Guniting thickness in-situ visualization method and system based on mixed reality

Abstract

The invention provides a mixed reality-based guniting thickness in-situ visualization method and system, relates to the field of civil engineering and intelligent construction, and aims to realize in-situ visualization of guniting thickness detection by using a mixed reality technology. The method comprises the steps of constructing a three-dimensional grid representation of a guniting operation site, and constructing a three-dimensional grid representation of the guniting operation site by using a space anchor point technology; a virtual mechanical arm corresponding to a physical mechanical arm in the guniting operation site is established, and the virtual mechanical arm and the physical mechanical arm are kept consistent in pose and spatial position; generating virtual guniting particles, and setting particle parameters for the virtual guniting particles; when it is detected that the virtual guniting particles collide with the environment grid every time, the three-dimensional coordinates of collision points are mapped to grid units in the two-dimensional height field grid, and the current deposition thickness value of the grid units is calculated; and based on the deposition thickness value of the grid unit, obtaining the spraying thickness of each position point in the grid unit, and rendering the spraying thickness of each position point to the target surface through a corresponding color.

Description

Technical Field

[0001] The present disclosure relates to the fields of civil engineering and intelligent construction, and in particular to a mixed reality-based in-situ visualization method and system for shotcrete thickness. Background Art

[0002] Shotcrete application, a core process in construction, tunneling, and mining engineering, aims to reinforce target surfaces by spraying concrete or mortar. In practice, the thickness of the sprayed concrete or mortar on the target surface must be measured to ensure that the application meets standards.

[0003] Existing methods for testing shotcrete thickness primarily include coring, laser scanning, and ultrasonic testing. While coring offers high accuracy, it is destructive, costly, and has limited detection points, making it inadequate for continuous monitoring of large areas. Laser scanning is significantly affected by ambient lighting conditions, significantly reducing accuracy in complex environments like tunnels, and incapable of real-time testing. Ultrasonic testing has strict requirements for surface roughness, resulting in low detection efficiency and difficulty adapting to the fast-paced demands of on-site operations.

[0004] In addition, traditional shotcrete thickness inspection can only be carried out after the entire construction process is completed. It lacks real-time and intuitive quality monitoring methods, resulting in problems such as uneven shotcrete, insufficient edge coverage or excessive shotcrete being difficult to detect and correct in a timely manner. Summary of the Invention

[0005] In a first aspect of the present disclosure, a mixed reality-based in-situ visualization method for shotcrete thickness is provided, the method comprising: Constructing a three-dimensional grid representation of the shotcrete operation site, the three-dimensional grid representation including an environment grid corresponding to the target surface to be sprayed; Using spatial anchor point technology, a virtual robotic arm corresponding to the physical robotic arm in the shotcrete operation site is established, and the virtual robotic arm and the physical robotic arm are kept consistent in posture and spatial position; generating virtual spraying particles, and setting particle parameters for the virtual spraying particles based on the posture of the physical manipulator during the spraying operation and the spraying parameters of the manipulator nozzle on the physical manipulator, the particle parameters including at least an initial velocity of the particles, a gravity influence factor, and a spraying angle of the particles; Whenever a collision between the virtual spraying particle and the environment grid is detected, the three-dimensional coordinates of the collision point are mapped to a corresponding grid cell in a two-dimensional height field grid, and a current deposition thickness value of the grid cell is obtained based on physical parameters of the virtual spraying particle; wherein the two-dimensional height field grid corresponds to the environment grid, and the physical parameters include at least a particle mass and an adhesion probability of the virtual spraying particle, wherein the adhesion probability represents a probability of the virtual spraying particle adhering to the target surface; Based on the accumulated deposition thickness value of each grid unit, the spraying thickness of each position point in the grid unit is obtained, and the spraying thickness of each position point is rendered into the target surface using a corresponding color; wherein the spraying thickness in different thickness ranges corresponds to different colors.

[0006] Exemplarily, the constructing of a three-dimensional grid representation of the spraying operation site, wherein the three-dimensional grid representation includes an environment grid corresponding to the target surface to be sprayed, includes: Automatically scanning depth information of the shotcrete operation site using a spatial mapping component of a mixed reality device, and generating the three-dimensional mesh representation based on the depth information; A collision volume component is added to the three-dimensional mesh representation; wherein the collision volume component is used to perform collision detection on the environment mesh.

[0007] Exemplarily, the use of spatial anchor technology to establish a virtual robotic arm corresponding to a physical robotic arm in the shotcrete operation site, and to keep the virtual robotic arm consistent with the physical robotic arm in posture and spatial position, includes: Constructing an initial virtual robotic arm and setting a virtual QR code for the virtual robotic arm; wherein the position of the virtual QR code on the virtual robotic arm is consistent with the position of the physical QR code on the physical robotic arm; detecting the position of the physical QR code in real time using an image acquisition device, and adjusting the pose of the virtual robotic arm in the three-dimensional grid representation according to the real-time position until the real-time poses of the virtual QR code and the physical QR code overlap in the mixed reality view, so that the virtual robotic arm is aligned with the physical robotic arm; A persistent spatial anchor is created for the alignment position of the virtual robotic arm when aligned with the physical robotic arm, and the virtual robotic arm is bound to the spatial anchor to lock and maintain the alignment relationship between the virtual robotic arm and the physical robotic arm.

[0008] Exemplarily, generating virtual spraying particles and setting particle parameters for the virtual spraying particles based on the posture of the physical manipulator during the spraying operation and the spraying parameters of the manipulator nozzle on the physical manipulator include: Creating a particle system in Unity and binding the particle system to the virtual model of the robotic arm nozzle; Setting the emission rate, initial velocity and emission angle of the virtual spraying particles according to the real-time position of the physical robotic arm during the spraying operation and the spraying parameters; Adding gravity, air resistance, life cycle and rebound coefficient to the virtual spraying particles to simulate the physical properties of real spraying materials; The life cycle represents the disappearance time of the virtual spraying particles that have not collided; and the rebound coefficient is negatively correlated with the adhesion coefficient of the virtual spraying particles.

[0009] Exemplarily, each time a collision between the virtual spraying particle and the environment grid is detected, mapping the three-dimensional coordinates of the collision point to a corresponding grid cell in the two-dimensional height field grid includes: Establishing a two-dimensional height field data structure to obtain the two-dimensional height field grid, and dividing the two-dimensional height field grid to obtain a plurality of grid units; Whenever a collision between the virtual spraying particle and the environment grid is detected, extracting the three-dimensional coordinates of the collision point; Converting the three-dimensional coordinates into relative coordinates relative to the origin of the two-dimensional height field grid; Based on the size of the grid unit and the relative coordinates, a grid unit to which the three-dimensional coordinate is mapped is obtained in the plurality of two-dimensional height field grids.

[0010] Exemplarily, the obtaining of the current deposition thickness value of the grid unit based on the physical parameters of the virtual spraying particles includes: The deposition contribution is obtained according to the following formula; ;in, is the mass of the particle, is the sticking probability, is the time factor; wherein, the time factor represents the length of time the robot arm nozzle continues spraying in the same posture.

[0011] The current deposition thickness value of the grid unit is obtained according to the deposition thickness value of the grid unit before the current collision and the deposition contribution.

[0012] Exemplarily, the obtaining, based on the accumulated deposition thickness value of each grid unit, the spraying thickness of each position point in the grid unit, and rendering the spraying thickness of each position point onto the target surface using a corresponding color, includes: Traversing the deposition thickness values ​​of the plurality of grid cells to obtain a maximum deposition thickness value and a minimum deposition thickness value; Normalizing the deposition thickness values ​​of the plurality of grid cells based on the maximum deposition thickness value and the minimum deposition thickness value to obtain a normalized thickness value of each grid cell; Based on the normalized thickness value of the grid unit, obtaining the spraying thickness of each position point in the grid unit; For each position point, the difference between the spraying thickness and the target deposition thickness value is determined by rendering the spraying thickness of the position point onto the target surface using a corresponding color; The target deposition thickness value includes the maximum deposition thickness value and the minimum deposition thickness value; or the target deposition thickness value is a preset thickness range.

[0013] Exemplarily, the grid unit is a polygon, and the normalized thickness value of the grid unit includes the normalized thickness value of each endpoint of the grid unit; obtaining the spraying thickness of each position point in the grid unit based on the normalized thickness value of the grid unit includes: Based on the normalized thickness values ​​of each endpoint of the grid unit, linear interpolation is performed between the maximum deposition thickness value and the minimum deposition thickness value to obtain the spraying thickness of each position point located in the grid unit.

[0014] Exemplarily, rendering the spraying thickness of each of the position points onto the target surface using a corresponding color includes: Rendering the corresponding color of each of the position points onto an environment grid, and holographically projecting the environment grid rendered with the color onto the target surface to display the spraying thickness of the target surface in a mixed reality manner; The particle parameters, the collision trajectory of the virtual spraying particles and at least one of the physical parameters are rendered into a generated display interface, and the display interface is holographically projected onto the spraying operation site to display the spraying operation process in mixed reality.

