Target object control method and device

Abstract

The invention discloses a target object control method and device, and belongs to the technical field of computers. The target object control method comprises the following steps: constructing at least one sampling path corresponding to a target to-be-measured point based on the target to-be-measured point in a plurality of to-be-measured points in a target area corresponding to a magnetic medium object; the sampling path comprises at least one sampling point; based on the at least one sampling point included in each sampling path corresponding to the target to-be-measured point, obtaining a magnetic physical quantity corresponding to the target to-be-measured point; and controlling the target object based on the magnetic physical quantity. The target object control method can be suitable for various computer surface geometric discrete formats, accurate, efficient and large-scale magnetic physical quantity solving can be completed in various scenes, and therefore the control precision and accuracy of virtual or entity objects are improved.

Description

Technical Field

[0001] This application belongs to the field of computer technology, and in particular relates to a method and apparatus for controlling a target object. Background Technology

[0002] In the field of artificial intelligence, the simulation of magnetic phenomena is an indispensable dimension in constructing AI "world models." For example, when controlling robot movement based on magnetic fields, the robot's motion path can be planned based on magnetic physical quantities. While some technologies exist that solve for magnetic physical quantities by solving large linear systems to control objects, these methods cannot accurately and efficiently solve for large-scale magnetic physical quantities in the triangular meshes, implicit surfaces, and high-order parametric surface geometries commonly found in industrial scenarios. This results in low precision and accuracy in controlling virtual or physical objects. Summary of the Invention

[0003] This application aims to address at least one of the technical problems existing in the related art. To this end, this application proposes a control method and apparatus for a target object, which is applicable to various computer surface geometry discretization formats, including but not limited to triangular meshes, implicit surfaces, and high-order parametric surface geometry formats. It can complete accurate, efficient, and large-scale solutions for magnetic physical quantities in various scenarios, meet the needs of a wide range of industrial applications, and has good compatibility, thereby improving the control precision and accuracy of virtual or physical objects.

[0004] Firstly, this application provides a method for controlling a target object, the method comprising:

[0005] Based on the target test point among multiple test points in the target region corresponding to the magnetic medium object, at least one sampling path is constructed corresponding to the target test point; the sampling path includes at least one sampling point.

[0006] Based on the at least one sampling point included in each sampling path corresponding to the target test point, the magnetic physical quantity corresponding to the target test point is obtained;

[0007] The target object is controlled based on the magnetic physical quantity.

[0008] According to the target object control method provided in the embodiments of this application, the magnetic physical quantity of the target test point is calculated by constructing the sampling path corresponding to the target test point. By reusing or modifying the existing algorithm tools in the ray tracing renderer, the magnetic physical quantity can be effectively calculated. It can be applied to various computer surface geometry discretization formats, including but not limited to triangular meshes, implicit surfaces and high-order parametric surface geometry formats. It can complete accurate, efficient and large-scale magnetic physical quantity solutions in various scenarios, meet the needs of a wide range of industrial applications, and has good compatibility, thereby improving the control precision and accuracy of virtual or physical objects.

[0009] One embodiment of the present application describes a method for controlling a target object, wherein obtaining a magnetic physical quantity corresponding to the target test point based on at least one sampling point included in each sampling path corresponding to the target test point includes:

[0010] Based on the at least one sampling point included in each sampling path corresponding to the target test point, the target potential energy corresponding to the target test point is obtained;

[0011] Based on the target potential energy of the target test point, the magnetic field strength corresponding to the target test point is obtained.

[0012] One embodiment of the present application describes a method for controlling a target object, wherein obtaining the target potential energy corresponding to the target test point based on at least one sampling point included in each sampling path corresponding to the target test point includes:

[0013] Based on the kernel function between the target test point and the target sampling point among the plurality of sampling points, the Monte Carlo algorithm is used to process at least one of the potential energy corresponding to the target sampling point, the internal magnetic susceptibility corresponding to the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point to obtain the target potential energy corresponding to the target test point; the target sampling point is determined from the plurality of sampling points based on the sampling probability.

[0014] One embodiment of the target object control method of this application includes a method for processing at least one of the following based on the kernel function between the target test point and the target sampling point among the plurality of sampling points: the potential energy corresponding to the target sampling point, the internal magnetic susceptibility of the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point, to obtain the target potential energy corresponding to the target test point:

[0015] Based on the kernel function between the target test point and the target sampling point, the Monte Carlo algorithm is used to process at least one of the potential energy corresponding to the target sampling point, the internal magnetic susceptibility of the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point to obtain the first potential energy corresponding to the target test point.

[0016] The target potential energy corresponding to the target test point is obtained by averaging at least one of the first potential energies.

[0017] A method for controlling a target object according to an embodiment of this application, wherein the method uses the Monte Carlo algorithm based on the kernel function between the target test point and the target sampling point to process at least one of the potential energy corresponding to the target sampling point, the internal magnetic susceptibility of the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point to obtain a first potential energy corresponding to the target test point, including:

[0018] Based on the target termination condition, determine the last sampling point in the sampling path corresponding to the target test point, and update the target sampling point to the last sampling point;

[0019] If the updated target sampling point is not the first sampling point after the target test point, based on the kernel function between the sampling point before the target sampling point and the target sampling point, at least one of the following is processed: the potential energy corresponding to the target sampling point, the internal magnetic susceptibility corresponding to the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point. This process yields the potential energy corresponding to the sampling point before the target sampling point, and the target sampling point is updated to the sampling point before the target sampling point. The step "based on the kernel function between the sampling point before the target sampling point and the target sampling point, process at least one of the following: the potential energy corresponding to the target sampling point, the internal magnetic susceptibility corresponding to the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point, to obtain the potential energy corresponding to the sampling point before the target sampling point, and update the target sampling point to the sampling point before the target sampling point" is repeated until the updated target sampling point is the first sampling point after the target test point.

[0020] Based on the kernel function between the target test point and the target sampling point, at least one of the potential energy corresponding to the target sampling point, the internal magnetic susceptibility corresponding to the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point is processed to obtain the first potential energy corresponding to the target test point.

[0021] One embodiment of the method for controlling a target object according to this application includes obtaining the magnetic field strength corresponding to the target test point based on the target potential energy of the target test point, comprising:

[0022] Using the Monte Carlo algorithm, the magnetic field strength corresponding to the target test point is obtained based on the target potential energy of the target test point.

