Adaptive tubular visualization method, system, terminal and medium for nonlinear data

Abstract

The application discloses a kind of self-adapting tubular visualization methods, systems, terminal and medium for nonlinear data, the method comprises: obtaining the data set of target object, data distribution characteristic density curve is generated according to data set, and is inverted processing, obtains initial pipe half profile;Initial pipe half profile is segmented and handled to obtain multiple pipe sections, and all pipe sections are subjected to equal-area transformation to obtain multiple target pipe sections;All target pipe sections are subjected to mirror image processing to obtain target pipe shape, and data set is visualized and exhibited according to target pipe shape to obtain data display result.The application automatically adjusts the shape of tubular graph by analyzing data distribution characteristics, shrinks pipe diameter in data dense area to expand display space, expands pipe diameter in data sparse area to save display space, generates tubular profile using data density curve, and realizes intuitive visual expression of data by equal-area segmented transformation.

Description

Technical Field

[0001] This invention relates to the field of data visualization technology, and in particular to an adaptive tubular visualization method, system, terminal, and computer-readable storage medium for nonlinear data. Background Technology

[0002] Data visualization plays an increasingly important role in modern information processing and analysis. Bar charts, as one of the most basic and widely used data visualization methods, encode and compare quantitative values ​​using rectangles of fixed width and their height. Research has found that aligned bar charts are easier to perceive than stacked bar charts, and that estimation accuracy decreases as the distance between bars increases.

[0003] However, with increasing data complexity, traditional bar charts have revealed several key problems in practical applications. When data is unevenly distributed—for example, when data is spread over a large range of values ​​or concentrated in a specific interval—traditional bar charts struggle to allocate limited display space reasonably, making spatial allocation and comparison of similar values ​​difficult. To address this issue, researchers have attempted to use nonlinear mapping to adaptively compress or stretch data. In cases of dispersed data distribution, nonlinear mapping can bring widely separated values ​​closer together, thus accurately representing the data without excessively stretching the chart. Similarly, for data concentrated in a small range, nonlinear mapping can expand the values, preventing excessive data clustering. However, nonlinear mapping, especially when exceeding commonly used logarithmic scales, is often difficult to understand intuitively, and data reading relies excessively on the coordinate system.

[0004] Therefore, existing technologies still need to be improved and developed. Summary of the Invention

[0005] The main objective of this invention is to provide an adaptive tubular visualization method, system, terminal, and medium for nonlinear data. This invention aims to solve the problems of existing technologies being unable to dynamically adjust the visualization effect according to the actual data distribution, and when processing data with a large numerical range and uneven distribution, either requiring too much vertical space or compressing the data, resulting in poor visual contrast.

[0006] To achieve the above objectives, the present invention provides an adaptive tubular visualization method for nonlinear data, which includes the following steps:

[0007] Obtain the dataset of the target object, generate a data distribution feature density curve based on the dataset, and invert the data distribution feature density curve to obtain the initial pipeline half-contour.

[0008] The initial pipe half-contour is segmented to obtain multiple pipe segments, and all pipe segments are subjected to equal area transformation to obtain multiple target pipe segments.

[0009] Mirroring is performed on all the target pipe segments to obtain the target pipe shape, and the dataset is visualized based on the target pipe shape to obtain the data display result.

[0010] Optionally, the adaptive tubular visualization method for nonlinear data, wherein acquiring the dataset of the target object, generating a data distribution feature density curve based on the dataset, and inverting the data distribution feature density curve to obtain an initial pipe half-contour, specifically includes:

[0011] Obtain the data visualization requirements of the target user, process the object of the data visualization requirements to obtain the target object, and obtain the dataset of the target object;

[0012] Clustering is performed on the data points of the dataset to obtain clustering results. Interval density values ​​are assigned to the data points based on the clustering results to obtain the assignment results.

[0013] Interpolation is performed on the density range of the assigned result to obtain the data distribution characteristic density curve, and the data distribution characteristic density curve is inverted to obtain the initial pipeline half profile.

