A sleeping cabin modeling method and system based on sub-surface

Abstract

The present invention discloses a sleeping cabin modeling method and system based on subdivision surfaces, the method comprising: obtaining sleeping cabin design parameters; discretizing the sleeping cabin into subdivision surfaces, and constructing a sleeping cabin geometric model based on the subdivision surfaces; obtaining a complexity index C of the sleeping cabin geometric model; and optimizing the control points P using a genetic algorithm. ij , to minimize the geometric complexity index C and the structural weight w, while satisfying that the vibration response acceleration a is less than the threshold a max and the maximum stress σ max The present invention can enhance design flexibility, achieve personalized customization, improve design efficiency and innovation, optimize ergonomic performance and enhance safety.

Description

Technical Field

[0001] The present invention belongs to the field of computer system engineering, and in particular relates to a sleeping cabin modeling method and system based on sub-surfaces. Background Art

[0002] Existing sleep pod design methods suffer from several key issues. First, traditional sleep pod designs generally lack specificity and flexibility, making it difficult to meet the individual needs of different groups. Existing methods typically only offer standardized sizes and shapes, failing to fully consider the user's physical characteristics and usage habits. This not only affects comfort but also degrades the user experience.

[0003] Secondly, existing sleeping pods lack scientific and ergonomic design guidance regarding their shape and dimensions. Some pods may suffer from low space utilization, excessive weight, and inability to provide optimal support and comfort. This not only affects comfort but also poses potential safety risks.

[0004] Furthermore, existing sleeping pod design methods rely too heavily on experience and traditional concepts, lacking innovation and advancement. Faced with rapidly evolving social needs and emerging technologies, these traditional methods struggle to adapt to future development trends and lead industry advancement. Summary of the Invention

[0005] In view of the above-mentioned defects in the prior art, the present invention provides a sleeping cabin modeling method based on sub-surfaces, comprising the following steps:

[0006] Step S101: Obtain sleeping cabin design parameters, including sleeping cabin dimensions L, W, H, surface curvature radius R, material elastic modulus E, and material density ρ;

[0007] Step S103: Discretize the sleeping cabin into sub-surfaces, and construct a sleeping cabin geometric model based on the subdivision sub-surfaces. The mathematical model is:

[0008] P(u,v)=∑ i ∑ j N i，k (u)*N j，l (v)*P i，j

[0009] Among them, P(u, v) is the coordinate (X, Y, Z) of any point on the surface, u, v are the surface parameter coordinates used to describe the position of any point on the surface, N i，k (u) and N j，l (v) is the Catmull-Clark subdivision basis function, k and l are the basis function orders, k = 3 or k = 4, l = 3 or l = 4, Pi，j is the coordinates (X i，j , Y i，j , Z i，j ), (i, j) represents a mesh surface after the sleeping cabin is subdivided into a surface, and the entire sleeping cabin surface is discretized into an m×n grid;

[0010] Step S105: Obtain the complexity index C of the sleeping cabin geometric model, using the following formula for calculation:

[0011] C=ΣΣ(|P i，j -P i+1,j |+|P i，j -P i，j+1 |) / (|P i，j +|P i+1，j |+|P i,j+1 |);

[0012] in, Among them L i =L / M,W j =W / N is the spacing of the control points in the x and y directions, H is the cabin height, R is the surface curvature radius, M and N are the number of control points, satisfying M×N=total number of control points,

[0013] and represents the control point P ij Euclidean distance to the origin (X0, Y0, Z0);

[0014] Step S107: Optimize the control point P using a genetic algorithm ij , in order to minimize the geometric complexity index C and the structural weight w, while satisfying that the vibration response acceleration a is less than the threshold a max and the maximum stress σ max , and obtain the final geometric model.

[0015] The Catmull-Clark subdivision basis function N in step S103 is i，k (u) and N j，l The calculation formula for (v) is:

[0016] N i，k (u)=∑B i，k (u)*P i ;

[0017] N j，l (v)=∑B j，l (v)*P j ;

[0018] Among them, Bi，k (u) and B j,l (v) is the B-spline basis function, P i and P j For control vertices.

