Optimization design method and system for detachable highway toll island

Abstract

The invention provides an optimization design method and system for a detachable expressway toll island, and relates to the technical field of expressway toll islands, and the method comprises the steps: obtaining first information, second information, third information, fourth information and fifth information; obtaining a preliminary function framework according to the first information; performing segmentation processing according to the second information and the preliminary function framework to obtain a module segmentation result; obtaining a module optimization result according to the second information and the module segmentation result; obtaining a universal wheel arrangement scheme according to the fourth information and a module optimization result; performing connection design according to the fifth information and the universal wheel arrangement scheme to obtain a module connection scheme; and obtaining a toll island design scheme according to the module optimization result, the universal wheel arrangement scheme and the module connection scheme. According to the method, a preliminary functional framework of the toll island is generated by adopting parametric modeling, and the module is subjected to segmentation and splicing mode design in combination with a component segmentation positioning algorithm and a topological optimization algorithm, so that the safety and stability of a modular structure are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of highway toll islands, and more particularly, to an optimized design method and system for a detachable highway toll island. Background Art

[0002] Highway toll islands are indispensable infrastructure in the modern traffic management system, widely used at highway entrances and exits to achieve standardized and safe management of vehicle passage. Existing toll islands mostly adopt a fixed cast-in-place base structure. Although this design can meet the basic needs of daily toll collection and vehicle guidance, there are still significant deficiencies in practical applications. Due to the limited lane width, over-width vehicles (such as emergency rescue vehicles and emergency material transportation vehicles) are often restricted when passing, and sometimes even part of the toll island needs to be demolished to complete special tasks, which not only consumes a large amount of manpower and material resources but also seriously affects traffic efficiency. In addition, the anti-collision performance and reusability of fixed toll islands are poor. Once damaged, they often need to be replaced or repaired as a whole, further increasing costs and wasting resources.

[0003] Currently, there are some simple improvement technologies trying to solve the above problems. For example, using a steel structure to replace the cast-in-place structure and installing universal wheels at the bottom of the toll island for easy movement. However, most of these improvements stay at the primary level of modular disassembly and assembly, lacking systematic optimization design for mechanical properties, anti-collision energy absorption performance, module splicing methods, and dynamic movement plans, and it is difficult to meet the multiple requirements of modern traffic management for flexibility, functionality, and economy. Summary of the Invention

[0004] The purpose of the present invention is to provide an optimized design method and system for a detachable highway toll island to improve the above problems. To achieve the above purpose, the technical solutions adopted by the present invention are as follows:

[0005] In a first aspect, the present application provides an optimized design method for a detachable highway toll island, including:

[0006] Obtain the first information, the second information, the third information, the fourth information, and the fifth information. The first information includes the space occupation data and geometric shape of the toll island. The second information includes functional requirements and mechanical requirements. The third information includes module splicing and connection requirements. The fourth information includes universal wheel layout requirements. The fifth information includes anti-collision performance requirements and economic objectives;

[0007] Design the basic shape of the toll island according to the first information, and control the growth direction, quantity, load capacity, and anti-collision performance of the module through parametric modeling to obtain a preliminary functional framework;

[0008] According to the second piece of information and the preliminary functional framework, the functional framework is segmented using a component segmentation and positioning algorithm. The module splicing method is determined through a topology optimization algorithm, and connection nodes and error tolerance zones are set according to the third piece of information to obtain a module segmentation result;

[0009] According to the second piece of information and the module segmentation result, the mechanical properties of the module are optimized using a graph neural network to obtain a module optimization result;

[0010] According to the fourth piece of information and the module optimization result, a linkage simulation is carried out. By dynamically adjusting the number, position, and load-bearing capacity of the casters, a caster layout plan is obtained;

[0011] According to the fifth piece of information and the caster layout plan, a connection design is carried out. The distribution and size of the pins are optimized through finite element analysis to obtain a module connection plan;

[0012] According to the module optimization result, the caster layout plan, and the module connection plan, multi-objective optimization processing is carried out. Through weight assignment and iterative optimization of the overall design of the toll island, a toll island design plan is obtained.

[0013] In a second aspect, the present application also provides an optimization design system for a detachable highway toll island, including:

[0014] An acquisition module, configured to acquire the first piece of information, the second piece of information, the third piece of information, the fourth piece of information, and the fifth piece of information. The first piece of information includes the spatial occupancy data and geometric form of the toll island. The second piece of information includes functional requirements and mechanical requirements. The third piece of information includes module splicing and connection requirements. The fourth piece of information includes caster layout requirements. The fifth piece of information includes anti-collision performance requirements and economic objectives;

[0015] A modeling module, configured to design the basic form of the toll island according to the first piece of information, and control the growth direction, quantity, load capacity, and anti-collision performance of the module through parametric modeling to obtain a preliminary functional framework;

[0016] A segmentation module, configured to segment the functional framework using a component segmentation and positioning algorithm according to the second piece of information and the preliminary functional framework, determine the module splicing method through a topology optimization algorithm, and set connection nodes and error tolerance zones according to the third piece of information to obtain a module segmentation result;

[0017] An optimization module, configured to optimize the mechanical properties of the module using a graph neural network according to the second piece of information and the module segmentation result to obtain a module optimization result;

[0018] A simulation module, configured to carry out a linkage simulation according to the fourth piece of information and the module optimization result. By dynamically adjusting the number, position, and load-bearing capacity of the casters, a caster layout plan is obtained;

[0019] A design module, configured to perform connection design according to the fifth information and the caster wheel layout scheme, optimize the distribution and size of pins through finite element analysis, and obtain a module connection scheme;

[0020] An output module, configured to perform multi-objective optimization processing according to the module optimization result, the caster wheel layout scheme, and the module connection scheme, and obtain a toll island design scheme through weight allocation and iterative optimization of the overall design of the toll island.

[0021] The beneficial effects of the present invention are as follows:

[0022] The present invention uses parametric modeling and morphological optimization algorithms to generate a preliminary functional framework of the toll island, combines component segmentation and positioning algorithms and topological optimization algorithms to design the refined segmentation and splicing methods of the modules, and ensures the safety and stability of the modular structure. Further, a graph neural network is used to optimize the mechanical properties of the modules, dynamically adjust the material distribution and connection strength, and improve the overall impact resistance. In terms of mobile design, combined with the interlocking simulation technology and the caster wheel layout optimization algorithm, the rapid switching between the stationary and mobile states of the toll island module is realized, significantly improving the applicability and flexibility. In addition, the distribution and size of pins are optimized through finite element analysis, the impact energy absorption performance between modules is enhanced, and a multi-objective optimization algorithm is used to comprehensively improve the modularity, anti-collision performance, mobility, and economy of the toll island. Description of the Drawings

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention, and therefore should not be regarded as limiting the scope. For those of ordinary skill in the art, other related drawings can be obtained based on these drawings without creative efforts.

[0024] Figure 1 It is a schematic flow chart of an optimization design method for a detachable highway toll island described in the embodiments of the present invention;

[0025] Figure 2 It is a schematic structural diagram of an optimization design system for a detachable highway toll island described in the embodiments of the present invention

[0026] Figure 3 It is a schematic structural diagram of an optimization design device for a detachable highway toll island described in the embodiments of the present invention.

[0027] Markings in the figure: 800, an optimized design device for a detachable highway toll island; 801, a processor; 802, a memory; 803, a multimedia component; 804, an I / O interface; 805, a communication component; 901, an acquisition module; 902, a modeling module; 903, a segmentation module; 904, an optimization module; 905, a simulation module; 906, a design module; 907, an output module. Detailed implementation manners

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. The components of the embodiments of the present invention described and illustrated herein generally can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0029] It should be noted that like reference numerals and letters denote like items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of the present invention, terms such as "first" and "second" are only used for descriptive distinction and cannot be construed as indicating or implying relative importance.

