A method for identifying and tracking particles in a particle group colliding with a wall surface and a measuring method

Abstract

The present application belongs to the field of micro-particle impact experiment, and particularly relates to a particle group impact wall particle identification tracking method and a measuring method, wherein the identification tracking method comprises the following steps: S1, marking a target particle in the particle group; S2, acquiring particle parameters of the target particle before the target particle impacts a wall; S3, acquiring a spatial position, a motion speed and an incident angle of the target particle in the last frame before the target particle impacts the wall; S4, constructing a particle impact motion trajectory prediction model, and calculating a predicted position of the target particle in n frames after the target particle impacts the wall; and S5, constructing a judgment model, acquiring images of the n frames, and judging particles located in the predicted position through the judgment model. The present application can effectively solve the key problem of identity loss caused by particle rebound direction, speed mutation, mutual shielding, leaving the field of view and other reasons when the particle group impacts the wall.

Description

Technical Field

[0001] This invention belongs to the field of microparticle impaction experiments, specifically involving a method for identifying, tracking, and measuring particles in a particle swarm impacting a wall. Background Technology

[0002] High-speed photography, when capturing micron-sized particles at high speeds (tens to hundreds of meters per second), places high demands on the parameters of the observation equipment. Extremely high frame rates and large magnification telephoto lenses are required to clearly capture the morphology and dynamics of the moving particles and their collision with the wall. This method results in images with low resolution and a very small field of view (approximately 1 mm). The focal plane is extremely narrow (tens of micrometers), and the trajectory of medium-to-high-speed micrometer-sized particles impacting the wall is difficult to control precisely under extreme jet conditions, exhibiting strong randomness. It is challenging to control the particles to land on the focal plane, resulting in difficulty obtaining high-quality, effective data. Therefore, in high-speed micrometer-sized particle impact tests, it is necessary to increase the particle flow rate to achieve particle swarm impacts on the wall, thereby increasing the probability of particles landing on the focal plane of the telephoto lens and improving experimental efficiency.

[0003] However, with increased particle quantity, it is difficult to align and track particles because they are non-spherical, small in size, and have high speed. In particular, after the particles bounce off the wall, the bounce path is not perfectly symmetrical with the incident path because the particle group is not a standard sphere. Furthermore, the particle speed changes abruptly before and after the collision, which can easily lead to the loss of tracking and identification of the particle group. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a highly reliable method and measurement method for identifying, tracking and measuring particles in a wall when the particles are non-spherical, small in size and high in velocity.

[0005] This invention provides a method for identifying and tracking particles in a particle swarm impacting a wall, comprising the following steps:

[0006] S1, marking the target particles in the particle group;

[0007] S2, obtain the particle parameters of the target particle in the particle swarm before it impacts the wall, including particle size and sphericity;

[0008] S3, obtain the spatial position, velocity and incident angle of the target particle in the last frame before it hits the wall;

[0009] S4. Construct a particle impact trajectory prediction model. The particle impact trajectory prediction model takes the particle sphericity, spatial position of the last frame, motion velocity and incident angle as inputs to calculate the predicted position of the target particle within n frames after the target particle impacts the wall.

[0010] S5. Construct a judgment model, acquire n frames of images, and judge the particles located in the predicted position using the judgment model. If the judgment model requirements are met, the particle is determined to be the same particle as the target particle before the impact.

[0011] Furthermore, the particle impact trajectory prediction model is as follows:

[0012] By utilizing the target particle attributes to obtain the symmetry of the particle's motion trajectory before and after the collision and the temporal correlation of the velocity change in the frame, data association and matching are performed, and the predicted position of the target particle within n frames after the target particle hits the wall is output.

[0013] Furthermore, in terms of the symmetry of particle trajectories before and after the collision, the higher the sphericity of the target particle, the more symmetrical the particle trajectories before and after the collision.

[0014] Furthermore, the judgment model is as follows:

[0015] If the particle size difference between the particle and the target particle is within a set range, the particle velocity difference between the particle and the target particle velocity before impacting the wall is within a set range, and the angle difference between the incident angle of the particle and the predicted rebound angle of the target particle is within a set range, then the judgment model requirements are met.

