Network segmentation method and device, electronic equipment and storage medium

Abstract

The invention provides a network segmentation method and device, electronic equipment and a storage medium. The network segmentation method comprises the following steps: acquiring a target value of a multi-dimensional characteristic parameter of each network node in a network; clustering the network nodes based on the target values of the multi-dimensional characteristic parameters, and taking each cluster obtained by clustering as a network segment; and selecting a central node of the network segment from the network nodes contained in the network segment as a control node of the network segment. In the embodiment of the invention, each network segment can be independently controlled, the network segments do not influence each other, and when a certain node fails, only the network segment where the node is located needs to be processed without influencing other network segments, so that the maintenance of a large-scale network is converted into the maintenance of a small-scale network segment, and the maintenance efficiency is improved. The complexity of network management is reduced, and the flexibility of the network is improved.

Description

Technical Field

[0001] The present invention relates to the field of network processing technology, and in particular to a network segmentation method, device, electronic equipment and storage medium. Background Art

[0002] Source routing is an important routing technology in computer networks. It allows the sender of a data packet to specify the path that the data packet takes in the network, rather than relying on network devices (such as routers) to select the best path on their own.

[0003] In the source routing method, the source node has the ability to build its own routing path to other nodes in the network, eliminating the complex routing tasks of the intermediate nodes and reducing the network overhead. The source routing method solves the problems of repeated lookups in traditional routing and excessive forwarding table overhead, and avoids the increase in network forwarding load caused by frequent label operations in multi-label protocols. However, with the continuous increase in network scale, the routing stability of the source routing method is poor, and the global stability of the routing is greatly affected by each node. The failure of a node will cause the failure of the entire path. The network maintenance overhead is relatively large, the network management is weak, and the flexibility is poor. Summary of the invention

[0004] In view of the above problems, embodiments of the present invention are proposed to provide a network segmentation method, device, electronic device and storage medium that overcome the above problems or at least partially solve the above problems.

[0005] In a first aspect, an embodiment of the present invention discloses a network segmentation method, the method comprising:

[0006] Obtain target values ​​of multidimensional characteristic parameters of each network node in the network;

[0007] Clustering the network nodes based on the target value of the multidimensional feature parameter, and taking each cluster obtained by clustering as a network segment;

[0008] A central node of the network segment is selected from the network nodes included in the network segment as a control node of the network segment.

[0009] Optionally, the multi-dimensional characteristic parameters include multiple ones of hop count, delay, physical distance, energy consumption, and mobile frequency.

[0010] Optionally, obtaining the target value of the multidimensional characteristic parameter of each network node in the network includes: for each of the network nodes, obtaining the initial value of the multidimensional characteristic parameter of the network node; normalizing the initial value of the multidimensional characteristic parameter to obtain the target value of the multidimensional characteristic parameter.

[0011] Optionally, the initial values ​​of the multidimensional feature parameters are normalized to obtain target values ​​of the multidimensional feature parameters, including: for each of the feature parameters, calculating the average value and standard deviation of the initial values ​​of the feature parameters of all network nodes; and normalizing the initial values ​​of the feature parameters based on the average value and the standard deviation to obtain the target value of the feature parameter.

[0012] Optionally, selecting the central node of the network segment from the network nodes included in the network segment, including: obtaining a target value of a multidimensional characteristic parameter of the centroid of the network segment; for each network node included in the network segment, calculating the distance between the network node and the centroid based on the target value of the multidimensional characteristic parameter of the network node and the target value of the multidimensional characteristic parameter of the centroid; and selecting at least one network node with the smallest moving frequency from a plurality of network nodes with the smallest distance to the centroid as the central node of the network segment.

[0013] Optionally, based on the target value of the multidimensional feature parameters of the network node and the target value of the multidimensional feature parameters of the centroid, the distance between the network node and the centroid is calculated, including: obtaining the weight of each feature parameter; performing weighted distance calculation based on the target value of the multidimensional feature parameters of the network node and the target value of the multidimensional feature parameters of the centroid according to the weight of the feature parameter, to obtain the distance between the network node and the centroid.

[0014] Optionally, the multidimensional feature parameter includes movement frequency, and the movement frequency has the highest weight.

[0015] In a second aspect, an embodiment of the present invention discloses a network segmentation device, the device comprising:

[0016] An acquisition module, used to acquire target values ​​of multi-dimensional characteristic parameters of each network node in the network;

[0017] A clustering module, used for clustering the network nodes based on the target value of the multi-dimensional feature parameter, and taking each cluster obtained by clustering as a network segment;

[0018] The selection module is used to select the central node of the network segment from the network nodes included in the network segment as the control node of the network segment.

[0019] Optionally, the multi-dimensional characteristic parameters include multiple ones of hop count, delay, physical distance, energy consumption, and mobile frequency.

