Multi-layer cloth synchronous positioning and accurate quilting method of quilting and embroidering all-in-one machine

Abstract

The invention provides a multi-layer cloth synchronous positioning and precise quilting method of a quilting and embroidering all-in-one machine, and aims to solve the service scene problems of insufficient sewing precision, low processing efficiency and poor dynamic change adaptability caused by posture deviation in multi-layer material processing. And a fusion solution of high-precision positioning and path optimization is realized. According to the method, material contour features are accurately extracted and attitude data are generated through a real-time image acquisition and edge detection algorithm, an offset correction vector is calculated in combination with a preset standard attitude, a corrected sewing path is generated through least square fitting, and meanwhile, material attitude changes are monitored in real time in the processing process, so that the processing accuracy is improved. And a dynamic adjustment signal update path is triggered to ensure the machining precision.

Description

Technical Field

[0001] This invention relates to the field of automatic quilting technology, and in particular to a method for synchronous positioning and precise quilting of multi-layer fabrics in an integrated quilting and embroidery machine. Background Technology

[0002] Multilayer material processing plays a vital role in modern manufacturing, particularly in textiles, apparel, and composite materials production, where processing precision and efficiency directly impact product quality and production costs. The importance of this field lies not in optimizing a single step, but in achieving multi-step collaboration in complex environments to ensure precise matching of each material layer and meet high-standard production requirements.

[0003] However, existing methods often struggle to address the challenges posed by material misalignment when processing multi-layered materials. Many traditional technologies lack real-time responsiveness to dynamic material changes, leading to decreased processing accuracy. This is especially true in multi-layered applications, where misalignment accumulates layer by layer, ultimately affecting the overall quality of the finished product. Furthermore, processing efficiency is limited by frequent downtime for adjustments, making it difficult to meet the dual demands of speed and flexibility in modern production.

[0004] A deeper technical challenge lies in effectively handling the dynamic posture changes of multi-layered materials during processing. This factor becomes a core issue affecting processing accuracy because different layers of material may have different physical properties, such as thickness, softness, or elasticity. These properties can cause each layer to exhibit inconsistent offset behavior during processing. For example, when sewing multiple layers of fabric, the bottom layer may wrinkle due to uneven stress, while the top layer may deviate from its intended position due to stretching. If these uncoordinated changes between layers are not detected and adjusted in time, it will lead to deviations in the sewing path, ultimately resulting in defective products. Further, when the processing environment changes, such as equipment vibration or changes in material humidity, these dynamic posture changes become even more complex, making traditional fixed-path planning inadequate and amplifying the problem.

[0005] Therefore, how to monitor and make differentiated adjustments to the dynamic posture changes of multi-layer materials in real time during the processing to ensure the accurate positioning of each layer and the stability of the sewing path has become a key problem that urgently needs to be solved. Summary of the Invention

[0006] To address the problems mentioned in the background art, this invention provides a method for synchronous positioning and precise quilting of multi-layer fabrics in a quilting and embroidery integrated machine. The method includes: S1, capturing real-time images of multi-layer materials on a processing platform using an image acquisition device, extracting material contour features, and obtaining current material posture data; S2, comparing the current material posture data with a preset standard posture, calculating the rotation offset angle, and determining an offset correction vector if the offset angle exceeds a preset offset angle threshold; S3, fitting the offset correction vector with the design pattern path using the least squares method to generate adjusted sewing trajectory parameters, obtaining the corrected sewing path; S4, from the corrected... S5. Extract key control points from the sewing path and determine whether the key control points are aligned with the material contour features. If aligned, the path is confirmed to be valid, and the verified path data is obtained. S6. Transmit the verified path data to the sewing equipment control module to obtain the robot arm motion command sequence and determine the final execution command. S7. Monitor the material posture changes in real time during the execution of the final execution command. By repeatedly capturing images and comparing them with the current posture data of the material, determine whether a new offset has occurred and obtain a dynamic adjustment signal. S8. Update the sewing trajectory parameters according to the dynamic adjustment signal, refit the path, and obtain the optimized sewing sequence.

[0007] Further, step S1 includes: step S11, the image acquisition device acquires an image of the multi-layer material on the processing platform, the real-time image containing the surface texture and boundary information of the multi-layer material; step S12, the real-time image is transmitted to the edge detection algorithm processing module, the edge detection algorithm identifies the material boundary in the real-time image to extract the material contour features; step S13, the material contour features are converted into material current posture data, the material current posture data including position coordinates and posture parameters.

[0008] Further, step S2 includes: step S21, superimposing and comparing the current posture data of the material with a preset standard posture to calculate the rotation offset angle; step S22, if the rotation offset angle exceeds a preset offset angle threshold, extracting key points in the material contour features, the key points being used to generate the offset correction vector; step S23, the offset correction vector recording translation and rotation parameters.

[0009] Further, step S3 includes: step S31, inputting the offset correction vector into the calculation module and decomposing the design pattern path into multiple path segments; step S32, calculating the adjustment coefficient corresponding to the offset correction vector for each path segment; step S33, outputting the adjusted sewing trajectory parameters according to the adjustment coefficient, wherein the corrected sewing path integrates the shape features of the design pattern path; and step S34, storing the corrected sewing path as a trajectory point sequence, wherein the trajectory point sequence is used for subsequent key control point extraction.

[0010] Further, step S4 includes: step S41, obtaining key control points by scanning the corrected sewing path, the key control points being located at path turning points; step S42, matching the key control points with material contour features one by one, and confirming alignment if the position of the key control point falls within the boundary of the material contour feature; step S43, the verified path data is generated from the corrected sewing path after alignment confirmation.

[0011] Furthermore, step S5 includes: step S51, converting the verified path data into an instruction format, the instruction format being compatible with the sewing equipment control module; step S52, the sewing equipment control module parsing the verified path data and generating a sequence of robotic arm motion instructions, the final execution instruction including speed and position control parameters.

[0012] Further, step S6 includes: step S61, when the sewing equipment control module executes the final execution command, the image acquisition device repeatedly captures images; step S62, real-time posture data is obtained through the repeatedly captured images, and the real-time posture data is compared with the current posture data of the material and the deviation is calculated; step S63, if the deviation exceeds a preset deviation threshold, it is determined that a new offset has occurred, and the dynamic adjustment signal carries the new offset information; step S64, the dynamic adjustment signal is transmitted to the path fitting module to trigger an update.

[0013] Further step S7 includes: step S71, parsing the dynamic adjustment signal into a new offset correction vector, and combining the new offset correction vector with the current sewing trajectory parameters; step S72, refitting the new offset correction vector with the existing path, and adjusting the stitch position through the optimized sewing sequence; step S73, replacing the trajectory data in the final execution instruction with the optimized sewing sequence.

[0014] The technical solutions provided by the embodiments of the present invention have the following beneficial effects:

[0015] This invention discloses a method for synchronous positioning and precise quilting of multi-layer fabrics in an integrated quilting and embroidery machine. Addressing the problems of insufficient sewing accuracy, low processing efficiency, and poor adaptability to dynamic changes caused by posture deviations in multi-layer material processing, this invention achieves a fusion solution of high-precision positioning and path optimization through image processing, posture correction, and dynamic adjustment techniques. The invention accurately extracts material contour features and generates posture data through real-time image acquisition and edge detection algorithms. It then calculates the deviation correction vector based on a preset standard posture and uses the least squares method to fit and generate the corrected sewing path. Simultaneously, it monitors material posture changes in real time during processing, triggering dynamic adjustment signals to update the path and ensure processing accuracy. The core innovation of this invention lies in the combination of a dynamic adjustment mechanism and a layered strategy. Differentiated thresholds and adjustment frequencies are set for the characteristics of different material layers, balancing accuracy and efficiency. Ultimately, this invention significantly improves the quality and stability of multi-layer material sewing, adapts to complex processing environments, reduces rework rates caused by deviations, and demonstrates superior technical performance. Attached Figure Description

[0016] Figure 1 This is a flowchart of the method for synchronous positioning and precise quilting of multi-layer fabrics in the quilting and embroidery machine of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. The described embodiments are merely some embodiments of the present invention.