[0016] Furthermore, obtaining the particle size of the target particle before it impacts the wall in the particle swarm includes:

[0017] Acquire three frames of images before and after the target particle impacts the wall. Calculate the equivalent particle size based on the pixel area occupied by the particle identification region using the area calculation formula. The average of the six calculated particle sizes is the final particle size.

[0018] Furthermore, obtaining the sphericity of the target particle before it impacts the wall in the particle swarm includes:

[0019] Acquire three frames of images before and after the target particle impacts the wall. Calculate the equivalent sphericity based on the pixels occupied by the particle identification area using the sphericity calculation formula. The average of the six sets of sphericities obtained is the final sphericity.

[0020] Furthermore, obtaining the velocity of the target particle in the last frame before impact with the wall includes:

[0021] The result is obtained by removing the bit of the geometric center particle of the target particle identification region between two consecutive frames, and then using the time span across frames.

[0022] Furthermore, obtaining the incident angle of the target particle in the last frame before impacting the wall includes:

[0023] Based on the identification, the vertical direction of the wall is a fixed direction, and the angle between the incident velocity and this direction is the incident angle.

[0024] The present invention also provides a method for measuring particles in a particle swarm impacting a wall, comprising marking all particles in the particle swarm located at the focal plane of a camera as target particles, tracking the target particles using the above-mentioned particle swarm impacting a wall identification and tracking method, and marking the target particles after they impact the wall.

[0025] Record and analyze all target particles before and after impacting the wall.

[0026] Furthermore, when measuring particle parameters in a particle swarm impacting a wall, the process also includes initializing global parameter particle detection, shape filtering, defocus reconstruction, and pre-collision multi-particle tracking and visualization.

[0027] The advantage of this invention is that the particle identification and tracking method provided by this invention can effectively solve the key problem of particle identification loss caused by particle rebound direction, speed change, mutual occlusion, and leaving the field of view when particle groups (especially high-speed and dense ones) collide with walls. It can accurately track target particles directly through video (image) data, and can specifically mark and continuously track the complete motion process of specific target particles before and after impacting the wall, capturing the changes in their dynamic parameters.

[0028] By employing particle impact trajectory prediction and judgment models to track and judge target particles, the workload of manually identifying and matching particles frame by frame is reduced. This significantly improves the automation level and efficiency of identification and tracking, especially for large particle groups or long-term sequence analysis. It also enhances the final reliability and provides accurate, individual-based basic data for subsequent research on the physical mechanisms of particle impact on walls (such as rebound characteristics, energy loss, wear mechanisms, etc.), making individual particle-based analysis more reliable.

[0029] Moreover, the particle identification and tracking method in this particle group impacting the wall is particularly suitable for particles of different sizes and sphericity. By introducing the sphericity parameter, the model can take into account the influence of particle shape on the collision rebound trajectory (the higher the sphericity, the more symmetrical the trajectory), thus improving the adaptability to particles of different shapes.

[0030] A particle impact trajectory prediction and judgment model is adopted, featuring a dual verification mechanism. This mechanism predicts the possible location range of the particle after impact based on its instantaneous state before impact (position, velocity, angle) and key physical properties (sphericity). Within the predicted location range, multiple key features (particle size difference, velocity difference, and rebound angle difference) are used for comprehensive judgment to ensure matching accuracy and effectively reduce false matches. Attached Figure Description

[0031] Appendix Figure 1This is a flowchart illustrating the method for identifying and tracking particles in a wall surface during particle swarm impacts, as described in this invention.

[0032] Appendix Figure 2 This is a scatter plot of particle impact parameters obtained by the particle impact measurement method in this invention.

[0033] Appendix Figure 3 This is one of the experimental images of the present invention;

[0034] Appendix Figure 4 This is the second experimental image of the present invention;

[0035] Appendix Figure 5 This is the third experimental image of the present invention;

[0036] Appendix Figure 6 This is the fourth experimental image of the present invention;

[0037] Appendix Figure 7 This is the fifth experimental image of the present invention;

[0038] Appendix Figure 8 This is the sixth experimental image of the present invention;

[0039] Appendix Figure 9 This is the seventh experimental image of the present invention. Detailed Implementation

[0040] 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 a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

