Backbone edge network scale compression method and system based on weighted graph stratified sampling

Abstract

The invention relates to a backbone edge network scale compression method and system based on weighted graph stratified sampling, and belongs to the field of network scale compression. According to the method, a topological structure and space flow of a weighted graph modeling test bed are adopted, and a backbone network and an edge network are segmented by designing a heuristic backbone edge node maximum weight segmentation classification technology; by calculating the shortest path of data flow propagation of edge network sampling sub-graph node pairs, the topology sampling and edge weight compression technology of the backbone network is designed, and balanced distribution of traffic of the backbone network and the edge network under the condition of scale compression is achieved. Technical support is provided for realistic deployment of the topological structure and the space flow of the large-scale compression simulation test bed.

Description

Technical Field

[0001] The present invention belongs to the field of network scale compression, and in particular relates to a backbone edge network scale compression method and system based on weighted graph layered sampling. Background Art

[0002] The R&D, testing, and evaluation of network technologies require the construction of simulated network testbeds. Compressing the size of the simulated network helps reduce testing costs and improve resource utilization efficiency. Spatial traffic can be modeled using edge weights to represent the average value of probe traffic over long time intervals (typically measured in days or weeks). Compared to spatiotemporal traffic, which typically has probe time intervals measured in seconds or minutes, spatial traffic is more suitable for simulation resource allocation and macro-deployment.

[0003] Existing testbed compression technologies focus on scaling specific topologies (such as autonomous system-level Internet topologies), ignoring the topological connections between other types of nodes, such as routers. Furthermore, they lack research on balanced deployment of spatial traffic across backbone and edge networks during the scaling process. In other words, existing weighted graph sampling methods can scale testbed topologies and spatial traffic, but they suffer from issues such as over-preferring high-weight edges and their adjacent nodes, and uneven edge weight distribution between backbone and edge networks. Summary of the Invention

[0004] The purpose of the present invention is to overcome the problems existing in the existing weighted graph sampling methods, such as excessive preference for high-weight edges and their adjacent nodes, and unbalanced edge weight distribution between the backbone network and the edge network. Therefore, a backbone edge network scale compression method and system based on weighted graph layered sampling is provided. The weighted graph is used to model the topological structure and spatial traffic of the test bed, and a heuristic maximum weight cut classification technology for the backbone edge nodes is designed to separate the backbone network and the edge network. By calculating the shortest path for data flow propagation of the edge network sampling subgraph node pairs, the topological sampling and edge weight compression technology of the backbone network is designed to achieve balanced distribution of backbone network and edge network traffic under scale compression conditions. The present invention provides technical support for the realistic deployment of the topological structure and spatial traffic of the scale compression simulation test bed.

[0005] To achieve the above objectives, the technical solution of the present invention is: a backbone edge network scale compression method based on weighted graph layered sampling, comprising:

[0006] Design a heuristic maximum weight cut classification method for backbone edge nodes to separate the backbone network from the edge network;

[0007] By calculating the shortest path for data flow propagation between pairs of sampling subgraph nodes in the edge network, a topology sampling and edge weight compression method for the backbone network is designed to achieve balanced distribution of traffic between the backbone network and the edge network under scale compression conditions.

[0008] Furthermore, the method comprises the following steps:

[0009] S10. Detect the topological structure and spatial flow of the original large-scale network, and use the weighted graph G = (V, E, W) to represent it, where V represents the node set, E represents the edge set, W(u, v) represents the weight of the edge (u, v) ∈ E, the nodes in the node set represent network devices, and the edges (u, v) in the edge set represent the data communication links between nodes u and v; let the expected number of edge nodes in the scale-compressed network be Go to S20;

[0010] S20, using the heuristic maximum weight cut classification method of backbone edge nodes to separate the backbone network and edge network:

[0011] S30, the backbone network and edge network divided by S20, set V b ={v1,v2,…,v I} is the backbone node set, V c =v I+1 ,v I+2 ,…,v n} is the edge node set; the expected number of edge nodes of the network is compressed according to the scale input by the user Using the weighted random walk strategy, in the edge node set V c Extracted by The set of child nodes consisting of nodes

[0012] S40. By calculating the shortest path between edge network sampling nodes, a topology sampling and edge weight compression method for the backbone network is designed to achieve balanced distribution of backbone network and edge network traffic under scale compression conditions.

