Space-time Kernel Density Visualization Method, System, Terminal and Readable Storage Medium Based on Module Compression

Abstract

The present invention relates to the field of geographic information science and technology, and discloses a method, system, terminal, and readable storage medium for visualizing spatiotemporal kernel density based on modular compression. The method comprises: constructing sample blocks based on a spatiotemporal dataset, determining a segmentation rule based on absolute error to segment the sample blocks, and obtaining a target sample block; scanning data points to obtain a scan result, and screening the target sample blocks based on the scan result to obtain a block set; compressing the 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 spatiotemporal kernel density approximation function for all pixel-time pairs based on the central data point in the block set. By compressing all data points in the target sample block, calculating the spatiotemporal kernel density approximation function based on the function, and then establishing a relationship with the absolute error, the present invention improves calculation speed while maintaining the spatiotemporal kernel density visualization results generated by the original dataset.

Description

Technical Field

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

[0002] Spatiotemporal kernel density visualization is a basic tool in geographic information systems. It 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] In order to solve the problem of generating spatiotemporal kernel density visualization, a more advanced method is to quickly calculate accurate spatiotemporal kernel density visualization by constructing a prefix matrix method (PREFIX, which means "spatiotemporal kernel density visualization method based on prefix matrix"), and smoothly reduce the time complexity from ( Represents the lowest time complexity of computing spatiotemporal kernel density visualization results among other methods. represents the number of spatiotemporal data points, Indicates the number of timestamps, and Both represent resolution) down to ( represents the time complexity of the PREFIX method for computing the spatiotemporal kernel density visualization results). However, the time complexity of PREFIX still depends on , is still very large, which also leads to a slow speed in calculating the spatiotemporal 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 method, system, terminal and computer-readable storage medium for spatiotemporal kernel density visualization based on module compression, aiming to solve the problem in the prior art that the process of calculating spatiotemporal kernel density visualization is slow when facing large-scale data.

[0006] To achieve the above object, the present invention provides a spatiotemporal kernel density visualization method based on module compression, which comprises the following steps:

[0007] Acquire a spatiotemporal dataset, construct a sample block according to the spatiotemporal dataset, determine a segmentation rule according to an absolute error input by a user, and segment the sample block according to the segmentation rule to obtain a plurality of target sample blocks;

[0008] Scanning all data points in the spatiotemporal dataset to obtain a scanning result, and screening all target sample blocks according to the scanning result to obtain a block set;

[0009] 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 spatiotemporal kernel density approximation function of all pixel-time pairs is calculated based on all the central data points in the block set.

[0010] Optionally, the spatiotemporal kernel density visualization method based on module compression, wherein the steps of acquiring a spatiotemporal dataset, constructing a sample block based on the spatiotemporal dataset, determining a segmentation rule based on an absolute error input by a user, and segmenting the sample block according to the segmentation rule to obtain a plurality of target sample blocks, further comprises:

[0011] The spatiotemporal dataset is scanned, and a plurality of data points at the most edge of the spatiotemporal dataset are extracted.

[0012] Optionally, the spatiotemporal kernel density visualization method based on module compression, wherein the steps of acquiring a spatiotemporal dataset, constructing a sample block based on the spatiotemporal dataset, determining a segmentation rule based on an absolute error input by a user, and segmenting the sample block according to the segmentation rule to obtain a plurality of target sample blocks, specifically include:

[0013] Acquire a spatiotemporal dataset, and construct a sample block according to all the data points at the outermost edges, wherein all the data points at the outermost edges are on the surface of the sample block;

[0014] Obtaining an absolute error input by a user, and determining a segmentation rule based on the absolute error;

[0015] The sample block is segmented 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 block.

[0016] Optionally, the spatiotemporal kernel density visualization method based on module compression, wherein the step of scanning all data points in the spatiotemporal dataset to obtain a scanning result, and screening all target sample blocks according to the scanning result to obtain a block set, specifically includes:

[0017] Scan all the data points in each target sample block to obtain the number of data points in each target sample block;

[0018] Filtering out a plurality of target sample blocks that meet preset conditions based on the number of all the data points;

[0019] A block set is constructed using all the screened target sample blocks.

