Method and system for visualizing cyberspace entity maps based on support library collaboration

Abstract

The present invention relates to the field of cyberspace surveying and mapping technology, and in particular to a method and system for visualizing a cyberspace entity map in collaboration with a support library. The method comprises: step 1: constructing a cyberspace support library; wherein the network support library comprises a model library, a symbol library, a style library, and a template library; step 2: constructing a cyberspace entity node graph based on the model library and performing preprocessing to obtain a cyberspace basic node graph; step 3: configuring symbols for the cyberspace basic node graph based on the symbol library to generate a cyberspace optimized node graph; step 4: bundling network edges of the cyberspace optimized node graph based on the model library to generate a cyberspace data graph; and step 5: overlaying the cyberspace data graph with a basic geographic information base map layer based on the template library to obtain a cyberspace entity map.

Description

Technical Field

[0001] The present invention relates to the technical field of cyberspace surveying and mapping, and in particular to a method and system for visualizing a cyberspace entity map in collaboration with a support library. Background Art

[0002] Cyberspace is an artificial space constructed based on information and communication technology (ICT) infrastructure to support various ICT-related activities. It is centered on the network, encompassing both the material foundation and the actors involved in these activities. Its connotation has expanded from the narrow computer internet to encompass all ubiquitous networks, including computer networks, telecommunications networks, and the Internet of Things. In the field of network information science, the focus is on analyzing and expressing the topological connections between cyberspace entities, while neglecting the spatial location information of these entities. Map visualization uses graphics and images combined with charts and text to intuitively and accurately represent the spatial distribution characteristics of geographic features. Currently, the application of visualization technology to construct cyberspace maps has become a key research area in fields such as cyberspace security, cyberspace mapping, and cyberspace geography.

[0003] However, it is not easy to construct a language, model and method system for the visualization of cyberspace maps. The main reasons are: first, the types of cyberspace entities are diverse and the concepts are abstract, and a map symbol representation system for cyberspace has not yet been formed; second, the number of cyberspace entities is huge and the relationships are complex, and the sharp increase in information load leads to serious visual confusion problems; third, the map visualization of cyberspace entities requires relatively high professional knowledge and skills in map making. Summary of the Invention

[0004] In response to the above-mentioned problems, the present invention provides a method and system for visualizing cyberspace entity maps with collaborative support libraries. Under the collaborative effect of model libraries, symbol libraries, style libraries, and template libraries, through steps such as data preprocessing, symbol configuration, bundled layout, and layer overlay, a fast and convenient visualization construction of cyberspace entity maps is achieved.

[0005] The present invention provides a network space entity map visualization method for supporting library collaboration, comprising:

[0006] Step 1: Construct a network space support library; wherein the network support library includes a model library, a symbol library, a style library and a template library;

[0007] Step 2: constructing a network space entity node graph based on the model library and performing preprocessing to obtain a network space basic node graph;

[0008] Step 3: configuring symbols for the network space basic node graph based on the symbol library to generate a network space optimized node graph;

[0009] Step 4: Based on the model library, the network space optimization node graph is network-bound and bundled to generate a network space data graph;

[0010] Step 5: Based on the template library, the cyberspace data map is superimposed with the basic geographic information base map layer to obtain a cyberspace entity map.

[0011] Furthermore, in step 1, the model library includes a cyberspace data processing model and a model access interface, the symbol library includes a cyberspace node symbol and a cyberspace edge symbol and a symbol access interface, the style library includes a cyberspace map layer style file and a style access interface, and the template library includes a plurality of template files and a template access interface;

[0012] Among them, the cyberspace data processing model includes an aggregation model, a rarefaction model and a layout model; the cyberspace map layer style file is composed based on the cyberspace node symbol and the cyberspace edge symbol called through the symbol access interface; the template file is composed based on the cyberspace map layer style file called through the style access interface and the cyberspace data processing model called through the model access interface.