[0020] Optionally, the acquisition module includes: an initial value acquisition unit, used to acquire the initial value of the multidimensional feature parameter of the network node for each of the network nodes; and a normalization unit, used to normalize the initial value of the multidimensional feature parameter to obtain the target value of the multidimensional feature parameter.

[0021] Optionally, the normalization unit is specifically used to calculate the average value and standard deviation of the initial values ​​of the characteristic parameters of all network nodes for each characteristic parameter; normalize the initial values ​​of the characteristic parameters based on the average value and the standard deviation to obtain the target value of the characteristic parameter.

[0022] Optionally, the selection module includes: a target value acquisition unit, used to acquire the target value of the multidimensional characteristic parameters of the centroid of the network segment; a distance calculation unit, used to calculate the distance between the network node and the centroid for each network node included in the network segment based on the target value of the multidimensional characteristic parameters of the network node and the target value of the multidimensional characteristic parameters of the centroid; a node selection unit, used to select at least one network node with the smallest moving frequency from a plurality of network nodes with the smallest distance to the centroid, as the central node of the network segment.

[0023] Optionally, the distance calculation unit is specifically used to obtain the weight of each characteristic parameter; according to the weight of the characteristic parameter, a weighted distance calculation is performed based on the target value of the multidimensional characteristic parameter of the network node and the target value of the multidimensional characteristic parameter of the centroid to obtain the distance between the network node and the centroid.

[0024] Optionally, the multidimensional feature parameter includes movement frequency, and the movement frequency has the highest weight.

[0025] In a third aspect, an embodiment of the present invention discloses an electronic device, comprising a processor and a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium; when the computer program is executed by the processor, the processor executes the network segmentation method as described in any one of the above items.

[0026] In a fourth aspect, an embodiment of the present invention discloses a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the processor executes the network segmentation method as described in any one of the above items.

[0027] In an embodiment of the present invention, the target value of the multidimensional characteristic parameter of each network node in the network is obtained; the network nodes are clustered based on the target value of the multidimensional characteristic parameter, and each cluster obtained by clustering is used as a network segment; the central node of the network segment is selected from the network nodes contained in the network segment as the control node of the network segment. It can be seen that in an embodiment of the present invention, network nodes with similar characteristics can be divided into the same network segment based on the multidimensional characteristic parameters of the network nodes, and the central node of the network segment is selected as the control node of the network segment. The control node is used to control the network segment, so that each network segment can be independently controlled, and the network segments do not affect each other. When a node fails, only the network segment where the node is located needs to be processed, and it will not affect other network segments, thereby converting the maintenance of a large-scale network into the maintenance of a small-scale network segment, reducing the complexity of network management and improving the flexibility of the network.

[0028] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the drawings required for use in the description of the embodiment of the present invention will be briefly introduced below. Obviously, the drawings described below are only some drawings of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0030] Figure 1 is a flowchart of the steps of a network segmentation method according to an embodiment of the present invention;

[0031] Figure 2 is a structural block diagram of a network segmentation device according to an embodiment of the present invention;

[0032] Figure 3 is a structural block diagram of an electronic device according to an embodiment of the present invention;

[0033] Figure 4 It is a structural block diagram of a computer-readable storage medium according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] The dynamic change of network topology caused by node movement is one of the key factors that reduce the performance of mobile ad hoc networks and limit the expansion of network scale. The established data transmission paths are often interrupted due to node movement. ① The general SD-MANET (Software-Defined Mobile Ad Hoc Networks) routing method has problems such as short flow table lifetime, frequent route repair, and self-control adjustment that consumes the limited network resources of the mobile ad hoc network. ② The upstream node discards the data packet that has timed out for route repair, which reduces the data packet delivery rate, and the route repair delay increases the entire end-to-end packet transmission delay. ③ The probability of path interruption increases with the increase in the number of forwarding hops, causing further deterioration of network performance and affecting the expansion of the network scale. The above problems make it difficult to deploy and apply large-scale wireless networks in practice.

[0036] Minimizing the number of path interruptions caused by node movement is of great significance to improving network performance, especially the expansion of large-scale mobile ad hoc networks.

[0037] The source node has the ability to build its own routing path to other nodes in the network, eliminating the complex routing tasks of the intermediate nodes and reducing the network overhead. The source routing method solves the problems of repeated search of traditional routing and excessive overhead of the forwarding table, and avoids the increase of network forwarding load caused by frequent label operations of multi-label protocols. The source routing protocol has been widely used in MANET (Mobile Ad Hoc Networks). The source routing algorithm is used to ensure that the routing is loop-free. The source routing protocol solves the problem of discovering reachable paths between the source node and the destination node. However, the forwarding data packet header carries a complete, ordered, and list of forwarding nodes that the packet passes through. ① In large-scale networking, the increase in jump paths will increase the additional consumption of data packets. ② With the continuous increase in the scale of the network, the routing stability of the source routing protocol algorithm is poor, and the global stability of the routing is greatly affected by each node. The failure of a node will cause the failure of the entire path. The network maintenance cost is relatively large, the network management is weak, and the flexibility is poor.

