Processing method, device and storage medium for data visualization analysis

Abstract

The present disclosure provides a data visualization analysis processing method and device and a storage medium, relates to the technical field of data visualization, and comprises the following steps: acquiring at least one group of source data in a database; drawing a first data analysis graph according to the source data; listening to a change operation of a user on the first data analysis graph; updating the first data analysis graph according to the change operation; and updating at least one group of the source data according to the updated first data analysis graph. The device and the storage medium apply the above method, and embodiments of the present disclosure realize the modification of the source data in the first data analysis graph through listening, and update the modified content to the database storing the source data, so that the source data in the database is modified through the relationship graph, and the new data analysis graph stores the modified content when the source data is read from the database for drawing next time, thereby realizing the accumulation of accurate and important data generated in the analysis process.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of data visualization, and in particular, to a data visualization analysis processing method, device and storage medium. BACKGROUND

[0002] The current mainstream data visualization analysis technology, especially in the fund analysis system, can convert the data source into a relationship graph through data mapping, more intuitively display the graphical information of the relationship graph corresponding to the data, and help users obtain the association relationship between personnel, bank account numbers and telephone numbers. SUMMARY

[0003] The inventor found that in the related art, a data visualization tool usually includes a processor, a visualization interface for interacting with a user, and a database for storing a data source. In an analysis process, one or more display interfaces (also referred to as canvases) can be created through the visualization interface to perform visualization analysis on the data. However, there are the following problems: either the graphical information of the relationship graph between each subject (such as personnel, bank account numbers, telephone numbers, etc.) in the canvas cannot be flexibly modified manually, or even if the graphical information of the relationship graph can be modified, the result of the modification only affects the currently modified canvas and has no effect on other canvases that exist in association, that is, the modification cannot be directly written back to the corresponding data source on the canvas, and the corresponding data in the data source can only be modified before the display on the canvas is updated again, so that accurate and important data generated in the analysis process cannot be accumulated.

[0004] The present disclosure aims to at least solve one of the technical problems existing in the related art. To this end, a data visualization analysis processing method, device and storage medium are proposed, which can accumulate accurate and important data generated in the analysis process.

[0005] According to the data visualization analysis processing method of the embodiment of the present disclosure, the method comprises: acquiring at least one group of source data in a database; drawing a first data analysis graph according to the source data; listening to a change operation of a user on the first data analysis graph; updating the first data analysis graph according to the change operation; and updating at least one group of the source data according to the updated first data analysis graph.

[0006] According to the above embodiment of the present disclosure, at least the following beneficial effects are achieved: by listening to the modification of the source data in the first data analysis graph and synchronously updating the modified content to the database storing the source data, the source data in the database is modified through the relationship graph, so that when the source data is read from the database for drawing next time, the newly drawn data analysis graph stores the modified content of the previous time, and the accumulation of accurate and important data generated in the analysis process is realized.

[0007] According to some embodiments of the data visualization analysis processing method of the present disclosure, the source data comprises a data subject and interaction information corresponding to the data subject, and the drawing of the first data analysis graph based on the source data comprises: obtaining a graph node parameter matched with the data subject in a cache; obtaining a graph connection parameter corresponding to the interaction information in the cache; and drawing the first data analysis graph in a preset canvas according to the graph node parameter and the graph connection parameter.

[0008] According to some embodiments of the data visualization analysis processing method of the present disclosure, the updating of the first data analysis graph based on the change operation comprises: updating attribute change data of the graph node parameter and / or attribute change data of the graph connection parameter corresponding in the cache based on the change operation; and updating the first data analysis graph based on the attribute change data; and the updating of at least one group of the source data based on the updated first data analysis graph comprises: writing back the at least one group of the source data in the database from the cache based on the attribute change data of the first data analysis graph.

[0009] According to some embodiments of the data visualization analysis processing method of the present disclosure, the data visualization analysis processing method further comprises: determining at least one second data analysis graph; the second data analysis graph and the first data analysis graph jointly refer to the same source data; and updating the second data analysis graph based on the attribute change data.

[0010] According to some embodiments of the data visualization analysis processing method of the present disclosure, the first data analysis graph and the second data analysis graph are respectively defined with a data entity cache interface and a data entity cache management class, and the data entity cache interface and the data entity cache management class are associated with the cache to record / monitor the attribute change data associated with the change operation.

[0011] According to some embodiments of the data visualization analysis processing method of the present disclosure, the processing method is applied to a data visualization analysis processing system, and the processing system comprises: a database; a cache configured to obtain the source data from the database; and a graph generation module configured to obtain the source data from the cache and generate a plurality of data analysis graphs based on the source data, wherein the plurality of data analysis graphs comprise the first data analysis graph; and the method further comprises: each data analysis graph and the cache receiving an event notification sent from the database, wherein the event notification comprises relevant content of the source data updated by the database; and each data analysis graph and the cache obtaining the source data updated by the database based on the event notification to realize refreshing.