[0023] One embodiment of the method for controlling a target object in this application includes constructing at least one sampling path corresponding to the target test point among multiple test points in the target region corresponding to the magnetic medium object.

[0024] At least one of the forward path tracing algorithm, the backward path tracing algorithm, and the bidirectional path tracing algorithm is used to construct the at least one sampling path corresponding to the target test point.

[0025] Secondly, this application provides a control device for a target object, comprising:

[0026] The first processing module is used to construct at least one sampling path corresponding to the target test point among multiple test points in the target region corresponding to the magnetic medium object; the sampling path includes at least one sampling point.

[0027] The second processing module is used to obtain the magnetic physical quantity corresponding to the target test point based on the at least one sampling point included in each sampling path corresponding to the target test point.

[0028] The third processing module is used to control the target object based on the magnetic physical quantity.

[0029] According to the target object control device provided in the embodiments of this application, the magnetic physical quantity of the target test point is calculated by constructing the sampling path corresponding to the target test point. By reusing or modifying the existing algorithm tools in the ray tracing renderer, the magnetic physical quantity is effectively calculated. It is applicable to various computer surface geometry discretization formats, including but not limited to triangular meshes, implicit surfaces and high-order parametric surface geometry formats. It can complete accurate, efficient and large-scale magnetic physical quantity solutions in various scenarios, meet the needs of a wide range of industrial applications, and has good compatibility, thereby improving the control precision and accuracy of virtual or physical objects.

[0030] Thirdly, this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the control method for the target object as described in the first aspect above.

[0031] Fourthly, this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the control method for the target object as described in the first aspect above.

[0032] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the control method for the target object as described in the first aspect above.

[0033] The above-described one or more technical solutions in the embodiments of this application have at least one of the following technical effects:

[0034] The magnetic physical quantities of the target test point are calculated by constructing the sampling path corresponding to the target test point. By reusing or modifying the existing algorithm tools in the ray tracing renderer, the magnetic physical quantities can be effectively calculated. It is applicable to a variety of computer surface geometry discretization formats, including but not limited to triangular meshes, implicit surfaces, and high-order parametric surface geometry formats. It can complete accurate, efficient, and large-scale magnetic physical quantity solutions in various scenarios, meet the needs of a wide range of industrial applications, and has good compatibility, thereby improving the control precision and accuracy of virtual or physical objects.

[0035] Furthermore, by using the Monte Carlo algorithm to solve the magnetic field integral equation, there is no need to rely on any complex linear system solvers, and the computational tools and underlying code used in the solution process can maintain logical consistency with the rendering. This allows the method to be seamlessly embedded as a plug-and-play module into various conventional physical simulation frameworks, thereby enriching the functionality of virtual simulation platforms. In addition, using the Monte Carlo algorithm to solve magnetic physical quantities is suitable for evaluating the uncertainty in solving the magnetic field of linear magnetic media objects. In AI systems, the decision-making process can be improved based on the quantification of uncertainty, enabling the formulation of more robust strategies by considering the possible range of changes.

[0036] Furthermore, by reusing the integration and sampling algorithms of the ray tracing renderer, the integral weights and luminescence terms in the rendering equations are modified, enabling the calculation of magnetic physical quantities based on the rendering equations. This optimizes the algorithm's integration and applicability while ensuring high efficiency and accuracy in the magnetic field solution process.

[0037] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0038] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0039] Figure 1 This is a flowchart illustrating the control method for the target object provided in the embodiments of this application;

[0040] Figure 2 This is a schematic diagram of the structure of the control device for the target object provided in the embodiments of this application;

[0041] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0042] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0043] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class and the number of objects is not limited; for example, a first object can be one or more. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0044] The following description, in conjunction with the accompanying drawings, details the target object control method, target object control device, electronic device, and readable storage medium provided in this application embodiment through specific embodiments and application scenarios.

[0045] The control method for the target object can be applied to the terminal, and can be executed by the hardware or software in the terminal.

[0046] The terminal includes, but is not limited to, portable communication devices such as mobile phones or tablets with touch-sensitive surfaces (e.g., touchscreen displays and / or touchpads). It should also be understood that, in some embodiments, the terminal may not be a portable communication device, but rather a desktop computer with touch-sensitive surfaces (e.g., touchscreen displays and / or touchpads).

[0047] The following embodiments describe a terminal including a display and a touch-sensitive surface. However, it should be understood that the terminal may include one or more other physical user interface devices such as a physical keyboard, mouse, and joystick.

[0048] The target object control method provided in this application embodiment can be executed by an electronic device or a functional module or functional entity in an electronic device that can implement the target object control method. The electronic devices mentioned in this application embodiment include, but are not limited to, mobile phones, tablets, computers, cameras and wearable devices. The target object control method provided in this application embodiment is described below using an electronic device as the execution subject.

[0049] like Figure 1 As shown, the control method for the target object includes steps 110, 120 and 130.

[0050] It should be noted that the control method for this target object can be applied to fields such as artificial intelligence, robotics, or metamaterial design.

[0051] For example, the control method of the target object can be embedded into a module and then integrated into a virtual simulation platform; or the robot's motion can be controlled based on the control method of the target object; or the magnetic properties of metamaterials can be precisely controlled based on the control method of the target object, etc., which are not limited in this application.

[0052] Step 110: Based on the target test point among multiple test points in the target region corresponding to the magnetic medium object, construct at least one sampling path corresponding to the target test point;

[0053] In this step, the magnetic medium is a substance whose internal state changes under the influence of an external magnetic field, which in turn affects the existence or distribution of the magnetic field.

[0054] Magnetic media can be: diamagnetic, paramagnetic, ferromagnetic, antiferromagnetic, and ferrimagnetic, etc.

[0055] Among them, diamagnetic materials include non-metals and insulators, such as copper, silver, gold, water, wood, and plastics.

[0056] Paramagnetic materials include substances containing ions or atoms with unpaired electrons, such as transition metals and rare earth metals.

[0057] Based on functions, internal and surface regions of magnetic media objects can be represented.

[0058] The target region can include the surface region of the magnetic medium object, and the target region can also include any region in three-dimensional space other than the interior and surface of the magnetic medium object.

[0059] Among them, the surface of the magnetic medium object has a normal vector and a tangent vector.

[0060] Multiple test points are points on the surface of a magnetic medium object where magnetic physical quantities need to be solved.

