A crepe digital texture pattern and a digital construction method thereof

Abstract

The application provides a crepe digital texture pattern and a digital construction method thereof, and belongs to the technical field of jacquard fabrics. The crepe digital texture pattern is an m*n binary matrix, m is the number of weft cycles, n is the number of warp cycles, element 0 in the matrix represents a weft float, element 1 represents a warp float, 1 is represented by black, and 0 is represented by white; the crepe texture pattern takes weft floats as the main body, and warp floats are randomly and uniformly distributed; the maximum length of continuous warp floats in the warp and weft directions is a preset value j, and the maximum length of continuous weft floats in the warp and weft directions is a preset value w. The application can realize full-digital construction and four-square continuous seamless splicing of the crepe digital texture pattern.

Description

Technical Field

[0001] This application relates to a digital texture pattern of crepe fabric and its digital construction method, belonging to the field of jacquard fabric technology. Background Technology

[0002] Crepe weave, also known as wool weave, is a classic weave structure that gives fabrics a unique appearance and feel. Its surface features irregular, intricately arranged, slightly uneven granules that create a crepe-like pattern, making it widely used in clothing and home textiles. Its core design requirements must simultaneously meet three points: first, the warp floats must be randomly and evenly distributed with no obvious regularity in the texture, ensuring a natural and full crepe effect; second, the warp and weft float lengths and interlacing times must be controlled to avoid an overly loose structure and ensure consistent warp shrinkage to meet manufacturing process requirements; and third, it must allow for continuous, seamless splicing in all four directions, adapting to the mass production needs of large-cycle jacquard weaving.

[0003] Existing crepe fabric design techniques are mainly divided into two categories: traditional design methods and digital design methods. I. Traditional design methods mainly rely on overlapping, rotation, selection, and simplification. Their core principle is to achieve a random crease effect by disrupting the inherent patterns of the basic structure. Therefore, they suffer from drawbacks such as repetitive texture patterns, insufficient crease effect, and poor randomness and naturalness. Furthermore, simplification design methods are complex, require high levels of expertise, necessitate repeated manual verification and correction, and have extremely low design efficiency. While crease-based CAD designs based on simplification have emerged in recent years, they still cannot adequately meet the three core requirements of crease design simultaneously.

[0004] Second, digital design methods, such as CN109377523A, pioneered the application of chaotic algorithms and Markov random fields to generate random textile textures. However, when handling seamless four-way continuous stitching, it employs an "iterative fitting" strategy based on outward extension. This "passive waiting" method based on probability matching has significant computational limitations and system risks. (1) Inefficiency and high uncertainty of latitudinal splicing: In the latitudinal boundary processing, it relies entirely on the random overlap between the extended generated state and the previous state. Since the generation of each organization point is constrained by the Markov random field and the floating length rule, the distribution of legal solutions is not uniform. This matching mechanism of "taking chances" through blind trial and error is not only extremely inefficient in computation, but also has significant uncertainty in generation time, which cannot meet the requirements of industrial software for stable algorithm output.

[0005] (2) Warp splicing is more difficult and carries the risk of program crashing or memory overflow: In warp splicing, the triggering conditions of existing technologies are more stringent, requiring the newly generated entire row sequence to be strictly identical to the target starting row. For the high-resolution matrix required for industrial jacquard, each row contains hundreds or thousands of pixels. The joint probability of a full match between two long-sequence high-dimensional discrete random vectors will decrease exponentially with the increase of matrix dimension. In practical engineering applications, the probability of such a natural match approaches zero, which can easily lead the algorithm into an invalid extension loop, thereby causing program crashing or memory overflow.

[0006] (3) "Logical deadlock" and infinite loop under small loop size: When the set texture size (loop period) is small, the fitting defects of the existing technology will be further amplified. Under the premise of a small loop size, the extended sequence can easily exceed 2n, resulting in the length of the fittable position exceeding the set length n, making it impossible to fit. This leads to a mathematical deadlock with no random matching solution, causing the algorithm to inevitably fall into an infinite extension dead loop, making it impossible to simultaneously satisfy local process constraints and global boundary matching conditions.

[0007] Given the limitations of the existing technologies, there is still a need to develop a fully automated, controllable, and seamlessly fitting digital design method for crease structures that is more efficient. Summary of the Invention

[0008] In view of this, the first objective of this application is to provide a crepe-structured digital texture pattern that can be fully digitally constructed, globally satisfying the preset constraints of maximum warp float length j and maximum weft float length w, and achieving continuous and seamless splicing in all four directions.

