Stitch quality recognition optimization system, sewing machine and sewing machine management system

Abstract

The invention provides a stitch quality identification optimization system, a sewing machine and a sewing machine management system, which are applied to the sewing machine and comprise a data acquisition module used for acquiring stitch data of a sewing fabric of the current sewing machine in real time; the data processing module is used for inputting the stitch data into a pre-trained stitch quality identification model for performing stitch quality identification so as to obtain a stitch quality identification result; and the control and adjustment module is used for analyzing according to the stitch quality identification result to obtain real-time optimization and adjustment parameters, and adjusting the current sewing machine based on the real-time optimization and adjustment parameters to optimize the stitch quality of the sewing fabric of the current sewing machine. The stitch quality in the sewing process of the sewing machine can be monitored and recognized in real time, the operation parameters of the sewing machine are adjusted according to the recognition result so that efficient optimization of the stitch quality can be achieved, the current sewing scene can be shared to other sewing machines, the sewing production efficiency can be improved, and the sewing maintenance cost can be reduced.

Description

Technical Field

[0001] This application relates to the field of sewing technology, and in particular to a stitch quality recognition and optimization system, a sewing machine, and a sewing machine management system. Background Technology

[0002] In the garment manufacturing industry, stitches are not only a basic component of garments but also a key factor affecting their appearance and quality. The quality and consistency of stitches directly affect the durability, comfort, and aesthetics of clothing. Traditional sewing machines, through the coordinated work of the needle and the hook-and-loop mechanism, create various types of stitches on the fabric, such as single-needle lockstitch, double-needle lockstitch, beaded stitch, single-needle chain stitch, and double-needle chain stitch.

[0003] However, existing sewing machines and production processes have some limitations in ensuring stitch quality:

[0004] (1) Human factors: The quality of the stitches is greatly affected by the skill level of the operators. Different skill levels of workers may lead to inconsistent stitch quality.

[0005] (2) Equipment limitations: The performance and precision of traditional sewing machines limit the consistency and aesthetics of the stitches.

[0006] (3) Production efficiency: Improving production efficiency while ensuring stitch quality is a challenge, especially in large-scale production.

[0007] (4) Quality inspection: Existing methods for inspecting the quality of stitches are mostly manual visual inspection, which is inefficient and prone to errors.

[0008] To address the aforementioned issues, although some automated and semi-automated solutions exist, they often fail to fully resolve the dual challenges of consistent stitch quality and production efficiency. Summary of the Invention

[0009] In view of the shortcomings of the prior art, the present invention provides a stitch quality identification and optimization system, a sewing machine, and a sewing machine management system to solve the technical problem that the prior art cannot fully address the dual challenges of stitch quality consistency and production efficiency.

[0010] To achieve the above and other related objectives, a first aspect of this application provides a stitch quality recognition and optimization system for a sewing machine, comprising: a data acquisition module for real-time acquisition of stitch data of the sewing fabric being sewn by the sewing machine; a data processing module connected to the data acquisition module for inputting the stitch data into a pre-trained stitch quality recognition model for stitch quality recognition to obtain stitch quality recognition results; and a control and adjustment module connected to both the data acquisition module and the data processing module for analyzing the stitch quality recognition results to obtain real-time optimization and adjustment parameters, and adjusting the sewing machine based on the real-time optimization and adjustment parameters to optimize the stitch quality of the sewing fabric being sewn by the sewing machine.

[0011] In some embodiments of the first aspect of this application, the data acquisition module includes any one or more combinations of: a stitch quality detection unit, a fabric identification unit, a thickness detection unit, a displacement sensor unit, a pressure sensor unit, and a network monitoring unit.

[0012] In some embodiments of the first aspect of this application, the pre-trained stitch quality recognition model is trained by using a visual detection algorithm to train the collected historical stitch data and construct the stitch quality recognition model.

