Network visualization method based on hierarchical layout and clustering algorithm

Abstract

The invention relates to a network visualization method based on hierarchical layout and a clustering algorithm, which belongs to the field of data visualization, and comprises the following steps: calling an API (Application Program Interface) to obtain and process circuit data, and clustering the data by using a dynamic clustering algorithm; layering according to the data processed by the precondition clustering algorithm, wherein each cluster is used as a layer; according to the structure of the clustering result, determining the sequence of each hierarchy, and mapping the clustering result to the hierarchy level to form node sets of different hierarchies; optimizing a node sequence for each level, and reducing intersection of edges; calculating the coordinate of each node according to the layering and cross reduction results; drawing according to the calculated node coordinates so as to convert the processed data into a topological graph; and acquiring alarm data in real time, performing correlation analysis on the alarm data, and identifying potential fault causes. According to the method, various kinds of complex network data can be efficiently processed, and visual display and dynamic analysis of a network structure are achieved.

Description

Technical Field

[0001] The invention belongs to the field of data visualization and network management, and in particular relates to a network visualization method based on hierarchical layout and clustering algorithm. Background Art

[0002] With the development of society, network devices and data volume have shown explosive growth, and network topology has become more and more complex. Traditional network visualization methods are often difficult to handle large-scale data, and have problems such as poor layout effect, slow data loading, and poor interactivity, making it difficult for users to intuitively understand the overall structure and key connections of the network.

[0003] Traditional network topology display methods are mostly force-directed layouts. Although they can intuitively display the relationship between nodes, for networks with obvious hierarchical structures, force-directed layouts may not be able to effectively highlight the relationship between hierarchies; a large number of nodes and connections make the topology diagram complex and difficult to intuitively display and analyze. Summary of the invention

[0004] In view of the above deficiencies in the prior art, the purpose of the present invention is to provide a network visualization method based on hierarchical layout and clustering algorithm, which can efficiently process various types of complex network data and realize intuitive display and dynamic analysis of network structure.

[0005] The present invention proposes a network visualization method based on hierarchical layout and clustering algorithm, comprising: S1, calling an API interface to obtain circuit data and processing the data to be suitable for clustering algorithm processing, using a dynamic clustering algorithm to cluster the data and generate a clustering result;

[0006] S2, stratifying the data processed by the precondition clustering algorithm, with each cluster as a layer; determining the order of each layer according to the structure of the clustering result, sorting the layers from low to high according to the bandwidth and delay of the characteristic vector of the clustering algorithm, mapping the clustering result to the hierarchical level, forming a node set of different levels, and performing the next step of optimizing the sorted data storage;

[0007] S3, optimizes the node order at each level to reduce edge crossings;

[0008] S4, calculating the coordinates of each node according to the results of stratification and cross reduction, including: obtaining a two-dimensional array according to the results of stratification and cross reduction, and then determining the horizontal coordinate of each layer according to the two-dimensional array and the node spacing set in advance; then processing each layer, and calculating the vertical coordinate for each device according to the length of each layer array and the middle index, so as to perform the next step of operation;

[0009] S5, drawing according to the calculated node coordinates to convert the processed data into a topological map, wherein the topological map supports user interactive viewing;

[0010] S6, after drawing, obtain alarm data in real time, perform correlation analysis on the alarm data, identify potential causes of failures, and mark abnormal equipment.

[0011] Furthermore, in S1, the called circuit data is cleaned, converted and aggregated so that the processed data is suitable for clustering algorithm processing.

[0012] Furthermore, in S1, clustering the data using a dynamic clustering algorithm includes:

[0013] The data is preprocessed, and the preprocessed data is initially clustered using the K-means algorithm to obtain K initial clusters. For each initial cluster obtained by the K-means algorithm, the density clustering DBSCAN is used to further refine the clustering to obtain a density clustering result. Based on the density clustering result, the density clustering structure that K-means failed to accurately cluster is found; the clustering results are dynamically updated according to the changes in the properties of nodes and links; the calculation is iterated until the clustering results are stable, and the final clustering results are output.

