Unmanned aerial vehicle flight task allocation method and device and terminal equipment

Abstract

The invention provides an unmanned aerial vehicle flight task allocation method and device and terminal equipment, and is suitable for the technical field of unmanned aerial vehicles, and the method comprises the steps: obtaining the current flight environment information, the current task demand information and the current flight process state information of an unmanned aerial vehicle; outputting initial flight task allocation information according to the current flight environment information, the current task demand information, the current flight process state information and a target flight task allocation model; in response to the output of the initial flight task allocation information, acquiring expert allocation task decision information; and obtaining target flight task allocation information of the unmanned aerial vehicle according to the initial flight task allocation information, the expert allocation task decision information and a preset task allocation decision weight. Self-adaptive distribution of the unmanned aerial vehicle flight tasks is realized, the reasonability of flight task distribution can be improved in combination with expert experience decision, and the execution efficiency and quality of the unmanned aerial vehicle flight tasks are improved.

Description

Technical Field

[0001] This application belongs to the technical field of unmanned aerial vehicles, and particularly relates to a method, device, and terminal device for allocating flight tasks of unmanned aerial vehicles. Background Art

[0002] With the continuous progress of unmanned aerial vehicle technology, swarm unmanned aerial vehicles have been widely used in multiple fields. In the military field, swarm unmanned aerial vehicles can perform complex tasks such as reconnaissance, strike, and interference, and improve combat effectiveness through multi-aircraft cooperation; in the civilian field, they play an important role in scenarios such as search and rescue, environmental monitoring, and logistics distribution. For example, in search and rescue, they can quickly search large areas, and in environmental monitoring, they can obtain environmental data in real time.

[0003] In the prior art, a rule-based path planning algorithm is usually used to automatically allocate the flight tasks of unmanned aerial vehicles, or a deep learning algorithm is directly used to allocate the flight tasks of unmanned aerial vehicles.

[0004] However, in the prior art, the rule-based path planning algorithm is difficult to cope with sudden changes in the environment and task requirements, and cannot achieve real-time adjustment of task allocation; using a deep learning algorithm usually requires a large amount of data for training. However, the training data usually has a limited range, resulting in deviations in the flight task allocation of the deep learning algorithm in new environments or new tasks, leading to low adaptability of flight task allocation. Summary of the Invention

[0005] In view of this, embodiments of this application provide a method, device, and terminal device for allocating flight tasks of unmanned aerial vehicles, aiming to solve the problem in unmanned aerial vehicle technology that it is difficult to cope with sudden changes in the flight environment and task requirements, resulting in low adaptability of the flight task allocation of unmanned aerial vehicles and inability to meet the military's need for flexible adjustment of environmental conditions in a timely manner.

[0006] The first aspect of the embodiments of this application provides a method for allocating flight tasks of unmanned aerial vehicles, including:

[0007] Obtain the current flight environment information, current task requirement information, and current flight process status information of the unmanned aerial vehicle;

[0008] According to the current flight environment information, current task requirement information, current flight process status information, and the target flight task allocation model, output the initial flight task allocation information;

[0009] In response to the output of the initial flight task allocation information, obtain the expert task allocation decision information;

[0010] According to the initial flight task allocation information, expert task allocation decision information, and the preset task allocation decision weight, obtain the target flight task allocation information of the unmanned aerial vehicle.

[0011] In a second aspect of the embodiments of the present application, a drone flight task allocation device is provided, including:

[0012] An information acquisition module, configured to acquire the current flight environment information, current task requirement information, and current flight process status information of the drone;

[0013] An initial flight task allocation information output module, configured to output initial flight task allocation information according to the current flight environment information, current task requirement information, current flight process status information, and a target flight task allocation model;

[0014] An expert task allocation decision information acquisition module, configured to acquire expert task allocation decision information in response to the output of the initial flight task allocation information; and

[0015] A target flight task allocation information determination module, configured to obtain the target flight task allocation information of the drone according to the initial flight task allocation information, expert task allocation decision information, and a preset task allocation decision weight.

[0016] In a third aspect of the embodiments of the present application, a terminal device is provided. The terminal device includes a memory and a processor. A computer program is stored on the memory and can run on the processor. When the processor executes the computer program, the steps of the drone flight task allocation method described in the first aspect above are implemented.

[0017] In a fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided, including: a computer program is stored, and when the computer program is executed by a processor, the steps of the drone flight task allocation method described in the first aspect above are implemented.