[0013] In some embodiments of the first aspect of this application, the process of analyzing the stitch quality identification result to obtain real-time optimization adjustment parameters includes: if the stitch quality identification result is an abnormal result, analyzing the abnormal result and the real-time operating parameters of the current sewing machine to obtain real-time optimization adjustment parameters.

[0014] In some embodiments of the first aspect of this application, the system further includes: a data storage module, connected to the control adjustment module and the data acquisition module respectively, for storing the operating parameters of the sewing machine after adjustment and the fabric information of the sewing fabric.

[0015] In some embodiments of the first aspect of this application, the control adjustment module includes: a display control unit for controlling the current sewing machine to display the stitch quality recognition result and to optimize and adjust parameters in real time.

[0016] In some embodiments of the first aspect of this application, the control adjustment module includes: an early warning control unit, configured to issue an early warning when the trace quality identification result is an abnormal result.

[0017] To achieve the above and other related objectives, a second aspect of this application provides a sewing machine including the stitch quality recognition and optimization system.

[0018] To achieve the above and other related objectives, a third aspect of this application provides a sewing machine management system, including one or more sewing machines and the stitch quality recognition and optimization system.

[0019] In some embodiments of the third aspect of this application, the sewing machine management system further includes a big data center and an Internet of Things (IoT) module; the big data center is connected to each sewing machine via the IoT module; and each sewing machine performs sewing based on the operating parameters stored in the big data center.

[0020] As described above, the stitch quality recognition and optimization system, sewing machine, and sewing machine management system provided in this application have the following beneficial effects: This invention can monitor, analyze, compare, and identify the stitch quality during the sewing process of a sewing machine in real time, and adjust the operating parameters of the sewing machine based on the identification results to achieve efficient optimization of stitch quality. At the same time, it can share the current sewing scene with other sewing machines to improve sewing production efficiency and reduce sewing maintenance costs. Attached Figure Description

[0021] Figure 1 The diagram shown is a structural schematic of a line quality recognition and optimization system according to an embodiment of this application.

[0022] Figure 2 The diagram shown is a structural schematic of a sewing machine management system according to an embodiment of this application.

[0023] Figure 3 The figure shown is a specific embodiment of a sewing machine management system according to one embodiment of this application. Detailed Implementation

[0024] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. This application can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, unless otherwise specified, the following embodiments and features in the embodiments can be combined with each other.

[0025] The stitch quality recognition and optimization system provided in this application has a wide range of applications in the sewing machine field, and can perform stitch quality recognition and optimization for different types of sewing machines. Common sewing machine types include: household sewing machines, industrial sewing machines, multi-functional sewing machines, automatic sewing machines, heavy-duty sewing machines, thin-duty sewing machines, embroidery machines, overlock machines, double-needle sewing machines, and cover sewing machines, etc. Household sewing machines are suitable for home users for everyday sewing tasks such as clothing repair and making small items; industrial sewing machines are used for large-scale production and are commonly found in garment factories, capable of high-speed continuous operation; multi-functional sewing machines have multiple sewing functions, including straight stitches, zigzag stitches, and overlocking; automatic sewing machines can automatically complete certain steps in the sewing process, such as automatic threading and automatic thread cutting; heavy-duty sewing machines are specifically designed for sewing heavy materials such as leather and canvas; thin-duty sewing machines are suitable for lightweight materials such as silk and tulle; embroidery machines are mainly used for embroidering patterns and can be either household or industrial grade; overlock machines are specifically used for overlocking to prevent fabric edges from fraying; double-needle sewing machines can use two needles and threads simultaneously for sewing and are often used for making garment edges; cover sewing machines are used for covering sewing, such as sewing pockets and collars on garments. The stitch quality recognition and optimization system in this application embodiment can automatically detect and adjust sewing parameters by analyzing the stitches during the sewing process to ensure the uniformity and strength of the sewing, reduce rework and scrap rates, and can be integrated into various types of sewing machines to improve production efficiency and product quality.

