Rigid body motion control method and system in ocean current, medium and equipment

Abstract

The invention provides a rigid body motion control method and system in ocean current, a medium and equipment, and the method comprises the steps: obtaining ocean current data, and constructing a layered fluid velocity model based on the ocean current data; obtaining a motion equation of a target rigid body in the ocean current; the motion equation comprises a translation equation and a rotation equation; determining discrete Lagrange points on the surface of the rigid body based on the motion equation; each Lagrange point comprises position information, surface force and a normal vector; a fluid domain corresponding to the Descartes grid discrete layering fluid velocity model with set resolution is adopted, boundary layer encryption is set, and a fluid grid is obtained; grid nodes of the fluid grid are used for storing fluid velocity and pressure; surface forces in the fluid grid and the Lagrange point are calculated through interpolation function coupling, and motion parameters of the target rigid body are output during convergence. According to the method, the grid does not need to be frequently reconstructed due to the complex shape and large displacement motion of the rigid body, and the problem of unstable numerical value caused by grid distortion in a traditional method is avoided.

Description

Technical Field

[0001] The present application relates to the field of deep-sea mining, and in particular to a method, system, medium and equipment for controlling rigid body motion in ocean currents. Background Art

[0002] Currently, deep-sea towed platforms must withstand multiple loads, including current disturbances and towing force fluctuations. Conventional fluid-structure interaction methods (such as the ALE method) employed in existing technologies suffer from low computational efficiency when dealing with large displacements of rigid bodies and struggle to handle complex boundary motions. Furthermore, they fail to fully account for the perturbations of deep-sea stratified currents on the towed body's posture, and lack synergistic modeling of the towed body's rigid motion with fluid friction and inertial effects. Therefore, precise control of the motion of rigid bodies in the deep sea remains a pressing technical challenge for those skilled in the art. Summary of the Invention

[0003] The purpose of this application is to provide a method, system, computer-readable storage medium and electronic device for controlling the motion of a rigid body in an ocean current, which can accurately calculate the motion tendency of a rigid body in an ocean current, thereby effectively adjusting the weight of the rigid body or the motion parameters, and realizing precise motion control of the rigid body.

[0004] To solve the above technical problems, the present application provides a method for controlling rigid body motion in ocean currents. The specific technical solution is as follows:

[0005] Acquiring ocean current data and constructing a layered fluid velocity model based on the ocean current data;

[0006] Obtaining a motion equation of a target rigid body located in an ocean current; the motion equation includes a translation equation and a rotation equation;

[0007] Determining discrete Lagrangian points on the surface of a rigid body based on the motion equation; each of the Lagrangian points includes position information, surface force, and normal vector;

[0008] The fluid domain corresponding to the layered fluid velocity model is discretized using a Cartesian grid with a set resolution, and boundary layer encryption is set to obtain a fluid grid; the grid nodes of the fluid grid are used to store fluid velocity and pressure;

[0009] The surface forces in the fluid grid and the Lagrangian points are calculated by coupling an interpolation function, and the motion parameters of the target rigid body are outputted upon convergence; the motion parameters are used to guide the target weight or motion control parameters of the target rigid body.

[0010] Optionally, obtaining the motion equation of a target rigid body located in an ocean current includes:

[0011] Acquire a three-dimensional model of the target rigid body;

[0012] Determining the translation equation according to the rigid body mass, rigid body center of mass velocity vector, fluid force, gravitational acceleration, buoyancy and fluid resistance included in the three-dimensional model;

[0013] The rotation equation is determined according to the moment of inertia tensor, the rigid body angular velocity vector, the fluid torque, and the control torque.

[0014] Optionally, obtaining the three-dimensional model of the target rigid body includes:

[0015] Establishing a three-dimensional model of the target rigid body and setting rigid body parameters of the target rigid body; the rigid body parameters include length, weight and buoyancy;

[0016] Accordingly, determining the discrete Lagrangian points on the rigid body surface based on the motion equation includes:

[0017] The surface of the target rigid body is discretized into Lagrange points based on the motion equation and the rigid body parameters.

