Sleep cabin modeling method and system based on sub-fractal surface

Abstract

The invention discloses a sleep cabin modeling method and system based on sub-fractal surfaces. The method comprises the steps that sleep cabin design parameters are acquired; performing sub-curved surface discretization on the sleep cabin, and constructing a geometric model of the sleep cabin based on a subvision sub-curved surface; obtaining a complexity index C of the geometric model of the sleep cabin; and optimizing the control point Pij by adopting a genetic algorithm so as to minimize the geometric complexity index C and the structural weight w and meet the conditions that the vibration response acceleration a is smaller than the threshold amax and the maximum stress sigma max, thereby obtaining a final geometric model. According to the method, the design flexibility can be enhanced, personalized customization is realized, the design efficiency and innovativeness are improved, the ergonomic performance is optimized, and the safety is improved.

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] There are some key problems with existing sleeping cabin design methods. First, traditional sleeping cabin designs generally lack specificity and flexibility, and it is difficult to meet the personalized needs of different groups of people. Existing methods usually only provide standardized sizes and shapes, and cannot fully consider the user's physical characteristics and usage habits. This not only affects comfort, but also reduces user experience.

[0003] Secondly, existing sleeping cabins lack scientific and ergonomic design guidance in terms of cabin shape and size. Some cabins may have problems such as low space utilization, excessive weight, and difficulty in providing optimal support and wrapping. This not only affects the comfort during use, but may also bring certain safety hazards.

[0004] Furthermore, the existing design methods of sleeping cabins are too dependent on experience or traditional concepts and lack innovation and advancement. Faced with rapidly developing social needs and emerging technologies, these traditional methods are difficult to adapt to future development trends and lead the progress of the industry. Summary of the invention

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

[0006] Step S101, obtaining 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] Where P(u, v) is the coordinates (X, Y, Z) of any point on the surface, u and v are surface parameter coordinates used to describe the position of any point on the surface, and 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 sub-surfaces, 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, Where 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 To the origin (X 0 , Y 0 , Z 0 )’s Euclidean distance;

[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, B i，k (u) and B j,l (v) is the B-spline basis function, P i and P j To control the vertex.

[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 chromosomes, each gene corresponds to the coordinates of a control point;

[0023] Step S1072, randomly generating 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 scheme;

[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, exchange their gene fragments, and 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 by 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] 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.

[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 by the following formula:

[0037] σ=E*ε=E*∑ i ∑ j (|P i，j -P i+1，j |+|P ij-P i，j+1 |) / (L i +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, comprising:

[0041] A parameter input module is used to obtain the design parameters of the sleeping cabin, including the sleeping cabin dimensions L, W, H, the surface curvature radius R, the material elastic modulus E, and the 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] Where P(u, v) is the coordinates (X, Y, Z) of any point on the surface, u and v are surface parameter coordinates used to describe the position of any point on the surface, and 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 sub-surfaces, 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] Where 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 To the origin (X 0 , Y 0 , Z 0 )’s Euclidean distance;

[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] The present invention adopts a more free sub-surface modeling method, which can design a cabin shape that conforms to ergonomics more flexibly. Compared with the traditional fixed design, this method has strong programmability and parameterization characteristics, which is convenient for optimization design and rapid iteration.

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

[0054] The present invention makes full use of computer-aided design technology, greatly improving design efficiency and innovation capabilities. R&D personnel can quickly explore new design solutions and improve innovation levels through parameter debugging and simulation verification.

[0055] The present invention adopts comprehensive human body feature modeling and analysis to achieve better ergonomic performance in cabin shape and structural design. The final product has better support, wrapping and human-computer interaction, greatly improving the user experience.

[0056] The invention can ensure the safety of use to the greatest extent in the optimization design of key parameters such as size and weight, which helps to reduce potential safety hazards and enhance the user's confidence in use. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

[0059] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

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

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

[0062] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after 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 determining" 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 term "includes", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the commodity or device including the elements.

