Template machine intelligent electronic thread control method and system

Abstract

This invention discloses an intelligent electronic thread clamping control method and system for template sewing machines, relating to the field of template sewing machine thread clamping technology. It aims to solve the problems of poor accuracy and consistency in existing mechanical thread clamping control methods, where thread clamping force adjustment relies on manual experience. The method includes: constructing a sewing parameter database, which can be updated and accessed in real time; collecting key data during the sewing process and preprocessing the key data to obtain sewing parameters, including sewing material parameters and tension parameters; adjusting the thread clamping force to an initial target value based on the matching results of the sewing parameters and the database; and dynamically adjusting the thread clamping actuator based on the collected sewing tension value to ensure the actual thread tension is within the optimal thread tension range. Based on preset control logic and collected sewing parameters, intelligent adjustment of the thread clamping force of the thread clamping actuator is achieved, improving sewing quality stability and significantly increasing production efficiency.

Description

Technical Field

[0001] This invention relates to the field of template machine wire clamping technology, specifically to an intelligent electronic wire clamping control method and system for template machines. Background Technology

[0002] During the operation of the template machine, the thread clamping mechanism plays a crucial role. The magnitude of its thread clamping force directly affects the tension of the sewing thread, which in turn has a decisive impact on the aesthetics, durability, and breakage of the sewn product.

[0003] Currently, most sewing machines use mechanical thread clamping control. This traditional method primarily involves manually adjusting the compression of a spring to change the clamping force. However, this method has several significant drawbacks. Firstly, operators rely on their experience to judge whether the clamping force is appropriate. Due to differences in experience levels among operators, it's difficult to guarantee the accuracy and consistency of the clamping force adjustment, leading to inconsistent stitch quality even within the same batch of sewn products. Secondly, during the sewing process, when the sewing material changes (e.g., switching from lightweight silk to heavy denim) or the stitch type changes (e.g., switching from straight stitch to zigzag stitch), the operator needs to stop the machine and manually readjust the clamping force. This not only interrupts the sewing workflow and reduces production efficiency but also increases the operator's workload due to frequent machine stops for adjustments.

[0004] Furthermore, traditional mechanical thread clamping control methods cannot monitor and dynamically adjust the clamping force in real time. During the sewing process, the tension of the thread may change due to factors such as thread wear and slight fluctuations in sewing speed. Mechanical thread clamping mechanisms cannot detect these changes and make timely adjustments, which can easily lead to problems such as the thread being too loose, resulting in loose stitches and wrinkles, or the thread being too tight, resulting in thread breakage and fabric damage, seriously affecting the quality of the sewn products.

[0005] For example, Chinese patent CN108660623A discloses an automatic thread clamping device for a template sewing machine, including a frame, a machine head, a thread clamping cylinder mounting plate, a pressure plate, a cylinder, and a solenoid reversing valve; the cylinder, the thread clamping cylinder mounting plate, and the pressure plate are all fixedly mounted on the machine head; it is mainly driven by a cylinder; but it does not involve intelligent automatic adjustment of the thread clamping. Summary of the Invention

[0006] This invention solves the problem that the adjustment of the clamping force in the existing mechanical clamping control method of template machine relies on human experience, resulting in poor accuracy and consistency. It proposes an intelligent electronic clamping control method and system for template machine, which realizes intelligent adjustment of the clamping force of the clamping actuator based on preset control logic and collected sewing parameters.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: a method for intelligent electronic wire clamping control of a template machine, comprising the following steps: S1, Construct a sewing parameter database, which can be updated and accessed in real time; S2, collect key data during the sewing process, and preprocess the key data to obtain sewing parameters, including sewing material parameters and tension parameters; S3, based on the matching results of the sewing parameters and the database, adjust the thread clamping force to the initial thread clamping force target value; S4, dynamically adjusts the thread clamping mechanism based on the collected sewing tension value, so that the actual thread tension value is within the optimal thread tension range.

