Design method of novel anti-crawling and anti-transverse moving structure

Abstract

The application discloses a design method of a novel anti-climbing and anti-lateral displacement structure, which comprises the following steps: (1) establishing an initial model of an anti-climbing device which meets parameter requirements according to initial performance requirements of the anti-climbing device and the shape and size of the anti-climbing device, setting an optimization area and a non-optimization area of the initial model of the anti-climbing device, and simultaneously establishing a multi-objective optimization function; (2) taking each of multiple optimization objectives in the multi-objective optimization function as a target function, taking the remaining objectives as constraint responses, and sequentially solving extreme values of multiple single targets; (3) performing weight assignment on the extreme values of the multiple single targets, and establishing a comprehensive target function of the multi-objective optimization; and (4) solving the comprehensive target function of the multi-objective optimization, and obtaining an optimal solution of the anti-climbing device structure. The anti-climbing tooth designed by the application is additionally provided with an anti-lateral displacement function, which can prevent vehicles from climbing each other, limit the vehicles from derailing in a zigzag shape due to an excessively large deflection angle, and maximally reduce the damage degree of accidents.

Description

Technical Field

[0001] This invention relates to the field of passive safety protection for trains, specifically to a design method for a novel anti-climbing and anti-lateral movement structure. Background Technology

[0002] Railways are a vital economic lifeline, and railway transportation safety is a matter of life and death. Because trains run on dedicated tracks, the likelihood of collisions is far lower than with other modes of transportation. However, railway trains are heavy and travel at high speeds, so any accidents can result in serious casualties and economic losses. Therefore, it is imperative to further improve the safety of railway vehicles.

[0003] Train safety protection technologies include active safety protection and passive safety protection. Passive safety protection refers to the technologies and measures that protect the lives and property of passengers after an accident. During a collision, relevant measures are taken to prevent the vehicle from climbing and to absorb impact energy through end energy-absorbing zones. This protects the integrity of the passenger area from large deformation and minimizes the degree of damage caused by the accident.

[0004] As a type of passive safety device, the anti-climb device is used to prevent adjacent carriages from climbing over during a collision. As an important component of the energy-absorbing structure at the end of the train body, the anti-climb device has a relatively complex structure, generally including anti-climb teeth, a buffer energy-absorbing structure, and a mounting plate. In a train collision, the anti-climb teeth mesh together to prevent the train from climbing over due to large vertical impact forces, while simultaneously transferring the longitudinal impact force to the rear energy-absorbing structure to absorb energy.

[0005] Since the force is transmitted between vehicles through the coupler connecting the two vehicles, and the coupler body can rotate horizontally (±25°) around the coupler seat, both longitudinal and lateral forces exist simultaneously during a collision. Existing anti-climbing tooth structures, when meshed, can only restrict the vertical displacement of the vehicles to prevent them from climbing each other, but do not restrict lateral movement. When the vehicle deflection angle is large (±15°), the vehicle is prone to zigzag derailment. Summary of the Invention

[0006] Based on the above problems, this invention designs and develops a novel anti-climb and anti-lateral movement structure design method. The purpose of this invention is to perform topology optimization design on the basis of the existing anti-climb tooth structure without changing the interface with the vehicle, so as to obtain the optimized size.

[0007] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0008] A design method for a novel anti-climb and anti-lateral movement structure includes the following steps:

[0009] Step 1: Based on the initial performance requirements and external dimensions of the anti-climb device, establish an initial model of the anti-climb device that meets the parameter requirements. Set optimization and non-optimization regions for the initial model of the anti-climb device and establish a multi-objective optimization function.

[0010] Step 2: Take each of the multiple optimization objectives in the multi-objective optimization function as the objective function, and the remaining objectives as the constraint responses, and solve for the extreme values ​​of multiple single objectives one by one;

[0011] Step 3: Assign weights to the extreme values ​​of the multiple single objectives to establish a comprehensive objective function for multi-objective optimization;

[0012] Step 4: Solve the comprehensive objective function of the multi-objective optimization to obtain the optimal solution for the anti-crawler structure.