[0009] Specifically, this application is implemented through the following scheme: A crepe-texture digital texture pattern, wherein the crepe-texture digital texture pattern is an m×n binary matrix, where m is the latitudinal cycle number and n is the longitudinal cycle number. In the matrix, element 0 represents latitudinal floating point and element 1 represents longitudinal floating point. 1 is represented by black and 0 by white. The crepe-texture pattern is mainly composed of latitudinal floating points, with longitudinal floating points randomly and evenly distributed. The latitudinal and longitudinal textures have no obvious regularity. It globally satisfies the preset constraints of maximum longitudinal floating length j and maximum latitudinal floating length w, achieving continuous and seamless splicing in all four directions (up, down, left, and right), and still satisfies the global preset constraints of maximum longitudinal floating length j and maximum latitudinal floating length w after splicing.

[0010] Furthermore, as a preferred option: The latitudinal cycle number m = 100 to 200, and the longitudinal cycle number n = 80 to 100.

[0011] The maximum longitude buoyancy j = 1 to 4, and the maximum latitude buoyancy w = 4 to 6.

[0012] In the crepe texture pattern, the proportion of weft floating points is ≥50%.

[0013] The applicant's second objective is to provide a method for digitally constructing the aforementioned crease-textured pattern, the steps of which are as follows: Step 1, Determination of basic constraint parameters: Based on the weaving process and texture design requirements of the crepe structure to be generated (including but not limited to weaving process parameters, yarn raw material characteristics, and fabric texture design requirements), determine the preset parameters for generating the crepe structure texture, namely the basic constraint parameters. The basic constraint parameters include the number of weft cycles m, the number of warp cycles n, the maximum warp float j, the maximum weft float w, and the minimum interval s of the initial crepe structure warp floats. Step 2, Global Weaving Constraint Model Construction: Based on the basic constraint parameters, a global manufacturing constraint model is constructed, clarifying the rules for selecting the legal warp floating-point candidate set and the rules for the ring generation mechanism, ensuring that the entire texture generation process meets the weaving process requirements, and avoiding the risk of exceeding the float length limit from the source; Step 3, Dynamic construction of the set of legal candidate floating-point textures: Based on the current texture generation state, the search range is dynamically determined. Combined with the global weaving constraint model in Step 2, the organization points are traversed within the search range, and the organization points that meet the requirements are selected to construct the set of legal candidate floating-point textures. Step 4, Construction of the ring generation mechanism rule: During the initial crepe organization matrix generation process, an extended matrix of size (m+w+1)×(n+w+1) is first constructed. Through the ring generation mechanism rule, when generating organization points at the right and lower boundaries, the constraint state information of the starting end on the opposite side can be obtained, thereby achieving boundary constraint fitting of the crepe organization matrix. Step 5, Initial Texture Iterative Generation: Generate a uniformly distributed pseudo-random sequence using a chaotic mapping algorithm. Combine the pseudo-random sequence with a decision mapping function to determine the generation position of a single floating-point number from the set of legal candidate floating-point numbers, thus completing the generation of a single floating-point number. Update the generation state of the texture based on the newly generated floating-point number, and repeat steps 3 to 5 until the preset iteration termination condition is met, thus completing the generation of the initial wrinkle organization matrix. Step 6, Output of m×n target matrix: Perform cut and replace processing on the optimized crepe texture extension matrix. Use columns n+1 to n+w+1 and rows m+1 to m+w+1 of the extension matrix to cut and replace the first w+1 columns and the first w+1 rows of the initial crepe texture matrix to obtain an m×n target size bit matrix, and output the organization diagram of the crepe texture digital texture pattern.

[0014] Through the aforementioned fully parameterized digital construction method, a closed-loop constraint mechanism is constructed, encompassing "dynamic determination of generation state, dynamic construction of candidate set, chaotic decision-making for point selection, iterative state update, global constraint optimization, and seamless fitting of ring-shaped generation boundary." This addresses the core pain point of the inability to reconcile randomness and process constraints in crepe fabric design from the source, achieving fully automated generation of crepe fabric textures. The generated crepe fabric textures are primarily based on weft floating points with randomly and evenly distributed warp floating points, globally satisfying preset warp and weft float length constraints. Simultaneously, it achieves seamless splicing in four directions while still conforming to weaving process constraints after splicing. This not only enables digital design of woven fabrics and allows the generated textures to be directly adapted to various digital jacquard weaving equipment without manual correction, but also significantly improves the texture's resistance to imitation and process adaptability, greatly enhancing the design efficiency of crepe fabrics. It solves the problems of low design efficiency, easy exceeding of float length limits, low seam pass rate, and insufficient originality in existing technologies, and is widely used in the design and production of crepe fabrics in the fields of clothing, home textiles, and other textile industries.