[0008] In the technical solution of this invention, a sewing parameter database is first constructed based on historical water fittings. This database can be updated periodically and accessed in real time. Then, relevant key data is collected by sensors and preprocessed to obtain sewing parameters. The sewing parameters are matched with the data in the sewing parameter database, and the thread clamping force is adjusted to the initial target value based on the matching result. During the sewing process, the thread clamping mechanism can be dynamically adjusted based on the real-time sewing tension value.

[0009] The present invention is further configured such that step S2 includes: Key data during the sewing process is collected in real time using sensors installed on the template machine; The type of fabric material is identified by an identification algorithm, thereby obtaining the sewing material parameters; The actual tension value is obtained after converting and processing the collected tension parameters.

[0010] In this technical solution, during the operation of the template machine, sensors installed on the template machine collect key parameters of the sewing process in real time. After collection, the sewing material is automatically identified to obtain the sewing material parameters.

[0011] The present invention is further configured such that step S1 includes: A parameter database is constructed based on the historical data of the template machine's thread clamping. The sewing parameter database contains the optimal thread clamping force parameters corresponding to different sewing materials and different thread types. The sewing parameter database supports automatic and manual updates.

[0012] The present invention is further configured such that step S3 includes: S31, based on the sewing parameters collected in real time, traverse the entire sewing parameter database to find the database parameter with the highest data matching degree; S32, parse the database parameters with the highest data matching degree to obtain the optimal thread clamping force parameter under the sewing parameter conditions obtained in real time, and use it as the initial thread clamping force target value; S33, send a control signal to the wire clamping actuator to adjust the wire clamping force to the initial wire clamping force target value.

[0013] In this technical solution, the clamping force is adjusted to the initial clamping force target value, thus completing the initial setting of the clamping force.

[0014] The present invention is further configured such that step S4 includes: S41, continuously collect the actual tension value of the suture and compare it with the optimal suture tension range under this sewing condition; S42, when the actual tension value is lower than the lower limit of the optimal suture tension range, increase the clamping force until the actual tension value returns to the optimal suture tension range; when the actual tension value is higher than the upper limit of the optimal suture tension range, decrease the clamping force until the actual tension value returns to the optimal suture tension range.

[0015] In this technical solution, during the sewing process, the actual tension value obtained in real time is dynamically compared with the optimal thread tension range to achieve dynamic adjustment of the thread clamping force.

[0016] The present invention is further configured such that step S42 includes: S421, if the current clamping force is too small, calculate the required increase in clamping force and send a control command to the clamping actuator to drive the clamping actuator to increase the clamping force; S422, when the current clamping force is too large, calculate the clamping force value that needs to be reduced, and send a control command to the clamping actuator to drive the clamping actuator to reduce the clamping force.

[0017] The present invention is further configured such that: the identification of the type of fabric material through the identification algorithm includes: Image data of the fabric surface is acquired, and wavelet transform is performed on the image data to obtain and extract relevant texture features. Combined with weaving features, a neural network recognition model is constructed, and the initial fabric material type is output through the recognition model. The final fabric material type is obtained through manual recognition.

[0018] In this technical solution, fabric identification can be performed using machine vision.

[0019] The present invention is further configured such that: the identification of the type of fabric material through the identification algorithm also includes: The type of fabric material is determined by spectral analysis. By analyzing the reflectance spectrum, the fiber composition ratio is directly determined, and thus the fabric material is identified.

[0020] In this technical solution, the type of fabric material can be identified by spectral analysis.

[0021] The present invention is further configured such that step S1 includes: viewing, modifying and adding to the database through the human-machine interface of the template machine, and also saving commonly used sewing parameter combinations as a custom mode according to actual production needs.

[0022] In this technical solution, the sewing parameter database can be viewed, manually modified, and added through a human-computer interaction interface.

[0023] A template machine intelligent electronic wire clamping control system, applicable to the aforementioned template machine intelligent electronic wire clamping control method, includes: A sewing parameter database, containing sewing parameter data, is connected to an electronic control unit; The electronic control unit receives sensor data and can send control signals to the wire clamping actuator to drive the wire clamping actuator. The wire clamping actuator adjusts the clamping force according to the control signal.

[0024] The system of this technical solution mainly includes a sewing parameter database, an electronic control unit, and a thread clamping mechanism. The sewing parameter database is connected to the electronic control unit, and the electronic control unit is connected to the thread clamping mechanism.

