WO2018120423A1 - 网络拓扑自适应的数据可视化方法、装置、设备和存储介质 - Google Patents

网络拓扑自适应的数据可视化方法、装置、设备和存储介质 Download PDF

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WO2018120423A1
WO2018120423A1 PCT/CN2017/076291 CN2017076291W WO2018120423A1 WO 2018120423 A1 WO2018120423 A1 WO 2018120423A1 CN 2017076291 W CN2017076291 W CN 2017076291W WO 2018120423 A1 WO2018120423 A1 WO 2018120423A1
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node
nodes
network topology
processing
data
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French (fr)
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余彬和
王建明
肖京
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Ping An Technology Shenzhen Co Ltd
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Ping An Technology Shenzhen Co Ltd
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Priority to AU2017341160A priority Critical patent/AU2017341160B2/en
Priority to JP2018516720A priority patent/JP6616893B2/ja
Priority to KR1020187015554A priority patent/KR102186864B1/ko
Priority to EP17857668.2A priority patent/EP3565181A4/en
Priority to SG11201803896PA priority patent/SG11201803896PA/en
Priority to US15/772,802 priority patent/US10749755B2/en
Publication of WO2018120423A1 publication Critical patent/WO2018120423A1/zh
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/248—Presentation of query results
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/14—Network analysis or design
    • H04L41/145—Network analysis or design involving simulating, designing, planning or modelling of a network
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/10—File systems; File servers
    • G06F16/17—Details of further file system functions
    • G06F16/174—Redundancy elimination performed by the file system
    • G06F16/1748—De-duplication implemented within the file system, e.g. based on file segments
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/08—Configuration management of networks or network elements
    • H04L41/0876—Aspects of the degree of configuration automation
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/12—Discovery or management of network topologies
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/22—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks comprising specially adapted graphical user interfaces [GUI]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/56—Provisioning of proxy services
    • H04L67/568—Storing data temporarily at an intermediate stage, e.g. caching
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
    • H04L41/50—Network service management, e.g. ensuring proper service fulfilment according to agreements
    • H04L41/5058—Service discovery by the service manager
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/50—Network services
    • H04L67/51—Discovery or management thereof, e.g. service location protocol [SLP] or web services

Definitions

  • the present invention relates to the field of data visualization technologies, and in particular, to a network topology adaptive data visualization method, apparatus, device, and storage medium.
  • Data visualization is the use of computer graphics to construct visual images to help people understand the larger and more complex scientific results or concepts in real life.
  • visualization technology is especially important to help present or interpret complex network data or models, and to discover patterns, features, and relationships.
  • the current data visualization process is mainly through the use of professional software tools for complex debugging and configuration parameters, and then to explore the visual visualization results.
  • professionals need to perform cumbersome data adjustment work, including adjustment of node color, node size and network shape, which is heavy and cumbersome.
  • the professional needs to re-adjust the data.
  • the overlapping work is cumbersome and cumbersome, and the data synchronization update cannot be realized.
  • there are overlapping nodes in the current data visualization process which makes some nodes unable to be completely presented, resulting in unclear topology, manual de-duplication processing, large workload and cumbersome, which is not conducive to improving data visualization efficiency.
  • the invention provides a network topology adaptive data visualization method, device, device and storage medium, so as to solve the problem of manual and cumbersome data adjustment work in the existing data visualization process.
  • a network topology adaptive data visualization method including:
  • the pre-processing node is processed by using a force-guided layout algorithm to form an initial network topology map
  • a target network topology map is formed based on the deduplication node.
  • the present invention provides a network topology adaptive data visualization apparatus, including:
  • a node preprocessing unit configured to preprocess the node and output the preprocessing node
  • An initial network topology map forming unit configured to process the pre-processing node by using a force-guided layout algorithm to form an initial network topology map
  • a de-reprocessing unit configured to perform de-duplication processing on the overlapping pre-processing nodes in the initial network topology diagram, and output a de-duplication node;
  • a target network topology map forming unit configured to form a target network topology map based on the deduplication node.
  • the present invention provides a network topology adaptive data visualization device, including a processor and a memory, the memory storing computer executable instructions, and the processor executing the computer executable instructions to perform the following steps :
  • the pre-processing node is processed by using a force-guided layout algorithm to form an initial network topology map
  • a target network topology map is formed based on the deduplication node.
  • a non-transitory computer readable storage medium storing one or more computer readable instructions, the computer readable instructions being executed by one or more processors, such that the one or more processes Performing the network extension A self-adaptive data visualization method.
  • the present invention has the following advantages: the network topology adaptive data visualization method, device, device and storage medium provided by the invention can realize data visualization automation and simplify data visualization processing flow without manual intervention. Effectively save labor intervention costs and improve processing efficiency. Moreover, in the network topology adaptive data visualization method, device, device and storage medium, the pre-processing nodes of the overlapping points are de-reprocessed to eliminate overlapping phenomena between nodes, so that each node can be completely rendered. The resulting target network topology map is clearly structured and highly displayable. Moreover, in the network topology adaptive data visualization method, device, device and storage medium, the data can be automatically synchronized and updated, so that the business requirement analysis and exploration are real-time.
  • FIG. 1 is a flow chart of a data topology adaptive data visualization method in a first embodiment of the present invention.
  • FIG. 2 is a specific flowchart of step S1 in the network topology adaptive data visualization method shown in FIG. 1.
  • FIG. 3 is a specific flowchart of step S3 in the network topology adaptive data visualization method shown in FIG. 1.
  • FIG. 4 is a schematic block diagram of a network topology adaptive data visualization apparatus in a second embodiment of the present invention.
  • FIG. 5 is a block diagram showing a specific principle of the network topology adaptive data visualization apparatus of FIG. 4.
  • FIG. 6 is a schematic diagram of a network topology adaptive data visualization device in a third embodiment of the present invention.
  • FIG. 1 is a flow chart showing a network topology adaptive data visualization method in the embodiment.
  • the network topology adaptive data visualization method can be performed on a network topology adaptive data visualization device equipped with professional software tools for data visualization.
  • the professional software tool can be Gephi, a complex network analysis software, which is mainly used in various networks and complex systems. It is an interactive tool for interactive visualization and detection of dynamic and layered graphs.
  • the network topology adaptive data visualization method comprises the following steps:
  • step S1 specifically includes the following steps:
  • the color_t attribute and the size_t attribute are added to each node.
  • the value can be set autonomously according to the actual business scenario, and the node color and node size of each node are generated according to the color_t attribute and the size_t attribute value of the node.
  • the professional software tool for performing the network topology adaptive data visualization method is Gephi, and the node color and node size of each node acquired are gexf file formats.
  • Gephi is an excellent complex network analysis software that supports importing files in multiple formats.
  • the gexf format is a Gephi recommended format and is a chart file created with the GEXF (Graph Exchange XML Format) language.
  • the GEXF language is a language that describes the structure of a network and is used in A diagram of the specified nodes and edges and user-defined attributes.
  • S12 Perform data standardization on the node color and the node size of each node, and obtain a standardized value of each node.
  • data normalization is to scale the data to a specific interval with a small value, to remove the unit limit of the data, and convert it into a pure value of infinite magnitude, so that the indicators of different units or orders of magnitude Ability to compare and weight.
  • Z-score normalization processing is performed to obtain the normalized value of each node.
  • Z-score standardization refers to standard deviation standardization, so that the processed data conforms to the standard positive distribution, that is, the mean value is 0, and the standard deviation is 1, so as to be compared or weighted based on the normalized value of the output.
  • the Z-score standardized conversion function is Where ⁇ is the mean of all sample data and ⁇ is the standard deviation of all standard data.
  • S13 Determine a section corresponding to the node according to the normalized value of each node and the partition threshold, and output the section corresponding to the node as the pre-processing node.
  • the partition threshold is used to divide the data into a plurality of intervals, and the normalized value of each node formed in step S12 is compared within a specific interval, and the normalized value of each node is compared with a preset partition threshold. It is determined in which interval the normalized value of the node is determined by the partition threshold, and the interval corresponding to the node is output as the pre-processing node.
  • the pre-processing node is processed by a force-guided layout algorithm to form an initial network topology map.
  • the force-guided layout algorithm (Fruchterman-Reingold algorithm, referred to as FR algorithm) is a physical model that enriches the two nodes, adding the electrostatic force between the nodes, and calculating the total energy of the system and minimizing the energy to achieve the layout. purpose.
  • the formula for the force-guided layout algorithm is as follows:
  • the spring potential model is used to calculate the elastic potential energy.
  • the spring model includes:
  • the energy model includes:
  • nodes i and j use d(i,j) to represent the Euclidean distance of the two nodes, s(i,j) represents the natural length of the spring, k is the elastic coefficient, and r represents the electrostatic force constant between the two nodes. , w is the weight between two nodes, E s is the elastic potential energy, and E is the dynamic potential energy.
  • the spring model is used to calculate the elastic potential energy of the pre-processing node; and based on the calculated elastic potential energy, the energy model is used to calculate the dynamic potential energy, and the calculated dynamic potential energy is used to process the pre-processing node to form an initial network topology.
  • the essence of the algorithm is to take an energy optimization problem, the difference is that the composition of the optimization function is different.
  • the optimization object includes the gravitational and repulsive parts, and different algorithms express different gravitational and repulsive forces.
  • the force-guided layout algorithm is easy to understand and easy to implement, can be used in most network datasets, and achieves better symmetry and local aggregation.
  • S3 Perform de-reprocessing on the overlapping pre-processing nodes in the initial network topology diagram, and output de-duplication nodes.
