Simulation flow field data production method and system for tomographic particle image velocimetry

Abstract

The invention relates to the technical field of vision measurement and fluid dynamics simulation, solves the technical problems that in the prior art, three-dimensional flow field data with true values are difficult to generate, the data manufacturing cost is high, and the efficiency is low, and particularly relates to a simulation flow field data manufacturing method for tomography particle image velocity measurement. Comprising the steps of creating a new three-dimensional simulation scene and generating a spherical particle model and a calibration plate three-dimensional model; placing the calibration board three-dimensional model into a three-dimensional simulation scene to realize calibration image acquisition under multiple visual angles; a camera array used for collecting calibration images is configured, and a Blender rendering engine is used for outputting a flow field image sequence under multiple camera visual angles. The method provided by the invention is high in controllability, supports complex scenes and multi-camera setting, can flexibly simulate various experimental environments and camera layouts, and meets the requirements of different application scenes. Meanwhile, the experiment cost is effectively reduced, and the data acquisition efficiency is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of visual measurement and fluid dynamics simulation, and particularly relates to a simulation flow field data production method and system for tomographic particle image velocimetry. BACKGROUND

[0002] Particle image velocimetry (PIV) is a non-contact measurement method for obtaining fluid motion information, which is widely used in the fields of aerodynamics, hydrodynamics and combustion science. Traditional PIV mainly includes two-dimensional two-component (2D2C) or two-dimensional three-component (2D3C) measurement methods, which can only provide velocity information of the flow field at a specific cross-section and cannot accurately reconstruct the real three-dimensional velocity vector distribution.

[0003] To meet the needs of more in-depth research on complex turbulent flow, vortex structure and other three-dimensional transient flow fields, tomographic PIV emerges as the times require. By uniformly scattering tracer particles in the flow field and imaging the particles from different angles using multiple cameras, the three-dimensional distribution of the particles in the flow field can be reconstructed, and the three-dimensional three-component (3D3C) velocity field of the flow field can be calculated. However, due to the complexity of the experimental environment, the high cost of the equipment and the limitation of the calibration accuracy, it is difficult to collect data in real scenarios, which cannot meet the large-scale data requirements based on deep learning and other methods. In addition, due to the small volume, large number and dynamic nature of the tracer particles, the error of the calculated tracer particle position information is large, and it is difficult to quantitatively verify and evaluate whether the measurement results of the flow field are accurate.

[0004] Currently, there are some technical solutions to obtain the true value data of the particles. For example, a particle true value data acquisition method suitable for particle image velocimetry technology is disclosed in CN116008589A. This method builds a three-dimensional calibration system in the Cartesian coordinate system, fixes the tracer particles by hanging lines and calculates their relative displacement by projection position, and obtains the true three-dimensional coordinates of the tracer particles by combining coordinate transformation. A plurality of groups of particle coordinates are obtained by moving the tracer particles to form a tracer particle dataset. The tracer particle positions obtained by this method are relatively accurate, but there are still unavoidable problems such as tedious true value data acquisition, inability of particles to block each other (which will affect the projection), and sparseness of particles.

[0005] In order to solve the problems of limited construction complexity, high cost of instruments and equipment, and limited accuracy of calibration, data acquisition in real scenes is difficult to meet the large-scale data requirements based on deep learning and other methods. Some invention patents have been proposed to solve this problem. For example, a method for making a light field structured light data set based on Blender is disclosed in CN114821232A. This method constructs a light field structured light data generation system based on Blender, solves the problem of high cost of light field structured light data set making in reality, and avoids the problem of difficulty in label making in light field structured light depth estimation based on deep learning. SUMMARY

[0006] In view of the deficiencies of the prior art, the present application provides a simulation flow field data making method and system for tomographic particle image velocimetry, which solves the technical problems of the prior art that it is difficult to generate three-dimensional flow field data with true values, and the data making cost is high and the efficiency is low.