[0014] Optionally, the adaptive tubular visualization method for nonlinear data, wherein the step of inverting the data distribution feature density curve to obtain an initial pipe half-profile, specifically includes:

[0015] Perform curve structure analysis on the data distribution characteristic density curve to obtain analysis results, and set the center line of the data distribution characteristic density curve based on the analysis results;

[0016] The data distribution characteristic density curve is inverted based on the centerline to obtain the constricted neck portion and the expanded abdomen portion, and the initial pipe half-profile is obtained based on the constricted neck portion and the expanded abdomen portion.

[0017] Optionally, the adaptive tubular visualization method for nonlinear data, wherein segmenting the initial pipe half-contour to obtain multiple pipe segments, and performing an equal-area transformation on all pipe segments to obtain multiple target pipe segments, specifically includes:

[0018] Obtain the segmentation requirements of the target user, and perform equal-height segmentation on the initial pipe half-profile according to the segmentation requirements to obtain multiple pipe segments;

[0019] The area of ​​all the pipe segments is transformed into equal areas according to the area transformation formula to obtain multiple target pipe segments.

[0020] Optionally, in the aforementioned adaptive tubular visualization method for nonlinear data, the area transformation formula is:

[0021] ;

[0022] in, For the first The height of the pipe section For weight parameters, This represents the total number of pipe sections. For the first The width of the pipe section.

[0023] Optionally, the adaptive tubular visualization method for nonlinear data, wherein mirroring all the target pipe segments to obtain the target pipe shape, and visualizing the dataset according to the target pipe shape to obtain the data display result, specifically includes:

[0024] All the target pipe segments are pipe-formed to obtain a target pipe half-outline, and the target pipe half-outline is mirrored to obtain the target pipe shape;

[0025] Based on the target pipe shape, the data-dense region of the dataset is formed into a narrow neck portion, and the data-dense region of the dataset is formed into a wide belly portion according to the target pipe shape;

[0026] The dataset is visualized based on the narrow neck portion and the wide belly portion to obtain the data display results.

[0027] Optionally, the adaptive tubular visualization method for nonlinear data further includes, after mirroring all the target pipe segments to obtain the target pipe shape, and visualizing the dataset according to the target pipe shape to obtain the data display result, the method further includes:

[0028] Obtain the pipe turning points in the data display results, mark the pipe turning points with scales to obtain the marking results, and optimize the data display results based on the marking results to obtain the target data display results.

[0029] Optionally, the adaptive tubular visualization method for nonlinear data, wherein the adaptive tubular visualization system for nonlinear data includes:

[0030] The pipeline contour generation module is used to acquire the dataset of the target object, generate a data distribution feature density curve based on the dataset, and invert the data distribution feature density curve to obtain the initial pipeline half contour.

[0031] The pipeline contour processing module is used to segment the initial pipeline half-contour to obtain multiple pipeline segments, and to perform an equal area transformation on all the pipeline segments to obtain multiple target pipeline segments.

[0032] The data visualization module is used to mirror all the target pipe segments to obtain the target pipe shape, and then visualize the dataset according to the target pipe shape to obtain the data display result.

[0033] Furthermore, to achieve the above objectives, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and an adaptive tubular visualization program for nonlinear data stored in the memory and executable on the processor, wherein when the adaptive tubular visualization program for nonlinear data is executed by the processor, it implements the steps of the adaptive tubular visualization method for nonlinear data as described above.

[0034] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores an adaptive tubular visualization program for nonlinear data, which, when executed by a processor, implements the steps of the adaptive tubular visualization method for nonlinear data as described above.