[0030] Embodiment 1:

[0031] This embodiment provides an optimized design method for a detachable highway toll island.

[0032] See Figure 1 , the figure shows that this method includes step S100 to step S700.

[0033] Step S100, acquire the first information, the second information, the third information, the fourth information, and the fifth information. The first information includes the space occupancy data and geometric form of the toll island. The second information includes functional requirements and mechanical requirements. The third information includes module splicing and connection requirements. The fourth information includes caster arrangement requirements. The fifth information includes anti-collision performance requirements and economic objectives;

[0034] It is understandable that the first information clarifies the physical space and appearance characteristics required for the toll island, which not only determines the segmentation scheme and growth direction of the module, but also needs to adapt to the specific size requirements of the toll island in different scenarios, such as the width limit of the highway entrance and exit or complex terrain; the second information ensures that the design of the toll island can meet the vehicle guidance and protection functions, and withstand various dynamic and static loads, including the impact force of vehicle collision and the static pressure of its own weight; the third information provides constraints for modular disassembly and assembly design, especially the strength of the connection nodes, the splicing form and the error tolerance range, which are crucial to achieving the stability and reusability of the modular structure; the fourth information, combined with the stress distribution at the bottom of the module, defines the position, number and load-bearing capacity of the universal wheels to ensure the flexible switching of the module between moving and stationary states; the fifth information directly affects the material selection of the module design, the optimization of the energy absorption structure, and the cost control of the overall solution.

[0035] Step S200: designing the basic form of the toll island according to the first information, and obtaining a preliminary functional framework by controlling the growth direction, quantity, load capacity and anti-collision performance of the modules through parametric modeling;

[0036] It should be noted that this step transforms the toll island from an abstract functional requirement into a specific structural design basis. Taking the highway toll island as an example, its space is long and narrow and needs to withstand complex traffic dynamic loads. Parametric modeling can not only quickly generate feasible module forms, but also flexibly adjust the design according to different spatial and functional requirements. The unique effect of this method is that it transforms the design from static to dynamic through algorithm-driven modeling, providing a highly flexible and accurate basic framework for subsequent optimization steps.

[0037] Step S300: segment the functional framework using a component segmentation and positioning algorithm according to the second information and the preliminary functional framework, determine the module splicing method using a topology optimization algorithm, and set connection nodes and error tolerance areas according to the third information to obtain a module segmentation result;

[0038] It can be understood that this step improves the disassembly and reuse capabilities of the toll island through modular segmentation, while the combination of topological optimization and graph theory optimization ensures the strength and economy of module splicing.

[0039] Step S400: Optimize the mechanical properties of the module using a graph neural network according to the second information and the module segmentation result to obtain a module optimization result;

[0040] It can be understood that through the optimization of the graph neural network in this step, the material distribution and connection strength inside the module have been comprehensively improved, and the anti-deformation ability of the module under dynamic loads and complex stress conditions has been significantly enhanced. In addition, the optimization of the graph neural network can also reduce unnecessary material use while ensuring mechanical properties, thus taking into account both economy and engineering applicability.

[0041] Step S500: Perform a linkage simulation based on the fourth information and the module optimization results, and obtain the caster arrangement plan by dynamically adjusting the number, position, and load-bearing capacity of the casters.

[0042] It should be noted that the optimized caster arrangement plan can not only effectively disperse the bottom stress of the module and avoid local overload, but also ensure that the module can quickly switch between dynamic movement and static fixation, providing strong support for the multi-scenario application of the toll island.

[0043] Step S600: Perform a connection design based on the fifth information and the caster arrangement plan, and optimize the distribution and size of the pins through finite element analysis to obtain the module connection plan.

[0044] It can be understood that the optimized module connection plan can ensure the structural safety and durability under the conditions of multiple disassembly and long-term use of the highway toll island, and at the same time significantly reduce the maintenance and replacement costs of the connection nodes.

[0045] Step S700: Perform multi-objective optimization processing based on the module optimization results, the caster arrangement plan, and the module connection plan, and obtain the toll island design plan by performing weight allocation and iterative optimization on the overall design of the toll island.

[0046] It should be noted that in this step, by comprehensively considering the optimization results of the module, the caster, and the connection, the local optimization and the overall design are organically combined to ensure that the toll island exhibits the best performance and economy under various complex scenarios (such as high traffic flow impact, frequent disassembly and assembly requirements, cost constraints, etc.).

[0047] Furthermore, step S200 includes steps S210 to S250.

[0048] Step S210: Initialize the shape of the toll island spatial data according to the first information, divide the target space into multiple functional areas through the Voronoi segmentation algorithm, and combine the geometric shape of the toll island to define the boundary conditions to obtain the initial functional area division result.

[0049] Specifically, the first step of form initialization is to construct a mathematical model of the toll island space, representing the target area as a geometric domain in two-dimensional or three-dimensional space. For example, in the toll island scenario, the lane width, entrance and exit positions, and surrounding geometric features are used as input conditions to generate a basic spatial framework describing the toll island. Then, the target space is divided into multiple functional areas through the Voronoi segmentation algorithm. The Voronoi algorithm is based on several initial points (called generating points) and divides the space into multiple polygonal regions. The distance from all points within each region to the corresponding generating point is less than the distance to other generating points. The selection of generating points is dynamically adjusted according to the functional requirements of the toll island (such as the number of lanes and traffic flow distribution) and the modular design goal to ensure that each functional area has a reasonable area and form. In addition, in order to make the segmentation result conform to the geometric form of the toll island, it is necessary to constrain the Voronoi segmentation result in combination with boundary conditions. For example, in the boundary area of the toll island, irregular geometric forms (such as arcs and hypotenuses) need to be used as constraint conditions to make the boundary of the segmented area consistent with the actual form of the toll island. At the same time, in the functional areas near toll booths or entrances and exits, the segmentation result needs to consider a higher traffic density, and the position of the generating points is dynamically adjusted to optimize the area division. This step provides a geometric basis for optimizing the growth direction of subsequent modules by converting abstract spatial data into a specific functional area distribution.

[0050] Step S220: Optimize the growth direction based on the initial functional area division result. Determine the growth path of the module within the divided area through the gradient descent method and the form optimization algorithm. With the objective of minimizing the overlapping and void areas of the functional areas, adjust the direction vector of the module to make the growth direction of the module match the force direction and functional requirements of the toll island, and obtain the module growth direction result;

[0051] Among them, the objective function formula is:

[0052]

[0053] Among them, represents the overall evaluation index of the module growth direction ; i represents the module number; n represents the total number of modules; j represents the number of overlapping or void areas; m represents the total number of overlapping and void areas; k represents the constraint condition number; p represents the total number of constraint conditions; represents the growth direction vector of the i-th module; represents the principal stress direction vector of the i-th module in the force analysis; A j represents the area of the j-th overlapping area or void area; A function representing the degree of deviation between the k-th constraint condition and the boundary constraint condition, which is used to limit the matching degree between the module growth direction and the boundary form; α, β, and γ respectively represent the weight coefficients.

[0054] Objective function Comprehensively evaluate the advantages and disadvantages of the module growth direction, which is divided into three parts:

[0055] The first item is Used to measure the module growth direction And the principal stress direction The matching degree. By minimizing the difference between the two, it is ensured that the module growth direction is consistent with the actual stress state, improving the mechanical properties of the module. It is applicable to dealing with the dynamic loads of highway toll plazas and the diverse stress distributions of complex shapes.

