Geographic time sequence visualization method, electronic equipment and medium

Abstract

The invention discloses a geographic time sequence visualization method, electronic equipment and a medium, and the method comprises the steps: obtaining geographic time sequence data, and constructing a space-time cube; converting the geographic time sequence data into space-time volume data by using a Kriging interpolation technology, and performing time smoothing processing on the space-time volume data by using a smoothing technology; and visualizing the space-time volume data by adopting a volume rendering technology. According to the method, discrete geographic time series data are converted into continuous volume data by adopting a Kriging interpolation technology, so that the data meet the characteristic requirements of a space-time cube; meanwhile, the discontinuity of the data in the time dimension is considered, a moving average method is introduced, and the noise of the volume data set in the time dimension is remarkably reduced. In addition, by introducing a volume rendering technology, the problem of shielding in three-dimensional visualization is reduced, and the applicability of the space-time cube to large-scale geographic time series data is re-evaluated. The method can be widely applied to the field of data visualization.

Description

Technical Field

[0001] The present invention relates to the field of data visualization, and in particular to a visualization method, electronic equipment and medium for geographic time series. Background Art

[0002] With the rapid development of sensor technology, large-scale geographic time series data has become increasingly common. Geographic time series refers to time series data related to a specific geographic location. Analyzing this form of data can discover potential and valuable spatiotemporal patterns in spatiotemporal related data, thereby providing valuable insights into understanding spatiotemporal phenomena, such as understanding the dynamic processes of spatiotemporal phenomena such as air pollution propagation, traffic conditions, and rainfall. Previous work has classified geographic time series data visualization methods into direct description type and summary overview type.

[0003] For direct descriptive visualization methods, previous work often uses line graphs to be drawn directly on the map, or another independent view associated with the map to present the data distribution trends in the spatial dimension and the temporal dimension respectively. However, under the premise that the time span and spatial span of large-scale time series data are both large, it will be difficult for analysts to intuitively obtain diverse and dynamically changing spatiotemporal characteristics (such as trends and correlations) from the vast space and extremely large time span. How to intuitively present as much and complex geographic time series data as possible in a limited view is the challenge faced by this method.

[0004] For summary and overview visualization, previous work has helped to provide analysts with clear spatiotemporal data features by preprocessing geographic time series data, extracting data features (such as correlation and clustering) at the data level, and visualizing the summarized data instead of the original time series data. However, these methods destroy the temporal orderliness in the original data, and the extracted features often fail to contain actual time information. Analysts cannot intuitively perceive the geographic location of the time series data and the changes in the data over time in space. For example, for the visualization of air pollution data, it is difficult for analysts to get answers to the following questions from these visualization methods: How fast does air pollution spread in a region? How long does it take for it to spread again? Is there a periodicity in the diffusion? Summary of the invention

[0005] In order to solve at least one of the technical problems existing in the prior art to a certain extent, the present invention aims to provide a visualization method, electronic device and medium for geographic time series based on space-time cube and volume rendering.

[0006] The first technical solution adopted by the present invention is:

[0007] A method for visualizing geographic time series, comprising the following steps:

[0008] Obtain geographic time series data and construct space-time cubes;

[0009] In the data conversion module, the geographic time series data is converted into spatiotemporal volume data using Kriging interpolation technology, and the spatiotemporal volume data is smoothed using smoothing technology;

[0010] In the visualization module, volume rendering technology is used to visualize spatiotemporal volume data.

[0011] Furthermore, the acquisition of geographic time series data and construction of a space-time cube includes:

[0012] Generate a space-time cube based on geographic time series data, where the xy plane represents geographic space and the z axis represents the time axis;

[0013] Each geographic time series is represented as a vertical line in the space-time cube according to its geographical location, and the temporal changes are mapped to the vertical direction of the vertical line, representing different time points of the time series.

[0014] Furthermore, the use of Kriging interpolation technology to convert geographic time series data into spatiotemporal volume data includes:

[0015] Divide the geographic space into n×m grids C = {c 1,1 ,c 1,2 ,…,c n,m}, each grid cell c n,m corresponds to a small area of ​​uniform size;

[0016] The geographic time series with T timestamps is divided into T time slices according to the timestamp; for the t-th time slice at timestamp t, (t≤T), there are observation data samples Z t ={z 1,t ,z 2,t ,…,z S}, where z i,t Represents the value of the i-th geographic time series at timestamp t;

[0017] According to the observed data sample Z t , using Kriging interpolation to predict each grid cell c at timestamp t x,y ∈C

[0018] For each time slice at each timestamp, a two-dimensional scalar field with n × m cells is obtained;

[0019] After performing the above operations on T time slices, the two-dimensional scalar field is stacked along the time dimension to obtain the complete n×m×T space-time volume data.

