Space-time kernel density visualization method and system based on module compression, terminal and readable storage medium

Abstract

The invention relates to the technical field of geographic information science, and discloses a space-time kernel density visualization method and system based on module compression, a terminal and a readable storage medium, and the method comprises the steps: constructing a sample block according to a space-time data set, determining a segmentation rule according to an absolute error to segment the sample block, and obtaining a target sample block; scanning the data points to obtain a scanning result, and screening the target sample blocks according to the scanning result to obtain a block set; compressing data points in the target sample blocks in the block set to obtain a central data point in each target sample block, and calculating a space-time kernel density approximation function of all pixel-time pairs according to the central data points in the block set. According to the method, all the data points in the target sample block are compressed, the space-time kernel density approximation function is calculated accordingly, then the relation between the space-time kernel density approximation function and the absolute error is constructed, and the calculation speed is increased under the condition that the space-time kernel density visualization result generated by an original data set is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of geographic information science, and in particular to a spatio-temporal kernel density visualization method, system, terminal and computer-readable storage medium based on module compression. Background Art

[0002] Spatio-temporal kernel density visualization, which is a basic tool of geographic information systems, has been used in applications such as traffic hotspot detection, traffic accident hotspot detection, and infectious disease hotspot detection, providing great convenience for information tracking.

[0003] To solve the problem of generating spatio-temporal kernel density visualization, a more advanced method is to quickly calculate accurate spatio-temporal kernel density visualization by constructing a prefix matrix method (PREFIX, representing "spatio-temporal kernel density visualization method based on prefix matrix"), and successfully reduce the time complexity from ( represents the lowest time complexity of calculating the spatio-temporal kernel density visualization result in other methods, represents the number of spatio-temporal data points, represents the number of timestamps, and both represent the resolution) to ( represents the time complexity of calculating the spatio-temporal kernel density visualization result by the PREFIX method). Nevertheless, the time complexity of PREFIX still depends on , which is still very large, resulting in a slow speed of calculating spatio-temporal kernel density visualization.

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

[0005] The main purpose of the present invention is to provide a spatio-temporal kernel density visualization method, system, terminal and computer-readable storage medium based on module compression, aiming to solve the problem that the process of calculating spatio-temporal kernel density visualization is slow when facing large-scale data in the existing technology.

[0006] To achieve the above object, the present invention provides a spatio-temporal kernel density visualization method based on module compression. The spatio-temporal kernel density visualization method based on module compression includes the following steps: Obtain a spatio-temporal data set, construct sample blocks according to the spatio-temporal data set, determine a segmentation rule according to the absolute error input by the user, and segment the sample blocks according to the segmentation rule to obtain a plurality of target sample blocks; Scan all data points in the spatio-temporal data set to obtain a scan result, and screen all the target sample blocks according to the scan result to obtain a block set; Compress all data points in each of the target sample blocks in the block set to obtain the central data points in each of the target sample blocks, and calculate the spatio-temporal kernel density approximation function of all pixel-time pairs based on all the central data points in the block set.

[0007] Optionally, in the spatio-temporal kernel density visualization method based on module compression, before obtaining the spatio-temporal data set, constructing sample blocks according to the spatio-temporal data set, determining a segmentation rule according to the absolute error input by the user, and segmenting the sample blocks according to the segmentation rule to obtain a plurality of target sample blocks, further including: Scan the spatio-temporal data set and extract a plurality of the outermost data points in the spatio-temporal data set.

[0008] Optionally, in the spatio-temporal kernel density visualization method based on module compression, the steps of obtaining the spatio-temporal data set, constructing sample blocks according to the spatio-temporal data set, determining a segmentation rule according to the absolute error input by the user, and segmenting the sample blocks according to the segmentation rule to obtain a plurality of target sample blocks specifically include: Obtain the spatio-temporal data set and construct sample blocks according to all the outermost data points, where all the outermost data points are on the surface of the sample blocks; Obtain the absolute error input by the user and determine the segmentation rule according to the absolute error; Segment the sample blocks according to the segmentation rule to obtain a plurality of target sample blocks, where the segmentation rule represents the length, width, and height of the target sample blocks.

