A method and system for offloading mobile edge computing tasks

Abstract

This invention discloses a method and system for offloading tasks in mobile edge computing. The method includes: configuring edge device information, task information, and latency / energy consumption weighting factors for each user; configuring task offloading system constraints; establishing a mobile edge computing system model, i.e., a Latency-Energy-based task offloading model, including latency, energy consumption, and total overhead models for local processing and task offloading; performing edge computing task offloading using a task offloading method based on an improved genetic algorithm; monitoring the task offloading process according to user-configured constraints; and outputting the final latency, energy consumption, total system overhead, and task offloading scheme results for edge computing. This invention effectively solves the task offloading problem in mobile edge computing, reduces system latency and energy consumption, and improves energy utilization.

Description

Technical Field

[0001] This invention belongs to the field of mobile edge computing, and in particular, it is a method and system for offloading mobile edge computing tasks. Background Art

[0002] With the development of mobile internet, more and more new applications have emerged, such as virtual reality, facial recognition, and IoT devices. These emerging applications and devices often have high requirements for latency and energy consumption. If traditional cloud computing is used to upload tasks to the cloud for processing, there will be high latency, which will affect the execution efficiency. To address this huge challenge, many researchers have proposed different solutions to achieve low latency and low energy consumption by flexibly allocating computing, storage, bandwidth and other resources.

[0003] Mobile edge computing, as a new 5G network architecture and key technology, migrates cloud computing resources to edge servers near users, allowing users to simply offload tasks to these servers for processing. Compared to cloud servers, edge servers offer greater flexibility and lower latency.

[0004] However, considering the limited computing power of edge servers and the potential for interference and increased transmission latency caused by offloading edge computing tasks in ultra-dense networks, not all computing tasks can be offloaded to edge servers; some tasks should be executed locally. Although local computing consumes more energy, it does not introduce transmission latency. Therefore, making effective offloading decisions to reduce the energy consumption and latency of offloading edge computing tasks is crucial. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by providing a method and system for offloading mobile edge computing tasks, which can effectively solve the problem of mobile edge computing task offloading, reduce latency and energy consumption, and improve energy utilization.

[0006] The technical solution to achieve the objective of this invention is: a method for offloading mobile edge computing tasks, the method comprising the following steps:

[0007] Step 1: Configure edge device information, task information, latency and energy consumption weighting factor for each user, and configure task offloading system constraints;

[0008] Step 2: Establish a mobile edge computing system model, namely a Latency-Energy task offloading model, including latency, energy consumption, and total overhead models for local processing and task offloading; and use a task offloading method based on an improved genetic algorithm to offload edge computing tasks.

[0009] Step 3: Monitor the task unloading process according to the constraints configured by the user;

[0010] Step 4: Output the final latency, energy consumption, total system overhead, and task offloading scheme results for edge computing.

[0011] A system for offloading mobile edge computing tasks, the system comprising:

[0012] The user configuration module is used to configure each user's edge device information, task information, latency and energy consumption weighting factor, and to configure task offloading system constraints.

[0013] The task offloading module establishes a mobile edge computing system model, namely the Latency-Energy task offloading model, including latency, energy consumption, and total overhead models for local processing and task offloading; and utilizes a task offloading method based on an improved genetic algorithm to offload edge computing tasks.

[0014] The performance monitoring module is used to monitor the task unloading process based on user-configured constraints.

[0015] The results output module is used to output the final latency, energy consumption, total system overhead, and task offloading scheme results of edge computing.

[0016] Furthermore, the system also includes:

[0017] The results query module is used to query the location information of task uninstallation and the latency, energy consumption and total cost of task processing based on the user and task number, and to display whether the task uninstallation was successful.

[0018] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps:

[0019] Step 1: Configure edge device information, task information, latency and energy consumption weighting factor for each user, and configure task offloading system constraints;

[0020] Step 2: Establish a mobile edge computing system model, namely a Latency-Energy task offloading model, including latency, energy consumption, and total overhead models for local processing and task offloading; and use a task offloading method based on an improved genetic algorithm to offload edge computing tasks.