[0020] Furthermore, the using of smoothing technology to perform time smoothing processing on the spatiotemporal volume data includes:

[0021] The space-time volume V of size n×m×T is divided into n×m time series according to the grid units; each time series V x,y (1≤x≤m, 1≤y≤n), all grid cells c x,y Geographic time series on , with T timestamps;

[0022] For each time series V ij A sliding average process is performed with a sliding window length equal to a preset time length.

[0023] Furthermore, the visualization of spatiotemporal volume data using volume rendering technology includes:

[0024] Volume rendering is achieved using ray-stepping technology, which projects rays from each pixel on the screen into the volume data, and then steps along the rays and samples points;

[0025] Each sampling point calculates the final color and opacity through the Phong lighting model based on the color and opacity information carried by the nearby voxel values;

[0026] The contributions of the sampling points along each ray are accumulated in order from front to back to synthesize the final color and opacity of each screen pixel; the synthesis formula is as follows:

[0027] α a =(1-α a )α s +α a , C a =(1-α a )C s +C a

[0028] In the formula, C a and α a represent the accumulated color and opacity respectively, and C s and α s Respectively represent the color and opacity of the sampling point.

[0029] Furthermore, the step of visualizing the spatiotemporal volume data using the volume rendering technology further includes the following steps:

[0030] By introducing the voxel threshold λ v , during the synthesis process, the value is less than λ v The voxels will be skipped.

[0031] Furthermore, the step of visualizing the spatiotemporal volume data using the volume rendering technology also includes the step of introducing the isosurface visualization technology:

[0032] When the transparency of the volume rendering makes it difficult for users to identify specific spatiotemporal details, isosurface visualization technology is used to allow users to set the isosurface threshold λ i , so that all values ​​in the spacetime volume are equal to λ i The voxel opacity is set to 1, forming an isosurface, which appears as a surface where voxel clusters are rendered.

[0033] Furthermore, the visualization method further comprises the following steps:

[0034] Through the interactive module, three-dimensional interaction is realized in the space-time cube, including time cutting, space cutting and anchor point selection.

[0035] The second technical solution adopted by the present invention is:

[0036] An electronic device comprises a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement a method for visualizing a geographic time series as described above.

[0037] The third technical solution adopted by the present invention is:

[0038] A computer-readable storage medium stores at least one instruction, at least one program, a code set or an instruction set, wherein the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement a method for visualizing a geographic time series as described above.

[0039] The fourth technical solution adopted by the present invention is:

[0040] A computer program product or a computer program includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the above-mentioned method for visualizing a geographic time series.

[0041] The beneficial effects of the present invention are as follows: the present invention can effectively render large-scale geographic time series in a space-time cube, and by adopting the Kriging interpolation technology, the discrete geographic time series data is converted into continuous volume data, so that the data meets the characteristic requirements of the space-time cube; at the same time, considering the discontinuity of the data in the time dimension, the sliding average method is introduced to significantly reduce the noise of the volume data set in the time dimension. In addition, by introducing the volume rendering technology, the occlusion problem in three-dimensional visualization is reduced, and the applicability of the space-time cube to large-scale geographic time series data is re-evaluated. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the embodiments of the present invention or the drawings of related technical solutions in the prior art are introduced below. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 is a flow chart of a visualization method of geographic time series based on space-time cube and volume rendering in an embodiment of the present invention; wherein, Figure 1 A in the figure is a schematic diagram showing that the geographic time series is placed in the three-dimensional space of the space-time cube; Figure 1 B in the middle is a schematic diagram of the space-time volume dataset after Kriging interpolation and time smoothing; Figure 1 Figure C in the middle is a schematic diagram of the rendering result after volume rendering and isosurface visualization;

[0044] Figure 2 is a schematic diagram of the interaction method in an embodiment of the present invention; wherein, Figure 2 A in the middle represents time selection based on time cutting; Figure 2 In the middle, B represents the spatial selection based on spatial cutting; Figure 2 The middle C represents the spatiotemporal selection based on the anchor point;

[0045] Figure 3 It is a time clipping function and a space clipping function effect diagram in the embodiment of the present invention;

[0046] Figure 4 This is an effect diagram of the isosurface visualization technology in an embodiment of the present invention;

[0047] Figure 5 This is a diagram showing the effect of the spatiotemporal analysis function based on anchor points in an embodiment of the present invention;

[0048] Figure 6 It is a flowchart of the steps of a visualization method of geographic time series based on space-time cube and volume rendering in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The embodiments of the present invention are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and are not to be construed as limitations of the present invention. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0050] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, front, back, left, right, etc., and orientations or positional relationships indicated are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0051] In the description of the present invention, "several" means one or more, "more" means more than two, "greater than", "less than", "exceed" etc. are understood as not including the number itself, and "above", "below", "within" etc. are understood as including the number itself. If there is a description of "first" or "second", it is only used for the purpose of distinguishing the technical features, and cannot be understood as indicating or implying the relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0052] In the description of the present invention, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in the present invention based on the specific content of the technical solution.