  • step S3 specifically includes:
  • the location of any pre-processing node can be determined by its coordinate data. If the coordinate data of any two preprocessing nodes are the same, that is, the x coordinate and the y coordinate are the same, the two preprocessing nodes overlap.
  • the placement of the same pre-processing node with the same coordinate data into the cache list includes: at least two pre-processing nodes having the same coordinate data as a set of cache node groups, and then putting at least one set of cache node groups into the cache list. in.
  • S33 traversing the cache list, selecting two pre-processing nodes with the same coordinate data, adding or subtracting the x-coordinates and y-coordinates of the two pre-processing nodes respectively to form two update nodes; sequentially iterating until the cache list There is no preprocessing node with the same coordinate data.
  • the random number is a number that is randomly generated not to be 0, and is set to k. It can be understood that if the random number k is 0, the x coordinate and the y coordinate of the two pre-processing nodes with the same coordinates are respectively added or subtracted from the random number, and two non-overlapping update nodes cannot be formed.
  • the coordinate data is the pre-processing nodes A and B of (x1, y1)
  • the x-coordinate and the y-coordinate of the pre-processing nodes A and B are respectively added or subtracted by the random number k1, the two formed.
  • the nodes A'(x1+k1, y1+k1) and B'(x1-k1, y1-k1) are updated such that the two pre-processing nodes A and B are evenly spread.
  • the coordinate data is the pre-processing nodes C, D, and E of (x2, y2)
  • the x and y coordinates of the pre-processing nodes C and D are respectively added or subtracted by the random number k2, and then two The nodes C'(x2+k2, y2+k2) and D'(x2-k2, y2-k2) are updated such that the three pre-processing nodes C, D and E (x2, y2) are evenly spread.
  • the coordinate data is the pre-processing nodes F, G, H, and I of (x3, y3)
  • the x-coordinate and the y-coordinate of the pre-processing nodes F and G are respectively added or subtracted by the random number k3, and the pre-processing node H is made.
  • the x and y coordinates of I are added or subtracted by the random number k4, respectively, and the four updated nodes F'(x3+k3, y3+k3), G'(x3-k3, y3-k3), H are formed.
  • the preprocessing node does not have at least two preprocessing nodes with the same coordinate data in the cache list.
  • S34 Determine whether there is a pre-processing node that is the same as the update node coordinate data; if yes, put the update node and the pre-processing node into the cache list; if not, the update node is output as the de-duplication node.
  • the update node formed by traversing the cache list may be the same as the coordinate data of other pre-processing nodes not put into the cache list, so that node overlap still exists, it is necessary to determine whether there is the same pre-processing as updating the node coordinate data. If there is, the update node and the pre-processing node are put into the cache list as a group of cache nodes, and step S33 is performed; if not, the update node is output as the de-duplication node to perform step S4. It can be understood that other pre-processing nodes in the initial network topology map that are not put into the cache list are also output as de-duplication nodes.
  • the computer executing the network topology adaptive data visualization method receives the deduplication node, and displays the target network topology map based on all the deduplication nodes in the browser to display the data visualization result.
  • the background data is updated, it is recalculated based on the steps S1-S4, so that the browser displays the data visualization result of the latest data, thereby realizing the synchronous update of the data without the need for the professional to perform data adjustment, which is beneficial to cost saving and data improvement.
  • Visual processing efficiency is beneficial to cost saving and data improvement.
  • the file format of the pre-processing node outputted in step S1 is the gexf file format
  • the initial network topology map and the deduplicated node are not subjected to file format conversion in steps S2 and S3, so that the output is de-duplicated.
  • the file format of the node is still the gexf file format.
  • the gexf file format has a large amount of network transmission data and a slow response time.
  • the step S3 and the step S4 of the network topology adaptive data visualization method further comprise: performing file format conversion on the deduplication node, and outputting the deduplication node in the json file format.
  • JSON JavaScript Object Notation
  • JSON uses a completely language-independent text format.
  • the gexf file format of the deduplication node is parsed, node information and edge information are acquired, and the deduplication node of the json file format is output based on the node information and the edge information. It can be understood that converting the deduplication node of the gexf file format into the deduplication node of the json file format can reduce the amount of data transmitted by the network, improve the response time, and is beneficial to improving the processing efficiency of the data visualization.
  • the network topology adaptive data visualization method provided by the embodiment can realize data visualization automation, simplify the data visualization processing flow, and save manual intervention cost and improve processing efficiency without manual intervention. Moreover, the network topology adaptive data visualization method performs de-reprocessing on the pre-processing nodes of the overlapping points to eliminate the overlapping phenomenon between the nodes, so that each node can be completely presented, so that the final target network topology is formed. The structure of the figure is clear and can be displayed. Moreover, the network topology adaptive data visualization method can realize automatic data synchronization and update, so that business requirement analysis and exploration are real-time.
  • the network topology adaptive data visualization device can be executed on a network topology adaptive data visualization device with professional software tools for data visualization.
  • the professional software tool can be Gephi, a complex network analysis software, which is mainly used in various networks and complex systems. It is an interactive tool for interactive visualization and detection of dynamic and layered graphs.
  • the network topology adaptive data visualization apparatus includes a node preprocessing unit 10, an initial network topology map forming unit 20, a deduplication processing unit 30, and a target network topology map forming unit 40.
  • the node pre-processing unit 10 is configured to pre-process the node and output the pre-processing node.
  • the node is preprocessed in the Gephi software tool, and the file format of the output preprocessing node is the gexf file format.
  • the node pre-processing unit 10 specifically includes a node acquisition sub-unit 11, a data normalization sub-unit 12, and a pre-processing node acquisition sub-unit 13.
  • the node acquisition subunit 11 is configured to acquire the node color and the node size of each node.
  • the color_t attribute and the size_t attribute are added to each node.
  • the value can be set autonomously according to the actual business scenario, and the node color and node size of each node are generated according to the color_t attribute and the size_t attribute value of the node.
  • the professional software tool for executing the network topology adaptive data visualization device is Gephi, and the node color and node size of each node acquired are gexf file formats.
  • Gephi is an excellent complex network analysis software that supports importing files in multiple formats.
  • the gexf format is a Gephi recommended format and is a chart file created with the GEXF (Graph Exchange XML Format) language.
  • the GEXF language is a language that describes the structure of a network, used to specify relational graphs of nodes and edges, and user-defined attributes.
  • the data normalization sub-unit 12 is configured to perform data standardization on the node color and the node size of each node, and obtain a standardized value of each node.
  • data normalization is to scale the data to a specific interval with a small value, to remove the unit limit of the data, and convert it into a pure value of infinite magnitude, so that the indicators of different units or orders of magnitude Ability to compare and weight.
  • Z-score normalization processing is performed to obtain the normalized value of each node.
  • Z-score standardization refers to standard deviation standardization, so that the processed data conforms to the standard positive distribution, that is, the mean value is 0, and the standard deviation is 1, so as to be compared or weighted based on the normalized value of the output.
  • the Z-score standardized conversion function is, where is the mean of all sample data, which is the standard deviation of all standard data.
  • the pre-processing node acquisition sub-unit 13 is configured to determine a section corresponding to the node according to the normalized value of each node and the partition threshold, and output the section corresponding to the node as the pre-processing node.
  • the partition threshold is used to divide the data into a plurality of intervals, and the normalized value of each node formed by the data normalization sub-unit 12 is normalized within a specific interval, and the normalized value of each node and the preset partition are used. By comparing the thresholds, it is determined in which interval the normalized value of the node is determined by the partition threshold, and the interval corresponding to the node is output as the pre-processing node.
  • the initial network topology map forming unit 20 is configured to process the pre-processing node by using a force-guided layout algorithm to form an initial network topology map.
  • the force-guided layout algorithm (Fruchterman-Reingold algorithm, referred to as FR algorithm) is a physical model that enriches the two nodes, adding the electrostatic force between the nodes, and calculating the total energy of the system and minimizing the energy to achieve the layout. purpose.
  • the formula for the force-guided layout algorithm is as follows:
  • the spring potential model is used to calculate the elastic potential energy.
  • the spring model includes:
  • the energy model includes:
  • nodes i and j use d(i,j) to represent the Euclidean distance of the two nodes, s(i,j) represents the natural length of the spring, k is the elastic coefficient, and r represents the electrostatic force constant between the two nodes. , w is the weight between two nodes, E s is the elastic potential energy, and E is the dynamic potential energy.
  • the spring model is used to calculate the elastic potential energy of the pre-processing node; and based on the calculated elastic potential energy, the energy model is used to calculate the dynamic potential energy, and the calculated dynamic potential energy is used to process the pre-processing node to form an initial network topology.
  • the essence of the algorithm is to take an energy optimization problem, the difference is that the composition of the optimization function is different.
  • the optimization object includes the gravitational and repulsive parts, and different algorithms express different gravitational and repulsive forces.
  • the force-guided layout algorithm is easy to understand and easy to implement, can be used in most network datasets, and achieves better symmetry and local aggregation.
  • the de-reprocessing unit 30 is configured to perform de-duplication processing on the pre-processing nodes that overlap in the initial network topology diagram, and output the de-duplication node.
  • the deduplication node in this embodiment refers to a non-overlapping node formed by the overlapping pre-processing nodes after performing deduplication processing.
  • the deduplication processing unit 30 specifically includes a coordinate data acquisition subunit 31, a data buffer subunit 32, a node deduplication processing subunit 33, and a deduplication node output subunit 34.