[0026] To facilitate understanding of the embodiments of this application, firstly, in conjunction with Figure 1 Detailed explanation. Figure 1 A schematic diagram of a line quality recognition and optimization system 100 according to an embodiment of the present invention is shown. The structure of the line quality recognition and optimization system 100 in this embodiment mainly includes:

[0027] The data acquisition module 110 is used to collect the stitch data of the sewing fabric of the current sewing machine in real time.

[0028] It should be noted that the data acquisition module 110 can collect real-time operating parameters and stitch data of the sewing fabric during the sewing process via sensors. The sensors can be high-precision cameras, capable of clearly capturing subtle changes in the stitches, which is crucial for detecting and analyzing stitch quality. Simultaneously, the camera's high resolution and high frame rate ensure the quality and real-time nature of the image data, providing a reliable foundation for subsequent image processing and analysis, thereby improving the system's stitch quality recognition capability.

[0029] It is important to emphasize that the main elements of garment quality include: style, pattern, fabric, color, matching, and stitching. Stitch quality plays a decisive role in garment quality; therefore, identifying and optimizing stitch quality during the sewing process is crucial. Specifically, sewing machine stitch types include, but are not limited to: single-needle lockstitch, double-needle lockstitch, beaded stitch, single-needle chain stitch, double-needle chain stitch, multi-needle stitch, embroidery stitch, wavy stitch, three-thread overlock stitch, four-thread overlock stitch, five-thread overlock stitch, six-thread overlock stitch, three-thread covered stitch, five-thread covered stitch, buttonhole stitch, bark stitch, decorative stitch, reverse stitch, zigzag stitch, and blind stitch.

[0030] Different stitch types exhibit different stitch quality identification characteristics; therefore, it is necessary to collect corresponding feature data through sensors. Taking lockstitch as an example, the stitch quality performance differs between lockstitches that use two different colors of thread for the bottom and top threads, and lockstitches that use the same color thread for both bottom and top threads.

[0031] In lockstitch, where the bottom and top threads are sewn with two different colored threads, the stitches on both sides of a normal lockstitch are essentially identical. If the fabric is thick, the knots can be completely hidden within it, preventing the appearance of knots of a different color on either side. If the fabric is relatively thin, the knots cannot be completely hidden, and a knot of a different color will be visible on one side of the fabric, protruding from the other side. To judge the stitch quality, one can check whether the shape, direction, and size of the knots are basically the same at each stitch, whether the knots on both sides of the fabric are basically consistent, or whether the length of each stitch is basically the same. The knots on the front and back should be different colors.

[0032] For lockstitch where the bottom and top threads are sewn with the same color thread, under normal stitch quality, the stitches and knots on both sides are basically the same.

[0033] High-precision sensors can capture subtle differences in stitches / knots, or the length of floating threads, in various situations as described above, which can be used to identify stitch quality later.

[0034] In one embodiment, the data acquisition module 110 includes any one or more combinations of a stitch quality detection unit, a fabric identification unit, a thickness detection unit, a displacement sensor unit, a pressure sensor unit, and a network monitoring unit.

[0035] In some examples, a stitch quality inspection unit is used to collect data on the stitch quality of the fabric currently being sewn by the sewing machine. This unit can perform detailed inspections of the stitches produced by the sewing machine. Depending on the type of stitches produced, the stitch quality inspection unit may include, but is not limited to, a fabric front stitch quality inspection subunit, a fabric back stitch quality inspection subunit, and a fabric side stitch quality inspection subunit. Each stitch quality inspection subunit can comprehensively inspect the stitches from different angles to ensure that the stitches are even and neat during the sewing process, without skipped stitches, loose threads, split threads, or other stitch defects.

