A method, system, device, and medium for rapid visualization of particle data

Abstract

This invention relates to the field of particle visualization, providing a method, system, device, and medium for rapid visualization of particle data. The method includes acquiring particle data; dividing the three-dimensional space of the particle data using a K-D tree and compressing it using a Gaussian mixture model at each leaf node to obtain compressed data; performing data transformation on the compressed data to obtain transformed data; projecting the transformed data onto a two-dimensional plane using a Jacobian matrix to obtain projected data; and performing splash rendering on the projected data to obtain visualized particle data. This invention effectively solves the problems of time-consuming reconstruction and large memory consumption in large-scale particle data visualization, achieving efficient visualization and time-series analysis.

Description

Technical Field

[0001] This invention relates to the field of particle visualization, and more specifically, to a method, system, device, and medium for rapid visualization of particle data. Background Technology

[0002] With the increasing computing power of supercomputers, the scale of scientific simulation data has exploded. Particle data in astronomical simulations often reaches tens of billions to trillions of data points, placing extremely high demands on the efficiency and effectiveness of data storage, visualization, and analysis methods. Existing visual analysis methods for large-scale particle data typically employ a "compression → storage → reconstruction → visualization" process. Among these, the reconstruction process is time-consuming, resource-intensive, and computationally demanding, becoming a bottleneck restricting the performance of visualization and analysis workflows for large-scale particle datasets. Summary of the Invention

[0003] The purpose of this invention is to provide a method, system, device, and medium for rapid visualization of particle data, in order to improve the above-mentioned problems.

[0004] To achieve the above objectives, the embodiments of this application provide the following technical solutions:

[0005] On one hand, embodiments of this application provide a method for rapid visualization of particle data, the method comprising:

[0006] Acquire particle data;

[0007] The three-dimensional space of the particle data is divided using a KD tree, and the Gaussian mixture model is used to compress the data at each leaf node to obtain the compressed data.

[0008] The compressed data is transformed to obtain the transformed data;

[0009] The transformed data is projected onto a two-dimensional plane using the Jacobian matrix to obtain the projected data;

[0010] The projected data is then splashed and rendered to obtain visualized particle data.

[0011] Secondly, embodiments of this application provide a rapid particle data visualization system, the system comprising:

[0012] The acquisition module is used to acquire particle data;

[0013] The first processing module is used to divide the three-dimensional space of particle data using a KD tree and to compress the data using a Gaussian mixture model at each leaf node to obtain compressed data.

[0014] The second processing module is used to perform data transformation on the compressed data to obtain transformed data;

[0015] The third processing module is used to project the transformed data onto a two-dimensional plane using the Jacobian matrix to obtain the projected data.

[0016] The fourth processing module is used to perform splash rendering on the projected data to obtain visualized particle data.

[0017] Thirdly, embodiments of this application provide a device for rapid visualization of particle data, the device including a memory and a processor. The memory is used to store a computer program; the processor is used to execute the computer program to implement the steps of the above-described rapid particle data visualization method.

[0018] Fourthly, embodiments of this application provide a readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for rapid visualization of particle data.

[0019] The beneficial effects of this invention are as follows:

[0020] This invention reduces data storage and I / O operations by acquiring particle data, partitioning the space using a KD tree, and combining it with a Gaussian mixture model for compression. Data transformation enables flexible adjustment from multiple perspectives, facilitating expert analysis. Projecting the data onto a two-dimensional plane using a Jacobian matrix, combined with the 3σ principle and splash rendering, improves rendering efficiency and reduces computational load. The overall workflow overcomes the bottleneck of traditional "reconstruction," supporting real-time interaction and multi-timestep analysis, effectively solving problems such as time-consuming reconstruction and high memory consumption in large-scale particle data visualization, achieving efficient visualization and time-series analysis.

[0021] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the particle data rapid visualization method described in an embodiment of the present invention.

[0024] Figure 2This is a schematic diagram of the particle data rapid visualization system structure described in an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram of the structure of the particle data rapid visualization device described in an embodiment of the present invention.

[0026] The diagram is labeled as follows: 800, particle data rapid visualization device; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component; 901, acquisition module; 902, first processing module; 903, second processing module; 904, third processing module; 905, fourth processing module. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0028] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0029] Example 1:

[0030] This embodiment provides a method for rapid visualization of particle data. It can be understood that a scenario can be set up in this embodiment, such as a scenario where particle data needs to be visualized quickly.