[0009] Optionally, in the spatio-temporal kernel density visualization method based on module compression, the steps of scanning all data points in the spatio-temporal data set to obtain a scan result and screening all the target sample blocks according to the scan result to obtain a block set specifically include: Scan all data points in each of the target sample blocks to obtain the number of data points in each of the target sample blocks; According to all the numbers of data points, screen out a plurality of target sample blocks that meet the preset conditions; Use all the screened target sample blocks to construct a block set.

[0010] Optionally, in the spatio-temporal kernel density visualization method based on module compression, the steps of compressing all data points in each of the target sample blocks in the block set to obtain the central data points in each of the target sample blocks and calculating the spatio-temporal kernel density approximation function of all pixel-time pairs based on all the central data points in the block set specifically include: Perform compression processing on all data points in each of the target sample blocks in the block set to obtain the central data point in each of the target sample blocks; Calculate the spatio-temporal kernel density approximation function of all pixel-time pairs in the block set according to all the central data points in the block set: ; wherein, represents the spatio-temporal kernel density approximation function, represents the number of all data points in the spatio-temporal data set, represents the target sample block, represents the block set, represents the number of data points in the target sample block, represents the spatial kernel function, represents the temporal kernel function, represents the pixel, represents the th time step of the pixel, represents the central data point of the target sample block, represents 's time step.

[0011] Optionally, in the spatio-temporal kernel density visualization method based on module compression, before calculating the spatio-temporal kernel density approximation function of all pixel-time pairs according to all the central data points in the block set, it further includes: Determine a plurality of pixel-time pairs according to all the data points in the block set.

[0012] Optionally, in the spatio-temporal kernel density visualization method based on module compression, the specific process of dividing the sample block according to the division rule to obtain a plurality of target sample blocks includes: Construct the relationship between the spatio-temporal kernel density approximation function and the absolute error: ; wherein, represents the spatio-temporal kernel density function, represents the spatio-temporal data set, represents the absolute error; Perform maximum cutting on the target sample block according to the relationship to obtain the final cutting result, wherein the speed and accuracy of the spatio-temporal kernel density approximation function are within a preset range under the final cutting result.

[0013] In addition, to achieve the above object, the present invention also provides a spatio-temporal kernel density visualization system based on module compression, wherein the spatio-temporal kernel density visualization system based on module compression includes: A sample block segmentation module, configured to obtain a spatio-temporal data set, construct sample blocks according to the spatio-temporal data set, determine a segmentation rule according to an absolute error input by a user, and segment the sample blocks according to the segmentation rule to obtain a plurality of target sample blocks; A sample block screening module, configured to scan all data points in the spatio-temporal data set to obtain a scan result, and screen all the target sample blocks according to the scan result to obtain a block set; A data point compression module, configured to compress all data points in each target sample block in the block set to obtain a central data point in each target sample block, and calculate a spatio-temporal kernel density approximation function of all pixel-time pairs according to all the central data points in the block set.

[0014] In addition, to achieve the above object, the present invention further provides a terminal, where the terminal includes: a memory, a processor, and a spatio-temporal kernel density visualization program based on module compression stored on the memory and executable on the processor. When the spatio-temporal kernel density visualization program based on module compression is executed by the processor, the steps of the spatio-temporal kernel density visualization method based on module compression as described above are implemented.

[0015] In addition, to achieve the above object, the present invention further provides a computer-readable storage medium, where the computer-readable storage medium stores a spatio-temporal kernel density visualization program based on module compression. When the spatio-temporal kernel density visualization program based on module compression is executed by a processor, the steps of the spatio-temporal kernel density visualization method based on module compression as described above are implemented.

