CN117807154B - Time sequence data visualization method, device and medium for display system - Google Patents
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Abstract
The invention discloses a time sequence data visualization method, equipment and medium for a display system, which relate to the technical field of image data processing, and are used for carrying out time sequence data fragment feature extraction on time sequence data so as to ensure the sequence correctness of the data in the conversion process, prevent the data from losing, position the time sequence data, construct a multi-dimensional visualization multi-resolution model, calculate a multi-resolution level corresponding to the time sequence data fragment mapping, set the multi-dimensional visualization multi-resolution model level according to the size of the time sequence data fragment, improve the accuracy of data mapping, search the positioning data of the level corresponding to the current time sequence data fragment and map the intersection node of the current level and the time sequence data fragment according to the visual data range in the mapping process, ensure the accuracy, the completeness and the efficiency of the data mapping.
Description
Technical Field
The invention relates to the technical field of image data processing, in particular to a time sequence data visualization method, device and medium for a display system.
Background
With the development of technology, better experience can be brought to audiences more intuitively by displaying data, and data visualization is a process of converting a large amount of data into a visual form, so that the data is converted into a two-dimensional image or a three-dimensional image, virtual display and digital display of display content are realized, and the characteristics and the relations of the data can be intuitively displayed by the data visualization, but the data visualization may have misleading property. If the visual representation is not accurate enough or the data processing process is problematic, the generated image and the data have differences, the data visualization is only a tool for data analysis, the depth data analysis cannot be replaced, and some important details of the data can be lost by the excessively simplified visualization. Therefore, when data visualization is performed, attention is required to be paid to limitations and application scenes of the data visualization, and accuracy and effectiveness of the data visualization are ensured. The defects of the prior art are mainly that when the data volume needing to be visualized becomes large, the existing visualization scheme is difficult to complete data positioning in a short time, high-resolution images cannot be loaded at one time, the quality of the visualized images is poor, interpretation of the data can be unclear, the problems of data loss and large data conversion deviation exist, and the display effect of the display is affected.
Disclosure of Invention
The invention aims to provide a time sequence data visualization method, equipment and medium for a display system, which are used for guaranteeing the sequence correctness of data in the conversion process by extracting time sequence data segment characteristics of time sequence data, preventing the data from being lost, constructing a multidimensional visualization multi-resolution model by locating the time sequence data, extracting the visual data by the multidimensional visualization multi-resolution model, carrying out visualization processing on a large amount of data, loading the high-resolution image at one time and improving the image precision after the data visualization.
The invention is realized by the following technical scheme:
The first aspect of the present invention provides a time series data visualization method for a display system, comprising the following specific steps:
acquiring target visual data, and extracting time sequence data segment characteristics of time sequence data;
Positioning time sequence data based on time sequence data segment characteristics, and constructing a multidimensional visual multi-resolution model;
calculating a multi-resolution level corresponding to the mapping of the time sequence data fragments, and setting a multi-dimensional visualized multi-resolution model level according to the size of the time sequence data fragments;
calculating a visual data range, and searching positioning data of a corresponding level of the current time sequence data segment according to the visual data range;
comparing the time sequence data segment with the node range, and determining an intersecting node of the current level and the time sequence data segment;
Mapping the time sequence data fragments into a multi-dimensional visual multi-resolution model according to the positioning data of the corresponding hierarchy and the intersecting nodes;
Extracting image mapping characteristics of time sequence data fragments, and extracting corresponding visual data from the multi-dimensional visual multi-resolution model;
The corresponding visual data is rendered into a visual image.
According to the method, sequential data fragment characteristics are extracted from the sequential data, so that the sequence correctness of the data in the conversion process is ensured, the data loss is prevented, a multi-dimensional visual multi-resolution model is constructed by positioning the sequential data, meanwhile, a multi-resolution level corresponding to the mapping of the sequential data fragments is calculated, and the multi-dimensional visual multi-resolution model level is set according to the size of the sequential data fragments; and mapping the time sequence data segment into a multi-dimensional visual multi-resolution model, improving the accuracy of data mapping, in the mapping process, searching positioning data of a level corresponding to the current time sequence data segment according to the visual data range by calculating the visual data range, searching intersecting nodes of the current level of the multi-dimensional visual multi-resolution model and the time sequence data segment, mapping, ensuring the accuracy, completeness and efficiency of data mapping, extracting visual data through the multi-dimensional visual multi-resolution model, carrying out visual processing on a large data volume, loading a high-resolution image at one time, and improving the image accuracy after data visualization.
