Textile burr removal degree identification method and system for textile production

Abstract

The invention discloses a textile burr removal degree identification method and system for textile production, and relates to the technical field of textiles. Then cutting and classifying the image by utilizing the size of the burr size parameter calculated by the image processing module to obtain a low-grade fabric class picture and a high-grade fabric class picture, positioning the positions of the two classes of pictures by using a positioning module, and then processing burrs at the corresponding positions of the pictures in combination with an execution module; then the component acquisition module is used for acquiring related data of the textile fabric, the burr removal degree proportion is calculated through the image processing module, whether the burr removal degree is qualified or not is determined according to the burr removal proportion, and the result of whether the burr removal degree is qualified or not is displayed through the display module, so that the effects that the burr position can be firstly determined and once removal is performed are achieved; and then whether burr removal is complete or not is identified.

Description

Technical Field

[0001] The invention relates to the field of textile technology, in particular to a method and system for identifying the degree of textile burr removal used in textile production. Background Art

[0002] After existing textile fabrics are woven and processed, due to the influence of the production and processing operations, irregular velvet burr structures will be generated and remain on the outer surface of the fabric after molding, affecting the overall appearance and effect of the fabric. In order to remove the irregular velvet and burrs, it is necessary to first locate the location of the burrs, and then use the removal structure to remove the velvet and burrs, and it is necessary to identify and judge whether the burrs are removed cleanly and completely. Summary of the invention

[0003] To address the deficiencies mentioned in the above background technology, the present invention aims to provide a method and system for identifying the degree of burr removal for textile production, which can first determine the burr position and remove it once, and then identify whether the burr removal is complete.

[0004] In a first aspect, the purpose of the present invention can be achieved by the following technical solutions: a textile burr removal degree identification system for weaving production, comprising: Image acquisition module: used to acquire textile fabric images and send the textile fabric images to the image processing module; Image processing module: used to perform grayscale processing on the textile fabric image to obtain the grayscale textile fabric image, perform image cutting on the grayscale textile fabric image, and classify the image, wherein the image classification standard is to classify according to the size of the burr size parameter obtained in the grayscale textile fabric image; obtain a low-level fabric category image and a high-level fabric image, send a high-level processing signal to the execution module based on the low-level fabric image, and send a low-level processing signal to the execution module based on the high-level fabric image; The burr size parameters are obtained by calculating the burr size based on the collected burr-related size data, wherein the burr-related size data includes the burr length, the burr thickness and the burr inner diameter; Positioning module: used to locate the position of low-level fabrics and the position of high-level fabrics according to the low-level fabric category pictures and the high-level fabric category pictures respectively; Execution module: used for performing high-level processing on the raw edge after receiving the high-level processing signal and combining the position of the low-level fabric in the positioning module, and performing low-level processing on the raw edge after receiving the low-level processing signal and combining the position of the high-level fabric in the positioning module; Component collection module: used to collect textile fabric related data processed by the execution module, and send the collected textile fabric related data to the component processing and analysis module, wherein the textile fabric related data includes: fabric gram weight, fabric fabric density, and knitted fabric coil length; Component processing and analysis module: used for preprocessing and data identification of textile fabric related data, using the identified textile fabric related data to calculate the quality of the textile fabric, and obtaining the fabric quality evaluation index, and calculating the degree of burr removal according to the calculated fabric quality evaluation index, and obtaining the proportion of burr removal, and determining whether the burr removal degree is qualified according to the calculated burr removal proportion, so as to send a qualified signal or a failed signal to the display module; Display module: after receiving a qualified signal, the display prompts the staff that the removal degree of the textile burrs is qualified at this time; after receiving an unqualified signal, the display prompts the staff that the removal degree of the textile burrs is unqualified at this time.

[0005] In combination with the first aspect, in some implementations of the first aspect, the system further includes: a marking process of the burr-related size data of the image processing module: The burr-related dimension data are marked, wherein the burr length is marked as Li, the burr thickness is marked as Hi, and the burr inner diameter is marked as Ni; wherein i is the number of times the image processing module obtains the burr-related dimension data, and i=1, 2, 3, ..., n, and n is the total number of times the image processing module obtains the burr-related dimension data.

[0006] In combination with the first aspect, in some implementations of the first aspect, the system further includes: a process in which the image processing module calculates the burr size: Using the formula Where Ci is the burr size parameter, k 1 is the correlation coefficient of burr length, k 2 is the correlation coefficient of burr thickness, k 3 is the correlation coefficient of the inner diameter of the burr, and ti is the burr category ratio coefficient obtained based on different categories of burrs.