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

[0042] Furthermore, in this invention, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0043] In this invention, unless otherwise explicitly specified and limited, the terms "connection," "fixed," etc., should be interpreted broadly. For example, "fixed" can mean a fixed connection, a detachable connection, or an integral part; it can mean a mechanical connection, an electrical connection, a physical connection, or a wireless communication connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean the internal communication of two elements or the interaction between two elements, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0044] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0045] As attached Figure 1 -Appendix Figure 9 As shown, the present invention provides a method for identifying and tracking particles in a particle swarm impacting a wall, comprising the following steps:

[0046] S1, marking the target particles in the particle group;

[0047] S2, obtain the particle parameters of the target particle in the particle swarm before it impacts the wall, including particle size and sphericity;

[0048] S3, obtain the spatial position, velocity and incident angle of the target particle in the last frame before it hits the wall;

[0049] S4. Construct a particle impact trajectory prediction model. The particle impact trajectory prediction model takes the particle sphericity, the spatial position of the last frame, the motion velocity and the incident angle as inputs to calculate the predicted position of the target particle within n frames after the target particle impacts the wall. Preferably, n frames can be the n frames after the last frame before the target particle impacts the wall. That is, when the last frame before the target particle impacts the wall is x frames, n frames are x+1 frames, x+2 frames, and x+n frames. The particle impact trajectory prediction model is used to calculate the predicted position of the target particle within this range.

[0050] S5. Construct a judgment model, acquire n frames of images, and judge the particles located in the predicted position using the judgment model. If the judgment model requirements are met, the particle is determined to be the same particle as the target particle before the impact.

[0051] The particle identification and tracking method provided by this invention can effectively solve the key problem of particle identification loss when a particle swarm (especially high-speed and dense) impacts a wall due to reasons such as particle rebound direction, sudden velocity change, mutual occlusion, and leaving the field of view. It can accurately track target particles directly through video (image) data, and can specifically mark and continuously track the complete motion process of a specific target particle before and after impacting the wall, capturing the changes in its dynamic parameters.

[0052] By employing particle impact trajectory prediction and judgment models to track and judge target particles, the workload of manually identifying and matching particles frame by frame is reduced. This significantly improves the automation level and efficiency of identification and tracking, especially for large particle groups or long-term sequence analysis. It also enhances the final reliability and provides accurate, individual-based basic data for subsequent research on the physical mechanisms of particle impact on walls (such as rebound characteristics, energy loss, wear mechanisms, etc.), making individual particle-based analysis more reliable.

[0053] Moreover, the particle identification and tracking method in this particle group impacting the wall is particularly suitable for particles of different sizes and sphericity. By introducing the sphericity parameter, the model can take into account the influence of particle shape on the collision rebound trajectory (the higher the sphericity, the more symmetrical the trajectory), thus improving the adaptability to particles of different shapes.

[0054] A particle impact trajectory prediction and judgment model is adopted, featuring a dual verification mechanism. This mechanism predicts the possible location range of the particle after impact based on its instantaneous state before impact (position, velocity, angle) and key physical properties (sphericity). Within the predicted location range, multiple key features (particle size difference, velocity difference, and rebound angle difference) are used for comprehensive judgment to ensure matching accuracy and effectively reduce false matches.

[0055] In one embodiment, the particle impact trajectory prediction model is as follows:

[0056] By leveraging the symmetry of the particle's trajectory before and after the collision and the temporal correlation of velocity abrupt changes across frames, data association matching is performed to output the predicted position of the target particle within n frames after its impact with the wall. In this embodiment, the prediction model integrates the symmetry of the collision trajectory determined by particle attributes (especially sphericity) with the specific number of frames where the velocity abrupt change occurs (temporal correlation). This transforms the impact physics mechanism and temporal information into an accurate data association matching strategy, effectively outputting the predicted position range of the target particle within a specified n frames after impact. Driven by physical laws, it significantly reduces the search space, lowers the risk of mismatches, and reduces computational complexity, greatly improving the efficiency and robustness of tracking target particles in complex impact scenarios.

[0057] In one embodiment, the higher the sphericity of the target particle's trajectory before and after the collision, the more symmetrical the particle's trajectory becomes. This embodiment can significantly improve the accuracy and reliability of trajectory prediction.