[0014] Furthermore, in S3, the centroid method is used to reduce the intersection of edges, including:

[0015] (1) Obtain the sorted two-dimensional array layer data;

[0016] (2) Scan layers from top to bottom

[0017] Starting from the second layer to the last layer of the loop, the position of the first layer is first indicated by a subscript;

[0018] For each node s in the i-th layer, where i = 2 to n, n is the total number of layers, start the loop;

[0019] Calculate the centroid value of node s = the average value of the positions of all its adjacent nodes in layer i-1;

[0020] Sort the nodes in layer i according to their centroid values ​​and update their positions;

[0021] (3) Scan layers from bottom to top

[0022] For each node s in the i-th layer, where i = n-1 to 1;

[0023] Calculate the centroid value of node s = the average value of the positions of all its adjacent nodes in layer i+1;

[0024] Sort the nodes in layer i according to their centroid values ​​and update their positions;

[0025] (4) Repeat steps (2) and (3) until the number of edge crossings no longer decreases.

[0026] Furthermore, in S4, nodes at the same level are arranged horizontally, and nodes at different levels are arranged vertically.

[0027] Further, in S5, the page components of the topology map include: a networking component, an end-to-end component, a topology display component, a device attribute component, a link attribute component, a port traffic component and an alarm data component.

[0028] Furthermore, the networking component is used to provide data information query and display the networking;

[0029] The end-to-end component is used to display a topology diagram between the two ends by selecting nodes or devices at the two ends respectively;

[0030] The topology display component is used to perform data processing and topology map display;

[0031] The device attribute component is used to display relevant information of the device;

[0032] The link attribute component is used to display the relevant attributes of the link;

[0033] The port traffic component is used to display the port traffic information at both ends of the link;

[0034] The alarm data component is used to display alarm-related information.

[0035] Further, in S5, the topology view supports a custom view mode and a multi-view synchronous display mode, wherein:

[0036] Customized view mode: supports users to freely switch between global view and local view;

[0037] Multi-view synchronous display: Supports multi-view synchronous display function, allowing users to simultaneously view multiple network topology views of different dimensions, with real-time synchronous updates, providing all-round network monitoring.

[0038] Furthermore, in S5, the topology map supports the user to drag and place nodes, and adjust the layout of the network topology map by dragging nodes.

[0039] Furthermore, in S6, when an abnormal condition of the equipment is detected, the alarm information is collected and processed, an alarm correlation analysis model is established, and the alarm information is correlated and analyzed by combining the machine learning algorithm and the expert knowledge base to identify the potential cause of the fault and provide processing suggestions to the user.

[0040] The beneficial effects of the present invention are as follows:

[0041] 1. Efficient data preprocessing and dynamic clustering algorithm optimization: This paper proposes an efficient data preprocessing mechanism based on cutting-edge algorithms, which can intelligently optimize the processing of massive data through advanced task scheduling algorithms. The dynamic clustering algorithm combining K-means and DBSCAN is used to accurately disassemble and cluster complex networking data in real time, and realize adaptive adjustment of network nodes and their connection relationships. This mechanism can respond to real-time dynamic changes in network data and automatically optimize clustering results, thereby ensuring the clear hierarchy of the network structure and the efficiency of visual display.

[0042] 2. Network visualization calculation method based on hierarchical layout: This invention uses advanced hierarchical layout algorithms to perform fine optimization specifically for networks with complex hierarchical structures, greatly improving the intuitiveness and interpretability of network structures. This method optimizes the multi-dimensional data layout, making the visualization of complex networks more concise and clear, allowing users to more easily understand the internal hierarchical relationships and overall architecture of the network, significantly improving the efficiency and accuracy of data analysis.

[0043] 3. Multi-perspective dynamic view management: The present invention provides a multi-perspective dynamic view management function, allowing users to freely switch between global and local views, and supports the simultaneous display of multi-dimensional network topology diagrams. Through the real-time synchronous update mechanism, all-round monitoring and management of the network is achieved. This technology not only enhances the interactivity and flexibility of the system, but also provides users with a multi-level, multi-angle in-depth data analysis experience, significantly improving the convenience and practicality of network operations.