[0015] A second aspect of the embodiments of the present disclosure provides an in-situ visualization system for shotcrete thickness based on mixed reality, comprising: An environmental perception module for constructing a three-dimensional grid representation of the spraying operation site, the three-dimensional grid representation including an environmental grid corresponding to the target surface to be sprayed; A spatial registration module is used to establish a virtual robotic arm corresponding to the physical robotic arm in the shotcrete operation site in the three-dimensional grid representation using spatial anchor technology, and to keep the virtual robotic arm consistent with the physical robotic arm in terms of posture and spatial position; a particle simulation module for generating virtual spraying particles and setting particle parameters for the virtual spraying particles based on the posture of the physical manipulator during the spraying operation and the spraying parameters of the manipulator nozzle on the physical manipulator, wherein the particle parameters include at least the initial velocity of the particles, the gravity influence factor, and the spraying angle of the particles; a thickness calculation module configured to, upon each detection of a collision between the virtual spraying particle and the environmental grid, map the three-dimensional coordinates of the collision point to a corresponding grid cell in a two-dimensional height field grid, and obtain a current deposition thickness value of the grid cell based on physical parameters of the virtual spraying particle; wherein the two-dimensional height field grid corresponds to the environmental grid, and the physical parameters include at least a particle mass and an adhesion probability of the virtual spraying particle, wherein the adhesion probability represents a probability of the virtual spraying particle adhering to the target surface; Visualization rendering module: used to obtain the spraying thickness of each position point in the grid unit based on the accumulated deposition thickness value of each grid unit, and render the spraying thickness of each position point to the target surface with corresponding colors; wherein, the spraying thickness in different thickness ranges corresponds to different colors.

[0016] Using the embodiments of the present disclosure, during the shotcrete process, the spray thickness at each location in the two-dimensional height field grid can be rendered onto the target surface using corresponding colors. Thus, the spray thickness at each location can be intuitively determined by the displayed color, making the spray thickness detection results visual. Furthermore, the spraying process performed by the physical robotic arm can be reproduced in real time through the environmental grid and virtual shotcrete particles, allowing the actual shotcrete process and the reproduction of the actual shotcrete process to proceed synchronously. This avoids the problem in related technologies where shotcrete thickness detection can only be performed after the entire construction process is complete.

[0017] On the other hand, since it is possible to scan and reconstruct a high-precision spatial grid model (three-dimensional grid representation) of the actual spraying operation site in real time, and use virtual spraying particles in the high-precision spatial grid model to simulate the spraying of real physical spraying particles during the spraying process, digital means are used to reproduce the spraying process of real physical spraying particles in the high-precision spatial grid model, which will not damage the sprayed surface, is not affected by ambient lighting conditions, and is not affected by the roughness of the target surface. This can improve its environmental adaptability and enable slurry thickness detection to be applied to a wider range of scenarios.

[0018] The above description is only an overview of the technical solution of the present disclosure. In order to more clearly understand the technical means of the present disclosure, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present disclosure more obvious and easy to understand, the specific implementation methods of the present disclosure are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure or related technologies, the following is a brief introduction to the drawings required for the description of the embodiments or related technologies. Obviously, the drawings described below are some embodiments of the present disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without inventive efforts. It should be noted that the scales in the drawings are for illustration only and do not represent the actual scale.

[0020] Figure 1 A schematic flow chart showing the steps of a mixed reality-based in-situ visualization method for shotcrete thickness according to an embodiment of the present disclosure is shown; Figure 2A 、 Figure 2B and Figure 3 Schematic diagrams of application scenarios of the mixed reality-based in-situ visualization method for shotcrete thickness according to an embodiment of the present disclosure are respectively shown; Figure 4 A schematic diagram of the steps for constructing a virtual robotic arm is shown; Figure 5 shows a schematic diagram of the particle parameter setting interface; Figure 6 A schematic flow chart showing the steps of determining corresponding grid cells in a two-dimensional height field grid is shown; Figure 7 A schematic diagram of the specific process of calculating the deposition thickness value is shown; Figure 8 A schematic diagram of the framework of the in-situ visualization system for shotcrete thickness based on mixed reality is shown.

[0021] Reference numerals: 600, environmental grid; 700, target surface; 601, grid unit; 800, display interface; 900, physical robotic arm. DETAILED DESCRIPTION

[0022] To make the above-mentioned purposes, features, and advantages of the present disclosure more clearly understood, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of them. All other embodiments obtained by persons of ordinary skill in the art based on the embodiments of the present disclosure without creative work are within the scope of protection of the present disclosure.

[0023] In related technologies, in order to improve the spraying quality of concrete or mortar materials and ensure that the spraying thickness meets the standards, local test spraying is generally carried out by manually operating the spraying equipment before construction, relying on experience to judge the coverage effect. This method has the following main defects: (1) It is divorced from the real environment: offline simulation relies on simplified models and cannot reflect the geometric complexity and dynamic changes of the real physical scene; (2) Construction personnel cannot directly observe the spraying effect and dynamically adjust the parameters at the real work site; (3) Physical test spraying and repeated corrections lead to a significant increase in material, time and labor costs.

[0024] At the same time, the spray thickness detection methods used in related technologies, such as the drilling coring method, laser scanning method and ultrasonic detection method, all have their own defects.

[0025] In view of this, the embodiments of the present disclosure propose an in-situ visualization method and system for spraying thickness based on mixed reality. The method provides an innovative direction for spraying simulation through mixed reality (MR) technology. The method first uses the depth sensor and spatial mapping function of the mixed reality device to scan and reconstruct a high-precision spatial grid model (three-dimensional grid representation) of the real spraying operation site in real time, providing a real physical basis for spraying simulation, and by generating virtual spraying particles. During the spraying process, according to the spraying parameters of the robotic arm nozzle on the physical robotic arm, the trajectory of the virtual spraying particles is rendered in real time, and the deposition effect (spraying thickness) of the virtual spraying particles on the target surface is obtained, and the deposition effect is rendered to the real target surface in real time through the corresponding color.

[0026] In this way, by establishing a physical interaction model between virtual shotcrete particles and the real target surface, the deposition thickness of the shotcrete material at different positions on the target surface can be accurately calculated, and the thickness distribution can be intuitively displayed in the form of color coding, allowing operators to grasp the shotcrete quality status in real time during the construction process.

[0027] Therefore, on the one hand, the spraying thickness of each position point on the target surface can be rendered in real time during the spraying operation, so that construction personnel can observe the spraying situation in real time, and positions that do not meet the standard thickness (hereinafter referred to as the preset thickness range) can be corrected in time, avoiding the problem in related technologies that the spraying thickness detection can only be carried out after the entire construction process is completed.

[0028] On the other hand, since the spray thickness of each position point in the two-dimensional height field can be rendered into the target surface through the corresponding color, the spray thickness of each position point can be intuitively judged by the displayed color, making the detection results of the spray thickness visualized.

[0029] On the other hand, by utilizing the depth sensor and spatial mapping function of the mixed reality device, a high-precision spatial grid model (three-dimensional grid representation) of the actual spraying operation site is scanned and reconstructed in real time. In the high-precision spatial grid model, virtual spraying particles are used to simulate the spraying of real physical spraying particles during the spraying process. Thus, digital means are used to reproduce the spraying process of real physical spraying particles in the high-precision spatial grid model, which will not damage the sprayed surface, is not affected by ambient lighting conditions, and is not affected by the roughness of the target surface. This can improve its environmental adaptability, and allows slurry thickness detection to be flexibly applied to a wider range of scenarios.

[0030] Below, the mixed reality-based in-situ visualization method and system for shotcrete thickness provided in the embodiments of the present application are exemplarily described with reference to the accompanying drawings.

[0031] Please refer to Figure 1 、 Figure 2A and Figure 3 As shown, Figure 1 A schematic flow chart of the steps of a mixed reality-based in-situ visualization method for shotcrete thickness according to an embodiment of the present disclosure is shown. Figure 2A 、 Figure 2B and Figure 3 Schematic diagrams of application scenarios of the in-situ visualization method of shotcrete thickness based on mixed reality according to an embodiment of the present disclosure are shown respectively. Figure 1-Figure 3 As shown, the in-situ visualization method of shotcrete thickness based on mixed reality in an embodiment of the present disclosure mainly includes the following steps: Step S100: constructing a three-dimensional grid representation of the shotcrete operation site; The three-dimensional grid represents an environment grid corresponding to the target surface to be sprayed.

[0032] In this embodiment, the spraying operation site refers to the environment where the target surface of the spraying operation is located, such as Figure 2A As shown, the construction spraying operation site can be inside a building structure such as a house or a tunnel, or can be the environment where the workpiece to be sprayed is located.

[0033] The target surface may refer to the surface to be sprayed, which may be a plane, a curved surface, a spherical surface, etc. The target surface may be a metal material, for example, the surface of a metal workpiece; or it may be a non-metallic material, such as the surface of a building made of ceramic, cement mortar, etc. The target surface is not limited here.

[0034] In some embodiments, the three-dimensional grid representation may refer to: a three-dimensional scene of the spraying operation site, such as a three-dimensional reproduction of the interior of a building structure such as a house or tunnel, which includes an environmental grid corresponding to the target surface to be sprayed.

[0035] For example, Figure 2A As shown, the environment grid 600 can be a three-dimensional representation of the target surface 700, which includes the depth information of the target surface, which can be used to reflect not only the two-dimensional plane of the target surface, but also the roughness of the target surface. Figure 2A and Figure 2B As shown, the environment grid may be a grid composed of multiple uneven three-dimensional grids.