[0018] The beneficial effects of the embodiments of the present application compared with the prior art are as follows: The present application combines multi-source information and outputs initial task allocation information by combining with a target model, and can adjust the task allocation information by considering special circumstances and potential risks in combination with expert experience, so as to improve the rationality of the drone flight task decision-making, thereby realizing the adaptive allocation of the drone flight task, improving the execution efficiency and quality of the drone flight task, and improving the overall effectiveness of the swarm drones. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 It is a schematic diagram of the implementation process of the UAV flight task allocation method provided in the first embodiment of this application;

[0021] Figure 2 It is a schematic diagram of the implementation process of the UAV flight task allocation method provided in the second embodiment of this application;

[0022] Figure 3 It is a schematic diagram of the implementation process of the UAV flight task allocation method provided in the third embodiment of this application;

[0023] Figure 4 It is a schematic diagram of the implementation process of the UAV flight task allocation method provided in the fourth embodiment of this application;

[0024] Figure 5 It is a schematic diagram of the implementation process of the UAV flight task allocation method provided in the fifth embodiment of this application;

[0025] Figure 6 It is a schematic diagram of the structure of the UAV flight task allocation device provided in the embodiments of this application;

[0026] Figure 7 It is a schematic diagram of the terminal device provided in the embodiments of this application. Detailed implementation manners

[0027] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of this application. However, those skilled in the art should clearly understand that this application can also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of this application.

[0028] In order to illustrate the technical solutions described in this application, the following will be described through specific embodiments.

[0029] Figure 1 The following shows the implementation flowchart of the UAV flight task allocation method provided in the first embodiment of this application, which is described in detail as follows:

[0030] Step S101, obtain the current flight environment information, current task requirement information, and current flight process status information of the UAV.

[0031] In this embodiment, the current flight environment information may be the external environmental conditions when the UAV is flying, including images, temperature, humidity, position, terrain, meteorological conditions, and the enemy's electronic warfare capabilities, etc. Among them, image data can be collected by the optoelectronic sensor carried by the UAV for identifying targets and obstacles; environmental data such as temperature and humidity are obtained by environmental sensors; position information is updated in real time with the help of GPS sensors; terrain data can be pre-stored in the database and called in combination with the UAV's real-time position; meteorological data can be obtained through information interaction with the meteorological department or the meteorological sensors carried by itself; information on the enemy's electronic warfare capabilities is collected and analyzed by electronic reconnaissance equipment. The current mission requirement information may be the specific requirements set by the user for the UAV mission, covering mission objectives, priorities, flight altitude, duration limit, and environmental restrictions, etc. For example, the mission objective may be to reconnoiter the enemy's position, interfere with the enemy's communication, etc.; the mission priority determines the order of mission execution; the flight altitude requirement is determined according to the mission nature and environment; the duration limit is set according to the urgency of the mission; environmental restrictions such as no-fly zones and enemy radar coverage areas can all be input into the system by the user before the mission starts. The current flight process status information may be used to reflect the real-time status of the UAV during flight, including UAV position, flight speed, flight attitude (pitch, roll, yaw), flight current, battery voltage, and remaining battery power, etc. Among them, the UAV position and flight speed can be updated and obtained by the GPS sensor every second; the flight attitude can be periodically collected by the attitude sensor; the flight current and battery voltage can be monitored in real time by the battery monitoring system; the remaining battery power can be calculated based on parameters such as battery voltage and is used to feedback the flight status of the UAV in real time to ensure flight safety and the smooth execution of the mission.

[0032] In this embodiment, the current flight environment information may include the current UAV distribution information, the current environmental temperature information, the current environmental wind speed information, and the current environmental air pressure information; the current mission requirement information may include the current target position information, the current mission type information, the current mission priority information, the current mission flight altitude information, the current mission execution time information, and the current mission execution environmental requirement information; the current flight process status information may include the current flight position information, the current flight speed information, the current flight attitude information, the current flight current information, the current flight voltage information, and the current remaining battery power information.

[0033] Step S102, according to the current flight environment information, the current mission requirement information, the current flight process status information, and the target flight mission allocation model, output the initial flight mission allocation information.

[0034] In this embodiment, the target flight mission allocation model can be a reinforcement learning model, a deep reinforcement learning model. The current flight environment information, the current mission requirement information, and the current flight process status information can be used as the input information of the target flight mission allocation model. The initial flight mission allocation information is output through the target flight mission allocation model, which is used to be combined with the expert-assigned mission decision information subsequently to generate the target flight mission allocation information. It can be understood that the target flight mission allocation model is a trained model and can be obtained through the past flight data of the UAV.