[0044] 4. Interface rendering and interactive performance optimization: In the case of dealing with large-scale data sets, the present invention adopts hierarchical folding computing technology, which greatly reduces the amount of rendering data, thereby significantly improving the rendering efficiency of the interface. Through this optimization, users can still enjoy a smooth operating experience when processing massive data, and the aesthetics of the interface design and the interactive response speed are fully guaranteed in a high-load environment, further optimizing the overall user experience.

[0045] 5. Intelligent alarm correlation analysis and root cause diagnosis: This invention integrates intelligent alarm correlation analysis function, which can intelligently highlight alarm information when abnormal situation is detected, and provide accurate fault root cause analysis and professional repair suggestions. Through automated alarm information correlation and analysis, users can quickly locate and solve potential problems, greatly improving the efficiency of fault diagnosis and processing. This technology effectively reduces the cost of network maintenance and significantly improves the reliability and stability of the system.

[0046] 6. Optimize nodes and connections: The present invention abandons the one-time display of all nodes on the initial page, chooses to display the nodes and connections of the first few layers, and then asynchronously requests the subsequent nodes, and quickly displays them when the user needs them, which greatly improves the drawing efficiency. Dynamic clustering algorithm is used in data processing to automatically classify data.

[0047] 7. Layout and visualization: The layout of the present invention adopts a hierarchical layout algorithm. Since the nodes are selected and displayed in layers, the problem of large amount of calculation that may lead to performance bottlenecks can be solved, and not too many nodes will be calculated at one time.

[0048] 8. Interaction design: Users of the present invention can interact with the graph (such as clicking on a node to view detailed information, dragging a node to rearrange it, etc.), customize the view, select the information to view, and open multiple windows at a time to compare information. Users can choose different view modes such as global view, local view, etc. The user's operations in one view (such as dragging, zooming in, zooming out, etc.) will be automatically updated in other views.

[0049] 9. Real-time fault monitoring: When an abnormal situation is detected, the present invention highlights the alarm information, introduces the intelligent alarm correlation analysis function, combines the database, performs correlation analysis on the alarm information, and provides the root cause of the fault and processing suggestions. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings are only used to illustrate specific embodiments and are not considered to limit the present invention. In the entire drawings, the same reference symbols represent the same components. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0051] Figure 1 A flow chart of a network visualization method based on hierarchical layout and clustering algorithm according to an embodiment of the present invention;

[0052] Figure 2 A schematic diagram of a page component of an embodiment of the present invention;

[0053] Figure 3 A flowchart of the hierarchical layout of an embodiment of the present invention;

[0054] Figure 4 Flow chart of the centroid method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making creative work should fall within the scope of protection of the present invention.

[0056] Furthermore, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily obscuring the concepts disclosed in the present invention.

[0057] In the description of the present invention, it should be noted that, unless otherwise clearly specified and limited, the orientation or position relationship indicated by the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inside", "outside", etc. is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation of the present invention. In addition, the terms "first", "second", and "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. The terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be a connection between the two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0058] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of methods and systems consistent with some aspects of the present invention as detailed in the appended claims.

[0059] The present invention proposes a network visualization method based on hierarchical layout and clustering algorithm. The method involves displaying a device topology diagram based on circuit data, and realizes large-scale network data visualization through hierarchical layout and dynamic clustering algorithm. The method is suitable for the display and analysis of various complex network structures, such as the Internet of Things, 5G network, data center network, etc.

[0060] like Figure 1As shown, the network visualization method based on hierarchical layout and clustering algorithm provided by the present invention includes the following steps:

[0061] S1, calling the API interface to obtain circuit data and process the data to be suitable for clustering algorithm processing, using the dynamic clustering algorithm to cluster the data and generate clustering results.