[0013] Furthermore, in step 2, the preprocessing includes removing or completing data with duplicate nodes and edges, incomplete attribute information, and missing spatial location information;

[0014] The repeated nodes and edges are identified based on spatial location distance, attribute field information and topological connection relationship.

[0015] Furthermore, the pre-processing also includes node aggregation;

[0016] The specific process of node aggregation is as follows:

[0017] Using a spatial clustering method to divide network nodes into multiple cluster sets, taking the center point of the cluster set as the aggregation node, and calculating the coordinates, in-degree value and out-degree value of the aggregation node; wherein the in-degree value represents the number of network edges whose end node is a network node in the current cluster set, and the out-degree value represents the number of network edges whose start node is a network node in the current cluster set;

[0018] The network edge between two aggregation nodes is processed based on the in-degree value and the out-degree value, and if there is an edge between nodes in two different cluster sets, an edge is established between the corresponding two aggregation nodes.

[0019] Furthermore, the preprocessing also includes data thinning; wherein the data thinning includes thinning according to the importance or attribute size of the network space nodes and thinning according to the weight or attribute size of the network space edges.

[0020] Furthermore, in step 3, symbols are configured for the network space basic node graph, specifically including:

[0021] Step 301: Perform symbol configuration on the network nodes in the network space basic node graph, and configure different types of symbols for different types of network nodes;

[0022] Step 302: Perform symbol configuration on the network edges in the network space basic node graph, and configure different types of line symbols for different types of network edges.

[0023] Furthermore, in step 4, the process of the network edge bundling layout is as follows:

[0024] Step 401: adding control points to the network edge; adding control points to the edge between two nodes according to a preset step distance;

[0025] Step 402: Calculate the kernel density of the control points. The kernel density calculation formula is as follows:

[0026]

[0027] Among them, K represents the Gaussian kernel function with bandwidth h, ρ(X) represents the kernel density of the control point X, and Y represents the other network edges l i The control point on l i represents other network edges except the network edge where the control point X is located, and n represents the number of other network edges;

[0028] Step 403: Estimate the density gradient Δρ(t) based on the kernel density obtained in step 402; wherein the density gradient is the difference between the kernel density at the current moment and the previous moment;

[0029] Step 404: Calculate the layout position of the control point based on the Euler integral and move the control point. The calculation formula for moving the control point is as follows:

[0030]

[0031] Where ε = 10 -5 , x represents the distance moved, t represents the current moment, h(t) represents the function of bandwidth decreasing over time, h(t) = λ t h max , where h max represents the initial bandwidth of the kernel density, λ t ∈[0.5,0.9];

[0032] Step 405: Applying the Laplace algorithm to the moved control points to perform edge smoothing;

[0033] Step 406: Repeat steps 401 to 405 multiple times.

[0034] Furthermore, the network edge bundling layout uses the edge symbol as a simple line symbol.

[0035] Furthermore, the step 5 also includes: configuring the network space entity map.

[0036] In a second aspect, the present invention provides a network space entity map visualization system supporting library collaboration, comprising:

[0037] A support library construction module is used to construct a network space support library, wherein the network support library includes a model library, a symbol library, a style library and a template library;

[0038] A network data preprocessing module is used to construct a network space entity node graph based on the model library and perform preprocessing to obtain a network space basic node graph;

[0039] A network symbol configuration module, configured to configure symbols for the network space basic node graph according to the symbol library to generate a network space optimized node graph;

[0040] A network edge bundling module, configured to perform network edge bundling on the network space optimization node graph according to the model library to generate a network space data graph;

[0041] The network layer overlay module is used to overlay the network space data map with the basic geographic information map layer according to the template library to obtain the network space entity map.

[0042] Beneficial effects of the present invention:

[0043] (1) Under the synergistic effect of the model library, symbol library, style library and template library, the present invention realizes the rapid and convenient construction of network space entity map visualization through the steps of data preprocessing, symbol configuration, bundled layout and layer overlay, providing a new method for the coupled representation of network space and geographic space. According to the edge bundling operation, the network edges are re-path-planned to highlight the high-order structure of the network and reduce visual clutter. In addition, the entire visualization process can be quickly constructed with the support of the template library, which provides the integrated management capability of the configuration parameter information of each step. Through built-in or customized templates, one-click rapid mapping can be achieved.