[0018] The technical solution of the present invention will be described in detail below with reference to specific embodiments, so that the purpose, technical solution and advantages of the present invention will be clearer.

[0019] like Figure 1 As shown, this invention provides a method for synchronous positioning and precise quilting of multi-layer fabrics in a quilting and embroidery machine. It aims to achieve high-precision positioning and path optimization of multi-layer materials during the sewing process through image processing, posture correction, and dynamic adjustment technologies, ensuring sewing quality and efficiency. The method will be described in detail below through specific steps and embodiments.

[0020] Specifically, the method includes: S1, capturing real-time images of multi-layer materials on a processing platform using an image acquisition device, processing the real-time images using an edge detection algorithm to extract material contour features, and obtaining the current posture data of the material.

[0021] This step is fundamental to the entire sewing process. Its purpose is to acquire the actual position and orientation information of the multi-layered material on the processing platform, providing data support for subsequent correction and path planning. The image acquisition device can be a high-definition camera mounted above the processing platform, or a combination of multiple cameras arranged at multiple angles to comprehensively capture the surface features of the material. The processing platform is typically a flat work surface. After the material is placed on it, the image acquisition device acquires real-time images at a fixed frequency or through a triggering mechanism. These images contain the boundaries, textures, and possible layering features of the multi-layered material, laying the foundation for subsequent processing. Specifically, in one implementation, the installation position and angle of the image acquisition device need to be adjusted according to the size of the processing platform and the characteristics of the material. For example, when processing larger textile materials, the camera can be installed at a higher position on the platform to cover the entire working area; while when processing small, precision materials, the camera needs to be closer to the platform and equipped with a macro lens to capture subtle features. During image acquisition, the impact of ambient light on image quality needs special attention. It is preferable to set up uniform lighting equipment around the processing platform to avoid shadows or reflections interfering with image clarity. The acquired images are typically stored at high resolution to ensure that subsequent algorithms can accurately identify material boundaries.

[0022] Optionally, this step also includes: step S11, where the image acquisition device acquires images of the multi-layer material on the processing platform, and the real-time image includes the surface texture and boundary information of the multi-layer material.

[0023] In this sub-step, the image acquisition device continuously captures images of the material on the processing platform according to preset time intervals or trigger conditions. Surface texture information reflects the material properties, such as the different texture features of cotton, leather, or composite materials, while boundary information directly relates to the shape and positional distribution of the material. During the acquisition process, the image acquisition device needs to ensure clear imaging of each layer of the multi-layered material, especially when the material layers are thick or similar in color. In such cases, it may be necessary to adjust the camera's focal length or aperture parameters to distinguish the boundaries between different layers. For example, in one possible implementation, suppose the processing platform contains three layers of textile material: a bottom layer of canvas, a middle layer of cotton, and a top layer of decorative fabric. The image acquisition device adjusts the focal length to capture clear images of each layer in sequence, obtaining image data containing the boundaries of the three layers. In the acquired images, the rough texture of the canvas, the fine texture of the cotton, and the pattern features of the decorative fabric are all fully recorded. This information is not only used for subsequent boundary extraction but also provides a reference for determining whether the material has slipped or deformed. In this way, it is ensured that the image data comprehensively reflects the actual state of the multi-layered material. Step S12: The real-time image is transmitted to the edge detection algorithm processing module, and the edge detection algorithm identifies the material boundaries in the real-time image to extract material contour features.

[0024] In this sub-step, the acquired real-time image is transmitted to the processing module for analysis. The edge detection algorithm determines the boundary position of the material by identifying regions of abrupt changes in pixel grayscale values ​​in the image. The processing module typically runs on a computing device connected to the image acquisition device and possesses high image processing capabilities. The edge detection process filters out noise in the image, such as shadow interference caused by uneven lighting, ensuring that the extracted contour features accurately reflect the actual shape of the material. Specifically, in one embodiment, the edge detection algorithm first preprocesses the image, including grayscale conversion and noise smoothing, to improve the accuracy of boundary recognition. Subsequently, the algorithm scans the pixels in the image to find areas with large changes in grayscale values ​​and marks these areas as possible boundary lines. For the characteristics of multi-layered materials, the algorithm further distinguishes the boundaries of different layers based on color or texture differences. For example, when processing three-layered textile materials, the algorithm identifies the rougher edges of the canvas layer, the smoother edges of the cotton layer, and the patterned edges of the decorative fabric layer, and generates corresponding contour feature data for each. These contour features are not only the basis for subsequent pose analysis but can also be used to determine whether the relative positions between the multi-layered materials meet expectations. Step S13: The material contour features are converted into the material's current posture data, which includes position coordinates and posture parameters.

[0025] In this sub-step, the extracted contour features are further processed and transformed into quantitative data that describes the material's position and orientation. Position coordinates are typically calculated with a fixed point on the processing platform as the origin, calculating the horizontal and vertical coordinates of key points in the material's contour features relative to that origin. Attitude parameters include information such as the material's rotation angle and tilt, used to describe whether the material's orientation on the platform matches the expected orientation. This data provides a direct basis for subsequent offset correction. For example, in one possible implementation, assuming the processing platform has its lower left corner as the origin, several key points in the material's contour features are marked as reference points. By calculating the positions of these reference points in the coordinate system, the coordinates of the material's center point and boundary range are obtained. Simultaneously, based on the overall shape of the contour features, the rotation angle of the material relative to the platform's horizontal direction is calculated; for example, it is found that the material has rotated 5 degrees clockwise. These position coordinates and attitude parameters together constitute the material's current attitude data, laying the foundation for subsequent comparison with a standard attitude. This data conversion method can intuitively reflect the actual state of the material on the processing platform, facilitating the implementation of subsequent correction operations. S2, compare the current posture data of the material with the preset standard posture, calculate the rotation offset angle, and if the offset angle exceeds the preset offset angle threshold, determine the offset correction vector.

[0026] This step is crucial for achieving precise material positioning. Its purpose is to determine whether the material's current posture meets design requirements and to provide a basis for subsequent path adjustments. The preset standard posture is typically an ideal state predefined based on design patterns or processing needs; for example, the material should be perfectly horizontal or aligned with the edge of the processing platform. By comparing the current posture with the standard posture, the direction and magnitude of adjustment required for the material are calculated. Specifically, in one implementation, the preset standard posture data is stored in the system's database, containing the material's position coordinates and posture parameters under ideal conditions. The comparison process overlays and analyzes the current posture data with the standard posture data, focusing on the material's rotation angle and position offset. The rotation offset angle is calculated based on key points in the material's contour features; for example, by comparing the angle difference between the current key point and the standard key point, the overall rotation degree of the material is determined. If this angle exceeds a preset offset angle threshold, such as 3 degrees, the system determines that correction is needed and generates a corresponding offset correction vector to guide subsequent adjustments.

[0027] Optionally, this step also includes: step S21, superimposing and comparing the current posture data of the material with the preset standard posture, and calculating the rotation offset angle.