[0018] Optionally, after obtaining the motion equation of the target rigid body in the ocean current, the following steps are further included:

[0019] Based on the motion equation, the rigid body posture of the target rigid body is updated using the quaternion method;

[0020] Recalculate the discrete Lagrangian points on the rigid body surface according to the updated rigid body posture.

[0021] Optionally, the coupling calculation of the fluid grid and the surface force in the Lagrangian point by using an interpolation function includes:

[0022] distributing the surface force to adjacent fluid grid nodes via a discrete function;

[0023] Calling a four-point interpolation kernel function to apply the surface force to four grid nodes around each Lagrange point;

[0024] Interpolating the fluid velocity to the Lagrange point and updating the position of the target rigid body;

[0025] The coupled calculation is performed until convergence, and the motion parameters of the target rigid body are output.

[0026] Optionally, when constructing a layered fluid velocity model based on the ocean current data, the method further includes:

[0027] A space-time disturbance function is generated by Gaussian white noise; the space-time disturbance function is used to simulate the turbulence effect in the ocean current.

[0028] Optionally, interpolating the fluid velocity to the Lagrange point and updating the position of the target rigid body includes:

[0029] Using grid nodes in the fluid grid as communication nodes, interpolating the fluid velocity to the Lagrange point using multi-node communication, and updating the position of the target rigid body;

[0030] Accordingly, interpolating the fluid velocity to the Lagrange point and updating the position of the target rigid body includes:

[0031] The graphics processor is called to interpolate the fluid velocity to the Lagrange point and output the updated position of the target rigid body.

[0032] The present application also provides a rigid body motion control system in ocean currents, comprising:

[0033] A flow diversion model building module, used for acquiring ocean current data and building a layered fluid velocity model based on the ocean current data;

[0034] A rigid body motion calculation module, used to obtain the motion equation of a target rigid body located in an ocean current; the motion equation includes a translation equation and a rotation equation;

[0035] A rigid body surface discretization module, configured to determine discrete Lagrangian points on the rigid body surface based on the motion equation; each of the Lagrangian points includes position information, surface force, and normal vector;

[0036] a fluid network generation module, configured to discretize the fluid domain corresponding to the layered fluid velocity model using a Cartesian grid of a set resolution, set boundary layer encryption, and obtain a fluid grid; grid nodes of the fluid grid are used to store fluid velocity and pressure;

[0037] A motion parameter output module is used to calculate the surface forces in the fluid grid and the Lagrangian points through interpolation function coupling, and output the motion parameters of the target rigid body when convergence; the motion parameters are used to guide the target weight or motion control parameters of the target rigid body. The present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method described above when executed by a processor.

[0038] The present application also provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of the above-mentioned method when calling the computer program in the memory.

[0039] The present application provides a method for controlling the motion of a rigid body in an ocean current, comprising: obtaining ocean current data and constructing a layered fluid velocity model based on the ocean current data; obtaining a motion equation of a target rigid body located in the ocean current; the motion equation includes a translation equation and a rotation equation; determining discrete Lagrangian points on the surface of the rigid body based on the motion equation; each of the Lagrangian points contains position information, surface force, and a normal vector; discretizing the fluid domain corresponding to the layered fluid velocity model using a Cartesian grid of a set resolution, setting boundary layer encryption, and obtaining a fluid grid; the grid nodes of the fluid grid are used to store fluid velocity and pressure; coupling and calculating the surface forces in the fluid grid and the Lagrangian points through an interpolation function, and outputting the motion parameters of the target rigid body upon convergence; the motion parameters are used to guide the target weight or motion control parameters of the target rigid body.