[0044] (2) The present invention also aggregates nodes with close spatial distances based on the set threshold parameters and the selected processing model, and processes the edge topological relationship of the aggregated nodes. According to the network space distribution and topological structure characteristics, the network space entity data is thinned out to remove unimportant nodes or edges to reduce the information load and improve the data signal-to-noise ratio. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 One of the flow diagrams of a method for visualizing a network space entity map in collaboration with a support library provided by an embodiment of the present invention;

[0046] Figure 2 A schematic diagram of the support library structure and calling relationship provided by an embodiment of the present invention;

[0047] Figure 3 A schematic diagram of a network edge bundling layout provided by an embodiment of the present invention;

[0048] Figure 4 A schematic diagram of the network edge bundling process provided by an embodiment of the present invention;

[0049] Figure 5 A schematic diagram of interpolating control points during network edge bundling processing provided by an embodiment of the present invention;

[0050] Figure 6 The second flowchart of a method for visualizing a network space entity map in collaboration with a support library provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0052] like Figure 1 As shown, an embodiment of the present invention provides a network space entity map visualization method for supporting library collaboration, including:

[0053] Step 1: Build a network space support library; the network support library includes a model library, a symbol library, a style library, and a template library;

[0054] Specifically, if Figure 2As shown in the figure, the model library includes cyberspace data processing models (aggregation models, sparse models and layout models, etc.) and model access interfaces, which can be used to obtain models and execute calls through a unified model access interface; the symbol library includes cyberspace node symbols and cyberspace edge symbols and symbol access interfaces, which can be used to obtain cyberspace map symbols through a unified symbol access interface; the style library includes cyberspace map layer style files and style access interfaces, which can be used to obtain map layer style file information through a unified style access interface; the template library includes several template files and template access interfaces, which can be used to obtain template information through a unified template access structure, thereby supporting the visualization of cyberspace entity maps; the template files reference the cyberspace map layer style files and cyberspace data processing models (including model parameters), and the template files determine the data processing models, cyberspace symbols, map rendering styles and other information used in the templates.

[0055] It is understood that network node symbols can be simple icons or graphics, used to intuitively represent entities in network space. For example, in a social network, a node may represent a user, and its symbol may be a user's profile picture. In the Internet of Things, a node may represent a device, and its symbol may be a device icon or model diagram. Network edge symbols represent relationships between nodes, such as social relationships and communication relationships. Edge symbols are typically represented by line segments or arrows, and attributes such as weight and direction can be annotated on the line segments. For example, in a social network, an edge can represent a friendly relationship between users, and the frequency or type of interaction can be annotated on the line segments. In a transportation network, an edge can represent a road connection, and the line segments can be annotated with traffic volume or travel time. It should be noted that the weight and relationship of an edge can also be represented by line segments of different styles.

[0056] Step 2: Construct a network space entity node graph based on the model library and perform preprocessing to obtain a network space basic node graph;

[0057] Specifically, the model library contains a cyberspace data processing model, which calls the target cyberspace entity node graph and performs preprocessing to obtain the cyberspace basic node graph;

[0058] Preprocessing includes removing or completing data that contains duplicate nodes and edges, incomplete attribute information, or lacks spatial location information.

[0059] Specifically, duplicate nodes and edges are identified based on spatial distance, attribute field information, and topological connection relationships;

[0060] The spatial position distance is identified according to formula (1). When the distance between two nodes is less than the threshold, the two nodes are judged to be duplicate nodes, and one of the nodes is removed.

