Clothing buttonhole precision processing device and processing method

Abstract

The present invention discloses a precise buttonhole and button sewing device and method for clothing, relating to the technical field of clothing processing. The device comprises: a buttonhole feature sequence acquisition module for identifying a buttonhole image sequence through a convolutional neural network to obtain a buttonhole feature sequence; a buttonhole feature sequence compensation module for performing buttonhole recognition error analysis and generating recognition compensation parameters to perform feature compensation on the buttonhole feature sequence; a button position optimization analysis module for performing iterative optimization analysis of button positions; and a mechanical button sewing module for controlling a button sewing device to mechanically sew buttons on target fabric according to an optimal button coordinate sequence and performing position adaptive correction based on trajectory monitoring results. The device solves the technical problems of inaccurate buttonhole positioning, insufficient error compensation, low button sewing efficiency, and inaccurate button sewing caused by fabric deformation, existing in the prior art. The device achieves the technical effects of improving buttonhole processing accuracy, button sewing efficiency, and quality, and optimizing button position.

Description

Technical Field

[0001] The present application relates to the technical field related to clothing processing, and in particular to a precise processing device and method for buttonholes and buttons on clothing. Background Art

[0002] Traditional clothing production relies on manual labor or semi-automated equipment, especially in button and buttonhole processing, which still presents many technical challenges. These include processing errors, inaccurate buttonhole positioning, and unstable button placement. These issues affect the quality of the finished product and production efficiency. Especially in mass production, it is difficult to ensure that the buttonholes and buttons of each garment are precisely aligned. Current buttonhole processing mostly uses mechanized methods, but its inherent drawbacks include difficulty in accurately identifying buttonholes to meet precision requirements and the inability to adapt to changes in the fabric in real time, hindering the realization of more efficient and accurate clothing processing.

[0003] Therefore, in the current related technologies, there are technical problems such as inaccurate buttonhole positioning, insufficient error compensation, low button sewing efficiency, and inaccurate button sewing caused by fabric deformation. Summary of the Invention

[0004] This application solves the technical problems of inaccurate buttonhole positioning, insufficient error compensation, low buttoning efficiency, and inaccurate buttoning caused by fabric deformation in the prior art by providing a precise processing device and method for buttonhole and button fastening on clothing, thereby achieving the technical effect of improving the processing accuracy of buttonholes, buttoning efficiency and quality, and optimizing button positions.

[0005] The present application provides a precise processing device for buttonholes and buttons on clothing, which includes: a buttonhole feature sequence acquisition module, which is used to collect buttonhole images in target fabric, identify the keyhole image sequence through a convolutional neural network, and obtain a keyhole feature sequence; a keyhole feature sequence compensation module, which is used to perform buttonhole recognition error analysis based on target fabric attribute information, generate recognition compensation parameters, perform feature compensation on the keyhole feature sequence, and obtain a corrected keyhole feature sequence; a button position optimization analysis module, which is used to perform iterative optimization analysis of button positions based on the corrected keyhole feature sequence, and generate an optimal button coordinate sequence; a mechanical buttoning operation module, which is used to control a buttoning device to mechanically sew buttons on the target fabric according to the optimal button coordinate sequence, and synchronously monitor the fabric movement trajectory during the buttoning operation, and perform position adaptive correction on the target fabric according to the trajectory monitoring results until the operation is completed.

[0006] In a possible implementation, the method for precise processing of buttonholes and buttons on clothing further performs the following processing: retrieving a set of sample keyhole images, and collecting keyhole features of sample keyhole images from different sample keyhole images to obtain a set of sample keyhole features, wherein the keyhole features include size, shape, and relative position; using the set of sample keyhole images and the set of sample keyhole features as supervision data, a convolutional neural network is trained in combination with a gradient descent algorithm until the mean square error loss function converges, thereby obtaining a keyhole feature recognition plug-in, and using the keyhole feature recognition plug-in to recognize a keyhole image sequence, and outputting the keyhole feature sequence.

[0007] In a possible implementation, the method for precise processing of buttonholes and buttons on clothing further performs the following processing: obtaining fabric attribute information, wherein the fabric attribute information includes at least fabric thickness and fabric elasticity; taking fabric thickness and fabric elasticity as independent variables and buttonhole recognition error as dependent variable, performing correlation tracing analysis based on a sample fabric thickness set, a sample fabric elasticity set, and a sample recognition error ratio set, and constructing an error analyzer, wherein the recognition error ratio includes a size error ratio, a shape error ratio, and a relative position error ratio; utilizing the error analyzer, performing buttonhole recognition error analysis based on the target fabric attribute information, outputting the target recognition error ratio, and calculating the recognition compensation parameter.

[0008] In a possible implementation, the method for precise buttonhole and button sewing on clothing further performs the following processing: selecting the first corrected buttonhole feature at the first position in the corrected buttonhole feature sequence, obtaining a first preset button sewing area and randomly setting the first button sewing coordinates; reading the predetermined button sewing parameters of the button sewing device, simulating button sewing according to the predetermined button sewing parameters, the button attribute characteristics, the first corrected buttonhole feature and the first button sewing coordinates, and evaluating to obtain the first button sewing quality coefficient; continuing to randomly select button sewing coordinates in the first preset button sewing area, and performing button sewing quality simulation analysis until a predetermined number of optimizations is reached, outputting the first optimal button coordinates, and adding them to the optimal button coordinate sequence.

[0009] In a possible implementation, the method for precise buttonhole and button-making on clothing further performs the following processing: mechanically buttoning the target fabric according to the optimal button coordinate sequence, and during the button-making process, monitoring and collecting button images of the target fabric, performing button position error analysis based on the button images, obtaining transverse dimension errors and longitudinal dimension errors, and continuously monitoring and generating transverse dimension error sequences and longitudinal dimension error sequences; predicting transverse dimension errors at future K processing points based on the transverse dimension error sequence, obtaining K transverse prediction errors, where K is an integer greater than 1; predicting longitudinal dimension errors at future K processing points based on the longitudinal dimension error sequence, obtaining K longitudinal prediction errors; and performing position adaptive correction on the target fabric based on the K transverse prediction errors and K longitudinal prediction errors.

