A dynamic regulation method for adaptive product form packaging design

Abstract

This invention relates to the field of packaging design control technology, specifically a dynamic control method for adaptive product form packaging design. The method includes: acquiring dynamic 3D model data and material physical property data of the target product through a product form data acquisition device and identifying its current form characteristics; inputting the dynamic 3D model data and form characteristics into a packaging form initial generator to obtain multiple initial packaging form schemes; using a collision detection algorithm to simulate and filter a set of candidate packaging forms based on the material physical property data; evaluating the form stability of the candidate schemes in conjunction with real-time usage scenario data; dynamically adjusting parameters based on the evaluation results; and outputting the final adaptive packaging form design data according to preset rules and sending it to the packaging production control system. This method enables the packaging form to adapt to dynamic changes in the product and usage scenarios, achieving seamless integration between design and production.

Description

Technical Field

[0001] This invention relates to the field of packaging design control technology, and in particular to a dynamic control method for packaging design that adapts to product form. Background Technology

[0002] In the packaging design and control process, existing technologies mostly rely on manual measurement or simple data acquisition equipment to obtain static dimensional data of the target product, generating a single packaging form scheme based on fixed parameters. This lacks targeted analysis of the product's material physical properties and fails to consider the dynamic changes in the product's form. After the packaging scheme is generated, adaptability is judged only through simple visual comparison, without conducting systematic form adaptability simulations or evaluating the stability of the packaging form in conjunction with the product's actual usage scenarios.

[0003] Static dimensional data cannot accurately reflect the dynamic form characteristics of a product. A single packaging solution is insufficient to adapt to the dynamic changes in product form, and the lack of consideration for material compatibility can easily lead to insufficient fit between packaging and product. The absence of adaptive simulation methods such as collision detection makes it impossible to select packaging solutions that truly fit the product, and the impact of usage scenarios on packaging stability is not considered. Packaging form cannot be dynamically adjusted according to actual needs, and the resulting packaging design data is difficult to directly integrate with the packaging production control system, hindering efficient integration between design and production.

[0004] It is necessary to obtain the product's dynamic 3D model and material physical property data, combine the material characteristics to conduct adaptive simulation screening of packaging solutions, evaluate the stability of packaging in real-time usage scenarios, dynamically adjust packaging parameters, and synchronize design data to the production control system to make up for the shortcomings of existing technologies in adaptability, dynamic control, and production integration. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a dynamic control method for packaging design that adapts to product form.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a dynamic control method for packaging design that adapts to product form, comprising: Based on the product form data acquisition equipment, dynamic three-dimensional model data and material physical property data of the target product are acquired, and the current form characteristics of the target product are identified; The dynamic 3D model data and the current morphological features are input into the packaging morphology initial generator to obtain multiple initial packaging morphology schemes; Based on the material physical property data, a collision detection algorithm is used to simulate the morphological adaptability of multiple initial packaging form schemes, and a set of candidate packaging forms that are dynamically adapted to the target product is selected. Based on real-time acquired product usage scenario data, the morphological stability of each option in the candidate packaging form set is evaluated, and evaluation results are generated. The parameters of the candidate packaging form set are dynamically adjusted based on the evaluation results, and the final selected adaptive packaging form design data is output according to the preset control rules. The adaptive packaging form design data is then sent to the packaging production control system.

[0007] As a further aspect of the present invention, the product form data acquisition device acquires dynamic three-dimensional model data and material physical property data of the target product, and identifies the current form characteristics of the target product, specifically as follows: Point cloud data of the target product in different poses are collected by a 3D laser scanning device. The point cloud data is then denoised and registered to generate a surface mesh model of the target product. Topological analysis and feature line extraction are performed on the surface mesh model to identify the key contour lines, curvature variation areas and deformable parts of the target product surface, thus forming a static morphological feature set of the target product. The motion capture unit in the product form data acquisition device is activated to record the form change sequence of the target product in a preset motion mode. Key frames are extracted from the form change sequence, and the deformation displacement field between adjacent key frames is calculated. Principal component analysis is performed on the deformation displacement field to obtain the principal component vector of morphological change, which describes the main mode of morphological change of the target product. The principal component vector of morphological change is fused with the static morphological feature set to generate the current morphological feature containing both static and dynamic features. Material samples of each component constituting the target product are obtained using material testing equipment. The elastic modulus, Poisson's ratio, density, and surface friction coefficient of the material samples are measured, and the parameters are integrated into material physical property data.

[0008] As a further aspect of the present invention, the working process of the collision detection algorithm is as follows: Based on the elastic modulus and Poisson's ratio in the material physical property data, calculate the local stiffness matrix of each part of the target product surface, and construct an adaptive spatial partitioning tree of the product based on the local stiffness matrix; In the adaptive spatial partitioning tree, the potential collision area where contact may occur is predicted based on the current morphological characteristics of the target product and the inner surface data of the initial packaging morphological scheme. Within the potential collision area, a continuous collision detection method is used to calculate the minimum distance between the product mesh and the inner wall mesh of the packaging in the next time step, and the tangential force between the contact points is calculated based on the surface friction coefficient in the material physical property data. When a collision is detected, a physics-based deformation response model is invoked. The deformation response model calculates the local strain distribution of the packaging shape caused by the collision based on the local stiffness matrix and the deformation displacement field. Based on the calculated local strain distribution, the geometry of the initial packaging form scheme is dynamically updated, and the collision detection and response process is iteratively executed until no new collisions are detected or the form changes tend to stabilize within the simulation time, thus completing the form adaptation simulation of the initial packaging form scheme.

[0009] As a further aspect of the present invention, based on real-time acquired product usage scenario data, the morphological stability of each option in the candidate packaging form set is evaluated, and an evaluation result is generated, specifically as follows: The system acquires real-time scenario data of the product's usage scenarios through environmental sensing devices. The scenario data includes ambient temperature, humidity, transportation vibration spectrum, and expected stacking pressure. Build a virtual testing environment and import each solution in the candidate packaging form set and the dynamic 3D model data of the target product into the virtual testing environment; In the virtual testing environment, environmental parameters are set based on real-time acquired scene data, and the target product is driven to perform motion simulation within the packaging scheme according to the principal component vector of the morphological change. During the motion simulation, the average contact pressure between the inner wall of the packaging form and the surface of the target product, the uniformity coefficient of the contact pressure distribution, and the volume change rate of the packaging form itself are monitored and recorded in real time. After the simulation, based on the recorded average contact pressure, uniformity coefficient and volume change rate, the comprehensive morphological stability score of each candidate packaging form scheme under the product usage scenario is calculated, and an evaluation result containing the scores of each scheme is generated.