  • the coordinate data acquisition sub-unit 31 is configured to acquire coordinate data of each pre-processing node in the initial network topology diagram, where the coordinate data includes an x coordinate and a y coordinate.
  • the location of any pre-processing node can be determined by its coordinate data. If the coordinate data of any two preprocessing nodes are the same, that is, the x coordinate and the y coordinate are the same, the two preprocessing nodes overlap.
  • the data cache sub-unit 32 is configured to put the pre-processing nodes with the same coordinate data into the cache list.
  • the placement of the same pre-processing node with the same coordinate data into the cache list includes: at least two pre-processing nodes having the same coordinate data as a set of cache node groups, and then putting at least one set of cache node groups into the cache list. in.
  • the node de-reprocessing sub-unit 33 is configured to traverse the cache list, select two pre-processing nodes with the same coordinate data, and add or subtract the random number from the x-coordinate and the y-coordinate of the two pre-processing nodes respectively to form two updates. Node; iterate sequentially until there is no preprocessing node with the same coordinate data in the cache list.
  • the random number is a number that is randomly generated not to be 0, and is set to k. It can be understood that if the random number k is 0, the x coordinate and the y coordinate of the two pre-processing nodes with the same coordinates are respectively added or subtracted from the random number, and two non-overlapping update nodes cannot be formed.
  • the coordinate data is the pre-processing nodes A and B of (x1, y1)
  • the x-coordinate and the y-coordinate of the pre-processing nodes A and B are respectively added or subtracted by the random number k1, the two formed.
  • the nodes A'(x1+k1, y1+k1) and B'(x1-k1, y1-k1) are updated such that the two pre-processing nodes A and B are evenly spread.
  • the coordinate data is the pre-processing nodes C, D, and E of (x2, y2)
  • the x and y coordinates of the pre-processing nodes C and D are respectively added or subtracted by the random number k2, and then two The nodes C'(x2+k2, y2+k2) and D'(x2-k2, y2-k2) are updated such that the three pre-processing nodes C, D and E (x2, y2) are evenly spread.
  • the coordinate data is the pre-processing nodes F, G, H, and I of (x3, y3)
  • the x-coordinate and the y-coordinate of the pre-processing nodes F and G are respectively added or subtracted by the random number k3, and the pre-processing node H is made.
  • the x and y coordinates of I are added or subtracted by the random number k4, respectively, and the four updated nodes F'(x3+k3, y3+k3), G'(x3-k3, y3-k3), H are formed.
  • the preprocessing node does not have at least two preprocessing nodes with the same coordinate data in the cache list.
  • the de-duty node output sub-unit 34 is configured to determine whether there is a pre-processing node that is the same as the update node coordinate data; if present, the update node and the pre-processing node are placed in the cache list; if not, the node is updated As a deduplication node output.
  • the update node formed by traversing the cache list may be the same as the coordinate data of other pre-processing nodes not put into the cache list, that is, there is still node overlap phenomenon, so it is necessary to determine whether there is a pre-processing node identical to the update node coordinate data. If yes, the update node and the pre-processing node are put into the cache list as a group of cache node groups, and jump to the node to re-process the sub-unit 33; if not, the update node is output as the de-duplication node, and the jump is performed. Go to the target network topology map forming unit 40. It can be understood that other pre-processing nodes in the initial network topology map that are not put into the cache list are also output as de-duplication nodes.
  • the target network topology map forming unit 40 is configured to form a target network topology map based on the deduplication node.
  • the computer executing the network topology adaptive data visualization device receives the deduplication node, and displays the target network topology map based on all the deduplication nodes in the browser to display the data visualization result.
  • the node pre-processing unit 10 the initial network topology map forming unit 20, the de-duplication processing unit 30, and the target network topology map forming unit 40 recalculate one time, so that the browser visualizes the data of the latest data. Display, so that data synchronization update, without the need for professional data adjustment, is conducive to cost savings and improve the efficiency of data visualization.
  • the file format of the pre-processing node output by the node pre-processing unit 10 is the gexf file format
  • the initial network topology map forming unit 20 and the de-duplication processing unit 30 do not perform the initial network topology map and the de-duplication node.
  • the file format is converted so that the file format of the deduplicated node that is output is still the gexf file format.
  • the gexf file format has a large amount of network transmission data and a slow response time.
  • the network topology adaptive data visualization device further includes: a format conversion unit 50, configured to perform file format conversion on the deduplication node output by the deduplication processing unit 30, and output a deduplication node in the json file format,
  • the deduplication node of the json file format is sent to the target network topology map forming unit 40.
  • JSON JavaScript Object Notation
  • JSON uses a completely language-independent text format.
  • the gexf file format of the deduplication node is parsed, node information and edge information are acquired, and the deduplication node of the json file format is output based on the node information and the edge information. It can be understood that converting the deduplication node of the gexf file format into the deduplication node of the json file format can reduce the amount of data transmitted by the network, improve the response time, and is beneficial to improving the processing efficiency of the data visualization.
  • the network topology adaptive data visualization device provided by the embodiment can realize data visualization automation, simplify the data visualization process, and eliminate manual intervention, thereby effectively saving labor intervention costs and improving processing efficiency. Moreover, the network topology adaptive data visualization device performs deduplication processing on the pre-processing nodes of the overlapping points to eliminate the overlapping phenomenon between the nodes, so that each node can be completely presented, so that the final target network topology is formed. The structure of the figure is clear and can be displayed. Moreover, the network topology adaptive data visualization device can realize automatic data synchronization and update, so that business requirement analysis and exploration are real-time.
  • FIG. 6 is a schematic structural diagram of a network topology adaptive data visualization device according to an embodiment of the present invention.
  • the device 600 in FIG. 6 may be a mobile terminal such as a mobile phone, a tablet computer, a personal digital assistant (PDA), and/or an on-board computer, or a terminal such as a desktop computer or a server.
  • the device 600 includes a radio frequency (RF) circuit 601, a memory 602, an input module 603, a display module 604, a processor 605, an audio circuit 606, a WiFi (Wireless Fidelity) module 607, and a power supply connected through a system bus. 608.
  • RF radio frequency
  • the input module 603 and the display module 604 are used as user interaction devices of the device 600 for implementing interaction between the user and the device 600, for example, receiving a data visualization request input by the user and displaying the corresponding target network topology map to implement data. Visual operation.
  • the input module 603 is configured to receive a data visualization request input by the user, and send the data visualization request to the processor 605, where the data visualization request includes the node.
  • the processor 605 is configured to acquire a target network topology map based on the received data visualization request, and send the target network topology map to the display module 604.
  • the display module 604 is configured to receive and display the target network topology map.
  • the input module 603 can be used to receive numeric or character information input by the user, as well as to generate signal inputs related to user settings and function control of the device 600.
  • the input module 603 can include a touch panel 6031.
  • the touch panel 6031 also referred to as a touch screen, can collect touch operations on or near the user (such as the operation of the user using any suitable object or accessory such as a finger or a stylus on the touch panel 6031), and according to the preset The programmed program drives the corresponding connection device.
  • the touch panel 6031 may include two parts of a touch detection device and a touch controller.
  • the touch detection device detects the touch orientation of the user, and detects a signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts the touch information into contact coordinates, and sends the touch information.
  • the processor 605 is provided and can receive commands from the processor 605 and execute them.
  • the touch panel 6031 can be implemented in various types such as resistive, capacitive, infrared, and surface acoustic waves.
  • the input module 603 may further include other input devices 6032.
  • the other input devices 6032 may include but are not limited to physical keyboards, function keys (such as volume control buttons, switch buttons, etc.), trackballs, mice, joysticks, and the like. One or more of them.
  • the touch panel 6031 can cover the display panel 6041 to form a touch display screen.
  • the touch display screen detects a touch operation on or near it, the touch display screen transmits to the processor 605 to determine the type of the touch event, and then the processor. 605 provides a corresponding visual output on the touch display depending on the type of touch event.
  • the touch panel 6031 can cover the display panel 6041 to form a touch display screen.
  • the touch display screen detects a touch operation on or near it, the touch display screen transmits to the processor 605 to determine the type of the touch event, and then the processor. 605 provides a corresponding visual output on the touch display depending on the type of touch event.
  • the touch display includes an application interface display area and a common control display area.
  • the arrangement manner of the application interface display area and the display area of the common control is not limited, and may be arranged up and down, left and right, etc. to distinguish two display areas. Arrangement.
  • the application interface display area can be used to display the interface of the application. Each interface can contain interface elements such as at least one application's icon and/or widget desktop control.
  • the application interface display area can also be an empty interface that does not contain any content.
  • the common control display area is used to display controls with high usage, such as setting buttons, interface numbers, scroll bars, phone book icons, and the like.
  • the WiFi module 607 serves as a network interface of the device 600, and can implement data interaction between the device 600 and other devices.
  • the network interface can be connected to the remote storage device and the external display device through network communication.
  • the network interface is configured to receive the node sent by the remote storage device, and send the node to the processor 605.
  • the network interface is further configured to receive the target network topology map sent by the processor 605. And transmitting the target network topology map to the external display device.
  • the remote storage device connected to the network interface through the WiFi network may be a cloud server or other database, where the node is stored on the remote storage device, and when the node needs to be visualized, The node is sent to the WiFi module 607 through the WiFi network, and the WiFi module 607 sends the acquired node to the processor 605.
  • the memory 602 includes a first memory 6021 and a second memory 6022.
  • the first memory 6021 can be a non-transitory computer readable storage medium having stored thereon an operating system, a database, and computer executable instructions.