[0010] In a possible implementation, the method for accurately processing buttonholes and buttons on clothing further performs the following processing: collecting a set of sample transverse error sequences at Q consecutive processing points in a historical processing record, and obtaining transverse dimension errors at subsequent K historical processing points of different sample transverse error sequences to obtain a set of sample transverse prediction error sequences; using the set of sample transverse error sequences and the set of sample transverse prediction error sequences as sample data, dividing them into N equal parts, selecting them with replacement N times to construct a first training set, iterating the selection N times to obtain N training sets; and using the N training sets to train long and short-term memory networks respectively. Until convergence, N lateral error prediction branches are harvested to form a lateral error prediction channel; the number of processing points in the lateral size error sequence is obtained, if the number of processing points is greater than or equal to Q, the number of branch selections is 1, if the number of processing points is less than Q, the ratio of the number of processing points to Q is set to P, the difference between 1 and P is multiplied by N and rounded to the integer to obtain the number of branch selections; lateral error prediction branches of the branch selection number are randomly selected from the N lateral error prediction branches, and lateral size errors of the future K processing points are predicted according to the lateral size error sequence, and the K lateral prediction errors are output.

[0011] In a possible implementation, the method for precisely processing buttonholes and buttons on clothing further performs the following processing: obtaining a button sewing operation standard, wherein the button sewing operation standard includes a transverse dimension error threshold and a longitudinal dimension error threshold; judging the K transverse prediction errors according to the transverse dimension error threshold, and if there is a situation where the transverse dimension error threshold is exceeded, locating the earliest processing point in time to obtain the earliest transverse warning processing point; judging the K longitudinal prediction errors according to the longitudinal dimension error threshold, and if there is a situation where the longitudinal dimension error threshold is exceeded, locating the earliest processing point in time to obtain the earliest longitudinal warning processing point; determining a warning processing point based on the earliest transverse warning processing point and the earliest longitudinal warning processing point, and performing orientation correction on the target fabric according to the initial orientation at the warning processing point.

[0012] The present application also provides a method for accurately processing buttonholes and buttons on clothing, which includes: collecting buttonhole images in target fabric, identifying a keyhole image sequence through a convolutional neural network, and obtaining a keyhole feature sequence; performing buttonhole recognition error analysis based on target fabric attribute information, generating recognition compensation parameters to perform feature compensation on the keyhole feature sequence, and obtaining a corrected keyhole feature sequence; performing iterative optimization analysis of button positions based on the corrected keyhole feature sequence to generate an optimal button coordinate sequence; controlling a button sewing device to mechanically sew buttons on the target fabric according to the optimal button coordinate sequence, and synchronously monitoring the fabric movement trajectory during the button sewing operation, and performing position adaptive correction on the target fabric according to the trajectory monitoring results until the operation is completed.

[0013] The device and method for accurately processing buttonholes and buttons on clothing proposed in this application include a buttonhole feature sequence acquisition module for identifying buttonhole image sequences through a convolutional neural network to obtain a buttonhole feature sequence; a buttonhole feature sequence compensation module for analyzing buttonhole recognition errors and generating recognition compensation parameters to compensate for the keyhole feature sequence; a button position optimization analysis module for iteratively optimizing button positions; and a mechanical buttoning operation module for controlling the buttoning equipment to mechanically sew buttons on target fabric according to the optimal button coordinate sequence and performing position adaptive correction based on trajectory monitoring results. This solves the technical problems of inaccurate buttonhole positioning, insufficient error compensation, low buttoning efficiency, and inaccurate buttoning caused by fabric deformation in the prior art, achieving the technical effect of improving buttonhole processing accuracy, buttoning efficiency, and quality, and optimizing button positions. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] To more clearly illustrate the technical solutions of the embodiments of the present disclosure, the accompanying drawings of the embodiments of the present disclosure are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the apparatus according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0015] Figure 1 A schematic diagram of the structure of a precise buttonhole and button processing device for clothing provided in an embodiment of the present application;

[0016] Figure 2 A schematic flow chart of the method for precisely processing buttonholes and buttons on clothing provided in an embodiment of the present application.

[0017] Description of the accompanying drawings: buttonhole feature sequence acquisition module 10, buttonhole feature sequence compensation module 20, button position optimization analysis module 30, mechanical button sewing operation module 40. DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms “first\second” involved are merely to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, device, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0021] The embodiment of the present application provides a precise processing device for buttonholes and buttons of clothing, such as Figure 1 As shown, the device includes:

[0022] The buttonhole feature sequence acquisition module 10 is used to collect buttonhole images in the target fabric, identify the buttonhole image sequence through a convolutional neural network, and obtain the buttonhole feature sequence.

[0023] Preferably, an image acquisition device (such as a camera, a webcam, etc.) is used to photograph the target fabric to accurately capture the image of the keyhole part on the surface of the fabric. For example, the details of the keyhole area of the fabric during the production process are photographed by a camera, and then the keyhole image sequence is recognized by a convolutional neural network. Specifically, the keyhole image is processed by a convolutional neural network to automatically identify the keyhole area, shape, size, position and other feature information in the image. The image sequence refers to multiple frames of images that may be taken continuously, and each frame of the image contains a keyhole pattern or image information of multiple keyholes; after the convolutional neural network completes the analysis and recognition of the keyhole image, it finally outputs a keyhole feature sequence, which contains detailed information of the keyhole, such as the shape, size, edge position, hole position, etc. of the keyhole.

[0024] Furthermore, the specific configuration of the keyhole feature sequence acquisition module 10 also includes: retrieving and acquiring a set of sample keyhole images, and collecting keyhole features of sample keyhole images from different sample keyhole images to acquire a set of sample keyhole features, wherein the keyhole features include size, shape and relative position; using the sample keyhole image set and the sample keyhole feature set as supervision data, combining the gradient descent algorithm to train the convolutional neural network until the mean square error loss function converges, thereby obtaining a keyhole feature recognition plug-in, and using the keyhole feature recognition plug-in to identify the keyhole image sequence and output the keyhole feature sequence.

[0025] Preferably, multiple images containing keyhole patterns are retrieved from multiple different sources (including different fabric types, different fabric surface conditions, different buttonhole processing methods, etc.) to form a set of sample keyhole images for training the convolutional neural network so that it can learn how to identify keyholes in different samples. The sample keyhole images contain keyholes of different sizes, shapes and arrangements, which are used to provide rich training data to help the neural network understand the various keyhole features. Then, relevant keyhole features are extracted from each keyhole image, mainly including size (the size of the keyhole, including the width and height of the keyhole hole, etc.), shape (the outer contour of the keyhole, such as round, oval, square, etc.) and relative position (the relative position of the keyhole on the fabric, which may refer to the distance between the keyholes, the position offset or its spatial relationship with other parts of the fabric), thereby forming a set of sample keyhole features.