[0021] Step 3: Monitor the task unloading process according to the constraints configured by the user;

[0022] Step 4: Output the final latency, energy consumption, total system overhead, and task offloading scheme results for edge computing.

[0023] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, performs the following steps:

[0024] Step 1: Configure edge device information, task information, latency and energy consumption weighting factor for each user, and configure task offloading system constraints;

[0025] Step 2: Establish a mobile edge computing system model, namely a Latency-Energy task offloading model, including latency, energy consumption, and total overhead models for local processing and task offloading; and use a task offloading method based on an improved genetic algorithm to offload edge computing tasks.

[0026] Step 3: Monitor the task unloading process according to the constraints configured by the user;

[0027] Step 4: Output the final latency, energy consumption, total system overhead, and task offloading scheme results for edge computing.

[0028] Compared with the prior art, the significant advantages of this invention are:

[0029] 1) The mobile edge computing task offloading system model of the present invention takes into account the remaining power of the edge device and uses latency and energy consumption weighting factors to calculate the total cost of the edge device, which has the advantages of extending the device usage time and reducing task offloading latency and energy consumption.

[0030] 2) The mobile edge computing task unloading algorithm based on the improved genetic algorithm proposed in this invention transforms the problem of finding the optimal unloading decision into the problem of finding the population optimal solution. The algorithm provides a more accurate solution, avoids getting trapped in local optima, effectively solves the task unloading problem, and is conducive to finding the best task unloading decision.

[0031] 3) This invention monitors task unloading latency, MEC server computing resources, and edge device remaining power, which can effectively prevent problems such as task unloading timeout, MEC server overload, and excessive edge device power consumption, and ensure the rationality of task unloading.

[0032] The present invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0033] Figure 1 This is a system architecture diagram for implementing mobile edge computing task offloading in one embodiment.

[0034] Figure 2 This is a schematic diagram of a system model in one embodiment.

[0035] Figure 3 A flowchart of the user configuration module in one embodiment.

[0036] Figure 4 This is a flowchart of the task unloading module in one embodiment.

[0037] Figure 5 This is a flowchart of the performance monitoring module in one embodiment.

[0038] Figure 6 This is a graph showing the relationship between total overhead and unloading strategy in one embodiment.

[0039] Figure 7 This is a schematic diagram comparing the convergence results of the algorithm of the present invention and the traditional genetic algorithm in one embodiment. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0041] In one embodiment, a method for offloading mobile edge computing tasks is provided, the method comprising the following steps:

[0042] Step 1: Configure edge device information, task information, latency and energy consumption weighting factor for each user, and configure task offloading system constraints;

[0043] Step 2: Establish a mobile edge computing system model, namely a Latency-Energy task offloading model, including latency, energy consumption, and total overhead models for local processing and task offloading; and use a task offloading method based on an improved genetic algorithm to offload edge computing tasks.

[0044] Step 3: Monitor the task unloading process according to the constraints configured by the user;

[0045] Step 4: Output the final latency, energy consumption, total system overhead, and task offloading scheme results for edge computing.

[0046] Furthermore, in one embodiment, combined with Figure 3 Step 1 involves configuring edge device information, task information, latency and energy consumption weighting factors for each user, and configuring task offloading system constraints. Specifically, this includes configuring relevant parameters based on the different latency and energy consumption requirements of each user's tasks, including:

[0047] Step 1-1: Set the latency and energy consumption weighting factor to represent the different users' preferences for latency and energy consumption;

[0048] Steps 1-2: Set the remaining power of the edge devices to modify the weighting factor and constrain the system energy consumption;

[0049] Steps 1-3 configure the task size range, the number of CPU cycles required for the task, and the maximum tolerable latency for the task, in order to generate a batch of tasks that meet the requirements.

