Unmanned aerial vehicle action consistency feedback optimization method and system, storage medium and electronic equipment

Abstract

The embodiment of the invention provides an unmanned aerial vehicle action consistency feedback optimization method and system, a storage medium and electronic equipment. The method comprises the steps that real-time flight state information and environment information in the process that an unmanned aerial vehicle executes a first task are collected; establishing a historical action database; generating a candidate action track of the first task based on the real-time flight state information and the environment information of the first task; based on a preset action consistency evaluation model, calculating a consistency evaluation value between the candidate action trajectory and a high-quality action trajectory in a historical action database; generating a reward signal based on the consistency evaluation value; the first control strategy is optimized according to the reward signal, and an optimized target action track is generated; and instructing the unmanned aerial vehicle to execute the first task according to the target action trajectory. According to the invention, the problem that it is difficult for the unmanned aerial vehicle to generate a high-quality motion trajectory in real time in a complex dynamic environment is solved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of unmanned aerial vehicle control, and more specifically, to a method, system, storage medium, and electronic device for optimizing the consistency of unmanned aerial vehicle actions through feedback. Background Technology

[0002] In recent years, with the rapid development of drone technology, drones have been widely used in many fields. In practical applications, drones often need to complete various tasks efficiently and stably in complex and ever-changing dynamic environments.

[0003] Existing technologies are increasingly employing reinforcement learning algorithms, enabling UAVs to optimize control strategies based on real-time feedback from historical experience and environmental interaction. However, existing reinforcement learning methods typically suffer from the following shortcomings: Firstly, the lack of an effective mechanism for utilizing historical motion trajectories makes it difficult for UAVs to quickly generate and optimize high-quality motion trajectories in new and complex environments. Secondly, the lack of effective quantification of the relationship between motion trajectory consistency and environmental complexity makes it impossible to dynamically adjust the optimization objective of UAV motion strategies in environments with varying degrees of complexity. Summary of the Invention

[0004] This invention provides a method, system, storage medium, and electronic device for optimizing drone motion consistency feedback, in order to at least solve the problem in related technologies that drones are difficult to generate high-quality motion trajectories in real time under complex dynamic environments.

[0005] According to an embodiment of the present invention, a method for optimizing the consistency of unmanned aerial vehicle (UAV) actions is provided, comprising: The system collects real-time flight status and environmental information of the UAV during the execution of its first mission; wherein the flight status information includes at least one of the UAV's position, attitude, speed, and battery level; and the environmental information includes at least one of the obstacle positions, motion status, weather information, and meteorological disturbance information. Establish a historical action database, wherein the historical action database stores the action trajectories of the drone in historical missions; Based on the real-time flight status information and environmental information of the first mission, a candidate action trajectory for the first mission is generated. Based on a preset motion consistency evaluation model, a consistency evaluation value is calculated between the candidate motion trajectory and the high-quality motion trajectories in the historical motion database; wherein, the calculation of the consistency evaluation value between the candidate motion trajectory and the high-quality motion trajectories in the historical motion database based on the preset motion consistency evaluation model includes: selecting from the historical motion database A set of high-quality historical action trajectories is used as a reference trajectory set; the Euclidean distance between the current task candidate action trajectory and each reference action trajectory is calculated; the overall consistency distance is determined based on the Euclidean distance; the overall consistency distance is converted into a consistency score through an exponential function; a reward signal is generated based on the consistency score and an environmental complexity index, wherein the environmental complexity index is obtained based on the number of obstacles in the current environment, the average speed of the obstacles, and the influence weight of weather and meteorological disturbances; A reward signal is generated based on the consistency evaluation value; The first control strategy is optimized based on the reward signal to generate an optimized target motion trajectory; and the UAV is instructed to perform the first task according to the target motion trajectory, wherein the first control strategy includes candidate motion trajectories.

[0006] In an exemplary embodiment, generating the candidate motion trajectory of the first task based on the real-time flight status information and the environmental information of the first task includes: Obtain real-time flight status information of drones and environmental information The candidate action trajectories for the first task are generated through the action consistency generation module. ; in, This represents the Transformer or diffusion model generating function. Indicates model parameters.