[0058] In one embodiment, the determination model is:

[0059] If the particle size difference between the target particle and the target particle is within a set range, the velocity difference between the target particle and the target particle before impact is within a set range, and the angle difference between the incident angle of the particle and the predicted rebound angle of the target particle is within a set range, then the judgment model requirements are met. In this embodiment, by setting a joint verification condition of three set ranges for particle size difference, velocity difference, and rebound angle difference, matching particles are accurately screened within multiple frames of predicted positions. The particle size difference ensures the uniqueness of the particle entity (excluding other particles); the velocity difference objectively reflects the reasonable range of collision energy loss; and the rebound angle difference verifies the consistency between the actual rebound direction and the predicted value based on physical laws. The strict joint judgment of these three highly correlated features effectively ensures the uniqueness, reliability, and physical rationality of the target particle identification in complex impact scenarios (such as dense rebound of multiple particles), providing highly reliable data for subsequent analysis.

[0060] In one embodiment, obtaining the particle size of the target particle before it impacts the wall in the particle swarm includes:

[0061] Three frames of images are acquired before and after the target particle impacts the wall. The equivalent particle size is calculated using the area calculation formula based on the pixel area occupied by the particle identification region. The average of the six calculated particle sizes is the final particle size. In this embodiment, since it is difficult to control the particle's trajectory in three-dimensional space to be parallel to the camera's focal plane, and the collision process may cause the particle's trajectory to deviate further from the focal plane, the average value is used to reduce errors when calculating the values ​​of various parameters of the particle impacting the wall. The actual spatial size corresponding to each pixel is calculated based on a scale of a certain length taken before observation.

[0062] In one embodiment, obtaining the sphericity of a target particle before it impacts a wall in a particle swarm includes:

[0063] Acquire three frames of images before and after the target particle impacts the wall. Calculate the equivalent sphericity based on the pixels occupied by the particle identification area using the sphericity calculation formula. The average of the six sets of sphericities obtained is the final sphericity.

[0064] In one embodiment, obtaining the velocity of the target particle in the last frame before impacting the wall includes:

[0065] The result is obtained by removing the bit of the geometric center particle of the target particle identification region between two consecutive frames, and then using the time span across frames.

[0066] In one embodiment, obtaining the incident angle of the target particle in the last frame before impacting the wall includes:

[0067] Based on the identification, the vertical direction of the wall is a fixed direction, and the angle between the incident velocity and this direction is the incident angle.

[0068] In one specific embodiment, because the particle group is not a standard sphere, the rebound path of the particle after hitting the wall is not completely symmetrical with the incident path, and the particle velocity changes abruptly before and after the collision, which can easily lead to the loss of tracking and identification of the particle group. Therefore, when tracking the particle group before and after the collision, the particle impact trajectory prediction model uses the target particle attributes to obtain the symmetry of the particle motion trajectory before and after the collision and the temporal correlation of the velocity change in the frame number for data association matching. Track a particle with a high speed (greater than 10 m / s, depending on the specific test conditions) moving towards the wall to the last frame (y) where the velocity does not change abruptly. At this time, the particle bounces off the wall and the velocity changes abruptly. Identify the new particles appearing in the images of frames y+1, 2, 3, etc. at the predicted position, and judge the following by the judgment model: 1. Whether the particle size is approximately (within 10 micrometers); 2. Whether the ratio of the particle velocity to the original high-speed particle velocity is less than 0.35 (according to the specific test conditions); 3. Whether the particle motion trajectory (angle) is near the original ideal rebound trajectory of the particle (angle less than 20 degrees; due to the randomness of non-spherical particles, the smaller the sphericity, the larger the angle can be set appropriately). If the above three conditions are met, the low-speed particle is considered to be the same particle as the original high-speed particle, and the same ID number is marked before tracking continues.

[0069] Reference Appendix Figure 3 -Appendix Figure 9 By using the aforementioned method for identifying and tracking particles in the wall after a particle swarm impacts, the final result will be... Figures 2-6 Particles marked as 2 (in) Figure 7 (The middle part consists of particles impacting the wall), and Figure 8 and Figure 9 Particles marked with 7 are identified as the same particle.

