Event-triggered tracking and control method for heterogeneous multi-UAV systems

Abstract

This invention discloses a tracking and control method for a heterogeneous multi-UAV system based on event triggering. A communication topology model of the heterogeneous multi-UAV system is established, comprising a leader and several heterogeneous followers. Based on this model, edge event triggering conditions for information interaction between followers and dynamic event triggering conditions for information interaction between the leader and followers are constructed. The leader's state is estimated using a compensator. Based on the estimated leader's state and the event triggering conditions, it is determined whether communication between UAVs is triggered. Followers that trigger communication are tracked and controlled by an event triggering controller. By combining edge and dynamic event triggering conditions, the overall communication requirements of the system are further reduced, significantly lowering the communication frequency between UAVs in the multi-UAV system. This effectively saves network bandwidth and energy consumption while reducing the communication burden.

Description

Technical Field

[0001] This invention relates to the cooperative control of multiple unmanned aerial vehicle (UAV) systems, specifically to a tracking and control method for heterogeneous multi-UAV systems based on event triggering. Background Technology

[0002] Cooperative control of multi-UAV systems has received continuous attention due to its wide application in distributed sensing, intelligent transportation, and unmanned system swarming. In practical applications, UAVs often exhibit heterogeneous dynamic characteristics and engage in both cooperation and competition, which places higher demands on the consistency control of the system. While traditional continuous-time control protocols can achieve consistency, they typically require continuous communication between UAVs, placing significant pressure on communication resources and energy consumption.

[0003] Especially in scenarios where communication topologies change over time and are not strongly connected, such as in mobile ad hoc networks or constrained communication environments, the system topology may only satisfy joint connectivity conditions, which further increases the complexity of distributed controller design. Most existing studies assume that UAVs have only cooperative relationships and usually use fixed topologies or fully connected networks, making it difficult to effectively handle real-world systems with mixed competition-cooperation relationships and dynamic topologies.

[0004] Event-triggered control can significantly reduce the system's communication burden by enabling communication and control updates only under specific triggering conditions. However, achieving binary output consistency for heterogeneous multi-UAV systems in symbolic directed graphs and jointly connected topologies still faces challenges such as triggering mechanism design, distributed compensator construction, and Zeno behavior avoidance. Summary of the Invention

[0005] Purpose of the invention: To address the above-mentioned shortcomings, this invention provides an event-triggered tracking and control method for heterogeneous multi-UAV systems that reduces the number of communications and avoids the Zeno phenomenon.

[0006] Technical Solution: To solve the above problems, this invention adopts an event-triggered heterogeneous multi-UAV system tracking and control method, including the following steps:

[0007] (1) Establish a communication topology model for a heterogeneous multi-UAV system, wherein the heterogeneous multi-UAV system includes a leader and several heterogeneous followers;

[0008] (2) Based on the communication topology model of the heterogeneous multi-UAV system, construct the event triggering conditions for communication between UAVs; the event triggering conditions include edge event triggering conditions for information interaction between followers and dynamic event triggering conditions for information interaction between leaders and followers;

[0009] (3) Construct a compensator and build an event triggering controller based on the compensator;

[0010] (4) Compensate for the follower's state using a compensator;

[0011] (5) Based on the state after compensation by the compensator, determine whether communication is triggered between UAVs based on the event triggering conditions, and track and control the follower that triggers communication through the event triggering controller.

[0012] Furthermore, the edge event triggering condition is as follows:

[0013] ;

[0014] ;

[0015] in, For follower drones With follower drones Between The moment when communication is triggered. For follower drones With follower drones Between The moment when communication is triggered. For follower drones With follower drones Event triggering functions between, Time-follower drone With follower drones Communication is triggered between them; For follower drones With follower drones Measurement error between For follower drones With follower drones Communication weights between them and Symbolic variables representing cooperative or competitive relationships. For follower drones Follower drones The estimated state, For follower drones Follower drones The estimated state, , and These are adjustment parameters for the edge event triggering conditions. For the current moment, This is the start time.