[0038] In view of the problems existing in the source routing method, the embodiment of the present invention introduces the idea of ​​segmentation into the source routing method, solves the problem from the design level of the routing protocol, and improves the performance of the network. It solves the problem of how to effectively divide a large-scale network into multiple different clusters and intelligently select the central node of each cluster as the control node, so that the network topology remains relatively stable, and the overall topology is divided into different network segments that are easy to manage, thereby realizing efficient, robust and adaptive segment routing. In large-scale self-organizing networks, segment routing improves routing efficiency and scalability by dividing the network into multiple smaller segments. Each segment is managed by one or more control nodes to achieve efficient operation of large-scale self-organizing networks.

[0039] The network segmentation method in the embodiment of the present invention is described in detail below.

[0040] Reference Figure 1 , shows a step flow chart of a network segmentation method according to an embodiment of the present invention.

[0041] like Figure 1 As shown, the network segmentation method may include the following steps:

[0042] Step 101: Obtain target values ​​of multi-dimensional characteristic parameters of each network node in the network.

[0043] For each network node in the network, characteristic parameters for describing relevant characteristics of the network node are obtained, and the network nodes can be clustered based on these characteristic parameters.

[0044] In an embodiment of the present invention, during the feature parameter selection process, feature parameters are obtained from multiple dimensions as much as possible, that is, multi-dimensional feature parameters of the network node are obtained, so as to more accurately and comprehensively describe the relevant characteristics of the network node, improve the stability of network segmentation, and improve the overall network performance.

[0045] Exemplarily, the multi-dimensional characteristic parameters may include but are not limited to the following: number of hops, delay, physical distance, energy consumption, movement frequency, etc. These characteristic parameters may be obtained through relevant equipment that monitors and manages network nodes.

[0046] In the embodiment of the present invention, in order to process the characteristic parameters of the network node more conveniently and accurately, the initial values ​​of the multidimensional characteristic parameters of the network node may be processed accordingly to obtain the target values ​​of the multidimensional characteristic parameters of the network node.

[0047] Exemplarily, the process of obtaining the target value of the multidimensional characteristic parameter of each network node in the network may include: for each network node, obtaining the initial value of the multidimensional characteristic parameter of the network node; normalizing the initial value of the multidimensional characteristic parameter to obtain the target value of the multidimensional characteristic parameter. The initial value refers to the value initially obtained, and the target value refers to the value obtained after the initial value is normalized.

[0048] Normalization is the process of converting feature parameters of different scales into the same scale, so that they have equal importance in subsequent algorithm processing. By normalizing feature parameters of different dimensions separately, the feature parameters of high-dimensional space are reduced to low-dimensional space, so that they have the same dimension, eliminating the influence of feature parameters of different scales on the calculation results.

[0049] Exemplarily, the process of normalizing the initial values ​​of the multidimensional feature parameters to obtain the target values ​​of the multidimensional feature parameters may include: for each of the feature parameters, calculating the average value and standard deviation of the initial values ​​of the feature parameters of all network nodes; normalizing the initial values ​​of the feature parameters based on the average value and the standard deviation to obtain the target value of the feature parameter.

[0050] Exemplarily, the process of normalizing the initial value of the characteristic parameter based on the mean value and the standard deviation to obtain the target value of the characteristic parameter may include: calculating the difference between the initial value of the characteristic parameter and the mean value corresponding to the characteristic parameter, and taking the ratio between the difference and the standard deviation corresponding to the characteristic parameter as the target value of the characteristic parameter.

[0051] For any characteristic parameter, the average value of the initial value of the characteristic parameter of all network nodes can be calculated by the following formula 1:

[0052]

[0053] Where μ represents the average value, n represents the total number of network nodes, and χ i Represents the initial value of the characteristic parameter of the i-th network node.

[0054] For any characteristic parameter, the standard deviation of the initial value of the characteristic parameter of all network nodes can be calculated by the following formula 2:

[0055]

[0056] Where σ represents the standard deviation, n represents the total number of network nodes, and χ i represents the initial value of the characteristic parameter of the ith network node, and μ represents the average value.

[0057] For any characteristic parameter, the target value of the characteristic parameter can be calculated by the following formula 3:

[0058]

[0059] Wherein, z represents the target value of the characteristic parameter, x represents the initial value of the characteristic parameter, μ represents the average value of the initial values ​​of the characteristic parameter of all network nodes, and σ represents the standard deviation of the initial values ​​of the characteristic parameter of all network nodes.