[0007] To solve the above technical problems, the present application provides the following technical solutions: a simulation flow field data making method for tomographic particle image velocimetry, which comprises the following steps: S1. Create a new three-dimensional simulation scene in Blender three-dimensional software, generate the required models through Python, including a spherical particle model and a calibration plate three-dimensional model; S2. Import the generated calibration plate three-dimensional model into the three-dimensional simulation scene, set the physical size, rotation angle and spatial position of the calibration plate three-dimensional model, and use it as a camera calibration and spatial reference; S3. Generate a spherical particle model of a tracer particle used for velocity measurement in the simulation experiment within the three-dimensional simulation scene, and randomly or regularly distribute it in the entire flow field range; S4. Place the calibration plate three-dimensional model in the three-dimensional simulation scene, set multiple key frames of the calibration plate three-dimensional model on the time axis, simulate its translation and rotation in space, and realize calibration image acquisition under multiple viewing angles; S5. Define a velocity field function based on a classical fluid mechanics model, calculate the position change of each spherical particle model between consecutive frames according to the velocity field function, and record the motion trajectory of each spherical particle model in the form of key frames; S6. Configure a camera array for collecting calibration images, and the configuration parameters include the number of cameras, focal length, sensor size, viewing angle, lens distortion and depth of field parameters; S7. Use the Blender rendering engine or plug-in interface to batch output flow field image sequences under multiple camera viewing angles, and export three-dimensional true coordinates and velocity displacement data files of all spherical particle models in each frame, forming a complete data set that can be used for supervised learning or algorithm evaluation.

[0008] Further, the step of generating a spherical particle model through Python is: checking whether there is an object with the same name in the three-dimensional simulation scene, if there is, deleting the object; creating a basic sphere and naming it; creating a material for the particle and setting the color of the material; applying the material to the particle to obtain a spherical particle model with different names.

[0009] Further, the step of generating a calibration plate three-dimensional model through Python is: deleting the old object and creating 2 materials, black and white materials; generating a grid plane according to the input number of rows and columns; assigning each grid a material at intervals, odd-numbered grids are white and even-numbered grids are black; combining all the grids into an object and naming it.

[0010] Further, the physical size of the calibration plate three-dimensional model is set to 50cm long, 40cm wide, and 5mm thick, with 108 grids evenly distributed on the surface, the grids being black and white alternately, forming 88 corner points; Place the calibration plate three-dimensional model at the center of the created three-dimensional simulation scene, and set the initial rotation angle to 0 degrees.

[0011] Further, in step S3, it includes: By arbitrarily specifying a three-dimensional region in the created three-dimensional simulation scene, 20000 spherical particle models with a diameter of 1mm are generated in a cuboid region with a length of 20cm, a width of 20cm, and a height of 15cm, simulating the tracer particles used for velocity measurement in the experiment.

[0012] Further, in step S4, the specific process includes the following steps: S41, set 30 key frames on the time axis to simulate the motion trajectory of the calibration plate three-dimensional model in three-dimensional space, including translation increments and rotation angle increments ; S42, calculate the pose of each key frame according to the translation increments and rotation angle increments , the calculation formula is: wherein, are the position and pose of each key frame respectively; is the initial pose of the calibration plate three-dimensional model in three-dimensional space; is the rotation matrix of the calibration plate three-dimensional model around the Z axis;​ to calibrate the angle increment of the three-dimensional model of the calibration board rotating around the Z axis; S43, calculate the translation vector of the three-dimensional model of the calibration board at each key frame in the three-dimensional space , that is, In the formula, is the initial position, , is the initial position along the Z axis; is the translation increment of each frame, ; denotes a key frame; S44, according to the pose and the translation vector , construct the extrinsic matrix of collecting any key frame calibration image , the expression is: In the formula, , are the translations of the three-dimensional model of the calibration board along the X axis and the Y axis, respectively; S45, according to the extrinsic matrix , calculate the projection matrix of the three-dimensional model of the calibration board under different positions and poses , the calculation formula is: In the formula, is the camera intrinsic matrix; S46, according to the projection matrix , transform the current pose of any key frame, and generate a flow field image sequence of the three-dimensional model of the calibration board under different camera perspectives through a rendering engine.

[0013] Further, in step S5, the specific process includes the following steps: S51, obtain the current position of any spherical particle model , and calculate the speed of the spherical particle model when moving from the k-th frame to the k+1-th frame, denoted as ; S52, according to the current position and the speed of the spherical particle model, calculate the position of the k+1-th frame spherical particle model , the calculation formula is: In the formula, is the time step from the k-th frame to the k+1-th frame; S53, repeat the above steps until 29 groups of speeds and positions of the spherical particle model motion are obtained.

