A method and system for dynamically configuring and generating a visual data view

Abstract

The present invention relates to the field of dynamic data processing, and is used to solve the problem that the risk level of data is lacking in dynamic configuration during visual data query, thus reflecting the risk magnitude of negative factors. Specifically, it is a method and system for dynamically configuring and generating visual data views, including a data receiving port, an original data parsing unit, a parameter data receiving port, an impact layer generating unit, a dynamic configuration generating unit, and an output statistics unit; the present invention parses the acquired original data, judges the risk level of the original data according to an automatic analysis program, and combines parameter data to automatically impact and generate the original data, dynamically updates the risk value of the data, then outputs the data in the form of a visual view, and stores all historical data visual views in the form of a time axis, facilitating the comparison and viewing of data visual views at different times.

Description

Technical Field

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

[0002] Data visualization is a scientific and technological research on the visual representation form of data. Generally, the representation form 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 units;

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

[0004] Currently, in the existing patent application CN202080074042.3, a technical solution is disclosed. This solution 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 guides to obtain nested query sets, thereby realizing the correlation visualization of query results. However, the technical solution in this solution can only improve the query results, and lacks effective in-depth mining and analysis means for the reliability and risk degree of data, resulting in a single function of the query means and being unable to automatically analyze data doubts when presenting data, and the system being unable to identify uncertain factors and reflect the risk level;

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

[0006] The present invention resolves the problem that when querying visual data, there is a lack of dynamic configuration of the risk level of the data, thus reflecting the risk magnitude of negative factors. It proposes a method and system for dynamically configuring and generating visual data views by parsing the obtained original data, judging the risk level of the original data according to an automatic analysis program, automatically influencing and generating the original data in combination with parameter data, dynamically updating the risk value of the data, outputting the data in the form of a visual view, and storing the visual views of all historical data in the form of a timeline for easy comparison and viewing of the visual views of data at different times.

[0007] The object of the present invention can be achieved by the following technical solutions:

[0008] A method for dynamically configuring and generating visual data views includes the following steps:

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

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

[0011] Step 3: Analyze through parameter data, construct a data influence layer, superimpose the data influence layer and the original data layer, and correct the visual data view, thereby realizing dynamic configuration of the visual data view through the data influence layer;

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

[0013] Step 5: When reaching the next time point, obtain new parameter data again, analyze according to the new parameter data, obtain a new data influence layer, superimpose the new data influence layer with the configured data view in Step 4, and correct and output the dynamically configured data view again;

[0014] Step 6: Add a node to the timeline again and store the newly generated dynamically configured data view in Step 5;

[0015] Step 7: Repeat Step 5 and Step 6 multiple times to form an ever-updating data view, and store the dynamically configured data views of all nodes on the timeline.

[0016] As a preferred embodiment of the present invention, the original data in step one is the data that needs to be analyzed for risks, and the parameter data is the external interference data and the set standard data that have an impact on the risks existing in the original data.

[0017] As a preferred embodiment of the present invention, in step two, 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.

[0018] A dynamic configuration generation system for visualization data views includes a data receiving port, which is used to receive the original data. The received original data includes i groups of original data groups, and each original data group contains y sub-data. Both i and y are non-zero natural numbers;

[0019] An original data parsing unit, which analyzes and combines the obtained original data to obtain a visualization data view of the original data group;

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

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

[0022] A dynamic configuration generation unit, which superimposes and combines the data influence layer with the visualization data view to perform dynamic configuration of the data view;

[0023] An output statistics unit, which saves and outputs the data view generated by the dynamic configuration generation unit.

[0024] As a preferred embodiment of the present invention, the original data parsing unit performs simulation analysis and calculation on the original data through a large model, 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 comparing all sub-data, the risk value J of an original data group is obtained through formula analysis, , where yb is the number of abnormal data;

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

[0026] As a preferred embodiment of the present invention, the interference data received by the parameter data receiving port are data with interference characteristics for the risk value, and the number of interference data is the same as that of the original data groups. The standard data is manually preset data for analyzing the risk value. The parameter data receiving port sends the interference data to the influence 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 to the set standard data to obtain i groups of interference coefficients, and generates a data influence layer with the interference coefficient as the vertical axis and the grouping number i of the interference coefficient as the horizontal axis, 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 as follows:

[0029] Overlap the abscissas, multiply the risk value and the interference coefficient with the same abscissa to obtain the corrected risk value. After all i groups of data are corrected, the image formed by the newly generated risk values is used as the configured data view.

