An equipment data acquisition system for overlock sewing machines based on the Internet of Things

Abstract

The invention relates to the technical field of overlock sewing machines, and in particular to an equipment data acquisition system of an overlock sewing machine based on the Internet of Things, comprising an overlock sewing module, an image feature acquisition module, a tension feature acquisition module, a data fitting module and an analysis module. The system acquires surface image information and side image information of an overlock sewing fabric to obtain a pattern interference feature value, so as to determine the actual distribution of a raised pattern. The system acquires the stretching rate of the fabric to be overlock sewn by the tension feature acquisition module to determine the tension of the fabric. The system comprehensively determines the actual situation of the fabric to be overlock sewn, so as to determine the operation parameters of the overlock sewing module in a targeted manner. The fabric is classified into complex categories based on complex overlock sewing parameters, and the overlock sewing module is controlled to process the fabric to be overlock sewn based on the complex categories of the fabric. The stitch length of the overlock sewing module is adjusted, and the wrinkle difference amount is determined according to the side image information. The suture tension of the overlock sewing module is subsequently adaptively adjusted to reduce the internal deformation of the fabric and improve the processing efficiency of the fabric to be overlock sewn.

Description

Technical Field

[0001] The present invention relates to the technical field of overlock sewing machines, and in particular to an equipment data acquisition system for overlock sewing machines based on the Internet of Things. Background Art

[0002] Overlock sewing machines are also called edge machines, edge machines, and bone machines. They are generally divided into three-line, four-line, and five-line machines. The main function is generally to prevent the seams of clothing from fuzzing. Overlock sewing machines can not only be used for edge binding, but also for sewing T-shirts, sportswear, underwear, knitwear and other fabrics. Overlock sewing machines can cut and sew at the same time, and the seams are like mesh, which is also suitable for elastic fabrics;

[0003] In today's advanced manufacturing industry, overlock machines are no longer traditional manual tools, but have successfully entered a new era of large-scale automated production. Modern overlock machine production lines are carried out in an orderly manner from the automatic loading of raw materials to the overlock process under the precise control of the automation system. Multiple overlock machines work together, and through advanced assembly line design and automated transmission devices, uninterrupted continuous production is achieved, greatly improving production efficiency.

[0004] Chinese patent publication number: CN118563503A, discloses a high-speed overlock sewing machine, including an overlock sewing machine body, a presser foot located on one side of the overlock sewing machine body, a workbench located at the position of the presser foot, and a pressing plate for pressing the fabric on the upper surface of the workbench, a long groove extending in a direction parallel to the side of the presser foot is provided on the workbench and close to the presser foot, a rectangular slider is provided in the long groove and the side wall is in contact with the side wall of the long groove, a cylindrical iron block is provided at the upper end of the slider, a magnet sleeve for the cylindrical iron block to be embedded and attracted is provided at the center of the lower surface of the pressing plate, and a pressing component for pushing the cylindrical iron block downward into the long groove after the hemming is completed is provided on the upper side of the pressing plate; it can be seen that the above technical scheme has the following problems: the same overlock sewing parameters are used for each type of fabric, and the overlock sewing parameters are not adjusted in a targeted manner according to the actual distribution of the raised pattern and the tension of the fabric, which affects the flatness of the fabric to be overlocked, and thus affects the processing efficiency of the fabric to be overlocked. Summary of the invention

[0005] To this end, the present invention provides an equipment data acquisition system for an overlock sewing machine based on the Internet of Things, so as to overcome the problem that the same overlock sewing parameters are used for various types of fabrics in the prior art, and the overlock sewing parameters are not adjusted in a targeted manner according to the actual distribution of the raised patterns and the tension of the fabric, which affects the flatness of the fabric to be sewn, and further affects the processing efficiency of the fabric to be sewn.