[0014] The technical scheme also provides a system applied to the simulation flow field data production method, and the system comprises the following modules: A calibration plate simulation module is configured to simulate calibration plate models of different sizes according to a scene; A particle simulation module is configured to generate and control simulated spherical particle models based on a Blender python API; A user-defined velocity field module is configured to define the motion velocity of each particle in a three-dimensional simulation scene in a mathematical form, and to construct a real physical field in combination with a classical fluid model; A multi-view camera setting module is configured to arrange a virtual imaging system and simulate a multi-camera shooting scene in a real experiment; A data export and analysis module is configured to record simulation image sequences and three-dimensional particle trajectory data and form a data set, and is applied to PIV algorithm verification and debugging.

[0015] By means of the above technical scheme, the simulation flow field data production method and system for tomographic particle image velocimetry are provided, and at least the following beneficial effects are achieved: The method has strong controllability, supports complex scenes and multi-camera settings, can flexibly simulate various experimental environments and camera layouts, and meets the needs of different application scenarios; The simulation particle trajectory has real physical significance and is calculated based on a classical fluid mechanics model, is suitable for data-driven model training, and improves the generalization ability of the model; The experimental cost is effectively reduced, expensive experimental equipment does not need to be purchased, a large-scale data set can be efficiently generated through simulation, and the data acquisition efficiency is improved; The method can be used to evaluate the precision performance of different 3D3C PIV algorithms in various scenes, provides reliable benchmark data for algorithm optimization, and assists in improving the algorithm performance; Through calibration plate animation control and multi-view image acquisition, the process of manually moving the calibration plate in a real experiment can be simulated, the geometric feature richness of the image is enhanced, and the robustness and precision of calibration are improved; The powerful three-dimensional modeling and rendering capability of Blender is utilized, the quality of the generated simulation image is high, the difference between the simulation image and a real particle image is small, the position coordinates of the particles are accurate, and this is conducive to the generalization application of the algorithm in a real scene. BRIEF DESCRIPTION OF DRAWINGS

[0016] The accompanying drawings, which are included to provide a further understanding of the application, form a part of the application and help to explain the application illustrate the application and do not limit the application. In the drawings: Figure 1 The flowchart of the simulation flow field data production method in the application; Figure 2 A schematic diagram for importing a calibration plate three-dimensional model into a three-dimensional simulation scene in the present application; Figure 3 A distribution schematic diagram of a spherical particle model in the present application; Figure 4 A schematic diagram for moving a calibration plate model in the present application; Figure 5 A final particle image effect diagram in the present application; Figure 6 A particle image effect diagram after binarization in the present application. DETAILED DESCRIPTION

[0017] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application will be further described in detail below in combination with the drawings and specific embodiments. The implementation process of how to apply technical means to solve technical problems and achieve technical effects can be fully understood and implemented by the present application.

[0018] The present embodiment is based on the technical defects of the prior art in the above background technology section, and combines existing research and application practice. The prior art has the following defects: Data collection in a real scene is difficult, and it is difficult to meet the large-scale data requirements based on deep learning and other methods; there is a lack of a controllable and automated simulation system, it is difficult to generate three-dimensional flow field images with true values, and it is impossible to quantitatively verify and evaluate whether the measurement results of the flow field are accurate; poor flexibility, strong dependence on the design and specifications of the calibration system, lack of flexibility, and difficult to adapt to various complex environments.

[0019] In order to overcome the above technical problems, the present embodiment proposes a simulation flow field data production method suitable for tomographic particle image velocimetry technology, which uses Blender to simulate and render flow field images, can accurately save the motion trajectory of each particle in the scene every frame, and obtain the three-dimensional three-component velocity field of the real flow field, so as to be used for constructing high-precision controllable PIV flow field data set, suitable for deep learning flow field prediction research and PIV algorithm verification scene. The method can effectively reduce the data production cost, improve the algorithm research efficiency and the reliability of the precision verification. As shown in Figure 1 The method comprises the following steps: S1, create a new three-dimensional simulation scene in the Blender three-dimensional software, generate the required model through Python, including a spherical particle model and a calibration plate three-dimensional model, etc. Which includes using Python script to control the generation of different models and different sizes of calibration plate three-dimensional model; using Python script to control the generation of reference particle model, including the size and shape of the particle; using Python script to control the camera parameter setting, animation key frame generation, etc.

[0020] In the present embodiment, the steps of generating the spherical particle model by Python are: checking whether there is an object with the same name in the three-dimensional simulation scene, if there is, deleting the object; creating a basic sphere and naming it; creating the material of the particle and setting the color of the material; applying the material to the particle to obtain a spherical particle model with different names.