[0007] In an exemplary embodiment, after generating the candidate motion trajectory of the first task based on the real-time flight state information and the environmental information of the first task, the method further includes: Calculate the action continuity loss and task completion loss of the candidate action sequence, and determine the total loss based on the action continuity loss and the task completion loss; Perform backpropagation based on the total loss to update the model parameters; Repeat the aforementioned steps until the generated candidate action trajectory reaches the preset loss threshold or the maximum number of iterations is completed.

[0008] In one exemplary embodiment, generating the reward signal based on the consistency score includes: Real-time computing environment complexity metrics;

[0009] Based on the environmental complexity index, the weighting coefficients of action consistency reward and task performance reward are dynamically determined; The reward signal is calculated based on the weighting coefficients.

[0010] In an exemplary embodiment, optimizing the first control strategy based on the reward signal includes: The first action is determined based on the current drone status information and the preset strategy function, and then the first action is executed. After performing the first action, the drone transitions to the next state and receives a reward signal; Update the value function parameters based on the reward signal and state transition; Update the policy parameters based on the value function parameters; Repeat the aforementioned steps until the policy parameters converge or the preset number of training iterations are reached, and generate the optimized target motion trajectory.

[0011] According to another embodiment of the present invention, a drone motion consistency feedback optimization system is provided, comprising: The status information acquisition module is used to collect real-time flight status information and environmental information of the UAV during the execution of its mission; The historical action database module is used to store the action trajectories of drones in historical missions; The motion consistency generation module is used to generate candidate motion trajectories for the first mission based on real-time flight status information and environmental information. The motion consistency evaluation module is used to calculate the consistency evaluation value between candidate motion trajectories and high-quality motion trajectories in the historical motion database; wherein, the calculation of the consistency evaluation value between the candidate motion trajectory and the high-quality motion trajectories in the historical motion database based on a preset motion consistency evaluation model includes: selecting from the historical motion database... A set of high-quality historical action trajectories is used as a reference trajectory set; the Euclidean distance between the current task candidate action trajectory and each reference action trajectory is calculated; the overall consistency distance is determined based on the Euclidean distance; the overall consistency distance is converted into a consistency score through an exponential function; the reward signal is generated based on the consistency score and the environmental complexity index, wherein the environmental complexity index is obtained based on the number of obstacles in the current environment, the average speed of the obstacles, and the influence weight of weather and meteorological disturbances; An adaptive reward module is used to generate reward signals based on consistent evaluation values; The strategy optimization module is used to optimize the current strategy based on the reward signal and output the optimized action trajectory. The flight control execution module is used to control the UAV to perform the first task based on the optimized motion trajectory.

[0012] In one exemplary embodiment, the adaptive reward module includes: An environmental complexity assessment unit is used to monitor and calculate environmental complexity indicators in real time. The weighting adjustment unit is used to dynamically adjust the weight ratio of consistency rewards and task performance rewards based on the environmental complexity index. The reward fusion unit is used to fuse consistency rewards and task performance rewards to generate the reward signal.

[0013] According to yet another embodiment of the present invention, a computer-readable storage medium is also provided, wherein a computer program is stored therein, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed.

[0014] According to yet another embodiment of the present invention, an electronic device is also provided, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0015] By incorporating historical motion data into the calculation and achieving dynamic control of UAV motion in complex environments through motion consistency evaluation, this invention can solve the problem of UAVs struggling to generate high-quality motion trajectories in real time in complex dynamic environments. This results in UAVs being able to quickly generate and optimize motion trajectories with high consistency and adaptability in complex dynamic environments. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating the steps of a drone motion consistency feedback optimization method according to an embodiment of the present invention. Figure 2 This is a schematic block diagram of the structure of a drone motion consistency feedback optimization system according to an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0018] In the following description, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0019] Furthermore, in this application, directional terms such as "upper," "lower," "left," and "right" may be defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms can be relative concepts, used for relative description and clarification, and may change accordingly depending on the orientation of the components in the accompanying drawings.

[0020] In this application, unless otherwise expressly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral part; it can be a direct connection or an indirect connection through an intermediate medium. Furthermore, the term "coupled" can refer to an electrical connection that enables signal transmission.

[0021] As used herein, “about,” “approximately,” or “approximately” includes the stated value and the average value within an acceptable range of deviation from the given value, wherein the acceptable range of deviation is determined by a person skilled in the art taking into account the measurement under discussion and the error associated with the measurement of the given quantity (i.e., the limitations of the measurement system).

