Visual data view dynamic configuration generation method and system

Abstract

The invention relates to the field of dynamic data processing, and in particular relates to a dynamic configuration generation method and system for a visual data view, which are used for solving the problem that the risk degree of negative factors is reflected due to lack of dynamic configuration on the risk degree of data during visual data query. Comprising a data receiving port, an original data analysis unit, a parameter data receiving port, an influence layer generation unit, a dynamic configuration generation unit and an output statistical unit, according to the method, the obtained original data is analyzed, the risk degree of the original data is judged according to an automatic analysis program, the original data is automatically influenced and generated in combination with the parameter data, the risk value of the data is dynamically updated, and the data is output in a visual view mode. And all historical data visualization views are stored in a time axis manner, so that the data visualization views at different moments can be compared and checked conveniently.

Description

Technical Field

[0001] The present invention relates to the field of dynamic data processing, and in particular to a method and system for dynamically configuring and generating a visual data view. Background Art

[0002] Data visualization is the scientific and technological study of the visual representation of data. Generally speaking, the representation of data visualization is defined as a kind of information abstracted in a certain summary form, including various attributes and variables of the corresponding information unit;

[0003] Data visualization is a concept that is constantly evolving and its boundaries are constantly expanding. It mainly refers to technically advanced technical methods that allow the use of graphics, image processing, computer vision, and user interfaces to visualize data through expression, modeling, and display of solids, surfaces, attributes, and animations. Compared with special technical methods such as solid modeling, data visualization mainly aims to convey and communicate information clearly and effectively with the help of graphical means. Data visualization is closely related to information graphics, information visualization, scientific visualization, and statistical graphics. At present, data visualization is an extremely active and critical aspect in the fields of research, teaching, and development;

[0004] At present, the existing patent application CN202080074042.3 discloses a technical solution, which queries the database through the selection dimensions of different elements to obtain the main result set, generates a table view of the result set, and then uses different guidelines to obtain a nested query set, thereby realizing the associated visualization of the query results. However, the technical solution in this solution can only improve the query results, but lacks effective deep mining and analysis methods for the reliability and risk level of the data, so that the query method has a single function and cannot automatically analyze the data doubts when presenting the data. The system cannot identify uncertain factors and reflect the risk level;

[0005] In view of the above technical problems, this application proposes a solution. Summary of the invention

[0006] The present invention analyzes the acquired raw data, determines the risk level of the raw data according to an automatic analysis program, automatically affects and generates the raw data in combination with parameter data, dynamically updates the risk value of the data, and then outputs the data in the form of a visualization view, and stores all historical data visualization views in the form of a timeline, which is convenient for comparing and viewing data visualization views at different times. This solves the problem of lack of dynamic configuration of the risk level of data during data visualization query, thereby reflecting the risk size of negative factors, and proposes a method and system for dynamically configuring and generating a visualization data view.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A method for dynamically configuring and generating a visual data view comprises the following steps:

[0009] Step 1: Obtain raw data and parameter data through data sources;

[0010] Step 2: Analyze the original data, calculate the risks contained in the original data, and build a visual data view based on the original data;

[0011] Step 3: Analyze the parameter data, build a data impact layer, superimpose the data impact layer and the original data layer, and modify the visualized data view, so as to realize dynamic configuration of the visualized data view through the data impact layer;

[0012] Step 4: Create a timeline, store the visual data view constructed by the original data at the starting node of the timeline, and store it at the corresponding time node according to the generation time of the dynamically configured data view at the subsequent nodes, and output the dynamically configured data view;

[0013] Step 5: When the next time point is reached, new parameter data is obtained again, and analysis is performed based on the new parameter data to obtain a new data impact layer, which is superimposed on the data view configured in step 4, and the dynamically configured data view is corrected and output again;

[0014] Step 6: Add another node on the timeline to store the dynamically configured data view newly generated in step 5;

[0015] Step 7: Repeat steps 5 and 6 multiple times to form a continuously updated data view, and store the dynamically configured data views of all nodes on the timeline.

[0016] As a preferred implementation of the present invention, the original data in step 1 are data that need to be risk analyzed, and the parameter data are external interference data that have an impact on the risk existing in the original data and set standard data.