[0012] According to some embodiments of the method for processing data visualization analysis according to the present disclosure, the change operation is a deletion operation or a merge operation; before updating at least one set of the source data, the method further comprises: when the change operation acts on the graph node parameter, updating the reference count corresponding to the graph node parameter according to the change operation; correspondingly, updating at least one set of the source data according to the updated first data analysis graph comprises: updating at least one set of the source data according to the reference count and the updated first data analysis graph.

[0013] According to some embodiments of the method for processing data visualization analysis according to the present disclosure, the processing method is applied to a processing system for data visualization analysis, the processing system comprising: a database; a graph generation module configured to generate a plurality of data analysis graphs according to the source data obtained from the database, wherein the plurality of data analysis graphs comprise the first data analysis graph; an interaction module configured to receive the change operation, the change operation being an addition operation or an import operation, the method further comprising: obtaining an analysis data set, the analysis data set being obtained after correlation analysis of the data to be analyzed added or imported by the user; determining whether each data analysis graph has a data subject consistent with a data subject in the analysis data set according to interaction information in the analysis data set; when it is determined that one or more data analysis graphs have a corresponding data subject, updating the data analysis graph associated with the corresponding data subject according to the corresponding data subject and the interaction information associated with the corresponding data subject in the analysis data set.

[0014] According to an apparatus according to the second aspect of the present disclosure, the apparatus comprises:

[0015] At least one processor, and a memory connected to the at least one processor in communication; wherein the memory stores instructions, the instructions are executed by the at least one processor, so that the at least one processor executes the instructions to realize the method for processing data visualization analysis according to any one of the aspects.

[0016] According to a storage medium according to the third aspect of the present disclosure, comprising computer executable instructions for executing the method for processing data visualization analysis according to any one of the aspects.

[0017] Other features and advantages of the present disclosure will be set forth in the following description, and in part will become apparent from the description, or will be learned by practice of the present disclosure. The objects and other advantages of the present disclosure will be realized and achieved by particularly pointed out in the specification, claims and drawings. BRIEF DESCRIPTION OF DRAWINGS

[0018] The above and / or additional aspects and advantages of the present disclosure will become apparent and more readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings in which:

[0019] Figure 1 is a structural schematic diagram of a system corresponding to the processing method of data visualization analysis provided by one embodiment of the present disclosure;

[0020] Figure 2 is a flow schematic diagram of a system corresponding to the processing method of data visualization analysis provided by one embodiment of the present disclosure;

[0021] Figure 3 is a flow schematic diagram of the processing method of data visualization analysis provided by one embodiment of the present disclosure;

[0022] Figure 4 is a display schematic diagram of a canvas of the processing method of data visualization analysis provided by one embodiment of the present disclosure;

[0023] Figure 5 is a schematic diagram of a source data and canvas mapping relationship of the processing method of data visualization analysis provided by one embodiment of the present disclosure;

[0024] Figure 6 is a schematic diagram of source data update of the processing method of data visualization analysis provided by one embodiment of the present disclosure;

[0025] Figure 7 is a flow schematic diagram of source data addition of the processing method of data visualization analysis provided by one embodiment of the present disclosure;

[0026] Figure 8 is a flow schematic diagram of database deletion of the processing method of data visualization analysis provided by one embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] Embodiments of the present disclosure are described in detail below with reference to the accompanying drawings, in which the same or similar components have the same or similar reference numbers throughout the several views. The embodiments described below are examples for explaining the present disclosure and are not intended to be limiting of the present disclosure.

[0028] In the description of the present disclosure, if there is a description to first, second, it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features or the sequence of indicated technical features.

[0029] The current mainstream data visualization analysis technology, especially in the fund analysis system, can convert the data source into a relationship diagram through data mapping, more directly display the diagram information of the relationship diagram corresponding to the data, and help the user to obtain the correlation between the personnel, bank account number and telephone number.

[0030] In the related art, the data visualization tool usually includes a processor, a visualization interface for interacting with the user, and a database for storing the data source. In the analysis process, one or more display interfaces (also called canvases) can be created through the visualization interface to visualize and analyze the data. However, either the diagram information of the relationship between the subjects in the canvas (such as personnel, bank account number, telephone number, etc.) cannot be flexibly modified manually, or even if the diagram information of the relationship can be modified, the result of the modification only affects the current modified canvas and has no effect on other canvases that exist in association, that is, the modification cannot be directly written back to the corresponding data source on the canvas, and the corresponding data in the data source can only be modified and then re-displayed on the canvas, so that the accurate and important data generated in the analysis process cannot be accumulated. Based on this, the present disclosure proposes a data visualization analysis processing method, device and storage medium, which can accumulate accurate and important data generated in the analysis process.

