A folding table adaptive design method and system based on stability evaluation

Abstract

This invention provides an adaptive design method and system for folding tables based on stability assessment, belonging to the field of furniture design technology. The method includes: physical principle-driven parametric design, automatically generating initial three-dimensional coordinates of the table legs based on input geometric parameters and physical constraints, and performing a pre-assessment of design quality; multi-dimensional quantitative stability assessment, constructing a comprehensive geometric and mechanical assessment model; robust hierarchical automatic optimization, employing a three-layer cascaded optimization strategy—direct optimization, multi-starting-point parallel optimization, and heuristic rule improvement—to iteratively adjust the spatial coordinates of the table legs and improve stability; and multi-view visualization analysis, automatically generating multi-dimensional charts such as support point distribution maps, height distribution maps, three-dimensional structure diagrams, and optimization comparison diagrams, providing a visual interpretation of design features, stability indicators, and optimization effects, and outputting a comprehensive report. This invention can provide key technical support for intelligent furniture manufacturing and personalized customization.

Description

Technical Field

[0001] This invention relates to the field of furniture design technology, specifically to an adaptive design method and system for folding tables based on stability assessment. Background Technology

[0002] As a type of furniture that combines practicality and space adaptability, folding tables need to be designed to ensure structural stability and safety while achieving convenient storage and flexible unfolding. However, existing folding table products and their design methods still have limitations in the following aspects:

[0003] First, regarding morphological adaptability and personalization, most products adopt fixed desktop shapes (such as circles or rectangles) and pre-set support structures, making it difficult to flexibly adjust according to diverse usage scenarios, spatial conditions, or user preferences, thus limiting their market applicability. Second, regarding the balance between structural performance and material economy, there is a significant contradiction between stability and material utilization. Existing design methods rely heavily on experience-based judgment and lack scientific tools for quantitatively weighing multiple constraints, making it difficult to obtain a globally optimal design solution. Third, regarding design process and efficiency, traditional methods heavily rely on manual experience and trial-and-error adjustments, resulting in long design cycles, cumbersome parameter adjustments, and poor reproducibility of results. Furthermore, there is a lack of an objective, systematic, and quantitative evaluation system for structural stability, making it difficult to scientifically compare and decide between different solutions. In addition, regarding multi-constraint design optimization, facing multiple constraints such as size, height, stability, and manufacturing processes, traditional methods often struggle to achieve globally automated optimization, resulting in limited optimization capabilities and failing to fully realize the structural performance potential of materials. Finally, in terms of design expression and verification, there is a general lack of intuitive and dynamic visualization simulation and analysis support, which affects the efficiency of form evaluation, motion coordination analysis and scheme communication during the design process.

[0004] Although existing research has involved geometric modeling or parameter calculation of folding tables, current technologies mostly handle these steps in isolation. Therefore, there is currently a lack of an efficient and reliable adaptive design system that can automatically generate optimized solutions from user input. Summary of the Invention

[0005] This invention provides an adaptive design method and system for folding tables based on stability assessment to solve the aforementioned problems. The adaptive design method and system for folding tables based on stability assessment provided by this invention offer important technical support for the structural stability design and parameter optimization of folding furniture. It integrates parametric modeling, multi-dimensional quantitative assessment, and hierarchical optimization strategies, combined with a comprehensive stability index system for collaborative optimization. Through robust multi-strategy fusion, it enhances design stability and material utilization, providing a scientific basis for furniture design and production, assisting designers in making more efficient and reliable design decisions, and ultimately improving the stability, safety, and economy of folding table products. This provides key technical support for intelligent furniture manufacturing and personalized customization.

[0006] This invention provides an adaptive design method for folding tables based on stability assessment, comprising the following steps:

[0007] S1. Receive the basic design parameters input, construct a parameter system including the size of the wooden board, the target height, the diameter of the tabletop, and physical constraints, and complete the generation and standardization of the initial table leg spatial coordinates through the hinge point convergence model and the length gradient algorithm.