[0060] Step 102: clustering the network nodes based on the target value of the multi-dimensional feature parameter, and taking each cluster obtained by clustering as a network segment.

[0061] Clustering refers to dividing the data points in a data set into multiple clusters, where the number of clusters is less than the total number of data points in the data set. Each cluster represents a cluster and satisfies the following conditions: each cluster contains at least one data point; each data point belongs to and only belongs to one cluster.

[0062] In the embodiment of the present invention, any suitable method can be selected to cluster the network nodes according to actual needs, and this embodiment does not limit this. For example, the k-means clustering algorithm, the k-medoids clustering algorithm, and the like can be selected. The k-means clustering algorithm assigns data points to clusters with the most similar centroids, and updates the centroids according to the cluster members. The k-medoids clustering algorithm is similar to the k-means clustering algorithm, but the k-medoids clustering algorithm uses the actual data points in the cluster as the centroid, which is more robust to noise and outliers.

[0063] The following is an introduction using the k-means clustering algorithm as an example.

[0064] (1) Initialization

[0065] In the K-means clustering algorithm, initialization is the process of selecting K initial centroids (the value of K depends on the specific network size and partitioning requirements). The centroid represents the center of a cluster. These centroids can be actual network nodes or virtual points in the feature parameter space.

[0066] K initial centroids are randomly selected, and the specific process is as follows: During the initialization process, K network nodes are randomly selected from the network nodes as the initial centroids.

[0067] (2) Iteration

[0068] The iterative process is the core content of the K-means clustering algorithm, which is a process that continuously adjusts the centroid position and network node allocation to eventually achieve K independent and stable clusters.

[0069] The specific implementation of the iterative process is as follows:

[0070] 1) Assign network nodes to the nearest centroid (divide clusters):

[0071] The distance between each network node and each centroid is calculated, and each network node is assigned to the cluster with the nearest centroid. Distance is a measure of the normalized feature parameters.

[0072] In an embodiment of the present invention, in order to distinguish the importance of different feature parameters and further improve the accuracy of clustering results, the preset weight of each feature parameter can be obtained, and then a weighted distance calculation is performed based on the target value of the multidimensional feature parameter of the network node and the target value of the multidimensional feature parameter of the centroid according to the weight of the feature parameter to obtain the distance between the network node and the centroid.

[0073] The weight of each characteristic parameter can be set according to actual needs, and the embodiment of the present invention does not limit this. For example, the characteristic parameter of mobile frequency has a greater impact on the network, so the weight of mobile frequency is set to be the highest, and so on. It can be set according to actual conditions.

[0074] The distance calculation formula can be shown as follows:

[0075]

[0076] Where d represents the distance, w1, w2, w3…w m represents the weight of each feature parameter, Represents the target value of each characteristic parameter of the network node, represents the target value of each characteristic parameter of the centroid, and m represents the number of types of characteristic parameters.

[0077] 2) Update the centroid (redetermine the centroid of each cluster):

[0078] Calculate the new centroid of each cluster. The new centroid is the average value of the characteristic parameter features of all network nodes in the cluster, that is, the spatial average position of the characteristic parameters of all network nodes. The position of the new centroid is recalculated in the feature space and is not the actual position of the network node.

[0079] The calculation formula of the new centroid can be shown as follows:

[0080]

[0081] Among them, μ j represents the new centroid of the jth cluster, S j represents the jth cluster, |S j| represents the total number of network nodes in the jth cluster, x i Represents the target value of the multidimensional feature parameter of the i-th network node in the j-th cluster.

[0082] 3) Repeat (update centroid) iteration:

[0083] The distance between each network node in each cluster and the new centroid is recalculated, and the network nodes are assigned again.

[0084] Repeat the process of updating the centroid and assigning network nodes until the centroid position no longer changes significantly or the change amplitude is less than the preset threshold, clustering is completed, and the final multiple clusters are obtained. The preset threshold here can be a small value, such as 0.001, indicating that the centroid moves a small distance and reaches a convergence state. It can be set according to actual needs.

[0085] In the embodiment of the present invention, through the above process, the network nodes are clustered based on the target value of the multidimensional feature parameter to obtain multiple clusters. The network nodes in each cluster have similar features, so each cluster obtained by clustering can be used as a network segment.

[0086] Step 103: Select a central node of the network segment from the network nodes included in the network segment as a control node of the network segment.

[0087] In the embodiment of the present invention, since the centroid in the network segment does not belong to the actual network node but is a virtual point in the feature space, after the network segment is divided, the centroid of the network segment can be further mapped to an actual network node in the network segment, that is, the central node of the network segment is selected from the network nodes included in the network segment, and the central node of the network segment is used as the control node of the network segment. The control node of the network segment can be used to perform relevant control on the network segment, for example, to send a routing table to other network nodes in the network segment, etc.

