Bullet structure generation method and device, computer device, and storage medium

Abstract

The application relates to a shell structure generation method and device, computer equipment and a storage medium. The method comprises the following steps: generating initial structure control parameters of a shell based on preset space constraint parameters; fitting based on the initial structure control parameters to obtain a shell three-dimensional model; the shell three-dimensional model is a shell three-dimensional model meeting preset geometric verification rules; performance simulation is performed on the shell three-dimensional model to obtain a performance simulation result; based on the performance simulation result, the initial structure control parameters are iteratively optimized to obtain target structure control parameters and a target shell three-dimensional model corresponding to the target structure control parameters. The method can improve shell design efficiency, reduce labor costs and improve shell performance.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a method, apparatus, computer device, and storage medium for generating a spring structure. Background Technology

[0002] With the development of electronic device integration and miniaturization, spring-loaded structures play an important role in the connection between flexible circuit board antennas, laser-formed antennas and circuit boards. However, traditional spring-loaded designs rely heavily on manual experience, resulting in low design efficiency and difficulty in meeting complex geometric constraints and performance requirements. They require repeated manual parameter adjustments and re-simulation, leading to low automation and high time and labor costs.

[0003] This shows that existing technologies still suffer from low efficiency in spring-loaded structure design and high labor costs. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, computer equipment, and storage medium for generating spring structures that can improve the efficiency of spring design, reduce labor costs, and improve spring performance, in order to address the above-mentioned technical problems.

[0005] In a first aspect, this application provides a method for generating a spring structure, the method comprising:

[0006] Based on preset spatial constraint parameters, the initial structural control parameters of the spring sheet are generated;

[0007] The initial structural control parameters are fitted to obtain a three-dimensional model of the spring; the three-dimensional model of the spring conforms to the preset geometric verification rules.

[0008] The performance of the three-dimensional model of the spring was simulated, and the performance simulation results were obtained.

[0009] Based on the performance simulation results, the initial structural control parameters are iteratively optimized to obtain the target structural control parameters and the target shrapnel three-dimensional model corresponding to the target structural control parameters.

[0010] In one embodiment, the initial structural control parameters include a set of control points; the initial structural control parameters for generating the spring sheet based on preset spatial constraint parameters include:

[0011] Within a preset simulation space, multiple seed control points that satisfy the preset space constraint parameters are generated;

[0012] Based on the multiple seed control points, multiple candidate control points are iteratively generated;

[0013] The set of control points is determined based on the candidate control points that satisfy the preset spatial constraint parameters.

[0014] In one embodiment, the fitting based on the initial structural control parameters to obtain the three-dimensional model of the spring sheet includes:

[0015] Based on the B-spline curve fitting algorithm, the initial structural control parameters are curve-fitted to obtain the spring sheet central axis curve that satisfies the preset curvature condition.

[0016] The central axis curve of the spring is subjected to normal plane offset and thickness stretching to obtain the three-dimensional model of the spring.

[0017] In one embodiment, the preset geometric verification rules include one or more of boundary constraint verification, self-intersection constraint verification, curvature constraint verification, and spacing constraint verification;

[0018] The boundary constraint verification includes verifying whether the 3D model of the spring is located within a preset simulation space and maintains a safe distance from the boundary of the preset simulation space, based on a preset polygonal region and a preset safety distance; the self-intersection constraint verification includes detecting whether the central axis curve has self-intersections except for the endpoints, based on a preset self-intersection judgment algorithm; the curvature constraint verification includes verifying whether the radius of curvature of the central axis curve is not less than a preset lower curvature limit, based on discrete points of the curve; and the spacing constraint verification includes verifying whether the distance between any two non-adjacent control points is not less than a preset minimum spacing, based on control point coordinates.

[0019] In one embodiment, the performance simulation of the three-dimensional model of the shrapnel includes:

[0020] Based on the material parameters of the three-dimensional model of the shrapnel and the preset mesh parameters, the three-dimensional model of the shrapnel is meshed to obtain the mesh model of the three-dimensional model of the shrapnel.

[0021] By applying loads or displacements based on the mesh model, the stiffness and maximum stress values ​​of the spring sheet in multiple directions are solved to obtain performance simulation results.

[0022] In one embodiment, the number of initial structural control parameters is multiple; the iterative optimization of the initial structural control parameters based on the performance simulation results includes:

[0023] Based on the performance simulation results of the three-dimensional models of the spring corresponding to the multiple initial structural control parameters and the preset performance evaluation function, the initial structural control parameters corresponding to the three-dimensional model of the spring with the best simulation results are taken as the current optimal control parameters.

[0024] Based on the weighted recombination operator, the current optimal control parameters are cross-operated to generate multiple candidate control parameters, which are used as the initial structure control parameters for the next round of optimization.

[0025] In one embodiment, the performance simulation of the three-dimensional model of the spring fragment to obtain the performance simulation results includes:

[0026] Based on each of the initial structure control parameters, the initial structure control parameters are allocated to the parallel computing task queue;

[0027] Based on multiple independent solver instances, performance simulations are performed synchronously on each of the three-dimensional models of the spring fragment to obtain performance simulation results.

[0028] Secondly, this application provides a spring-loaded structure generating apparatus, the apparatus comprising:

[0029] The parameter generation module is used to generate the initial structural control parameters of the spring sheet based on preset spatial constraint parameters.

[0030] The model generation module is used to fit the initial structural control parameters to obtain a three-dimensional model of the spring; the three-dimensional model of the spring is a three-dimensional model of the spring that conforms to the preset geometric verification rules;

[0031] The performance simulation module is used to perform performance simulation on the three-dimensional model of the spring and obtain the performance simulation results.

[0032] The parameter optimization module is used to iteratively optimize the initial structural control parameters based on the performance simulation results, so as to obtain the target structural control parameters and the target spring three-dimensional model corresponding to the target structural control parameters.

[0033] Thirdly, this application provides a computer device including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described above.

[0034] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described above.

[0035] The aforementioned method, apparatus, computer equipment, and storage medium for generating spring structures generate initial structural control parameters for the spring based on preset spatial constraint parameters; fit these initial structural control parameters to obtain a three-dimensional model of the spring; the three-dimensional model of the spring conforms to preset geometric verification rules; perform performance simulation on the three-dimensional model of the spring to obtain performance simulation results; iteratively optimize the initial structural control parameters based on the performance simulation results to obtain target structural control parameters and a target three-dimensional model of the spring corresponding to the target structural control parameters. By automating parameter configuration to reduce reliance on human experience, achieving rapid modeling through geometric fitting, and effectively avoiding significant geometric defects in the automatically designed three-dimensional model of the spring through preset geometric verification rules, a closed-loop optimization mechanism is formed by combining simulation feedback, outputting model results that meet the requirements of spatial assembly, manufacturing process, and electrical performance. This achieves the technical effects of improving design efficiency, reducing labor costs, and optimizing the performance of the spring. Attached Figure Description

[0036] Figure 1 This is an application environment diagram of the spring structure generation method in one embodiment;

[0037] Figure 2 This is a flowchart illustrating a method for generating a spring structure in one embodiment;

[0038] Figure 3 This is a structural block diagram of a spring structure generating device in one embodiment;

[0039] Figure 4 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0041] The spring-loaded structure generation method provided in this application embodiment can be applied to, for example, Figure 1In the application environment shown, terminal 102 communicates with server 104 via a network. A data storage system can store the data that server 104 needs to process. The data storage system can be integrated onto server 104 or placed on a cloud or other network server. Terminal 102 generates initial structural control parameters for the spring based on preset spatial constraint parameters; it then fits these initial structural control parameters to obtain a three-dimensional model of the spring; the three-dimensional model of the spring conforms to preset geometric verification rules; it performs performance simulation on the three-dimensional model of the spring to obtain performance simulation results; based on the performance simulation results, iteratively optimizes the initial structural control parameters to obtain target structural control parameters and a target three-dimensional model of the spring corresponding to the target structural control parameters. Terminal 102 can be, but is not limited to, various personal computers, laptops, smartphones, and tablets. Server 104 can be implemented using a standalone server or a server cluster composed of multiple servers.

[0042] In one embodiment, such as Figure 2 As shown, a method for generating a spring structure is provided, the method including:

[0043] Step S100: Generate the initial structural control parameters of the spring sheet based on the preset spatial constraint parameters.

[0044] The preset spatial constraint parameters can be a set of parameters that constrain the spatial range during the spring design process. These parameters provide basic spatial limitations for the spring structure design, ensuring that the generated structure meets physical assembly requirements. For example, the preset spatial constraint parameters may include one or more parameters such as spatial boundaries, length limits, width limits, and height limits. In this embodiment, the preset spatial constraint parameters can be input by the user or extracted from a configuration file pre-stored in the electronic device.

[0045] The initial structural control parameters can be fundamental parameters controlling the generation process of the 3D model of the spring fragment. They can also be parameters that work in conjunction with the generation algorithm, enabling the algorithm to generate a defined 3D model based on these initial structural control parameters. In one exemplary embodiment, the initial structural control parameters can be a set of parameters composed of one or more control points, with the 3D model of the spring fragment generated using these control points as the basis for its shape. In another exemplary embodiment, the initial structural control parameters can also be control curves describing the overall orientation of the 3D model of the spring fragment, with the 3D model obtained by transforming and extending these control curves. The initial structural control parameters can also be other parameters used to control the generation process of the 3D model of the spring fragment, which are not limited in this embodiment.

[0046] The initial structural control parameters for the spring can be generated by determining the design boundary based on preset spatial constraint parameters and generating parameters to control the constraints of the spring's 3D model modeling. For example, corresponding parameters can be extracted from a pre-stored parameter mapping table using a lookup table, or initial values ​​can be generated through mathematical functions. This achieves automated initial configuration of the spring's structural parameters, reducing the workload of manual setting.