[0031] See Figure 1 The figure shows that the method includes steps S1, S2, S3, S4, and S5, which specifically include:

[0032] Step S1: Acquire particle data;

[0033] Step S2: Use a KD tree to divide the three-dimensional space of the particle data, and use a Gaussian mixture model to compress the data at each leaf node to obtain the compressed data;

[0034] Step S2 further includes steps S21, S22, and S23, which specifically include:

[0035] Step S21: Use a KD tree to divide the three-dimensional space of the particle data and generate leaf node partitioning results, wherein the leaf node partitioning results include a set of particle coordinates.

[0036] In this step, the three-dimensional space of the particle data is divided into uniform leaf nodes containing an approximate number of particles using the KD tree algorithm, achieving a spatially balanced partition. This provides a structured data foundation for the subsequent local fitting of the Gaussian mixture model (GMM), facilitating improved compression efficiency and computational uniformity.

[0037] Step S22: Based on the particle coordinates within the leaf nodes, use a Gaussian mixture model to fit the particle density to obtain the weights, mean, and covariance matrix of each Gaussian distribution;

[0038] In this step, the specific process of fitting the particle density is as follows:

[0039] ;

[0040] In the above formula, Represents coordinates in three-dimensional space The particle density distribution function at the location; L represents the number of Gaussian components in each Gaussian mixture model, which is taken as 2 in this step; This represents the weight of the i-th Gaussian component; Let represent the Gaussian distribution function, where and Let represent the mean vector and covariance matrix of the i-th Gaussian distribution, respectively.

[0041] Step S23: Replace the particle data with the weights, mean, and covariance matrices of each Gaussian distribution to obtain compressed data.

[0042] In this step, the weights, means, and covariance matrices of each Gaussian distribution are used to replace the particle data, significantly reducing data storage and I / O load. This also lays the foundation for subsequent parametric rendering in the visualization stage.

[0043] Step S3: Perform data transformation on the compressed data to obtain transformed data;

[0044] Step S3 further includes steps S31, S32, S33, S34, and S35, which specifically include:

[0045] Step S31: Determine the first information based on the compressed data. The first information includes the total number of particles and the number of particles corresponding to a Gaussian mixture model.

[0046] Step S32: Calculate the global weight parameters based on the first information;

[0047] In this step, the specific calculation process for the global weight parameters is as follows:

[0048] ;

[0049] In the above formula, Represents the global weight parameters; This represents the weight of a Gaussian kernel in its corresponding Gaussian mixture model. The formula represents the number of particles corresponding to a Gaussian mixture model; M represents the total number of particles. The Gaussian kernel weights are adjusted using the formula above to balance the particle distribution ratio of different GMM models and avoid density distortion when the compressed data is visualized.

[0050] It should be noted that if the original weights are used directly, the overall density may be biased due to the different number of particles covered by each GMM. This formula can map the proportion of particles in the local GMM to the global weights to ensure that the GMMs of different leaf nodes maintain density consistency in visualization, thereby eliminating the bias.

[0051] Step S33: Obtain second information, which includes preset three-dimensional center point coordinates and scaling factor;

[0052] Step S34: Transform the compressed data according to the second information to obtain the scaled parameters;

[0053] In this step, the specific process of transforming the compressed data is as follows:

[0054] ;

[0055] ;

[0056] In the above formula, and Let represent the mean vector and covariance matrix after scaling transformation, respectively; and These represent the original mean vector and covariance matrix, respectively; Indicates the scaling factor; This represents the preset coordinates of the 3D center point, which serves as the reference point for scaling transformation.

[0057] Step S35: Rotate the scaled parameters to obtain the transformed data.

[0058] In this step, the specific process of rotating the scaled parameters is as follows:

[0059] ;

[0060] ;

[0061] In this step, and The mean vector and covariance matrix after rotation are respectively, and R represents the preset rotation matrix.

[0062] It is understood that the order of scaling and rotation does not affect the particle visualization results of this invention.

[0063] In this embodiment, after fitting the particle density using a Gaussian mixture model, the model parameters (mean, covariance, weights) are directly geometrically transformed. This eliminates the need to reconstruct the original particle coordinates, significantly reducing computational overhead and laying the foundation for real-time visualization and time-series analysis of large-scale particle data.

[0064] It is understandable that existing technologies generally believe that compressed model parameters can only represent statistical distributions and cannot be directly used for visualization. This invention can achieve high-precision visualization without reconstructing the original data, thus avoiding the CPU and memory consumption of the reconstruction step.

