[0086] In one embodiment, step S14 includes:

[0087] Step S41, based on the target area information, determining whether one of the first drones can complete the task to be assigned;

[0088] Step S42, if one of the first drones can complete the task to be assigned, then based on the task data structure of the task to be assigned, the current state models of all the first drones and the preset particle swarm algorithm, determine a second drone that meets the set requirements, wherein the set requirements are that the flight path of each first drone meets the set path requirements and the energy consumption meets the set energy consumption requirements;

[0089] Step S43, if a drone cannot complete the task to be assigned, the target area information of the task to be assigned is divided based on a preset area division rule to obtain a plurality of block areas, and a corresponding block task is determined based on each of the block areas, each of which includes a block task data structure;

[0090] Step S44: for each of the block tasks, based on the block task data structure corresponding to the block task, the current state models of all the first drones and a preset particle swarm algorithm, determine a second drone that meets the set requirements.

[0091] The ability of a single UAV to complete the task is determined based on the target area information of the task to be assigned. If a single UAV can complete the task, the particle swarm algorithm is further used to determine the second UAV with the optimal flight path and energy consumption that meets the set requirements, thereby ensuring that the task is completed efficiently and energy-efficiently. If a single UAV cannot complete the task, the target area is divided into multiple block tasks, and a second UAV that meets the path and energy consumption requirements is determined for each block task, achieving effective decomposition and efficient allocation of complex tasks.

[0092] Optionally, the determining the second drone that meets the set requirements based on the task data structure of the task to be assigned, the current state models of all the first drones, and a preset particle swarm algorithm includes:

[0093] Initializing a particle swarm based on the task data structure of the task to be assigned and the current state models of all the first UAVs, the particle swarm comprising a plurality of particles, each of the particles representing a task assignment scheme, and the task assignment scheme representing a UAV flight path;

[0094] Based on the preset objective function, calculate the fitness value of each particle;

[0095] Based on the fitness value, current position and current speed of each particle, updating the speed and position of each particle;

[0096] The updated speed and position of each particle are used as the current speed and current position of each particle in the particle swarm of the next iteration, and the cycle is repeated until the fitness value is less than the set threshold. The task allocation scheme corresponding to the particle that meets the set path requirements and energy consumption requirements is selected from the particle swarm of the current iteration cycle as the target task allocation scheme;

[0097] Based on the target task allocation plan, a first drone that executes the target task allocation plan is selected as the second drone.

[0098] Wherein, the preset objective function may be: F(x)=w1×L(x)+w2×E(x);

[0099] F(x) is the objective function, x represents the task allocation and flight path plan, w1 and w2 are weight coefficients, L(x) and E(x) are the sub-functions corresponding to the path length and energy consumption, respectively.

[0100] Through the global search capability and parallel computing characteristics of the particle swarm algorithm, it is possible to find a better task allocation and flight path solution in a relatively short time, thereby determining the second UAV that meets the set requirements. By adjusting the weight coefficient in the objective function, multiple performance indicators such as path length and energy consumption can be balanced to adapt to different application scenarios.

[0101] Step S15: performing task assignments on all the second UAVs based on the task data structure of the tasks to be assigned.

[0102] In one embodiment, assigning tasks to all the second drones based on the task data structure of the tasks to be assigned includes:

[0103] For any of the second drones, determining a task execution order of the second drone based on the task priority of the task to be assigned and the priority of the task information to be executed of the second drone;

[0104] For any of the second drones, tasks are assigned to the second drone based on the task execution order.

[0105] In the above implementation, the priority of the task information to be executed includes the tasks that the drone has received but not yet completed and the priorities of these tasks.

[0106] By comparing the priority of the task to be assigned and the priority of the task information to be executed by the second UAV, the task execution order of the second UAV is determined. Generally, the task with a higher priority will be executed by the second UAV first.