[0050] Furthermore, in one embodiment, step 2, establishing the mobile edge computing system model, is based on the Latency-Energy task offloading model, including latency, energy consumption, and total overhead models for local processing and task offloading, specifically including:

[0051] (1) First, establish a latency model to obtain the computation latency of each subtask on the local machine and the edge server, specifically:

[0052] The local computation delay t of the i-th subtask l Represented as:

[0053]

[0054] Where, x i0 This indicates that task i is computed on the local device, c i f0 represents the CPU cycles required for the i-th subtask, and f0 represents the CPU frequency of the local device.

[0055] The computation latency t of the i-th subtask on the edge server c Represented as:

[0056]

[0057] Where, x ik This indicates that the i-th task is offloaded to the k-th edge server for computation, c i f represents the number of CPU cycles required for the i-th subtask. k This indicates the CPU frequency of edge server k;

[0058] The transmission delay of the i-th subtask is t u Represented as:

[0059]

[0060] Where, d i r represents the data size of the i-th task. k This represents the transfer rate at which the task is unloaded to the k-th edge server;

[0061] The total latency of the i-th subtask is the maximum value T of the local computation latency and the latency generated by unloading, expressed as:

[0062] T = max(t) l ,t c +t u )

[0063] (2) Then, an energy consumption model is established to obtain the energy consumption of each subtask on the local and edge servers, specifically:

[0064] The local energy consumption of the i-th subtask is e l Represented as:

[0065]

[0066] Among them, the energy consumption coefficient κ is a constant related to the chip structure of the mobile device;

[0067] The energy consumption e for the transmission of the i-th subtask c Represented as:

[0068]

[0069] Where p is the transmission power of the device;

[0070] The total energy consumption of the i-th subtask is the sum of local energy consumption and transmission energy consumption, E, expressed as:

[0071] E = e l +e c

[0072] (3) Based on the required time delay energy consumption, the total system cost model is derived using the remaining power weighting factor, as follows:

[0073] The remaining power weighting factor includes time delay and energy consumption weighting factors, where the time delay weighting factor w t Represented as:

[0074] w t =ln[λ(e w -1)+1]

[0075] Where w is a user-defined latency weighting factor, and λ is the remaining battery percentage of the device;

[0076] Energy consumption weighting factor w e Represented as:

[0077] w e =1-w t

[0078] The total system overhead W is expressed as:

[0079] W = w t T+w e E.

[0080] Furthermore, in one embodiment, the improved genetic algorithm in step 2 includes four novel crossover mutation methods: single-unit decomposition, two-unit combination, spacer exchange, and two-unit crossover, specifically:

[0081] (1) After a chromosome undergoes monosomy, it produces two chromosomes. A random decomposition point is selected from the original chromosome to produce two new chromosomes. The dynamic execution probability p1 of monosomy decomposition with respect to individual fitness is expressed as:

[0082]

[0083] In the formula, l max and l min Let represent the maximum and minimum fitness in the population, and l represent the fitness of the current individual. avg Indicates the average fitness of the population;

[0084] (2) Dimorphic combination is a chromosome combination operation involving two chromosomes. Two chromosomes are selected, and a combination point is randomly generated. The new chromosome inherits some genes from the original chromosomes, resulting in a new chromosome. The dynamic execution probability p2 of dimorphic combination with population evolution is expressed as:

[0085]

[0086] Where, p s2 p is the initial execution probability value. e2 The execution probability value is used to determine the end of the process. Iter represents the current generation number of the population. max This represents the maximum number of iterations for the algorithm.

[0087] (3) Crossing over refers to the process of two chromosomes translocating. Two interspacer sites are selected from the original chromosomes, and the genes at the corresponding sites are exchanged to generate two new chromosomes. The dynamic probability p3 of crossing over with population evolution is expressed as:

[0088]

[0089] In the formula, l1 and l2 represent the fitness of the two chromosomes, respectively, and the number of chromosomes u1 involved in crossing over translocation is calculated as follows:

[0090]

[0091] (4) Double crossover occurs when several genes at the same position on two chromosomes C1 and C2 cross over and interchange. For two chromosomes, a crossover point is randomly selected, and the gene positions after the crossover point are interchanged to generate two new chromosomes. The dynamic execution probability p4 of double crossover is expressed as:

[0092] p4 = 1 - p1 - p2 - p3

[0093] The number of chromosomes u2 involved in crossing over translocation is calculated as follows:

[0094]

[0095] Furthermore, in one embodiment, combined with Figure 4 Step 2 utilizes a task offloading method based on an improved genetic algorithm to offload edge computing tasks. The specific process includes:

[0096] Step 2-1: Initialize the relevant parameters of the improved genetic algorithm and generate an initial population, and generate an initial unloading strategy;

[0097] Step 2-2: Calculate the latency, energy consumption, and total system overhead of all tasks on the local machine and edge server, and use this to generate a new population and adjust the crossover and mutation probabilities of the genetic algorithm.

[0098] Steps 2-3: Determine if the output conditions have been met. When the maximum number of iterations has been reached, return the optimal unloading strategy.

[0099] Furthermore, in one embodiment, combined with Figure 5 Step 3 involves monitoring the task unloading process based on user-configured constraints. The specific process includes:

[0100] Step 3-1: Monitor the task processing latency and determine whether it exceeds the set maximum tolerable latency. If it does, return to step 2 to change the unloading strategy; otherwise, proceed to the next step.

[0101] Step 3-2: Monitor the computing resources of the mobile edge computing (MEC) server to determine whether the computing load of the current offloading strategy is overloaded. If it is overloaded, return to step 2 to change the offloading strategy; otherwise, proceed to the next step.

[0102] Step 3-3: Determine if the remaining power of the user's edge device is sufficient. If not, return to step 2 to change the unloading strategy; otherwise, it means that the unloading decision obtained by the current task unloading algorithm is reasonable.

[0103] In one embodiment, combined Figure 1 and Figure 2 A system for offloading mobile edge computing tasks is provided, the system comprising:

[0104] The user configuration module is used to configure each user's edge device information, task information, latency and energy consumption weighting factor, and to configure task offloading system constraints.

[0105] The task offloading module establishes a mobile edge computing system model, namely the Latency-Energy task offloading model, including latency, energy consumption, and total overhead models for local processing and task offloading; and utilizes a task offloading method based on an improved genetic algorithm to offload edge computing tasks.

[0106] The performance monitoring module is used to monitor the task unloading process based on user-configured constraints.

[0107] The results output module is used to output the final latency, energy consumption, total system overhead, and task offloading scheme results of edge computing.

[0108] Furthermore, in one embodiment, the system further includes:

[0109] The results query module is used to query the location information of task unloading and the latency, energy consumption, and total overhead information of task processing based on the user and task number. It also displays whether the task unloading was successful and shows the changes in system latency, energy consumption, and total overhead under different power levels. Figure 6 As shown. Combined with Figure 7 The results show the effectiveness of the improved algorithm and the genetic algorithm in solving the problem. It can be seen that the algorithm of the present invention can obtain a better mobile edge computing task offloading strategy under the same number of iterations.

[0110] For specific limitations regarding the system for implementing mobile edge computing task offloading, please refer to the limitations on the methods for implementing mobile edge computing task offloading mentioned above, which will not be repeated here. Each module in the aforementioned system for implementing mobile edge computing task offloading can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0111] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0112] Step 1: Configure edge device information, task information, latency and energy consumption weighting factor for each user, and configure task offloading system constraints;

[0113] Step 2: Establish a mobile edge computing system model, namely a Latency-Energy task offloading model, including latency, energy consumption, and total overhead models for local processing and task offloading; and use a task offloading method based on an improved genetic algorithm to offload edge computing tasks.

[0114] Step 3: Monitor the task unloading process according to the constraints configured by the user;

[0115] Step 4: Output the final latency, energy consumption, total system overhead, and task offloading scheme results for edge computing.

[0116] For specific limitations on each step, please refer to the limitations on the method for implementing mobile edge computing task offloading mentioned above, which will not be repeated here.