[0020] Optionally, the spatiotemporal kernel density visualization method based on module compression, wherein compressing all data points in each target sample block in the block set to obtain a central data point in each target sample block, and calculating the spatiotemporal kernel density approximation function of all pixel-time pairs based on all the central data points in the block set, specifically includes:

[0021] Performing compression processing on all data points in each target sample block in the block set to obtain a central data point in each target sample block;

[0022] According to all the central data points in the block set, the spatiotemporal kernel density approximation function of all pixel-time pairs in the block set is calculated:

[0023] ;

[0024] in, represents the space-time kernel density approximation function, represents the number of all data points in the spatiotemporal dataset, represents the target sample block, Represents a collection of blocks, represents the number of data points in the target sample block, represents the spatial kernel function, represents the time kernel function, represents pixels, Indicates the pixel time steps, represents the central data point of the target sample block, express time step.

[0025] Optionally, the spatiotemporal kernel density visualization method based on module compression, wherein the spatiotemporal kernel density approximation function of all pixel-time pairs is calculated based on all the central data points in the block set, further comprises:

[0026] Based on all of the data points in the set of blocks, a plurality of pixel-time pairs are determined.

[0027] Optionally, the spatiotemporal kernel density visualization method based on module compression, wherein the step of segmenting the sample block according to the segmentation rule to obtain a plurality of target sample blocks, specifically includes:

[0028] Construct the relationship between the spatiotemporal kernel density approximation function and the absolute error:

[0029] ;

[0030] in, represents the space-time kernel density function, represents a spatiotemporal dataset, represents the absolute error;

[0031] The target sample block is maximally cut according to the relationship to obtain a final cutting result, wherein the speed and accuracy of the spatiotemporal kernel density approximation function are within a preset range under the final cutting result.

[0032] In addition, to achieve the above-mentioned purpose, the present invention further provides a spatiotemporal kernel density visualization system based on module compression, wherein the spatiotemporal kernel density visualization system based on module compression includes:

[0033] A sample block segmentation module is used to obtain a spatiotemporal dataset, construct a sample block according to the spatiotemporal dataset, determine a segmentation rule according to an absolute error input by a user, and segment the sample block according to the segmentation rule to obtain multiple target sample blocks;

[0034] a sample block screening module, configured to scan all data points in the spatiotemporal dataset to obtain a scanning result, and screen all the target sample blocks according to the scanning result to obtain a block set;

[0035] A data point compression module is used 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 the spatiotemporal kernel density approximation function of all pixel-time pairs based on all the central data points in the block set.

[0036] In addition, to achieve the above-mentioned purpose, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and a module-compression-based spatiotemporal kernel density visualization program stored on the memory and runnable on the processor, and when the module-compression-based spatiotemporal kernel density visualization program is executed by the processor, the steps of the module-compression-based spatiotemporal kernel density visualization method as described above are implemented.

[0037] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a spatiotemporal kernel density visualization program based on module compression, and when the spatiotemporal kernel density visualization program based on module compression is executed by a processor, the steps of the spatiotemporal kernel density visualization method based on module compression as described above are implemented.