[0021] The steps of generating the three-dimensional model of the calibration plate by Python are: deleting the old object and creating 2 materials, black and white materials; generating a grid plane according to the input number of rows and columns; assigning each grid a material at intervals, odd-numbered grids are white and even-numbered grids are black; combining all the grids into an object and naming it.

[0022] S2, import the generated three-dimensional model of the calibration plate into the three-dimensional simulation scene, set the physical size, rotation angle and spatial position of the three-dimensional model of the calibration plate, and use it as the camera calibration and spatial reference datum. In the present embodiment, the three-dimensional model of the calibration plate is imported into the three-dimensional simulation scene, the physical size of the three-dimensional model of the calibration plate is set to 50 cm long, 40 cm wide and 5 mm thick, the surface is uniformly distributed with 108 grids, the grids are black and white, and a total of 88 corner points are formed. The three-dimensional model of the calibration plate is placed at the center position of the created three-dimensional simulation scene, and the initial rotation angle is set to 0 degrees, as shown in Figure 2 .

[0023] S3, generate the spherical particle model of the tracer particle used for velocity measurement in the simulation experiment in the three-dimensional simulation scene, and randomly or regularly distribute it in the entire flow field range. In the present embodiment, a three-dimensional region is arbitrarily specified in the created three-dimensional simulation scene, 20000 spherical particle models with a diameter of 1 mm are generated in a cubic region with a length of 20 cm, a width of 20 cm and a height of 15 cm, simulating the tracer particles used for velocity measurement in the simulation experiment, and the distribution of the generated spherical particle models is as shown in Figure 3 .

[0024] S4, place the three-dimensional model of the calibration plate into the three-dimensional simulation scene, set multiple key frames of the three-dimensional model of the calibration plate on the time axis according to the basic principle of Zhang Zhengyou calibration method, simulate the translation and rotation of the three-dimensional model of the calibration plate in space, and realize the generation of flow field image sequence by collecting calibration images from multiple angles.

[0025] This embodiment calculates the projection matrix of the calibration board's 3D model at different positions and orientations based on the Zhang Zhengyou calibration method. Thirty keyframes are set on the time axis to simulate the motion trajectory of the calibration board's 3D model in 3D space, including translation and rotation. Between every two keyframes, the calibration board's 3D model translates 2cm along the X-axis, translates 2cm along the Y-axis, and rotates 15 degrees around the Z-axis. Finally, a sequence of flow field images of the calibration board's 3D model from different camera perspectives is rendered and used as input data for the calibration algorithm.

[0026] In this embodiment, the given parameters are 30 keyframes, and the transformation of each frame relative to the previous frame is as follows: X translation +2 cm (0.02 m) Y translation +2 cm (0.02 m) Rotate the Z-axis by +15° (π / 12 rad). Based on this, the extrinsic parameter matrix for each frame can be generated sequentially. Then, based on the principle of Zhang Zhengyou's calibration method, the projection matrix of the calibration plate's 3D model under different positions and orientations is calculated. (External parameter matrix) The calculation process is as follows: Increment of rotation angle of the calibration plate 3D model around the Z-axis The calculation formula is: Translation vector for each keyframe The calculation formula is: In the formula, The initial position, , This represents the initial position along the Z-axis; For each frame's translation increment, ; This indicates a keyframe.

[0027] Final extrinsic matrix The expression is: In the formula, , These represent the translations of the calibration plate's 3D model along the X and Y axes, respectively.

[0028] like Figure 4 The diagram shows the movement of the calibration plate model. Only one calibration plate model is shown in the diagram, representing all the positions that the calibration plate model has moved to in the 3D simulation scene.

[0029] S5, define the velocity field function based on the classical fluid mechanics model, calculate the position change of each spherical particle model between continuous frames according to the velocity field function, and record the motion trajectory of each spherical particle model in the form of key frame. The specific process includes the following steps: S51, obtain the current position of any spherical particle model , and calculate the velocity of the spherical particle model when moving from the kth frame to the k+1th frame, denoted as ; S52, calculate the position of the k+1th frame of the spherical particle model according to the current position and the velocity , and the calculation formula is: In the formula, is the time step from the kth frame to the k+1th frame; S53, repeat the above steps until 29 groups of velocity and position of the spherical particle model motion are obtained.