[0117] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0118] Step 1: Configure edge device information, task information, latency and energy consumption weighting factor for each user, and configure task offloading system constraints;

[0119] Step 2: Establish a mobile edge computing system model, namely a Latency-Energy task offloading model, including latency, energy consumption, and total overhead models for local processing and task offloading; and use a task offloading method based on an improved genetic algorithm to offload edge computing tasks.

[0120] Step 3: Monitor the task unloading process according to the constraints configured by the user;

[0121] Step 4: Output the final latency, energy consumption, total system overhead, and task offloading scheme results for edge computing.

[0122] For specific limitations on each step, please refer to the limitations on the method for implementing mobile edge computing task offloading mentioned above, which will not be repeated here.

[0123] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for offloading mobile edge computing tasks, characterized in that, The method includes the following steps: Step 1: Configure edge device information, task information, latency and energy consumption weighting factor for each user, and configure task offloading system constraints; Step 2: Establish a mobile edge computing system model, namely a Latency-Energy task offloading model, including latency, energy consumption, and total overhead models for local processing and task offloading; and use a task offloading method based on an improved genetic algorithm to offload edge computing tasks. Step 3: Monitor the task unloading process according to the constraints configured by the user; Step 4: Output the final latency, energy consumption, total system overhead, and task offloading scheme results for edge computing; Step 1 involves configuring edge device information, task information, latency and energy consumption weighting factors for each user, and configuring task offloading system constraints. Specifically, this includes configuring relevant parameters based on the different latency and energy consumption requirements of each user's tasks, including: Step 1-1: Set the latency and energy consumption weighting factor to represent the different users' preferences for latency and energy consumption; Steps 1-2: Set the remaining power of the edge devices to modify the weighting factor and constrain the system energy consumption; Steps 1-3 configure the task size range, the number of CPU cycles required for the task, and the maximum tolerable latency for the task to generate a batch of tasks that meet the requirements. Step 2, establishing the mobile edge computing system model, is based on the Latency-Energy task offloading model, including latency, energy consumption, and total overhead models for local processing and task offloading. Specifically, it includes: (1) First, establish a latency model to obtain the computation latency of each subtask on the local machine and the edge server, specifically: The local computation delay t of the i-th subtask l Represented as: Where, x i0 This indicates that task i is computed on the local device, c i f0 represents the CPU cycles required for the i-th subtask, and f0 represents the CPU frequency of the local device. The computation latency t of the i-th subtask on the edge server c Represented as: Where, x ik This indicates that the i-th task is offloaded to the k-th edge server for computation, c i f represents the number of CPU cycles required for the i-th subtask. k This indicates the CPU frequency of edge server k; The transmission delay of the i-th subtask is t u Represented as: Where, d i r represents the data size of the i-th task. k This represents the transfer rate at which the task is unloaded to the k-th edge server; The total latency of the i-th subtask is the maximum value T of the local computation latency and the latency generated by unloading, expressed as: T=max(t l ,t c +t u ) (2) Then, an energy consumption model is established to obtain the energy consumption of each subtask on the local and edge servers, specifically: The local energy consumption of the i-th subtask is e l Represented as: Among them, the energy consumption coefficient κ is a constant related to the chip structure of the mobile device; The energy consumption e for the transmission of the i-th subtask c Represented as: Where p is the transmission power of the device; The total energy consumption of the i-th subtask is the sum of local energy consumption and transmission energy consumption, E, expressed as: And=and l +e c (3) Based on the required time delay energy consumption, the total system cost model is derived using the remaining power weighting factor, as follows: The remaining power weighting factor includes time delay and energy consumption weighting factors, where the time delay weighting factor w t Represented as: w t ln[λ(e w -1)+1] Where w is a user-defined latency weighting factor, and λ is the remaining battery percentage of the device; Energy consumption weighting factor w e Represented as: In e =1-in t The total system overhead W is expressed as: W=w t T+w e E。 2. The method for offloading mobile edge computing tasks according to claim 1, characterized in that, The improved genetic algorithm described in step 2 includes four novel crossover and mutation methods: single-unit decomposition, two-unit combination, spacer exchange, and two-unit crossover. Specifically: (1) After the chromosome undergoes monolysis, it produces two chromosomes. A random decomposition point is selected in the original chromosome to decompose it into two new chromosomes. The dynamic execution probability p1 of single-unit decomposition with respect to individual fitness is expressed as: In the formula, l max and l min Let represent the maximum and minimum fitness in the population, and l represent the fitness of the current individual. avg Indicates the average fitness of the population; (2) Dimorphic combination is a chromosome combination operation involving two chromosomes. Two chromosomes are selected, and a combination point is randomly generated. The new chromosome inherits some genes from the original chromosomes, resulting in a new chromosome. The dynamic execution probability p2 of dimorphic combination with population evolution is expressed as: Where, p s2 p is the initial execution probability value. e2 The execution probability value is used to determine the end of the process. Iter represents the current generation number of the population. max This represents the maximum number of iterations for the algorithm. (3) Crossing over refers to the process of two chromosomes translocating. Two interstitial sites are selected from the original chromosomes, and the genes at the corresponding sites are exchanged to generate two new chromosomes. The dynamic probability p3 of crossing over with population evolution is expressed as: In the formula, l1 and l2 represent the fitness of the two chromosomes, respectively, and the number of chromosomes u1 involved in crossing over translocation is calculated as follows: (4) Double crossover occurs when several genes at the same position on two chromosomes C1 and C2 cross over and interchange. For two chromosomes, a crossover point is randomly selected, and the gene positions after the crossover point are interchanged to generate two new chromosomes. The dynamic execution probability p4 of double crossover is expressed as: p4 = 1 - p1 - p2 - p3 The number of chromosomes u2 involved in crossing over translocation is calculated as follows:

3. The method for offloading mobile edge computing tasks according to claim 2, characterized in that, Step 2 utilizes a task offloading method based on an improved genetic algorithm to offload edge computing tasks. The specific process includes: Step 2-1: Initialize the relevant parameters of the improved genetic algorithm and generate an initial population, and generate an initial unloading strategy; Step 2-2: Calculate the latency, energy consumption, and total system overhead of all tasks on the local and edge servers, and use this to generate a new population and adjust the crossover and mutation probabilities of the genetic algorithm. Steps 2-3: Determine if the output conditions have been met. When the maximum number of iterations has been reached, return the optimal unloading strategy.

4. The method for offloading mobile edge computing tasks according to claim 3, characterized in that, Step 3 involves monitoring the task unloading process based on user-configured constraints. The specific process includes: Step 3-1: Monitor the task processing latency and determine whether it exceeds the set maximum tolerable latency. If it does, return to step 2 to change the unloading strategy; otherwise, proceed to the next step. Step 3-2: Monitor the computing resources of the mobile edge computing (MEC) server to determine whether the computing load of the current offloading strategy is overloaded. If it is overloaded, return to step 2 to change the offloading strategy; otherwise, proceed to the next step. Step 3-3: Determine if the remaining power of the user's edge device is sufficient. If not, return to step 2 to change the unloading strategy; otherwise, it means that the unloading decision obtained by the current task unloading algorithm is reasonable.

5. A system for offloading mobile edge computing tasks based on the method of any one of claims 1 to 4, characterized in that, The system includes: The user configuration module is used to configure each user's edge device information, task information, latency and energy consumption weighting factor, and to configure task offloading system constraints. The task offloading module establishes a mobile edge computing system model, namely the Latency-Energy task offloading model, including latency, energy consumption, and total overhead models for local processing and task offloading; and utilizes a task offloading method based on an improved genetic algorithm to offload edge computing tasks. The performance monitoring module is used to monitor the task unloading process based on user-configured constraints. The results output module is used to output the final latency, energy consumption, total system overhead, and task offloading scheme results of edge computing.

6. The system for offloading mobile edge computing tasks according to claim 5, characterized in that, The system also includes: The results query module is used to query the location information of task uninstallation and the latency, energy consumption and total cost of task processing based on the user and task number, and to display whether the task uninstallation was successful.

7. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

8. A computer-readable storage medium having a computer program stored thereon, 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 4.

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