[0070] Parameter Calculation: Since it's difficult to control the particle's trajectory in three-dimensional space to be parallel to the camera's focal plane, and the collision process may cause the particle's trajectory to deviate further from the focal plane, the average value is used to reduce error when calculating the parameters for particle collisions. The actual spatial size corresponding to each pixel is calculated based on a scale of a certain length taken before observation.

[0071] Particle size: Based on the pixel area occupied by the particle recognition region according to the formula ( The equivalent particle size is calculated, and the average value of three (6) images before and after the collision is taken as the final particle size.

[0072] Motion velocity: It is obtained by removing the position of the geometric center particle of the particle identification area in the two consecutive frames and spanning the frame time. The average of the three particle motion velocities before the particle velocity suddenly becomes 0 is regarded as the incident velocity, and the average of the three particle motion velocities after the sudden becoming 0 is regarded as the exit velocity.

[0073] Motion angle: Based on the identified vertical direction of the wall as a fixed direction, the angle between the motion speed and this direction is the motion angle. The average of the three incident angles and the exit angles before and after the collision is calculated as the final angle.

[0074] Tangential and normal velocities before and after the collision: The corresponding component velocities are obtained by multiplying the motion velocity by the cosine and sine of the incident angle, and the average value is taken as the final calculation result.

[0075] Rebound recovery coefficient: The formula for calculating the rebound recovery coefficient is as follows: The calculation methods for the tangential and normal restitution coefficients are the same.

[0076] Sphericity: The sphericity of the identified particles in each frame of the image is calculated according to the formula, and the average of the sphericity of the three particles before and after hitting the wall is taken as the sphericity of the target particle.

[0077] Finally, save the data: Save the data obtained from processing each group of images into an Excel spreadsheet with the same name as the subfolder.

[0078] In summary, the method for identifying and tracking particles in a particle swarm impacting a wall integrates the physical laws of particle impact (especially the influence of sphericity on symmetry), sophisticated image recognition technology (multi-frame key state acquisition, multi-frame average calculation of particle size / sphericity), intelligent matching logic with multi-feature joint verification (particle impact trajectory prediction model + triple feature judgment model), and the utilization of temporal correlation. Ultimately, it achieves high-precision and robust continuous tracking of a specific target particle from before to after impact in a complex and transient scenario such as a particle swarm impacting a wall.

[0079] The present invention also provides a method for measuring particles in a particle swarm impacting a wall, comprising marking all particles in the particle swarm located at the focal plane of a camera as target particles, tracking the target particles using the above-mentioned particle swarm impacting a wall identification and tracking method, and marking the target particles after they impact the wall.

[0080] Record and analyze all target particles before and after impacting the wall.

[0081] The method for measuring particles in the impact wall of this particle swarm also includes initial global parameter particle detection, shape filtering, defocus reconstruction, and pre-collision multi-particle tracking and visualization.

[0082] Initialize global parameters including: input target root directory and generate output directory, scan and read all subfolders containing image sequences and generate corresponding folders with the same name in the output directory, and sort according to the numerical frame number in the image sequence name (data saved before and after).

[0083] Particle detection includes: preprocessing the image frame by frame, converting the image into a grayscale image, and identifying particles whose grayscale values ​​are greater than a certain threshold (90) and whose connected regions have an area greater than a threshold (1000). The area is treated as a particle to remove the noise effect (the image data is poor, and a lot of invalid data is imaged as gray-black background noise).

[0084] Shape filtering includes: the image also contains rectangular shadow wall regions that are not spherical, according to The formula calculates sphericity, and areas with sphericity below a certain threshold or aspect ratio above a certain threshold are considered walls, preventing walls from being mistakenly marked as particles when identifying and tracking particles.

[0085] Defocus reconstruction includes: using the point spread function (PSF) to perform multiple deconvolutions on the blurred observation particle image. The parameters are set to PSF kernel pixel size, Gaussian kernel pixel standard deviation, and number of convolution iterations, which can be set according to the reconstruction effect until the image is relatively clear.

[0086] Pre-collision multi-particle tracking and visualization include:

[0087] Since multiple valid particles may exist in the same image frame, trajectory prediction and association are required for each particle. A new trajectory is created based on the detected particles. The position in the next frame is predicted based on the position + velocity * time of the previous frame. The predicted position is matched with the Euclidean distance between the predicted position and the nearest particle's position using a distance matrix (nearest neighbor algorithm). A maximum displacement threshold is set to limit the matching range. If a match is successful, the trajectory is updated; otherwise, a new trajectory is created. The outlines of the identified particles corresponding to each particle's motion trajectory are displayed according to a grayscale threshold and labeled for storage.