[0031] Reference Figure 1 As shown in the figure, the present disclosure proposes a data visualization analysis processing system, which includes a database 100, a processor 200 and a display module 300. The processor 200 is in communication connection with the database 100, and the display module 300 is connected with the processor 200. The display module 300 is used for interacting with the user and displaying the visualization result processed by the processor 200.

[0032] It should be noted that the processor 200 obtains at least one set of source data for drawing a first data analysis diagram from the database 100. The processor 200 converts the source data into graph node parameters and graph connection line parameters for drawing the first data analysis diagram, and then displays it on the display module 300. The user's change operation on the first data analysis diagram is listened to, and the processor 200 is notified to change the first data analysis diagram according to the change operation. When the user triggers to save the changed first data analysis diagram or after a predetermined time, the processor 200 will update the changed data to the database 100.

[0033] It should be noted that the database 100, the processor 200 and the display module 300 can be independent devices or integrated on the same device. For example, the data visualization analysis processing system can be loaded on a server or a terminal. The server can include the database 100 and the processor 200, for example: the server can include an application processor (AP), a modem processor, a graphics processing unit (GPU), an image signal processor (ISP), a controller, a memory, a video codec, a digital signal processor (DSP), a baseband processor, and / or a neural-network processing unit (NPU), etc. Different processing units can be independent devices or integrated in one or more processors. The terminal can be a mobile terminal device or a non-mobile terminal device. The mobile terminal device can be a mobile phone, a tablet computer, a notebook computer, a palm computer, a vehicle terminal device, a wearable device, a super mobile personal computer, a netbook, a personal digital assistant, a CPE, etc. The non-mobile terminal device can be a personal computer, a television, a teller machine or a self-service machine, etc. The embodiments of the present disclosure are not limited specifically.

[0034] For example, in the canvas 1, the processor 200 imports the data bodies of the multiple source data associated with the created drawing information on the canvas 1 into the cache from the database 100 at the beginning of the program, the user selects the filtering conditions and the data body selection for drawing the first data analysis graph in the canvas 1, and the processor 200 determines the referenced data bodies in the cache according to the filtering conditions and the data body selection, and imports other data (such as interaction information) for drawing the first data analysis graph according to the referenced data bodies; thereby the drawing of the first data analysis graph can be performed. Referring to Figure 2 As shown in the drawing, after the first data analysis graph is drawn, the change operation on the first data analysis graph is listened to, and when the user saves the change data corresponding to the change operation, the change data in the cache is written back to the database, and the canvas 2 or the canvas 3 related to the change data is triggered to be refreshed. Before the user triggers the change data corresponding to the change operation to be saved, only the change data in the cache is updated, that is, the corresponding data is marked as change data, and the canvases 1, 2 and 3 are temporarily presented as data visualization icons.

[0035] Those skilled in the art can understand that, Figure 1The illustrated system does not constitute a limitation on the embodiments of the present disclosure, and can include more or fewer components than illustrated, or combine certain components, or arrange the components differently.

[0036] The following refers to Figures 3 to 8 Various embodiments of the processing method of data visualization analysis of the embodiments of the present disclosure are presented.

[0037] The processing method of data visualization analysis of the present disclosure comprises:

[0038] Step S100, obtaining at least one set of source data in a database.

[0039] It should be noted that the source data includes data subjects and interaction information, the interaction information is used to represent the records between different data subjects, one set of source data corresponds to multiple records in the database, the data subjects have subject information, and the source data is obtained by filtering and extracting the original data according to certain rules. For example, the original data is bank flow, the data subject is represented by bank card number, the subject information is the sender account number or the receiver account number, one record can obtain two data subjects, one data subject is the bank card number corresponding to the sender account number, and one data subject is the bank card number corresponding to the receiver account number. The relationship description is refined to transfer in funds and transfer out funds, and the corresponding interaction information is the flow record.

[0040] In some embodiments, the processing method of data visualization analysis loads the relevant record data in the database into the cache according to the user's filtering conditions and the selection of the data subjects, and the canvas realizes graphical processing by matching the data subjects in the cache based on certain rules. In some embodiments, by listening to the range change of the filtering conditions in the canvas or the subject information of the newly added or modified data subjects, the relevant information of the source data can be quickly obtained from the database and stored in the cache for graphical processing. Still taking the bank flow as an example, the user filters the bank card numbers starting with 6212 and filters the bank flow of the bank card numbers in 2012-2013 for processing, and the source data is the corresponding data subjects and interaction information of the bank flow of the bank card numbers starting with 6212 in 2012-2013.