[0088] In an optional implementation, the process of selecting the central node of the network segment from the network nodes included in the network segment may include: obtaining the target value of the multidimensional characteristic parameter of the centroid of the network segment; for each network node included in the network segment, calculating the distance between the network node and the centroid based on the target value of the multidimensional characteristic parameter of the network node and the target value of the multidimensional characteristic parameter of the centroid; selecting at least one network node with the smallest distance from the centroid as the central node of the network segment. In this method, based on the distance to the centroid, at least one network node closest to the centroid is selected as the central node. The closer the distance to the centroid, the closer it is to the center of the network segment, and therefore the higher the stability of the network node.

[0089] Specifically, after calculating the distance between each network node and the centroid, the network nodes may be sorted according to the distance, and then at least one network node with the smallest distance to the centroid is selected as the central node of the network segment.

[0090] In an optional implementation, the process of selecting the central node of the network segment from the network nodes included in the network segment may include: obtaining the target value of the multidimensional characteristic parameter of the centroid of the network segment; for each network node included in the network segment, calculating the distance between the network node and the centroid based on the target value of the multidimensional characteristic parameter of the network node and the target value of the multidimensional characteristic parameter of the centroid; and selecting at least one network node with the smallest movement frequency from multiple network nodes with the smallest distance to the centroid as the central node of the network segment. In this method, the characteristic parameter of movement frequency is further combined with the distance to the centroid, and the movement frequency can characterize the position stability of the network node, thereby further ensuring the stability of the central node.

[0091] Specifically, after calculating the distance between each network node and the centroid, the network nodes can be sorted according to the distance, and then multiple network nodes with the smallest distance from the centroid are selected; then the selected multiple network nodes with the smallest distance from the centroid are sorted according to the movement frequency, and then at least one network node with the smallest movement frequency is selected as the central node of the network segment.

[0092] Exemplarily, the process of calculating the distance between the network node and the centroid based on the target value of the multidimensional feature parameters of the network node and the target value of the multidimensional feature parameters of the centroid may include: obtaining the weight of each feature parameter; performing weighted distance calculation based on the target value of the multidimensional feature parameters of the network node and the target value of the multidimensional feature parameters of the centroid according to the weight of the feature parameter, to obtain the distance between the network node and the centroid.

[0093] The distance calculation formula can be shown as follows:

[0094]

[0095] Among them, D i represents the distance between the ith network node and the centroid, w j represents the weight of the jth feature parameter, x ij represents the target value of the jth characteristic parameter of the i-th network node, C j represents the target value of the jth feature parameter of the centroid, and m represents the number of types of feature parameters.

[0096] In the embodiment of the present invention, network nodes with similar characteristics can be divided into the same network segment based on the multi-dimensional characteristic parameters of the network nodes, and the central node of the network segment is selected as the control node of the network segment. The control node is used to control the network segment, so that each network segment can be independently controlled without affecting each other. When a node fails, only the network segment where the node is located needs to be processed, and other network segments will not be affected, thereby converting the maintenance of large-scale networks into the maintenance of small-scale network segments, reducing the complexity of network management and improving the flexibility of the network.

[0097] The following is an introduction to the network segmentation method using a specific example.

[0098] Assume that there are 10 network nodes, and the characteristic parameters of each network node include the number of hops, delay, physical distance, energy consumption, and mobile frequency. Of course, other characteristic parameters may also be included, which can be set according to actual needs, and this embodiment does not limit this.

[0099] The initial values ​​of the characteristic parameters of each network node are shown in Table 1 below:

[0100] Table 1

[0101]

[0102]

[0103] The following calculations are performed based on the above Table 1.

[0104] The average value of the number of hops μ 跳数 as follows:

[0105]

[0106] The standard deviation of the number of hops σ 跳数 as follows:

[0107]

[0108] Among them, x i跳数 represents the number of hops of the i-th network node. The average delay μ 延时 as follows:

[0109]

[0110] The standard deviation of the delay σ 延时 as follows:

[0111]

[0112] Among them, x i延时represents the delay of the i-th network node.

[0113] The average value of physical distance μ 物理距离 as follows:

[0114]

[0115] The standard deviation of the physical distance σ 物理距离 as follows:

[0116]

[0117] Among them, x i物理距离 Represents the physical distance of the ith network node. The average value of energy consumption μ 能耗 as follows:

[0118]

[0119] Standard deviation of energy consumption σ 能耗 as follows:

[0120]

[0121] Among them, x i能耗 represents the energy consumption of the i-th network node.

