A method, medium and computing device for quickly making a 3D model of a fabric

Abstract

The application relates to a fabric 3D model rapid plate making method, and the technical scheme points are as follows: by setting an AI knowledge graph, different drape and stretch fabrics can be correlated, in the process of using new fabrics for 3D simulation and simulation, the drape data and the stretch data of the fabrics stored in the AI knowledge graph are compared with each other, and the 3D data model corresponding to the most similar data is selected, so that the most similar 3D data model can be generated according to the drape and stretch characteristics of the fabric when the fabric is used to make a 3D model, the reality of the 3D data model is improved, the plate making speed is higher than that in the traditional process of cutting and plate making by using actual fabrics, the research and development design cycle is shorter, and the method is more suitable for the increasingly short clothing iteration cycle.

Description

Technical Field

[0001] This invention relates to the field of clothing design technology, and more specifically, to a method, medium, and computing device for rapid pattern making of 3D fabric models. Background Technology

[0002] In the current clothing design process, fabric is usually made into a pattern according to the design. However, the current clothing industry is iterating rapidly. According to the conventional pattern making, cutting and sewing process, it takes a long time to complete the design of a garment. During this period, it is necessary to make modifications, which is very time-consuming and wastes fabric.

[0003] Some existing software or systems can fill fabrics according to pattern models to generate simulated clothing patterns. However, existing simulated clothing patterns are usually fixed and cannot be modified according to the type and characteristics of the fabric. As a result, the clothing models produced are all fixed in appearance and cannot reflect different textures according to the characteristics of the fabric. Summary of the Invention

[0004] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method for rapid pattern making of fabric 3D models, so as to solve the problem that the existing 3D models used for fabric pattern making cannot reflect the different garment textures according to the characteristics of the fabric.

[0005] The above-mentioned technical objective of the present invention is achieved through the following technical solution: a method for rapid pattern making of fabric 3D models; comprising the following steps:

[0006] S1. Measure the fabric to be tested and generate the fabric data to be tested corresponding to the fabric to be tested.

[0007] S2. Take a picture of the fabric to be tested to obtain a texture map of the fabric per unit area;

[0008] S3. Input the fabric data to be tested into a preset AI knowledge graph; the AI ​​knowledge graph stores multiple fabric data and their corresponding 3D data models;

[0009] S4. Compare the fabric data to be tested with each fabric data in the AI ​​knowledge graph to generate a corresponding data similarity set; the data similarity set contains multiple data similarities and their corresponding fabric data and 3D data models;

[0010] S5. Filter out the fabric data and 3D data model corresponding to the data with the highest data similarity in the data similarity set, and denot them as the maximum fabric data and the maximum 3D data model, respectively.

[0011] S6. Determine whether the data similarity corresponding to the maximum fabric data is greater than the similarity threshold. If yes, proceed to step S7; otherwise, proceed to step S8.

[0012] S7. Fill the maximum 3D data model with the unit area texture map to generate the pattern making model corresponding to the fabric to be patterned.

[0013] S8. Design a corresponding model to be filled based on the fabric data to be patterned, and fill the model to be filled with the unit area texture map to generate the patterning model corresponding to the fabric to be patterned.

[0014] Optionally, the fabric data to be tested includes drape and stretch; the weighting ratio of drape and stretch is 1:1.

[0015] Optionally, the data similarity includes drape similarity and stretch similarity; the similarity threshold includes drape similarity threshold and stretch similarity threshold; the data similarity corresponding to the largest fabric data being greater than the similarity threshold includes: the drape similarity being greater than the drape similarity threshold and the stretch similarity being greater than the stretch similarity threshold; the data similarity corresponding to the largest fabric data being less than the similarity threshold includes: the drape similarity being less than the drape similarity threshold and / or the stretch similarity being less than the stretch similarity threshold.

[0016] Optionally, step S9 is also included: storing the fabric data to be patterned and the corresponding pattern-making model into an AI knowledge graph.

[0017] Optionally, the measurement of the fabric to be tested includes measuring drape; this includes the following steps:

[0018] S1A. Hang the fabric of the predetermined size to be printed with one end fixed.

[0019] S1B, The fabric to be tested is blown by an airflow at a predetermined flow rate;

[0020] S1C. Measure and record the distance between the bottom of the fabric to be tested and the plumb line.