[0025] The intelligent electronic wire clamping control method and system for template machines of the present invention can bring the following beneficial effects: 1. The present invention relates to an intelligent electronic thread clamping control method for a template machine, which, based on preset control logic and real-time acquired sewing parameters, drives the thread clamping actuator through an electronic control unit to achieve intelligent adjustment of the thread clamping force, thereby improving the stability of sewing quality and reducing the difficulty of operation; 2. The present invention relates to an intelligent electronic thread clamping control system for a template machine, which, through the coordinated action of a sewing parameter database, an electronic control unit, and a thread clamping actuator, quickly adjusts the thread clamping force, significantly improving production efficiency. Attached Figure Description

[0026] Figure 1 This is a flowchart of an intelligent electronic wire clamping control method for a template machine according to the present invention.

[0027] Figure 2 This is a flowchart illustrating the dynamic adjustment of an intelligent electronic wire clamping control method for a template machine according to the present invention. Detailed Implementation

[0028] Example 1 To address the technical problems of existing template sewing machines' mechanical thread clamping control methods, such as reliance on manual experience for thread clamping force adjustment leading to poor accuracy and consistency, the need for manual adjustment during material or stitch type changes, low production efficiency, and the inability to monitor and dynamically adjust thread clamping force in real time, resulting in sewing quality issues, this embodiment proposes an intelligent electronic thread clamping control method for template sewing machines. (Refer to...) Figure 1 and Figure 2 It mainly includes the following steps.

[0029] Step S1: First, construct a sewing parameter database that can be updated and accessed in real time.

[0030] For step S1 above, the sewing parameter database is specifically constructed by integrating historical thread clamping data from the template machine. The sewing parameter database includes the optimal thread clamping force parameters corresponding to different sewing materials and different thread types. Furthermore, the sewing parameter database can support both automatic and manual update modes.

[0031] In this embodiment, the sewing materials include, but are not limited to, silk, cotton, denim, and leather, and the sewing thread types include, but are not limited to, cotton thread, polyester thread, and silk thread.

[0032] More specifically, the sewing parameter database connects to an external human-machine interface or remote communication device via a port. The human-machine interface includes, but is not limited to, touchscreens, allowing users to view, modify, and add data to the database. Furthermore, frequently used sewing parameter combinations can be saved as custom patterns for quick access later, based on actual production needs. Remote communication devices include, but are not limited to, mobile phones, smartwatches, and laptops.

[0033] The sewing parameter database in this embodiment includes not only historical data on the thread clamping of the template machine, but also historical data on other sewing parameters, all of which are stored in the form of timestamps plus specific historical data.

[0034] After completing step S1, proceed to step S2, collect key data during the sewing process, and preprocess the key data to obtain sewing parameters, including sewing material parameters and tension parameters.

[0035] More specifically, step S2 includes the following sub-steps.

[0036] Step S21: Collect key data during the sewing process using sensors installed on the template machine.

[0037] Step S22: Identify the type of fabric material using relevant recognition algorithms to obtain sewing material parameters.

[0038] In this embodiment, the type of fabric material is identified by an identification algorithm, which can be achieved through machine vision. Specifically, the process includes the following steps: first, image data of the fabric surface is acquired; then, wavelet transform is performed on the image data to obtain and extract relevant texture features; the texture features are combined with weaving features to construct a neural network identification model; the initial fabric material type is output through the identification model; and finally, the final fabric material type is obtained through manual identification.

[0039] More specifically, high-precision cameras are used to obtain image data of the fabric surface. To ensure image accuracy, images are taken from different angles and using light sources of different colors. Wavelet transform is applied to the captured image data to obtain relevant texture features, such as fabric roughness and contrast. Then, historical experience data is used in conjunction with the image data to obtain weaving features. The texture features and weaving features are weighted and weighted to obtain fused data. This fused data is used to construct a neural network recognition model based on a neural network. The trained neural network recognition model can output the initial fabric material type. Finally, manual verification is used to obtain the final fabric material type.