[0061] The point to be measured can be a specific point used to simulate or analyze the interaction behavior of light. For example, the point to be measured can be a camera lens, a detector, or other device or location that needs to receive or measure light.

[0062] Magnetic physical quantities include magnetic induction intensity, magnetic flux, and magnetic field strength.

[0063] The target test point is any one of multiple test points.

[0064] At least one sampling path can be constructed based on the target point to be measured and the point with the maximum external magnetic field strength.

[0065] In solving for magnetic physical quantities, the function g(x) = 2λH can be constructed based on the internal magnetic susceptibility of the magnetic medium, the external magnetic field strength of the magnetic medium, and the normal vector of the surface of the magnetic medium: ext (x)·(x), where, Here are the integral coefficients related to magnetic susceptibility derived from the data, where x is the point to be measured, and H is the coefficient of the integral term. ext (x) represents the external magnetic field strength, n(x) represents the normal vector, and the points in the region with larger g(x) values ​​are the points with the maximum external magnetic field strength.

[0066] The sampling path includes at least one sampling point.

[0067] During the sampling process, sampling points can be obtained from the target point to be measured, or from the point with the maximum external magnetic field strength, or from both the target point to be measured and the point with the maximum external magnetic field strength simultaneously. Sampling points are obtained based on the probability distribution, and sampling is stopped based on the termination condition, resulting in at least one sampling point. The sampling method can be selected based on the requirements, and this application does not limit it.

[0068] The sampling point may be located at the point of maximum external magnetic field strength, the surface of the magnetic medium object, or any other location in the space where the magnetic medium object is located; this application does not limit this.

[0069] Multiple samplings can be performed to obtain multiple sampling paths corresponding to the target test point.

[0070] In some embodiments, step 110 may include:

[0071] At least one of the forward path tracing algorithm, backward path tracing algorithm, and bidirectional path tracing algorithm is used to construct at least one sampling path corresponding to the target test point.

[0072] In this embodiment, at least one sampling path can be constructed using a forward path tracing algorithm, a backward path tracing algorithm, or a bidirectional path tracing algorithm.

[0073] Alternatively, any two of the forward path tracing algorithm, backward path tracing algorithm, and bidirectional path tracing algorithm can be selected to construct at least one sampling path.

[0074] Alternatively, forward path tracing, backward path tracing, and bidirectional path tracing algorithms can be used to construct at least one sampling path.

[0075] Based on user needs, at least one sampling path can be constructed by selecting any of the three tracking algorithms mentioned above, or it can be constructed based on any other algorithm; this application does not impose any limitations.

[0076] It is understood that the method of constructing the sampling path in this application is the same as the method of constructing the light path in the ray tracing rendering algorithm. In the ray tracing rendering algorithm, the point with the maximum value of the external magnetic field strength is the light source point. Based on the forward path tracing algorithm, a light ray can be emitted from the target test point (such as the camera or the viewpoint) in a certain direction, and then the interaction between this light ray and the objects in the scene (such as reflection, refraction or scattering) is tracked until the light ray reaches the light source point or the energy decays to a sufficiently low level.

[0077] In forward path tracing, multiple rays can be emitted from the target point to obtain multiple sampling paths.

[0078] Based on the backward path tracing algorithm, light rays can be emitted from the light source into the scene and tracked until they reach the target point or the energy decays to a sufficiently low level.

[0079] In this application, a backward path tracing algorithm is adopted to achieve importance sampling in magnetic field problems: when calculating magnetic physical quantities, the intersection of the light emitted from the light source point and the surface of the magnetic medium object is ignored and geometric occlusion is calculated. The point to be measured is selected from the intersection, which corresponds to the next point of the reverse sampling path.

[0080] Based on the bidirectional path tracing algorithm, light rays can be emitted from both the target point to be measured and the light source point, and then connected in the scene to form a complete sampling path.

[0081] In actual implementation, it can be started from the target measurement point x. 0 Build path x 0 x 1 …x t Then from the point y where the external magnetic field strength is at its maximum 0 Build path y 0 y 1 …ys .

[0082] x 0 x 1 …x t Any point on can be connected to y 0 y 1 …y s Connect any point on the path to construct the path x. 0 x 1 …x t′ y s′ …y 1 y 0 (t′≤t, s′≤s).

[0083] In some embodiments, at least one sampling path can be constructed for the target measurement point based on importance sampling, surface uniform sampling, or Russian roulette.

[0084] In this embodiment, importance sampling is a technique for selecting sampling points based on a probability distribution.

[0085] Russian roulette can be used to randomly terminate sampling; for example, a termination probability can be set, and the decision to continue sampling can be based on the termination probability.

[0086] Uniform surface sampling is a method of uniformly distributing sampling points on the surface of a magnetic medium object. Uniform surface sampling can be used to select the location where light intersects with the surface of the magnetic medium object.

[0087] According to the target object control method provided in the embodiments of this application, at least one sampling path corresponding to the target test point is constructed by employing at least one of the forward path tracing algorithm, the backward path tracing algorithm, and the bidirectional path tracing algorithm. The appropriate tracing algorithm can be selected to construct the sampling path according to the specific application scenario and requirements, which is highly flexible and improves sampling efficiency and sampling quality.

[0088] Step 120: Based on at least one sampling point included in each sampling path corresponding to the target test point, obtain the magnetic physical quantity corresponding to the target test point.

[0089] In this step, the magnetic physical quantities corresponding to the target measurement point may include potential energy and magnetic field strength.

[0090] Potential energy is a scalar quantity used to characterize the energy an object possesses due to its position in a conservative force field.

[0091] Magnetic field strength is used to characterize the magnitude of the magnetic field around a magnetic field.

[0092] The magnetic field strength corresponding to the target test point may include at least one of the following: the magnetic field strength at the target test point on the surface of the magnetic medium object, the magnetic field strength at the target test point along the normal direction, and the magnetic field strength at the target test point along the tangential direction.

[0093] By processing at least one sampling point included in the sampling path, the magnetic physical quantity corresponding to the target measurement point can be obtained.

[0094] In some embodiments, step 120 may include:

[0095] Based on at least one sampling point included in each sampling path corresponding to the target test point, the target potential energy corresponding to the target test point is obtained.

[0096] Based on the target potential energy at the target point, the magnetic field strength corresponding to the target point is obtained.

