Hierarchical network topology layout method and device based on topology potential

Abstract

The embodiment of the invention provides a hierarchical network topology layout method and device based on topology potential, and the method comprises the steps: calculating the topology potential value of a node according to the position information, connection relation and service attribute of a known network node, and positioning a key node based on the topology potential value; performing clustering layered layout on the network nodes according to the key nodes and the service areas where the key nodes are located to obtain a plurality of clustering points; and taking the plurality of clustering points as a first hierarchy of the network topology, visually displaying representative nodes of the first hierarchy, and performing hierarchical topology display on a subnet region where the representative nodes are located based on an interactive operation of a user on the clustering points.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of network topology structure analysis. Specifically, it relates to a hierarchical network topology layout method, device, and computer-readable storage medium storing a computer program based on topological potential. Background Art

[0002] Network security situation awareness is to identify potential security threats, vulnerabilities, and attack behaviors by real-time monitoring, analyzing, and responding to security data in the network environment. Usually, the perception objects are enterprise-level networks. In enterprise-level networks, there are many network device nodes, and the connection relationships are complex, having both hierarchy and dependence. Moreover, each department or business unit usually has an independent logical partition, which makes the display of the entire network structure very complex, and the network topology structure may present diverse and dynamic characteristics. In real-time situation awareness, the network topology needs to be able to dynamically adapt to network changes, which poses higher requirements for the automatic perception and display of the topology. In some cases, the internal structure of the network is unknown, and only node type, connection relationship data, and other network attribute information are available. It is necessary to clarify the internal structure of network objects through topological analysis of the existing data and realize the visualization of the network topology through an automatic layout method.

[0003] Therefore, how to efficiently and accurately present these device nodes and their interconnected topological structures is the key to network management and security monitoring. Summary of the Invention

[0004] To improve the layout efficiency of large-scale complex network topologies and the user experience, and better provide a visualization basis for network security situation assessment, the embodiments described herein provide a hierarchical network topology layout method, device, and computer-readable storage medium storing a computer program based on topological potential.

[0005] According to a first aspect of the present disclosure, a hierarchical network topology layout method based on topological potential is provided, including: calculating the topological potential value of a node according to the position information, connection relationship, and service attribute of known network nodes, and positioning key nodes based on the topological potential value; performing clustering hierarchical layout on network nodes according to the key nodes and their service areas where they are located to obtain multiple aggregation points; using the multiple aggregation points as the first level of the network topology, visually displaying the representative nodes of the first level, and hierarchically topologically displaying the subnet area where the representative nodes are located based on the user's interaction operation on the aggregation points.

[0006] In some embodiments of the present disclosure, calculating the topological potential value of a node based on the location information, connection relationship, and service attributes of network nodes, and locating key nodes based on the topological potential value includes: constructing a network topology G(N, V) according to the locations and connection relationships of known network nodes, where N represents a non-empty finite set of nodes in the network, and V is the set of edges of the links between nodes;

[0007] Calculating the topological potential φ(i) at node i:

[0008]

[0009] In the formula, m j represents the mass of node j, d j→i represents the shortest path length from node j to node i, σ is the influence factor, and n is the number of nodes;

[0010] Optimizing the influence factor according to the topological potential entropy value, and calculating the topological potential value of the node according to the optimized influence factor and the attribute evaluation matrix of the node to obtain the importance of each node; and locating the key nodes in the network topology according to the sorting of the importance of the nodes.

[0011] In some embodiments of the present disclosure, optimizing the influence factor according to the topological potential entropy value, and calculating the topological potential value of the node according to the optimized influence factor and the attribute evaluation matrix of the node to obtain the importance of each node includes:

[0012] Calculating the topological potential entropy value through the following formula:

[0013]

[0014] In the formula, represents the normalized probability of the topological potential of node i, and selects the optimized influence factor by minimizing the topological potential entropy value;

[0015] Weight the inherent attributes of the node according to user preferences to form an attribute evaluation matrix, and use the attribute evaluation matrix as the mass of the node. The inherent attributes of the node include node type, node value, type of business software installed on the node, node access volume, and node egress bandwidth; calculate the topological potential value of each node in the network according to the optimized influence factor and the attribute evaluation matrix to obtain the importance of each node.