[0040] This application constructs a layered fluid velocity model based on actual ocean current data, accurately reflecting the actual flow velocity and characteristics of ocean currents at different depths in deep-sea environments. This model, combined with the equations of motion for rigid bodies that account for both translational and rotational motion, and the determination of discrete Lagrange points on the rigid body's surface, enables more accurate and detailed analysis of the target rigid body's motion in the ocean current, effectively reducing errors in towed body attitude angle prediction and providing more reliable simulation results. The fluid domain is discretized using a Cartesian grid, with boundary layer density settings. Interpolation functions are used to couple the fluid grid with surface forces at the Lagrange points. This eliminates the need for frequent mesh reconstruction due to the complex shape and large displacement of the rigid body, avoiding the numerical instability caused by mesh distortion in traditional methods and better handling fluid-rigid body interactions under complex boundary conditions. The target rigid body's motion parameters are output upon convergence. Compared to traditional calculation methods, this method significantly improves computational efficiency and reduces resource consumption. It can solve large-scale, complex fluid-rigid body coupling problems within a reasonable timeframe, making it more suitable for the computational efficiency requirements of practical engineering scenarios.

[0041] The present application also provides a rigid body motion control system in ocean currents, a computer-readable storage medium, and an electronic device, which have the above-mentioned beneficial effects and will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without any creative work.

[0043] Figure 1 A flowchart of a method for controlling rigid body motion in ocean currents provided in an embodiment of the present application;

[0044] Figure 2 A schematic diagram of the structure of a rigid body motion control system in ocean currents provided by an embodiment of the present application;

[0045] Figure 3 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0046] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0047] Please refer to Figure 1 , Figure 1 A flowchart of a method for controlling rigid body motion in ocean currents provided in an embodiment of the present application, the method comprising:

[0048] S101: Acquire ocean current data, and construct a layered fluid velocity model based on the ocean current data;

[0049] S102: Obtaining a motion equation of a target rigid body located in an ocean current; the motion equation includes a translation equation and a rotation equation;

[0050] S103: Determine discrete Lagrangian points on the surface of the rigid body based on the motion equation; each of the Lagrangian points includes position information, surface force, and normal vector;

[0051] S104: discretizing the fluid domain corresponding to the layered fluid velocity model using a Cartesian grid of a set resolution, setting boundary layer encryption, and obtaining a fluid grid; grid nodes of the fluid grid are used to store fluid velocity and pressure;

[0052] S105: Calculating the surface forces in the fluid grid and the Lagrangian points through coupling of an interpolation function, and outputting motion parameters of the target rigid body upon convergence; the motion parameters are used to guide target weighting or motion control parameters of the target rigid body.

[0053] The motion of the fluid is described by the Navier-Stokes equations. Let the fluid velocity field be, the fluid density be, and the pressure be , the fluid viscosity is , the external disturbance force is. For incompressible fluid, the Navier-Stokes equation is:

[0054] ;

[0055] Inlet: stratified flow rate input;

[0056] Outlet: pressure outlet;

[0057] Rigid surface: no-slip wall, boundary force is applied through IBM.

[0058] In order to adapt to the complex deep-sea environment, ocean currents not only have external disturbance forces, but also need to consider the stratification effect of flow velocity at different depths. In the deep sea, ocean currents are usually divided into multiple different flow layers, and the flow velocity and turbulence intensity of each layer are different. Therefore, the fluid velocity can be represented by a layered model:

[0059] ;

[0060] in, is the velocity distribution of layer ii, It is a disturbance function based on time and space, used to represent the ocean current dynamics in different layers.

[0061] The motion of the dragged body is exactly described by the Newton-Euler equations:

[0062] The translational equation, according to Newton's second law, the translational motion of a rigid body is driven by a net external force. The translational equation can be determined based on the rigid body mass, the rigid body center of mass velocity vector, the fluid force, the gravitational acceleration, the buoyancy, and the fluid resistance contained in the three-dimensional model. The formula is:

[0063] ;

[0064] Where m is the mass of the rigid body (unit: kg); v is the velocity vector of the rigid body's center of mass (unit: m / s);

[0065] is the fluid force, calculated by integrating pressure and shear force (unit: N);

[0066] =mg, represents gravity (unit: N), .