[0030] In this embodiment, the velocity field function is defined as a fluid mechanics model. The displacement and velocity change of each spherical particle model between continuous frames are calculated, and the time step is set to 0.005 seconds. The three-dimensional motion trajectory of each spherical particle model in 30 frames is recorded in the form of key frame.

[0031] S6, configure a camera array for collecting calibration images, and the configuration parameters include the number of cameras, focal length, sensor size, viewing angle, lens distortion and depth of field parameters, etc. The camera layout can be flexibly adjusted according to different experimental requirements. In this embodiment, 6 groups of camera arrays are configured, each group containing 4 cameras, the camera focal length is 50mm, the sensor size is 24mm×36mm, the lens distortion coefficient is 0.03, and the depth of field range is 10cm to 25cm. The camera layout is arranged in a hemispherical shape, and the viewing angle covers the entire flow field area.

[0032] S7, use Blender rendering engine or plug-in interface to batch output flow field image sequences under multi-camera viewing angle, and export three-dimensional real coordinates and velocity displacement data files of all spherical particle models in each frame to form a complete data set that can be used for supervised learning or algorithm evaluation.

[0033] In this embodiment, the rendering parameters are set, including resolution of 2560×1920 and frame rate of 24fps. Batch rendering generates 30 frames of three-dimensional image sequences with 24 angles. At the same time, three-dimensional real coordinates and velocity displacement data files of all 20000 particles in each frame are exported to form a complete data set that can be used for supervised learning or algorithm evaluation. The rendered particle image effect is shown in Figure 5 , and the particle effect after binarization is as follows​Figure 6 as shown.

[0034] The method has strong controllability, supports complex scenes and multi-camera settings, can flexibly simulate various experimental environments and camera layouts, meets the needs of different application scenarios, is calculated based on a classical fluid mechanics model, is suitable for model training driven by data, and improves the generalization ability of the model.

[0035] The method effectively reduces experimental costs, does not require the purchase of expensive experimental equipment, can efficiently generate large-scale data sets through simulation, and improves data acquisition efficiency. It can be used to evaluate the accuracy of different 3D3C PIV algorithms in various scenarios, providing reliable benchmark data for algorithm optimization and assisting in improving algorithm performance.

[0036] The method can simulate the process of manually moving the calibration plate in real experiments, enhance the geometric feature richness of the images, and improve the robustness and accuracy of the calibration. The powerful three-dimensional modeling and rendering capabilities of Blender are used to generate simulation images with high quality and little difference from real particle images, and the position coordinates of the particles are accurate, which is conducive to the generalization of the algorithm in real scenarios.

[0037] Those skilled in the art can understand that all or part of the steps of the above-mentioned embodiment methods can be completed by programs instructing related hardware, therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) containing computer usable program codes.

[0038] Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts of each embodiment can be referred to each other. For each of the above embodiments, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant part can be referred to the part of the method embodiment.

[0039] The above embodiments have been described in detail, and the principles and implementation methods of the present application have been described by applying specific examples. The above embodiment is only used to help understand the method of the present application and its core idea; at the same time, for those skilled in the art, according to the idea of the present application, the specific implementation method and application range will be changed, and the above description should not be understood as a limitation of the present application.

Claims

1. A method for generating simulated flow field data for tomographic particle image velocimetry, characterized in that, The method comprises the following steps: S1, creating a new three-dimensional simulation scene in Blender three-dimensional software, generating the required model through Python, including a spherical particle model and a calibration plate three-dimensional model; S2, importing the generated calibration plate three-dimensional model into the three-dimensional simulation scene, setting the physical size, rotation angle and spatial position of the calibration plate three-dimensional model, and taking it as the camera calibration and spatial reference; S3, generating the spherical particle model of the tracer particle for velocity measurement in the simulation experiment in the three-dimensional simulation scene, and randomly or regularly distributing it in the entire flow field range; S4, placing the calibration plate three-dimensional model in the three-dimensional simulation scene, setting multiple key frames of the calibration plate three-dimensional model on the time axis, and realizing the acquisition of calibration images under multiple viewing angles by simulating the translation and rotation of the calibration plate three-dimensional model in space; S5, defining a velocity field function based on a classical fluid mechanics model, calculating the position change of each spherical particle model between consecutive frames according to the velocity field function, and recording the motion trajectory of each spherical particle model in the form of key frames; S6, configuring a camera array for acquiring calibration images, and the configuration parameters include the number of cameras, focal length, sensor size, viewing angle, lens distortion and depth of field parameters; S7, using the Blender rendering engine or plug-in interface to batch output flow field image sequences under multiple camera viewing angles, and simultaneously exporting three-dimensional real coordinate and velocity displacement data files of all spherical particle models in each frame to form a complete data set that can be used for supervised learning or algorithm evaluation.