[0047] Step S200: Fit the initial structural control parameters to obtain the three-dimensional model of the spring. The three-dimensional model of the spring conforms to the preset geometric verification rules.

[0048] The 3D model of the spring fragment can be a geometric structure of the spring fragment expressed in 3D digital form. In this embodiment, the 3D model of the spring fragment can be constructed based on initial structural control parameters. It is understood that since the initial structural control parameters determine the basic shape and / or extension direction of the 3D model of the spring fragment, it can be constructed by fitting based on the basic shape and / or extension direction to obtain the 3D model of the spring fragment. Furthermore, the 3D model of the spring fragment can include, but is not limited to, a linear spring fragment model, a U-shaped spring fragment model, a spiral spring fragment model, etc.

[0049] The preset geometric verification rules can be a set of rules used to determine whether the 3D model of the spring-loaded projectile meets the basic geometric feasibility requirements, ensuring that the generated 3D model possesses manufacturability and structural integrity. In a specific embodiment, the preset geometric verification rules can verify preset structural control parameters and the 3D model of the spring-loaded projectile according to a preset algorithm. For example, it can determine whether the generated 3D model of the spring-loaded projectile meets the requirements of preset spatial constraint parameters, and whether the 3D model of the spring-loaded projectile has problems such as excessively small curvature or curve intersections that obviously indicate manufacturing difficulties and / or low performance, thereby verifying whether it meets the preset geometric conditions. For example, the preset geometric verification rules may include, but are not limited to, one or more of the following: minimum radius of curvature rules, structural continuity rules, and no self-intersection rules.

[0050] The three-dimensional model of the spring is obtained by fitting the initial structural control parameters. Alternatively, the fitted three-dimensional model can be obtained by fitting the initial structural control parameters and then verifying the fitted three-dimensional model through preset geometric verification rules. The model that meets the preset geometric verification rules is then output as the three-dimensional model of the spring. This ensures that the generated three-dimensional model of the spring basically meets the requirements of the geometric rules and avoids the waste of computing resources by performing performance simulation on models that are obviously not physically feasible.

[0051] Step S300: Perform performance simulation on the three-dimensional model of the spring fragment to obtain the performance simulation results.

[0052] The performance simulation results can be data output from the physical performance analysis of the three-dimensional model of the spring, used to evaluate the performance of the spring under actual working conditions. In this embodiment, the performance simulation results can be used to calculate the mechanical properties using finite element analysis software. By solving the mechanical response of the spring, performance indicators such as stiffness and maximum stress can be extracted, allowing for the evaluation of the spring's performance without the need for physical testing, thus shortening the verification cycle. Furthermore, the performance simulation results may include, but are not limited to, one or more of the following: stress distribution results, stiffness simulation results, springback simulation results, and contact resistance simulation results.

[0053] Step S400: Based on the performance simulation results, the initial structural control parameters are iteratively optimized to obtain the target structural control parameters and the three-dimensional model of the target spring corresponding to the target structural control parameters.

[0054] The target structure control parameters can be a set of spring structure parameters that meet performance requirements after iterative optimization, used to generate the final usable 3D model of the spring. In an exemplary embodiment, the target structure control parameters can be continuously adjusted by an optimization control module using iterative optimization algorithms such as evolutionary strategy algorithms based on performance simulation results to obtain the optimal solution that meets multiple target performance requirements. Furthermore, the target structure control parameters can include, but are not limited to, one or more of the following: target length parameters, target width parameters, target height parameters, and target curvature distribution.

[0055] The target shrapnel 3D model can be the final shrapnel 3D structure that meets spatial constraints and performance requirements, serving as a design output ready for manufacturing. In this embodiment, the target shrapnel 3D model can be a 3D solid model regenerated by calling geometric modeling methods based on the target structure control parameters.

[0056] Based on performance simulation results, the initial structural control parameters are iteratively optimized. For example, simulation results can be used as feedback to drive parameter adjustments, search for better design schemes, and regenerate the model. For instance, gradient descent, genetic algorithms, or other methods can be used to search for better parameter combinations. Each iteration updates the parameters and regenerates the model, thereby achieving automatic parameter optimization and reducing repeated manual adjustments.

[0057] Taking the design of antenna springs for smart wearable devices as an example, when designing the connection spring between the flexible circuit board antenna and the laser direct forming antenna of a smartwatch, the spatial boundary inside the watch case can be input as a preset spatial constraint parameter. The terminal 102 generates initial structural control parameters within this spatial boundary and fits a three-dimensional model of the spring. After the model passes the geometric verification rule check, performance simulation is performed to obtain the performance simulation results. Based on the simulation results and key performance indicators, the terminal 102 adjusts the initial structural control parameters such as curvature, and after multiple iterations, outputs a target three-dimensional model of the spring that meets the key performance indicators.

[0058] This embodiment provides a method for generating spring structures. It generates initial structural control parameters for the spring based on preset spatial constraint parameters; fits these initial structural control parameters to obtain a three-dimensional model of the spring; the three-dimensional model conforms to preset geometric verification rules; performs performance simulation on the three-dimensional model to obtain simulation results; iteratively optimizes the initial structural control parameters based on the simulation results to obtain target structural control parameters and a target three-dimensional model of the spring corresponding to these parameters. This method reduces reliance on manual experience through automated parameter configuration, achieves rapid modeling through geometric fitting, effectively avoids significant geometric defects in the automatically designed three-dimensional model of the spring through preset geometric verification rules, and forms a closed-loop optimization mechanism combined with simulation feedback. It outputs model results that meet the requirements of spatial assembly, manufacturing process, and electrical performance, thereby achieving the technical effects of improving design efficiency, reducing labor costs, and optimizing spring performance.

[0059] In one embodiment, the initial structural control parameters include a set of control points; based on preset spatial constraint parameters, the initial structural control parameters for generating the spring sheet include:

[0060] Within the preset simulation space, multiple seed control points that satisfy the preset spatial constraint parameters are generated;

[0061] Based on multiple seed control points, multiple candidate control points are generated iteratively.

[0062] A set of control points is determined based on candidate control points that meet the preset spatial constraint parameters.

[0063] Seed control points can be basic control points that satisfy spatial constraints and are initialized within a preset simulation space, serving as the starting point for the distribution of candidate control points. In this embodiment, seed control points can be generated by distributing them within the allowed design area based on preset spatial constraint parameters, ensuring that the positions of the control points do not violate boundary restrictions or avoidance zone requirements. Furthermore, seed control points can be generated using random distribution, regular grid distribution, or other methods to achieve spatial coverage of the initial point locations.

[0064] Generating multiple seed control points that satisfy preset spatial constraint parameters can be achieved by distributing initial points in the simulation space according to the feasible region defined by the preset spatial constraint parameters, ensuring that all seed control points are within the allowable distribution range and the defined boundary range. Alternatively, a Poisson disk sampling algorithm can be used to generate multiple seed control points that satisfy minimum point spacing and boundary safety distance within the polygonal design region specified by the preset spatial constraint parameters. Furthermore, fixed starting points of key connection regions can be included as anchor points in the sampling results, thereby improving the rationality of the initial point distribution and space utilization.

[0065] Candidate control points can be potential control points generated from seed control points, enriching the geometric representation of the control point set. Furthermore, candidate control points can be new points generated based on seed control points through local perturbations, directional expansion, topological derivation strategies, etc., and obtained after geometric verification. For example, directional expansion can involve vector offset along the normal or tangential direction, while local perturbations can introduce random directional perturbations to increase morphological diversity.

[0066] The control point set can be an ordered or unordered set of candidate control points that satisfy preset spatial constraint parameters, and can be used as a basic data structure to describe the geometry of the spring. In this embodiment, the control point set is formed by selecting points that fully meet the spatial constraint conditions from the generated candidate control points and organizing them according to geometric order or topological relationship, and is used to determine the orientation and shape of the spring's central axis.

[0067] Based on candidate control points that satisfy preset spatial constraint parameters, the control point set is determined. This can be achieved by performing spatial compliance checks on all generated candidate control points, filtering out points that meet boundary, avoidance zone, and curvature constraints, and organizing them into the final control point set according to geometric continuity or topological order. Furthermore, Boolean operations can be used to determine whether a point is located outside the boundary-defined area, and connectivity analysis can be combined to eliminate isolated point groups, thereby ensuring the geometric feasibility of the control point set.

[0068] This embodiment provides a method for generating spring-loaded structures. By generating seed control points that satisfy preset spatial constraint parameters within a preset simulation space, iteratively generating multiple candidate control points based on multiple seed control points, and then determining a set of control points based on the candidate control points that satisfy the preset spatial constraint parameters, a legal and diverse control point structure is gradually constructed through a hierarchical generation mechanism. This ensures that the generation process of the initial structure control parameters of the spring-loaded structure has spatial compliance assurance and morphological exploration capabilities. It can achieve the technical effects of improving the degree of design automation, enhancing structural adaptability and design flexibility, reducing manual intervention, and improving the effectiveness of generating spring-loaded models in complex geometric environments.

[0069] In one embodiment, fitting based on initial structural control parameters yields a three-dimensional model of the spring sheet, including:

[0070] Based on the B-spline curve fitting algorithm, the initial structural control parameters are fitted to obtain the spring sheet central axis curve that meets the preset curvature conditions.

[0071] The 3D model of the spring is obtained by offsetting the normal plane and stretching the thickness of the central axis curve.

[0072] In this embodiment, the construction of the three-dimensional model of the spring can be achieved by fitting the initial structural control parameters to generate a smooth central axis parameter curve, and then offsetting along the normal of the central axis curve to construct a three-dimensional spring solid model with thickness and width.

[0073] The B-spline curve fitting algorithm can generate a parametric curve of the spring's central axis by fitting control points. Based on the number and distribution of control points, it calculates the nodal vectors of the spline curve to ensure a continuous and smooth transition of the curve segments. This can be used to generate a smooth central axis path that meets manufacturing requirements, avoiding performance degradation due to abrupt curvature changes. For example, the B-spline curve fitting algorithm may include, but is not limited to, cubic B-spline algorithms, non-uniform B-spline algorithms, or rational B-spline algorithms.