[0016] In the present invention, a spatio-temporal data set is obtained, sample blocks are constructed according to the spatio-temporal data set, a segmentation rule is determined according to an absolute error input by a user, and the sample blocks are segmented according to the segmentation rule to obtain a plurality of target sample blocks; all data points in the spatio-temporal data set are scanned to obtain a scan result, and all the target sample blocks are screened according to the scan result to obtain a block set; all data points in each target sample block in the block set are compressed to obtain a central data point in each target sample block, and a spatio-temporal kernel density approximation function of all pixel-time pairs is calculated according to all the central data points in the block set; a relationship between the spatio-temporal kernel density approximation function and the absolute error is constructed, and the target sample blocks are maximally cut according to the relationship to obtain a final cut result, and the average betweenness centrality of the simulation network is calculated. By compressing all data points in the target sample blocks, calculating the spatio-temporal kernel density approximation function accordingly, and then constructing a relationship with the absolute error, the present invention improves the calculation speed and calculation accuracy while ensuring the spatio-temporal kernel density visualization result generated by the original data set. Description of the Drawings

[0017] Figure 1 is a flowchart of a preferred embodiment of the spatio-temporal kernel density visualization method based on module compression of the present invention; Figure 2 is a schematic diagram of spatio-temporal kernel density visualization of Area A in a preferred embodiment of the spatio-temporal kernel density visualization method based on module compression of the present invention; Figure 3 is a distribution diagram of position data points of a dataset in a certain place in three-dimensional space-time in a preferred embodiment of the spatio-temporal kernel density visualization method based on module compression of the present invention; Figure 4 is a schematic diagram of data point compression in a preferred embodiment of the spatio-temporal kernel density visualization method based on module compression of the present invention; Figure 5 is a comparison diagram of the accuracy of different methods in spatio-temporal kernel density visualization in a preferred embodiment of the spatio-temporal kernel density visualization method based on module compression of the present invention; Figure 6 is a schematic diagram of the maximum error of spatio-temporal kernel density visualization in a preferred embodiment of the spatio-temporal kernel density visualization method based on module compression of the present invention; Figure 7 is a schematic diagram of the number of target sample blocks at a fixed absolute error in a preferred embodiment of the spatio-temporal kernel density visualization method based on module compression of the present invention; Figure 8 is a first time schematic diagram of spatio-temporal kernel density visualization in a preferred embodiment of the spatio-temporal kernel density visualization method based on module compression of the present invention; Figure 9 is a second time schematic diagram of spatio-temporal kernel density visualization in a preferred embodiment of the spatio-temporal kernel density visualization method based on module compression of the present invention; Figure 10 is a third time schematic diagram of spatio-temporal kernel density visualization in a preferred embodiment of the spatio-temporal kernel density visualization method based on module compression of the present invention; Figure 11 is a fourth time schematic diagram of spatio-temporal kernel density visualization in a preferred embodiment of the spatio-temporal kernel density visualization method based on module compression of the present invention; Figure 12 is a fifth time schematic diagram of spatio-temporal kernel density visualization in a preferred embodiment of the spatio-temporal kernel density visualization method based on module compression of the present invention; Figure 13 is a structural diagram of a preferred embodiment of the spatio-temporal kernel density visualization system based on module compression of the present invention; Figure 14 is a structural diagram of a preferred embodiment of the terminal of the present invention. Detailed implementation manners

[0018] To make the objectives, technical solutions, and advantages of the present invention clearer and more definite, the following further elaborates on the present invention with reference to the accompanying drawings and by way of examples. It should be understood that the specific examples described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] The method for visualizing spatio-temporal kernel density based on module compression according to a preferred embodiment of the present invention is as Figure 1 shown. The method for visualizing spatio-temporal kernel density based on module compression includes the following steps: Step S10: Obtain a spatio-temporal data set, construct a sample block according to the spatio-temporal data set, determine a segmentation rule according to the absolute error input by the user, and segment the sample block according to the segmentation rule to obtain a plurality of target sample blocks.

[0020] Among them, scan the spatio-temporal data set and extract a plurality of outermost data points in the spatio-temporal data set. For the obtained data points, they can be first converted into the results of spatio-temporal kernel density visualization, as Figure 2 shown, that is, Figure 2 in which (a) represents the data set of location A, Figure 2 in (b), Figure 2 in (c), Figure 2 in (d), and Figure 2 in (e) represent the results of spatio-temporal kernel density visualization of location A in different times in this embodiment. Among them, different colors represent the hot spot positions or cold spot positions of the data to be tracked.