[0041] Step S200, drawing a first data analysis graph according to the source data.

[0042] It should be noted that the source data is processed to obtain the corresponding graphical properties to be displayed, so as to draw the source data. For example, taking a bank card number and a transaction flow as an example, through step S100, according to the action of the user operating on the canvas, the relevant data is obtained from the database, and the corresponding data body and interaction information of the bank flow of the bank card number starting with 6212 in 2012-2013 are obtained. After graphical processing, a plurality of pattern styles of the bank card number as the data body are displayed in the corresponding canvas. Referring to FIG. 8, after the data is graphically processed, a plurality of icons recording the bank card number, the holder and other information are displayed in the canvas. The pattern style of the icon is a human shape, and the transaction flow involved is a graphical style of a line with an arrow and text, which is used to represent the direction and total amount of the transaction. Figure 4

[0043] Step S300, listen to the user's change operation on the first data analysis graph.

[0044] It should be noted that the first data analysis graph is drawn and displayed on the canvas. The node attribute change event is set to listen on the canvas. According to the position and operation triggered, it is judged which graphical element in the first data analysis graph is modified, so as to determine the change content of the source data, such as judging which record is added, deleted, modified or merged.

[0045] It should be noted that each canvas can draw one data analysis graph, or can draw multiple data analysis graphs, which is not limited by the present disclosure. In order to be more convenient to understand, the following examples are all taken as an example of one canvas corresponding to one data analysis graph. Of course, one data analysis graph in the present case description can also refer to one canvas.

[0046] Step S400, updating the first data analysis graph according to the change operation.

[0047] It should be noted that in some embodiments, the data contained in the data analysis graphs formed by the plurality of canvases is derived from the same database, that is, there is at least one second data analysis graph and the first data analysis graph derived from the same source data. When the first data analysis graph is drawn according to the corresponding cache data (such as graphical node parameters, graphical connection parameters) in the cache of the source data, the corresponding cache data in the cache is updated first, so that the second data analysis graph can be updated in real time. When the corresponding source data of the change operation is referenced in multiple canvases or multiple data analysis graphs, the change of the cache data corresponding to the source data of any one of the canvases or data analysis graphs can trigger the synchronous update of the other related data analysis graphs. For example, Figure 5 ​As shown, data 1 in the source data (referred to as source data 1) is referenced by canvas 1 and canvas 2, data 2 in the source data (referred to as source data 2) is only referenced by canvas 2, and data 3 in the source data (referred to as source data 3) is only referenced by canvas 1. When the change operation is for source data 1, both canvas 1 and canvas 2 are synchronously updated, and when the change operation changes source data 3, only canvas 1 is updated.

[0048] Step S500, updating at least one set of source data according to the updated first data analysis graph.

[0049] It should be noted that the updating of the source data can be triggered by the user clicking to save the first data analysis graph, and the changed data is written back to the corresponding position in the database. Alternatively, the user can preset an automatic saving time, and when the changed data has been changed for the automatic saving time, it will be automatically written back to the database.

[0050] Therefore, by listening to the operation in the first data analysis graph, the changed content can be synchronously updated to the database storing the source data based on the save operation, the modification of the relational graph / analysis graph based on the canvas is realized to modify the source data in the database, and thus the new data analysis graph drawn in the next reading of the source data from the database stores the content of the previous modification, and the accumulation of accurate and important data generated in the analysis process is realized.

[0051] It can be understood that the source data includes a data subject and interaction information corresponding to the data subject, and the data subject and the interaction information corresponding to the data subject can be obtained. Step S200, drawing a first data analysis graph according to the source data, comprising: obtaining a graph node parameter matched with the data subject in the cache; obtaining a graph connection parameter corresponding to the interaction information in the cache; drawing the first data analysis graph in the preset canvas according to the graph node parameter and the graph connection parameter.

[0052] It should be noted that the graph node (such as an icon in Figure 4 ) displayed in the canvas contains the graph node parameter, which includes the subject information of the data subject and the graph attribute of the data subject, etc. The same graph node with the same meaning in different canvases involves the same information; the graph connection parameter represents the sender account, the receiver account, the involved content (such as the amount), the graph attribute, etc. contained in the connection between two graph nodes. A connection corresponds to a record. Taking a bank transaction record as an example, the data subject to be drawn in the canvas is a bank card number, as shown in Figure 4 , two graph nodes are drawn, and the graph node parameter corresponding to each graph node contains the bank card number, the holder corresponding to the bank card number, and the graph attribute of the visual icon, etc. There are two connections between Zhang San and Li Si, and the graph connection parameters of the two connections actually correspond to two flow records.