[0008] S2. Starting with the initial design scheme generated by physical principles, a three-level cascaded optimization system of "direct global optimization, multi-starting point parallel optimization, and heuristic rule improvement" is constructed to optimize and coordinate the dynamic stability of the table leg coordinates in multiple dimensions, and evaluate the improvement contribution of design variables based on the optimization history.

[0009] S3. A comprehensive stability scoring system is constructed using four types of indicators: support area, center of gravity height, overturning angle, and safety factor. The design scheme is systematically evaluated through weighted comprehensive scoring and stability level classification.

[0010] S4 integrates multi-dimensional visualization charts such as support point distribution map, height distribution map, 3D structure map and optimization comparison map to provide a visual interpretation of design features, stability performance and optimization effect. Through comprehensive scoring and stability level, the design quality is accurately located and the correlation mechanism between structural parameters and stability indicators is verified.

[0011] Further, step S1 includes:

[0012] Receive basic design parameters for the folding table input by the user, including the length of the wooden board, the width of the wooden board, the target height, and the diameter of the tabletop;

[0013] A parameter system is constructed for generating the initial scheme, which includes multi-dimensional design parameters such as the number of table leg pairs, the width of the wooden strips, the folding angle, the angle compression factor, and the length gradient factor.

[0014] The parameter system is parametrically designed, and the process includes: calculating the hinge point distribution coordinates based on the folding angle and the desktop radius, calculating the reference length of the table legs based on the target height, calculating the actual length of each table leg based on the gradient factor, and calculating the three-dimensional coordinates of the table leg endpoints based on the geometric projection relationship.

[0015] Generate an initial table leg coordinate matrix and call the stability assessment module for pre-assessment, outputting key indicators such as support area, center of gravity height, overturning angle, and safety factor.

[0016] Further, step S2 specifically includes: receiving the coordinate matrix of the table legs, extracting the set of projection points on the ground plane, and calculating the support area using the convex hull algorithm; if the convex hull calculation fails, the area of ​​the boundary rectangle is used as a substitute; using the support area to evaluate the structural support range; using the center of gravity height to evaluate the overall stability; using the overturning angle to evaluate the overturning resistance; using the safety factor to evaluate the design margin; according to the preset piecewise scoring function, scoring the support area, center of gravity height, overturning angle, and safety factor respectively, and performing weighted summation according to weights to obtain a comprehensive stability score, and dividing it into five stability levels: "Excellent", "Good", "Average", "Needs Improvement", and "Poor" according to preset thresholds, to achieve a systematic graded evaluation of design quality.

[0017] Furthermore, the robust hierarchical automatic optimization described in step S3 includes starting with the initial design scheme and its comprehensive score, and sequentially executing a three-layer cascade optimization strategy, namely differential evolution algorithm, multi-starting point parallel optimization algorithm, and heuristic rule improvement algorithm, to iteratively adjust the spatial coordinates of the table legs, and taking a significant improvement in stability score as the optimization goal, automatically making decisions and outputting the optimal design scheme and optimization report;

[0018] Furthermore, the visualization charts in step S4 should at least include a support point distribution chart, a height distribution chart, a 3D structure chart, and an indicator comparison. Figure 4 Types.

[0019] Furthermore, this invention also proposes an adaptive design system for folding tables based on stability assessment, comprising:

[0020] The physical principle-driven parametric design module is configured to perform the following process: receive input basic design parameters, automatically generate an initial three-dimensional coordinate scheme for the table legs with reasonable structure and excellent stability based on geometric and mechanical principles through parametric modeling, pre-evaluate the initial scheme, and output key stability indicators;

[0021] The multi-dimensional quantitative stability assessment module is configured to perform the following process: for the input table leg coordinate scheme, comprehensively calculate the support area, center of gravity height, stability margin and overturning angle, construct a comprehensive assessment model covering geometric and mechanical indicators, and output the quantitative stability score and grade of the scheme through a weighted scoring and grading system.