[0021] Specifically, obtain a spatio-temporal data set, construct a sample block according to all the outermost data points, where all the outermost data points are on the surface of the sample block; obtain the absolute error input by the user, and determine a segmentation rule according to the absolute error; segment the sample block according to the segmentation rule to obtain a plurality of target sample blocks, where the segmentation rule represents the length, width, and height of the target sample block.

[0022] Among them, for the obtained data points, their distribution in three-dimensional spatio-temporal can be obtained, as Figure 3 shown, which represents the distribution of location data points of a telephone location data set in a certain place in three-dimensional spatio-temporal. Among them, T represents the time stamp when the telephone location data set of this place is obtained, and both X and Y represent the resolution of the data points.

[0023] Furthermore, for the data points distributed in three-dimensional spatio-temporal, a sample block containing all the data points in the spatio-temporal data set can be extracted, where the outermost data points in the spatio-temporal data set are on the surface of the sample block. Then, according to the segmentation rule, the sample block is segmented into a plurality of target sample blocks with a size of , as Figure 4 shown, where represents the bottom edge of the target sample block, represents the height of the target sample block.

[0024] Step S20: Scan all data points in the spatio-temporal dataset to obtain a scan result, and screen all the target sample blocks according to the scan result to obtain a block set.

[0025] Among them, after the segmentation of the sample blocks is completed, it is necessary to detect the data points in the target sample blocks to screen out the target sample blocks that meet the rules.

[0026] Specifically, scan all the data points in each target sample block to obtain the number of data points in each target sample block; screen out multiple target sample blocks that meet the preset conditions according to all the data point numbers; use all the screened target sample blocks to construct a block set.

[0027] Among them, for the target sample blocks that do not contain data points, they are excluded; further, if a certain data point is on the surfaces of multiple target sample blocks at the same time, this data point is randomly assigned to a target sample block. Preferably, this data point can be assigned to the target sample block with more data points, which can reduce the subsequent calculation time and improve the calculation speed and the accuracy of the calculation result. When the screening of the target sample blocks is completed, the remaining target sample blocks are constructed into a block set for calculating the subsequent spatio-temporal kernel density approximation function.

[0028] Step S30: Compress all the data points in each target sample block in the block set to obtain the central data points in each target sample block, and calculate the spatio-temporal kernel density approximation function of all pixel-time pairs according to all the central data points in the block set.

[0029] Specifically, perform compression processing on all the data points in each target sample block in the block set to obtain the central data points in each target sample block; calculate the spatio-temporal kernel density approximation function of all pixel-time pairs in the block set according to all the central data points in the block set: ; Among them, represents the spatio-temporal kernel density approximation function, represents the number of all data points in the spatio-temporal dataset, represents the target sample block, represents the block set, represents the number of data points in the target sample block, represents the spatial kernel function, represents the temporal kernel function, Represents a pixel, Represents the time step of the pixel, and represents the central data point of the target sample block, Represents the time step of

[0030] Among them, for all data points in the target sample block, compression processing is first performed. Instead of directly calculating the spatio-temporal kernel density visualization result of the pixel-time pairs converted according to the spatio-temporal data set, the data points in each target sample block are first compressed into the central data point at the center. For example, Figure 4 in when it is 4, it means there are 4 data points. Compressing these 4 data points results in the central data point . These blocks also store the number of data points inside. By designing an effective data compression method, the number of data points is reduced as much as possible, and it is theoretically ensured that the spatio-temporal kernel density visualization gives a visualization result similar to that in the original data after compression, thereby massively compressing the data set and greatly improving the speed when processing large-scale data.

[0031] Furthermore, according to all the data points in the block set, a plurality of pixel-time pairs are determined.

[0032] Among them, after obtaining the block set, all the data points in the block set can be further converted into the form of pixel-time pairs for subsequent spatio-temporal kernel density approximate visualization.

[0033] Furthermore, the preset condition means that an accurate spatio-temporal kernel density visualization result can be obtained. To ensure that the spatio-temporal kernel density approximation function does not deviate too far from the original spatio-temporal kernel density visualization (as shown in Figure 5 , Figure 5 in (a), Figure 5 in (b), Figure 5 in (c), and Figure 5 in (d). The four figures above respectively represent the spatio-temporal kernel density visualization results obtained according to the original spatio-temporal kernel density function at different times. Figure 5 in (a), Figure 5 in (b), Figure 5 in (c), and Figure 5 in (d). The four figures below respectively represent the spatio-temporal kernel density visualization results obtained according to the spatio-temporal kernel density approximation function at different times), an absolute error needs to be constructed to ensure that the difference between the spatio-temporal kernel density approximation function and the original spatio-temporal kernel density function does not exceed this absolute error.

