Network space entity map visualization method and system supporting library collaboration

Abstract

The invention relates to the technical field of cyberspace surveying and mapping, in particular to a cyberspace entity map visualization method and system supporting library collaboration. The method comprises the following steps: step 1, constructing a network space support library; wherein the network support library comprises a model library, a symbol library, a style library and a template library; 2, constructing a network space entity node graph based on the model library, and performing preprocessing to obtain a network space basic node graph; 3, configuring symbols for the network space basic node graph based on the symbol library, and generating a network space optimization node graph; 4, carrying out network connection edge binding on the network space optimization node graph based on the model library to generate a network space data graph; and 5, based on the template library, superposing the cyberspace data graph and a basic geographic information base graph layer to obtain a cyberspace entity map.

Description

Technical Field

[0001] The present invention relates to the technical field of cyberspace 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 built on the basis of information and communication technology infrastructure to support people to carry out various activities related to information and communication technology in this space. It is centered on the network and involves the material basis and the subjects of activities. Its connotation has expanded from the narrow sense of computer Internet to the broad sense of all ubiquitous networks including computer networks, telecommunications networks, and the Internet of Things. In the field of network information science, the focus is on the analysis and expression of the topological connection relationship between cyberspace entities, but the spatial location information of entities is ignored. Map visualization uses graphics and images combined with charts and text to intuitively and accurately express the spatial distribution characteristics of geographic elements. At present, the application of visualization technology to construct cyberspace maps has become a key research direction in many 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 carrier volume has led 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 view of the above-mentioned problems, the present invention provides a method and system for visualizing a cyberspace entity map in collaboration with a support library. With the collaborative effect of a model library, a symbol library, a style library, and a template library, a fast and convenient visualization construction of a cyberspace entity map is achieved through steps such as data preprocessing, symbol configuration, bundling layout, and layer overlay.

[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: construct a network space entity node graph based on the model library and perform 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 edge-bound 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] Further, 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 based on the cyberspace node symbols and cyberspace edge symbols called through the symbol access interface; the template file is 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 lack of 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 preprocessing 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 an 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 of 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 control point X, and Y represents 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 density gradient △ρ(t) according to 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 time, 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 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 according to the model library and perform preprocessing to obtain a network space basic node graph;

[0039] A network symbol configuration module, used 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, 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;

[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 fast and convenient visualization construction of cyberspace entity maps through steps such as data preprocessing, symbol configuration, bundling layout and layer overlay, and provides a new method for the coupling representation of cyberspace and geographic space. The network edges are re-planned according to the edge bundling operation to highlight the high-order structure of the network and reduce visual confusion. In addition, the entire visualization process can be quickly constructed with the support of the template library. The template library provides the integrated management capability of the configuration parameter information of each step, and one-click rapid mapping can be achieved through built-in or customized templates.

[0044] (2) The present invention also aggregates nodes with close spatial distances according to 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 a 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 in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution in the embodiment of the present invention will be clearly described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[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, Figure 2As shown, the model library includes cyberspace data processing models (aggregation models, sparse models, and layout models, etc.) and model access interfaces, and the models can be obtained and called through a unified model access interface; the symbol library includes cyberspace node symbols and cyberspace edge symbols and symbol access interfaces, and the cyberspace map symbols can be obtained through a unified symbol access interface; the style library includes cyberspace map layer style files and style access interfaces, and the map layer style file information can be obtained through a unified style access interface; the template library includes several template files and template access interfaces, and the template information can be obtained through a unified template access structure, so as to support the visualization of cyberspace entity maps; the template file references the cyberspace map layer style file and the cyberspace data processing model (including model parameters), and the template file determines the data processing model, cyberspace symbols, map rendering style and other information used in the template.

[0055] It can be understood that the cyberspace node symbol can be a simple icon or graphic to intuitively represent entities in the cyberspace. For example, in a social network, a node may represent a user, and its symbol may be a user's avatar picture. In the Internet of Things, a node may represent a device, and its symbol may be an icon or model diagram of the device. The cyberspace edge symbol represents the relationship between nodes, such as social relationships, communication relationships, etc. The edge symbol is usually represented by a line segment or an arrow, and the line segment can be labeled with attributes such as weight, direction, etc. For example, in a social network, an edge can represent a friendly relationship between users, and the frequency or type of interaction can be labeled on the line segment; in a transportation network, an edge can represent a road connection, and the line segment can be labeled with traffic volume or travel time. It should be noted that the weight and relationship of the 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 and preprocesses the target cyberspace entity node graph to obtain the cyberspace basic node graph;

[0058] The preprocessing includes removing or completing data with duplicate nodes and edges, incomplete attribute information, and missing spatial location information;

[0059] Specifically, repeated nodes and edges are identified based on spatial distance positions, 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 a cyberspace entity 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 numeric 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] The 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 completed by matching the coordinates of the place name based on the information.