[0015] Preferred: The selection rules for the set of valid candidate floating points are as follows: the horizontal and vertical intervals between the candidate point and the generated floating points are both ≥s, and the candidate point is in the same row, with the length of the continuous floating points between the existing floating points on the left and right ≤w; the candidate point is in the same column, with the length of the continuous floating points between the existing floating points above and below ≤w.

[0016] The ring generation mechanism follows these rules: Construct an extended matrix of size (m+w+1)×(n+w+1). For each row, when the (w+1)th column of the row is generated, copy the values ​​from the 1st to the (w+1)th column of the row and replace the values ​​from the (n+1)th to the (n+w+1)th column of the row. When the matrix has generated the (w+1)th row, copy the values ​​from the 1st to the (w+1)th row and replace the values ​​from the (m+1)th to the (m+w+1)th row.

[0017] In step three: The initial generation state is based on the preset starting point of the texture matrix, i.e. the top left corner (1,1), to determine the initial search range. The initial search range is the horizontal [1,w+1] of the point (1,1).

[0018] The generated state includes the position distribution of the generated floating points, the continuous latitudinal floating point lengths of the row and column where the current point is located, and the marked constraint influence range.

[0019] In step five: The chaotic mapping algorithm is selected from any one of the following: Logistic mapping, Skew Tent Map, Henon mapping, and Lorenz chaotic system. The sequences generated by each chaotic system need to be preprocessed to remove the initial unstable iteration values ​​and mapped to the interval [0,1) to ensure that the pseudo-random sequence used as the input to the decision mapping function has good uniform distribution characteristics.

[0020] More preferably, The iterative formula for the Logistic mapping is: Where μ is the chaos control parameter, μ∈(3.57,4], and the initial value corresponding to n=0 is denoted as x 0 , x 0 For each integer ∈(0,1), the first L unstable results are skipped during iteration, generating a stable pseudo-random sequence within the interval [0,1). More preferably, L≥1000, the uniformity deviation of the generated pseudo-random sequence is ≤5%.

[0021] The iterative formula for the Skew Tent Map is as follows: .

[0022] in, P ∈ (0,1), which is the control parameter. The slanted tent mapping has a uniform steady-state probability density distribution in the interval [0,1], which does not require additional inverse trigonometric function homogenization and has low computational complexity.

[0023] The Henon mapping, as a two-dimensional discrete-time dynamical system, has the following iterative formula: .

[0024] The x or y sequence is extracted and normalized to serve as the pseudo-random sequence in this application. Its classic chaotic parameters are a=1.4 and b=0.3.

[0025] The Lorenz chaotic system, as a continuous-time three-dimensional chaotic system, can be discretized and solved using numerical integration algorithms such as the Runge-Kutta method. By extracting any one-dimensional data sequence from the state variables x, y, z and normalizing it, a pseudo-random sequence with high unpredictability can be obtained.

[0026] The decision mapping function satisfies: ,in, x n This is the current value of the pseudo-random sequence. kThe number of elements in the current candidate floating-point set, and the output value of the decision mapping function. X ( x n , k () represents the index of the selected floating-point number in the candidate floating-point set.

[0027] The preset iteration termination condition is: the set of valid candidates via floating-point numbers is empty.

[0028] Compared with the prior art, this application has the following significant advantages: 1) This application achieves a balance between randomness and process constraints, addressing the core pain points of the industry from the source. The dynamic iterative generation closed-loop mechanism with pre-embedded constraints in the entire process embeds weaving constraints into every step of warp floating point generation, rather than post-verification and correction. This ensures both the random uniformity of warp floating point distribution, achieving a natural and full crepe effect, and ensures 100% global compliance with warp and weft float length constraints. The generated crepe structure digital texture pattern requires no manual correction, improving design efficiency by more than 95% compared to the traditional provincial heddle design method.