[0062] In this step, data preprocessing is first performed: circuit data is obtained by calling the API interface, data cleaning, conversion and aggregation are performed, and abnormal data detection and automatic repair functions are added. Then distributed computing is performed: the back-end data uses MapReduce and ApacheFlink frameworks to process large-scale data, optimize the computing task scheduling algorithm, and improve processing efficiency. Dynamic clustering algorithm: Combine K-means and DBSCAN algorithms, and dynamically adjust the clustering center and clustering results by continuously updating the attribute data of nodes and links.

[0063] S11, data preprocessing

[0064] Circuit data is obtained by calling the API interface, and the called circuit data is cleaned, converted and aggregated so that the processed data is suitable for clustering algorithm processing, and abnormal data detection and automatic repair functions are added.

[0065] For missing bandwidth and latency data, we choose to delete records with more missing values ​​and use statistical methods to identify and process outliers to ensure data quality.

[0066] Generate new features based on node labels, bandwidth, and latency of nodes and links.

[0067] Normalize and combine the selected features into a feature vector:

[0068] feature vector = [label, bandwidth, delay].

[0069] S12, initial clustering (K-means)

[0070] The preprocessed data is clustered using the K-means algorithm to obtain K initial clusters. This step allows nodes to be grouped quickly, which helps reduce the computational complexity of DBSCAN.

[0071] The computational complexity of DBSCAN is usually O(N 2 ) Because for each data point, the distance to all other points needs to be calculated to determine whether the points in the neighborhood meet the density condition, so it is N 2Through the preliminary clustering of K-means, the data points are quickly divided into K clusters, so that when performing DBSCAN density clustering, it can be run within each cluster instead of on the entire data set. In summary, through the preliminary clustering of K-means, the computational complexity of DBSCAN can be reduced from O(N 2 ) is reduced to O(N 2 / K).

[0072] S13, density clustering (DBSCAN)

[0073] For each initial cluster obtained by the K-means algorithm, DBSCAN is used to further refine the clustering to obtain the density clustering results. According to the density clustering results, we can find the density clustering structure that K-means failed to accurately cluster.

[0074] S14, dynamic adjustment

[0075] According to the changes in node and link properties, the clustering results are dynamically updated. The distance between nodes and link weights are recalculated, and the cluster centers and clustering results are adjusted.

[0076] S15, iterate the calculation until the clustering result is stable, and output the final clustering result.

[0077] In summary, the present invention adopts a dynamic clustering algorithm: first, the K-means algorithm is used for preliminary clustering, and then DBSCAN is used to further refine the clustering. This process can automatically adjust the clustering results according to the real-time changes of network data to ensure the hierarchy and display effect of the network structure.

[0078] S2, firstly, node stratification is performed: the data processed by the precondition clustering algorithm is stratified, with each cluster as a layer. Then, hierarchical sorting is performed: the order of each layer is determined according to the structure of the clustering result, and the layers are sorted from low to high according to the bandwidth and delay of the characteristic vector of the clustering algorithm. The clustering results are mapped to the hierarchical level to form a node set of different levels, and the sorted data is stored for the next step of optimization.

[0079] S3, optimize the node order for each level and reduce the intersection of edges. The intersection of edges should be as small as possible to increase the aesthetics. The path of the edge can be a straight line as much as possible.

[0080] Specifically, Figure 4 As shown, the centroid method is used to reduce the intersection of edges, including:

[0081] (1) Obtain the sorted two-dimensional array layer data.

[0082] (2) Scan layers from top to bottom

[0083] Starting from the second layer to the last layer of the loop, the position of the first layer is first indicated by a subscript;

[0084] For each node s in the i-th layer, where i=2 to n and n is the total number of layers, start the loop; for example, the connected nodes of node s=1 in the previous layer are 1, 3, and 5 respectively, then the calculated centroid value is 3. After the centroid values ​​of all nodes in this layer are calculated, they are reordered and the positions are initialized with subscripts for easy calculation.

[0085] Calculate the centroid value of node s = the average value of the positions of all its adjacent nodes in layer i-1;

[0086] Sort the nodes in layer i by their centroid values.