[0019] Among them, B i，k (u)=(uu i ) / (u i+k -u i )*B i，k-1 (u)+(u i+k+1 -u) / (u i+k+1 -u i+1 )*B i+1，k-1 (u); and

[0020] B j,l (v)=(vv j ) / (v j+l -v j )*B j，l-1 (v)+(v j+l+1 -v) / (v j+l+1 -v j+1 )*B j+1，l-1 (v)

[0021] Wherein, the step S107 includes the following steps:

[0022] Step S1071: Set the control point P ij Encoded as a chromosome, each gene corresponds to the coordinates of a control point;

[0023] Step S1072: randomly generate an initial population, including multiple chromosomes;

[0024] Step S1073: The fitness value of each chromosome is calculated by the first fitness function, which represents the quality of the control point solution;

[0025] Step S1074: Use the roulette wheel selection method to select the individual with the highest fitness value to enter the next generation;

[0026] Step S1075: Randomly select two individuals and exchange their gene fragments to generate two new individuals;

[0027] Step S1076: Randomly mutate individual genes with a certain probability to simulate the mutation process of natural evolution;

[0028] Step S1077: When the maximum number of iterations is reached or the fitness function converges to the required accuracy, the algorithm terminates. At this time, the control point P corresponding to the optimal individual ij That’s what you want.

[0029] The first fitness function in step S1073 is expressed using the following formula:

[0030] minf=C (α1) *W (α2) *(a max -a allow ) (α3) *(σ max -σ allow ) (α4) ; Where C is the geometric complexity index, W is the structural weight, a max a_max maximum vibration response acceleration,

[0031] a allow a_allow-maximum allowed vibration response acceleration σ max is the maximum stress, σ allow is the maximum allowable stress, α1, α2, α3, α4 are nonlinear exponential factors.

[0032] The final structural weight of the geometric model in step S107 is ω=∑ i ∑ j ρ*V ij , where V ij =L i *W j *H is the unit volume, and ρ is the material density.

[0033] The vibration response acceleration a of the final geometric model in step S107 satisfies:

[0034] a=∑ i ∑ j |P i，j -(P i+1，j +P i，j+1 +P i-1，j +P i，j-1 ) / 4| / Δt 2 ≤a max ,

[0035] Among them, a max is the preset maximum vibration response acceleration threshold, and Δt is the sampling time interval.

[0036] The material stress σ of the final geometric model in step S107 is expressed as follows:

[0037] σ=E*ε=E*∑ i ∑ j (|P i，j -P i+1，j |+|P ij -P i，j+1 |) / (Li +W j );

[0038] Among them, σ is the material stress, ε is the material strain, and E is the material elastic modulus.

[0039] Wherein, the material of the sleeping cabin geometric model is memory sponge or memory foam

[0040] The present invention also proposes a sleeping cabin modeling system based on sub-surfaces, including

[0041] Parameter input module, which is used to obtain sleeping cabin design parameters, including sleeping cabin dimensions L, W, H, surface curvature radius R, material elastic modulus E, and density ρ;

[0042] The discretization module is used to discretize the sleeping cabin into sub-surfaces and construct a sleeping cabin geometric model based on the subdivision sub-surfaces. The mathematical model is:

[0043] P(u, v) = ∑ i ∑ j N i，k (u)*N j，l (v)*P i，j ;

[0044] Among them, P(u, v) is the coordinate (X, Y, Z) of any point on the surface, u, v are the surface parameter coordinates used to describe the position of any point on the surface, N i，k (u) and N j，l (v) is the Catmull-Clark subdivision basis function, k and l are the basis function orders, k = 3 or k = 4, l = 3 or 1 = 4, P i，j is the coordinates (X i，j , Y i，j , Z i，j ), (i, j) represents a mesh surface after the sleeping cabin is subdivided into a surface, and the entire sleeping cabin surface is discretized into an m×n grid;

[0045] The complexity index determination module is used to obtain the complexity index C of the sleeping cabin geometric model, which is calculated using the following formula:

[0046] C=∑∑(|P i，j -P i+1，j |+|P i，j -P i，j+1 |) / (|P i，j |+|P i+1，j |+|P i，j+1 |);

[0047] in,

[0048] Among them L i =L / M,W j =W / N is the spacing of the control points in the x and y directions, H is the cabin height, R is the surface curvature radius, M and N are the number of control points, satisfying M×N=total number of control points,

[0049] and represents the control point P ij Euclidean distance to the origin (X0, Y0, Z0);

[0050] Model output module, which is used to optimize the control point P using genetic algorithm ij , in order to minimize the geometric complexity index C and the structural weight w, while satisfying that the vibration response acceleration a is less than the threshold a max and the maximum stress σ max , and obtain the final geometric model.