[0036] In some examples, a fabric recognition unit is used to identify the fabric being sewn by the current sewing machine to obtain the corresponding fabric information. This fabric information is crucial for selecting appropriate sewing parameters and subsequent garment processing. Fabric information includes: fabric composition, fabric density, fabric thickness, fabric elasticity, fabric coefficient of friction, and fabric resistance. Fabric composition includes cotton, wool, silk, polyester, etc.; fabric density refers to the number of yarns per unit area, reflecting the fabric's strength and appearance; fabric thickness is determined by measuring the thickness of the fabric within a specific area; fabric elasticity refers to the fabric's ability to return to its original shape after being subjected to external force; the fabric coefficient of friction refers to the frictional force when the fabric comes into contact with other surfaces; and fabric resistance refers to the different resistance encountered by the sewing machine needle when penetrating different fabrics during the sewing process. The magnitude of this resistance affects the stitch quality and the smoothness of the sewing process; the fabric's structure, fiber characteristics, and pretreatment all influence this resistance.

[0037] In some examples, a thickness detection unit is used to detect the thickness of the fabric at the current sewing position of the sewing machine, ensuring the consistency of the fabric during the sewing process and avoiding sewing quality problems caused by uneven fabric thickness.

[0038] In some examples, a displacement sensor unit is used to detect the movement of mechanical parts, ensuring that the sewing machine needle and other components operate in the correct positions. The movement of the sewing machine can be precisely controlled based on the detected displacement information to ensure the accuracy and consistency of sewing.

[0039] In some examples, a pressure sensor unit is used to dynamically detect the presser foot pressure information of the sewing machine during the sewing process. When sewing different types of fabrics, the presser foot pressure needs to be adjusted to ensure strong and even stitches. For example, for lightweight silk fabrics, a smaller presser foot pressure is needed to avoid damaging the fabric; while for heavy wool fabrics, a larger presser foot pressure is needed to ensure strong stitches. If improper presser foot adjustment is detected during the detection process, sewing should be stopped immediately and the appropriate adjustments made.

[0040] In some examples, a network monitoring unit is used to monitor the network connection status of the sewing machine, ensuring real-time data transmission and processing. This facilitates intelligent management and remote monitoring of the sewing machine's stitch quality. If the network monitoring unit detects a problem with the system network, it displays the relevant information on the monitor, such as a flashing network icon, to alert staff to the network anomaly.

[0041] The data processing module 120, connected to the data acquisition module 110, is used to input the line stitch data into a pre-trained line stitch quality recognition model for line stitch quality recognition in order to obtain line stitch quality recognition results.

[0042] It should be noted that the stitch data of the sewing fabric currently being sewn by the sewing machine, which is collected in real time by the data acquisition module 110, is input into the pre-trained stitch quality recognition model in the data processing module 120. The stitch quality recognition model judges the stitch quality of the stitch data and obtains the stitch quality recognition result. The stitch quality recognition result includes normal stitch quality and abnormal stitch quality.

[0043] In one embodiment, the pre-trained stitch quality recognition model is trained as follows: a visual detection algorithm is used to train and construct the stitch quality recognition model from collected historical stitch data. The visual detection algorithm employs deep learning technology, which improves the ability to identify stitch defects by training on a large amount of stitch image data. Deep learning technology is characterized by its high speed and accuracy, enabling real-time target detection, which is crucial for real-time monitoring of stitch quality.

[0044] By collecting diverse data such as front, back, and side photos of stitches formed during the sewing process using sensors, skipped stitch photos, split stitch photos, stitch float photos, and stitch defect photos, a historical stitch database is created. Using historical stitch data from this database, a visual detection algorithm is trained to improve the accuracy and performance of the stitch quality recognition model.

[0045] The control and adjustment module 130, connected to both the data acquisition module 110 and the data processing module 120, analyzes the stitch quality identification results to obtain optimized adjustment parameters, and adjusts the current sewing machine based on these parameters to optimize the stitch quality of the sewing fabric. The data acquisition module 110 collects the real-time operating parameters of the current sewing machine and sends them to the control and adjustment module 130, while the data processing module 120 transmits the stitch quality identification results to the control and adjustment module 130.