[0034] Specifically, construct the relationship between the spatio-temporal kernel density approximation function and the absolute error: ; wherein, represents the spatio-temporal kernel density function, represents the spatio-temporal data set, represents the absolute error; the target sample block is maximally cut according to the relationship to obtain a final cutting result, wherein, under the final cutting result, the speed and accuracy of the spatio-temporal kernel density approximation function are within a preset range.

[0035] wherein, as Figure 6 shown, under the changed theoretical absolute error, the relationship between the absolute error and the actual maximum error of the approximation method in generating the spatio-temporal kernel density visualization can be found; further, as Figure 7 shown, it can be found that when the target sample block is very large, each target sample block will contain a lot of data points, and the number of target sample blocks will decrease, which also leads to a faster speed of calculating the spatio-temporal kernel density approximation function, but the error (absolute error) will increase. When each target sample block becomes smaller, it will contain fewer data points, and the number of target sample blocks will increase, which will correspondingly bring the effect of slower calculation speed, but this will improve the calculation accuracy. Therefore, in this embodiment, by maximizing and , the difference between the spatio-temporal kernel density approximation function and the spatio-temporal kernel density function does not exceed the absolute error. Through this process, the time for generating the spatio-temporal kernel density visualization can be reduced in this compressed data set and a higher compression rate can be maintained, and the result of the spatio-temporal kernel density visualization generated in the compressed data set and the result of the spatio-temporal kernel density visualization generated in the original data set can also be kept within the error range.

[0036] Further, in this embodiment, based on the above method, the speed and accuracy of the spatio-temporal kernel density visualization can be significantly improved. As Figure 8 shown, where SWS represents the method of "Sliding-Window-based Spatio-Temporal Kernel Density Visualization", Sliding-Window-based Solution, represents the SWS method compressed by the method described in the present invention, represents the PREFIX method compressed by the method described in the present invention. Through Figure 8 it can be found that after the compression method in the present invention, the speed of the result of the spatio-temporal kernel density visualization obtained is faster; and in subsequent changes in resolution (as Figure 8 shown), changes in the number of timestamps (as Figure 9 shown), changes in the spatial bandwidth (as Figure 10 shown), changes in the temporal bandwidth (as Figure 11 shown), and changes in the theoretical absolute error (asFigure 12 When the situation shown in the figure is present, the compression method in the present invention can achieve a faster calculation speed.

[0037] The present invention compresses all data points in the target sample block, calculates the spatio-temporal kernel density approximation function accordingly, then constructs the relationship with the absolute error, and improves the calculation speed while ensuring the spatio-temporal kernel density visualization result generated by the original data set.

[0038] Furthermore, as Figure 13 shown, based on the above spatio-temporal kernel density visualization method based on module compression, the present invention also correspondingly provides a spatio-temporal kernel density visualization system based on module compression. Among them, the spatio-temporal kernel density visualization system based on module compression includes: A sample block segmentation module 51, configured to obtain a spatio-temporal data set, construct sample blocks according to the spatio-temporal data set, determine a segmentation rule according to the absolute error input by the user, and segment the sample blocks according to the segmentation rule to obtain a plurality of target sample blocks; A sample block screening module 52, configured to scan all data points in the spatio-temporal data set to obtain a scan result, and screen all the target sample blocks according to the scan result to obtain a block set; A data point compression module 53, configured to compress all data points in each target sample block in the block set to obtain the central data points in each target sample block, and calculate the spatio-temporal kernel density approximation function of all pixel-time pairs according to all the central data points in the block set.

[0039] Furthermore, as Figure 14 shown, based on the above spatio-temporal kernel density visualization method and system based on module compression, the present invention also correspondingly provides a terminal, and the terminal includes a processor 10, a memory 20, and a display 30. Figure 14 Only some components of the terminal are shown, but it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively.