[0006] To achieve the above object, the present invention provides an equipment data acquisition system for an overlock sewing machine based on the Internet of Things, comprising:

[0007] An overlock module, which is used to process the fabric to be overlocked;

[0008] An image feature acquisition module, comprising a first image detector arranged above the overlock module for acquiring surface image information of the fabric to be overlocked and a second image detector arranged at one side of the overlock module for acquiring side image information of the fabric to be overlocked;

[0009] A tension feature acquisition module, which is used to stretch the fabric to be sewn to obtain the stretching rate of the fabric to be sewn;

[0010] A data fitting module, connected to the image feature acquisition module and the tension feature acquisition module respectively, for determining the pattern interference feature value based on the surface image information and the side image information, and determining the complex parameters of the overlock in combination with the stretch rate;

[0011] An analysis module, which is respectively connected to the overlock module, the image feature acquisition module and the data fitting module, is used to classify the complexity of the fabric based on the complexity parameters of the overlock, and control the overlock module to process the fabric to be overlocked based on the complexity category of the fabric, including:

[0012] Adjust the stitch length of the sewing module based on the complex parameters of the sewing, determine the wrinkle difference according to the side image information, and determine whether to correct the thread tension of the sewing module according to the wrinkle difference; or control the sewing module to continue to use the predetermined stitch length to process the fabric to be sewn.

[0013] Furthermore, the tension feature acquisition module is used to obtain the stretching rate of the fabric to be sewn, including:

[0014] Respectively obtaining the original length of the fabric to be sewn before stretching and the stretched length after stretching;

[0015] Solve for the difference between the stretched length and the original length, and calculate the ratio of the difference to the original length to obtain the stretch rate.

[0016] Furthermore, the data fitting module is used to determine the pattern interference feature value based on the surface image information and the side image information, including:

[0017] Cloth contour features for obtaining side image information;

[0018] Acquire a convex feature in the cloth contour feature, determine the maximum convex thickness of the convex feature, and calculate a thickness ratio of the maximum thickness to an average thickness of the cloth contour feature;

[0019] Obtaining pattern features of surface image information, determining the total area of ​​each pattern feature, and solving the area ratio of the total area to the area of ​​the fabric to be sewn;

[0020] The sum of the calculated area ratio and thickness ratio is determined as the pattern interference characteristic value.

[0021] Furthermore, the data fitting module is used to determine the complex parameters of the overlock based on the pattern interference characteristic value and the stretch rate, including:

[0022] The ratio of the pattern interference characteristic value of the fabric to be sewn to the average pattern interference characteristic value of each fabric to be sewn in history is determined as the interference variable;

[0023] The ratio of the stretching rate of the fabric to be sewn to the average stretching rate of each fabric to be sewn in history is determined as a stretching variable;

[0024] The interference variables and stretching variables are assigned corresponding coefficients and summed up to obtain the complex parameters of the seam.

[0025] Furthermore, the analysis module is used to classify the complexity of fabrics into categories based on the complexity parameters of the seams, including:

[0026] If the overlock complexity parameter is less than or equal to the preset overlock complexity parameter, the fabric to be overlocked is classified as a low-complexity fabric category;

[0027] If the overlock complexity parameter is greater than the preset overlock complexity parameter, the fabric to be overlocked is classified as a fabric with high complexity.

[0028] Furthermore, the analysis module is used to control the overlocking module to process the overlocking fabric based on the complexity category of the fabric, including:

[0029] If the fabric to be sewn is of low complexity, the sewing module is controlled to continue to process the fabric to be sewn using a predetermined stitch length;

[0030] If the fabric to be sewn is of a high-complexity category, the needle length of the sewing module is adjusted based on the sewing complexity parameter, the wrinkle difference is determined based on the side image information, and whether to correct the thread tension of the sewing module is determined based on the wrinkle difference.

[0031] Further, the analysis module is used to adjust the stitch length of the overlock module based on the complex overlock parameters, wherein:

[0032] The needle distance of the overlock sewing module determined based on the overlock sewing complex parameters is proportional to the overlock sewing complex parameters.