[0028] In this sub-step, the system compares the position coordinates and attitude parameters in the current attitude data with the standard attitude data one by one. The overlay comparison process is usually implemented graphically, that is, the contour features of the current attitude and the contour features of the standard attitude are overlaid and displayed in the same coordinate system, visually showing the differences between the two. The calculation of the rotation offset angle is based on the overall direction of the contour features, for example, by comparing the angle between the central axes of the two sets of contour features to obtain the specific offset angle value. For example, in one embodiment, assuming the standard attitude requires the long side of the material to be completely parallel to the horizontal direction of the processing platform, while the current attitude data shows that the long side of the material has an angle with the horizontal direction. Through overlay comparison, the system calculates that this angle is 4.5 degrees, indicating that the material has rotated clockwise. Such a rotation offset angle directly affects the accuracy of the subsequent sewing path, so it is necessary to further determine whether it exceeds a preset offset angle threshold in order to decide whether to perform a correction operation. Through this intuitive comparison method, deviations in the material's attitude can be quickly identified. Step S22: If the rotation offset angle exceeds the preset offset angle threshold, key points in the material contour features are extracted, and these key points are used to generate an offset correction vector.

[0029] In this sub-step, if the system determines that the rotation offset angle exceeds a preset offset angle threshold, such as 3 degrees, it will extract several key points from the material contour features for subsequent correction vector calculation. These key points are usually corner points, endpoints, or center points in the contour features, representing the overall shape and positional characteristics of the material. The purpose of extracting key points is to provide a reference for the correction process, ensuring that the correction vector accurately reflects the direction and magnitude of the material adjustment. Specifically, in one possible implementation, the system selects several representative key points from the material contour features, such as the four corner points of a rectangular material or the center point of a circular material. Assuming the current material is a rectangular textile, the system extracts the coordinate data of the four corner points and compares them with the corresponding corner point coordinates in the standard posture, calculating the distance and direction that each corner point needs to move. The offset data of these key points provides the basis for the subsequent generation of correction vectors, ensuring that the correction process can specifically address the material posture deviation problem. Step S23: The offset correction vector records the translation and rotation parameters.

[0030] In this sub-step, based on the extracted keypoint data, the system calculates an offset correction vector, which includes two main parameters: translation and rotation. Translation represents the distance the material needs to move laterally or longitudinally, such as moving 2 cm to the right or 1.5 cm upwards. Rotation represents the angle and direction the material needs to rotate, such as rotating 4 degrees counterclockwise. These parameters together constitute the offset correction vector, providing specific guidance for subsequent sewing path adjustments. For example, in one embodiment, assuming that keypoint comparison reveals the material has shifted 1.8 cm to the right and rotated 4.2 degrees clockwise, the system will generate an offset correction vector recording the translation as 1.8 cm to the left and the rotation as 4.2 degrees counterclockwise. Such a correction vector intuitively reflects the necessary adjustments to the material, ensuring the subsequent sewing path aligns with the material's actual position. This effectively avoids sewing errors caused by material posture deviations. S3, the least squares method is used to fit the offset correction vector to the design pattern path, generating adjusted sewing trajectory parameters to obtain the corrected sewing path.

[0031] This step is the core of sewing path optimization. Its purpose is to adjust the original design pattern path based on the offset correction vector, ensuring that the sewing trajectory perfectly matches the actual posture of the material. Least squares is a commonly used mathematical optimization method that calculates the sewing trajectory parameters that best meet actual requirements by minimizing path deviation. The corrected sewing path will serve as the direct basis for subsequent processing, ensuring the accuracy and stability of the sewing process. Specifically, in one implementation, the design pattern path is an ideal sewing trajectory pre-drawn according to processing requirements, such as a straight line, a set of curves, or a complex pattern. The offset correction vector provides the specific direction and magnitude of material posture adjustment. The system decomposes the design pattern path into multiple segments, and for each segment, it calculates the adjusted trajectory point position by combining the translation and rotation of the offset correction vector. The application of least squares is to ensure, through optimization calculation, that the deviation between the adjusted trajectory point and the original design pattern path is minimized, while simultaneously satisfying the constraints of the material's current posture.

[0032] Optionally, this step also includes: step S31, inputting the offset correction vector into the calculation module and decomposing the design pattern path into multiple path segments.

[0033] In this sub-step, the offset correction vector is transmitted to a dedicated calculation module for fitting calculations with the design pattern path. The design pattern path is typically stored in vector form, containing multiple consecutive trajectory points. The system decomposes these trajectory points into several path segments according to certain rules, such as decomposing a long curve into multiple short curve segments, or decomposing a complex pattern into multiple basic shape units. The purpose of decomposing the path segments is to facilitate segment-by-segment adjustments, ensuring a more precise correction process. For example, in one possible implementation, suppose the design pattern path is a complex pattern curve containing multiple turning points and arc segments. The system will decompose this curve into 10 path segments, each corresponding to a smaller arc or straight line portion. After the translation and rotation parameters in the offset correction vector are input into the calculation module, the system will calculate the adjustment direction and magnitude for each path segment. This decomposition method ensures that the correction process will not produce large errors due to path complexity. Step S32: Calculate the adjustment coefficient corresponding to the offset correction vector for each path segment, where the adjustment coefficient minimizes the path deviation.

[0034] In this sub-step, the system calculates the corresponding adjustment coefficient for each decomposed path segment, combining it with the offset correction vector. The adjustment coefficient quantifies the translation or rotation range required for each path segment, ensuring that the adjusted path segment aligns with the actual posture of the material while preserving the shape characteristics of the original design pattern as much as possible. Minimizing path deviation is the core objective of the adjustment coefficient calculation. Through optimization algorithms, the system ensures that the difference between the adjusted path segment and the ideal path is minimized. Specifically, in one embodiment, assuming a path segment is a straight line, the original design requires this line to be parallel to the horizontal direction of the processing platform. However, due to material offset, the actual path needs to be adjusted 1.5 cm to the left and rotated 3 degrees counterclockwise. The system calculates the corresponding adjustment coefficients, including translation and rotation adjustment coefficients, to ensure that the path segment maintains its straight-line characteristics after adjustment while aligning with the material contour features. In this way, path deviation can be effectively reduced, improving the accuracy of the sewing trajectory. Step S33: Based on the adjustment coefficients, the adjusted sewing trajectory parameters are output using the least squares method, and the corrected sewing path is integrated with the shape characteristics of the design pattern path.

[0035] In this sub-step, based on the adjustment coefficients calculated above, the system generates adjusted sewing trajectory parameters using the least squares method. These parameters contain the specific position and direction information of each path segment, ensuring that the corrected sewing path conforms to the actual posture of the material while preserving the original shape characteristics of the design pattern path as much as possible. The corrected sewing path is output in the form of a continuous trajectory, providing a basis for the subsequent extraction of key control points. For example, in one possible implementation, assume that the original design pattern path is a circular pattern containing multiple continuous arc segments. After fitting using the least squares method, the adjusted sewing trajectory parameters fine-tune each arc segment, for example, shifting the position of some arc segments 1.2 cm to the left, while adjusting the curvature of the arc segments to adapt to the rotational offset of the material. The corrected sewing path still maintains the characteristics of a circular pattern overall, but the positions of specific trajectory points are now perfectly matched with the actual posture of the material. This fusion method ensures that the sewing result is both aesthetically pleasing and accurate. Step S34: The corrected sewing path is stored as a trajectory point sequence, which is used for subsequent key control point extraction.

[0036] In this sub-step, the corrected sewing path is transformed into an ordered sequence of trajectory points, each containing specific coordinate values ​​and direction information. These trajectory point sequences are stored in the system as data files, facilitating subsequent extraction of key control points and path verification. The trajectory point sequences are typically stored according to the continuity of the path, ensuring smooth reading and parsing by subsequent processing modules. S4: Key control points are extracted from the corrected sewing path. It is determined whether the key control points are aligned with the material contour features. If aligned, the path is confirmed as valid, and the verified path data is obtained.