[0061]

[0062] Among them, (x i ,y i ) represents node e i The coordinates of (x j ,y j ) represents node e j The coordinates of , σ represents the set close distance threshold;

[0063] The attribute field information is identified according to formula (2),

[0064]

[0065] Among them, d(e i ,e j ) represents the cyberspace entity e i and cyberspace entities j The similarity of attribute field information. Generally speaking, When the cyberspace entity i and e j When all k-th attributes are null or one of them is null Represents cyberspace entity e i and e j The similarity of the kth attribute is integrated into the overall similarity d(e i ,e j ) has no meaning. When k is a categorical or binary attribute, comparing e i and e j In the value of the kth attribute, if they are equal, then otherwise When k is a numerical attribute, calculate the maximum and minimum values ​​of the kth attribute, then When k is a character attribute, the proportion of the same number of characters is calculated.

[0066] Topological connection relationship similarity is identified based on the degree or centrality index of the nodes.

[0067] Specifically, incomplete attribute information and lack of spatial location information are both problems caused by incomplete field information. The field information can be supplemented based on the entities with the same name identified when processing duplicate nodes and edges, and the record can be deleted if there is no entity with the same name. In the case of lack of spatial location information, if there is place name location information describing the network space entity in the attribute field, the information can be supplemented by matching coordinates with the place name based on this information.

[0068] Step 3: Based on the symbol library, configure symbols for the network space basic node graph to generate the network space optimized node graph;

[0069] The configuration symbols for the network space basic node graph specifically include:

[0070] Step 301: Perform symbol configuration on the network nodes in the network space basic node graph, and configure different types of symbols for different types of network nodes;

[0071] Specifically, cyberspace node symbols are primarily designed for cyberspace entities such as routers, servers, and computers. Configuration parameters include symbol type, size, color, angle, or offset. In addition to static symbol parameter configuration, the node symbol library also supports dynamic effects such as flashing, scaling, and brightness to represent changes in the state of cyberspace entities.

[0072] Step 302: Perform symbol configuration on the network edges in the network space basic node graph, and configure different types of line symbols for different types of network edges.

[0073] Specifically, cyberspace edge symbols primarily represent the connections between cyberspace entities and are typically expressed using line symbols. These symbols include simple line symbols, line symbols with icons, meteor line symbols, flow line symbols, and trajectory line symbols. Different symbols require different configuration parameters. Table 1 provides the types of cyberspace edge symbols and their main parameter settings.

[0074] Table 1 Types and main parameters of network edge symbols

[0075]

[0076]

[0077] Step 4: Based on the model library, the network space optimization node graph is bundled with network edges to generate a network space data graph;

[0078] Specifically, the network edge bundling layout is used for network space edges whose edge symbols are simple linear symbols. The network edge bundling layout mainly replans the paths of the edges and bundles the edges with high similarity in direction, distance, length, etc., so as to highlight the edges. Figure 3 The high-order skeleton structure of the network shown reduces the visual clutter caused by the interlaced edges.

[0079] like Figure 4 As shown, the process of network edge bundling layout is as follows:

[0080] Step 401: Figure 5 As shown, add control points to the network edges;

[0081] Specifically, based on the length of the edge between two nodes, a step distance is preset, and edge control points are added to the edge between the two nodes. It should be noted that the step distance can be set according to the actual situation. A smaller step distance will add more edge control points, while a larger step distance will add fewer edge control points.

[0082] Step 402: Calculate the kernel density of the control points. The kernel density calculation formula is as follows:

[0083]

[0084] Among them, K represents the Gaussian kernel function with bandwidth h, ρ(X) represents the kernel density of the control point X, and Y represents the other network edges l i The control point on l i represents other network edges except the network edge where the control point X is located, and n represents the number of other network edges;

[0085] Step 403: Estimate the density gradient Δρ(t) based on the kernel density obtained in step 402; wherein the density gradient is the difference between the kernel density at the current moment and the previous moment;

[0086] Specifically, the density gradient △ρ(t) is the difference in kernel density between the current moment and the previous moment, that is, the difference before and after the control point moves, and then the difference is normalized to obtain the value.