[0017] As a preferred implementation of the present invention, in step 2, when drawing a visualization view, the horizontal axis is the original data group, the vertical axis is the risk value, and a risk standard line is drawn in the visualization data view. The visualization data view is divided into two parts by the risk standard line to obtain a low-risk area and a high-risk area.

[0018] A visual data view dynamic configuration generation system includes a data receiving port, wherein the data receiving port is used to receive original data, wherein the received original data includes i groups of original data groups, each original data group contains y sub-data, and i and y are both non-zero natural numbers;

[0019] A raw data analysis unit, which analyzes and combines the acquired raw data to obtain a visual data view of the raw data group;

[0020] A parameter data receiving port, wherein the parameter data receiving port is used to receive parameter data, wherein the parameter data is divided into interference data and standard data;

[0021] An influence layer generation unit, wherein the influence layer generation unit generates a data influence layer after obtaining interference data;

[0022] A dynamic configuration generation unit, wherein the dynamic configuration generation unit overlays and combines the data impact layer with the visual data view to perform dynamic configuration of the data view;

[0023] The output statistics unit stores and outputs the data view generated by the dynamic configuration generation unit.

[0024] As a preferred embodiment of the present invention, the raw data analysis unit simulates and analyzes the raw data through a large model, compares each sub-data with a set normal interval, and records it as normal data if the sub-data is within the normal interval; if the sub-data is not within the normal interval, it is recorded as abnormal data. After all sub-data are compared, a risk value J of a raw data group is obtained through formula analysis. , where yb is the number of abnormal data;

[0025] The raw data analysis unit combines the raw data groups, generates the combined raw data groups through a data visualization model, and obtains a visualized data view of the raw data groups. When generating the visualized data view, the horizontal axis is i raw data groups, the vertical axis is the risk value, and a risk standard line is set on the set standard risk value.

[0026] As a preferred embodiment of the present invention, the interference data received by the parameter data receiving port is data that has interference characteristics on the risk value, the number of interference data is the same as that of the original data group, the standard data is manually preset and is used to analyze the risk value, and the parameter data receiving port sends the interference data to the impact layer generation unit.

[0027] As a preferred embodiment of the present invention, after obtaining the interference data, the influence layer generation unit numbers the interference data, calculates the ratio of each group of interference data with the set standard data, obtains i groups of interference coefficients, and uses the interference coefficient as the vertical axis and the group number i of the interference coefficient as the horizontal axis to generate a data influence layer, and sends the data influence layer to the dynamic configuration generation unit.

[0028] As a preferred embodiment of the present invention, the method for the dynamic configuration generation unit to perform superposition combination is:

[0029] The horizontal coordinates are overlapped, and the risk value with the same horizontal coordinate is multiplied by the interference coefficient to obtain the corrected risk value. After all i groups of data are corrected, the image formed by the newly generated risk value is used as the configured data view.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. The present invention analyzes the acquired raw data, thereby judging the risk level of the raw data according to an automatic analysis program, thereby realizing a preliminary judgment of the size of risk factors, and when acquiring new parameter data, automatically considering the impact on the raw data according to the new parameter data, and dynamically updating the judged risk value according to the impact result, thereby improving the accuracy of analyzing the size of the risk contained in the data.

[0032] 2. In the present invention, after the risk analysis of the data is completed, the data is output in the form of a visual view, and a large amount of data is automatically divided by set standard lines to form high-risk areas and low-risk areas, making the data presentation more intuitive and improving the degree of data visualization.

[0033] 3. In the present invention, when the data is dynamically configured and output for visualization, the time point of each dynamic configuration of the data is recorded, and all historical data visualization views are stored in the form of a timeline, which is convenient for comparing and viewing the data visualization views at different times. At the same time, the automatic collection and output of data risk change trends can be achieved through nested comparison template software or algorithms. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0035] Figure 1 is a system block diagram of the present invention;

[0036] Figure 2 It is a system flow chart of the present invention. DETAILED DESCRIPTION

[0037] The technical scheme of the present invention will be described clearly and completely in conjunction with the embodiments below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] Embodiment 1:

[0039] See also Figure 1 - Figure 2 As shown, a method for dynamically configuring and generating a visual data view includes the following steps:

[0040] Step 1: Obtain original data and parameter data through data sources, where original data refers to data that needs to be risk analyzed, and parameter data refers to external interference data that affects the risks in the original data and set standard data;

[0041] Step 2: Analyze the original data, calculate the risks contained in the original data, and build a visual data view based on the original data, where the horizontal axis of the visual view is data and the vertical axis is risk value. Draw a risk standard line in the visual data view, and divide the visual data view into two parts through the risk standard line to obtain a low-risk area and a high-risk area;

[0042] Step 3: Analyze the parameter data, build a data impact layer, superimpose the data impact layer and the original data layer, and modify the visualized data view, so as to realize dynamic configuration of the visualized data view through the data impact layer;

[0043] Step 4: Output the dynamically configured data view and create a timeline at the same time. Store the visualized data view constructed by the original data at the starting node of the timeline. Store the data view at the corresponding time node according to the generation time of the dynamically configured data view at the subsequent nodes.

[0044] Step 5: When the next time point is reached, new parameter data is obtained again, and analysis is performed based on the new parameter data to obtain a new data impact layer, which is superimposed on the data view configured in step 4, and the dynamically configured data view is corrected and output again;

[0045] Step 6: Add another node on the timeline to store the dynamically configured data view newly generated in step 5;

[0046] Step 7: Repeat steps 5 and 6 multiple times to form a continuously updated data view, and store the dynamically configured data views of all nodes on the timeline.

[0047] Embodiment 2:

[0048] See also Figure 1 - Figure 2 As shown, a visual data view dynamic configuration generation system includes a data receiving port, a raw data parsing unit, a parameter data receiving port, an impact layer generation unit, a dynamic configuration generation unit and an output statistics unit;

[0049] The data receiving port is used to receive the original data, and send the received original data to the original data parsing unit, wherein the original data includes i groups of original data groups, each original data group contains y sub-data, and i and y are both non-zero natural numbers;

[0050] The raw data analysis unit analyzes the acquired raw data, simulates and analyzes the raw data through the large model, and compares each sub-data with the set normal interval. If the sub-data is within the normal interval, it is recorded as normal data. If the sub-data is not in the normal interval, it is recorded as abnormal data. After all sub-data are compared, the risk value J of a raw data group is obtained through formula analysis. , where yb is the number of abnormal data, and the original data groups are combined at the same time, and the combined original data groups are generated through the data visualization model to obtain a visualized data view of the original data groups. When generating the visualized data view, the horizontal axis is i original data groups, and the vertical axis is the risk value. At the same time, a risk standard line is set on the set standard risk value;

[0051] The raw data analysis unit sends the risk and raw data to the dynamic configuration generation unit;

[0052] The parameter data receiving port is used to receive the parameter data and perform preliminary analysis on the parameter data, wherein the parameter data is divided into interference data and standard data, wherein the interference data is data that has interference characteristics on the risk value, and the number of the interference data is the same as that of the original data group, and the standard data is manually preset and used for analyzing the risk value;

[0053] The parameter data receiving port sends the interference data to the influence layer generation unit. After obtaining the interference data, the influence layer generation unit numbers the interference data, calculates the ratio of each group of interference data to the set standard data, obtains i groups of interference coefficients, and generates a data influence layer with the interference coefficient as the vertical axis and the group number i of the interference coefficient as the horizontal axis, and sends the data influence layer to the dynamic configuration generation unit;

[0054] The dynamic configuration generation unit overlays and combines the data impact layer with the visual data view to dynamically configure the data view;

[0055] The method for dynamically configuring the generation unit to perform superposition and combination is as follows:

[0056] The horizontal coordinates are overlapped, and the risk value with the same horizontal coordinate is multiplied by the interference coefficient to obtain the corrected risk value. After all i groups of data are corrected, the image formed by the newly generated risk value is used as the configured data view.

[0057] The output statistics unit saves and outputs the data view generated by the dynamic configuration generation unit.