  • Computer executable instructions are executable by processor 605 for implementing a network topology adaptive data visualization method of the embodiment as illustrated in FIGS. 1-3.
  • the database on the first memory 6021 is used to store various types of data, for example, various data involved in the network topology adaptive data visualization method, such as a target network topology map and node data.
  • the node data may be a node generated by the device 600 and stored in the database, or a node sent by the remote storage device received through the network interface.
  • the second memory 6021 can be an internal memory of the device 600 that provides a cached operating environment for operating systems, databases, and computer executable instructions in a non-transitory computer readable storage medium.
  • processor 605 is the control center of device 600, which connects various portions of the entire handset using various interfaces and lines, by running or executing computer executable instructions and/or databases stored in first memory 6021. The data, performing various functions of the device 600 and processing data, thereby overall monitoring the device 600.
  • processor 605 can include one or more processing modules.
  • the processor 605 by executing the computer executable instructions stored in the first memory 6021 and/or the data in the database, the processor 605 is configured to perform the following steps: pre-processing the node, outputting the pre-processing node; Directing the layout algorithm to process the pre-processing node to form an initial network topology map; performing de-reprocessing on the overlapping pre-processing nodes in the initial network topology map, outputting a de-duplication node; forming a target network based on the de-duplication node Topology.
  • preprocessing the node and outputting the preprocessing node includes:
  • the spring potential model is used to calculate the elastic potential energy, the spring model comprising:
  • an energy model is used to calculate the dynamic potential energy, and the energy model includes:
  • nodes i and j use d(i,j) to represent the Euclidean distance of the two nodes, s(i,j) represents the natural length of the spring, k is the elastic coefficient, and r represents the electrostatic force constant between the two nodes. , w is the weight between two nodes, is the elastic potential energy, is the dynamic potential energy.
  • the performing de-duplication processing on the overlapping pre-processing nodes in the initial network topology diagram, and outputting the de-duplication nodes includes:
  • the processor 605 further performs the following steps: performing file format conversion on the deduplication node, and outputting a deduplication node in the json file format.
  • the network topology adaptive data visualization device 600 provided by the embodiment can realize data visualization automation, simplify the data visualization process, and eliminate manual intervention, thereby effectively saving labor intervention costs and improving processing efficiency. Moreover, in the device 600, the pre-processing node of the overlapping point is subjected to de-reprocessing to eliminate the overlapping phenomenon between the nodes, so that each node can be completely presented, so that the final formed target network topology structure is clear. Strong display. Moreover, in the device 600, automatic data synchronization update can be implemented, so that business requirement analysis and exploration are real-time.
  • the embodiment provides a non-transitory computer readable storage medium storing one or more computer readable instructions that are executed by one or more processors such that the one or more processes
  • the network topology adaptive data visualization method described in the first embodiment is executed. To avoid repetition, details are not described herein again.
  • modules and algorithm steps of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the solution. A person skilled in the art can use different methods for implementing the described functions for each particular application, but such implementation should not be considered to be beyond the scope of the present invention.
  • the disclosed apparatus and method may be implemented in other manners.
  • the device embodiments described above are merely illustrative.
  • the division of the modules is only a logical function division.
  • there may be another division manner for example, multiple modules or components may be combined or Can be integrated into another system, or some features can be ignored or not executed.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, device or module, and may be electrical, mechanical or otherwise.
  • the modules described as separate components may or may not be physically separated.