[0022] The robust hierarchical automatic optimization module is configured to perform the following process: starting from the initial scheme, it sequentially adopts a three-layer strategy of global search based on differential evolution algorithm, parallel optimization at multiple starting points, and improvement based on heuristic rules to iteratively adjust the spatial coordinates of the table legs, and takes a significant improvement in stability score as the optimization goal, automatically makes decisions and outputs the optimal design scheme and optimization report;

[0023] The multi-view visualization analysis module is configured to perform the following processes: automatically generate charts such as support point distribution map, height distribution map, 3D structure map, evaluation result map and optimization comparison map, intuitively display the design geometric features, performance indicators and optimization effects, and output a structured design report.

[0024] Furthermore, the present invention also proposes a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the present invention.

[0025] Meanwhile, the present invention also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed, it implements the steps of the method described in the present invention.

[0026] Finally, the present invention provides a computer program product comprising a computer program / instructions that, when executed by a processor, implement the steps of the method described in the present invention.

[0027] Compared with the prior art, the beneficial effects of the present invention are:

[0028] 1. This invention, through a parameter-driven physical principle designer, can automatically generate a structurally sound initial 3D design scheme within milliseconds based on user-inputted basic dimensional parameters (such as the length and width of the wooden board, the target height, and the diameter of the tabletop). This changes the traditional design model that relies on human experience, trial and error, and tedious manual calculations, shortening the design cycle from hours or even days to near real-time, significantly improving design efficiency, and ensuring the repeatability and consistency of the design process.

[0029] 2. The multi-dimensional quantitative stability assessment model constructed in this invention, through precise calculation of support area, estimation of center of gravity height, analysis of stability margin and overturning angle, and the introduction of a safety factor, forms a complete geometric-mechanical comprehensive evaluation index system. Based on a weighted scoring function and grading rules, it provides an objective quantitative score and clear grade of 0-100 for the design scheme. This solves the problem of difficulty in quantifying stability and reliance on subjective judgment in traditional design, providing a scientific and accurate decision-making basis for the selection and comparison of design schemes.

[0030] 3. The three-layer cascaded robust optimization strategy employed in this invention sequentially utilizes a differential evolution algorithm for global search, multi-starting-point parallel optimization to avoid local optima, and heuristic rules for baseline improvement. This strategy ensures that the system can find an optimization scheme with significantly improved stability under any initial design conditions. The optimization process uses quantitative scoring as the objective for automatic supervision and decision-making, effectively handling multiple constraints such as plate size and target height. This solves the problems of limited optimization effects and difficulty in finding a globally optimal solution in traditional methods, fully exploring the performance potential of materials and structures.

[0031] 4. This invention constructs a complete workflow from parameter input to automatic output of optimization solutions and reports. This system achieves full automation and intelligence in the design process of folding tables, not only improving the efficiency of individual steps but, more importantly, ensuring the optimal overall design in terms of structure, stability, economy, and aesthetics through inter-module collaboration and data flow. This provides a reliable technical platform for the intelligent design and manufacturing of personalized, scenario-based folding tables. Attached Figure Description

[0032] Figure 1 The flowchart of an adaptive design system for a folding table based on stability assessment, which is the subject of this invention;

[0033] Figure 2 This is a diagram showing the initial and optimized support point distribution in the embodiment.

[0034] Figure 3 These are the initial and optimized height distribution maps involved in the embodiment;

[0035] Figure 4 These are the initial and optimized 3D visualizations related to the embodiments;

[0036] Figure 5 This is a comparison chart of key performance indicators before and after optimization in the embodiments. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0038] Example 1:

[0039] This embodiment provides a specific implementation of an adaptive design method for folding tables based on stability assessment, referring to... Figure 1 The process shown includes the following steps:

[0040] Step 1: Physical Principles Driven Parametric Design

[0041] The design paradigm combines parameter-driven and physical constraint approaches. By receiving basic parameters input by the user, it automatically generates an initial folding table design scheme that performs well in terms of both structural rationality and static stability based on geometric principles and structural mechanics analysis.