[0013] Preferably, in step one, the optimized region is the tooth height, size, and arrangement cycle of the anti-climb teeth at the anti-climb device interface, and the non-optimized region is the bottom size of the anti-climb device. The multi-objective optimization function is:

[0014] MaxF h = f(T,H,D,t,h,d),

[0015] MaxF c = f(T,H,D,t,h,d),

[0016] MinG=ρV(L,W,B,T,H,D,t,h,d),

[0017] F h ≥F h0 ,

[0018] F c ≥F c0 ,

[0019] G≤G0;

[0020] Among them, F h For lateral bearing capacity, F c For vertical bearing capacity, F h0 For minimum lateral bearing capacity, F c0 The minimum vertical load-bearing capacity is given by G, the weight of the anti-climb device is given by G0, the maximum weight of the anti-climb device is given by G0, the overall length of the anti-climb teeth is given by L, the distance between the vertical anti-climb teeth is given by T, the overall thickness of the anti-climb teeth is given by B, the vertical tooth height is given by H, the vertical tooth width is given by D, the overall width of the anti-climb teeth is given by W, the distance between the anti-lateral movement teeth is given by t, the height of the anti-lateral movement teeth is given by h, the width of the anti-lateral movement teeth is given by d, the density of the anti-climb tooth material is given by ρ, and the volume of the anti-climb device is given by V.

[0021] Preferably, in step two, each of the multiple optimization objectives in the multi-objective optimization function is taken as the objective function, and the remaining objectives are taken as constraint responses. The extrema of the multiple single objectives are then solved one by one, specifically as follows:

[0022] F h As the objective function, F c If G is the constraint response, then it is:

[0023] MaxF h = f(T,H,D,t,h,d),

[0024] F h ≥F h0 ,

[0025] F c ≥F c0 ,

[0026] G≤G0,

[0027] T = (WD) / (n-1),

[0028] 1 / 3·B≤H≤2 / 3·B,

[0029] 1 / 4·T≤D≤1 / 2·T,

[0030] t = (Ld) / (m-1),

[0031] 1 / 4·t≤d≤1 / 2·t

[0032] 1 / 3·B≤h≤2 / 3·B,

[0033] H+h≤3 / 4·B,

[0034] 3≤n≤5,

[0035] 3≤m≤8,

[0036] D = d;

[0037] F c As the objective function, F h If G is the constraint response, then it is:

[0038] MaxF c =f(T,H,D,t,h,d)

[0039] F h ≥F h0 ,

[0040] F c ≥F c0 ,

[0041] G≤G0,

[0042] T = (WD) / (n-1),

[0043] 1 / 4·T≤D≤1 / 2·T,

[0044] 1 / 3·B≤H≤2 / 3·B,

[0045] t = (Ld) / (m-1),

[0046] 1 / 4·t≤d≤1 / 2·t,

[0047] 1 / 3·B≤h≤2 / 3·B,

[0048] H+h≤3 / 4·B,

[0049] 3≤n≤5,

[0050] 3≤m≤8,

[0051] D = d;

[0052] F c As the objective function, F h If G is the constraint response, then it is:

[0053] MinG=ρV(L,W,B,T,H,D,t,h,d)

[0054] F h ≥F h0 ,

[0055] F c ≥F c0 ,

[0056] G≤G0,

[0057] T = (WD) / (n-1),

[0058] 1 / 4·T≤D≤1 / 2·T,

[0059] 1 / 3·B≤H≤2 / 3·B,

[0060] t = (Ld) / (m-1),

[0061] 1 / 4·t≤d≤1 / 2·t,

[0062] 1 / 3·B≤h≤2 / 3·B,

[0063] H+h≤3 / 4·B,

[0064] 3≤n≤5

[0065] 3≤m≤8,

[0066] D = d;

[0067] Among them, F h For lateral bearing capacity, F c For vertical bearing capacity, F h0 For minimum lateral bearing capacity, F c0 For the minimum vertical load-bearing capacity, G is the weight of the anti-climb device, G0 is the maximum weight of the anti-climb device, L is the overall length of the anti-climb teeth, T is the distance between the vertical anti-climb teeth, B is the overall thickness of the anti-climb teeth, H is the vertical tooth height, D is the vertical tooth width, W is the overall width of the anti-climb teeth, t is the distance between the anti-lateral movement teeth, h is the height of the anti-lateral movement teeth, d is the width of the anti-lateral movement teeth, ρ is the density of the anti-climb tooth material, V is the volume of the anti-climb device, n is the number of vertical anti-climb teeth, and m is the number of lateral anti-climb teeth.