[0040] In this embodiment, the type of fabric material can be identified by an identification algorithm, and it can also be achieved through spectral analysis. Specifically, the process includes the following steps: determining the type of fabric material through spectral analysis, directly determining the fiber composition ratio by analyzing the reflectance spectrum, and thus determining the fabric material.

[0041] Different materials exhibit unique absorption and reflection characteristics of NIR light due to their different molecular structures. By analyzing the reflection spectrum, the composition ratio of the fiber can be directly determined.

[0042] In this embodiment, the type of fabric material can also be further identified by combining machine vision with spectral analysis.

[0043] Step S23: After converting and processing the collected tension parameters, the actual tension value is obtained.

[0044] In this technical solution, during the operation of the template machine, sensors installed on the template machine collect key parameters of the sewing process in real time. After collection, the sewing material is automatically identified to obtain the sewing material parameters.

[0045] Step S3: Based on the matching results of the sewing parameters and the database, adjust the thread clamping force to the initial thread clamping force target value.

[0046] Step S3 above mainly includes the following sub-steps.

[0047] Step S31: Based on the sewing parameters collected in real time, traverse the entire sewing parameter database to find the database parameter with the highest data matching degree.

[0048] Step S32: Analyze the database parameters with the highest data matching degree to obtain the optimal thread clamping force parameter under the sewing parameter conditions obtained in real time, and use it as the initial thread clamping force target value.

[0049] Step S33: By sending a control signal to the wire clamping actuator, the wire clamping force is adjusted to the initial wire clamping force target value.

[0050] In this embodiment, after the electronic control unit receives the sewing parameters from the sensor, it matches the sewing parameters with a preset sewing parameter database. Specifically, firstly, based on the collected sewing material type, the optimal clamping force parameter is searched in the database as the initial clamping force target value. Then, the electronic control unit sends a control signal to the clamping actuator to drive the clamping actuator to adjust the clamping force to the initial clamping force target value, thus completing the initial setting of the clamping force. Specifically, the clamping actuator includes, but is not limited to, a clamping electromagnet.

[0051] Step S4: The thread clamping mechanism is dynamically adjusted based on the collected sewing tension value, so that the actual thread tension value is within the optimal thread tension range.

[0052] For step S4 above, refer to Figure 2 Specifically, it includes the following sub-steps.

[0053] Step S41: During the sewing process, the sensor continuously collects the actual tension value of the thread and compares it with the optimal thread tension range under this sewing condition to determine whether adjustment is needed to ensure that the thread clamping force is at its best during the sewing process. In this embodiment, the sensor is a tension sensor.

[0054] In step S42, if the actual tension value is less than the lower limit of the optimal suture tension range, it indicates that the current clamping force is too small and the clamping force needs to be increased until the actual tension value returns to the optimal suture tension range; if the actual tension value is greater than the upper limit of the optimal suture tension range, it indicates that the current clamping force is too large and the clamping force needs to be reduced until the actual tension value returns to the optimal suture tension range.

[0055] Furthermore, in step S421, when the actual tension value obtained is less than the lower limit of the optimal suture tension range, i.e. the current suture clamping force is too small, the electronic control unit calculates the suture clamping force value that needs to be increased and sends a control command to the suture clamping actuator to drive the suture clamping actuator to increase the suture clamping force.

[0056] Step S422: When the actual tension value collected is greater than the upper limit of the optimal suture tension range, that is, the current suture clamping force is too large, the electronic control unit calculates the suture clamping force value that needs to be reduced and sends a control command to the suture clamping actuator to drive the suture clamping actuator to reduce the suture clamping force.

[0057] In the method of this invention, the constructed sewing parameter database is specifically a two-dimensional optimal thread clamping force parameter database covering "sewing material-thread type," which can achieve accurate matching of parameters under different working conditions and solve the problem of traditional mechanical control lacking standardized parameter references. Furthermore, the database also has a custom mode function: it allows operators to save commonly used parameter combinations as custom modes, which can be quickly accessed through a human-machine interface, shortening production preparation time and improving operational convenience.

[0058] The tension sensor of this invention uses a pressure sensor to ensure the real-time and accuracy of tension data, balancing acquisition precision and material protection. The clamping adjustment process of the present invention includes initial adjustment for precise matching and dynamic adjustment for closed-loop control.