[0097] In this embodiment, the target potential energy of the target test point can be obtained based on at least one sampling point included in each sampling path corresponding to the target test point.

[0098] The calculation methods for the magnetic field strength in different directions of the target point to be measured may be different. For example, if the target potential energy of the target point to be measured is obtained, the magnetic field strength can be directly derived based on the formula, or the magnetic field strength can be calculated based on the algorithm. This application does not limit this.

[0099] In actual execution, the internal region of the magnetic medium object in the simulated scene can be denoted as Ω = Ω. + And the surface of the magnetic medium object is denoted as The surface normal vector n of the magnetic medium object points outward from the magnetic medium object, the surface tangent vector of the magnetic medium object is denoted as τ, and the external domain of the magnetic medium object is denoted as τ. The external permeability of the magnetic medium is μ0, the internal susceptibility is a constant χ, and the internal permeability is μ=(1+)μ0.

[0100] Given an external magnetic field H ext We can define a potential energy scalar u on Γ that satisfies:

[0101]

[0102] in, The coefficients of the integral term related to magnetic susceptibility are derived, where x and y are points on surface Γ, y is the integration variable, and d... y For a given area element, the global integral is performed on the surface region Γ as an area integral. Let x be the directional derivative along the normal at x.

[0103] Among them, the Green's function G(x,y) and its partial derivatives have analytical forms:

[0104]

[0105]

[0106]

[0107] Where x and y are points on surface Γ, and G(x,y) is the Green's function. Let x be the gradient of the Green's function. Let n be the directional derivative of the Green's function at x along the normal. x Let x be the normal vector at point x.

[0108] Using this potential energy, the magnetic field strength H at any point in the scene can be calculated:

[0109]

[0110] Where H(x) is the magnetic field strength at point x, and x is a point in three-dimensional space excluding the interior and surface of the magnetic medium object. ext (x) represents the external magnetic field strength, and Γ represents the surface of the magnetic medium object. Let d be the gradient of the Green's function with respect to x, y be the integration variable, and d be the gradient of the Green's function with respect to x. y It is a micro-element of area.

[0111]

[0112] Where, [H(x)·] + Let x be the magnetic field strength along the normal direction at point x, x be a point on the surface of the magnetic medium object, χ be the internal magnetic susceptibility, and u(x) be the potential energy at point x.

[0113]

[0114] Where, [H(x)·τ()] + Let H be the magnetic field strength along the tangential direction at point x, where x is a point on the surface of the magnetic medium object. ext τ(x) represents the external magnetic field strength, τ(x) represents the tangent vector at x, and Γ represents the surface of the magnetic medium object. Let x be the gradient of the Green's function. Let G(x,y) be the directional derivative along the normal at x, G(x,y) be the Green's function, y be the integration variable, and d be the directional derivative along the normal at x. y It is a micro-element of area.

[0115] The force exerted by a magnetic field can be determined from its strength. For example, the pressure exerted by a magnetic field on the surface of an object can be calculated based on its strength.

[0116]

[0117] Where, p m (x) represents the surface pressure at point x on surface Γ caused by the magnetic field strength H(x), μ0 is the free permeability, χ is the internal magnetic susceptibility, H(x) is the magnetic field strength at x, and n(x) is the normal vector at x.

[0118] The above formula can be applied to scenarios such as magnetic levitation or magnetic confinement to solve for the forces exerted on magnetic materials in a magnetic field.

[0119] Based on at least one sampling point included in a sampling path corresponding to the target test point, the potential energy corresponding to the target test point can be obtained. If multiple potential energies are obtained, the average value or other statistical processing can be performed on the multiple potential energies to obtain the final potential energy of the target test point.

[0120] Then the magnetic field strength at the target test point can be obtained based on the potential energy.

[0121] For each of the multiple test points, the magnetic physical quantities can be solved.

[0122] Step 130: Control the target object based on magnetic physical quantities.

[0123] In this step, the target object can include virtual objects and physical entities.

[0124] Virtual objects can include three-dimensional or two-dimensional models, which can be used in a variety of applications, such as games, simulation software, virtual reality (VR) or augmented reality (AR) experiences.

[0125] For example, a virtual object can be a virtual character in a game, or a virtual avatar in virtual reality.

[0126] Virtual environments can be built using game engines, graphics libraries, or custom software, and then target objects that need to be controlled by magnetic fields can be created within these virtual environments.

[0127] The physical entity can be a robot, which can be applied to fields such as medicine, exploration, and manufacturing.

[0128] Among them, robots can be micro-robots, smart home appliances, or physical embodiments.

[0129] By controlling the movement of a target object using magnetic physical quantities, it is possible to manipulate the object without contact with it. For example, it is possible to control the movement of a micro-robot that needs to operate in a sterile or hard-to-reach environment.

[0130] In practice, magnetic fields can apply force and torque in multiple directions, allowing for the simultaneous control of multiple microrobots to perform precise group operations and collaboration. Combined with magnetic field sensors, microrobots can perceive their surroundings, including determining the positions of other robots and obstacles, thus enabling collision-free path planning.

[0131] In this application, the movement of a target object is controlled by magnetic physical quantities, which can manipulate the movement of the target object without contact. It can be applied to scenarios that require precise operation and high efficiency, and achieves flexible, reliable and efficient control of the target object. Furthermore, based on the control method of the target object, a flexible robot capable of performing multiple tasks can be designed, thereby adapting to changing environmental conditions.

[0132] This application is compatible with various computer surface geometry discretization formats, such as particles, triangular meshes, implicit surfaces, and high-order parametric surfaces, so it can be combined with simulation algorithms for fluids, solids, and elastic bodies to simulate the behavior of various magnetic materials. In addition, it can accurately describe various boundary conditions (such as boundary conditions at infinity and the jump of the magnetic field at the interface), and the accuracy of the magnetic physical quantities obtained is high.

[0133] According to the target object control method provided in the embodiments of this application, the magnetic physical quantity of the target test point is calculated by constructing the sampling path corresponding to the target test point. By reusing or modifying the existing algorithm tools in the ray tracing renderer, the magnetic physical quantity can be effectively calculated. It can be applied to various computer surface geometry discretization formats, including but not limited to triangular meshes, implicit surfaces and high-order parametric surface geometry formats. It can complete accurate, efficient and large-scale magnetic physical quantity solutions in various scenarios, meet the needs of a wide range of industrial applications, and has good compatibility, thereby improving the control precision and accuracy of virtual or physical objects.