[0051] Compared with the prior art, the present invention has the following advantages:

[0052] This method uses a more flexible sub-surface modeling approach, enabling more flexible design of ergonomic cabin shapes. Compared to traditional fixed designs, this method offers greater programmability and parameterization, facilitating design optimization and rapid iteration.

[0053] The present invention can quickly generate targeted cabin sizes and shapes based on the physical characteristics of different users, which helps to significantly improve user comfort and meet the personalized needs of different groups.

[0054] This invention fully utilizes computer-aided design technology, greatly improving design efficiency and innovation capabilities. R&D personnel can quickly explore new design solutions and enhance innovation through parameter debugging and simulation verification.

[0055] This invention utilizes comprehensive human body modeling and analysis to achieve superior ergonomics in cabin shape and structural design. The resulting product offers superior support, support, and human-computer interaction, significantly improving the user experience.

[0056] The present invention optimizes the design of key parameters such as size and weight to ensure maximum safety in use, which helps reduce potential safety hazards and enhances user confidence. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The above and other objects, features and advantages of the exemplary embodiments of the present disclosure will become readily understood by reading the following detailed description with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present disclosure are shown in an illustrative and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0058] Figure 1 2 is a flow chart showing a method for modeling a sleeping cabin based on sub-surfaces according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] To make the objectives, technical solutions, and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings. It is apparent that the embodiments described are only some, not all, of the present invention. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are intended to fall within the scope of protection of the present invention.

[0060] The terms used in the embodiments of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The singular forms "a," "an," "the," and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, and unless the context clearly indicates otherwise, "a plurality" generally includes at least two.

[0061] It should be understood that although the terms "first," "second," "third," etc. may be used to describe "...," these "..." should not be limited to these terms. These terms are merely used to distinguish "...." For example, "first..." could also be referred to as "second...", and similarly, "second..." could also be referred to as "first..." without departing from the scope of the present invention.

[0062] It should be understood that the term "and / or" as used herein is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0063] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0064] It should also be noted that the terms "include," "comprises," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a product or device comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such product or device. In the absence of further limitations, an element defined by the phrase "comprises a..." does not exclude the presence of other identical elements in the product or device comprising the element.

[0065] The optional embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0066] Example 1

[0067] like Figure 1 As shown, the present invention discloses a sleeping cabin modeling and evaluation method based on sub-surface, comprising the following steps:

[0068] Step S10 1. Obtain sleeping cabin design parameters, including sleeping cabin dimensions L, W, H, surface curvature radius R, material elastic modulus E, and material density ρ;

[0069] Step S103: discretize the sleeping cabin into sub-surfaces based on subdivision n The sub-surface is used to construct the sleeping cabin geometric model, and its mathematical model is:

[0070] P(u, v) = ∑ i ∑ j N i，k (u)*N j，l (v)*P i，j ;

[0071] Among them, P(u, v) is the coordinate (X, Y, Z) of any point on the surface, u, v are the surface parameter coordinates used to describe the position of any point on the surface, N i，k (u) and N j，l (v) is the Catmull-Clark subdivision basis function, k and l are the basis function orders, k = 3 or k = 4, l = 3 or 1 = 4, P i，j is the coordinates (X i，j , Y i，j , Z i，j ), (i, j) represents a mesh surface after the sleeping cabin is subdivided into a surface, and the entire sleeping cabin surface is discretized into an m×n grid;

[0072] Step S105: Obtain the complexity index C of the sleeping cabin geometric model, using the following formula for calculation:

[0073] C=∑∑(|Pi，j -P i+1，j |+|P i，j -P i，j+1 |) / (|P i，j |+|P i+1，j |+|P i，j+1 |);

[0074] in,

[0075] Among them L i =L / M,W j =W / N is the spacing of the control points in the x and y directions, H is the cabin height, R is the surface curvature radius, M and N are the number of control points, satisfying M×N=total number of control points,

[0076] and represents the control point P ij Euclidean distance to the origin (X0, Y0, Z0);

[0077] Step S107: Optimize the control point P using a genetic algorithm ij , in order to minimize the geometric complexity index C and the structural weight w, while satisfying that the vibration response acceleration a is less than the threshold a max and the maximum stress σ max , and obtain the final geometric model.