[0053] Technical explanation:

[0054] Space-time cube: Space-time cube is a traditional three-dimensional geographic time series visualization method. This method maps spatial information to a two-dimensional plane (i.e., the xy plane), takes time as the third dimension (i.e., the z-axis), and integrates and displays space-time information in a three-dimensional form. Space-time cube intuitively displays space-time patterns by displaying the spatiotemporal positions of space-time entities, allowing users to perceive changes in time and space at the same time, significantly reducing the context switching cost of users between geographic information and time information. Bach et al. reviewed the existing interaction methods of space-time cubes and proposed a descriptive framework for interacting with space-time cubes.

[0055] Volume rendering: Volume rendering technology is a three-dimensional visualization method that can intuitively display the internal structure and distribution characteristics of an object, provide richer visualization effects, and is often used to display complex three-dimensional data sets. The core of this technology is to simulate the propagation process of light in three-dimensional space, thereby generating intuitive images of three-dimensional data on a two-dimensional plane. In volume rendering, the rendered three-dimensional data set is called a volume data set, and a data point in the volume data set is called a voxel. Existing work integrates volume data and space-time cubes in video visualization, because the frame sequence in the video essentially constitutes a volume data set. These visualizations are designed to effectively capture activities in the video and identify areas of interest for the audience. Subsequently, this combination was applied to the field of trajectory visualization, converting trajectories in the space-time cube into volume density representations through 3D kernel density estimation (KDE), and the same method was also used to represent crime aggregation. However, these literatures all use small data sets, without considering the application and interaction in large-scale geographic time series data sets. At the same time, the literatures all use density estimation techniques to obtain space-time volume data sets, which reflects the density of discrete observations rather than recovering the true value. This means that they provide a way to estimate the frequency or intensity of events in a given area, rather than accurately reproducing each individual event.

[0056] The present invention aims to solve the occlusion problem and interaction difficulty of existing visualization methods based on space-time cube when processing large-scale geographic time series. The present invention reduces the occlusion problem in three-dimensional visualization by introducing volume rendering technology, and re-evaluates the applicability of space-time cube to large-scale geographic time series data. The present invention follows the framework proposed by Bach et al., designs flexible interaction methods from the perspectives of time, space and space-time, enables users to explore space-time data more intuitively in the space-time cube, reduces the context switching cost of users between geographic and time information, effectively reveals hidden space-time patterns, and makes up for the deficiencies in the prior art.

[0057] Existing visualization methods based on space-time cubes are more suitable for small-scale datasets. When applied to large-scale geographic time series, a series of problems and limitations will arise, including:

[0058] 1) Intuitiveness problem: As the scale of geographic time series data increases, the traditional space-time cube method is difficult to effectively handle large amounts of space-time data. First, the three-dimensional visualization of the space-time cube is prone to occlusion problems. For example, when multiple space-time entities are drawn in the same cube, they tend to occlude each other, resulting in visual confusion and difficulty in clearly presenting hidden space-time patterns. The space-time cube method proposed in the existing literature can effectively handle a small amount of timestamp data, but when the timestamp exceeds 30, it will have serious occlusion problems and is difficult to use.

[0059] 2) Interaction and navigation issues: The space-time cube generated by large-scale geographic time series often has a large volume. When exploring a huge space-time cube, users may only be interested in some areas or time periods. However, the space-time cube rendered by the system may be quite different from the actual map, making it difficult for users to accurately select the area of ​​interest on the space-time cube. At the same time, since the vertical dimension (Z axis) of the space-time cube is the time dimension, when the user adjusts the pitch angle of the camera, the distortion of the space-time cube in the camera view will make it more difficult for users to accurately select the time period in the vertical dimension.

[0060] Therefore, the present invention respectively realizes a data conversion method based on Kriging interpolation, a visualization method based on volume rendering, and an interactive method combining volume rendering technology with space-time cube characteristics.

[0061] 1) Data conversion method based on Kriging interpolation: a) Kriging interpolation method is used to predict the true value of the data rather than the probability density, and discrete geographic time series data are converted into continuous volume data to construct a volume data set so that the data meets the characteristics of the space-time cube; b) Considering the discontinuity of the data in the time dimension, the sliding average method is introduced to significantly reduce the noise of the volume data set in the time dimension.