[0016] In some embodiments of the present disclosure, the mass of a node is equal to the sum of the weighted scores of the node on each inherent attribute:

[0017] m j = w 1 p 1 (j) + w 2 p 2 (j) + … + w n pn (j); where p 1 (j), p 2 (j), …, p n (j) represents the scores of node j on different attributes, w 1 , w 2 , …, w n represents the weights of the corresponding attributes; m j is the quality score of node j.

[0018] In some embodiments of the present disclosure, the network nodes are clustered and hierarchically laid out according to the key nodes and their business regions, and multiple aggregation points are obtained as follows: If the key nodes have clear business partitions, then according to the business logic and network topology of the key nodes, the key nodes and their subnets are used as an aggregation point to represent the subnet area through the aggregation point; in the case of no business partition or business logic, then according to the topological potential value of each node, the breadth-first search algorithm is used to cluster the network nodes to obtain multiple aggregation points.

[0019] In some embodiments of the present disclosure, according to the topological potential value of each node, the breadth-first search algorithm is used to cluster the network nodes to obtain multiple aggregation points, including:

[0020] Select the node with the largest topological potential value, and use the breadth-first search algorithm with the node with the largest potential value as the center to find the set of shortest path nodes directly connected to it N = {N 1 , N 2 ,..., N m}; Taking each node in the node set as the center, continue to perform breadth-first search downward until all leaf nodes without subsequent connections are found, and a topological cluster is formed by the subset of nodes represented by N i ; By calculating the distances between the representative node N i and other representative nodes, the shortest distances between different topological clusters are determined, so that the nodes of each layer can be regionally optimized according to the shortest distances between different topological clusters.

[0021] In some embodiments of the present disclosure, multiple aggregation points are used as the first level of the network topology, the representative nodes of the first level are visually displayed, and based on user interaction operations, the subnet area hierarchical topology where the representative nodes are located is visually displayed, including: According to the network topology including nodes, links, and subnet areas, the key nodes of multiple aggregation points are used as representative nodes and displayed as the first level of the network topology, and the subnet content of the representative nodes is folded; According to user interaction operations, based on the B / S architecture, the network topology hierarchical layout is visually displayed, and the layout calculation of the subnet content and the subnet topology loading display are performed on the representative nodes of the first level.

[0022] In some embodiments of the present disclosure, according to user interaction operations, a network topology hierarchical layout is visually displayed based on the B / S architecture. The layout calculation and subnet topology loading and display of the representative nodes at the first level include: the front end listens for user interaction events, requests the back-end API, obtains the subnet topology data of the nodes, and uses a visualization library to render and display the topology structure of the subnet. Among them, the user interaction events include node click and mouse hover events. Set the number of subnet levels and the maximum number of nodes that can be displayed in each level to control the display effect in the same-level canvas; the back end calculates and returns the corresponding subnet topology data according to the front-end request and the user settings.

[0023] According to a second aspect of the present disclosure, a hierarchical network topology layout device based on topological potential is provided. The device includes at least one processor; and at least one memory storing a computer program. When the computer program is executed by the at least one processor, the device is caused to: calculate the topological potential value of a node according to the position information, connection relationship, and service attributes of known network nodes, and locate key nodes based on the topological potential value; perform clustered hierarchical layout on network nodes according to the key nodes and their business regions where they are located to obtain multiple aggregation points; and use the multiple aggregation points as the first level of the network topology, visually display the representative nodes of the first level, and perform hierarchical topology display on the subnet area where the representative nodes are located based on the user's interaction operations on the aggregation points.

[0024] According to a third aspect of the present disclosure, a computer-readable storage medium storing a computer program is provided, wherein the computer program, when executed by a processor, implements the steps of the method for hierarchical network topology layout based on topological potential according to the first aspect of the present disclosure.

