Power system time-delay multilink complex dynamic network topology identification method based on improved compressed sensing

Abstract

The invention discloses a power system time-delay multi-link complex dynamic network topology identification method based on improved compressed sensing, and belongs to the field of power system time-delay multi-link complex dynamic network topology structure identification. Setting a threshold for triggering random pulse driving, and preventing network nodes from internal synchronization or state convergence to a fixed value; and then cleaning and integrating the sampling data, constructing a sensing matrix and a sampling signal which are suitable for the compressed sensing method so as to establish a mathematical model of the compressed sensing identification network topology, and solving the model to obtain a network topology signal so as to recover a network topology structure. According to the method, the problem of recognition failure caused by internal synchronization or state convergence of a complex network can be avoided, and compared with a traditional method based on self-adaptive synchronization, the control cost is lower, and disturbance to the system is smaller.

Description

Technical Field

[0001] The present invention belongs to the field of topological structure recognition of complex dynamic networks with time delays and multi-links in power systems, and more specifically, relates to a topological structure recognition method of complex dynamic networks with time delays and multi-links in power systems based on improved compressed sensing. Background Art

[0002] The power system has the characteristics of time-delay multi-links. Different links have different electrical parameters. The multi-link characteristics lead to complex distribution patterns of power flow in the network. The topology identification of complex dynamic networks with time-delay multi-links in power systems refers to the process of inverting or restoring the topology of the network based on observable network data for networked systems with unknown or difficult-to-measure topologies. Research on the topology identification of complex dynamic networks with time-delay multi-links in power systems is of great significance for improving the stability of the entire power network, optimizing the power flow distribution of the system, and enhancing the fault diagnosis and fault tolerance capabilities of the entire power system.

[0003] At present, most of the complex network topology identification methods of power systems are based on adaptive synchronization, which requires the design of auxiliary networks of the same size, or requires the network to meet the assumption of linear independence of synchronization trends, or requires the network to be controlled at all times. However, in actual applications, the above assumptions are difficult to meet, and applying control at all times will also cause greater disturbances to the system itself. Summary of the invention

[0004] In response to the above defects or improvement needs of the prior art, the present invention provides a method for identifying time-delay multi-link complex dynamic network topology in an electric power system based on improved compressed sensing. Compared with the existing methods, the method provided by the present invention has lower control cost, less disturbance to the system itself, and can solve the problem of identification failure caused by internal synchronization or state convergence in the complex network.

[0005] To achieve the above object, according to a first aspect of the present invention, a method for identifying a time-delay multi-link complex dynamic network topology of a power system based on improved compressed sensing is provided, comprising:

[0006] S1, real-time collection and determination of whether the range of the lth state component of the network node i is lower than a preset range;

[0007] S2, if yes, trigger the random pulse and return to S1, otherwise determine whether the current acquisition duration reaches the preset duration, if yes, enter S4, otherwise enter S3; wherein the random pulse applied to each node uses the node number and time as the random seed;

[0008] S3, calculating the similarity E between the state vectors of all nodes in the network at the current sampling time and the state vectors at the previous sampling time, if E is greater than the corresponding threshold, triggering the random pulse and returning to S1, otherwise directly returning to S1;

[0009] S4, collect the state quantity of network node i within a preset time, and perform data cleaning to delete the state quantity generated by node i under the action of the random pulse; use the cleaned data to construct the sensor matrix Φ and the measurement signal matrix y to establish a compressed sensing mathematical model The topological signal matrix T of the network is obtained by solving and calculating it; wherein the component of the topological signal matrix T is x, and the component of the measurement signal matrix y is u.

[0010] According to a second aspect of the present invention, there is provided an electronic device, comprising: a computer-readable storage medium and a processor;

[0011] The computer-readable storage medium is used to store executable instructions;

[0012] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the method as described in the first aspect.

[0013] According to a third aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause a processor to execute the method as described in the first aspect.

[0014] According to a fourth aspect of the present invention, there is provided a computer program product, comprising a computer program or instructions, wherein when the computer program or instructions are executed by a processor, the method according to the first aspect is implemented.

