CN-115329162-B - Data visualization processing method, device and storage medium

CN115329162BCN 115329162 BCN115329162 BCN 115329162BCN-115329162-B

Abstract

The application provides a data visualization processing method, a device and a storage medium, relates to the field of data processing, and is used for improving the data visualization effect. The method includes acquiring a plurality of high-dimensional data. The plurality of high-dimensional data is classified, and a first data set is determined, wherein the first data set comprises the high-dimensional data corresponding to the target type. Splitting the first data set according to a decision tree algorithm and a plurality of preset features, determining a plurality of feature sets, wherein one preset feature corresponds to one feature set, and the plurality of preset features are features of high-dimensional data in the first data set. And determining a target set from a plurality of feature sets according to the target features, wherein the target set is a feature set with the matching degree with the target features being greater than a preset similarity threshold value in the plurality of feature sets. And rendering the target set.

Inventors

CHEN LIBO
SUN KANG
HUO SIYU
REN GAOYUAN
LI XI
GAO JING

Assignees

中国联合网络通信集团有限公司
中讯邮电咨询设计院有限公司

Dates

Publication Date: 20260508
Application Date: 20220819

Claims (9)

1. A method of processing data visualization, applied to a first server, the method comprising: Acquiring a plurality of high-dimensional data; Classifying the plurality of high-dimensional data, and determining a first data set, wherein the first data set comprises high-dimensional data corresponding to a target type; splitting the first data set according to a decision tree algorithm and a plurality of preset features, and determining a plurality of feature sets, wherein one preset feature corresponds to one feature set, and the plurality of preset features are features of high-dimensional data in the first data set; determining a first information entropy according to the preset features and the first data set, wherein the first information entropy is used for indicating the matching degree between high-dimensional data in the first data set and target features; Determining a plurality of second information entropies according to the plurality of preset features and the plurality of feature sets, wherein the second information entropies are used for indicating the matching degree between the feature sets and the target features, and one feature set corresponds to one second information entropy; determining a plurality of information gains according to the first information entropy and the plurality of second information entropy, wherein the information gains are used for indicating the matching degree between the feature set and the target feature; Comparing the information gains to determine a target gain, wherein the target gain is the maximum information gain in the information gains; taking a feature set corresponding to the target gain as a target set, wherein the target set is a feature set, of the feature sets, of which the matching degree with the target feature is larger than a preset similarity threshold value; And rendering the target set.
2. The method of claim 1, wherein prior to said rendering the set of targets, the method further comprises: Determining rendering task information corresponding to the target set, wherein the rendering task information comprises the number of rendering tasks and task duration of each rendering task; The rendering process for the target set includes: And if the number of the rendering tasks is smaller than a first preset threshold and the task duration of each rendering task is smaller than a second preset threshold, rendering the target set.
3. The method of claim 2, wherein the target set comprises a second data set and a third data set, the method further comprising: If the number of the rendering tasks is greater than the first preset threshold and the task time length of each rendering task is greater than the second preset threshold, rendering the second data set; Sending a rendering message to a second server, the rendering message including the third data set, the rendering message being for instructing the second server to render the third data set; and receiving rendering data from the second server, wherein the rendering data is data after the third data set is rendered.
4. A processing apparatus for data visualization, for application to a first server, the apparatus comprising: the acquisition module is used for acquiring a plurality of high-dimensional data; the processing module is used for classifying the plurality of high-dimensional data and determining a first data set, wherein the first data set comprises high-dimensional data corresponding to a target type; The processing module is further configured to split the first data set according to a decision tree algorithm and a plurality of preset features, determine a plurality of feature sets, one of the preset features corresponds to one of the feature sets, and the plurality of preset features are features of high-dimensional data in the first data set; The processing module is specifically configured to determine a first information entropy according to the multiple preset features and the first data set, where the first information entropy is used to indicate a degree of matching between high-dimensional data in the first data set and a target feature; The processing module is specifically configured to determine a plurality of second information entropies according to the plurality of preset features and the plurality of feature sets, where the second information entropies are used to indicate a degree of matching between the feature sets and the target features, and one feature set corresponds to one second information entropy; The processing module is specifically configured to determine a plurality of information gains according to the first information entropy and the plurality of second information entropy, where the information gains are used to indicate a degree of matching between the feature set and the target feature; The processing module is specifically configured to compare the plurality of information gains, determine a target gain, where the target gain is a maximum information gain of the plurality of information gains; The processing module is specifically configured to take a feature set corresponding to the target gain as a target set, where the target set is a feature set, in the multiple feature sets, having a matching degree with the target feature greater than a preset similarity threshold; the processing module is further used for rendering the target set.
5. The apparatus of claim 4, wherein the device comprises a plurality of sensors, The processing module is specifically configured to determine rendering task information corresponding to the target set, where the rendering task information includes the number of rendering tasks and task duration of each rendering task; The processing module is further configured to perform rendering processing on the target set if the number of rendering tasks is less than a first preset threshold and a task duration of each rendering task is less than a second preset threshold.
6. The apparatus of claim 5, wherein the target set comprises a second data set and a third data set; The processing module is specifically configured to perform rendering processing on the second data set if the number of the rendering tasks is greater than the first preset threshold and the task time length of each rendering task is greater than the second preset threshold; the processing module is further configured to send a rendering message to a second server, where the rendering message includes the third data set, and the rendering message is configured to instruct the second server to render the third data set; The processing module is further configured to receive rendering data from the second server, where the rendering data is data after rendering the third dataset.
7. A data visualization processing device comprising a processor and a memory, the processor and the memory being coupled, the memory being configured to store one or more programs, the one or more programs comprising computer-executable instructions that, when executed by the data visualization processing device, cause the data visualization processing device to perform the data visualization processing method of any of claims 1-3.
8. A computer-readable storage medium having instructions stored therein, wherein when the instructions are executed by a computer, the computer performs the method of processing data visualization according to any one of claims 1-3.
9. A computer program product comprising a computer program, characterized in that the computer program, when executed by a processor, implements a method of processing a data visualization according to any of claims 1-3.