[0025] The method and device for hierarchical network topology layout based on topological potential provided by the embodiments of the present disclosure not only improve the visualization effect of the network topology diagram, but also optimize the user interaction experience, especially in the folding and unfolding operations of subnets. Its core advantage lies in using topological potential and service attribute analysis to identify key nodes, and effectively organizing nodes and subnets through a hierarchical layout method. The interactive display method further enhances the user's operation experience. Especially during the process of network security incident response, real-time topology visualization can provide quick support for decision-making, helping the team understand the attack path, potentially affected nodes and their connections, and then taking reasonable countermeasures. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments will be briefly described below. It should be understood that the following described drawings only relate to some embodiments of the present disclosure and do not limit the present disclosure, where:

[0027] Figure 1It is an exemplary flowchart of a hierarchical network topology layout method 100 based on topological potential according to an embodiment of the present disclosure;

[0028] Figure 2 It is a schematic block diagram of a hierarchical network topology layout device 200 based on topological potential according to an embodiment of the present disclosure.

[0029] It should be noted that the elements in the drawings are schematic and not drawn to scale. Detailed implementation manners

[0030] In order to make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments obtained by those skilled in the art based on the described embodiments of the present disclosure without creative efforts shall also fall within the scope of protection of the present disclosure.

[0031] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those skilled in the art to which the subject matter of the present disclosure belongs. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the context of the specification and the relevant art, and will not be interpreted in an idealized or overly formal form unless expressly defined herein otherwise. Additionally, terms such as "first" and "second" are only used to distinguish one component (or a part of a component) from another component (or another part of a component).

[0032] Topological potential is a concept that constructs a virtual potential field in a network topology space, aiming to describe the importance and potential influence of nodes in the network through a mathematical model. This influence not only depends on the attributes of the nodes themselves but also is affected by neighboring nodes.

[0033] The embodiments of the present disclosure provide a hierarchical network topology layout method based on topological potential. Through physical and logical analysis of network nodes and connection relationships, key nodes are located, and based on this information, logical partitioning and hierarchical layout of the network are achieved. By introducing the concepts of topological potential and topological potential entropy, the algorithm can optimize the visualization and layout efficiency of the network topology while considering the service attributes of nodes, avoiding problems such as excessive node accumulation or chaotic layout.

[0034] To further elaborate on the embodiments of the present disclosure in detail, Figure 1 It is an exemplary flowchart of a hierarchical network topology layout method 100 based on topological potential according to an embodiment of the present disclosure.

[0035] At Figure 1At the frame S102, calculate the topological potential of the node based on the location information, connection relationship, and service attributes of the network node, and locate the key node based on the topological potential value.

[0036] A typical network topology includes: Nodes: Represent devices or services in the network, usually including routers, switches, servers, terminals, etc. Links: Connections between nodes, indicating data transmission paths. Service attributes: Attributes related to nodes and links, such as bandwidth, latency, node type, service type, traffic volume, fault tolerance, etc. In a large-scale network, the network can be divided into multiple layers according to the requirements of functions and data traffic. Each layer has different roles and functions, for example, including the core layer, aggregation layer, and access layer. The core layer is responsible for high-speed data forwarding between different regions. The devices in the core layer usually have very high bandwidth and low latency. The node types are core switches and core routers. The aggregation layer is used to aggregate the traffic from the access layer to the core layer, usually for policy control, load balancing, and traffic filtering. The node types are aggregation switches, distributed routers, and load balancers. The access layer provides services for end users or devices to access the network, connecting user devices (such as computers, IP phones, wireless devices, etc.) to the network. The node types are access switches, wireless access points (APs), and terminal devices.

[0037] In some embodiments of the present disclosure, according to the known location and connection relationship of network nodes, a network topology G(N, V) is constructed, where N represents a non-empty finite set of nodes in the network, including all nodes in the network, and V is the edge set composed of links between nodes. The topological potential φ(i) of each node i is determined by the quality of the node, the shortest path to other nodes, and the influence factor. The topological potential φ(i) at node i can be calculated based on the following formula:

[0038]

[0039] In the formula, m j represents the quality of node j, which can be defined according to the attributes of the node (such as data traffic, degree, etc.), and d j→i represents the shortest path length from node j to node i, σ is the influence factor, controlling the range of influence between nodes, and n is the number of nodes.