[0074] The central axis curve of the spring can be a one-dimensional parametric curve describing the path of the spring's geometric center. By offsetting the central axis curve along its normal plane, the upper and lower boundary curves of the spring can be generated. Furthermore, by stretching the boundary curves into upper and lower surfaces according to a set thickness, a three-dimensional solid model with closed thickness can be generated. In this embodiment, the initial central axis curve of the spring is generated by calling the B-spline formula, and geometric validity verification is performed to ensure that the model meets curvature constraints.

[0075] Preset curvature conditions can be limiting conditions on the local bending degree of the central axis curve of the spring sheet, used to avoid excessive bending that leads to stress concentration or manufacturing difficulties, and can be used to ensure that the generated central axis curve has good manufacturability and mechanical stability. In a specific embodiment, the preset curvature conditions can be preset according to material properties, processing technology, and reliability requirements, setting a minimum curvature radius or a maximum curvature change rate threshold in advance. For example, preset curvature conditions may include, but are not limited to, one or more of the following: minimum curvature radius threshold, curvature continuity level, curvature change smoothness requirements, etc.

[0076] Furthermore, by performing normal plane offset and thickness stretching on the central axis curve of the spring, the local width of the spring can be changed by controlling the function of the normal offset distance along the curve, thus achieving dynamic adjustment of the width.

[0077] This embodiment provides a method for generating spring-loaded structures. It uses a B-spline curve fitting algorithm to fit the initial structural control parameters, obtaining a spring-loaded central axis curve that satisfies preset curvature conditions. Then, it performs normal plane offset and thickness stretching on the spring-loaded central axis curve to obtain a three-dimensional model of the spring-loaded structure. By using the B-spline curve fitting algorithm to fit control points and generate a continuous and smooth central axis path, combined with curvature constraints for verification, geometric rationality is improved. Finally, through normal offset and thickness stretching, accurate modeling from parametric curves to a three-dimensional solid is achieved. This method can automatically generate high-performance, manufacturable spring-loaded structures in complex and compact spaces, achieving the technical effects of improving the automation level of modeling and design quality, and reducing the need for manual intervention.

[0078] In one embodiment, the preset geometric verification rules include one or more of boundary constraint verification, self-intersection constraint verification, curvature constraint verification, and spacing constraint verification.

[0079] The boundary constraint verification includes verifying whether the 3D model of the spring is located within a preset simulation space and maintains a safe distance from the boundary of the preset simulation space, based on a preset polygonal region and a preset safety distance. The preset polygonal region can be a closed geometric region used to define the space where the 3D model of the spring can exist, providing a spatial reference for boundary constraint verification and ensuring that the spring structure does not exceed the allowed physical range of arrangement. For example, the preset polygonal region can be extracted from the outline boundary of the installation area from the electronic device structural design file, or a specified area can be drawn by the user through a graphical interface. The preset polygonal region can participate in the boundary constraint verification together with the preset safety distance. The preset safety distance can be the minimum distance that needs to be maintained between the spring structure and the boundary of the simulation space, used to prevent the spring from touching surrounding devices or structural components due to manufacturing or assembly deviations. In an exemplary embodiment, the preset safety distance can be obtained based on empirical or calculated values ​​set according to manufacturing tolerances, material thermal expansion coefficients, or assembly errors. Boundary constraint verification can be achieved quickly by combining point and polygon inclusion relationship judgment with distance field calculation, thereby ensuring that the spring structure is within the specified space and has sufficient assembly margin, improving design feasibility.

[0080] Self-intersection constraint verification includes detecting whether the central axis curve has self-intersections other than its endpoints, based on a preset self-intersection determination algorithm. In this embodiment, self-intersection constraint verification can be based on the parametric expression of the central axis curve or a discrete set of line segments, using a preset self-intersection determination algorithm to detect whether there are non-endpoint intersections. For example, the preset self-intersection determination algorithm can employ one or more of the following: line segment intersection detection algorithm, bounding box pruning algorithm, parametric equation solving algorithm, etc., thereby avoiding the generation of self-intersection structures and ensuring the geometric rationality and manufacturability of the spring sheet.

[0081] Curvature constraint verification includes verifying, based on discrete points on the curve, whether the radius of curvature of the central axis curve is not less than a preset lower limit of curvature. These discrete points can be a set of points sampled along the central axis curve at a fixed step size or in an adaptive manner, or they can be control points used during the fitting process of the central axis curve, used to numerically evaluate the local geometric properties of the central axis curve, such as the radius of curvature. Furthermore, the discrete points can be used to calculate the radius of curvature in curvature constraint verification. The preset lower limit of curvature can be the minimum allowable radius of curvature threshold for the central axis curve of the spring, used to prevent stress concentration or processing fracture due to excessive bending of the spring. For example, the preset lower limit of curvature can be set according to the material's yield strength, minimum bending process capability, fatigue life requirements, etc. Furthermore, the preset lower limit of curvature can participate in curvature constraint verification together with the discrete points on the curve. It is understood that curvature constraint verification can use differential geometry to calculate the curvature distribution of the curve and verify whether the radius of curvature is greater than the specified lower limit everywhere, thereby preventing excessive bending of the spring from causing material damage or signal transmission distortion.

[0082] Spacing constraint verification includes verifying, based on control point coordinates, whether the distance between any two non-adjacent control points is not less than a preset minimum spacing. This preset minimum spacing can be the minimum distance requirement that any two non-adjacent control points must meet, used to avoid excessively dense control point distribution leading to local structural overlap or manufacturing difficulties. In an exemplary embodiment, the preset minimum spacing can be set based on factors such as the spring material thickness, etching process resolution, and structural stability indicators. Furthermore, the preset minimum spacing can participate in spacing constraint verification together with control point coordinates. Further, spacing constraint verification can utilize KD-trees to accelerate nearest-neighbor search, reducing computational complexity. Through spacing constraint verification, structural anomalies caused by unreasonable control point distribution can be effectively avoided, improving the stability of parametric modeling.

[0083] This embodiment provides a method for generating spring-loaded structures. By employing one or more of the following processes—boundary constraint verification, self-intersection constraint verification, curvature constraint verification, and spacing constraint verification—a multi-dimensional geometric verification mechanism is introduced. This mechanism automatically verifies the spatial adaptability, structural continuity, bending rationality, and parameter stability of the spring-loaded model. It can effectively intercept infeasible designs, reduce invalid simulation iterations, and further improve the robustness and output quality of automated design, thereby enhancing the reliability and efficiency of spring-loaded structure design.

[0084] In one embodiment, performance simulation of the three-dimensional model of the shrapnel includes:

[0085] Based on the material parameters and preset mesh parameters of the 3D model of the shrapnel, the 3D model of the shrapnel is meshed to obtain the mesh model of the 3D model of the shrapnel.

[0086] By applying loads or displacements based on a mesh model, the stiffness and maximum stress values ​​of the spring sheet in multiple directions are solved to obtain performance simulation results.

[0087] The material parameters can be a set of physical properties of the materials used in the elastic 3D model, applied to the performance simulation calculation of the 3D model of the spring. For example, the material parameters can include, but are not limited to, one or more of the following: elastic modulus, Poisson's ratio, yield strength, etc.

[0088] Preset mesh parameters can be configuration parameters used to control the density and quality of finite element mesh generation, and can affect the fineness of the mesh model and the efficiency of simulation calculation. In an exemplary embodiment, the preset mesh parameters can be set according to the mesh generation settings such as element type and size during the simulation stage. Furthermore, shell elements or solid elements can be selected for configuration. Exemplarily, the preset mesh parameters may include, but are not limited to, one or more of the following: element size parameters, mesh growth rate parameters, boundary layer number parameters, etc.

[0089] Mesh models can be numerical computational models that discretize the three-dimensional model of electronic component contact points into a finite number of elements and nodes. They can be used as computational carriers for performance simulation and applied to the numerical solution of mechanical equations.

[0090] The three-dimensional model of the contact piece of the electronic component is meshed based on material parameters and preset mesh parameters. In an exemplary embodiment, the material properties and mesh settings can be combined to call the finite element analysis interface tool to generate a mesh model containing nodes and element structures corresponding to the three-dimensional model of the contact piece, thereby ensuring that the simulation model has sufficient geometric fidelity and computational stability and improving the credibility of the simulation results.

[0091] Loads and displacements can be external excitation conditions that simulate the mechanical actions experienced by a shrapnel during assembly or use. For example, depending on the method of application, loads can include, but are not limited to, concentrated force loads, distributed pressure loads, etc., and displacements can include forced displacement boundary conditions.

[0092] By applying loads or displacements to a mesh model, the stiffness and maximum stress values ​​can be solved. This can be achieved by applying fixed constraints and displacement or force loads to the mesh model, controlling the solver to perform static analysis, and calculating the stiffness and maximum stress values ​​of the spring in multiple directions. Furthermore, by applying load combinations under different working conditions step by step, multi-scenario simulations can be achieved, thereby enabling a multi-dimensional quantitative evaluation of the mechanical properties of the spring.

[0093] This embodiment provides a method for generating a spring-loaded structure. By dividing the three-dimensional model of the spring-loaded structure into a mesh model based on material parameters and preset mesh parameters, a performance simulation result is obtained by applying loads or displacements based on the mesh model to solve for stiffness values ​​and maximum stress values ​​in multiple directions. By introducing material parameters and preset mesh parameters to divide the mesh model and combining load or displacement conditions, a quantitative analysis of the mechanical properties of the spring-loaded structure can be achieved, thereby enhancing the reliability of the simulation results.