[0078] Example 2

[0079] The present invention proposes a sleeping cabin modeling method based on sub-surfaces, comprising the following steps:

[0080] Step S10 1. Obtain sleeping cabin design parameters, including sleeping cabin dimensions L, W, H, surface curvature radius R, material elastic modulus E, and density ρ;

[0081] Step S103: discretize the sleeping cabin into sub-surfaces based on subdivision n The sub-surface is used to construct the sleeping cabin geometric model, and its mathematical model is:

[0082] P(u, v) = ∑ i ∑ j N i，k (u)*N j，l (v)*P i，j ;

[0083] Among them, P(u, v) is the coordinate (X, Y, Z) of any point on the surface, u, v are the surface parameter coordinates used to describe the position of any point on the surface, N i，k (u) and N j，l(v) is the Catmull-Clark subdivision basis function, k and l are the basis function orders, k = 3 or k = 4, l = 3 or 1 = 4, P i，j is the coordinates (X i，j , Y i，j , Z i，j ), (i, j) represents a mesh surface after the sleeping cabin is subdivided into a surface, and the entire sleeping cabin surface is discretized into an m×n grid;

[0084] Step S105: Obtain the complexity index C of the sleeping cabin geometric model, using the following formula for calculation:

[0085] C=∑∑(|P i，j -P i+1，j |+|P i，j -P i，j+1 |) / (|P i，j |+|P i+1，j |+|P i，j+1 |);

[0086] in,

[0087] Among them L i =L / M,W j =W / N is the spacing of the control points in the x and y directions, H is the cabin height, R is the surface curvature radius, M and N are the number of control points, satisfying M×N=total number of control points,

[0088] and represents the control point P ij Euclidean distance to the origin (X0, Y0, Z0);

[0089] Step S107: Optimize the control point P using a genetic algorithm ij , in order to minimize the geometric complexity index C and the structural weight w, while satisfying that the vibration response acceleration a is less than the threshold a max and the maximum stress σ max , and obtain the final geometric model.

[0090] The Catmull-Clark subdivision basis function N in step S103 is i，k (u) and N j，l The calculation formula for (v) is:

[0091] N i，k (u)=∑B i，k (u)*P i ;

[0092] N j，l (v)=∑B j，l (v)*P j ;

[0093] Among them, B i，k (u) and B j，l (v) is the B-spline basis function, P i and P j For control vertices.

[0094] Among them, B i，k (u)=(uu i ) / (u i+k -u i )*B i，k-1 (u)+(u i+k+1 -u) / (u i+k+1 -u i+1 )*B i+1，k-1 (u); and

[0095] B j，l (v)=(vv j ) / (v j+l -v j )*B j，l-1 (v)+(v j+l+1 -v) / (v j+l+1 -v j+1 )*B j+1,l-1 (v)

[0096] Wherein, the step S107 includes the following steps:

[0097] Step S1071: Set the control point P ij Encoded as a chromosome, each gene corresponds to the coordinates of a control point;

[0098] Step S1072: randomly generate an initial population, including multiple chromosomes;

[0099] Step S1073: The fitness value of each chromosome is calculated by the first fitness function, which represents the quality of the control point solution;

[0100] Step S1074: Use the roulette wheel selection method to select the individual with the highest fitness value to enter the next generation;

[0101] Step S1075: Randomly select two individuals and exchange their gene fragments to generate two new individuals;

[0102] Step S1076: Randomly mutate individual genes with a certain probability to simulate the mutation process of natural evolution;

[0103] Step S1077: When the maximum number of iterations is reached or the fitness function converges to the required accuracy, the algorithm terminates. At this time, the control point P corresponding to the optimal individual ij That’s what you want.

[0104] The first fitness function in step S1073 is expressed using the following formula:

[0105] minf=C (α1) *W (α2) *(a max -a allow ) (α3) *(σ max -σ allow ) (α4) Among them, C

[0106] is the geometric complexity index, W is the structural weight, a max a_max maximum vibration response acceleration,

[0107] a allow a_allow-maximum allowed vibration response acceleration σ max is the maximum stress, σ allow is the maximum allowable stress, α1, α2, α3, α4 are nonlinear exponential factors.