[0035] In this invention, a dataset of the target object is acquired, a data distribution feature density curve is generated based on the dataset, and the data distribution feature density curve is inverted to obtain an initial pipe semi-outline. The initial pipe semi-outline is segmented to obtain multiple pipe segments, and all pipe segments are subjected to equal area transformation to obtain multiple target pipe segments. All target pipe segments are mirrored to obtain the target pipe shape, and the dataset is visualized based on the target pipe shape to obtain the data display result. This invention automatically adjusts the shape of the tubular graphic by analyzing data distribution characteristics, shrinking the pipe diameter in data-dense areas to expand the display space, and expanding the pipe diameter in data-sparse areas to save display space. It uses the data density curve to generate the tubular outline and achieves intuitive visualization of the data through equal area segmentation transformation. Attached Figure Description

[0036] Figure 1 This is a flowchart of a preferred embodiment of the adaptive tubular visualization method for nonlinear data of the present invention;

[0037] Figure 2This is a schematic diagram of the overall process of the adaptive tubular visualization method for nonlinear data according to the present invention;

[0038] Figure 3 This is a structural diagram of a preferred embodiment of the adaptive tubular visualization system for nonlinear data of the present invention;

[0039] Figure 4 This is a schematic diagram of the operating environment of a preferred embodiment of the terminal of the present invention. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this invention clearer and more explicit, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0041] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0042] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0043] The adaptive tubular visualization method for nonlinear data described in the preferred embodiment of the present invention, such as... Figure 1 As shown, the adaptive tubular visualization method for nonlinear data includes the following steps:

[0044] Step S10: Obtain the dataset of the target object, generate a data distribution feature density curve based on the dataset, and invert the data distribution feature density curve to obtain the initial pipeline half-profile.

[0045] Step S10 includes:

[0046] Step S11: Obtain the data visualization requirements of the target user, process the object of the data visualization requirements to obtain the target object, and obtain the dataset of the target object;

[0047] Step S12: Perform clustering processing on the data points of the dataset to obtain clustering results, and assign interval density values ​​to the data points according to the clustering results to obtain the assignment results;

[0048] Step S13: Interpolate the density range of the assignment result to obtain the data distribution characteristic density curve, and invert the data distribution characteristic density curve to obtain the initial pipeline half profile.

[0049] Specifically, in this embodiment of the invention, to address the problem that existing technologies cannot dynamically adjust visualization effects according to actual data distribution, and that when processing data with a large numerical range and uneven distribution, either excessive vertical space is required or data compression leads to poor visual contrast, an adaptive tubular visualization method for nonlinear data is proposed. The corresponding processing flow is as follows: Figure 2 As shown, firstly, the data visualization needs of the target user are obtained, and the object of the data visualization needs is processed to obtain the target object (e.g., the financial situation of Company A in the past year), and the dataset of the target object is obtained; a density curve is generated based on the distribution characteristics of the input dataset, and the initial pipeline half-profile is obtained by inverting the density curve, specifically, as follows: Figure 2 Parts a to c of the diagram involve acquiring the dataset of the target object and generating a density curve reflecting the data distribution characteristics, i.e., a data distribution characteristic density curve. The curve generation methods include non-parametric methods such as histograms or kernel density estimation, and clustering-based methods. The advantage of non-parametric methods is that they do not require assumptions about the underlying data distribution and can directly estimate density from the data. However, they also have limitations; when estimating at a fine granular level, they may become overly complex, leading to unnecessary visual complexity. If the neighborhood definition is too broad, the ability to represent density details will be weakened. The advantage of clustering-based methods is that they can successfully capture the clustering characteristics of data points within high-density intervals, maintaining visual simplicity while accurately representing density.

[0050] The invention employs a clustering-based method, which achieves a better balance between visual simplicity and density representation. The specific curve generation process involves: clustering the data points of the dataset to obtain clustering results; assigning interval density values ​​to the data points based on the clustering results, i.e., assigning larger density values ​​to clustered intervals and smaller density values ​​to non-clustered intervals; then, interpolating the density intervals of the assigned results to obtain a data distribution characteristic density curve. This curve describes the density of data distribution in different intervals, where dense data intervals correspond to larger density values ​​and sparse data intervals correspond to smaller density values. Finally, the data distribution characteristic density curve is inverted relative to the centerline to form an initial pipe semi-contour with a constricted neck and an expanded belly. Specifically, curve structure analysis is performed on the data distribution characteristic density curve to obtain analysis results, and the centerline of the curve is set based on these results; the curve is then inverted relative to the centerline to obtain the constricted neck and expanded belly portions, and the initial pipe semi-contour is obtained from these portions.