[0058] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for dynamically configuring and generating a visual data view, characterized in that: The following steps are involved: Step 1: Obtain raw data and parameter data through data sources; Step 2: Analyze the original data, calculate the risks contained in the original data, and build a visual data view based on the original data; Step 3: Analyze the parameter data, build a data impact layer, superimpose the data impact layer and the original data layer, and modify the visualized data view, so as to realize dynamic configuration of the visualized data view through the data impact layer; Step 4: Create a timeline, store the visual data view constructed by the original data at the starting node of the timeline, and store it at the corresponding time node according to the generation time of the dynamically configured data view at the subsequent nodes, and output the dynamically configured data view; Step 5: When the next time point is reached, new parameter data is obtained again, and analysis is performed based on the new parameter data to obtain a new data impact layer, which is superimposed on the data view configured in step 4, and the dynamically configured data view is corrected and output again; Step 6: Add another node on the timeline to store the dynamically configured data view newly generated in step 5; Step 7: Repeat steps 5 and 6 multiple times to form a continuously updated data view, and store the dynamically configured data views of all nodes on the timeline.

2. A method for dynamically configuring and generating a visual data view according to claim 1, characterized in that: The original data in step 1 are data that need to be risk analyzed, and the parameter data are external interference data that have an impact on the risk existing in the original data and set standard data.

3. A method for dynamically configuring and generating a visual data view according to claim 1, characterized in that: In the step 2, when drawing the visualization view, the horizontal axis is the original data group, the vertical axis is the risk value, and a risk standard line is drawn in the visualization data view. The visualization data view is divided into two parts by the risk standard line to obtain a low-risk area and a high-risk area.

4. A visual data view dynamic configuration generation system, applicable to a visual data view dynamic configuration generation method according to claim 1, characterized in that: A data receiving port is included, and the data receiving port is used to receive original data, the received original data includes i groups of original data groups, each original data group contains y sub-data, and i and y are both non-zero natural numbers; A raw data analysis unit, which analyzes and combines the acquired raw data to obtain a visual data view of the raw data group; A parameter data receiving port, wherein the parameter data receiving port is used to receive parameter data, wherein the parameter data is divided into interference data and standard data; An influence layer generation unit, wherein the influence layer generation unit generates a data influence layer after obtaining interference data; A dynamic configuration generation unit, wherein the dynamic configuration generation unit overlays and combines the data impact layer with the visual data view to perform dynamic configuration of the data view; The output statistics unit stores and outputs the data view generated by the dynamic configuration generation unit.

5. A visual data view dynamic configuration generation system according to claim 4, characterized in that: The raw data analysis unit simulates and analyzes the raw data through a large model, and compares each sub-data with the set normal interval. If the sub-data is within the normal interval, it is recorded as normal data. If the sub-data is not within the normal interval, it is recorded as abnormal data. After all sub-data are compared, a risk value J of a raw data group is obtained through formula analysis. , where yb is the number of abnormal data; The raw data analysis unit combines the raw data groups, generates the combined raw data groups through a data visualization model, and obtains a visualized data view of the raw data groups. When generating the visualized data view, the horizontal axis is i raw data groups, the vertical axis is the risk value, and a risk standard line is set on the set standard risk value.

6. A visual data view dynamic configuration generation system according to claim 4, characterized in that: The interference data received by the parameter data receiving port is data that has interference characteristics on the risk value. The number of interference data is the same as that of the original data group. The standard data is manually preset and is used to analyze the risk value. The parameter data receiving port sends the interference data to the impact layer generation unit.

7. A visual data view dynamic configuration generation system according to claim 4, characterized in that: After obtaining the interference data, the influence layer generation unit numbers the interference data, calculates the ratio of each group of interference data to the set standard data, obtains i groups of interference coefficients, and uses the interference coefficient as the vertical axis and the group number i of the interference coefficient as the horizontal axis to generate a data influence layer, and sends the data influence layer to the dynamic configuration generation unit.

8. A visual data view dynamic configuration generation system according to claim 4, characterized in that: The method for the dynamic configuration generation unit to perform superposition and combination is: The horizontal coordinates are overlapped, and the risk value with the same horizontal coordinate is multiplied by the interference coefficient to obtain the corrected risk value. After all i groups of data are corrected, the image formed by the newly generated risk value is used as the configured data view.

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