  • the components displayed as modules may or may not be physical modules, that is, may be located in one place, or may be distributed to multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the embodiment.
  • each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.
  • the functions, if implemented in the form of software functional modules and sold or used as separate products, may be stored in a computer readable storage medium.
  • the technical solution of the present invention which is essential or contributes to the prior art, or a part of the technical solution, may be embodied in the form of a software product, which is stored in a storage medium, including a number of instructions to make a computer device (which can be a personal computer, The server, or network device, etc.) performs all or part of the steps of the method described in various embodiments of the present invention.
  • the foregoing storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk, or an optical disk.

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Abstract

本方案公开了一种网络拓扑自适应的数据可视化方法、装置、设备和存储介质。该网络拓扑自适应的数据可视化方法包括：对节点进行预处理，输出预处理节点；采用力引导布局算法对所述预处理节点进行处理，形成初始网络拓扑图；对所述初始网络拓扑图中重叠的预处理节点进行去重处理，输出去重节点；基于所述去重节点形成目标网络拓扑图。该网络拓扑自适应的数据可视化方法可实现数据可视化自动化，简化数据可视化处理流程，无需人工干预，可有效节省人工干预成本，并提高处理效率。

Description

网络拓扑自适应的数据可视化方法、装置、设备和存储介质 技术领域
本发明涉及数据可视化技术领域，尤其涉及一种网络拓扑自适应的数据要视化方法、装置、设备和存储介质。
背景技术
数据可视化是利用计算机图形学来构建视觉图像，以帮助人们理解实际生活中规模较大且结构较复杂的科学结果或概念。对于复杂网络研究来说，可视化技术尤为重要，能有助于呈现或解释复杂网络数据或模型，进而从中发现各种模式、特点和关系。
当前数据可视化过程主要是通过专业软件工具进行复杂的调试和配置参数，再对直观的可视化结果进行目标探索。在当前数据可视化过程中，需专业人员进行繁琐的数据调整工作，包括对节点颜色、节点大小及网络形状等进行调整，工作量大且繁琐。在数据更新后，需专业人员重新进行数据调整，重叠工作较多且繁琐，无法实现数据同步更新。另外，当前数据可视化过程中存在节点重叠，使得部分节点不能完整呈现，导致拓扑结构不清晰现象，需进行人工去重处理，工作量大且繁琐，不利于提高数据可视化效率。
发明内容
本发明提供一种网络拓扑自适应的数据可视化方法、装置、设备和存储介质，以解决现有数据可视化过程中需人工进行繁琐的数据调整工作的问题。
本发明解决其技术问题所采用的技术方案是：
第一方面，提供一种网络拓扑自适应的数据可视化方法，包括：
对节点进行预处理，输出预处理节点；
采用力引导布局算法对所述预处理节点进行处理，形成初始网络拓扑图；
对所述初始网络拓扑图中重叠的预处理节点进行去重处理，输出去重节点；
基于所述去重节点形成目标网络拓扑图。
第二方面，本发明提供一种网络拓扑自适应的数据可视化装置，包括：
节点预处理单元，用于对节点进行预处理，输出预处理节点；
初始网络拓扑图形成单元，用于采用力引导布局算法对所述预处理节点进行处理，形成初始网络拓扑图；
去重处理单元，用于对所述初始网络拓扑图中重叠的预处理节点进行去重处理，输出去重节点；
目标网络拓扑图形成单元，用于基于所述去重节点形成目标网络拓扑图。
第三方面，本发明提供一种网络拓扑自适应的数据可视化设备，包括处理器及存储器，所述存储器存储有计算机可执行指令，所述处理器执行所述计算机可执行指令以执执行如下步骤：
对节点进行预处理，输出预处理节点；
采用力引导布局算法对所述预处理节点进行处理，形成初始网络拓扑图；
对所述初始网络拓扑图中重叠的预处理节点进行去重处理，输出去重节点；
基于所述去重节点形成目标网络拓扑图。
第四方面，一种非易失性计算机可读存储介质，存储有一个或多个计算机可读指令，所述计算机可读指令被一个或多个处理器执行，使得所述一个或多个处理器执行所述网络拓 扑自适应的数据可视化方法。
本发明与现有技术相比具有如下优点：本发明所提供的网络拓扑自适应的数据可视化方法、装置、设备和存储介质，可实现数据可视化自动化，简化数据可视化处理流程，无需人工干预，可有效节省人工干预成本，并提高处理效率。而且，该网络拓扑自适应的数据可视化方法、装置、设备和存储介质中，通过对重叠点的预处理节点进行去重处理，以消除节点之间重叠现象，使每一节点均能完整的呈现，使得最终形成的目标网络拓扑图结构清晰，可展示性强。而且，该网络拓扑自适应的数据可视化方法、装置、设备和存储介质中，可实现数据自动同步更新，使业务需求分析和探索具有实时性。
附图说明
下面将结合附图及实施例对本发明作进一步说明，附图中：
图1是本发明第一实施例中网络拓扑自适应的数据可视化方法的一流程图。
图2是图1所示网络拓扑自适应的数据可视化方法中步骤S1的一具体流程图。
图3是图1所示网络拓扑自适应的数据可视化方法中步骤S3的一具体流程图。
图4是本发明第二实施例中网络拓扑自适应的数据可视化装置的一原理框图。
图5是图4中网络拓扑自适应的数据可视化装置的一具体原理框图。
图6是本发明第三实施例中网络拓扑自适应的数据可视化设备的一示意图。
具体实施方式
为了对本发明的技术特征、目的和效果有更加清楚的理解，现对照附图详细说明本发明的具体实施方式。
第一实施例
图1示出本实施例中网络拓扑自适应的数据可视化方法的流程图。该网络拓扑自适应的数据可视化方法可在安装有数据可视化的专业软件工具的网络拓扑自适应的数据可视化设备上执行。其中，专业软件工具可以是Gephi这一复杂网络分析软件，主要用于各种网络和复杂系统，是用于进行动态和分层图的交互可视化和探测开源工具。如图1所示，该网络拓扑自适应的数据可视化方法包括如下步骤：
S1：对节点进行预处理，输出预处理节点。
本实施例中，在Gephi软件工具中对节点进行预处理，输出的预处理节点的文件格式是gexf文件格式。如图2所示，步骤S1具体包括如下步骤：
S11：获取每一节点的节点颜色和节点大小。
即给每一节点添加color_t属性和size_t属性，如下所示，value可根据实际业务场景自主设定，并根据该节点的color_t属性和size_t属性的值生成每一节点的节点颜色和节点大小。
Figure PCTCN2017076291-appb-000001
本实施例中，执行该网络拓扑自适应的数据可视化方法的专业软件工具为Gephi，获取到的每一节点的节点颜色和节点大小均为gexf文件格式。Gephi是一款优秀的复杂网络分析软件，支持导入多种格式的文件。gexf格式是Gephi推荐的格式，是用GEXF(Graph Exchange XML Format)语言创建的图表文件。GEXF语言是一种描述网络结构的语言，用于 指定的节点和边的关系图和以及用户定义的属性。
S12：对每一节点的节点颜色和节点大小进行数据标准化，获取每一节点的标准化值。
其中，数据标准化(normalization)是将数据按比例缩放，使其落入数值较小的特定区间，用于去除数据的单位限制，将其转化为无量级的纯数值，使得不同单位或数量级的指标能够进行比较和加权。
本实施例中，对每一节点的节点颜色对应的color_t属性和节点大小对应的size_t属性进行排序后，再进行Z-score标准化(zero-mean normalization)处理，以获取每一节点的标准化值。其中，Z-score标准化是指标准差标准化，以使经过处理的数据符合标准正太分布，即均值为0，标准差为1，以便基于输出的标准化值进行比较或加权。具体地，Z-score标准化的转化函数为
Figure PCTCN2017076291-appb-000002
其中，μ为所有样本数据的均值，σ为所有标准数据的标准差。
S13：根据每一节点的标准化值和分区阈值确定节点对应的区间，并将节点对应的区间作为预处理节点输出。
其中，分区阈值用于将数据划分成多个区间，而步骤S12中形成的每一节点的标准化值在一特定区间内，将每一节点的标准化值与预设的分区阈值进行比较，即可确定该节点的标准化值在分区阈值确定的哪一区间内，并将该节点对应的区间作为预处理节点输出。
S2：采用力引导布局算法对预处理节点进行处理，形成初始网络拓扑图。
力引导布局算法(Fruchterman-Reingold算法，简称FR算法)是一种丰富两节点之间的物理模型，加入节点之间的静电力，通过计算系统的总能量并使得能量最小化，从而达到布局的目的。力引导布局算法的计算公式如下：
采用弹簧模型计算弹性势能，弹簧模型包括：
Figure PCTCN2017076291-appb-000003
基于弹性势能，采用能量模型计算动力势能，能量模型包括：
Figure PCTCN2017076291-appb-000004
其中，节点i和j，用d(i,j)表示两个节点的欧式距离，s(i,j)表示弹簧的自然长度，k是弹力系数，r表示两个节点之间的静电力常数，w是两个节点之间的权重，Es为弹性势能，E为动力势能。
本实施例中，先采用弹簧模型计算预处理节点的弹性势能；并基于计算得到的弹性势能，采用能量模型计算动力势能，利用计算得到的动力势能对预处理节点进行处理，以形成初始网络拓扑图。
无论是弹簧模型还是能量模型，其算法的本质是要接一个能量优化问题，区别在于优化函数的组成不同。优化对象包括引力和斥力部分，不同算法对引力和斥力的表达方式不同。力引导布局算法易于理解、容易实现，可以用于大多数网络数据集，而且实现的效果具有较好的对称性和局部聚合性。
S3：对初始网络拓扑图中重叠的预处理节点进行去重处理，输出去重节点。
由于经过力引导布局算法对预处理节点进行处理后形成的初始网络拓扑图中可能存在节点重叠，为避免节点重叠导致初始网络拓扑图结构不清晰，对初始网络拓扑图中重叠的预处理节点进行去处理，使重叠节点均匀散开。本实施例中的去重节点是指重叠的预处理节点进行去重处理后形成的不重叠的节点。如图3所示，步骤S3具体包括：