[0013] S50, output scale-compressed weighted subgraph in Represents a node set, represents the edge set, Represents an edge The weight of .

[0014] Furthermore, S20 is specifically implemented as follows:

[0015] S201. For each node v∈V, calculate the node strength Where N(v) = {u∈V|(u,v)∈E} represents the set of nodes adjacent to v; the nodes v∈V are arranged in descending order according to S(v) as v1, v2, ..., v n , where n is the number of all nodes in V; let the constant in represents the smallest integer not less than x, and then goes to S202;

[0016] S202. Initialize the step size Initialize the variable i ← 0, initialize the upper bound value U ← M, and initialize two edge weight statistics W e ← 0 and Go to S203;

[0017] S203. If i ≥ U, then set I = i, set v1, v2, …, v I as the backbone nodes, and set v I+1 , v I+2 , …, v n as the edge nodes, and go to S30;

[0018] If i < U and then update U ← i, i ← i - L, and go to S204;

[0019] If i < U and then update and go to S204;

[0020] S204. Calculate the sum of two edge weights where N(v j ) = {u ∈ V|(u, v j ) ∈ E} represents the set of nodes adjacent to the node v j :

[0021]

[0022]

[0023] Update Update i ← i + L, and go to S203.

[0024] Furthermore, S30 is specifically implemented as follows:

[0025] S301. Initialize the edge network sampling node set where represents the empty set, and define to represent the number of nodes included, and go to S302;

[0026] S302. Uniformly draw a node v from V c , and update and go to S303;

[0027] S303. If and then simulate the weighted random walk process of the traveler: Set the current state of the traveler to be located at the node v, and calculate the probability distribution D for the traveler to walk to the next node u ∈ V c :

[0028]

[0029] Randomly select a node u with probability distribution D and update And update v←u, indicating that the location node v of the traveler's current state is updated to node u, and go to S303;

[0030] like and Then go to S302; if Then go to S40.

[0031] Furthermore, S40 is specifically implemented as follows:

[0032] S401: Initialize the backbone network sampling node set Construct an unweighted subgraph of the original graph G = (V, E, W) in Represents a node set, Represents an edge set, and then goes to S402;

[0033] S402: Sampling node sets on edge networks Any node pair in

[0034] Calculate u,v in the unweighted subgraph A shortest path p u,v , where p u,v It represents the shortest path among all the paths with nodes u and v as the two endpoints in the graph G. The length of the path is defined as the number of edges contained in the path. Define V(p u,v ) is p u,v The set of all nodes in ; let the node set renew

[0035] Completed After all the node pairs are in the network, the backbone network sampling node set is output Go to S403;

[0036] S403: Sampling node set from backbone network and edge network sampling node set Generate a scale-compressed weighted subgraph of the original weighted graph G = (V, E, W) in Represents a node set, represents the edge set, Represents an edge The weight on ; go to S50.

[0037] Furthermore, in S402 , if the number of shortest paths with nodes u and v as two endpoints is greater than 1, then any one of these shortest paths is selected as the shortest path among all paths with nodes u and v as two endpoints.

[0038] Furthermore, in S403, The calculation method is as follows:

[0039] Set the edges Decompose into subsets and subsets

[0040] For each edge calculate

[0041]

[0042] For each edge calculate

[0043]

[0044] Among them, the statistics and The calculation method is as follows:

[0045] Let G unweighted =(V,E) is the unweighted graph obtained by removing the edge weights in the weighted graph G=(V,E,W),

[0046] Let S(V c ,G unweighted ) represents the node set V c All node pairs in G unweighted The set of shortest paths where each node pair x,y∈V c Corresponding unweighted graph G unweighted A shortest path with nodes x and y as two endpoints;

[0047] set up Represents a set of sampling nodes All pairs of nodes in the unweighted graph The set of shortest paths where each node Corresponding unweighted graph A shortest path with nodes x and y as two endpoints;

[0048] Statistics Representing a collection The number of shortest paths passing through edge (u,v);

[0049] Statistics Denotes the set S(V c ,G unweighted) is the number of shortest paths passing through the edge (u,v).