[0035] Step S103: In response to the output of the initial flight mission allocation information, obtain the expert-assigned mission decision information.

[0036] In this embodiment, after the initial flight mission allocation information is output, military experts in a specific field can review the current flight environment information, the current mission requirement information, the current flight process status information, and the initial flight mission allocation information. Then, a set of mission decision information for the UAV is output by the specific military experts, thereby generating the expert-assigned mission decision information, which is used to be combined with the initial flight mission allocation information.

[0037] Step S104: Obtain the target flight mission allocation information of the UAV according to the initial flight mission allocation information, the expert-assigned mission decision information, and the preset mission allocation decision weight.

[0038] In this embodiment, the preset mission allocation decision weight can be set manually. The initial flight mission allocation information and the expert-assigned mission decision information can be weighted and summed according to the mission allocation decision weight, and the summation result is used as the target flight mission allocation information of the UAV. It can be understood that mainly based on expert experience, the weight of the expert-assigned mission decision information can be set larger, so that the expert-assigned mission decision information is considered more in the finally obtained target flight mission allocation information of the UAV.

[0039] The UAV flight mission allocation method provided by the embodiment of the present application combines multi-source information and outputs the initial mission allocation information through the target model. It can consider special situations and potential risks in combination with expert experience to adjust the mission allocation information, so as to improve the rationality of the UAV flight mission decision-making, thereby realizing the adaptive allocation of the UAV flight mission, improving the execution efficiency and quality of the UAV flight mission, and enhancing the overall effectiveness of the cluster UAVs.

[0040] Figure 2 The implementation flowchart of the UAV flight mission allocation method provided by the second embodiment of the present application is shown. The difference from the first embodiment above is as follows:

[0041] The target flight mission allocation model is obtained through the following steps:

[0042] Step S201, obtain historical flight environment information, historical mission requirement information, and historical flight status process information.

[0043] In this embodiment, the historical flight environment information, historical mission requirement information, and historical flight status process information can all be records and integrations of data related to the past flight missions of the UAV. Among them, the historical flight environment information can refer to the data of the external environment conditions in which the UAV was located during past flight missions, including images, temperature, humidity, location, terrain, meteorological conditions, and the enemy's electronic warfare capabilities, etc.; the historical mission requirement information can be the task requirement data set by the user when performing tasks in the past, covering task objectives, priorities, flight altitude, duration limits, and environmental limits, etc.; the historical flight status process information can be the real-time status data reflecting the UAV during past flight missions, including UAV position, flight speed, flight attitude (pitch, roll, yaw), flight current, battery voltage, and remaining battery power, etc.

[0044] Step S202, obtain flight mission allocation response information according to the historical flight environment information, historical mission requirement information, historical flight status process information, and the initial flight mission allocation model.

[0045] In this embodiment, the initial flight mission allocation model can be an untrained reinforcement learning model, or an untrained deep reinforcement learning model, or an untrained deep learning model. The historical flight environment information, historical mission requirement information, and historical flight status process information can be used as the input information of the initial flight mission allocation model, and the flight mission allocation response information is output through the initial flight mission allocation model.

[0046] Step S203, obtain the flight mission allocation metric function value according to the flight mission allocation response information and the preset flight mission allocation weight information.

[0047] In this embodiment, the preset flight mission allocation weight information can be set manually. According to the flight mission allocation weight information, a weighted sum of the flight mission allocation response information can be calculated, and the calculation result is used as the flight mission allocation metric function value.

[0048] Step S204, determine whether the flight mission allocation metric function value is greater than the preset metric function threshold; if so, proceed to step S205; if not, proceed to step S207.

[0049] In this embodiment, the preset metric function threshold can be set manually. When the flight mission allocation metric function value is greater than the preset metric function threshold, it indicates that the initial flight mission allocation model has not yet reached the standard for field application, and thus the parameters of the initial flight mission allocation model need to be adjusted, and then continue to be trained based on the historical information of the UAVs; when the flight mission allocation metric function value is less than or equal to the preset metric function threshold, it indicates that the initial flight mission allocation model has reached the standard for field application, and thus there is no need to continue the training.

[0050] Step S205: Adjust the parameters of the initial flight mission allocation model to obtain an intermediate flight mission allocation model.