[0059] Among these features, the initial adjustment is precise and matched: the electronic control unit (ECU) quickly determines the initial clamping force target value by automatically matching collected parameters with the database, and drives the clamping actuator to complete the initial setting without manual intervention, solving the problem of traditional manual adjustment relying on experience. Dynamic adjustment closed-loop control: based on real-time feedback from the tension sensor, a closed-loop control logic of "tension detection - deviation judgment - force adjustment - tension regression" is established. When the tension exceeds the optimal range, the ECU automatically calculates the adjustment amount and drives the actuator to ensure that the suture tension remains stable within the optimal range, avoiding stitch problems.

[0060] Example 2 This embodiment proposes an intelligent electronic wire clamping control method for a template machine, referencing... Figure 1 It includes the following steps.

[0061] Step S1: First, construct a sewing parameter database that can be updated and accessed in real time.

[0062] For step S1 above, the sewing parameter database is specifically constructed by integrating historical thread clamping data from the template machine. The sewing parameter database includes the optimal thread clamping force parameters corresponding to different sewing materials and different thread types. Furthermore, the sewing parameter database can support both automatic and manual update modes.

[0063] In this embodiment, the sewing materials include, but are not limited to, silk, cotton, denim, and leather, and the sewing thread types include, but are not limited to, cotton thread, polyester thread, and silk thread.

[0064] More specifically, the sewing parameter database connects to an external human-machine interface or remote communication device via a port. The human-machine interface includes, but is not limited to, touchscreens, allowing users to view, modify, and add data to the database. Furthermore, frequently used sewing parameter combinations can be saved as custom patterns for quick access later, based on actual production needs. Remote communication devices include, but are not limited to, mobile phones, smartwatches, and laptops.

[0065] The sewing parameter database in this embodiment includes not only historical data on the thread clamping of the template machine, but also historical data on other sewing parameters, all of which are stored in the form of timestamps plus specific historical data.

[0066] After completing step S1, proceed to step S2, collect key data during the sewing process, and preprocess the key data to obtain sewing parameters, including sewing material parameters and tension parameters.

[0067] More specifically, step S2 includes the following sub-steps.

[0068] Step S21: Collect key data during the sewing process using sensors installed on the template machine.

[0069] Step S22: Identify the type of fabric material using relevant recognition algorithms to obtain sewing material parameters.

[0070] In this embodiment, the type of fabric material is identified by an identification algorithm, which can be achieved through machine vision. Specifically, the process includes the following steps: first, image data of the fabric surface is acquired; then, wavelet transform is performed on the image data to obtain and extract relevant texture features; the texture features are combined with weaving features to construct a neural network identification model; the initial fabric material type is output through the identification model; and finally, the final fabric material type is obtained through manual identification.

[0071] More specifically, high-precision cameras are used to obtain image data of the fabric surface. To ensure image accuracy, images are taken from different angles and using light sources of different colors. Wavelet transform is applied to the captured image data to obtain relevant texture features, such as fabric roughness and contrast. Then, historical experience data is used in conjunction with the image data to obtain weaving features. The texture features and weaving features are weighted and weighted to obtain fused data. This fused data is used to construct a neural network recognition model based on a neural network. The trained neural network recognition model can output the initial fabric material type. Finally, manual verification is used to obtain the final fabric material type.

[0072] In this embodiment, the type of fabric material can be identified by an identification algorithm, and it can also be achieved through spectral analysis. Specifically, the process includes the following steps: determining the type of fabric material through spectral analysis, directly determining the fiber composition ratio by analyzing the reflectance spectrum, and thus determining the fabric material.

[0073] Different materials exhibit unique absorption and reflection characteristics of NIR light due to their different molecular structures. By analyzing the reflection spectrum, the composition ratio of the fiber can be directly determined.

[0074] In this embodiment, the type of fabric material can also be further identified by combining machine vision with spectral analysis.

[0075] Step S23: After converting and processing the collected tension parameters, the actual tension value is obtained.