[0036] Among them, such as Figure 2B As shown, the environment grid 600 may include a plurality of grid units 601 ( Figure 2B In some examples, the shape of the grid cells in the environment grid may be different from that of the target surface. For example, Figure 2B As shown, the target surface is a rectangular surface, and the grid cells in the environment grid may be triangular pyramid cells.

[0037] in, Figure 2A The following example shows a scene diagram of mixed reality display. Figure 2A In this paper, only the environment mesh of a part of the target surface is used as an example to facilitate intuitive understanding of the relationship between the environment mesh and the target surface in mixed reality.

[0038] in, Figure 2B A schematic diagram of an environment grid covering the entire target surface is shown, illustrating the relationship between the environment grid and the shotcrete operation site. For example, the target surface at the shotcrete operation site corresponds to the environment grid. The sizes and shapes of the multiple grid cells included in the environment grid may not be exactly the same.

[0039] In some examples, when constructing the three-dimensional mesh representation, a sensor may be used to collect depth information of the shotcrete operation site and convert the depth information into the three-dimensional mesh representation.

[0040] The sensor may include an image acquisition device, such as a 3D camera, or may include a laser device, which is not limited here.

[0041] Step S200: Using the spatial anchor point technology, a virtual robotic arm corresponding to the physical robotic arm in the shotcrete operation site is established, and the virtual robotic arm and the physical robotic arm are kept consistent in posture and spatial position.

[0042] In this embodiment, the physical robotic arm in the spraying operation site refers to the robotic arm used to perform the spraying operation, such as Figure 3 As shown, the physical robotic arm 900 refers to a device that actually exists at the spraying operation site.

[0043] Among them, the space anchor technology can be used to establish a virtual robotic arm corresponding to the physical robotic arm. The virtual robotic arm can be understood as a mapping of the physical robotic arm in the virtual space.

[0044] In this embodiment, the created virtual robotic arm can track the posture of the physical robotic arm and maintain the same posture and spatial position as the physical robotic arm. Therefore, the virtual robotic arm can reproduce the posture changes of the physical robotic arm during the spraying operation in real time in the virtual space.

[0045] Among them, the posture can be understood as the motion posture of the physical robotic arm, and the spatial position can be understood as the positional relationship between the physical robotic arm and the target surface.

[0046] Step S300: Generate virtual spraying particles and set particle parameters for the virtual spraying particles based on the posture of the physical robotic arm during the spraying operation and the spraying parameters of the robotic arm nozzle on the physical robotic arm. The particle parameters include at least the initial velocity of the particles, the gravity influence factor, and the spraying angle of the particles.

[0047] In this embodiment, the virtual spraying particles correspond to the physical spraying particles sprayed from the nozzle of the robotic arm. The physical spraying particles refer to the spraying particles that actually exist at the spraying operation site and are sprayed onto the target surface. The virtual spraying particles can be understood as the mapping of the physical spraying particles in the virtual space.

[0048] like Figure 3 As shown, a robotic arm nozzle is installed on the physical robotic arm, and the posture change of the physical robotic arm is related to the spraying cone direction of the robotic arm nozzle. For example, the posture of the physical robotic arm determines the spraying cone direction of the robotic arm nozzle. Therefore, in the aforementioned step S200, the spraying cone direction of the robotic arm nozzle can be tracked in real time by binding the virtual robotic arm to the physical robotic arm, thereby obtaining the spraying position of the spray particles on the target surface.

[0049] After generating virtual spraying particles, the initial velocity, gravity influence factor, particle spraying angle, etc. of the virtual spraying particles can be set based on the posture of the physical robotic arm during the spraying operation and the spraying parameters of the robotic arm nozzle, so as to reproduce the real spraying operation process in the virtual space.

[0050] Among them, the initial velocity of the virtual spraying particles can be determined according to the spraying parameters of the robot arm nozzle. If the spraying parameters of the robot arm nozzle include the initial spraying velocity of the physical spraying particles, the initial velocity of the virtual spraying particles is the initial spraying velocity of the physical spraying particles.

[0051] The gravity influence factor represents the effect of gravity on the physical spraying particles during the spraying operation. For example, a gravity influence factor of 1 indicates that the particles are completely affected by gravity. It is understood that the gravity influence factor is related to the initial spraying velocity. The higher the initial spraying velocity, the smaller the gravity influence factor, indicating that the physical spraying particles are less affected by gravity.

[0052] The particle spraying angle may refer to the emission angle of the spray cone of the robot arm nozzle, which may represent the positional relationship with the target surface. The emission angle is related to the deposition position of the physical spraying particles on the target surface.

[0053] Therefore, by setting the particle parameters, the virtual spraying particles can reproduce the spraying trajectory of the physical spraying particles in the spraying operation site in real time.

[0054] In this embodiment, setting the gravity influence factor can make the virtual spraying particles have more realistic physical effects, thereby improving the accuracy of reproducing the spraying trajectory of the physical spraying particles in the spraying operation site.

[0055] Step S400: Whenever a virtual spraying particle is detected to collide with an environmental grid, the three-dimensional coordinates of the collision point are mapped to a corresponding grid cell in a two-dimensional height field grid, and a current deposition thickness value of the grid cell is obtained based on the physical parameters of the virtual spraying particle. The two-dimensional height field grid corresponds to the environmental grid, and the physical parameters include at least a particle mass and an adhesion probability of the virtual spraying particle, where the adhesion probability represents the probability of the virtual spraying particle adhering to the target surface.

[0056] In this embodiment, a two-dimensional height field grid corresponding to the environment grid can be obtained. This two-dimensional height field grid can be understood as a two-dimensional layer corresponding to the environment grid. This two-dimensional layer is used to calculate the deposition thickness value of the virtual spraying particles in each grid cell of the environment grid. This two-dimensional layer serves to associate the environment grid with the target surface. It can map the deposition position and thickness value of the virtual spraying particles impacting the environment grid to the corresponding deposition position on the target surface.

[0057] The two-dimensional height field grid may also include multiple grid cells. The number of grid cells in the two-dimensional height field grid may be determined based on the size of the target surface and the accuracy of the spray thickness detection. For example, if the accuracy requirement for the spray thickness detection is high, the size of the grid cells may be smaller, and the two-dimensional height field grid may include a larger number of grid cells.

[0058] The shape of the grid cells in the two-dimensional height field grid can be polygonal, which can be adapted to the shape of the grid cells in the environment grid. For example, the shape of the grid cells in the two-dimensional height field grid is a triangle, and the shape of the grid cells in the environment grid is a triangular pyramid.

[0059] The sizes of the grid cells in the two-dimensional height field grid may be different. For example, different grid cells in the two-dimensional height field grid may have different sizes.

[0060] The size may refer to the area of ​​a grid unit.

[0061] In this embodiment, a collision detection component may be provided, and the collision detection component may be used to detect collisions between virtual spraying particles and the environment grid.

[0062] When a collision between a virtual spraying particle and an environment grid is detected, the three-dimensional coordinates of the collision point can be obtained. The three-dimensional coordinates refer to the coordinates of the environment grid in the three-dimensional grid representation. For example, the three-dimensional coordinates can be expressed as .

[0063] Among them, the three-dimensional coordinates of the collision point can be mapped to the corresponding grid unit in the two-dimensional height field grid. Specifically, the grid unit corresponding to the three-dimensional coordinates in the two-dimensional height field grid can be determined based on the three-dimensional coordinates of the collision point, so that the three-dimensional coordinates of the collision point are mapped to the two-dimensional height field grid.

[0064] In this embodiment, the two-dimensional height field grid can be associated with the environment grid and the target surface, and the collision events detected on the environment grid can be mapped to the target surface in situ through the two-dimensional height field grid, so that a mapping relationship between the environment grid and the target surface is established through the two-dimensional height field grid, and the spray thickness is calculated in the two-dimensional height field grid.

[0065] After determining the grid cell corresponding to the three-dimensional coordinate in the two-dimensional height field grid, the current deposition thickness value of the mapped grid cell can be obtained based on the physical parameters of the virtual spraying particles. The physical parameters may include the particle mass and adhesion probability of the virtual spraying particles. The adhesion probability represents the probability of the virtual spraying particles adhering to the target surface.

[0066] The particle mass of the virtual spraying particles may be related to the deposition thickness. For example, a larger mass may contribute more to the deposition thickness.

[0067] The adhesion probability of the virtual spraying particles is also related to the deposition thickness. The greater the adhesion probability, the greater the contribution to the deposition thickness.

[0068] Therefore, in this embodiment, the current deposition thickness value of the grid cell in the two-dimensional height field grid may be determined based on at least the particle mass of the virtual spraying particle and the adhesion probability of the virtual spraying particle.

[0069] The current deposition thickness value of a grid cell in the two-dimensional height field grid is a continuously accumulated process. For example, when a virtual grouting particle is detected to collide with the environment grid for the first time, the current deposition thickness value of the grid cell can be obtained through calculation. When the virtual grouting particle is detected to collide with the environment grid for the second time, the current deposition thickness value of the grid cell can be obtained by adding the deposition thickness value calculated the second time to the first deposition thickness value of the grid cell. This process is repeated in this way, thus obtaining the current deposition thickness value of the grid cell after continuous accumulation.

[0070] In this embodiment, the adhesion probability and particle mass of the virtual spraying particles are consistent with the adhesion probability and particle mass of the physical spraying particles sprayed by the nozzle of the robot arm.