[0026] Preferably, a set of sample keyhole images and a set of sample keyhole features are used as input data for supervised learning of a convolutional neural network, that is, the convolutional neural network is trained using these data, wherein each keyhole image has a corresponding label (that is, the keyhole feature corresponding to the image) as the target output of the convolutional neural network learning. Specifically, the convolutional neural network is trained in combination with a gradient descent algorithm. During the training process, the convolutional neural network (CNN) continuously adjusts the weights and biases in the network to minimize the error between the predicted output and the true keyhole feature. The gradient descent algorithm is used to optimize the neural network, and by calculating the gradient of the error and updating the network parameters, the loss function (such as the mean square error) value is gradually reduced. The mean square error is a commonly used loss function that measures the difference between the network's predicted value and the actual value. After continuous training and adjustment until the loss function converges, it means that the convolutional neural network has learned how to extract the correct keyhole features from the input keyhole image, and then obtains the keyhole recognition plug-in (the trained convolutional neural network model), which can output the corresponding keyhole features (such as size, shape, position, etc.) based on the input keyhole image; finally, the keyhole feature recognition plug-in is used to identify the keyhole image sequence, that is, the keyhole image sequence is input into the keyhole feature recognition plug-in for analysis and processing, and the corresponding keyhole feature sequence is output, that is, a series of specific parameters about the keyhole (such as the size, shape, relative position, etc. of each keyhole).

[0027] The buttonhole feature sequence compensation module 20 is used to perform buttonhole recognition error analysis based on target fabric attribute information, generate recognition compensation parameters, perform feature compensation on the buttonhole feature sequence, and obtain a corrected buttonhole feature sequence.

[0028] Preferably, the buttonhole recognition error analysis is performed based on the target fabric attribute information (such as the material, thickness, elasticity, texture characteristics, color reflection characteristics, etc. of the fabric), that is, the deviation between the actual recognition result and the expected result is evaluated, and the errors caused by the fabric characteristics are found, such as the blurred buttonhole boundary due to the complex texture of the fabric, and the deformation of the buttonhole shape due to the elasticity or stretching of the fabric, and then the recognition compensation parameters are generated. Among them, the recognition compensation parameters refer to the adjustment parameters generated to reduce the error obtained through historical data training and machine learning model prediction, which may include size compensation parameters (used to adjust the buttonhole size change caused by stretching or scaling), shape compensation parameters (used to correct the buttonhole boundary recognition deviation) and position compensation parameters. Parameters (used to correct the offset of the identified buttonhole position); the generated error recognition compensation parameters are then used to perform feature compensation on the buttonhole feature sequence (the buttonhole information set output by the convolutional neural network recognition), that is, to adjust the feature data in the buttonhole feature sequence. For example, if the recognition error indicates that the width of the buttonhole is generally smaller than the true value, the width feature is uniformly amplified for compensation. If the center position of the buttonhole is offset from the expected position, its coordinate value is offset and corrected by the compensation parameters to ensure that the buttonhole feature is closer to the actual situation of actual fabric processing, and then a corrected buttonhole feature sequence is generated with higher accuracy and consistency, thereby significantly improving the precision and effect of subsequent operations (such as mechanical button sewing).

[0029] Furthermore, the specific configuration of the buttonhole feature sequence compensation module 20 also includes obtaining fabric attribute information, wherein the fabric attribute information includes at least fabric thickness and fabric elasticity; taking fabric thickness and fabric elasticity as independent variables and keyhole recognition error as dependent variable, performing correlation tracing analysis based on the sample fabric thickness set, the sample fabric elasticity set and the sample recognition error ratio set, and constructing an error analyzer, wherein the recognition error ratio includes the size error ratio, the shape error ratio and the relative position error ratio; using the error analyzer, performing keyhole recognition error analysis according to the target fabric attribute information, outputting the target recognition error ratio, and calculating the recognition compensation parameter.

[0030] Preferably, fabric attribute information is obtained, wherein the fabric attribute information is a key factor affecting the accuracy of buttonhole recognition, and mainly includes two attributes: fabric thickness and fabric elasticity. Fabric thickness refers to the physical size of the fabric in the vertical direction, usually measured in millimeters (mm). The thickness of the fabric directly affects the mechanical behavior during the buttonhole processing process, such as the resistance when threading the needle and the appearance of the buttonhole shape on the fabric. Fabric elasticity refers to the deformation ability of the fabric when subjected to external force. Fabric with good elasticity will cause changes in the shape and position of the buttonhole due to stretching or deformation, thereby affecting the recognition accuracy; using the two independent variables of fabric thickness and fabric elasticity, an error analyzer is constructed by performing correlation and traceability analysis on historical sample data to predict the buttonhole recognition error based on fabric properties. Specifically, a sample fabric thickness set, a sample fabric elasticity set, and a sample recognition error ratio set are collected from a large amount of historical sample data of different fabrics to establish a relationship between the error and the fabric attributes. Each sample includes the thickness and elasticity of the fabric, as well as the buttonhole recognition error identified by the convolutional neural network.

[0031] Preferably, through correlation tracing analysis, the correlation between fabric thickness, elasticity and buttonhole recognition error is found, and then an error analyzer is constructed to predict the recognition error of a new target fabric; wherein the recognition error ratio includes the size error ratio (the ratio of the size deviation of the buttonhole to the expected size), the shape error ratio (the deviation ratio between the shape of the buttonhole, such as circle, ellipse, square, etc., and the actual shape; fabric with strong elasticity may cause the edge of the buttonhole to be irregular) and the relative position error ratio (the position offset ratio of the buttonhole relative to other parts of the fabric; the elasticity, thickness or physical unevenness of the fabric may cause the buttonhole position to shift); the target fabric attribute information (fabric thickness and elasticity) is input as input data into the error analyzer to perform buttonhole recognition error analysis, that is, predict possible errors in buttonhole recognition, including the size error, shape error and position error of the buttonhole, and then output the target recognition error ratio based on the output result of the target fabric attribute; then, the recognition error compensation parameters are calculated, including size compensation parameters, shape compensation parameters and position compensation parameters, which are used to compensate for the buttonhole error, so that the size, shape and position of the buttonhole are more accurate.

[0032] Preferably, the size compensation parameters are used to correct size errors to ensure that the actual size of the keyhole is consistent with the predetermined size. For example, if the error analyzer predicts a size error of 10%, the compensation parameters will adjust the recognition result to ensure the accuracy of the keyhole size. The shape compensation parameters are used to correct shape errors to make the shape of the keyhole meet the design requirements. If the elasticity of the fabric causes the keyhole to deform, the compensation parameters will adjust the keyhole recognition result to restore the predetermined shape. The position compensation parameters are used to correct position errors to ensure the accuracy of the keyhole position. For example, if the error analyzer outputs a position error of 5mm, the compensation parameters will adjust the coordinate value of the keyhole to accurately match the predetermined position. By calculating the compensation parameters to correct these errors, the accuracy of the buttonhole processing is ensured, and the error caused by the physical properties of the fabric (such as thickness and elasticity) is avoided, thereby improving the quality and processing accuracy of the finished product.