[0010] As a further aspect of the present invention, the step of dynamically adjusting the parameters of the candidate packaging form set based on the evaluation results specifically includes: The evaluation results are analyzed, the comprehensive morphological stability score of each scheme in the candidate packaging form set is extracted, and the packaging form schemes that have a score lower than the preset threshold or whose score fluctuation exceeds the allowable range are identified and need to be adjusted. For each packaging form scheme to be adjusted, strain distribution data of the key deformation areas that lead to poor form stability are retrieved from the form adaptability simulation process. Meanwhile, the product contact pressure and vibration response spectrum corresponding to the key deformation area were extracted from the motion simulation records of the virtual test environment. The strain distribution data, product contact pressure and vibration response spectrum are input into a morphological parameter optimizer. The morphological parameter optimizer, based on the mechanical constitutive relationship of the packaging material, reversely deduce the required adjustment amount and direction of the local geometric parameters of the packaging shape. Based on the adjustment amount and direction, a geometric correction instruction is generated for the proposed adjustment schemes in the candidate packaging form set, and the data of the candidate packaging form set is updated using the geometric correction instruction.

[0011] As a further aspect of the present invention, the step of outputting the finally selected adaptive packaging form design data according to the preset control rules specifically includes: The preset control rules include the form adaptation priority rule, the material saving rule, and the production feasibility rule; For the candidate packaging form set after dynamic parameter adjustment, calculate the adaptation score of each scheme under the form adaptation priority rule, the material utilization score under the material saving rule, and the process complexity score under the production feasibility rule. For each candidate packaging form scheme, its adaptability score, material utilization score and process complexity score are weighted and integrated to calculate the comprehensive control score of the candidate packaging form scheme. From the pool of candidate packaging forms, the scheme with the highest comprehensive regulation score is selected and marked as the optimal candidate scheme; The optimal candidate solution is subjected to a final interference check and dimensional accuracy verification. After the verification is passed, its complete three-dimensional model data, material parameters and structural parameters are packaged into adaptive packaging form design data.

[0012] As a further aspect of the present invention, topological analysis and feature line extraction are performed on the surface mesh model, specifically as follows: The surface curvature of the surface mesh model is calculated, and mesh regions with curvature values ​​exceeding a preset threshold are identified and marked as high curvature feature regions. The feature line tracing algorithm is applied to grow from the boundary of the high curvature feature region along the direction of the greatest curvature change, and connects to form a feature line network that describes the product outline. The feature line network is simplified and smoothed, removing short branches with lengths less than a threshold and connections with abnormal angles to form clear product feature skeleton lines. Calculate the normal direction and radius of curvature at each point on the product feature skeleton line, and compare them with the orientation of the facets in the adjacent region to verify the geometric consistency of the feature skeleton line.

[0013] As a further aspect of the present invention, the invocation of a physics-based deformation response model, which calculates the local strain distribution of the packaging shape caused by the collision based on the local stiffness matrix and the deformation displacement field, specifically: Based on the material's physical properties, a constitutive relationship of local stress and strain at the contact point where a collision is detected is established for the packaging material. The contact force calculated by the collision detection algorithm is applied as a boundary condition to the local stress-strain constitutive relation to solve the local stress field at the moment of collision. Based on the local stress field and the local stiffness matrix, the nodal displacement of the packaging shape in the neighborhood of the contact point is calculated by the finite element method, that is, the deformation displacement field increment. The deformation displacement field increment is superimposed onto the current geometric coordinates of the packaging shape to update the surface mesh of the packaging shape; Based on the updated surface mesh node positions, the curvature and normal of the packaging surface are recalculated to complete the morphological response caused by a collision and output the local strain distribution data of the region.

[0014] As a further aspect of the present invention, in the virtual testing environment, environmental parameters are set based on real-time acquired scene data, and the target product is driven to perform motion simulation within the packaging scheme according to the principal component vector of the morphological change, specifically as follows: In the virtual testing environment, a world coordinate system consistent with the real environment is established, and the corresponding physical parameters of the packaging materials and product materials are set based on the real-time acquired ambient temperature and humidity data. Based on the real-time acquired transportation vibration spectrum data, a corresponding basic vibration excitation signal is generated and applied as an environmental load to the packaging-product assembly in the entire virtual test system. Based on the real-time acquired expected stacking pressure data, a uniformly distributed static pressure load is applied to the top of the packaging structure. Read the principal component vector of the morphological change and use it as a driving signal to control the movement mode and deformation sequence of the target product inside the packaging, thereby simulating the real morphological changes of the product during transportation or use. Under conditions of applied environmental loads and product motion, a dynamics solver for a virtual test environment is run to simulate the interaction between the packaging and the product over a set time period.

[0015] As a further aspect of the present invention, the optimal candidate solution is subjected to a final interference check and dimensional accuracy verification, specifically as follows: Import the 3D model of the optimal candidate solution and the dynamic 3D model data of the target product into a digital assembly environment; In a digital assembly environment, the product is simulated to move inside the packaging of the optimal candidate solution according to its complete morphological change sequence. A continuous collision detection algorithm is used to calculate the minimum gap between the product surface and the inner wall of the packaging in real time. Record the change curve of the minimum gap during the entire motion simulation process, and check whether the minimum gap is always greater than the preset safety gap threshold to complete the dynamic interference check; On the 3D model of the optimal candidate solution, all key mating dimensions and positioning feature dimensions are selected and compared with the equipment processing accuracy range of the packaging production control system. If all critical dimensions are within the equipment's machining accuracy range and the dynamic interference check is passed, then the dimensional accuracy verification is deemed qualified. If any condition is not met, the optimal candidate solution is returned to the parameter dynamic adjustment step for reprocessing.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are as follows: Based on product form data acquisition equipment, dynamic 3D model data and material physical property data of the target product are acquired. The current form characteristics of the target product are identified, and the dynamic 3D model data and current form characteristics are input into a packaging form initial generator to obtain multiple initial packaging form schemes. Compared with conventional technologies that only acquire static product size data and generate a single packaging scheme, dynamic 3D model data can accurately capture the dynamic changes in product form, and material physical property data provides the foundation for packaging adaptability. Multiple initial schemes break the limitations of a single scheme, enabling the initial packaging form to fit the actual form characteristics of the product. The adaptability flexibility of the packaging scheme is optimized, avoiding the problem of insufficient packaging-product fit caused by static data.