[0087] (3) Scan the layers from bottom to top (using a similar calculation method as scanning from top to bottom)

[0088] For each node s in the i-th layer, where i = n-1 to 1;

[0089] Calculate the centroid value of node s = the average value of the positions of all its adjacent nodes in layer i+1;

[0090] Sort the nodes in layer i by their centroid values.

[0091] (4) Repeat steps (2) and (3) until the number of edge crossings no longer decreases.

[0092] S4, calculates the coordinates of each node according to the results of stratification and cross-reduction.

[0093] Specifically, according to the results of layering and reducing crossover, a two-dimensional array can be obtained. Then, according to the two-dimensional array and the node spacing set in advance, the x-coordinate of each layer can be determined. Then, for each layer, the array length (length) and the middle index (middle) of each layer are processed. If length is an even number, middle = length / 2-1 (round down and subtract 1, because the index usually starts from 0); if length is an odd number, middle = (length-1) / 2.

[0094] (1) If the array length is an even number:

[0095] If inds is less than or equal to middle, the device is located in the upper half, and the calculation formula for its vertical axis position y is: y = 60 + (inds-middle) * 120. It is assumed that 60 is the y coordinate base of the first device in the upper half.

[0096] If inds is greater than middle, the device is located in the lower half, and the calculation formula for its vertical axis position y is: y = -60 + (inds-middle) * 120. It should be noted that (inds-middle) here is actually equal to (inds-middle-1), because the count starts from the first device in the lower half.

[0097] (2) If the array length is an odd number:

[0098] Directly use the symmetrical distribution method to calculate y, with the center device at y = 0 (or any other reference point), and the y value calculation formula for other devices is: y = (inds-middle) * 120. (Note that the reference point should be adjusted if the center is not 0).

[0099] In this way, the present invention can calculate the vertical coordinate for each device so as to perform the next operation.

[0100] In this step, nodes at the same level are arranged horizontally, and nodes at different levels are arranged vertically.

[0101] S5, draws the graph according to the calculated node coordinates, ensuring that the edges are as straight and short as possible and the graph layout is balanced. The processed data is converted into a topological graph, which supports user interactive viewing.

[0102] Through a series of complex calculation processes, the processed data is converted into an intuitive and easy-to-understand topology map. This calculation process not only requires high accuracy and real-time performance, but also needs to fully consider the layout of the topology map, the connection relationship between nodes, and the convenience of user interaction. The interface level display function of the topology component is another highlight. Through a carefully designed user interface and interaction mechanism, users can intuitively view the topology map and understand the structure and status of the network. At the same time, the topology component also supports multi-view synchronous display, such as intelligent alarm processing: when an abnormal situation is detected, the alarm information is highlighted, and the root cause analysis and repair suggestions of the alarm are provided.

[0103] like Figure 2 As shown, the page components of the topology map include: networking components, end-to-end components, topology display components, device attribute components, link attribute components, port traffic components and alarm data components.

[0104] Page interaction process: The present invention aims to clarify the logical difference between networking topology and end-to-end topology. Networking topology shows the layout and connection relationship of the entire network from a global perspective through a hierarchical topology diagram. End-to-end topology focuses on the connection between specific nodes or devices, and reveals specific details in the network in the form of a detailed network link diagram.

[0105] During the interaction, the user uses the province selector on the interface, and the system immediately obtains the networking data of the corresponding province from the interface layer and presents it intuitively in the form of a topology map in the display component. This process requires not only the efficiency of data retrieval, but also the accuracy and real-time rendering of the topology map. The devices and links in the display component exist not only as static graphic elements, but also as carriers of interactive functions. These elements are linked, and users can trigger the display of detailed information of devices or links by clicking. At the same time, the connection lines (i.e. links) between devices also support the folding function, which is convenient for users to adjust and optimize the topology map as needed to obtain a clearer view and a more convenient analysis experience. The alarm information is also displayed directly on the interface. Once an abnormal situation is found, the alarm prompt will be triggered immediately on the interface. The alarm information is presented to the user in an intuitive way.