[0046] In one embodiment, the process of analyzing the stitch quality identification result to obtain real-time optimization adjustment parameters includes: if the stitch quality identification result is an abnormal result, analyzing the abnormal result and the real-time operating parameters of the current sewing machine to obtain real-time optimization adjustment parameters.

[0047] It should be explained that when the data processing module 120 determines that the stitch quality of the sewing fabric of the current sewing machine is abnormal, it analyzes the abnormal result and adjusts the real-time operating parameters of the current sewing machine to obtain real-time optimization adjustment parameters. For example, it adjusts operating parameters such as thread tension, speed, and trajectory in real time to improve the stitch quality of the current stitch. Sewing continues based on the adjusted operating parameters. In this embodiment, the stitch quality of the sewing fabric of the sewing machine is constantly monitored in real time. Once an abnormal state is detected, the operating parameters of the sewing machine are adjusted in real time to optimize the stitch quality.

[0048] like Figure 2 As shown, the control adjustment module 130 includes any one or more combinations of a line tension control unit, a trajectory control unit, a speed control unit, and a pressure control unit.

[0049] In some examples, the thread tension control unit is used to control the adaptive adjustment of the corresponding tension in the sewing machine. The thread tension control unit also includes: a first thread tension control subunit, a second thread tension control subunit, a third thread tension control subunit, and an Nth thread tension control subunit, etc. Each thread tension control subunit is set for complex sewing needs, but this embodiment does not limit it.

[0050] In some examples, the trajectory control unit is used to control various fabric feed trajectories of the sewing machine, enabling sewing of different shapes and styles by changing the sewing trajectory. Examples include elliptical trajectories, rectangular trajectories, front triangular trajectories, and rear triangular trajectories.

[0051] In some examples, the speed control unit is used to control the speed of the sewing machine's power source, spindle, and other auxiliary operating mechanisms. It can adjust the sewing speed according to the sewing needs of different sewing machines to adapt to the sewing requirements of finished and semi-finished garments of different qualities, in different scenarios, with different fabrics, and using different processes.

[0052] In some examples, the pressure control unit is used to control the adjustment of the presser foot pressure of the sewing machine. Presser foot pressure can prevent the fabric from slipping or wrinkling during sewing, and also helps to form even stitches.

[0053] It should be noted that when identifying abnormal stitch quality, different stitch quality abnormalities require different sewing parameter adjustments. Furthermore, different real-time optimization adjustment parameters exist depending on the current real-time operating parameters of the sewing machine, necessitating adaptive adjustments based on the actual situation. For example, if a sensor detects differences in stitch / knot length or the length of the float thread, the control will optimize the thread tension. The specific optimization adjustment parameters must be calculated based on the current real-time operating parameters of the thread tension.

[0054] In one embodiment, the system further includes a data storage module 140, which is connected to the control adjustment module 130 and the data acquisition module 110 respectively, for storing the operating parameters of the sewing machine after adjustment and the fabric information of the sewing fabric.

[0055] It should be noted that after the control and adjustment module 130 adjusts the current sewing machine to optimize the stitch quality of the sewing fabric and obtains a sewing state with normal stitch quality, the data acquisition module 110 collects the real-time operating parameters of the sewing machine and transmits them to the data storage module 140 for storage. The corresponding fabric information also needs to be stored synchronously. Different fabrics will definitely have different operating parameters in the same sewing machine. Storing this data can make it convenient to directly retrieve the corresponding operating parameters when sewing the same fabric in the future.