[0017] Based on the material and physical properties of the target product, a collision detection algorithm is used to simulate the morphological adaptability of multiple initial packaging form schemes, selecting a set of candidate packaging forms that dynamically adapt to the target product. Based on real-time product usage scenario data, the morphological stability of each scheme in the candidate packaging form set is evaluated. The parameters of the candidate packaging form set are dynamically adjusted according to the evaluation results, and the final selected adaptive packaging form design data is output according to preset control rules and sent to the packaging production control system. The collision detection algorithm accurately determines the degree of compatibility between the packaging and the product, and the selected candidate schemes are more closely aligned with the product's material characteristics. The integration of real-time usage scenario data makes the packaging stability assessment more aligned with actual application needs. Dynamic parameter adjustment allows the packaging form to flexibly adapt to product changes and usage scenarios. The design data is directly sent to the production control system, achieving seamless integration between packaging design and production, thus optimizing both the adaptability of the packaging form and the efficiency of production integration. Attached Figure Description

[0018] Figure 1 This is a flowchart of a dynamic control method for packaging design that adapts to product form, as described in this invention. Figure 2 A flowchart for acquiring product data and identifying morphological features; Figure 3 This is a flowchart illustrating the working process of a collision detection algorithm. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0021] See Figure 1 A dynamic control method for adaptive product form packaging design is proposed, with the following overall implementation scheme: Dynamic 3D model data and material physical property data of the target product are acquired using a product form data acquisition device, and the current form characteristics of the target product are identified. The dynamic 3D model data and the current form characteristics are input into a packaging form initial generator to generate multiple initial packaging form schemes. Based on the material physical property data, a collision detection algorithm is used to simulate the form adaptability of the multiple initial packaging form schemes, and a set of candidate packaging forms that dynamically adapt to the target product is selected. Based on real-time acquired product usage scenario data, the form stability of each scheme in the candidate packaging form set is evaluated, and an evaluation result is generated. The parameters of the candidate packaging form set are dynamically adjusted according to the evaluation result, and the final selected adaptive packaging form design data is output according to preset control rules. This design data is then sent to the packaging production control system.

[0022] In one embodiment of the present invention, see [reference] Figure 2Point cloud data of the target product in different poses is acquired using a 3D laser scanning device. The point cloud data is then denoised and registered to generate a surface mesh model of the target product. The surface mesh model's curvature is calculated, and mesh regions with curvature values ​​exceeding a preset threshold are identified and marked as high-curvature feature regions. A feature line tracing algorithm is applied, starting from the boundary of the high-curvature feature regions and growing along the direction of greatest curvature change, connecting to form a feature line network describing the product's contour. This feature line network is simplified and smoothed, removing short branches shorter than a threshold and connections with abnormal angles to form a clear product feature skeleton line. The normal vector direction and radius of curvature at each point on the product feature skeleton line are calculated and compared with the orientation of adjacent areas to verify the geometric consistency of the feature skeleton line. Through topological analysis and feature line extraction, key contour lines, curvature variation regions, and deformable parts of the target product surface are identified, constituting a static morphological feature set of the target product. The motion capture unit in the product form data acquisition device is activated to record the form change sequence of the target product under a preset motion mode. Keyframes are extracted from the form change sequence, and the deformation displacement field between adjacent keyframes is calculated. Principal component analysis is performed on the deformation displacement field to obtain the form change principal component vector describing the main pattern of form change of the target product. The form change principal component vector is fused with the static form feature set to generate the current form feature containing both static and dynamic features. Material samples of each component constituting the target product are obtained through material testing equipment, and the elastic modulus, Poisson's ratio, density, and surface friction coefficient of the material samples are measured. The parameters are integrated into material physical property data.

[0023] In the specific implementation, a deformable soft robot was used as the target product. Point cloud data of the deformable soft robot at 10 different positions was collected using a 3D laser scanning device. The point cloud data contained an average of 2,000,000 data points. The point cloud data was denoised and registered. The denoising process adopted the statistical outlier removal method, and the registration process adopted the iterative nearest point algorithm to generate a surface mesh model of the deformable soft robot. The surface mesh model is composed of 500,000 triangular facets. In some embodiments, surface curvature calculation is performed on the surface mesh model. The surface curvature calculation adopts a joint evaluation method of Gaussian curvature and average curvature to identify mesh regions with curvature values ​​exceeding a preset threshold. The preset threshold is set to 0.05 and marked as high curvature feature regions. The number of high curvature feature regions is 120. When the preset threshold is adjusted to 0.03, the number of high curvature feature regions increases to 180. A feature line tracing algorithm is applied to grow from the boundary of the high curvature feature regions along the direction of the greatest curvature change, connecting to form a feature line network describing the contour of the deformable soft robot. The feature line network initially contains 300 line segments.

[0024] Optionally, the feature line network is simplified and smoothed, removing short branches less than 0.5 mm in length and connections with anomalies exceeding 30 degrees, forming a clear feature skeleton line for the deformable soft robot. The simplified feature skeleton line contains 150 line segments. The normal vector direction and radius of curvature at each point on the feature skeleton line of the deformable soft robot are calculated and compared with the orientation of the facets in adjacent regions. The consistency comparison uses dot product operation to verify that the deviation of the normal vector direction is less than 5 degrees, verifying the geometric consistency of the feature skeleton line. Through topology analysis and feature line extraction, the key contour lines, curvature change regions, and deformable parts on the surface of the deformable soft robot are identified, constituting the static morphological feature set of the deformable soft robot. The static morphological feature set contains 20 key contour line segments, 15 curvature change region markers, and 5 deformable part identifiers.