[0007] In combination with the first aspect, in some implementations of the first aspect, the system further includes: a process in which the image processing module performs classification according to the size of the burr size parameter: Set the standard burr size parameter C0; Compare the calculated raw edge size parameter Ci with the set standard raw edge size parameter C0, and determine the textile fabric category based on the comparison result: If Ci≥C0, the textile fabric image category at this time is marked as a low-level fabric category image, and a high-level processing signal is sent to the execution module; If Ci<C0, the textile fabric image category at this time is marked as a high-level fabric category image, and a low-level processing signal is sent to the execution module.

[0008] In combination with the first aspect, in certain implementations of the first aspect, the system also includes: the component processing and analysis module performs data identification on the processed textile fabric related data, wherein the fabric gram weight is marked as Gj, the fabric density is marked as Wj, and the knitted fabric coil length is marked as Rj, wherein j is the number of times the textile fabric related data is collected by the component collection module, and j=1, 2, 3, ..., m, and m is the total number of times the textile fabric related data is collected by the component collection module.

[0009] In combination with the first aspect, in some implementations of the first aspect, the system further includes: the calculation process of the textile fabric quality calculation of the component processing analysis module is as follows: Using the formula The fabric quality evaluation index Qj is calculated, where G0 is the preset standard fabric weight coefficient, W0 is the preset standard fabric density coefficient, R0 is the preset standard knitted fabric coil length coefficient, and x 1 is the fabric weight influence coefficient, x 2 is the fabric density influence coefficient, x 3 is the influence coefficient of knitted fabric loop length.

[0010] In combination with the first aspect, in certain implementations of the first aspect, the system further includes: a burr removal degree ratio calculation process of the component processing analysis module: In the formula, Blj is the burr removal ratio, Q0 is the fabric quality calibration value; According to the calculated burr removal ratio, determine whether the burr removal degree is qualified: If 80%≤Blj<100%, send a qualified signal to the display module; If 0<Blj<80%, send a failed signal to the display module.

[0011] In a second aspect, in order to achieve the above-mentioned object, the present invention discloses a method for identifying the degree of textile burr removal for weaving production, the method comprising the following steps: Acquire a textile fabric image, perform grayscale processing on the textile fabric image to obtain a grayscale textile fabric image, perform image cutting on the grayscale textile fabric image, and classify the image, wherein the classification standard of the image is to classify according to the size of the burr size parameter obtained in the grayscale textile fabric image; the burr size parameter is obtained by calculating the burr size based on the burr-related size data obtained; The grayscale textile fabric image is cut and classified into low-level fabric category images and high-level fabric category images according to the size of the rough edge size parameter, and the position of the low-level fabric category image and the position of the high-level fabric category image are located; high-level processing is performed according to the position of the low-level fabric category image, and low-level processing is performed according to the position of the high-level fabric category image; Acquire processed textile fabric related data, pre-process the textile fabric related data to obtain processed textile fabric related data, label the processed textile fabric related data, calculate the textile fabric quality using the labeled textile fabric related data, and obtain a fabric quality evaluation index; The burr removal degree is calculated based on the calculated fabric quality evaluation index to obtain the burr removal degree ratio. Based on the calculated burr removal ratio, it is determined whether the burr removal degree is qualified.

[0012] Beneficial effects of the present invention: The present invention collects textile fabric images through an image acquisition module, and then uses the size of the burr size parameters calculated by the image processing module to cut and classify the images to obtain low-level fabric category pictures and high-level fabric category pictures, uses a positioning module to locate the positions of the two types of pictures, and then combines with the execution module to process the burrs at the corresponding positions of the pictures, and then uses the component acquisition module to collect textile fabric related data, and calculates the burr removal degree ratio through the image processing module, and then determines whether the burr removal degree is qualified according to the size of the burr removal ratio, and displays the qualified result through the display module, thereby realizing the function of first determining the burr position and removing it once, and then identifying whether the burr removal is complete. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative work. Figure 1 It is a schematic diagram of the system structure of the present invention; Figure 2 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION

[0014] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of 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.