[0108] The final structural weight of the geometric model in step S107 is ω=∑ i ∑ j ρ*V ij , where V ij =L i *W j *H is the unit volume, and ρ is the material density.

[0109] The vibration response acceleration a of the final geometric model in step S107 satisfies:

[0110] a=∑ i ∑ j |P i，j -(P i+1，j +P i，j+1 +P i-1，j +P i，j-1 ) / 4| / Δt 2 ≤a max ,

[0111] Among them, a max is the preset maximum vibration response acceleration threshold, and Δt is the sampling time interval.

[0112] The material stress σ of the final geometric model in step S107 is expressed as follows:

[0113] σ=E*ε=E*Σ i Σ j (|P i,j -P i+1，j |+|P ij -P i，j+1 |) / (L i +W j );

[0114] Among them, σ is the material stress, ε is the material strain, and E is the material elastic modulus.

[0115] Wherein, the material of the sleeping cabin geometric model is memory sponge or memory foam.

[0116] In step S1074, the probability of each individual being selected in the Roulette Wheel Selection is proportional to its fitness:

[0117] Where P(i) is the probability that individual i is selected, f(i) is the fitness value of individual i, and Q is the population size.

[0118] In step S1075, binary coding mutation is used with a certain mutation probability p m Randomly flip certain bits on the individual's gene string, for example: the original individual is 10101 → after mutation it is 10001.

[0119] The mutation operation is performed with probability P m Randomly perturb the genes of an individual, the formula is as follows:

[0120]

[0121] in is the new gene value after mutation, is the old gene value before mutation, σ is the standard deviation of mutation, and N(0,1) is a standard normal distribution random number.

[0122] In step S1076, a single-point crossover is used, i.e., a random crossover point is selected on the gene strings of the two parent individuals, and the gene sequences after the crossover point are exchanged to obtain two offspring individuals. Alternatively, a double-point crossover is used, i.e., two random crossover points are selected on the gene strings of the two parent individuals, and the gene sequences between the two crossover points are exchanged to obtain two offspring individuals.

[0123] Example 3:

[0124] The present invention also proposes a sleeping cabin modeling device based on sub-surfaces.

[0125] Parameter input module, which is used to obtain sleeping cabin design parameters, including sleeping cabin dimensions L, W, H, surface curvature radius R, material elastic modulus E, and density ρ;

[0126] The discretization module is used to discretize the sleeping cabin into sub-surfaces and construct a sleeping cabin geometric model based on the subdivision sub-surfaces. The mathematical model is:

[0127] P(u, v) = Σ i Σ j N i，k (u)*N j，l (v)*P i，j ;

[0128] Among them, P(u, v) is the coordinate (X, Y, Z) of any point on the surface, u, v are the surface parameter coordinates used to describe the position of any point on the surface, N i，k (u) and N j，l (v) is the Catmull-Clark subdivision basis function, k and l are the basis function orders, k = 3 or k = 4, l = 3 or 1 = 4, P i，j is the coordinates (X i，j , Y i，j , Z i，j ), (i, j) represents a mesh surface after the sleeping cabin is subdivided into a surface, and the entire sleeping cabin surface is discretized into an m×n grid;

[0129] The complexity index determination module is used to obtain the complexity index C of the sleeping cabin geometric model, which is calculated using the following formula:

[0130] C=Σ∑(|P i，j -P i+1，j |+|P i，j -P i，j+1 |) / (|P i，j |+|P i+1，j |+|P i，j+1 |);

[0131] in,

[0132] Among them L i =L / M,W j =W / N is the spacing of the control points in the x and y directions, H is the cabin height, R is the surface curvature radius, M and N are the number of control points, satisfying M×N=total number of control points,

[0133] and represents the control point P ijEuclidean distance to the origin (X0, Y0, Z0);

[0134] Model output module, which is used to optimize the control point P using genetic algorithm ij , in order to minimize the geometric complexity index C and the structural weight w, while satisfying that the vibration response acceleration a is less than the threshold a max and the maximum stress σ max , and obtain the final geometric model.