[0068] Preferably, in step two, each of the multiple optimization objectives in the multi-objective optimization function is taken as the objective function, and the remaining objectives are taken as the constraint responses. The extrema of the multiple single objectives are solved one by one using OptiStruct.

[0069] Preferably, the comprehensive objective function for multi-objective optimization in step three is:

[0070]

[0071] Among them, F hmin F cmin G represents the minimum lateral and vertical load of the anti-climb device. max For the maximum weight of the anti-climb device, and F hmin =F h0 F cmin =F c0 G max =G0;

[0072] σ is the objective function that combines vertical load, lateral load, and weight.

[0073] W1, W2, and W3 are the weight values ​​of the three objectives, and W1 + W2 + W3 = 1;

[0074] F hmax F cmax G represents the maximum lateral and vertical load capacity of the anti-climb device. min This is the minimum weight for the anti-climb device.

[0075] Preferably, F hmin The value is 100kN, F cmin The value is 130kN, G max The value is 50Kg.

[0076] Preferably, W1 = W2 = W3 = 1 / 3.

[0077] Preferably, in step two, OptiStruct is used to solve for the extreme values ​​of multiple single targets.

[0078] Advantages and beneficial effects of the present invention: In a train collision, the anti-climbing teeth mesh with each other, and the car body is subjected to both longitudinal and lateral forces. The anti-climbing teeth designed in this invention have added anti-lateral displacement function, which not only prevents vehicles from climbing each other, but also limits the car body from derailing in a zigzag pattern due to excessive deflection angle. This ensures that the force at the collision interface is transmitted longitudinally along the car body, maximizes the utilization rate of the end energy absorption area, ensures the integrity of the vehicle, and minimizes the degree of damage caused by the accident. Attached Figure Description

[0079] Figure 1 This is a flowchart illustrating the design method of the novel anti-climbing and anti-lateral movement structure described in this invention.

[0080] Figure 2 This is a schematic diagram of the overall anti-climb device that meets the energy absorption parameter requirements of the present invention.

[0081] Figure 3 This is a schematic diagram of the initialization structure with anti-climbing and anti-lateral movement functions described in this invention.

[0082] Figure 4 This is a schematic diagram of the optimized region described in this invention.

[0083] Figure 5a This is a schematic diagram of the novel anti-climbing and anti-lateral movement structure described in this invention.

[0084] Figure 5b This is a schematic diagram of the novel anti-climbing and anti-lateral movement structure described in this invention.

[0085] Figure 6a This is a stress cloud diagram after simulation calculation and verification of the novel anti-climbing and anti-lateral movement structure described in this invention.

[0086] Figure 6b This is a displacement cloud diagram after simulation calculation and verification of the novel anti-climbing and anti-lateral movement structure described in this invention. Detailed Implementation

[0087] The present invention will now be described in further detail with reference to the accompanying drawings, so that those skilled in the art can implement it based on the description.

[0088] like Figure 1As shown, this invention provides a novel design method for an anti-climb and anti-lateral movement structure, demonstrating how to add a function to restrict lateral movement while meeting the requirements of structural strength and anti-climb functionality. An ideal anti-climb device possesses maximum lateral and vertical load-bearing capacity and minimum weight. To achieve the design of this novel anti-climb device, a multi-objective optimization method is introduced.

[0089] The main process of this method is as follows:

[0090] First, the multiple multi-objectives are transformed into corresponding single-objective optimizations;

[0091] Secondly, a finite model is established using Hypermesh, and the single-objective optimal solution of the model is solved using Optistruct. The optimization parameters of the anti-crawler are solved, and CAD data is directly generated using the OSSmooth tool to obtain the anti-crawler structure.

[0092] Next, establish a comprehensive objective function for multi-objective optimization with average weights;

[0093] Finally, the comprehensive objective function of multi-objective optimization is used as the minimum objective function. A finite model is established using Hypermesh, and the model is solved using Optistruct. After the optimization objective and optimization parameters are input into Optistruct, the software automatically solves the problem based on the existing constraints. When the optimization objective is minimized, the corresponding value in the constraint parameters is found, and the optimal anti-crawler structure size is obtained.