[0122] The average value of the moving frequency μ 移动频率 as follows:

[0123]

[0124] The standard deviation of the moving frequency σ 移动频率 as follows:

[0125]

[0126] Among them, x i移动频率 represents the movement frequency of the i-th network node.

[0127] For each characteristic parameter of each network node in Table 1, the above formula 3 is used to perform normalization processing.

[0128] For example, the hop count of network node A is normalized to obtain the target value z of the hop count of network node A. 跳数 as follows:

[0129]

[0130] After normalization, the target values ​​of the characteristic parameters of each network node are shown in Table 2 below:

[0131] Table 2

[0132]

[0133] Accordingly, the coordinates of each network node (i.e., the target value of the characteristic parameter) are expressed as follows:

[0134] 'A':[-1.00,-1.17,-1.28,-1.11,0.16]

[0135] 'B':[0.33,-0.06,0.41,0.67,-1.44]

[0136] 'C':[-0.33,-0.34,-0.15,-0.22,0.96]

[0137] 'D':[1.00,1.34,1.25,1.11,-0.64]

[0138] 'E':[-1.67,-1.45,-1.56,-1.56,1.76]

[0139] 'F':[-0.33,-0.47,-0.44,-0.67,0.16]

[0140] 'G':[1.67,1.06,0.97,0.89,-0.64]

[0141] 'H':[-1.00,-0.75,-0.86,-0.89,0.16]

[0142] 'I':[1.00,1.62,1.54,1.56,-1.44]

[0143] 'J':[0.33,0.22,0.13,0.22,0.96]

[0144] First, the coordinates of the initial centroid selected from the above network nodes (i.e., the target value of the characteristic parameter) are expressed as follows:

[0145] 'A':[-1.00,-1.17,-1.28,-1.11,0.16]

[0146] 'D':[1.00,1.34,1.25,1.11,-0.64]

[0147] 'G':[1.67,1.06,0.97,0.89,-0.64]

[0148] Then, the distance between each network node and each centroid is calculated according to the above formula 6. In this example, the weight of each feature parameter is 1.

[0149] for example:

[0150] The distance between network node A and centroid A is:

[0151]

[0152] The distance between network node A and centroid D is:

[0153]

[0154] The distance between network node A and centroid G is:

[0155]

[0156] Each network node is then assigned to the cluster to which the nearest centroid lies.

[0157] For example, suppose the initial allocation results are as follows:

[0158] Cluster 1 (centroid A): A, E, H

[0159] Cluster 2 (centroid D): B, D, I

[0160] Cluster 3 (centroid G): C, F, G, J

[0161] Then, the new centroid of each cluster is recalculated. The new centroid is the average value of the characteristic parameters of all network nodes in the cluster, that is, the spatial average position of the characteristic parameters of all network nodes.

[0162] The new centroid can be expressed as follows:

[0163]

[0164] Among them, α i represents the target value of the hop count of the i-th network node in the cluster, β i represents the target value of the delay of the i-th network node in the cluster, γ i represents the target value of the physical distance of the i-th network node in the cluster, δ i represents the target value of energy consumption of the i-th network node in the cluster, θ i represents the target value of the mobility frequency of the i-th network node in the cluster, and N represents the total number of network nodes in the cluster.

[0165] The new centroid of cluster 1 is as follows:

[0166]

[0167] For example, the new centroid of cluster 2 is as follows:

[0168]

[0169] For example, the new centroid of cluster 3 is as follows:

[0170]

[0171] According to the new centroid of each cluster, the distance between each network node and each centroid is recalculated, and each network node is assigned to the cluster where the nearest centroid is located. The new centroid of each cluster is recalculated again, and the process of updating the centroid and assigning network nodes is repeated until the position of the centroid no longer changes significantly or the change is less than the preset threshold, and finally multiple clusters are obtained.

[0172] Each cluster obtained by clustering is regarded as a network segment. For each network segment, a target value of a multidimensional characteristic parameter of the centroid of the network segment is obtained; for each network node included in the network segment, a distance between the network node and the centroid is calculated based on the target value of the multidimensional characteristic parameter of the network node and the target value of the multidimensional characteristic parameter of the centroid; from a plurality of network nodes having the smallest distance to the centroid, at least one network node having the smallest moving frequency is selected as the central node of the network segment.

[0173] The embodiments of the present invention can achieve the following beneficial effects:

[0174] Improve network stability: By comprehensively considering the movement frequency and other characteristic parameters of network nodes, relatively stable network nodes are preferentially selected as central nodes to reduce the impact of topology changes caused by frequent movement of network nodes on network performance.

[0175] Optimize routing efficiency: By reasonably dividing the network segments, the number of nodes in each network segment is reduced, making routing calculation more efficient. At the same time, communication between network segments is carried out through pre-calculated optimal paths, reducing the delay and overhead of cross-segment communication.