[0029] 2) This application achieves seamless four-way continuous splicing at the process level, completely solving the problem of low seam pass rate. This application uses a ring generation mechanism of pre-boundary replication to embed the constraints of the left, right and top and bottom boundaries into the generation process, which not only achieves visually seamless splicing, but also ensures that the warp and weft floats at the seam after splicing still strictly meet the preset constraints, and is fully compatible with the mass production of large-cycle digital jacquard.

[0030] 3) This application constructs a fully parameterized weaving constraint system with extremely high adaptability. All core design steps in this application are parameterized and controlled, allowing parameters to be quickly adjusted according to different weaving equipment, different yarn materials (silk, cotton, chemical fibers, etc.), and different texture requirements, thereby achieving standardized and automated rapid generation of crepe textures.

[0031] 4) This application significantly improves the originality and anti-copying ability of textures. This application uses chaotic mapping such as Logistic to generate pseudo-random sequences, which have the initial value sensitivity and unpredictability of chaotic systems. The generated textures have no obvious regularity, are extremely original, and have a much better anti-copying ability than the regular design methods of existing technologies.

[0032] 5) The digital construction method of this application is fully adapted to the core design requirements of crepe fabrics and has a wide range of applications. This application is customized to meet the core requirements of crepe fabrics such as "randomness and naturalness, weaving controllability, and seamless splicing adaptability" and can be adapted to the design requirements of various crepe fabrics. Attached Figure Description

[0033] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application.

[0034] Figure 1 This is a diagram of the digital texture pattern of crepe fabric with dimensions of 200×80 in this application.

[0035] Figure 2 for Figure 1 A four-dimensional continuous stitched diagram of the organization chart.

[0036] Figure 3 This is a diagram of the crepe fabric digital texture pattern with dimensions of 100×100 used in this application.

[0037] Figure 4 for Figure 3 A four-dimensional continuous stitched diagram of the organization chart. Detailed Implementation

[0038] To make the technical problems, technical solutions, and beneficial effects to be solved by this application clearer, the technical solutions in the embodiments of this application will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit the technical solutions of this application. All other embodiments obtained by those skilled in the art based on the embodiments in this application without creative effort are within the scope of protection of this application.

[0039] This embodiment provides a crepe-textured digital texture pattern. The embodiment of this application is described below with reference to the accompanying drawings.

[0040] As a specific solution, the method for digitally constructing crepe-textured patterns in this embodiment is as follows: Step 1: Determine the basic constraint parameters.

[0041] Based on the weaving process requirements and texture design needs of crepe fabric, the basic constraint parameters are determined as follows: Texture matrix dimensions: weft cycle number m=200, warp cycle number n=80, maximum warp float length j=1, maximum weft float length w=6, initial crease fabric warp float minimum interval s=1.

[0042] Step 2: Constructing the global weaving constraint model.

[0043] Based on the above basic constraint parameters, a full-process constraint rule system is constructed: (1) Initial crease organization legal floating point candidate set screening rules: the latitudinal and vertical intervals between the candidate points and the generated floating points are between 1 and 6 (including the end values ​​1 and 6).

[0044] (2) Ring generation mechanism rules: Generate an initial 207×87 (i.e., 200+6+1=207, 80+6+1=87) extended matrix. In a row, after the generation of the 7th (i.e., 6+1) point is completed, copy the 7 points on the left boundary to replace the 7 points on the right boundary to constrain the right boundary. When the matrix generates the 7th (i.e., 6+1) row, copy the 7 rows on the upper boundary to replace the 7 rows on the lower boundary to constrain the lower boundary.

[0045] Step 3: Valid candidates are dynamically constructed using a floating-point set.

[0046] Initialize a 207×87 binary matrix of all zeros (0 represents latitude floating point, 1 represents longitude floating point), with the initial generation state being: no longitude floating points generated; take the upper left corner point (1,1) of the matrix as the initial reference point, determine the initial search range as horizontal [0,7], and based on the filtering rules in step two, filter the organization points that meet the constraints within the search range to form the initial set of legal candidate longitude floating points.

[0047] Complete the first floating-point A a,b After generation, the search range is dynamically updated to the current horizontal range [b+1, b+7] based on the position of the generated floating point.

[0048] Step 4: Iterative generation of the initial texture.

[0049] (1) Pseudo-random sequence generation: Pseudo-random sequences are generated through Logistic chaotic mapping, and the iterative formula is: .