[0033] Furthermore, the analysis module is used to determine the wrinkle difference amount according to the side image information, including:

[0034] Controlling the second image detector to respectively obtain the original side image information of the fabric to be overlocked and the product side image information of the fabric after overlocking;

[0035] Randomly determine a number of calibration points in the cloth contour features of the side image information;

[0036] Calculate the variance of the thickness of the cloth contour feature corresponding to each calibration point in the original side image information, and determine it as the original variance;

[0037] Calculate the variance of the thickness of the cloth contour feature corresponding to each calibration point in the product side image information, and determine it as the product variance;

[0038] The wrinkle difference is obtained by solving the ratio of product variance to original variance.

[0039] Furthermore, the analysis module is used to determine whether to correct the suture tension of the overlock module according to the wrinkle difference, including:

[0040] If the wrinkle difference is less than or equal to the predetermined wrinkle difference, the overlock sewing module is controlled to continue to use the predetermined sewing thread tension to process the overlock sewing fabric;

[0041] If the wrinkle difference amount is greater than a predetermined wrinkle difference amount, the thread tension of the overlock sewing module is corrected based on the wrinkle difference amount.

[0042] Further, the analysis module is used to correct the suture tension of the overlock module based on the wrinkle difference, wherein:

[0043] The stitch tension of the overlock module determined based on the wrinkle difference is inversely proportional to the wrinkle difference.

[0044] Compared with the prior art, the beneficial effect of the present invention lies in that the present invention obtains the surface image information and the side image information of the overlock fabric to obtain the pattern interference characteristic value to determine the actual distribution of the raised pattern, obtains the stretching rate of the fabric to be overlocked through the tension feature acquisition module to determine the tension of the fabric, comprehensively determines the actual situation of the fabric to be overlocked, and determines the operation parameters of the overlock module in a targeted manner, the analysis module divides the fabric complexity category based on the overlock complex parameters, controls the overlock module to process the fabric to be overlocked based on the fabric complexity category, adjusts the needle length of the overlock module, determines the wrinkle difference amount according to the side image information, and subsequently adaptively adjusts the suture tension of the overlock module to reduce the internal deformation of the fabric and improve the processing efficiency of the fabric to be overlocked.

[0045] Furthermore, the data fitting module determines the complex parameters of the seam based on the pattern interference eigenvalue and the stretch rate. The thickness ratio of the fabric contour feature in the pattern interference eigenvalue characterizes the protrusion of the raised pattern in the fabric to be seamed, and the area ratio of the surface image information characterizes the distribution of the raised pattern. In actual situations, the raised pattern will cause the thickness of the fabric to be seamed to be uneven. The seam parameters used by the seam sewing machine when sewing materials of different thicknesses are different. However, for patterned fabrics, only a single seam parameter can be used to process the fabric to ensure processing efficiency. In order to balance the seam parameters of fabrics with uneven thickness, the pattern interference eigenvalue is introduced to determine the difficulty of the seam: in the actual seam process The tension of the fabric during the sewing process will affect the sewing part due to the stretching situation; further, the present invention calculates the complex parameters of the seam based on the pattern interference characteristic value and the expansion rate, characterizes the actual uneven resistance phenomenon inside the fabric to be sewn, provides data support for classifying the complex categories of the fabric, and facilitates the subsequent adaptive targeted monitoring of the seam process that is prone to abnormal situations, obtains the wrinkle difference amount, determines whether wrinkles exist during the seam process, and timely further adjusts the thread tension, reduces the unevenness caused by the internal tension of the fabric to be sewn and the uneven internal resistance caused by the distribution of raised patterns, improves the flatness of the fabric to be sewn, and thus improves the processing efficiency of the fabric to be sewn. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 It is a module block diagram of the equipment data acquisition system of the overlock sewing machine based on the Internet of Things according to an embodiment of the present invention;

[0047] Figure 2 It is a logic decision diagram of the analysis module of the embodiment of the present invention for classifying the complex categories of fabrics based on the complex parameters of the overlock;

[0048] Figure 3 It is a logic decision diagram of the analysis module in the embodiment of the present invention controlling the overlock module to process the overlock fabric based on the complexity category of the fabric;

[0049] Figure 4 This is a logic decision diagram for the analysis module of the embodiment of the present invention to determine whether to correct the suture tension of the overlock module according to the wrinkle difference amount. DETAILED DESCRIPTION