[0051] In this embodiment, when the initial flight mission allocation model is a deep reinforcement learning model, the adjustable parameters in the initial flight mission allocation model can include the number of neurons, the number of network layers, the learning rate, etc. The initial flight mission allocation model after parameter adjustment is used as the intermediate flight mission allocation model for further operations during the training process.

[0052] Step S206: Use the intermediate flight mission allocation model as the initial flight mission allocation model and return to step S202.

[0053] In this embodiment, the initial flight mission allocation model after parameter adjustment continues to be trained to quickly learn the rules related to UAV mission allocation, facilitating the adaptive adjustment of the UAV mission allocation strategy during the actual application process.

[0054] Step S207: Use the initial flight mission allocation model as the target flight mission allocation model.

[0055] In this embodiment, since the initial flight mission allocation model has been trained, the initial flight mission allocation model can be used as the target flight mission allocation model to allocate flight missions to UAVs in real time in the actual military field.

[0056] The UAV flight mission allocation method provided by the embodiments of this application comprehensively obtains the historical flight environment, mission requirements, and flight status process information, which is used to reflect the flight conditions of UAVs in various actual scenarios, provides a rich data basis for the initial flight mission allocation model, enables the initial flight mission allocation model to learn the optimal mission allocation strategies under different environments and mission requirements, enhances the practicability and adaptability of the flight mission allocation model, provides clear optimization goals and evaluation criteria for the model output by introducing a metric function and threshold judgment, and uses an iterative training mechanism to make the flight mission allocation model gradually approach the optimal solution during the process of repeated learning and adjustment, improving the accuracy and stability in the process of UAV flight mission allocation.

[0057] Figure 3 The flowchart shows the implementation of the UAV flight mission allocation method provided in the third embodiment of this application. The difference from the second embodiment above is that:

[0058] The flight mission allocation response information includes the number of UAVs allocated for the flight mission response information, the flight path allocation response information, and the flight mission energy allocation response information;

[0059] Step S203 specifically includes:

[0060] Step S301: Generate optimized flight path allocation information based on the flight path allocation response information and a preset flight path optimization model.

[0061] In this embodiment, the preset path optimization model can be linear programming (LP), mixed integer programming (MIP), or genetic algorithm (GA). The flight path allocation response information can be used as the input information of the flight path optimization model, and the optimized flight path allocation information is output through the flight path optimization model.

[0062] Step S302: Calculate the flight path energy consumption information based on the optimized flight path allocation information and a preset flight path energy consumption calculation function.

[0063] In this embodiment, the preset flight path energy consumption calculation function can be set manually. Among them, the objective function of the optimized flight path allocation information can be expressed as:

[0064]

[0065] Among them, is the th derivative of the segment flight path allocation response information, and the goal is to minimize , that is, to minimize the integral of the higher-order derivative of the flight path allocation response information.

[0066] Taking into account the trajectory optimization objective function and the flight path energy consumption information of the cluster UAVs, calculate the system contribution rate of the value of the flight path allocation response information, and the calculation formula is

[0067]

[0068] Among them, is the immediate reward, , are the discount factors, Represents a mapping from a linear space to a non - linear space, and is used to quantify the energy consumption information of the flight path 。

[0069] Step S303: According to the preset weight information for flight mission allocation, perform a weighted sum on the drone quantity response information for the flight mission, the flight path allocation response information, and the flight path energy consumption information to obtain the flight mission allocation metric function value

[0070] In this embodiment, the preset weight information for flight mission allocation can be set manually. Based on the weight information for flight mission allocation, a weighted sum is performed on the drone quantity response information for the flight mission, the flight path allocation response information, and the flight path energy consumption information, and the calculation result is used as the flight mission allocation metric function value

[0071] In this embodiment, the drone quantity response information for the flight mission may include the drone quantity response information for the reconnaissance mission, the drone quantity response information for the attack mission, and the drone quantity response information for the electronic jamming mission; the flight mission energy allocation response information may include the drone energy allocation response information for the reconnaissance mission, the drone energy allocation response information for the attack mission, and the drone quantity response information for the electronic jamming mission