[0056] The second item is Used to minimize the areas A of the overlapping region and the void region j . This item optimizes the compactness of the functional area division, avoids wasting space, and at the same time ensures that the module boundaries do not interfere with each other.

[0057] The third item is Used to limit the degree of deviation between the module growth direction and the geometric boundary form of the toll plaza. By constraining the module growth direction near the irregular boundary, the global consistency and geometric constraint adaptability of the segmentation result are ensured.

[0058] The update formula of the gradient descent method is:

[0059]

[0060] Among them, Represents the growth direction vector of the i-th module in the t-th iteration; Represents the growth direction vector of the i-th module after the (t + 1)-th iteration; η represents the learning rate, which is used to control the update amplitude of each iteration; Represents the gradient of the objective function with respect to the growth direction vector . After each round of iteration, the growth direction vector of the module is adjusted to gradually approach the target direction, while optimizing the global overlap and void problems.

[0061] Step S230: Optimize the number of modules according to the module growth direction results. Determine the number of modules through the module division and configuration model based on the genetic algorithm, with the module load capacity and space utilization efficiency as the optimization objectives. Through the crossover, mutation, and selection operations in the genetic algorithm, obtain the module number configuration result;

[0062] It can be understood that through the results of the module growth direction, the preliminary division range of the functional area and the characteristics of the force distribution can be clarified. On this basis, the genetic algorithm is optimized. By randomly generating a variety of module configuration schemes as the initial population, and using the fitness function to evaluate the performance of each scheme in terms of load capacity balance and space utilization rate. Through the selection operation, the high-fitness schemes are retained. The crossover operation combines the characteristics of excellent schemes to generate new schemes. The mutation operation randomly adjusts the number or distribution of modules to increase the diversity of the population. Finally, it iteratively converges to the optimal configuration scheme. This process can dynamically adapt to complex geometric shapes and force distribution requirements. For example, increasing the number of modules in high-stress areas to improve the load-bearing capacity, and reducing the number of modules in low-stress areas to reduce material costs and splicing complexity. The optimized number of modules can achieve load balance, reduce the void area, improve the space utilization efficiency, and at the same time provide a more reasonable segmentation basis for module splicing and form design.

[0063] Step S240: Perform simulations based on the module quantity configuration result. By adjusting the edge shape and size of the modules, with the optimization goals of maximizing the impact energy absorption and minimizing the structural deformation, simulate the stress distribution and energy absorption performance under different vehicle impacts to obtain a preliminary functional framework.

[0064] Specifically, in this step, first combine the module quantity configuration result, take the edge shape and size of each module as adjustable variables, and use finite element analysis to dynamically simulate the stress distribution of the modules under impact loads. The impact simulation needs to consider not only static loads but also dynamic loads (such as vehicle impacts) to comprehensively evaluate the structure of the modules. By adjusting the edge shape of the modules, optimize the contact surface distribution between the modules and the impact area, reduce the stress concentration area, and improve the energy absorption efficiency of the modules. And the adjustment of the module size aims to ensure that the geometric shape of each module not only meets the structural strength requirements but also does not cause unnecessary material waste.

[0065] During the simulation process, by gradually iteratively adjusting the edge shape and size, the optimization goal is to maximize the impact energy absorption capacity of the modules while minimizing the deformation degree under the action of the load. For example, for the modules near the lane edge of the toll island, the edge shape can be designed as a nested style with a buffer structure to better disperse the impact force. And in the internal module area, the shape can be simplified to reduce material use. This method can ensure that each module has excellent anti-impact performance and meets the requirements of economy and manufacturing feasibility.

[0066] Furthermore, step S300 includes steps S310 to S340.

[0067] Step S310: Delineate the module segmentation area according to the second information and the preliminary functional framework, perform cluster analysis on the geometric area of the functional framework, combine mechanical requirements as clustering features, set the minimum functional unit constraint of the module, and obtain the preliminary module segmentation area;

[0068] It should be noted that cluster analysis uses multidimensional data such as the geometric form, force distribution and functional requirements of the functional framework as feature input, and groups and divides the framework area with the force uniformity and functional consistency of each module as the goal. In this process, module segmentation must meet specific minimum functional unit constraints, that is, each module must independently bear its functional requirements, such as carrying traffic loads or absorbing energy and avoiding collisions. Combined with mechanical requirements, clustering features focus on stress concentration areas, boundary geometric features, and logical relationships between functional partitions. For example, in areas with concentrated forces, module segmentation must ensure that the module size is moderate to avoid excessive stress concentration; in areas with relatively simple functions (such as central traffic areas), larger modules can be divided to reduce the number of splicing points. This step divides the complex functional framework into reasonable preliminary module areas through a clustering algorithm, and ensures that each module retains its geometric and mechanical properties during the segmentation process to avoid performance loss or increased splicing complexity due to improper segmentation.

[0069] Step S320, optimizing the module boundary shape according to the preliminary module segmentation area, dynamically adjusting the module edge morphology, minimizing the module boundary complexity and maximizing the mechanical stability, combining the gradient descent method to iteratively adjust the module boundary smoothness and structural strength, and obtaining the module boundary shape optimization result;

[0070] First, according to the boundary shape characteristics of the preliminary module segmentation area, combined with the functional requirements and force analysis of the toll island, the key feature points and parts that need to be optimized in the boundary shape are identified. For example, in areas with high boundary complexity or sharp corners, these features may cause stress concentration and affect the overall strength of the module. Subsequently, the gradient descent method is used to iteratively optimize the boundary, and the module boundary is smoother and unnecessary complex shapes are reduced by gradually adjusting the position and curvature of the boundary points. During the boundary optimization process, the smoothness of the boundary shape is defined as part of the objective function to limit the sudden change of the boundary curvature while ensuring the geometric continuity and manufacturing feasibility of the boundary shape. At the same time, in order to improve the mechanical stability of the module, the boundary shape is combined with the mechanical distribution within the module during the optimization process. For example, the boundary shape is optimized in high stress areas to disperse the load and prevent local stress concentration from damaging the overall performance of the module; while in low stress areas, the boundary can be simplified to reduce the material usage and splicing complexity. The partial derivatives of the objective function are iteratively updated by the gradient descent method, and gradually converge to the optimal boundary shape that meets the minimum complexity and maximum stability.

[0071] The objective function formula is as follows:

[0072] F(B) = F smooth (B) + F stress (B) + F thickness (B);

[0073]

[0074] Among them, F(B) represents the objective function for boundary optimization; B represents the set of boundary shape vectors of the module; F smooth (B) represents the boundary smoothness optimization formula; F stress (B) represents the stress distribution uniformity optimization formula; F thickness (B) represents the material thickness smoothness optimization formula; μ represents the weight of boundary smoothness; B i represents the shape function of the boundary of the i-th module; represents the boundary of the i-th module; u represents the parameter coordinates on the boundary; v represents the weight of stress distribution uniformity optimization; l represents the region number on the module boundary; r represents the total number of boundary regions divided in all modules; σ l represents the stress value on the boundary of the l-th module; represents the boundary of the l-th module; represents the total length of the boundary of the l-th module; w represents the region number of the material thickness change area on the module boundary; s represents the total number of regions involving material thickness change in all module boundaries; ξ represents the weight of material thickness smoothness optimization; z represents the boundary normal coordinate; H w represents the material thickness distribution function of the boundary of the w-th module; represents the boundary of the w-th module.