[0067] = , buoyancy (unit: N), is the fluid density, and V is the displacement volume.

[0068] = , represents the fluid resistance (unit: N), Cd is the drag coefficient, A is the reference area, and M is the mass of the dragged body.

[0069] The rotation equation is determined based on the moment of inertia tensor, rigid body angular velocity vector, fluid torque, and control torque:

[0070] ;

[0071] Where I is the moment of inertia tensor (unit: kg·m²), which is a diagonal matrix in the principal axis coordinate system:

[0072] ;

[0073] : rigid body angular velocity vector (unit: rad / s);

[0074] : fluid torque, calculated by integrating the surface pressure and the shear torque (unit: N·m);

[0075] : Control torque (such as ballast adjustment torque), such as proportional control:

[0076] ;

[0077] in is the proportionality coefficient (unit: N·m / rad), is the attitude angle deviation.

[0078] In a feasible implementation, to avoid the gimbal lock problem of Euler angles, the rigid body posture of the target rigid body can be updated using the quaternion method based on the motion equation, thereby recalculating the discrete Lagrangian points on the rigid body surface according to the updated rigid body posture:

[0079] ;

[0080] is the angular velocity matrix:

[0081] ;

[0082] During the numerical solution, this embodiment of the application uses the Immersed Boundary Method (IBM) to achieve rigid-fluid coupling. The IBM is a numerical method for handling the interaction between fluid and rigid bodies. It embeds the influence of the rigid boundary into the fluid equations through a virtual force source term, eliminating the need for dynamic adjustment of the fluid mesh. The specific steps are as follows:

[0083] Step 1: Fluid domain discretization: Use a fixed Cartesian grid (Eulerian grid) to discretize the fluid domain;

[0084] Step 2: Discrete the rigid body boundary: discretize the rigid body surface into Lagrangian points;

[0085] Step 3: Force and displacement transmission: bidirectional coupling between rigid body surface force and fluid grid is achieved through interpolation function.

[0086] When dividing the fluid grid, the fluid domain uses a uniform Cartesian grid with a typical resolution of Δx=Δy=Δz=0.1m. Among them, the grid nodes store the fluid velocity and pressure .

[0087] When calculating the discreteness of the rigid body surface, the drag body surface is divided into Lagrange points (spacing ≤ 0.05m), and the position of each point is stored. and surface forces .

[0088] For example, if the towed body is 3.24m long and 0.95m wide, its surface is discretized into about 2000 Lagrange points.

[0089] In the process of boundary force transmission mechanism, the rigid body surface force is calculated first:

[0090] The forces exerted by the fluid on the rigid body include pressure and viscous shear force:

[0091] ;

[0092] in, is the surface normal vector of the rigid body, is the dynamic viscosity of the fluid.

[0093] The surface forces are then interpolated from the Lagrange points to the fluid mesh:

[0094] Through the discrete Dirac delta function The rigid body surface force Distribute to adjacent fluid mesh nodes:

[0095] ;

[0096] In the above formula, is the surface element area corresponding to the Lagrange point;

[0097] Function form (taking three dimensions as an example):

[0098] ;

[0099] in is the fluid grid spacing;

[0100] It is a four-point interpolation kernel function, covering four grid nodes around each Lagrange point (in one-dimensional case), and expanding to 4×4×4=64 nodes in three dimensions.

[0101] Four-point interpolation kernel function The expression of is a piecewise function that aims to balance interpolation accuracy and computational efficiency:

[0102] ;

[0103] Key features include:

[0104] The support range is the force affecting the four surrounding mesh nodes (one-dimensional) at each Lagrange point to ensure locality.