[0025] In the specific implementation, the motion capture unit in the product form data acquisition device is activated to record the morphological change sequence of the deformable soft robot under preset motion modes. The preset motion modes include bending, twisting, and stretching. The morphological change sequence contains 1000 frames of data. Keyframes are extracted from the morphological change sequence based on an inter-frame deformation energy threshold. Fifty keyframes are extracted, and the deformation displacement field between adjacent keyframes is calculated. The deformation displacement field is represented as a three-dimensional vector field. Principal component analysis is performed on the deformation displacement field, organizing the deformation displacement field data into a matrix form. Let the deformation displacement field data matrix be... Its dimensions are ,in Indicates the number of keyframes. Representing the total number of displacement components, principal component analysis (PCA) obtains principal component vectors describing the main patterns of morphological changes in a deformable soft robot by solving for the eigenvectors of the covariance matrix. The first principal component vector explains 65% of the deformation variance, and the second principal component vector explains 20%. This can be understood as fusing the morphological change principal component vectors with the static morphological feature set. This fusion operation is achieved through vector concatenation and normalization, generating a current morphological feature that includes both static and dynamic features. The current morphological feature is a feature vector containing 100 dimensions.

[0026] Material samples of each component constituting the deformable soft robot are obtained using material testing equipment. These samples include the silicone body and internal sensor components. The elastic modulus, Poisson's ratio, density, and surface friction coefficient of the material samples are measured. The silicone body has an elastic modulus of 1.2 MPa, a Poisson's ratio of 0.49, a density of 1100 kg / m³, and a surface friction coefficient of 0.8. These parameters are integrated into material physical property data, which is stored in a structured table. In some embodiments, the scanning accuracy of the 3D laser scanning device is 0.1 mm, the sampling frequency of the motion capture unit is 100 Hz, and the material testing equipment uses a universal testing machine and a friction coefficient tester. Optionally, in the point cloud data denoising process, the number of neighborhood points removed from outliers is set to 20, and the standard deviation factor is set to 2.0. It can be understood that the keyframe extraction of the morphological change sequence and the inter-frame deformation energy threshold are calculated based on the sum of squared grid vertex displacements, with the threshold set to 0.1 mm².

[0027] In one embodiment of the present invention, see [reference] Figure 3 Based on the elastic modulus and Poisson's ratio in the material's physical property data, the local stiffness matrix of each part of the target product's surface is calculated, and an adaptive spatial partitioning tree for the product is constructed based on the local stiffness matrix. In the adaptive spatial partitioning tree, the potential collision area is predicted based on the current morphological characteristics of the target product and the inner surface data of the initial packaging morphology. Within the potential collision area, a continuous collision detection method is used to calculate the minimum distance between the product mesh and the inner wall mesh of the packaging in the next time step, and the tangential force between the contact points is calculated based on the surface friction coefficient in the material's physical property data. When a collision is detected, a physics-based deformation response model is invoked. At the contact point where a collision is detected, a local stress-strain constitutive relation of the packaging material at the contact point is established based on the material's physical property data. The contact force calculated by the collision detection algorithm is applied as a boundary condition to the local stress-strain constitutive relation to solve for the local stress field at the instant of collision. Based on the local stress field and the local stiffness matrix, the nodal displacement of the packaging morphology in the neighborhood of the contact point, i.e., the deformation displacement field increment, is calculated using the finite element method.

[0028] The deformation displacement field increment is superimposed onto the current geometric coordinates of the packaging form to update the surface mesh of the packaging form. Based on the updated surface mesh node positions, the curvature and normal of the packaging form surface are recalculated to complete the morphological response caused by a collision, and the local strain distribution data of the region is output. Based on the calculated local strain distribution, the geometry of the initial packaging form scheme is dynamically updated, and the collision detection and response process is iteratively executed until no new collisions are detected or the morphological changes tend to stabilize within the simulation time, thus completing the morphological adaptation simulation of the initial packaging form scheme.

[0029] In specific implementation, we continue to use deformable soft robots and their packaging design as an example scenario. Based on the material physical property data obtained from the deformable soft robot, the elastic modulus of the silicone body material is 1.2 MPa and the Poisson's ratio is 0.49. The local stiffness matrix of each part of the target product surface is calculated based on the elastic modulus and Poisson's ratio. The local stiffness matrix is ​​constructed based on a tetrahedral element linear elastic model with an element size of 2 mm. An adaptive spatial partitioning tree for the product is constructed based on the local stiffness matrices of all elements, with a depth of 8 layers. In some embodiments, within the adaptive spatial partitioning tree, based on the current morphological characteristics of the target product and the inner surface data of an initial packaging form scheme (which contains 15,000 mesh patches), potential collision areas are predicted through boundary box intersection tests, identifying three potential collision areas.

[0030] A continuous collision detection method is used. Within the potential collision area, the minimum distance between the deformable soft robot mesh and the inner wall mesh of the packaging is calculated in the next time step. The time step is set to 0.01 seconds, and the calculated minimum distance is 0.15 mm. Based on the surface friction coefficient of 0.8 in the material physical property data, the tangential force between the contact points is calculated using the Coulomb friction model. When a collision is detected, a physics-based deformation response model is invoked. At the contact point where a collision is detected, a local stress-strain constitutive relationship of the packaging material is established based on the material physical property data. The packaging material is assumed to be a linear elastic foam with an elastic modulus of 0.5 MPa and a Poisson's ratio of 0.3. The local stress-strain constitutive relationship is as follows: ; in: Represents the Cauchy stress tensor. This represents the fourth-order elastic tensor. This represents the infinitesimal strain tensor. It can be understood that the contact force calculated by the collision detection algorithm, with a value of 5 Newtons, is applied as a boundary condition to the local stress-strain constitutive relation. The local stress field at the instant of collision is obtained by solving the equilibrium equations. Based on the local stress field and the local stiffness matrix of the deformable soft robot, the displacement of the packaging shape at 15 nodes in the neighborhood of the contact point is calculated using the finite element method, i.e., the deformation displacement field increment. The maximum value of the deformation displacement field increment is 0.3 mm. The deformation displacement field increment is superimposed onto the current geometric coordinates of the packaging shape, updating the surface mesh of the packaging shape. Optionally, based on the updated surface mesh node positions, the curvature and normal of the packaging shape surface in the region around the contact point are recalculated. The curvature calculation uses the moving least squares method to complete the shape response caused by a collision and output the local strain distribution data of this region, which includes the strain values ​​at 5 Gaussian integration points.