[0051] Step S20: Segment the initial pipe half-contour to obtain multiple pipe segments, and perform an equal area transformation on all the pipe segments to obtain multiple target pipe segments.

[0052] Step S20 includes:

[0053] Step S21: Obtain the segmentation requirements of the target user, and perform equal-height segmentation on the initial pipe half-contour according to the segmentation requirements to obtain multiple pipe segments;

[0054] Step S22: Perform an equal area transformation on all the pipe segments according to the area transformation formula to obtain multiple target pipe segments.

[0055] Specifically, after obtaining the initial pipe half-profile, it is necessary to divide the initial pipe half-profile into segments of equal height and perform an equal area transformation, adjusting the height while maintaining the original width. Specifically, as follows: Figure 2In the section from d to e, the segmentation requirements of the target user are obtained. Based on these requirements, the initial pipe half-contour is segmented at equal heights to obtain multiple pipe segments. Specifically, the initial pipe half-contour is divided vertically into K horizontal segments of equal height, each corresponding to an equal data interval. The number of segments K can be adjusted according to actual application requirements to balance display effect and computing performance. Then, these pipe segments undergo equal area transformation. While maintaining the original width of each segment, their height is adjusted to make the area of ​​each segment equal. Specifically, all the pipe segments are transformed using the area transformation formula to obtain multiple target pipe segments. The corresponding area transformation formula is: ;in, For the first The height of the pipe section For weight parameters, This represents the total number of pipe sections. For the first The width of the pipe segment is changed, and this transformation causes the narrow segment (corresponding to the data-dense area) to be stretched in the vertical direction, while the wide segment (corresponding to the data-sparse area) remains relatively short.

[0056] Step S30: Mirror all the target pipe segments to obtain the target pipe shape, and visualize the dataset according to the target pipe shape to obtain the data display result.

[0057] Step S30 includes:

[0058] Step S31: Form all the target pipe segments into pipes to obtain a target pipe half-outline, and mirror the target pipe half-outline to obtain the target pipe shape;

[0059] Step S32: According to the target pipe shape, form a narrow neck portion of the data-dense region of the dataset, and form a wide belly portion of the data-dense region of the dataset according to the target pipe shape;

[0060] Step S33: Visualize the dataset based on the narrow neck portion and the wide belly portion to obtain the data display result.

[0061] Specifically, in this embodiment of the invention, after obtaining the target pipe segment, it is necessary to form the final pipe. This final pipe is formed through mirroring, specifically as follows: Figure 2In part f, all the target pipe segments are formed into pipes to obtain a target pipe half-outline, and the target pipe half-outline is mirrored to obtain the target pipe shape. Based on the target pipe shape, the data-dense regions of the dataset are formed into narrow neck sections, and the data-dense regions of the dataset are formed into wide belly sections. The narrow neck sections in the data-dense regions provide more vertical display space, facilitating comparison of similar values; while the wide belly sections in the data-sparse regions effectively save vertical space. Then, the dataset is visualized based on the narrow neck sections and the wide belly sections to obtain the data display results. This invention achieves an effective mapping from data distribution characteristics to visualization forms, ensuring the accuracy of data representation and the efficiency of space utilization. Furthermore, the adaptive shape design based on data distribution is particularly suitable for processing the visualization needs of datasets with uneven distribution.

[0062] Furthermore, in this embodiment of the invention, in order to enhance the readability and usability of the pipeline, localized marking is implemented. Specifically, the pipeline turning points in the data display results are obtained, the pipeline turning points are marked with scales to obtain marking results, and the data display results are optimized based on the marking results to obtain the target data display results.