[0033] The button position optimization analysis module 30 is used to perform iterative optimization analysis of button positions based on the corrected buttonhole feature sequence to generate an optimal button coordinate sequence.

[0034] Preferably, an iterative optimization analysis of the button position is performed based on the corrected keyhole feature sequence, that is, based on the known keyhole features, the optimal button position is found through continuous adjustment and calculation. Specifically, the coordinates of the initial position of the button are calculated based on the coordinates of the center point of the keyhole and the overall layout of the fabric. For example, the button usually needs to be located on the symmetry axis of the center of the keyhole, and then the optimization target is determined to ensure the alignment accuracy between the button and the keyhole while meeting the processing requirements. For example, the position of the button needs to be located at the optimal point in the center of the keyhole to ensure visual effect and mechanical strength. In the case of fabric elasticity or position offset, it is necessary to ensure that the button can naturally reset to the ideal position after processing; then an iterative algorithm (such as gradient descent, genetic algorithm, etc.) is used to continuously adjust the candidate position of the button, and according to the position The accuracy (distance from the center of the buttonhole), smoothness of the processing trajectory, overall arrangement symmetry, etc. are taken into consideration to calculate the fitness of each candidate position. Through iterative optimization of the button position, the optimal button coordinate sequence is output, which contains a set of precise button coordinate data to guide the operation of the mechanical button sewing equipment. Specifically, the position coordinates of each button on the fabric (such as X and Y coordinates on a two-dimensional plane) and the arrangement order of the coordinates, such as the sequential positions of a row of buttons. Among them, the optimal button coordinate sequence can ensure that the position of each button is accurately aligned with the buttonhole, avoiding button installation offset, and taking into account the characteristics of the fabric (such as elasticity and deformation), so that the button sewing effect remains in the best position when the fabric returns to its natural state, thereby significantly improving the accuracy and efficiency of the button sewing process and ensuring that the button installation meets the design requirements.

[0035] Furthermore, the specific configuration of the button position optimization analysis module 30 also includes selecting the first correction buttonhole feature at the first position in the correction buttonhole feature sequence, obtaining the first preset button sewing area and randomly setting the first button sewing coordinates; reading the predetermined button sewing parameters of the button sewing equipment, simulating button sewing according to the predetermined button sewing parameters, the button attribute characteristics, the first correction buttonhole feature and the first button sewing coordinates, and evaluating to obtain the first button sewing quality coefficient; continuing to randomly select button sewing coordinates in the first preset button sewing area, and performing button sewing quality simulation analysis until the predetermined optimization times are reached, outputting the first optimal button coordinates, and adding them to the optimal button coordinate sequence.

[0036] Preferably, a correction keyhole feature at a position is randomly selected from the correction keyhole feature sequence as the correction keyhole feature (detailed data of the keyhole after error compensation, including the keyhole position coordinates, the shape and size of the keyhole, and the relative position relationship of the keyhole on the fabric), and a first preset buttonhole sewing area is obtained, that is, a predetermined area around the first correction keyhole feature, which represents the effective range within which the buttonhole sewing operation is allowed. The size and shape of this area can be set according to the characteristics of the keyhole (such as size, shape) and the properties of the fabric (such as elasticity, thickness). It is usually a rectangular or circular area with the center of the keyhole as the reference point. Then, a point is randomly selected in the preset buttonhole sewing area as the initial buttonhole position, and multiple candidate coordinates are randomly selected to generate within the area. The optimal position is found through simulation analysis and optimization algorithm.

[0037] Preferably, the predetermined button sewing parameters of the button sewing equipment are read. The button sewing parameters refer to key process indicators that affect processing accuracy and quality, including punching force (the pressure applied by the mechanical equipment when sewing the button, which affects the fixing strength of the button and the fabric), thread tension (the tension maintained by the sewing thread during the button sewing process, which determines the firmness of the button), stitch spacing (the distance between the stitches of the stitches, which affects the stability and aesthetics of the button sewing), and needle bar running speed (the moving speed of the needle bar of the equipment during the button sewing process, which affects efficiency and quality). The button sewing equipment is used in clothing production. Mechanical equipment for fixing buttons to fabrics, passing buttons through fabrics through a specific process, and firmly fixing buttons to fabrics by sewing, stamping or crimping, etc., mainly includes a workbench, a needle bar mechanism (controls the up and down movement of the needle), a sewing device (passes the thread through the fabric and fixes the button, including a needle, a thread reel, a needle plate, a needle and thread tension device, etc.), a stamping mechanism (such as using a press to stamp the button through the fabric), a control system (PLC controller, servo motor and sensor, etc.), a feeding device (precise fabric feeding), and a position calibration device.

[0038] Preferably, based on the correction characteristics of the buttonhole, the randomly generated button sewing coordinates, the button attribute characteristics (such as button size, material) and the button sewing equipment parameters, the button sewing simulation is performed, that is, the working process of the button sewing equipment is simulated by computer virtual simulation, and its button sewing effect is evaluated. Specifically, the predetermined button sewing parameters (such as punching force, stitch spacing, etc.), the button attribute characteristics, the first correction buttonhole characteristics and the first button sewing coordinates are input into the simulation model, and the actual operation process of the button sewing equipment under these parameters is simulated, including how the needle rod of the button sewing equipment passes through the fabric and the buttonhole, how the fabric deforms during the button sewing process, and how the suture passes through the buttonhole and button to form a fixed shape, thereby generating multiple simulation results, such as whether the fixed position of the button is aligned with the buttonhole, the firmness of the button sewing (such as the button Whether the button is loose or the fabric is damaged), the aesthetics of the buttoning (such as whether the stitch distribution is uniform), and the simulation results are evaluated and quantified to obtain the corresponding first buttoning quality coefficient, which usually includes a comprehensive evaluation of the buttoning accuracy (whether the button is accurately positioned at the center of the buttonhole), firmness (whether the strength of the bond between the button and the fabric meets the requirements), aesthetics (whether the overall visual effect after buttoning meets the requirements, such as uniform stitches) and fabric protection (whether the fabric is damaged during the buttoning process, such as tearing or excessive deformation). The higher the quality coefficient, the more suitable the current combination of buttoning parameters, buttonhole features and buttoning coordinates is for actual production, so that the optimal buttoning position and process parameters can be found without actual processing, reducing the trial and error cost and improving the buttoning efficiency and quality.