[0031] The deformation response model calculates the local strain distribution of the packaging shape caused by collision based on the local stiffness matrix and deformation displacement field. According to the calculated local strain distribution, the geometry of the initial packaging shape is dynamically updated, the coordinates of 20 nodes around the contact area are corrected, and the collision detection and response process is iteratively executed. The number of iterations is set to 10, until no new collision is detected within 0.1 seconds of simulation time or the maximum displacement change rate of each node of the packaging shape is less than 0.01 mm / s, and the shape change tends to stabilize, completing the shape adaptation simulation of the initial packaging shape. It can be understood that the above shape adaptation simulation process is executed in parallel for multiple initial packaging shape schemes to select a candidate packaging shape set that dynamically adapts to the deformable soft robot. The candidate packaging shape set contains 8 schemes. In some embodiments, the packaging material model can also adopt a hyperelastic model, in which case the constitutive relation is nonlinear.

[0032] In one embodiment of the present invention, scene data of the product's usage scenario is acquired in real time through an environmental sensing device. This scene data includes ambient temperature, humidity, transportation vibration spectrum, and expected stacking pressure. A virtual testing environment is constructed, importing each scheme from the candidate packaging form set and the dynamic 3D model data of the target product into the virtual testing environment. In the virtual testing environment, a world coordinate system consistent with the real environment is established, and the corresponding physical parameters of the packaging material and product material are set based on the real-time acquired ambient temperature and humidity data. Based on the real-time acquired transportation vibration spectrum data, a corresponding basic vibration excitation signal is generated and applied as an environmental load to the packaging-product assembly in the entire virtual testing system. Based on the real-time acquired expected stacking pressure data, a uniformly distributed static pressure load is applied to the top of the packaging form. The principal component vector of the morphological change is read and used as a driving signal to control the motion mode and deformation sequence of the target product inside the packaging, simulating the real morphological changes of the product during transportation or use. Under the conditions of applied environmental load and product motion, the dynamic solver of the virtual testing environment is run to simulate the interaction process between the packaging and the product within a set time period. During the motion simulation, the average contact pressure between the inner wall of the packaging design and the surface of the target product, the uniformity coefficient of the contact pressure distribution, and the volume change rate of the packaging itself are monitored and recorded in real time. After the simulation, based on the recorded average contact pressure, uniformity coefficient, and volume change rate, the comprehensive morphological stability score of each candidate packaging design under the product usage scenario is calculated, and an evaluation result containing the scores of each design is generated.

[0033] In the specific implementation, we continue to use the deformable soft robot and its candidate packaging form set as an example scenario. Real-time scenario data of the product's usage scenario is acquired through environmental sensing devices, including temperature and humidity sensors and a three-axis accelerometer. The acquired scenario data includes an ambient temperature of 25 degrees Celsius, an ambient humidity of 60%, a transport vibration spectrum, and an expected stacking pressure. The dominant frequency of the transport vibration spectrum is 5 Hz to 200 Hz, and the expected stacking pressure is 500 Pascals. A virtual testing environment is constructed based on a physics engine. Each scheme in the candidate packaging form set and the dynamic 3D model data of the deformable soft robot are imported into the virtual testing environment. The candidate packaging form set contains 8 schemes. In the virtual testing environment, a world coordinate system consistent with the real environment is established. The world coordinate system adopts a right-handed Cartesian coordinate system. Based on the real-time acquired ambient temperature of 25 degrees Celsius and ambient humidity of 60%, the corresponding physical parameters of the packaging material and the product material are set. The elastic modulus of the packaging foam is corrected to 0.48 MPa based on temperature and humidity.

[0034] In some embodiments, based on real-time acquired transportation vibration spectrum data, which includes three main peaks at 5 Hz, 50 Hz, and 150 Hz, a corresponding basic vibration excitation signal is generated. This basic vibration excitation signal is synthesized by superimposing multiple sine waves of different frequencies and amplitudes, and is applied as an environmental load to the packaging-product assembly in the entire virtual testing system. Optionally, based on real-time acquired expected stacking pressure data, a uniformly distributed static pressure load of 500 Pascals is applied to the top of the packaging. The principal component vector of the deformable soft robot's morphological changes is read and used as a driving signal. This principal component vector includes a first principal component and a second principal component, controlling the motion mode and deformation sequence of the deformable soft robot inside the packaging. This simulates the bending and torsional morphological changes of the deformable soft robot during transportation, with a motion simulation duration of 10 seconds. Under the applied environmental load and product motion conditions, the dynamics solver of the virtual testing environment is run. The dynamics solver uses an explicit time integration method with a time step of 0.001 seconds to simulate the interaction between the packaging and the product within 10 seconds.

[0035] In practice, during motion simulation, the average contact pressure between the inner wall of the packaging form and the surface of the deformable soft robot, the uniformity coefficient of the contact pressure distribution, and the volume change rate of the packaging form itself are monitored and recorded in real time. The average contact pressure is recorded every 0.1 seconds, the uniformity coefficient is calculated based on the standard deviation of the contact pressure, and the volume change rate is calculated based on the volume of the packaging form's surrounding box. Refer to Table 1, which shows some of the monitoring records for candidate packaging form scheme A1 during the simulation.

[0036] Table 1. Monitoring Records of Scheme A1 during the Simulation Process: After the simulation, based on the recorded average contact pressure, uniformity coefficient, and volume change rate, the comprehensive morphological stability score of each candidate packaging form scheme under the product usage scenario was calculated. The calculation method is as follows: ; in: This indicates the overall morphological stability score. This represents the weighting coefficient for the i-th monitoring time point. This represents the average contact pressure at the i-th monitoring time point. This represents the uniformity coefficient at the i-th monitoring time point. This represents the rate of volume change at the i-th monitoring time point. Indicates will A function that maps to individual ratings. This represents the total number of monitoring time points and generates an evaluation result containing scores for each scheme, stored in list form. In some embodiments, the sampling frequency of the average contact pressure can be set to 1000 Hz. Optionally, the uniformity coefficient is calculated using the coefficient of variation method. The accuracy of the kinetic solver can be controlled by adjusting the time step, which can be set to 0.0005 seconds.

[0037] In one embodiment of the present invention, the evaluation results are analyzed to extract the comprehensive morphological stability score of each scheme in the candidate packaging form set, and packaging form schemes with scores below a preset threshold or score fluctuations exceeding the allowable range are identified as needing adjustment. For each packaging form scheme to be adjusted, strain distribution data of the key deformation areas that lead to poor morphological stability are retrieved from the morphological adaptability simulation process. Simultaneously, the product contact pressure and vibration response spectrum corresponding to the key deformation areas are extracted from the motion simulation records of the virtual test environment. The strain distribution data, product contact pressure, and vibration response spectrum are input into a morphological parameter optimizer, which, based on the mechanical constitutive relationship of the packaging material, reverse-engineers the required adjustment amount and direction of the local geometric parameters of the packaging form. Based on the adjustment amount and direction, geometric correction instructions are generated for the schemes to be adjusted in the candidate packaging form set, and the data of the candidate packaging form set is updated using the geometric correction instructions.