[0016] Furthermore, the dynamic event triggering condition is as follows:

[0017] ;

[0018] ;

[0019] in, For follower drones Between the leader drone The moment when communication is triggered. For follower drones Between the leader drone The moment when communication is triggered. For follower drones Event triggering functions between the leader drone and the leader drone. Time-follower drone Trigger communication with the leader's drone; For follower drones Measurement error between the leader drone and the leader drone This is an adaptive parameter in the dynamic event triggering condition. , and These are adjustment parameters for dynamic event triggering conditions. For the current moment, This is the start time.

[0020] Furthermore, the adaptive parameters in the dynamic event triggering conditions The update law is:

[0021] ;

[0022] in, For adaptive parameters The first derivative; These are design parameters.

[0023] Furthermore, the compensator is:

[0024]

[0025] in, For follower drones The state after compensation. The coefficient matrix represents the position and state space of the leader drone. For positive integers, The total number of follower drones, For follower drones Communication weight between the leader drone, For follower drones The estimated value of the position state vector. For follower drones The mean of the estimated states for all follower drones, For follower drones The mean of the estimated states for all follower drones.

[0026] Furthermore, follower drones The mean of the estimated states for all follower drones The expression is:

[0027] ;

[0028] ;

[0029] in, For follower drones exist The state vector at time t.

[0030] Furthermore, the event triggering controller is:

[0031] ;

[0032] in, For follower drones The control input vector, For follower drones State estimation, For follower drones The state after compensation. and This is the controller gain matrix.

[0033] Furthermore, the follower drone is estimated by constructing a state feedback observer. The state, and the state feedback observer are:

[0034]

[0035] in, The observer gain matrix is... For follower drones The actual output.

[0036] The present invention also employs a computer device, 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 implement the steps of the above method.

[0037] The present invention also employs a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above method.

[0038] Beneficial effects: Compared with the prior art, the significant advantages of this invention are:

[0039] (1) A combination of edge event triggering mechanism and dynamic event triggering mechanism is proposed. The edge event triggering mechanism is used for information interaction between followers, while the dynamic event triggering mechanism is used for information interaction between the leader and followers. This further reduces the overall communication requirements of the system and significantly reduces the communication frequency between UAVs in a multi-UAV system. Compared with traditional periodic sampling control, this method only communicates when the triggering condition is met, effectively saving network bandwidth and energy consumption, while reducing the communication burden, making it more suitable for resource-constrained practical application scenarios. The dynamic event triggering mechanism introduces internal dynamic variables, which can adaptively adjust the triggering threshold according to the system state, achieving higher communication efficiency while ensuring control performance.

[0040] (2) Fully distributed control is achieved under jointly connected directed switching topology, without relying on global topology information. The system can achieve binary output consistency tracking of heterogeneous multi-UAV systems under the condition of time-varying and non-fully connected communication topology, effectively handle the complex interaction relationship of cooperation and competition among UAVs, and rigorously prove that the system does not have Zeno behavior. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating the interception method of the present invention.

[0042] Figure 2 This is a schematic diagram of the joint connected topology in this invention. Detailed Implementation

[0043] Current cooperative control methods for multi-UAV systems largely rely on continuous-time communication and fixed topology assumptions, making them ill-suited for real-world scenarios with limited communication resources or dynamically changing topologies. Particularly in heterogeneous linear multi-UAV systems, existing research often overlooks the potential competitive-cooperative hybrid relationships among UAVs and mostly employs fully connected or fixed topologies, limiting their application in dynamic environments such as mobile ad hoc networks. Furthermore, traditional event-triggered control methods often face challenges in achieving binary output consistency in jointly connected directed graphs, such as Zeno behavior exclusion and distributed compensator design.

[0044] To improve the cooperative control performance of multi-UAV systems in dynamic topology and resource-constrained environments, this invention proposes an event-triggered tracking control method for heterogeneous multi-UAV systems. To demonstrate the feasibility and effectiveness of this invention, two specific embodiments will be described below.