[0076] In this technical solution, during the operation of the template machine, sensors installed on the template machine collect key parameters of the sewing process in real time. After collection, the sewing material is automatically identified to obtain the sewing material parameters.

[0077] Step S3: Based on the matching results of the sewing parameters and the database, adjust the thread clamping force to the initial thread clamping force target value.

[0078] Step S3 above mainly includes the following sub-steps.

[0079] Step S31: Based on the sewing parameters collected in real time, traverse the entire sewing parameter database to find the database parameter with the highest data matching degree.

[0080] Step S32: Analyze the database parameters with the highest data matching degree to obtain the optimal thread clamping force parameter under the sewing parameter conditions obtained in real time, and use it as the initial thread clamping force target value.

[0081] Step S33: By sending a control signal to the wire clamping actuator, the wire clamping force is adjusted to the initial wire clamping force target value.

[0082] In this embodiment, after the electronic control unit receives the sewing parameters from the sensor, it matches the sewing parameters with a preset sewing parameter database. Specifically, firstly, based on the collected sewing material type, the optimal clamping force parameter is searched in the database as the initial clamping force target value. Then, the electronic control unit sends a control signal to the clamping actuator to drive the clamping actuator to adjust the clamping force to the initial clamping force target value, thus completing the initial setting of the clamping force. Specifically, the clamping actuator includes, but is not limited to, a clamping electromagnet.

[0083] Step S4: The thread clamping mechanism is dynamically adjusted based on the collected sewing tension value, so that the actual thread tension value is within the optimal thread tension range.

[0084] For step S4 above, refer to Figure 2 Specifically, it includes the following sub-steps.

[0085] Step S41: During the sewing process, the sensor continuously collects the actual tension value of the sewing thread and compares it with the optimal sewing thread tension range under the sewing conditions to determine whether to make adjustments to ensure that the thread clamping force is in the best state during the sewing process.

[0086] In step S42, if the actual tension value is less than the lower limit of the optimal suture tension range, it indicates that the current suture clamping force is too small. The electronic control unit calculates the required increase in suture clamping force and sends a control command to the suture clamping actuator to increase the clamping force until the actual tension value returns to the optimal suture tension range. If the actual tension value is greater than the upper limit of the optimal suture tension range, it indicates that the current suture clamping force is too large. The electronic control unit calculates the required decrease in suture clamping force and sends a control command to the suture clamping actuator to decrease the clamping force until the actual tension value returns to the optimal suture tension range.

[0087] Based on the aforementioned intelligent electronic thread clamping control method for a template sewing machine, this embodiment proposes an intelligent electronic thread clamping control system for a template sewing machine. This system includes a sewing parameter database, an electronic control unit, and a thread clamping actuator. The sewing parameter database is connected to the electronic control unit, which in turn is connected to the thread clamping actuator. The sewing parameter database mainly contains sewing parameter data. The electronic control unit receives data from sensors and, after a series of judgments, sends corresponding execution control signals to the thread clamping actuator, thereby driving the actuator to adjust the tension. The thread clamping actuator can dynamically adjust the thread clamping force according to the control signals from the electronic control unit.

[0088] The technical solution of this embodiment can bring about the following technical effects, mainly including the following three aspects.

[0089] 1. Improve the stability of sewing quality; Specifically, through a preset sewing parameter database and real-time thread tension monitoring, it is possible to accurately adjust the thread clamping force, avoiding errors caused by manual adjustment, and ensuring that the thread tension is always kept within the optimal range under different sewing conditions. This effectively solves sewing quality problems such as loose stitches, wrinkles, and thread breakage, and improves the consistency and stability of sewn product quality.

[0090] 2. Significantly improve production efficiency; Specifically, when it is necessary to change the sewing material, thread type or stitch type during the sewing process, this technical solution can automatically collect new sewing parameters through sensors and quickly adjust the thread clamping force through the electronic control unit, without the need for operators to stop the machine for manual adjustment, which greatly shortens the production preparation time and the interruption time during the sewing process, and significantly improves production efficiency.

[0091] 3. Reduced operational difficulty and labor intensity: Specifically, operators no longer need to manually adjust the clamping force based on experience. They can simply call or set parameters through the human-machine interface, which reduces the skill requirements for operators and also reduces repetitive work, effectively alleviating labor intensity.