[0040] Then, optimize the influence factor according to the topological potential entropy value, and calculate the topological potential value of the node according to the optimized influence factor and the attribute evaluation matrix of the node to obtain the importance of each node.

[0041] Among them, the topological potential entropy H(σ) is used to measure the distribution uniformity of the node topological potential in the network. When the value of σ is different, the influence degree between nodes changes, thereby affecting the potential distribution of the network. By making the topological potential entropy H(σ) reach the minimum value, the influence factor σ is selected to achieve an optimized layout effect. The topological potential entropy value can be calculated by the following formula:

[0042]

[0043] In the formula, represents the normalized probability of the topological potential of node i. By minimizing the topological potential entropy value, a suitable influence factor is selected to optimize the hierarchical layout of the network topology. The optimal value of σ can effectively balance the influence degree between nodes and avoid a too uniform or too concentrated potential distribution.

[0044] According to the user preferences, the inherent attributes of the nodes are weighted to form an attribute evaluation matrix. The attribute evaluation matrix is used as the quality of the nodes. The inherent attributes of the nodes include node type (such as server node, router or switch node, terminal node), node value, type of service software installed on the node, node access volume, node egress bandwidth, etc.

[0045] That is to say, the node quality is not a fixed value, but is calculated and mapped according to various inherent attributes of the nodes. Different users or different application scenarios may assign different weights to these attributes. For example, for a data center network, the node value and egress bandwidth may be assigned higher weights; for a service-oriented network, the node type and access volume may be more important. After weighting the above-mentioned inherent attribute scores, an attribute evaluation matrix can be generated. Each row of this matrix represents a node, and each column represents an attribute or evaluation criterion. The quality of the node is equal to the sum of the weighted scores of the node on each attribute:

[0046] m j = w 1 p 1 (j) + w 2 p 2 (j) + … + w n p n (j)

[0047] Among them, p 1 (j), p 2 (j), …, p n (j) represent the scores of node j on different attributes, and w 1 , w 2 , …, w n represent the weights of the corresponding attributes; m j is the quality score of node j.

[0048] According to the optimized influence factors and attribute evaluation matrix, calculate the topological potential value of each node in the network to obtain the importance of each node. The importance of a node determines the hierarchical position and display method of the node in the network diagram. Locate the key nodes in the network topology according to the sorting of the node importance.

[0049] Subsequently, at Figure 1 frame S104, perform a clustered hierarchical layout on the network nodes according to the key nodes and their business regions to obtain multiple focus points.

[0050] To more effectively display the network topology, the hierarchical layout often combines with the regional optimization strategy to optimize the node positions and connection relationships while considering the node levels, avoiding intersections and overlaps.

[0051] In some embodiments of the present disclosure, if the key nodes have clear business partitions, then according to the business logic of the key nodes and the network topology structure, take the key nodes and their subnets as a focus point, fold up the subnet content representing the nodes, and use the focus point to represent the subnet area. Among them, the basis for node aggregation can include the data volume and access frequency. The larger the data volume processed or transmitted by a node, it usually represents its importance in the network, and these nodes are given priority. A high access frequency between nodes indicates that they are active points in the network and need to be better displayed in the layout. Through aggregation, some nodes with relatively close relationships and similar functions are combined together to reduce complexity. The aggregation methods include multiple dimensions such as physical location and functional role. Layout the aggregated nodes hierarchically according to their importance, business attributes, and topological positions. High-level nodes (such as core nodes) are located in the upper layer of the layout, while low-level nodes (such as edge nodes, terminal devices) are located in the lower layer. In this way, it is convenient for users to quickly understand the node relationships at different levels when viewing the network topology.

[0052] In the case of no business partition or business logic, then according to the topological potential value of each node, use the breadth-first search algorithm to cluster the network nodes to obtain multiple focus points.

[0053] In some embodiments of the present disclosure, select the node with the largest topological potential value, take the node with the largest potential value as the center, and use the breadth-first search algorithm to find the set of shortest path nodes N = {N 1 , N 2 ,..., N m} directly connected to it. Take each node in the node set as the center and continue to perform breadth-first search downward until all leaf nodes without subsequent connections are found. A subset with N i as the representative node forms a topological cluster. By calculating the representative node N iDetermine the shortest distance between different topological clusters based on the distances between other representative nodes. Based on the shortest distance between different topological clusters, the nodes in each layer can be regionally optimized, that is, the edge crossings between nodes can be reduced and unnecessary overlaps can be avoided.