[0038] In the present invention, a spatiotemporal dataset is obtained, sample blocks are constructed based on the spatiotemporal dataset, and a segmentation rule is determined based on the absolute error input by the user. The sample blocks are segmented according to the segmentation rule to obtain multiple target sample blocks. All data points in the spatiotemporal dataset are scanned to obtain a scan result, and all target sample blocks are screened based on the scan result to obtain a block set. All data points in each target sample block in the block set are compressed to obtain the central data point in each target sample block. Based on all the central data points in the block set, a spatiotemporal kernel density approximation function is calculated for all pixel-time pairs. A relationship between the spatiotemporal kernel density approximation function and the absolute error is constructed, and the target sample block is maximized based on the relationship to obtain a final cut result to calculate the average betweenness centrality of the simulated network. By compressing all data points in the target sample block, calculating the spatiotemporal kernel density approximation function based on the function, and then establishing a relationship with the absolute error, the present invention improves calculation speed and accuracy while maintaining the spatiotemporal kernel density visualization results generated by the original dataset. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 It is a flow chart of a preferred embodiment of the spatiotemporal kernel density visualization method based on module compression of the present invention;

[0040] Figure 2 1 is a schematic diagram of the spatiotemporal kernel density visualization of location A according to a preferred embodiment of the spatiotemporal kernel density visualization method based on module compression of the present invention;

[0041] Figure 3 It is a distribution diagram of location data points of a certain location data set in three-dimensional space and time in a preferred embodiment of the spatiotemporal kernel density visualization method based on module compression of the present invention;

[0042] Figure 4 Schematic diagram of data point compression of a preferred embodiment of the spatiotemporal kernel density visualization method based on module compression of the present invention;

[0043] Figure 5 This is a comparison chart of the accuracy of spatiotemporal kernel density visualization using different methods of a preferred embodiment of the spatiotemporal kernel density visualization method based on module compression of the present invention;

[0044] Figure 6 Schematic diagram of maximum error of spatiotemporal kernel density visualization of a preferred embodiment of the spatiotemporal kernel density visualization method based on module compression of the present invention;

[0045] Figure 7 1 is a schematic diagram of the number of target sample blocks when the absolute error is fixed in a preferred embodiment of the spatiotemporal kernel density visualization method based on module compression of the present invention;

[0046] Figure 8 This is a first time schematic diagram of the spatiotemporal kernel density visualization of a preferred embodiment of the spatiotemporal kernel density visualization method based on module compression of the present invention;

[0047] Figure 9 1 is a second time schematic diagram of the spatiotemporal kernel density visualization of a preferred embodiment of the spatiotemporal kernel density visualization method based on module compression of the present invention;

[0048] Figure 10 3 is a third time schematic diagram of the spatiotemporal kernel density visualization of a preferred embodiment of the spatiotemporal kernel density visualization method based on module compression of the present invention;

[0049] Figure 11 4 is a schematic diagram of the spatiotemporal kernel density visualization of a preferred embodiment of the spatiotemporal kernel density visualization method based on module compression according to the present invention;

[0050] Figure 12 is a fifth time schematic diagram of the spatiotemporal kernel density visualization of a preferred embodiment of the spatiotemporal kernel density visualization method based on module compression of the present invention;

[0051] Figure 13 1 is a structural diagram of a preferred embodiment of a spatiotemporal kernel density visualization system based on module compression according to the present invention;

[0052] Figure 14 FIG. 4 is a structural diagram of a preferred embodiment of the terminal of the present invention. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solutions and advantages of the present invention more clear and distinct, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0054] The spatiotemporal kernel density visualization method based on module compression described in the preferred embodiment of the present invention is as follows: Figure 1 As shown, the spatiotemporal kernel density visualization method based on module compression includes the following steps:

[0055] Step S10: Acquire a spatiotemporal dataset, construct a sample block according to the spatiotemporal dataset, determine a segmentation rule according to an absolute error input by a user, segment the sample block according to the segmentation rule, and obtain a plurality of target sample blocks.

[0056] The spatiotemporal dataset is scanned and multiple data points at the edge of the spatiotemporal dataset are extracted. The obtained data points can be converted into spatiotemporal kernel density visualization results, such as Figure 2 As shown, Figure 2 (a) in the figure represents the dataset of location A. Figure 2(b) Figure 2 (c) Figure 2 (d) and Figure 2 (e) in FIG. 5 represents the results of the spatiotemporal kernel density visualization of location A at different times in this embodiment, where different colors represent the hot spots or cold spots of the data to be tracked.