[0040] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as the hard disk or memory of the terminal. In some other embodiments, the memory 20 may also be an external storage device of the terminal, such as a plug-in hard disk equipped on the terminal, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Further, the memory 20 may also include both the internal storage unit of the terminal and the external storage device. The memory 20 is used to store application software installed on the terminal and various types of data, such as the program code of the installed terminal, etc. The memory 20 may also be used to temporarily store data that has been output or will be output. In one embodiment, a spatio-temporal kernel density visualization program 40 based on module compression is stored on the memory 20, and the spatio-temporal kernel density visualization program 40 based on module compression can be executed by the processor 10, so as to implement the spatio-temporal kernel density visualization method based on module compression in the present application.

[0041] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor or other data processing chips, and is used to run the program code stored in the memory 20 or process data, such as executing the spatio-temporal kernel density visualization method based on module compression, etc.

[0042] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch liquid crystal display, and an OLED (Organic Light-Emitting Diode) toucher, etc. 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 through a system bus.

[0043] In one embodiment, when the processor 10 executes the spatio-temporal kernel density visualization program 40 in the memory 20, the steps of the spatio-temporal kernel density visualization method based on module compression as described above are implemented.

[0044] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a spatio-temporal kernel density visualization program based on module compression, and when the spatio-temporal kernel density visualization program based on module compression is executed by a processor, the steps of the spatio-temporal kernel density visualization method based on module compression as described above are implemented.

[0045] In summary, the present invention provides a spatio-temporal kernel density visualization method and related devices based on module compression. The method includes: obtaining a spatio-temporal data set, constructing sample blocks according to the spatio-temporal data set, determining a segmentation rule according to the absolute error input by a user, segmenting the sample blocks according to the segmentation rule to obtain a plurality of target sample blocks; scanning all data points in the spatio-temporal data set to obtain a scanning result, and screening all the target sample blocks according to the scanning result to obtain a block set; compressing all data points in each target sample block in the block set to obtain central data points in each target sample block, and calculating a spatio-temporal kernel density approximation function for all pixel-time pairs according to all the central data points in the block set. By compressing all data points in the target sample blocks, calculating the spatio-temporal kernel density approximation function accordingly, and then constructing the relationship with the absolute error, the present invention improves the calculation speed and calculation accuracy while ensuring the spatio-temporal kernel density visualization result generated by the original data set.

[0046] It should be noted that in this article, the terms "including", "comprising" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or terminal. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or terminal including that element.

[0047] Certainly, those of ordinary skill in the art can understand that all or part of the processes of implementing the above method embodiments can be completed by instructing relevant hardware (such as a processor, a controller, etc.) through a computer program. The program can be stored in a computer-readable storage medium readable by a computer. When the program is executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be a memory, a magnetic disk, an optical disc, etc.

[0048] It should be understood that the application of the present invention is not limited to the above examples. For those of ordinary skill in the art, improvements or transformations can be made according to the above description. All such improvements and transformations should fall within the protection scope of the appended claims of the present invention.

Claims

1. A spatio-temporal kernel density visualization method based on module compression, characterized in that, The spatio-temporal kernel density visualization method based on module compression includes: Obtain a spatio-temporal data set, construct sample blocks according to the spatio-temporal data set, determine a segmentation rule according to the absolute error input by the user, and segment the sample blocks according to the segmentation rule to obtain a plurality of target sample blocks; Scan all data points in the spatio-temporal data set to obtain a scan result, and screen all the target sample blocks according to the scan result to obtain a block set; Compress all data points in each target sample block in the block set to obtain the central data points in each target sample block, and calculate the spatio-temporal kernel density approximation function of all pixel-time pairs according to all the central data points in the block set.

2. The spatio-temporal kernel density visualization method based on module compression according to claim 1, wherein Before the step of obtaining a spatio-temporal data set, constructing sample blocks according to the spatio-temporal data set, determining a segmentation rule according to the absolute error input by the user, and segmenting the sample blocks according to the segmentation rule to obtain a plurality of target sample blocks, further includes: Scan the spatio-temporal data set and extract a plurality of the outermost data points in the spatio-temporal data set.