[0065] Step S4: Project the transformed data onto a two-dimensional plane using the Jacobian matrix to obtain the projected data;

[0066] In this step, projecting the transformed data onto a two-dimensional plane using the Jacobian matrix is ​​a well-known technique and will not be elaborated upon here.

[0067] Step S5: Perform splash rendering on the projected data to obtain visualized particle data.

[0068] Step S5 further includes steps S51, S52, S53, and S54, which specifically include:

[0069] Step S51: Calculate the projected rendering area based on the 3σ principle of Gaussian distribution to obtain the coordinate range of the two-dimensional rendering area;

[0070] Step S51 further includes steps S511, S512, S513, S514, S515, and S516, which specifically include:

[0071] Step S511: Determine the covariance matrix based on the projected data;

[0072] Step S512: Calculate the corresponding eigenvalue information and eigenvector information based on the covariance matrix;

[0073] In this step, the corresponding eigenvalues ​​are calculated based on the covariance matrix to obtain two eigenvalues, denoted as the first eigenvalue. Second eigenvalue Based on the covariance matrix, the corresponding eigenvector information is calculated, resulting in two eigenvectors, denoted as the first eigenvector. Second eigenvector .

[0074] Step S513: Calculate the angle between the two feature vectors to obtain the angle information;

[0075] In this step, the formula for calculating the included angle information is:

[0076] ;

[0077] In the above formula, The ordinate of the first eigenvector is represented by its vertical axis. The x-coordinate represents the second eigenvector.

[0078] Step S514: Calculate the maximum and minimum values ​​of each Gaussian kernel on the X-axis in the two-dimensional plane based on the feature value information and the included angle information to obtain the third information;

[0079] In this step, the specific calculation process for the third information is as follows:

[0080] ;

[0081] ;

[0082] In the above formula, and These represent the maximum and minimum values ​​of each Gaussian kernel on the X-axis in the two-dimensional plane, respectively. This represents the component of the Gaussian kernel mean after two-dimensional projection on the X-axis. and These represent the first eigenvalue and the second eigenvalue, respectively. This indicates the included angle information.

[0083] Step S515: Calculate the maximum and minimum values ​​of each Gaussian kernel on the Y-axis in the two-dimensional plane based on the feature value information and the included angle information to obtain the fourth information;

[0084] In this step, the specific calculation process for the fourth piece of information is as follows:

[0085] ;

[0086] ;

[0087] In the above formula, and These represent the maximum and minimum values ​​of each Gaussian kernel along the y-axis in the two-dimensional plane, respectively. This represents the y-axis component of the Gaussian kernel mean after two-dimensional projection.

[0088] Step S516: Determine the rendered area after projection based on the third information and the fourth information.

[0089] In this step, a minimum axis-aligned rectangle containing the 3σ region of the Gaussian kernel is generated based on the third and fourth information. This rectangle serves as the effective pixel range for subsequent rendering, avoiding invalid calculations on all planar pixels, achieving efficient reduction of rendering load while ensuring visualization accuracy.

[0090] Step S52: Calculate the Mahalanobis distance between each pixel within the coordinate range of the two-dimensional rendering area and the current Gaussian kernel;

[0091] In this step, the specific calculation process for the square of the Mahalanobis distance is as follows:

[0092] ;

[0093] In the above formula, Represents the square of the Mahalanobis distance; Represents the pixel coordinates in the j-th row and k-th column of a two-dimensional plane; This represents the mean vector of the Gaussian kernel projected onto a two-dimensional plane. This represents a two-dimensional covariance matrix.

[0094] Step S53: Filter out the set of pixels within the 3σ region based on the Mahalanobis distance;

[0095] In this step, when If the value is greater than 9, then the pixel is outside the 3σ region and can be directly excluded. All remaining pixels within the 3σ region are then selected to obtain a pixel set. By filtering out invalid pixels, the amount of rendering computation is reduced while ensuring the accuracy of the visualization results.

[0096] Step S54: Render based on the pixel set to obtain visualized particle data.

[0097] Step S54 further includes steps S541, S542, S543, S544, and S545, which specifically include:

[0098] Step S541: Calculate the rendering weight corresponding to each pixel in the pixel set to obtain a rendering weight set;

[0099] In this step, the specific calculation process for rendering weights is as follows:

[0100]

[0101] In the above formula, This represents the square of the Mahalanobis distance from the pixel to the center of the Gaussian kernel; Representing a very small positive number is used to prevent =0, ensuring computational stability and making the weight of the Gaussian kernel center position finite and computable; This represents the decay exponent parameter, q>0; This indicates the rendering weight of the current pixel.