[0030] Compared with the prior art, the beneficial effects of the present invention are:

[0031] 1. The present invention parses the obtained original data, thereby judging the risk degree of the original data according to the automatic analysis program, realizing a preliminary judgment of the size of the risk factors, and when obtaining new parameter data, automatically considering the influence result on the original data according to the new parameter data, and dynamically updating the judged risk value according to the influence result, so as to improve the accuracy when analyzing the risk size contained in the data.

[0032] 2. In the present invention, after the analysis of the risk size 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 the set standard line, thereby forming a high-risk area and a low-risk area, making the data presentation more intuitive and improving the data visualization degree.

[0033] 3. In the present invention, when visualizing and dynamically configuring the output of data, the time points of each dynamic configuration of the data are 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, by nesting a comparison template software or algorithm, the automatic collection and output of the data risk change trend can be realized. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] For the convenience of those skilled in the art to understand, the present invention will be further described below in conjunction with the accompanying drawings.

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

[0036] Figure 2 is the system flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] Embodiment 1:

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

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

[0041] Step 2: Parse the original data, calculate the risks contained in the original data, construct a visual data view based on the original data. Among them, the horizontal axis of the visual view is the data, the vertical axis is the risk value, and a risk standard line is drawn in the visual data view. The visual data view is divided into two parts by the risk standard line to obtain a low-risk area and a high-risk area;

[0042] Step 3: Analyze through the parameter data, construct a data influence layer, superimpose the data influence layer and the original data layer, and correct the visual data view, so as to realize the dynamic configuration of the visual data view through the data influence layer;

[0043] Step 4: Output the dynamically configured data view, and create a timeline. Store the visualization data view constructed from the original data at the starting node of the timeline. At subsequent nodes, store it at the corresponding time nodes according to the generation time of the dynamically configured data view.

[0044] Step 5: When reaching the next time point, obtain new parameter data again, analyze it according to the new parameter data to obtain a new data impact layer, overlay the new data impact layer with the configured data view in Step 4, and correct and output the dynamically configured data view again.

[0045] Step 6: Add nodes to the timeline again and store the newly generated dynamically configured data view in Step 5.

[0046] Step 7: Repeat Step 5 and Step 6 multiple times to form an ever-updating data view, and store the dynamically configured data views of all nodes on the timeline.

[0047] Embodiment 2:

[0048] Please refer to Figure 1 - Figure 2 As shown in the figure, a system for dynamically generating a visualization data view includes a data receiving port, an original data parsing unit, a parameter data receiving port, an impact layer generating unit, a dynamic configuration generating unit, and an output statistics unit.

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

[0050] The original data parsing unit analyzes the obtained original data, performs simulation analysis and calculation on the original data through a large model, compares each sub-data with the set normal range. If the sub-data is within the normal range, it is recorded as normal data. If the sub-data is not within the normal range, it is recorded as abnormal data. After comparing all sub-data, a risk value J of an original data group is obtained through formula analysis. , where yb is the number of abnormal data. At the same time, combine the original data groups, generate the combined original data groups through a data visualization model to obtain the visualization data view of the original data group. When generating the visualization data view, the horizontal axis is i original data groups, the vertical axis is the risk value, and a risk standard line is set at the set standard risk value.

[0051] The original data parsing unit sends the riskiness and the original data to the dynamic configuration generating unit.