[0042] (1) Parameter initialization and constraint setting

[0043] Define and receive core basic parameters, including plank length. (unit: ), width of the wooden board (unit: ), target height (unit: ), desktop diameter (Diameter of the circular tabletop, unit: Simultaneously, physical constraint parameters, including the width of the wood strips, are set based on an optimized balance between material specifications and manufacturing processes. (unit: ) and the number of table legs The number of table legs The optimized configuration was determined after weighing multiple objectives, including structural stability, processing complexity, and material utilization. Compared to the 10 pairs of legs commonly used in traditional folding table designs, this system reduces the number of support units by 20%. By optimizing the layout of support points and the force transmission path, it improves support efficiency and material utilization while ensuring or even enhancing overall stability.

[0044] (2) Folding angle optimization and hinge point distribution design

[0045] Using a fixed folding angle as the design benchmark, the optimal folding angle is determined based on torque balance and stability analysis. This angle achieves the optimal balance between the maximum support area and the minimum center of gravity height under a given height constraint. For the... hinge points Its position is determined by the polar angle parameterization calculation formula:

[0046]

[0047] in, Indicates the first Polar angles corresponding to each hinge point (unit: radians); Indicates the number of table legs.

[0048] The final three-dimensional coordinates of the hinge point are calculated using a convergence design algorithm to achieve intelligent inward convergence of the support point. The formula is as follows:

[0049] .

[0050] in For the first Three-dimensional coordinates of each hinge point (unit: ); Desktop radius (unit: ); This represents the angular compression factor, which controls the degree to which the hinge point converges inward; express Directional compression factor, used to optimize the distribution of lateral support points.

[0051] (3) Gradient design of table leg length

[0052] Based on geometric relationships, the formula for calculating the theoretical reference length of the table leg is: To achieve a progressive change in support stiffness from the center of the desktop outwards, conforming to the laws of mechanics, the length gradient factor is defined as follows: ,in, Indicates the first The length gradient factor of each table leg (dimensionless). This is the minimum length factor for the inner table legs; This is the maximum length factor of the outer table leg. Therefore, the... The formula for calculating the actual length of each table leg is: .

[0053] (4) Calculation of spatial coordinates of the end points of the table legs

[0054] The coordinates of the endpoint (contact point) of each table leg are determined by the coordinates of its corresponding hinge point, the length of the table leg, and the folding direction. Coordinates from hinge point The coordinates are obtained by adding the projection of the table leg onto the horizontal direction. The calculation formula is: ; The coordinates (i.e., the height of the table legs) are determined by the length of the table legs and the folding angle, using the following formula: ; Coordinate preservation and hinge points With the same coordinates, the formula is: Therefore, the first The complete coordinates of each table leg endpoint are: .

[0055] A highly efficient symmetric generation algorithm is used to automatically construct the complete support structure of the folding table. For each hinge point, the corresponding coordinates of the left table leg are generated. ;pass Inverting the coordinates achieves symmetry on the right side: This symmetric transformation can be represented by a matrix as follows: .

[0056] Finally, generate Coordinate matrix of the corner points of the table:

[0057] .

[0058] (5) Design quality pre-assessment and parameter output

[0059] The system automatically invokes the stability assessment module to pre-evaluate the generated design, and outputs the assessment results as initial quantitative indicators of design quality to the subsequent optimization module. Key indicators obtained from the pre-evaluation include: support area. Center of gravity height Overturning angle Safety factor Overall score Stability level .

[0060] Step 2: Multi-dimensional quantitative stability assessment:

[0061] A comprehensive geometric-mechanical evaluation model is established to transform the traditional experience-based qualitative stability judgment into a scientific, quantitative, and comparable indicator system. Through multi-dimensional quantitative evaluation methods, the stability of folding table design schemes is scientifically and objectively analyzed and evaluated.

[0062] (1) Data Input and Initialization. Receive table leg position data generated by the physics designer. The input data format is as follows: ,in, This represents the total number of table leg points. Coordinate separation is performed to extract coordinate vectors for each dimension: And calculate the position of all table legs on the ground plane. The projection onto the plane forms a two-dimensional point set used to calculate the supporting area: .

[0063] (2) Quantitative calculation of support area. As the core indicator for evaluating stability, the support area is calculated using two methods: convex hull area calculation and boundary rectangle area calculation. The convex hull area more accurately reflects the actual support range, while the boundary rectangle serves as a conservative estimate to ensure the robustness of the calculation.