[0102] Step S542: Calculate the normalization factor based on the set of rendering weights;

[0103] In this step, the specific process for calculating the normalization factor is as follows:

[0104] ;

[0105] In the above formula, This represents the rendering weight of the j-th pixel; This represents the square of the Mahalanobis distance from the j-th pixel to the center of the Gaussian kernel.

[0106] Step S543: Calculate the particle density corresponding to each pixel based on the normalization factor;

[0107] In this step, the specific formula for calculating the particle density at each pixel is as follows:

[0108] ;

[0109] In the above formula, This represents the rendering weight of the i-th pixel; It represents the square of the Mahalanobis distance from the i-th pixel to the center of the Gaussian kernel.

[0110] Step S544: Calculate the color value based on particle density, total number of particles, and global weight;

[0111] In this step, the specific calculation process for the color value is as follows:

[0112] ;

[0113] In the above formula, The transfer function is represented by M; the total number of particles is represented by M. This represents the global weight of the m-th Gaussian kernel; This indicates that the m-th Gaussian kernel is located at pixel [number]. The particle density at that location; This represents the set of all Gaussian kernels corresponding to the current rendering plane that contain this pixel; This represents the i-th pixel.

[0114] Step S545: Render the pixel set according to the color value.

[0115] In this step, once the color value is calculated, each pixel can be rendered.

[0116] It should be noted that, in order to improve rendering efficiency, this invention employs parallel computing technology to optimize the entire rendering process. This process mainly consists of two stages: the first stage calculates the normalization factor for each Gaussian distribution, and the second stage calculates the final density value for each pixel. Although there is some overlap in calculations between the two stages, considering that each Gaussian distribution may affect thousands of pixels, the memory overhead of storing intermediate variables is actually higher than the cost of directly recalculating.

[0117] In the CUDA implementation, this invention employs a parallel strategy of processing a Gaussian distribution with a single thread block. Multiple threads within each thread block process the pixel calculations corresponding to that Gaussian distribution in parallel. This design fully leverages the isomorphism of pixel calculations within the Gaussian distribution, making it naturally suitable for multi-threaded parallel processing. Simultaneously, the atomic accumulation operation within the thread block ensures the correctness of the normalization coefficients and avoids data race issues, significantly improving computational efficiency.

[0118] Example 2:

[0119] like Figure 2 As shown, this embodiment provides a rapid particle data visualization system. The system includes an acquisition module 901, a first processing module 902, a second processing module 903, a third processing module 904, and a fourth processing module 905, specifically including:

[0120] Acquisition module 901 is used to acquire particle data;

[0121] The first processing module 902 is used to divide the three-dimensional space of the particle data using a KD tree and to compress the data using a Gaussian mixture model at each leaf node to obtain compressed data.

[0122] The second processing module 903 is used to perform data transformation on the compressed data to obtain transformed data;

[0123] The third processing module 904 is used to project the transformed data onto a two-dimensional plane using the Jacobian matrix to obtain the projected data.

[0124] The fourth processing module 905 is used to perform splash rendering on the projected data to obtain visualized particle data.

[0125] In one specific embodiment of this disclosure, the first processing module further includes a first processing unit, a second processing unit, and a third processing unit, specifically including:

[0126] The first processing unit is used to divide the three-dimensional space of particle data using a KD tree and generate leaf node partitioning results, wherein the leaf node partitioning results include a set of particle coordinates.

[0127] The second processing unit is used to fit the particle density using a Gaussian mixture model based on the particle coordinates within the leaf nodes to obtain the weights, mean, and covariance matrix of each Gaussian distribution.

[0128] The third processing unit is used to replace the particle data with the weights, mean, and covariance matrices of each Gaussian distribution to obtain compressed data.

[0129] In one specific embodiment of this disclosure, the second processing module further includes a fourth processing unit, a first calculation unit, an acquisition unit, a fifth processing unit, and a sixth processing unit, specifically including:

[0130] The fourth processing unit is used to determine first information based on the compressed data, the first information including the total number of particles and the number of particles corresponding to a Gaussian mixture model;

[0131] The first calculation unit is used to calculate global weight parameters based on the first information;

[0132] The acquisition unit is used to acquire second information, which includes preset three-dimensional center point coordinates and scaling factor;

[0133] The fifth processing unit is used to transform the compressed data according to the second information to obtain scaled parameters;

[0134] The sixth processing unit is used to rotate the scaled parameters to obtain the transformed data.

