Data asset display analysis method and system based on data visualization

Abstract

The invention provides a data asset display analysis method and system based on data visualization, and belongs to the technical field of data asset display analysis. Then, carrying out visualization dimension mapping processing to obtain a visualization dimension set comprising a structure dimension, an association dimension and a circulation dimension, and constructing a multi-level display template comprising a basic display layer, an association display layer and a dynamic display layer according to the visualization dimension set; according to the method, the multi-level display template is subjected to interaction adaptation processing according to user analysis requirements to generate a visual interaction interface, operation track data of a user in the visual interaction interface is tracked, the multi-level display template is adjusted based on the operation track data, the visual interaction interface is updated, and comprehensive, visual and interactive display analysis of data assets is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of data asset display and analysis, and in particular to a data asset display and analysis method and system based on data visualization. Background Art

[0002] In today's digital age, data assets have become a core resource for businesses and organizations, and their value is becoming increasingly prominent. To fully tap into and leverage the value of data assets, effective display and analysis are necessary. Currently, common methods for displaying and analyzing data assets rely primarily on traditional reports and charts. While these methods can present certain data information, they have many limitations.

[0003] On the one hand, traditional display methods can often only present a single feature of a data asset, and it is difficult to fully display the multi-dimensional features of the data asset, such as its structure, associations, and flows. For example, when displaying the structure of a data asset, it may only present the fields and types of the data in a simple table format, and it is impossible to clearly display the hierarchical relationships and complex structures between the data; when displaying data associations, it is also difficult to intuitively present the inherent connections and influences between different data assets. On the other hand, traditional display methods lack interactivity and dynamism. Users can only passively view preset reports and charts, and cannot flexibly interact and dynamically adjust the display according to their own analysis needs. As a result, when analyzing data assets, users find it difficult to deeply explore the potential information and value behind the data, and are unable to meet the increasingly complex and changing data analysis needs. Summary of the Invention

[0004] In view of the above-mentioned problems, in combination with the first aspect of the present invention, an embodiment of the present invention provides a data asset display and analysis method based on data visualization, the method comprising: Collecting original feature information of data assets, including structural features, association features, and flow features of data assets; Performing visualization dimension mapping processing on the original feature information to obtain a visualization dimension set of the data asset, wherein the visualization dimension set includes a structure dimension, an association dimension, and a flow dimension; Constructing a multi-level presentation template for data assets based on the visualization dimension set, wherein the multi-level presentation template includes a basic presentation layer, a related presentation layer, and a dynamic presentation layer; According to the user's analysis needs, the multi-level display template is interactively adapted to generate a visual interactive interface for the data assets; Tracking the user's operation trajectory data in the visual interactive interface, adjusting the multi-level display template based on the operation trajectory data, and updating the visual interactive interface.

[0005] On the other hand, an embodiment of the present invention also provides a data asset display and analysis system based on data visualization, including a processor and a machine-readable storage medium, wherein the machine-readable storage medium is connected to the processor, the machine-readable storage medium is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the machine-readable storage medium to implement the above method.

[0006] Based on the above aspects, embodiments of the present invention collect raw feature information such as structural, correlation, and flow characteristics of data assets and perform visualization dimension mapping processing to obtain a visualization dimension set containing structural, correlation, and flow dimensions. This achieves comprehensive extraction and integration of the multi-dimensional features of data assets. A multi-level display template constructed based on the visualization dimension set, including a basic display layer, a correlation display layer, and a dynamic display layer, displays data assets from different levels and perspectives, meeting users' diverse analytical needs and enabling users to gain a more comprehensive and in-depth understanding of all aspects of data assets. The multi-level display template is interactively adapted based on the user's analytical needs to generate a visual interactive interface for the data asset, enhancing the interactivity between the user and the data asset and enabling users to flexibly view and analyze data according to their preferences. User operation trajectory data in the visualization interactive interface is tracked and, based on this data, the multi-level display template is adjusted and the visualization interactive interface is updated, achieving dynamic and personalized display analysis. The display effect can be optimized in real time based on the user's operating habits and analytical focus, significantly improving the efficiency and accuracy of data asset display analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] Figure 1 It is a schematic diagram of the execution flow of the data asset display and analysis method based on data visualization provided by an embodiment of the present invention.

[0008] Figure 2 Schematic diagram of exemplary hardware and software components of a data asset display and analysis system based on data visualization provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0009] The present invention will be described in detail below with reference to the accompanying drawings. Figure 1 This is a flow chart of a data asset display and analysis method based on data visualization provided by an embodiment of the present invention. The data asset display and analysis method based on data visualization is introduced in detail below.

[0010] Step S110: Collecting original feature information of data assets, where the original feature information includes structural features, association features, and flow features of the data assets.

[0011] In this example, we use data assets within an enterprise customer relationship management system (CRM) as an application scenario to collect raw feature information. First, we need to define the scope of these data assets, which include basic customer information datasets, transaction record datasets, and customer feedback datasets. These data assets are stored and circulated in various forms within the CRM.

[0012] Step S111: accessing the data asset management system, and retrieving metadata records of all data assets stored in the data asset management system, wherein the metadata records include the field composition, format type, and storage path of the data assets.

[0013] In an enterprise customer relationship management system, the data asset management system records metadata information for all data assets. To access this data asset management system, a connection must be established through the interface provided by the data asset management system. This interface uses a standard communication protocol to ensure stable and secure data transmission. Once connected, a retrieval request is sent, containing the scope of the metadata records to be retrieved, namely, all data assets related to customer relationship management.

[0014] After receiving a request, the data asset management system can retrieve internally stored metadata records. For the customer basic information dataset, the fields in its metadata record include customer name, gender, age, contact information, industry, etc.; the format type is structured data, stored in table form; the storage path is the customer information table path in the system database. The fields of the transaction record dataset include transaction number, customer ID, transaction time, transaction amount, transaction goods, etc.; the format type is also structured data; the storage path is the transaction information table path. The fields of the customer feedback dataset include feedback number, customer ID, feedback time, feedback content, feedback type, etc.; the format type includes structured data (such as feedback number, customer ID, etc.) and unstructured data (such as text information of feedback content); the storage paths correspond to the feedback information table and text storage directory respectively.

[0015] Step S112: extracting the hierarchical relationship of fields, the dependency relationship between fields, and the compatible attributes of format types from the metadata record, and integrating them to form the structural characteristics of the data asset, which are presented in a hierarchical tree format.

[0016] For the customer basic information dataset, we analyze the hierarchical relationships of the fields in the metadata records. The customer basic information node serves as the root node, which is divided into two subnodes: personal information and business information. The personal information subnode includes fields such as name, gender, age, and contact information; the business information subnode includes fields such as company name, industry, and company size.

[0017] In terms of dependencies between fields, the customer identification field is the key to linking the customer basic information dataset with other datasets. In the customer basic information dataset, the contact information field depends on the existence of the customer name field. That is, each customer record must have a customer name before it can record the corresponding contact information.

[0018] Regarding the format type compatibility properties, the customer name field is in text format, the gender field is in enumeration format (Male / Female / Other), and the age field is in integer format. Certain compatibility rules exist between these formats. For example, an integer-formatted age field cannot be directly operated on with a text-formatted name field, but text concatenation can be performed during display.

[0019] After integrating the above information, a hierarchical tree structure is formed. The root node is the customer basic information dataset, which is divided into personal information and corporate information sub-nodes. Each sub-node contains corresponding fields as leaf nodes. The tree structure also uses specific tags to indicate the dependencies and format compatibility between fields.

[0020] Step S113: Query the association mapping table in the data asset management system, extract the reference relationship, sharing relationship and combination relationship between different data assets, and integrate them into the association characteristics of the data assets, where the reference relationship refers to the field call relationship of one data asset to another data asset, the sharing relationship refers to the basic field set shared by multiple data assets, and the combination relationship refers to the aggregation form formed by multiple data assets according to the set rules.

[0021] The association mapping table in the data asset management system records the associations between various data assets. A query of this table reveals a reference relationship between the basic customer information dataset and the transaction record dataset. The customer ID field in the transaction record dataset references the customer ID field in the basic customer information dataset, enabling association queries between the two datasets.

[0022] In terms of sharing relationships, the customer basic information dataset, transaction record dataset, and customer feedback dataset share a customer identification field. This customer identification field is a basic field shared by these three datasets and is used to uniquely identify customers and enable cross-dataset customer information tracking.