[0063] This invention significantly improves the accuracy of data visualization, especially in tasks involving comparison of similar values. It also performs exceptionally well when dealing with densely distributed data, exhibiting higher accuracy in reading values ​​when the data is concentrated. This is particularly valuable for scenarios requiring precise analysis of dense data regions. Furthermore, it employs an intuitive visual metaphor based on a liquid container, enabling users to understand and interpret the data more naturally.

[0064] Furthermore, such as Figure 3 As shown, based on the above-described adaptive tubular visualization method for nonlinear data, this invention also provides an adaptive tubular visualization system for nonlinear data, wherein the adaptive tubular visualization system for nonlinear data includes:

[0065] The pipeline contour generation module 51 is used to acquire the dataset of the target object, generate a data distribution feature density curve based on the dataset, and reverse the data distribution feature density curve to obtain an initial pipeline half contour.

[0066] The pipe contour processing module 52 is used to segment the initial pipe half contour to obtain multiple pipe segments, and to perform an equal area transformation on all the pipe segments to obtain multiple target pipe segments.

[0067] The data visualization module 53 is used to mirror all the target pipe segments to obtain the target pipe shape, and to visualize the dataset according to the target pipe shape to obtain the data display result.

[0068] Furthermore, such as Figure 4 As shown, based on the above-mentioned adaptive tubular visualization method for nonlinear data, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 4 Only some of the terminal components are shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0069] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory. In other embodiments, the memory 20 may be an external storage device of the terminal, such as a plug-in hard disk, smart media card (SMC), secure digital card (SD), flash card, etc. Further, the memory 20 may include both internal and external storage devices. The memory 20 is used to store application software and various types of data installed on the terminal, such as program code installed on the terminal. The memory 20 can also be used to temporarily store data that has been output or will be output. In one embodiment, the memory 20 stores an adaptive tubular visualization program 40 for nonlinear data, which can be executed by the processor 10 to implement the adaptive tubular visualization method for nonlinear data in this application.

[0070] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, used to run program code stored in the memory 20 or process data, such as executing the adaptive tubular visualization method for nonlinear data.

[0071] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 30 is used to display information on the terminal and to display a visual user interface. The components of the terminal communicate with each other via a system bus.

[0072] In one embodiment, when processor 10 executes an adaptive tubular visualization program 40 for nonlinear data in memory 20, the following steps are performed:

[0073] Obtain the dataset of the target object, generate a data distribution feature density curve based on the dataset, and invert the data distribution feature density curve to obtain the initial pipeline half-contour.

[0074] The initial pipe half-contour is segmented to obtain multiple pipe segments, and all pipe segments are subjected to equal area transformation to obtain multiple target pipe segments.

[0075] Mirroring is performed on all the target pipe segments to obtain the target pipe shape, and the dataset is visualized based on the target pipe shape to obtain the data display result.

[0076] The step of acquiring the dataset of the target object, generating a data distribution feature density curve based on the dataset, and inverting the data distribution feature density curve to obtain an initial pipeline half-profile specifically includes:

[0077] Obtain the data visualization requirements of the target user, process the object of the data visualization requirements to obtain the target object, and obtain the dataset of the target object;

[0078] Clustering is performed on the data points of the dataset to obtain clustering results. Interval density values ​​are assigned to the data points based on the clustering results to obtain the assignment results.

[0079] Interpolation is performed on the density range of the assigned result to obtain the data distribution characteristic density curve, and the data distribution characteristic density curve is inverted to obtain the initial pipeline half profile.

[0080] Specifically, the step of inverting the data distribution feature density curve to obtain the initial pipeline half-profile includes:

[0081] Perform curve structure analysis on the data distribution characteristic density curve to obtain analysis results, and set the center line of the data distribution characteristic density curve based on the analysis results;

[0082] The data distribution characteristic density curve is inverted based on the centerline to obtain the constricted neck portion and the expanded abdomen portion, and the initial pipe half-profile is obtained based on the constricted neck portion and the expanded abdomen portion.