[0094] According to the requirements for anti-creep devices in CRRC's standard-designed metro vehicles, the vertical load-bearing capacity must be no less than 130kN. During train operation, due to curves or terrain, the vehicle may experience a ±15° deflection angle, resulting in approximately 100mm of lateral displacement. Under these conditions, the train is prone to zigzag derailment. To prevent lateral displacement, the anti-creep device must withstand a lateral force of no less than 100kN. Simultaneously, meeting this requirement, the new anti-creep structure can also limit lateral movement when the coupler experiences significant deflection during a collision.

[0095] For optimization designs that achieve multiple objectives, the commonly used methods are the empirical method and the single-objective transformation method.

[0096] The experience-based method refers to senior engineers using similar past cases as a basis to first provide finite design dimensions, and then verify them through testing or finite element analysis to evaluate whether the design requirements are met. This method relies on the engineer's experience, involves a large amount of verification work, and the final result is only feasible, making it difficult to achieve the optimal result.

[0097] The single-objective transformation method involves selecting one objective from multiple objectives for optimization, then using the others as constraints, and finally performing the optimization design. If different objectives are chosen as the final optimization objective, the final optimization results will differ. If improvements are needed for some of the objectives that are transformed into constraints, the constraints must be made more stringent, such as limiting the weight to no more than 80% of the original, but this requires experience and repeated trials. Therefore, this method is not the optimal solution. For multiple equally important objectives, different selections of optimization objectives can cause significant deviations in the optimization results.

[0098] Multi-objective optimization refers to optimizing multiple sub-objectives simultaneously, and its mathematical expression is usually represented in the following form:

[0099] The constraint formula is as follows:

[0100] MaxF m (x)m=1,2,3,…,M,

[0101] MinG n (x)n=1,2,3,…,N,

[0102] stg j (x)≤0j=1,2,3,…,J,

[0103] h k (x) = 0, k = 1, 2, 3, ..., K

[0104]

[0105] In the formula, F is the structural force vector required to support the anti-climb device, G is the weight of the anti-climb device, and F m (x) and G n (x) represents the sub-objective function to be optimized, and m and n are the number of optimization design variables; g j (x) and h k (x) represents the inequality constraints; x i The design variable takes the values ​​0 and 1 (0 represents deleting the unit, and 1 represents keeping the unit); L and U are the design variables and their upper and lower limits.

[0106] Lateral and vertical load-bearing capacity are important indicators for evaluating this mechanism. For an ideal anti-climb device, maximum vertical load-bearing capacity is required.

[0107] And lateral load, minimum weight requirement, therefore the constraint objective is:

[0108] MaxF h =f(T,H,D,t,h,d)

[0109] MaxF c = f(T,H,D,t,h,d),

[0110] MinG=ρV(L,W,B,T,H,D,t,h,d),

[0111] F h ≥F h0 ,

[0112] F c ≥F c0 ,

[0113] G≤G0;

[0114] Among them, F h For lateral bearing capacity, F c For vertical bearing capacity, F h0 For minimum lateral bearing capacity, F c0 The minimum vertical load-bearing capacity is given by G, the weight of the anti-climb device is given by G0, the maximum weight of the anti-climb device is given by G0, the overall length of the anti-climb teeth is given by L, the distance between the vertical anti-climb teeth is given by T, the overall thickness of the anti-climb teeth is given by B, the vertical tooth height is given by H, the vertical tooth width is given by D, the overall width of the anti-climb teeth is given by W, the distance between the anti-lateral movement teeth is given by t, the height of the anti-lateral movement teeth is given by h, the width of the anti-lateral movement teeth is given by d, the density of the anti-climb tooth material is given by ρ, and the volume of the anti-climb device is given by V.

[0115] One of the multiple objectives is treated as a single optimization objective, while the other optimization objectives are treated as constraints.

[0116] F h As the primary optimization objective function, other F c If G is used as a constraint function, then:

[0117] MaxF h = f(T,H,D,t,h,d),

[0118] F h ≥F h0 ,

[0119] F c ≥F c0 ,

[0120] G≤G0,

[0121] T = (WD) / (n-1),

[0122] 1 / 4·T≤D≤1 / 2·T,

[0123] 1 / 3·B≤H≤2 / 3·B,

[0124] t = (Ld) / (m-1),

[0125] 1 / 4·t≤d≤1 / 2·t,

[0126] 1 / 3·B≤h≤2 / 3·B,

[0127] H+h≤3 / 4·B,

[0128] 3≤n≤5,

[0129] 3≤m≤8,

[0130] D = d;

[0131] Using OptiStruct to solve, we can obtain F. hmax .