[0037] This step is crucial for ensuring a perfect match between the sewing path and the actual material's posture. Its purpose is to confirm the accuracy of the corrected sewing path in actual processing by extracting and verifying key control points. Key control points are typically important locations along the path, such as turning points, start points, or end points, directly affecting the accuracy and stability of the sewing trajectory. By determining whether these points are aligned with the material's contour features, sewing errors caused by path deviations can be effectively avoided. In one implementation, the corrected sewing path is stored as a sequence of trajectory points, from which the system selects representative key control points. These key control points are not only crucial for determining the path's shape but are also closely related to the material's boundaries or important feature points. The verification process compares the positions of the key control points one by one with the material's contour features. If a key control point falls within the boundary range of the material's contour features, the alignment is considered successful, and the path's validity is confirmed. The verified path data serves as the basis for subsequent sewing equipment execution, ensuring the accuracy of the processing.

[0038] Optionally, this step also includes: step S41, obtaining key control points by scanning the corrected sewing path, the key control points being located at path turning points.

[0039] In this sub-step, the system performs a comprehensive scan of the corrected sewing path, identifying locations in the trajectory point sequence with significant directional changes or shape features. These locations are typically turning points in the path, such as the intersection of straight lines and curves, curve corners, or the start and end points of the path. The extraction of key control points is automatically performed based on the geometric characteristics of the path, ensuring that the selected points represent the main shape features of the path and provide a reliable basis for subsequent alignment verification. For example, in one possible implementation, suppose the corrected sewing path is a complex pattern containing combinations of multiple straight and curved segments. When scanning the trajectory point sequence, the system identifies the turning points where each straight and curved segment connects, as well as corners with significant curvature changes within the curved segments, marking these points as key control points. Assuming the path has 50 key control points, distributed across various important locations in the pattern, these points comprehensively reflect the overall shape of the path. By extracting these key control points, accurate data support can be provided for subsequent verification. Step S42: Match the key control points with the material contour features one by one. If the position of the key control point falls within the boundary of the material contour feature, the alignment is confirmed.

[0040] In this sub-step, the system compares the extracted key control points with the material contour features to determine whether each key control point is within the boundary of the material contour features. Material contour features are typically stored as closed curves or polygons, containing the material's outer boundary and internal feature lines. The matching process calculates the distance from the key control point to the contour feature boundary. If the distance is less than a preset range, such as 0.5 cm, the key control point is considered aligned with the contour feature. Specifically, in one embodiment, assuming the material is a rectangular textile piece, its contour feature is a rectangular boundary containing four corner points and four side lines. The system compares each of the 50 extracted key control points with this rectangular boundary, calculating the vertical distance from each key control point to the nearest side line. If the distance from a key control point to a side line is 0.3 cm, which is less than the preset range, the point is considered aligned with the contour feature. By verifying all key control points one by one, it can be ensured that the corrected sewing path perfectly matches the actual position of the material, avoiding deviations during sewing. In step S43, the verified path data is generated from the corrected sewing path after alignment confirmation.

[0041] In this sub-step, if all or most critical control points pass alignment verification, the system confirms the validity of the corrected sewing path and generates verified path data. This path data is stored in a standard format, containing a complete sequence of trajectory points and critical control point information, providing a reliable basis for subsequent transmission to the sewing equipment control module. If some critical control points fail verification, the system may trigger a path recalibration process to ensure the accuracy of the final path data. For example, in one possible implementation, suppose that during the verification process, 48 ​​out of 50 critical control points are found to be aligned with the material contour features, and the deviation of the remaining 2 points is within an acceptable range. The system will determine that the corrected sewing path is valid and integrate the complete sequence of trajectory points and critical control point information into verified path data. This data is stored in file form, containing the coordinate values ​​and direction parameters of each trajectory point, ensuring that subsequent equipment can accurately read and execute it. This verification method can effectively improve the reliability of the sewing path. S5, for the verified path data, it is transmitted to the sewing equipment control module to obtain the robot arm motion command sequence and determine the final execution command.

[0042] This step is crucial in converting the verified path data into actual processing instructions, ensuring that the sewing equipment can accurately execute processing tasks according to the corrected path. The sewing equipment control module is typically the core control unit of the sewing equipment, responsible for parsing the path data and generating a sequence of motion instructions for the robotic arm. The final execution instructions contain the specific action parameters of the robotic arm during processing, ensuring the successful completion of the sewing task. Specifically, in one implementation, the verified path data is transmitted to the sewing equipment control module in a standard format. The module decomposes the path data into a series of continuous motion instructions based on the equipment's hardware characteristics and processing requirements. These instructions include not only the robotic arm's movement path but also parameters such as movement speed and stitch length control. The generation process of the final execution instructions fully considers the equipment's physical limitations, such as the robotic arm's maximum range of motion and speed limit, ensuring the executability of the instructions.

[0043] Optionally, this step also includes: step S51, converting the verified path data into an instruction format, the instruction format being compatible with the sewing equipment control module.

[0044] In this sub-step, the verified path data is converted into an instruction format compatible with the sewing equipment control module. This format is typically a standard data structure defined by the equipment manufacturer, containing information such as path point coordinates, direction parameters, and processing parameters. The conversion process adjusts the path data appropriately according to the specific requirements of the equipment, such as adjusting the density of trajectory points or optimizing the path sequence to improve equipment execution efficiency. For example, in one possible implementation, assuming the sewing equipment control module supports a specific instruction format requiring path data to be stored at a density of one trajectory point per millimeter, the system interpolates the verified path data to increase the number of trajectory points, ensuring compliance with equipment requirements. The converted instruction format file contains the coordinate values ​​and direction information of each trajectory point, which can be directly read and parsed by the control module. This conversion method ensures seamless integration between the path data and the equipment hardware. In step S52, the sewing equipment control module parses the verified path data to generate a sequence of robotic arm motion instructions, with the final execution instructions containing speed and position control parameters.

[0045] In this sub-step, the sewing equipment control module parses the converted instruction format file, extracts the trajectory point information and processing parameters, and generates a sequence of motion instructions for the robotic arm. These instruction sequences contain the specific position, speed, and direction of the robotic arm at each time point, ensuring the sewing needle moves accurately on the material. The final execution instruction also sets appropriate speed control parameters based on processing requirements, such as reducing speed at turning points to improve accuracy. Specifically, in one embodiment, assuming the sewing equipment is a multi-axis industrial sewing machine, its control module generates a sequence of motion instructions containing 5000 instructions after parsing the path data. Each instruction contains the position parameters and speed parameters of the robotic arm in the processing platform coordinate system, such as a uniform speed of 5 cm / s on straight sections, reducing to 2 cm / s at turning points to ensure stitch accuracy. This meticulous instruction design effectively improves the stability and processing quality of the sewing process. S6, during the execution of the final execution instruction, the material's posture changes are monitored in real time. By repeatedly capturing images and comparing them with the current material posture data, it is determined whether a new offset has occurred, and a dynamic adjustment signal is obtained.

[0046] This step is the core of achieving dynamic optimization of the sewing process. Its purpose is to monitor the material's posture in real time during processing, ensuring timely detection and adjustment even in the event of unexpected deviations. During sewing, the material may change position due to machine vibration, stitch tension, or external interference. Real-time image acquisition and comparison can quickly identify these changes and generate dynamic adjustment signals. Specifically, in one implementation, while the sewing equipment executes its final command, the image acquisition device repeatedly captures images of the material on the processing platform at a fixed frequency. After processing, these images extract the current material posture data and compare it with the initial material posture data. If a significant difference is found, such as the material's overall translation or rotation exceeding a preset range, the system determines a new deviation and generates a dynamic adjustment signal to trigger subsequent path updates. This real-time monitoring mechanism effectively addresses unexpected situations during processing.