[0039] Preferably, within the first preset button-stitching area, new button-stitching coordinates are continuously randomly selected, button-stitching and quality evaluation are repeatedly simulated, and genetic algorithms, particle swarm optimization or gradient descent are used to gradually find the button-stitching position with the highest quality coefficient through multiple iterative analyses (reaching a predetermined number of optimization times), and then the first optimal button-stitching coordinates within the preset area are output, that is, the optimal button position under the current buttonhole characteristics, which can ensure that the button-stitching operation achieves the best effect in terms of firmness, accuracy and aesthetics, and the calculated first optimal button-stitching coordinates are added to the overall optimal button coordinate sequence. The optimal button coordinate sequence is a collection of the optimal button positions under all buttonhole positions, and finally guides the button-stitching equipment to complete accurate and efficient button-stitching operations, which not only ensures the accuracy of the button position, but also optimizes production efficiency and finished product quality.

[0040] The mechanical button sewing operation module 40 is used to control the button sewing equipment to mechanically sew buttons on the target fabric according to the optimal button coordinate sequence, and synchronously monitor the fabric movement trajectory during the button sewing operation, and perform position adaptive correction on the target fabric according to the trajectory monitoring results until the operation is completed.

[0041] Preferably, the coordinates of the optimal button coordinate sequence are applied to the button sewing device, and the button sewing device accurately installs each button at a designated position on the fabric according to these coordinates. Specifically, based on the optimal button coordinate sequence, the button sewing device performs the button sewing operation through precise control, including automatically identifying the current state and position of the fabric to ensure that the fabric is fixed in the button sewing area. According to the optimal button coordinates, the button sewing device passes the button through the buttonhole of the fabric and accurately completes sewing or crimping, thereby ensuring the quality of the finished garment and avoiding button deviation or fabric damage. At the same time, the movement trajectory of the fabric is synchronously monitored during the button sewing operation. Specifically, the fabric may undergo slight movement changes (such as stretching, sliding or deformation) during the button sewing process. The movement of the fabric is monitored in real time by integrating sensors, cameras or laser scanning devices, which work synchronously with the button sewing device to capture information such as fabric position, shape, speed, etc. Update the motion state of the fabric, obtain the trajectory monitoring results, and adjust the button sewing operation according to the trajectory monitoring results, that is, perform position adaptive correction on the target fabric, including the button sewing equipment dynamically adjusting the position of the fabric according to the trajectory monitoring results. For example, if the fabric is offset, the equipment will automatically adjust the positioning to ensure that the buttonhole is aligned with the button. By dynamically adjusting the fabric position, it is ensured that each button sewing is performed accurately, and the button can be fixed in the best position even when the fabric moves or changes; until the operation is completed, that is, after the button sewing equipment completes the sewing of each button, it automatically determines whether the target position has been reached. If so, it enters the next button sewing position; if not, it will continue to adjust until it reaches the correct position, until all buttons are sewn and the fabric is processed, indicating that the operation is completed, making the button sewing process not only efficient but also precise, ensuring the high quality and consistency of the final garment product.

[0042] Furthermore, the specific configuration of the mechanical button sewing operation module 40 also includes mechanically sewing buttons on the target fabric according to the optimal button coordinate sequence, and during the button sewing operation process, monitoring and collecting button sewing images of the target fabric, performing button sewing position error analysis based on the button sewing image, obtaining lateral size error and longitudinal size error, and continuously monitoring and generating lateral size error sequence and longitudinal size error sequence; predicting lateral size errors at future K processing points based on the lateral size error sequence, obtaining K lateral prediction errors, where K is an integer greater than 1; predicting longitudinal size errors at future K processing points based on the longitudinal size error sequence, obtaining K longitudinal prediction errors; and performing position adaptive correction on the target fabric based on the K lateral prediction errors and K longitudinal prediction errors.

[0043] Preferably, the target fabric is mechanically buttoned according to the optimal button coordinate sequence, that is, the buttoning device accurately installs each button to the designated position on the fabric according to the optimal button coordinate sequence to ensure that each button is accurately positioned on the fabric, and an image acquisition system (such as a camera or sensor) installed on the buttoning device is used to monitor the fabric image during the buttoning process in real time, that is, the button image of the target fabric, to capture the alignment of the button position on the fabric surface and the buttonhole, that is, to obtain the actual position of the button after buttoning, and then perform button position error analysis based on the button image, that is, analyze the actual position of the button to determine the error. The deviation from the optimal predetermined position is determined to obtain the lateral size error and longitudinal size error, wherein the lateral size error refers to the deviation of the button in the horizontal direction, that is, the error of the button position relative to the predetermined coordinate in the lateral (X-axis) direction; the longitudinal size error refers to the deviation of the button in the vertical direction, that is, the error of the button position relative to the predetermined coordinate in the longitudinal (Y-axis) direction. Then, two corresponding error sequences are generated through continuous real-time monitoring, namely, the lateral size error sequence (recording the change of the lateral error in each buttoning operation) and the longitudinal size error sequence (recording the change of the longitudinal error in each buttoning operation).

[0044] Preferably, historical error data (i.e., a sequence of transverse dimension errors) is used to predict the transverse errors of the next K processing points through data analysis and prediction models (such as regression analysis, time series prediction, or machine learning algorithms), that is, to obtain K transverse prediction errors, where K is an integer greater than 1, representing the total number of button sewing processing points where errors will occur in the future. Similarly, based on the historical error data (i.e., a sequence of longitudinal dimension errors), a corresponding prediction model is used to predict the longitudinal errors of the next K processing points, that is, to obtain K longitudinal prediction errors, which helps to understand the longitudinal deviations that may occur in the future production process. Finally, the position of the fabric is adjusted according to the predicted values of the transverse and longitudinal errors to ensure that each button sewing operation is as accurate as possible. For example, based on the predicted transverse error, the transverse position of the fabric is adjusted to ensure that the button can be accurately located at the center of the buttonhole during the next button sewing operation. Similarly, based on the predicted longitudinal error, the longitudinal position of the fabric is adjusted to avoid the longitudinal offset affecting the button sewing accuracy, thereby ensuring the stability and consistency of the button sewing accuracy throughout the entire production process, greatly improving the accuracy, efficiency, and quality of the finished product of the automated production.