[0107] Based on the priority of the task to be assigned and the priority of the task information to be executed of the second UAV, the task execution order of the second UAV can be reasonably determined, thereby achieving efficient and orderly task allocation, ensuring that high-priority tasks are executed first, and improving the overall efficiency of task execution and resource utilization.

[0108] As an optional implementation of the embodiment of the present application, after allocating tasks to all the second drones based on the task data structure of the tasks to be allocated, the method further includes:

[0109] Step S16, obtaining flight status information and currently executed mission information of each second UAV;

[0110] In one embodiment, for any second drone, the position signal information, posture information, flight speed information, device operation information, and device configuration information of the drone are obtained through a sensor on the second drone;

[0111] For any of the second UAVs, a flight status assessment is performed based on the position signal information, posture information, flight speed information, device operation information, and device configuration information to determine the flight status information of the UAV.

[0112] Step S17: for any second UAV, determining a safe flight threshold of the second UAV based on the currently executed mission information;

[0113] Step S18, for any of the second UAVs, comparing the flight status information of the second UAV with a corresponding safe flight threshold;

[0114] Step S19: For any of the second UAVs, if the flight status information of the second UAV exceeds the corresponding safe flight threshold, an alarm message is sent to the command center.

[0115] By obtaining the flight status information of the second UAV and its current mission information in real time, and determining the safe flight threshold based on the current mission information, the flight status information is compared with the safe flight threshold to promptly detect anomalies, thereby effectively ensuring the safe operation of the UAV, and sending warning information to the command center in a timely manner under abnormal circumstances to ensure the reliability of mission execution.

[0116] By acquiring the current state model of the drone and performing structured processing on the tasks to be assigned, this method can quickly understand the task requirements and the capabilities of the drone, thereby efficiently performing task assignment. Based on the task type and observation requirements, combined with factors such as the current position and remaining power of the drone, multiple first drones are determined, and then at least one suitable second drone is selected to perform the task according to the preset task assignment rules, thereby achieving scientific and reasonable drone task assignment and improving the efficiency and success rate of overall task execution.

[0117] Figure 2 A structural block diagram of a UAV task allocation device 200 provided in one embodiment of the present invention.

[0118] like Figure 2 As shown, a UAV task allocation device 200 includes:

[0119] The acquisition module 201 is used to acquire the current state model of the task to be assigned and all drones, and the current state model of each drone includes the current position, remaining power, sensor type, observation capability and information of the task to be executed;

[0120] The structured processing module 202 is used to perform structured processing on the task to be assigned to obtain a task data structure of the task to be assigned, wherein the task data structure includes a task type, a task name, a task priority, target area information and observation requirements, and the task type is point continuous monitoring, line continuous observation, single polygonal area imaging, discretely distributed multiple polygonal area imaging or annular area imaging;

[0121] A first determination module 203 is used to determine a plurality of first drones that meet the task type and the observation requirement from among all the drones based on current state models of all the drones;

[0122] A second determination module 204 is used to determine at least one second drone that executes the task to be assigned from each of the first drones based on the task data structure of the task to be assigned, the current state models of all the first drones, and a preset task assignment rule;

[0123] The task allocation module 205 is used to allocate tasks to all the second drones based on the task data structure of the tasks to be allocated.

[0124] Optionally, the first determining module 203 is specifically configured to:

[0125] Determine the sensor type, observation capability and power requirement of the UAV that performs the task to be assigned according to the task type and observation requirements of the task to be assigned;

[0126] Determine a first set of drones based on the sensor types, observation capabilities, and power requirements of the required drones;

[0127] Determine, according to the to-be-executed task information of each drone in the first drone set, a second drone set that meets the task priority of the to-be-assigned task;

[0128] Obtaining historical completion efficiency of each drone in the second drone set performing tasks of the task type;

[0129] Determine, according to the historical completion efficiency of each drone in the second drone set, a third drone set that meets the efficiency threshold corresponding to the task priority of the task to be assigned;

[0130] All drones in the third drone set are used as first drones.