[0050] The present invention also provides a backbone edge network scale compression system based on weighted graph layered sampling, including a data acquisition module, a compression calculation module and a test bed resource deployment module, wherein:

[0051] The data collection module collects the topological connection relationship of network devices and the spatial flow of each data communication link, where the spatial flow is the average value of the detection flow in the time interval of a day or a week. A simple undirected graph is used to represent the topological connection relationship, where nodes represent network devices and edges represent the connection relationship between network devices. Edge weights are used to represent the spatial flow on the edges. A weighted graph G = (V, E, W) is generated, where V represents the node set, E represents the edge set, and W(u, v) represents the weight of the edge (u, v) ∈ E. The nodes in the node set represent network devices, and the edges (u, v) in the edge set represent the data communication links between nodes u and v.

[0052] The compression calculation module uses the weighted graph G and the expected number of edge nodes in the scale compression network as As input, use any of the above method steps to output a scale-compressed weighted subgraph

[0053] Testbed resource deployment module, based on scale-compressed weighted subgraphs Call network devices from the resource pool to generate small-scale network topology connection relationships, and Allocate process generation resources deployed on network devices so that the mean flow of data communication links is close to the given edge weight.

[0054] Furthermore, network devices include routers, firewalls, and computing devices.

[0055] The present invention also provides a computer-readable storage medium on which computer program instructions that can be executed by a processor are stored. When the processor executes the computer program instructions, any of the method steps described above can be implemented.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] The network scale compression method and system of the present invention comprehensively consider the topology structure and spatial traffic, and can be applied to the scale compression of communication network topology structure and spatial traffic outside the autonomous system-level Internet topology structure (with the autonomous system domain of the Internet as the node); compared with weighted graph sampling methods such as weighted independence sampling (Weighted Independence Sampling) and weighted random walk (Weighted Random Walk), this system software method solves the problems of excessive preference for high-weight edges and their adjacent nodes in the scale compression network, and unbalanced edge weight distribution between the backbone network and the edge network, and provides support for the realistic deployment of simulation test bed topology structure and spatial traffic under scale compression conditions (reducing test bed construction cost and test running time) (improving test accuracy).

[0058] The method and system of the present invention are applicable to a wide range of communication network topologies and spatial traffic scale compression, and solve problems such as excessive preference for high-weight edges and their adjacent nodes in scale compression networks, and unbalanced edge weight distribution between backbone networks and edge networks, thereby improving the fidelity of simulation test bed topologies and spatial traffic deployment under scale compression conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a flow chart of the test bed topology and spatial traffic scale compression method of the present invention;

[0060] Figure 2 Schematic diagram of the structure of the network-scale compression system of the present invention. DETAILED DESCRIPTION

[0061] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings.

[0062] The present invention provides a backbone edge network scale compression method based on weighted graph layered sampling, comprising:

[0063] Design a heuristic maximum weight cut classification method for backbone edge nodes to separate the backbone network from the edge network;

[0064] By calculating the shortest path for data flow propagation between pairs of sampling subgraph nodes in the edge network, a topology sampling and edge weight compression method for the backbone network is designed to achieve balanced distribution of traffic between the backbone network and the edge network under scale compression conditions.

[0065] The following is a specific implementation process of the present invention.

[0066] like Figure 1 As shown, the present invention provides a backbone edge network scale compression method based on weighted graph layered sampling, which is divided into five steps: S10, S20, S30, S40 and S50:

[0067] S10. Detect the topology structure and spatial traffic of the original large-scale network, which is characterized by a weighted graph G = (V, E, W), where V represents the set of nodes (nodes represent network devices such as routers), E represents the set of edges (edges represent data communication links between nodes, and the direction of the edges is not considered), and W(u, v) represents the weight of the edge (u, v) ∈ E (the weight represents the average value of the detected traffic on the edge (u, v) in a time interval of days or weeks); Let the expected number of edge nodes in the scale-compressed network be (input by the user), go to S20;

[0068] S20. Adopt the maximum weight cut classification technology of heuristic backbone edge nodes to split the backbone network and the edge network:

[0069] S201. For each node v ∈ V, calculate the node strength S(v) = ∑ u∈N(v) W(u, v), where N(v) = {u ∈ V|(u, v) ∈ E} represents the set of nodes adjacent to v; Arrange the nodes v ∈ V in descending order of S(v) as v1, v2,..., v n , where n is the total number of nodes in V; Let the constant where represents the smallest integer not less than x, go to S202;