[0045] Furthermore, the specific configuration of the mechanical button fastening operation module 40 also includes collecting a set of sample transverse error sequences at Q consecutive processing points in the historical processing records, and obtaining the transverse dimension errors at subsequent K historical processing points of different sample transverse error sequences to obtain a set of sample transverse prediction error sequences; dividing the set of sample transverse error sequences and the set of sample transverse prediction error sequences into N equal parts as sample data, selecting them N times with replacement to construct a first training set, iteratively selecting them N times to obtain N training sets; and using the N training sets to train the long and short-term memory networks respectively until convergence. N lateral error prediction branches are harvested to form a lateral error prediction channel; the number of processing points in the lateral size error sequence is obtained, if the number of processing points is greater than or equal to Q, the number of branches selected is 1, if the number of processing points is less than Q, the ratio of the number of processing points to Q is set to P, the difference between 1 and P is multiplied by N and rounded to the integer to obtain the number of branches selected; lateral error prediction branches of the branch selection number are randomly selected from the N lateral error prediction branches, and lateral size errors at the next K processing points are predicted according to the lateral size error sequence, and the K lateral prediction errors are output.

[0046] Preferably, the lateral dimension errors of Q consecutive processing points are extracted from the historical processing records to form a sample lateral error sequence set, which reflects the changing trend of the lateral error during the button fastening process and serves as the input data for model prediction. At the same time, the actual lateral dimension errors of these sample lateral error sequences at the subsequent K processing points are obtained to form a sample lateral prediction error sequence set as the target value (prediction output) of the model prediction, wherein Q is an integer greater than 1, representing the length of the historical error sequence (the total number of button fastening processing points). Then, the sample data (sample lateral error sequence set and prediction error sequence set) are equally divided into N parts to increase the diversity of the data and the robustness of the training. N times of random selection with replacement are made to construct the first training set, and then N training sets are iteratively selected to obtain N training sets. Each training set includes input (historical error sequence) and output (corresponding prediction error sequence), wherein N is a positive integer, which helps the model cope with the randomness and diversity of error data and improves the prediction accuracy and generalization ability.

[0047] Preferably, N training sets are used to train long short-term memory networks separately, wherein the long short-term memory network (LSTM) is a deep learning model for processing time series data, which is good at capturing long-term and short-term dependencies in sequence data, and is used to learn the changing pattern of lateral errors, thereby predicting future errors. Specifically, each training set is used to train an independent long short-term memory network model until the model converges (that is, the prediction error is small enough), and then N lateral error prediction branches are generated according to the training of the N training sets, which are responsible for learning and predicting errors based on different sample data, and the N lateral error prediction branches are combined into a lateral error prediction channel for lateral error prediction.

[0048] Preferably, the number of processing points in the lateral dimension error sequence is obtained, wherein the more data in the lateral dimension error sequence and the more reliable it is, the higher the prediction accuracy is, the fewer branches are selected, the analysis efficiency is improved, and computing power is saved; conversely, the less data, the more branches are selected, that is, if the number of processing points in the current lateral dimension error sequence is ≥Q (that is, the historical data is long enough), only one branch needs to be selected for prediction; if the number of processing points is <Q (that is, the historical data is short), the number of selected branches needs to be adjusted according to the proportion. Specifically, the ratio of the number of processing points to Q is taken as the proportion P, and 1-P is used to represent the insufficient proportion. The result is multiplied by N and rounded to the integer to obtain the number of branches that need to be additionally selected; finally, a lateral error prediction branch with the calculated number of branch selections is randomly selected from the N lateral error prediction branches as a candidate lateral error prediction branch. The candidate lateral error prediction branch is used to predict the lateral dimension errors of the future K processing points according to the current lateral dimension error sequence, and the corresponding K lateral prediction errors are output. Each error value corresponds to the lateral dimension error of a future processing point, thereby improving the accuracy of error prediction and production efficiency.

[0049] Furthermore, the specific configuration of the mechanical button sewing operation module 40 also includes obtaining a button sewing operation standard, wherein the button sewing operation standard includes a transverse dimension error threshold and a longitudinal dimension error threshold; judging the K transverse prediction errors according to the transverse dimension error threshold, and if there is a situation where the transverse dimension error threshold is exceeded, locating the earliest processing point in time to obtain the earliest transverse warning processing point; judging the K longitudinal prediction errors according to the longitudinal dimension error threshold, and if there is a situation where the longitudinal dimension error threshold is exceeded, locating the earliest processing point in time to obtain the earliest longitudinal warning processing point; determining a warning processing point according to the earliest transverse warning processing point and the earliest longitudinal warning processing point, and at the warning processing point, performing orientation correction on the target fabric according to the initial orientation.

[0050] Preferably, the button-stitching operation standard, i.e., the transverse dimension error threshold and the longitudinal dimension error threshold, is obtained, wherein the transverse dimension error threshold refers to the maximum allowable deviation of the transverse position error in the button-stitching operation, and the longitudinal dimension error threshold refers to the maximum allowable deviation of the longitudinal position error in the button-stitching operation; then, a judgment is made based on the transverse dimension error threshold, i.e., if a certain predicted transverse error exceeds the preset transverse dimension error threshold, it means that the button-stitching at this processing point may have position deviation, and needs to be adjusted, and then the processing point where the transverse error first exceeds the standard is located, i.e., the processing point where the error exceeds the threshold for the first time, as the transverse warning processing point; similarly, a judgment is made based on the longitudinal dimension error threshold, i.e., if a certain longitudinal predicted error exceeds the set longitudinal dimension error threshold, it means that the button-stitching position of this processing point may also deviate, and needs to be adjusted, and then the processing point where the longitudinal error first occurs is located. The processing point where the error exceeds the standard, that is, the longitudinal processing point where the error exceeds the threshold for the first time, is used as the longitudinal warning processing point; then, based on the earliest horizontal warning processing point and the earliest longitudinal warning processing point, that is, comparing the earliest horizontal warning processing point and the earliest longitudinal warning processing point, the earlier one is determined as the final warning processing point; finally, under the warning processing point, the target fabric is oriented according to the initial orientation to ensure the accuracy of the button sewing operation, which specifically includes manual correction (the operator manually adjusts the position of the fabric to ensure that the fabric returns to the standard direction and standard position) and mechanical correction (the mechanical equipment automatically adjusts the position of the fabric to ensure that the fabric returns to the standard direction and standard position). After correction, the direction and position of the fabric should be restored to the preset standard state (standard direction and position) to ensure the accuracy and consistency of the button sewing operation, greatly reduce the button sewing deviation, and improve production efficiency and product quality.

[0051] In the above, refer to Figure 1 The precise processing device for buttonholes and buttons of clothing according to the embodiment of the present invention is described in detail. Figure 2 A method for accurately processing buttonholes and buttons on clothing according to an embodiment of the present invention is described.