[0131] Optionally, the second determining module is specifically configured to perform the following steps:

[0132] Step S41, based on the target area information, determining whether one of the first drones can complete the task to be assigned;

[0133] Step S42, if one of the first drones can complete the task to be assigned, then based on the task data structure of the task to be assigned, the state models of all the first drones and the preset particle swarm algorithm, determine a second drone that meets the set requirements, wherein the set requirements are that the flight path of each first drone meets the set path requirements and the energy consumption meets the set energy consumption requirements;

[0134] Step S43, if a drone cannot complete the task to be assigned, the target area information of the task to be assigned is divided based on a preset area division rule to obtain a plurality of block areas, and a corresponding block task is determined based on each of the block areas, each of which includes a block task data structure;

[0135] Step S44: for each of the block tasks, based on the block task data structure corresponding to the block task, the state models of all the first drones and a preset particle swarm algorithm, determine a second drone that meets the set requirements.

[0136] Optionally, the second determining module executes step S42 including:

[0137] Initializing a particle swarm based on the task data structure of the task to be assigned and the state models of all the first drones, the particle swarm comprising a plurality of particles, each of the particles representing a task assignment scheme;

[0138] Based on the preset objective function, calculate the fitness value of each particle;

[0139] Based on the fitness value, current position and current speed of each particle, updating the speed and position of each particle;

[0140] The updated speed and position of each particle are used as the current speed and current position of each particle of the particle swarm of the next iteration, and the cycle is repeated until the fitness value is less than the set threshold value. The task allocation scheme corresponding to the particle that meets the set path requirements and energy consumption requirements is selected from the particle swarm of the current iteration cycle as the second UAV that executes the task to be assigned, and the task allocation scheme includes the flight path of the UAV.

[0141] Optional, task allocation module, specifically used for:

[0142] For any of the second drones, determining a task execution order of the second drone based on the task priority of the task to be assigned and the priority of the task information to be executed of the second drone;

[0143] For any of the second drones, tasks are assigned to the second drone based on the task execution order.

[0144] Optionally, the drone task allocation device 200 further includes a monitoring module, which includes:

[0145] an acquisition submodule, configured to acquire flight status information and currently executed task information of each second UAV after assigning tasks to the second UAVs based on the task data structure of the task to be assigned;

[0146] a determination submodule, configured to determine, for any second UAV, a safe flight threshold of the second UAV based on the currently executed mission information;

[0147] a comparison submodule, configured to compare the flight status information of the second UAV with a corresponding safe flight threshold for any of the second UAVs;

[0148] The sending submodule is used to send an alarm message to the command center if the flight status information of any of the second UAVs exceeds the corresponding safe flight threshold.

[0149] Optionally, get submodules, specifically for:

[0150] For any second drone, obtaining the location signal information, posture information, flight speed information, device operation information, and device configuration information of the drone through sensors on the second drone;

[0151] For any of the second UAVs, a flight status assessment is performed based on the position signal information, posture information, flight speed information, device operation information, and device configuration information to determine the flight status information of the UAV.

[0152] In one example, the module in any of the above devices may be one or more integrated circuits configured to implement the above method, such as one or more application specific integrated circuits (ASICs), or one or more digital signal processors.

[0153] (digital signal processor, DSP), or, one or more field programmable gate arrays (field programmable gate array, FPGA), or a combination of at least two of these integrated circuit forms.

[0154] For another example, when the modules in the device can be implemented in the form of a processing element scheduling program, the processing element can be a general-purpose processor, such as a central processing unit (CPU) or other processor that can call a program. For another example, these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC).

[0155] Various objects such as various messages / information / equipment / network elements / systems / devices / actions / operations / processes / concepts that may appear in this application are named. It can be understood that these specific names do not constitute a limitation on the relevant objects. The names assigned may change with factors such as scenarios, contexts or usage habits. The understanding of the technical meaning of the technical terms in this application should be mainly determined from the functions and technical effects embodied / executed in the technical scheme.

[0156] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0157] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0158] Figure 3 This is a structural block diagram of an electronic device 300 according to an embodiment of the present application.