[0105] Continuity: In The function value is continuous at , ensuring smooth interpolation.

[0106] Normalization: Satisfaction , strictly conserving the total force.

[0107] when : The Lagrange point is close to a grid node, and the force is mainly distributed to the nearest node.

[0108] when : The force gradually decays to farther nodes to avoid numerical oscillations.

[0109] As the displacement is transferred from the fluid mesh to the rigid body surface, the fluid velocity is interpolated to the Lagrange point and the rigid body position is updated:

[0110] ;

[0111] in, is the Lagrange point velocity, used to update the rigid body position .

[0112] The bidirectional coupling iterative calculation process includes the following steps:

[0113] First perform initialization and set the initial fluid field ,pressure , and the initial position of the rigid body .

[0114] The iterative process is as follows (nth time step):

[0115] Step 1, solve the fluid equation:

[0116] Add the rigid body surface force F_boundary to the Navier-Stokes equation;

[0117] Use the finite volume method to solve for the updated velocity and pressure .

[0118] Step 2, interpolate the fluid velocity to the rigid body surface:

[0119] according to , calculate the Lagrange point velocity ;

[0120] Update rigidbody position ;

[0121] Step 3, solve the rigid body dynamics equation:

[0122] According to the fluid force , solve the Newton-Euler equations;

[0123] Update rigidbody velocity and angular velocity .

[0124] Step 4, determine convergence:

[0125] Calculate residuals based on flow rate changes ;

[0126] like , enter the next time step; otherwise, repeat steps 1-3.

[0127] By setting a time step limit, convergence is achieved when the CFL (Courant-Friedrichs-Lewy) condition is met:

[0128] ;

[0129] Typical value: Δt=0.001s.

[0130] It should be noted that during the computational process, the fluid grid partitions can be computed in parallel, using MPI for multi-node communication. At the same time, the rigid body surface force interpolation process can be accelerated on a GPU, improving computational efficiency.

[0131] After convergence, you can analyze the convergence results. These typically include the rigid body's motion parameters, such as position, velocity, attitude angle, and angular velocity, as well as the fluid forces and torques acting on the rigid body. These reflect the towed body's motion state and force under the current weighting and control parameters. Check the towed body's attitude changes in the fluid, such as the fluctuation amplitude of the pitch, roll, and yaw angles. Large fluctuations in attitude angles indicate insufficient stability, which may require adjustment of the weighting or control parameters. You can also analyze the towed body's parameters, such as velocity and acceleration, to assess its maneuverability and responsiveness in the fluid. If the towed body's motion performance does not meet expectations, such as slow acceleration or steering, you may need to optimize the weight distribution or adjust the control strategy.

[0132] When setting the towed vehicle's counterweight, the ideal center of gravity position can be determined based on the towed vehicle's design requirements and motion demands. Generally speaking, a reasonable center of gravity position can improve the towed vehicle's stability and maneuverability. Based on the rigid body's posture and motion response from the convergence results, combined with the deviation between the current center of gravity position and the target center of gravity position, the required counterweight adjustment is calculated. For example, adding or reducing the counterweight in certain areas can shift the center of gravity toward the target position. The counterweight blocks are arranged rationally on the towed vehicle, increasing or reducing the weight in specific areas to achieve center of gravity adjustment. It is important to note that when adjusting the counterweight, the symmetry of the towed vehicle should be maintained as much as possible to avoid new imbalances caused by uneven weight distribution.