Claims

1. A method for intelligent electronic wire clamping control of a template machine, characterized in that, Includes the following steps: S1, Construct a sewing parameter database, which can be updated and accessed in real time; S2, collect key data during the sewing process, and preprocess the key data to obtain sewing parameters, including sewing material parameters and tension parameters; S3, based on the matching results of the sewing parameters and the database, adjust the thread clamping force to the initial thread clamping force target value; S4, dynamically adjusts the thread clamping mechanism based on the collected sewing tension value, so that the actual thread tension value is within the optimal thread tension range.

2. The intelligent electronic wire clamping control method for a template machine according to claim 1, characterized in that, Step S2 includes: Key data during the sewing process is collected in real time using sensors installed on the template machine; The type of fabric material is identified by an identification algorithm, thereby obtaining the sewing material parameters; The actual tension value is obtained after converting and processing the collected tension parameters.

3. The intelligent electronic wire clamping control method for a template machine according to claim 1 or 2, characterized in that, Step S1 includes: A parameter database is constructed based on the historical data of the template machine's thread clamping. The sewing parameter database contains the optimal thread clamping force parameters corresponding to different sewing materials and different thread types. The sewing parameter database supports automatic and manual updates.

4. The intelligent electronic wire clamping control method for a template machine according to claim 3, characterized in that, Step S3 includes: S31, based on the sewing parameters collected in real time, traverse the entire sewing parameter database to find the database parameter with the highest data matching degree; S32, parse the database parameters with the highest data matching degree to obtain the optimal thread clamping force parameter under the sewing parameter conditions obtained in real time, and use it as the initial thread clamping force target value; S33, send a control signal to the wire clamping actuator to adjust the wire clamping force to the initial wire clamping force target value.

5. A method for intelligent electronic wire clamping control of a template machine according to claim 1, 2, or 4, characterized in that, Step S4 includes: S41, continuously collect the actual tension value of the suture and compare it with the optimal suture tension range under this sewing condition; S42, when the actual tension value is lower than the lower limit of the optimal suture tension range, increase the clamping force until the actual tension value returns to the optimal suture tension range; when the actual tension value is higher than the upper limit of the optimal suture tension range, decrease the clamping force until the actual tension value returns to the optimal suture tension range.

6. The intelligent electronic wire clamping control method for a template machine according to claim 5, characterized in that, Step S42 includes: S421, if the current clamping force is too small, calculate the required increase in clamping force and send a control command to the clamping actuator to drive the clamping actuator to increase the clamping force; S422, when the current clamping force is too large, calculate the clamping force value that needs to be reduced, and send a control command to the clamping actuator to drive the clamping actuator to reduce the clamping force.

7. The intelligent electronic wire clamping control method for a template machine according to claim 2, characterized in that, The identification of fabric material type through the recognition algorithm includes: Image data of the fabric surface is acquired, and wavelet transform is performed on the image data to obtain and extract relevant texture features. Combined with weaving features, a neural network recognition model is constructed, and the initial fabric material type is output through the recognition model. The final fabric material type is obtained through manual recognition.

8. The intelligent electronic wire clamping control method for a template machine according to claim 2, characterized in that, The method of identifying the type of fabric material through an identification algorithm also includes: The type of fabric material is determined by spectral analysis. By analyzing the reflectance spectrum, the fiber composition ratio is directly determined, and thus the fabric material is identified.

9. The intelligent electronic wire clamping control method for a template machine according to claim 3, characterized in that, Step S1 further includes: viewing, modifying and adding to the database through the human-machine interface of the template machine, and saving commonly used sewing parameter combinations as a custom mode according to actual production needs.

10. A template machine intelligent electronic wire clamping control system, applicable to the template machine intelligent electronic wire clamping control method according to any one of claims 1-9, characterized in that, include: A sewing parameter database, containing sewing parameter data, is connected to an electronic control unit; The electronic control unit receives sensor data and can send control signals to the wire clamping actuator to drive the wire clamping actuator. The wire clamping actuator adjusts the clamping force according to the control signal.

Images