[0159] like Figure 3 As shown, the electronic device 300 includes a processor 301 and a memory 302 , and may further include one or more of an information input / information output (I / O) interface 303 , a communication component 304 , and a communication bus 305 .

[0160] The processor 301 is used to control the overall operation of the electronic device 300 to complete all or part of the steps in the above-mentioned drone task allocation method; the memory 302 is used to store various types of data to support the operation of the electronic device 300, and these data may include, for example, instructions for any application or method operated on the electronic device 300, and data related to the application. The memory 302 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0161] The I / O interface 303 provides an interface between the processor 301 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 304 is used to test the wired or wireless communication between the electronic device 300 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 304 can include: Wi-Fi components, Bluetooth components, NFC components.

[0162] The communication bus 305 may include a path to transmit information between the above components. The communication bus 305 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The communication bus 305 may be divided into an address bus, a data bus, a control bus, etc.

[0163] The electronic device 300 can be implemented by one or more application-specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the drone task allocation method given in the above embodiment.

[0164] The computer-readable storage medium provided in an embodiment of the present application is introduced below. The computer-readable storage medium described below and the drone task allocation method described above can be referenced to each other.

[0165] The present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned drone task allocation method are implemented.

[0166] The computer-readable storage medium may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0167] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus.

[0168] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of application involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned application concept. For example, the above features are replaced with (but not limited to) technical features with similar functions applied in the present application.

Claims

1. A method for allocating unmanned aerial vehicle tasks, characterized in that: include: Obtaining the current state models of the tasks to be assigned and all drones, wherein the current state model of each drone includes the current position, remaining power, sensor type, observation capability, and information of the tasks to be performed; Performing structured processing on the tasks to be assigned to obtain a task data structure of the tasks to be assigned, wherein the task data structure includes a task type, a task name, a task priority, target area information, and observation requirements, wherein the task type is point continuous monitoring, line continuous observation, single polygonal area imaging, discretely distributed multiple polygonal area imaging, or annular area imaging; Based on the current state models of all the drones, determining a plurality of first drones that meet the mission type and the observation requirement from among all the drones; Determine at least one second drone that executes the task to be assigned from each of the first drones based on the task data structure of the task to be assigned, the current state models of all the first drones, and a preset task assignment rule; Based on the task data structure of the tasks to be assigned, tasks are assigned to all the second drones.

2. A method for allocating unmanned aerial vehicle tasks according to claim 1, characterized in that: The determining of a plurality of first drones based on the task type, the observation requirement and the current state models of all the drones comprises: Determine the sensor type, observation capability and power requirement of the UAV that performs the task to be assigned according to the task type and observation requirements of the task to be assigned; Determine a first set of drones based on the sensor types, observation capabilities, and power requirements of the required drones; Determine, according to the to-be-executed task information of each drone in the first drone set, a second drone set that meets the task priority of the to-be-assigned task; Obtaining historical completion efficiency of each drone in the second drone set performing tasks of the task type; Determine, according to the historical completion efficiency of each drone in the second drone set, a third drone set that meets the efficiency threshold corresponding to the task priority of the task to be assigned; All drones in the third drone set are used as first drones.

3. A method for allocating unmanned aerial vehicle tasks according to claim 2, characterized in that: The step of determining at least one second drone to execute the task to be assigned based on the task data structure of the task to be assigned, current state models of all first drones, and a preset task assignment rule comprises: Step S41, based on the target area information, determining whether one of the first drones can complete the task to be assigned; Step S42, if one of the first drones can complete the task to be assigned, then based on the task data structure of the task to be assigned, the current state models of all the first drones and the preset particle swarm algorithm, determine a second drone that meets the set requirements, wherein the set requirements are that the flight path of each first drone meets the set path requirements and the energy consumption meets the set energy consumption requirements; Step S43, if a drone cannot complete the task to be assigned, the target area information of the task to be assigned is divided based on a preset area division rule to obtain a plurality of block areas, and a corresponding block task is determined based on each of the block areas, each of which includes a block task data structure; Step S44: for each of the block tasks, based on the block task data structure corresponding to the block task, the current state models of all the first drones and a preset particle swarm algorithm, determine a second drone that meets the set requirements.