[0045] Example 1

[0046] This invention proposes an event-triggered tracking control method for heterogeneous multi-UAV systems, specifically designed for cooperative tracking control of heterogeneous linear multi-UAV systems. The core innovation lies in constructing a highly efficient, fully distributed control architecture for UAV swarms with heterogeneous characteristics and complex interactions involving both cooperation and competition within the swarm. Specifically, this invention innovatively designs a two-layer event-triggered mechanism, introducing a unique edge event-triggered mechanism on the communication link between follower UAVs, fundamentally avoiding redundant communication. Combined with dynamic event triggering between leader and follower UAVs, this significantly reduces network channel load and onboard computing resource consumption. Furthermore, by fusing a distributed binary compensator with output feedback, accurate estimation of the leader's state and consistent binary output tracking are achieved under directed switching topologies. This method strictly avoids Zeno behavior, ensuring the system's engineering feasibility. It is particularly suitable for complex, highly dynamic scenarios with adversarial or mixed cooperative-competitive relationships, such as cooperative interception under enemy-friendly confrontation and encirclement by multi-task swarms, significantly improving the autonomous cooperative intelligence and mission robustness of heterogeneous UAV swarms.

[0047] like Figure 1 As shown in this embodiment, a tracking and control method for a heterogeneous multi-UAV system based on event triggering includes the following steps:

[0048] Step 1: Establish a communication topology model for a heterogeneous multi-UAV system, which includes a leader and several heterogeneous followers;

[0049] Step 2: Based on the communication topology model of the heterogeneous multi-UAV system, construct the event triggering conditions for communication between UAVs; the event triggering conditions include edge event triggering conditions for information interaction between followers and dynamic event triggering conditions for information interaction between the leader and followers;

[0050] Step 3: Build the compensator and build an event-triggered controller based on the compensator;

[0051] Step 4: Compensate for the follower's state using a compensator;

[0052] Step 5: Based on the state after compensation by the compensator, determine whether communication is triggered between drones based on the event triggering conditions, and track and control the follower that has triggered communication through the event triggering controller.

[0053] Specifically, the implementation process in this embodiment is as follows: The parameters of the multi-UAV system in this embodiment are designed as follows: , , , , , ;in, For the number of drones tracking, For the leader's state dimension, For output dimensions, For compensator gain, For topology switching cycle, Parameters are designed for the event triggering mechanism; the leader dynamic matrix is... The follower system matrix is The communication topology consists of four jointly connected symbolic directed graphs, and the switching sequence cycles according to a predetermined period; the initial state of the leader is... The initial state of the follower is The initial state of the compensator is The goal is to achieve a binary consistency between the output of all followers and the output of the leader, i.e. ,in This indicates a cooperative or competitive relationship between drones and leaders.

[0054] Step 1: Establish the communication topology model of the heterogeneous multi-UAV system. The communication topology of the heterogeneous multi-UAV system is defined as a symbolically united connected directed switching graph, with a balanced system structure. The communication topology structure of the multi-UAV system is as follows: It means that, among them represent A collection of drones Represents the edge set. For ,like This indicates that the drone is... Able to use drones Receive information. Symbol Subgraph The weighted adjacency matrix, whose elements For the edge The weighting. Note that... .also, or Describes drones With drones The system involves cooperative or adversarial relationships between nodes. It has a leader node labeled Node 0, and its augmenting system is denoted as [node name missing]. ,in Define a diagonal matrix. ,in This indicates the communication weight between the leader node and its followers. Note that... , indicating drone It can only receive information directly from the leader node. (Subgraph) The Laplace matrix is ​​defined as , among which when hour, ,and, The leader-follower matrix of the system is denoted as... If a graph has directed paths from at least one root node to all other nodes, then the graph is said to have a directed spanning tree.

[0055] If symbolic subgraph Allow the node set to be divided into and (satisfy ,and ), such that for any have , and for any , have If so, the symbol diagram is said to be structurally balanced. Its symbol matrix is ​​denoted as... ,in And for symbols and same.

[0056] A directed graph with switching symbols is defined as follows: ,in For switching signals. Consider a sequence of an infinite number of non-empty bounded time intervals. ..., start time And for all ( The set of positive integers satisfies Furthermore, in each interval Within, there exist a finite number of non-overlapping subintervals. ,in, , In each sub-interval Inside, a directed graph is defined as The adjacency matrix, Laplace matrix, and leader-follower matrix of the directed graph with switching symbols are denoted as follows:

[0057] Figure 2 The figure shows a schematic diagram of the jointly connected directed topology proposed in this embodiment. This topology consists of one leader and four follower drone nodes, with the leader numbered 0 and the followers numbered 1-4. The directed edges in the topology represent communication links between the drone nodes, and the arrows indicate the direction of information flow. Specifically, this topology uses symbolic weights; solid lines represent positive weights (cooperative relationships), and dashed lines represent negative weights (competitive relationships), reflecting the structural balance characteristics of the system.