[0075] Step S330: According to the module boundary shape optimization result and the second information, design the module splicing method, determine the best connection scheme of the modules through the topology optimization algorithm, with the goal of minimizing the stress concentration at the splicing position and the module splicing volume, adjust the number and distribution of connection points, and obtain the module splicing method result;

[0076] Specifically, the design of module splicing needs to consider two aspects: First, based on the optimization results of the boundary shape of the module, identify areas suitable for connection, such as areas with smoother boundaries and uniform stress distribution, which are more suitable as the basis for the distribution of splicing points; Second, according to the mechanical requirements in the second information, ensure that the splicing points can effectively transfer loads and avoid failures caused by local stress concentration. On this basis, through the topology optimization algorithm, dynamically adjust the number and position of splicing points. The topology optimization takes minimizing the stress concentration at the splicing position and the module splicing volume as the objective function, and by repeatedly iterating to adjust the layout of the connection points, find the best solution. For example, in the boundary area of the module with large force, the density of splicing points can be increased to disperse the load; while in the low-force area, the splicing points can be reduced to reduce material usage and splicing costs.

[0077] In this process, the core of the topology optimization algorithm lies in its ability to optimize the global layout of splicing points based on the actual geometric shape and mechanical distribution of the module, and at the same time, combined with the complexity of the boundary shape, dynamically adapt to the changes in the splicing area. This method can minimize the splicing volume and material waste while ensuring the structural strength.

[0078] Step S340: According to the module splicing method result and the third information, design the connection nodes and error tolerance areas of the module, and determine the global distribution and redundancy of the connection nodes through graph theory algorithms to obtain the module segmentation result.

[0079] It should be noted that in this step, first, according to the module splicing method result, extract the information of the connection area between modules, including the initial distribution of splicing points, boundary shape characteristics, and the force transfer path between modules. Combine the requirements for the strength, quantity, and error tolerance of connection nodes in the third information to clarify the basic design constraints of connection nodes. The key to the design of the error tolerance area is to ensure that even if there are certain machining errors or installation deviations during the assembly process of the module, the connection strength and stability can still be guaranteed. Next, optimize the global distribution of connection nodes through graph theory algorithms. The module connection is modeled as a graph structure, where nodes represent the connection points of the module, and edges represent the splicing relationship and force transfer path between modules. By analyzing the characteristics of the graph, preferably such as the minimum spanning tree, path optimality, etc., determine the distribution and redundancy of the connection nodes. The redundancy optimization is to ensure that the module can still maintain the overall structural integrity in the case of the failure of some connection nodes.

[0080] Furthermore, step S400 includes steps S410 to S440.

[0081] Step S410: Based on the second information and the module segmentation result, preliminarily model the mechanical properties of the modules. Through finite element analysis, simulate the force conditions of each module, map the stress-strain characteristics of the modules to node attributes, and map the connection strength and force transmission direction to edge attributes to generate an initial mechanical graph structure model;

[0082] Specifically, first use finite element analysis to conduct a detailed simulation of the force conditions of each module. By inputting the module segmentation result into finite element software, simulate the stress-strain distribution of the module under static and dynamic loads (such as traffic impact force and self-weight load), so as to capture the mechanical properties of the module in the actual application scenario. In finite element analysis, the key force-bearing areas and structural deformation characteristics of the module are accurately calculated, and specific data are provided for subsequent modeling. Subsequently, map the results of the finite element analysis to the initial mechanical graph structure model. The node attributes of the model represent the local mechanical properties of the module (such as stress, strain, compressive strength, etc.), and these properties reflect the response ability of the module under different force states; the edge attributes represent the connection characteristics and force transmission paths between modules, including connection strength, directionality, and force transmission efficiency. Through this abstraction of nodes and edges, the complex mechanical behavior of the module is transformed into a graph structure that is easy to analyze and optimize. This step highly abstracts the mechanical behavior of the module by using the graph structure, enabling the mechanical correlation between modules to be intuitively presented from a global perspective.

[0083] Step S420: Perform structural optimization processing based on the initial mechanical graph structure model. Through the spectral clustering algorithm, partition the mechanical graph, aiming to reduce node connection redundancy and optimize the edge weight distribution. Divide the mechanical graph into several subgraphs, and redefine the node and edge attributes within the subgraphs to obtain a mechanical subgraph structure;

[0084] It should be noted that the initial mechanical graph structure model consists of nodes (local mechanical properties of modules, such as stress and strain) and edges (force transmission properties in the module connection area, such as connection strength and transmission efficiency). Since there may be unnecessary connection redundancy or uneven distribution of force transmission paths in the mechanical behavior of modules, which directly affects the performance and cost of the overall structure, it is necessary to optimize the graph structure. The spectral clustering algorithm is applied in this process. Its core idea is to perform eigenvalue decomposition on the Laplacian matrix of the mechanical graph to find the similarity measure between nodes and partition the graph into several subgraphs accordingly. In actual processing, the spectral clustering algorithm first calculates the incidence matrix of the nodes and edges of the mechanical graph, combines the edge weights (such as force transmission intensity) and node attributes (such as local stress concentration), and constructs the Laplacian matrix of the graph. By performing eigenvalue decomposition on the Laplacian matrix, sets of closely related nodes in the graph are identified and these sets are partitioned into independent subgraphs. The partitioned subgraphs have the following characteristics: Nodes and edges within each subgraph have higher correlation (such as similar stress distribution and force transmission direction). Connections between subgraphs are reduced, optimizing the edge weight distribution and avoiding unnecessary mechanical transmission paths. After the subgraphs are generated, the attributes of the nodes and edges within each subgraph are redefined. For example, the edge weights within a subgraph can be readjusted to the average force transmission intensity, while the node attributes are aggregated into the overall stress distribution characteristics of the area. In this way, each subgraph independently describes the local mechanical properties of the module and reduces the complexity of the global graph. The optimized mechanical subgraph structure can effectively reduce redundancy in module connection design, improve force transmission efficiency and structural stability

[0085] Step S430: According to the mechanical subgraph structure, predict and optimize the node material distribution and edge connection strength of the module through a graph neural network. With the goal of minimizing the force uniformity of the module and the edge connection strength under dynamic loads, use a message passing network to iteratively update the attributes of the nodes and edges to obtain the material distribution - connection strength optimization result;

[0086] It is understandable that the core of the graph neural network in this step lies in using the Message Passing Network (MPN). Through the interaction between nodes and neighbor nodes and edges, the material distribution and connection strength attributes are gradually updated. Node updates optimize local material properties to adapt to dynamic loads, resulting in more uniform stress; edge updates adjust the force transmission path and strength to avoid redundancy. In multiple rounds of iteration, the graph neural network gradually propagates global and local information, converging the mechanical graph to an optimal state. Through this optimization, the stress uniformity inside the module is significantly improved, reducing the risk of local stress concentration; the consumption of connecting materials is reduced, lowering costs; the optimization results can flexibly adapt to the dynamic load requirements of complex traffic scenarios, providing higher safety, stability, and economy for the toll island module design. This optimization method based on the graph neural network fully reflects the global and local balance of the mechanical properties of the toll island module and is applicable to the actual deployment of complex modular structures.

[0087] Step S440: According to the material distribution - connection strength optimization results, comprehensively optimize the overall mechanical performance of the module, and adjust the global distribution of the module shape and materials through a multi - objective genetic algorithm to generate the module optimization results.

[0088] It should be noted that the multi - objective genetic algorithm integrates various performance requirements such as dynamic loads, static loads, and structural stability within the optimization framework through a global search of the shape and material distribution. The optimization objectives include maximizing the impact resistance performance of the module to make it more stable in complex stress environments; minimizing the non - uniformity of the module material distribution to ensure that the material distribution better meets the stress requirements; and reducing the material usage to control costs.