[0132] Similarly, F c As the primary optimization objective function, other F h If G is used as a constraint function, then:

[0133] MaxF c =f(T,H,D,t,h,d)

[0134] F h ≥F h0 ,

[0135] F c ≥F c0 ,

[0136] G≤G0,

[0137] T = (WD) / (n-1),

[0138] 1 / 4·T≤D≤1 / 2·T,

[0139] 1@3·B≤H≤2 / 3·B,

[0140] t = (Ld) / (m-1),

[0141] 1 / 4·t≤d≤1 / 2·t

[0142] 1 / 3·B≤h≤2 / 3·B,

[0143] H+h≤3 / 4·B,

[0144] 3≤n≤5,

[0145] 3≤m≤8,

[0146] D = d;

[0147] Using OptiStruct to solve, we can obtain F. cmax .

[0148] Similarly, using G as the primary optimization objective function, and other F... h Fc As a constraint function, it is:

[0149] MinG=ρV(L,W,B,T,H,D,t,h,d)

[0150] F h ≥F h0 ,

[0151] F c ≥F c0 ,

[0152] G≤G0,

[0153] T = (WD) / (n-1),

[0154] 1 / 4·T≤D≤1 / 2·T

[0155] 1 / 3·B≤H≤2 / 3·B,

[0156] t = (Ld) / (m-1),

[0157] 1 / 4·t≤d≤1 / 2·t,

[0158] 1 / 3·B≤h≤2 / 3·B,

[0159] H+h≤3 / 4·B,

[0160] 3≤n≤5

[0161] 3≤m≤8,

[0162] D = d;

[0163] Using OptiStruct to solve, we can obtain G. min .

[0164] In the above formula, F h For lateral bearing capacity, F c For vertical bearing capacity, F h0 For minimum lateral bearing capacity, F c0 For the minimum vertical load-bearing capacity, G is the weight of the anti-climb device, G0 is the maximum weight of the anti-climb device, L is the overall length of the anti-climb teeth, T is the distance between the vertical anti-climb teeth, B is the overall thickness of the anti-climb teeth, H is the vertical tooth height, D is the vertical tooth width, W is the overall width of the anti-climb teeth, t is the distance between the anti-lateral movement teeth, h is the height of the anti-lateral movement teeth, d is the width of the anti-lateral movement teeth, ρ is the density of the anti-climb tooth material, V is the volume of the anti-climb device, n is the number of vertical anti-climb teeth, and m is the number of lateral anti-climb teeth.

[0165] The multi-objective optimization of the anti-climb device considers the optimization of three objectives: vertical load, lateral load, and weight, and uses a comprehensive objective function for multi-objective optimization.

[0166]

[0167] In the formula, σ is the objective function that integrates vertical load, lateral load, and weight; W1, W2, and W3 are the weight values ​​of the three objectives, W1 + W2 + W3 = 1, and W1 = W2 = W3 = 1 / 3. hmax F cmax G represents the maximum lateral and vertical load capacity of the anti-climb device. min To minimize the weight of the anti-climb device, a single-objective optimization is required; F hmin F cmin G represents the minimum lateral and vertical load of the anti-climb device. max This represents the maximum weight of the anti-climb device; the value here is the minimum design requirement.

[0168] F hmin =F h0 ,

[0169] F cmin =F c0 ,

[0170] G max =G0.

[0171] Example

[0172] Taking the CRRC's standardized Metro Type A vehicle as an example, the energy absorption parameters of the anti-climb device are required as follows: plastic deformation force of 600kN, energy absorption stroke of 380mm; load-bearing capacity: lateral force not less than 100kN, vertical force not less than 130kN; anti-climb device dimensions (L×W×H) = 1025×360×220 (mm); anti-climb tooth dimensions (L×W×B) = 250×220×40 (mm); weight not exceeding 50kg. After design, the overall anti-climb device model that meets the energy absorption parameter requirements is as follows: Figure 2 As shown.