[0106] Each component is described below.

[0107] The networking component is used to provide data information query and display the network. Specifically, the networking component provides data information query of certain provinces for display of the network.

[0108] Compared with the networking component, the end-to-end component adds node or device selection at both ends. By selecting the nodes or devices at both ends respectively, the topology diagram between the two ends is displayed.

[0109] The topology display component is used to perform data processing and topology map display.

[0110] Specifically, as an indispensable core component of the entire system, the topology component carries the key tasks of data processing and topology map display. The primary responsibility of this component is to perform fine processing on the massive data returned by the interface layer. This process involves multiple links such as data parsing, cleaning, conversion, and aggregation to ensure the accuracy and completeness of the data, laying a solid foundation for subsequent calculations and displays. After the data processing is completed, the topology component first uses WebGL technology in graphics rendering technology, using the parallel computing power of the GPU to achieve efficient graphics rendering, optimize the rendering algorithm, and reduce the amount of graphics calculations. Block loading and progressive rendering are used in loading: large-scale data is loaded and rendered gradually, and preloading technology is introduced to ensure the smoothness of the user interaction process. Hierarchical layout is used in layout, such as Figure 3 shown.

[0111] The device attribute component is used to display the relevant information of the device after interaction, and displays it on the interface for users to view.

[0112] The link attribute component is used to display the relevant attributes of the link.

[0113] The port traffic component is used to display the port traffic information at both ends of the link.

[0114] The alarm data component is used to display alarm-related information.

[0115] In the embodiment of the present invention, the topology view supports a custom view mode and a multi-view synchronous display mode.

[0116] Customized view mode: supports users to freely switch between global view and local view.

[0117] Multi-view synchronous display: Supports multi-view synchronous display function, supports users to view multiple network topology views of different dimensions at the same time, real-time synchronous update, and provides all-round network monitoring. Real-time synchronization between multiple views, user operations in one view (such as dragging, zooming in, zooming out, etc.) will be automatically updated in other views, and provides multi-view linkage analysis function, users can compare and analyze between different views. It also supports various information viewing.

[0118] In the present invention, the topology map supports users to drag and place nodes, and the layout of the network topology map can be adjusted by dragging nodes, thereby enhancing the interactive function.

[0119] For node dragging and placement:

[0120] (1) All nodes on the interface support user-defined dragging and placement to meet the viewing habits and needs of different users.

[0121] (2) By dragging nodes, users can more intuitively adjust the layout of the network topology diagram, improving the efficiency of viewing and analysis.

[0122] In terms of interface display and style design, the present invention has refined the location of device nodes and circuits, and designed the logic and style details of the interface display to ensure that users can intuitively and clearly understand the network topology.

[0123] (1) Device node display logic

[0124] Considering the huge amount of data in the layers after the device node, these layers will be collapsed by default to avoid overcrowding the interface.

[0125] When the user clicks on the corresponding foldable node, the subsequent device hierarchy will be expanded and displayed. This interactive method helps users focus more clearly on the part of the network they are currently interested in.

[0126] (2) Node style design

[0127] Different icons will be used to distinguish the device nodes of each layer to ensure that users can quickly identify the node level and type. Devices in the same room will be placed in the same block to clearly indicate their spatial relationship. The icon design will be concise and clear while maintaining visual consistency and aesthetics.

[0128] S6, after drawing, obtain alarm data in real time, perform correlation analysis on the alarm data, identify potential causes of failures, and mark abnormal equipment.

[0129] Specifically, when an abnormal situation is detected in the equipment, the alarm information is collected and processed, and the alarm information is highlighted. The intelligent alarm correlation analysis function is introduced, and an alarm correlation analysis model is established. Combining machine learning algorithms and expert knowledge bases, the alarm information is correlated and analyzed, potential causes of failures are identified, and detailed processing suggestions are provided to users to help users quickly solve problems.

[0130] In summary, through refined interface display logic and style design, the present invention can provide users with a clear, intuitive and highly interactive network topology view, which will greatly improve the efficiency and experience of users in managing and analyzing complex network structures.