[0089] The optimization process of the genetic algorithm starts from randomly generating multiple groups of module shape and material distribution schemes as the initial population. Each group of schemes consists of geometric parameters (such as size, curvature) and material parameters (such as density, elastic modulus) of the module. The performance of each group of schemes in terms of impact resistance performance, stress uniformity, and material utilization rate is evaluated through a fitness function. Individuals with higher fitness are selected for crossover operations to combine the advantages of two excellent schemes to generate new candidate schemes; at the same time, mutation operations randomly adjust the parameters of individuals to introduce diversity and avoid falling into local optima. After multiple rounds of iteration, the algorithm gradually converges to the global optimal solution, finally generating the optimization results of the module.

[0090] Furthermore, step S500 includes steps S510 to S540.

[0091] Step S510: According to the fourth information and the module optimization results, analyze the stress distribution at the bottom of the module. By identifying the high - stress areas and uniformly loaded areas at the bottom, and combining the symmetry constraints of the module edge and center, delimit the preliminary universal wheel arrangement area to obtain the preliminary result of stress analysis;

[0092] It is understandable that this step is based on the module optimization results, and the finite element analysis method is used to calculate the stress distribution at the bottom of the module, identify the high-stress areas and uniform load-bearing areas at the bottom. High-stress areas usually appear at positions where the bottom load is concentrated or the structure changes suddenly, while the uniform load-bearing areas reflect the relatively stable stress state of the module. Through this classification, the mechanical properties of the bottom of the module can be effectively divided. Based on the stress distribution analysis, combined with the symmetry constraint conditions at the edges and center of the module, the layout area of the casters is further optimized. The symmetry constraint aims to ensure the geometric and mechanical balance of the caster layout, and avoid tilting or load imbalance of the module during movement due to uneven layout. For example, in the edge area of the module, the casters can be arranged near the high-stress points to disperse the load; in the center area of the module, the symmetry of the layout needs to be ensured to maintain the overall stability.

[0093] Step S520: According to the preliminary results of the stress analysis, dynamically distribute points in the candidate area through the iterative point layout algorithm, and optimize the caster layout position in combination with the distribution of the module connection nodes to obtain a preliminary caster layout plan;

[0094] It is understandable that the candidate area is delimited by the identified high-stress areas and uniform load-bearing areas. The iterative point layout algorithm uses the candidate area as the search space. By dynamically adjusting the positions of the distributed points, the casters are preferentially distributed in the high-stress areas to disperse the load and reduce stress concentration, while ensuring that the distributed points in the low-stress areas do not affect the overall mechanical properties. During the point layout process, the distribution of the module connection nodes is taken into account for optimization. The connection nodes are usually the key parts where the module is stressed. The caster layout in these areas should avoid interference with splicing and provide additional support to improve the stability of the connection nodes. The iterative point layout algorithm dynamically optimizes the point layout plan by gradually adjusting the positions of the distributed points in the candidate area. In each iteration, the positions and quantities of the distributed points are re-evaluated according to the objective function of stress dispersion and load balance. The distributed points that contribute the most to the structural stability are preferentially selected, and redundant points are eliminated to improve the layout efficiency.

[0095] Step S530: According to the preliminary caster layout plan, discretely optimize the number and load-bearing capacity of the casters, and allocate the load-bearing ratio through the integer linear programming algorithm to generate a caster load-bearing allocation plan;

[0096] Specifically, the local stress and force requirements at each caster position are obtained through the preliminary caster layout scheme. These positions are regarded as discrete variables, with each variable corresponding to a caster and its possible load-bearing capacity options. The load-bearing capacity is represented by discrete values, such as standard load ranges (e.g., 200 kg, 400 kg, 600 kg, etc.). The optimization objective function is to maximize the stress uniformity at the bottom of the module while minimizing the total number of casters and the risk of overload, ensuring that the load distribution of each caster not only meets the local requirements but also achieves global balance. During the optimization process, the integer linear programming algorithm dynamically adjusts the number and distribution of casters by selecting the optimal combination of load-bearing capacities, making the overall load-bearing more uniform and reducing the risk of local stress concentration and vibration during dynamic movement.

[0097] Step S540: Simulate the kinematic performance of the connection of the casters according to the caster load distribution scheme. Through Lagrangian dynamics modeling, simulate the movement path and load uniformity of the module and optimize the connection structure of the casters to obtain the final caster layout scheme.

[0098] It can be understood that Lagrangian dynamics modeling is used to describe the movement behavior of the module and its caster system. This model comprehensively considers the inertia of the module, the supporting force of the casters, the force conditions at the connection points, and the external forces acting on the module during the movement path (such as steering force and impact force). By introducing the Lagrangian function, the kinetic energy and potential energy of the system are combined within a unified mathematical framework to establish the dynamic equations of the whole module. During the dynamic simulation process, the model focuses on analyzing the smoothness of the movement path and the load uniformity. The smoothness of the movement path indicates whether the casters can maintain a stable movement trajectory under different terrain and load conditions, avoiding tilting or deviation of the module caused by uneven force distribution. The load uniformity indicates whether the instantaneous load changes of each caster during dynamic movement are uniform, especially whether the casters in the high-load area can effectively disperse the force. During the simulation process, the design of the caster connection structure is also incorporated into the optimization objective. By adjusting the distribution of the connection points, the flexible characteristics of the connection device, and the connection angle, ensure that the casters can quickly adapt to the force changes at the bottom of the module and smoothly transition different directions and load distributions during movement. The optimization results are obtained through a step-by-step iterative method to ensure that the scheme after each adjustment is improved in terms of movement smoothness and load uniformity.

[0099] Furthermore, step S600 includes steps S610 to S640.

[0100] Step S610: Analyze according to the fifth information and the caster layout scheme. Based on the stress path analysis algorithm, extract the main force transmission paths between the modules, and combine the boundary conditions and mechanical requirements of the modules to delimit the initial distribution positions of the pins to obtain a preliminary pin layout scheme;

[0101] It can be understood that stress path analysis captures the main paths of force transmission from one module to another by identifying the key force-bearing chains in the module structure. These paths are usually concentrated in the splicing areas with large forces or the parts with strong boundary condition constraints of the module. This process can clarify the areas that need to be focused on for the pin distribution, such as the high stress concentration points under dynamic loads and the splicing areas with frequent force transmission. Under the constraints of the module boundary conditions, the pins need to be distributed in the areas that can disperse the forces to the greatest extent, while avoiding interfering with the geometric characteristics of the module edge or reducing the connection strength of the module. For example, at the module splicing, pins should be preferentially arranged at the force balance points to reduce connection failures caused by excessive local forces. Inside the module, the pin layout needs to match the mechanical requirements to optimize the load transfer efficiency between modules.

[0102] Step S620: Optimize the size and shape of the pins according to the preliminary pin layout plan. By dynamically generating multiple groups of pin design parameter combinations and combining with the performance simulation results of the pins under different stress states, the optimized pin size results are obtained.

[0103] Through this optimization process, the pin design can more accurately adapt to the dynamic mechanical requirements of the toll island module, providing a key guarantee for the efficiency and durability of the overall modular design.

[0104] Step S630: According to the optimized pin size results, use the multi-objective particle swarm optimization algorithm to iteratively optimize the strength, energy absorption, and distribution density of the pins to obtain the optimized pin distribution results.

[0105] It can be understood that the multi-objective particle swarm optimization algorithm dynamically searches and iterates the pin distribution plan by constructing a fitness function. Each particle represents a pin distribution plan, including the distribution information of specific positions, strength, and energy absorption parameters. The algorithm simulates the movement of particles in the search space and continuously adjusts the positions and velocities of the particles in combination with the multi-objective constraint conditions of strength, energy absorption, and density. In each iteration, the fitness of the particles is evaluated according to the weights of the objective functions, the best-performing plan is retained, and other particles are guided to approach the optimal solution through the global optimal and local optimal information.