[0050] Logistic chaotic mapping parameters: Chaotic control parameter μ=4, n=0 is the initial random number, denoted as [missing information]. x 0 , x 0 = 0.3275, initial iteration skip count L=1000, skip the first 1000 unstable iteration results, generate a pseudo-random sequence in the [0,1) interval with a length of 2000.

[0051] (2) First floating-point generation: The first floating-point position is selected from the initial candidate set as (1,1) by the decision mapping function and the first pseudo-random number is used to complete the initialization.

[0052] (3) Floating-point iterative generation: Select the floating-point position from the current candidate set through the decision mapping function, and after completing the generation of a single floating-point, update the generation state and the range of influence of constraints.

[0053] Repeat steps three and four until the circular generation mechanism of step two is triggered, performing a circular replacement. After the circular replacement is completed, repeat steps three and four until the set of valid candidates is empty, satisfying the iteration termination condition, thus completing the generation of the initial texture matrix. The initial texture matrix with a specification of 200×80 obtained by the above method is as follows: Figure 1 As shown.

[0054] Step 5: Fitting a continuous seamless boundary in four directions.

[0055] Boundary constraint fitting is achieved through a ring generation mechanism: The regions from rows 201 to 207 and columns 81 to 87 of the shearing extension matrix are copied, and rows 1 to 7 and columns 1 to 7 of the extension matrix are replaced respectively, resulting in a final 200×80 crepe-like digital texture pattern. Its seamlessly stitched four-sided texture diagram is shown below. Figure 2 As shown.

[0056] When the latitudinal cycle number m=100 and the longitudinal cycle number n=100, repeat the above steps to obtain the following result: Figure 3 The 100×100 digital texture pattern of crepe fabric shown is an example of a seamless, continuous fabric pattern created by stitching together four sides. Figure 4 As shown.

[0057] The above results show that the crepe texture generated in this embodiment is mainly composed of weft floats on the front side, with warp floats randomly and evenly distributed, without continuous warp floats, and no obvious regularity in the warp and weft directions, thus possessing a natural and full crepe effect; the maximum warp and weft float length constraint is satisfied 100% globally, and there is no excess float length or texture break at the seam after continuous splicing in four directions, which can be directly used for the digital jacquard mass production of silk crepe fabrics, fully achieving the design expectations.

[0058] In actual industrial software deployment, the full-process parameterization algorithm of this application, due to the high decoupling between the core algorithm module and the front-end interactive interface, allows for flexible replacement of the random number generator core based on the underlying computing power. The chaotic mapping algorithm in step five can also be replaced by Skew Tent Map, Henon Map, or the Lorenz chaotic system.

[0059] (1) Skew Tent Map: Its iterative formula is as follows .

[0060] in, P ∈ (0,1), which is the control parameter. The slanted tent mapping has a uniform steady-state probability density distribution in the interval [0,1], which does not require additional inverse trigonometric function homogenization and has low computational complexity.

[0061] (2) Henon mapping: As a two-dimensional discrete-time dynamical system, its iterative formula is: .

[0062] The x or y sequence is extracted and normalized to serve as the pseudo-random sequence in this application. Its classic chaotic parameters are a=1.4 and b=0.3.

[0063] (3) Lorenz chaotic system: As a continuous-time three-dimensional chaotic system, its differential equation system can be discretized and solved by numerical integration algorithms such as the Runge-Kutta method. The one-dimensional data sequence of any one dimension in the state variables x, y, z can be extracted and normalized to obtain a pseudo-random sequence with high unpredictability.

[0064] The embodiments described above are merely examples of several feasible implementations of this application. While the descriptions are specific and detailed, they should not be construed as limiting the scope of the patent application. Furthermore, the embodiments are not intended to limit the scope of protection outlined in the claims. Those skilled in the art can make various modifications and improvements without departing from the concept of this application. All equivalent implementations or changes that do not depart from the scope of this application should be included in the technology of this application.

Claims

1. A crepe-textured digital texture pattern, characterized in that: The crepe texture pattern is an m×n binary matrix, where m is the latitudinal cycle number and n is the longitudinal cycle number. In the matrix, element 0 represents latitudinal floating points and element 1 represents longitudinal floating points. 1 is represented by black and 0 by white. The crepe texture pattern is mainly composed of latitudinal floating points, with longitudinal floating points randomly and evenly distributed. The maximum length of continuous longitudinal floating points in the longitudinal and latitudinal directions is a preset value j, and the maximum length of continuous latitudinal floating points in the longitudinal and latitudinal directions is a preset value w. After the crepe texture pattern is continuously spliced ​​in four directions, the length of continuous longitudinal floating points at the seam does not exceed j, and the length of continuous latitudinal floating points does not exceed w, achieving seamless splicing.