[0015] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0016] The method provided by the present invention samples the states of the nodes of the complex dynamic network of multi-links with time lag in the power system, calculates the evaluation index of internal synchronization and state convergence in the process of network evolution, sets the threshold for triggering random pulse driving, and prevents the network nodes from internal synchronization or state convergence to a fixed value; then cleans and integrates the sampled data, constructs a sensor matrix and sampling signals suitable for the compressed sensing method, so as to establish a mathematical model for compressed sensing identification of network topology, obtains the network topology signal by solving the model, and then restores the network topology structure. This method can avoid the problem of recognition failure caused by internal synchronization or state convergence of the complex dynamic network of multi-links with time lag in the power system, has lower control cost and less disturbance to the system itself than the traditional method based on adaptive synchronization; it can improve the stability of the entire power network, optimize the power flow distribution of the system, and enhance the fault diagnosis and fault tolerance of the entire power system.

[0017] As a preferred solution, the present invention designs a solution method for the mathematical model of the compressed sensing network topology recognition based on the greedy idea. Compared with the existing compressed sensing methods, the amount of sampled data required when identifying the topological structure is less, which can further improve the calculation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flow chart of a method for identifying a time-delay multi-link complex dynamic network topology in a power system based on improved compressed sensing provided by an embodiment of the present invention;

[0019] Figure 2 A flowchart of random pulse driving triggered by network status provided by an embodiment of the present invention;

[0020] Figure 3 A flowchart of constructing a sensing matrix, a sampling signal matrix and a topological signal matrix provided in an embodiment of the present invention;

[0021] Figure 4 A flowchart for solving a network topology signal matrix provided by an embodiment of the present invention;

[0022] Figure 5 (a) and (b) are schematic diagrams of the evolution of the network node state and the driving triggering situation before and after the driving strategy provided by the embodiment of the present invention is applied to the network, respectively. Figure 5 (c) is a schematic diagram of the triggering of the driving strategy over time;

[0023] Figure 6 (a) and (b) are schematic diagrams of the Laplacian matrices of link 1 and link 2 of a 10-node network shown in formula (3) provided in an embodiment of the present invention, respectively. Figure 6(c) and (d) are schematic diagrams of the adjacency matrices of link 1 and link 2 of the 10-node network shown in formula (5) provided in an embodiment of the present invention;

[0024] Figure 7 (a) and (b) are schematic diagrams of Laplacian matrix identification results of link 1 and link 2 of a 10-node network shown in formula (3) provided in an embodiment of the present invention, respectively. Figure 7 (c) and (d) are schematic diagrams of the recognition results of the adjacency matrices of link 1 and link 2 of the 10-node network shown in formula (5) provided in an embodiment of the present invention;

[0025] Figure 8 A schematic diagram of the topology identification results of a 30-node multi-link complex dynamic network provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0027] At present, some studies have proposed to use compressed sensing methods to identify network topology, but the research objects are all simple networks with single-link connections between nodes. In addition, the occurrence of network synchronization will cause the sampling data to be unavailable, making it difficult to accurately restore the network topology. Based on this, an embodiment of the present invention provides a method for identifying the topology of a time-delay multi-link complex dynamic network in a power system based on improved compressed sensing. Before introducing this method, some basic terms about network graphs are first introduced. A single-layer complex dynamic network can be represented by a graph G = (V, E), where the vertex set V = {v1, v2, ..., v n} represents all nodes in the complex network, and the edge set E = {e1,e2,...,e m} represents all the information transmission links between network nodes. If it is an undirected graph, information can be transmitted between the nodes at the two vertices of the undirected edge e, and these two nodes are called neighbor nodes. If it is a directed graph, the edge e = (v i ,v j ) indicates a v j Point to v i One-way edge, in the network, node v i With v j Only one-way information transmission can occur, that is, node v j The information can be passed to node v i But node vi The information cannot be delivered to node v j . Respectively represent the state vector and the derivative of the state vector of node i; ω ij represents the edge relationship between nodes i and j, ω ij =0 means there is no edge between nodes i and j, otherwise it means there is an edge between nodes i and j; f i (x i ) represents the self-dynamic function of node i, C P ,Ξ (P) They represent the intra-layer coupling strength and intra-coupling matrix of the Pth sub-link of the network respectively. They represent the vectors consisting of each node's own dynamics, state vector, and the derivative of the state vector.