[0053] It can be understood that the step S400 of updating the first data analysis graph according to the change operation includes: updating the attribute change data of the corresponding graph node parameter and / or the attribute change data of the graph connection parameter in the cache according to the change operation; and updating the first data analysis graph according to the attribute change data. The step S500 of updating at least one set of source data according to the updated first data analysis graph includes: writing back at least one set of source data in the database from the cache according to the attribute change data of the first data analysis graph.

[0054] It should be noted that, in some embodiments, the change operation represents an operation of adding, modifying, deleting, or merging, and the user performs corresponding operations on the icons, lines, and information displayed by the icons and lines in the canvas, and actually performs corresponding operations, such as deleting, adding, merging, or modifying, on the corresponding records in the graph node parameter or the graph connection parameter. The attribute change data of the graph node parameter records the modification content of the graph node in the data analysis graph in the canvas; and the attribute change data of the graph connection parameter records the modification content (such as data to be refreshed and a presentation effect) of the graph connection in the data analysis graph in the canvas. The attribute change data changed by the change operation made by the user based on the canvas first exists in the cache, and is written back to the database from the cache after triggering saving, and the source data associated with the attribute change data in the database is updated at once.

[0055] It can be understood that, as shown in Figure 5 It can be understood that, as shown in

[0056] It should be noted that, after the modified content of the first data analysis graph is submitted, the modified content is first stored in the cache, and an attribute change event is triggered in time. For a second data analysis graph that references the same data object of the corresponding graph node, the second data analysis graph can be updated and the interface can be refreshed through a broadcast mode. The second data analysis graph is immediately updated based on the attribute change data in the first data analysis graph, and the corresponding graph nodes and the connections between the graph nodes in the second data analysis graph are updated. In some embodiments, a red dot can be displayed on a tab or a list item of the canvas to remind the user that the data analysis graph in the canvas has been changed. In other embodiments, the user can actively click to save to trigger real-time updating of the associated canvas. Preferably, in the embodiments of the present disclosure, the updating of the second data analysis graph is automatically triggered after the first data analysis graph is saved.

[0057] In some embodiments, the first data analysis graph and the second data analysis graph respectively define a data entity cache interface and a data entity cache management class, and the data entity cache interface and the data entity cache management class are associated with the cache to record / monitor attribute change data associated with the change operation. The same graph node and connection in different data analysis graphs are refreshed. For example, in some embodiments, the analysis system defines a data entity cache interface, a data entity cache management class, a graph node class, a graph connection class, and an analysis graph in the program. The data entity cache management class provides data entity operation interfaces such as adding, modifying, deleting, and querying, and data entity change records, so that each data object added to the cache stores the reference count and data entity change state associated therewith. The data entity cache interface is associated with the data entity cache management class and includes a unique identifier and an attribute change event. The graph node class is used to create a graph node based on a data subject, inherits the entity cache interface, and includes a unique identifier, subject information, graph attributes, attribute change event data to be refreshed, and presentation effects. The graph connection class is used to create a connection between data subjects based on the relationship description of the data subject, inherits the entity cache interface, and includes a unique identifier, interaction information, data subjects, graph attributes, attribute change event data to be refreshed, and presentation effects. The subject information includes information to be displayed by the data subject, the graph attributes are the appearance styles of the icons, the unique identifier is the UUID (Universally Unique Identifier) corresponding to the source data in the database, and each record corresponds to a unique identifier UUID. Referring to Figure 4 For example, based on the bank card number as the data subject, each record of the source data based on the bank card number is stored based on a certain data format, such as including the transfer-in bank card number, the transfer-out bank card number, the involved amount, the transaction time, the unique identifier code, and the like. The defined analysis graph includes a graph node set and a graph connection set attribute, is used to monitor node attribute change events and add / delete node events, and provides data import functions and data analysis functions. For example, when the data import function option is used in the current canvas interface, multiple new bank card number information stream records are imported based on a certain data format, the new data is stored in the cache, and when there is no graph node associated with the newly imported bank card number information, a corresponding graph node is generated in the canvas based on the data subject of the new bank card number information. According to the imported new data, a connection between the associated graph nodes is created in the canvas. For example, when a graph node (such as a new bank card number) is added in the current canvas interface, a graph node object is obtained through operation, and a corresponding record is prompted based on the addition. The mapping between the added graph node and the added record is realized by the data entity cache management class, and the data reference consistency is ensured.