[0058] Based on the jointly connected directed topology, the position and state space model of heterogeneous multi-UAVs is established as follows:

[0059]

[0060]

[0061] The multi-UAV system consists of one leader and N followers; the UAV swarm forms a heterogeneous linear system, whose dynamic equations are described by the aforementioned position-state space model; the leader's dynamics are defined by matrices A0 and C0. This represents the leader's positional state vector. for The derivative of represents the leader's velocity-state vector. Its output vector; follower drone The dynamics are determined by matrix A i B i and C i describe, The range of values ​​is Furthermore, the system satisfies the assumptions that it is stable, detectable, and that the output regulation equation is solvable. For the first follower drone The position state vector, for The derivative of the follower drone represents the derivative of the follower drone. velocity state vector, To control its input vector, Its output vector; the communication topology between UAVs is a symbolically united connected directed switching graph, and the system structure is balanced.

[0062] In this embodiment, a state-space modeling method for heterogeneous multi-UAV systems based on jointly connected directed topology is adopted, effectively solving the technical challenges of stringent communication topology requirements and difficulty in adapting to dynamically changing environments in traditional cooperative control. By introducing a symbolically jointly connected directed switching graph, this model can not only accurately describe the information interaction relationships of UAV swarms in complex scenarios such as asymmetric communication links and intermittent interruptions, but also ensure the stability of the system when there is a mixed cooperative-competitive relationship through the structural balance assumption.

[0063] Step 2: Based on the communication topology model of the heterogeneous multi-UAV system, construct the event triggering conditions for communication between UAVs. According to the jointly connected directed topology graph and combined with the position-state space model of the multi-UAV system, establish an edge event triggering mechanism for communication between followers as follows:

[0064]

[0065]

[0066] in, It is the communication topology edge of the drone The event triggering function, It is the communication topology edge of the drone No. The moment this event is triggered The measurement error between UAVs i and j; For communication weights; Symbolic variables representing cooperative or competitive relationships; Let i be the estimated state of UAV i to j; These are the design parameters for the event triggering mechanism.

[0067] Design a dynamic event triggering mechanism for communication between leaders and followers as follows:

[0068]

[0069]

[0070] in, It is the communication topology edge of the drone The event triggering function, For drones and Measurement error between For dynamic variables The first derivative, and The update law for the adjustment parameters in the dynamic event triggering mechanism is designed as follows:

[0071]

[0072] in, , Design parameters, dynamic variables It is an adaptive parameter in the dynamic event triggering mechanism.

[0073] In this embodiment, due to the efficient communication characteristics of the event-triggered mechanism, a distributed event-triggered strategy is used to regulate information interaction between multiple UAV systems. Introducing the event-triggered mechanism significantly reduces the system's communication burden and energy consumption, enabling intelligent communication scheduling and control updates through dynamic triggering conditions, effectively mitigating network congestion and avoiding resource waste. This mechanism not only improves the scalability of the control system but also effectively ensures consistency performance, enabling the multi-UAV system to operate stably under resource-constrained and topology-varying conditions, adapting to complex collaborative task requirements.

[0074] Step 3: Construct a compensator and build an event-triggered controller based on the compensator. In this embodiment, the compensator is a distributed binary compensator, which is used to balance the dimensional differences between agent states, transforming the heterogeneous UAV cooperation problem into a homogeneous problem, and an output feedback type distributed event-triggered controller is designed based on this compensator.

[0075] First, follower drones The distributed binary compensator is designed as follows:

[0076]

[0077] in, For drones The state after compensation by the compensator . and The definition is as follows:

[0078]

[0079]

[0080] in, and This refers to the defined event trigger time. Note that... and They are respectively and The open-loop estimate.

[0081] Secondly, based on this compensator, an output feedback-type distributed event-triggered controller is designed. The conditions for satisfying the binary output consistency are given as follows:

[0082]

[0083] in, For follower drones The actual output, For the leader's output, The symbolic variable representing the cooperative or competitive relationship, whose value is determined by the system's structural balance, is given by the output consistency error function: .