[0056] In one embodiment, combined with Figure 2 The control and adjustment module 130 includes a display control unit for controlling the current sewing machine to display the stitch quality identification result and real-time optimization adjustment parameters. The sewing machine's display can show the stitch quality identification result in real time, i.e., the real-time stitch status of the currently sewn fabric. If the stitch quality identification result is abnormal, the display shows the specific abnormality type, such as skipped stitches, floating dots, split threads, stitch defects, etc., and can also display corresponding stitch optimization suggestions, such as real-time optimization adjustment parameters. In this embodiment, the identification and optimization system can also, based on the stitch optimization suggestions displayed on the display and according to preset requirements, allow for manual decision-making on whether to perform optimization, or automatic optimization.

[0057] In one embodiment, combined with Figure 2 The control adjustment module 130 includes an early warning control unit, used to issue an early warning when the stitch quality identification result is abnormal. The early warning is provided via a display, speaker, indicator lights, etc., to alert staff to the abnormal stitch quality.

[0058] This invention also provides a sewing machine, including the stitch quality recognition and optimization system described above. The stitch quality recognition and optimization system allows for real-time monitoring of the stitch quality of the current sewing machine.

[0059] This invention also provides a sewing machine management system, including one or more sewing machines and the stitch quality recognition and optimization system described above. Figure 1 and Figure 2 As shown, sewing machines A, B, ..., N are each connected to a corresponding stitch quality recognition and optimization system. The real-time operating parameters of the stitch quality recognition system for each sewing machine are different after the stitch quality is adjusted.

[0060] The sewing machine management system also includes a big data center 160 and an Internet of Things (IoT) module 150. The big data center 160 is connected to each sewing machine via the IoT module 150. Each sewing machine performs sewing based on the operating parameters stored in the big data center. The current sewing scene is recorded and saved, and uploaded to the data center via the IoT. Specifically, the operating parameters obtained by the stitch quality recognition system corresponding to each sewing machine and the parameters when the stitch quality is normal are stored in the big data center 160.

[0061] In the sewing machine management system, when other sewing machines within the same IoT network sew the same scenario again, they can download the adjustment parameters from the shared database and adaptively adjust to the same parameters. That is, when any sewing machine sews a certain fabric, it can retrieve the operating parameters of the corresponding sewing machine when the stitch quality is normal, thus improving sewing efficiency. When the network monitoring unit in the stitch quality recognition and optimization system detects a network shutdown or anomaly, it pauses data retrieval or download from the big data center. It can then use the data stored in the data storage module of the stitch quality recognition and optimization system for stitch quality recognition and judgment, or resume sewing based on the stored operating parameters.

[0062] To facilitate the demonstration of the line quality recognition and optimization system in this application, combined with Figure 3 The following specific embodiments are provided for illustration:

[0063] When the stitch quality recognition and optimization system detects an abnormal stitch quality recognition result, the control and adjustment module within the system controls the current sewing machine to make adjustments to optimize the stitch quality of the fabric being sewn. If the stitch quality recognition and optimization system detects a normal stitch quality recognition result, the system continues to monitor the stitch quality during the sewing machine's operation.

[0064] Once the sewing machine has completed its sewing action and stopped, the operator can decide whether to optimize the process, or optimize it automatically, or download the best optimization data for the current sewing fabric from the big data center via the Internet of Things module and optimize the stitch quality based on that data.

[0065] In the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with essentially the same function and effect, without limiting their order. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that the terms "first" and "second" do not necessarily imply that they are different.

[0066] It should be noted that, in the embodiments of this application, the words "exemplary" or "for example" indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0067] In this application embodiment, "at least one" refers to one or more, and "more than one" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple.

[0068] It should be understood that the specific process of each module performing the above-mentioned steps has been described in detail in the above method embodiments, and will not be repeated here for the sake of brevity.

[0069] It should also be understood that the module division in the embodiments of this application is illustrative and only represents a logical functional division; in actual implementation, there may be other division methods. Furthermore, the functional modules in the various embodiments of this application can be integrated into a single processor, exist as separate physical entities, or be integrated into a single module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0070] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0071] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0072] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0073] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0074] In the above embodiments, the functions of each functional unit can be implemented entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. A computer program product includes one or more computer instructions (programs). When the computer program instructions (programs) are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., high-density digital video discs, DVDs), or semiconductor media (e.g., solid-state disks, SSDs, etc.).