[0058] It can be understood that the processing method is applied to a processing system for data visualization analysis, and the processing system comprises a database, a cache for obtaining source data from the database, and a graph generation module for obtaining the source data from the cache and generating a plurality of data analysis graphs according to the source data, wherein the plurality of data analysis graphs comprise a first data analysis graph. When the first data analysis graph, the second data analysis graph and the cache are present, the processing method for data visualization analysis further comprises: each data analysis graph and the cache receive an event notification sent from the database, and the event notification contains related content of updated source data of the database; and each data analysis graph and the cache obtain the updated source data of the database based on the event notification to realize refreshing.

[0059] It should be noted that, with reference to the embodiment shown in Figure 1 The functions of the graph generation module are implemented in the processor 200, and in other embodiments, part of the functions of the graph generation module are implemented in the processor 200 and the other part of the functions are implemented in the display module 100 (such as data view rendering). The embodiments of the present disclosure do not limit this.

[0060] It should be noted that after the database is updated, the cache and each data analysis graph are notified of the change based on the event notification mechanism. The cache determines whether to reload data based on the UUID of the updated record carried in the event notification sent by the database, that is, the UUID of the changed record recorded in the event notification, and the canvas determines whether to update based on the UUID. After reloading the data, the graph node parameters matching the data subject in the current cache, the graph connection parameters corresponding to the interaction information, etc. need to be obtained again to realize refreshing of each data analysis graph. For example, as shown in Figure 6 After the corresponding attribute update data saved in the cache is updated synchronously with the source data 1 in the database, the database sends an event notification to the cache, the canvas 1 and the canvas 2. When it is determined according to the UUID recorded in the event notification that the cache needs to update the data, the cache is triggered to reload the updated source data. When it is determined according to the UUID recorded in the event notification that the canvas 1 and the canvas 2 both need to be updated, the related data is reloaded from the cache and the screen is refreshed. The cache, the canvas 1 and the canvas 2 determine whether to reload the updated data of the database based on the UUID corresponding to each record, whether there is the same data subject and other conditions, so that the data remains consistent when the data changes.

[0061] It can be understood that the change operation is a deletion operation or a merge operation; before step S500, the processing method of the data visualization analysis further includes: when the change operation acts on the graphic node parameter, updating the reference count corresponding to the graphic node parameter according to the change operation. Correspondingly, step S500, updating at least one set of source data according to the updated first data analysis graph, includes: updating at least one set of source data according to the reference count and the updated first data analysis graph.

[0062] It should be noted that the reference count represents the number of times the data object corresponding to the graphic node is referenced by different data analysis graphs. Deleting the graphic node on the canvas means deleting the graphic node parameter corresponding to the graphic node on the canvas. In cache management, the reference count of the data object of the graphic node is reduced by one. Only when the reference count of the data object corresponding to the graphic node (i.e. the graphic node parameter) is equal to 0, the data object corresponding to the graphic node is not referenced by any data analysis graph, and then it can be directly identified as a deleted data object. The chart is saved and the record is synchronized to the deletion database. For example, Figure 6 In the example, if canvas 1 and canvas 2 both display an icon of a data object Zhang San (or a bank card number corresponding to Zhang San), the reference count of the data object Zhang San is 2. If the icon of the data object Zhang San is deleted on canvas 1, the reference count is reduced by one, and at this time the reference count is equal to 1. If the icon of the data object Zhang San is further deleted on canvas 2, at this time the reference count is equal to 0, that is, the data object Zhang San is not referenced by any data analysis graph, and then Zhang San can be identified as a deleted data object.

[0063] It can be understood that the deletion operation can also be an operation directly performed on the database, deleting a certain source data, and being able to realize the update of each data analysis graph. Referring to Figure 8 If the deletion operation is directly performed on the database, the processing method of the data visualization analysis further includes:

[0064] Step S710, listening to the deletion operation on the database to at least one set of source data.

[0065] Step S720, updating the data analysis graph related to the deleted source data according to the deletion operation.

[0066] It should be noted that in some embodiments, after the data in the database is deleted, the update of the information in the cache is triggered based on the event notification and the UUID recorded in the event notification. The corresponding data in the cache is synchronously deleted, and the data analysis graph associated therewith. For example, taking the fifth data analysis graph as the first data analysis graph as an example, referring to Figure 5 The source data 1 is deleted, and the data analysis graphs in canvas 1 and canvas 2 are updated.