[0028] The nodes of the time-delay multi-link complex dynamic network of the power system are the generators or loads in the power network.

[0029] The embodiment of the present invention provides a method for identifying a complex dynamic network topology of a power system with time delay and multi-links based on improved compressed sensing, comprising:

[0030] S1, real-time collection and determination of whether the range of the lth state component of the network node i is lower than a preset range;

[0031] S2, if yes, trigger the random pulse and return to S1, otherwise determine whether the current acquisition duration reaches the preset duration, if yes, enter S4, otherwise enter S3; wherein the random pulse is a random pulse applied to each node, with the number and time of each node as the random seed;

[0032] S3, calculate the similarity E between the state vectors of all nodes in the network at the current sampling time and the state vectors at the previous sampling time, if E is greater than the corresponding threshold, trigger the random pulse and return to S1, otherwise directly return to S1.

[0033] The compressed sensing method requires sampling of enough linearly independent data, but synchronization is widely present in complex dynamic networks. Once internal synchronization occurs in a complex network, the sampled data will be linearly correlated, and accurate topological information cannot be restored. To this end, the state information of each node in the network is first sampled, and the range of each state component is calculated. The specific calculation method is as follows:

[0034]

[0035] where x ilrepresents the lth component of the state vector of node i. When a component converges to a smaller interval, no other judgment is required and a pulse control is immediately triggered to break the internal synchronization of the network. The pulse signal set to node i at time t is represented by h i (i, t), represents a random pulse generated with i, t as a random seed. When the signal is applied to the network (3a), the dynamic expression of the network is shown in (3b). When the above control is not triggered, the cosine similarity between the node state vector at the sampling moment and the node state vector at the previous sampling moment is calculated. The calculation method is as follows:

[0036]

[0037] Considering the indicator E, when the calculated result E is greater than a certain threshold, a pulse control is triggered; if the above two judgments do not trigger the control, resampling is performed and the algorithm is executed again.

[0038] like Figure 2 As shown, in step S1, the state data of the network node is sampled, and the range of each component of the node state at the sampling time is calculated. If the range of a certain component is lower than the set threshold value, then in step S2, a random pulse drive is triggered, wherein the random signal applied to each node uses the number and time of the node as a random seed, thereby ensuring that the drive signal of each node is different; otherwise, it is determined whether the current acquisition time reaches the preset time, if so, enter S3, otherwise return to S1;

[0039] In step S3, the similarity E between the state vector of all nodes in the network at the current sampling time and the state vector at the previous sampling time is calculated, and the comparison index E is compared to see whether it exceeds the threshold. If it exceeds the threshold, the random pulse drive is triggered, and the random signal applied to each node also uses the node number and sampling time as the random seed; if it does not exceed the threshold, the random pulse drive will not be triggered, and the next round of node state sampling will be performed again.

[0040] S4, collect the state quantity of network node i within a preset time, and perform data cleaning to delete the state quantity generated by node i under the action of the random pulse; use the cleaned data to construct the sensor matrix Φ and the measurement signal matrix y to establish a compressed sensing mathematical model The topological signal matrix T of the network is obtained by solving and calculating it; wherein the component of the topological signal matrix T is x, and the component of the measurement signal matrix y is u.

[0041] It is understandable that if no random pulse is triggered in steps S2-S3, no data cleaning is required in step S4, and the sensor matrix Φ and the measurement signal matrix y are directly constructed based on the state quantity of the network node i collected within the preset time.

[0042] For a time-delay multi-link complex network with dynamics as shown in equation (3a), after modeling model transformation, the expression shown in equation (4) is obtained, where X 1:M is the sensing matrix, is the sampling signal, is the topological signal that needs to be solved.