[0057] Specifically, a spatiotemporal dataset is obtained, and a sample block is constructed based on all the data points at the outermost edges, wherein all the data points at the outermost edges are on the surface of the sample block; an absolute error input by a user is obtained, and a segmentation rule is determined based on the absolute error; the sample block is segmented according to the segmentation rule to obtain multiple target sample blocks, wherein the segmentation rule represents the length, width and height of the target sample block.

[0058] Among them, for the acquired data points, its distribution in three-dimensional space and time can be obtained, such as Figure 3 As shown, it represents the distribution of location data points of a telephone location dataset of a certain place in three-dimensional space and time, where T represents the timestamp when the telephone location dataset of the place is obtained, and X and Y both represent the resolution of the data points.

[0059] Furthermore, for the data points distributed in three-dimensional space-time, a sample block containing all the data points in the space-time dataset can be extracted, where the data points at the edge of the space-time dataset are on the surface of the sample block. Then, according to the segmentation rule, the sample block is divided into multiple blocks of size The target sample block, such as Figure 4 As shown, represents the bottom edge of the target sample block, Indicates the height of the target sample block.

[0060] Step S20: Scan all data points in the spatiotemporal dataset to obtain a scan result, and screen all target sample blocks according to the scan result to obtain a block set.

[0061] After the sample blocks are segmented, it is necessary to detect the data points in the target sample blocks to filter out target sample blocks that meet the rules.

[0062] Specifically, all the data points in each target sample block are scanned to obtain the number of data points in each target sample block; based on the number of all the data points, a plurality of target sample blocks that meet preset conditions are screened out; and a block set is constructed using all the screened target sample blocks.

[0063] Target sample blocks that do not contain data points are removed. Furthermore, if a data point is on the surface of multiple target sample blocks, it is randomly assigned to a target sample block. Preferably, it can be assigned to a target sample block containing more data points. This can reduce subsequent calculation time, improve calculation speed and accuracy of calculation results. After the target sample blocks are screened, the remaining target sample blocks are constructed into a block set for the subsequent calculation of the spatiotemporal kernel density approximation function.

[0064] Step S30: 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 the spatiotemporal kernel density approximation function of all pixel-time pairs based on all the central data points in the block set.

[0065] Specifically, 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 based on all the central data points in the block set, a spatiotemporal kernel density approximation function of all pixel-time pairs in the block set is calculated:

[0066] ;

[0067] in, represents the space-time kernel density approximation function, represents the number of all data points in the spatiotemporal dataset, represents the target sample block, Represents a collection of blocks, represents the number of data points in the target sample block, represents the spatial kernel function, represents the time kernel function, represents pixels, Indicates the pixel time steps, represents the central data point of the target sample block, express time step.

[0068] Among them, for all data points in the target sample block, compression processing is first performed. Instead of directly calculating the spatiotemporal kernel density visualization results of the pixel-time pairs converted according to the spatiotemporal dataset, the data points in each target sample block are first compressed into the most central central data point, such as Figure 4 in When it is 4, it means there are 4 data points. By compressing these 4 data points, the central data point is obtained. , these blocks also store the number of data points in them; by designing effective data compression methods to reduce the number of data points as much as possible, and theoretically ensuring that the spatiotemporal kernel density visualization gives similar visualization results in the compressed data as in the original data, thereby compressing the data set on a large scale and greatly improving the speed of processing large-scale data.

[0069] Furthermore, a plurality of pixel-time pairs are determined based on all the data points in the block set.

[0070] After obtaining the block set, all data points in the block set can be further converted into pixel-time pairs for subsequent spatiotemporal kernel density approximation visualization.