[0021] Optionally, the measurement of the fabric to be tested includes measuring drape; this includes the following steps:

[0022] S1D: Hang the fabric of the predetermined size to be printed with one end fixed.

[0023] S1E: Use airflow to blow the fabric to be hit so that the distance between the fabric to be hit and the plumb line reaches a preset distance.

[0024] S1F, Measure and record the velocity of the airflow.

[0025] Optionally, the measurement of the fabric to be tested, including its elasticity, includes the following steps:

[0026] S1G, Fix both ends of the fabric to be patterned to the predetermined size respectively;

[0027] S1H: Apply opposite forces to the fabric to be tested, causing the fabric to be tested to stretch a predetermined distance to both ends;

[0028] S1I, Measure and record the magnitude of the applied force.

[0029] Optionally, the measurement of the fabric to be tested, including its elasticity, includes the following steps:

[0030] S1J, Fix both ends of the fabric to be patterned to the predetermined size respectively;

[0031] S1K: Stretch both ends of the fabric to be patterned using a predetermined force.

[0032] S1L, Measure and record the deformation length of the fabric to be tested.

[0033] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0034] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0035] In summary, the present invention has the following beneficial effects: By establishing an AI knowledge graph, fabrics with different drape and elasticity can be interconnected. When a new fabric needs to be used for 3D simulation, the drape and elasticity data of the fabric are compared with those pre-stored in the AI ​​knowledge graph, and the 3D data model corresponding to the most similar data is selected. This ensures that when making a 3D model of the fabric, the most similar 3D data model is generated according to the characteristics of the fabric such as drape and elasticity, which improves the realism of the 3D data model. Compared with the traditional process of cutting and pattern making using actual fabrics, the sampling speed is faster, the R&D design cycle is shorter, and it is more suitable for the increasingly shorter iteration cycle of clothing. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the process of the present invention;

[0037] Figure 2 This is a schematic diagram of the AI ​​knowledge graph structure of the present invention;

[0038] Figure 3This is a schematic diagram of another form of AI knowledge graph structure of the present invention;

[0039] Figure 4 This is a flowchart illustrating the steps involved in measuring droopiness according to the present invention.

[0040] Figure 5 This is a flowchart of another step in the present invention for measuring droop.

[0041] Figure 6 This is a flowchart illustrating the steps involved in measuring elasticity according to the present invention.

[0042] Figure 7 This is another flowchart of the steps for measuring elasticity according to the present invention;

[0043] Figure 8 This is a schematic diagram of the structure of a rapid pattern-making system for fabric 3D models according to the present invention;

[0044] Figure 9 This is an internal structural diagram of a computer device in an embodiment of the present invention.

[0045] In the diagram: 1. Fabric data conversion module; 2. Fabric photography module; 3. Fabric data storage module; 4. Fabric data matching module; 5. Similarity comparison module; 6. Pattern making model filling module; 7. 3D data model generation module. Detailed Implementation

[0046] To make the objectives, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein.

[0047] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances. The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature.

[0048] In this invention, unless otherwise expressly specified and limited, "above" or "below" a second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of a second feature includes the first feature being directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" of a second feature includes the first feature being directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature. The terms "vertical," "horizontal," "left," "right," "above," "below," and similar expressions are for illustrative purposes only and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed or operated in a specific orientation, and therefore should not be construed as limiting the invention.

[0049] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0050] This invention provides a method for rapid pattern making from a 3D fabric model, such as... Figure 1 As shown, it includes the following steps:

[0051] S1. Measure the fabric to be tested and generate the fabric data to be tested corresponding to the fabric to be tested.

[0052] S2. Take a picture of the fabric to be tested to obtain a texture map of the fabric per unit area;

[0053] S3. Input the fabric data to be tested into a preset AI knowledge graph; the AI ​​knowledge graph stores multiple fabric data and their corresponding 3D data models;

[0054] S4. Compare the fabric data to be tested with each fabric data in the AI ​​knowledge graph to generate a corresponding data similarity set; the data similarity set contains multiple data similarities and their corresponding fabric data and 3D data models;

[0055] S5. Filter out the fabric data and 3D data model corresponding to the data with the highest data similarity in the data similarity set, and denot them as the maximum fabric data and the maximum 3D data model, respectively.