[0064] The formula for calculating the area of ​​the convex hull support is: Pair set The formula for constructing a convex hull and calculating the area of ​​the convex hull polygon is as follows: ,in Let be the number of vertices of the convex hull, by convention. When the point sets are too collinear or the convex hull algorithm fails unexpectedly, the boundary rectangle method is used, and the calculation formula is as follows: ,in Points for all table legs and The maximum and minimum values ​​of the coordinates.

[0065] (3) Estimation of center of gravity height. Based on the simplified center of gravity model, assuming that the table mass is uniformly distributed and the main mass is concentrated in the connection structure between the tabletop and the legs, the average height of the table legs approximately reflects the center of gravity height. The formula for estimating the center of gravity height is: ,in, Indicates the first Height of each table leg (unit: ).

[0066] (4) Stability margin calculation. The stability margin is defined as the distance from the projection of the center of gravity to the nearest support point, representing the safe boundary at which the center of gravity can move without causing overturning. The coordinates of the projected center of gravity are: The formula for calculating the stability margin is: .

[0067] (5) Overturning angle calculation. Based on geometric relationships, the formula for calculating the overturning angle is: Angle normalization is performed using the following formula: The normalized overturning angle is limited to to Within a reasonable range.

[0068] (6) Safety factor calculation. The safety factor is defined as the ratio of the actual overturning angle to the design standard angle, and the formula is: ,in This indicates the design standard overturning angle. The engineering judgment criterion is: if... If so, the safety requirements are not met; Then it is basically safe; Therefore, the safety margin is sufficient.

[0069] (7) Multi-indicator comprehensive scoring system. A four-dimensional weighted comprehensive scoring model is established, mapping the four key indicators to standard scores, and summing them according to their importance: The weighting allocation includes the weight of the supporting area. Center of gravity and height weight Overturning angle weight Safety factor weight .

[0070] The scoring functions for each indicator are defined as follows.

[0071] The area scoring function is: ;

[0072] The high score function is:

[0073] The angle scoring function is: ;

[0074] Safety rating:

[0075] (8) Stability Level Classification. The stability level is determined based on the comprehensive score. The stability level classification function is as follows:

[0076]

[0077] This rating system provides an intuitive evaluation of design quality, allowing designers to quickly understand the overall stability of the design.

[0078] Step 3: Robust hierarchical automatic optimizer:

[0079] Based on the Differential Evolution algorithm, a three-level cascaded optimization strategy and robustness guarantee mechanism are adopted to perform stable and effective iterative optimization of the initial folding table design scheme generated by the physical principle designer.

[0080] First, the optimizer receives the initial table leg position data generated by the physics designer. ,in Indicates the first The three-dimensional coordinates of each table leg point are used. A stability evaluator is invoked to quantitatively evaluate the initial design, obtaining the following initial performance index set:

[0081]

[0082] in, Indicates the initial support area, Indicates the initial center of gravity height, Indicates the initial stability margin, Indicates the initial overturning angle, Indicates the initial safety factor, This indicates the initial overall score.

[0083] Initialize optimization parameters and configure the maximum number of iterations. The initial scoring criteria are Optimize the historical record set to .

[0084] Next, the optimization process is executed in the following three levels, with the result of the previous level serving as the trigger condition or input for the next level, ensuring optimization efficiency and success rate.

[0085] (1) The first layer uses a direct optimization strategy based on the differential evolution algorithm as the primary optimization method, aiming to find the optimal solution through global search. First, the coordinate matrix of the table legs is flattened into a design variable vector: We define the optimization objective function as a negative comprehensive score, thus transforming the problem of maximizing the score into a minimization problem: ,in, The operation reconstructs the vector into The position matrix; This represents the overall stability score.

[0086] To ensure the physical rationality and convergence efficiency of the optimization, boundaries are set for each coordinate component: ,in, Indicates all table leg points The mean of the coordinates; They are respectively Maximum allowable range of direction adjustment; This refers to the reasonable range for the height of the table legs.