[0022] This embodiment provides a method for optimizing the consistency of drone actions through feedback. Figure 1 This is a flowchart of a drone motion consistency feedback optimization method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S11: Collect real-time flight status information and environmental information of the UAV during the execution of the first task; wherein, the flight status information includes at least one of the UAV's position, attitude, speed and battery level information; the environmental information includes at least one of the obstacle position, motion status, weather information and meteorological disturbance information; the first task can be a task such as inspection, logistics delivery, or aerial photography in a complex environment; Step S12: Establish a historical action database, wherein the historical action database stores the action trajectories of the UAV in historical missions; Step S13: Generate candidate action trajectories for the first mission based on the real-time flight status information and environmental information of the first mission; In this embodiment, the action consistency generation module can be implemented using a Transformer or diffusion model. The specific action generation algorithm operation steps include: S131. Obtain real-time flight status information of the UAV. and environmental information The candidate action trajectory for the first task is generated through the action consistency generation module. ; in, This represents the Transformer or diffusion model generating function. Indicates model parameters; S132. Calculate the action continuity loss of the candidate action sequence. and task completion losses And thereby obtain the total loss. : The loss of continuity of action is: ; in, Indicates the first Action vector at time step, Indicates the first The vector of the rate of change of motion at any given moment; The method for calculating the task completion loss is as follows: ; in, This represents the final state after the candidate action trajectory is executed. This indicates the target state that the current task hopes to achieve; The overall loss function of the action consistency generation module is: ; in, The hyperparameter, which balances action continuity with task completion performance, has a value range of [value range missing]. ; S133. Update model parameters through backpropagation. : ; in, For learning rate, This represents the gradient of the loss function with respect to the model parameters. S134. Repeat steps S131 to S133 until the generated candidate motion trajectory reaches the preset loss threshold or completes the maximum number of iterations, and then output the final optimized motion trajectory to the flight control execution module.

[0023] Step S14: Based on the preset action consistency evaluation model, calculate the consistency evaluation value between the candidate action trajectory and the high-quality action trajectory in the historical action database; In this embodiment, the evaluation process of the action consistency evaluation model includes: S141. Select from the historical action database A set of high-quality historical motion trajectories as reference trajectories: ; S142. Calculate the candidate action trajectories for the current task. Euclidean distance between each reference motion trajectory: ; S143. Calculate the average of the Euclidean distances to obtain the overall consistency distance: ; S144. Convert the overall consistency distance into a consistency score using an exponential function. : ;

[0024] S145. The calculated consistency score is used to generate subsequent reward signals to guide the reinforcement learning algorithm in optimizing the current strategy of the UAV.

[0025] Step S15: Generate a reward signal based on the consistency evaluation value. The reward signal is used to guide the reinforcement learning algorithm to optimize the first control strategy of the first task. The first control strategy includes candidate action trajectories. In this embodiment, the generation of the reward signal includes: S151. Real-time computing environment complexity index, defined as follows: : ; in, Indicates the number of obstacles in the current environment; Indicates the average speed of the obstacle; Indicates the weight of the impact of weather and meteorological disturbances; , , These are weighting coefficients for assessing environmental complexity; for example, in an urban inspection scenario, γ1=0.4, γ2=0.3, and γ3=0.3, while in an open logistics scenario, γ1=0.2, γ2=0.2, and γ3=0.6; weather impact weighting. The quantitative standard is: sunny day =0.1, cloudy =0.3, light rain / light breeze =0.5, heavy rain / strong wind =0.8, heavy rain / strong wind =1.0 S152. Based on environmental complexity indicators Dynamically determine the weighting coefficients for action consistency rewards and task performance rewards. : ; ; S153, Calculate the comprehensive reward signal : ; in, The score is used to assess the consistency of movement. It indicates the current task performance score, such as task progress, energy consumption, or how close it is to the target state; Then the above adaptive reward signal Used for reinforcement learning algorithms to optimize drone motion control strategies in real time.

[0026] Step S16: Optimize the first control strategy based on the reward signal using the reinforcement learning algorithm to generate an optimized target motion trajectory; and instruct the UAV to execute the first task based on the target motion trajectory.