[0071] In this embodiment, during the process of the physical robotic arm performing the spraying operation, the deposition thickness value corresponding to each grid unit in the two-dimensional height field grid can be continuously updated.

[0072] In some examples, the deposition thickness value corresponding to each grid cell in the two-dimensional height field grid may refer to: the deposition thickness value at the endpoint of the grid cell. In this way, when calculating the deposition thickness value, the deposition thickness value at the edge of the grid cell can be used to calculate to reduce the amount of calculation.

[0073] Step S500: Based on the accumulated deposition thickness value of each grid unit, obtain the spraying thickness of each position point in the grid unit, and render the spraying thickness of each position point to the target surface with the corresponding color; wherein, the spraying thickness in different thickness ranges corresponds to different colors.

[0074] In this embodiment, each grid unit in the two-dimensional height field grid may include multiple position points, and the multiple position points may correspond to multiple different positions in a certain position area of ​​the target surface.

[0075] In the process of the physical robotic arm performing the spraying operation, the spraying thickness of each position point in the grid unit can be determined based on the deposition thickness value corresponding to the grid unit in the two-dimensional height field grid.

[0076] As described in the aforementioned embodiment, the deposition thickness value corresponding to each grid cell may be the deposition thickness value at the endpoint of the grid cell. In this way, the spraying thickness at each position point in the grid cell can be calculated based on the deposition thickness value at the endpoint of the grid cell, such as by performing linear or nonlinear interpolation on the deposition thickness value at the endpoint of the grid cell.

[0077] After obtaining the spray thickness at each location in the grid cell, the spray thickness at each location can be rendered onto the target surface using the corresponding color, thereby achieving a mixed reality display effect. In this way, the spray thickness of the physical spray particles on the target surface can be displayed on the target surface in a more intuitive way through visualization.

[0078] Among them, the position points in the two-dimensional height field grid correspond one-to-one to the position points on the target surface. In this way, the spraying thickness of the virtual spraying particles at the corresponding position points of the grid unit can be displayed in situ at the deposition position of the physical spraying particles corresponding to the virtual spraying particles on the target surface, thereby making the spraying thickness visualized in situ.

[0079] For example, Figure 3 As shown, what is shown is the picture seen from the perspective of the workers observing the spraying operation site. In this picture, in addition to the building surface in the spraying operation site, the color rendered by the above steps S100-S500 can also be seen attached to the building surface. Therefore, the construction workers do not need to leave the spraying operation site, and there is no need to return to the electronic device to observe the spraying thickness, that is, there is no need for the construction workers to switch the perspective back and forth between the building surface and the electronic device. Therefore, it does not affect the construction workers' construction work, and allows the construction workers to observe the spraying thickness in real time, and can timely adjust the nozzle distance, pressure and other construction parameters according to the spraying thickness in real time to spray and correct the locations where the thickness does not meet the standards.

[0080] In this embodiment, the spraying thicknesses in different thickness ranges correspond to different colors. For example, the spraying thickness in the first thickness range corresponds to dark blue, the spraying thickness in the second thickness range corresponds to light blue, the spraying thickness in the third thickness range corresponds to light purple, the spraying thickness in the fourth thickness range corresponds to dark purple, and the spraying thickness in the fifth thickness range corresponds to red.

[0081] The first thickness range is smaller than the second thickness range, the second thickness range is smaller than the third thickness range, the third thickness range is smaller than the fourth thickness range, and the fourth thickness range is smaller than the fifth thickness range.

[0082] In this way, the position point with the thinnest thickness corresponds to dark blue, and the position point with the largest thickness corresponds to red. Thus, the spraying thickness is converted into a thermal map representation through the gradient of dark blue-light blue-light purple-dark purple-red, thereby improving the intuitiveness of thickness observation.

[0083] The in-situ visualization method for shotcrete thickness of this embodiment allows the spray thickness of each location point in the two-dimensional height field grid to be rendered in real time onto the target surface using corresponding colors during the shotcrete operation. Thus, the spray thickness at each location point can be intuitively judged by the displayed color, making the spray thickness detection results visual. Furthermore, the spraying operation process performed by the physical robotic arm can be reproduced in real time using the environmental grid and virtual shotcrete particles, allowing the actual shotcrete operation process and the reproduction of the actual shotcrete operation process to proceed synchronously. Thus, while spraying, construction workers can observe the spray thickness of the spraying operation in real time on the target surface. This avoids the problem in related technologies where shotcrete thickness detection can only be performed after the entire construction process is completed.

[0084] On the other hand, by utilizing the depth sensor and spatial mapping function of the mixed reality device, a high-precision spatial grid model (three-dimensional grid representation) of the actual spraying operation site is scanned and reconstructed in real time. In the high-precision spatial grid model, virtual spraying particles are used to simulate the spraying of real physical spraying particles during the spraying process. Thus, digital means are used to reproduce the spraying process of real physical spraying particles in the high-precision spatial grid model, which will not damage the sprayed surface, is not affected by ambient lighting conditions, and is not affected by the roughness of the target surface. This can improve its environmental adaptability and enable slurry thickness detection to be applied to a wider range of scenarios.

[0085] In some embodiments, in the above step S100, the spatial mapping component of the mixed reality device can be used to automatically scan the depth information of the shotcrete operation site, and generate a three-dimensional mesh representation based on the depth information; and, a collision body component can be added to the three-dimensional mesh representation; wherein the collision body component is used to perform collision detection of the environment mesh.

[0086] Specifically, in the Unity development environment, you can call the device's spatial mapping component IMixedRealitySpatialAwarenessMeshObserver. This spatial mapping component will automatically scan the surrounding environment of the shotcrete operation site and generate a geometric mesh. The scanned meshes are enumerated through the API, and the MeshFilter data is serialized into a model file type to obtain the above-mentioned three-dimensional mesh representation.

[0087] Among them, for the scanned three-dimensional mesh representation, a Mesh Collider component (collision body component) can be added to it in Unity to enable collision detection with virtual spray particles.

[0088] The Unity development environment is a real-time 3D interactive content creation and operation platform. All creators, including those in game development, art, architecture, automotive design, and film and television, use Unity to turn their ideas into reality. The Unity development environment provides a comprehensive set of software solutions for creating, operating, and monetizing any real-time interactive 2D and 3D content, supporting platforms including mobile phones, tablets, PCs, game consoles, augmented reality, and virtual reality devices.

[0089] IMixedRealitySpatialAwarenessMeshObserver is the core component for spatially aware mesh observation in the Microsoft MixedReality Toolkit (MRTK). It is responsible for acquiring spatial mesh data in the environment in real time and providing interactive capabilities. Its core functions include real-time spatial awareness, collision detection, and dynamic interaction.

[0090] Among them, real-time spatial perception tracks the device position and posture through IMU (Inertial Measurement Unit) and VIO (Visual Inertial Odometry) technology to build an environmental space grid model. Collision detection supports ray detection, camera collision detection, and object interaction response, such as triggering events (such as triggering OnTriggerEnter / OnTriggerExit events when a ray enters / exits the object range). Dynamic Interaction adds physical properties (such as material and mass) to mesh objects, enabling more realistic interaction feedback.

[0091] In some embodiments, as Figure 4 As shown, Figure 4 The following is a flow chart showing the steps for building a virtual robotic arm: Figure 4 As shown, in step S200, the following steps may be included: Step S201: constructing an initial virtual robotic arm and setting a virtual QR code for the virtual robotic arm; wherein the position of the virtual QR code on the virtual robotic arm is consistent with the position of the physical QR code on the physical robotic arm; Step S202: Detecting the position of the physical QR code in real time using an image acquisition device, and adjusting the pose of the virtual robotic arm in the three-dimensional grid representation according to the real-time position until the real-time poses of the virtual QR code and the physical QR code coincide in the mixed reality view, thereby aligning the virtual robotic arm with the physical robotic arm; Step S203: creating a persistent spatial anchor point for the alignment position of the virtual robotic arm and the physical robotic arm, and binding the virtual robotic arm to the spatial anchor point to lock and maintain the alignment relationship between the virtual robotic arm and the physical robotic arm.

[0092] In this embodiment, a physical QR code may be attached to a fixed reference position of the physical robotic arm as an identifier of the physical robotic arm.

[0093] In the Unity editor, you can create a virtual QR code model, set it as a child object of the virtual robotic arm, and precisely adjust its local position and posture so that it is completely consistent with the relative position and orientation of the physical QR code on the physical robotic arm.

[0094] The image acquisition device may be a camera, and the position of the camera may not be limited, as long as it can capture the physical robotic arm and the physical QR code pasted on the physical robotic arm.

[0095] In this embodiment, the image of the physical QR code can be captured in real time by the camera, and the posture of the physical robotic arm can be obtained based on the captured image of the physical QR code, so as to automatically adjust the posture of the virtual robotic arm in the virtual space, and overlap the real-time posture of the virtual QR code and the physical QR code detected by the camera in the mixed reality view, thereby completing the precise alignment of the virtual robotic arm and the physical robotic arm.