4. A method for allocating unmanned aerial vehicle tasks according to claim 3, characterized in that: The determining of the second UAV that meets the set requirements based on the task data structure of the task to be assigned, the current state models of all the first UAVs and the preset particle swarm algorithm includes: Initializing a particle swarm based on the task data structure of the task to be assigned and the current state models of all the first UAVs, the particle swarm comprising a plurality of particles, each of the particles representing a task assignment scheme, and the task assignment scheme representing a UAV flight path; Based on the preset objective function, calculate the fitness value of each particle; Based on the fitness value, current position and current speed of each particle, updating the speed and position of each particle; The updated speed and position of each particle are used as the current speed and current position of each particle in the particle swarm of the next iteration, and the cycle is repeated until the fitness value is less than the set threshold. The task allocation scheme corresponding to the particle that meets the set path requirements and energy consumption requirements is selected from the particle swarm of the current iteration cycle as the target task allocation scheme; Based on the target task allocation plan, a first drone that executes the target task allocation plan is selected as the second drone.

5. The method for allocating unmanned aerial vehicle tasks according to claim 1, characterized in that: The step of allocating tasks to all the second UAVs based on the task data structure of the tasks to be allocated includes: For any of the second drones, determining a task execution order of the second drone based on the task priority of the task to be assigned and the priority of the task information to be executed of the second drone; For any of the second drones, tasks are assigned to the second drone based on the task execution order.

6. A method for allocating unmanned aerial vehicle tasks according to claim 1, characterized in that: After allocating tasks to all the second UAVs based on the task data structure of the tasks to be allocated, the method further includes: Obtaining flight status information and current mission information of each second UAV; For any second UAV, determining a safe flight threshold of the second UAV based on the currently executed mission information; For any of the second UAVs, comparing the flight status information of the second UAV with a corresponding safe flight threshold; For any of the second UAVs, if the flight status information of the second UAV exceeds the corresponding safe flight threshold, an alarm message is sent to the command center.

7. A method for allocating unmanned aerial vehicle tasks according to claim 6, characterized in that: The obtaining of the flight status information of each second UAV includes: For any second drone, obtaining the location signal information, posture information, flight speed information, device operation information, and device configuration information of the drone through sensors on the second drone; For any of the second UAVs, a flight status assessment is performed based on the position signal information, posture information, flight speed information, device operation information, and device configuration information to determine the flight status information of the UAV.

8. A drone task allocation device, characterized in that: include: An acquisition module is used to acquire the current state model of the tasks to be assigned and all drones, wherein the current state model of each drone includes the current position, remaining power, sensor type, observation capability and information of the tasks to be performed; A structured processing module is used to perform structured processing on the task to be assigned to obtain a task data structure of the task to be assigned, wherein the task data structure includes a task type, a task name, a task priority, target area information and observation requirements, and the task type is point continuous monitoring, line continuous observation, single polygonal area imaging, discretely distributed multiple polygonal area imaging or annular area imaging; A first determination module is used to determine a plurality of first drones that meet the task type and the observation requirement from among all the drones based on current state models of all the drones; A second determination module is used to determine at least one second drone that executes the task to be assigned from each of the first drones based on the task data structure of the task to be assigned, the current state models of all the first drones, and a preset task assignment rule; A task allocation module is used to allocate tasks to all the second drones based on the task data structure of the tasks to be allocated.

9. An electronic device, characterized in that: comprising a processor coupled to a memory; The processor is configured to execute a computer program stored in the memory, so that the electronic device executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The method comprises a computer program or an instruction, and when the computer program or the instruction is executed on a computer, the computer is caused to execute the method according to any one of claims 1 to 7.

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