[0050] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0051] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0052] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is merely for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0053] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0054] See also Figure 1 , Figure 2 , Figure 3 as well as Figure 4 As shown, they are respectively a module block diagram of an equipment data acquisition system for an overlock sewing machine based on the Internet of Things according to an embodiment of the present invention, a logic determination diagram for the analysis module to classify the complex categories of fabrics based on the complex parameters of overlock sewing, a logic determination diagram for the analysis module to control the overlock sewing module to process the fabric to be overlock sewn based on the complex categories of fabrics, and a logic determination diagram for the analysis module to determine whether to correct the suture tension of the overlock sewing module according to the wrinkle difference amount; an equipment data acquisition system for an overlock sewing machine based on the Internet of Things according to an embodiment of the present invention comprises:

[0055] An overlock module, which is used to process the fabric to be overlocked;

[0056] An image feature acquisition module, comprising a first image detector arranged above the overlock module for acquiring surface image information of the fabric to be overlocked and a second image detector arranged at one side of the overlock module for acquiring side image information of the fabric to be overlocked;

[0057] A tension feature acquisition module, which is used to stretch the fabric to be sewn to obtain the stretching rate of the fabric to be sewn;

[0058] A data fitting module, connected to the image feature acquisition module and the tension feature acquisition module respectively, for determining the pattern interference feature value based on the surface image information and the side image information, and determining the complex parameters of the overlock in combination with the stretch rate;

[0059] An analysis module, which is respectively connected to the overlock module, the image feature acquisition module and the data fitting module, is used to classify the complexity of the fabric based on the complexity parameters of the overlock, and control the overlock module to process the fabric to be overlocked based on the complexity category of the fabric, including:

[0060] Adjust the stitch length of the sewing module based on the complex parameters of the sewing, determine the wrinkle difference according to the side image information, and determine whether to correct the thread tension of the sewing module according to the wrinkle difference; or control the sewing module to continue to use the predetermined stitch length to process the fabric to be sewn.

[0061] Specifically, the specific structure of the tension feature acquisition module is not limited, and it can include a first clamping mechanism and a second clamping mechanism for respectively fixing the fabric to be sewn, each clamping device is connected to a slide rail through a slider arranged on its base, and each slider is connected to a motor for controlling its movement along the slide rail. The movement directions of each slider are opposite. It can be understood that the operating power of each motor is the same. The original length of the fabric to be sewn before stretching and the stretched length after stretching are respectively obtained through the image acquirer, the difference between the stretched length and the original length is solved, and the ratio of the difference to the original length is calculated to obtain the stretching rate.

[0062] Specifically, the specific structure of the overlock module is not limited, and it only needs to be able to realize the corresponding function. For example, it may include a sewing machine head for overlocking the cloth and a cloth feeding device for continuously pushing the cloth. Commonly, the sewing machine head can adjust parameters such as thread tension, stitch length, and presser foot pressure. Those skilled in the art can select the corresponding sewing machine head according to specific needs, which will not be repeated here.

[0063] Specifically, the present invention obtains surface image information and side image information of the overlock fabric and obtains pattern interference feature values ​​to determine the actual distribution of the raised patterns, obtains the stretching rate of the fabric to be overlocked through a tension feature acquisition module to determine the tension of the fabric, comprehensively determines the actual situation of the fabric to be overlocked, and specifically determines the operating parameters of the overlock module. The analysis module divides the fabric complexity categories based on the overlock complex parameters, controls the overlock module to process the fabric to be overlocked based on the fabric complexity categories, adjusts the needle length of the overlock module, determines the wrinkle difference amount based on the side image information, and subsequently adaptively adjusts the suture tension of the overlock module to reduce the internal deformation of the fabric and improve the processing efficiency of the fabric to be overlocked.

[0064] Specifically, the tension feature acquisition module is used to obtain the stretch rate of the fabric to be sewn, including:

[0065] Respectively obtaining the original length of the fabric to be sewn before stretching and the stretched length after stretching;

[0066] Solve for the difference between the stretched length and the original length, and calculate the ratio of the difference to the original length to obtain the stretch rate.