[0015] Embodiment 1: The following is an introduction to the relevant terms involved in the embodiments of the present application: Image segmentation: Image segmentation is the technology and process of dividing an image into several specific regions with unique properties and proposing targets of interest. It is a key step from image processing to image analysis. Existing image segmentation methods are mainly divided into the following categories: threshold-based segmentation methods, region-based segmentation methods, edge-based segmentation methods, and segmentation methods based on specific theories. From a mathematical point of view, image segmentation is the process of dividing a digital image into non-overlapping regions. The image segmentation process is also a labeling process, that is, pixels belonging to the same region are assigned the same number.

[0016] Coil length of knitted fabrics: Coil length of knitted fabrics refers to the length of yarn required to form a coil. It consists of the coil stem and the extension line of the coil, usually measured in millimeters (mm). Coil length not only affects the density of knitted fabrics, but also has an important impact on its shedding, wear resistance, elasticity, stretch, pilling resistance, snagging and other properties. Therefore, it is an important physical indicator of knitted fabrics.

[0017] Fabric density refers to the number of yarns per unit length in the warp or weft direction of the fabric, usually expressed as yarns / 10cm or yarns / inch. This parameter is one of the important indicators of textile fabrics and is directly related to the quality, appearance style and performance of the fabric. The determination of fabric density is of great significance to ensure product quality, improve production efficiency and meet customer needs.

[0018] like Figure 1 As shown, a textile burr removal degree recognition system for weaving production includes: Image acquisition module, image processing module, positioning module, component acquisition module, component processing and analysis module, execution module and display module; The image acquisition module is used to acquire the textile fabric image, and then send the acquired textile fabric image to the image processing module for processing; After receiving the textile fabric image sent by the data acquisition module, the image processing module performs image processing. Specifically, the processing process of the image processing module includes the following steps: Performing grayscale processing on the textile fabric image to obtain a grayscale textile fabric image; There are many methods for grayscale processing of images, including linear transformation, piecewise linear transformation, etc. These methods aim to enhance the useful information in the image and improve the visual effect of the image by adjusting the brightness and contrast of the image. In addition, HDR tone and exposure adjustment are also effective tools to reduce the grayscale in the image and make the picture more vivid. In this embodiment, the grayscale processing of the image is mainly to reduce the brightness and contrast of the image, so that the textile fabric in the image can be more conveniently inspected as a whole, and more accurate textile fabric images at each position can be obtained by graying the textile fabric, thereby achieving a more accurate positioning effect; Performing image segmentation and classification on the grayscale textile fabric image, wherein the image classification standard is to classify the image according to the size of the rough edge size parameter obtained in the grayscale textile fabric image; Specifically, it should be noted that the process of image segmentation and classification of grayscale textile fabric images includes the following steps: The process of obtaining the burr size parameters is as follows: Obtain burr-related dimension data, including burr length, burr thickness and burr inner diameter; The burr-related dimension data are marked, wherein the burr length is marked as Li, the burr thickness is marked as Hi, and the burr inner diameter is marked as Ni; wherein i is the number of times the image processing module obtains the burr-related dimension data, and i=1, 2, 3, ..., n, and n is the total number of times the image processing module obtains the burr-related dimension data; The burr size is calculated using the burr-related size data to obtain the burr size parameters. The burr size calculation process is as follows: Using the formula Where Ci is the burr size parameter, k 1 is the correlation coefficient of burr length, k 2 is the correlation coefficient of burr thickness, k 3 is the correlation coefficient of the inner diameter of the burr, ti is the burr category ratio coefficient obtained according to different categories of burrs; Among them, in this embodiment, the burr length correlation coefficient, the burr thickness correlation coefficient and the burr inner diameter correlation coefficient are calculated by comprehensive evaluation based on external factors when the application obtains the relevant data of the burr size on a daily basis, including human factors, detection and factors affected by knitted fabric materials, etc. Human factors are caused by human operation or improper scanning; Set the standard burr size parameter C0; The calculated burr size parameter Ci is compared with the set standard burr size parameter C0, the textile fabric category is determined according to the comparison result, and different levels of processing signals are sent to the execution module. The process is as follows: If Ci≥C0, it means that the burr size exceeds the preset burr size, the textile fabric image category is marked as a low-level fabric category image, and the image processing module sends a high-level processing signal to the execution module; If Ci<C0, it means that the burr size does not exceed the preset burr size at this time, and the textile fabric image category at this time is marked as a high-level fabric category image, and the image processing module sends a low-level processing signal to the execution module; Low-grade fabric category means that the size of the raw edges is large, and the raw edges need to be treated more thoroughly. High-grade fabric category means that the size of the raw edges is small, and only simple treatment is needed. Count the number of marked low-level fabric category images and high-level fabric category images respectively, and send the corresponding low-level fabric category images and high-level fabric category images to the positioning module; The positioning module is used to locate the position of the low-level fabric and the position of the high-level fabric according to the low-level fabric category picture and the high-level fabric category picture respectively, so that the subsequent execution module can process the raw edges after combining the position of the low-level fabric and the position of the high-level fabric.