S31：获取初始网络拓扑图中每一预处理节点的坐标数据，坐标数据包括x坐标和y 坐标。
在初始网络拓扑图中，任一预处理节点的位置均可通过其坐标数据确定。若任意两个预处理节点的坐标数据相同，即x坐标和y坐标均相同，则两个预处理节点重叠。
S32：将坐标数据相同的预处理节点放入缓存列表。
本实施例中，若坐标数据为(x1，y1)的预处理节点有两个，坐标数据为(x2，y2)的预处理节点有3个，坐标数据为(x3，y3)的预处理节点有四个……，将坐标数据相同的预处理节点放入缓存列表包括：将坐标数据相同的至少两个预处理节点作为一组缓存节点组，再将至少一组缓存节点组放入缓存列表中。
S33：遍历缓存列表，选取两个坐标数据相同的预处理节点，使两个预处理节点的x坐标和y坐标分别加上或减去随机数，形成两个更新节点；依次迭代，直至缓存列表中不存在坐标数据相同的预处理节点。
其中，随机数是随机生成不为0的数，设为k。可以理解地，若随机数k为0，则使两个坐标相同的预处理节点的x坐标和y坐标分别加上或减去随机数，无法形成两个不重叠的更新节点。
本实施例中，若坐标数据为(x1，y1)的预处理节点A和B，使预处理节点A和B的x坐标和y坐标分别加上或减去随机数k1，则形成的两个更新节点A’(x1+k1,y1+k1)和B’(x1-k1,y1-k1)，从而使两个预处理节点A和B均匀散开。相应地，若坐标数据为(x2，y2)的预处理节点C、D和E，使预处理节点C和D的x坐标和y坐标分别加上或减去随机数k2，则形成的两个更新节点C’(x2+k2,y2+k2)和D’(x2-k2,y2-k2)，从而使三个预处理节点C、D和E(x2，y2)均匀散开。若坐标数据为(x3，y3)的预处理节点F、G、H和I，使预处理节点F和G的x坐标和y坐标分别加上或减去随机数k3，并使预处理节点H和I的x坐标和y坐标分别加上或减去随机数k4，则形成的四个更新节点F’(x3+k3,y3+k3)、G’(x3-k3,y3-k3)、H’(x3+k4,y3+k4)和I’(x3-k4,y3-k4)，从而使四个预处理节点F、G、H和I均匀散开……遍历缓存列表中所有坐标数据相同的预处理节点，直至缓存列表中不存在坐标数据相同的至少两个预处理节点。
S34：判断是否存在与更新节点坐标数据相同的预处理节点；若存在，则将更新节点与预处理节点放入所述缓存列表；若不存在，则将更新节点作为去重节点输出。
由于遍历缓存列表所形成的更新节点可能会与其他未放入缓存列表中的预处理节点的坐标数据相同，使其仍存在节点重叠现象，因此需判断是否存在与更新节点坐标数据相同的预处理节点；若存在，则将更新节点与预处理节点作为一组缓存节点组放入缓存列表中，执行步骤S33；若不存在，则将更新节点作为去重节点输出，以执行步骤S4。可以理解地，初始网络拓扑图中的其他未放入缓存列表中的预处理节点也作为去重节点输出。
S4：基于去重节点形成目标网络拓扑图。
具体地，执行该网络拓扑自适应的数据可视化方法的计算机接收到去重节点，在浏览器中基于所有去重节点显示目标网络拓扑图，以展示数据可视化结果。当后台数据有更新时，再基于步骤S1-S4重新计算一遍，使得浏览器将最新数据的数据可视化结果显示，从而实现数据同步更新，而无需专业人员进行数据调整，有利于节省成本并提高数据可视化的处理效率。
进一步地，由于步骤S1中输出的预处理节点的文件格式为gexf文件格式，步骤S2和步骤S3中并没有对输出的初始网络拓扑图和去重节点进行文件格式转换，使得其输出的去重节点的文件格式仍为gexf文件格式，在基于去重节点形成目标网络拓扑图的过程中，gexf文件格式的网络传输数据量大，响应时间较慢。
为克服上述问题，该网络拓扑自适应的数据可视化方法的步骤S3与步骤S4之间还包括：对去重节点进行文件格式转换，输出json文件格式的去重节点。JSON(JavaScript Object  Notation)是一种轻量级的数据交换格式。JSON采用完全独立于语言的文本格式，这些特性使JSON成为理想的数据交换语言，具有易于人阅读和编写，同时也易于机器解析和生成的优点。
具体地，对去重节点的gexf文件格式进行解析，获取节点(node)信息和边缘(edge)信息，并基于节点(node)信息和边缘(edge)信息，输出json文件格式的去重节点。可以理解地，将gexf文件格式的去重节点转换成json文件格式的去重节点，可减小网络传输数据量，提高响应时间，有利于提高数据可视化的处理效率。
本实施例所提供的网络拓扑自适应的数据可视化方法可实现数据可视化自动化，简化数据可视化处理流程，无需人工干预，可有效节省人工干预成本，并提高处理效率。而且，该网络拓扑自适应的数据可视化方法中通过对重叠点的预处理节点进行去重处理，以消除节点之间重叠现象，使每一节点均能完整的呈现，使得最终形成的目标网络拓扑图结构清晰，可展示性强。而且，该网络拓扑自适应的数据可视化方法可实现数据自动同步更新，使业务需求分析和探索具有实时性。
第二实施例
图4和图5示出本实施例中网络拓扑自适应的数据可视化装置的原理框图。该网络拓扑自适应的数据可视化装置可在安装有数据可视化的专业软件工具的网络拓扑自适应的数据可视化设备上执行。其中，专业软件工具可以是Gephi这一复杂网络分析软件，主要用于各种网络和复杂系统，是用于进行动态和分层图的交互可视化和探测开源工具。如图4所示，该网络拓扑自适应的数据可视化装置包括节点预处理单元10、初始网络拓扑图形成单元20、去重处理单元30和目标网络拓扑图形成单元40。
节点预处理单元10，用于对节点进行预处理，输出预处理节点。
本实施例中，在Gephi软件工具中对节点进行预处理，输出的预处理节点的文件格式是gexf文件格式。如图5所示，节点预处理单元10具体包括节点获取子单元11、数据标准化子单元12、和预处理节点获取子单元13。
节点获取子单元11，用于获取每一节点的节点颜色和节点大小。
即给每一节点添加color_t属性和size_t属性，如下所示，value可根据实际业务场景自主设定，并根据该节点的color_t属性和size_t属性的值生成每一节点的节点颜色和节点大小。
Figure PCTCN2017076291-appb-000005
本实施例中，执行该网络拓扑自适应的数据可视化装置的专业软件工具为Gephi，获取到的每一节点的节点颜色和节点大小均为gexf文件格式。Gephi是一款优秀的复杂网络分析软件，支持导入多种格式的文件。gexf格式是Gephi推荐的格式，是用GEXF(Graph Exchange XML Format)语言创建的图表文件。GEXF语言是一种描述网络结构的语言，用于指定的节点和边的关系图和以及用户定义的属性。
数据标准化子单元12，用于对每一节点的节点颜色和节点大小进行数据标准化，获取每一节点的标准化值。
其中，数据标准化(normalization)是将数据按比例缩放，使其落入数值较小的特定区间，用于去除数据的单位限制，将其转化为无量级的纯数值，使得不同单位或数量级的指标能够进行比较和加权。
本实施例中，对每一节点的节点颜色对应的color_t属性和节点大小对应的size_t属性进行排序后，再进行Z-score标准化(zero-mean normalization)处理，以获取每一节点的标准化值。其中，Z-score标准化是指标准差标准化，以使经过处理的数据符合标准正太分布，即均值为0，标准差为1，以便基于输出的标准化值进行比较或加权。具体地，Z-score标准化的转化函数为，其中，为所有样本数据的均值，为所有标准数据的标准差。
预处理节点获取子单元13，用于根据每一节点的标准化值和分区阈值确定节点对应的区间，并将节点对应的区间作为预处理节点输出。
其中，分区阈值用于将数据划分成多个区间，通过数据标准化子单元12进行数据标准化后形成的每一节点的标准化值在一特定区间内，将每一节点的标准化值与预设的分区阈值进行比较，即可确定该节点的标准化值在分区阈值确定的哪一区间内，并将该节点对应的区间作为预处理节点输出。
初始网络拓扑图形成单元20，用于采用力引导布局算法对预处理节点进行处理，形成初始网络拓扑图。
力引导布局算法(Fruchterman-Reingold算法，简称FR算法)是一种丰富两节点之间的物理模型，加入节点之间的静电力，通过计算系统的总能量并使得能量最小化，从而达到布局的目的。力引导布局算法的计算公式如下：
采用弹簧模型计算弹性势能，弹簧模型包括：
Figure PCTCN2017076291-appb-000006
基于弹性势能，采用能量模型计算动力势能，能量模型包括：
Figure PCTCN2017076291-appb-000007
其中，节点i和j，用d(i,j)表示两个节点的欧式距离，s(i,j)表示弹簧的自然长度，k是弹力系数，r表示两个节点之间的静电力常数，w是两个节点之间的权重，Es为弹性势能，E为动力势能。
本实施例中，先采用弹簧模型计算预处理节点的弹性势能；并基于计算得到的弹性势能，采用能量模型计算动力势能，利用计算得到的动力势能对预处理节点进行处理，以形成初始网络拓扑图。
无论是弹簧模型还是能量模型，其算法的本质是要接一个能量优化问题，区别在于优化函数的组成不同。优化对象包括引力和斥力部分，不同算法对引力和斥力的表达方式不同。力引导布局算法易于理解、容易实现，可以用于大多数网络数据集，而且实现的效果具有较好的对称性和局部聚合性。
去重处理单元30，用于对初始网络拓扑图中重叠的预处理节点进行去重处理，输出去重节点。
由于经过力引导布局算法对预处理节点进行处理后形成的初始网络拓扑图中可能存在节点重叠，为避免节点重叠导致初始网络拓扑图结构不清晰，对初始网络拓扑图中重叠的预处理节点进行去处理，使重叠节点均匀散开。本实施例中的去重节点是指重叠的预处理节点进行去重处理后形成的不重叠的节点。如图5所示，去重处理单元30具体包括坐标数据获取子单元31、数据缓存子单元32、节点去重处理子单元33和去重节点输出子单元34。
坐标数据获取子单元31，用于获取初始网络拓扑图中每一预处理节点的坐标数据，坐标数据包括x坐标和y坐标。
在初始网络拓扑图中，任一预处理节点的位置均可通过其坐标数据确定。若任意两个预处理节点的坐标数据相同，即x坐标和y坐标均相同，则两个预处理节点重叠。
数据缓存子单元32，用于将坐标数据相同的预处理节点放入缓存列表。
本实施例中，若坐标数据为(x1，y1)的预处理节点有两个，坐标数据为(x2，y2)的预处理节点有3个，坐标数据为(x3，y3)的预处理节点有四个……，将坐标数据相同的预处理节点放入缓存列表包括：将坐标数据相同的至少两个预处理节点作为一组缓存节点组，再将至少一组缓存节点组放入缓存列表中。
节点去重处理子单元33，用于遍历缓存列表，选取两个坐标数据相同的预处理节点，使两个预处理节点的x坐标和y坐标分别加上或减去随机数，形成两个更新节点；依次迭代，直至缓存列表中不存在坐标数据相同的预处理节点。
其中，随机数是随机生成不为0的数，设为k。可以理解地，若随机数k为0，则使两个坐标相同的预处理节点的x坐标和y坐标分别加上或减去随机数，无法形成两个不重叠的更新节点。