[0072] The drone flight mission allocation method provided by the embodiments of the present application comprehensively considers the number of drones allocated for the flight mission, the flight path allocation, and the flight mission energy allocation, avoids the one - sidedness of single - factor decision - making, can measure the rationality of the flight mission allocation plan as a whole, improves the overall efficiency of flight mission execution, combines the flight path energy consumption calculation function to obtain the corresponding energy consumption information, fully considers path optimization and energy consumption when planning the flight path, reduces energy waste, improves flight efficiency, reduces the cost of task execution, enhances the endurance and task - continuous execution capabilities of the drone in complex environments, and by performing a weighted sum on each response information through the preset weight information for flight mission allocation, can flexibly adjust the importance of each factor in the metric function according to the task characteristics and actual requirements, making the metric function more suitable for different task scenarios and improving the scientificity and accuracy of decision - making for drone flight missions

[0073] Figure 4 Shows the implementation flowchart of the drone flight mission allocation method provided by the fourth embodiment of the present application. The difference from the first embodiment above is that the step S102 specifically includes:

[0074] Step S401: According to the current flight environment information and the current task requirement information, obtain the current environment and task requirement mapping information

[0075] In this embodiment, it may be to establish a mapping relationship between the current flight environment information and the current task requirement information through a deep reinforcement learning model to obtain the mapping information of the current environment and task requirements.

[0076] Step S402: Output initial flight task allocation information according to the mapping information of the current environment and task requirements, the current flight process status information, and the target flight task allocation model.

[0077] In this embodiment, it may be to use the mapping information of the current environment and task requirements and the current flight process status information as the input information of the target flight task allocation model, and output the initial flight task allocation information after the calculation of the target flight task allocation model.

[0078] The unmanned aerial vehicle flight task allocation method provided by the embodiment of the present application maps the current flight environment information and the current task requirement information, deeply mines the internal connection between the current flight environment information and the current task requirement information, and is used to generate more targeted mapping information of the current environment and task requirements. Combining the current flight process status information and the target flight task allocation model to output the initial flight task allocation information, so as to ensure the close fit between the flight task allocation and the current actual situation, and at the same time improve the accuracy and professionalism of the decision-making by means of the target model, which helps to improve the efficiency and quality of the unmanned aerial vehicle task execution, and enables the unmanned aerial vehicle to better complete tasks in a complex and changeable environment.

[0079] Figure 5 The implementation flowchart of the unmanned aerial vehicle flight task allocation method provided by the fifth embodiment of the present application is shown. The difference from the first embodiment above is that after the step S104, it further includes:

[0080] Step S501: Obtain task execution information.

[0081] In this embodiment, the task execution information may be obtained from the flight data transmitted back to the monitoring system by the unmanned aerial vehicle after the unmanned aerial vehicle executes the assigned flight task.

[0082] Step S502: Obtain task execution contribution degree information according to the task execution information, the target flight task allocation information, and the preset task execution contribution degree quantification rule.

[0083] In this embodiment, the preset quantization rule for the contribution degree of task execution can be set manually. It can take the success rate of task completion, the proportion of resource consumption, and the combat time as the core elements. Among them, in different tasks such as reconnaissance, attack, and interference, the success rate of task completion reflects the probability of discovering, destroying the target, or effectively interfering; the proportion of resource consumption covers aspects such as energy consumption and UAV damage; the combat time is related to the timeliness of intelligence acquisition and the control of the combat rhythm. It can be to construct a system contribution rate measurement function by combining tactical rules, auxiliary decision-making algorithms, and decision-making losses. Among them, the auxiliary decision-making algorithm is used to quantify the system contribution rate of different plans, and the decision-making loss is measured from dimensions such as resource consumption and task failure risk. Design a measurement function with the success rate of task completion, the proportion of resource consumption, and the combat time as key factors. The weight coefficient is determined by expert experience or data analysis, and the weight is adjusted according to the task scenario to achieve optimal resource allocation and improve the overall efficiency of the cluster UAV combat system. It can be to first rely on task execution information, such as task completion degree, interference effect, flight path, etc., which reflects the actual execution of the task. Then combine the target flight task allocation information, that is, the task allocation plan determined through optimization and decision-making. Finally, according to the preset quantization rule for the contribution degree of task execution, comprehensively consider key factors such as the success rate of task completion, the proportion of resource consumption, and the combat time, as well as the input-output index mapping relationship of different task types, and construct a system contribution rate measurement function for calculation. Through the quantization and weighted calculation of each factor, the task execution contribution degree information is finally obtained to comprehensively evaluate the effect of task execution and the contribution degree to the overall task.