[0052] Precision processing method of buttonholes and buttons on clothing, such as Figure 2 As shown, the method includes: collecting keyhole images in the target fabric, identifying the keyhole image sequence through a convolutional neural network, and obtaining a keyhole feature sequence; performing keyhole recognition error analysis based on the target fabric attribute information, generating recognition compensation parameters to perform feature compensation on the keyhole feature sequence, and obtaining a corrected keyhole feature sequence; performing iterative optimization analysis of button positions based on the corrected keyhole feature sequence to generate an optimal button coordinate sequence; controlling the button sewing equipment to mechanically sew buttons on the target fabric according to the optimal button coordinate sequence, and synchronously monitoring the fabric motion trajectory during the button sewing operation, and performing position adaptive correction on the target fabric according to the trajectory monitoring results until the operation is completed.

[0053] In a possible implementation, the method for precise processing of buttonholes and buttons on clothing further performs the following processing: retrieving a set of sample keyhole images, and collecting keyhole features of sample keyhole images from different sample keyhole images to obtain a set of sample keyhole features, wherein the keyhole features include size, shape, and relative position; using the set of sample keyhole images and the set of sample keyhole features as supervision data, a convolutional neural network is trained in combination with a gradient descent algorithm until the mean square error loss function converges, thereby obtaining a keyhole feature recognition plug-in, and using the keyhole feature recognition plug-in to recognize a keyhole image sequence, and outputting the keyhole feature sequence.

[0054] In a possible implementation, the method for precise processing of buttonholes and buttons on clothing further performs the following processing: obtaining fabric attribute information, wherein the fabric attribute information includes at least fabric thickness and fabric elasticity; taking fabric thickness and fabric elasticity as independent variables and buttonhole recognition error as dependent variable, performing correlation tracing analysis based on a sample fabric thickness set, a sample fabric elasticity set, and a sample recognition error ratio set, and constructing an error analyzer, wherein the recognition error ratio includes a size error ratio, a shape error ratio, and a relative position error ratio; utilizing the error analyzer, performing buttonhole recognition error analysis based on the target fabric attribute information, outputting the target recognition error ratio, and calculating the recognition compensation parameter.

[0055] In a possible implementation, the method for precise buttonhole and button sewing on clothing further performs the following processing: selecting the first corrected buttonhole feature at the first position in the corrected buttonhole feature sequence, obtaining a first preset button sewing area and randomly setting the first button sewing coordinates; reading the predetermined button sewing parameters of the button sewing device, simulating button sewing according to the predetermined button sewing parameters, the button attribute characteristics, the first corrected buttonhole feature and the first button sewing coordinates, and evaluating to obtain the first button sewing quality coefficient; continuing to randomly select button sewing coordinates in the first preset button sewing area, and performing button sewing quality simulation analysis until a predetermined number of optimizations is reached, outputting the first optimal button coordinates, and adding them to the optimal button coordinate sequence.

[0056] In a possible implementation, the method for precise buttonhole and button-making on clothing further performs the following processing: mechanically buttoning the target fabric according to the optimal button coordinate sequence, and during the button-making process, monitoring and collecting button images of the target fabric, performing button position error analysis based on the button images, obtaining transverse dimension errors and longitudinal dimension errors, and continuously monitoring and generating transverse dimension error sequences and longitudinal dimension error sequences; predicting transverse dimension errors at future K processing points based on the transverse dimension error sequence, obtaining K transverse prediction errors, where K is an integer greater than 1; predicting longitudinal dimension errors at future K processing points based on the longitudinal dimension error sequence, obtaining K longitudinal prediction errors; and performing position adaptive correction on the target fabric based on the K transverse prediction errors and K longitudinal prediction errors.

[0057] In a possible implementation, the method for accurately processing buttonholes and buttons on clothing further performs the following processing: collecting a set of sample transverse error sequences at Q consecutive processing points in a historical processing record, and obtaining transverse dimension errors at subsequent K historical processing points of different sample transverse error sequences to obtain a set of sample transverse prediction error sequences; using the set of sample transverse error sequences and the set of sample transverse prediction error sequences as sample data, dividing them into N equal parts, selecting them with replacement N times to construct a first training set, iterating the selection N times to obtain N training sets; and using the N training sets to train long and short-term memory networks respectively. Until convergence, N lateral error prediction branches are harvested to form a lateral error prediction channel; the number of processing points in the lateral size error sequence is obtained, if the number of processing points is greater than or equal to Q, the number of branch selections is 1, if the number of processing points is less than Q, the ratio of the number of processing points to Q is set to P, the difference between 1 and P is multiplied by N and rounded to the integer to obtain the number of branch selections; lateral error prediction branches of the branch selection number are randomly selected from the N lateral error prediction branches, and lateral size errors of the future K processing points are predicted according to the lateral size error sequence, and the K lateral prediction errors are output.

[0058] In a possible implementation, the method for precisely processing buttonholes and buttons on clothing further performs the following processing: obtaining a button sewing operation standard, wherein the button sewing operation standard includes a transverse dimension error threshold and a longitudinal dimension error threshold; judging the K transverse prediction errors according to the transverse dimension error threshold, and if there is a situation where the transverse dimension error threshold is exceeded, locating the earliest processing point in time to obtain the earliest transverse warning processing point; judging the K longitudinal prediction errors according to the longitudinal dimension error threshold, and if there is a situation where the longitudinal dimension error threshold is exceeded, locating the earliest processing point in time to obtain the earliest longitudinal warning processing point; determining a warning processing point based on the earliest transverse warning processing point and the earliest longitudinal warning processing point, and performing orientation correction on the target fabric according to the initial orientation at the warning processing point.

[0059] Although the present application makes various references to certain modules in the apparatus according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0060] The above specific embodiments do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application shall be included within the scope of protection of this application.

Claims

1. The precise processing device for buttonholes and buttons on clothing is characterized by: The clothing buttonhole and button precision processing device comprises: A keyhole feature sequence acquisition module is used to collect keyhole images from the target fabric, identify the keyhole image sequence through a convolutional neural network, and obtain the keyhole feature sequence; A buttonhole feature sequence compensation module is used to analyze buttonhole recognition errors based on target fabric property information, generate recognition compensation parameters, perform feature compensation on the buttonhole feature sequence, and obtain a corrected buttonhole feature sequence; A button position optimization analysis module, configured to perform iterative optimization analysis of button positions based on the corrected buttonhole feature sequence to generate an optimal button coordinate sequence; a mechanical button sewing operation module, configured to control the button sewing equipment to mechanically sew buttons on the target fabric according to the optimal button coordinate sequence, synchronously monitor the fabric motion trajectory during the button sewing operation, and adaptively correct the position of the target fabric based on the trajectory monitoring results until the operation is completed; The button position optimization analysis module includes: A first button-stitching coordinate setting unit is configured to select a first correction buttonhole feature at a first position in the correction buttonhole feature sequence, obtain a first preset button-stitching area, and randomly set first button-stitching coordinates; a button sewing simulation unit, configured to read predetermined button sewing parameters of a button sewing device, simulate button sewing according to the predetermined button sewing parameters, based on the button attribute characteristics, the first corrected buttonhole characteristics, and the first button sewing coordinates, and evaluate and obtain a first button sewing quality coefficient; The first optimal button coordinate output unit is used to continue to randomly select button coordinates in the first preset button area and perform button quality simulation analysis until a predetermined number of optimization times is reached, output the first optimal button coordinates, and add them to the optimal button coordinate sequence.