2. The digital texture pattern of crepe fabric according to claim 1, characterized in that: m=100~200, n=80~100, j=1~4, w=4~6.

3. A method for digitally constructing the crepe texture pattern as described in claim 1, characterized in that, The steps are as follows: Step 1: Determine the basic constraint parameters based on the weaving process and texture design requirements of the crepe structure to be generated. The basic constraint parameters include the weft cycle number m, the warp cycle number n, the maximum warp float j, the maximum weft float w, and the minimum interval s of the initial crepe structure warp floats. Step 2: Based on the basic constraint parameters, construct a global manufacturing constraint model and clarify the rules for selecting the legal floating-point candidate set and the rules for the ring generation mechanism; Step 3: Based on the current texture generation state, dynamically determine the search range, combine the global weaving constraint model, filter out the required organization points within the search range, and construct a set of legal candidate floating-point points; Step 4: First, construct an extended matrix of (m+w+1)×(n+w+1). Then, using the ring generation mechanism rule, while generating organization points at the right and lower boundaries, obtain the constraint state information of the starting end on the opposite side to achieve boundary constraint fitting of the crepe organization matrix. Step 5: Generate a uniformly distributed pseudo-random sequence using a chaotic mapping algorithm. Combine the pseudo-random sequence with the decision mapping function to determine the generation position of the floating point from the set of legal candidate floating points, thus completing the generation of a single floating point. Update the generation state of the texture based on the newly generated floating point, and repeat steps 3 to 5 until the preset iteration termination condition is met, thus completing the generation of the initial wrinkle organization matrix. Step 6: Use columns n+1 to n+w+1 and rows m+1 to m+w+1 of the extended matrix to cut and replace the first w+1 columns and rows of the initial crepe matrix to obtain the target bit matrix of m×n, and output the organization diagram of the crepe digital texture pattern.

4. The method for digitally constructing a crepe-textured digital texture pattern according to claim 3, characterized in that, The filtering rules for the set of legal candidate floating points are as follows: the horizontal and vertical intervals between the candidate point and the generated floating points are both ≥s, and the candidate point is in the same row, with the length of the continuous floating points between the existing floating points on the left and right ≤w; the candidate point is in the same column, with the length of the continuous floating points between the existing floating points above and below ≤w.

5. The method for digitally constructing a crepe-textured digital texture pattern according to claim 3, characterized in that, The ring generation mechanism rules are as follows: construct an extended matrix of size (m+w+1)×(n+w+1). When the (w+1)th column of the current row is generated, copy and replace the values ​​of the (n+1)th to (n+w+1)th columns of the current row. When the matrix is ​​generated to the (w+1)th row, copy and replace the values ​​of the (m+1)th to (m+w+1)th rows of the current row.

6. The method for digitally constructing a crepe-textured digital texture pattern according to claim 3, characterized in that: In step three, the initial generation state of the current texture generation state is based on the top left corner point (1,1) of the extended matrix, and the corresponding initial retrieval range is the horizontal [1,w+1] of point (1,1).

7. The method for digitally constructing a crepe-textured digital texture pattern according to claim 3, characterized in that: The chaotic mapping algorithm is selected from any one of the Logistic mapping, the slanted tent mapping, the Henon mapping, and the Lorenz chaotic system.

8. The method for digitally constructing a crepe-textured digital texture pattern according to claim 7, characterized in that, The iterative formula for the Logistic mapping is: ， Where μ is the chaotic control parameter, μ∈(3.57,4], and the initial value corresponding to n=0 is denoted as x 0 , x 0 ∈(0,1), skip the first L unstable results in the iteration and generate a stable pseudo-random sequence in the interval [0,1).

9. The method for digitally constructing a crepe-textured digital texture pattern according to claim 8, characterized in that: For L≥1000, the uniformity deviation of the generated pseudo-random sequence is ≤5%.

10. The method for digitally constructing a crepe-textured digital texture pattern according to claim 3, characterized in that, The decision mapping function satisfies: ,in, x n This is the current value of the pseudo-random sequence. k The number of elements in the current candidate floating-point set, and the output value of the decision mapping function. X ( x n , k () represents the index of the selected floating-point number in the candidate floating-point set.

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