[0083] Specifically, the process of segmenting the initial pipe half-contour to obtain multiple pipe segments, and performing an equal-area transformation on all pipe segments to obtain multiple target pipe segments, includes:

[0084] Obtain the segmentation requirements of the target user, and perform equal-height segmentation on the initial pipe half-profile according to the segmentation requirements to obtain multiple pipe segments;

[0085] The area of ​​all the pipe segments is transformed into equal areas according to the area transformation formula to obtain multiple target pipe segments.

[0086] The area transformation formula is as follows:

[0087] ;

[0088] in, For the first The height of the pipe section For weight parameters, This represents the total number of pipe sections. For the first The width of the pipe section.

[0089] Specifically, the process of mirroring all the target pipe segments to obtain the target pipe shape, and then visualizing the dataset based on the target pipe shape to obtain the data display result, includes:

[0090] All the target pipe segments are pipe-formed to obtain a target pipe half-outline, and the target pipe half-outline is mirrored to obtain the target pipe shape;

[0091] Based on the target pipe shape, the data-dense region of the dataset is formed into a narrow neck portion, and the data-dense region of the dataset is formed into a wide belly portion according to the target pipe shape;

[0092] The dataset is visualized based on the narrow neck portion and the wide belly portion to obtain the data display results.

[0093] The process includes mirroring all target pipe segments to obtain the target pipe shape, visualizing the dataset based on the target pipe shape to obtain the data display result, and then further including:

[0094] Obtain the pipe turning points in the data display results, mark the pipe turning points with scales to obtain the marking results, and optimize the data display results based on the marking results to obtain the target data display results.

[0095] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores an adaptive tubular visualization program for nonlinear data, and the adaptive tubular visualization program for nonlinear data, when executed by a processor, implements the steps of the adaptive tubular visualization method for nonlinear data as described above.

[0096] In summary, this invention provides an adaptive tubular visualization method, system, terminal, and medium for nonlinear data. The method includes: acquiring a dataset of the target object; generating a data distribution characteristic density curve based on the dataset; inverting the data distribution characteristic density curve to obtain an initial pipe semi-contour; segmenting the initial pipe semi-contour to obtain multiple pipe segments; performing an equal-area transformation on all pipe segments to obtain multiple target pipe segments; mirroring all target pipe segments to obtain target pipe shapes; and visualizing the dataset based on the target pipe shapes to obtain data display results. This invention automatically adjusts the shape of the tubular graphic by analyzing data distribution characteristics, shrinking the pipe diameter in data-dense areas to expand the display space, and expanding the pipe diameter in data-sparse areas to save display space. It utilizes the data density curve to generate the tubular contour and achieves intuitive visualization of the data through equal-area segmented transformation.

[0097] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal that includes that element.

[0098] Of course, those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.). The program can be stored in a computer-readable storage medium, and when executed, it can include the processes described in the above method embodiments. The computer-readable storage medium can be a memory, magnetic disk, optical disk, etc.