[0023] In terms of composition, the customer value analysis dataset is formed by aggregating the customer basic information dataset, transaction record dataset, and customer feedback dataset according to predefined rules. The aggregation rules are: Relevant records in the three datasets are matched based on customer identification, and information such as basic customer information, total transaction amount, transaction frequency, and feedback satisfaction is extracted to form comprehensive data for customer value assessment.

[0024] After integrating these reference relationships, sharing relationships, and combination relationships, the association characteristics of the data assets are formed and presented in the form of a clear relationship list, indicating the name of the data set involved, the association type, and the specific association content.

[0025] Step S114: Obtain the transfer records of data assets between different processing nodes, extract the node identification, transfer time and processing status during the transfer process, and generate the flow characteristics of the data assets. The node identification includes the generation node, processing node and storage node, and the processing status includes pending, processing and completed.

[0026] The flow of customer feedback datasets in the system has clear transfer records. Its generation node is the customer feedback submission module. After the customer submits feedback information through the customer feedback submission module, the data first enters the generation node.

[0027] The data is then transferred to the processing node, known as the customer service processing module. The transfer time is the time from when the data is sent from the generating node to when it is received by the processing node. At the processing node, customer service personnel process the feedback, and the processing status is now in progress.

[0028] After processing is completed, the data is transferred to the storage node, that is, the database storage module. The transfer time is the time from when the processing node sends the data to when the storage node receives the data, and the processing status becomes completed.

[0029] In addition, after the transaction record data set is generated from the generation node (transaction system module), it is first passed to the processing node (transaction analysis module) for data analysis. The processing status is processing. It is then passed to the storage node (transaction database) for storage. The processing status becomes completed. The transmission time of each node is recorded accordingly.

[0030] These node identifications, transmission times, and processing status information are extracted and arranged in chronological order to generate the flow characteristics of each data asset.

[0031] Step S115: Deduplication verification is performed on the structural features, associated features, and flow features to remove duplicate feature information, and the verified feature information is associated according to the unique code of the data asset to generate original feature information.

[0032] After collecting structural features, association features, and flow features, deduplication verification is required. For structural features, check whether there are duplicate field hierarchical relationship descriptions. For example, if there are two identical tree-structured records in the structural features of a customer basic information dataset, remove the duplicate records.

[0033] The deduplication check of association features mainly checks whether there are duplicate association relationship records, such as whether the reference relationship between the customer basic information dataset and the transaction record dataset is recorded multiple times. If there are duplicates, only one record is retained.

[0034] The deduplication check of the flow characteristics targets duplicate records of the same data asset in the same processing status at the same node. For example, whether there are duplicate transfer records of the customer feedback dataset in the completed status at the storage node, the duplicates will be removed.

[0035] After deduplication and verification, each data asset has a unique code, such as DS001 for the customer basic information dataset and DS002 for the transaction record dataset. Verified structural features, association features, and flow features are associated with these unique codes, so that each code corresponds to a complete set of feature information, thereby generating original feature information.

[0036] Step S120: Perform visual dimension mapping processing on the original feature information to obtain a visual dimension set of the data asset, where the visual dimension set includes a structural dimension, an association dimension, and a flow dimension.

[0037] After obtaining the original feature information, it needs to be mapped to the visualization dimension in order to subsequently build the display template. This process requires processing the structural features, association features, and flow features separately to ensure that the mapped dimensions can accurately reflect the various characteristics of the data assets.

[0038] Step S121: Analyze the structural features in the original feature information, identify the hierarchical depth, the number of nodes and the connection strength between nodes in the hierarchical tree form, map the hierarchical depth to the hierarchical axis of the structural dimension, map the number of nodes to the scale axis of the structural dimension, and map the connection strength between nodes to the association axis of the structural dimension to form a structural dimension.

[0039] Analyzing the structural characteristics of the customer basic information dataset, its hierarchical tree structure has three levels of depth: the root node is the first level, the personal information and enterprise information subnodes are the second level, and the leaf nodes of each field are the third level. This level of depth is mapped to the level axis of the structural dimension. The scale of the level axis corresponds to the level depth value, and the scale on the axis can intuitively reflect the hierarchical structure.

[0040] In terms of node number, the hierarchical tree structure of the customer basic information dataset has one root node, two second-level nodes, and eight third-level nodes, for a total of 11 nodes. The number of nodes is mapped to the scale axis of the structural dimension. The value range of the scale axis corresponds to the number of nodes. The more nodes there are, the further to the right they are on the scale axis.

[0041] The strength of connections between nodes is determined by the dependencies and association frequency between fields. For example, the connection strength between the customer name field and the contact information field is strong because they are directly dependent and frequently queried together. However, the connection strength between the gender field and the company size field is weak, as there is almost no direct correlation between the two. These connection strengths are mapped to the association axis of the structural dimension, with the value of the association axis corresponding to the strength of the connection.

[0042] The structural dimensions of the customer basic information dataset are composed of the hierarchy axis, scale axis and association axis. The structural dimensions of other data assets are generated in the same way.

[0043] Step S122: Analyze the associated features in the original feature information, extract the frequency of the reference relationship, the coverage of the sharing relationship and the complexity of the combination relationship, the frequency of the reference relationship refers to the number of references per unit time, the coverage of the sharing relationship refers to the proportion of the shared fields in the total fields, and the complexity of the combination relationship refers to the number of data assets required for the combination, and map them to the frequency axis, coverage axis and complexity axis of the associated dimension respectively to form the associated dimension.

[0044] Analyze the reference relationship between the basic customer information dataset and the transaction record dataset. Count the number of times the customer ID field in the transaction record dataset references the customer ID field in the basic customer information dataset within a unit of time to obtain the frequency of the reference relationship. Map this frequency to the frequency axis of the associated dimension. Higher frequencies are ranked higher on the frequency axis.

[0045] For a shared relationship, for example, the customer ID field accounts for a certain percentage of the total number of fields in the customer basic information dataset, another percentage in the total number of fields in the transaction record dataset, and yet another percentage in the total number of fields in the customer feedback dataset. The average of these percentages is taken as the coverage of the shared relationship. The coverage is mapped to the coverage axis of the associated dimension. Higher coverage indicates a higher position on the coverage axis.

[0046] In terms of the complexity of the combination relationship, the customer value analysis dataset is composed of three data assets, so the complexity of its combination relationship is 3. This complexity is mapped to the complexity axis of the associated dimension, and the value of the complexity axis corresponds to the number of data assets required for the combination.

[0047] The association dimension, which is composed of the frequency axis, coverage axis, and complexity axis, is used to describe the association characteristics between data assets.

[0048] Step S123: Process the flow features in the original feature information, extract the conversion sequence of node identifiers, the interval distribution of transfer time and the conversion probability of processing status. The conversion sequence of node identifiers refers to the order of nodes that data assets pass through, the interval distribution of transfer time refers to the time difference distribution between adjacent nodes, and the conversion probability of processing status refers to the possibility of converting from one state to another. They are mapped to the node axis, time axis and state axis of the flow dimension to form the flow dimension.

[0049] The transformation sequence for processing the flow characteristics of the customer feedback dataset is generation node, processing node, and storage node. This transformation sequence is mapped to the node axis of the flow dimension, with each node labeled in order to illustrate the flow path of the data asset.

[0050] The interval distribution of delivery time refers to the distribution of the time differences between the generation node and the processing node, and between the processing node and the storage node, for a customer feedback dataset. For example, most time differences are concentrated within a certain range, while a few are outside this range. This distribution is mapped onto a time axis in the flow dimension, where the time axis is divided into different intervals to represent the distribution of time differences.

[0051] Regarding the transition probabilities of processing states, the customer feedback dataset has a high probability of transitioning from the pending state to the processing state, and a high probability of transitioning from the processing state to the completed state. However, the probability of transitioning from the completed state to any other state is extremely low. These transition probabilities are mapped onto the state axis of the flow dimension. The distance between states on the state axis corresponds to the magnitude of the transition probability; the closer the distance, the higher the transition probability.

[0052] The flow dimension is composed of the node axis, time axis and status axis, which is used to reflect the flow characteristics of data assets.

[0053] Step S124: performing dimension calibration processing on the structural dimension, the associated dimension, and the flow dimension, and integrating the calibrated structural dimension, the associated dimension, and the flow dimension into a visualization dimension set.