3. The spatio-temporal kernel density visualization method based on module compression according to claim 2, wherein The step of obtaining a spatio-temporal data set, constructing sample blocks according to the spatio-temporal data set, determining a segmentation rule according to the absolute error input by the user, and segmenting the sample blocks according to the segmentation rule to obtain a plurality of target sample blocks specifically includes: Obtain a spatio-temporal data set and construct sample blocks according to all the outermost data points, wherein all the outermost data points are on the surface of the sample blocks; Obtain the absolute error input by the user and determine a segmentation rule according to the absolute error; Segment the sample blocks according to the segmentation rule to obtain a plurality of target sample blocks, wherein the segmentation rule represents the length, width and height of the target sample blocks.

4. The spatio-temporal kernel density visualization method based on module compression according to claim 1, wherein The step of scanning all data points in the spatio-temporal data set to obtain a scan result, and screening all the target sample blocks according to the scan result to obtain a block set specifically includes: Scan all data points in each target sample block to obtain the number of data points in each target sample block; Screen out a plurality of target sample blocks that meet the preset conditions according to all the numbers of data points; Construct a block set by using all the screened target sample blocks.

5. The spatio-temporal kernel density visualization method based on module compression according to claim 1, wherein The step of compressing all data points in each target sample block in the block set to obtain the central data points in each target sample block, and calculating the spatio-temporal kernel density approximation function of all pixel-time pairs according to all the central data points in the block set specifically includes: Perform compression processing on all data points in each target sample block in the block set to obtain the central data points in each target sample block; Calculate the spatio-temporal kernel density approximation function of all pixel-time pairs according to all the central data points in the block set: ； Among them, represents the spatio-temporal kernel density approximation function, represents the number of all data points in the spatio-temporal dataset, represents the target sample block, represents the block set, represents the number of data points in the target sample block, represents the spatial kernel function, represents the temporal kernel function, represents a pixel, represents the th time step of the pixel, represents the central data point of the target sample block, represents 's time step.

6. The spatio-temporal kernel density visualization method based on module compression according to claim 5, wherein Before the step of calculating the spatio-temporal kernel density approximation function of all pixel-time pairs according to all the central data points in the block set, further includes: Determine a plurality of pixel-time pairs according to all the data points in the block set.

7. The spatio-temporal kernel density visualization method based on module compression according to claim 6, characterized in that, Dividing the sample block according to the division rule to obtain a plurality of target sample blocks, specifically including: Constructing the relationship between the spatio-temporal kernel density approximation function and the absolute error: ； Among them, represents the spatio-temporal kernel density function, represents the spatio-temporal data set, represents the absolute error; Performing maximum cutting on the target sample block according to the relationship to obtain a final cutting result, wherein the speed and accuracy of the spatio-temporal kernel density approximation function are within a preset range under the final cutting result.

8. A spatio-temporal kernel density visualization system based on module compression, characterized in that, The spatio-temporal kernel density visualization system based on module compression includes: A sample block division module, configured to obtain a spatio-temporal data set, construct a sample block according to the spatio-temporal data set, determine a division rule according to the absolute error input by a user, and divide the sample block according to the division rule to obtain a plurality of target sample blocks; A sample block screening module, configured to scan all data points in the spatio-temporal data set to obtain a scan result, and screen all the target sample blocks according to the scan result to obtain a block set; A data point compression module, configured to compress all data points in each target sample block in the block set to obtain central data points in each target sample block, and calculate a spatio-temporal kernel density approximation function of all pixel-time pairs according to all the central data points in the block set.

9. A terminal, characterized in that, The terminal includes: a memory, a processor, and a spatio-temporal kernel density visualization program based on module compression stored on the memory and executable on the processor. When the spatio-temporal kernel density visualization program based on module compression is executed by the processor, the steps of the spatio-temporal kernel density visualization method according to any one of claims 1-7 are implemented.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a spatio-temporal kernel density visualization program based on module compression. When the spatio-temporal kernel density visualization program based on module compression is executed by a processor, the steps of the spatio-temporal kernel density visualization method according to any one of claims 1-7 are implemented.

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