[0176] Enhanced network scalability: Dynamically adding multiple feature parameters allows the solution to adapt to networks of different sizes and make adaptive adjustments as the network scale expands, maintaining high routing efficiency and stability.

[0177] Balanced network load: When dividing the network into segments, the energy consumption of network nodes is considered to ensure that the load of each network node in the network segment is balanced, to prevent some network nodes from quickly running out of energy due to excessive use, and to increase the life of the entire network.

[0178] Improve network robustness: By normalizing different feature parameters, the network can adapt to a variety of different environments and application scenarios, improving the network's robustness and anti-interference capabilities.

[0179] Improve communication quality: By comprehensively considering latency and physical distance, ensure that the selected central nodes and divided network segments can provide lower communication latency and higher communication quality to meet the needs of applications with high real-time requirements.

[0180] Simplified management and maintenance: Through intelligent central node selection and network segmentation, the need for manual intervention is reduced, making network management and maintenance easier and reducing operating costs.

[0181] Enhanced adaptability: The network segmentation and central node selection can be dynamically adjusted according to the actual operation of the network, ensuring that the network can adaptively respond to various changes and challenges and maintain good performance.

[0182] Reference Figure 2 , shows a structural block diagram of a network segmentation device according to an embodiment of the present invention.

[0183] like Figure 2 As shown, the network segmentation device may include the following modules:

[0184] An acquisition module 201 is used to acquire a target value of a multi-dimensional characteristic parameter of each network node in the network;

[0185] A clustering module 202, configured to cluster the network nodes based on the target value of the multi-dimensional feature parameter, and use each cluster obtained by clustering as a network segment;

[0186] The selection module 203 is used to select the central node of the network segment from the network nodes included in the network segment as the control node of the network segment.

[0187] Optionally, the multi-dimensional characteristic parameters include multiple ones of hop count, delay, physical distance, energy consumption, and mobile frequency.

[0188] Optionally, the acquisition module 201 includes: an initial value acquisition unit, used to acquire the initial value of the multidimensional feature parameter of the network node for each of the network nodes; and a normalization unit, used to normalize the initial value of the multidimensional feature parameter to obtain the target value of the multidimensional feature parameter.

[0189] Optionally, the normalization unit is specifically used to calculate the average value and standard deviation of the initial values ​​of the characteristic parameters of all network nodes for each characteristic parameter; normalize the initial values ​​of the characteristic parameters based on the average value and the standard deviation to obtain the target value of the characteristic parameter.

[0190] Optionally, the selection module 202 includes: a target value acquisition unit, used to acquire the target value of the multidimensional characteristic parameter of the centroid of the network segment; a distance calculation unit, used to calculate the distance between the network node and the centroid for each network node included in the network segment based on the target value of the multidimensional characteristic parameter of the network node and the target value of the multidimensional characteristic parameter of the centroid; a node selection unit, used to select at least one network node with the smallest moving frequency from a plurality of network nodes with the smallest distance to the centroid, as the central node of the network segment.

[0191] Optionally, the distance calculation unit is specifically used to obtain the weight of each characteristic parameter; according to the weight of the characteristic parameter, a weighted distance calculation is performed based on the target value of the multidimensional characteristic parameter of the network node and the target value of the multidimensional characteristic parameter of the centroid to obtain the distance between the network node and the centroid.

[0192] Optionally, the multidimensional feature parameter includes movement frequency, and the movement frequency has the highest weight.

[0193] In the embodiment of the present invention, network nodes with similar characteristics can be divided into the same network segment based on the multi-dimensional characteristic parameters of the network nodes, and the central node of the network segment is selected as the control node of the network segment. The control node is used to control the network segment, so that each network segment can be independently controlled without affecting each other. When a node fails, only the network segment where the node is located needs to be processed, and other network segments will not be affected, thereby converting the maintenance of large-scale networks into the maintenance of small-scale network segments, reducing the complexity of network management and improving the flexibility of the network.

[0194] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0195] In an embodiment of the present invention, an electronic device is further provided. The electronic device may include a processor and a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium; when the computer program is executed by the processor, the processor executes the network segmentation method of any of the above embodiments.

[0196] Reference Figure 3 , shows a structural block diagram of an electronic device according to an embodiment of the present invention. Figure 3 As shown, the electronic device 30 includes a processor 301 and a computer-readable storage medium 302 , on which a computer program 3021 is stored.

[0197] The processor 301 is used to execute the computer program 3021 stored on the computer-readable storage medium 302. When executing the computer program 3021, the processor 301 implements the network segmentation method of any of the above embodiments and can achieve the same technical effect. To avoid repetition, it will not be described here.