[0087] Step 404: Calculate the layout position of the control point based on the Euler integral and move the control point. The calculation formula for moving the control point is as follows:

[0088]

[0089] Where ε = 10 -5 , x represents the distance moved, t represents the current moment, h(t) represents the function of bandwidth decreasing over time, h(t) = λ t h max , where h max represents the initial bandwidth of the kernel density, λ t ∈[0.5,0.9]; Step 405: Apply the Laplace algorithm to the moved edge control points to perform edge smoothing.

[0090] Step 406: Repeat steps 401 to 405 multiple times.

[0091] Specifically, there is no limit to the number of repeated cycles. The more cycles there are, the tighter the binding result will be. Generally, 8-10 repetitions are enough to achieve the following results. Figure 3 The effect shown.

[0092] Step 5: Based on the template library, overlay the cyberspace data map with the basic geographic information base map layer to obtain the cyberspace entity map.

[0093] Specifically, the cyberspace data map and the basic geographic information map layer are superimposed to display the location of the cyberspace entity in the real geographic map, and obtain the visualization result of the network space-time entity map.

[0094] The method provided in the embodiment of the present invention, under the synergistic effect of the model library, symbol library, style library and template library, realizes the fast and convenient visualization construction of cyberspace entity map through steps such as data preprocessing, symbol configuration, bundled layout and layer overlay, and provides a new method for the coupled representation of cyberspace and geographic space.

[0095] Furthermore, in order to avoid visual confusion caused by the aggregation of adjacent nodes, the preprocessing in step 1 also includes network space node aggregation.

[0096] Specifically, network space node aggregation mainly fuses nodes that are close to each other to avoid visual confusion caused by the aggregation of adjacent nodes. The main process of network space node aggregation is as follows:

[0097] Use the spatial clustering method to divide the network nodes into multiple cluster sets, and use the center point of the cluster set as the aggregation node; calculate the coordinates, in-degree value, and out-degree value of the aggregation node; the in-degree value represents the number of network edges whose end node is a network node in the current cluster set, and the out-degree value represents the number of network edges whose start node is a network node in the current cluster set. The calculation process is as follows:

[0098]

[0099] Among them, v(x,y) represents the coordinates of the center point of the cluster set, m represents that the cluster set has m nodes, Deg in Indicates the in-degree value of the aggregation node, Deg out represents the out-degree value of the aggregation node, represents the coordinates of the starting node, Indicates the coordinates of the terminal node;

[0100] Furthermore, calculate the in-degree value Deg in and out-degree value Deg out The sum of Deg is used as the total amount of edges between the two aggregation nodes to calculate the weight of the edges between the aggregation nodes.

[0101] The network edges between two aggregation nodes are processed based on the in-degree value and out-degree value. If there are edges between nodes in two different cluster sets, a network edge is established between the corresponding two aggregation nodes.

[0102] Furthermore, the network edge weight between the two aggregation nodes is calculated based on the sum of the in-degree and out-degree values ​​of the two aggregation nodes as the total number of edges between the two aggregation nodes. In the subsequent steps of configuring network edge symbols, different line thicknesses or brightnesses can be configured according to the weight to distinguish the size of network space traffic / interaction volume. The calculation formula is as follows:

[0103]

[0104] Among them, Inter pq Indicates that the starting node is in the cluster set C p and the end node is in C q The number of edges in Weight pq Indicates C p and C q The weight of the edge, Indicates the number of all current network edges.

[0105] Furthermore, in order to further optimize the network space basic node graph, the preprocessing also includes network space data rarefaction;

[0106] Specifically, network data thinning primarily involves filtering out unimportant structures based on the importance of network nodes or network edges, thereby compressing the network data size and improving the signal-to-noise ratio. This includes thinning based on the importance or attribute size of network nodes and thinning based on the weight or attribute size of network edges.

[0107] It is understood that the importance or attribute size of a node in a network space can be evaluated based on metrics such as degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. These metrics are commonly used to evaluate the importance of a node in a network structure and are common knowledge in this field, so they will not be elaborated on here.

[0108] Furthermore, step 5 also includes configuring the network space entity map.