[0134] In some embodiments, obtaining the target potential energy corresponding to the target test point based on at least one sampling point included in each sampling path corresponding to the target test point may include:

[0135] Based on the kernel function between the target test point and the target sampling point among multiple sampling points, the Monte Carlo algorithm is used to process at least one of the potential energy corresponding to the target sampling point, the internal magnetic susceptibility of the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point to obtain the target potential energy corresponding to the target test point.

[0136] In this embodiment, the target sampling point can be determined from multiple sampling points based on the sampling probability.

[0137] Starting from the target point to be measured, the target sampling point can be determined from multiple sampling points based on the sampling probability.

[0138] The sampling probability can be determined based on the sampling strategy. For example, the sampling strategy can be uniform surface sampling or importance sampling, or other sampling strategies. This application does not limit the specific sampling strategy.

[0139] When multiple sampling points are obtained based on uniform surface sampling, the sampling probability can be: p(r i+1 | i ) = 1 / (Γ), where x i For the target measurement point, x i+1 Γ represents the target sampling point, and Γ represents the surface of the magnetic medium object.

[0140] Kernel functions are used to characterize the interaction between the target measurement point and the target sampling point.

[0141] The kernel function can be determined based on the geometric information of the target measurement point and the target sampling point, such as the position and normal of the target measurement point and the target sampling point.

[0142] The Monte Carlo algorithm is a method for estimating mathematical integrals through random sampling.

[0143] Internal magnetic susceptibility is used to characterize the degree of magnetization of a magnetic medium object under the action of an external magnetic field.

[0144] Inside a magnetic medium object, due to the magnetization of the object itself, an additional magnetic field is generated. This additional magnetic field can be superimposed on the external magnetic field to form the total magnetic field inside the object.

[0145] The surface normal vector corresponding to the target point to be measured is a vector perpendicular to the surface, and the surface normal vector is perpendicular to the tangent plane where the target point to be measured is located.

[0146] In actual execution, the surface integral form (i.e., H(x) and [H(x)·τ()]) is used to calculate the magnetic field strength corresponding to the target measurement point. + The calculation formula and the rendering equation are both in the form of: f(x)=g(x)+∫ Γ K(x,y)f(y)d y By modifying the integral weight K(x,y) and the luminescence term g(x) in the rendering equation, the magnetic field strength can be calculated in the above form:

[0147] In the process of calculating the potential energy scalar u, the integration weights can be modified as follows: The luminescence term is modified to: g(x) = 2λH ext If (x)·(x), then the formula for calculating potential energy is: u(x)=g(x)+∫ ΓK(x,y)u(y)d y ,in, The coefficients of the integral term related to magnetic susceptibility are derived, where x and y are points on surface Γ, y is the integration variable, and d... y For the area of ​​a microelement, Let x be the directional derivative along the normal at x.

[0148] It is understandable that in the formula for calculating potential energy, both sides of the equal sign have 'u', and the potential energy can be solved iteratively. For example, it can be solved in the form of a sampling path, starting from the target point to be measured to obtain a sampling path, and then performing integration calculation.

[0149] According to the target object control method provided in the embodiments of this application, by reusing the integration and sampling algorithms of the ray tracing renderer, the integral weights and luminescence terms in the rendering equation are modified, so that magnetic physical quantities can be calculated based on the rendering equation. This optimizes the integration and wide applicability of the algorithm, while ensuring high efficiency and accuracy in the magnetic field solution process.

[0150] In some embodiments, based on the kernel function between the target test point and the target sampling point among multiple sampling points, a Monte Carlo algorithm is used to process at least one of the potential energy corresponding to the target sampling point, the internal magnetic susceptibility of the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point to obtain the target potential energy corresponding to the target test point. This may include:

[0151] Based on the kernel function between the target test point and the target sampling point, the Monte Carlo algorithm is used to process at least one of the potential energy corresponding to the target sampling point, the internal magnetic susceptibility of the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point to obtain the first potential energy corresponding to the target test point.

[0152] The target potential energy corresponding to the target measurement point is obtained by averaging at least one first potential energy.

[0153] In this embodiment, for each sampling path, the Monte Carlo algorithm can be used to obtain a first potential energy corresponding to the target test point.

[0154] Multiple samplings can yield multiple sampling paths, and thus multiple first potential energies.

[0155] Statistical processing can be performed on at least one first potential energy. For example, the average value of at least one first potential energy can be calculated to obtain the target potential energy corresponding to the target measurement point.

[0156] In some embodiments, based on the kernel function between the target test point and the target sampling point, a Monte Carlo algorithm is used to process at least one of the potential energy corresponding to the target sampling point, the internal magnetic susceptibility of the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point to obtain the first potential energy corresponding to the target test point, which may include:

[0157] Based on the target termination condition, determine the last sampling point in the sampling path corresponding to the target test point, and update the target sampling point to the last sampling point;

[0158] If the updated target sampling point is not the first sampling point after the target test point, based on the kernel function between the sampling points before the target sampling point and the target sampling point, at least one of the following is processed: the potential energy corresponding to the target sampling point, the internal magnetic susceptibility of the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point. This process yields the potential energy corresponding to the sampling points before the target sampling point, and the target sampling point is updated to be the sampling point before the target sampling point. The process of "based on the kernel function between the sampling points before the target sampling point and the target sampling point, processing at least one of the following: the potential energy corresponding to the target sampling point, the internal magnetic susceptibility of the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point, yielding the potential energy corresponding to the sampling points before the target sampling point, and updating the target sampling point to be the sampling point before the target sampling point" is repeated until the updated target sampling point is the first sampling point after the target test point.

[0159] Based on the kernel function between the target test point and the target sampling point, at least one of the potential energy corresponding to the target sampling point, the internal magnetic susceptibility of the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point is processed to obtain the first potential energy corresponding to the target test point.

[0160] In this embodiment, the target termination condition is used to determine when to stop sampling.

[0161] The target termination conditions may include truncation termination conditions and Russian Roulette termination conditions, or other termination conditions, which can be selected based on user needs, and this application does not limit them.

[0162] Among them, based on the truncation termination condition, it is possible to terminate at i=m. We obtain a biased estimate of u.