[0135] Example 4:

[0136] In one embodiment, assuming a sleeping cabin suitable for an adult with a height of 180 cm and a weight of 90 kg, the specific parameters are as follows:

[0137] Assumed dimensions: L = 2.2m, W = 1.0m, H = 0.8m;

[0138] Surface curvature radius: R = 0.5m;

[0139] Material elastic modulus: E = 200 GPa;

[0140] Material density: ρ = 2700 kg / m 3 ;

[0141] The Catmull-Clark subdivision method is used to construct the initial surface mesh with 16x8 control points. The coordinates of each control point (X ij ,Y ij ,Z ij ) is solved by the optimization algorithm. According to the above formula, the vector differences between all adjacent control points are accumulated to obtain the geometric complexity index C = 3.24.

[0142] Genetic algorithm is used to optimize the coordinates of the control points to minimize C and the structural weight W while satisfying the vibration response acceleration a<0.5m / s 2 The constraints are as follows: after optimization, C=2.86, W=85kg.

[0143] According to the optimized control point coordinates, the final subdivision surface geometric model is output, which has better ergonomic performance and structural stability.

[0144] Example 5

[0145] An embodiment of the present disclosure provides a non-volatile computer storage medium, wherein the computer storage medium stores computer-executable instructions, and the computer-executable instructions can execute the method steps described in the above embodiment.

[0146] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wires, optical cables, RF (radio frequency), etc., or any suitable combination thereof.

[0147] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0148] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0149] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0150] The units involved in the embodiments described in this disclosure may be implemented in software or hardware, wherein the name of a unit does not necessarily limit the unit itself.

[0151] The above introduces the preferred embodiments of the present invention, which is intended to make the spirit of the present invention clearer and easier to understand, and is not intended to limit the present invention. Any modifications, replacements, and improvements made within the spirit and principles of the present invention should be included in the scope of protection outlined by the claims attached to the present invention.

Claims

1. A sleeping cabin modeling method based on sub-surface, characterized in that: The following steps are involved: Step S101: Obtain sleeping cabin design parameters, including sleeping cabin dimensions L, W, H, surface curvature radius R, material elastic modulus E, and material density ρ; Step S103: discretize the sleeping cabin into sub-surfaces, and construct a sleeping cabin geometric model based on the subdivision surface. The mathematical model is: P(u,v)=∑ i ∑ j N i，k (u)*N j，l (v)*P i，j ; Among them, P(u, v) is the coordinate (X, Y, Z) of any point on the surface, u, v are the surface parameter coordinates used to describe the position of any point on the surface, N i，k (u) and N j，l (v) is the Catmull-Clark subdivision basis function, k and l are the basis function orders, k = 3 or k = 4, l = 3 or 1 = 4, P i，j is the coordinates (X i，j , Y i，j , Z i，j ), (i, j) represents a mesh surface after the sleeping cabin is subdivided into a surface, and the entire sleeping cabin surface is discretized into an m×n grid; Step S105: Obtain the complexity index C of the sleeping cabin geometric model, using the following formula for calculation: C=∑Σ(|P i，j -P i+1，j |+|P i，j -P i，j+1| ) / (|P i，j |+|P i+1，j |+|P i，j+1 |); in, Among them L i =L / M,W j =W / N is the spacing of the control points in the x and y directions, H is the cabin height, R is the surface curvature radius, M and N are the number of control points, satisfying M×N=total number of control points, and represents the control point P ij Euclidean distance to the origin (X0, Y0, Z0); Step S107: Optimize the control point P using a genetic algorithm ij , in order to minimize the geometric complexity index C and the structural weight w, while satisfying that the vibration response acceleration a is less than the threshold a max and the maximum stress σ max , and obtain the final geometric model.

2. The method according to claim 1, wherein: The Catmull-Clark subdivision basis function N in step S103 i，k (u) and N j，l The calculation formula for (v) is: N i，k (u)=∑B i，k (u)*P i ; N j，l (v)=∑B j，l (v)*P j ; Among them, B i，k (u) and B j，l (v) is the B-spline basis function, P i and P j For control vertices.