[0054] After completing the hierarchical layout and regional optimization of the nodes, at Figure 1 in block S106, multiple aggregation points are used as the first layer of the network topology, the representative nodes of the first layer are visually displayed, and based on the user's interaction operations on the aggregation points, the hierarchical topology of the subnet area where the representative nodes are located is displayed.

[0055] The visualization of the network hierarchical topology can be implemented based on the B / S architecture using web technologies (such as HTML, CSS, JavaScript, D3.js). The front end can achieve zooming in, zooming out, dragging of the topological structure and viewing of node information. The back end uses lightweight frameworks such as Flask or Django to be responsible for reading the data set and calculating the network topology layout. When calculating the network topology layout, a hierarchical layout algorithm (such as described in step S104) is adopted, and an API interface is provided to return the result of the layout. The back end also calculates the node distribution of each layer according to the number of subnet levels set by the user and the maximum number of nodes that can be displayed in each level, and controls the display effect of each level of the canvas.

[0056] In some embodiments of the present disclosure, according to the network topology including nodes, links and subnet areas, the key nodes of multiple aggregation points are used as representative nodes and displayed as the first layer of the network topology, and the subnet content of the representative nodes is folded. Based on the user's interaction operations, the hierarchical layout of the network topology is visually displayed based on the B / S architecture, and the layout calculation and subnet topology loading display of the subnet content of the representative nodes in the first layer are performed.

[0057] Specifically, the front end is used to listen for user interaction events, request the back-end API, obtain the subnet topology data of the nodes, and use the visualization library to render and display the topology structure of the subnet. Among them, the user interaction events include node click and mouse hover events, setting the number of subnet levels and the maximum number of nodes that can be displayed in each level, and controlling the display effect in the same level of the canvas; the back end is used to calculate and return the corresponding subnet topology data according to the front-end request and the user's settings.

[0058] By means of interaction methods such as clicking on the aggregation point or mouse hovering, the subnet topology of each representative node is expanded. The user can enter the subnet area where the representative node is located and view the detailed topology inside the subnet. The expanded subnet area can display the detailed topology structure of the current subnet, and the display of nodes and links is flexibly adjusted according to the subnet level and topology structure. In addition, the expansion process should support interactive functions such as animation transition, zooming and panning, so that users can easily browse the detailed structure of the network.

[0059] That is to say, in the visualization interface of the network topology, the functions of folding and unfolding subnets are provided, and the detailed structure of subnets can be hidden or displayed as needed, further improving the user experience. When a subnet is unfolded, the topology inside the subnet can be clearly displayed, avoiding congestion and information overload when displaying a large network. In this way, users can focus on a certain part of the network according to specific needs, view details or adjust the layout. Through hierarchical topology calculation, the topology structure of each subnet or area is displayed layer by layer. During the hierarchical process, the display methods of different levels of topology are different. For the core level, key elements such as connection nodes and links are displayed; for the end nodes, more detailed node information and traffic conditions are displayed.

[0060] Test with actual network topology examples and compare the network layout effects before and after applying the algorithm. Different types of network topologies (such as communication networks, data center networks, etc.) can be selected for verification, and indicators such as the accuracy of the algorithm, layout effect, and user experience are evaluated, including:

[0061] Clarity of topology structure: Through hierarchical layout, verify whether the layout between nodes has been effectively optimized and whether the problems of chaos and overlap have been solved.

[0062] Computing efficiency: Evaluate the computing efficiency of the algorithm in large-scale network topologies and whether it meets the requirements of real-time computing.

[0063] User experience: Through interactive operation tests, verify whether the display of the network topology is smooth and easy to understand during the operation process of users, and whether it can effectively improve the efficiency of fault troubleshooting and network management.