[0038] In specific implementation, we continue to use the deformable soft robot and its candidate packaging form set as an example scenario. We analyze the evaluation results, which are a list containing the comprehensive morphological stability scores of eight candidate schemes. We extract the comprehensive morphological stability score for each scheme in the candidate packaging form set, with the score ranging from 0 to 100. We identify packaging form schemes with scores below a preset threshold or whose score fluctuations exceed the allowable range, requiring adjustment. The preset threshold is set at 70 points, and the allowable score fluctuation range is ±5 points. In some embodiments, for each identified packaging form scheme requiring adjustment, such as schemes B3, C1, and D2, we retrieve strain distribution data of the key deformation areas that lead to poor morphological stability from the morphological adaptation simulation process. Key deformation areas are defined by positions with strain values ​​greater than 0.1. Simultaneously, we extract the product contact pressure and vibration response spectrum corresponding to the key deformation areas from the motion simulation records of the virtual test environment. The product contact pressure is time-history data, and the vibration response spectrum is power spectral density data. Refer to Table 2, which shows some key data extracted from the three schemes requiring adjustment mentioned above.

[0039] Table 2: Key Deformation Area Data Extraction Table for Packaging Shape Schemes to be Adjusted It is understandable that strain distribution data, product contact pressure, and vibration response spectrum are input into a morphological parameter optimizer. The morphological parameter optimizer employs a gradient-based optimization algorithm. Based on the mechanical constitutive relationship of the packaging material (assuming it is linearly elastic foam with a known stress-strain relationship), the optimizer reverse-engineers the required adjustment amount and direction for the local geometric parameters of the packaging shape. These local geometric parameters include the radius of curvature and wall thickness of key areas. For region 1 of scheme B3, the morphological parameter optimizer calculates an adjustment amount of increasing the radius of curvature by 2 mm, with the adjustment direction being to increase the curvature. In some embodiments, the core operation of the morphological parameter optimizer involves an objective function: ; in: Represents a vector of specific geometric parameters The optimization target value, These are weighting coefficients. This represents the strain energy term calculated from strain distribution data. This represents the pressure uniformity term calculated from product contact pressure data. This represents the resonance risk term calculated from vibration response spectrum data. The optimization process minimizes... To find the optimal parameter adjustment amount Optionally, based on the calculated adjustment amount and direction, for example, increasing the wall thickness of region 1 in scheme C1 by 1.5 mm and increasing the radius of curvature of region 1 in scheme D2 by 3 mm while increasing the wall thickness by 1.0 mm, geometric correction instructions for the schemes to be adjusted in the candidate packaging shape set are generated. These geometric correction instructions exist in the form of a parametric script. The data of the candidate packaging shape set is updated using the geometric correction instructions, specifically by modifying the coordinates and connectivity of corresponding vertices in the 3D mesh models of schemes B3, C1, and D2, thereby updating the candidate packaging shape set. It is understood that the shape parameter optimizer can also be implemented using a genetic algorithm or a particle swarm optimization algorithm. In some embodiments, the weight coefficients... It can be configured according to different design focuses, such as increasing the size when focusing on shock absorption. The value of .

[0040] In one embodiment of the present invention, the preset control rules include a form adaptation priority rule, a material saving rule, and a production feasibility rule. For the candidate packaging form set after dynamic parameter adjustment, the adaptation score under the form adaptation priority rule, the material utilization score under the material saving rule, and the process complexity score under the production feasibility rule are calculated for each scheme. For each candidate packaging form scheme, its adaptation score, material utilization score, and process complexity score are weighted and fused to calculate the comprehensive control score of the candidate packaging form scheme. The scheme with the highest comprehensive control score in the candidate packaging form set is selected and marked as the optimal candidate scheme. The three-dimensional model of the optimal candidate scheme and the dynamic three-dimensional model data of the target product are imported into a digital assembly environment. In the digital assembly environment, the product is simulated to move inside the packaging of the optimal candidate scheme according to its complete form change sequence. A continuous collision detection algorithm is used to calculate the minimum gap between the product surface and the inner wall of the packaging in real time. The change curve of the minimum gap during the entire motion simulation is recorded, and it is checked whether the minimum gap is always greater than a preset safety gap threshold to complete the dynamic interference check.

[0041] On the 3D model of the optimal candidate solution, all key mating dimensions and positioning feature dimensions are selected and compared with the machining accuracy range of the packaging production control system. If all key dimensions are within the machining accuracy range and the dynamic interference check passes, the dimensional accuracy verification is deemed qualified. The optimal candidate solution undergoes a final interference check and dimensional accuracy verification. After passing the verification, its complete 3D model data, material parameters, and structural parameters are encapsulated into adaptive packaging form design data.

[0042] In specific implementation, we continue to use the candidate packaging form set of deformable soft robots after dynamic parameter adjustment as an example scenario. The preset control rules include the form adaptation priority rule, the material saving rule, and the production feasibility rule. For the candidate packaging form set after dynamic parameter adjustment, which contains 8 schemes, we calculate the adaptation score under the form adaptation priority rule, the material utilization score under the material saving rule, and the process complexity score under the production feasibility rule for each scheme. The adaptation score under the form adaptation priority rule is calculated based on the average contact pressure and uniformity coefficient in the virtual test; the material utilization score under the material saving rule is calculated based on the ratio of the net volume of the packaging form to the minimum bounding box volume; and the process complexity score under the production feasibility rule is calculated based on the average Gaussian curvature and the number of chamfers of the packaging form's surface. In some embodiments, for each candidate packaging form scheme, its adaptation score, material utilization score, and process complexity score are weighted and fused to calculate the comprehensive control score of the candidate packaging form scheme. The calculation method is as follows: ; in: This indicates the overall regulatory score. This represents the normalized fitness score. This represents the normalized material utilization score. This represents the normalized process complexity score. These represent the corresponding weight coefficients, and From the set of candidate packaging forms, the scheme with the highest comprehensive regulation score is selected and marked as the optimal candidate scheme. For example, the comprehensive regulation score of scheme E4 is 92.5 points, which is higher than other schemes.