[0163] Based on the termination conditions of Russian roulette, and based on probability 1-p rr Continue the recursive calculation based on the first probability p rr Stop sampling:

[0164]

[0165] We obtain an unbiased estimate of u, where, As the first potential energy, p rr Let x be the first probability. i For the target measurement point, x i+1 For the target sampling point, K(x) i ,x i+1 p(x) is the kernel function between the target test point and the target sampling point. i+1 |x i ) represents the sampling probability. This represents the potential energy corresponding to the target sampling point.

[0166] If the last sampling point is determined to be the point of maximum external magnetic field strength based on the Russian Roulette termination condition, the maximum external magnetic field strength can be determined as the potential energy corresponding to the last sampling point; if the last sampling point reaches infinite space based on the Russian Roulette termination condition, the potential energy corresponding to the last sampling point can be determined as 0.

[0167] If sampling stops and the last sampling point in the sampling path is obtained, the target sampling point can be updated to the last sampling point. If the updated target sampling point is not the first sampling point after the target test point, the potential energy corresponding to the sampling point before the target sampling point is calculated, and the target sampling point is updated to the sampling point before the target sampling point, until the updated target sampling point is the first sampling point after the target test point.

[0168] When the updated target sampling point is the first sampling point after the target test point, the first potential energy corresponding to the target test point can be calculated based on the formula:

[0169]

[0170] in, As the first potential energy, x i For the target measurement point, x i+1 For the target sampling point, K(x) i ,x i+1 p(x) is the kernel function. i+1 |x i ) represents the sampling probability. Let g(x) be the potential energy corresponding to the target sampling point. i ) is a function determined based on the internal magnetic susceptibility, the external magnetic field strength, and the surface normal vector.

[0171] Given the surface Γ of a magnetic medium object, the algorithm needs to find the set of points S = {x0, ..., x} in space. nThe magnetic physical quantities at point} are {{u(x)|x∈S∩Γ},{H(x)|x∈S}} (where, the magnetic pressure p) m (x) can be calculated based on H(x).

[0172] In estimation At that time, sampling can be performed on surface Γ based on a sampling strategy, with sampling probability p(x) i+1 |x i ) to obtain x i+1 Recursive estimation

[0173] For example, in the case of a sampling strategy of uniform surface sampling, p(x) i+1 |x i ) = 1 / Area(Γ).

[0174] In this application, from the perspective of ray tracing, a sampling path can be constructed: x 0 x 1 …x n The first potential energy is obtained based on a sampling path. Then, the average value of at least one first potential energy obtained based on at least one sampling path is calculated to estimate the target potential energy u(x). 0 ).

[0175] According to the target object control method provided in the embodiments of this application, the magnetic field integral equation is solved by using the Monte Carlo algorithm without relying on any complex linear system solver. Moreover, the computing tools and underlying code used in the solution process can maintain logical consistency with the rendering, making the method a plug-and-play module that can be seamlessly embedded into various conventional physical simulation frameworks, thereby enriching the functionality of the virtual simulation platform. In addition, the use of the Monte Carlo algorithm to solve magnetic physical quantities is suitable for evaluating the uncertainty in the solution of the magnetic field of linear magnetic media objects. In AI systems, the decision-making process can be improved based on the quantification of uncertainty, and more robust strategies can be formulated by considering the possible range of changes.

[0176] In some embodiments, obtaining the magnetic field strength corresponding to the target test point based on the target potential energy of the target test point may include:

[0177] The Monte Carlo algorithm is used to obtain the magnetic field strength corresponding to the target point based on the target potential energy.

[0178] In this embodiment, a formula for calculating the magnetic field strength can be constructed, and the magnetic field strength can be obtained based on the target potential energy.

[0179] The Monte Carlo integration algorithm can be used to calculate the magnetic field strength at the target measurement point, as well as the magnetic field strength along the tangential direction at the target measurement point.

[0180] In some embodiments, given the target potential energy at the target test point, the magnetic field strength along the normal direction at the target test point can be directly derived.

[0181] In actual execution, the magnetic field strength {(x)·n()|∈S∩Γ} at the target test point along the normal direction can be directly derived from u(x).

[0182] In calculation During the process, the integral weights can be modified as follows: Modify the luminescent term to: g(x) = ext Here, integrating over the vector f() (independent in each dimension), we obtain the formula for calculating the magnetic field strength at x: H()=g()+∫ Γ K(x,y)u(y)d y Where x is a point in three-dimensional space excluding the interior and surface of the magnetic medium object, and H ext (x) represents the external magnetic field strength, and Γ represents the surface of the magnetic medium object. Let d be the gradient of the Green's function with respect to x, y be the integration variable, and d be the gradient of the Green's function with respect to x. y It is a micro-element of area.

[0183] In the process of calculating H(x)·τ()(∈Γ), the integration weights can be modified as follows: Modifying the luminescent term to: gx = Hextx·τx, we can obtain the formula for calculating the magnetic field strength along the tangential direction at x: H(t)·τ(t) = g(x) + ∫ Γ K(x,y)u(y)d y Where x is a point on the surface of the magnetic medium object, and H ext τ(x) represents the external magnetic field strength, τ(x) represents the tangent vector at x, and Γ represents the surface of the magnetic medium object. Let x be the gradient of the Green's function. Let G(x,y) be the directional derivative along the normal at x, G(x,y) be the Green's function, y be the integration variable, and d be the directional derivative along the normal at x. y It is a micro-element of area.

[0184] K(x,y) can be determined based on the geometric information of x and y (such as position and normal), and g(x) is a physical quantity that does not depend on integration and can be directly calculated from scene data.

[0185] It should be noted that the integral form of the magnetic field strength at point x and the magnetic field strength along the tangent at point x is f(x) = g(x) + ∫ Γ K(x,y)u(y)d ySince there are no identical variables on both sides of the equals sign, there is no need to perform optical path integration. The magnetic field strength can be obtained by performing one integration based on the Monte Carlo algorithm. In the Monte Carlo algorithm, it is only used as the integral formula for the first step. Subsequent sampling steps use the integral formula of u(x).

[0186] To calculate the magnetic field strength at point x For example, the first step of Monte Carlo integration uses:

[0187]

[0188] in The calculation can reuse the algorithm used above to calculate potential energy.

[0189] Similarly, a sampling path can be constructed: x 0 x 1 …x n , where x 1 x 2 …x n The construction method is the same as the algorithm used to calculate the potential energy above (and both are derived from sampling on the surface Γ of the magnetic medium object), only the first step of the optical path x0x1 is different.