[0064] Figure 2 It is a schematic block diagram of a hierarchical network topology layout device based on topological potential according to an embodiment of the present disclosure. As Figure 2 shown, the device 200 may include a processor 210 and a memory 220 storing a computer program. When the computer program is executed by the processor 210, the device 200 can execute the steps of the method 100 as Figure 1 shown. In one example, the device 200 can calculate the topological potential values of nodes based on the position information, connection relationships, and service attributes of known network nodes, locate key nodes based on the topological potential values; perform clustering and hierarchical layout on network nodes according to the key nodes and their service areas to obtain multiple clustering points; and use the multiple clustering points as the first level of the network topology, visually display the representative nodes of the first level, and perform hierarchical topology display on the subnet areas where the representative nodes are located based on the user's interactive operations on the clustering points.

[0065] In some embodiments of the present disclosure, the apparatus 200 may construct a network topology G(N, V) according to the positions and connection relationships of known network nodes, where N represents a non-empty finite set of nodes in the network, and V is the set of edges of the links between the nodes; calculate the topological potential φ(i) at node i:

[0066]

[0067] In the formula, m j represents the mass of node j, d j→i represents the shortest path length from node j to node i, σ is the influence factor, and n is the number of nodes; optimize the influence factor according to the topological potential entropy value, and calculate the topological potential value of the node according to the optimized influence factor and the attribute evaluation matrix of the node to obtain the importance of each node. And the apparatus 200 may locate the key nodes in the network topology according to the importance ranking of the nodes. These key nodes have relatively high topological potential values and play a key role in the stability, efficiency, and management of the network.

[0068] In some embodiments of the present disclosure, the apparatus 200 may calculate the topological potential entropy value through the following formula:

[0069]

[0070] In the formula, represents the normalized probability of the topological potential of node i, and select the optimized influence factor by minimizing the topological potential entropy value; weight the inherent attributes of the nodes according to user preferences to form an attribute evaluation matrix, and use the attribute evaluation matrix as the mass of the node. The inherent attributes of the node include node type, node value, type of service software installed on the node, node access volume, and node egress bandwidth. Among them, the mass of the node is equal to the sum of the weighted scores of the node on each inherent attribute:

[0071] m j = w 1 p 1 (j) + w 2 p 2 (j) + … + w n p n (j), p 1 (j), p 2 (j), …, p n (j) represent the scores of node j on different attributes, w 1 , w 2 , …, w n represent the weights of the corresponding attributes; m j is the mass score of node j. The apparatus 200 may calculate the topological potential value of each node in the network according to the optimized influence factor and the attribute evaluation matrix to obtain the importance of each node.

[0072] In some embodiments of the present disclosure, the apparatus 200 may select the node with the largest topological potential value, and with the node having the largest potential value as the center, use the breadth-first search algorithm to find the set of shortest-path nodes N = {N 1 , N 2 ,..., N m} that are directly connected to it; with each node in the node set as the center, continue to perform breadth-first search downward until all leaf nodes without subsequent connections are found, and a subset of nodes represented by N i forms a topological cluster; and by calculating the distances between the representative node N i and other representative nodes, determine the shortest distances between different topological clusters, so that the nodes of each layer can be regionally optimized according to the shortest distances between different topological clusters.

[0073] In some embodiments of the present disclosure, the apparatus 200 may, according to a network topology including nodes, links, and subnet regions, use the key nodes of multiple aggregation points as representative nodes and display them as the first layer of the network topology, and fold the subnet content of the representative nodes; and based on user interaction operations, visually display the hierarchical layout of the network topology based on the B / S architecture, and perform layout calculation and subnet topology loading display on the subnet content of the representative nodes of the first layer.

[0074] In an embodiment of the present disclosure, the processor 210 may be, for example, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a processor based on a multi-core processor architecture, etc. The memory 220 may be any type of memory implemented using data storage technology, including but not limited to random access memory, read-only memory, semiconductor-based memory, flash memory, disk memory, etc.