[0109] Specifically, drawing configuration is to complete the position layout and style settings of drawing elements such as the drawing name, legend, compass, scale, and illustrations.

[0110] like Figure 6As shown, the present invention provides a method for visualizing cyberspace entity maps in collaboration with a support library. The model library supports data preprocessing, cyberspace node aggregation, and cyberspace data thinning. The symbol library and style library support network node and edge symbol configuration. The model library supports network edge bundling layout. The template library supports layer overlay and drawing configuration. Furthermore, the entire visualization process can be rapidly constructed with the support of the template library, which provides integrated management of configuration parameter information for each step. Rapid mapping can be achieved through built-in or custom templates.

[0111] An embodiment of the present invention further provides a network space entity map visualization system supporting library collaboration, comprising:

[0112] A support library construction module is used to construct a network space support library, wherein the network support library includes a model library, a symbol library, a style library and a template library;

[0113] The network data preprocessing module is used to construct a network space entity node graph based on the model library and perform preprocessing to obtain a network space basic node graph;

[0114] The network symbol configuration module is used to configure symbols for the network space basic node graph according to the symbol library to generate the network space optimized node graph;

[0115] The network edge bundling module is used to perform network edge bundling on the network space optimization node graph according to the model library to generate a network space data graph;

[0116] The network layer overlay module is used to overlay the network space data map with the basic geographic information map layer according to the template library to obtain the network space entity map.

[0117] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A network space entity map visualization method supporting library collaboration, characterized in that: include: Step 1: Build a cyberspace support library; The cyberspace support library includes a model library, a symbol library, a style library, and a template library; the model library includes a cyberspace data processing model and a model access interface, the symbol library includes cyberspace node symbols and cyberspace edge symbols and a symbol access interface, the style library includes cyberspace map layer style files and a style access interface, and the template library includes multiple template files and a template access interface; The cyberspace data processing model includes an aggregation model, a rarefaction model, and a layout model; the cyberspace map layer style file is composed based on the cyberspace node symbol and the cyberspace edge symbol called through the symbol access interface; the template file is composed based on the cyberspace map layer style file called through the style access interface and the cyberspace data processing model called through the model access interface; Step 2: constructing a network space entity node graph based on the model library and performing preprocessing to obtain a network space basic node graph; Step 3: configuring symbols for the network space basic node graph based on the symbol library to generate a network space optimized node graph; Step 4: Based on the model library, the network space optimization node graph is network-bound and bundled to generate a network space data graph; The process of the network edge bundling layout is as follows: Step 401: adding control points to the network edge; adding control points to the edge between two nodes according to a preset step distance; Step 402: Calculate the kernel density of the control points. The kernel density calculation formula is as follows: Among them, K represents the Gaussian kernel function with bandwidth h, ρ(X) represents the kernel density of the control point X, and Y represents the other network edges l i The control point on l i represents other network edges except the network edge where the control point X is located, and n represents the number of other network edges; Step 403: Estimate the density gradient Δρ(t) based on the kernel density obtained in step 402; wherein the density gradient is the difference between the kernel density at the current moment and the previous moment; Step 404: Calculate the layout position of the control point based on the Euler integral and move the control point. The calculation formula for moving the control point is as follows: Where ε = 10 -5 , x represents the distance moved, t represents the current moment, h(t) represents the function of bandwidth decreasing over time, h(t) = λ t h max , where h max represents the initial bandwidth of the kernel density, λ t ∈[0.5,0.9]; Step 405: Applying the Laplace algorithm to the moved control points to perform edge smoothing; Step 406: Repeat steps 401 to 405 multiple times; Step 5: Based on the template library, the cyberspace data map is superimposed with the basic geographic information base map layer to obtain a cyberspace entity map.

2. A network space entity map visualization method for supporting library collaboration according to claim 1, characterized in that: In step 2, the preprocessing includes removing or completing data with duplicate nodes and edges, incomplete attribute information, and missing spatial location information; The repeated nodes and edges are identified based on spatial location distance, attribute field information and topological connection relationship.