[0019] The component collection module is used to collect the textile fabric related data processed by the execution module, and send the collected textile fabric related data to the component processing and analysis module for processing and analysis, wherein the textile fabric related data includes: fabric gram weight, fabric fabric density, and knitted fabric coil length; Among them, the method for measuring the coil length of knitted fabrics includes the unraveling method and the projection estimation method. The unraveling method is to draw lines along the longitudinal rows of coils on the fabric, unravel them after counting a certain number of coils, and measure their lengths; the projection estimation method is to estimate the length of the coils based on their projections on the plane. The determination of coil length is crucial for determining the weaving process of the rough cloth and controlling the performance of the knitted fabric. This embodiment adopts the projection estimation method because this method is more convenient for calculating the coil length of the knitted fabric. ‌

[0020] After receiving the fabric-related data sent by the component acquisition module, the component processing and analysis module performs component processing and analysis. Specifically, the processing and analysis process of the component processing and analysis module includes the following steps: Preprocessing the textile fabric related data to obtain processed textile fabric related data; The pretreatment mainly includes: Data cleaning, data integration, data transformation, data reduction, etc. These data processing technologies are used before data mining, which greatly improves the quality of data mining patterns and reduces the time required for actual mining.

[0021] In this embodiment, data cleaning is mainly performed on textile fabric related data. The data cleaning routine "cleans" the data by filling in missing values, smoothing noisy data, identifying or removing outliers, and resolving inconsistencies. The main goals are to achieve the following: format standardization, abnormal data removal, error correction, and duplicate data removal.

[0022] The processed textile fabric related data are marked with data, wherein the fabric gram weight is marked as Gj, the fabric density is marked as Wj, and the knitted fabric coil length is marked as Rj, wherein j is the number of times the textile fabric related data collected by the component collection module is collected, and j=1, 2, 3, ..., m, and m is the total number of times the textile fabric related data is collected by the component collection module; The textile fabric quality is calculated using the relevant data of the marked textile fabrics to obtain the fabric quality evaluation index. Specifically, the calculation process of the textile fabric quality calculation is as follows: Using the formula The fabric quality evaluation index Qj is calculated, where G0 is the preset standard fabric weight coefficient, W0 is the preset standard fabric density coefficient, R0 is the preset standard knitted fabric coil length coefficient, and x 1 is the fabric weight influence coefficient, x 2 is the fabric density influence coefficient, x 3 is the influence coefficient of knitted fabric loop length; Furthermore, in the specific implementation process, the preset standard fabric gram weight coefficient, the preset standard fabric density coefficient and the preset standard knitted fabric loop length coefficient are obtained by collecting textile fabric data daily, performing multiple simulation calculations and taking data averages; Among them, in this embodiment, the fabric weight influence coefficient, the fabric density influence coefficient and the knitted fabric coil length influence coefficient are obtained by comprehensive evaluation and calculation based on the influence of external factors when the application obtains data related to textile fabrics on a daily basis, including human factors, detection factors and factors affected by knitted fabric materials, etc.; human factors and detection factors are caused by human operation or improper detection scanning; The burr removal degree is calculated based on the calculated fabric quality evaluation index Qj, and the burr removal degree ratio is obtained; calculation process: In the formula, Blj is the burr removal ratio, Q0 is the fabric quality calibration value; According to the calculated burr removal ratio, determine whether the burr removal degree is qualified: If 80%≤Blj<100%, it means that the burr removal degree is qualified, and the component processing and analysis module sends a qualified signal to the display module; If 0<Blj<80%, it means that the burr removal degree is unqualified; the component processing analysis module sends an unqualified signal to the display module; The execution module performs execution processing after receiving the processing signals of different levels sent by the image processing module, wherein after receiving the high-level processing signal, the high-level burning processing is performed on the burrs in combination with the position of the low-level fabric in the positioning module, and after receiving the low-level processing signal, the low-level cutting processing is performed on the burrs in combination with the position of the high-level fabric in the positioning module; wherein, during the burning processing, the burning mechanism is used to wrap the burrs and then the burning processing is performed; After receiving the qualified signal sent by the component processing and analysis module, the display module displays a prompt to the staff that the degree of removal of textile burrs is qualified at this time. After receiving the unqualified signal sent by the component processing and analysis module, the display module displays a prompt to the staff that the degree of removal of textile burrs is unqualified at this time.