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

[0066] Embodiment 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 the design parameters of the sleeping cabin, including the sleeping cabin dimensions L, W, H, the surface curvature radius R, the material elastic modulus E and the material density ρ;

[0069] Step S103: discretize the sleeping cabin into sub-surfaces based on subdivision n The sub-surfaces are 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 coordinates (X, Y, Z) of any point on the surface, u, v are 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 sub-surfaces, 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] Where 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 To the origin (X 0 , Y 0 , Z 0 )’s Euclidean distance;

[0077] Step S107: Optimize 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.

[0078] Embodiment 2

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

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

[0081] Step S103: discretize the sleeping cabin into sub-surfaces based on subdivision n The sub-surfaces are 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 coordinates (X, Y, Z) of any point on the surface, u, v are 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 sub-surfaces, 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] Where 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 To the origin (X 0 , Y 0 , Z 0 )’s Euclidean distance;

[0089] Step S107: Optimize 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.

[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) = ∑Bi，k (u)*P i ;

[0092] N j，l (v) = ∑B j，l (v)*P j ;

[0093] where B i，k (u) and B j，l (v) are B-spline basis functions, and P i and P j are control points.

[0094] where B i，k (u) = (u - u 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) = (v - v 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] where the step S107 includes the following steps:

[0097] Step S1071: Encode the control point P ij as a chromosome, with each gene corresponding to the coordinates of a control point;

[0098] Step S1072: Randomly generate an initial population containing multiple chromosomes;

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

[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, exchange their gene segments, and 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 by 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 allowable 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] 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.

[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 by the following formula:

[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] Among them, 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 gene string, for example: the original individual 10101 → 10001 after mutation.

[0119] The mutation operation is performed with probability P m The individual's genes are randomly perturbed, and 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 crossover point is randomly 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 crossover points are randomly 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] Embodiment 3

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

[0125] A parameter input module is used to obtain the design parameters of the sleeping cabin, including the sleeping cabin dimensions L, W, H, the surface curvature radius R, the material elastic modulus E, and the 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 coordinates (X, Y, Z) of any point on the surface, u, v are 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 sub-surfaces, 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] Where 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] while represents the Euclidean distance from the control point P ij to the origin (X 0 , Y 0 , Z 0 );

[0134] The model output module is used to optimize the control point 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 , to obtain the final geometric model

[0135] Example 4

[0136] In a certain embodiment, it is assumed that for a sleep pod applicable to adults with a height of 180 cm and a weight of 90 kg, the specific parameters are as follows

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

[0138] Surface curvature radius: R = 0.5 m

[0139] Material elastic modulus: E = 200 GPa

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

[0141] Using the Catmull-Clark subdivision method, an initial surface mesh is constructed with 16x8 control points, and the coordinates of each control point (X ij , Y ij , Z ij ) are solved by an 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] Using a genetic algorithm to optimize the control point coordinates to minimize C and the structural weight W, while satisfying the constraint condition that the vibration response acceleration a < 0.5 m / s 2 , the optimized C = 2.86, W = 85 kg

[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 disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, 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 containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit 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 of the above.

[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 separate 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 (AN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0149] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square 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 square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a 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 the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, constitute a limitation on 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, but is not intended to limit the present invention. All modifications, substitutions, 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, obtaining 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 sub-surfaces, and its 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 coordinates (X, Y, Z) of any point on the surface, u, v are 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 sub-surfaces, 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, Where 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, characterized in that: 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 To control the vertex.

3. The method according to claim 2, characterized in that: 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, characterized in that: The step S107 includes the following steps: Step S1071: Set the control point P ij Encoded as chromosomes, each gene corresponds to the coordinates of a control point; Step S1072, randomly generating 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 scheme; 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, exchange their gene fragments, and 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, characterized in that: In step S1073, the first fitness function is expressed by 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 allowable 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, characterized in that 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, characterized in that: 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, characterized in that 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, characterized in that: 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: A parameter input module is used to obtain the design parameters of the sleeping cabin, including the sleeping cabin dimensions L, W, H, the surface curvature radius R, the material elastic modulus E, and the 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 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 sub-surfaces, 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, Where 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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