[0054] Dimension calibration primarily ensures consistency and coordination in the display of structural, association, and flow dimensions. For the hierarchy, scale, and association axes of the structural dimension, the scale ranges of each axis are adjusted to ensure that the structural dimensions of different data assets are comparable within the same range. For example, by setting the hierarchy axis scale for all data assets to a uniform range of 0 to 5, a customer basic information dataset with a hierarchy depth of 3 and a transaction record dataset with a hierarchy depth of 2 can be displayed on the same axis.

[0055] The frequency, coverage, and complexity axes of the associated dimensions also need to be calibrated to unify the metrics for each axis. For example, the frequency axis should be standardized to times per hour, the coverage axis should have a range of values ​​from 0 to 1, and the complexity axis should have a range adjusted based on the maximum number of combined data assets.

[0056] The calibration of the node axis, time axis, and state axis of the flow dimension mainly involves unifying the format of node markings, the time unit of the time axis (such as minutes), and the representation of transition probabilities on the state axis.

[0057] After calibration, the structural dimension, association dimension and flow dimension are integrated together to form a visualization dimension set.

[0058] Step S130: constructing a multi-level presentation template of the data asset according to the visualization dimension set, wherein the multi-level presentation template includes a basic presentation layer, a related presentation layer, and a dynamic presentation layer.

[0059] Based on the visualization dimension set, we begin to build a multi-level display template. This process requires making full use of the information of the structural dimension, the associated dimension, and the flow dimension to respectively build the basic display layer, the associated display layer, and the dynamic display layer. We also set the corresponding hierarchical switching rules so that each display layer can be organically combined to provide users with a comprehensive visualization display.

[0060] Step S131: Based on the structural dimension in the visualization dimension set, a basic display layer is constructed. The basic display layer uses a tree diagram to display the structural characteristics of the data asset. The root node of the tree diagram corresponds to the highest level of the data asset, and the child node corresponds to the next level. The size of the node is determined according to the scale axis of the structural dimension, and the thickness of the connection line between the nodes is determined according to the association axis of the structural dimension.

[0061] A tree diagram for the basic display layer is constructed based on the structural dimensions of the basic customer information dataset. The root node of the tree diagram is the basic customer information dataset, corresponding to the highest level. The child nodes under this root node are personal information and company information, corresponding to the next level. The child nodes under the personal information subnode include fields such as name, gender, age, and contact information. The child nodes under the company information subnode include fields such as company name, industry, and company size.

[0062] The size of a node is determined by the scale axis of the structural dimension. The larger the scale axis value, the larger the node. The total number of nodes in the customer basic information dataset is 11, and there is a corresponding value on the scale axis. The size of the root node is determined by this value and is larger than the size of its child nodes because the number of nodes corresponding to the child nodes is smaller.

[0063] The thickness of the lines between nodes is determined by the association axis of the structural dimension. A larger association axis value indicates a thicker line. The connection strength between the customer name field and the contact information field is higher on the association axis, resulting in a thicker line. The connection strength between the gender field and the company size field is lower, resulting in a thinner line.

[0064] Through the above-mentioned tree diagram format, the structural characteristics of the customer basic information data set are clearly displayed on the basic display layer. The structural characteristics of other data assets are also displayed on the basic display layer in the same way.

[0065] Step S132: Based on the associated dimensions in the visualization dimension set, an associated display layer is constructed on the basis of the basic display layer. The associated display layer is superimposed on the tree diagram in the form of a network diagram. The nodes in the network diagram correspond to data assets, and the lines between the nodes represent the association relationship. The length of the line is determined according to the frequency axis of the associated dimension, the color of the line is determined according to the coverage axis of the associated dimension, and the shape of the node is determined according to the complexity axis of the associated dimension.

[0066] On top of the tree diagram of the basic display layer, a network diagram-based association display layer is superimposed. The nodes in the network diagram correspond to the customer basic information dataset, transaction record dataset, customer feedback dataset, and customer value analysis dataset, etc.

[0067] The lines between nodes represent the association relationship. The length of the line between the customer basic information dataset and the transaction record dataset is determined by the frequency axis of the association dimension. Since the reference relationship between the two is high, the frequency axis value is large, so the line is short.

[0068] The color of the connection line is determined by the coverage axis of the associated dimension. The customer basic information dataset, transaction record dataset, and customer feedback dataset share the customer identification field, and its coverage axis value is high, so the color of the connection line between the three is dark; the coverage axis values ​​of other associations are low, and the connection color is light.

[0069] The shape of the node is determined by the complexity axis of the associated dimension. The combination relationship complexity of the customer value analysis dataset is 3, and the complexity axis value is large, so its node shape is hexagonal; nodes with smaller complexity axis values, such as the customer basic information dataset, are circular in shape.

[0070] Through the above-mentioned network diagram-based association display layer, the association relationship between various data assets is clearly displayed on the basis of the basic display layer.

[0071] Step S133: Combine the flow dimension in the visualization dimension set to construct a dynamic display layer. The dynamic display layer uses a timeline animation to display the flow characteristics of the data assets. The scale of the timeline is determined according to the timeline of the flow dimension. The icons in the timeline animation represent data assets. The movement path of the icons is determined according to the node axis of the flow dimension, and the color change of the icons is determined according to the status axis of the flow dimension.

[0072] We build a dynamic display layer based on the flow dimension, taking the flow of a customer feedback dataset as an example. The scale of the time axis is determined by the time axis of the flow dimension, and each scale on the time axis represents a certain time interval (such as 10 minutes).

[0073] In the timeline animation, specific icons are used to represent customer feedback datasets. The icon's movement path is determined by the node axis of the flow dimension, that is, it moves from the generation node icon to the processing node icon, and then to the storage node icon, clearly showing the flow path.

[0074] The icon's color changes based on the status axis of the flow dimension. When in the pending state, the icon is gray; when in the processing state, the icon turns blue; and when the processing is complete and the state is completed, the icon turns green. During the state transition, the color transitions smoothly to reflect the state change.

[0075] The same process is used to display the flow of other data assets, such as transaction record datasets, in the dynamic display layer using a timeline animation. For example, the transaction record dataset icon moves from the transaction system module icon (generation node) to the transaction analysis module icon (processing node), and then to the transaction database icon (storage node). Its color changes from gray (pending) to blue (processing), and finally to green (completed).

[0076] Step S134: Set the hierarchical switching rules of the multi-level display template. The basic display layer is the default display layer. The preset switching instructions can be used to switch to the associated display layer or the dynamic display layer. The switching process uses a smooth transition animation to integrate the basic display layer, associated display layer, dynamic display layer and hierarchical switching rules into a multi-level display template.

[0077] The hierarchical switching rules of multi-level display templates need to be clear and easy to operate. The basic display layer is set as the default display layer. When the user opens the visualization interface, the tree diagram of the basic display layer is the first thing presented.

[0078] Preset switching commands include shortcut keys and interface buttons. For example, users can press the "G" key on the keyboard or click the "Associated Views" button at the top of the interface to switch from the base display layer to the associated display layer; press the "D" key or click the "Dynamic View" button to switch to the dynamic display layer; press the "B" key or click the "Base View" button to switch back to the base display layer from any other display layer.

[0079] The switching process uses a smooth transition animation. When switching from the basic display layer to the associated display layer, the tree diagram gradually fades, and the network diagram gradually becomes clearer. The entire transition process lasts for a certain period of time (such as 0.5 seconds) to avoid user discomfort caused by sudden interface changes. When switching from the associated display layer to the dynamic display layer, the network diagram gradually fades, and the timeline animation gradually appears. When switching from the dynamic display layer to other display layers, the animation gradually stops and fades, and the content of the target display layer gradually becomes clearer.

[0080] The basic display layer, associated display layer, dynamic display layer and the above-mentioned level switching rules are integrated together to form a complete multi-level display template.

[0081] Step S140: According to the user's analysis requirements, the multi-level display template is interactively adapted to generate a visual interactive interface for the data assets.

[0082] After obtaining a multi-level display template, it is necessary to interactively adapt it according to the user's analysis needs to make the display content more suitable for the user's usage scenario and improve the efficiency of the user's analysis of data assets. This process involves analyzing user needs, selecting display layers, processing focus objects, adjusting display accuracy, and adding interactive controls.

[0083] Step S141: Receive the analysis requirement instruction input by the user, and parse the analysis type, focus object and display accuracy requirements contained in the analysis requirement instruction. The analysis type includes structural analysis, association analysis and flow analysis. The focus object refers to the data assets that the user is concerned about. The display accuracy requirement refers to the level of detail of the display content.