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

[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, the cyberspace node symbols are mainly for cyberspace entities, such as routers, servers, and computers, and the configured parameters include the symbol type, size, color, angle or offset, etc. In addition to the static symbol parameter configuration, the node symbol library also supports dynamic special effects, such as flashing, scaling, brightness, etc., to represent the 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, the cyberspace edge symbols are mainly for the connection relationship between cyberspace entities, usually expressed by line symbols. Edge symbols include simple line symbols, line symbols with icons, meteor line symbols, flow line symbols, and trajectory line symbols. Different symbols have 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 with high similarity in direction, distance, length, etc. 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 in the figure, 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, according to 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 actual conditions. A smaller step distance means more edge control points are added, and a larger step distance means fewer edge control points are added.

[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 control point X, and Y represents 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 density gradient △ρ(t) according to 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 time, 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 repetitions. The more repetitions, the tighter the binding result. Generally, 8-10 repetitions are sufficient 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 a cyberspace entity map.

[0093] Specifically, the cyberspace data map and the basic geographic information map layer are overlaid to display the location of the cyberspace entity in the real geographic map, thereby obtaining 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 the steps of 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, the network space node aggregation mainly fuses nodes with close distances 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 the network node in the current cluster set, and the out-degree value represents the number of network edges whose start node is the 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, the in-degree value Deg in And the out-degree value Deg out The sum of Deg is used as the total amount of edges between 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 value and the out-degree value of the two aggregation nodes as the total amount of edges between the two aggregation nodes. When configuring the network edge symbols in the subsequent steps, line symbols with different line thickness or brightness can be configured according to the weight size 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 Represents 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 thinning;

[0106] Specifically, network space data thinning mainly filters out unimportant structures based on the importance of network space nodes or network space edges, so as to compress the scale of network data and improve the signal-to-noise ratio of network data. Network space data thinning includes thinning based on the importance or attribute size of network space nodes and thinning based on the weight or attribute size of network space edges.

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

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

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

[0110] like Figure 6As shown, the present invention provides a method for visualizing a network space entity map in collaboration with a support library, wherein data preprocessing, network space node aggregation, and network space data thinning are supported by a model library, network node symbol configuration and network edge symbol configuration are supported by a symbol library and a style library, network edge bundling layout is supported by a model library, and layer overlay and drawing configuration are supported by a template library. In addition, the entire visualization process can also be quickly constructed with the support of a template library, which provides an integrated management capability for configuration parameter information of each step, and can achieve rapid mapping through built-in or customized templates.

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

[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 according to the model library and perform preprocessing to obtain a network space basic node graph;

[0114] A network symbol configuration module is used to configure symbols for the network space basic node graph according to the symbol library to generate a 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 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 network support library includes a model library, a symbol library, a style library and a template library; 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; 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 edge-bound to generate a network space data graph; 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 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; 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 based on the cyberspace node symbols and cyberspace edge symbols called through the symbol access interface; the template file is 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.

3. The method for visualizing a network space entity map based on support 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.

4. The method for visualizing a network space entity map in collaboration with a support library according to claim 3 is characterized in that: The preprocessing also includes node aggregation; The specific process of node aggregation is as follows: Using a spatial clustering method to divide the network nodes into multiple cluster sets, taking the center point of the cluster set as an aggregation node, and calculating 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.

5. The method for visualizing a network space entity map in collaboration with a support library according to claim 3 is 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.

6. The method for visualizing a network space entity map in collaboration with a support library according to claim 1, characterized in that: In step 3, symbols are configured 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.

7. The method for visualizing a network space entity map in collaboration with a support library according to claim 1, characterized in that: In step 4, 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 of 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 control point X, and Y represents 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 density gradient △ρ(t) according to 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 time, 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 Laplace algorithm to the moved control points to perform edge smoothing; Step 406: Repeat steps 401 to 405 multiple times.

8. A network space entity map visualization method for supporting library collaboration according to claim 7, characterized in that: The network edge bundling layout uses the edge symbol as a simple line symbol.

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

10. A network space entity map visualization system supporting library collaboration, characterized in that: include: 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; A network data preprocessing module is used to construct a network space entity node graph according to the model library and perform preprocessing to obtain a network space basic node graph; A network symbol configuration module, used 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, 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; 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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