本实施例中，若坐标数据为(x1，y1)的预处理节点A和B，使预处理节点A和B的x坐标和y坐标分别加上或减去随机数k1，则形成的两个更新节点A’(x1+k1,y1+k1)和B’(x1-k1,y1-k1)，从而使两个预处理节点A和B均匀散开。相应地，若坐标数据为(x2，y2)的预处理节点C、D和E，使预处理节点C和D的x坐标和y坐标分别加上或减去随机数k2，则形成的两个更新节点C’(x2+k2,y2+k2)和D’(x2-k2,y2-k2)，从而使三个预处理节点C、D和E(x2，y2)均匀散开。若坐标数据为(x3，y3)的预处理节点F、G、H和I，使预处理节点F和G的x坐标和y坐标分别加上或减去随机数k3，并使预处理节点H和I的x坐标和y坐标分别加上或减去随机数k4，则形成的四个更新节点F’(x3+k3,y3+k3)、G’(x3-k3,y3-k3)、H’(x3+k4,y3+k4)和I’(x3-k4,y3-k4)，从而使四个预处理节点F、G、H和I均匀散开……遍历缓存列表中所有坐标数据相同的预处理节点，直至缓存列表中不存在坐标数据相同的至少两个预处理节点。
去重节点输出子单元34，用于判断是否存在与更新节点坐标数据相同的预处理节点；若存在，则将更新节点与预处理节点放入所述缓存列表；若不存在，则将更新节点作为去重节点输出。
由于遍历缓存列表所形成的更新节点可能会与其他未放入缓存列表中的预处理节点的坐标数据相同，即仍存在节点重叠现象，因此需判断是否存在与更新节点坐标数据相同的预处理节点；若存在，则将更新节点与预处理节点作为一组缓存节点组放入缓存列表中，跳转到节点去重处理子单元33；若不存在，则将更新节点作为去重节点输出，跳转到目标网络拓扑图形成单元40。可以理解地，初始网络拓扑图中的其他未放入缓存列表中的预处理节点也作为去重节点输出。
目标网络拓扑图形成单元40，用于基于去重节点形成目标网络拓扑图。
具体地，执行该网络拓扑自适应的数据可视化装置的计算机接收到去重节点，在浏览器中基于所有去重节点显示目标网络拓扑图，以展示数据可视化结果。当后台数据有更新时，再基于节点预处理单元10、初始网络拓扑图形成单元20、去重处理单元30和目标网络拓扑图形成单元40重新计算一遍，使得浏览器将最新数据的数据可视化结果显示，从而实现数据同步更新，而无需专业人员进行数据调整，有利于节省成本并提高数据可视化的处理效率。
进一步地，由于节点预处理单元10输出的预处理节点的文件格式为gexf文件格式，初始网络拓扑图形成单元20和去重处理单元30中并没有对输出的初始网络拓扑图和去重节点进行文件格式转换，使得其输出的去重节点的文件格式仍为gexf文件格式，在基于去重节点形成目标网络拓扑图的过程中，gexf文件格式的网络传输数据量大，响应时间较慢。
为克服上述问题，该网络拓扑自适应的数据可视化装置中还包括：格式转换单元50，用于对去重处理单元30输出的去重节点进行文件格式转换，输出json文件格式的去重节点，并将json文件格式的去重节点发送给目标网络拓扑图形成单元40。JSON(JavaScript  Object Notation)是一种轻量级的数据交换格式。JSON采用完全独立于语言的文本格式，这些特性使JSON成为理想的数据交换语言，具有易于人阅读和编写，同时也易于机器解析和生成的优点。
具体地，对去重节点的gexf文件格式进行解析，获取节点(node)信息和边缘(edge)信息，并基于节点(node)信息和边缘(edge)信息，输出json文件格式的去重节点。可以理解地，将gexf文件格式的去重节点转换成json文件格式的去重节点，可减小网络传输数据量，提高响应时间，有利于提高数据可视化的处理效率。
本实施例所提供的网络拓扑自适应的数据可视化装置可实现数据可视化自动化，简化数据可视化处理流程，无需人工干预，可有效节省人工干预成本，并提高处理效率。而且，该网络拓扑自适应的数据可视化装置中通过对重叠点的预处理节点进行去重处理，以消除节点之间重叠现象，使每一节点均能完整的呈现，使得最终形成的目标网络拓扑图结构清晰，可展示性强。而且，该网络拓扑自适应的数据可视化装置可实现数据自动同步更新，使业务需求分析和探索具有实时性。
第三实施例
图6是本发明实施例的网络拓扑自适应的数据可视化设备的结构示意图。具体地，图6中的设备600可以为手机、平板电脑、个人数字助理(PersonalDigital Assistant，PDA)和/或车载电脑等移动终端、或者台式电脑、服务器等终端。如图6所示，设备600包括通过系统总线连接的射频(RadioFrequency，RF)电路601、存储器602、输入模块603、显示模块604、处理器605、音频电路606、WiFi(WirelessFidelity)模块607和电源608。
输入模块603和显示模块604作为设备600的用户交互装置，用于实现用户与设备600之间的交互，例如，接收用户输入的数据可视化请求并显示对应的所述目标网络拓扑图，以实现数据可视化操作。输入模块603用于接收用户输入的数据可视化请求，并将所述数据可视化请求发送给所述处理器605，所述数据可视化请求包括所述节点。所述处理器605用于基于接收到的所述数据可视化请求，获取目标网络拓扑图，并将所述目标网络拓扑图发送给所述显示模块604。显示模块604用于接收并显示所述目标网络拓扑图。
在一些实施例中，输入模块603可用于接收用户输入的数字或字符信息，以及产生与设备600的用户设置以及功能控制有关的信号输入。在一些实施例中，该输入模块603可以包括触控面板6031。触控面板6031，也称为触摸屏，可收集用户在其上或附近的触摸操作(比如用户使用手指、触笔等任何适合的物体或附件在触控面板6031上的操作)，并根据预先设定的程式驱动相应的连接装置。可选地，触控面板6031可包括触摸检测装置和触摸控制器两个部分。其中，触摸检测装置检测用户的触摸方位，并检测触摸操作带来的信号，将信号传送给触摸控制器；触摸控制器从触摸检测装置上接收触摸信息，并将它转换成触点坐标，再送给该处理器605，并能接收处理器605发来的命令并加以执行。此外，可以采用电阻式、电容式、红外线以及表面声波等多种类型实现触控面板6031。除了触控面板6031，输入模块603还可以包括其他输入设备6032，其他输入设备6032可以包括但不限于物理键盘、功能键(比如音量控制按键、开关按键等)、轨迹球、鼠标、操作杆等中的一种或多种。
可以理解，触控面板6031可以覆盖显示面板6041，形成触摸显示屏，当该触摸显示屏检测到在其上或附近的触摸操作后，传送给处理器605以确定触摸事件的类型，随后处理器605根据触摸事件的类型在触摸显示屏上提供相应的视觉输出。
可以理解，触控面板6031可以覆盖显示面板6041，形成触摸显示屏，当该触摸显示屏检测到在其上或附近的触摸操作后，传送给处理器605以确定触摸事件的类型，随后处理器605根据触摸事件的类型在触摸显示屏上提供相应的视觉输出。
触摸显示屏包括应用程序界面显示区及常用控件显示区。该应用程序界面显示区及该常用控件显示区的排列方式并不限定，可以为上下排列、左右排列等可以区分两个显示区的 排列方式。该应用程序界面显示区可以用于显示应用程序的界面。每一个界面可以包含至少一个应用程序的图标和/或widget桌面控件等界面元素。该应用程序界面显示区也可以为不包含任何内容的空界面。该常用控件显示区用于显示使用率较高的控件，例如，设置按钮、界面编号、滚动条、电话本图标等应用程序图标等。
WiFi模块607作为设备600的网络接口，可以实现设备600与其他设备的数据交互，本实施例中，网络接口可与远端存储设备和外部显示设备通过网络通信相连。所述网络接口用于接收所述远端存储设备发送的所述节点，并将所述节点发送给所述处理器605；还用于接收所述处理器605发送的所述目标网络拓扑图，并将所述目标网络拓扑图发送给所述外部显示设备。本实施例中，与该网络接口通过WiFi网络相连的远端存储设备可以是云服务器或其他数据库，该远端存储设备上存储有所述节点，在需对所述节点进行可视化处理时，可将所述节点通过WiFi网络发送给WiFi模块607，WiFi模块607将获取到的所述节点发送给所述处理器605。
存储器602包括第一存储器6021及第二存储器6022。在一些实施例中，第一存储器6021可为非易失性计算机可读存储介质，其上存储有操作系统、数据库及计算机可执行指令。计算机可执行指令可被处理器605所执行，用于实现如图1-3所示的实施例的网络拓扑自适应的数据可视化方法。第一存储器6021上的数据库用于存储各类数据，例如，上述网络拓扑自适应的数据可视化方法中所涉及的各种数据，如目标网络拓扑图及节点数据等。该节点数据可以是设备600生成并存储在数据库中的节点，也可以通过网络接口接收到的远端存储设备发送的节点。第二存储器6021可为设备600的内存储器，为非易失性计算机可读存储介质中操作系统、数据库和计算机可执行指令提供高速缓存的运行环境。
在本实施例中，处理器605是设备600的控制中心，利用各种接口和线路连接整个手机的各个部分，通过运行或执行存储在第一存储器6021内的计算机可执行指令和/或数据库内的数据，执行设备600的各种功能和处理数据，从而对设备600进行整体监控。可选地，处理器605可包括一个或多个处理模块。
在本实施例中，通过执行存储该第一存储器6021内的计算机可执行指令和/或数据库内的数据，处理器605用于执行如下步骤：对节点进行预处理，输出预处理节点；采用力引导布局算法对所述预处理节点进行处理，形成初始网络拓扑图；对所述初始网络拓扑图中重叠的预处理节点进行去重处理，输出去重节点；基于所述去重节点形成目标网络拓扑图。
优选地，对节点进行预处理，输出预处理节点，包括：
获取每一所述节点的节点颜色和节点大小；
对每一所述节点进行数据标准化，获取每一所述节点的标准化值；
根据每一所述节点的标准化值和分区阈值确定所述节点对应的区间，并将所述节点对应的区间作为所述预处理节点输出。
优选地，采用弹簧模型计算弹性势能，所述弹簧模型包括：
Figure PCTCN2017076291-appb-000008
基于所述弹性势能，采用能量模型计算动力势能，所述能量模型包括：
Figure PCTCN2017076291-appb-000009
其中，节点i和j，用d(i,j)表示两个节点的欧式距离，s(i,j)表示弹簧的自然长度，k是弹力系数，r表示两个节点之间的静电力常数，w是两个节点之间的权重，为弹性势能，为动力势能。
优选地，所述对所述初始网络拓扑图中重叠的预处理节点进行去重处理，输出去重节点，包括：
获取所述初始网络拓扑图中每一预处理节点的坐标数据，所述坐标数据包括x坐标和y坐标；
将坐标数据相同的所述预处理节点放入缓存列表；
遍历所述缓存列表，选取两个坐标数据相同的所述预处理节点，使两个所述预处理节点的x坐标和y坐标分别加上或减去随机数，形成两个更新节点；依次迭代，直至所述缓存列表中不存在坐标数据相同的所述预处理节点；
判断是否存在与所述更新节点坐标数据相同的预处理节点；若存在，则将所述更新节点与所述预处理节点放入所述缓存列表；若不存在，则将所述更新节点作为所述去重节点输出。
优选地，所述处理器605还执行如下步骤：对所述去重节点进行文件格式转换，输出json文件格式的去重节点。
本实施例所提供的网络拓扑自适应的数据可视化设备600，可实现数据可视化自动化，简化数据可视化处理流程，无需人工干预，可有效节省人工干预成本，并提高处理效率。而且，该设备600中，通过对重叠点的预处理节点进行去重处理，以消除节点之间重叠现象，使每一节点均能完整的呈现，使得最终形成的目标网络拓扑图结构清晰，可展示性强。而且，该设备600中，可实现数据自动同步更新，使业务需求分析和探索具有实时性。
第四实施例