[0047] Optionally, this step also includes: step S61, when the sewing equipment control module executes the final execution instruction, the image acquisition device repeatedly captures images.

[0048] In this sub-step, the sewing equipment drives the robotic arm to perform processing according to the final execution command, while the image acquisition device continuously captures images of the material on the processing platform at a preset frequency, such as 5 times per second. These images contain real-time position and posture information of the material, reflecting whether the material has moved or deformed during processing. The settings of the image acquisition device are consistent with those in step S1 to ensure stable image quality. For example, in one possible implementation, assuming the sewing equipment is processing a multi-layered textile material, the image acquisition device is mounted above the equipment and captures images at a frequency of 5 times per second. During processing, the material may slip slightly due to stitch tension, and the image acquisition device can record these changes in a timely manner. The acquired images contain boundary and surface texture information of the material, providing reliable data for subsequent posture comparison. This high-frequency acquisition method ensures real-time perception of material posture changes. In step S62, real-time posture data is obtained by processing the repeatedly captured images using an edge detection algorithm, and the real-time posture data is compared with the current posture data of the material to calculate the deviation.

[0049] In this sub-step, the repeatedly captured images are transmitted to the processing module, where the current contour features of the material are extracted using an edge detection algorithm and converted into real-time posture data. This data includes the material's position coordinates and posture parameters. The real-time posture data is compared with the material's current posture data obtained in step S1 to calculate the deviation between the two sets of data. The deviation calculation mainly focuses on the material's translation and rotation angle to ensure accurate identification of posture changes. Specifically, in one embodiment, it is assumed that the repeatedly captured images show the material has shifted 0.8 cm to the right and rotated 2 degrees clockwise. The system extracts the current contour features using an edge detection algorithm, generates real-time posture data, and compares it with the initial material current posture data to calculate a translation deviation of 0.8 cm and a rotation deviation of 2 degrees. This deviation data provides a direct basis for subsequent judgment on whether adjustments are needed. Through this comparison method, material offset problems during processing can be quickly detected. In step S63, if the deviation exceeds a preset deviation threshold, a new offset is determined, and a dynamic adjustment signal carrying the new offset information is sent.

[0050] In this sub-step, the system determines whether the calculated deviation value exceeds a preset deviation threshold, such as a translational deviation exceeding 0.5 cm or a rotational deviation exceeding 1 degree. If the deviation threshold is exceeded, the material is considered to have shifted significantly, requiring path adjustment. The system generates a dynamic adjustment signal containing specific information about the new offset, such as the translational direction, translational distance, and rotational angle, providing guidance for subsequent path updates. For example, in one possible implementation, suppose the calculated deviation shows that the material has shifted 0.9 cm to the right, exceeding the preset deviation threshold of 0.5 cm. The system determines that a new offset has occurred and generates a dynamic adjustment signal, recording the correction information of a 0.9 cm shift to the left. The dynamic adjustment signal is stored in the form of a data packet, containing specific parameters of the offset, ensuring that subsequent modules can accurately read and execute adjustment operations. In this way, changes in material during processing can be addressed promptly. In step S64, the dynamic adjustment signal is transmitted to the path fitting module to trigger an update.

[0051] In this sub-step, the generated dynamic adjustment signal is transmitted to the path fitting module, triggering the update process of the sewing trajectory parameters. The path fitting module is responsible for recalculating the sewing path based on the new offset information, ensuring that the processing trajectory is aligned with the latest posture of the material. The transmission of the dynamic adjustment signal is usually achieved through the system's internal data interface, ensuring that the signal can be delivered and processed quickly. Specifically, in one embodiment, assuming that the dynamic adjustment signal contains correction information of 0.9 cm to the left, the system transmits it to the path fitting module. After receiving the signal, the module initiates the path update process, combining the current sewing trajectory parameters and the new offset information to recalculate the adjusted trajectory point sequence. Through this fast response mechanism, it can be ensured that the sewing path always maintains consistency with the material posture, avoiding processing errors. S7, the sewing trajectory parameters are updated according to the dynamic adjustment signal, and the path is refitted using the least squares method to obtain the optimized sewing sequence.

[0052] This step is the final stage of dynamic optimization of the sewing path. Its purpose is to readjust the sewing trajectory parameters based on the new offset information in the dynamic adjustment signal, ensuring that the processing can adapt to changes in material posture. The least squares method is again used for path fitting, generating an optimized sewing sequence through optimization calculations, providing precise guidance for subsequent processing. Specifically, in one implementation, the new offset information in the dynamic adjustment signal is input into the path fitting module, which, combined with the current sewing trajectory parameters, recalculates the adjusted trajectory point positions. The application of the least squares method ensures that the adjusted path deviation is minimized while preserving as much of the shape characteristics of the original design pattern as possible. The optimized sewing sequence is output as a new trajectory point sequence, directly replacing the trajectory data in the currently executed instruction, ensuring the continuity and accuracy of the processing.

[0053] Optionally, this step also includes: step S71, dynamically adjusting the signal parsing into a new offset correction vector, which is then combined with the current sewing trajectory parameters.

[0054] In this sub-step, the dynamic adjustment signal is parsed into a new offset correction vector, containing translation and rotation parameters. These parameters are combined with the current sewing trajectory parameters to guide the subsequent path fitting process. The new offset correction vector records specific information about the latest posture change of the material, ensuring that the adjusted path is aligned with the actual position of the material. For example, in one possible implementation, assuming the dynamic adjustment signal shows that the material has translated 0.9 cm to the right, the system parses a new offset correction vector, recording the correction information for a 0.9 cm translation to the left. This vector, combined with the current sewing trajectory parameters, forms the basic data for path adjustment. This parsing and combination method ensures that the subsequent fitting process accurately reflects the latest posture change of the material. In step S72, the new offset correction vector is refitted with the existing path using the least squares method, and the stitch position is adjusted using the optimized sewing sequence.

[0055] In this sub-step, the system uses the least squares method to refit the new offset correction vector and the existing path, calculating the adjusted trajectory point positions. The fitting process adjusts the stitch positions for each path segment, combining the new offset correction vector to ensure the sewing sequence perfectly matches the latest posture of the material. The optimized sewing sequence contains the adjusted trajectory point information, providing precise guidance for subsequent processing. Specifically, in one embodiment, assuming the existing path is a straight line segment, the new offset correction vector requires a 0.9 cm shift to the left. The system refits the path using the least squares method, adjusting the entire straight line segment to the left by 0.9 cm while maintaining its straight-line characteristics. The optimized sewing sequence records the adjusted stitch positions, ensuring the robotic arm can execute the processing task according to the new path. This dynamic adjustment method effectively addresses material offset issues during processing. Step S73 replaces the trajectory data in the final execution instruction with the optimized sewing sequence.