[0067] For example, referring to Figure 7As shown, the processing method is applied to a processing system of data visualization analysis, the processing system comprising: a database; a graph generation module configured to generate a plurality of data analysis graphs according to source data obtained from the database, wherein the plurality of data analysis graphs comprises a first data analysis graph; and an interaction module configured to receive a change operation, wherein when the change operation is an adding operation or an importing operation, that is, when the data in the cache is changed, the analysis data set can be reacquired for processing. The processing method of data visualization analysis further comprises steps S610-S630 as follows:

[0068] Step S610, obtaining an analysis data set. The analysis data set is obtained after the correlation relationship of the imported data to be analyzed by the user is analyzed. It should be noted that the data to be analyzed is the original data of the data subject without extraction, such as the original bank flow record table. The analysis data set is obtained by extracting the data to be analyzed according to the data subject and the interaction information. The data to be analyzed can be imported from the database or imported from the canvas, or added from the database or added from the canvas, and the data in the cache has changed. After the database is imported, the database processing is performed, such as data cleaning, generating UUID for the imported records and saving, and the data to be analyzed imported from the database can be loaded into the cache. For example, the records imported / added from the canvas are first saved to the cache. It should be noted that after the records imported / added from the canvas are triggered to be saved to the database, the database generates UUID for each new record, and the cache is loaded again.

[0069] Step S620, judging whether each data analysis graph exists a data subject corresponding to the data subject in the analysis data set according to the interaction information in the analysis data set. It should be noted that the judgment of the associated data subject is to synchronize the update of the associated data analysis graph. The data subject associated with the imported / added record can be referenced by one or more data analysis graphs, such as Zhang San as a data subject existing in multiple data analysis graphs, and the data of Zhang San exists in the imported / added record, then the corresponding data analysis graph needs to be updated, so that the user can intuitively know the change of the analysis graph.

[0070] Step S630, when it is judged that one or more data analysis graphs exist corresponding data subjects, updating the data analysis graph associated with the corresponding data subject according to the corresponding data subject and the interaction information associated with the corresponding data subject in the analysis data set. It can be understood that the imported / added bank flow record, the subject information of the data subject in the analysis data set is a bank card number, and there is a case that the bank card number already belongs to the graph node corresponding to a person on the data analysis graph, that is, the bank card ownership relationship exists in fact, then according to the existing graph node, the corresponding graph connection line is added, and the imported / added bank flow record is intuitively displayed.

[0071] In addition, the imported / additional bank flow record is an analysis chart in a certain format, and the analysis chart includes analysis chart data. The analysis chart data is obtained by analyzing the analysis chart, and the analysis chart data is associated with at least one set of source data in the database, so that the update of the analysis chart can be synchronized to the source data.

[0072] It should be noted that the third data analysis chart is editable after being imported, and the third data analysis chart includes a plurality of graphic nodes and a plurality of connection lines, the plurality of graphic nodes correspond to graphic node parameters, the plurality of connection lines correspond to graphic connection parameters, and actually include a plurality of records. When the same data exists in the database, it indicates that the analysis chart data and the source data are obtained by processing the same original data, and when the third data analysis chart is changed, it can be updated to the database. For example, the first data analysis chart is edited, and the first data analysis chart can be exported. When editing is needed again, the saved first data analysis chart can be imported, and the imported data analysis chart can be edited and updated to at least one set of source data in step S100. It can be understood that the above step of associating the analysis chart data with at least one set of source data in the database includes: loading the recorded source data in the database into the cache; in the cache, the data subjects in the analysis chart data are compared and de-duplicated with the data subjects loaded in the cache; when the content of the imported third data analysis chart is changed, the data analysis chart associated with the changed data is also updated in real time.

[0073] It can be understood that the data visualization analysis processing method further includes: receiving a user change query request for the first data analysis chart; and exporting a change record of the first data analysis chart according to the change query request.

[0074] It should be noted that in some embodiments, when a change operation on the set of graphic nodes and the set of graphic connection lines in the canvas is listened to, such as deletion, merging, adding, and the like, the corresponding change operation is recorded. When the user change query request is received; according to the change query request, the change record is exported to facilitate user approval.

[0075] On the other hand, the present disclosure also provides an electronic device, comprising:

[0076] at least one processor, and a memory connected to the at least one processor in communication; wherein the memory stores instructions, and the instructions are executed by the at least one processor to make the at least one processor execute the data visualization analysis processing method when the instructions are executed;

[0077] The memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory can optionally include a memory disposed remotely with respect to the processor, which can be connected to the processor through a network. Examples of the above network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0078] For example, the processor performs steps S100-S500 as shown in FIG. 1; for another example, the processor performs steps S100-S500, steps S610-S630, and steps S710-S720 as shown in FIG. 2. Figure 3 Figure 3 Figure 7 Figure 8 For example, the processor performs steps S100-S500 as shown in FIG. 1; for another example, the processor performs steps S100-S500, steps S610-S630, and steps S710-S720 as shown in FIG. 2.

[0079] On the other hand, the present disclosure also provides a storage medium comprising computer-executable instructions stored therein, the computer-executable instructions being used to execute the processing method of data visualization analysis.