[0087] Next, differential evolution algorithm is used for optimization, and key parameters including population size are configured. Crossover probability Variable factors (Adaptive strategy) and fixed random number seed .

[0088] During the algorithm iteration process, for the first... Each individual in the population Generate test individuals The steps are as follows:

[0089] First, randomly select three distinct and different... individual Generate mutation vector Secondly, experimental vectors are generated by binomial crossover. ,in The dimension indices are randomly selected to ensure that at least one dimension of the experimental vector comes from the mutated vector. Finally, the objective function values ​​of the experimental vector and the original vector are compared, and a greedy selection is performed. Record the optimal solution and its performance metrics for each generation to form an optimization history. ,in, Indicates the first The optimal solution in the next iteration; This indicates the corresponding overall score; Indicates the supporting area; Indicates the height of the center of gravity; This indicates the actual number of iterations.

[0090] After optimization, the direct optimization results were evaluated: the best optimization solution was... The optimality index is The improvement in rating is And the conditions for successful determination are: .

[0091] (2) Second layer, multi-starting point parallel optimization strategy. If direct optimization does not achieve the expected results (i.e. If the initial design fails, this strategy is activated, aiming to avoid getting trapped in local optima by exploring different initial regions. First, in the initial design... Attachment generation Two different initial starting points: ,in Represents the random perturbation matrix; This represents the standard deviation of the disturbance. Then, for each starting point... It independently executes an optimization process similar to the direct optimization strategy to obtain the corresponding optimization results. and its rating Finally, from all parallel optimization results, the design scheme with the highest comprehensive score is selected as the output of this layer: , , .

[0092] (3) The third layer is the heuristic rule improvement strategy. If the first two layers of strategies do not produce significant improvements (such as...), If this condition is not met, the deterministic improvement rules based on engineering experience at this layer will be activated to provide a minimum improvement. To increase the support area, the table legs located on both sides of the average position will be moved outward by a fixed distance. : To lower the center of gravity and improve stability, the height of all table legs was proportionally adjusted. reduce: To increase stability margin, all table legs were tested. Coordinates are scaled up enlarge: After applying the above rules, an improved design is obtained. And calculate its improvement amount. .

[0093] Automated decision-making is performed based on the output of the three-layer strategy to determine the final optimal solution. A strategy-effect set is constructed. The final strategy is selected based on the preset decision-making logic. A typical decision-making logic is as follows: ,in and Improved thresholds for different levels According to the selection strategy Determine the final optimized design scheme and its corresponding performance index set Calculate the overall optimization effect: absolute improvement. Relative improvement rate Stability level changes Finally, a structured optimization results report is generated. The optimization results show that the initial design has a comprehensive score of 78.0 points and a stability level of "good"; the optimized design has a comprehensive score of 86.0 points and a stability level of "excellent"; the absolute improvement is 8.0 points, the relative improvement is 10.3%, and the quality assessment shows that the design has reached the excellent level and can be directly used for production.

[0094] Step 4: Multi-view Visualization Analysis:

[0095] Employing a multi-dimensional, multi-perspective, and hierarchical visualization approach, key data, evaluation results, and optimization effects from the folding table design process are presented intuitively in professional graphical charts. Initial and optimized support point distribution maps are generated to visually demonstrate the planar layout characteristics of the folding table's support points, such as... Figure 2 As shown; the generated initial and optimized height distribution maps illustrate the distribution of the heights of each table leg, such as... Figure 3 As shown; the generated initial and optimized 3D visualizations intuitively display the three-dimensional space of the folding table design, such as... Figure 4 As shown in the indicator comparison chart, the changes in key performance indicators before and after optimization are analyzed. Figure 5 As shown.

[0096] The stability evaluation results of the initial design and the optimized design are shown in Table 1.