Description

Data visualization processing method, device and storage medium Technical Field The present application relates to the field of data processing, and in particular, to a data visualization processing method, apparatus, and storage medium. Background The rapid development of big data provides a great deal of convenience for the production and life of people. In order to further improve the usability of the data, the data can be clearly and effectively communicated with information, and the data visualization is an effective technology. The data visualization may present relatively boring data in a manner that is more easily understood and accepted by the user. During the data visualization process, the server may obtain a data set containing a large amount of data. And then, the server can classify the data in the data set according to the requirements to obtain various types of data. Then, the server performs rendering processing on the data of the target type meeting the demand, forming an image suitable for the demand. However, in the current technical scheme, the data meeting the requirements can be obtained only through one-time classification, which may result in lower accuracy of the obtained data and influence the visual effect. Disclosure of Invention The application provides a data visualization processing method, a data visualization processing device and a storage medium, which are used for improving a data visualization effect. In order to achieve the above purpose, the application adopts the following technical scheme: In a first aspect, the present application provides a method of processing data visualization. In this method, a processing device (which may be simply referred to as a "processing device") for data visualization acquires a plurality of high-dimensional data. The processing device may classify the plurality of high-dimensional data to determine a first data set, the first data set including high-dimensional data corresponding to the target type. Then, the processing device may split the first data set according to a decision tree algorithm and a plurality of preset features, determine a plurality of feature sets, where one preset feature corresponds to one feature set, and the plurality of preset features are features of high-dimensional data in the first data set. And then, the processing device can determine a target set from a plurality of feature sets according to the target features, wherein the target set is a feature set with the matching degree with the target features being larger than a preset similarity threshold value in the plurality of feature sets. Thereafter, the processing device may perform rendering processing on the target set. Optionally, the processing device may determine a first information entropy according to the plurality of preset features and the first data set, where the first information entropy is used to indicate a matching degree between the high-dimensional data in the first data set and the target feature. And then, the processing device can determine a plurality of second information entropies according to a plurality of preset features and a plurality of feature sets, wherein the second information entropies are used for indicating the matching degree between the feature sets and the target features, and one feature set corresponds to one second information entropy. The processing means may then determine a plurality of information gains from the first information entropy and the plurality of second information entropy, the information gains being indicative of a degree of matching between the feature set and the target feature. The method for determining the target set from the plurality of feature sets comprises the step that the processing device can compare the plurality of information gains to determine the target gain, wherein the target gain is the largest information gain in the plurality of information gains. The processing device may then use the feature set corresponding to the target gain as the target set. Optionally, the method further comprises the step that the processing device can determine rendering task information corresponding to the target set, wherein the rendering task information comprises the number of rendering tasks and task duration of each rendering task. The method for rendering the target set comprises the step that if the number of rendering tasks is smaller than a first preset threshold and the task duration of each rendering task is smaller than a second preset threshold, the processing device can render the target set. Optionally, the target set comprises a second data set and a third data set. The method further comprises the step that if the number of the rendering tasks is larger than a first preset threshold value and the task time length of each rendering task is larger than a second preset threshold value, the processing device can conduct rendering processing on the second data set. The processing de