[0096] For example, in Unity, you can use the ARMarkerManager component to scan with the device camera. When a physical QR code matching a preset pattern is detected, the markersChanged event is triggered. By subscribing to this event, the newly detected ARMarker object can be obtained in the callback function of the C# script. The position and posture (transform.position and transform.rotation) of this ARMarker object are the precise position of the physical QR code in Unity world space. This posture is obtained and the posture of the virtual robotic arm root object is adjusted in real time to make the posture of the virtual QR code coincide with that of the ARMarker, thereby completing the automatic alignment of the virtual and physical robotic arms.

[0097] In this embodiment, after the virtual robotic arm is aligned with the physical robotic arm, a persistent spatial anchor point can be created at the current alignment position, and the virtual robotic arm model can be bound to the spatial anchor point to lock and maintain the established virtual-real space registration relationship, thereby achieving stable tracking that does not rely on continuous recognition of the QR code (physical QR code).

[0098] For example, after completing the alignment of the virtual robotic arm and the physical robotic arm, you can add a WorldAnchor component to the virtual robotic arm to lock it, call the WorldAnchorStore.GetAsync() asynchronous method, get an instance of the world anchor storage area WorldAnchorStore on the device, call its Save() method, and save the spatial anchor information of the current alignment position to the device (that is, create a persistent spatial anchor at the current alignment position), thereby realizing the persistence of the alignment relationship between the physical robotic arm and the virtual robotic arm.

[0099] This method allows for stable tracking without relying on continuous recognition of physical QR codes.

[0100] In some embodiments, in step S300, a particle system can be created in Unity and bound to a virtual model of the robotic arm nozzle; the emission rate, initial velocity, and emission angle of the virtual spraying particles are set according to the real-time position of the physical robotic arm during the spraying operation and the spraying parameters; and gravity, air resistance, life cycle, and rebound coefficient are added to the virtual spraying particles to simulate the physical properties of real spraying materials; wherein the life cycle represents the disappearance time of virtual spraying particles that have not collided; and the rebound coefficient is negatively correlated with the adhesion coefficient of the virtual spraying particles.

[0101] In this embodiment, the emission rate, initial velocity and emission angle of the virtual spraying particles can be set dynamically according to the real-time position and spraying parameters of the physical robotic arm during the spraying operation; when the real-time position and spraying parameters of the physical robotic arm during the spraying operation change, the emission rate, initial velocity and emission angle of the virtual spraying particles can be set according to the changed real-time position and spraying parameters.

[0102] Among them, the emission rate, initial velocity and emission angle of the virtual spraying particles can be consistent with the emission rate, initial velocity and emission angle of the physical spraying particles ejected from the robot arm nozzle to simulate the actual spraying trajectory of the physical spraying particles.

[0103] In this embodiment, please refer to Figure 5 As shown, Figure 5 A schematic diagram of the particle parameter setting interface is shown in Figure 5 As shown, you can set the emission rate in the particle system's Emission module, set the angle and radius of the emission cone in the Shape module, and set the initial velocity in the particle system's main module.

[0104] In this example, to simulate the physical effects of real-world spraying particles, you can enable the Gravity Modifier in the particle system's main module and set an appropriate value (e.g., 1.0 means it's completely affected by gravity). At the same time, enable Drag in the Limit Velocity over Lifetime module to simulate the velocity decay of physical spraying particles flying through the air.

[0105] In this example, the Start Lifetime can be set in the particle system's main module to ensure that uncolliding particles disappear after a certain period of time (i.e., the disappearance time of uncolliding virtual spray particles), thus saving computing resources. In the Collision module, set the Type to World and the Bounce coefficient to a very small value (such as 0.1) to simulate the physical property of spraying material, where most of the material adheres to the target surface rather than rebounding.

[0106] Among them, the rebound coefficient is negatively correlated with the adhesion coefficient of the virtual spraying particles, that is, the larger the rebound coefficient, the smaller the adhesion coefficient of the virtual spraying particles, and the less likely the spraying particles are to adhere to the target surface; the smaller the rebound coefficient, the larger the adhesion coefficient of the virtual spraying particles, and the easier it is for the spraying particles to adhere to the target surface.

[0107] Therefore, through the above settings, the physical properties of the virtual spraying particles can be made consistent with the physical properties of the physical spraying particles, so that the spraying trajectory of the virtual spraying particles is highly consistent with the spraying trajectory of the physical spraying particles, so that the spraying trajectory of the physical spraying particles can be accurately reproduced in the virtual space, thereby improving the accuracy of the spraying thickness detection.

[0108] Among them, the gravity, air resistance, life cycle and rebound coefficient of the virtual spraying particles can be determined based on the gravity, air resistance, life cycle and rebound coefficient faced by the physical spraying particles. For example, in practice, the above-mentioned physical properties of the physical spraying particles can be detected and set as the physical properties of the virtual spraying particles.

[0109] In step S400, whenever a virtual spraying particle is detected to collide with the environment grid, the three-dimensional coordinates of the collision point are mapped to the corresponding grid cells in the two-dimensional height field grid. Figure 6 The steps shown determine the corresponding grid cells in the two-dimensional height field grid.

[0110] Step S401: Establish a two-dimensional height field data structure, obtain a two-dimensional height field grid, and divide the two-dimensional height field grid to obtain multiple grid units; wherein the two-dimensional height field data structure is represented as ;in, 、 is the horizontal coordinate.

[0111] In the C# script, a two-dimensional floating-point array can be created as a two-dimensional height field grid for storing thickness data. The array dimension and coverage of the data structure are determined according to the actual working area. The two-dimensional height field grid can be divided into multiple grid cells. The sizes of the multiple grid cells may not be exactly the same, and the shapes of the multiple grid cells may be the same.

[0112] As described in the previous embodiment, a collision body component can be added to the 3D mesh representation to perform collision detection on the environment mesh. In this embodiment, an OnParticleCollision(GameObject other) callback function can be created in a C# script to respond to collision events between virtual spray particles and the environment mesh. Then, in the callback function, you can use particleSystem.GetCollisionEvents(other,collisionEvents) to get all collision events.

[0113] Step S402: When a collision between a virtual spraying particle and an environment grid is detected, the three-dimensional coordinates of the collision point are extracted; wherein the three-dimensional coordinates are expressed as .

[0114] Step S403: Convert the three-dimensional coordinates into relative coordinates relative to the origin of the two-dimensional height field grid. The relative coordinates are expressed as .

[0115] Step S404: Based on the size and relative coordinates of the grid cells, the grid cells to which the three-dimensional coordinates are mapped are obtained in the plurality of two-dimensional height field grids.

[0116] Among them, for three-dimensional coordinates, the corresponding components in the relative coordinate vector can be selected according to the preset grid orientation, and these two components are divided by the preset grid unit size to convert them into the index of the two-dimensional height field grid , thus obtaining the grid unit to which the three-dimensional coordinates are mapped.

[0117] Specifically, based on the three-dimensional coordinates , get the collision point relative to the grid origin The relative coordinates of . Among them, the grid origin It can refer to the geometric center of the grid cell where the collision point is located in the environment grid.

[0118] Then, based on the size and relative coordinates of the grid cells, the grid cells to which the three-dimensional coordinates are mapped can be obtained in multiple two-dimensional height field grids according to the following formula (1): ; Formula (1); Where i and j in formula (1) represent the index of the two-dimensional height field grid, for example, represents the grid cell in the i-th row and j-th column.

[0119] The cell size in formula (1) represents the size of the grid cell, which may refer to the area of ​​the grid cell.

[0120] In this embodiment, in step S400, when obtaining the current deposition thickness value of the grid unit based on the physical parameters of the virtual spraying particles, the deposition contribution can be obtained according to the following formula (2): Formula (2); In formula (2), is the mass of the particle, is the sticking probability, is the time factor, where the time factor represents the duration of continuous spraying of the robot nozzle in the same posture.

[0121] In this embodiment, the time factor may be set in the particle system, or the time factor may be introduced separately when calculating the deposition contribution.

[0122] Among them, the time factor can represent the length of time that the physical spraying particles continue to act on the same position.

[0123] Here, “×” represents a multiplication operation.

[0124] Accordingly, the current deposition thickness value of the grid cell can be obtained according to the deposition thickness value and deposition contribution of the grid cell before the current collision.

[0125] For example, the current deposition thickness value of the grid cell can be obtained according to the following formula (3): H[i,j]=h[i,j]+H Formula (3) In formula (3), H is the deposition contribution of a single particle, H[i,j] represents the current deposition thickness value of the grid unit, and h[i,j] represents the deposition thickness value of the grid unit before the current collision.

[0126] In step S500, it is necessary to obtain the spraying thickness of each position point in the grid unit based on the accumulated deposition thickness value of each grid unit, and render the spraying thickness of each position point to the target surface with the corresponding color. In some embodiments, it can be combined with Figure 7 As shown, Figure 7 The specific process diagram of step S500 is shown in FIG. Figure 7 As shown, the following steps may be specifically included: Step S501: traverse the deposition thickness values ​​of multiple grid cells to obtain the maximum deposition thickness value and the minimum deposition thickness value.

[0127] In this embodiment, the maximum deposition thickness value refers to the deposition thickness value corresponding to the grid cell with the largest deposition thickness value among the multiple grid cells.

[0128] The minimum deposition thickness value refers to the deposition thickness value corresponding to the grid cell with the smallest deposition thickness value among the multiple grid cells.

[0129] Step S502: normalizing the deposition thickness values ​​of multiple grid cells based on the maximum deposition thickness value and the minimum deposition thickness value to obtain a normalized thickness value of each grid cell.