[0062] 2) Visualization method based on volume rendering: a) Volume rendering based on ray casting algorithm is implemented, and transparency is introduced to allow users to perceive the voxel values ​​inside the voxel cluster, significantly reducing the impact of visual occlusion on the user's analysis process; b) While retaining all the information of the geographic time series, rich visual effects such as color and lighting are provided to significantly enhance the readability of the data; c) The isosurface visualization technology is introduced to render the surface of the voxel cluster and enhance the lighting information, making the shape and boundary of the voxel cluster clearer, providing users with more intuitive visual depth information, and also helping users to more clearly identify and analyze spatiotemporal patterns. d) The voxel threshold function is introduced to skip the rendering of voxels with values ​​less than the threshold, helping users to more easily analyze voxel clusters with higher values ​​and obtain potential spatiotemporal patterns more clearly and intuitively.

[0063] 3) An interactive method combining volume rendering technology with the characteristics of space-time cube: a) Implementing slice-based spatial and temporal selection, which can slice the space-time volume data in time and space dimensions, and quickly and accurately locate the area or time of interest; b) Implementing anchor-based space-time selection interaction to help users quickly locate specific space-time patterns.

[0064] Example 1

[0065] like Figure 1 and Figure 6As shown, this embodiment provides a visualization method for geographic time series based on space-time cube and volume rendering, including the following steps:

[0066] S1. Obtain geographic time series data and construct a space-time cube.

[0067] S2. In the data conversion module, the geographic time series data is converted into spatiotemporal volume data using Kriging interpolation technology, and the spatiotemporal volume data is temporally smoothed using smoothing technology.

[0068] S3. In the visualization module, volume rendering technology is used to visualize the spatiotemporal volume data.

[0069] S4. Through the interactive module, three-dimensional interaction is realized in the space-time cube, including time cutting, space cutting and anchor point selection.

[0070] The specific implementation process of the method of this embodiment is as follows: First, the method generates a space-time cube, in which the xy plane represents the geographic space and the z axis represents the time axis. Each geographic time series is represented as a vertical line in the space-time cube according to its geographic location, such as Figure 1 As shown in A, the time changes are mapped to the vertical direction of the vertical line, indicating different time points of the time series. In this cube, the space-time area is evenly divided into voxels, i.e. small cube units.

[0071] In the data conversion module, in order to achieve data continuity, this embodiment uses Kriging interpolation and sliding window smoothing technology to estimate the value v of each voxel with a spatiotemporal position (x, y, z), so that the values ​​between adjacent voxels tend to be continuous, thereby forming complete spatiotemporal volume data, such as Figure 1 As shown in B.

[0072] In the visualization module, spatiotemporal volume data are displayed through volume rendering and isosurface visualization techniques, such as Figure 1 As shown in C, the voxel threshold function is implemented, and the spatiotemporal context information is presented using maps and timelines.

[0073] In the interactive module, the method of this embodiment implements an efficient and accurate three-dimensional interactive mode in the space-time cube, including time clipping, space clipping and anchor point selection, so that users can efficiently explore large-scale geographic time series.

[0074] The above contents are explained in detail below with reference to the accompanying drawings and specific implementation methods.

[0075] (1) Data conversion module

[0076] 1) Kriging interpolation. The embodiment of the present invention uses Kriging interpolation technology to convert geographic time series data into spatiotemporal volume data. Kriging interpolation is a geostatistical method commonly used for spatial interpolation. It uses spatial autocorrelation to predict any point in space based on discrete observation data Z = {z1, z2, ...} in space. The value of Kriging interpolation is performed using the following formula:

[0077]

[0078] in, is the value at the predicted position p, z i is the value of the i-th observation point, λ i is the weight coefficient. Weight Λ={λ1,λ2,λ3,…,λ n}Calculated by the optimal coefficient to minimize the predicted value and the true value z p The variance of the difference Get a series of weight coefficients λ i .

[0079] Specifically, first, this method divides the geographic space into n×m grids C = {c 1,1 ,c 1,2 ,…,c n,m}, each grid cell c n,m corresponds to a small region of uniform size, e.g. Figure 2 The geographic space is divided into a 3×3 grid in A, with a total of 9 cells. Secondly, the geographic time series with T timestamps is divided into T time slices according to the timestamp. For the tth time slice (t≤T) at timestamp t, there are observation data samples Z t ={z 1,t ,z 2,t ,…,z S}, where z i,t Represents the value of the i-th geographic time series at timestamp t. According to the observed data sample Z t , using Kriging interpolation to predict each grid c at time stamp t x,y ∈C For each time slice of each timestamp, a two-dimensional scalar field with n×m units is obtained. After performing the above operations on T time slices, the two-dimensional scalar field is stacked along the time dimension, and finally the complete n×m×T spatiotemporal volume data is output.