[0067] Specifically, the data fitting module is used to determine the pattern interference feature value based on the surface image information and the side image information, including:

[0068] Cloth contour features for obtaining side image information;

[0069] Acquire a convex feature in the cloth contour feature, determine the maximum convex thickness of the convex feature, and calculate a thickness ratio of the maximum thickness to an average thickness of the cloth contour feature;

[0070] Obtaining pattern features of surface image information, determining the total area of ​​each pattern feature, and solving the area ratio of the total area to the area of ​​the fabric to be sewn;

[0071] The sum of the calculated area ratio and thickness ratio is determined as the pattern interference characteristic value.

[0072] Specifically, it can be understood that the maximum protrusion thickness is the distance between the upper surface and the lower surface along the vertical direction of the cloth corresponding to the maximum protrusion point in the protrusion feature, which will not be elaborated here.

[0073] Specifically, there is no limitation on the specific method of identifying the contour features of the cloth. The contour features of the cloth can be determined by identifying the edge contour of the cloth, and then the recognition of the raised features can be achieved. The relevant algorithm can be imported into the logic processing unit to obtain the edge contour features in the side image information. It can be understood that the raised features can be determined as raised features by calculating the average thickness of the cloth, and the area with a thickness greater than the average thickness is determined as a raised feature. This will not be repeated.

[0074] Specifically, there is no limitation on the specific method of acquiring pattern features. The pattern contour features can be determined by identifying the edge contour of the pattern in the fabric, and then the pattern features can be recognized. The relevant algorithm can be imported into the logic processing unit to acquire the pattern features in the surface image information. This is the existing technology and will not be elaborated on.

[0075] Specifically, the data fitting module is used to determine the complex parameters of the overlock based on the pattern interference characteristic value and the stretch rate, including:

[0076] The ratio of the pattern interference characteristic value of the fabric to be sewn to the average pattern interference characteristic value of each fabric to be sewn in history is determined as the interference variable;

[0077] The ratio of the stretching rate of the fabric to be sewn to the average stretching rate of each fabric to be sewn in history is determined as a stretching variable;

[0078] The interference variables and stretching variables are assigned corresponding coefficients and summed up to obtain the complex parameters of the seam.

[0079] Specifically, the corresponding coefficient assigned to the interference variable is the interference variable weight coefficient, and the corresponding coefficient assigned to the stretching variable is the stretching variable weight coefficient. The interference variable weight coefficient is 0.35, and the stretching variable weight coefficient is 0.3. The process of assigning the corresponding coefficient can be multiplying the variable with the corresponding weight coefficient.

[0080] Specifically, the analysis module is used to classify the complexity of fabrics based on the complexity parameters of the seams, including:

[0081] If the overlock complexity parameter is less than or equal to the preset overlock complexity parameter, the fabric to be overlocked is classified as a low-complexity fabric category;

[0082] If the overlock complexity parameter is greater than the preset overlock complexity parameter, the fabric to be overlocked is classified as a fabric with high complexity.

[0083] The preset complex parameter N0 of the seam is selected in the interval [1.3, 1.45].

[0084] Specifically, the analysis module is used to control the overlocking module to process the overlocking fabric based on the complexity of the fabric, including:

[0085] If the fabric to be sewn is of low complexity, the sewing module is controlled to continue to process the fabric to be sewn using a predetermined stitch length;

[0086] If the fabric to be sewn is of a high-complexity category, the needle length of the sewing module is adjusted based on the sewing complexity parameter, the wrinkle difference is determined based on the side image information, and whether to correct the thread tension of the sewing module is determined based on the wrinkle difference.

[0087] Specifically, the analysis module is used to adjust the stitch length of the overlock module based on the complex overlock parameters, wherein:

[0088] The needle distance of the overlock sewing module determined based on the overlock sewing complex parameters is proportional to the overlock sewing complex parameters.

[0089] In this embodiment, optionally,

[0090] The overlock complexity parameter N is compared with the first preset complexity comparison threshold N1 and the second preset complexity comparison threshold N2.