[0071] Furthermore, the preset condition means that accurate spatiotemporal kernel density visualization results can be obtained. In order to ensure that the spatiotemporal kernel density approximation function does not differ too much from the original spatiotemporal kernel density visualization (such as Figure 5 As shown, Figure 5 (a) Figure 5 (b) Figure 5 (c) and Figure 5 The four figures above (d) represent the visualization results of the space-time kernel density at different times based on the original space-time kernel density function. Figure 5 (a) Figure 5 (b) Figure 5 (c) and Figure 5 The four figures below (d) in the figure represent the visualization results of the space-time kernel density obtained according to the space-time kernel density approximation function at different times. It is necessary to construct an absolute error to ensure that the difference between the space-time kernel density approximation function and the original space-time kernel density function does not exceed this absolute error.

[0072] Specifically, the relationship between the spatiotemporal kernel density approximation function and the absolute error is constructed:

[0073] ;

[0074] in, represents the space-time kernel density function, represents a spatiotemporal dataset, represents an absolute error; and maximally cutting the target sample block according to the relationship to obtain a final cutting result, wherein the speed and accuracy of the spatiotemporal kernel density approximation function under the final cutting result are within a preset range.

[0075] Among them, such as Figure 6As shown in , under the change of theoretical absolute error, the relationship between absolute error and the actual maximum error of the approximate method in generating spatiotemporal kernel density visualization can be found; further, as Figure 7 As shown, it can be found that when the target sample block is large, each target sample block will contain many data points, and the number of target sample blocks will decrease, which also leads to a faster speed in calculating the spatiotemporal kernel density approximation function, but the error (absolute error) will become larger. 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 lead to a slower calculation speed, but this will improve the calculation accuracy. Therefore, in this embodiment, by maximizing and , so that the difference between the space-time kernel density approximation function and the space-time kernel density function does not exceed the absolute error. Through this process, the time to generate space-time kernel density visualization in this compressed dataset can be reduced and a higher compression rate can be maintained. In addition, the space-time kernel density visualization generated by the compressed dataset and the space-time kernel density visualization generated by the original dataset can be kept within the error.

[0076] Furthermore, in this embodiment, based on the above method, the speed and accuracy of spatiotemporal kernel density visualization can be significantly improved, such as Figure 8 As shown, SWS represents the method of "Sliding-Window-based Solution". It represents the SWS method after compression by the method described in the present invention, It represents the PREFIX method after compression by the method described in the present invention, through Figure 8 It can be found that after the compression method of the present invention, the speed of obtaining the spatiotemporal kernel density visualization result is faster; and in the subsequent change of resolution (such as Figure 8 As shown), changing the number of timestamps (as shown Figure 9 As shown), changing the spatial bandwidth (as Figure 10 As shown), changing the time bandwidth (as Figure 11 as shown) and changing the theoretical absolute error (as Figure 12 As shown), the compression method in the present invention can achieve faster calculation speed.

[0077] The present invention compresses all data points in the target sample block, calculates the spatiotemporal kernel density approximation function based on it, and then constructs a relationship with the absolute error, thereby improving the calculation speed while ensuring the spatiotemporal kernel density visualization result generated by the original data set.

[0078] Further, if Figure 13As shown, based on the above-mentioned module compression-based spatiotemporal kernel density visualization method, the present invention also provides a module compression-based spatiotemporal kernel density visualization system, wherein the module compression-based spatiotemporal kernel density visualization system includes:

[0079] The sample block segmentation module 51 is used to obtain a spatiotemporal dataset, construct a sample block according to the spatiotemporal dataset, determine a segmentation rule according to an absolute error input by a user, and segment the sample block according to the segmentation rule to obtain multiple target sample blocks;

[0080] a sample block screening module 52 for scanning all data points in the spatiotemporal dataset to obtain a scanning result, and screening all the target sample blocks according to the scanning result to obtain a block set;

[0081] The data point compression module 53 is used to compress 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, and calculate the spatiotemporal kernel density approximation function of all pixel-time pairs based on all the central data points in the block set.