[0052] The parameter data receiving port is used to receive parameter data and perform preliminary parsing on the parameter data. The parameter data is divided into interference data and standard data. The interference data is data with interference characteristics for the risk value, and 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;

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

[0054] The dynamic configuration generation unit superimposes and combines the data impact layer with the visual data view for dynamic configuration of the data view;

[0055] The method by which the dynamic configuration generation unit performs the superimposing and combining is as follows:

[0056] Coincide the abscissas, multiply the risk value with the same abscissa 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 values 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 illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor limit the invention to only the specific implementation manners. Obviously, many modifications and variations can be made according to the content of this specification. These embodiments are selected and specifically described in this specification in order to better explain the principles and practical applications of the present invention, so that those skilled in the relevant technical fields can well understand and utilize the present invention. The present invention is only limited 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, Including the following steps: Step 1: Obtain the original data and parameter data from the data source; The original data in Step 1 is the data that needs to be risk-analyzed, and the parameter data is the external interference data that affects the risks existing in the original data and the set standard data; Step 2: Analyze the original data, calculate the risks contained in the original data, and construct a visual data view based on the original data; Step 3: Analyze through the parameter data, construct a data influence layer, superimpose the data influence layer and the original data layer, and correct the visual data view, so as to realize the dynamic configuration of the visual data view through the data influence layer; Step 4: Create a timeline, store the visual data view constructed from the original data at the starting node of the timeline, and at subsequent nodes, store it at the corresponding time nodes according to the generation time of the dynamically configured data view, and output the dynamically configured data view; Step 5: When reaching the next time point, obtain new parameter data again, analyze according to the new parameter data, obtain a new data influence layer, superimpose the new data influence layer with the configured data view in Step 4, and correct and output the dynamically configured data view again; Step 6: Add a node again on the timeline and store the newly generated dynamically configured data view in Step 5; Step 7: Repeat Step 5 and Step 6 multiple times to form an ever-updating 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, wherein In Step 2, when drawing the visual 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 visual data view. The visual data view is divided into two parts by the risk standard line to obtain a low-risk area and a high-risk area.

3. A visual data view dynamic configuration generation system, applicable to the visual data view dynamic configuration generation method described in claim 1, characterized in that, Including a data receiving port, which is used to receive the original data. The received original data includes i groups of original data groups, and each original data group contains y sub-data. Both i and y are non-zero natural numbers; An original data parsing unit, which analyzes and combines the obtained original data to obtain a visual data view of the original data group; A parameter data receiving port, which is used to receive the parameter data, and the parameter data is divided into interference data and standard data; An influence layer generation unit, which generates a data influence layer after obtaining the interference data; A dynamic configuration generation unit, which superimposes and combines the data influence layer and the visual data view to perform dynamic configuration of the data view; An output statistics unit, which saves and outputs the data view generated by the dynamic configuration generation unit.

4. A visual data view dynamic configuration generation system according to claim 3, characterized in that The original data parsing unit performs simulation analysis and calculation on the original data through a large model, compares each sub-data with a set normal range. If the sub-data is within the normal range, it is recorded as normal data; if the sub-data is not within the normal range, it is recorded as abnormal data. After comparing all sub-data, a risk value J of an original data group is obtained through formula analysis, , where yb is the number of abnormal data; The original data parsing unit combines the original data groups, generates them through a data visualization model to obtain a visual data view of the original data group. When generating the visual data view, the horizontal axis is i groups of original data groups, the vertical axis is the risk value, and a risk standard line is set at the set standard risk value.

5. The visual data view dynamic configuration generation system according to claim 3, wherein The interference data received by the parameter data receiving port are data with interference characteristics for the risk value, and the number of interference data is the same as that of the original data group. The standard data are manually preset data for analyzing the risk value, and the parameter data receiving port sends the interference data to the impact layer generation unit.

6. A visual data view dynamic configuration generation system according to claim 3, characterized in that After obtaining the interference data, the impact layer generation unit numbers the interference data, calculates the ratio of each group of interference data to the set standard data to obtain i groups of interference coefficients, and generates a data impact layer with the interference coefficient as the vertical axis and the grouping number i of the interference coefficient as the horizontal axis, and sends the data impact layer to the dynamic configuration generation unit.

7. A visual data view dynamic configuration generation system according to claim 3, characterized in that The method for the dynamic configuration generation unit to perform superposition combination is as follows: Coincide the abscissas, multiply the risk value with the same abscissa 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 values is used as the configured data view.

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