[0133] This embodiment of the present application constructs a layered fluid velocity model based on actual ocean current data, accurately reflecting the actual flow velocity and characteristics of ocean currents at different depths in deep-sea environments. This model, combined with the rigid body motion equations that account for both translational and rotational motion, determines discrete Lagrange points on the rigid body's surface. This makes subsequent analysis of the target rigid body's motion in the ocean current more accurate and detailed, effectively reducing errors in the towed body's attitude angle prediction and providing more reliable simulation results. The fluid domain is discretized using a Cartesian grid, with boundary layer density settings. Interpolation functions are used to couple the fluid grid with the surface forces at the Lagrange points. This eliminates the need for frequent mesh reconstruction due to the complex shape and large displacement of the rigid body, avoiding the numerical instability caused by mesh distortion in traditional methods and better handling fluid-rigid body interactions under complex boundary conditions. The target rigid body's motion parameters are output upon convergence, significantly improving computational efficiency and reducing resource consumption compared to traditional methods. This method can solve large-scale, complex fluid-rigid body coupling problems within a reasonable timeframe, making it more suitable for the computational efficiency requirements of practical engineering scenarios.

[0134] The following is an exemplary calculation process to illustrate the method for controlling rigid body motion in ocean currents provided by this application:

[0135] Step 1: Model establishment and parameter setting:

[0136] The towed body geometry modeling can be done using CAD software (such as SolidWorks) to create a 3D model of the towed body and export it to STL format; key parameters include: length 3.24 m, weight 1158.58 kg, and buoyancy 1204.53 kg.

[0137] Fluid domain and grid division: The fluid domain size is 500 m × 200 m × 1000 m (length × width × depth); Cartesian grid division is adopted with a resolution of 0.1 m, and the boundary layer is refined to 0.01 m.

[0138] Rigid body surface discretization: the towed body surface is discretized into Lagrange points (spacing ≤ 0.05 m), a total of about 2000 points; each point stores the position , surface forces and normal vector .

[0139] Step 2: Layered ocean current field configuration:

[0140] Velocity layered modeling: Divide the ocean current into N layers (e.g., N=5), and the velocity distribution of each layer is:

[0141] ;

[0142] Parameter example:

[0143] Surface layer (0~100 m): u 10 =1.5 m / s, z0=10 m

[0144] Middle layer (100~500 m): u 20 =0.8 m / s, z0=50 m.

[0145] Turbulence disturbance superposition, generating space-time disturbance function through Gaussian white noise , the amplitude is 10%~20% of the laminar flow velocity.

[0146] Step 3: Bidirectional coupling simulation process:

[0147] Assume initialization condition: initial velocity of fluid field ,pressure = hydrostatic pressure; initial position of the towed body: 10 m above the water surface, speed =0, attitude angle =0°.

[0148] Time step iteration (Δt=0.001 s):

[0149] Step 1: When solving the fluid, add the rigid body surface force F_boundary to the Navier-Stokes equations; use the finite volume method to solve the updated and .

[0150] Step 2: Interpolate the fluid velocity to the Lagrange point and calculate , and update the rigid body position .

[0151] Step 3: Calculate the fluid forces when solving rigid body dynamics ; Then solve the Newton-Euler equation and update the speed and angular velocity .

[0152] Step 4: Perform convergence judgment. If the residual , enter the next time step; otherwise, repeat steps 1 to 3.

[0153] Termination conditions: The total simulation time is 60 s, or the towed body motion tends to be stable (attitude angle fluctuation is less than 1°).

[0154] It can be seen that the key to this embodiment is to use the immersed boundary method (IBM) coupling framework to achieve efficient fluid-rigid body coupling through bidirectional force transmission between a fixed Cartesian grid (Eulerian grid) and the Lagrangian points on the rigid body surface, thereby avoiding the grid distortion problem of the traditional ALE method.

[0155] Core formula:

[0156] ;

[0157] in, is the discrete Dirac function, is the surface element area.

[0158] In addition, by constructing a stratified ocean current disturbance model, that is, by using the stratified flow velocity function Quantify the non-uniform disturbance of ocean currents at different depths and significantly improve the adaptability to deep-sea environments. Parameter example: surface current velocity u 10 =1.5 m / s, middle layer velocity u 20 =0.8 m / s. Based on the Newton-Euler equations, the towed body's translation and rotation are accurately described. The quaternion method is introduced to update the attitude angle to avoid the singularity problem of traditional Euler angles.