[0043]

[0044] For a time-delay multi-link complex network whose dynamics is shown in equation (5), after modeling transformation, the expression shown in equation (6) is obtained, where is the sensing matrix, is the sampling signal, is the topological signal that needs to be solved.

[0045]

[0046] like Figure 3 As shown, in step S4, the network node state within a certain period of time is sampled, and the external triggering situation at the sampling moment is obtained; combined with the external driving situation, the data disturbed by the external signal (i.e., random pulse) is cleaned, that is, the dynamic data generated by the external disturbance imposed on the network node state during the evolution process is deleted, and then the cleaned data is used to construct the sensor matrix and measurement signal, and the compressed sensing mathematical model is obtained, and then solved.

[0047] For the network shown in equation (3a), the sensing matrix is ​​X 1:M :=[X(t-τ (1) ); X(t-τ (2) );…;X(t-τ (M) )], the measurement signal matrix is For the network shown in equation (5), the sensing matrix is The measurement signal matrix is

[0048] Assuming the sensing matrix as Φ, the component of the topological signal matrix T as x, and the component of the measurement signal matrix y as u, the topological identification problem of multi-link complex networks is transformed into the following zero-norm minimization problem, namely the compressed sensing mathematical model:

[0049]

[0050] Since x is a sparse signal, u can be regarded as a linear combination of some column vectors in the matrix Φ. Therefore, solving the above problem is transformed into the problem of finding the support set of signal u from the column vectors of the matrix Φ.

[0051] By solving the above compressed sensing mathematical model, the topological signal matrix T of the network can be obtained.

[0052] The above minimization problem can be solved by existing methods, for example, relaxing the 0-norm minimization problem to a 1-norm minimization problem. However, considering that when using this solution method, the 0-norm minimization problem is converted into a 1-norm minimization problem with additional conditions and is difficult to verify, as a further optimization of the present invention, a 0-norm minimization solution method is designed based on a greedy algorithm, that is, the above problem is solved in the following manner:

[0053] A1, initialize the collection The residual signal v = y(k), calculate the inner product of each column vector of Φ and the residual signal, and take the first 2K with the largest inner product k The serial numbers of the column vectors of Φ are added to Υ in order;

[0054] A2, using the submatrix formed by the column vectors in Φ corresponding to the serial numbers in Y and y(k), calculate the estimated value of the kth row vector of the topological signal based on the batch least squares method And according to the formula Calculate the estimated value of the kth column vector of the measurement signal matrix y Determine the current residual signal Is the 2-norm of less than the corresponding threshold? If so, end; otherwise, proceed to A3; where K k for The sparsity of

[0055] A3, for set Υ, retain The first K in k The sequence number corresponding to the maximum value is removed from the set Y;

[0056] A4, calculate the inner product of each column vector of Φ and the current residual signal, and select the first K with the largest inner product k Add the sequence numbers of the column vectors to Y and return A2.

[0057] Preferably, the following method is used to obtain The sparsity K k :

[0058] B1, initialize the collection K k =1, and the sensor matrix Φ is normalized to obtain calculate The inner product of each column vector of with the kth column vector of y, and sort by the size of the inner product After sorting the column vectors in descending order, The column vector numbers are added

[0059] B2, judged by The first K in k The matrix consisting of column vectors corresponding to the serial numbers Is it satisfied? Where y(k) is the kth column vector of y;

[0060] B3, if yes, then update The sparsity is K k , otherwise let K k =K k +1, return to B2.

[0061] Preferably, Where Γ=diag(||Φ1||2 -1 ,||Φ2||2 -1 ,…,||Φ n ||2 -1 ), n is the length of the restored topology signal.

[0062] Specifically, Figure 4 As shown, the process of solving the network topology signal is as follows:

[0063] (1) Data initialization: Obtain the measurement signal matrix y and the sensor matrix Φ, and determine the length n of the topology signal to be restored based on the scale of the network and the state dimension of each node.

[0064] (2) Unitized sensor matrix.

[0065] Calculate the normalized matrix Γ = diag(||Φ1||2 -1 ,||Φ2||2 -1 ,…,||Φ n ||2 -1 ), normalize the sensor matrix

[0066] (3) Estimate the sparsity of the topological signal.