[0056] S6. Determine whether the data similarity corresponding to the maximum fabric data is greater than the similarity threshold. If yes, proceed to step S7; otherwise, proceed to step S8.

[0057] S7. Fill the maximum 3D data model with the unit area texture map to generate the pattern making model corresponding to the fabric to be patterned.

[0058] S8. Design a corresponding model to be filled based on the fabric data to be patterned, and fill the model to be filled with the unit area texture map to generate the patterning model corresponding to the fabric to be patterned.

[0059] In practical applications, this invention aims to address the problem that existing infill models are fixed formats set by others. Even when filling different fabric patterns, the generated 3D data models cannot effectively represent the different drapes and stretchability of different fabrics. To address this issue, this application establishes a knowledge graph that links different fabrics through the interrelationships between feature points. For example, given five different fabrics A, B, C, D, and E, each corresponding to stretchability data A1, B1, C1, D1, E1 and drape data A2, B2, C2, D2, E2, a corresponding knowledge graph can be generated. Figure 2 As shown, data is stored in relation to each other in an AI knowledge graph; when some data are equal or located in the same interval and can be approximated as equal, multiple nodes can be overlapped and grouped into one node, such as... Figure 3 As shown, A1 / C1 / E1 can be approximated as the same data. Overlapping these three nodes allows for the retrieval of corresponding fabric information when searching for related data. When multiple fabrics have similar drape and stretch, they can be tagged using the same 3D data model. However, since the colors and textures of the two fabrics may differ, they cannot be lumped together. Similarly, the 3D data model corresponding to the fabric and its unit area image can be linked and stored as nodes in the AI ​​knowledge graph. This allows for more intuitive retrieval of fabric data when searching for related information.

[0060] Specifically, the steps for designing the corresponding filling model based on the fabric data to be tested are as follows:

[0061] 1. Based on several existing 3D data models, and assuming that the drape remains unchanged, establish a curve function model of how the first variable in the 3D data model changes with the fabric's elasticity.

[0062] 2. Based on several existing 3D data models, and assuming that the stretchability remains unchanged, establish a curve function model of how the second variable in the 3D data model changes with the fabric drape.

[0063] 3. Based on the change curves of the first and second changes, make corresponding modifications to the existing 3D model to generate the corresponding infilled model;

[0064] As can be seen from the above steps, those skilled in the art can generate a fabric to be filled model under the condition of covering more fabric data.

[0065] Furthermore, the fabric data to be tested includes drape and stretch; the weighting ratio of drape and stretch is 1:1.

[0066] Furthermore, the data similarity includes vertical similarity and scalability similarity;

[0067] The similarity thresholds include: vertices similarity threshold and scalability similarity threshold;

[0068] The data similarity corresponding to the maximum fabric data is greater than the similarity threshold, including: the drape similarity is greater than the drape similarity threshold and the stretch similarity is greater than the stretch similarity threshold.

[0069] The data similarity corresponding to the maximum fabric data is less than the similarity threshold, including: the drape similarity is less than the drape similarity threshold and / or the stretch similarity is less than the stretch similarity threshold.

[0070] In practical applications, the weights of drape and stretch are equal. Specifically, this applies to the following scenario: When comparing data for fabric A, there are two fabrics B / C whose similarity to fabric A is greater than a similarity threshold (in this embodiment, the similarity threshold is assumed to be 0.92). That is, the drape similarity is greater than the drape similarity threshold of 0.92, and the stretch similarity is greater than the stretch similarity threshold of 0.92. However, the two similarity values ​​corresponding to the two fabrics are different. For example, the drape similarity B1 between fabric A and fabric B is 0.96, and the stretch similarity B2 between fabric A and fabric B is 0.94; the drape similarity C1 between fabric A and fabric C is 0.95, and the stretch similarity C2 between fabric A and fabric C is 0.98. Therefore, a comprehensive similarity needs to be calculated based on the weights of drape and stretch to select the 3D model corresponding to the fabric most similar to fabric A. Similarly, different weights can be set as needed, which will not be elaborated further here. Furthermore, in this embodiment, if the drape and stretch similarity of fabrics B and C are the same as those of fabric A, then the 3D data models used for fabrics B and C are the same, and there is no need to separate the TOP13D data model.