[0080] 2) Temporal smoothing. Since Kriging interpolation can capture spatial autocorrelation, each two-dimensional scalar field generated is smooth in geographic space. However, due to many reasons such as unstable sensor data transmission and inherent uncertainty in interpolation, noise will inevitably appear in the time dimension of the space-time body, which manifests as a sudden discontinuity between two two-dimensional scalar fields at any adjacent timestamps. This discontinuity will cause visual confusion, which in turn hinders users from performing analysis. To this end, the present invention performs temporal smoothing on volume data through a sliding average technique. First, the space-time body V of size n×m×T is divided into n×m time series according to grid cells. Each time series V x,y (1≤x≤m, 1≤y≤n), all are unit c x,y A geographic time series on V with T timestamps. Then, for each time series V ij The sliding average processing with a sliding window length of 24 hours is performed respectively.

[0081] (2) Visualization module

[0082] 1) Volume rendering. First, the method of this embodiment uses volume rendering technology to visualize spatiotemporal volume data. The method of this embodiment uses ray stepping technology to implement volume rendering, projecting light from each pixel on the screen into the volume data, stepping forward along the light and sampling points. Each sampling point calculates the final color and opacity through the Phong lighting model based on the color and opacity information carried by the nearby voxel values. The contributions of the sampling points along each ray are accumulated in order from front to back to synthesize the final color and opacity of each screen pixel. The synthesis formula is as follows:

[0083] α a =(1-α a )α s +α a , C a =(1-α a )C s +C a

[0084] Among them, C a and α a represent the accumulated color and opacity respectively, and C s and α s Respectively represent the color and opacity of the sampling point. Therefore, the larger the value, the redder the color and the more opaque it is, and vice versa, the greener the color and the more transparent it is.

[0085] 2) Voxel threshold. In addition, the present invention introduces the voxel threshold λ v During the synthesis process, the value is less than λ v The voxels with λ will be skipped. The user can interactively adjust λ via the slider vIn this way, the potential spatiotemporal patterns in geographic time series can be easily analyzed based on groups of voxels with larger values.

[0086] 3) Isosurface visualization technology. This embodiment also introduces isosurface visualization technology. When the transparency of volume rendering makes it difficult for users to identify specific spatiotemporal details, isosurface visualization technology allows users to set the isosurface threshold λ i , so that all values ​​in the spacetime volume are equal to λ i The voxel opacity is set to 1 to form an isosurface, which is the surface of the rendered voxel cluster. This function allows users to observe the shape and fine structure of the voxel cluster more clearly, and thus analyze the spatiotemporal patterns more intuitively and clearly.

[0087] 4) Spatiotemporal context information. In order to enhance the visualization effect of the space-time cube, the present invention also provides spatiotemporal context information. In the space-time cube, the geographical map is displayed on the xy plane, and the time axis is displayed on the z axis. The timestamp of each time period is marked at the bottom of the axis, so that the user can see the geographical and time information at any time when rotating the viewing angle.

[0088] (3) Interaction module

[0089] 1) Time selection based on time cutting

[0090] The time cropping function allows users to select a specific time range on the time axis (z axis) of the space-time cube. The time range is represented by t1 and t2, and the corresponding vertical position of the z axis is [z(t1), z(t2)]. The operation process is as follows: a) The user first drags the geographic map (xy plane) perpendicular to the z axis to the z(t1) position of the z axis to hide the voxels below the plane. b) Then, the user selects another plane and drags it to the z(t2) position to filter out the voxels above z(t2). This process is equivalent to slicing the space-time cube on the time axis, showing only the volume data within the time range of interest to the user. During the dragging process, the vertical position of the plane will display the corresponding time information, allowing users to more easily associate the visualization content with time and space. In addition, users can also drag two planes at the same time through the time range slider to browse the changes in spatial distribution within a fixed time range.

[0091] 2) Space selection based on space cutting

[0092] The present invention provides a method for spatial selection in a space-time cube, in particular, a function based on spatial clipping, which allows users to select a specific geographic space area on the xy plane. The following are the specific implementation steps of the method: a) The user can select a circular space range by projecting a circular beam similar to a spotlight in the cube space. This circular range is defined by three parameters: the radius r and the center position (c x ,c y ), the user can directly specify the center position of the circular range by clicking on the map. Then, by using the mouse wheel, the user can flexibly adjust the radius of the circular range to accurately select the spatial area of ​​interest. b) For voxels outside the selected circular range, that is, those that meet the condition (xc x ) 2 +(yc y ) 2 >r 2 , their opacity will be set to 0, which means that these voxels will not be displayed in the visualization, making the voxels within the circle highlighted.