[0094] In one embodiment, there are multiple initial structural control parameters; iterative optimization of the initial structural control parameters based on performance simulation results includes:

[0095] Based on the performance simulation results of the three-dimensional model of the spring corresponding to multiple initial structural control parameters and the preset performance evaluation function, the initial structural control parameters corresponding to the three-dimensional model of the spring with the best simulation results are taken as the current optimal control parameters.

[0096] Based on the weighted recombination operator, the current optimal control parameters are cross-operated to generate multiple candidate control parameters, which are used as the initial structural control parameters for the next round of optimization.

[0097] The preset performance evaluation function can be a comprehensive fitness function constructed based on multi-objective and multi-constraint requirements. It guides the search for design schemes that satisfy the constraints through a penalty term, thereby providing a comparable performance scoring basis for different design schemes. In one exemplary embodiment, the preset performance evaluation function can map one or more simulation indicators, including stiffness, stress, springback, and contact resistance, to a single score value, and achieve quantitative evaluation of multi-objective performance through a weighted combination of indicators. In some other embodiments, the preset performance evaluation function can also be a threshold judgment function, a graded evaluation function, etc., which is not limited in this embodiment.

[0098] The current optimal control parameter can be the initial structural control parameter with the highest performance score in the current optimization round, and can be used to generate the next generation of candidate control parameters. In an exemplary embodiment, the current optimal control parameter can be extracted from the performance simulation results corresponding to multiple initial structural control parameters, based on the score of a preset performance evaluation function, and the candidate control parameter corresponding to the highest score is selected.

[0099] Weighted recombination operators can be a combination of methods that update parameter vectors based on numerical weights, thereby exploring new parameter combinations while maintaining excellent design characteristics. In a specific embodiment, the weighted recombination operator can assign different weights to the parameters of each dimension of the current optimal control parameters, generating a new parameter vector through linear combination or nonlinear mapping. For example, the weighted recombination operator can employ linear weighting operators, Gaussian perturbation weighting operators, adaptive weighting operators, etc.

[0100] In this embodiment, candidate control parameters can be a set of potentially feasible parameters generated through weighted recombination, which can be used as initial structural control parameters for the next round of optimization. For example, a weighted recombination mechanism can be used to weightedly combine the control point location information of multiple elite individuals to generate a new solution. Simultaneously, a random mutation following a normal distribution is applied to the current optimal control parameters, and a mutation rejection mechanism is used to ensure the legitimacy of the offspring, generating multiple candidate control parameters. This allows for the exploration of the neighboring design space while preserving high-performance design characteristics, thereby improving optimization convergence efficiency.

[0101] This embodiment provides a method for generating spring structures. By setting multiple initial structural control parameters in the initial stage and automatically selecting the current optimal control parameters by combining performance simulation results and preset performance evaluation functions, a weighted recombination operator is used to combine and perturb the current optimal control parameters to generate new candidate control parameters. This can effectively avoid invalid simulation rounds and improve the convergence speed and result quality of the optimization process under multi-objective and multi-constraint conditions. It can achieve the technical effect of improving the efficiency of spring design and reducing the dependence on the experience of designers.

[0102] In one embodiment, performance simulation is performed on the three-dimensional model of the shrapnel, and the performance simulation results include:

[0103] Based on each initial structure control parameter, the initial structure control parameters are allocated to the parallel computing task queue;

[0104] Based on multiple independent solver instances, performance simulations are performed synchronously on the 3D model of each spring fragment to obtain performance simulation results.

[0105] In this embodiment, the parallel computing task queue can be a scheduling structure for managing performance simulation tasks, used to achieve centralized management and resource scheduling of multiple tasks, thereby improving the organizational efficiency of the simulation process. In this embodiment, the parallel computing task queue can receive simulation tasks corresponding to the initial structure control parameters and distribute the tasks to multiple independent solver instances. Furthermore, the parallel computing task queue can also be a static task queue, a dynamic load-balanced queue, or a priority-ordered task queue, etc.

[0106] Assigning initial structural control parameters to the parallel computing task queue can be achieved by encapsulating each set of initial structural control parameters into a standardized simulation task unit and injecting them into the parallel computing task queue sequentially or based on load status, awaiting execution. In a specific embodiment, tasks can be assigned using a polling method, or the assignment strategy can be dynamically adjusted based on the current load of each solver instance. This enables automated batch submission of simulation tasks, avoiding manual initiation one by one and improving task management efficiency.

[0107] Independent solver instances can be simulation engine processes running in independent memory spaces or on computing nodes, used to perform finite element analysis or other physics field solutions. By processing multiple independent solver instances, performance simulations of multiple 3D models of spring fragments can be performed simultaneously, reducing the overall simulation time.

[0108] In one exemplary embodiment, multiple independent simulation software solver processes can be launched by calling open-source tool interfaces. Each instance loads the mesh model and executes the boundary conditions and load settings in the performance simulation, independently completing the mechanical performance simulation of the three-dimensional model of the spring fragment.

[0109] Based on multiple independent solver instances, performance simulations of each spring fragment's 3D model are performed synchronously. This can be achieved by the task scheduling module triggering multiple independent solver instances to concurrently read the spring fragment's 3D model from the task queue. Each instance calls a mesh generation function to complete the mesh generation, then sets material properties, boundary conditions, and loads through model analysis functions, and finally calls the simulation software solver to perform static calculations, generating performance simulation results including stiffness and maximum stress in each direction. By leveraging operating system-level parallelism to achieve truly synchronous simulation, the overall simulation time for multiple scenarios can be significantly shortened, improving the overall response speed of the iterative optimization process.

[0110] This embodiment provides a method for generating spring structures. By allocating initial structure control parameters to a parallel computing task queue based on each initial structure control parameter, and synchronously executing performance simulations on each spring 3D model based on multiple independent solver instances, the method obtains performance simulation results. By centrally managing the simulation tasks corresponding to multiple initial structure control parameters in a parallel computing task queue and synchronously executing performance simulations using multiple independent solver instances, the method can effectively overcome the time bottleneck of single-process serial simulation, reduce waiting time, improve hardware resource utilization, and achieve the technical effect of improving the automation efficiency and response speed of spring structure generation.

[0111] To more clearly illustrate the technical solution of this application, a detailed embodiment is also provided.

[0112] The applicant's research revealed that current mainstream VCM spring designs largely rely on empirical structures (such as four-corner double-arm springs and S-shaped springs), exhibiting relative conservatism in geometric expansion and limiting design space due to human experience. Lacking automated design generation methods, engineers often fine-tune dimensions from existing mature solutions to meet new requirements, making it difficult to identify groundbreaking new structures in a timely manner. For example, to balance vertical compliance and horizontal stability, a classic design involves adding slender elastic arms to the four sides of a square frame to form a "cross" or "frame + cantilever" structure. However, this pre-defined topology may not be optimal, merely an empirical choice. The lack of topology optimization methods can lead to a rigid spring topology, with performance approaching its bottleneck. Furthermore, the numerous and interdependent shape parameters of the spring make it difficult to gain a comprehensive understanding through manual parameter tuning. Designers often prioritize ensuring stiffness and stroke meet targets, while neglecting stress concentration and modal characteristics, leaving potential problems. For instance, thinning local thicknesses to increase stroke in some springs may introduce stress hotspots, leading to a decrease in fatigue life. Due to varying designer experience, spring performance differs significantly between different teams. It is evident that human design is inadequate in exploring complex geometry, and design solutions are limited to a narrow scope.

[0113] Meanwhile, performance simulation is also a time-consuming and labor-intensive step in the traditional spring design process. Each time the geometry is modified, the mesh must be remodeled and finite element analysis run to evaluate stiffness, resonant frequency, stress, etc. If electromagnetic driving force needs to be considered, magnetic field simulation must also be coupled, making the process complex. The lack of an automated toolchain means this process relies entirely on manual intervention, with each iteration potentially taking several days to complete. This directly lengthens the design cycle and limits the number of possible solutions. Furthermore, different simulation conditions and objectives (static stiffness, dynamic modes, fatigue life, etc.) are often calculated by different software or modules, requiring manual trade-offs between the results. Since spring design involves multi-objective optimization—for example, increasing stiffness increases stress, while increasing elasticity reduces stability—manual adjustments often fail to simultaneously satisfy the optimal values ​​for multiple indicators, frequently requiring multiple rounds of trial and error to achieve a balance. In addition, the accuracy and complexity of the simulation model itself also present challenges. For example, with spring thicknesses of only tens of micrometers, a mesh that is too coarse affects accuracy, while a mesh that is too fine results in a large computational load; also, nonlinear large deformations, contact or eddy current losses occur after the spring is subjected to force, requiring high-order simulation solutions. In engineering practice, simplification is often employed, but this may not reflect reality, necessitating repeated trial production to verify and correct the model, further slowing down the design process. It is evident that the existing process, where simulation analysis and design adjustments are disconnected, is inefficient and prone to overlooking problems, increasing costs and time, thus becoming a bottleneck in the spring optimization process.

[0114] For the reasons mentioned above, the development of new VCM spring solutions typically involves a long cycle and heavily relies on the experience of senior engineers. From conceptual design to finalization, it often requires multiple cycles of conceptual design, simulation verification, prototype manufacturing, testing feedback, and solution revision. In this process, human decision-making dominates, such as judging which area of ​​the spring needs strengthening or thinning based on experience. The limitations of this experience-driven model are twofold: firstly, the human brain struggles to globally optimize multiple parameters, often resulting in suboptimal rather than globally optimal designs; secondly, industry experience is primarily held by a few experts, leading to long training periods for newcomers and hindering innovation. Furthermore, human design is easily influenced by subjective preferences, resulting in significant differences in solutions provided by different engineers and making it difficult to standardize design quality. Due to insufficient design capabilities and experience, repeated trial and error are necessary to catch up, but the long cycle of manual design is clearly at odds with the rapid iteration pace of consumer electronics, making it difficult to meet the needs of OEMs. Therefore, how to reduce reliance on personal experience, shorten design cycles, and improve the first-time success rate of designs has become a pressing issue for current technologies. In summary, the current VCM spring design process suffers from insufficient automation and intelligence, severely impacting product performance improvement and development efficiency.