[0084] Step S503, determine whether the task execution contribution degree information is greater than the preset task execution contribution degree threshold. If so, obtain an optimized target flight task allocation model according to the target flight task allocation information, task execution information, and the target flight task allocation model; if not, perform storage processing on the target flight task allocation information and task execution information.

[0085] In this embodiment, the preset task execution contribution degree threshold can be set manually. When the task execution contribution degree information is greater than the preset task execution contribution degree threshold, it indicates that the execution of this flight task meets the standard, and the information of this flight task can be used to optimize the target flight task allocation model to obtain an optimized target flight task allocation model for making flight task allocation decisions for UAVs in actual applications next time. When the task execution contribution degree information is less than or equal to the preset task execution contribution degree threshold, it indicates that the execution of this flight task does not meet the standard, and the information of this flight task cannot be used to optimize the target flight task allocation model. Then, perform storage processing on the target flight task allocation information and task execution information for future analysis and processing.

[0086] The UAV flight task allocation method provided by the embodiment of the present application obtains task execution contribution degree information through task execution information, target flight task allocation information, and a preset quantization rule, comprehensively and objectively evaluates the execution effect of the flight task, and the quantization method makes the evaluation more scientific, and can clearly judge the contribution degree of task execution to the overall goal. When the task execution contribution degree information is greater than the preset threshold, the target flight task allocation model is optimized, so that the flight task decision-making process can continuously automatically learn experience from the successful flight task execution situation, improve the adaptability of the generated flight task allocation information to the flight environment, and thus more effectively respond to complex and changeable flight environments and task requirements.

[0087] Corresponding to the method in the above embodiment, Figure 6 The structural block diagram of the UAV flight task allocation device provided by the embodiment of the present application is shown. For the sake of convenience of description, only the parts related to the embodiment of the present application are shown. Figure 6 The exemplary UAV flight task allocation device may be the execution main body of the UAV flight task allocation method provided in the foregoing Embodiment 1.

[0088] Referring to Figure 6 , the UAV flight task allocation device includes:

[0089] An information acquisition module 610, configured to acquire the current flight environment information, current task requirement information, and current flight process status information of the UAV;

[0090] An initial flight task allocation information output module 620, configured to output initial flight task allocation information according to the current flight environment information, current task requirement information, current flight process status information, and a target flight task allocation model;

[0091] An expert assignment task decision information acquisition module 630, configured to acquire expert assignment task decision information in response to the output of the initial flight task allocation information; and

[0092] A target flight task allocation information determination module 640, configured to obtain the target flight task allocation information of the UAV according to the initial flight task allocation information, expert assignment task decision information, and a preset task assignment decision weight.

[0093] For the process of each module in the UAV flight task allocation device provided by the embodiment of the present application to implement its respective functions, reference may specifically be made to the description of Embodiment 1 shown above, and details are not described herein again. Figure 1 It should be understood that the magnitudes of the sequence numbers of the above steps do not mean the order of execution is prior or posterior, and the execution order of each process should be determined according to its function and internal logic, and should not constitute any limitation to the implementation process of the embodiment of the present application.

[0094] ​

[0095] It should be understood that, as used in the specification of this application and the appended claims, the term "comprising" indicates the presence of the described features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or their groups.

[0096] It should also be understood that the term "and / or" as used in the specification of this application and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0097] As used in the specification of this application and the appended claims, the term "if" may be construed, depending on the context, as "when" or "once" or "in response to determining" or "in response to detecting". Similarly, the phrases "if determined" or "if [the described condition or event] is detected" may be construed, depending on the context, as meaning "once determined" or "in response to determining" or "once [the described condition or event] is detected" or "in response to detecting [the described condition or event]".

[0098] In addition, in the description of the specification of this application and the appended claims, the terms "first", "second", "third", etc. are used only for differentiating descriptions and cannot be construed as indicating or implying relative importance. It should also be understood that although the terms "first", "second", etc. are used in the text in some embodiments of this application to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first table may be named a second table, and similarly, a second table may be named a first table, without departing from the scope of the various described embodiments. The first table and the second table are both tables, but they are not the same table.

[0099] Reference to "an embodiment" or "some embodiments" or the like described in the specification of this application means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of this application. Thus, statements such as "in an embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0100] The UAV flight mission allocation method provided by the embodiments of this application can be applied to terminal devices such as mobile phones, tablet computers, wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, laptop computers, ultra-mobile personal computers (UMPCs), netbooks, personal digital assistants (PDAs), etc. The embodiments of this application do not impose any restrictions on the specific types of terminal devices.