[0070] S202. Initialize the step size Initialize the variable i ← 0, initialize the upper bound value U ← M, initialize two edge weight statistics W e ← 0 and go to S203;

[0071] S203. If i ≥ U, then let I = i, set v1, v�, v I as backbone nodes, set v I+1 , v I+2 ,..., v n as edge nodes, go to S30;

[0072] If i < U and then update U ← i, i ← i - L, go to S204;

[0073] If i < U and then update go to S204;

[0074] S204. Calculate the two edge weights and where N(v j ) = {u ∈ V|(u, v j ) ∈ E} represents the set of nodes adjacent to the node v j :

[0075]

[0076]

[0077] renew Update i←i+L, and go to S203; (Note: when i=0, let {v1,v2,…,v i} is an empty set)

[0078] S30, set V b ={v1,v2,…,v I} is the backbone node set, V c ={v I+1 ,v I+2 ,…,v n} is the edge node set; the expected number of edge nodes of the network is compressed according to the scale input by the user Using the weighted random walk strategy, in the edge node set V c Extracted by The set of child nodes consisting of nodes (Unlike the existing weighted random walk strategy, this invention only applies this strategy to node sampling in the edge network after segmentation, and does not apply it to node sampling in the backbone network)

[0079] S301: Initialize edge network sampling node set in represents the empty set, and defines express The number of nodes included, go to S302;

[0080] S302, from V c Uniformly extract a node v from the , update Go to S303;

[0081] S303, if and Then simulate the weighted random walk process of the traveler: suppose the current state of the traveler is located at node v, calculate the traveler's walk to the next node u∈V c The probability distribution D is:

[0082]

[0083] Randomly select a node u with probability distribution D and update And update v←u (the location node v representing the current state of the traveler is updated to node u) and go to S303;

[0084] like and Then go to S302; if Then go to S40;

[0085] (where N(v) = {u∈V|(u,v)∈E} represents the set of nodes adjacent to node v)

[0086] S40. Design the backbone network's topology sampling and edge weight compression technology by calculating the shortest path between edge network sampling nodes:

[0087] S401: Initialize the backbone network sampling node set Construct an unweighted subgraph of the original graph G = (V, E, W) in represents a node set, Represents an edge set, and then goes to S402;

[0088] S402: Sampling node sets on edge networks Any node pair in

[0089] Calculate u,v in the unweighted subgraph A shortest path p u,v , where p u,v It represents the shortest path among all the paths with nodes u and v as the two endpoints in the graph G. The length of the path is defined as the number of edges contained in the path. Define V(p u,v ) is p u,v The set of all nodes in ; let the node set renew

[0090] Completed After all the node pairs are in the network, the backbone network sampling node set is output Go to S403;

[0091] (Note: If the number of shortest paths with nodes u and v as two endpoints is greater than 1, then any one of these shortest paths is selected)

[0092] S403, by sampling node set and Generate a scale-compressed weighted subgraph of the original weighted graph G = (V, E, W) in represents a node set, represents the edge set, Represents an edge The weight on ; The calculation method is as follows:

[0093] Set the edges Decompose into subsets and subsets

[0094] For each edge calculate

[0095]

[0096] For each edge calculate

[0097]

[0098] The statistics and The calculation method is as follows:

[0099] Let G unweighted =(V,E) is the unweighted graph obtained by removing the edge weights in the weighted graph G=(V,E,W),

[0100] Let S(V c ,G unweighted ) represents the node set V c All node pairs in G unweighted The set of shortest paths where each node pair x,y∈V c Corresponding unweighted graph G unweighted A shortest path with nodes x and y as two endpoints;

[0101] set up Represents a set of sampling nodes All pairs of nodes in the unweighted graph The set of shortest paths where each node Corresponding unweighted graph A shortest path with nodes x and y as two endpoints;

[0102] Statistics Representing a collection The number of shortest paths passing through edge (u,v);

[0103] Statistics Denotes the set S(V c ,G unweighted ) the number of shortest paths passing through edge (u,v);

[0104] Turn S50;

[0105] S50, output scale-compressed weighted subgraph

[0106] like Figure 2 As shown, the present invention also provides a backbone edge network scale compression system based on weighted graph layered sampling, including a data acquisition module 100, a compression calculation module 200 and a test bed resource deployment module 300.