[0080] Those of ordinary skill in the art can understand that all or some steps in the above disclosed method and system can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, which can include computer storage media (or non-transitory media) and communication media (or transitory media). As known to those of ordinary skill in the art, the term computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. In addition, it is known to those of ordinary skill in the art that communication media typically includes computer readable instructions, data structures, program modules or other data in modulated data signals such as carrier waves or other transport mechanisms, and can include any information delivery medium.

[0081] ​​​Although the embodiments of the present disclosure have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, alternatives, and variations can be made thereto without departing from the principles and spirit of the present disclosure, and the scope of the present disclosure is defined by the claims and their equivalents.

[0082] The embodiments of the present disclosure are described in detail above with reference to the accompanying drawings, but the present disclosure is not limited to the above-described embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the spirit of the present disclosure.

Claims

1. A data visualization analysis processing method, characterized in that, The method includes: Obtain at least one set of source data from the database; the source data includes a data subject and interaction information corresponding to the data subject; Based on the source data, draw a first data analysis chart; Monitor user changes to the first data analysis graph; Based on the change operation, obtain the attribute change data of the first data analysis graph and update the first data analysis graph; Change the data according to the attributes, and update at least one set of the source data; At least one second data analysis graph is identified; the second data analysis graph and the first data analysis graph jointly reference a portion of the source data; the second data analysis graph and the first data analysis graph are located on different canvases; Update the second data analysis graph based on the changes in the attribute data; Obtain the analysis dataset, which is obtained by parsing the relationships between the data to be analyzed that is added or imported by the user; and the data to be analyzed is the original data without the main data subject extracted. Based on the interaction information in the analysis dataset, determine whether each data analysis graph contains a data subject that is consistent with the data subject in the analysis dataset; When it is determined that one or more data analysis graphs have corresponding data subjects, the data analysis graphs associated with the corresponding data subjects are updated based on the corresponding data subjects, the interaction information associated with the corresponding data subjects, and the analysis dataset.

2. The data visualization analysis processing method according to claim 1, characterized in that, The step of drawing a first data analysis chart based on the source data includes: Obtain the graph node parameters that match the data body from the cache; Obtain the graphics connection parameters corresponding to the interaction information from the cache; Based on the graph node parameters and the graph connection parameters, a first data analysis graph is drawn on a preset canvas.

3. The data visualization analysis processing method according to claim 2, characterized in that, The step of obtaining the attribute change data of the first data analysis graph and updating the first data analysis graph according to the change operation includes: According to the change operation, update the attribute change data of the corresponding graph node parameters and / or the attribute change data of the graph connection parameters in the cache; Update the first data analysis graph based on the changes in the attribute data; The step of changing the data according to the attribute and updating at least one set of the source data includes: Based on the attribute changes, at least one set of the source data in the database is written back from the cache to update it.

4. The data visualization analysis processing method according to claim 2, characterized in that, The first data analysis graph and the second data analysis graph respectively define a data entity cache interface and a data entity cache management class. Both the data entity cache interface and the data entity cache management class are associated with the cache to record / listen to the attribute change data associated with the change operation.

5. The data visualization analysis processing method according to claim 1, characterized in that, The processing method is applied to a data visualization analysis processing system, the processing system comprising: database; A cache is used to retrieve the source data from the database; A graph generation module is used to acquire source data from the cache and generate multiple data analysis graphs based on the source data, wherein the multiple data analysis graphs include the first data analysis graph; The method further includes: Each data analysis graph and the cache receive event notifications sent from the database, and the event notifications contain relevant content about the source data updated by the database; Each data analysis graph and the cache obtain the source data for database updates based on event notifications to achieve refresh.

6. The data visualization analysis processing method according to claim 2, characterized in that, The change operation is either a deletion operation or a merge operation; Before updating at least one set of the source data, the method further includes: When the change operation is applied to the graphical node parameter, the reference count corresponding to the graphical node parameter is updated according to the change operation; Correspondingly, the step of updating at least one set of source data according to the attribute change data includes: Update at least one set of the source data based on the reference count and the attribute change data.

7. The data visualization analysis processing method according to claim 1, characterized in that, The processing method is applied to a data visualization analysis processing system, the processing system comprising: database; A graph generation module is used to generate multiple data analysis graphs based on the source data obtained from the database, wherein the multiple data analysis graphs include the first data analysis graph; An interaction module is used to receive the change operation, which is either a new operation or an import operation.

8. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to the at least one processor; wherein the memory stores instructions which are executed by the at least one processor to cause the at least one processor to implement the data visualization analysis processing method as described in any one of claims 1 to 7 when executing the instructions.

9. A computer storage medium, characterized in that, It includes storing computer-executable instructions for performing a data visualization analysis processing method as described in any one of claims 1 to 7.

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