[0097] Table 1: Stability Assessment Results

[0098] Evaluation indicators Initial Design Optimized design Support area ( ) 1454.9 1878.3 Center of gravity height ( ) 53.0 50.3 stability margin ( ) 16.6 19.6 Overturning angle ( ) 17.4° 21.3° Safety factor 1.16 1.42 Overall score 78.0 86.0 Stability level good excellent

[0099] The table shows that the initial design has an excellent support area, but the center of gravity is too high, so the height of the table needs to be reduced. The overturning angle is acceptable and the safety factor is basically up to standard. The optimized design will be improved based on the results of the initial design.

[0100] Example 2:

[0101] This embodiment proposes an adaptive design system for folding tables based on stability assessment, including:

[0102] The physical principle-driven parametric design module is configured to perform the following process: receive input basic design parameters, automatically generate an initial three-dimensional coordinate scheme for the table legs with reasonable structure and excellent stability based on geometric and mechanical principles through parametric modeling, pre-evaluate the initial scheme, and output key stability indicators;

[0103] The multi-dimensional quantitative stability assessment module is configured to perform the following process: for the input table leg coordinate scheme, comprehensively calculate the support area, center of gravity height, stability margin and overturning angle, construct a comprehensive assessment model covering geometric and mechanical indicators, and output the quantitative stability score and grade of the scheme through a weighted scoring and grading system.

[0104] The robust hierarchical automatic optimization module is configured to perform the following process: starting from the initial scheme, it sequentially adopts a three-layer strategy of global search based on differential evolution algorithm, parallel optimization at multiple starting points, and improvement based on heuristic rules to iteratively adjust the spatial coordinates of the table legs, and takes a significant improvement in stability score as the optimization goal, automatically makes decisions and outputs the optimal design scheme and optimization report;

[0105] The multi-view visualization analysis module is configured to perform the following process: automatically generate multi-view, multi-dimensional analysis charts based on design data, including a planar distribution diagram of the support points for the initial and optimized schemes (such as...). Figure 2 ), Histogram of table leg height distribution (e.g.) Figure 3 ), a three-dimensional spatial structure diagram (such as Figure 4 ) and a comprehensive indicator comparison bar chart (such as Figure 5 These visualizations visually present the geometric features, performance differences, and optimization effects of the design schemes, and output a structured design report.

[0106] Example 3:

[0107] This embodiment proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the control method as described in this invention.

[0108] Example 4:

[0109] This embodiment proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed, it implements the steps of the method described in this invention.

[0110] Example 5:

[0111] This invention proposes a computer program product, including a computer program / instructions, which, when executed by a processor, implement the steps of the method described in this invention.

[0112] It should be noted that the processing flow of embodiments 2-5 corresponds to the specific steps of the method provided in embodiment 1 of the present invention, and has the corresponding functional modules and beneficial effects of the method. Technical details not described in detail in this embodiment can be found in the method provided in embodiment 1 of the present invention.

[0113] The program code used to implement the methods of this application may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0114] The specific implementation schemes described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific implementation schemes of the present invention and are not intended to limit the scope of the present invention. Any equivalent changes and modifications made by those skilled in the art without departing from the concept and principles of the present invention should fall within the scope of protection of the present invention.

Claims

1. An adaptive design method for folding tables based on stability assessment, characterized in that, Includes the following steps: S1: Receives the basic design parameters, constructs the parameter system, and generates and standardizes the initial table leg spatial coordinates through the hinge point convergence model and length gradient algorithm; S2: Starting with the initial design scheme generated by physical principles, a three-tiered cascaded optimization system is constructed, consisting of "direct global optimization, multi-starting-point parallel optimization, and heuristic rule improvement". S3: A comprehensive stability scoring system is constructed using four types of indicators: support area, center of gravity height, overturning angle, and safety factor. The design scheme is systematically evaluated through weighted comprehensive scoring and stability level classification. S4: Integrates multi-dimensional visualization charts such as support point distribution map, height distribution map, 3D structure map and optimization comparison map to provide a visual interpretation of design features, stability performance and optimization effect. It accurately locates the design quality through comprehensive scoring and stability level and verifies the correlation mechanism between structural parameters and stability indicators.

2. The adaptive design method for folding tables based on stability assessment according to claim 1, characterized in that, The parameter system in step S1 includes the size of the wooden board, the target height, the diameter of the tabletop, and physical constraints.