[0106] Step S640: Verify the overall connection performance of the module according to the optimized pin distribution results. Simulate the dynamic force performance of the pins through finite element analysis, and use dynamic impact and static loads as test conditions to evaluate the overall impact resistance and long-term stability of the module connection to obtain the final module connection plan.

[0107] It should be noted that in the dynamic impact condition, high-dynamic load scenarios such as simulating vehicle impacts and sudden vibrations are carried out. By analyzing the force changes, deformation conditions, and energy absorption performance of the pin under the impact force, it is verified whether its anti-impact ability meets the requirements. In the static load condition, the self-weight and long-term stress conditions of the module during daily use are simulated to evaluate the stress distribution and fatigue life of the pin to ensure the long-term stability of the connection.

[0108] Further, step S700 includes steps S710 to S740.

[0109] Step S710: According to the module optimization results, the universal wheel layout plan, and the module connection plan, model the overall structural performance. Establish a performance index system for the toll island design through a weighted objective function model. Take anti-impact performance, mobility, durability, and economy as objectives and assign different weight coefficients to generate the overall optimization objective function of the toll island and obtain the comprehensive performance model.

[0110] It can be understood that the quantification of anti-impact performance ensures the stability of the modular design of the toll island in high-dynamic load scenarios; the mobility assessment improves the applicability of the toll island during disassembly and assembly and in complex terrains; the durability analysis extends the service life of the module and the connector; the economic optimization reduces the overall cost. Through this model, the toll island design can not only meet diverse application requirements but also achieve a high degree of coordination among performance, cost, and reliability, providing a solid foundation for the global optimization of the overall design.

[0111] Step S720: Optimize the parameters according to the comprehensive performance model. Iteratively adjust the module size, the position of the universal wheel, and the distribution of connection nodes through the nonlinear programming method to obtain the initial structural optimization result.

[0112] It should be noted that the optimization of the module size reduces material waste while maintaining mechanical properties. The adjustment of the universal wheel position improves the dynamic movement performance and load distribution uniformity of the module. The optimization of the connection nodes improves the strength and anti-impact ability at the splicing location. The overall optimization result provides an accurate basic configuration for the subsequent global design.

[0113] Step S730: According to the initial structural optimization result, optimize the module splicing sequence and layout method through the simulated annealing algorithm to minimize the connection stress and installation angle deviation, and combine the evaluation of the load distribution to assess the change of the target value to obtain the global layout optimization result of the module.

[0114] It is understandable that the simulated annealing algorithm adjusts the splicing order and layout position of the modules through random perturbations, and gradually reduces the probability of large-scale adjustments according to the decrease of the annealing temperature. After each adjustment, the objective function value is calculated. If the objective value improves, the new scheme is accepted; if the objective value deteriorates, it is decided whether to accept it according to the annealing temperature to jump out of the local optimum. The finally generated global layout optimization result of the modules provides a more efficient and reliable splicing scheme for the modular design of the toll island, ensuring the long-term stability of the modules in a complex mechanical and dynamic environment, while simplifying the actual construction process and reducing the manufacturing and installation costs.

[0115] Step S740: According to the global layout optimization result of the modules, comprehensively optimize the overall design scheme of the toll island through a multi-objective genetic algorithm. With the anti-impact performance, mobility performance, and cost control as the objectives, the module material distribution, structural form, and mobility configuration are used as optimization variables. Optimization solutions are generated through selection, crossover, and mutation operations, and the fitness function is combined to evaluate the performance of the scheme to obtain the final overall design scheme of the toll island.

[0116] Embodiment 2:

[0117] As Figure 2 shown, this embodiment provides an optimized design system for a detachable highway toll island. The system includes:

[0118] A module acquisition unit 901, configured to acquire the first information, the second information, the third information, the fourth information, and the fifth information. The first information includes the space occupancy data and geometric form of the toll island. The second information includes functional requirements and mechanical requirements. The third information includes module splicing and connection requirements. The fourth information includes the arrangement requirements of universal wheels. The fifth information includes anti-collision performance requirements and economic objectives;

[0119] A modeling module 902, configured to design the basic form of the toll island according to the first information, and control the growth direction, quantity, load capacity, and anti-collision performance of the module through parametric modeling to obtain a preliminary functional framework;

[0120] A segmentation module 903, configured to perform segmentation processing on the functional framework according to the second information and the preliminary functional framework by using a component segmentation and positioning algorithm, determine the module splicing method through a topology optimization algorithm, and set connection nodes and error tolerance areas according to the third information to obtain a module segmentation result;

[0121] An optimization module 904, configured to optimize the mechanical performance of the module according to the second information and the module segmentation result by using a graph neural network to obtain a module optimization result;

[0122] A simulation module 905, configured to perform a chain simulation according to the fourth information and the module optimization result, and obtain a universal wheel arrangement scheme by dynamically adjusting the quantity, position, and load-bearing capacity of the universal wheels;

[0123] A design module 906, configured to perform connection design according to the fifth information and the caster arrangement plan, optimize the distribution and size of pins through finite element analysis, and obtain a module connection plan;

[0124] An output module 907, configured to perform multi-objective optimization processing according to the module optimization result, the caster arrangement plan, and the module connection plan, and obtain a toll island design plan through weight allocation and iterative optimization of the overall design of the toll island.

[0125] In a specific embodiment disclosed by the present invention, the modeling module 902 includes:

[0126] A first division unit, configured to perform morphological initialization on the toll island spatial data according to the first information, divide the target space into multiple functional regions through the Voronoi segmentation algorithm, and combine the geometric shape of the toll island to define boundary conditions, so as to obtain an initial functional region division result;

[0127] A first optimization unit, configured to perform growth direction optimization processing according to the initial functional region division result, determine the growth path of the module in the divided region through the gradient descent method and the morphological optimization algorithm, use minimizing the overlap of functional regions and the void area as the objective function, adjust the direction vector of the module, and make the module growth direction match the force direction and functional requirements of the toll island, so as to obtain a module growth direction result;

[0128] A second optimization unit, configured to optimize the number of modules according to the module growth direction result, determine the number of modules through a module division and configuration model based on the genetic algorithm, use the module load capacity and space utilization efficiency as the optimization objectives, and obtain a module number configuration result through the crossover, mutation, and selection operations in the genetic algorithm;

[0129] A first simulation unit, configured to perform simulation according to the module number configuration result, adjust the edge shape and size of the module, use maximizing the impact energy absorption and minimizing the structural deformation as the optimization objectives, simulate the stress distribution and energy absorption performance under different vehicle impacts, and obtain a preliminary functional framework.

[0130] In a specific embodiment disclosed by the present invention, the segmentation module 903 includes:

[0131] A first clustering unit, configured to delimit the module segmentation region according to the second information and the preliminary functional framework, perform clustering analysis on the geometric region of the functional framework, combine the mechanical requirements as the clustering features, and set the minimum functional unit constraint conditions of the module, so as to obtain a preliminary module segmentation region;

[0132] The third optimization unit is used to optimize the boundary shape of the module according to the preliminary module segmentation region. By dynamically adjusting the module edge form, aiming at minimizing the module boundary complexity and maximizing the mechanical stability, the smoothness and structural strength of the module boundary are iteratively adjusted by combining the gradient descent method to obtain the optimized result of the module boundary shape;

[0133] The fourth optimization unit is used to design the module splicing method according to the optimized result of the module boundary shape and the second information. By using the topology optimization algorithm to determine the best connection scheme of the modules, aiming at minimizing the stress concentration at the splicing position and the module splicing volume, the number and distribution of the connection points are adjusted to obtain the module splicing method result;

[0134] The first design unit is used to design the connection nodes and error tolerance areas of the modules according to the module splicing method result and the third information. By using the graph theory algorithm to determine the global distribution and redundancy of the connection nodes, the module segmentation result is obtained.