[0115] To address the aforementioned technical issues, in one embodiment, taking VCM spring design as an example, a spring structure generation method is provided and applied to an automatic spring generation system. The automatic spring generation system adopts a modular architecture, dividing the fully automated VCM spring design process into several decoupled sub-modules, forming a closed-loop process from parameter configuration, shape generation, simulation evaluation to optimization decision-making. Each module interacts through a clear interface, executes sequentially, and feeds back data, achieving automated iterative optimization.

[0116] The overall architecture of the automatic spring fragment generation system includes: a geometric parameter configuration module for defining the design space and constraints; a control point sampling module for randomly generating initial structural parameters; a curve generation and contour construction module for generating the 3D model of the spring fragment; a geometric validity verification module for ensuring structural effectiveness; a finite element simulation automation module for performance simulation calculations; an optimization control module for updating the design based on simulation results; and a parallel acceleration and data output module for improving computational efficiency and exporting design results. These modules are loosely coupled, allowing for independent debugging while maintaining a tightly linked workflow: the optimization control module drives the other modules to work sequentially, ultimately achieving the automatic generation and optimization of the spring fragment structure. Data interfaces are used between modules to exchange information such as geometric parameter objects, control point sets, curve coordinates, meshes, and performance indicators, ensuring smooth information flow and consistency.

[0117] The spring fragment structure generation method can include: a parameter configuration module determines the design boundary conditions, a sampling module generates candidate designs (control point set), a curve / contour module constructs the spring fragment geometry, a geometry validity verification module screens the geometry, a simulation module calculates the mechanical properties, and finally, an optimization control module adjusts the next batch of design schemes accordingly, iterating until the convergence condition is met. The overall process of the automatic spring fragment generation system achieves fully automatic closed-loop optimization, from design input to optimization output without manual intervention. The architecture design emphasizes module decoupling and anomaly control. For example, the simulation module has error handling to ensure that the system can automatically retry or skip when a single design simulation fails without affecting the overall iteration process; the optimization control module checks the validity of the received data to ensure that the output of each link meets the interface requirements, thus giving the system high robustness and stability.

[0118] Furthermore, the GeometryParameters module is responsible for defining the overall parameters and constraints of the spring design, providing a unified design input for other modules. This module reads user-provided design requirements as needed, such as the spring's spatial extent (polygonal outline of the design area), fixed starting point position, material thickness, tolerance range, and geometric / physical constraints, and encapsulates these into a global parameter object for subsequent use. Centralized configuration ensures that all sub-modules operate under unified constraints, avoiding parameter inconsistencies.

[0119] The input parameters for the geometry parameter configuration module may include: the polygonal region of the design space (such as the installation boundary of the camera module spring), the starting control point or fixed end position, material and dimensional parameters (thickness, upper and lower width limits, etc.), and target performance index thresholds (anisotropic stiffness requirements, stress upper limits, etc.).

[0120] The output parameters of the geometry parameter configuration module may include: parameter data structures containing the design settings mentioned above, such as the set of vertices in the design region, the coordinates of the initial control points, and the lower curvature limit κ. min Control point spacing limit d min Parameters such as boundary distance and safety distance ε are provided. An output interface allows other modules to query the required parameters.

[0121] The geometry parameter configuration module includes multiple parameter acquisition interfaces, such as `get_design_domain()` for obtaining the initial space, `get_start_point()` for obtaining control points, and `get_thickness()` for obtaining thickness. These are used by the sampling and modeling modules to obtain design boundaries and initial conditions. The module first reads and verifies the completeness and validity of the input parameters (e.g., checking the closure of the design region polygons and the reasonableness of the parameter range), and then generates a global parameter object during system initialization and broadcasts it to all modules. Once initialized, the parameters remain unchanged throughout the optimization process or are only adjusted by the user. By centralizing design conditions in the geometry parameter configuration module, parameter configuration and algorithm logic are decoupled, facilitating subsequent maintenance and modification of design requirements without altering the core algorithm code.

[0122] Furthermore, the control point sampling module (PoissonPolygonSampler) can randomly generate a set of control points within a specified polygonal design area using the Poisson disk sampling algorithm. These points serve as the initial control vertices for the spring's central axis curve. Random sampling ensures initial topological diversity while strictly satisfying spatial distribution constraints, thus avoiding invalid initial geometry from the outset. The characteristic of Poisson disk sampling is that it generates uniformly distributed points within the region, maintaining a minimum distance between any two points, thereby providing a good point set foundation for subsequent curve generation.

[0123] The input parameters for the control point sampling module can include: the design area polygon (which can be provided by the geometry parameter configuration module) and sampling parameters, such as the number or desired density of control points, minimum point spacing d, and constraint thresholds such as minimum distance from the boundary ε. The module can also accept existing anchor points (such as fixed starting points) as partial input to ensure that key points are included in the sampling results.

[0124] The output parameters of the control point sampling module may include a list of control point coordinates that meet the constraints, typically represented in two-dimensional coordinates within the design plane. This output point set will be used for subsequent B-spline curve fitting. For cases involving a fixed starting point, the output ensures that the point is contained within it and that other points are uniformly distributed relative to it.

[0125] The control point sampling module generates n points in O(n log n) complexity using efficient Poisson sampling methods such as the Bridson algorithm. The process includes: randomly selecting seed points within the design polygon, then iteratively generating new points, ensuring that each new point is at least d away from existing points and at least ε away from the polygon boundary. Sampling terminates if no suitable new point is found after a certain number of attempts. Throughout the process, the module checks boundary and spacing constraints in real time: using the inclusion test within the polygon to ensure points fall within the boundary and leave a safe margin ε, and using spatial indexing to quickly determine if the nearest distance between a new point and an existing point meets the d requirement. After sampling, the initial control point set is output. An interface such as sample_points(polygon, n, d, ε) is provided to generate the point set, and it can be called multiple times to obtain schemes with different random distributions. During optimization, this module can also be used to generate new points in the mutation phase: for example, when the optimization control module attempts to adjust the control points, this module can be called to resample local areas to replace some invalid points, thus ensuring that the mutated scheme still meets the constraints. By using Poisson sampling, this system avoids topological limitations in the initial design stage, allowing the generation of control point layouts with arbitrary distributions, laying the foundation for exploring diverse shapes.

[0126] Furthermore, the curve generation and contour construction modules (SplineBuilder, StripBuilder) can include two sub-modules: the Spline curve generation module SplineBuilder and the solid contour construction module StripBuilder. The former can generate a smooth central axis curve of the spring sheet based on the control point set, while the latter constructs a three-dimensional solid contour of the spring sheet with thickness and width along the central axis curve. This module converts discrete control points into continuous geometry and forms a solid model that can be used for finite element analysis.

[0127] The SplineBuilder module can use a cubic B-spline algorithm to fit control points and generate a parametric curve for the centerline of the spring sheet. The SplineBuilder module first calculates the nodal vectors of the spline curve based on the number and distribution of control points, ensuring a continuous and smooth transition of the curve segments (satisfying certain G...). 2 (Continuity). Then, an initial central axis curve is generated by calling the B-spline formula, and an interface is provided to adjust the control points and regenerate the curve to optimize its shape. The module's function interface, such as the spline curve construction function `build_spline(points)`, allows the generation of parametric equations or discrete point sequences representing the central axis curve by inputting a list of control points. For example, by inputting a set of control point coordinates (provided by the sampling module or optimization control module), the output is the spline center curve, which can be discretized into polylines for subsequent modeling. `SplineBuilder` ensures that the curve meets curvature smoothing requirements by calculating the second derivative of the curve to verify that the local curvature is not lower than the threshold κ.min If an acute-angle inflection point with excessively small curvature appears, it can be corrected by adding control points or adjusting their positions to ensure a smooth central axis curve. Furthermore, SplineBuilder supports fine-tuning of the curve as needed: if a section of curvature or shape needs improvement during optimization, the module's adjustment interface can be called to update local control points, and then the spline can be regenerated to optimize the curve's shape.

[0128] After obtaining the central axis curve, the StripBuilder solid contour building module can expand it into a 3D spring sheet solid model with thickness and width. Input parameters include a description of the central axis curve (such as a discrete point array), a preset spring sheet thickness value, and a width distribution function or parameter that may vary along the curve. The output is a 3D solid geometric model of the spring sheet. For example, the 3D solid geometric model can be represented as a CAD surface or solid, and can be exported as an IGES file. For example, the StripBuilder module can offset the curve along the central axis curve in its normal plane direction to generate the upper and lower boundary curves of the spring sheet, then stretch the boundary curves into upper and lower surfaces according to the set thickness, finally generating a closed-thickness solid. Furthermore, to improve design flexibility, StripBuilder can also support parameterized dynamic adjustment of the width, that is, changing the local width of the spring sheet by controlling a function of the offset distance along the curve's normal direction.

[0129] Furthermore, in the early stages of optimization, a larger width perturbation can be applied (e.g., using Gaussian random perturbation, but truncated to the range [min_width, max_width]) to broadly explore possible cross-sectional changes; in the later stages, the perturbation amplitude is reduced to fine-tune the width, achieving more refined shape optimization. The position calculation of the width and central axis curve adopts a coupling mechanism, that is, each width change is recalculated through the curve normal, which can ensure smooth transition of the elastic edge without abrupt changes. After generating the solid model, StripBuilder calls the geometry verification module to check the solid, such as whether the upper and lower surfaces have self-intersections or whether they exceed the design boundaries, etc. Only the model that passes the verification will proceed to the next simulation step. The curve generation and contour construction module provides interfaces such as the function build_strip(curve, thickness, width_func) to build spline curves, which internally calls the results of SplineBuilder and superimposes the thickness and width parameters to generate the final CAD model. Through SplineBuilder and StripBuilder, the system can transform discrete design variables into continuous solids, preparing for simulation analysis and ensuring that the model geometrically meets the design requirements.