Further, the time sequence data extracting the time sequence data segment features specifically includes:
acquiring target visual data, performing hierarchical clustering on the target visual data, and integrating time sequence data under the same cluster according to a clustering result;
and dividing time sequence data according to the clusters to obtain time sequence data fragments, and extracting characteristics of the time sequence data fragments.
Further, the positioning the time series data based on the time series data segment features specifically includes:
Positioning the time sequence data according to random sampling, and dividing the time sequence data according to positioning to obtain a data block;
And constructing a single multi-dimensional visualized multi-resolution model of each data block in parallel, and merging the single multi-dimensional visualized multi-resolution models of each data block based on the time sequence data segment characteristics to obtain the multi-dimensional visualized multi-resolution model.
Further, the dividing the time sequence data according to the positioning to obtain the data block specifically includes:
positioning the time sequence data according to random sampling to obtain positioning data;
Dividing a plurality of equal areas according to the number of positioning points of the positioning data and the positioning space;
counting the number of locating points contained in each area;
If the number of locating points in the region is within the threshold value range, judging that the region can be a data block.
Further, the process of mapping the time sequence data segment to the multidimensional visual multi-resolution model further comprises calculating a mapping error, and correcting the mapping error, and the specific steps comprise:
Acquiring an estimated value of a data error and an error in the data error estimation process, and obtaining uncertainty of the data error;
Acquiring uncertainty generated when the time sequence data segment corresponds to the hierarchy, uncertainty of the size of the data block and uncertainty of the time step, and obtaining uncertainty of a model error;
Obtaining a mapping error according to the uncertainty of the data error and the uncertainty of the model error;
And judging the convergence in the data calculation by adopting a control variable method, and correcting the mapping error by adopting a weighted least square method if the convergence condition is met.
Further, the extracting the image mapping feature of the time sequence data segment extracts corresponding visual data from the multidimensional visual multi-resolution model, and specifically includes:
According to the positioning data in the current level node, searching out a visible node;
Extracting image mapping characteristics of time sequence data fragments according to the position information of the nodes;
And acquiring node information according to the image mapping characteristics, obtaining the position and size information of the positioning data of the visual nodes in the hierarchical data file, and extracting the visual data.
Further, the extracting the image mapping feature of the time sequence data segment according to the position information of the node specifically includes:
marking the characteristic points corresponding to the time sequence data fragments, and displaying the characteristic points corresponding to the time sequence data fragments by using the scatter diagram;
and arranging the feature points according to the similarity degree to obtain the image mapping features of the time sequence data fragments.
Further, the rendering the corresponding visual data into a visual image specifically includes:
Calculating intensity values in a Gaussian point spread function pixel grid of the visual data by adopting Gaussian rendering;
And performing pseudo-color processing according to the intensity value in the pixel grid to obtain a visible image.
A second aspect of the invention provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing a time series data visualization method for a presentation system when executing the program.
A third aspect of the invention provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements a time series data visualization method for a presentation system.
Compared with the prior art, the invention has the following advantages and beneficial effects:
The sequential data segment feature extraction is carried out on the sequential data to ensure the sequence correctness of the data in the conversion process, prevent the data from losing, the multi-dimensional visual multi-resolution model is constructed by positioning the sequential data, the multi-resolution level corresponding to the mapping of the sequential data segment is calculated, and the multi-dimensional visual multi-resolution model level is set according to the size of the sequential data segment; and mapping the time sequence data segment into a multi-dimensional visual multi-resolution model, improving the accuracy of data mapping, in the mapping process, searching positioning data of a level corresponding to the current time sequence data segment according to the visual data range by calculating the visual data range, searching intersecting nodes of the current level of the multi-dimensional visual multi-resolution model and the time sequence data segment, mapping, ensuring the accuracy, completeness and efficiency of data mapping, extracting visual data through the multi-dimensional visual multi-resolution model, carrying out visual processing on a large data volume, loading a high-resolution image at one time, and improving the image accuracy after data visualization.