[0075] In addition, in an embodiment of the present disclosure, the apparatus 200 may also include an input device 230, such as a keyboard, a mouse, etc., for inputting node positions, connection relationships, service attributes, etc., in order to calculate topological potential values. Additionally, the apparatus 200 may further include an output device 240, such as a display, etc., for visually displaying, first, the aggregation points (representative nodes of the first layer), and each aggregation point will present the cluster node information of this layer. Through interaction operations by the user on the aggregation points, the subnet where a certain aggregation point is located can be folded or unfolded, that is, the specific network topology to which the aggregation point belongs is displayed through user interaction. The interactive display can not only view the first layer of the aggregation points, but also, as needed, drill down to view the specific nodes and connection relationships in the subnet. When the user clicks on an aggregation point, the system will display a more detailed topological structure based on the cluster and layer it represents, and the nodes and service flows in the subnet will be displayed layer by layer.

[0076] In other embodiments of the present disclosure, there is also provided a computer-readable storage medium storing a computer program, wherein the computer program, when executed by a processor, can implement the steps of the hierarchical network topology layout method 100 based on topological potential as shown in Figure 1 Figure 238.

[0077] In summary, according to the hierarchical network topology layout method and apparatus based on topological potential in the embodiments of the present disclosure, the problems of chaos and node overlap existing in the traditional network topology layout are effectively solved. This algorithm not only improves the visualization effect of the network topology diagram but also optimizes the user interaction experience, especially in the folding and unfolding operations of subnets. Its core advantage lies in using topological potential and service attribute analysis to identify key nodes and effectively organizing nodes and subnets through a hierarchical layout method. The interactive display method further enhances the user's operation experience. Finally, through verification tests, it is confirmed that this algorithm can effectively solve the chaos problem in network topology layout and improve the work efficiency of users in network management and fault troubleshooting.

[0078] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of apparatuses and methods according to multiple embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of an instruction, and the module, program segment, or part of an instruction contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0079] Unless the context clearly indicates otherwise, the singular forms of words used in this specification and the appended claims include the plural, and vice versa. Thus, when referring to the singular, the corresponding plural is usually included. Similarly, the terms "comprising" and "including" will be interpreted as inclusive rather than exclusive. Likewise, the term "including" and "or" should be interpreted as inclusive, unless such an interpretation is clearly prohibited in this specification. Where the term "example" is used in this specification, especially when it is located after a group of terms, the "example" is merely exemplary and illustrative and should not be considered exclusive or extensive.

[0080] Further aspects and scope of adaptability will become apparent from the description provided herein. It should be understood that various aspects of the present application can be implemented alone or in combination with one or more other aspects. It should also be understood that the description and specific embodiments herein are for illustrative purposes only and are not intended to limit the scope of the present application.

[0081] The above has described several embodiments of the present disclosure in detail. However, it is obvious that those skilled in the art can make various modifications and variations to the embodiments of the present disclosure without departing from the spirit and scope of the present disclosure. The protection scope of the present disclosure is defined by the appended claims.

Claims

1. A hierarchical network topology layout method based on topological potential, characterized in that: include: Calculate the topological potential value of the node according to the location information, connection relationship and service attributes of the known network nodes, and locate the key nodes based on the topological potential value; Clustering and layering the network nodes according to the key nodes and the business areas where they are located to obtain multiple clustering points; as well as The multiple clusters are used as the first level of the network topology, and representative nodes of the first level are visually displayed. Based on the user's interactive operations on the clusters, a hierarchical topology display of the subnet area where the representative nodes are located is performed.

2. The method for topological layout of a hierarchical network based on topological potential according to claim 1, characterized in that: The calculating the topological potential value of the node according to the location information, connection relationship and service attribute of the network node, and locating the key node based on the topological potential value includes: Based on the locations and connection relationships of known network nodes, a network topology G(N,V) is constructed, where N represents a non-empty finite set of nodes in the network and V is the edge set of links between nodes. Calculate the topological potential φ(i) at node i: In the formula, m j represents the quality of node j, d j→i represents the shortest path length from node j to node i, σ is the impact factor, and n is the number of nodes; Optimizing the impact factor according to the topological potential entropy value, and calculating the topological potential value of the node according to the optimized impact factor and the attribute evaluation matrix of the node to obtain the importance of each node; and The key nodes in the network topology are located according to the order of their importance.