[0198] The processor 301 mentioned above may include but is not limited to: a central processing unit (CPU), a network processor (NP), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0199] The computer-readable storage medium 302 mentioned above may include but is not limited to: Read Only Memory (ROM), Random Access Memory (RAM), Compact Disc Read Only Memory (CD-ROM), Electronic Erasable Programmable Read Only Memory (EEPROM), hard disk, floppy disk, flash memory, etc.

[0200] In an embodiment of the present invention, a computer-readable storage medium is also provided, on which a computer program is stored. The computer program can be executed by a processor of an electronic device. When the computer program is executed by the processor, the processor executes the network segmentation method described in any of the above embodiments.

[0201] Reference Figure 4 , shows a block diagram of a computer-readable storage medium according to an embodiment of the present invention. Figure 4 As shown, a computer program 401 is stored on a computer-readable storage medium 40. When the computer program 401 is executed by a processor, the processor executes the network segmentation method described in any of the above embodiments and can achieve the same technical effect. To avoid repetition, it will not be described here.

[0202] It should be noted that all actions of acquiring signals, information or data in the present invention are performed in compliance with the corresponding data protection laws and policies of the country where the device is located and with the authorization given by the owner of the corresponding device.

[0203] The various embodiments in this specification are interrelated, and each embodiment is described in a progressive manner. Each embodiment focuses on the differences from other embodiments, and the same or similar parts between the various embodiments can be referenced to each other.

[0204] It will be appreciated by those skilled in the art that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the embodiments of the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the embodiments of the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0205] The embodiments of the present invention are described with reference to the flowcharts and / or block diagrams of the methods, terminal devices (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing terminal device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing terminal device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0206] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing terminal device to operate in a specific manner, so that the instructions stored in the computer readable memory produce a manufactured product including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0207] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal device so that a series of operating steps are executed on the computer or other programmable terminal device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable terminal device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0208] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0209] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0210] The network segmentation method, device, electronic device and storage medium provided by the present invention are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. For those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A network segmentation method, characterized in that: The method comprises: Obtain target values ​​of multidimensional characteristic parameters of each network node in the network; Clustering the network nodes based on the target value of the multidimensional feature parameter, and taking each cluster obtained by clustering as a network segment; A central node of the network segment is selected from the network nodes included in the network segment as a control node of the network segment.

2. The method according to claim 1, characterized in that The multi-dimensional characteristic parameters include multiple ones of hop count, delay, physical distance, energy consumption, and mobile frequency.

3. The method according to claim 1, characterized in that Obtain the target value of the multidimensional characteristic parameters of each network node in the network, including: For each of the network nodes, obtaining an initial value of a multidimensional characteristic parameter of the network node; The initial value of the multidimensional feature parameter is normalized to obtain the target value of the multidimensional feature parameter.

4. The method according to claim 3, characterized in that Normalizing the initial value of the multidimensional feature parameter to obtain the target value of the multidimensional feature parameter includes: For each of the characteristic parameters, calculating the average value and standard deviation of the initial values ​​of the characteristic parameters of all network nodes; The initial value of the characteristic parameter is normalized based on the average value and the standard deviation to obtain a target value of the characteristic parameter.

5. The method according to claim 1, characterized in that Selecting a central node of the network segment from the network nodes included in the network segment includes: Obtaining target values ​​of multidimensional feature parameters of the centroid of the network segment; For each network node included in the network segment, based on a target value of a multidimensional feature parameter of the network node and a target value of a multidimensional feature parameter of the centroid, calculating a distance between the network node and the centroid; From the plurality of network nodes with the smallest distance from the centroid, at least one network node with the smallest movement frequency is selected as the central node of the network segment.

6. The method according to claim 5, characterized in that Calculating the distance between the network node and the centroid based on the target value of the multidimensional feature parameter of the network node and the target value of the multidimensional feature parameter of the centroid includes: Get the weight of each feature parameter; According to the weight of the characteristic parameter, a weighted distance calculation is performed based on the target value of the multidimensional characteristic parameter of the network node and the target value of the multidimensional characteristic parameter of the centroid to obtain the distance between the network node and the centroid.

7. The method according to claim 6, characterized in that The multi-dimensional feature parameters include movement frequency, and the movement frequency has the highest weight.

8. A network segmentation device, characterized in that: The device comprises: An acquisition module, used to acquire target values ​​of multi-dimensional characteristic parameters of each network node in the network; A clustering module, used for clustering the network nodes based on the target value of the multi-dimensional feature parameter, and taking each cluster obtained by clustering as a network segment; The selection module is used to select the central node of the network segment from the network nodes included in the network segment as the control node of the network segment.

9. An electronic device, characterized in that: The electronic device includes a processor and a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium; when the computer program is executed by the processor, the processor executes the network segmentation method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor executes the network segmentation method according to any one of claims 1 to 7.

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