[0190] The average value of multiple magnetic field strengths obtained from multiple sampling paths can be calculated, and this average value can be used as H(x). 0 (estimated).

[0191] The algorithm for calculating the magnetic field strength at x along the tangent direction {(x)·τ()|∈S∩Γ} is similar to the algorithm for calculating the magnetic field strength at x, and will not be elaborated here.

[0192] In some embodiments, after step 120, the method for controlling the target object may further include:

[0193] A noise reduction algorithm is used to optimize the acquired two-dimensional planar image to obtain the target result.

[0194] In this embodiment, a noise reduction algorithm from the rendering technique can be used to optimize the two-dimensional planar image.

[0195] In the task of visualizing magnetic fields, the point set S = {x0,…,x} n By taking the screen pixels, noise reduction techniques in the rendering process can be applied directly.

[0196] For example, Gaussian filtering or joint bilateral filtering based on signal convolution processing can be used, or denoising techniques based on deep learning, such as denoising autoencoders or deep learning supersampling, can be used.

[0197] In more general magnetic field calculation tasks, noise reduction can be achieved using filtering or interpolation algorithms based on spatial convolution kernels.

[0198] Irradiance caching can also be used for noise reduction. This involves sampling an auxiliary point set S′ in the scene and calculating and caching the results, which are then used to approximate the calculation results of S, thereby improving computational efficiency and noise reduction.

[0199] The auxiliary point set consists of additional sampling points selected in the scene that do not intersect with the real point set, and the size of the auxiliary point set is smaller than that of the real point set.

[0200] The estimated values ​​of the auxiliary point set can be used to estimate the calculated values ​​of the real point set through numerical integration. In rendering technology, the irradiance is cached using auxiliary scene points, which can be used to assist in calculating the irradiance of the target point.

[0201] Quasi-random sequences or blue noise sampling can also be used to improve sampling efficiency.

[0202] The control device for the target object provided in this application is described below. The control device for the target object described below can be referred to in correspondence with the control method for the target object described above.

[0203] The target object control method provided in this application can be executed by a target object control device. This application uses the example of a target object control device executing the target object control method to illustrate the target object control device provided in this application.

[0204] This application also provides a control device for a target object.

[0205] like Figure 2 As shown, the control device for the target object includes: a first processing module 210, a second processing module 220 and a third processing module 230.

[0206] The first processing module 210 is used to construct at least one sampling path corresponding to the target test point based on the target test point among multiple test points in the target region corresponding to the magnetic medium object; the sampling path includes at least one sampling point.

[0207] The second processing module 220 is used to obtain the magnetic physical quantity corresponding to the target test point based on at least one sampling point included in each sampling path corresponding to the target test point.

[0208] The third processing module 230 is used to control virtual objects based on magnetic physical quantities.

[0209] According to the target object control device provided in the embodiments of this application, the magnetic physical quantity of the target test point is calculated by constructing the sampling path corresponding to the target test point. By reusing or modifying the existing algorithm tools in the ray tracing renderer, the magnetic physical quantity is effectively calculated. It is applicable to various computer surface geometry discretization formats, including but not limited to triangular meshes, implicit surfaces and high-order parametric surface geometry formats. It can complete accurate, efficient and large-scale magnetic physical quantity solutions in various scenarios, meet the needs of a wide range of industrial applications, and has good compatibility, thereby improving the control precision and accuracy of virtual or physical objects.

[0210] In some embodiments, the second processing module 220 may also be used for:

[0211] Based on at least one sampling point included in each sampling path corresponding to the target test point, the target potential energy corresponding to the target test point is obtained.

[0212] Based on the target potential energy at the target point, the magnetic field strength corresponding to the target point is obtained.

[0213] In some embodiments, the second processing module 220 may also be used for:

[0214] Based on the kernel function between the target test point and the target sampling point among multiple sampling points, the Monte Carlo algorithm is used to process at least one of the potential energy corresponding to the target sampling point, the internal magnetic susceptibility of the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point to obtain the target potential energy corresponding to the target test point; the target sampling point is determined from multiple sampling points based on the sampling probability.

[0215] In some embodiments, the second processing module 220 may also be used for:

[0216] Based on the kernel function between the target test point and the target sampling point, the Monte Carlo algorithm is used to process at least one of the potential energy corresponding to the target sampling point, the internal magnetic susceptibility of the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point to obtain the first potential energy corresponding to the target test point.

[0217] The target potential energy corresponding to the target measurement point is obtained by averaging at least one first potential energy.

[0218] In some embodiments, the second processing module 220 may also be used for:

[0219] Based on the target termination condition, determine the last sampling point in the sampling path corresponding to the target test point, and update the target sampling point to the last sampling point;

[0220] If the updated target sampling point is not the first sampling point after the target test point, based on the kernel function between the sampling points before the target sampling point and the target sampling point, at least one of the following is processed: the potential energy corresponding to the target sampling point, the internal magnetic susceptibility of the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point. This process yields the potential energy corresponding to the sampling points before the target sampling point, and the target sampling point is updated to be the sampling point before the target sampling point. The process of "based on the kernel function between the sampling points before the target sampling point and the target sampling point, processing at least one of the following: the potential energy corresponding to the target sampling point, the internal magnetic susceptibility of the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point, yielding the potential energy corresponding to the sampling points before the target sampling point, and updating the target sampling point to be the sampling point before the target sampling point" is repeated until the updated target sampling point is the first sampling point after the target test point.

[0221] Based on the kernel function between the target test point and the target sampling point, at least one of the following is processed: the potential energy corresponding to the target sampling point, the internal magnetic susceptibility of the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point, to obtain the first potential energy corresponding to the target test point.

[0222] In some embodiments, the second processing module 220 may also be used for:

[0223] The Monte Carlo algorithm is used to obtain the magnetic field strength corresponding to the target point based on the target potential energy.

[0224] In some embodiments, the first processing module 210 may also be used for:

[0225] At least one of the forward path tracing algorithm, backward path tracing algorithm, and bidirectional path tracing algorithm is used to construct at least one sampling path corresponding to the target test point.