本实施例提供一种非易失性计算机可读存储介质，存储有一个或多个计算机可读指令，所述计算机可读指令被一个或多个处理器执行，使得所述一个或多个处理器执行第一实施例所述的网络拓扑自适应的数据可视化方法，为避免重复，这里不再赘述。
本领域普通技术人员可以意识到，结合本文中所公开的实施例描述的各示例的模块及算法步骤，能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能，但是这种实现不应认为超出本发明的范围。
所属领域的技术人员可以清楚地了解到，为描述的方便和简洁，上述描述的系统、装置和模块的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。
在本申请所提供的实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述模块的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个模块或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或模块的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的模块可以是或者也可以不是物理上分开的，作为模块显示的部件可以是或者也可以不是物理模块，即可以位于一个地方，或者也可以分布到多个网络模块上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
另外，在本发明各个实施例中的各功能模块可以集成在一个处理模块中，也可以是各个模块单独物理存在，也可以两个或两个以上模块集成在一个模块中。
所述功能如果以软件功能模块的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本发明的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机， 服务器，或者网络设备等)执行本发明各个实施例所述方法的全部或部分步骤。而前述的存储介质包括：U盘、移动硬盘、ROM、RAM、磁碟或者光盘等各种可以存储程序代码的介质。
以上所述，仅为本发明的具体实施方式，但本发明的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本发明揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本发明的保护范围之内。因此，本发明的保护范围应以权利要求的保护范围为准。

Claims (19)

  1. 一种网络拓扑自适应的数据可视化方法，其特征在于，包括：
    对节点进行预处理，输出预处理节点；
    采用力引导布局算法对所述预处理节点进行处理，形成初始网络拓扑图；
    对所述初始网络拓扑图中重叠的预处理节点进行去重处理，输出去重节点；
    基于所述去重节点形成目标网络拓扑图。
  2. 根据权利要求1所述的网络拓扑自适应的数据可视化方法，其特征在于，所述对节点进行预处理，输出预处理节点，包括：
    获取每一所述节点的节点颜色和节点大小；
    对每一所述节点进行数据标准化，获取每一所述节点的标准化值；
    根据每一所述节点的标准化值和分区阈值确定所述节点对应的区间，并将所述节点对应的区间作为所述预处理节点输出。
  3. 根据权利要求1所述的网络拓扑自适应的数据可视化方法，其特征在于，所述力引导布局算法包括：
    采用弹簧模型计算弹性势能，所述弹簧模型包括：
    Figure PCTCN2017076291-appb-100001
    基于所述弹性势能，采用能量模型计算动力势能，所述能量模型包括：
    Figure PCTCN2017076291-appb-100002
    其中，节点i和j，用d(i,j)表示两个节点的欧式距离，s(i,j)表示弹簧的自然长度，k是弹力系数，r表示两个节点之间的静电力常数，w是两个节点之间的权重，Es为弹性势能，E为动力势能。
  4. 根据权利要求1所述的网络拓扑自适应的数据可视化方法，其特征在于，所述对所述初始网络拓扑图中重叠的预处理节点进行去重处理，输出去重节点，包括：
    获取所述初始网络拓扑图中每一预处理节点的坐标数据，所述坐标数据包括x坐标和y坐标；
    将坐标数据相同的所述预处理节点放入缓存列表；
    遍历所述缓存列表，选取两个坐标数据相同的所述预处理节点，使两个所述预处理节点的x坐标和y坐标分别加上或减去随机数，形成两个更新节点；依次迭代，直至所述缓存列表中不存在坐标数据相同的所述预处理节点；
    判断是否存在与所述更新节点坐标数据相同的预处理节点；若存在，则将所述更新节点与所述预处理节点放入所述缓存列表；若不存在，则将所述更新节点作为所述去重节点输出。
  5. 根据权利要求1-4任一项所述的网络拓扑自适应的数据可视化方法，其特征在于，还包括：对所述去重节点进行文件格式转换，输出json文件格式的去重节点。
  6. 一种网络拓扑自适应的数据可视化装置，其特征在于，包括：
    节点预处理单元，用于对节点进行预处理，输出预处理节点；
    初始网络拓扑图形成单元，用于采用力引导布局算法对所述预处理节点进行处理，形成初始网络拓扑图；
    去重处理单元，用于对所述初始网络拓扑图中重叠的预处理节点进行去重处理，输出去重节点；
    目标网络拓扑图形成单元，用于基于所述去重节点形成目标网络拓扑图。
  7. 根据权利要求6所述的网络拓扑自适应的数据可视化装置，其特征在于，所述节点预处理单元包括：
    节点获取子单元，用于获取每一所述节点的节点颜色和节点大小；
    数据标准化子单元，用于对每一所述节点进行数据标准化，获取每一所述节点的标准化值；
    预处理节点获取子单元，用于根据每一所述节点的标准化值和分区阈值确定所述节点对应的区间，并将所述节点对应的区间作为所述预处理节点输出。
  8. 根据权利要求6所述的网络拓扑自适应的数据可视化装置，其特征在于，所述力引导布局算法包括：
    采用弹簧模型计算弹性势能，所述弹簧模型包括：
    Figure PCTCN2017076291-appb-100003
    基于所述弹性势能，采用能量模型计算动力势能，所述能量模型包括：
    Figure PCTCN2017076291-appb-100004
    其中，节点i和j，用d(i,j)表示两个节点的欧式距离，s(i,j)表示弹簧的自然长度，k是弹力系数，r表示两个节点之间的静电力常数，w是两个节点之间的权重，Es为弹性势能，E为动力势能。
  9. 根据权利要求6所述的网络拓扑自适应的数据可视化装置，其特征在于，所述去重处理单元包括：
    坐标数据获取子单元，用于获取所述初始网络拓扑图中每一预处理节点的坐标数据，所述坐标数据包括x坐标和y坐标；
    数据缓存子单元，用于将坐标数据相同的所述预处理节点放入缓存列表；
    节点去重处理子单元，用于遍历所述缓存列表，选取两个坐标数据相同的所述预处理节点，使两个所述预处理节点的x坐标和y坐标分别加上或减去随机数，形成两个更新节点；依次迭代，直至所述缓存列表中不存在坐标数据相同的所述预处理节点；
    去重节点输出子单元，用于判断是否存在与所述更新节点坐标数据相同的预处理节点；若存在，则将所述更新节点与所述预处理节点放入所述缓存列表；若不存在，则将所述更新节点作为所述去重节点输出。
  10. 根据权利要求6-9任一项所述的网络拓扑自适应的数据可视化装置，其特征在于，还包括格式转换单元，用于对所述去重节点进行文件格式转换，输出json文件格式的去重节点。
  11. 一种网络拓扑自适应的数据可视化设备，其特征在于，包括处理器及存储器，所述存储器存储有计算机可执行指令，所述处理器执行所述计算机可执行指令以执行如下步骤：
    对节点进行预处理，输出预处理节点；
    采用力引导布局算法对所述预处理节点进行处理，形成初始网络拓扑图；
    对所述初始网络拓扑图中重叠的预处理节点进行去重处理，输出去重节点；
    基于所述去重节点形成目标网络拓扑图。
  12. 根据权利要求11所述的设备，其特征在于，所述对节点进行预处理，输出预处理节点，包括：
    获取每一所述节点的节点颜色和节点大小；
    对每一所述节点进行数据标准化，获取每一所述节点的标准化值；
    根据每一所述节点的标准化值和分区阈值确定所述节点对应的区间，并将所述节点对应的区间作为所述预处理节点输出。
  13. 根据权利要求11所述的设备，其特征在于，所述力引导布局算法包括：
    采用弹簧模型计算弹性势能，所述弹簧模型包括：
    Figure PCTCN2017076291-appb-100005
    基于所述弹性势能，采用能量模型计算动力势能，所述能量模型包括：
    Figure PCTCN2017076291-appb-100006
    其中，节点i和j，用d(i,j)表示两个节点的欧式距离，s(i,j)表示弹簧的自然长度，k是弹力系数，r表示两个节点之间的静电力常数，w是两个节点之间的权重，Es为弹性势能，E为动力势能。
  14. 根据权利要求11所述的设备，其特征在于，所述对所述初始网络拓扑图中重叠的预处理节点进行去重处理，输出去重节点，包括：
    获取所述初始网络拓扑图中每一预处理节点的坐标数据，所述坐标数据包括x坐标和y坐标；
    将坐标数据相同的所述预处理节点放入缓存列表；
    遍历所述缓存列表，选取两个坐标数据相同的所述预处理节点，使两个所述预处理节点的x坐标和y坐标分别加上或减去随机数，形成两个更新节点；依次迭代，直至所述缓存列表中不存在坐标数据相同的所述预处理节点；
    判断是否存在与所述更新节点坐标数据相同的预处理节点；若存在，则将所述更新节点与所述预处理节点放入所述缓存列表；若不存在，则将所述更新节点作为所述去重节点输出。
  15. 根据权利要求11-14任一项所述的设备，其特征在于，所述处理器还执行如下步骤：对所述去重节点进行文件格式转换，输出json文件格式的去重节点。
  16. 根据权利要求11所述的设备，其特征在于，所述设备还包括与所述处理器相连的用户交互装置，所述用户交互装置用于接收用户输入的数据可视化请求，并将所述数据可视化请求发送给所述处理器，所述数据可视化请求包括所述节点；
    所述处理器用于基于接收到的所述数据可视化请求，获取目标网络拓扑图，并将所述目标网络拓扑图发送给所述用户交互装置；
    所述用户交互装置，用于接收并显示所述目标网络拓扑图。
  17. 根据权利要求11所述的设备，其特征在于，所述设备还包括与所述处理器相连的网络接口，所述网络接口与远端存储设备和外部显示设备相连；所述网络接口用于接收所述远端存储设备发送的所述节点，并将所述节点发送给所述处理器；还用于接收所述处理器发送的所述目标网络拓扑图，并将所述目标网络拓扑图发送给所述外部显示设备。
  18. 根据权利要求11所述的设备，其特征在于，所述存储器中存储有数据库，用于存储所述目标网络拓扑图。
  19. 一种非易失性计算机可读存储介质，存储有一个或多个计算机可读指令，所述计 算机可读指令被一个或多个处理器执行，使得所述一个或多个处理器执行权利要求1-5任一项所述的网络拓扑自适应的数据可视化方法。
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