[0091] If N≤N1, the stitch length of the overlock module is determined to be the first stitch length L1, and L1=1.12L0;

[0092] If N1<N≤N2, the stitch length of the overlock module is determined to be the second stitch length L2, and L2=1.17L0;

[0093] If N>N2, the needle distance of the overlock module is determined to be the third needle distance L3, and L3=1.28L0;

[0094] Among them, L0 is the initial needle distance, N1=1.21N0, Y2=2.38N0.

[0095] Specifically, the analysis module is used to determine the wrinkle difference amount according to the side image information, including:

[0096] Controlling the second image detector to respectively obtain the original side image information of the fabric to be overlocked and the product side image information of the fabric after overlocking;

[0097] Randomly determine a number of calibration points in the cloth contour features of the side image information;

[0098] Calculate the variance of the thickness of the cloth contour feature corresponding to each calibration point in the original side image information, and determine it as the original variance;

[0099] Calculate the variance of the thickness of the cloth contour feature corresponding to each calibration point in the product side image information, and determine it as the product variance;

[0100] The wrinkle difference is obtained by solving the ratio of product variance to original variance.

[0101] Specifically, the number of calibration points and the calibration method are not limited, and the marking points can be calibrated at the same intervals in the cloth contour features of the side image information; the method for obtaining the thickness of the corresponding cloth contour feature for a single calibration point can be to obtain the thickness of the cloth contour feature perpendicular to the cloth contour feature with the calibration point as the reference, which will not be repeated here.

[0102] Specifically, the analysis module is used to determine whether to correct the suture tension of the overlock module according to the wrinkle difference, including:

[0103] If the wrinkle difference is less than or equal to the predetermined wrinkle difference, the overlock sewing module is controlled to continue to use the predetermined sewing thread tension to process the overlock sewing fabric;

[0104] If the wrinkle difference amount is greater than a predetermined wrinkle difference amount, the thread tension of the overlock sewing module is corrected based on the wrinkle difference amount.

[0105] The predetermined wrinkle difference amount Z0 is selected within the interval [1.21, 1.39].

[0106] Specifically, the analysis module is used to correct the suture tension of the overlock module based on the wrinkle difference, wherein:

[0107] The stitch tension of the overlock module determined based on the wrinkle difference is inversely proportional to the wrinkle difference.

[0108] In this embodiment, optionally,

[0109] Compare the wrinkle difference Z with a first preset wrinkle comparison threshold Z1 and a second preset wrinkle comparison threshold Z2;

[0110] If Z≤Z1, the thread tension of the overlock module is determined as the first thread tension A1, and A1=0.92A0;

[0111] If Z1<Z≤Z2, the thread tension of the overlock module is determined as the second thread tension A2, and A2=0.83A0;

[0112] If Z>Z2, the thread tension of the overlock module is determined as the third thread tension A3, and A3=0.72A0 is set;

[0113] Among them, A0 is the initial suture tension, Z1=1.18Z0, X2=1.51Z0.

[0114] Specifically, the data fitting module determines the complex parameters of the seam based on the pattern interference eigenvalue and the stretch rate. The thickness ratio of the fabric contour feature in the pattern interference eigenvalue characterizes the protrusion of the raised pattern in the fabric to be seamed, and the area ratio of the surface image information characterizes the distribution of the raised pattern. In actual situations, the raised pattern will cause the thickness of the fabric to be seamed to be uneven. The seam parameters used by the seam sewing machine when sewing materials of different thicknesses are different. However, for patterned fabrics, in order to ensure processing efficiency, only a single seam parameter can be used to process the fabric. In order to balance the seam parameters of fabrics with uneven thickness, the pattern interference eigenvalue is introduced to determine the difficulty of the seam: in the actual seam process, the seam parameters of the fabric are determined by the seam length. The tension of the fabric during the sewing process will affect the sewing part due to the stretching situation; further, the present invention calculates the complex parameters of the seam based on the pattern interference characteristic value and the expansion rate, characterizes the actual uneven resistance phenomenon inside the fabric to be sewn, provides data support for classifying the complex categories of the fabric, and facilitates the subsequent adaptive targeted monitoring of the seam process that is prone to abnormal situations, obtains the wrinkle difference amount, determines whether wrinkles exist during the seam process, and timely further adjusts the thread tension, reduces the unevenness caused by the internal tension of the fabric to be sewn and the uneven internal resistance caused by the distribution of raised patterns, improves the flatness of the fabric to be sewn, and thus improves the processing efficiency of the fabric to be sewn.