3. The method for topological layout of a hierarchical network based on topological potential according to claim 2, characterized in that: The influencing factor is optimized according to the topological potential entropy value, and the topological potential value of the node is calculated according to the optimized influencing factor and the attribute evaluation matrix of the node to obtain the importance of each node, including: The topological potential entropy value is calculated by the following formula: In the formula, Represents the normalized probability of the topological potential of node i, and the optimized influencing factor is selected by minimizing the topological potential entropy value; Weighting the inherent attributes of the node according to user preferences to form an attribute evaluation matrix, and using the attribute evaluation matrix as the quality of the node, the inherent attributes of the node including node type, node value, type of business software installed on the node, node access volume, and node export bandwidth; and According to the optimized influencing factors and the attribute evaluation matrix, the topological potential value of each node in the network is calculated to obtain the importance of each node.

4. The method for topological layout of a hierarchical network based on topological potential according to claim 3 is characterized in that: The quality of the node is equal to the sum of the weighted scores of each intrinsic attribute of the node: m j =w1p1(j)+w2p2(j)+…+w n p n (j) Among them, p1(j),p2(j),…,p n (j) represents the score of node j on different attributes, w1,w2,…,w n Represents the weight of the corresponding attribute; m j is the quality score of node j.

5. The method for topological layout of a hierarchical network based on topological potential according to claim 1, characterized in that: The clustering and hierarchical layout of the network nodes according to the key nodes and the service areas where they are located, to obtain multiple cluster points includes: If the key node has a clear business partition, the key node and its subnet are regarded as a cluster point according to the business logic of the key node and the network topology structure, and the cluster point represents the subnet area; and In the absence of business partitions or business logic, the network nodes are clustered using a breadth-first search algorithm based on the topological potential value of each node to obtain multiple clusters.

6. The method for topological layout of a hierarchical network based on topological potential according to claim 5, characterized in that: According to the topological potential value of each node, the network nodes are clustered using a breadth-first search algorithm to obtain multiple cluster points including: Select the node with the largest topological potential value, take the node with the largest potential value as the center, and use the breadth-first search algorithm to find the shortest path node set N = {N1, N2, ..., N m }; Taking each node in the node set as the center, continue to perform breadth-first search downward until all leaf nodes without subsequent connections are found, with N i forming a topological cluster for a subset of representative nodes; and By calculating the representative node N i The distances to other representative nodes determine the shortest distances between different topological clusters, so that the nodes at each layer can be regionally optimized based on the shortest distances between different topological clusters.

7. The method for topological layout of a hierarchical network based on topological potential according to claim 1, characterized in that: The method of using the plurality of clusters as the first level of the network topology, visually displaying the representative nodes of the first level, and displaying the hierarchical topology of the subnet area where the representative nodes are located based on user interaction operations includes: According to the network topology including nodes, links and subnet areas, the key nodes of the plurality of clusters are represented as representative nodes, displayed as the first level of the network topology, and the subnet contents of the representative nodes are collapsed; and According to user interaction operations, the hierarchical layout of the network topology is visualized based on the B / S architecture, and the layout calculation of the subnet content and the subnet topology loading and display are performed on the representative nodes of the first level.

8. The method for topological layout of a hierarchical network based on topological potential according to claim 7, characterized in that: The visual display of the hierarchical layout of the network topology based on the B / S architecture according to the user interaction operation, and the subnet content layout calculation and subnet topology loading display for the representative node of the first level include: The front-end listens to user interaction events, requests the back-end API, obtains the subnet topology data of the node, and uses the visualization library to render and display the subnet topology structure, where the user interaction events include node click and mouse hover events, sets the number of subnet levels and the maximum number of nodes that can be displayed at each level, and controls the display effect in the same level canvas; The backend calculates and returns corresponding subnet topology data according to the frontend request and the user's settings.

9. A hierarchical network topology layout device based on topological potential, characterized in that: The device comprises: at least one processor; and at least one memory storing a computer program; Wherein, when the computer program is executed by the at least one processor, the device is enabled to perform the steps of the hierarchical network topology layout method based on topological potential according to any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the computer program implements the steps of the hierarchical network topology layout method based on topological potential according to any one of claims 1 to 8.

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