[0159] See also Figure 2 , Figure 2 This is a schematic diagram of a rigid body motion control system in ocean currents provided by an embodiment of the present application, the system comprising:

[0160] A flow diversion model building module, used for acquiring ocean current data and building a layered fluid velocity model based on the ocean current data;

[0161] A rigid body motion calculation module, used to obtain the motion equation of a target rigid body located in an ocean current; the motion equation includes a translation equation and a rotation equation;

[0162] A rigid body surface discretization module, configured to determine discrete Lagrangian points on the rigid body surface based on the motion equation; each of the Lagrangian points includes position information, surface force, and normal vector;

[0163] a fluid network generation module, configured to discretize the fluid domain corresponding to the layered fluid velocity model using a Cartesian grid of a set resolution, set boundary layer encryption, and obtain a fluid grid; grid nodes of the fluid grid are used to store fluid velocity and pressure;

[0164] A motion parameter output module is used to calculate the surface forces in the fluid grid and the Lagrangian points through interpolation function coupling, and output the motion parameters of the target rigid body when convergence; the motion parameters are used to guide the target weight or motion control parameters of the target rigid body. The present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method described above when executed by a processor.

[0165] This application also provides a computer-readable storage medium having a computer program stored thereon. When executed, the computer program can implement the steps provided in the above embodiments. The storage medium may include: a USB flash drive, a mobile hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, among other media capable of storing program code.

[0166] This application also provides an electronic device, see Figure 3 , a structural diagram of an electronic device provided in an embodiment of the present application, such as Figure 3 As shown, a processor 1410 and a memory 1420 may be included.

[0167] The processor 1410 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 1410 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), or PLA (Programmable Logic Array). The processor 1410 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also known as a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state. In some embodiments, the processor 1410 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the display screen. In some embodiments, the processor 1410 may also include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0168] The memory 1420 may include one or more computer-readable storage media, which may be non-transitory. The memory 1420 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In this embodiment, the memory 1420 is at least used to store the following computer program 1421, wherein, after the computer program is loaded and executed by the processor 1410, it can implement the relevant steps in the method performed by the electronic device side disclosed in any of the aforementioned embodiments. In addition, the resources stored in the memory 1420 may also include an operating system 1422 and data 1423, etc., and the storage method may be temporary storage or permanent storage. Among them, the operating system 1422 may include Windows, Linux, Android, etc.

[0169] In some embodiments, the electronic device may further include a display screen 1430 , an input / output interface 1440 , a communication interface 1450 , a sensor 1460 , a power supply 1470 , and a communication bus 1480 .

[0170] certainly, Figure 3 The structure of the electronic device shown does not constitute a limitation on the electronic device in the embodiment of the present application. In actual applications, the electronic device may include Figure 3 More or fewer components than shown, or combinations of certain components.

[0171] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems provided in the embodiments, since they correspond to the methods provided in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0172] This document uses specific examples to illustrate the principles and implementation methods of this application. The description of the above examples is only intended to help understand the method and core ideas of this application. It should be noted that for those skilled in the art, without departing from the principles of this application, various improvements and modifications can be made to this application, and such improvements and modifications also fall within the scope of protection of this application.

[0173] It should also be noted that, in this specification, 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 such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. 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, article, or apparatus comprising the element.

Claims

1. A method for controlling rigid body motion in ocean currents, characterized in that: include: Acquiring ocean current data and constructing a layered fluid velocity model based on the ocean current data; Obtaining a motion equation of a target rigid body located in an ocean current; the motion equation includes a translation equation and a rotation equation; Determining discrete Lagrangian points on the surface of a rigid body based on the motion equation; each of the Lagrangian points includes position information, surface force, and normal vector; The fluid domain corresponding to the layered fluid velocity model is discretized using a Cartesian grid with a set resolution, and boundary layer encryption is set to obtain a fluid grid; the grid nodes of the fluid grid are used to store fluid velocity and pressure; The surface forces in the fluid grid and the Lagrangian points are calculated by coupling an interpolation function, and the motion parameters of the target rigid body are outputted upon convergence; the motion parameters are used to guide the target weight or motion control parameters of the target rigid body.