[0130] The geometric validity verification module runs throughout the design generation process, performing a series of rigorous constraint checks on the generated control point sets, curves, and solid models. This ensures that each candidate control parameter, i.e., the candidate design, meets the pre-set geometric constraints before entering finite element simulation. This embodiment employs a "strong constraint" verification strategy, meaning that any design that does not meet the constraints will be immediately identified, eliminated, or corrected. This avoids sending infeasible designs into the simulation and causing computational interruption, thus ensuring that the entire optimization process always takes place within the valid feasible region.

[0131] The geometric validity verification module checks boundary constraints, self-intersection constraints, curvature constraints, and spacing constraints, among which:

[0132] Boundary constraints: These are used to ensure that all control points and generated curves are within the design polygon, and that the curves maintain a safe distance of at least ε from the design area boundary. Polygon inclusion tests and nearest-neighbor distance calculations can be used to ensure that the curves are completely within the allowable range.

[0133] Self-intersection constraint: Used to check whether the central axis curve of the spring is a simple curve, that is, it does not have any self-intersections except for the fixed endpoints at both ends. Algorithmically, it can use pairwise line segment intersection detection or ray detection to determine whether the curves have self-intersections or overlaps. If self-intersections are found, the module will mark the design as illegal.

[0134] Curvature constraint: Used to calculate the curvature distribution of a curve using differential geometry, verifying whether the radius of curvature is greater than a specified lower limit κ at every point. min To avoid overly sharp bends, any minute segment of the curve whose curvature violates the requirements is considered invalid.

[0135] Spacing constraint: For discrete control points, further check whether the distance between non-adjacent points is not less than the set minimum value d. min This is to prevent excessively dense control points from causing local spline jitter or excessively narrow areas of the solid. Spatial indexing is used to accelerate the calculation of distances between points, thereby improving detection efficiency.

[0136] The geometric validity verification module can be invoked immediately after each curve generation / modification to review the current design. For example, the module can execute all the above checks at once through the validity verification function `is_valid(design)`, returning a boolean result or specific violation information. If an invalid result is returned, the optimization control module will abandon the design and trigger alternative solutions (such as resampling control points or adjusting parameters). In parallel simulation scenarios, the verification module can also be embedded in the simulation subprocess to prevent some designs from suddenly failing to meet constraints due to the "cliff effect" and crashing midway. This strong constraint verification strategy differs from traditional soft constraint processing (such as penalty function methods that allow violations but impose penalties). By directly rejecting infeasible solutions and resampling new solutions, optimization stagnation caused by the inability to compute the objective function due to infeasible designs is fundamentally avoided, achieving precise control over the feasible region and providing a reliable foundation for subsequent optimization.

[0137] The finite element simulation automation module integrates mesh generation, physical modeling, and solution analysis functions. Utilizing the Python interface of existing software, it can automatically calculate the mechanical properties of spring-loaded structures. The module comprises two main parts: MeshBuilder (the mesh generation unit) and ModelAnalysis (the modeling and solving unit). By leveraging open-source tools to achieve programmatic control of the independent solver, it can perform fully automated stiffness and stress simulation evaluations for each candidate design, reducing the time-consuming steps of manual simulation to the second level.

[0138] The MeshBuilder unit is responsible for discretizing the spring's geometric model into a mesh model required for finite element analysis. The input parameters for the MeshBuilder module can include the 3D spring model output by StripBuilder, material properties (such as Young's modulus, Poisson's ratio, etc.), and mesh generation settings (element type, size, etc.), thereby obtaining the corresponding finite element mesh model of the spring's 3D model (which may include node and element information). For example, the mesh can be generated through command interfaces provided by open-source tools. For instance, commands can be called to create geometric entities (such as reconstructing the spring shape by connecting key points, stretching patches, etc.), then specifying the shell element or solid element type, and meshing to obtain the mesh model. In this embodiment, to improve efficiency, a simplified shell element model can be generated during the optimization and coarse screening stage, and then switched to a solid element model during the fine evaluation stage. The MeshBuilder unit can provide interfaces such as the mesh model function `mesh_model(geometry, element_type)`, allowing selection of the element type as needed and automatic meshing. During the meshing process, the module can also control the mesh quality, such as checking the element distortion rate, to ensure solution accuracy.

[0139] The ModelAnalysis unit can, after obtaining the mesh, set boundary conditions, loading conditions, and solve for the mechanical response of the spring. For example, based on the mesh model provided by the mesh generation unit, loading and constraint conditions (such as fixed support positions, applied electromagnetic forces or displacements), and the type of results to be extracted, the ModelAnalysis unit solves for the performance indicators of the spring design, including stiffness values ​​in each direction and maximum stress values. The ModelAnalysis unit can also control an independent solver through open-source tools, specifying material properties for the mesh model, applying boundary conditions (e.g., fixing the spring mounting frame area to simulate actual assembly constraints), and applying loads or displacements to simulate the effect of the VCM on the spring during operation (e.g., applying displacement in the optical axis direction to calculate Z-axis stiffness, and applying lateral forces in the X / Y directions to calculate transverse stiffness), thereby solving the static problem. After the independent solver completes the calculation, the results are read, including the equivalent stiffness (force / displacement) and stress distribution contour maps of the spring in each loading direction, and the maximum stress value is extracted. The results are obtained through open-source tools, eliminating the need for manual access to the solver interface. The modeling and solving unit (ModelAnalysis) encapsulates a series of preset solver commands through the analysis function `run_analysis(mesh, load_case)`. For each spring design, ModelAnalysis can store key results in a data structure available to the optimization control module, such as stiffness and stress values ​​in the X, Y, and Z directions. To shorten the simulation time per run and meet optimization requirements, ModelAnalysis can also run multiple simulation instances simultaneously under the scheduling of the parallel acceleration module. After solving, the results are returned to the optimization control module via shared memory or temporary files, achieving full automation and seamless integration of the simulation process. It links modeling, partitioning, loading, solving, and post-processing in a chain, executing them automatically by the program. This avoids repetitive manual operations, eliminates file import / export waiting time, and reduces the time spent on structural evaluation.

[0140] The optimization control module (Optimizer) can globally optimize the spring sheet structure design using an evolution strategy algorithm. Based on performance feedback from the simulation module, it continuously adjusts design parameters and iteratively searches for the optimal solution that meets multiple performance objectives. Unlike traditional local optimization methods that rely on manual experience, the optimization control module can achieve constraint-driven global optimization, autonomously evolving new structural topologies and shapes in a high-dimensional design space.

[0141] For example, the optimization control module can encode the design degrees of freedom of the spring geometry into a set of optimization variables, including the coordinate positions of control points and possibly the width distribution parameters along the length direction of the spring. Furthermore, these variables can be represented in real number form. The initial population generates multiple sets of random candidate solutions using methods such as Poisson sampling, ensuring the diversity and feasibility of the initial solution set. Each candidate solution, i.e., candidate control parameters, corresponds to a set of control points, which are used to generate a spring solid model via the spline generation module SplineBuilder and the solid contour construction module StripBuilder.

[0142] Furthermore, the optimization control module can also maintain a specific population list to control the overall optimization iteration. The population list can include all design individuals and their performance evaluations in the current iteration.

[0143] The optimization control module involves multiple objectives and constraints, namely, it needs to simultaneously satisfy constraints such as X / Y direction stiffness ≥ preset threshold, Z direction stiffness within a preset range, maximum stress in each direction ≤ material tolerance, and geometric non-self-intersection and curvature smoothness. In this embodiment, the optimization control module transforms the above constraints into a comprehensive objective function or fitness function. By setting penalty terms for cases where stiffness is insufficient or stress exceeds the limit, these cases are included in the objective function evaluation, causing the algorithm to automatically favor directions that satisfy the constraints during the search.

[0144] Furthermore, since geometric constraints have been verified by the legality verification module, infeasible designs will not proceed to the objective evaluation stage. In this embodiment, multiple objectives are unified into a single fitness value through a weighted summation strategy, or different objectives are alternately optimized during the evolutionary process, thereby achieving automatic balancing optimization of performance such as stiffness and strength. For example, the objective function can be defined as f(P) = -(α·k) Z (P) + β·k XY (P)-γ·max(σ(P)-σ max ,0)...), where α, β, γ are weights, P is the parameter set, k Z (P) represents the overall stiffness of the structure in the Z direction, k XY (P) represents the overall stiffness of the structure in the XY plane, and σ(P) represents the stress in the structure. max This represents the maximum allowable stress of the material, thus balancing the requirements of increasing stiffness and reducing excessive stress. Different design preferences can be achieved by adjusting the weights or the form of the objective function according to design requirements.

[0145] The optimization control module can employ an (μ, λ) evolutionary strategy, generating λ candidate offspring designs from μ parents in each generation. Exemplarily, the evolutionary strategy and iteration can include selection, mutation, recombination, and survivor selection. Selection can involve choosing several elite individuals (e.g., μ) from the current population based on fitness for reproduction, applying random mutations to the design parameters of these elite individuals to generate new candidate designs. Mutation can involve adding small random perturbations (random vectors following a normal distribution) to the coordinates of the control points. The perturbation magnitude σ is larger in the early stages of the algorithm to encourage broad exploration, and gradually decreases in the later stages for more detailed searching. Furthermore, the optimization control module can employ a mutation rejection mechanism; if a mutation is deemed by the geometric validity verification module to no longer satisfy the geometric constraints, the mutation is rejected and resampling is performed to ensure that the generated offspring are 100% valid. Additionally, the optimization control module can employ a weighted recombination mechanism; that is, the control point location information of multiple excellent individuals can be weighted and combined to generate new solutions, integrating the advantages of different solutions to improve population diversity and the inheritance of superior genes. Each newly generated individual's performance is immediately evaluated through the aforementioned simulation module. After all λ candidates in a generation have been evaluated, the optimization control module, based on a survivor retention strategy, selects the μ individuals with the best performance from the total set of parents and offspring to enter the next generation, eliminating inferior designs. This process is repeated iteratively, continuously approaching the optimal solution. The entire evolutionary process continues until a termination condition is met, such as reaching a preset generation limit or the increase in population fitness being less than a preset condition.