[0101] For example, the terminal device can be a station (STAION, ST) in a WLAN, a cellular phone, a cordless phone, a Session Initiation Protocol (SIP) phone, a Wireless Local Loop (WLL) station, a Personal Digital Assistant (PDA) device, a handheld device with wireless communication capabilities, a computing device, or other processing devices connected to a wireless modem, a vehicle-mounted device, a vehicle networking terminal, a computer, a laptop computer, a handheld communication device, a handheld computing device, a satellite wireless device, a wireless modem card, a television set-top box (set top box, STB), a customer premise equipment (CPE), and / or other devices for communicating on a wireless system, as well as next-generation communication systems, such as mobile terminals in a 5G network or mobile terminals in a future evolved Public Land Mobile Network (PLMN) network.

[0102] By way of example and not limitation, when the terminal device is a wearable device, the wearable device can also be a general term for devices developed by applying wearable technology to the intelligent design of daily wear, such as glasses, gloves, watches, clothing, and shoes. A wearable device is a portable device that is either directly worn on the body or integrated into the user's clothing or accessories. A wearable device is not just a hardware device, but also realizes powerful functions through software support, data interaction, and cloud interaction. Broadly speaking, wearable intelligent devices include those with complete functions and large sizes that can achieve complete or partial functions without relying on a smart phone, such as smart watches or smart glasses, as well as those that only focus on a certain type of application function and need to cooperate with other devices such as smart phones, such as various smart bracelets and smart jewelry for physical sign monitoring.

[0103] Figure 7It is a schematic structural diagram of a terminal device provided by an embodiment of the present application. As Figure 7 shown, the terminal device 7 of this embodiment includes: at least one processor 70 ( Figure 7 only one is shown in the figure), and a memory 71. A computer program 72 that can run on the processor 70 is stored in the memory 71. When the processor 70 executes the computer program 72, the steps in the above-mentioned embodiments of various unmanned aerial vehicle flight task allocation methods are implemented, for example Figure 1 the steps S101 to S104 shown. Alternatively, when the processor 70 executes the computer program 72, the functions of each module / unit in the above-mentioned device embodiments are implemented, for example Figure 6 the functions of the modules 610 to 640 shown.

[0104] The terminal device 7 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor 70 and a memory 71. Those skilled in the art can understand that Figure 7 it is only an example of the terminal device 7 and does not constitute a limitation on the terminal device 7. It may include more or fewer components than those shown in the figure, or combine some components, or different components. For example, the terminal device may further include an input sending device, a network access device, a bus, etc.

[0105] The so-called processor 70 may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0106] In some embodiments, the memory 71 may be an internal storage unit of the terminal device 7, such as a hard disk or memory of the terminal device 7. The memory 71 may also be an external storage device of the terminal device 7, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc., equipped on the terminal device 7. Further, the memory 71 may also include both the internal storage unit and the external storage device of the terminal device 7. The memory 71 is used to store an operating system, application programs, a BootLoader, data, and other programs, such as program codes of the computer program. The memory 71 may also be used to temporarily store data that has been sent or will be sent.

[0107] In addition, in each embodiment of the present application, each functional unit may be integrated in one processing unit, may exist separately physically for each unit, or two or more units may be integrated in one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0108] An embodiment of the present application further provides a terminal device. The terminal device includes at least one memory, at least one processor, and a computer program stored in the at least one memory and executable on the at least one processor. When the processor executes the computer program, the terminal device implements the steps in any of the above method embodiments.

[0109] An embodiment of the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments can be implemented.

[0110] An embodiment of the present application provides a computer program product. When the computer program product runs on a terminal device, the terminal device is caused to implement the steps in any of the above method embodiments when executed.

[0111] When the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, to implement all or part of the processes in the above-described embodiment methods of the present application, it can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc.

[0112] In the above embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0113] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present application.

[0114] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they can be located in one place, or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0115] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for allocating unmanned aerial vehicle flight tasks, characterized in that, Including: Obtain the current flight environment information, current task requirement information, and current flight process status information of the unmanned aerial vehicle (UAV); Output initial flight task allocation information according to the current flight environment information, current task requirement information, current flight process status information, and the target flight task allocation model; In response to the output of the initial flight task allocation information, obtain expert task allocation decision information; Obtain the target flight task allocation information of the UAV according to the initial flight task allocation information, expert task allocation decision information, and a preset task allocation decision weight.