2. The precise processing device for buttonholes and buttons on clothing according to claim 1, characterized in that: The keyhole feature sequence acquisition module includes: a sample keyhole feature set acquisition unit, configured to retrieve and acquire a sample keyhole image set, and collect keyhole features of sample keyhole images from different sample keyhole images to acquire a sample keyhole feature set, wherein the keyhole features include size, shape, and relative position; A keyhole feature sequence output unit is used to train a convolutional neural network using the sample keyhole image set and the sample keyhole feature set as supervision data in combination with a gradient descent algorithm until the mean square error loss function converges, thereby obtaining a keyhole feature recognition plug-in, and using the keyhole feature recognition plug-in to recognize a keyhole image sequence and output the keyhole feature sequence.

3. The precise processing device for buttonholes and buttons on clothing according to claim 1, characterized in that: The keyhole feature sequence compensation module includes: a cloth attribute information acquiring unit, configured to acquire cloth attribute information, wherein the cloth attribute information includes at least cloth thickness and cloth elasticity; The error analyzer construction unit is used to construct an error analyzer by performing correlation and source tracing analysis based on the sample fabric thickness set, the sample fabric elasticity set, and the sample recognition error ratio set, using fabric thickness and fabric elasticity as independent variables and buttonhole recognition error as dependent variable. The recognition error ratio includes the size error ratio, the shape error ratio, and the relative position error ratio. The buttonhole recognition error analysis unit is used to use the error analyzer to perform buttonhole recognition error analysis according to the target fabric attribute information, output the target recognition error ratio, and calculate the recognition compensation parameter.

4. The precise processing device for buttonholes and buttons on clothing according to claim 1, characterized in that: The mechanical buttoning operation module includes: a size error acquisition unit, configured to mechanically sew buttons on the target fabric according to the optimal button coordinate sequence, and during the buttoning operation, monitor and capture button images of the target fabric, perform button position error analysis based on the button images, acquire transverse size errors and longitudinal size errors, and continuously monitor and generate transverse size error sequences and longitudinal size error sequences; a lateral dimension error prediction unit, configured to predict lateral dimension errors at future K processing points based on the lateral dimension error sequence, and obtain K lateral prediction errors, where K is an integer greater than 1; A longitudinal dimension error prediction unit is used to predict longitudinal dimension errors at future K processing points based on the longitudinal dimension error sequence to obtain K longitudinal prediction errors; A position adaptive correction unit is used to perform position adaptive correction on the target cloth based on the K lateral prediction errors and the K longitudinal prediction errors.

5. The precise processing device for buttonholes and buttons on clothing according to claim 4, characterized in that: The mechanical buttoning operation module includes: Collect a set of sample lateral error sequences at Q consecutive processing points in the historical processing records, and obtain the lateral dimension errors at subsequent K historical processing points of different sample lateral error sequences to obtain a set of sample lateral prediction error sequences; a sample data equal-value division unit, configured to divide the sample horizontal error sequence set and the sample horizontal prediction error sequence set as sample data into N equal-value parts, select them with replacement N times to construct a first training set, and iterate the selection N times to obtain N training sets; A lateral error prediction channel forming unit is used to respectively train the long short-term memory network using the N training sets until convergence, and obtain N lateral error prediction branches to form a lateral error prediction channel; a branch selection number obtaining unit, configured to obtain the number of processing points in the transverse dimension error sequence; if the number of processing points is greater than or equal to Q, the branch selection number is 1; if the number of processing points is less than Q, the ratio of the number of processing points to Q is set to P, and the difference between 1 and P is multiplied by N and rounded to the integer to obtain the branch selection number; A lateral error prediction branch selection unit is used to randomly select the lateral error prediction branches of the branch selection number from the N lateral error prediction branches, perform lateral dimension error prediction at the next K processing points according to the lateral dimension error sequence, and output the K lateral prediction errors.

6. The precise processing device for buttonholes and buttons on clothing according to claim 4, characterized in that: The mechanical buttoning operation module includes: a button-stitching operation standard acquiring unit, configured to acquire a button-stitching operation standard, wherein the button-stitching operation standard includes a transverse dimension error threshold and a longitudinal dimension error threshold; an earliest lateral warning processing point obtaining unit, configured to judge the K lateral prediction errors according to the lateral dimension error threshold, and if any of the errors exceeds the lateral dimension error threshold, locate the earliest processing point in time to obtain the earliest lateral warning processing point; an earliest longitudinal warning processing point obtaining unit, configured to judge the K longitudinal prediction errors according to the longitudinal dimension error threshold, and if any of the errors exceeds the longitudinal dimension error threshold, locate the earliest processing point in time to obtain the earliest longitudinal warning processing point; The warning processing point determining unit is used to determine a warning processing point according to the earliest horizontal warning processing point and the earliest longitudinal warning processing point, and perform orientation correction on the target cloth according to the initial orientation at the warning processing point.

7. A method for precisely processing buttonholes and buttons on clothing, the method being applied to the precise processing device for buttonholes and buttons on clothing according to any one of claims 1 to 6, the method comprising: Collect keyhole images from the target fabric, identify the keyhole image sequence using a convolutional neural network, and obtain the keyhole feature sequence; Performing buttonhole recognition error analysis based on target fabric attribute information, generating recognition compensation parameters to perform feature compensation on the buttonhole feature sequence, and obtaining a corrected buttonhole feature sequence; Performing iterative optimization analysis of button positions based on the corrected buttonhole feature sequence to generate an optimal button coordinate sequence; According to the optimal button coordinate sequence, the button sewing equipment is controlled to mechanically sew buttons on the target fabric, and the fabric movement trajectory is synchronously monitored during the button sewing operation. The position of the target fabric is adaptively corrected according to the trajectory monitoring result until the operation is completed.

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