[0084] In this embodiment, an event-triggered control scheme based on a distributed binary compensator and output feedback effectively solves the cooperative control challenge of heterogeneous UAV systems under conditions of limited communication and only measurable outputs. The compensator estimates the leader state using local neighbor information, reducing reliance on global communication. The output feedback controller, designed based on this, combined with an event-triggered mechanism, updates communication and control only when specific conditions are met, significantly saving communication and computing resources and improving adaptability and robustness in adversarial environments.

[0085] Furthermore, based on the system dynamics and output regulation equations, the control protocol is designed as follows:

[0086]

[0087] in, For follower drones The control input vector, For follower drones State estimation, For compensators for follower drones The state after compensation. and For the controller gain matrix, by making For Hurwitz matrices, and select... To determine, and The solution to the following regulation equation is output.

[0088] ,

[0089] in , , , , It is the coefficient matrix of the system state equation.

[0090] In this embodiment, the cooperative tracking control problem of heterogeneous UAV systems is effectively solved by introducing a composite control structure that combines state feedback and output regulation. The gain matrix in this protocol is carefully designed to ensure the stability of each UAV's closed-loop system while achieving accurate tracking of the leader reference signal through the output regulation mechanism. This design enables heterogeneous UAV swarms to maintain good tracking performance and control accuracy under resource-constrained conditions, providing reliable technical support for cooperative interception missions in complex battlefield environments.

[0091] Furthermore, the follower drone is estimated by constructing a state feedback observer. The state;

[0092] The state feedback observer is:

[0093]

[0094] in is the observer gain matrix.

[0095] According to the control protocol, the observer error is defined. and state tracking error Through observer error and state tracking error The process of obtaining the system parameters converged to zero includes:

[0096] Define the observer error ; Observer error Taking the derivative, we obtain the dynamic equation for the observer error as follows:

[0097]

[0098] in, For the observer gain matrix, design such that Since it is a Hurwitz matrix, we have Thus ensuring The exponent converges to zero.

[0099] definition ,right Taking the derivative, we obtain its first derivative as:

[0100]

[0101] According to the Input State Stability Theorem (ISS) and That is, it must satisfy:

[0102]

[0103] By designing a distributed event-triggered controller and parameter conditions, And ensure the system is asymptotically stable.

[0104] Step 4: Compensate for the follower's state using a compensator;

[0105] Step 5: Based on the state after compensation by the compensator, determine whether communication is triggered between drones based on the event triggering conditions, and track and control the follower that has triggered communication through the event triggering controller.

[0106] In this embodiment, by explicitly defining observer error and state tracking error, a quantitative basis is provided for system stability analysis and controller design. The cooperative control architecture built upon these two types of errors not only enables real-time evaluation of system performance but also lays the theoretical foundation for the design of event triggering conditions. This error-driven control strategy effectively enhances the adaptability of heterogeneous UAV swarms in dynamic environments, providing reliable assurance for cooperative interception missions under complex battlefield conditions.

[0107] In this embodiment, the designed distributed output feedback event-triggered controller effectively solves the challenge of collaborative control of heterogeneous UAV systems in resource-constrained environments. This controller relies solely on measurable output information for control decisions through an output feedback mechanism, reducing reliance on full-state measurements. Combined with a distributed architecture, each UAV only needs to communicate with its neighboring nodes, significantly reducing the system's communication burden.

[0108] Furthermore, through the aforementioned event triggering mechanism and controller, it is proven that the system does not exhibit Zeno behavior:

[0109] First, define the total error between agents as: Taking the norm of both sides, we can obtain... .

[0110] in, .right Perform the upper Dini derivative operation:

[0111]

[0112] Among them, parameters Defined as This can be guaranteed by the following formula. :

[0113]

[0114] Therefore, it can be deduced that:

[0115]

[0116]

[0117] Combining the event triggering mechanism, we can obtain:

[0118] ,

[0119] Therefore, we can obtain Therefore, this method can avoid the Zeno phenomenon between drones.