2. The UAV flight task allocation method according to claim 1, wherein: The target flight task allocation model is obtained through the following steps: Obtain historical flight environment information, historical task requirement information, and historical flight status process information; Obtain flight task allocation response information according to the historical flight environment information, historical task requirement information, historical flight status process information, and the initial flight task allocation model; Obtain a flight task allocation metric function value according to the flight task allocation response information and a preset flight task allocation weight information; Judge whether the flight task allocation metric function value is greater than a preset metric function threshold; If so, adjust the parameters of the initial flight task allocation model to obtain an intermediate flight task allocation model; Take the intermediate flight task allocation model as the initial flight task allocation model, and return to the step of obtaining flight task allocation response information according to the historical flight environment information, historical task requirement information, historical flight status process information, and the initial flight task allocation model; If not, take the initial flight task allocation model as the target flight task allocation model.

3. The UAV flight task allocation method according to claim 2, wherein: The flight task allocation response information includes the number of UAVs for flight task allocation response information, flight path allocation response information, and flight task energy allocation response information; The step of obtaining a flight task allocation metric function value according to the flight task allocation response information and a preset flight task allocation weight information specifically includes: Generate flight path allocation optimization information according to the flight path allocation response information and a preset flight path optimization model; Calculate flight path energy consumption information according to the flight path allocation optimization information and a preset flight path energy consumption calculation function; Perform weighted summation on the number of UAVs for flight task allocation response information, flight path allocation response information, and flight path energy consumption information according to the preset flight task allocation weight information to obtain a flight task allocation metric function value.

4. The UAV flight task allocation method according to claim 3, wherein: The number of UAVs for flight task allocation response information includes the number of UAVs for reconnaissance task response information, the number of UAVs for attack task response information, and the number of UAVs for electronic jamming task response information; The flight mission energy distribution response information includes the energy distribution response information of the reconnaissance mission UAV, the energy distribution response information of the attack mission UAV, and the quantity response information of the electronic jamming mission UAV.

5. The method for assigning drone flight tasks according to claim 1, wherein, The step of outputting the initial flight mission allocation information according to the current flight environment information, the current mission requirement information, the current flight process status information, and the target flight mission allocation model specifically includes: Obtaining the current environment and mission requirement mapping information according to the current flight environment information and the current mission requirement information; Outputting the initial flight mission allocation information according to the current environment and mission requirement mapping information, the current flight process status information, and the target flight mission allocation model.

6. The UAV flight mission allocation method according to claim 1, wherein The current flight environment information includes the current UAV distribution information, the current environment temperature information, the current environment wind speed information, and the current environment air pressure information; The current mission requirement information includes the current target location information, the current mission type information, the current mission priority information, the current mission flight altitude information, the current mission execution time information, and the current mission execution environment requirement information; The current flight process status information includes the current flight location information, the current flight speed information, the current flight attitude information, the current flight current information, the current flight voltage information, and the current remaining battery power information.

7. The method for allocating unmanned aerial vehicle flight missions according to claim 1, wherein, After the step of obtaining the target flight mission allocation information of the UAV according to the initial flight mission allocation information, the expert assigned task decision information, and the preset task allocation decision weight, it further includes: Obtaining the mission execution information; Obtaining the mission execution contribution degree information according to the mission execution information, the target flight mission allocation information, and the preset mission execution contribution degree quantization rule; When the mission execution contribution degree information is greater than the preset mission execution contribution degree threshold, then obtaining the optimized target flight mission allocation model according to the target flight mission allocation information, the mission execution information, and the target flight mission allocation model.

8. An unmanned aerial vehicle flight mission allocation device, characterized in that, Including: An information acquisition module, configured to acquire the current flight environment information, the current mission requirement information, and the current flight process status information of the UAV; An initial flight mission allocation information output module, configured to output the initial flight mission allocation information according to the current flight environment information, the current mission requirement information, the current flight process status information, and the target flight mission allocation model; An expert assigned task decision information acquisition module, configured to acquire the expert assigned task decision information in response to the output of the initial flight mission allocation information; And A target flight mission allocation information determination module, configured to obtain the target flight mission allocation information of the UAV according to the initial flight mission allocation information, the expert assigned task decision information, and the preset task allocation decision weight.

9. A terminal device, characterized in that, The terminal device includes a memory and a processor, and a computer program that can run on the processor is stored on the memory. When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 7.

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