[0067] For each topological signal that needs to be restored, that is, the kth row vector of the topological signal matrix, denoted as T(k), its sparsity is K k , perform the following steps:

[0068] 1) Initialize the collection Calculating the Matrix The inner product of each column vector of and the kth column vector in the measurement signal matrix y is calculated, and the inner product is calculated according to the size of the inner product. Sort the column vectors in descending order.

[0069] 2) According to the order in 1), correspond the inner product results to The column vector of indices to add to the set In the example, we initialize the topological signal sparsity, i.e. K k is 1.

[0070] 3) Check the sequence number formed by the set Submatrix of Is it satisfied? If satisfied, update the sparsity to K k , go to step (4); otherwise, let K=K+1 and repeat step (3).

[0071] (4) Restore the topological signal. For each topological signal that needs to be restored, define the topological signal matrix as T, the kth row vector of the topological signal matrix as T(k), and perform the following steps:

[0072] 1) Initialize the set Υ to an empty set and define is the estimated value of T(k), defined using When T(k) is substituted, the estimated value of the kth column vector of the measurement signal matrix y is The residual signal is defined as Initialize the residual signal to the measured signal, that is, set v = y(k). Calculate the inner product of each column vector of the matrix Φ and the residual signal, and take the first 2K vectors with the largest inner product as k The serial numbers of the column vectors of Φ are added to the set Y.

[0073] 2) Using the submatrix formed by the column vectors in Φ corresponding to the serial numbers in the set Y, the estimated value of the kth row vector of the topological signal is calculated based on the batch least squares method renew Calculate the new residual signal Check whether the 2-norm of the residual signal is less than an acceptable threshold; if acceptable, end step 4 and obtain a topology estimation signal. If not, continue to step 3).

[0074] 3) For set Y, retain the first K estimated signals k The sequence numbers corresponding to the maximum values ​​are removed from the set Y.

[0075] 4) Calculate the inner product of each column vector of the matrix Φ and the current residual signal. k Add the serial number of the column vector to the set Y and repeat step 2).

[0076] The method provided by the present invention is further illustrated by taking a specific example as follows.

[0077] In this example, a time-delay multi-link complex network with dynamics as shown in equation (5) is used, M = 2, N = 10, Figure 5(a) shows the node dynamics evolution of a multi-link complex dynamic network as shown in the following dynamic equation:

[0078]

[0079] from Figure 5 As can be seen from (a) in the figure, the node states quickly tend to be internally synchronized. At this point, the traditional topology recognition method based on the adaptive synchronization method needs to construct an auxiliary network of the same size and apply continuous control signals to each node, while the existing compressed sensing method cannot recognize it because it cannot sample enough linearly independent data. Figure 5 (b) shows the evolution of the network node state after applying the driving strategy of the present invention. Figure 5 (c) in the figure shows the triggering of the external driving strategy over time. It can be seen that the present invention successfully breaks the internal synchronization of the network with a smaller number of external driving times. Compared with the adaptive synchronization method, it reduces the control cost and the disturbance to the network to be identified.

[0080] The method provided by the present invention is compared with the existing method based on adaptive control and compressed sensing method for topological identification of complex dynamic networks. As shown in Table 1, when identifying networks with different nodes, the 0-norm minimization solution method based on the greedy algorithm provided by the present invention requires fewer sampling times than the relaxation method that uses 1-norm minimization instead of 0-norm minimization in previous studies. It can be seen that the method provided by the embodiment of the present invention effectively reduces the number of sampling times, especially on large-scale networks.

[0081] Table 1 Comparison of the number of sampling times required by the proposed method and the 1-norm relaxation solution to completely identify the topological structure

[0082]

[0083] An embodiment of the present invention provides an electronic device, including: a computer-readable storage medium and a processor;

[0084] The computer-readable storage medium is used to store executable instructions;

[0085] The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the method described in any of the above embodiments.

[0086] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the method described in any of the above embodiments.