[0084] The system receives the user's analysis request instructions through an input box, drop-down menu, or voice input module on the interface. For example, the user enters "Analyze the relationship between customer basic information and transaction records, focusing on customer A, and displaying detailed information" in the input box.

[0085] Parsing this instruction, we know that the analysis type is association analysis; the focus is on the data assets corresponding to customer A, namely, customer A's basic information dataset and related transaction record dataset; the display accuracy requirement is detailed, requiring the display of more fields and association details.

[0086] If the user enters "View the flow of the customer feedback dataset and display brief information", the analysis type is flow analysis, the focus is the customer feedback dataset, and the display accuracy requirement is brief. Only key flow nodes and status need to be displayed.

[0087] Step S142: Determine the corresponding display layer in the multi-level display template according to the analysis type. If it is a structural analysis, the basic display layer is mainly used; if it is an association analysis, the association display layer is mainly used; if it is a flow analysis, the dynamic display layer is mainly used.

[0088] When the analysis type is structural analysis, the system prioritizes the basic display layer. For example, if a user needs to analyze the field hierarchy of a customer basic information dataset, the interface will primarily present a tree diagram of the basic display layer, clearly displaying the relationships between nodes and fields at each level.

[0089] If the analysis type is association analysis, for example, if the user needs to analyze the reference relationship between the customer basic information dataset and the transaction record dataset, the association display layer will be the main focus. The interface will mainly present a network diagram superimposed on a tree diagram, highlighting the connection and association attributes between the two.

[0090] When the analysis type is flow analysis, such as analyzing the transmission process of customer feedback data sets, the system mainly uses a dynamic display layer. The interface mainly presents a timeline animation to show the flow of data assets at each node.

[0091] Step S143: highlighting the object of interest in the corresponding display layer, wherein the highlighting methods include enlarging the display, changing the color, and adding a border.

[0092] The basic information dataset and related transaction record dataset for customer A are highlighted in the associated display layer. The nodes for customer A's basic information dataset and related transaction record dataset are enlarged and displayed proportionally larger than other nodes. The node color is changed to a striking orange to distinguish it from the other nodes, and a red border is added around the node to further highlight it.

[0093] If the object of interest is a customer feedback dataset, in the dynamic display layer, the icon representing the dataset is enlarged, the color changes to yellow, and a blue border is added to enable users to quickly identify it.

[0094] Step S144: adjusting the detail display level of the display layer according to the display accuracy requirement.

[0095] When the display accuracy requirement is detailed, in the basic display layer, each node of the tree diagram not only displays the field name, but also displays additional information such as the field format type and length; in addition to the thickness of the connection between nodes indicating the connection strength, the connection lines also indicate the specific dependency description.

[0096] In the association display layer, the lines in the network diagram are marked with specific values ​​of the reference frequency, the names of shared fields, a brief description of the combination rules, etc.; in addition to the shape distinction, the nodes also display the unique code of the data asset and the number of main fields.

[0097] If the display accuracy requirement is concise, the tree diagram of the basic display layer only displays the main hierarchical nodes and key field names; the network diagram of the association display layer only retains important association lines and does not display specific values ​​and descriptions; the timeline animation of the dynamic display layer only displays the main flow nodes and simplifies the details of state transitions.

[0098] Step S145: Add interactive controls to the visual interactive interface. The interactive controls include zoom controls, filter controls, detail controls, and comparison controls. The zoom controls are used to adjust the size of the displayed content. The filter controls are used to filter data assets with specific conditions. The detail controls are used to view detailed information about data assets. The comparison controls are used to compare the characteristics of multiple data assets.

[0099] Step S1451: Add a zoom control in the edge area of ​​the visual interactive interface, wherein the zoom control includes a zoom-in button, a zoom-out button, and a zoom slider. The zoom-in button triggers the display content to be enlarged according to a preset ratio, and the zoom-out button triggers the display content to be reduced according to a preset ratio. The zoom slider continuously adjusts the zoom ratio of the display content by dragging the position, and the zoom range is limited between the preset minimum zoom ratio and the maximum zoom ratio.

[0100] Add zoom controls to the right edge of the interface. The zoom-in button is an upward arrow icon, the zoom-out button is a downward arrow icon, and the zoom slider is a rectangular block that can be dragged up and down.

[0101] When the user clicks the zoom-in button, the displayed content is enlarged according to the preset ratio (such as 20% each time) to make the details clearer; when the user clicks the zoom-out button, it is zoomed out according to the same ratio to display more overall content.

[0102] The range of the zoom slider corresponds to the zoom ratio range, with a preset minimum zoom ratio of 50% and a maximum zoom ratio of 200%. When the slider is at the bottom, the content is displayed at the minimum zoom ratio; dragging it to the top is the maximum zoom ratio, and the middle position corresponds to the corresponding intermediate zoom ratio.

[0103] Step S1452: Add a filter control in the top area of ​​the visual interactive interface. The filter control includes a structure filter option, an association filter option, and a flow filter option. The structure filter option allows the user to select a specific structure level or format type. The association filter option allows the user to set the frequency threshold or coverage threshold of the association relationship. The flow filter option allows the user to specify the flow node or processing status. After selecting the filter option, the display layer automatically hides the data assets that do not meet the filter conditions.

[0104] In the drop-down menu for setting up filter controls at the top of the interface, the drop-down menu for structure filtering options includes structure level options such as "Level 1," "Level 2," and "Level 3," as well as format type options such as "Structured Data," "Unstructured Data," "Text Format," and "Integer Format." When you select an option, data asset nodes in the display layer that don't match the selected structure level or format type are hidden.

[0105] In the association filtering options, users can use the input box to set the frequency threshold of the reference relationship (for example, entering "5" means that only associations with a frequency higher than 5 times / hour are displayed), and use the slider to set the coverage threshold of the sharing relationship (for example, sliding to 80% means that only sharing relationships with a coverage higher than 80% are displayed).

[0106] The drop-down menu for the flow filter options includes flow node options such as "Generate Node," "Process Node," and "Store Node," as well as processing status options such as "Pending," "Processing," and "Completed." After the user selects a node, data assets that do not contain the specified node or status will be hidden in the display layer.

[0107] Step S1453: Add a details control in the right area of ​​the visual interactive interface. The details control is in the form of a floating button. When the user clicks on a data asset node in the display layer, the details control is automatically activated and displays the original feature information of the data asset. The original feature information includes the complete hierarchy of structural features, detailed relationships of associated features, and a full record of flow features. The details control also contains a close button, which can be clicked to hide the details information.

[0108] The details control is a floating button with the text "Details" located in the middle of the right side of the interface. It is initially gray and unclickable. When the user clicks the customer basic information dataset node in the presentation layer, the details control turns blue and clickable, and an information panel automatically pops up.

[0109] The information panel displays raw feature information in the order of structural features, associated features, and flow features. The structural features section displays a complete hierarchical tree diagram, including all fields and dependencies; the associated features section lists details of references, sharing, and combination relationships with other datasets; and the flow features section displays the entire transfer record in chronological order, including the identifier of each node, transfer time, and processing status.

[0110] There is an "×"-shaped close button in the upper right corner of the information panel. When the user clicks it, the panel is hidden and the detail control returns to a gray and unclickable state.

[0111] Step S1454: Add a comparison control in the bottom area of ​​the visual interactive interface. The comparison control includes an Add Compare button and a comparison panel. When the user clicks the Add Compare button, the currently selected data asset is added to the comparison list. The comparison panel displays the feature comparison of all data assets in the comparison list, and presents the indicator values ​​of each data asset in the structural dimension, association dimension and flow dimension in a tabular form. The comparison panel also includes a Clear button for clearing the comparison list.

[0112] At the bottom of the interface, the Add Compare button is a button with the text "+ Compare" and the Compare panel is an expandable rectangular area.

[0113] After the user selects the customer basic information dataset node and clicks the Add Comparison button, the dataset is added to the comparison list; then selects the transaction record dataset node and clicks the Add Comparison button, and the transaction record dataset is also added to the comparison list.

[0114] At this time, expand the comparison panel, and the comparison content is displayed in a table. The columns of the table are data asset name, structure dimension (hierarchy axis, scale axis, association axis), association dimension (frequency axis, coverage axis, complexity axis), and flow dimension (node ​​axis, time axis, status axis). Each row corresponds to the indicator value of a data asset.

[0115] There is a "Clear" button in the lower right corner of the comparison panel. When the user clicks it, all data assets in the comparison list are removed and the table contents are cleared.