[0099] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. An adaptive tubular visualization method for nonlinear data, characterized in that, The adaptive tubular visualization method for nonlinear data includes: Obtain the dataset of the target object, generate a data distribution feature density curve based on the dataset, and invert the data distribution feature density curve to obtain the initial pipeline half-contour. The process of acquiring the dataset of the target object, generating a data distribution feature density curve based on the dataset, and inverting the data distribution feature density curve to obtain an initial pipeline half-profile specifically includes: Obtain the data visualization requirements of the target user, process the object of the data visualization requirements to obtain the target object, and obtain the dataset of the target object; Clustering is performed on the data points of the dataset to obtain clustering results. Interval density values ​​are assigned to the data points based on the clustering results to obtain the assignment results. Interpolate the density range of the assigned result to obtain the data distribution characteristic density curve, and invert the data distribution characteristic density curve to obtain the initial pipeline half profile. The initial pipe half-contour is segmented to obtain multiple pipe segments, and all pipe segments are subjected to equal area transformation to obtain multiple target pipe segments. The process of segmenting the initial pipe half-profile to obtain multiple pipe segments, and then performing an equal-area transformation on all the pipe segments to obtain multiple target pipe segments, specifically includes: Obtain the segmentation requirements of the target user, and perform equal-height segmentation on the initial pipe half-profile according to the segmentation requirements to obtain multiple pipe segments; Based on the area transformation formula, all the pipe segments are transformed into equal areas to obtain multiple target pipe segments; Mirroring all the target pipe segments yields the target pipe shape, and the dataset is visualized based on the target pipe shape to obtain the data display result.

2. The adaptive tubular visualization method for nonlinear data according to claim 1, characterized in that, The process of inverting the data distribution feature density curve to obtain the initial pipe half-profile specifically includes: Perform curve structure analysis on the data distribution characteristic density curve to obtain analysis results, and set the center line of the data distribution characteristic density curve based on the analysis results; The data distribution characteristic density curve is inverted based on the centerline to obtain the constricted neck portion and the expanded abdomen portion, and the initial pipe half-profile is obtained based on the constricted neck portion and the expanded abdomen portion.

3. The adaptive tubular visualization method for nonlinear data according to claim 1, characterized in that, The area transformation formula is: ； in, For the first The height of the pipe section For weight parameters, This represents the total number of pipe sections. For the first The width of the pipe section.

4. The adaptive tubular visualization method for nonlinear data according to claim 1, characterized in that, The process of mirroring all the target pipe segments to obtain the target pipe shape, and then visualizing the dataset based on the target pipe shape to obtain the data display result, specifically includes: All the target pipe segments are pipe-formed to obtain a target pipe half-outline, and the target pipe half-outline is mirrored to obtain the target pipe shape; Based on the target pipe shape, the data-dense region of the dataset is formed into a narrow neck portion, and the data-dense region of the dataset is formed into a wide belly portion according to the target pipe shape; The dataset is visualized based on the narrow neck portion and the wide belly portion to obtain the data display results.

5. The adaptive tubular visualization method for nonlinear data according to claim 1, characterized in that, The process involves mirroring all the target pipe segments to obtain the target pipe shape, and then visualizing the dataset based on the target pipe shape to obtain the data display result. The process further includes: Obtain the pipe turning points in the data display results, mark the pipe turning points with scales to obtain the marking results, and optimize the data display results based on the marking results to obtain the target data display results.

6. An adaptive tubular visualization system for nonlinear data, characterized in that, The adaptive tubular visualization system for nonlinear data is used to implement the adaptive tubular visualization method for nonlinear data according to any one of claims 1-5, wherein the adaptive tubular visualization system for nonlinear data comprises: The pipeline contour generation module is used to acquire the dataset of the target object, generate a data distribution feature density curve based on the dataset, and invert the data distribution feature density curve to obtain the initial pipeline half contour. The pipeline contour processing module is used to segment the initial pipeline half-contour to obtain multiple pipeline segments, and to perform an equal area transformation on all the pipeline segments to obtain multiple target pipeline segments. The data visualization module is used to mirror all the target pipe segments to obtain the target pipe shape, and then visualize the dataset according to the target pipe shape to obtain the data display result.

7. A terminal, characterized in that, The terminal includes: a memory, a processor, and an adaptive tubular visualization program for nonlinear data stored in the memory and executable on the processor. When executed by the processor, the adaptive tubular visualization program for nonlinear data implements the steps of the adaptive tubular visualization method for nonlinear data as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores an adaptive tubular visualization program for nonlinear data, which, when executed by a processor, implements the steps of the adaptive tubular visualization method for nonlinear data as described in any one of claims 1-5.

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