2. The method according to claim 1, characterized in that The method of obtaining the motion equation of the target rigid body located in the ocean current includes: Acquire a three-dimensional model of the target rigid body; Determining the translation equation according to the rigid body mass, rigid body center of mass velocity vector, fluid force, gravitational acceleration, buoyancy and fluid resistance included in the three-dimensional model; The rotation equation is determined according to the moment of inertia tensor, the rigid body angular velocity vector, the fluid torque, and the control torque.

3. The method according to claim 2, characterized in that Acquiring the three-dimensional model of the target rigid body includes: Establishing a three-dimensional model of the target rigid body and setting rigid body parameters of the target rigid body; the rigid body parameters include length, weight and buoyancy; Accordingly, determining the discrete Lagrangian points on the rigid body surface based on the motion equation includes: The surface of the target rigid body is discretized into Lagrange points based on the motion equation and the rigid body parameters.

4. The method according to claim 1, wherein After obtaining the motion equation of the target rigid body located in the ocean current, it also includes: Based on the motion equation, the rigid body posture of the target rigid body is updated using the quaternion method; Recalculate the discrete Lagrangian points on the rigid body surface according to the updated rigid body posture.

5. The method according to claim 1, wherein The coupling calculation of the fluid grid and the surface force in the Lagrangian point by using an interpolation function comprises: distributing the surface force to adjacent fluid grid nodes via a discrete function; Calling a four-point interpolation kernel function to apply the surface force to four grid nodes around each Lagrange point; Interpolating the fluid velocity to the Lagrange point and updating the position of the target rigid body; The coupled calculation is performed until convergence, and the motion parameters of the target rigid body are output.

6. The method according to claim 5, characterized in that When constructing a layered fluid velocity model based on the ocean current data, the method further includes: A space-time disturbance function is generated by Gaussian white noise; the space-time disturbance function is used to simulate the turbulence effect in the ocean current.

7. The method according to claim 5, characterized in that Interpolating the fluid velocity to the Lagrange point and updating the position of the target rigid body includes: Using grid nodes in the fluid grid as communication nodes, interpolating the fluid velocity to the Lagrange point using multi-node communication, and updating the position of the target rigid body; Accordingly, interpolating the fluid velocity to the Lagrange point and updating the position of the target rigid body includes: The graphics processor is called to interpolate the fluid velocity to the Lagrange point and output the updated position of the target rigid body.

8. A rigid body motion control system in ocean currents, characterized in that: include: A flow diversion model building module, used for acquiring ocean current data and building a layered fluid velocity model based on the ocean current data; A rigid body motion calculation module, used to obtain the motion equation of a target rigid body located in an ocean current; the motion equation includes a translation equation and a rotation equation; A rigid body surface discretization module, configured to determine discrete Lagrangian points on the rigid body surface based on the motion equation; each of the Lagrangian points includes position information, surface force, and normal vector; a fluid network generation module, configured to discretize the fluid domain corresponding to the layered fluid velocity model using a Cartesian grid of a set resolution, set boundary layer encryption, and obtain a fluid grid; grid nodes of the fluid grid are used to store fluid velocity and pressure; A motion parameter output module is used to calculate the surface forces in the fluid grid and the Lagrangian points through interpolation function coupling, and output the motion parameters of the target rigid body when convergence; the motion parameters are used to guide the target weight or motion control parameters of the target rigid body.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. An electronic device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, the steps of the method according to any one of claims 1 to 7 are implemented.

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