[0082] Further, if Figure 14 As shown, based on the above-mentioned module compression-based spatiotemporal kernel density visualization method and system, the present invention also provides a terminal, which includes a processor 10, a memory 20 and a display 30. Figure 14 Only some of the components of the terminal are shown, but it should be understood that implementation of all of the shown components is not required, and more or fewer components may be implemented instead.

[0083] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal. In other embodiments, the memory 20 may also be an external storage device of the terminal, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the terminal. Furthermore, the memory 20 may include both an internal storage unit of the terminal and an external storage device. The memory 20 is used to store application software installed on the terminal and various types of data, such as program code of the installation terminal. The memory 20 may also be used to temporarily store data that has been output or is about to be output. In one embodiment, the memory 20 stores a spatiotemporal kernel density visualization program 40 based on module compression. The spatiotemporal kernel density visualization program 40 based on module compression can be executed by the processor 10, thereby implementing the spatiotemporal kernel density visualization method based on module compression in the present application.

[0084] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor, or other data processing chip, configured to execute program codes or process data stored in the memory 20, such as executing the module compression-based spatiotemporal kernel density visualization method.

[0085] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display 30 is used to display information on the terminal and to display a visual user interface. The components of the terminal communicate with each other via a system bus.

[0086] In one embodiment, when the processor 10 executes the module compression-based spatiotemporal kernel density visualization program 40 in the memory 20 , the steps of the module compression-based spatiotemporal kernel density visualization method are implemented.

[0087] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a spatiotemporal kernel density visualization program based on module compression, and when the spatiotemporal kernel density visualization program based on module compression is executed by a processor, the steps of the spatiotemporal kernel density visualization method based on module compression as described above are implemented.

[0088] In summary, the present invention provides a spatiotemporal kernel density visualization method and related equipment based on module compression. The method includes: obtaining a spatiotemporal dataset, constructing sample blocks based on the spatiotemporal dataset, determining a segmentation rule based on an absolute error input by a user, segmenting the sample blocks according to the segmentation rule to obtain multiple target sample blocks; scanning all data points in the spatiotemporal dataset to obtain a scan result, and screening all the target sample blocks based on the scan result to obtain a block set; compressing all data points in each target sample block in the block set to obtain a central data point in each target sample block, and calculating the spatiotemporal kernel density approximation function for all pixel-time pairs based on all the central data points in the block set. The present invention improves calculation speed and accuracy by compressing all data points in the target sample block, calculating the spatiotemporal kernel density approximation function based on the function, and then establishing a relationship with the absolute error, while ensuring the spatiotemporal kernel density visualization result generated by the original dataset.

[0089] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or terminal comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or terminal comprising the element.

[0090] Of course, those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware (such as a processor, controller, etc.) through a computer program. The program can be stored in a computer-readable storage medium that can be read by a computer. When executed, the program can include the processes in the above-described method embodiments. The computer-readable storage medium can be a memory, a magnetic disk, an optical disk, etc.

[0091] It should be understood that the application of the present invention is not limited to the above examples. For those skilled in the art, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A spatiotemporal kernel density visualization method based on module compression, characterized in that: The spatiotemporal kernel density visualization method based on module compression includes: Scanning a spatiotemporal dataset and extracting a plurality of data points at the outermost edges of the spatiotemporal dataset to obtain the spatiotemporal dataset, constructing a sample block based on the spatiotemporal dataset, determining a segmentation rule based on an absolute error input by a user, segmenting the sample block based on the segmentation rule to obtain a plurality of target sample blocks, wherein the spatiotemporal kernel density approximation function and the original spatiotemporal kernel density function do not exceed the absolute error, and the data points represent the distribution of location data points of a telephone location dataset of a certain location in three-dimensional space-time; Scanning all data points in the spatiotemporal dataset to obtain a scanning result, and screening all 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 a central data point in each target sample block, and calculating a spatiotemporal kernel density approximation function of all pixel-time pairs based on all the central data points in the block set; The compressing all data points in each target sample block in the block set to obtain a central data point in each target sample block, and calculating a spatiotemporal kernel density approximation function of all pixel-time pairs based on all the central data points in the block set, specifically includes: Performing compression processing on all data points in each target sample block in the block set to obtain a central data point in each target sample block; According to all the central data points in the block set, the spatiotemporal kernel density approximation function of all pixel-time pairs is calculated: ； in, represents the space-time kernel density approximation function, represents the number of all data points in the spatiotemporal dataset, represents the target sample block, Represents a collection of blocks, represents the number of data points in the target sample block, represents the spatial kernel function, represents the time kernel function, represents pixels, Indicates the pixel time steps, represents the central data point of the target sample block, express time step.