[0135] Embodiment 3:

[0136] Corresponding to the above method embodiment, in this embodiment, an optimized design device for a detachable highway toll island is also provided. An optimized design device for a detachable highway toll island described below can be correspondingly referred to the optimized design method for a detachable highway toll island described above.

[0137] Figure 3 It is a block diagram of an optimized design device 800 for a detachable highway toll island shown according to an exemplary embodiment. As Figure 3 shown, the optimized design device 800 for a detachable highway toll island may include: a processor 801, a memory 802. The optimized design device 800 for a detachable highway toll island may further include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0138] Among them, the processor 801 is used to control the overall operation of the optimized design device 800 for a detachable highway toll island to complete all or part of the steps in the above-mentioned optimized design method for a detachable highway toll island. The memory 802 is used to store various types of data to support the operation of the optimized design device 800 for a detachable highway toll island. These data may include, for example, instructions for any application program or method operating on the optimized design device 800 for a detachable highway toll island, as well as application program-related data, such as contact data, sent and received messages, pictures, audio, video, and so on. The memory 802 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc. The multimedia component 803 may include a screen and an audio component. Among them, the screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone, and the microphone is used to receive external audio signals. The received audio signal may be further stored in the memory 802 or sent through the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the optimized design device 800 for a detachable highway toll island and other devices. Wireless communication, such as Wi-Fi, Bluetooth, near field communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them. Therefore, the corresponding communication component 805 may include: a Wi-Fi module, a Bluetooth module, and an NFC module.

[0139] In an exemplary embodiment, an optimized design device 800 for a detachable highway toll island can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, and is used to execute the above-mentioned optimized design method for a detachable highway toll island.

[0140] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When the program instructions are executed by a processor, the steps of the above-mentioned optimized design method for a detachable highway toll island are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions can be executed by the processor 801 of an optimized design device 800 for a detachable highway toll island to complete the above-mentioned optimized design method for a detachable highway toll island.

[0141] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention.

Claims

1. An optimized design method for a detachable highway toll island, characterized in that, Including: Obtain the first information, the second information, the third information, the fourth information, and the fifth information. The first information includes the space occupancy data and geometric form of the toll island. The second information includes functional requirements and mechanical requirements. The third information includes module splicing and connection requirements. The fourth information includes caster arrangement requirements. The fifth information includes anti-collision performance requirements and economic objectives; Design the basic form of the toll island according to the first information, control the growth direction, quantity, load capacity, and anti-collision performance of the module through parametric modeling, and obtain a preliminary functional framework; According to the second information and the preliminary functional framework, use the component segmentation and positioning algorithm to segment the functional framework, determine the module splicing method through the topology optimization algorithm, and set the connection nodes and error tolerance areas according to the third information to obtain the module segmentation result; According to the second information and the module segmentation result, use the graph neural network to optimize the mechanical performance of the module to obtain the module optimization result; Conduct a catenation simulation according to the fourth information and the module optimization result, and obtain the caster arrangement plan by dynamically adjusting the quantity, position, and load-bearing capacity of the casters; Conduct a connection design according to the fifth information and the caster arrangement plan, and optimize the distribution and size of the pins through finite element analysis to obtain the module connection plan; Conduct multi-objective optimization processing according to the module optimization result, the caster arrangement plan, and the module connection plan, and obtain the toll island design plan by performing weight allocation and iterative optimization on the overall design of the toll island; 2. The optimized design method of a detachable highway toll island according to claim 1, characterized in that Design the basic form of the toll island according to the first information, control the growth direction, quantity, load capacity, and anti-collision performance of the module through parametric modeling, and obtain a preliminary functional framework, including: Perform form initialization on the toll island space data according to the first information, divide the target space into multiple functional areas through the Voronoi segmentation algorithm, and combine the geometric form of the toll island to define the boundary conditions to obtain the initial functional area division result; Perform growth direction optimization processing according to the initial functional area division result, determine the growth path of the module within the divided area through the gradient descent method and the form optimization algorithm, use the minimization of functional area overlap and void area as the objective function, adjust the direction vector of the module, and make the module growth direction match the force direction and functional requirements of the toll island to obtain the module growth direction result; Optimize the module quantity according to the module growth direction result, determine the module quantity through the module division and configuration model based on the genetic algorithm, use the module load capacity and space utilization efficiency as the optimization objectives, and obtain the module quantity configuration result through the crossover, mutation, and selection operations in the genetic algorithm; Conduct a simulation according to the module quantity configuration result, adjust the edge shape and size of the module, use the maximization of impact energy absorption and the minimization of structural deformation as the optimization objectives, simulate the stress distribution and energy absorption performance under different vehicle impacts, and obtain the preliminary functional framework.

3. The optimized design method of a detachable highway toll island according to claim 1, characterized in that According to the second information and the preliminary functional framework, the functional framework is segmented using a component segmentation and positioning algorithm. The module splicing method is determined through a topology optimization algorithm, and connection nodes and error tolerance areas are set according to the third information to obtain a module segmentation result, including: Based on the second information and the preliminary functional framework, the module segmentation area is delimited. By performing clustering analysis on the geometric area of the functional framework, combining mechanical requirements as clustering features, and setting the constraint conditions for the minimum functional unit of the module, a preliminary module segmentation area is obtained; Based on the preliminary module segmentation area, the boundary shape of the module is optimized. By dynamically adjusting the module edge form, with the goal of minimizing the complexity of the module boundary and maximizing the mechanical stability, the smoothness and structural strength of the module boundary are iteratively adjusted using the gradient descent method to obtain the optimized result of the module boundary shape; Based on the optimized result of the module boundary shape and the second information, the module splicing method is designed. The optimal connection scheme of the module is determined through a topology optimization algorithm, with the goal of minimizing the stress concentration at the splicing position and the module splicing volume, and adjusting the number and distribution of connection points to obtain the result of the module splicing method; Based on the result of the module splicing method and the third information, the connection nodes and error tolerance areas of the module are designed. The global distribution and redundancy of the connection nodes are determined through a graph theory algorithm to obtain the module segmentation result.

4. The optimized design method of a detachable highway toll island according to claim 1, characterized in that According to the second information and the module segmentation result, the mechanical properties of the module are optimized using a graph neural network to obtain a module optimization result, including: Based on the second information and the module segmentation result, a preliminary model of the mechanical characteristics of the module is established. Through finite element analysis, the force conditions of each module are simulated, the stress-strain characteristics of the module are mapped to node attributes, and the connection strength and force transmission direction are mapped to edge attributes to generate an initial mechanical graph structure model; Based on the initial mechanical graph structure model, structural optimization is performed. The mechanical graph is partitioned through a spectral clustering algorithm, with the goal of reducing node connection redundancy and optimizing the edge weight distribution. The mechanical graph is divided into several subgraphs, and the node and edge attributes within the subgraphs are redefined to obtain a mechanical subgraph structure; Based on the mechanical subgraph structure, the node material distribution and edge connection strength of the module are predicted and optimized through a graph neural network. With the goal of minimizing the force uniformity of the module under dynamic load and the edge connection strength, the message passing network is used to iteratively update the node and edge attributes to obtain the optimization result of the material distribution-connection strength; Based on the optimization result of the material distribution-connection strength, the overall mechanical properties of the module are comprehensively optimized. The global distribution of the module shape and material is adjusted through a multi-objective genetic algorithm to generate the module optimization result.