[0023] Specifically, the scheme of the present invention is further described below through an embodiment: the display module prompts the staff whether the degree of burr removal is qualified through dual prompts of the display screen and voice, so that it can be determined more conveniently and quickly.

[0024] Embodiment 2: Figure 2 As shown, a method for identifying the degree of removal of textile burrs for weaving production comprises the following steps: S101: Acquire a textile fabric image, perform grayscale processing on the textile fabric image to obtain a grayscale textile fabric image, perform image segmentation on the grayscale textile fabric image, and classify the image, wherein the classification standard of the image is to classify according to the size of the burr size parameter obtained in the grayscale textile fabric image; the burr size parameter is obtained by calculating the burr size based on the burr-related size data obtained; S102: cutting and classifying the grayscale textile fabric image into a low-level fabric category image and a high-level fabric category image according to the size of the rough edge size parameter, and locating the position of the low-level fabric category image and the position of the high-level fabric category image; performing high-level processing according to the position of the low-level fabric category image, and performing low-level processing according to the position of the high-level fabric category image; S103: Acquire processed textile fabric related data, pre-process the textile fabric related data to obtain processed textile fabric related data, mark the processed textile fabric related data, calculate the textile fabric quality using the marked textile fabric related data, and obtain a fabric quality evaluation index; S104: Calculating the burr removal degree according to the calculated fabric quality evaluation index to obtain a burr removal degree ratio, and determining whether the burr removal degree is qualified according to the calculated burr removal ratio.

[0025] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is used to implement one or more instructions, specifically for loading and executing one or more instructions in a computer storage medium to implement the above method.

[0026] It should be further explained that, based on the same inventive concept, the present invention also provides a computer storage medium, on which a computer program is stored, and the computer program is executed by the processor to execute the above method. The storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: 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 invention, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0027] The above formulas are all calculated by removing dimensions and taking numerical values. The formula is a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data.

[0028] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present disclosure. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0029] The above shows and describes the basic principles, main features and advantages of the present disclosure. Those skilled in the art should understand that the present disclosure is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present disclosure. Without departing from the spirit and scope of the present disclosure, the present disclosure may have various changes and improvements, and these changes and improvements fall within the scope of the present disclosure to be protected.

Claims

1. A textile burr removal degree recognition system for weaving production, characterized in that: include: Image acquisition module: used to acquire textile fabric images and send the textile fabric images to the image processing module; Image processing module: used to perform grayscale processing on the textile fabric image to obtain the grayscale textile fabric image, perform image cutting on the grayscale textile fabric image, and classify the image, wherein the image classification standard is to classify according to the size of the burr size parameter obtained in the grayscale textile fabric image; obtain a low-level fabric category image and a high-level fabric image, send a high-level processing signal to the execution module based on the low-level fabric image, and send a low-level processing signal to the execution module based on the high-level fabric image; The burr size parameters are obtained by calculating the burr size based on the collected burr-related size data, wherein the burr-related size data includes the burr length, the burr thickness and the burr inner diameter; Positioning module: used to locate the position of low-level fabrics and the position of high-level fabrics according to the low-level fabric category pictures and the high-level fabric category pictures respectively; Execution module: used for performing high-level processing on the raw edge after receiving the high-level processing signal and combining the position of the low-level fabric in the positioning module, and performing low-level processing on the raw edge after receiving the low-level processing signal and combining the position of the high-level fabric in the positioning module; Component collection module: used to collect textile fabric related data processed by the execution module, and send the collected textile fabric related data to the component processing and analysis module, wherein the textile fabric related data includes: fabric gram weight, fabric fabric density, and knitted fabric coil length; Component processing and analysis module: used for preprocessing and data identification of textile fabric related data, using the identified textile fabric related data to calculate the quality of the textile fabric, and obtaining the fabric quality evaluation index, and calculating the degree of burr removal according to the calculated fabric quality evaluation index, and obtaining the proportion of burr removal, and determining whether the burr removal degree is qualified according to the calculated burr removal proportion, so as to send a qualified signal or a failed signal to the display module; Display module: after receiving a qualified signal, the display prompts the staff that the removal degree of the textile burrs is qualified at this time; after receiving an unqualified signal, the display prompts the staff that the removal degree of the textile burrs is unqualified at this time.