[0120] In this embodiment, the absence of Zeno behavior in the system is rigorously proven, ensuring the engineering feasibility of the event-triggered control strategy and effectively preventing the abnormal phenomenon of the controller triggering an unlimited number of times within a finite time. This proof not only theoretically verifies the reliability of the control system but also provides a crucial guarantee for the long-term stable operation of heterogeneous UAV swarms in real combat environments, effectively preventing risks such as communication resource depletion or controller failure due to excessive triggering.

[0121] Example 2

[0122] The parameters of the multi-UAV system designed in this embodiment are as follows: , , Where N is the number of drones, p is the leader state dimension, q is the output dimension, μ is the compensator gain, and T is the topology switching period. These are the parameters for the event triggering mechanism; the leader's dynamic matrix is ​​A0=[0,1;-1,0], C0=[1,0]; the follower system matrix is... The communication topology consists of four jointly connected symbolic directed graphs, and the switching sequence cycles according to a predetermined period; the initial state of the leader is... The initial state of the follower is The initial state of the compensator is The goal is to achieve a binary consistency between the output of all followers and the output of the leader, i.e. .

Claims

1. A tracking and control method for a heterogeneous multi-UAV system based on event triggering, characterized in that, Includes the following steps: (1) Establish a communication topology model for a heterogeneous multi-UAV system, wherein the heterogeneous multi-UAV system includes a leader and several heterogeneous followers; (2) Based on the communication topology model of the heterogeneous multi-UAV system, construct the event triggering conditions for communication between UAVs; the event triggering conditions include edge event triggering conditions for information interaction between followers and dynamic event triggering conditions for information interaction between leaders and followers; (3) Construct a compensator and build an event triggering controller based on the compensator; (4) Compensate for the follower's state using a compensator; (5) Based on the state after compensation by the compensator, determine whether communication is triggered between UAVs based on the event triggering conditions, and track and control the follower that triggers communication through the event triggering controller; The triggering condition for the edge event is: ； ； in, For follower drones With follower drones Between The moment when communication is triggered. For follower drones With follower drones Between The moment when communication is triggered. For follower drones With follower drones Event triggering functions between, Time-follower drone With follower drones Communication is triggered between them; For follower drones With follower drones Measurement error between For follower drones With follower drones Communication weights between them and Symbolic variables representing cooperative or competitive relationships. For follower drones Follower drones The estimated state, For follower drones Follower drones The estimated state, , and These are adjustment parameters for the edge event triggering conditions. For the current moment, The start time; The dynamic event triggering condition is as follows: ； ； in, For follower drones Between the leader drone The moment when communication is triggered. For follower drones Between the leader drone The moment when communication is triggered. For follower drones Event triggering functions between the leader drone and the leader drone. Time-follower drone Trigger communication with the leader's drone; For follower drones Measurement error between the leader drone and the leader drone This is an adaptive parameter in the dynamic event triggering condition. , and These are adjustment parameters for dynamic event triggering conditions. For the current moment, This is the start time.

2. The event-triggered heterogeneous multi-UAV system tracking and control method according to claim 1, characterized in that, The adaptive parameters in the dynamic event triggering conditions The update law is: ； in, For adaptive parameters The first derivative; These are design parameters.

3. The event-triggered heterogeneous multi-UAV system tracking and control method according to claim 2, characterized in that, The compensator is: ； in, For follower drones The state after compensation. The coefficient matrix represents the position and state space of the leader drone. For positive integers, The total number of follower drones, For follower drones Communication weight between the leader drone, For follower drones The estimated value of the position state vector. For follower drones The mean of the estimated states for all follower drones, For follower drones The mean of the estimated states for all follower drones.

4. The event-triggered heterogeneous multi-UAV system tracking and control method according to claim 3, characterized in that, Follower drone The mean of the estimated states for all follower drones The expression is: ； ； in, For follower drones exist The state vector at time t.

5. The event-triggered heterogeneous multi-UAV system tracking and control method according to claim 4, characterized in that, The event triggering controller is: ； in, For follower drones The control input vector, For follower drones State estimation, For follower drones The state after compensation. and This is the controller gain matrix.

6. The event-triggered heterogeneous multi-UAV system tracking and control method according to claim 5, characterized in that, Estimate follower drones by constructing a state feedback observer. The state, and the state feedback observer are: ； in, The observer gain matrix is... For follower drones The actual output, , , It is the coefficient matrix of the system state equation.

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 6.

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 6.

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