2. The spatiotemporal kernel density visualization method based on module compression according to claim 1, characterized in that: The step of acquiring a spatiotemporal dataset, constructing a sample block according to the spatiotemporal dataset, determining a segmentation rule according to an absolute error input by a user, and segmenting the sample block according to the segmentation rule to obtain a plurality of target sample blocks specifically includes: Acquire a spatiotemporal dataset, and construct a sample block according to all the data points at the outermost edges, wherein all the data points at the outermost edges are on the surface of the sample block; Obtaining an absolute error input by a user, and determining a segmentation rule based on the absolute error; The sample block is segmented 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 block.

3. The spatiotemporal kernel density visualization method based on module compression according to claim 1, characterized in that: Scanning all data points in the spatiotemporal dataset to obtain a scanning result, and screening all target sample blocks according to the scanning result to obtain a block set, specifically includes: Scan all the data points in each target sample block to obtain the number of data points in each target sample block; Filtering out a plurality of target sample blocks that meet preset conditions based on the number of all the data points; A block set is constructed using all the screened target sample blocks.

4. The spatiotemporal kernel density visualization method based on module compression according to claim 1, characterized in that: The step of calculating the spatiotemporal kernel density approximation function of all pixel-time pairs based on all the central data points in the block set further includes: Based on all of the data points in the set of blocks, a plurality of pixel-time pairs are determined.

5. The spatiotemporal kernel density visualization method based on module compression according to claim 4, characterized in that: The segmenting of the sample block according to the segmentation rule to obtain a plurality of target sample blocks specifically includes: Construct the relationship between the spatiotemporal kernel density approximation function and the absolute error: ； in, represents the space-time kernel density function, represents a spatiotemporal dataset, represents the absolute error; The target sample block is maximally cut according to the relationship to obtain a final cutting result, wherein the speed and accuracy of the spatiotemporal kernel density approximation function are within a preset range under the final cutting result.

6. A spatiotemporal kernel density visualization system based on module compression, characterized in that: The module compression-based spatiotemporal kernel density visualization system is applied to the module compression-based spatiotemporal kernel density visualization method according to any one of claims 1 to 5, and the module compression-based spatiotemporal kernel density visualization system includes: A sample block segmentation module is used to obtain a spatiotemporal dataset, construct a sample block according to the spatiotemporal dataset, determine a segmentation rule according to an absolute error input by a user, and segment the sample block according to the segmentation rule to obtain multiple target sample blocks; a sample block screening module, configured to scan all data points in the spatiotemporal dataset to obtain a scanning result, and screen all the target sample blocks according to the scanning result to obtain a block set; A data point compression module is used 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 the spatiotemporal kernel density approximation function of all pixel-time pairs based on all the central data points in the block set.

7. A terminal, characterized in that: The terminal includes: a memory, a processor, and a module-compression-based spatiotemporal kernel density visualization program stored in the memory and runnable on the processor. When the module-compression-based spatiotemporal kernel density visualization program is executed by the processor, the steps of the module-compression-based spatiotemporal kernel density visualization method as described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a spatiotemporal kernel density visualization program based on module compression, and when the spatiotemporal kernel density visualization program based on module compression is executed by a processor, the steps of the spatiotemporal kernel density visualization method based on module compression as described in any one of claims 1 to 5 are implemented.

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