[0071] Furthermore, it also includes step S9: storing the fabric data to be patterned and the corresponding pattern-making model into an AI knowledge graph.

[0072] Furthermore, such as Figure 4 As shown, the measurement of the fabric to be tested includes measuring the drape; and includes the following steps:

[0073] S1A. Hang the fabric of the predetermined size to be printed with one end fixed.

[0074] S1B, The fabric to be tested is blown by an airflow at a predetermined flow rate;

[0075] S1C. Measure and record the distance between the bottom of the fabric to be tested and the plumb line.

[0076] Furthermore, such as Figure 5 As shown, the measurement of the fabric to be tested includes measuring the drape; and includes the following steps:

[0077] S1D: Hang the fabric of the predetermined size to be printed with one end fixed.

[0078] S1E: Use airflow to blow the fabric to be hit so that the distance between the fabric to be hit and the plumb line reaches a preset distance.

[0079] S1F, Measure and record the velocity of the airflow.

[0080] In practical applications, drape is primarily influenced by the weight per unit area of ​​the fabric. The heavier the fabric per unit area, the more pronounced the drape, the less likely the fabric is to be blown up by the wind, the less prone it is to wrinkle, and the smoother the surface. Therefore, by using airflow to move the bottom of the fabric and measuring the displacement distance caused by a predetermined airflow velocity, or by using airflow to move the bottom of the fabric a predetermined distance and recording the airflow speed, the drape of the fabric can be quantified. This recorded data can then be stored in an AI knowledge graph to achieve fabric data recording.

[0081] Furthermore, such as Figure 6 As shown, the measurement of the fabric to be tested, including its elasticity, includes the following steps:

[0082] S1G, Fix both ends of the fabric to be patterned to the predetermined size respectively;

[0083] S1H: Apply opposite forces to the fabric to be tested, causing the fabric to be tested to stretch a predetermined distance to both ends;

[0084] S1I, Measure and record the magnitude of the applied force.

[0085] Furthermore, such as Figure 7 As shown, the measurement of the fabric to be tested, including its elasticity, includes the following steps:

[0086] S1J, Fix both ends of the fabric to be patterned to the predetermined size respectively;

[0087] S1K: Stretch both ends of the fabric to be patterned using a predetermined force.

[0088] S1L, Measure and record the deformation length of the fabric to be tested.

[0089] In practical applications, stretchability is mainly based on the materials used in the fabric and the weave pattern of the fabric. The stretchability is formed after pre-shrinking, dehydration and drying. The fabric stretchability is tested by stretching both ends of the fabric using a unit width and unit length of fabric. The deformation of the fabric under a certain tension can be recorded, as well as the magnitude of the tension required to cause a certain deformation of the fabric.

[0090] In summary, this application, through the establishment of an AI knowledge graph in the actual production and design process, can interconnect fabrics with different drapes and stretch properties. When a new fabric needs to be used for 3D simulation, the drape and stretch data of the fabric are compared with those pre-stored in the AI ​​knowledge graph, and the 3D data model corresponding to the most similar data is selected. This ensures that when creating a 3D model of the fabric, the most similar 3D data model is generated based on the fabric's drape and stretch properties, improving the realism of the 3D data model. Compared with the traditional process of cutting and pattern making using actual fabrics, the sampling speed is faster, the R&D design cycle is shorter, and it is more suitable for increasingly shorter garment iteration cycles.

[0091] like Figure 8 As shown, the present invention also provides a rapid pattern-making system for fabric 3D models, comprising:

[0092] Fabric data conversion module 1: It is used to convert the measured fabric data into corresponding drape data and stretch data; that is, it is used to convert the detected airflow speed or the offset distance at the bottom of the fabric into the drape data of the fabric; it can also be used to convert the recorded tensile force or the elongation deformation of the fabric into the stretch data of the fabric.