[0043] In a specific implementation, a final interference check and dimensional accuracy verification are performed on the optimal candidate solution. The 3D model of the optimal candidate solution E4 and the dynamic 3D model data of the deformable soft robot are imported into a digital assembly environment. In the digital assembly environment, simulate the movement of the deformable soft robot inside the package of the optimal candidate solution E4 according to its complete morphological change sequence. Adopt a continuous collision detection algorithm to calculate the minimum clearance between the surface of the deformable soft robot and the inner wall of the package in real time. The calculation accuracy of the minimum clearance is 0.01 mm. Record the change curve of the minimum clearance during the entire motion simulation process. The change curve of the minimum clearance contains 1000 data points, and check whether the minimum clearance is always greater than the preset safety clearance threshold. The safety clearance threshold is set to 1.0 mm to complete the dynamic interference check. On the 3D model of the optimal candidate solution E4, select all key mating dimensions and positioning feature dimensions. The key mating dimensions include 8 inner cavity diameters, and the positioning feature dimensions include the diameters and heights of 5 positioning posts. Compare them with the equipment processing accuracy range of the packaging production control system. The equipment processing accuracy range is ±0.2 mm. If all key dimensions are within the equipment processing accuracy range and the dynamic interference check passes, it is determined that the dimensional accuracy verification is qualified. It can be understood that the setting of the safety clearance threshold in the dynamic interference check is based on the surface material of the product and the expected vibration amplitude. Optionally, if any one is not satisfied, for example, a certain inner cavity diameter exceeds the processing accuracy range by 0.1 mm, the optimal candidate solution E4 is returned to the parameter dynamic adjustment step for reprocessing. In some embodiments, after the dimensional accuracy verification is qualified, its complete 3D model data, material parameters, and structure parameters are encapsulated as adaptive packaging form design data. The adaptive packaging form design data is encapsulated using the standard STEP format and XML parameter files. It can be understood that the motion simulation duration in the digital assembly environment can cover a complete working cycle of the product.

[0044] Above, it is only a preferred embodiment of the present invention, and it does not limit the present invention in other forms. Any person skilled in this field may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments according to the technical essence of the present invention still belong to the protection scope of the technical solution of the present invention.

Claims

1. A method for dynamically adjusting packaging design to adapt to product form, characterized in that, Includes the following steps: Based on the product form data acquisition equipment, dynamic three-dimensional model data and material physical property data of the target product are acquired, and the current form characteristics of the target product are identified; The dynamic 3D model data and the current morphological features are input into the packaging morphology initial generator to obtain multiple initial packaging morphology schemes; Based on the material physical property data, a collision detection algorithm is used to simulate the morphological adaptability of multiple initial packaging form schemes, and a set of candidate packaging forms that are dynamically adapted to the target product is selected. Based on real-time acquired product usage scenario data, the morphological stability of each option in the candidate packaging form set is evaluated, and evaluation results are generated. The parameters of the candidate packaging form set are dynamically adjusted based on the evaluation results, and the final selected adaptive packaging form design data is output according to the preset control rules. The adaptive packaging form design data is then sent to the packaging production control system.

2. The method for dynamic control of packaging design for adaptive product form according to claim 1, characterized in that, The product form data acquisition device acquires dynamic 3D model data and material physical property data of the target product, and identifies the current form characteristics of the target product, specifically: Point cloud data of the target product in different poses are collected by a 3D laser scanning device. The point cloud data is then denoised and registered to generate a surface mesh model of the target product. Topological analysis and feature line extraction are performed on the surface mesh model to identify the key contour lines, curvature variation areas and deformable parts of the target product surface, thus forming a static morphological feature set of the target product. The motion capture unit in the product form data acquisition device is activated to record the form change sequence of the target product in a preset motion mode. Key frames are extracted from the form change sequence, and the deformation displacement field between adjacent key frames is calculated. Principal component analysis is performed on the deformation displacement field to obtain the principal component vector of morphological change, which describes the main mode of morphological change of the target product. The principal component vector of morphological change is fused with the static morphological feature set to generate the current morphological feature containing both static and dynamic features. Material samples of each component constituting the target product are obtained using material testing equipment. The elastic modulus, Poisson's ratio, density, and surface friction coefficient of the material samples are measured, and the parameters are integrated into material physical property data.

3. The method for dynamic control of packaging design for adaptive product form according to claim 2, characterized in that, The specific working process of the collision detection algorithm is as follows: Based on the elastic modulus and Poisson's ratio in the material physical property data, calculate the local stiffness matrix of each part of the target product surface, and construct an adaptive spatial partitioning tree of the product based on the local stiffness matrix; In the adaptive spatial partitioning tree, the potential collision area where contact may occur is predicted based on the current morphological characteristics of the target product and the inner surface data of the initial packaging morphological scheme. Within the potential collision area, a continuous collision detection method is used to calculate the minimum distance between the product mesh and the inner wall mesh of the packaging in the next time step, and the tangential force between the contact points is calculated based on the surface friction coefficient in the material physical property data. When a collision is detected, a physics-based deformation response model is invoked. The deformation response model calculates the local strain distribution of the packaging shape caused by the collision based on the local stiffness matrix and the deformation displacement field. Based on the calculated local strain distribution, the geometry of the initial packaging form scheme is dynamically updated, and the collision detection and response process is iteratively executed until no new collisions are detected or the form changes tend to stabilize within the simulation time, thus completing the form adaptation simulation of the initial packaging form scheme.

4. The method for dynamic control of packaging design for adaptive product form according to claim 2, characterized in that, Based on real-time acquired product usage scenario data, the morphological stability of each option in the candidate packaging form set is evaluated, and evaluation results are generated, specifically: The system acquires real-time scenario data of the product's usage scenarios through environmental sensing devices. The scenario data includes ambient temperature, humidity, transportation vibration spectrum, and expected stacking pressure. Build a virtual testing environment and import each solution in the candidate packaging form set and the dynamic 3D model data of the target product into the virtual testing environment; In the virtual testing environment, environmental parameters are set based on real-time acquired scene data, and the target product is driven to perform motion simulation within the packaging scheme according to the principal component vector of the morphological change. During the motion simulation, the average contact pressure between the inner wall of the packaging form and the surface of the target product, the uniformity coefficient of the contact pressure distribution, and the volume change rate of the packaging form itself are monitored and recorded in real time. After the simulation, based on the recorded average contact pressure, uniformity coefficient and volume change rate, the comprehensive morphological stability score of each candidate packaging form scheme under the product usage scenario is calculated, and an evaluation result containing the scores of each scheme is generated.