[0146] In this embodiment, the optimization control module can start the entire process by launching the optimization function `run_optimization()` through a unified scheduling interface. The optimization control module can periodically call the interfaces of each module: calling the sampling and modeling modules when generating the initial population, calling the simulation module in batches to obtain results when evaluating individuals, and calling the sampling module or its own built-in functions to generate perturbations when applying mutation operations. During the optimization process, the optimization control module monitors the constraint satisfaction and performance indicators in real time and records the best design and fitness value for each generation. To facilitate analysis and tracing, the optimization control module can automatically save the input parameters and output results of each iteration to the population list. Implementation is achieved through interface function calls rather than coupled code, and the interaction with the independent solver is also encapsulated through an abstraction layer (e.g., the unified design evaluation function `evaluate_design(design)` interface), thus allowing for easy replacement of the underlying algorithm or simulation tool without affecting the overall process. By combining evolutionary algorithms with engineering constraints, the optimization control module can achieve automatic exploration of VCM spring design. Compared with traditional manual parameter tuning methods, it can discover globally optimal or near-optimal solutions, avoiding getting trapped in local suboptimal traps.

[0147] The parallel acceleration and data output module is used to improve simulation calculation efficiency and process and output final design data. Multi-core parallel computing significantly shortens optimization iteration time, while the latter exports design results that can be provided for engineering applications and demonstrations. Specifically, the parallel acceleration unit provides high-speed simulation support during each optimization evaluation stage, and the data output unit provides result saving and visualization functions at the end or intermediate stages of optimization.

[0148] Furthermore, multi-core parallel computing can be used to address situations where a large number of candidate designs need to be evaluated in each generation of optimization. This involves utilizing open-source tools to achieve multi-process parallel solving. For example, at the start of an optimization generation, the optimization control module sends the set of individual designs to be evaluated to a parallel computing task queue. This queue automatically schedules multiple independent solver kernel instances (kernel instances can correspond to the number of CPU cores, such as 16 cores) to undertake simulation tasks for different designs. The module ensures balanced task distribution, avoiding overload of any process. Each independent solver instance concurrently executes the modeling, meshing, and solving processes, using shared memory or files to send the results back to the main controller. Once all parallel tasks are completed, the optimization control module aggregates the results for the next evolutionary computation. This multi-core parallel computing mechanism significantly alleviates simulation computation bottlenecks, resulting in a substantial reduction in overall optimization time.

[0149] Furthermore, to ensure the smooth operation of the parallel process, a pre-check can be performed using a geometric verification module before task distribution to filter out illegal designs and avoid wasting computational resources. Simultaneously, constraint monitoring can be embedded within each process. If an anomaly occurs in a spring design during simulation, such as material non-convergence or geometric distortion, an error handling mechanism will capture it and notify the main process to retry or skip. Furthermore, flexible core configuration and automatic process management enable good scalability of the simulation process. In one specific embodiment, in a 16-core parallel environment, the actual speedup can reach approximately 12 times, and the simulation time per run can be reduced from 15–30 seconds serially to 1–3 seconds. Furthermore, this embodiment, through a hierarchical simulation strategy of coarsely screening shell elements and finely tuning solid elements, can complete the simulation workload equivalent to 14 days in traditional techniques within 2–3 hours, fully leveraging the performance potential of multi-core hardware and enabling the global optimization algorithm to complete high-intensity computational tasks within an acceptable timeframe.

[0150] After the optimization process is completed, the data output unit organizes and exports the final optimized spring design and process data for engineering applications and decision-making reference. Specifically, the data output unit can call the StripBuilder spline generation module to regenerate the detailed CAD model of the final design and provides IGES file export functionality, facilitating the import of the design shape into a CAD / CAM system for detailed design and manufacturing. Furthermore, it can also synchronously summarize and output the results of the simulation module (such as anisotropic stiffness values ​​and stress distribution).

[0151] Furthermore, for the performance simulation results, key performance indicators can be generated into reports or tables, and visualizations of the simulation results can be exported, such as stress cloud diagrams of spring pieces and deformation morphology diagrams.

[0152] In addition, the data output module can organize historical data from the optimization process, including parameters and values ​​for each generation of the population, thereby constructing a traceable optimization history database. This provides data backup for future analysis of optimization convergence trends or for improving the algorithm. The data output module can be called when the optimization control module issues a termination command through interfaces such as the export_results(design) function and the save_history(log) function.

[0153] Furthermore, the data output module can also add corresponding metadata descriptions to the output data, such as coordinate units, material assumptions, and constraints, to facilitate user understanding and reproduction of the design, and to present the design process data in an engineering-friendly format.

[0154] This embodiment provides a method for generating spring structures that, through global design space exploration and topological innovation, overcomes the limitations of traditional methods that rely on fixed initial topological structures for local adjustments. By employing Poisson random sampling combined with B-spline parameterization, it enables the exploration of the global design space. Unlike existing technologies that can only fine-tune near the initial structure, this embodiment's method can freely generate entirely new spring shapes within a given design domain, including topological forms that were previously difficult to conceive of manually (such as evolving a traditional wave shape into a new "Ω" shape). This broadens the design freedom, covers feasible areas that traditional methods cannot reach, and thus significantly improves the diversity of design solutions, helping to discover structures with superior performance. By employing a strong constraint handling mechanism, including Poisson sampling generation and mutation rejection strategies, this algorithm ensures from the outset that all candidate designs necessarily satisfy hard constraints. This differs from existing algorithms that commonly use penalty functions or constraint violation punishment methods (which require assigning virtual inferior values ​​to solutions that do not meet constraints, potentially leading to unmeasurable failures during optimization). Instead, it directly eliminates illegal solutions and resamples, preventing infeasible solutions from participating in the evolution process. This improves the reliability and efficiency of the optimization process when handling geometrically discrete constraints (such as self-intersection, boundary crossings, and other "cliff effect" constraints). The entire optimization process is free from simulation interruptions due to constraint violations, guaranteeing the continuity of algorithm convergence and the reliability of the results. Furthermore, because the search always occurs within the feasible region, the algorithm avoids wasting computational resources on infeasible directions, significantly improving effective search efficiency. By employing a weighted recombination evolutionary strategy, multi-objective synchronous optimization is achieved through multi-objective collaborative optimization and automatic balancing. Unlike existing technologies that often rely on manual weight adjustments or separate optimizations followed by manual compromises, this method embeds a multi-objective balancing mechanism within the evolutionary algorithm. By setting reasonable objective weights and recombination operators, the algorithm can simultaneously consider requirements such as increasing stiffness in the X / Y directions, controlling stiffness in the Z direction within a range, and reducing maximum stress in a single run, achieving automatic trade-offs among various objectives. For example, the weighted recombination of elite individuals during the optimization process generates new solutions, allowing design genes that excel in both stiffness and stress to be combined, producing offspring that balance both aspects. Compared to the traditional method of optimizing and rebalancing item by item, the collaborative evolutionary strategy in this embodiment can find a design scheme that simultaneously meets all key performance targets, significantly improving the overall design performance. It can also solve the problem of excessive stress caused by simply increasing stiffness in traditional design approaches.By introducing a dual acceleration strategy of hierarchical solution and multi-core parallelism in the simulation stage, a two-stage simulation scheme of shell element coarse screening and solid element fine tuning is adopted. First, the stiffness / stress performance of a large number of candidate designs is approximated by the computationally fast shell element, and inferior solutions are eliminated. Then, the shortlisted designs are simulated and verified by high-precision solid element simulation. Unlike the existing single-precision simulation, this can significantly reduce the total amount of computation while ensuring that the accuracy of the final result is not lost. At the computing hardware level, the parallel interface of open source tools can be used to realize multi-threaded and multi-process parallel simulation: multiple independent solver instances can be run at the same time to evaluate different individuals in the population. Compared with the traditional serial one-by-one simulation, it can effectively improve the simulation efficiency on multi-core CPUs without sacrificing accuracy. Instead, the reliability of the results is ensured through fine simulation verification, thereby greatly improving the timeliness and practical value of the system. Through end-to-end automated integration, all aspects of spring design (including parametric modeling, geometry generation, simulation evaluation, and result processing) are seamlessly integrated into a single automated platform. Compared to the discrete processes in traditional technologies, such as manual sketching, manual import of simulation software, and multiple manual adjustments, this embodiment connects the CAD and CAE processes using open-source tools, eliminating manual intervention and file interaction delays. Simulation input and output are all processed by the program, eliminating the need for manual waiting. This makes the entire design process digital and continuous, reducing reliance on human experience and minimizing human error. It also allows for uninterrupted automatic design iteration, significantly improving R&D efficiency. Furthermore, the exception handling mechanisms in this embodiment, such as automatic retrying of simulation failures and automatic data saving, ensure robustness for long-term unattended operation. Thus, the entire process of VCM spring design and verification is intelligent, achieving a dual improvement in design efficiency and quality.

[0155] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0156] Based on the same inventive concept, this application also provides a spring structure generating apparatus for implementing the spring structure generating method described above. The solution provided by this apparatus is similar to the implementation described in the above method; therefore, the specific limitations in one or more embodiments of the spring structure generating apparatus provided below can be found in the limitations of the spring structure generating method described above, and will not be repeated here.

[0157] In one embodiment, such as Figure 3 As shown, a spring-loaded structure generating apparatus is provided, the apparatus comprising:

[0158] The parameter generation module 100 is used to generate the initial structural control parameters of the spring sheet based on preset spatial constraint parameters.