[0116] Step S146: Set the response rules for the interactive controls. When the user operates the interactive controls, the corresponding display layer is updated according to the preset rules. The filter controls trigger the hiding of data assets that do not meet the conditions. The adapted display layer, interactive controls, and response rules are integrated into a visual interactive interface.

[0117] Step S1461: For the zoom control, set the proportion adjustment rules. When the user clicks the zoom-in button, the zoom ratio of the displayed content increases by a preset increment on the current basis, and the increment is the same for each click until the maximum zoom ratio is reached and no longer responds. When the user clicks the zoom-out button, the zoom ratio of the displayed content decreases by a preset increment on the current basis and no longer responds after the minimum zoom ratio is reached. When the user drags the zoom slider, the zoom ratio changes linearly with the position of the slider. When the zoom slider is at the far left end, it is the minimum zoom ratio, and when it is at the far right end, it is the maximum zoom ratio. The center position of the displayed content remains unchanged during the zooming process.

[0118] The default increment is 20%, the maximum zoom ratio is 200%, and the minimum zoom ratio is 50%. When the current zoom ratio is 100%, the user clicks the zoom button, and the ratio increases to 120%. Clicking it again increases it to 140%, and after reaching 200%, further clicks of the zoom button will not change the ratio.

[0119] When you click the zoom out button, the current scale of 100% changes to 80%, and when you click it again, it changes to 60%, and then it stops responding after reaching 50%.

[0120] As you drag the zoom slider, the slider position and the zoom ratio are linearly related. The zoom ratio changes evenly from the leftmost end (50%) to the rightmost end (200%). Throughout the zoom process, the center of the displayed content remains at the center of the interface to prevent content shifting.

[0121] Step S1462: For the filter control, set the hiding rules. When the user selects the structure filter option, the data asset nodes and their connections in the display layer that do not belong to the selected structure level or format type are automatically hidden. The hiding process uses a fading animation, and the remaining nodes automatically adjust their positions. When the user selects the association filter option, the connections in the display layer whose association relationship frequency or coverage is lower than the corresponding preset threshold are automatically hidden, and the data asset nodes are retained but the color becomes light. When the user selects the flow filter option, the data asset icons and their moving paths that do not contain the specified flow nodes or processing status in the display layer are automatically hidden, and the timeline range is adjusted according to the flow time of the remaining data assets.

[0122] When users select "Level 2" and "Structured Data" in the structure filter options, the data asset nodes and their connections in Levels 1 and 3, as well as unstructured data, begin to fade out in the display layer. This animation lasts for 0.3 seconds before disappearing completely. The remaining nodes automatically adjust their positions to optimize the interface layout and avoid blank areas.

[0123] When the user sets the frequency threshold to 5 times / hour in the association filtering option, all the connections in the association display layer with a reference frequency lower than 5 times / hour gradually disappear, and the corresponding node color changes to light gray, which contrasts with the color of the nodes that meet the conditions.

[0124] After the user selects the "Processing" status in the flow filtering option, the data asset icons and their movement paths in the pending and completed states in the dynamic display layer are hidden, and the start and end times of the timeline are adjusted according to the flow time of the remaining data assets in the processing state, making the display content more focused.

[0125] Step S1463: Regarding the details control, set the display rules. When the user clicks on a data asset node, the details control slides out from the right side of the interface. The sliding-out animation lasts for a preset time. The details information is loaded and displayed in the order of structural features, associated features, and flow features. When the user clicks the close button, the details control slides to the right and is hidden. The sliding-in animation lasts for a preset time, and the data asset node is deselected at the same time.

[0126] The default slide-out and slide-in animation duration is 0.4 seconds. When the user clicks the customer feedback dataset node, the details control slides out from the right edge of the interface, gradually revealing the complete information panel. The information in the panel first loads the hierarchical tree diagram of the structural features, then the relationship list of the associated features, and finally the transfer history of the flow features. Each section loads with a 0.1-second interval.

[0127] After the user clicks the Close button, the information panel slides to the right and gradually disappears at the edge of the interface. The animation lasts for 0.4 seconds. At the same time, the selection of the customer feedback dataset node is removed, the node highlight effect disappears, and the display returns to normal.

[0128] Step S1464: For the comparison control, set the list update rules. When the user clicks the Add Comparison button, if the data asset is not in the comparison list, it will be added and a corresponding row of data will be added to the comparison panel; if it is already in the comparison list, a prompt message will pop up to indicate that the data asset is already in the comparison list; when the user clicks the Clear button, the comparison list is cleared, the table content in the comparison panel is cleared, and the selection status of all data asset nodes is released.

[0129] The user selects the customer basic information dataset and clicks the Add to Comparison button. Since the dataset is not in the comparison list, the system adds it to the list and adds a new row of indicator values ​​for the customer basic information dataset to the table in the comparison panel.

[0130] If the user selects the customer basic information dataset again and clicks the Add Comparison button, the system will pop up a prompt box saying "This data asset is already in the comparison list" and the prompt box will automatically disappear after 3 seconds.

[0131] When the user clicks the Clear button, all data assets in the comparison list are removed, the contents in the table are cleared, and the highlight effects of all selected data asset nodes disappear, returning to normal.

[0132] The interactively adapted display layer, added interactive controls, and set response rules are integrated together to form a visual interactive interface for data assets, making it easier for users to perform analytical operations on data assets.

[0133] Step S150: Tracking the user's operation trajectory data in the visual interactive interface, adjusting the multi-level presentation template based on the operation trajectory data, and updating the visual interactive interface.

[0134] In order to make the visual interactive interface more in line with user habits, it is necessary to track the user's operation trajectory, analyze the user's behavioral preferences, and then adjust the multi-level display templates, update the interface content, and improve the user experience.

[0135] Step S151: embedding a trajectory collection module in the visual interactive interface. The trajectory collection module records all user operations in real time, including mouse clicks, mouse movements, keyboard inputs, and control operations.

[0136] The trajectory acquisition module is embedded in the visual interactive interface as a code plug-in. Once started, it runs in the background without affecting the user's normal operations. It captures all user actions on the interface, including mouse clicks on any location or element on the interface, mouse movement on the interface, any characters entered via the keyboard, and operations on various interactive controls.

[0137] The module generates a record for each operation, including the type of operation, the time of occurrence, the objects involved, and other information, and stores it in a local log file in chronological order.

[0138] Step S152: When the user performs a mouse click operation, the click position coordinates, the clicked object identifier, and the click time are recorded. The clicked object identifier refers to the unique identifier of the clicked interactive control or data asset node.

[0139] When a user clicks a customer basic information dataset node in the associated display layer with the mouse, the trajectory acquisition module records the location coordinates of the click operation (X-axis and Y-axis coordinates with the upper left corner of the interface as the origin). The clicked object is identified by the unique code of the customer basic information dataset (such as DS001), and the click time is the system time accurate to milliseconds.

[0140] When the user clicks the zoom button, the recorded position coordinates are the coordinates of the zoom button on the interface, the object identifier is "zoom button", and the click time is recorded at the same time.

[0141] Step S153: When the user moves the mouse, the coordinate sequence of the movement path of the mouse pointer in the interface and the coordinates and dwell time of the stop point during the movement are recorded. The stop point refers to the position where the movement speed of the mouse pointer is lower than a preset threshold.

[0142] The mouse movement speed threshold is preset to a certain pixel / millisecond. When the user moves the mouse from the zoom control to the customer feedback dataset node, the trajectory acquisition module records the series of coordinates passed by the mouse pointer to form a movement path coordinate sequence.

[0143] During movement, if the mouse pointer's movement speed near a customer basic information dataset node falls below a preset threshold, that location is identified as a dwell point. The trajectory acquisition module records the X- and Y-axis coordinates of the dwell point and measures the dwell time from the time the mouse pointer reaches that point until it leaves it. For example, if a user hovers over a customer basic information dataset node for three seconds while viewing the node, the module will accurately record the three-second duration and the corresponding coordinate position.

[0144] Step S154: When the user performs keyboard input operation, the input character content, input start time and input end time are recorded. The character content includes the filter conditions or search keywords entered in the filter control.

[0145] When a user enters "Transaction amount greater than a certain value" as a filter condition in the filter control's input box, the trajectory acquisition module records the complete text of the character input. It also records the time the user presses the first character as the input start time, and the time the user presses the Enter key or clicks outside the input box to confirm the input as the input end time. If the user enters "Customer B" in the search box to search, the module also records the character content and the corresponding input start and end times.