[0107] Data collection module 100: collects the topological connectivity of network devices (e.g., routers, firewalls, computing devices, etc.) and the spatial flow of each data transmission link, where the spatial flow is the average of the detected flow over a daily or weekly time interval. A simple undirected graph is used to represent the topological connectivity, where nodes represent network devices, edges represent the connections between network devices, and edge weights represent the spatial flow on edges, generating a weighted graph G = (V, E, W).

[0108] Compression calculation module 200: The expected number of edge nodes of the weighted graph G and the scale compression network is As input, a network-scale compression software method (such as Figure 1 The method of the present invention described above) outputs a scale-compressed weighted subgraph

[0109] Testbed resource deployment module 300: weighted subgraphs based on scale compression Call network devices from the resource pool to generate small-scale network topology connection relationships, and Allocate process generation resources deployed on network devices so that the mean flow of data transmission links (edges) is close to the given edge weight.

[0110] The present invention also provides a computer-readable storage medium on which computer program instructions that can be executed by a processor are stored. When the processor executes the computer program instructions, any of the method steps described above can be implemented.

[0111] The above are preferred embodiments of the present invention. Any changes made according to the technical solution of the present invention, as long as the resulting functions and effects do not exceed the scope of the technical solution of the present invention, shall fall within the scope of protection of the present invention.

Claims

1. A backbone edge network scale compression method based on weighted graph layered sampling, characterized in that: include: Design a heuristic maximum weight cut classification method for backbone edge nodes to separate the backbone network from the edge network; By calculating the shortest path for data flow propagation between pairs of sampling subgraph nodes in the edge network, a topology sampling and edge weight compression method for the backbone network is designed to achieve balanced distribution of traffic between the backbone network and the edge network under scale compression conditions.

2. The backbone edge network scale compression method based on weighted graph layered sampling according to claim 1 is characterized in that: The method comprises the following steps: S10. Detect the topological structure and spatial flow of the original large-scale network, and use the weighted graph G = (V, E, W) to represent it, where V represents the node set, E represents the edge set, W(u, v) represents the weight of the edge (u, v) ∈ E, the nodes in the node set represent network devices, and the edges (u, v) in the edge set represent the data communication links between nodes u and v; let the expected number of edge nodes in the scale-compressed network be Go to S20; S20, using the heuristic maximum weight cut classification method of backbone edge nodes to separate the backbone network and edge network: S30, the backbone network and edge network divided by S20, set V b ={v1,v2,…,v I } is the backbone node set, V c =v I+1 ,v I+2 ,…,v n } is the edge node set; the expected number of edge nodes of the network is compressed according to the scale input by the user Using the weighted random walk strategy, in the edge node set V c Extracted by The set of child nodes consisting of nodes S40. By calculating the shortest path between edge network sampling nodes, a topology sampling and edge weight compression method for the backbone network is designed to achieve balanced distribution of backbone network and edge network traffic under scale compression conditions. S50, output scale-compressed weighted subgraph in Represents a node set, represents the edge set, Represents an edge The weight of .

3. The backbone edge network scale compression method based on weighted graph layered sampling according to claim 2 is characterized in that: S20, specifically implemented as follows: S201. For each node v∈V, calculate the node strength S(v)=∑ u∈N(v) W(u,v), where N(v) = {u∈V|(u,v)∈E} represents the set of nodes adjacent to v; arrange the nodes v∈V in descending order according to S(v) as v1, v2, ..., v n , where n is the number of all nodes in V; let the constant in represents the smallest integer not less than x, and then goes to S202; S202, initialization step length Initialize the variable i←0, initialize the upper bound value U←M, and initialize the two edge weight statistics W e ←0 and Go to S203; S203. If i≥U, let I=i, and let v1,v2,…,v I is the backbone node, and v I+1 ,v I+2 ,…,v n For edge nodes, go to S30; If i < U and then update U ← i, i ← i - L in sequence, go to S204; If i < U and then update Go to S204; S204, calculate the sum of two edge weights Where N(v j )={u∈V|(u,v j )∈E} represents the node v j Adjacent node sets: renew Update i←i+L and go to S203.