3. The method according to claim 2, wherein: in B i，k (u)=(u) i ) / (in i+k -in i )*B i,k-1 (in)+(in) i+k+1 -u) / (u i+k+1 -in i+1 )*B i+1，k-1 (in); and B j，l (v)=(vv j ) / (v j+l -v j )*B j，l-1 (v)+(v j+l+1 -v) / (v j+l+1 -v j+1 )*B j+1，l-1 (v)。 4. The method according to claim 1, wherein: The step S107 includes the following steps: Step S1071: Set the control point P ij Encoded as a chromosome, each gene corresponds to the coordinates of a control point; Step S1072: randomly generate an initial population, including multiple chromosomes; Step S1073: The fitness value of each chromosome is calculated by the first fitness function, which represents the quality of the control point solution; Step S1074: Use the roulette wheel selection method to select the individual with the highest fitness value to enter the next generation; Step S1075: Randomly select two individuals and exchange their gene fragments to generate two new individuals; Step S1076: Randomly mutate individual genes with a certain probability to simulate the mutation process of natural evolution; Step S1077: When the maximum number of iterations is reached or the fitness function converges to the required accuracy, the algorithm terminates. At this time, the control point P corresponding to the optimal individual ij That’s what you want.

5. The method according to claim 1, wherein: The first fitness function in step S1073 is expressed using the following formula: minf=C (α1) *W (α2) *(a max -a allow ) (α3) *(σ max -σ allow ) (α4) ; Where C is the geometric complexity index, W is the structural weight, a max a_max maximum vibration response acceleration, a allow a_allow-maximum allowed vibration response acceleration σ max is the maximum stress, σ allow is the maximum allowable stress, α1, α2, α3, α4 are nonlinear exponential factors.

6. The method according to claim 5, wherein: The structural weight of the final geometric model in step S107 is ω=∑ i ∑ j ρ*V ij , where V ij =L i *W j *H is the unit volume, and ρ is the material density.

7. The method according to claim 5, wherein: The vibration response acceleration a of the final geometric model in step S107 satisfies: a=∑ i ∑ j |P i，j -(P i+1，j +P i，j+1 +P i-1，j +P i，j-1 ) / 4| / Δt 2 ≤a max , Among them, a max is the preset maximum vibration response acceleration threshold, and Δt is the sampling time interval.

8. The method according to claim 5, wherein: The material stress σ of the final geometric model in step S107 is expressed as follows: σ=E*ε=E*∑ i ∑ j (|P i，j -P i+1，j |+|P ij -P i，j+1 |) / (L i +W j ); Among them, σ is the material stress, ε is the material strain, and E is the material elastic modulus.

9. The method according to claim 1, wherein: The material of the sleeping cabin geometric model is memory sponge or memory foam.

10. A sleeping cabin modeling device based on sub-surface, comprising Parameter input module, which is used to obtain sleeping cabin design parameters, including sleeping cabin dimensions L, W, H, surface curvature radius R, material elastic modulus E, and density ρ; The discretization module is used to discretize the sleeping cabin into sub-surfaces and construct a sleeping cabin geometric model based on the subdivision sub-surfaces. The mathematical model is: P(u,v)=∑ i ∑ j N i，k (u)*N j，l (v)*P i，j ; in, P(u, v) is the coordinates (X, Y, Z) of any point on the surface, u and v are the surface parameter coordinates used to describe the position of any point on the surface, N i，k (u) and N j，l (v) is the Catmull-Clark subdivision basis function, k and l are the basis function orders, k = 3 or k = 4, l = 3 or 1 = 4, P i，j is the coordinates (X i，j , Y i，j , Z i，j ), (i, j) represents a mesh surface after the sleeping cabin is subdivided into a surface, and the entire sleeping cabin surface is discretized into an m×n grid; The complexity index determination module is used to obtain the complexity index C of the sleeping cabin geometric model, which is calculated using the following formula: C=∑∑(|P i，j -P i+1，j |+|P i，j -P i，j+1 |) / (|P i，j |+|P i+1，j |+|P i，j+1 |); in, Among them L i =L / M,W j =W / N is the spacing of the control points in the x and y directions, H is the cabin height, R is the surface curvature radius, M and N are the number of control points, satisfying M×N=total number of control points, and represents the control point P ij Euclidean distance to the origin (X0, Y0, Z0); Model output module, which is used to optimize the control point P using genetic algorithm ij , in order to minimize the geometric complexity index C and the structural weight w, while satisfying that the vibration response acceleration a is less than the threshold a max and the maximum stress σ max , and obtain the final geometric model.

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