[0135] In one specific embodiment of this disclosure, the fourth processing module further includes a seventh processing unit, a second computing unit, an eighth processing unit, and a ninth processing unit, specifically comprising:

[0136] The seventh processing unit is used to calculate the projected rendering area based on the 3σ principle of Gaussian distribution, and obtain the coordinate range of the two-dimensional rendering area.

[0137] The second calculation unit is used to calculate the Mahalanobis distance between each pixel within the coordinate range of the two-dimensional rendering area and the current Gaussian kernel;

[0138] The eighth processing unit is used to filter out a set of pixels within a 3σ region based on the Mahalanobis distance;

[0139] The ninth processing unit is used to render based on the pixel set to obtain visualized particle data.

[0140] In one specific embodiment of this disclosure, the seventh processing unit further includes a tenth processing unit, a third calculation unit, a fourth calculation unit, a fifth calculation unit, a ninth calculation unit, and an eleventh processing unit, specifically comprising:

[0141] The tenth processing unit is used to determine the covariance matrix based on the projected data;

[0142] The third calculation unit is used to calculate the corresponding eigenvalue information and eigenvector information based on the covariance matrix.

[0143] The fourth calculation unit is used to calculate the angle between two feature vectors and obtain the angle information.

[0144] The fifth calculation unit is used to calculate the maximum and minimum values ​​of each Gaussian kernel on the X-axis in the two-dimensional plane based on the feature value information and the included angle information, and to obtain the third information;

[0145] The sixth calculation unit is used to calculate the maximum and minimum values ​​of each Gaussian kernel on the Y-axis in the two-dimensional plane based on the feature value information and the included angle information, so as to obtain the fourth information;

[0146] The eleventh processing unit is used to determine the rendered area after projection based on the third information and the fourth information.

[0147] In one specific embodiment of this disclosure, the ninth processing unit further includes a seventh calculation unit, an eighth calculation unit, a ninth calculation unit, a tenth calculation unit, and a twelfth processing unit, specifically comprising:

[0148] The seventh calculation unit is used to calculate the rendering weight corresponding to each pixel in the pixel set, so as to obtain a rendering weight set;

[0149] The eighth calculation unit is used to calculate the normalization factor based on the set of rendering weights;

[0150] The ninth calculation unit is used to calculate the particle density corresponding to each pixel based on the normalization factor;

[0151] The tenth calculation unit is used to calculate the color value based on particle density, total number of particles, and global weights;

[0152] The twelfth processing unit is used to render the pixel set according to the color value.

[0153] It should be noted that the specific methods by which each module performs operations in the system described in the above embodiments have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0154] Example 3:

[0155] Corresponding to the above method embodiments, this embodiment also provides a particle data rapid visualization device. The particle data rapid visualization device described below and the particle data rapid visualization method described above can be referred to in correspondence.

[0156] Figure 3 This is a block diagram illustrating a particle data rapid visualization device 800 according to an exemplary embodiment. Figure 3 As shown, the particle data rapid visualization device 800 may include: a processor 801 and a memory 802. The particle data rapid visualization device 800 may also include one or more of the following: a multimedia component 803, an I / O interface 804, and a communication component 805.

[0157] The processor 801 controls the overall operation of the particle data rapid visualization device 800 to complete all or part of the steps in the aforementioned particle data rapid visualization method. The memory 802 stores various types of data to support the operation of the particle data rapid visualization device 800. This data may include, for example, instructions for any application or method operating on the particle data rapid visualization device 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as a keyboard, mouse, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the particle data rapid visualization device 800 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0158] In an exemplary embodiment, the particle data rapid visualization device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the particle data rapid visualization method described above.

[0159] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, which, when executed by a processor, implement the steps of the aforementioned rapid particle data visualization method. For example, the computer-readable storage medium may be the aforementioned memory 802 including program instructions, which may be executed by the processor 801 of the rapid particle data visualization device 800 to complete the aforementioned rapid particle data visualization method.

[0160] Example 4:

[0161] Corresponding to the above method embodiments, this embodiment also provides a readable storage medium. The readable storage medium described below can be referred to in conjunction with the particle data fast visualization method described above.

[0162] A readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the particle data rapid visualization method described in the above method embodiments.

[0163] Specifically, the readable storage medium can be a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, or any other readable storage medium capable of storing program code.