[0088] The above description is merely an embodiment and does not constitute any limitation on the present invention. Any person skilled in the art can make many possible variations, modifications, or alterations to the technical solutions of the present invention without departing from the scope of the present invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention, without departing from the scope of the present invention, should fall within the protection scope of the present invention.

Claims

1. A method for identifying and tracking particles in a wall surface during particle swarm impact, characterized in that, Includes the following steps: S1, marking the target particles in the particle group; S2, obtain the particle parameters of the target particle in the particle swarm before it impacts the wall, including particle size and sphericity; S3, obtain the spatial position, velocity and incident angle of the target particle in the last frame before it hits the wall; S4. Construct a particle impact trajectory prediction model. The particle impact trajectory prediction model takes the particle sphericity, spatial position of the last frame, motion velocity and incident angle as inputs to calculate the predicted position of the target particle within n frames after the target particle impacts the wall. S5. Construct a judgment model, acquire n frames of images, and judge the particles located within the predicted position using the judgment model. If the judgment model requirements are met, the particle is determined to be the same particle as the target particle before the impact. The particle impact trajectory prediction model is as follows: By utilizing the target particle attributes to obtain the symmetry of the particle's motion trajectory before and after the collision and the temporal correlation of the velocity change in the frame, data association and matching are performed to output the predicted position of the target particle within n frames after the target particle hits the wall. The judgment model is: If the particle size difference between the particle and the target particle is within a set range, the particle velocity difference between the particle and the target particle velocity before impacting the wall is within a set range, and the angle difference between the incident angle of the particle and the predicted rebound angle of the target particle is within a set range, then the judgment model requirements are met.

2. The method for identifying and tracking particles in a wall surface during particle swarm impact as described in claim 1, characterized in that, In terms of the symmetry of particle trajectories before and after a collision, the higher the sphericity of the target particle, the more symmetrical the particle trajectories before and after the collision.

3. The method for identifying and tracking particles in a wall surface during particle swarm impact as described in claim 1, characterized in that, Obtaining the particle size of the target particle before it impacts the wall in the particle swarm includes: Acquire three frames of images before and after the target particle impacts the wall. Calculate the equivalent particle size based on the pixel area occupied by the particle identification region using the area calculation formula. The average of the six calculated particle sizes is the final particle size.

4. The method for identifying and tracking particles in a wall surface during particle swarm impact as described in claim 1, characterized in that, Obtaining the sphericity of a target particle before it impacts the wall in a particle swarm includes: Acquire three frames of images before and after the target particle impacts the wall. Calculate the equivalent sphericity based on the pixels occupied by the particle identification area using the sphericity calculation formula. The average of the six sets of sphericities obtained is the final sphericity.

5. The method for identifying and tracking particles in a wall surface after a particle swarm impacts as described in claim 1, characterized in that, Obtaining the velocity of the target particle in the last frame before impact with the wall includes: The result is obtained by removing the bit of the geometric center particle of the target particle identification region between two consecutive frames, and then using the time span across frames.

6. The method for identifying and tracking particles in a wall surface during particle swarm impact as described in claim 1, characterized in that, The incident angle of the target particle in the last frame before impacting the wall includes: Based on the identification, the vertical direction of the wall is a fixed direction, and the angle between the incident velocity and this direction is the incident angle.

7. A method for measuring the impact of a particle swarm on particles in a wall surface, characterized in that, This includes marking all particles in the particle group located at the camera's focal plane as target particles, tracking the target particles using the particle group impact wall identification and tracking method as described in any one of claims 1-6, and marking the target particles after they impact the wall. Record and analyze all target particles before and after impacting the wall.

8. The method for measuring particles impacting a wall surface as described in claim 7, characterized in that, Prior to the method for measuring particles in a particle swarm impacting a wall, the method also includes initializing global parameters for particle detection, shape filtering, defocus reconstruction, and pre-collision multi-particle tracking and visualization.

Images