5. The method for dynamic control of packaging design for adaptive product form according to claim 4, characterized in that, The parameters for dynamically adjusting the candidate packaging form set based on the evaluation results are as follows: The evaluation results are analyzed, the comprehensive morphological stability score of each scheme in the candidate packaging form set is extracted, and the packaging form schemes that need to be adjusted are identified if the score is lower than the preset threshold or the score fluctuation exceeds the allowable range. For each packaging form scheme to be adjusted, strain distribution data of the key deformation areas that lead to poor form stability are retrieved from the form adaptability simulation process. Meanwhile, the product contact pressure and vibration response spectrum corresponding to the key deformation area were extracted from the motion simulation records of the virtual test environment. The strain distribution data, product contact pressure and vibration response spectrum are input into a morphological parameter optimizer. The morphological parameter optimizer, based on the mechanical constitutive relationship of the packaging material, reversely deduce the required adjustment amount and direction of the local geometric parameters of the packaging shape. Based on the adjustment amount and direction, a geometric correction instruction is generated for the proposed adjustment schemes in the candidate packaging form set, and the data of the candidate packaging form set is updated using the geometric correction instruction.

6. The method for dynamic control of packaging design for adaptive product form according to claim 5, characterized in that, The step of outputting the final selected adaptive packaging form design data according to the preset control rules is as follows: The preset control rules include the form adaptation priority rule, the material saving rule, and the production feasibility rule; For the candidate packaging form set after dynamic parameter adjustment, calculate the adaptation score of each scheme under the form adaptation priority rule, the material utilization score under the material saving rule, and the process complexity score under the production feasibility rule. For each candidate packaging form scheme, its adaptability score, material utilization score and process complexity score are weighted and integrated to calculate the comprehensive control score of the candidate packaging form scheme. From the pool of candidate packaging forms, the scheme with the highest comprehensive regulation score is selected and marked as the optimal candidate scheme; The optimal candidate solution is subjected to a final interference check and dimensional accuracy verification. After the verification is passed, its complete three-dimensional model data, material parameters and structural parameters are packaged into adaptive packaging form design data.

7. The method for dynamic control of packaging design for adaptive product form according to claim 2, characterized in that, The surface mesh model is subjected to topological analysis and feature line extraction, specifically as follows: The surface curvature of the surface mesh model is calculated, and mesh regions with curvature values ​​exceeding a preset threshold are identified and marked as high curvature feature regions. The feature line tracing algorithm is applied to grow from the boundary of the high curvature feature region along the direction of the greatest curvature change, and connects to form a feature line network that describes the product outline. The feature line network is simplified and smoothed, removing short branches with lengths less than a threshold and connections with abnormal angles to form clear product feature skeleton lines. Calculate the normal direction and radius of curvature at each point on the product feature skeleton line, and compare them with the orientation of the facets in the adjacent region to verify the geometric consistency of the feature skeleton line.

8. The method for dynamic control of packaging design for adaptive product form according to claim 3, characterized in that, The invocation of the physics-based deformation response model, which calculates the local strain distribution of the packaging shape caused by the collision based on the local stiffness matrix and the deformation displacement field, specifically: Based on the material's physical properties, a constitutive relationship of local stress and strain at the contact point where a collision is detected is established for the packaging material. The contact force calculated by the collision detection algorithm is applied as a boundary condition to the local stress-strain constitutive relation to solve the local stress field at the moment of collision. Based on the local stress field and the local stiffness matrix, the nodal displacement of the packaging shape in the neighborhood of the contact point is calculated by the finite element method, that is, the deformation displacement field increment. The deformation displacement field increment is superimposed onto the current geometric coordinates of the packaging shape to update the surface mesh of the packaging shape; Based on the updated surface mesh node positions, the curvature and normal of the packaging surface are recalculated to complete the morphological response caused by a collision and output the local strain distribution data of the region.

9. The method for dynamic control of packaging design for adaptive product form according to claim 4, characterized in that, In the virtual testing environment, environmental parameters are set based on real-time acquired scene data, and the target product is driven to simulate motion within the packaging scheme according to the principal component vector of the morphological change. Specifically: In the virtual testing environment, a world coordinate system consistent with the real environment is established, and the corresponding physical parameters of the packaging materials and product materials are set based on the real-time acquired ambient temperature and humidity data. Based on the real-time acquired transportation vibration spectrum data, a corresponding basic vibration excitation signal is generated and applied as an environmental load to the packaging-product assembly in the entire virtual test system. Based on the real-time acquired expected stacking pressure data, a uniformly distributed static pressure load is applied to the top of the packaging structure. Read the principal component vector of the morphological change and use it as a driving signal to control the movement mode and deformation sequence of the target product inside the packaging, thereby simulating the real morphological changes of the product during transportation or use. Under conditions of applied environmental loads and product motion, a dynamics solver for a virtual test environment is run to simulate the interaction between the packaging and the product over a set time period.

10. The method for dynamic control of packaging design for adaptive product form according to claim 6, characterized in that, The optimal candidate solution undergoes a final interference check and dimensional accuracy verification, specifically as follows: Import the 3D model of the optimal candidate solution and the dynamic 3D model data of the target product into a digital assembly environment; In a digital assembly environment, the product is simulated to move inside the packaging of the optimal candidate solution according to its complete morphological change sequence. A continuous collision detection algorithm is used to calculate the minimum gap between the product surface and the inner wall of the packaging in real time. Record the change curve of the minimum gap during the entire motion simulation process, and check whether the minimum gap is always greater than the preset safety gap threshold to complete the dynamic interference check; On the 3D model of the optimal candidate solution, all key mating dimensions and positioning feature dimensions are selected and compared with the equipment processing accuracy range of the packaging production control system. If all critical dimensions are within the equipment's machining accuracy range and the dynamic interference check is passed, then the dimensional accuracy verification is deemed qualified. If any condition is not met, the optimal candidate solution is returned to the parameter dynamic adjustment step for reprocessing.

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