[0093] 3) Anchor-based spatiotemporal selection

[0094] a) Anchor point detection

[0095] When analyzing geographic time series, domain experts often focus on areas with significantly high values, such as abnormal hot spots or obvious propagation paths. Therefore, anchor points are defined as voxel values ​​in the spatiotemporal region that exceed a preset threshold λ. a To identify these regions, we first need to remove voxel clusters with values ​​less than or equal to λ. a Next, the DBSCAN clustering algorithm is used to perform density-based clustering on the remaining voxels to group them according to their 3D spatial locations. Finally, each voxel cluster is identified as an anchor point.

[0096] b) Anchor-based interaction

[0097] Corresponding to the 3D position of each voxel cluster in the cubic space, a transparent but interactive hexahedron is added. When the user clicks on this hexahedron, the voxel group associated with the hexahedron will be retrieved, so that volume slicing and spotlight effects are automatically performed, with the selected time and space ranges respectively. The time range is determined by λ a The spatial extent is a circular area in the xy plane that covers the projection of the voxel cluster on this plane.

[0098] In summary, the present invention includes three modules: a data conversion module, a rendering module and an interaction module, which effectively render large-scale geographic time series in a space-time cube and provide an efficient and intuitive interaction method to help users quickly and accurately explore the space-time patterns in geographic time series.

[0099] 1) In the data conversion module, the Kriging interpolation method and time smoothing technology are used to more clearly display the continuous change process of the spatiotemporal phenomena corresponding to the spatiotemporal volume data. The specific analysis is as follows:

[0100] a) The present invention uses Kriging interpolation to convert discrete geographic time series data into continuous spatiotemporal volume data, and by predicting the true value of each voxel rather than just calculating the event density, the continuity of large-scale geographic event sequences in the spatiotemporal dimension can be reflected. This method can more realistically display the continuous spatiotemporal changes of spatiotemporal phenomena such as air pollution and rainfall.

[0101] b) The present invention introduces the sliding average time smoothing technology to eliminate the discontinuity of the space-time volume data in the time dimension, making the rendering result of the space-time volume smoother in the time dimension, thereby reducing visual noise and interference, enhancing the readability and analysis accuracy of the data, and better reflecting the continuity of space-time phenomena in time.

[0102] 2) In the rendering module, the use of volume rendering technology, voxel threshold function, and isosurface visualization technology can alleviate the problem of mutual occlusion between space-time entities and provide intuitive and clear rendering effects for users to analyze. The specific analysis is as follows:

[0103] a) The present invention uses volume rendering technology to allow users to observe the internal structure of the data. This can not only intuitively display the overall picture of the spatiotemporal data, but also use transparency rendering to alleviate the problem of mutual occlusion between spatiotemporal entities in the spatiotemporal cube.

[0104] b) The present invention introduces a voxel threshold function, which allows users to filter out voxels below the threshold. This method allows users to focus on spatiotemporal phenomena with higher values, more effectively analyze spatiotemporal patterns in geographic time series data, and avoid interference from irrelevant data in the analysis.

[0105] c) The present invention introduces isosurface visualization technology. Isosurfaces can clearly display the shape and boundaries of voxel clusters, and use illumination to enhance the visual depth information of voxel clusters, thereby better reflecting the changing trend of spatiotemporal patterns and helping users to more intuitively identify and analyze complex spatiotemporal patterns.

[0106] 3) In the interactive module, it is used to adopt interactive methods designed from the perspectives of time, space and spatiotemporal, so that users can locate and analyze spatiotemporal patterns more intuitively and efficiently. The specific analysis is as follows:

[0107] a) The present invention provides a time cutting function (such as Figure 2 The map (shown in A) allows users to select data within a certain time period for detailed analysis by dragging the map, providing users with the ability to select time. This function can reduce information overload for users in a large time span, help them focus on the time window of interest, and thus improve the efficiency of data analysis.

[0108] b) The present invention realizes the space cutting function (such as Figure 2 The feature allows users to highlight a certain geographic area and provide users with the ability to select space to focus on analyzing the spatiotemporal patterns in that area. This feature helps users quickly locate the geographic area of ​​interest in complex spatiotemporal data and eliminate interference from other areas, thereby analyzing spatiotemporal patterns more accurately.