[0056] In this sub-step, the optimized sewing sequence is transmitted to the sewing equipment control module, directly replacing the original trajectory data in the final execution instruction. This replacement process ensures the continuity of the processing task; for example, after pausing at the current stitch position, new trajectory data is immediately loaded, and subsequent processing continues. In this way, real-time updates to the sewing path are achieved, ensuring that processing accuracy is unaffected by changes in material posture. For example, in one possible implementation, suppose the sewing equipment is processing the middle section of a curved path. The system detects material offset and generates an optimized sewing sequence. The control module pauses processing at the current stitch position, loads the new trajectory data, and continues execution from the pause point along the adjusted path. This seamless replacement method ensures that the processing task is not interrupted by path updates, while maintaining the continuity and consistency of the sewing results. This dynamic adjustment mechanism significantly improves the adaptability and stability of the processing process. In one embodiment, for complex processing scenarios involving multi-layered materials, the system sets different deviation thresholds and adjustment strategies based on the material characteristics of different layers. For example, when processing three-layer textile materials, the deviation threshold for the bottom canvas can be set higher because its material is thicker, and small deviations have less impact on processing; while the deviation threshold for the top decorative fabric is set lower because its pattern alignment requirements are higher. When the system detects a 0.6 cm deviation in the top fabric, it immediately triggers a dynamic adjustment signal and generates a new offset correction vector to refit the path. This layered adjustment strategy ensures that each layer of the multi-layer material is processed accurately. Specifically, in another implementation, assuming the processing scenario is high-precision garment sewing, the material includes a three-layer structure: lining, fabric, and decorative layer. During real-time monitoring, the system detects that the decorative layer has shifted 0.7 cm to the left due to stitch tension, exceeding the preset deviation threshold of 0.4 cm. A dynamic adjustment signal is generated and transmitted to the path fitting module, which adjusts the sewing path corresponding to the decorative layer according to the new offset correction vector, while keeping the lining and fabric paths unchanged. The optimized sewing sequence is loaded into the control module to ensure that the pattern stitches of the decorative layer are perfectly aligned with the design pattern. This targeted adjustment effectively improves the overall quality of multi-layer material processing. For example, in one possible implementation, for processing heavy materials, the system increases the image acquisition frequency of the image acquisition device, for instance, from 5 times per second to 10 times per second, to detect material deviations more promptly. Simultaneously, the logic for generating the dynamic adjustment signal is optimized based on the material weight; for example, a higher deviation threshold is set for heavy materials to avoid decreased processing efficiency due to frequent adjustments. Suppose that when processing a multi-layered leather material, the system detects that the material has shifted by 0.4 cm, which is less than the preset deviation threshold of 0.6 cm, therefore no adjustment signal is triggered, and processing continues. This flexible setting allows for both precision and processing efficiency to be maintained.In one embodiment, for path fitting during dynamic adjustment, the system selects different fitting strategies based on the processing progress. For example, in the early stages of processing, path fitting prioritizes the adjustment of the overall trajectory to ensure a comprehensive path update after material posture correction; while in the later stages, the fitting strategy focuses more on local adjustments, only fine-tuning the remaining unprocessed paths to avoid affecting completed stitches. Assuming the processing progress is 70% complete, if the system detects a material offset of 0.8 cm, the path fitting module only adjusts the remaining 30% of the path, generating a locally optimized sewing sequence. This phased strategy effectively reduces the impact of adjustments on the overall processing. Specifically, in another embodiment, assuming the processing task is sewing a complex pattern with multiple intersecting lines and turning points, the system detects that the material has rotated 2.5 degrees in the middle of processing, exceeding the preset offset angle threshold of 1 degree. After the dynamic adjustment signal is triggered, the path fitting module, in conjunction with the processing progress, only adjusts the path for the unfinished intersecting lines, refitting the turning point positions to ensure that subsequent stitches are aligned with the material posture. The optimized sewing sequence is loaded into the control module, and the robotic arm continues processing along the new path, ensuring the integrity and aesthetics of the pattern. This localized adjustment method maintains high-quality completion of the processing task even during dynamic changes. For example, in one possible implementation, for long-term continuous processing scenarios, the system sets a frequency limit for dynamic adjustments to avoid overloading the equipment due to frequent adjustments. For instance, when processing a large, multi-layered material, the system is set to trigger a path update at most once per minute. Even if multiple small deviations are detected, the deviation is accumulated and adjusted uniformly in the next update. Assuming that the material has cumulatively shifted by 0.9 cm within one minute, the system generates a dynamic adjustment signal at the start of the next minute to refit the path. This frequency control method extends the equipment's lifespan while ensuring processing accuracy. In one embodiment, the system optimizes the generation logic of the final execution command based on the characteristics of different types of sewing equipment. For example, for high-speed sewing machines, the command sequence prioritizes the smooth transition of speed parameters to avoid sudden speed changes due to path adjustments; for multi-needle sewing machines, the command sequence focuses on optimizing the multi-needle collaborative path to ensure that the needle trajectories do not conflict after adjustment. When processing multi-layered materials using a multi-needle sewing machine, if the system detects material offset and updates the path, the instruction sequence will redistribute the sewing areas of each needle to ensure that the adjusted path does not cause stitch overlap. This device adaptation method effectively improves the compatibility and stability of the processing task. Specifically, in another implementation, assuming the processing scenario is the sewing of precision leather goods, a double-needle sewing machine is used.During dynamic adjustment, the system detects a 0.7 cm shift of the material to the right. After the path fitting module generates an optimized sewing sequence, the control module adjusts the track spacing and synchronization parameters of the two needles based on the characteristics of the double-needle sewing machine. This ensures that the adjusted path does not cause the stitches of the two needles to cross or misalign. This meticulous instruction optimization ensures that precision machining tasks maintain high quality even after dynamic adjustment, while preventing equipment malfunctions due to instruction conflicts. For example, in one possible implementation, the system sets different priorities for the generation and transmission of dynamic adjustment signals based on the complexity of the processing environment. In a relatively stable processing environment, the generation priority of the dynamic adjustment signal is low, triggering an update only when the deviation is large. In a complex processing environment where the material is prone to slippage, the signal generation priority is high, triggering adjustments promptly even with small deviations. For instance, when processing a piece of slippery silk material, if the system detects a 0.3 cm shift, although this does not exceed the preset shift threshold of 0.5 cm, a dynamic adjustment signal is still generated due to the high complexity of the environment to ensure processing accuracy. This priority setting allows for flexible adaptation to the needs of different processing scenarios. In one embodiment, for processing multi-layered materials, the system sets a dynamically adjusted differentiated strategy based on the processing requirements of different layers. For example, when processing the bottom layer material, the dynamic adjustment mainly focuses on the stability of the overall path to ensure the firmness of the bottom layer stitches; when processing the top layer material, the adjustment focuses more on the pattern alignment accuracy to ensure the top layer decorative effect. Assuming that when processing three layers of material, the system detects a 0.6 cm offset in the top decorative layer, it immediately triggers an adjustment signal and updates the path, while a 0.4 cm offset in the bottom canvas layer has a smaller impact on the overall process and does not trigger an adjustment. This differentiated strategy achieves a balance between accuracy and efficiency in multi-layered material processing. Specifically, in another embodiment, assuming the processing task is the sewing of multiple layers of curtain fabric, including a bottom blackout fabric, a middle sound insulation fabric, and a top decorative fabric, when processing the top decorative fabric, the system detects a 0.5 cm offset, exceeding the preset offset threshold of 0.3 cm, immediately generates a dynamic adjustment signal and updates the sewing path to ensure that the pattern stitches of the decorative fabric are aligned with the design pattern. During the processing of the bottom layer of light-blocking fabric, a deviation of 0.6 cm was detected. However, because the preset deviation threshold was 0.8 cm, no adjustment signal was triggered, and processing continued. This layered adjustment method ensures that the processing quality of each layer in multi-layer material processing is specifically guaranteed. For example, in one possible implementation, the system selects different fitting parameters based on the complexity of the processing task to determine the path fitting accuracy during dynamic adjustment. For instance, when processing simple straight paths, the fitting parameters are set more leniently, allowing for a certain degree of deviation to improve adjustment speed; while when processing complex patterned paths, the fitting parameters are set more strictly to ensure that the adjusted path is highly consistent with the design pattern.When processing a complex pattern, if the system detects a material offset, the path fitting module recalculates the trajectory point positions with high-precision parameters to ensure that each turning point and arc segment is aligned with the design pattern. This parameter optimization method allows for a dynamic balance between accuracy and efficiency across different processing tasks. In one embodiment, the system sets different trigger conditions for the dynamic adjustment signal based on the processing progress and material characteristics. For example, in the early stages of processing, the trigger conditions are more lenient, allowing for small offsets to avoid frequent adjustments affecting efficiency; in the later stages, the trigger conditions are stricter to ensure the accuracy of the final stitch. For instance, when processing a multi-layered cotton fabric, if an offset of 0.4 cm is detected in the early stages, which is below the lenient threshold of 0.6 cm, no adjustment signal is triggered; however, if an offset of 0.3 cm is detected in the later stages, exceeding the strict threshold of 0.2 cm, an adjustment signal is triggered. This phased triggering mechanism allows for a reasonable allocation of accuracy and efficiency throughout the entire processing flow. Specifically, in another implementation, assuming the processing task is sewing multi-layered bed sheet fabric, the system initially uses a lenient threshold of 0.7 cm as the trigger condition. If a material offset of 0.5 cm is detected, no adjustment signal is triggered, and processing continues. Later in the processing phase, the system adjusts the strict threshold to 0.3 cm. If a material offset of 0.4 cm is detected, a dynamic adjustment signal is immediately generated to update the sewing path, ensuring the accuracy and aesthetics of the final stitches. This