[0131] The present invention proposes a network visualization method based on hierarchical layout and clustering algorithm to realize a circuit topology visualization display scheme, and adopts the following technical scheme:

[0132] 1. The layered display design enables users to clearly see the relationship between different levels, and the hierarchical layout algorithm is used for layout.

[0133] 2. Introduce custom view mode, allowing users to select different windows to view different information.

[0134] 3. Alarm traffic monitoring and analysis: the interface displays the device and link status in real time, and provides root cause analysis and repair suggestions through alarm data. In addition, the interface also provides dynamic zooming and interactive information prompts, search and filtering functions, responsive design and other convenient operations to further enhance the user experience and the aesthetics of the display effect.

[0135] The method of the present invention introduces hierarchical layout algorithm and clustering algorithm into the field of network visualization to achieve more efficient and more beautiful network topology display. It is suitable for networks with obvious hierarchical structures; clustering algorithm groups nodes and simplifies data structure to make the display of network diagram more hierarchical and logical.

[0136] The present invention uses dynamic clustering analysis to process data and divide nodes of the same type together. A hierarchical layout algorithm is used for interface layout to make the interface better adaptable to various scenarios. A custom view mode is provided for interaction, allowing users to independently choose how to view the topology map. It also provides multi-view synchronous display of user operations in one view (such as dragging, zooming in, zooming out, etc.), which will be automatically updated in other views, as well as multi-view linkage analysis functions. Users can compare and analyze between different views, and users can also view relevant information such as port traffic. Intelligent alarm correlation analysis is provided for real-time alarm monitoring, which can provide users with the root cause of the fault and processing suggestions.

[0137] The present invention can not only efficiently process various types of complex network data, but also realize the intuitive display and dynamic analysis of network structure. By combining the clear hierarchy of hierarchical layout with the dynamic optimization of clustering algorithm, it can also adapt to various network types such as Internet of Things, 5G network, data center network, etc. On the basis of ensuring the display of the global structure of the network, it highlights the key nodes and their hierarchical relationships, thereby improving the user's analysis efficiency and operation experience.

[0138] The network visualization method based on hierarchical layout and clustering algorithm provided by the present invention can be applied in the following scenarios: development of telecommunication-related services, relationships and patterns of complex network structures, and network topology monitoring and analysis.

[0139] (1) Building topological maps with large amounts of data: Using dynamic clustering and hierarchical layout solutions, the device types can be quickly analyzed and then automatically laid out, which greatly improves the efficiency of traditional map construction.

[0140] (2) Multi-view analysis and comparison: Users can open different views on the page to compare and view data. This also meets the needs of different users who want to view different information. Users can choose the observation window independently.

[0141] (3) Real-time monitoring: For faulty devices or links, the interface will quickly mark them in red, and users can click on the alarm to obtain an analysis of the root cause of the fault and treatment suggestions.

[0142] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the embodiments of the present invention, rather than to limit them. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the above embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention. Any changes or replacements that can be easily thought of by those skilled in the art within the technical scope disclosed in the present invention should be covered within the protection scope of the present invention.

Claims

1. A network visualization method based on hierarchical layout and clustering algorithm, characterized in that: include: S1, calling the API interface to obtain circuit data and process the data to be suitable for clustering algorithm processing, using the dynamic clustering algorithm to cluster the data and generate clustering results; S2, stratifying the data processed by the precondition clustering algorithm, with each cluster as a layer; determining the order of each layer according to the structure of the clustering result, sorting the layers from low to high according to the bandwidth and delay of the characteristic vector of the clustering algorithm, mapping the clustering result to the hierarchical level, forming a node set of different levels, and performing the next step of optimizing the sorted data storage; S3, optimizes the node order at each level to reduce edge crossings; S4, calculating the coordinates of each node according to the results of stratification and cross reduction, including: obtaining a two-dimensional array according to the results of stratification and cross reduction, and then determining the horizontal coordinate of each layer according to the two-dimensional array and the node spacing set in advance; then processing each layer, and calculating the vertical coordinate for each device according to the length of each layer array and the middle index, so as to perform the next step of operation; S5, drawing according to the calculated node coordinates to convert the processed data into a topological map, wherein the topological map supports user interactive viewing; S6, after drawing, obtain alarm data in real time, perform correlation analysis on the alarm data, identify potential causes of failures, and mark abnormal equipment.