[0109] c) The present invention also introduces anchor selection interaction (such as Figure 2 The anchor selection method allows users to quickly jump to the identified spatiotemporal patterns and provides users with the ability to select spatiotemporal patterns. Anchor selection helps users efficiently locate and analyze important spatiotemporal patterns, greatly improving the efficiency of exploring large-scale data sets.

[0110] Example 2

[0111] This embodiment is illustrated by taking the 2018 China air pollution dataset as an example. The dataset covers monitoring data from 448 monitoring sites in China, forming 448 geographic time series, covering almost the entire territory of China. Each geographic time series records in detail the air quality index (AQI) measurement data from January 1 to July 3, 2018, and the data is recorded in hours. There are a total of 448 geographic time series and 4416 timestamps in the dataset, generating a large number of records, with AQI values ​​ranging from 0 to 500.

[0112] In the implementation process, the geographic time series data is first processed. Through the data conversion module (such as Figure 1 The Kriging interpolation and smoothing algorithms are used to transform the data to generate a space-time volume dataset suitable for space-time cube rendering.

[0113] After the converted spatiotemporal volume dataset is imported into the system, the results obtained through visualization rendering are as follows: Figure 3 As shown in A, we can preliminarily observe the temporal and spatial distribution of air pollution. In certain months, some areas will experience severe air pollution, such as Figure 3In order to more clearly display these highly polluted areas, the threshold is set to 150 through the voxel threshold tool, and only voxels with AQI values ​​exceeding 150 are displayed, as shown in Figure 3 As shown in B, the high-pollution area is more prominent, while the low-pollution area is effectively hidden.

[0114] Furthermore, by activating the spatial clipping tool, we can identify high-pollution areas in Xinjiang, western China, such as Figure 3 As shown in Figure C, the air pollution level in this area remains high. Figure 3 As shown in D, it can be seen that since April, the air pollution level in the area has been relatively low, with AQI values ​​generally below 150.

[0115] The time trimming tool can be used to analyze air pollution during a specific period of time. Figure 3 E1 and Figure 3 As shown in E2, the air pollution level in most parts of the country was low during this period, and only from February 15 to February 17 did some parts of central and northern China experience moderate air pollution. Figure 3 Analysis of the corresponding period of G, such as Figure 3 G1 and Figure 3 As shown in G2, it was found that moderate air pollution existed in the northwest and central regions during this period.

[0116] In addition, isosurface visualization techniques can render the shapes of voxel clusters more clearly. Figure 4 As shown in Figure B, before the isosurface visualization is turned on, the shape of the voxel cluster is not obvious; after turning on this function, Figure 4 As shown in middle C, the shapes of voxel clusters are clearly rendered, which helps to determine the propagation path of air pollution events.

[0117] Finally, the anchor selection feature can be triggered by clicking on a voxel cluster. Figure 5 As shown in A, when the user clicks Figure 5 After the voxel cluster shown in A1 is obtained, the anchor selection function is activated. At this time, spatial cropping and temporal cropping will be automatically performed to select the spatiotemporal area covered by the voxel cluster, such as Figure 5 In this mode, users can clearly analyze spatiotemporal patterns, such as Figure 5 As shown in C1 to C5, these images show the detailed path of the spatiotemporal pattern, including the starting location, the ending location, and the propagation path in between.

[0118] Example 3

[0119] An embodiment of the present invention further provides an electronic device, the electronic device comprising a processor and a memory, the memory storing at least one instruction, at least one program, a code set or an instruction set, the at least one instruction, the at least one program, the code set or the instruction set being loaded and executed by the processor to implement the following Figure 1 and Figure 6 A method for visualizing geographic time series is shown in Figure 1.

[0120] It is understandable that the memory may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory includes a non-transitory computer-readable storage medium. The memory may be used to store instructions, programs, codes, code sets, or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function, instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data created according to the use of the server, etc.

[0121] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect the various parts of the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory. Optionally, the processor can be implemented in at least one hardware form of digital signal processing (DSP), field programmable gate array (FPGA), and programmable logic array (PLA). The processor can integrate one or a combination of a central processing unit (CPU) and a modem. Among them, the CPU mainly processes the operating system and application programs; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor, but implemented separately through a chip.

[0122] Since the electronic device is an electronic device corresponding to a method for visualizing geographic time series in an embodiment of the present invention, and the principle of solving the problem by the electronic device is similar to that of the method, the implementation of the electronic device can refer to the implementation process of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0123] Example 4

[0124] The embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, the at least one program, the code set or instruction set is loaded and executed by a processor to implement the following Figure 1 and Figure 6 A method for visualizing geographic time series is shown in Figure 1.