[0173] Establish a multi-objective optimization function:

[0174] MaxF h =f(T,H,D,t,h,d)

[0175] MaxF c = f(T,H,D,t,h,d),

[0176] MinG=ρV(250,220,40,T,H,D,t,h,d),

[0177] F h ≥100kN,

[0178] F c≥130kN,

[0179] G≤50kg;

[0180] like Figure 3 As shown, the initial topology model obtained by topology optimization design according to the above constraints has the function of restricting lateral and vertical movement, but the existing tooth size and weight are not the optimal structure.

[0181] F h As the primary optimization objective function, other F c If G is used as a constraint function, then:

[0182] MaxF h =f(T,H,D,t,h,d)

[0183] F h ≥100kN,

[0184] F c ≥130kN,

[0185] G≤50kg,

[0186] H+h≤30,

[0187] n=4,

[0188] m = 5,

[0189] T=(WD) / (n-1), T=(220-D) / 3,

[0190] 1 / 4·T≤D≤1 / 2·T,

[0191] 1 / 3·B≤H≤2 / 3·B,

[0192] t=(Ld) / (m-1),t=(250-d) / 4,

[0193] 1 / 4·t≤d≤1 / 2·t,

[0194] 1 / 3·B≤h≤2 / 3·B,

[0195] H+h≤3 / 4·B,

[0196] D = d;

[0197] Using OptiStruct to solve, we can obtain: where D = d = 22.48, T = 65.84, t = 56.88, H = 13.74, h = 7.51, F hmax = 159.40 kN; Similarly, F cmax =182.27kN, G min= 40.27kg. The areas requiring optimization are tooth height, dimensions, and arrangement cycle; the non-optimized area is the bottom dimensions of the anti-climb device, as detailed below. Figure 4 As shown, Figure 4 The area at point A in the middle is the optimization region.

[0198] Establish a comprehensive objective function for multi-objective optimization:

[0199]

[0200] W1+W2+W3=1;

[0201] Right now

[0202] W1 = W2 = W3 = 1 / 3;

[0203] Using OptiStruct to solve the problem, the lateral bearing capacity F can be obtained. h =133.42kN, vertical bearing capacity F c =169.71kN; anti-climb device weight G = 43.59kg. Taking these three as targets, the final optimization was achieved through iterative analysis of the anti-climb device's relevant parameters. When the optimization targets reached their optimal values, the final optimized anti-climb teeth were obtained: D = d = 26.5, T = 64.5, t = 55.875, H = 16.68, h = 11.53. The final optimized anti-climb teeth are shown below. Figure 5a , 5b As shown.

[0204] Finally, simulation calculations were performed to verify the optimized anti-climb device. A vertical load of 130kN and a lateral load of 100kN were simultaneously applied to the teeth of the anti-climb device, and the stress and deformation results of the anti-climb teeth were extracted. Figure 6a , 6b As shown, the optimized anti-climb teeth meet the requirements.

[0205] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and illustrations shown and described herein.