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In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the drawings that are needed in the examples will be briefly described below, it being understood that the following drawings only illustrate some examples of the present invention and therefore should not be considered as limiting the scope, and that other related drawings may be obtained from these drawings without inventive effort for a person skilled in the art. In the drawings:
Fig. 1 is a flow chart of a visualization method in an embodiment of the present invention.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present invention, the present invention will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present invention and the descriptions thereof are for illustrating the present invention only and are not to be construed as limiting the present invention.
As a possible embodiment, as shown in fig. 1, a first aspect of the present embodiment provides a time-series data visualization method for a display system, including the following specific steps: acquiring target visual data, and extracting time sequence data segment characteristics of time sequence data; positioning time sequence data based on time sequence data segment characteristics, and constructing a multidimensional visual multi-resolution model; calculating a multi-resolution level corresponding to the mapping of the time sequence data fragments, and setting a multi-dimensional visualized multi-resolution model level according to the size of the time sequence data fragments;
Calculating a visual data range, and searching positioning data of a corresponding level of the current time sequence data segment according to the visual data range; comparing the time sequence data segment with the node range, and determining an intersecting node of the current level and the time sequence data segment; mapping the time sequence data fragments into a multi-dimensional visual multi-resolution model according to the positioning data of the corresponding hierarchy and the intersecting nodes; extracting image mapping characteristics of time sequence data fragments, and extracting corresponding visual data from the multi-dimensional visual multi-resolution model; the corresponding visual data is rendered into a visual image. According to the embodiment, sequential data fragment characteristic extraction is carried out on the sequential data, so that the sequence correctness of the data in the conversion process is ensured, data loss is prevented, a multi-dimensional visual multi-resolution model is constructed by positioning the sequential data, meanwhile, a multi-resolution level corresponding to the mapping of the sequential data fragments is calculated, and the multi-dimensional visual multi-resolution model level is set according to the size of the sequential data fragments; and mapping the time sequence data segment into a multi-dimensional visual multi-resolution model, improving the accuracy of data mapping, in the mapping process, searching positioning data of a level corresponding to the current time sequence data segment according to the visual data range by calculating the visual data range, searching intersecting nodes of the current level of the multi-dimensional visual multi-resolution model and the time sequence data segment, mapping, ensuring the accuracy, completeness and efficiency of data mapping, extracting visual data through the multi-dimensional visual multi-resolution model, carrying out visual processing on a large data volume, loading a high-resolution image at one time, and improving the image accuracy after data visualization.
In some possible embodiments, the present embodiment determines, after selecting a time-series data set to be analyzed, representative time-series data for performing analysis of time-series data visualization according to a target and a task of the data to be converted, and generates a corresponding visualization result.
In some possible embodiments, performing time series data segment feature extraction on time series data specifically includes: acquiring target visual data, performing hierarchical clustering on the target visual data, and integrating time sequence data under the same cluster according to a clustering result; and dividing time sequence data according to the clusters to obtain time sequence data fragments, and extracting characteristics of the time sequence data fragments. Hierarchical clustering has visualization capability and universality, and can generate a nested hierarchical structure according to a similarity matrix between sequences, so that time sequence data under the same cluster are integrated according to a clustering result, and the characteristics of the time sequence data are displayed.
In some possible embodiments, obtaining the time-series data segment HIA includes: finding all maximum value points and minimum value points in time sequence data under the same cluster, fitting by using a cubic spline interpolation method to obtain an upper envelope line of the maximum value points and a lower envelope line of the minimum value points, taking an average value of the maximum value points and the minimum value points, picking out the average value from an original signal to obtain a new time sequence, replacing the original time sequence, obtaining an eigenmode function IMF when the obtained new sequence meets the requirement of an eigenmode function, separating a first eigenmode function from the original signal, taking the residual sequence as the new sequence, repeating the step of obtaining the new sequence until the eigenmode function cannot be screened out, circularly stopping, and obtaining the remainder of the last remaining original signal, namely a residual sequence, wherein the final original sequence is the sum of a plurality of eigenmode functions and the residual sequence.