[0164] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0165] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for fast visualization of particle data, characterized by, The method comprises the following steps: acquiring particle data; dividing the three-dimensional space of the particle data by using a K-D tree, and compressing each leaf node by using a Gaussian mixture model to obtain compressed data; performing data transformation on the compressed data to obtain transformed data; projecting the transformed data onto a two-dimensional plane by using a Jacobian matrix to obtain projected data; performing splash rendering on the projected data to obtain visualized particle data; wherein the step of dividing the three-dimensional space of the particle data by using a K-D tree and compressing each leaf node by using a Gaussian mixture model comprises the following steps: dividing the three-dimensional space of the particle data by using a K-D tree to generate a leaf node division result, wherein the leaf node division result comprises a particle coordinate set; fitting particle density by using a Gaussian mixture model based on the particle coordinates in the leaf node to obtain the weight, mean value and covariance matrix of each Gaussian distribution; replacing the particle data with the weight, mean value and covariance matrix of each Gaussian distribution to obtain compressed data.

2. The method of claim 1, wherein, The step of performing data transformation on the compressed data to obtain transformed data comprises the following steps: determining first information according to the compressed data, wherein the first information comprises the total number of particles and the number of particles corresponding to one Gaussian mixture model; calculating a global weight parameter according to the first information; acquiring second information, wherein the second information comprises a preset three-dimensional center point coordinate and a scaling factor; transforming the compressed data according to the second information to obtain scaled parameters; rotating the scaled parameters to obtain the transformed data.

3. The method of claim 1, wherein, The step of performing splash rendering on the projected data to obtain visualized particle data comprises the following steps: calculating the rendering area of the projected data based on the 3σ principle of Gaussian distribution to obtain a two-dimensional rendering area coordinate range; calculating the Mahalanobis distance between each pixel in the two-dimensional rendering area coordinate range and the current Gaussian kernel; screening out a pixel set in the 3σ area according to the Mahalanobis distance; performing rendering based on the pixel set to obtain visualized particle data.

4. A system for fast visualization of particle data, characterized by The method comprises the following steps: an acquisition module configured to acquire particle data; a first processing module configured to divide the three-dimensional space of the particle data by using a K-D tree, and compress each leaf node by using a Gaussian mixture model to obtain compressed data; a second processing module configured to perform data transformation on the compressed data to obtain transformed data; a third processing module configured to project the transformed data onto a two-dimensional plane by using a Jacobian matrix to obtain projected data; a fourth processing module configured to perform splash rendering on the projected data to obtain visualized particle data; wherein the first processing module comprises the following steps: a first processing unit configured to divide the three-dimensional space of the particle data by using a K-D tree to generate a leaf node division result, wherein the leaf node division result comprises a particle coordinate set; a second processing unit configured to fit particle density by using a Gaussian mixture model based on the particle coordinates in the leaf node to obtain the weight, mean value and covariance matrix of each Gaussian distribution; a third processing unit configured to replace the particle data with the weight, mean value and covariance matrix of each Gaussian distribution to obtain compressed data.

5. The particle data fast visualization system of claim 4, wherein, The second processing module comprises: A fourth processing unit configured to determine first information according to the compressed data, the first information comprising a total number of particles and a number of particles corresponding to a Gaussian mixture model; A first calculation unit configured to calculate a global weight parameter according to the first information; An acquisition unit configured to acquire second information, the second information comprising a preset three-dimensional center point coordinate and a scaling factor; A fifth processing unit configured to transform the compressed data according to the second information to obtain scaled parameters; A sixth processing unit configured to rotate the scaled parameters to obtain the transformed data.

6. The particle data fast visualization system of claim 4, wherein, The fourth processing module comprises: A seventh processing unit configured to calculate a projected rendering area based on a 3σ principle of Gaussian distribution to obtain a two-dimensional rendering area coordinate range; A second calculation unit configured to calculate a Mahalanobis distance between each pixel in the two-dimensional rendering area coordinate range and a current Gaussian kernel; An eighth processing unit configured to filter out a pixel set in a 3σ area according to the Mahalanobis distance; A ninth processing unit configured to perform rendering based on the pixel set to obtain visualized particle data.

7. A particle data fast visualization device, characterized by The computer program is stored on the readable storage medium and is executed by the processor to implement the steps of the particle data fast visualization method according to any one of claims 1 to 3. The computer program is stored on the readable storage medium and is executed by the processor to implement the steps of the particle data fast visualization method according to any one of claims 1 to 3. ​ 8. A readable storage medium characterized by: ​

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