Claims

1. A design method for an anti-climbing and anti-lateral movement structure, characterized in that, Includes the following steps: Step 1: Based on the initial performance requirements and external dimensions of the anti-climb device, establish an initial model of the anti-climb device that meets the parameter requirements. Set optimization and non-optimization regions for the initial model of the anti-climb device and establish a multi-objective optimization function. Step 2: Take each of the multiple optimization objectives in the multi-objective optimization function as the objective function, and the remaining objectives as the constraint responses, and solve for the extreme values ​​of multiple single objectives one by one; Step 3: Assign weights to the extreme values ​​of the multiple single objectives to establish a comprehensive objective function for multi-objective optimization; Step 4: Solve the comprehensive objective function of the multi-objective optimization to obtain the optimal solution for the anti-crawler structure; In step one, the optimization region is the size and arrangement cycle of the anti-climb teeth at the interface of the anti-climb device, and the non-optimization region is the bottom size of the anti-climb device. The multi-objective optimization function is: MaxF h =f(T,H,D,t,h,d), MaxF c =f(T,H,D,t,h,d), MinG=ρV(L,W,B,T,H,D,t,h,d), F h ≥F h0 , F c ≥F c0 , G≤G0; Among them, F h For lateral bearing capacity, F c For vertical bearing capacity, F h0 For minimum lateral bearing capacity, F c0 For the minimum vertical load-bearing capacity, G is the weight of the anti-climb device, G0 is the maximum weight of the anti-climb device, L is the overall length of the anti-climb teeth, T is the distance between the vertical anti-climb teeth, B is the overall thickness of the anti-climb teeth, H is the vertical tooth height, D is the vertical tooth width, W is the overall width of the anti-climb teeth, t is the distance between the anti-lateral movement teeth, h is the anti-lateral movement tooth height, d is the anti-lateral movement tooth width, ρ is the density of the anti-climb tooth material, and V is the volume of the anti-climb device. In step two, each of the multiple optimization objectives in the multi-objective optimization function is taken as the objective function, and the remaining objectives are taken as constraint responses. The extrema of each of the multiple single objectives are then solved sequentially. Specifically: F h As the objective function, F c If G is the constraint response, then it is: MaxF h =f(T,H,D,t,h,d), F h ≥F h0 , F c ≥F c0 , G≤G0, T = (WD)(n-1), 1 / 3·B≤H≤2 / 3·B, 1 / 4·T≤D≤1 / 2·T, t = (Ld)(m-1), 1 / 4·t≤d≤1 / 2·t 1 / 3·B≤h≤2 / 3·B, H+h≤3 / 4·B, 3≤n≤5， 3≤m≤8， D = d; F c As the objective function, F h If G is the constraint response, then it is: MaxF c =f(T,H,D,t,h,d), F h ≥F h0 , F c ≥F c0 , G≤G0, T = (WD)(n-1), 1 / 4·T≤D≤1 / 2·T, 1 / 3·B≤H≤2 / 3·B, t = (Ld)(m-1), 1 / 4·t≤d≤1 / 2·t, 1 / 3·B≤h≤2 / 3·B, H+h≤3 / 4·B, 3≤n≤5， 3≤m≤8， D = d; Taking G as the objective function, F h F c As a constraint response, it is: MinG=ρV(L,W,B,T,H,D,t,h,d), F h ≥F h0 , F c ≥F c0 , G≤G0, T = (WD) / (n-1), 1 / 4·T≤D≤1 / 2·T, 1 / 3·B≤H≤2 / 3·B, t = (Ld) / (m-1), 1 / 4·t≤d≤1 / 2·t, 1 / 3·B≤h≤2 / 3·B, H+h≤3 / 4·B, 3≤n≤5， 3≤m≤8， D = d; Among them, F h For lateral bearing capacity, F c For vertical bearing capacity, F h0 For minimum lateral bearing capacity, F c0 For the minimum vertical load-bearing capacity, G is the weight of the anti-climb device, G0 is the maximum weight of the anti-climb device, L is the overall length of the anti-climb teeth, T is the distance between the vertical anti-climb teeth, B is the overall thickness of the anti-climb teeth, H is the vertical tooth height, D is the vertical tooth width, W is the overall width of the anti-climb teeth, t is the distance between the anti-lateral teeth, h is the anti-lateral tooth height, d is the anti-lateral tooth width, ρ is the density of the anti-climb tooth material, V is the volume of the anti-climb device, n is the number of vertical anti-climb teeth, and m is the number of lateral anti-climb teeth. The comprehensive objective function for the multi-objective optimization in step three is: Among them, F hmin F cmin G represents the minimum lateral and vertical load of the anti-climb device. max For the maximum weight of the anti-climb device, and F hmin =F h0 F cmin =F c0 G max =G0; σ is the objective function that combines vertical load, lateral load, and weight; W1, W2, and W3 are the weight values ​​of the three objectives, W1 + W2 + W3 = 1; F hmax F cmax G represents the maximum lateral and vertical load capacity of the anti-climb device. min This is the minimum weight for the anti-climb device.

2. The design method of the anti-climb and anti-lateral movement structure according to claim 1, characterized in that, In step two, each of the multiple optimization objectives in the multi-objective optimization function is taken as the objective function, and the remaining objectives are taken as the constraint responses. The extrema of multiple single objectives are solved one by one using OptiStruct.

3. The design method of the anti-climb and anti-lateral movement structure according to claim 2, characterized in that, F hmin The value is 100kN, F cmin The value is 130kN, G max The value is 50Kg.

4. The design method of the anti-climb and anti-lateral movement structure according to claim 1, characterized in that, W1 = W2 = W3 = 1 / 3.

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