In some possible embodiments, locating the time series data based on the time series data segment features specifically includes: positioning the time sequence data according to random sampling, and dividing the time sequence data according to positioning to obtain a data block; and constructing a single multi-dimensional visualized multi-resolution model of each data block in parallel, and merging the single multi-dimensional visualized multi-resolution models of each data block based on the time sequence data segment characteristics to obtain the multi-dimensional visualized multi-resolution model. Dividing time sequence data according to positioning to obtain a data block specifically comprises: carrying out batch processing on the time sequence data, dividing the time sequence data into data blocks in batches, positioning the time sequence data according to random sampling, and obtaining positioning data; setting an initial value according to the number of positioning points, constructing a multi-dimensional visual multi-resolution model according to the maximum accommodation amount of the data blocks by an exact hierarchy, and dividing a plurality of equal areas according to the number of positioning points of positioning data and a positioning space; counting the number of locating points contained in each area; if the number of locating points in the region is within the threshold value range, judging that the region can be a data block. The judgment rule is to judge whether the sum of the numbers of the positioning points is larger than a threshold value in the n multiplied by n adjacent areas, and if so, the n multiplied by n adjacent areas are regarded as data blocks, and then, the data blocks take the positions of the data blocks in the hierarchical structure as file names of the data blocks, so that the positioning and mapping of the subsequent time sequence data are convenient. Repeating the steps until all time sequence data are divided into data blocks. When a large data volume is segmented into data blocks, the n value of the data blocks needs to be set according to requirements, the data blocks need to be ensured to be small enough to be loaded into a memory at the same time, and meanwhile, the size of the data blocks is ensured not to influence the speed of the built multidimensional visual multi-resolution model to be slow. When the data blocks are built, nodes in the areas among the father node and the ancestor node are scheduled to ensure that the resolution of the image rendered by the child node is not reduced when the child node areas are visualized, and the data in the data blocks are completely stored in the quadtree hierarchy after the multi-resolution hierarchy of each data block is built.
In some possible embodiments, when the visual data is located on a large scale, that is, when a large amount of data is processed, the corresponding multi-resolution level is required to be acquired to determine the maximum value and the minimum value of the visual data range, so that mapping errors are generated in the process of mapping the time series data segment to the multi-dimensional visual multi-resolution model, and therefore, the mapping errors need to be calculated and corrected, and the specific steps include:
Acquiring an estimated value of a data error and an error in the data error estimation process, and obtaining uncertainty of the data error;
Acquiring uncertainty generated when the time sequence data segment corresponds to the hierarchy, uncertainty of the size of the data block and uncertainty of the time step, and obtaining uncertainty of a model error;
Obtaining a mapping error according to the uncertainty of the data error and the uncertainty of the model error;
And judging the convergence in the data calculation by adopting a control variable method, and correcting the mapping error by adopting a weighted least square method if the convergence condition is met.
In some possible embodiments, extracting image mapping features of the time series data segments, extracting corresponding visual data from the multi-dimensional visual multi-resolution model, specifically includes: according to the positioning data in the current level node, searching out a visible node;
Extracting image mapping characteristics of time sequence data fragments according to the position information of the nodes; and acquiring node information according to the image mapping characteristics, obtaining the position and size information of the positioning data of the visual nodes in the hierarchical data file, and extracting the visual data.
In some possible embodiments, extracting the image mapping feature of the time series data segment according to the position information of the node specifically includes: marking the characteristic points corresponding to the time sequence data fragments, and displaying the characteristic points corresponding to the time sequence data fragments by using the scatter diagram; and arranging the feature points according to the similarity degree to obtain the image mapping features of the time sequence data fragments.
In some possible embodiments, the image is a set of pixel values, so the key of rendering is to convert anchor point information into pixel values, gaussian rendering first converts the position, positioning accuracy, peak intensity information of each anchor point into an array of intensity values centered on the anchor point position, rendering the corresponding visual data into a visual image specifically includes: calculating intensity values in a Gaussian point spread function pixel grid of the visual data by adopting Gaussian rendering; and performing pseudo-color processing according to the intensity value in the pixel grid to obtain a visible image.
A second aspect of the present embodiment provides an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing a time series data visualization method for a presentation system when executing the program.