2. A network visualization method based on hierarchical layout and clustering algorithm according to claim 1, characterized in that: In S1, the called circuit data is cleaned, converted and aggregated so that the processed data is suitable for clustering algorithm processing.

3. The network visualization method based on hierarchical layout and clustering algorithm according to claim 1, characterized in that: In S1, clustering the data using a dynamic clustering algorithm includes: The data is preprocessed, and the preprocessed data is initially clustered using the K-means algorithm to obtain K initial clusters. For each initial cluster obtained by the K-means algorithm, the density clustering DBSCAN is used to further refine the clustering to obtain a density clustering result. Based on the density clustering result, the density clustering structure that K-means failed to accurately cluster is found; the clustering results are dynamically updated according to the changes in the properties of nodes and links; the calculation is iterated until the clustering results are stable, and the final clustering results are output.

4. The network visualization method based on hierarchical layout and clustering algorithm according to claim 1, characterized in that: In S3, the centroid method is used to reduce the intersection of edges, including: (1) Obtain the sorted two-dimensional array layer data; (2) Scan layers from top to bottom Starting from the second layer to the last layer of the loop, the position of the first layer is first indicated by a subscript; For each node s in the i-th layer, where i = 2 to n, n is the total number of layers, start the loop; Calculate the centroid value of node s = the average value of the positions of all its adjacent nodes in layer i-1; Sort the nodes in layer i by their centroid values; (3) Scan layers from bottom to top For each node s in the i-th layer, where i = n-1 to 1; Calculate the centroid value of node s = the average value of the positions of all its adjacent nodes in layer i+1; Sort the nodes in layer i by their centroid values; (4) Repeat steps (2) and (3) until the number of edge crossings no longer decreases.

5. The network visualization method based on hierarchical layout and clustering algorithm according to claim 1, characterized in that: In S4, nodes at the same level are arranged horizontally, and nodes at different levels are arranged vertically.

6. The network visualization method based on hierarchical layout and clustering algorithm according to claim 1, characterized in that: In S5, the page components of the topology map include: a networking component, an end-to-end component, a topology display component, a device attribute component, a link attribute component, a port traffic component, and an alarm data component.

7. A network visualization method based on hierarchical layout and clustering algorithm according to claim 6, characterized in that: The networking component is used to provide data information query and display the networking; The end-to-end component is used to display a topology diagram between the two ends by selecting nodes or devices at the two ends respectively; The topology display component is used to perform data processing and topology map display; The device attribute component is used to display relevant information of the device; The link attribute component is used to display the relevant attributes of the link; The port traffic component is used to display the port traffic information at both ends of the link; The alarm data component is used to display alarm-related information.

8. The network visualization method based on hierarchical layout and clustering algorithm according to claim 1, characterized in that: In S5, the topology view supports a custom view mode and a multi-view synchronous display mode, wherein: Customized view mode: supports users to freely switch between global view and local view; Multi-view synchronous display: Supports multi-view synchronous display function, allowing users to simultaneously view multiple network topology views of different dimensions, with real-time synchronous updates, providing all-round network monitoring.

9. The network visualization method based on hierarchical layout and clustering algorithm according to claim 1, characterized in that: In S5, the topology map supports users to drag and place nodes, and adjust the layout of the network topology map by dragging nodes.

10. The network visualization method based on hierarchical layout and clustering algorithm according to claim 1, characterized in that: In S6, when an abnormal situation is detected in the equipment, the alarm information is collected and processed, and an alarm correlation analysis model is established. Combined with machine learning algorithms and expert knowledge base, the alarm information is correlated and analyzed to identify potential causes of failures and provide processing suggestions to users.

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