5. The optimized design method of a detachable highway toll island according to claim 1, characterized in that According to the fourth information and the module optimization result, a linkage simulation is performed. By dynamically adjusting the number, position, and load-bearing capacity of the casters, a caster layout plan is obtained, including: Analyze the stress distribution at the bottom of the module based on the fourth information and the module optimization results. By identifying the high-stress areas and evenly loaded areas at the bottom, and combining the symmetry constraints of the module edge and center, delimit the preliminary caster arrangement area to obtain the preliminary result of stress analysis; Based on the preliminary result of stress analysis, dynamically distribute points within the candidate area through the iterative point-placement algorithm, and optimize the caster arrangement positions in combination with the distribution of module connection nodes to obtain the preliminary caster arrangement plan; According to the preliminary caster arrangement plan, discretely optimize the number and load-bearing capacity of the casters, and allocate the load-bearing ratio through the integer linear programming algorithm to generate the caster load distribution plan; Simulate the kinematic performance of the caster connections according to the caster load distribution plan. Through Lagrangian dynamics modeling, simulate the movement path and load uniformity of the module and optimize the caster connection structure to obtain the final caster arrangement plan.

6. The optimized design method of a detachable highway toll island according to claim 1, characterized in that Conduct connection design according to the fifth information and the caster arrangement plan. Optimize the distribution and size of the pins through finite element analysis to obtain the module connection plan, including: Analyze according to the fifth information and the caster arrangement plan. Based on the stress path analysis algorithm, extract the main force transmission paths between the modules. Combine the boundary conditions and mechanical requirements of the modules to delimit the initial distribution positions of the pins to obtain the preliminary pin layout plan; Optimize the size and shape of the pins according to the preliminary pin layout plan. Dynamically generate multiple groups of pin design parameter combinations, and combine the performance simulation results of the pins under different stress states to screen and obtain the pin size optimization result; According to the pin size optimization result, iteratively optimize the strength, energy absorption, and distribution density of the pins through the multi-objective particle swarm optimization algorithm to obtain the pin distribution optimization result; Verify the overall connection performance of the module according to the pin distribution optimization result. Simulate the dynamic stress performance of the pins through finite element analysis. Using dynamic impact and static load as test conditions, evaluate the overall impact resistance and long-term stability of the module connection to obtain the final module connection plan.

7. The optimized design method of a detachable highway toll island according to claim 1, characterized in that Conduct multi-objective optimization processing according to the module optimization result, the caster arrangement plan, and the module connection plan. Through weight allocation and iterative optimization of the overall design of the toll island, obtain the toll island design plan, including: Based on the module optimization result, the caster arrangement plan, and the module connection plan, model the overall structural performance. Establish a performance index system for toll island design through a weighted objective function model. Take impact resistance, mobility, durability, and economy as objectives and allocate different weight coefficients to generate the overall optimization objective function for the toll island to obtain the comprehensive performance model; Conduct parameter optimization according to the comprehensive performance model. Iteratively adjust the module size, caster position, and connection node distribution through the nonlinear programming method to obtain the initial structural optimization result; According to the initial structure optimization result, the module splicing sequence and layout are optimized by a simulated annealing algorithm to minimize the connection stress and installation angle deviation, and the change of the target value of the load distribution is evaluated to obtain the global layout optimization result of the module; According to the global layout optimization result of the module, the comprehensive performance of the overall design scheme of the toll island is optimized by a multi-objective genetic algorithm. With the anti-impact performance, mobility performance and cost control as the objectives, the module material distribution, structural form and mobile configuration are used as the optimization variables. Optimization solutions are generated through selection, crossover and mutation operations, and the performance of the scheme is evaluated in combination with the fitness function to obtain the final overall design scheme of the toll island.

8. An optimized design system for a detachable highway toll island, characterized in that, Including: An acquisition module, used to acquire the first information, the second information, the third information, the fourth information and the fifth information. The first information includes the space occupancy data and geometric form of the toll island. The second information includes functional requirements and mechanical requirements. The third information includes module splicing and connection requirements. The fourth information includes the arrangement requirements of casters. The fifth information includes anti-collision performance requirements and economic objectives; A modeling module, used to design the basic form of the toll island according to the first information, and control the growth direction, quantity, load capacity and anti-collision performance of the module through parametric modeling to obtain a preliminary functional framework; A segmentation module, used to segment the functional framework by using a component segmentation and positioning algorithm according to the second information and the preliminary functional framework, determine the module splicing method through a topology optimization algorithm, and set connection nodes and error tolerance areas according to the third information to obtain the module segmentation result; An optimization module, used to optimize the mechanical performance of the module by using a graph neural network according to the second information and the module segmentation result to obtain the module optimization result; A simulation module, used to perform a linkage simulation according to the fourth information and the module optimization result, and obtain the caster arrangement plan by dynamically adjusting the quantity, position and load-bearing capacity of the casters; A design module, used to perform connection design according to the fifth information and the caster arrangement plan, and optimize the distribution and size of pins through finite element analysis to obtain the module connection plan; An output module, used to perform multi-objective optimization processing according to the module optimization result, the caster arrangement plan and the module connection plan, and obtain the toll island design plan through weight assignment and iterative optimization of the overall design of the toll island.

9. The optimized design system of a detachable highway toll island according to claim 8, characterized in that, The modeling module includes: A first division unit, used to initialize the form of the toll island space data according to the first information, divide the target space into multiple functional areas by using the Voronoi segmentation algorithm, and define the boundary conditions in combination with the geometric form of the toll island to obtain the initial functional area division result; A first optimization unit, used to perform growth direction optimization processing according to the initial functional area division result, determine the growth path of the module in the divided area through the gradient descent method and the form optimization algorithm, take minimizing the overlap of functional areas and the void area as the objective function, and adjust the direction vector of the module to make the module growth direction match the force direction and functional requirements of the toll island to obtain the module growth direction result; A second optimization unit, configured to optimize the number of modules according to the module growth direction result, determine the number of modules through a module division and configuration model based on a genetic algorithm, take the module load capacity and space utilization efficiency as optimization objectives, and obtain a module number configuration result through crossover, mutation, and selection operations in the genetic algorithm; A first simulation unit, configured to perform simulation according to the module number configuration result, adjust the edge shape and size of the module, take maximizing impact energy absorption and minimizing structural deformation as optimization objectives, simulate the stress distribution and energy absorption performance under different vehicle impacts, and obtain a preliminary functional framework.

10. An optimized design system for a detachable highway toll island according to claim 8, characterized in that, The segmentation module includes: A first clustering unit, configured to delimit a module segmentation area according to the second information and the preliminary functional framework, perform clustering analysis on the geometric area of the functional framework, combine mechanical requirements as clustering features, and set the minimum functional unit constraint conditions of the module to obtain a preliminary module segmentation area; A third optimization unit, configured to optimize the boundary shape of the module according to the preliminary module segmentation area, dynamically adjust the module edge form, take minimizing the module boundary complexity and maximizing the mechanical stability as objectives, and iteratively adjust the smoothness and structural strength of the module boundary in combination with the gradient descent method to obtain a module boundary shape optimization result; A fourth optimization unit, configured to design a module splicing method according to the module boundary shape optimization result and the second information, determine the best connection scheme of the module through a topology optimization algorithm, take minimizing the stress concentration at the splicing position and the module splicing volume as objectives, and adjust the number and distribution of connection points to obtain a module splicing method result; A first design unit, configured to design the connection nodes and error tolerance areas of the module according to the module splicing method result and the third information, determine the global distribution and redundancy of the connection nodes through a graph theory algorithm, and obtain a module segmentation result.

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