[0146] Step S155: When the user performs a control operation, the control type, operation parameters, and interface state changes before and after the operation are recorded. The operation parameters include the zoom ratio change value of the zoom control and the filter condition setting value of the filter control.

[0147] When a user presses the zoom button of a zoom control, the control type is recorded as "Zoom Control - Zoom Button," and the operation parameter is the change in the current zoom ratio (e.g., from 100% to 120%, the change is 20%). The display status of the interface before the operation (e.g., the size and position of each node) and after the operation are also recorded.

[0148] When the user sets the coverage threshold of the sharing relationship to 80% in the filter control, the control type is recorded as "Filter Control-Associated Filter Option", the operation parameter is "Coverage Threshold = 80%", and the display status changes of data asset nodes and connections in the presentation layer before and after the operation are recorded.

[0149] Step S156: Integrate the above recorded operation behaviors in chronological order to generate operation trajectory data including operation type, operation object, operation time and operation parameters. The operation trajectory data is stored in the form of a log, and each log entry contains a unique trajectory number.

[0150] The trajectory acquisition module arranges the records of mouse clicks, mouse movements, keyboard input, and control operations in chronological order. Each record contains the operation type (such as "mouse click", "keyboard input", etc.), operation object (such as "customer basic information dataset node", "zoom in button", etc.), operation time (accurate to milliseconds) and operation parameters (such as coordinates, character content, scale change value, etc.).

[0151] The generated operation trace data is stored as log files in a system-specified directory. Each log entry has a unique trace number, consisting of date, time, and a random sequence, to ensure uniqueness. For example, a log entry might be "Trace number: 20240520153022001 | Operation type: Mouse click | Operation object: Customer feedback dataset node | Operation time: 2024-05-20 15:30:22.123 | Operation parameters: X=300, Y=200."

[0152] Step S157: Analyze the mouse click records in the operation trajectory data, count the click frequencies of different interactive controls and data asset nodes, and determine objects with click frequencies higher than a preset click threshold as high-frequency operation objects.

[0153] For example, step S1571: all mouse click records are filtered out from the operation trajectory data, each mouse click record includes the clicked object identifier, click time, and click location coordinates.

[0154] From the stored operation trace logs, filter out all records with the "mouse click" operation type. Each of these records clearly contains the clicked object identifier (e.g., "DS001," "zoom in button," etc.), the specific time the click occurred, and the location coordinates at the time of the click.

[0155] For example, the filtered records may include "Object ID: DS001, Click Time: 2024-05-20 15:30:22.123, Coordinates: X=300, Y=200", "Object ID: Zoom In Button, Click Time: 2024-05-20 15:31:05.456, Coordinates: X=500, Y=100", etc.

[0156] Step S1572: Group the mouse click records according to the object identifier, and group all click records with the same object identifier into one group.

[0157] Group the filtered mouse click records based on their object IDs. Group all click records with the object ID "DS001" (the customer basic information dataset node) into one group; group all click records with the object ID "zoom in button" into another group; and so on, ensuring that click records corresponding to each object ID are concentrated in their own group.

[0158] Step S1573: For each click record group corresponding to the object identifier, count the total number of records, calculate the ratio of the total number of clicks to the time length covered by the records, and obtain the click frequency of the object. The time length is the difference between the earliest click time and the latest click time in the click record group.

[0159] For example, consider the click record group with the object ID "DS001." The total number of records in this group is 20. The earliest click time in this group is 2024-05-20 15:30:22.123, and the latest click time is 2024-05-20 16:30:22.123, with a duration of 1 hour (3600 seconds). Therefore, the click frequency for this object is 20 times / 3600 seconds, which is the click frequency per second.

[0160] Similarly, for the click record group of the "Zoom In Button", if the total number is 15 and the time length is 2 hours (7200 seconds), the click frequency is 15 times / 7200 seconds.

[0161] Step S1574: Set a preset click threshold, which is the average click frequency of all objects.

[0162] Calculate the average click frequency of all objects (including data asset nodes and interactive controls) and set this average as the preset click threshold. For example, if the total click frequency of all objects is 100 times / second and there are 20 objects in total, the preset click threshold is 5 times / second.

[0163] Step S1575: Compare the click frequency of each object with a preset click threshold. If the click frequency is higher than the preset click threshold, mark the object as a candidate high-frequency operation object.

[0164] The click frequency of each object is compared with the preset click threshold (5 times / second) one by one. If the click frequency of "DS001" is 6 times / second, which is higher than the threshold, it will be marked as a candidate high-frequency operation object; if the click frequency of "zoom in button" is 3 times / second, which is lower than the threshold, it will not be marked.

[0165] Step S1576: Perform time distribution analysis on the click records of the candidate high-frequency operation object. If the proportion of the click frequency in the most recent preset time period to the total click frequency is higher than the preset time proportion, it is determined to be a high-frequency operation object.

[0166] The preset duration is 1 hour, and the preset time ratio is 60%. For candidate high-frequency operation object "DS001," analyzing its click history reveals 12 clicks in the last hour and 20 total clicks. (12 / 20 = 60%, meeting the preset time ratio.) Therefore, "DS001" is identified as a high-frequency operation object.

[0167] If another candidate high-frequency operation object has a click frequency of 50% but less than 60% in the last hour, it will not be determined as a high-frequency operation object.

[0168] Step S1577: Create an identification file for the frequently operated object, wherein the identification file includes the object identification, click frequency, most recent click time and location information in the interface.

[0169] An identification file is created for the frequently operated object "DS001". The file records the object identification as "DS001", the click frequency as 6 times / second, the most recent click time as 2024-05-20 16:30:22.123, and the position coordinates in the interface as X=300, Y=200.

[0170] Step S158: Adjust the display properties of frequently operated objects in the multi-level display template. For interactive controls that are frequently operated, increase their display size in the interface and adjust their positions to areas that are easy to operate. For data asset nodes that are frequently operated, enhance their highlighting effects in the corresponding display layer.

[0171] For interactive controls that are frequently operated, such as "Filter Control-Associated Filter Options", their display size in the interface is increased by a certain proportion, with both width and height increased by a certain percentage, making them easier for users to click; at the same time, their position is adjusted from the top edge of the interface to the top middle area, which is within a range that is easier for users to operate.

[0172] For the data asset node "DS001" with high frequency operations, its highlight effect is enhanced in the basic display layer, associated display layer and dynamic display layer. In addition to the original amplification, color change and border addition, a flashing effect (flashing once every 1 second) is added to make it more prominent in the interface.

[0173] Step S159: Analyze the mouse movement records in the operation trajectory data, extract the main path of the mouse movement and the distribution of the stay points, and determine the user's visual attention area, which refers to the interface area where the total stay time accounts for more than a preset proportion.

[0174] Extract all mouse movement records from the operation trajectory data, including the coordinate sequence of the movement path, the coordinates of the dwell points, and the dwell time. By analyzing the movement paths, identify the main paths of the user's mouse movement. These paths are the routes that users frequently move within the interface, such as from the zoom control on the left to the customer basic information dataset node in the middle, and then to the details control on the right.

[0175] The dwell time at all dwell points is counted, and the sum of dwell time within each area is calculated. A default ratio of 40% is set, and the interface area where the total dwell time exceeds 40% is determined to be the user's visual attention area. For example, if the total dwell time in the center-left area of ​​the interface accounts for 45%, this area is determined to be the visual attention area.

[0176] Step S1510: Adjust the layout of the multi-level presentation template, prioritize the placement of core data asset nodes and important interactive controls within the visual focus area, and optimize the arrangement of nodes in the presentation layer.

[0177] Move core data asset nodes (such as customer basic information dataset nodes and transaction record dataset nodes) and important interactive controls (such as filter controls and detail controls) to a specific visual focus area to ensure that users can quickly capture these key elements visually.

[0178] At the same time, the arrangement of nodes in the display layer is optimized. For the tree diagram of the basic display layer, high-frequency operation nodes and core nodes are placed in the upper and middle positions of the tree diagram to reduce the user's mouse movement distance; for the network diagram of the associated display layer, the distribution of nodes is adjusted so that nodes with close associations are clustered together to reduce the crossing of connections.

[0179] Step S1511: Analyze the control operation records in the operation trajectory data, extract the user's commonly used filtering conditions, zoom ratios and comparison combinations and save them as default configurations. When the user opens the visual interactive interface again, the default configurations are automatically applied.

[0180] Analyzing the control operation records, we found that users often set filtering conditions such as "sharing relationship coverage higher than 80%" and "processing status is in progress" in the filter control; the commonly used zoom ratio in the zoom control is 120%; and the customer basic information dataset and transaction record dataset are often compared and combined in the comparison control.