[0226] The control device for the target object in this application embodiment can be an electronic device or a component within an electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal or other devices besides a terminal. For example, the electronic device can be a mobile phone, tablet computer, laptop computer, PDA, in-vehicle electronic device, mobile internet device (MID), augmented reality (AR) / virtual reality (VR) device, robot, wearable device, ultra-mobile personal computer (UMPC), netbook, or personal digital assistant (PDA), etc. It can also be a server, network attached storage (NAS), personal computer (PC), television (TV), ATM, or self-service machine, etc. This application embodiment does not specifically limit the scope of the device.

[0227] The control device for the target object in this application embodiment can be a device with an operating system. This operating system can be Android, iOS, or other possible operating systems; this application embodiment does not specifically limit the specific operating system used.

[0228] The control device for the target object provided in this application embodiment can achieve... Figure 1 The various processes implemented in the method implementation examples will not be described again here to avoid repetition.

[0229] In some embodiments, such as Figure 3 As shown, this application embodiment also provides an electronic device 300, including a processor 301, a memory 302, and a computer program stored in the memory 302 and executable on the processor 301. When the program is executed by the processor 301, it implements the various processes of the above-described control method embodiment for the target object and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0230] It should be noted that the electronic devices in the embodiments of this application include the mobile electronic devices and non-mobile electronic devices described above.

[0231] On the other hand, this application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the various processes of the above-described control method embodiment for the target object and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0232] In another aspect, this application also provides a non-transitory computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements various processes of the above-described control method embodiment for the target object and achieves the same technical effect. To avoid repetition, it will not be described again here.

[0233] On another front, this application also provides a chip, which includes a processor and a communication interface. The communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned target object control method embodiments and achieve the same technical effect. To avoid repetition, it will not be described again here.

[0234] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0235] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0236] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the parts that contribute to the related technology, can be embodied in the form of software products. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0237] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for controlling a target object, characterized in that, include: Based on the target test point among multiple test points in the target region corresponding to the magnetic medium object, at least one sampling path corresponding to the target test point is constructed; The sampling path includes at least one sampling point; Based on the at least one sampling point included in each sampling path corresponding to the target test point, the magnetic physical quantity corresponding to the target test point is obtained; The target object is controlled based on the magnetic physical quantity.

2. The method for controlling the target object according to claim 1, characterized in that, The step of obtaining the magnetic physical quantity corresponding to the target test point based on at least one sampling point included in each sampling path corresponding to the target test point includes: Based on the at least one sampling point included in each sampling path corresponding to the target test point, the target potential energy corresponding to the target test point is obtained; Based on the target potential energy of the target test point, the magnetic field strength corresponding to the target test point is obtained.

3. The method for controlling the target object according to claim 2, characterized in that, The step of obtaining the target potential energy corresponding to the target test point based on at least one sampling point included in each sampling path corresponding to the target test point includes: Based on the kernel function between the target test point and the target sampling point among the plurality of sampling points, the Monte Carlo algorithm is used to process at least one of the potential energy corresponding to the target sampling point, the internal magnetic susceptibility corresponding to the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point to obtain the target potential energy corresponding to the target test point; the target sampling point is determined from the plurality of sampling points based on the sampling probability.

4. The method for controlling the target object according to claim 3, characterized in that, The method, based on the kernel function between the target test point and the target sampling point among the plurality of sampling points, uses a Monte Carlo algorithm to process at least one of the potential energy corresponding to the target sampling point, the internal magnetic susceptibility of the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point, to obtain the target potential energy corresponding to the target test point, including: Based on the kernel function between the target test point and the target sampling point, the Monte Carlo algorithm is used to process at least one of the potential energy corresponding to the target sampling point, the internal magnetic susceptibility of the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point to obtain the first potential energy corresponding to the target test point. The target potential energy corresponding to the target test point is obtained by averaging at least one of the first potential energies.

5. The method for controlling the target object according to claim 4, characterized in that, The method, based on the kernel function between the target test point and the target sampling point, uses the Monte Carlo algorithm to process at least one of the potential energy corresponding to the target sampling point, the internal magnetic susceptibility of the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point, to obtain the first potential energy corresponding to the target test point, including: Based on the target termination condition, determine the last sampling point in the sampling path corresponding to the target test point, and update the target sampling point to the last sampling point; If the updated target sampling point is not the first sampling point after the target test point, based on the kernel function between the sampling point before the target sampling point and the target sampling point, at least one of the following is processed: the potential energy corresponding to the target sampling point, the internal magnetic susceptibility corresponding to the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point, to obtain the potential energy corresponding to the sampling point before the target sampling point. Then, the target sampling point is updated to the sampling point before the target sampling point. The step "based on the kernel function between the sampling point before the target sampling point and the target sampling point, at least one of the following is processed: the potential energy corresponding to the target sampling point, the internal magnetic susceptibility corresponding to the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point, to obtain the potential energy corresponding to the sampling point before the target sampling point, and the target sampling point is updated to the sampling point before the target sampling point" is repeated until the updated target sampling point is the first sampling point after the target test point. Based on the kernel function between the target test point and the target sampling point, at least one of the potential energy corresponding to the target sampling point, the internal magnetic susceptibility corresponding to the magnetic medium object, the external magnetic field strength, and the surface normal vector corresponding to the target test point is processed to obtain the first potential energy corresponding to the target test point.

6. The method for controlling a target object according to claim 2, characterized in that, The process of obtaining the magnetic field strength corresponding to the target test point based on the target potential energy includes: Using the Monte Carlo algorithm, the magnetic field strength corresponding to the target test point is obtained based on the target potential energy of the target test point.

7. The method for controlling a target object according to any one of claims 1-6, characterized in that, The construction of at least one sampling path corresponding to the target test point among multiple test points in the target region corresponding to the magnetic medium object includes: At least one of the forward path tracing algorithm, the backward path tracing algorithm, and the bidirectional path tracing algorithm is used to construct the at least one sampling path corresponding to the target test point.

8. A control device for a target object, characterized in that, include: The first processing module is used to construct at least one sampling path corresponding to the target test point based on the target test point among multiple test points in the target region corresponding to the magnetic medium object; The sampling path includes at least one sampling point; The second processing module is used to obtain the magnetic physical quantity corresponding to the target test point based on the at least one sampling point included in each sampling path corresponding to the target test point. The third processing module is used to control the target object based on the magnetic physical quantity.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the control method for the target object as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the control method for the target object as described in any one of claims 1-7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the control method for the target object as described in any one of claims 1-7.

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