[0087] An embodiment of the present invention provides a computer program product, including a computer program or instructions, and when the computer program or instructions are executed by a processor, the method described in any of the above embodiments is implemented.

[0088] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for identifying time-delay multi-link complex dynamic network topology in power system based on improved compressed sensing, characterized in that: include: S1, real-time collection and determination of whether the range of the lth state component of the network node i is lower than a preset range; S2, if yes, trigger the random pulse and return to S1, otherwise determine whether the current acquisition duration reaches the preset duration, if yes, enter S4, otherwise enter S3; wherein the random pulse applied to each node uses the node number and time as the random seed; S3, calculating the similarity E between the state vectors of all nodes in the network at the current sampling time and the state vectors at the previous sampling time, if E is greater than the corresponding threshold, triggering the random pulse and returning to S1, otherwise directly returning to S1; S4, collect the state quantity of network node i within a preset time, and perform data cleaning to delete the state quantity generated by node i under the action of the random pulse; use the cleaned data to construct the sensor matrix Φ and the measurement signal matrix y to establish a compressed sensing mathematical model The topological signal matrix T of the network is obtained by solving and calculating it; wherein the component of the topological signal matrix T is x, and the component of the measurement signal matrix y is u.

2. The method according to claim 1, characterized in that If the dynamics formula of the network is: Then its sensing matrix Φ=[X(t-τ (1) ); X(t-τ (2) );…;X(t-τ (M) )], measurement signal matrix If the dynamics formula of the network is: Then its sensing matrix Φ=[X(t-τ (1) );…;X(t-τ (M) ), X(t)], measurement signal matrix Among them, x i is the state vector of node i, f i (x i ) is the self-dynamic function of node i, x j (t-τ (p) ) is the node j at t-τ (p) The state vector at time τ (p) is the time delay of the p-th sub-link, X(t-τ (M) ) is the sum of all nodes in the network at t-τ (M) The vector of state vectors at time τ (M) is the time delay of the Mth sub-link, M is the number of network time delay links, N is the number of nodes in the network, C P ,Ξ (p) are the intra-layer coupling strength and intra-coupling matrix of the th sub-link in the network, is the connection weight of node i,j on the pth link.

3. The method according to claim 1 or 2, characterized in that Step S4 includes: A1, initialize the collection The residual signal v = y(k), calculate the inner product of each column vector of Φ and the residual signal, and take the first 2K with the largest inner product k The serial numbers of the column vectors of Φ are added to Υ in order; A2, using the submatrix formed by the column vectors in Φ corresponding to the serial numbers in Y and y(k), calculate the estimated value of the kth row vector of the topological signal based on the batch least squares method And according to the formula Calculate the estimated value of the kth column vector of the measurement signal matrix y Determine the current residual signal Is the 2-norm of less than the corresponding threshold? If so, end; otherwise, proceed to A3; where K k for The sparsity of A3, for set Υ, retain The first K in k The sequence number corresponding to the maximum value is removed from the set Y; A4, calculate the inner product of each column vector of Φ and the current residual signal, and select the first K with the largest inner product k Add the sequence numbers of the column vectors to Y and return A2.

4. The method according to claim 3, characterized in that Obtain it as follows The sparsity K k : B1, initialize the collection K k =1, and the sensor matrix Φ is normalized to obtain calculate The inner product of each column vector of with the kth column vector of y, and sort by the size of the inner product After sorting the column vectors in descending order, The column vector numbers are added B2, judged by The first K in k The matrix consisting of column vectors corresponding to the serial numbers Is it satisfied? Where y(k) is the kth column vector of y; B3, if yes, then update The sparsity is K k , otherwise let K k =K k +1, return to B2.

5. The method according to claim 4, characterized in that Where Γ=diag(||Φ1||2 -1 ,||Φ2||2 -1 ,…,||Φ n ||2 -1 ), n is the length of the restored topology signal.

6. An electronic device, characterized in that: include: A computer readable storage medium and a processor; The computer-readable storage medium is used to store executable instructions; The processor is used to read the executable instructions stored in the computer-readable storage medium and execute the method according to any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to execute the method according to any one of claims 1 to 5.

8. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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