[0130] In this embodiment, when the deposition thickness values ​​of multiple grid cells are normalized based on the maximum deposition thickness value and the minimum deposition thickness value, the maximum deposition thickness value can be normalized to 1 and the minimum deposition thickness value can be normalized to 0. According to the deposition thickness value of the grid cell and the difference between the maximum deposition thickness value and the minimum deposition thickness value, the deposition thickness value of the grid cell is normalized to a value between 0 and 1, thereby obtaining the normalized thickness value of the grid cell.

[0131] For example, assuming the maximum deposition thickness is 50mm and the minimum deposition thickness is 10mm, 50mm is normalized to 1 and 10mm is normalized to 0. Therefore, the difference of 40mm between the maximum and minimum deposition thickness values ​​is converted to a difference of 1. On average, each 1mm corresponds to a step size of 0.025 in the (0, 1) range. Assuming the deposition thickness of the current grid is 12mm, the normalized thickness value is 0.05.

[0132] Of course, there are other ways to perform normalization, which will not be described here.

[0133] Step S503: Based on the normalized thickness value of the grid unit, the spraying thickness of each position point in the grid unit is obtained.

[0134] In this embodiment, after normalizing the deposition thickness values ​​of each grid cell, the deposition thickness values ​​of each grid cell can be calibrated to the same numerical range, so that the deposition thicknesses of multiple grid cells can be compared at the same scale, thereby improving the accuracy of subsequent color rendering.

[0135] In this embodiment, since the grid unit deposition thickness value refers to the deposition thickness value at the end point of the grid unit, in practice, in order to improve the refined detection of the spraying thickness, the spraying thickness of each position point in the grid unit can be obtained based on the normalized thickness value at the end point of the grid unit.

[0136] Because a grid cell includes multiple locations, specifically, in a virtual scene, a grid cell generally includes multiple pixels. Each pixel can be considered a location. Therefore, the normalized thickness value at the grid cell endpoint can be used to obtain the normalized spray thickness of each pixel in the grid cell. The corresponding location is color-coded based on the normalized spray thickness of each pixel.

[0137] For example, a normalized thickness value of 0 corresponds to pure blue (RGB: 0, 0, 255), representing the thinnest area. A normalized thickness value of 1 corresponds to pure red (RGB: 255, 0, 0), representing the thickest area. For any normalized thickness value between 0 and 1, the corresponding color is obtained by linearly interpolating between blue and red. This means that as the thickness gradually increases, the color smoothly transitions from blue to purple, magenta, and finally red.

[0138] In some examples, the spraying thickness at each position point in the grid unit may be obtained by performing linear interpolation between the maximum deposition thickness value and the minimum deposition thickness value based on the normalized thickness values ​​of each endpoint of the grid unit.

[0139] In this embodiment, the normalized thickness value t of all endpoints of the grid cell can be passed to the custom shader, and a color mapping function can be implemented in the fragment shading function of the shader. , this function will perform linear interpolation between the maximum deposition thickness value and the minimum deposition thickness value based on the input normalized thickness value t, calculate the color that each pixel in the grid unit should display, that is, calculate the normalized spray thickness of each position point, and then obtain the display color of each position point based on the normalized spray thickness.

[0140] According to the input normalized thickness value t, linear interpolation is performed between the normalized value 1 corresponding to the maximum deposition thickness value and the normalized value 0 corresponding to the minimum deposition thickness value.

[0141] Exemplarily, the grid unit includes three endpoints, and the normalized thickness values ​​t of the three endpoints are assumed to be 0.05, 0.075 and 0.05 respectively. The maximum deposition thickness value is 50 mm, and the minimum deposition thickness value is 10 mm. The corresponding normalized values ​​are 1 and 0 respectively. Then, linear interpolation of 0.05, 0.075, 0.05, 1 and 0 can be used to obtain the normalized spray thickness of each position point in the grid unit.

[0142] For example, a grid unit includes 10 pixels, that is, 10 position points. The normalized spray thicknesses of the 10 position points can be linearly interpolated using 0.05, 0.075, 0.05, 0, and 1, respectively, so that the obtained spray thicknesses are 0.06, 0.065, 0.055, etc.

[0143] For example, taking the grid unit as a triangle, the shader can use the values ​​of these three endpoints to perform linear interpolation from blue to red.

[0144] For example, if a grid cell contains 10 pixels, the shader will calculate an accurate normalized spray thickness for each of these 10 pixels based on its relative position within the grid cell. For example, if a pixel has a normalized spray thickness of 0.2, its color will be blue. Another pixel has a normalized spray thickness of 0.5, and its color will be closer to purple than the former, falling into the medium thickness range. Another pixel has a normalized spray thickness of 0.8, and its color will be closer to red, indicating a thicker thickness.

[0145] In this way, the shader can render the corresponding color representation for each position point according to the normalized spray thickness of each position point. This process can be called a color encoding process.

[0146] The process of linear interpolation in this embodiment can refer to the linear interpolation in related technologies, and will not be described in detail here.

[0147] Step S504: for each position point, the difference between the spraying thickness and the target deposition thickness value is used to render the spraying thickness of the position point onto the target surface using a corresponding color; The target deposition thickness value includes a maximum deposition thickness value and a minimum deposition thickness value; or, the target deposition thickness value is a preset thickness range.

[0148] In this embodiment, after obtaining the spraying thickness of each position point, the color corresponding to the position point can be determined based on the difference between the spraying thickness of the position point and the target deposition thickness value, and the color can be rendered on the target surface.

[0149] In some examples, the target deposition thickness value may include a maximum deposition thickness value and a minimum deposition thickness value. The maximum deposition thickness value may be colored red, while the minimum deposition thickness value may be colored blue. The spraying thickness at a location generally lies between the maximum and minimum deposition thickness values, and the color may be a mixture of red and blue in a corresponding ratio, such as a gradient purple.

[0150] The difference between the spraying thickness at the location point and the target deposition thickness value may include a first difference between the spraying thickness at the location point and the maximum deposition thickness value, and a second difference between the spraying thickness at the location point and the minimum deposition thickness value. A color ratio between blue and red may be determined based on the first and second differences, thereby obtaining the color corresponding to the location point.

[0151] The ratio between red (the color corresponding to the maximum deposition thickness value) and blue (the color corresponding to the minimum deposition thickness value) can be inversely proportional to the ratio of the first difference to the second difference. For example, if the first difference is greater than the second difference, indicating that the spray thickness at the location is closer to the minimum deposition thickness value, the color can be closer to blue, increasing the blue ratio and decreasing the red ratio, resulting in a light purple color at the location.

[0152] Of course, in some examples, the target deposition thickness value may include a preset thickness range, the color corresponding to the preset thickness range may be green, the color smaller than the preset thickness range may be blue, and the color greater than the preset thickness range may be red.

[0153] The preset thickness range may refer to a standard deposition thickness of the spray material sprayed on the target surface; the specific thickness may be determined according to the spraying requirements of the target surface and is not limited here.

[0154] In some examples, the color of each position point can be rendered into a two-dimensional height field grid, and the two-dimensional height field grid rendered with the color can be projected onto the target surface using holographic projection technology.

[0155] In some examples, the colors of each location point can be rendered onto the surface of an environment mesh, and the colored environment mesh can be projected onto a target surface using holographic projection technology. This allows the rendered colors to appear more three-dimensional on the target surface, matching the surface roughness of the target surface, achieving a stereoscopic viewing effect and thus optimizing the mixed reality display.

[0156] In some embodiments, as Figure 3 As shown, at least one of the particle parameters, collision trajectories of virtual spraying particles, and physical parameters can also be rendered into the generated display interface 800, and the display interface 800 can be holographically projected onto the spraying operation site to display the spraying operation process in a mixed reality manner.

[0157] For example, display interface 800 can be understood as a virtual display interface of the shotcrete operation site. This virtual display interface includes multiple windows, each displaying particle parameters, collision trajectories of virtual shotcrete particles, and physical parameters. Holographic projection technology can then be used to project the display interface onto the shotcrete operation site, thus achieving a mixed reality operating interface.

[0158] like Figure 3 As shown, from the perspective of a construction worker, in addition to being able to see the color representing the thickness rendered on the wall, one can also see a projected display interface 800, in which particle parameters, collision trajectories of virtual spraying particles, and physical parameters can be displayed in different windows.

[0159] In some examples, a gesture control function may also be provided. For example, a construction worker may perform gesture control on a display interface displayed at the spraying operation site.

[0160] For example, an image acquisition device can be used to capture the gestures of construction workers at the shotcrete work site and recognize the captured gestures. Based on the recognized gestures, the specific operation the construction worker intends to perform can be determined. For example, whether it is a click operation or a slide operation, the electronic device can be controlled to execute the control logic corresponding to the operation, so that the view displayed on the display interface of the shotcrete work site shows the view change corresponding to the operation. This not only satisfies the visualization of shotcrete thickness detection, but also allows construction workers to set and view the physical parameters and particle parameters of virtual shotcrete particles without relying on input devices.