[0075] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0076] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0077] In summary, this application provides a stitch quality recognition and optimization system, a sewing machine, and a sewing machine management system, applied to a sewing machine. The system includes: a data acquisition module for real-time acquisition of stitch data from the fabric being sewn by the sewing machine; a data processing module for inputting the stitch data into a pre-trained stitch quality recognition model for stitch quality recognition to obtain stitch quality recognition results; and a control and adjustment module for analyzing the stitch quality recognition results to obtain real-time optimization and adjustment parameters, and adjusting the sewing machine based on these parameters to optimize the stitch quality of the fabric being sewn. This invention can monitor and identify stitch quality in real-time during the sewing process and adjust the sewing machine's operating parameters based on the recognition results to achieve efficient stitch quality optimization. It can also share the current sewing scenario with other sewing machines to improve sewing production efficiency and reduce sewing maintenance costs. Therefore, this application effectively overcomes the various shortcomings of the prior art and has high industrial application value.

[0078] The above embodiments are merely illustrative of the principles and effects of this application and are not intended to limit this application. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of this application. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in this application should still be covered by the claims of this application.

Claims

1. A stitch quality recognition and optimization system, applied to a sewing machine, characterized in that, include: The data acquisition module is used to collect the stitch data of the sewing fabric being sewn by the sewing machine in real time; The data processing module, connected to the data acquisition module, is used to input the line stitch data into a pre-trained line stitch quality recognition model for line stitch quality recognition, so as to obtain line stitch quality recognition results. The control and adjustment module is connected to the data acquisition module and the data processing module respectively. It is used to analyze the stitch quality identification results to obtain real-time optimization adjustment parameters, and adjust the current sewing machine based on the real-time optimization adjustment parameters to optimize the stitch quality of the sewing fabric of the current sewing machine.

2. The line quality identification and optimization system according to claim 1, characterized in that, The data acquisition module includes any one or more combinations of the following: a stitch quality detection unit, a fabric identification unit, a thickness detection unit, a displacement sensor unit, a pressure sensor unit, and a network monitoring unit.

3. The line quality identification and optimization system according to claim 1, characterized in that, The pre-trained line quality recognition model is trained as follows: a visual detection algorithm is used to train the collected historical line data and construct the line quality recognition model.

4. The line quality identification and optimization system according to claim 1, characterized in that, The process of analyzing the stitch quality identification result to obtain real-time optimization adjustment parameters includes: if the stitch quality identification result is an abnormal result, analyzing the abnormal result and the real-time operating parameters of the sewing machine to obtain real-time optimization adjustment parameters.

5. The line quality identification and optimization system according to claim 1, characterized in that, The system further includes a data storage module, which is connected to the control adjustment module and the data acquisition module respectively, for storing the operating parameters of the sewing machine after adjustment and the fabric information of the sewing fabric.

6. The line quality identification and optimization system according to claim 1, characterized in that, The control and adjustment module includes a display control unit, which controls the current sewing machine to display the stitch quality recognition result and optimize and adjust parameters in real time.

7. The line quality identification and optimization system according to claim 1, characterized in that, The control adjustment module includes an early warning control unit, used to issue an early warning when the trace quality identification result is abnormal.

8. A sewing machine, characterized in that, Including the trace quality recognition and optimization system as described in any one of claims 1 to 7.

9. A sewing machine management system, characterized in that, It includes one or more sewing machines and a stitch quality recognition and optimization system as described in any one of claims 1 to 7.

10. The sewing machine management system according to claim 9, characterized in that, The sewing machine management system also includes a big data center and an Internet of Things (IoT) module; the big data center is connected to each sewing machine through the IoT module; each sewing machine performs sewing based on the operating parameters stored in the big data center.

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