dynamic threshold adjustment method effectively adapts to the needs of different stages of the processing task, ensuring overall processing quality. For example, in one possible implementation, to improve data transmission efficiency during the dynamic adjustment process, the system optimizes the data packet structure based on the processing equipment's processing capacity. For example, for equipment with high processing capacity, the data packet contains a complete sequence of trajectory points and adjustment parameters to ensure adjustment accuracy; for equipment with low processing capacity, the data packet simplifies the data structure, containing only key adjustment information to improve transmission speed. Assuming a sewing machine with limited processing capacity is used, after the system detects material offset, the generated dynamic adjustment signal data packet only contains adjustment information for key control points, reducing the data volume to ensure the equipment can respond quickly. This data optimization method enables smooth execution of dynamic adjustments across different devices. In one embodiment, for dynamic adjustments in multi-layer material processing, the system sets adjustment priorities based on the order of processing layers. For example, when processing the bottom layer of material, the adjustment priority is low, and updates are triggered only when the deviation is large to ensure processing efficiency; when processing the top layer of material, the adjustment priority is high, and updates are triggered immediately even if the deviation is small to ensure decorative effect. For instance, when processing a multi-layered curtain fabric, if the bottom blackout fabric shifts by 0.7 cm, which does not exceed the lower priority shift threshold of 0.8 cm, no adjustment signal is triggered; however, if the top decorative fabric shifts by 0.4 cm, which exceeds the higher priority shift threshold of 0.3 cm, an adjustment signal is immediately triggered.This prioritization ensures the processing quality of critical layers in multi-layer material processing. Specifically, in another implementation, suppose the processing task is the sewing of a multi-layer sofa fabric, including a bottom reinforcing fabric, a middle cushioning fabric, and a top decorative fabric. When processing the top decorative fabric, if the system detects an offset of 0.5 cm, exceeding the higher-priority offset threshold of 0.3 cm, it immediately generates a dynamic adjustment signal and updates the path to ensure the pattern stitches of the decorative fabric align with the design. However, when processing the bottom reinforcing fabric, if an offset of 0.9 cm is detected, but due to its lower priority (offset threshold of 1.0 cm), no adjustment signal is triggered, and processing continues. This layered priority adjustment method achieves rational resource allocation and stable processing quality in multi-layer material processing. For example, in one possible implementation, the system sets different update frequencies based on the urgency of the processing task to determine the path update speed during dynamic adjustment. For instance, when processing ordinary textile materials, the update frequency is set to once per minute to ensure the equipment is not overloaded; when processing high-value precision materials, the update frequency is increased to once every 30 seconds to ensure timely correction of deviations. Suppose that when processing a piece of high-value silk material, the system detects a material offset of 0.4 cm, immediately triggers an adjustment signal, and completes a path update within 30 seconds to ensure processing accuracy. This frequency optimization method enables flexible and efficient dynamic adjustments across different processing tasks. In one embodiment, the system sets different response mechanisms for the generation of dynamic adjustment signals and path updates based on the stability of the processing environment. For example, in a stable processing environment, the response mechanism is set to delayed response, triggering an update only when the deviation accumulates to a certain level; in an unstable processing environment, the response mechanism is set to immediate response, triggering an update immediately upon detecting a deviation. Suppose that in an unstable processing environment, the system detects a material offset of 0.3 cm, immediately generates a dynamic adjustment signal, and updates the path to ensure processing accuracy. This response mechanism optimization method effectively adapts to the needs of different processing environments. Specifically, in another implementation, suppose the processing task is the sewing of multi-layered curtain fabric, and the processing environment is unstable due to equipment vibration. The system's response mechanism is set to instantaneous. Upon detecting a material offset of 0.4 cm during processing, it immediately generates a dynamic adjustment signal and transmits it to the path fitting module. This module updates the path within 10 seconds, ensuring the processing task is unaffected by vibration. The optimized sewing sequence is loaded into the control module, and the robotic arm continues processing along the new path, ensuring stitch accuracy. This instantaneous response method maintains high-quality completion of processing tasks even in unstable environments. For example, in one possible implementation, the system sets different storage strategies based on the scale of the processing task for data storage and retrieval during the dynamic adjustment process.For example, when processing small materials, data storage uses a temporary cache to reduce storage space usage; when processing large materials, data storage uses permanent storage to ensure data traceability. For instance, when processing a large, multi-layered curtain fabric, if the system detects a material offset, the generated dynamic adjustment signal and optimized sewing sequence are permanently stored in the system database for review and optimization in subsequent processing tasks. This optimized storage strategy enables flexibility and reliability in data management across tasks of varying scales. In one embodiment, for dynamic adjustments in multi-layered material processing, the system sets different adjustment ranges based on the complexity of the processing task. For example, when processing simple patterns, the adjustment range is set to local adjustment, updating only the offset area; when processing complex patterns, the adjustment range is set to global adjustment, updating the entire path. For example, when processing a multi-layered material with a complex pattern, if the system detects a material offset of 0.6 cm and triggers a dynamic adjustment signal, the path fitting module globally adjusts the entire sewing path to ensure the overall consistency of the pattern. This optimized adjustment range enables both accuracy and efficiency in dynamic adjustments across tasks of varying complexity. Specifically, in another implementation, assuming the processing task involves sewing complex patterns on multi-layered bed sheet fabric, the system detects a material offset of 0.7 cm during processing, exceeding the preset offset threshold of 0.5 cm. After a dynamic adjustment signal is triggered, the path fitting module sets the adjustment range to global adjustment, refitting the entire pattern path to ensure that each turning point and arc segment is aligned with the latest material posture. The optimized sewing sequence is loaded into the control module, and the robotic arm continues processing along the new path, ensuring the integrity and aesthetics of the pattern. This global adjustment method effectively addresses the material offset problem in complex pattern processing. For example, in one possible implementation, for equipment load management during dynamic adjustment, the system sets different adjustment timings based on the operating status of the processing equipment. For instance, when the equipment load is low, the adjustment timing is set to immediate adjustment, updating the path immediately upon detecting a deviation; when the equipment load is high, the adjustment timing is set to delayed adjustment, waiting for the equipment load to decrease before updating the path. Assuming that when processing a multi-layered material, the system detects a material offset of 0.5 cm, but the equipment load is high, the adjustment timing is set to delayed adjustment, completing the path update after the equipment load decreases. This load management method ensures processing accuracy while preventing equipment overload due to frequent adjustments. In one embodiment, the system sets different adjustment accuracies based on the progress of the processing task for the triggering of dynamic adjustment signals and path updates. For example, in the early stages of processing, the adjustment accuracies are set to lower to allow for a certain degree of deviation to improve efficiency; in the later stages of processing, the adjustment accuracies are set to higher to ensure the accuracy of the final stitch.Suppose that when processing a multi-layered cotton fabric, a deviation of 0.5 cm is detected in the early stages of processing, indicating low adjustment precision and no adjustment signal is triggered. However, in the later stages, a deviation of 0.3 cm is detected, indicating higher adjustment precision, triggering an adjustment signal and updating the sewing path. This precision adjustment method achieves a dynamic balance between efficiency and precision throughout the entire processing flow. Specifically, in another implementation, assuming the processing task is sewing multi-layered curtain fabric, the system sets the adjustment precision to a low threshold of 0.7 cm in the early stages of processing. When a material deviation of 0.6 cm is detected, no adjustment signal is triggered, and processing continues. In the later stages of processing, the system increases the adjustment precision, raising the threshold to 0.3 cm. When a material deviation of 0.4 cm is detected, a dynamic adjustment signal is immediately generated, updating the sewing path to ensure the precision and aesthetics of the final stitches. This dynamic precision adjustment method effectively adapts to the needs of different stages of the processing task, ensuring overall processing quality. For example, in one possible implementation, the system sets different fitting speeds based on the urgency of the processing task to improve path fitting efficiency during the dynamic adjustment process. For example, when processing ordinary materials, the fitting speed is set to the standard speed to ensure adjustment accuracy; when processing urgent tasks, the fitting speed is set to high speed to prioritize adjustment speed. Suppose that when processing a multi-layered material for an urgent task, the system detects a material offset of 0.5 cm, the path fitting module recalculates the trajectory point position in high-speed mode, completing the path update within 5 seconds to ensure the processing task is completed on time. This speed optimization method achieves high efficiency in dynamic adjustments during urgent tasks. In one embodiment, for dynamic adjustments in multi-layered material processing, the system sets different adjustment frequencies based on the characteristics of the processing layers. For example, when processing the bottom layer of material, the adjustment frequency is lower to avoid frequent adjustments affecting efficiency; when processing the top layer of material, the adjustment frequency is higher to ensure the accuracy of the decorative effect. Suppose that when processing a multi-layered curtain fabric, the adjustment frequency of the bottom blackout fabric is set to once per minute, and no adjustment signal is triggered when an offset of 0.6 cm is detected; while the adjustment frequency of the top decorative fabric is set to once every 30 seconds, and an adjustment signal is triggered immediately when an offset of 0.4 cm is detected. This frequency setting method achieves rational resource allocation and stable processing quality in multi-layered material processing. Specifically, in another implementation, assuming the processing task involves sewing a multi-layer sofa fabric, including a bottom reinforcing fabric, a middle cushioning fabric, and a top decorative fabric, the system adjusts the frequency every 30 seconds when processing the top decorative fabric. Upon detecting a deviation of 0.5 cm, a dynamic adjustment signal is immediately generated and the path is updated to ensure the pattern stitches of the decorative fabric align with the design. When processing the bottom reinforcing fabric, the adjustment frequency is set to once per minute; if a deviation of 0.8 cm is detected, no adjustment signal is triggered, and processing continues. This layered frequency adjustment method achieves a balance between accuracy and efficiency in multi-layer material processing.