[0161] In summary, this embodiment has the following advantages and positive effects: (1) By utilizing mixed reality spatial perception and height field thickness calculation technology, the virtual shotcrete thickness is precisely integrated with the real physical environment. By real-time scanning of the environment to generate a three-dimensional grid representation and establishing a collision detection system, the deposition process of the shotcrete particles and the thickness accumulation effect strictly follow the physical laws of the real shotcrete process, and the cumulative thickness value of each grid position is accurately recorded based on the two-dimensional height field grid. This method avoids the problems of destructive sampling and delayed feedback in traditional thickness detection methods, ensures the real-time thickness calculation and the accuracy of construction quality monitoring, and improves the operator's ability to predict the shotcrete coverage effect and the control accuracy.

[0162] (2) Through dynamic color mapping and in-situ heat map rendering technology, efficient and intuitive thickness visualization performance is achieved. Height field data is updated in real time based on particle collision events, and the spray thickness is converted into an intuitive heat map display through a color gradient algorithm. Combined with the threshold warning mechanism and dynamic rendering optimization, the system maintains stable visualization effects when running on mixed reality devices. This efficient visualization method not only reduces the cognitive load of construction workers, but also significantly improves the efficiency of quality monitoring in complex construction environments. It is particularly suitable for in-situ operation scenarios with complex lighting conditions such as tunnels and foundation pits.

[0163] (3) With the advantages of real-time thickness monitoring and in-situ decision support, preventive control of shotcrete quality and dynamic parameter adjustment are achieved. By recording the collision position, deposition thickness and time data of virtual shotcrete particles, a dynamic thickness distribution map covering the target surface is generated. Construction personnel can identify areas with insufficient or excessive thickness in real time and adjust construction parameters such as nozzle distance and pressure in a timely manner. This method transforms traditional post-process quality inspection into real-time monitoring with process control, reducing material waste and rework costs, and shortening the time to discover construction quality problems from hours to seconds.

[0164] In summary, the disclosed embodiments have constructed a high-precision in-situ monitoring system for shotcrete thickness through the deep integration of mixed reality technology, real-time thickness calculation and intelligent visualization. The technical difficulties of real-time calculation and in-situ visualization of shotcrete thickness have been overcome, significantly improving the scientific nature and operability of construction quality control. In the fields of tunnel support, foundation pit reinforcement, etc., construction personnel can use mixed reality equipment to monitor the distribution of shotcrete thickness in situ and optimize the construction process without relying on traditional coring or offline detection equipment. This technology reduces the risk of project quality and improves construction efficiency in complex environments. At the same time, it provides a reusable technical framework for intelligent construction and digital construction, and promotes the accelerated development of the engineering construction field towards refinement and intelligence.

[0165] Based on the same inventive concept, a mixed reality-based in-situ visualization system for shotcrete thickness is also provided. Figure 8 As shown, Figure 8 The schematic diagram of the framework of the in-situ visualization system of shotcrete thickness based on mixed reality is shown in Figure 8 As shown, the system mainly includes the following modules: An environmental perception module for constructing a three-dimensional grid representation of the spraying operation site, the three-dimensional grid representation including an environmental grid corresponding to the target surface to be sprayed; A spatial registration module is used to establish a virtual robotic arm corresponding to the physical robotic arm in the shotcrete operation site in the three-dimensional grid representation using spatial anchor technology, and to keep the virtual robotic arm consistent with the physical robotic arm in terms of posture and spatial position; a particle simulation module for generating virtual spraying particles and setting particle parameters for the virtual spraying particles based on the posture of the physical manipulator during the spraying operation and the spraying parameters of the manipulator nozzle on the physical manipulator, wherein the particle parameters include at least the initial velocity of the particles, the gravity influence factor, and the spraying angle of the particles; a thickness calculation module configured to, upon each detection of a collision between the virtual spraying particle and the environmental grid, map the three-dimensional coordinates of the collision point to a corresponding grid cell in a two-dimensional height field grid, and obtain a current deposition thickness value of the grid cell based on physical parameters of the virtual spraying particle; wherein the two-dimensional height field grid corresponds to the environmental grid, and the physical parameters include at least a particle mass and an adhesion probability of the virtual spraying particle, wherein the adhesion probability represents a probability of the virtual spraying particle adhering to the target surface; Visualization rendering module: used to obtain the spraying thickness of each position point in the environmental grid based on the accumulated deposition thickness value of each grid unit, and render the spraying thickness of each position point into the environmental grid using corresponding colors; wherein, the spraying thickness in different thickness ranges corresponds to different colors.

[0166] Exemplarily, the environment perception module includes: a scanning unit, configured to automatically scan depth information of the shotcrete operation site using a spatial mapping component of a mixed reality device, and generate the three-dimensional mesh representation based on the depth information; A component adding unit is used to add a collision body component to the three-dimensional grid representation; wherein the collision body component is used to perform collision detection on the environment grid.

[0167] Exemplarily, the spatial registration module includes: a QR code setting unit, configured to construct an initial virtual robotic arm and set a virtual QR code for the virtual robotic arm; wherein the position of the virtual QR code on the virtual robotic arm is consistent with the position of the physical QR code on the physical robotic arm; a posture alignment unit, configured to detect the position of the physical QR code in real time by an image acquisition device, and adjust the posture of the virtual robotic arm in the three-dimensional grid representation according to the real-time position, until the real-time postures of the virtual QR code and the physical QR code coincide in the mixed reality view, so as to align the virtual robotic arm with the physical robotic arm; An anchor binding unit is used to create a persistent spatial anchor for the alignment position of the virtual robotic arm when it is aligned with the physical robotic arm, and bind the virtual robotic arm to the spatial anchor to lock and maintain the alignment relationship between the virtual robotic arm and the physical robotic arm.

[0168] Exemplarily, the particle simulation module includes: A particle generation unit, used to create a particle system in Unity and bind the particle system to the virtual model of the robotic arm nozzle; A first setting unit is used to set the emission rate, initial velocity and emission angle of the virtual spraying particles according to the real-time position of the physical robotic arm during the spraying operation and the spraying parameters; A second setting unit is used to add gravity, air resistance, life cycle and rebound coefficient to the virtual spraying particles to simulate the physical properties of real spraying materials; The life cycle represents the disappearance time of the virtual spraying particles that have not collided; and the rebound coefficient is negatively correlated with the adhesion coefficient of the virtual spraying particles.

[0169] Exemplarily, the thickness calculation module includes a position mapping submodule, and the position mapping submodule includes: A data structure generating unit is used to establish a two-dimensional height field data structure, obtain the two-dimensional height field grid, and divide the two-dimensional height field grid to obtain a plurality of grid units; a coordinate acquisition unit, configured to extract the three-dimensional coordinates of the collision point whenever a collision between the virtual spraying particle and the environment grid is detected; A coordinate conversion unit, configured to convert the three-dimensional coordinates into relative coordinates relative to the origin of the two-dimensional height field grid; A coordinate mapping unit is configured to obtain, in the plurality of two-dimensional height field grids, a grid unit to which the three-dimensional coordinate is mapped based on a size of the grid unit and the relative coordinates.

[0170] Exemplarily, the thickness calculation module includes a thickness calculation submodule, and the thickness calculation submodule is specifically configured to: obtain a deposition contribution according to the following formula; ;in, is the mass of the particle, is the sticking probability, is a time factor; wherein the time factor represents the duration of continuous spraying of the robot arm nozzle in the same posture; and according to the thickness value deposited by the grid unit before the current collision and the deposition contribution, the current deposition thickness value of the grid unit is obtained.

[0171] Exemplarily, the visualization rendering module includes: A traversal unit is used to traverse the deposition thickness values ​​of a plurality of the grid cells to obtain a maximum deposition thickness value and a minimum deposition thickness value; a normalizing unit, configured to normalize the deposition thickness values ​​of the plurality of grid cells based on the maximum deposition thickness value and the minimum deposition thickness value, to obtain a normalized thickness value of each grid cell; A position point thickness acquisition unit, configured to acquire the spraying thickness of each position point in the grid unit based on the normalized thickness value of the grid unit; a rendering unit, configured to render the spraying thickness of each position point onto the target surface using a corresponding color according to a difference between the spraying thickness of the position point and the target deposition thickness value; The target deposition thickness value includes the maximum deposition thickness value and the minimum deposition thickness value; or the target deposition thickness value is a preset thickness range.

[0172] Exemplarily, the grid unit is a polygon, and the normalized thickness value of the grid unit includes the normalized thickness values ​​of each endpoint of the grid unit; the position point thickness acquisition unit is specifically used to: based on the normalized thickness values ​​of each endpoint of the grid unit, perform linear interpolation between the maximum deposition thickness value and the minimum deposition thickness value to obtain the spraying thickness of each position point located in the grid unit.

[0173] Exemplarily, the rendering unit includes: a first rendering subunit, configured to render the corresponding color of each of the position points onto an environment grid, and holographically project the environment grid rendered with the color onto the target surface, so as to display the spraying thickness of the target surface in a mixed reality manner; The second rendering subunit is used to render the particle parameters, the collision trajectory of the virtual spraying particles and at least one of the physical parameters into a generated display interface, and holographically project the display interface onto the spraying operation site to display the spraying operation process in a mixed reality.

[0174] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0175] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity, or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, commodity, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, commodity, or device that includes the element.

[0176] The above is a detailed introduction to the in-situ visualization method and system for shotcrete thickness based on mixed reality provided by the present disclosure. Specific examples are used in this article to illustrate the principles and implementation methods of the present disclosure. The description of the above embodiments is only used to help understand the method and core ideas of the present disclosure. At the same time, for those skilled in the art, according to the ideas of the present disclosure, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present disclosure.