A third aspect of the present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a time-series data visualization method for a presentation system.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (9)
1. A time series data visualization method for a display system, comprising the following specific steps:
acquiring target visual data, and extracting time sequence data segment characteristics of time sequence data;
positioning time sequence data based on time sequence data segment characteristics, and constructing a multidimensional visual multi-resolution model, wherein the method comprises the following steps:
Positioning the time sequence data according to random sampling, and dividing the time sequence data according to positioning to obtain a data block;
Constructing a single multi-dimensional visualized multi-resolution model of each data block in parallel, and merging the single multi-dimensional visualized multi-resolution models of each data block based on the time sequence data segment characteristics to obtain a multi-dimensional visualized multi-resolution model;
calculating a multi-resolution level corresponding to the mapping of the time sequence data fragments, and setting a multi-dimensional visualized multi-resolution model level according to the size of the time sequence data fragments;
calculating a visual data range, and searching positioning data of a corresponding level of the current time sequence data segment according to the visual data range;
comparing the time sequence data segment with the node range, and determining an intersecting node of the current level and the time sequence data segment;
Mapping the time sequence data fragments into a multi-dimensional visual multi-resolution model according to the positioning data of the corresponding hierarchy and the intersecting nodes;
Extracting image mapping characteristics of time sequence data fragments, and extracting corresponding visual data from the multi-dimensional visual multi-resolution model;
The corresponding visual data is rendered into a visual image.
2. The method for visualizing time series data for a display system as in claim 1, wherein said time series data for time series data segment feature extraction specifically comprises:
acquiring target visual data, performing hierarchical clustering on the target visual data, and integrating time sequence data under the same cluster according to a clustering result;
and dividing time sequence data according to the clusters to obtain time sequence data fragments, and extracting characteristics of the time sequence data fragments.
3. The method for visualizing data in time series for a display system as in claim 1, wherein said dividing time series data according to positioning to obtain data blocks, specifically comprises:
positioning the time sequence data according to random sampling to obtain positioning data;
Dividing a plurality of equal areas according to the number of positioning points of the positioning data and the positioning space;
counting the number of locating points contained in each area;
If the number of locating points in the region is within the threshold value range, judging that the region can be a data block.
4. A method for visualizing time series data for a presentation system as in claim 3, wherein said mapping of time series data segments into a multi-dimensional visualization multi-resolution model further comprises calculating a mapping error, and correcting the mapping error, the steps comprising:
Acquiring an estimated value of a data error and an error in the data error estimation process, and obtaining uncertainty of the data error;
Acquiring uncertainty generated when the time sequence data segment corresponds to the hierarchy, uncertainty of the size of the data block and uncertainty of the time step, and obtaining uncertainty of a model error;
Obtaining a mapping error according to the uncertainty of the data error and the uncertainty of the model error;
And judging the convergence in the data calculation by adopting a control variable method, and correcting the mapping error by adopting a weighted least square method if the convergence condition is met.
5. A time series data visualization method for a presentation system as claimed in claim 3 wherein the extracting image mapping features of the time series data segments extracts corresponding visual data from a multi-dimensional visual multi-resolution model, comprising:
According to the positioning data in the current level node, searching out a visible node;
Extracting image mapping characteristics of time sequence data fragments according to the position information of the nodes;
And acquiring node information according to the image mapping characteristics, obtaining the position and size information of the positioning data of the visual nodes in the hierarchical data file, and extracting the visual data.
6. The method for visualizing the temporal data of the presentation system of claim 5, wherein said extracting the image mapping characteristics of the temporal data segment based on the location information of the nodes, in particular, comprises:
marking the characteristic points corresponding to the time sequence data fragments, and displaying the characteristic points corresponding to the time sequence data fragments by using the scatter diagram;
and arranging the feature points according to the similarity degree to obtain the image mapping features of the time sequence data fragments.
7. The method for visualizing time-series data for a presentation system as in claim 1, wherein said rendering corresponding visual data into a visual image, in particular comprises:
Calculating intensity values in a Gaussian point spread function pixel grid of the visual data by adopting Gaussian rendering;
And performing pseudo-color processing according to the intensity value in the pixel grid to obtain a visible image.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements a time series data visualization method for a presentation system as claimed in any one of claims 1 to 7 when the program is executed by the processor.
9. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a time series data visualization method for a presentation system as claimed in any one of claims 1 to 7.
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