2. The textile burr removal degree recognition system for weaving production according to claim 1, characterized in that: The marking process of the burr-related dimension data of the image processing module: The burr-related dimension data are marked, wherein the burr length is marked as Li, the burr thickness is marked as Hi, and the burr inner diameter is marked as Ni; wherein i is the number of times the image processing module obtains the burr-related dimension data, and i=1, 2, 3, ..., n, and n is the total number of times the image processing module obtains the burr-related dimension data.

3. The textile burr removal degree identification system for weaving production according to claim 2, characterized in that: The image processing module performs the following process to calculate the burr size: Using the formula In the formula, Ci is the burr size parameter, k1 is the burr length correlation coefficient, k2 is the burr thickness correlation coefficient, k3 is the burr inner diameter correlation coefficient, and ti is the burr category ratio coefficient obtained according to different categories of burrs.

4. The textile burr removal degree recognition system for weaving production according to claim 3, characterized in that: The image processing module performs classification according to the size of the burr size parameter: Set the standard burr size parameter C0; Compare the calculated raw edge size parameter Ci with the set standard raw edge size parameter C0, and determine the textile fabric category based on the comparison result: If Ci≥C0, the textile fabric image category at this time is marked as a low-level fabric category image, and a high-level processing signal is sent to the execution module; If Ci<C0, the textile fabric image category at this time is marked as a high-level fabric category image, and a low-level processing signal is sent to the execution module.

5. The textile burr removal degree recognition system for weaving production according to claim 1, characterized in that: The component processing and analysis module performs data identification on the processed textile fabric related data, wherein the fabric gram weight is marked as Gj, the fabric density is marked as Wj, and the knitted fabric coil length is marked as Rj, wherein j is the number of times the textile fabric related data collected by the component collection module is collected, and j=1, 2, 3, ..., m, and m is the total number of times the textile fabric related data is collected by the component collection module.

6. The textile burr removal degree recognition system for weaving production according to claim 5, characterized in that: The calculation process of the textile fabric quality calculation of the component processing analysis module is as follows: Using the formula The fabric quality evaluation index Qj is calculated, where G0 is the preset standard fabric gram weight coefficient, W0 is the preset standard fabric fabric density coefficient, R0 is the preset standard knitted fabric coil length coefficient, x1 is the fabric weight influence coefficient, x2 is the fabric density influence coefficient, and x3 is the knitted fabric coil length influence coefficient.

7. The textile burr removal degree recognition system for weaving production according to claim 6, characterized in that: The calculation process of the ratio of burr removal degree in the component processing analysis module is as follows: In the formula, Blj is the burr removal ratio, Q0 is the fabric quality calibration value; According to the calculated burr removal ratio, determine whether the burr removal degree is qualified: If 80%≤Blj<100%, send a qualified signal to the display module; If 0<Blj<80%, send a failed signal to the display module.

8. A method for identifying the degree of removal of textile burrs for weaving production, characterized in that: The method comprises the following steps: Acquire a textile fabric image, perform grayscale processing on the textile fabric image to obtain a grayscale textile fabric image, perform image cutting on the grayscale textile fabric image, and classify the image, wherein the classification standard of the image is to classify according to the size of the burr size parameter obtained in the grayscale textile fabric image; the burr size parameter is obtained by calculating the burr size based on the burr-related size data obtained; The grayscale textile fabric image is cut and classified into low-level fabric category images and high-level fabric category images according to the size of the rough edge size parameter, and the position of the low-level fabric category image and the position of the high-level fabric category image are located; high-level processing is performed according to the position of the low-level fabric category image, and low-level processing is performed according to the position of the high-level fabric category image; Acquire processed textile fabric related data, pre-process the textile fabric related data to obtain processed textile fabric related data, label the processed textile fabric related data, calculate the textile fabric quality using the labeled textile fabric related data, and obtain a fabric quality evaluation index; The burr removal degree is calculated based on the calculated fabric quality evaluation index to obtain the burr removal degree ratio. Based on the calculated burr removal ratio, it is determined whether the burr removal degree is qualified.

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