[0177] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein.

[0178] It will be understood that the present disclosure is not limited to the exact construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof.

[0179] References herein to "one embodiment," "an embodiment," or "one or more embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Furthermore, please note that instances of the phrase "in one embodiment" do not necessarily all refer to the same embodiment.

[0180] In the description provided herein, numerous specific details are described. However, it is understood that embodiments of the present disclosure may be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0181] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present disclosure, rather than to limit them. Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present disclosure.

Claims

1. A mixed reality-based in-situ visualization method for shotcrete thickness, characterized in that: The method comprises: Constructing a three-dimensional grid representation of the shotcrete operation site, the three-dimensional grid representation including an environment grid corresponding to the target surface to be sprayed; Using spatial anchor point technology, a virtual robotic arm corresponding to the physical robotic arm in the shotcrete operation site is established, and the virtual robotic arm and the physical robotic arm are kept consistent in posture and spatial position; generating virtual spraying particles, and setting particle parameters for the virtual spraying particles based on the posture of the physical manipulator during the spraying operation and the spraying parameters of the manipulator nozzle on the physical manipulator, the particle parameters including at least an initial velocity of the particles, a gravity influence factor, and a spraying angle of the particles; Whenever a collision between the virtual spraying particle and the environment grid is detected, the three-dimensional coordinates of the collision point are mapped to a corresponding grid cell in a two-dimensional height field grid, and a current deposition thickness value of the grid cell is obtained based on physical parameters of the virtual spraying particle; wherein the two-dimensional height field grid corresponds to the environment grid, and the physical parameters include at least a particle mass and an adhesion probability of the virtual spraying particle, wherein the adhesion probability represents a probability of the virtual spraying particle adhering to the target surface; Based on the accumulated deposition thickness value of each grid unit, the spraying thickness of each position point in the grid unit is obtained, and the spraying thickness of each position point is rendered into the target surface using a corresponding color; wherein the spraying thickness in different thickness ranges corresponds to different colors.

2. The in-situ visualization method for shotcrete thickness based on mixed reality according to claim 1, characterized in that: The constructing of a three-dimensional grid representation of the shotcrete operation site includes: Automatically scanning depth information of the shotcrete operation site using a spatial mapping component of a mixed reality device, and generating the three-dimensional mesh representation based on the depth information; A collision volume component is added to the three-dimensional mesh representation; wherein the collision volume component is used to perform collision detection on the environment mesh.

3. The in-situ visualization method for shotcrete thickness based on mixed reality according to claim 1, characterized in that: The method of using the spatial anchor point technology to establish a virtual robotic arm corresponding to the physical robotic arm in the shotcrete operation site, and making the virtual robotic arm consistent with the physical robotic arm in posture and spatial position, includes: Constructing an initial virtual robotic arm and setting a virtual QR code for the virtual robotic arm; wherein the position of the virtual QR code on the virtual robotic arm is consistent with the position of the physical QR code on the physical robotic arm; detecting the position of the physical QR code in real time using an image acquisition device, and adjusting the pose of the virtual robotic arm in the three-dimensional grid representation according to the position until the real-time poses of the virtual QR code and the physical QR code coincide in the mixed reality view, so as to align the virtual robotic arm with the physical robotic arm; A persistent spatial anchor is created for the alignment position of the virtual robotic arm when aligned with the physical robotic arm, and the virtual robotic arm is bound to the spatial anchor to lock and maintain the alignment relationship between the virtual robotic arm and the physical robotic arm.

4. The in-situ visualization method for shotcrete thickness based on mixed reality according to claim 1, characterized in that: The generating of virtual spraying particles and setting particle parameters for the virtual spraying particles based on the posture of the physical manipulator during the spraying operation and the spraying parameters of the manipulator nozzle on the physical manipulator include: Creating a particle system in Unity and binding the particle system to the virtual model of the robotic arm nozzle; Setting the emission rate, initial velocity and emission angle of the virtual spraying particles according to the real-time position of the physical robotic arm during the spraying operation and the spraying parameters; Adding gravity, air resistance, life cycle and rebound coefficient to the virtual spraying particles to simulate the physical properties of real spraying materials; The life cycle represents the disappearance time of the virtual spraying particles that have not collided; and the rebound coefficient is negatively correlated with the adhesion coefficient of the virtual spraying particles.

5. The in-situ visualization method for shotcrete thickness based on mixed reality according to claim 1, characterized in that: The method of mapping the three-dimensional coordinates of the collision point to a corresponding grid cell in the two-dimensional height field grid each time the virtual spraying particle is detected to collide with the environment grid comprises: Establishing a two-dimensional height field data structure to obtain the two-dimensional height field grid, and dividing the two-dimensional height field grid to obtain a plurality of grid units; Whenever a collision between the virtual spraying particle and the environment grid is detected, extracting the three-dimensional coordinates of the collision point; Converting the three-dimensional coordinates into relative coordinates relative to the origin of the two-dimensional height field grid; Based on the size of the grid unit and the relative coordinates, a grid unit to which the three-dimensional coordinate is mapped is obtained in the plurality of two-dimensional height field grids.

6. The in-situ visualization method for shotcrete thickness based on mixed reality according to claim 1, characterized in that: The obtaining of the current deposition thickness value of the grid unit based on the physical parameters of the virtual spraying particles includes: The deposition contribution is obtained according to the following formula; ;in, is the mass of the particle, is the sticking probability, is a time factor; wherein, the time factor represents the duration of continuous spraying of the robot nozzle in the same posture; The current deposition thickness value of the grid unit is obtained according to the deposition thickness value of the grid unit before the current collision and the deposition contribution.

7. The in-situ visualization method for shotcrete thickness based on mixed reality according to claim 1, characterized in that: The step of obtaining the spraying thickness of each position point in the grid unit based on the accumulated deposition thickness value of each grid unit, and rendering the spraying thickness of each position point to the target surface using a corresponding color, includes: Traversing the deposition thickness values ​​of the plurality of grid cells to obtain a maximum deposition thickness value and a minimum deposition thickness value; Normalizing the deposition thickness values ​​of the plurality of grid cells based on the maximum deposition thickness value and the minimum deposition thickness value to obtain a normalized thickness value of each grid cell; Based on the normalized thickness value of the grid unit, obtaining the spraying thickness of each position point in the grid unit; For each position point, the difference between the spraying thickness and the target deposition thickness value is determined by rendering the spraying thickness of the position point onto the target surface using a corresponding color; The target deposition thickness value includes the maximum deposition thickness value and the minimum deposition thickness value; or the target deposition thickness value is a preset thickness range.

8. The in-situ visualization method for shotcrete thickness based on mixed reality according to claim 7, characterized in that: The grid unit is a polygon, and the normalized thickness value of the grid unit includes the normalized thickness value of each endpoint of the grid unit; obtaining the spraying thickness of each position point in the grid unit based on the normalized thickness value of the grid unit includes: Based on the normalized thickness values ​​of each endpoint of the grid unit, linear interpolation is performed between the maximum deposition thickness value and the minimum deposition thickness value to obtain the spraying thickness of each position point located in the grid unit.

9. The in-situ visualization method for shotcrete thickness based on mixed reality according to claim 1, characterized in that: The step of rendering the spraying thickness of each of the position points onto the target surface using a corresponding color includes: Rendering the corresponding color of each of the position points onto an environment grid, and holographically projecting the environment grid rendered with the color onto the target surface to display the spraying thickness of the target surface in a mixed reality manner; The particle parameters, the collision trajectory of the virtual spraying particles and at least one of the physical parameters are rendered into a generated display interface, and the display interface is holographically projected onto the spraying operation site to display the spraying operation process in mixed reality.

10. A mixed reality based in-situ visualization system for shotcrete thickness, characterized by: include: An environmental perception module for constructing a three-dimensional grid representation of the spraying operation site, the three-dimensional grid representation including an environmental grid corresponding to the target surface to be sprayed; A spatial registration module is used to establish a virtual robotic arm corresponding to the physical robotic arm in the shotcrete operation site in the three-dimensional grid representation using spatial anchor technology, and to keep the virtual robotic arm consistent with the physical robotic arm in terms of posture and spatial position; a particle simulation module for generating virtual spraying particles and setting particle parameters for the virtual spraying particles based on the posture of the physical manipulator during the spraying operation and the spraying parameters of the manipulator nozzle on the physical manipulator, wherein the particle parameters include at least the initial velocity of the particles, the gravity influence factor, and the spraying angle of the particles; a thickness calculation module configured to, upon each detection of a collision between the virtual spraying particle and the environmental grid, map the three-dimensional coordinates of the collision point to a corresponding grid cell in a two-dimensional height field grid, and obtain a current deposition thickness value of the grid cell based on physical parameters of the virtual spraying particle; wherein the two-dimensional height field grid corresponds to the environmental grid, and the physical parameters include at least a particle mass and an adhesion probability of the virtual spraying particle, wherein the adhesion probability represents a probability of the virtual spraying particle adhering to the target surface; Visualization rendering module: used to obtain the spraying thickness of each position point in the grid unit based on the accumulated deposition thickness value of each grid unit, and render the spraying thickness of each position point to the target surface with corresponding colors; wherein, the spraying thickness in different thickness ranges corresponds to different colors.

Images