[0093] Fabric photography module 2; used to photograph the fabric to be tested and obtain a texture map of the fabric per unit area;

[0094] Fabric data storage module 3: Inputs the fabric data to be patterned into a preset AI knowledge graph; the AI ​​knowledge graph stores multiple fabric data and their corresponding 3D data models;

[0095] Fabric data matching module 4: used to compare the fabric data to be patterned with each fabric data in the AI ​​knowledge graph to generate a corresponding data similarity set; the data similarity set contains multiple data similarities and their corresponding fabric data and 3D data models;

[0096] Similarity comparison module 5: Used to compare similarity with similarity threshold;

[0097] Pattern Making Model Filling Module 6: Fills the maximum 3D data model with the unit area texture map to generate the pattern making model corresponding to the fabric to be patterned;

[0098] 3D data model generation module 7: used to design the corresponding filling model based on the fabric data to be tested;

[0099] Specific limitations regarding the rapid pattern-making system for 3D fabric models can be found in the above description of the system's limitations and will not be repeated here. Each module in the aforementioned rapid pattern-making system for 3D fabric models can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the computer device's memory as software, allowing the processor to call and execute the corresponding operations of each module.

[0100] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. When the computer program is executed by the processor, it implements a method for rapid pattern making of a 3D fabric model.

[0101] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0102] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0103] S1. Measure the fabric to be tested and generate the fabric data to be tested corresponding to the fabric to be tested.

[0104] S2. Take a picture of the fabric to be tested to obtain a texture map of the fabric per unit area;

[0105] S3. Input the fabric data to be tested into a preset AI knowledge graph; the AI ​​knowledge graph stores multiple fabric data and their corresponding 3D data models;

[0106] S4. Compare the fabric data to be tested with each fabric data in the AI ​​knowledge graph to generate a corresponding data similarity set; the data similarity set contains multiple data similarities and their corresponding fabric data and 3D data models;

[0107] S5. Filter out the fabric data and 3D data model corresponding to the data with the highest data similarity in the data similarity set, and denot them as the maximum fabric data and the maximum 3D data model, respectively.

[0108] S6. Determine whether the data similarity corresponding to the maximum fabric data is greater than the similarity threshold. If yes, proceed to step S7; otherwise, proceed to step S8.

[0109] S7. Fill the maximum 3D data model with the unit area texture map to generate the pattern making model corresponding to the fabric to be patterned.

[0110] S8. Design a corresponding model to be filled based on the fabric data to be patterned, and fill the model to be filled with the unit area texture map to generate the patterning model corresponding to the fabric to be patterned.

[0111] In one embodiment, the fabric data to be tested includes drape and stretch; the weighting ratio of drape and stretch is 1:1.

[0112] In one embodiment, the data similarity includes vertical similarity and scalability similarity;

[0113] The similarity thresholds include: vertices similarity threshold and scalability similarity threshold;

[0114] The data similarity corresponding to the maximum fabric data is greater than the similarity threshold, including: the drape similarity is greater than the drape similarity threshold and the stretch similarity is greater than the stretch similarity threshold.

[0115] The data similarity corresponding to the maximum fabric data is less than the similarity threshold, including: the drape similarity is less than the drape similarity threshold and / or the stretch similarity is less than the stretch similarity threshold.

[0116] In one embodiment, the method further includes step S9: storing the fabric data to be patterned and the corresponding pattern-making model into an AI knowledge graph.

[0117] In one embodiment, measuring the fabric to be tested includes measuring drape; and includes the following steps:

[0118] S1A. Hang the fabric of the predetermined size to be printed with one end fixed.

[0119] S1B, The fabric to be tested is blown by an airflow at a predetermined flow rate;

[0120] S1C. Measure and record the distance between the bottom of the fabric to be tested and the plumb line.

[0121] In one embodiment, measuring the fabric to be tested includes measuring drape; and includes the following steps:

[0122] S1D: Hang the fabric of the predetermined size to be printed with one end fixed.

[0123] S1E: Use airflow to blow the fabric to be hit so that the distance between the fabric to be hit and the plumb line reaches a preset distance.

[0124] S1F, Measure and record the velocity of the airflow.

[0125] In one embodiment, measuring the fabric to be tested, including its elasticity, includes the following steps:

[0126] S1G, Fix both ends of the fabric to be patterned to the predetermined size respectively;

[0127] S1H: Apply opposite forces to the fabric to be tested, causing the fabric to be tested to stretch a predetermined distance to both ends;

[0128] S1I, Measure and record the magnitude of the applied force.

[0129] In one embodiment, measuring the fabric to be tested, including its elasticity, includes the following steps:

[0130] S1J, Fix both ends of the fabric to be patterned to the predetermined size respectively;

[0131] S1K: Stretch both ends of the fabric to be patterned using a predetermined force.