2. The method of claim 1, wherein The step of generating the spherical particle model through Python is: checking whether there is an object with the same name in the three-dimensional simulation scene, if there is, deleting the object; creating a basic sphere and naming it; creating a particle material and setting the color of the material; applying the material to the particle to obtain a spherical particle model with a different name.

3. The method of claim 1, wherein The step of generating the calibration plate three-dimensional model through Python is: deleting the old object and creating two materials, black and white; generating a grid plane according to the input number of rows and columns; assigning each grid a material at intervals, with odd-numbered grids being white and even-numbered grids being black; combining all the grids into an object and naming it.

4. The method of claim 1, wherein The physical size of the calibration plate three-dimensional model is set to 50 cm long, 40 cm wide and 5 mm thick, and 108 grids are uniformly distributed on the surface, with black and white grids forming 88 corner points; Place the calibration plate three-dimensional model at the center of the created three-dimensional simulation scene, and set the initial rotation angle to 0 degrees.

5. The method of claim 1, wherein In step S3, it includes: By arbitrarily specifying a three-dimensional region in the created three-dimensional simulation scene, selecting a 20 cm long, 20 cm wide and 15 cm high cubic region, and generating 20,000 spherical particle models with a diameter of 1 mm in the cubic region according to uniform distribution, the tracer particles for velocity measurement in the simulation experiment.

6. The method of claim 1, wherein In step S4, the specific process includes the following steps: S41, 30 groups of key frames are set on the time axis to simulate the motion trajectory of the three-dimensional model of the calibration plate in the three-dimensional space, including translation increments and rotation angle increments ; S42、According to the translation increment and the rotation angle increment Calculate the pose of each key frame The calculation formula is:​ wherein, are the position and pose of each key frame, respectively; is the initial pose of the three-dimensional model of the calibration plate in the three-dimensional space; is the rotation matrix of the three-dimensional model of the calibration plate around the Z axis; is the angle increment of the rotation of the three-dimensional model of the calibration plate around the Z axis; S43, calculate the translation vector of the calibration board three-dimensional model at each key frame in the three-dimensional space That is: wherein is the initial position, , is the initial position along the Z axis; is the translation increment per frame, ; denotes a key frame; S44, according to the pose and the translation vector constructing an extrinsic parameter matrix of collecting any key frame calibration image , the expression is: In the formula, , are the translations of the three-dimensional model of the calibration plate along the X-axis and the Y-axis, respectively. S45、According to the external parameter matrix Calculate the projection matrix of the three-dimensional model of the calibration board at different positions and attitudes The calculation formula is: In the formula, is the camera intrinsic matrix; S46、According to the projection matrix Transform the current pose of any key frame and generate the flow field image sequence of the calibration board three-dimensional model under different camera perspectives through the rendering engine.

7. The simulation flow field data production method according to claim 6, wherein In step S5, the specific process includes the following steps: S51, obtaining the current position of any spherical particle model and calculating the speed of the spherical particle model when moving from the kth frame to the k+1th frame, denoted as ; S52, current position of the spherical particle model and velocity calculating the position of the k+1 frame spherical particle model , the calculation formula is: In the formula, is the time step for the kth frame to move to the k+1th frame; S53, repeat the above steps until 29 groups of velocity and position of the spherical particle model motion are obtained.

8. A system for use in the method of claim 1 to 7, wherein It includes: Calibration board simulation module, which simulates calibration board models of different sizes according to the scene; Particle simulation module, which generates and controls simulated spherical particle models based on Blender python API; Custom velocity field module, which defines the motion velocity of each particle in the three-dimensional simulation scene in mathematical form, and combines with the classical fluid model to construct the real physical field; Multi-view camera setting module, which is used to arrange the virtual imaging system and simulate the scene shooting by multiple cameras in the real experiment; Data export and analysis module, which is used to record the simulation image sequence, three-dimensional particle trajectory data and form data sets, and is applied to PIV algorithm verification and debugging.

Images