[0159] The model generation module 200 is used to fit the initial structural control parameters to obtain a three-dimensional model of the spring. The three-dimensional model of the spring conforms to the preset geometric verification rules.

[0160] The performance simulation module 300 is used to perform performance simulation on the three-dimensional model of the spring and obtain the performance simulation results.

[0161] The parameter optimization module 400 is used to iteratively optimize the initial structural control parameters based on the performance simulation results, so as to obtain the target structural control parameters and the target spring three-dimensional model corresponding to the target structural control parameters.

[0162] In one embodiment, the initial structural control parameters include a set of control points; the parameter generation module 100 is further configured to:

[0163] Within a preset simulation space, multiple seed control points that satisfy the preset space constraint parameters are generated;

[0164] Based on the multiple seed control points, multiple candidate control points are iteratively generated;

[0165] The set of control points is determined based on the candidate control points that satisfy the preset spatial constraint parameters.

[0166] In one embodiment, the model generation module 200 is further configured to:

[0167] Based on the B-spline curve fitting algorithm, the initial structural control parameters are fitted to obtain the spring sheet central axis curve that satisfies the preset curvature condition.

[0168] The central axis curve of the spring is subjected to normal plane offset and thickness stretching to obtain the three-dimensional model of the spring.

[0169] In one embodiment, the preset geometric verification rules include one or more of boundary constraint verification, self-intersection constraint verification, curvature constraint verification, and spacing constraint verification;

[0170] The boundary constraint verification includes verifying whether the 3D model of the spring is located within a preset simulation space and maintains a safe distance from the boundary of the preset simulation space, based on a preset polygonal region and a preset safety distance; the self-intersection constraint verification includes detecting whether the central axis curve has self-intersections except for the endpoints, based on a preset self-intersection judgment algorithm; the curvature constraint verification includes verifying whether the radius of curvature of the central axis curve is not less than a preset lower curvature limit, based on discrete points of the curve; and the spacing constraint verification includes verifying whether the distance between any two non-adjacent control points is not less than a preset minimum spacing, based on control point coordinates.

[0171] In one embodiment, the performance simulation module 300 is further configured to:

[0172] Based on the material parameters of the three-dimensional model of the shrapnel and the preset mesh parameters, the three-dimensional model of the shrapnel is meshed to obtain the mesh model of the three-dimensional model of the shrapnel.

[0173] By applying loads or displacements based on the mesh model, the stiffness and maximum stress values ​​of the spring sheet in multiple directions are solved to obtain performance simulation results.

[0174] In one embodiment, the number of initial structural control parameters is multiple; the parameter optimization module 400 is further configured to:

[0175] Based on the performance simulation results of the three-dimensional models of the spring corresponding to the multiple initial structural control parameters and the preset performance evaluation function, the initial structural control parameters corresponding to the three-dimensional model of the spring with the best simulation results are taken as the current optimal control parameters.

[0176] Based on the weighted recombination operator, the current optimal control parameters are cross-operated to generate multiple candidate control parameters, which are used as the initial structure control parameters for the next round of optimization.

[0177] In one embodiment, the parameter optimization module 400 is further configured to:

[0178] Based on each of the initial structure control parameters, the initial structure control parameters are allocated to the parallel computing task queue;

[0179] Based on multiple independent solver instances, performance simulations are performed synchronously on each of the three-dimensional models of the spring fragment to obtain performance simulation results.

[0180] Each module in the aforementioned spring structure generation device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the operations corresponding to each module.

[0181] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 4 As shown, the computer device includes a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for generating a spring-loaded structure. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0182] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0183] In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the spring structure generation method of any of the above embodiments.

[0184] Based on preset spatial constraint parameters, the initial structural control parameters of the spring sheet are generated;

[0185] The initial structural control parameters are fitted to obtain a three-dimensional model of the spring; the three-dimensional model of the spring conforms to the preset geometric verification rules.

[0186] The performance of the three-dimensional model of the spring was simulated, and the performance simulation results were obtained.

[0187] Based on the performance simulation results, the initial structural control parameters are iteratively optimized to obtain the target structural control parameters and the target shrapnel three-dimensional model corresponding to the target structural control parameters.

[0188] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the spring structure generation method of any of the above embodiments:

[0189] Based on preset spatial constraint parameters, the initial structural control parameters of the spring sheet are generated;

[0190] The initial structural control parameters are fitted to obtain a three-dimensional model of the spring; the three-dimensional model of the spring conforms to the preset geometric verification rules.

[0191] The performance of the three-dimensional model of the spring was simulated, and the performance simulation results were obtained.

[0192] Based on the performance simulation results, the initial structural control parameters are iteratively optimized to obtain the target structural control parameters and the target shrapnel three-dimensional model corresponding to the target structural control parameters.

[0193] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0194] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0195] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0196] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for generating a spring-loaded structure, characterized in that, The method for generating the spring structure includes: Based on preset spatial constraint parameters, initial structural control parameters for the spring are generated; the initial structural control parameters include a set of control points; the generation of initial structural control parameters for the spring based on preset spatial constraint parameters includes: generating multiple seed control points that satisfy the preset spatial constraint parameters within a preset simulation space; iteratively generating multiple candidate control points based on the multiple seed control points; and determining the set of control points based on the candidate control points that satisfy the preset spatial constraint parameters. The initial structural control parameters are fitted to obtain a three-dimensional model of the spring; the three-dimensional model of the spring conforms to the preset geometric verification rules. The performance of the three-dimensional model of the spring was simulated, and the performance simulation results were obtained. Based on the performance simulation results, the initial structural control parameters are iteratively optimized to obtain the target structural control parameters and the target shrapnel 3D model corresponding to the target structural control parameters; the number of initial structural control parameters is multiple; the iterative optimization of the initial structural control parameters based on the performance simulation results includes: based on the performance simulation results of the shrapnel 3D models corresponding to the multiple initial structural control parameters and a preset performance evaluation function, the initial structural control parameter corresponding to the shrapnel 3D model with the best simulation result is taken as the current optimal control parameter; based on a weighted recombination operator, the current optimal control parameter is subjected to a cross operation to generate multiple candidate control parameters, which are used as the initial structural control parameters for the next round of optimization.

2. The method for generating a spring structure according to claim 1, characterized in that, The fitting process based on the initial structural control parameters to obtain the three-dimensional model of the spring sheet includes: Based on the B-spline curve fitting algorithm, the initial structural control parameters are curve-fitted to obtain the spring sheet central axis curve that satisfies the preset curvature condition. The central axis curve of the spring is subjected to normal plane offset and thickness stretching to obtain the three-dimensional model of the spring.

3. The method for generating a spring structure according to claim 1, characterized in that, The preset geometric verification rules include one or more of the following: boundary constraint verification, self-intersection constraint verification, curvature constraint verification, and spacing constraint verification. The boundary constraint verification includes verifying whether the 3D model of the spring is located within a preset simulation space and maintains a safe distance from the boundary of the preset simulation space, based on a preset polygonal region and a preset safety distance; the self-intersection constraint verification includes detecting whether the central axis curve has self-intersections except for the endpoints, based on a preset self-intersection judgment algorithm; the curvature constraint verification includes verifying whether the radius of curvature of the central axis curve is not less than a preset lower curvature limit, based on discrete points of the curve; and the spacing constraint verification includes verifying whether the distance between any two non-adjacent control points is not less than a preset minimum spacing, based on control point coordinates.

4. The method for generating a spring structure according to claim 1, characterized in that, The performance simulation of the three-dimensional model of the shrapnel includes: Based on the material parameters of the three-dimensional model of the shrapnel and the preset mesh parameters, the three-dimensional model of the shrapnel is meshed to obtain the mesh model of the three-dimensional model of the shrapnel. By applying loads or displacements based on the mesh model, the stiffness and maximum stress values ​​of the spring sheet in multiple directions are solved to obtain performance simulation results.

5. The method for generating a spring structure according to claim 1, characterized in that, The performance simulation of the three-dimensional model of the spring fragment yielded the following results: Based on each of the initial structure control parameters, the initial structure control parameters are allocated to the parallel computing task queue; Based on multiple independent solver instances, performance simulations are performed synchronously on each of the three-dimensional models of the spring fragment to obtain performance simulation results.

6. A spring-loaded structure generating device, characterized in that, The device includes: A parameter generation module is used to generate initial structural control parameters for a spring sheet based on preset spatial constraint parameters. The initial structural control parameters include a set of control points. Generating the initial structural control parameters for the spring sheet based on the preset spatial constraint parameters includes: generating multiple seed control points that satisfy the preset spatial constraint parameters within a preset simulation space; iteratively generating multiple candidate control points based on the multiple seed control points; and determining the set of control points based on the candidate control points that satisfy the preset spatial constraint parameters. The model generation module is used to fit the initial structural control parameters to obtain a three-dimensional model of the spring; the three-dimensional model of the spring is a three-dimensional model of the spring that conforms to the preset geometric verification rules; The performance simulation module is used to perform performance simulation on the three-dimensional model of the spring and obtain the performance simulation results. The parameter optimization module is used to iteratively optimize the initial structural control parameters based on the performance simulation results to obtain target structural control parameters and a target shrapnel 3D model corresponding to the target structural control parameters. The number of initial structural control parameters is multiple. The iterative optimization of the initial structural control parameters based on the performance simulation results includes: using the performance simulation results of the multiple shrapnel 3D models corresponding to the initial structural control parameters and a preset performance evaluation function, selecting the initial structural control parameter corresponding to the shrapnel 3D model with the best simulation results as the current optimal control parameter; and performing a cross-operation on the current optimal control parameter based on a weighted recombination operator to generate multiple candidate control parameters, which are then used as the initial structural control parameters for the next round of optimization.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method of any one of claims 1 to 5.

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