[0132] S1L, Measure and record the deformation length of the fabric to be tested.

[0133] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0134] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0135] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for rapid pattern making from a 3D fabric model, characterized in that, Includes the following steps: S1. Measure the fabric to be tested and generate the fabric data to be tested corresponding to the fabric to be tested. S2. Take a picture of the fabric to be tested to obtain a texture map of the fabric per unit area; S3. Input the fabric data to be tested into a preset AI knowledge graph; the AI ​​knowledge graph stores multiple fabric data and their corresponding 3D data models; S4. Compare the fabric data to be tested with each fabric data in the AI ​​knowledge graph to generate a corresponding data similarity set; the data similarity set contains multiple data similarities and their corresponding fabric data and 3D data models; S5. Filter out the fabric data and 3D data model corresponding to the data with the highest data similarity in the data similarity set, and denot them as the maximum fabric data and the maximum 3D data model, respectively. S6. Determine whether the data similarity corresponding to the maximum fabric data is greater than the similarity threshold. If yes, proceed to step S7; otherwise, proceed to step S8. S7. Fill the maximum 3D data model with the unit area texture map to generate the pattern making model corresponding to the fabric to be patterned. S8. Design a corresponding model to be filled based on the fabric data to be patterned, and fill the model to be filled with the unit area texture map to generate a pattern-making model corresponding to the fabric to be patterned; the fabric data to be patterned includes drape and stretch; the weight ratio of drape and stretch is 1:

1. The specific steps for designing the corresponding filling model based on the fabric data to be tested are as follows: S8A. Based on several existing 3D data models, and assuming that the drape remains unchanged, establish a curve function model of how the first variable in the 3D data model changes with the fabric's elasticity. S8B. Based on several existing 3D data models, and assuming that the stretchability remains unchanged, establish a curve function model of how the second variable in the 3D data model changes with the fabric drape. S8C. Based on the change curves of the first and second changes, make corresponding modifications to the existing 3D model to generate the corresponding model to be filled. The data similarity includes vertical similarity and scalability similarity; The similarity thresholds include: vertical similarity threshold and scalability similarity threshold; The data similarity corresponding to the maximum fabric data is greater than the similarity threshold, including: the drape similarity is greater than the drape similarity threshold and the stretch similarity is greater than the stretch similarity threshold. The data similarity corresponding to the maximum fabric data is less than the similarity threshold, including: the drape similarity is less than the drape similarity threshold and / or the stretch similarity is less than the stretch similarity threshold; the measurement of the fabric to be patterned includes measuring the drape; including the following steps: S1A. Hang the fabric of the predetermined size to be printed with one end fixed. S1B, The fabric to be tested is blown by an airflow at a predetermined flow rate; S1C. Measure and record the distance between the bottom of the fabric to be tested and the plumb line.

2. The method for rapid pattern making of a 3D fabric model according to claim 1, characterized in that: It also includes step S9: storing the fabric data to be patterned and the corresponding pattern-making model into an AI knowledge graph.

3. The method for rapid pattern making of a 3D fabric model according to claim 1, characterized in that: The measurement of the fabric to be tested includes measuring its drape; and includes the following steps: S1D: Hang the fabric of the predetermined size to be printed with one end fixed. S1E: Use airflow to blow the fabric to be hit so that the distance between the fabric to be hit and the plumb line reaches a preset distance. S1F, Measure and record the velocity of the airflow.

4. The method for rapid pattern making of a 3D fabric model according to claim 1, characterized in that: The measurement of the fabric to be tested, including its elasticity, includes the following steps: S1G, Fix both ends of the fabric to be patterned to the predetermined size respectively; S1H: Apply opposite forces to the fabric to be tested, causing the fabric to be tested to stretch a predetermined distance to both ends; S1I, Measure and record the magnitude of the applied force.

5. The method for rapid pattern making of a 3D fabric model according to claim 1, characterized in that: The measurement of the fabric to be tested, including its elasticity, includes the following steps: S1J, Fix both ends of the fabric to be patterned to the predetermined size respectively; S1K: Stretch both ends of the fabric to be patterned using a predetermined force. S1L, Measure and record the deformation length of the fabric to be tested.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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