[0125] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0126] Since the storage medium is a storage medium corresponding to a method for visualizing a geographic time series in an embodiment of the present invention, and the principle of solving the problem by the storage medium is similar to that of the method, the implementation of the storage medium can refer to the implementation process of the above-mentioned method embodiment, and the repeated parts will not be repeated.

[0127] Example 5

[0128] In some possible implementations, various aspects of the method of the embodiment of the present invention may also be implemented in the form of a program product, which includes a program code. When the program product is run on a computer device, the program code is used to enable the computer device to execute the steps of a method for visualizing a geographic time series according to various exemplary embodiments of the present application described above in this specification. Among them, the executable computer program code or "code" for executing each embodiment may be written in a high-level programming language such as C, C++, C#, Smalltalk, Java, JavaScript, Visual Basic, structured query language (e.g., Transact-SQL), Perl, or in various other programming languages.

[0129] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0130] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0131] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable ordinary technicians in the field to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made based on the essence of the content of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for visualizing geographic time series, characterized in that: The following steps are involved: Obtain geographic time series data and construct space-time cubes; The geographic time series data is converted into space-time volume data using Kriging interpolation technology, and the space-time volume data is smoothed using smoothing technology; Visualize spatiotemporal volume data using volume rendering techniques.

2. A method for visualizing geographic time series according to claim 1, characterized in that: The acquisition of geographic time series data and construction of a space-time cube includes: Generate a space-time cube from geographic time series data, where the xy plane represents geographic space and the z axis represents the time axis; Each geographic time series is represented as a vertical line in the space-time cube according to its geographical location, and the temporal changes are mapped to the vertical direction of the vertical line, representing different time points of the time series.

3. A method for visualizing geographic time series according to claim 1, characterized in that: The method of converting geographic time series data into spatiotemporal volume data using Kriging interpolation technology includes: Divide the geographic space into n×m grids C = {c 1，1 , c 1，2 , ..., c n，m }, each grid cell C n，m corresponds to a small area of ​​uniform size; The geographic time series with T timestamps is divided into T time slices according to the timestamp; for the t-th time slice at timestamp t, there are observation data samples Z t ={z 1,t , z 2，t , ..., z S }, where z i，t Represents the value of the i-th geographic time series at timestamp t; According to the observed data sample Z t , using Kriging interpolation to predict each grid cell c at timestamp t x，y ∈C For each time slice at each timestamp, a two-dimensional scalar field with n × m cells is obtained; After performing the above operations on T time slices, the two-dimensional scalar field is stacked along the time dimension to obtain the complete n×m×T space-time volume data.

4. A method for visualizing geographic time series according to claim 3, characterized in that: The method of performing time smoothing processing on the spatiotemporal volume data by using a smoothing technology includes: The space-time volume V of size n×m×T is divided into n×m time series according to the grid units; each time series V x，y , are all grid cells c x,y Geographic time series on , with T timestamps; For each time series V ij A sliding average process is performed with a sliding window length equal to a preset time length.

5. A method for visualizing geographic time series according to claim 1, characterized in that: The method of visualizing spatiotemporal volume data using volume rendering technology includes: Volume rendering is achieved using ray-stepping technology, which projects rays from each pixel on the screen into the volume data, and then steps along the rays and samples points; Each sampling point calculates the final color and opacity through the Phong lighting model based on the color and opacity information carried by the nearby voxel values; The contributions of the sampling points along each ray are accumulated in order from front to back to synthesize the final color and opacity of each screen pixel; the synthesis formula is as follows: a a =(1-a a )a s +a a ,C a =(1-a a )C s +C a In the formula, C a and α a represent the accumulated color and opacity respectively, and C s and α s Respectively represent the color and opacity of the sampling point.

6. A method for visualizing geographic time series according to claim 5, characterized in that: The step of visualizing the spatiotemporal volume data using volume rendering technology also includes the following steps: By introducing the voxel threshold λ v , during the synthesis process, the value is less than λ v The voxels will be skipped.

7. A method for visualizing geographic time series according to claim 1, characterized in that: The step of visualizing the spatiotemporal volume data using volume rendering technology also includes the step of introducing isosurface visualization technology: When the transparency of the volume rendering makes it difficult for users to identify specific spatiotemporal details, isosurface visualization technology is used to allow users to set the isosurface threshold λ i , so that all values ​​in the space-time volume are equal to λ i The voxel opacity is set to 1, forming an isosurface, which appears as a surface where voxel clusters are rendered.

8. A method for visualizing geographic time series according to claim 1, characterized in that: The visualization method further comprises the following steps: Through the interactive module, three-dimensional interaction is realized in the space-time cube, including time cutting, space cutting and anchor point selection.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method described in any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the method according to any one of claims 1 to 8.

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