[0027] In this embodiment, the reinforcement learning algorithm is one of the following: Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), or Soft Actor-Critic (SAC) algorithm. The process of the reinforcement learning algorithm optimizing the current policy includes: S161. Based on the current drone status information Reinforcement learning algorithms employ policy functions. Select Action And execute: ; S162, Execution Action Afterwards, the drone's state transitions to the next state. and obtain adaptive reward signals. ; S163, Utilizing Actions ,award State transition Update the Critic parameter: For the PPO algorithm, advantage function estimation is used. The optimization objective is: ; For the DDPG algorithm, the Critic's value function The update target is: ; For the SAC algorithm, optimize the Critic function. The objective is: ; in, The clip range hyperparameter for the PPO algorithm; Discount factor; S164. Update the Actor policy parameters based on the feedback from the value function parameters. : PPO strategy parameter update: ; DDPG policy parameter update: ; ; SAC strategy parameter update: ; ; in, The learning rate of the policy network. This is the entropy adjustment coefficient in the SAC algorithm.

[0028] S165. Repeat steps S161 to S164 until the policy parameters converge or the preset number of training iterations are reached, and generate the optimized motion trajectory. .

[0029] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0030] This embodiment also provides a UAV action consistency feedback optimization system, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0031] Figure 2 This is a structural block diagram of a drone motion consistency feedback optimization system according to an embodiment of the present invention, such as... Figure 2 As shown, the system includes: The status information acquisition module 21 is used to collect real-time flight status information and environmental information of the UAV during the execution of its mission; Historical Action Database Module 22 is used to store the action trajectories in the historical missions of the UAV; The motion consistency generation module 23 is used to generate candidate motion trajectories for the first mission based on real-time flight status information and environmental information. The motion consistency evaluation module 24 is used to calculate the consistency evaluation value between the candidate motion trajectory and the high-quality motion trajectory in the historical motion database. Adaptive reward module 25 is used to generate reward signals based on consistency evaluation values; The strategy optimization module 26 is used to optimize the current strategy based on the reward signal and output the optimized action trajectory; Flight control execution module 27 is used to control the UAV to perform the first task based on the optimized motion trajectory.

[0032] In an optional embodiment, the adaptive reward module includes: An environmental complexity assessment unit is used to monitor and calculate environmental complexity indicators in real time. The weighting adjustment unit is used to dynamically adjust the weight ratio of consistency rewards and task performance rewards based on the environmental complexity index. The reward fusion unit is used to fuse consistency rewards and task performance rewards to generate the reward signal.

[0033] It should be noted that the above modules can be implemented by software or hardware. For the latter, they can be implemented in the following ways, but are not limited to: all the above modules are located in the same processor; or, the above modules are located in different processors in any combination.

[0034] Embodiments of the present invention also provide a computer-readable storage medium storing a computer program, wherein the computer program is configured to perform the steps in any of the above method embodiments when executed.

[0035] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard disk, magnetic disk, or optical disk.

[0036] Embodiments of the present invention also provide an electronic device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0037] In one exemplary embodiment, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0038] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.

[0039] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0040] The units described as separate components may or may not be physically separate. A component shown as a unit can be one or more physical units; that is, it can be located in one place or distributed in multiple different locations. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0041] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0042] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, in essence, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0043] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for optimizing the consistency of drone actions through feedback, characterized in that, include: The system collects real-time flight status and environmental information of the UAV during the execution of its first mission; wherein the flight status information includes at least one of the UAV's position, attitude, speed, and battery level; and the environmental information includes at least one of the obstacle positions, motion status, weather information, and meteorological disturbance information. Establish a historical action database, wherein the historical action database stores the action trajectories of the drone in historical missions; Based on the real-time flight status information and environmental information of the first mission, a candidate action trajectory for the first mission is generated. Based on a preset motion consistency evaluation model, a consistency evaluation value is calculated between the candidate motion trajectory and the high-quality motion trajectories in the historical motion database; wherein, the calculation of the consistency evaluation value between the candidate motion trajectory and the high-quality motion trajectories in the historical motion database based on the preset motion consistency evaluation model includes: selecting from the historical motion database A set of high-quality historical action trajectories is used as a reference trajectory set; the Euclidean distance between the current task candidate action trajectory and each reference action trajectory is calculated; the overall consistency distance is determined based on the Euclidean distance; the overall consistency distance is converted into a consistency score through an exponential function; a reward signal is generated based on the consistency score and an environmental complexity index, wherein the environmental complexity index is obtained based on the number of obstacles in the current environment, the average speed of the obstacles, and the influence weight of weather and meteorological disturbances; A reward signal is generated based on the consistency evaluation value; The first control strategy is optimized based on the reward signal to generate an optimized target motion trajectory; and the UAV is instructed to perform the first task according to the target motion trajectory, wherein the first control strategy includes candidate motion trajectories.