3. The adaptive design method for folding tables based on stability assessment according to claim 1, characterized in that, Step S1 specifically includes: Receive basic design parameters for the folding table input by the user, including the length of the wooden board, the width of the wooden board, the target height, and the diameter of the tabletop; A parameter system is constructed for generating the initial scheme, which includes multi-dimensional design parameters such as the number of table leg pairs, the width of the wooden strips, the folding angle, the angle compression factor, and the length gradient factor. The parameter system is parametrically designed, and the process includes: calculating the hinge point distribution coordinates based on the folding angle and the desktop radius, calculating the reference length of the table legs based on the target height, calculating the actual length of each table leg based on the gradient factor, and calculating the three-dimensional coordinates of the table leg endpoints based on the geometric projection relationship. Generate an initial table leg coordinate matrix and call the stability assessment module for pre-assessment, outputting key indicators such as support area, center of gravity height, overturning angle, and safety factor.

4. The adaptive design method for folding tables based on stability assessment according to claim 1, characterized in that, Step S2 specifically includes: The system receives the coordinate matrix of the table legs, extracts the set of projection points on the ground plane, and calculates the support area using a convex hull algorithm. If the convex hull calculation fails, the area of ​​the boundary rectangle is used as a substitute. The support area is used to evaluate the structural support range; the center of gravity height is used to evaluate the overall stability; the overturning angle is used to evaluate the overturning resistance; and the safety factor is used to evaluate the design margin. Based on a preset piecewise scoring function, the support area, center of gravity height, overturning angle, and safety factor are scored separately, and weighted summation is performed according to weights to obtain a comprehensive stability score. Based on a preset threshold, the score is divided into five stability levels: "Excellent," "Good," "Average," "Needs Improvement," and "Poor," thus achieving a systematic graded evaluation of design quality.

5. The adaptive design method for folding tables based on stability assessment according to claim 1, characterized in that, The robust hierarchical automatic optimization described in step S3 includes starting with the initial design scheme and its comprehensive score, and sequentially executing a three-layer cascade optimization strategy, namely differential evolution algorithm, multi-starting point parallel optimization algorithm, and heuristic rule improvement algorithm, to iteratively adjust the spatial coordinates of the table legs. With a significant improvement in stability score as the optimization goal, the system automatically makes decisions and outputs the optimal design scheme and optimization report.

6. The adaptive design method for folding tables based on stability assessment according to claim 1, characterized in that, The visualization charts in step S4 include at least four types: support point distribution chart, height distribution chart, three-dimensional structure chart, and indicator comparison chart.

7. An adaptive design system for a folding table based on stability assessment, characterized in that, include: The physical principle-driven parametric design module is configured to perform the following process: receive input basic design parameters, automatically generate an initial three-dimensional coordinate scheme for the table legs with reasonable structure and excellent stability based on geometric and mechanical principles through parametric modeling, pre-evaluate the initial scheme, and output key stability indicators; The multi-dimensional quantitative stability assessment module is configured to perform the following process: for the input table leg coordinate scheme, comprehensively calculate the support area, center of gravity height, stability margin and overturning angle, construct a comprehensive assessment model covering geometric and mechanical indicators, and output the quantitative stability score and grade of the scheme through a weighted scoring and grading system. The robust hierarchical automatic optimization module is configured to perform the following process: starting from the initial scheme, it sequentially adopts a three-layer strategy of global search based on differential evolution algorithm, parallel optimization at multiple starting points, and improvement based on heuristic rules to iteratively adjust the spatial coordinates of the table legs, and takes a significant improvement in stability score as the optimization goal, automatically makes decisions and outputs the optimal design scheme and optimization report; The multi-view visualization analysis module is configured to perform the following processes: automatically generate charts such as support point distribution map, height distribution map, 3D structure map, evaluation result map and optimization comparison map, intuitively display the design geometric features, performance indicators and optimization effects, and output a structured design report.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1 to 6.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the computer program is executed, it implements the steps of the method as described in any one of claims 1 to 6.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 6.

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