3. The method for visualizing a network space entity map based on support library collaboration according to claim 2, characterized in that: The pre-processing also includes node aggregation; The specific process of node aggregation is as follows: Use spatial clustering method to divide network nodes into multiple cluster sets, take the center point of the cluster set as the aggregation node, and calculate the coordinates, in-degree value and out-degree value of the aggregation node; The in-degree value represents the number of network edges whose end nodes are network nodes in the current cluster set, and the out-degree value represents the number of network edges whose start nodes are network nodes in the current cluster set; The network edge between two aggregation nodes is processed based on the in-degree value and the out-degree value, and if there is an edge between nodes in two different cluster sets, an edge is established between the corresponding two aggregation nodes.

4. The method for visualizing a network space entity map based on support library collaboration according to claim 2, characterized in that: The preprocessing also includes data thinning; wherein the data thinning includes thinning according to the importance or attribute size of the network space nodes and thinning according to the weight or attribute size of the network space edges.

5. The method for visualizing a network space entity map based on support library collaboration according to claim 1, characterized in that: In step 3, configure symbols for the network space basic node graph, including: Step 301: Perform symbol configuration on the network nodes in the network space basic node graph, and configure different types of symbols for different types of network nodes; Step 302: Perform symbol configuration on the network edges in the network space basic node graph, and configure different types of line symbols for different types of network edges.

6. The method for visualizing a network space entity map based on support library collaboration according to claim 1, characterized in that: The network edge bundling layout uses the edge symbol as a simple line symbol.

7. The method for visualizing a network space entity map based on support library collaboration according to claim 1, characterized in that: The step 5 also includes: configuring the network space entity map.

8. A network space entity map visualization system supporting library collaboration, characterized by: include: A support library construction module is used to construct a cyberspace support library, wherein the cyberspace support library includes a model library, a symbol library, a style library, and a template library; the model library includes a cyberspace data processing model and a model access interface, the symbol library includes cyberspace node symbols and cyberspace edge symbols and a symbol access interface, the style library includes cyberspace map layer style files and a style access interface, and the template library includes multiple template files and a template access interface; The cyberspace data processing model includes an aggregation model, a rarefaction model, and a layout model; the cyberspace map layer style file is composed based on the cyberspace node symbol and the cyberspace edge symbol called through the symbol access interface; the template file is composed based on the cyberspace map layer style file called through the style access interface and the cyberspace data processing model called through the model access interface; A network data preprocessing module is used to construct a network space entity node graph based on the model library and perform preprocessing to obtain a network space basic node graph; A network symbol configuration module, configured to configure symbols for the network space basic node graph according to the symbol library to generate a network space optimized node graph; A network edge bundling module, configured to perform network edge bundling on the network space optimization node graph according to the model library to generate a network space data graph; The process of the network edge bundling layout is as follows: Step 401: adding control points to the network edge; adding control points to the edge between two nodes according to a preset step distance; Step 402: Calculate the kernel density of the control points. The kernel density calculation formula is as follows: Among them, K represents the Gaussian kernel function with bandwidth h, ρ(X) represents the kernel density of the control point X, and Y represents the other network edges l i The control point on l i represents other network edges except the network edge where the control point X is located, and n represents the number of other network edges; Step 403: Estimate the density gradient Δρ(t) based on the kernel density obtained in step 402; wherein the density gradient is the difference between the kernel density at the current moment and the previous moment; Step 404: Calculate the layout position of the control point based on the Euler integral and move the control point. The calculation formula for moving the control point is as follows: Where ε = 10 -5 , x represents the distance moved, t represents the current moment, h(t) represents the function of bandwidth decreasing over time, h(t) = λ t h max , where h max represents the initial bandwidth of the kernel density, λ t ∈[0.5,0.9]; Step 405: Applying the Laplace algorithm to the moved control points to perform edge smoothing; Step 406: Repeat steps 401 to 405 multiple times; The network layer overlay module is used to overlay the network space data map with the basic geographic information map layer according to the template library to obtain the network space entity map.

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