[0181] Save these commonly used filter conditions, zoom ratios, and comparison combinations as default configurations. When the user reopens the visualization interface, the system automatically applies these default configurations. The presentation layer displays data assets according to commonly used filter conditions, the zoom ratio is automatically adjusted to 120%, and the customer basic information dataset and transaction record dataset are automatically added to the comparison list.

[0182] Step S1512: updating the layout, display attributes and default configuration of the visual interactive interface according to the adjusted multi-level display template, and using a smooth transition animation during the updating process to complete the updating of the visual interactive interface.

[0183] The visual interaction interface has been updated based on the adjusted multi-level display template. In terms of layout, core nodes and important controls have been moved to areas of visual interest, and node arrangement has been optimized. In terms of display properties, the size, position, and highlighting of frequently operated objects have been adjusted. In terms of default configuration, commonly used filter conditions, zoom ratios, and comparison combinations have been applied.

[0184] During the update process, smooth transition animations are used to animate the movement, resizing, and display state of interface elements. The animations last for 0.5 seconds to avoid sudden changes that affect the user experience. After the animation is complete, the visual interactive interface is updated to a state that better suits user operating habits.

[0185] Figure 2 The following diagram illustrates exemplary hardware and software components of a data asset display and analysis system 100 based on data visualization, which can implement the concepts of the present application, as provided in some embodiments of the present application. For example, the processor 120 can be used in the data asset display and analysis system 100 based on data visualization to perform the functions of the present application.

[0186] The data asset display and analysis system 100 based on data visualization can be a general-purpose server or a special-purpose server, both of which can be used to implement the data asset display and analysis method based on data visualization of this application. Although only one server is shown in this application, for convenience, the functions described in this application can be implemented in a distributed manner on multiple similar platforms to balance the processing load.

[0187] For example, the data asset display and analysis system 100 based on data visualization may include a network port 110 connected to a network, one or more processors 120 for executing program instructions, a communication bus 130, and storage media 140 in different forms, such as a disk, ROM, or RAM, or any combination thereof. Exemplarily, the data asset display and analysis system 100 based on data visualization may also include program instructions stored in ROM, RAM, or other types of non-transitory storage media, or any combination thereof. The method of the present application may be implemented according to these program instructions. The data asset display and analysis system 100 based on data visualization also includes an I / O interface 150 between the computer and other input and output devices.

[0188] For ease of explanation, only one processor is described in the data asset display and analysis system 100 based on data visualization. However, it should be noted that the data asset display and analysis system 100 based on data visualization in the present application may also include multiple processors, so the steps performed by one processor described in the present application may also be performed jointly or individually by multiple processors. For example, if the processor of the data asset display and analysis system 100 based on data visualization executes step A and step B, it should be understood that step A and step B may also be executed jointly by two different processors or individually in one processor. For example, the first processor executes step A, the second processor executes step B, or the first processor and the second processor execute steps A and B together.

[0189] In addition, an embodiment of the present invention further provides a readable storage medium, in which computer-executable instructions are preset. When a processor executes the computer-executable instructions, the above-mentioned data asset display and analysis method based on data visualization is implemented.

[0190] It should be noted that in order to simplify the description of the present invention and thus help understand one or more embodiments of the invention, in the foregoing description of the embodiments of the present invention, multiple features are sometimes combined into one embodiment, figure or description thereof.

Claims

1. A data asset display and analysis method based on data visualization, characterized in that: The method comprises: Collecting original feature information of data assets, including structural features, association features, and flow features of data assets; Performing visualization dimension mapping processing on the original feature information to obtain a visualization dimension set of the data asset, wherein the visualization dimension set includes a structure dimension, an association dimension, and a flow dimension; Constructing a multi-level presentation template for data assets based on the visualization dimension set, wherein the multi-level presentation template includes a basic presentation layer, a related presentation layer, and a dynamic presentation layer; According to the user's analysis needs, the multi-level display template is interactively adapted to generate a visual interactive interface for the data assets; Tracking the user's operation trajectory data in the visual interactive interface, adjusting the multi-level display template based on the operation trajectory data, and updating the visual interactive interface.

2. The data asset display and analysis method based on data visualization according to claim 1 is characterized in that: The original feature information of the collected data assets includes: Accessing the data asset management system and retrieving metadata records of all data assets stored in the data asset management system, wherein the metadata records include the field composition, format type and storage path of the data assets; Extracting the hierarchical relationship of fields, the dependency relationship between fields, and the compatible attributes of format types from the metadata record, and integrating them to form the structural characteristics of the data asset, wherein the structural characteristics are presented in a hierarchical tree format; Query the association mapping table in the data asset management system to extract the reference, sharing, and combination relationships between different data assets, and integrate them into the association characteristics of data assets. The reference relationship refers to the field call relationship between one data asset and another data asset; the sharing relationship refers to the basic field set shared by multiple data assets; and the combination relationship refers to the aggregation form formed by multiple data assets according to set rules. Obtain the transfer records of data assets between different processing nodes, extract the node identification, transfer time and processing status during the transfer process, and generate the flow characteristics of data assets. The node identification includes the generation node, processing node and storage node, and the processing status includes pending, processing and completed. Deduplication and verification are performed on structural features, associated features and flow features to eliminate duplicate feature information, and the verified feature information is associated according to the unique code of the data asset to generate original feature information.

3. The data asset display and analysis method based on data visualization according to claim 2 is characterized in that: The performing of visualization dimension mapping processing on the original feature information to obtain a visualization dimension set of the data asset includes: parsing structural features in the original feature information, identifying the hierarchical depth, the number of nodes, and the connection strength between nodes in the hierarchical tree form, mapping the hierarchical depth to the hierarchical axis of the structural dimension, mapping the number of nodes to the scale axis of the structural dimension, and mapping the connection strength between nodes to the association axis of the structural dimension to form a structural dimension; Analyze the association features in the original feature information, extract the frequency of reference relationships, the coverage of sharing relationships, and the complexity of combination relationships. The frequency of reference relationships refers to the number of references per unit time, the coverage of sharing relationships refers to the proportion of shared fields in the total fields, and the complexity of combination relationships refers to the number of data assets required for the combination. These are mapped to the frequency axis, coverage axis, and complexity axis of the association dimension, respectively, to form an association dimension. Processing the flow features in the original feature information, extracting the node identification conversion sequence, the interval distribution of the transfer time, and the conversion probability of the processing state. The node identification conversion sequence refers to the order of nodes that the data asset passes through, the interval distribution of the transfer time refers to the distribution of the time difference between adjacent nodes, and the conversion probability of the processing state refers to the possibility of converting from one state to another. These are mapped to the node axis, time axis, and state axis of the flow dimension to form a flow dimension. Perform dimension calibration on the structural dimension, associated dimension and flow dimension, and integrate the calibrated structural dimension, associated dimension and flow dimension into a visualization dimension set.

4. The data asset display and analysis method based on data visualization according to claim 3 is characterized in that: The step of constructing a multi-level display template for data assets based on the visualization dimension set includes: Based on the structural dimension in the visualization dimension set, a basic display layer is constructed. The basic display layer uses a tree diagram to display the structural characteristics of the data asset. The root node of the tree diagram corresponds to the highest level of the data asset, and the child nodes correspond to the next level. The size of the node is determined according to the scale axis of the structural dimension, and the thickness of the connection line between the nodes is determined according to the association axis of the structural dimension. Based on the associated dimensions in the visualization dimension set, an associated display layer is constructed on the basis of the basic display layer. The associated display layer is superimposed on the tree diagram in the form of a network diagram. The nodes in the network diagram correspond to data assets, and the lines between the nodes represent the association relationship. The length of the line is determined by the frequency axis of the associated dimension, the color of the line is determined by the coverage axis of the associated dimension, and the shape of the node is determined by the complexity axis of the associated dimension. In combination with the flow dimension in the visualization dimension set, a dynamic display layer is constructed. The dynamic display layer uses a timeline animation to display the flow characteristics of data assets. The scale of the timeline is determined according to the timeline of the flow dimension. The icons in the timeline animation represent data assets. The movement path of the icons is determined according to the node axis of the flow dimension. The color change of the icons is determined according to the state axis of the flow dimension. Set the hierarchical switching rules for the multi-level display template. The basic display layer is the default display layer. You can switch to the associated display layer or dynamic display layer through the preset switching instructions. The switching process uses a smooth transition animation. The basic display layer, associated display layer, dynamic display layer and hierarchical switching rules are integrated into a multi-level display template.