4. The backbone edge network scale compression method based on weighted graph layered sampling according to claim 3 is characterized in that: S30, specifically implemented as follows: S301: Initialize edge network sampling node set in represents the empty set, and defines express The number of nodes included, go to S302; S302, from V c Uniformly extract a node v from the , update Go to S303; S303, if and Then simulate the weighted random walk process of the traveler: suppose the current state of the traveler is located at node v, calculate the traveler's walk to the next node u∈V c The probability distribution D is: Randomly select a node u with probability distribution D and update And update v←u, indicating that the location node v of the traveler's current state is updated to node u, and go to S303; like and Then go to S302; if Then go to S40.

5. The backbone edge network scale compression method based on weighted graph layered sampling according to claim 4 is characterized in that: S40, specifically implemented as follows: S401: Initialize the backbone network sampling node set Construct an unweighted subgraph of the original graph G = (V, E, W) in Represents a node set, Represents an edge set, and then goes to S402; S402: Sampling node sets on edge networks Any node pair in Calculate u,v in the unweighted subgraph A shortest path p u,v , where p u,v It represents the shortest path among all the paths with nodes u and v as the two endpoints in the graph G. The length of the path is defined as the number of edges contained in the path. Define V(p u,v ) is p u,v The set of all nodes in ; let the node set renew Completed After all the node pairs are in the network, the backbone network sampling node set is output Go to S403; S403: Sampling node set from backbone network and edge network sampling node set Generate a scale-compressed weighted subgraph of the original weighted graph G = (V, E, W) in Represents a node set, represents the edge set, Represents an edge The weight on ; go to S50.

6. The backbone edge network scale compression method based on weighted graph layered sampling according to claim 5 is characterized in that: In S402 , if the number of shortest paths with nodes u and v as two endpoints is greater than 1, then any one of these shortest paths is selected as the shortest path among all paths with nodes u and v as two endpoints.

7. The backbone edge network scale compression method based on weighted graph layered sampling according to claim 5 is characterized in that: In S403, The calculation method is as follows: Set the edges Decompose into subsets and subsets For each edge calculate For each edge calculate Among them, the statistics and The calculation method is as follows: Let G unweighted =(V,E) is the unweighted graph obtained by removing the edge weights in the weighted graph G=(V,E,W), Let S(V c ,G unweighted ) represents the node set V c All node pairs in G unweighted The set of shortest paths where each node pair x,y∈V c Corresponding unweighted graph G unweighted A shortest path with nodes x and y as two endpoints; set up Represents a set of sampling nodes All pairs of nodes in the unweighted graph The set of shortest paths where each node Corresponding unweighted graph A shortest path with nodes x and y as two endpoints; Statistics Representing a collection The number of shortest paths passing through edge (u,v); Statistics Denotes the set S(V c ,G unweighted ) is the number of shortest paths passing through the edge (u,v).

8. A backbone edge network scale compression system based on weighted graph layered sampling, characterized in that: It includes data acquisition module, compression calculation module and test bed resource deployment module, among which, The data collection module collects the topological connection relationship of network devices and the spatial flow of each data communication link, where the spatial flow is the average value of the detection flow in the time interval of a day or a week. A simple undirected graph is used to represent the topological connection relationship, where nodes represent network devices and edges represent the connection relationship between network devices. Edge weights are used to represent the spatial flow on the edges. A weighted graph G = (V, E, W) is generated, where V represents the node set, E represents the edge set, and W(u, v) represents the weight of the edge (u, v) ∈ E. The nodes in the node set represent network devices, and the edges (u, v) in the edge set represent the data communication links between nodes u and v. The compression calculation module uses the weighted graph G and the expected number of edge nodes in the scale compression network as As input, the method steps of any one of claims 1 to 7 are used to output a scale-compressed weighted subgraph Testbed resource deployment module, based on scale-compressed weighted subgraphs Call network devices from the resource pool to generate small-scale network topology connection relationships, and Allocate process generation resources deployed on network devices so that the mean flow of data communication links is close to the given edge weight.

9. The backbone edge network scale compression system based on weighted graph layered sampling according to claim 8, characterized in that: Network equipment includes routers, firewalls, and computing devices.

10. A computer-readable storage medium storing computer program instructions that can be executed by a processor, wherein when the processor executes the computer program instructions, the method steps according to any one of claims 1 to 7 can be implemented.

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