2. The method according to claim 1, characterized in that, The generation of candidate action trajectories for the first mission based on the real-time flight status information and the environmental information of the first mission includes: Obtain real-time flight status information of drones and environmental information The candidate action trajectories for the first task are generated through the action consistency generation module. ； in, This represents the Transformer or diffusion model generating function. Indicates model parameters.

3. The method according to claim 1, characterized in that, After generating the candidate motion trajectory for the first mission based on the real-time flight status information and the environmental information of the first mission, the method further includes: Calculate the action continuity loss and task completion loss of the candidate action sequence, and determine the total loss based on the action continuity loss and the task completion loss; Perform backpropagation based on the total loss to update the model parameters; Repeat the aforementioned steps until the generated candidate action trajectory reaches the preset loss threshold or the maximum number of iterations is completed.

4. The method according to claim 1, characterized in that, The step of generating the reward signal based on the consistency score includes: Real-time computing environment complexity metrics; Based on the environmental complexity index, the weighting coefficients of action consistency reward and task performance reward are dynamically determined; The reward signal is calculated based on the weighting coefficients.

5. The method according to claim 1, characterized in that, The optimization of the first control strategy based on the reward signal includes: The first action is determined based on the current drone status information and the preset strategy function, and then the first action is executed. After performing the first action, the drone transitions to the next state and receives a reward signal; Update the value function parameters based on the reward signal and state transition; Update the policy parameters based on the value function parameters; Repeat the aforementioned steps until the policy parameters converge or the preset number of training iterations are reached, and generate the optimized target motion trajectory.

6. A drone motion consistency feedback optimization system, characterized in that, The system includes: The status information acquisition module is used to collect real-time flight status information and environmental information of the UAV during the execution of its mission; The historical action database module is used to store the action trajectories of drones in historical missions; The motion consistency generation module is used to generate candidate motion trajectories for the first mission based on real-time flight status information and environmental information. The motion consistency evaluation module is used to calculate the consistency evaluation value between candidate motion trajectories and high-quality motion trajectories in the historical motion database; wherein, the calculation of the consistency evaluation value between the candidate motion trajectory and the high-quality motion trajectories in the historical motion database based on a preset motion consistency evaluation model includes: selecting from the historical motion database... A set of high-quality historical action trajectories is used as a reference trajectory set; the Euclidean distance between the current task candidate action trajectory and each reference action trajectory is calculated; the overall consistency distance is determined based on the Euclidean distance; the overall consistency distance is converted into a consistency score through an exponential function; a reward signal is generated based on the consistency score and an environmental complexity index, wherein the environmental complexity index is obtained based on the number of obstacles in the current environment, the average speed of the obstacles, and the influence weight of weather and meteorological disturbances; An adaptive reward module is used to generate reward signals based on consistent evaluation values; The strategy optimization module is used to optimize the current strategy based on the reward signal and output the optimized action trajectory. The flight control execution module is used to control the UAV to perform the first task based on the optimized motion trajectory.

7. The system according to claim 6, characterized in that, The adaptive reward module includes: An environmental complexity assessment unit is used to monitor and calculate environmental complexity indicators in real time. The weighting adjustment unit is used to dynamically adjust the weight ratio of consistency rewards and task performance rewards based on the environmental complexity index. The reward fusion unit is used to fuse consistency rewards and task performance rewards to generate the reward signal.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, wherein the computer program is configured to perform the method described in any one of claims 1 to 5 when executed.

9. An electronic device comprising a memory and a processor, characterized in that, The memory stores a computer program, and the processor is configured to run the computer program to perform the method as described in any one of claims 1 to 5.

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