5. The data asset display and analysis method based on data visualization according to claim 1 is characterized in that: The interactive adaptation processing of the multi-level display template according to the user's analysis requirements to generate a visual interactive interface for data assets includes: Receive an analysis requirement instruction input by the user, and parse the analysis type, focus object, and display accuracy requirement contained in the analysis requirement instruction. The analysis type includes structure analysis, association analysis, and flow analysis. The focus object refers to the data asset that the user is interested in. The display accuracy requirement refers to the level of detail of the displayed content. Determine the corresponding display layer in the multi-level display template based on the analysis type. If it is a structural analysis, the basic display layer is the main one; if it is an association analysis, the association display layer is the main one; if it is a flow analysis, the dynamic display layer is the main one; For the object of interest, highlight it in the corresponding display layer. The highlighting methods include enlarging the display, changing the color, and adding a border. Adjust the level of detail displayed in the display layer based on display accuracy requirements; Add interactive controls to the visual interactive interface. These controls include zoom, filter, detail, and comparison. Zoom controls are used to adjust the size of displayed content. Filter controls are used to filter data assets under specific conditions. Detail controls are used to view detailed information about data assets. Comparison controls are used to compare the characteristics of multiple data assets. Set response rules for interactive controls. When users operate interactive controls, the corresponding display layer is updated according to the preset rules. The filter controls trigger the hiding of data assets that do not meet the conditions. The adapted display layer, interactive controls, and response rules are integrated into a visual interactive interface.

6. The data asset display and analysis method based on data visualization according to claim 5 is characterized in that: Adding interactive controls to the visual interactive interface includes: Adding zoom controls to the edge of the visual interactive interface. The zoom controls include a zoom-in button, a zoom-out button, and a zoom slider. The zoom-in button triggers the display content to be enlarged according to a preset ratio, while the zoom-out button triggers the display content to be reduced according to a preset ratio. The zoom slider continuously adjusts the zoom ratio of the display content by dragging the position, and the zoom range is limited to a preset minimum zoom ratio and a maximum zoom ratio. Add filter controls to the top area of ​​the visual interactive interface. These filter controls include structure filter options, association filter options, and flow filter options. The structure filter option allows users to select a specific structure level or format type, the association filter option allows users to set a frequency threshold or coverage threshold for association relationships, and the flow filter option allows users to specify a flow node or processing status. After selecting a filter option, the display layer automatically hides data assets that do not meet the filter conditions. Add a details control in the right area of ​​the visual interactive interface. The details control is in the form of a floating button. When the user clicks a data asset node in the presentation layer, the details control automatically activates and displays the original feature information of the data asset. The original feature information includes the complete hierarchy of structural features, the detailed relationship of associated features, and the full record of flow features. The details control also contains a close button, which can be clicked to hide the details information. A comparison control is added to the bottom area of ​​the visual interactive interface. The comparison control includes an Add Compare button and a comparison panel. When the user clicks the Add Compare button, the currently selected data asset is added to the comparison list. The comparison panel displays the feature comparison of all data assets in the comparison list, and presents the indicator values ​​of each data asset in the structural dimension, association dimension and flow dimension in a tabular form. The comparison panel also includes a Clear button for clearing the comparison list.

7. The data asset display and analysis method based on data visualization according to claim 6 is characterized in that: The response rules for setting interactive controls are as follows: when a user operates an interactive control, the corresponding display layer is updated according to the preset rules, and the filtering control triggers the hiding of data assets that do not meet the conditions, including: For the zoom control, set the scale adjustment rules. When the user clicks the zoom-in button, the zoom ratio of the displayed content increases by a preset increment based on the current scale, with the same increment for each click until the maximum scale is reached, at which point the control stops responding. When the user clicks the zoom-out button, the zoom ratio of the displayed content decreases by a preset increment based on the current scale, until the minimum scale is reached, at which point the control stops responding. When the user drags the zoom slider, the zoom ratio changes linearly with the slider position. When the slider is at the far left end, the zoom ratio is the minimum scale, and when it is at the far right end, the zoom ratio is the maximum scale. The center position of the displayed content remains unchanged during the zooming process. For filter controls, set hiding rules. When the user selects the structure filter option, data asset nodes and their connections in the display layer that do not belong to the selected structural level or format type are automatically hidden. The hiding process uses a gradual fading animation, and the remaining nodes automatically adjust their positions. When the user selects the association filter option, connections in the display layer whose association frequency or coverage is lower than the corresponding preset threshold are automatically hidden, and the data asset nodes are retained but their colors are changed to lighter tones. When the user selects the flow filter option, data asset icons and their movement paths in the display layer that do not contain the specified flow node or processing status are automatically hidden, and the timeline range is adjusted according to the flow time of the remaining data assets. For the details control, set the display rules. When the user clicks a data asset node, the details control slides out from the right side of the interface. The sliding-out animation lasts for a preset time, and the detailed information is loaded and displayed in the order of structural features, association features, and flow features. When the user clicks the close button, the details control slides in to the right and hides. The sliding-in animation lasts for a preset time, and the data asset node is deselected. For the comparison control, set the list update rules. When the user clicks the Add Comparison button, if the data asset is not in the comparison list, it will be added and a new row of corresponding data will be added to the comparison panel. If it is already in the comparison list, a prompt message will pop up to indicate that the data asset is in the comparison list. When the user clicks the Clear button, the comparison list is cleared, the table content in the comparison panel is cleared, and the selection status of all data asset nodes is deselected.

8. The data asset display and analysis method based on data visualization according to claim 6 is characterized in that: The tracking of the user's operation trajectory data in the visual interactive interface includes: Embed a trajectory acquisition module in the visual interactive interface. The trajectory acquisition module records all user operations in real time, including mouse clicks, mouse movements, keyboard input, and control operations. When a user clicks the mouse, the click location coordinates, the clicked object ID, and the click time are recorded. The clicked object ID refers to the unique ID of the clicked interactive control or data asset node. When the user moves the mouse, the coordinate sequence of the mouse pointer's movement path in the interface and the coordinates and dwell time of the stop point during the movement are recorded. The stop point refers to the position where the mouse pointer's movement speed is lower than the preset threshold; When the user performs keyboard input operations, the input character content, input start time and input end time are recorded. The character content includes the filter conditions or search keywords entered in the filter control; When the user operates a control, the control type, operation parameters, and interface state changes before and after the operation are recorded. The operation parameters include the zoom ratio change value of the zoom control and the filter condition setting value of the filter control. The recorded operation behaviors are integrated in chronological order to generate operation trajectory data including operation type, operation object, operation time and operation parameters. The operation trajectory data is stored in the form of logs, and each log entry contains a unique trajectory number.

9. The data asset display and analysis method based on data visualization according to claim 6 is characterized in that: The adjusting the multi-level presentation template based on the operation trajectory data and updating the visual interaction interface includes: Analyze the mouse click records in the operation trajectory data, count the click frequencies of different interactive controls and data asset nodes, and determine objects with click frequencies higher than a preset click threshold as high-frequency operation objects; Adjust the display properties of frequently operated objects in multi-level display templates. For frequently operated interactive controls, increase their display size in the interface and reposition them in an easily accessible area. For frequently operated data asset nodes, enhance their highlighting effect in the corresponding display layer. Analyze the mouse movement records in the operation trajectory data, extract the main path of the mouse movement and the distribution of the dwell points, and determine the user's visual attention area, where the visual attention area refers to the interface area where the total dwell time accounts for more than a preset proportion; Adjust the layout of multi-level display templates, prioritize the placement of core data asset nodes and important interactive controls within the visual focus area, and optimize the arrangement of nodes in the display layer; Analyze the control operation records in the operation trajectory data, extract the user's commonly used filtering conditions, zoom ratios and comparison combinations, and save them as default configurations. When the user opens the visual interactive interface again, the default configurations are automatically applied; According to the adjusted multi-level display template, the layout, display properties and default configuration of the visual interactive interface are updated. During the update process, a smooth transition animation is used to complete the update of the visual interactive interface.

10. A data asset display and analysis system based on data visualization, characterized in that: It includes a processor and a memory, the memory is connected to the processor, the memory is used to store programs, instructions or codes, and the processor is used to execute the programs, instructions or codes in the memory to implement the data asset display and analysis method based on data visualization as described in any one of claims 1 to 9 above.

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