[0057] The above embodiments are merely one of the preferred embodiments of the present invention and should not be used to limit the scope of protection of the present invention. Any modifications or refinements made to the main design concept and spirit of the present invention that are not of substantial significance, but solve the same technical problem as the present invention, should be included within the scope of protection of the present invention.

Claims

1. A method for synchronous positioning and precise quilting of multi-layer fabrics in a quilting and embroidery integrated machine, characterized in that: The method includes: S1, capturing real-time images of multi-layer materials on a processing platform using an image acquisition device, extracting material contour features, and obtaining current material posture data; S2, comparing the current material posture data with a preset standard posture, calculating the rotation offset angle, and determining an offset correction vector if the offset angle exceeds a preset offset angle threshold; S3, fitting the offset correction vector with the design pattern path using the least squares method to generate adjusted sewing trajectory parameters and obtain a corrected sewing path; S4, extracting key control points from the corrected sewing path, determining whether the key control points are aligned with the material contour features, and confirming the path validity if aligned, obtaining verified path data; S5, transmitting the verified path data to the sewing equipment control module to obtain a robotic arm motion command sequence and determine the final execution command; S6, monitoring material posture changes in real time during the execution of the final execution command, repeatedly capturing images and comparing them with the current material posture data to determine if a new offset occurs and obtaining a dynamic adjustment signal; S7, updating the sewing trajectory parameters according to the dynamic adjustment signal, refitting the path, and obtaining an optimized sewing sequence.

2. The method as described in claim 1, characterized in that, Step S1 includes: Step S11, the image acquisition device acquires an image of the multi-layer material on the processing platform, the real-time image containing the surface texture and boundary information of the multi-layer material; Step S12, the real-time image is transmitted to the edge detection algorithm processing module, the edge detection algorithm identifies the material boundary in the real-time image to extract the material contour features; Step S13, the material contour features are converted into material current posture data, the material current posture data including position coordinates and posture parameters.

3. The method as described in claim 1, characterized in that, Step S2 includes: Step S21, superimposing and comparing the current posture data of the material with a preset standard posture to calculate the rotation offset angle; Step S22, if the rotation offset angle exceeds a preset offset angle threshold, extracting key points from the material contour features, the key points being used to generate the offset correction vector; Step S23, the offset correction vector recording translation and rotation parameters.

4. The method as described in claim 1, characterized in that, Step S3 includes: Step S31, inputting the offset correction vector into the calculation module and decomposing the design pattern path into multiple path segments; Step S32, calculating the adjustment coefficient corresponding to the offset correction vector for each path segment; Step S33, outputting the adjusted sewing trajectory parameters according to the adjustment coefficient, wherein the corrected sewing path integrates the shape features of the design pattern path; Step S34, storing the corrected sewing path as a trajectory point sequence, wherein the trajectory point sequence is used for subsequent key control point extraction.

5. The method as described in claim 1, characterized in that, Step S4 includes: Step S41, obtaining key control points by scanning the corrected sewing path, the key control points being located at path turning points; Step S42, matching the key control points with material contour features one by one, and confirming alignment if the position of the key control point falls within the boundary of the material contour feature; Step S43, generating the verified path data from the corrected sewing path after alignment confirmation.

6. The method as described in claim 1, characterized in that, Step S5 includes: Step S51, converting the verified path data into an instruction format, the instruction format being compatible with the sewing equipment control module; Step S52, the sewing equipment control module parsing the verified path data and generating a sequence of robotic arm motion instructions, the final execution instruction including speed and position control parameters.

7. The method as described in claim 1, characterized in that, Step S6 includes: Step S61, when the sewing equipment control module executes the final execution command, the image acquisition device repeatedly captures images; Step S62, real-time posture data is obtained through the repeatedly captured images, and the real-time posture data is compared with the current posture data of the material and the deviation is calculated; Step S63, if the deviation exceeds a preset deviation threshold, it is determined that a new offset has occurred, and the dynamic adjustment signal carries the new offset information; Step S64, the dynamic adjustment signal is transmitted to the path fitting module to trigger an update.

8. The method as described in claim 1, characterized in that, Step S7 includes: Step S71, the dynamic adjustment signal is parsed into a new offset correction vector, and the new offset correction vector is combined with the current sewing trajectory parameters; Step S72, the new offset correction vector is refitted with the existing path, and the stitch position is adjusted by the optimized sewing sequence; Step S73, the optimized sewing sequence replaces the trajectory data in the final execution instruction.

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