[0115] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0116] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An equipment data acquisition system for an overlock sewing machine based on the Internet of Things, characterized in that: include: An overlock module, which is used to process the fabric to be overlocked; An image feature acquisition module, comprising a first image detector disposed above the overlock module for acquiring surface image information of the fabric to be overlocked and a second image detector disposed at one side of the overlock module for acquiring side image information of the fabric to be overlocked; A tension feature acquisition module, which is used to stretch the fabric to be sewn to obtain the stretching rate of the fabric to be sewn; A data fitting module is connected to the image feature acquisition module and the tension feature acquisition module respectively, and is used to determine the pattern interference feature value based on the surface image information and the side image information, and determine the complex parameters of the overlock in combination with the stretch rate; The analysis module is connected to the overlock module, the image feature acquisition module and the data fitting module respectively, and is used to classify the complex categories of fabrics based on the complex parameters of the overlock, and control the overlock module to process the fabric to be overlocked based on the complex categories of the fabrics, including: Adjust the stitch length of the overlock module based on the complex parameters of the overlock, determine the wrinkle difference according to the side image information, and determine whether to correct the thread tension of the overlock module according to the wrinkle difference; The step of determining the pattern interference characteristic value comprises: Obtaining cloth contour features from side image information; Acquire a convex feature in the cloth contour feature, determine the maximum convex thickness of the convex feature, and calculate a thickness ratio of the maximum thickness to an average thickness of the cloth contour feature; Obtaining pattern features of surface image information, determining the total area of ​​each pattern feature, and solving the area ratio of the total area to the area of ​​the fabric to be sewn; The sum of the calculated area ratio and thickness ratio is determined as the pattern interference characteristic value; The method of determining the complex parameters of the seam wrapping comprises: The ratio of the pattern interference characteristic value of the fabric to be sewn to the average pattern interference characteristic value of each fabric to be sewn in history is determined as the interference variable; The ratio of the stretching rate of the fabric to be sewn to the average stretching rate of each fabric to be sewn in history is determined as a stretching variable; The interference variables and stretching variables are assigned corresponding coefficients and summed up to obtain the complex parameters of the seam wrapping; The determining of the wrinkle difference amount comprises: Controlling the second image detector to respectively obtain the original side image information of the fabric to be overlocked and the product side image information of the fabric after overlocking; Randomly determine a number of calibration points in the cloth contour features of the side image information; Calculate the variance of the thickness of the cloth contour feature corresponding to each calibration point in the original side image information, and determine it as the original variance; Calculate the variance of the thickness of the cloth contour feature corresponding to each calibration point in the product side image information, and determine it as the product variance; The wrinkle difference is obtained by solving the ratio of product variance to original variance.

2. According to the device data acquisition system of the Internet of Things-based overlock sewing machine according to claim 1, it is characterized in that: The tension feature acquisition module is used to obtain the stretch rate of the fabric to be sewn, including: Respectively obtaining the original length of the fabric to be sewn before stretching and the stretched length after stretching; Solve for the difference between the stretched length and the original length, and calculate the ratio of the difference to the original length to obtain the stretch rate.

3. The device data acquisition system for an overlock sewing machine based on the Internet of Things according to claim 2 is characterized in that: The analysis module is used to classify the complexity of fabrics into categories based on the complexity parameters of the seams, including: If the overlock complexity parameter is less than or equal to the preset overlock complexity parameter, the fabric to be overlocked is classified as a low-complexity fabric category; If the overlock complexity parameter is greater than the preset overlock complexity parameter, the fabric to be overlocked is classified as a fabric with high complexity.

4. The device data acquisition system for an overlock sewing machine based on the Internet of Things according to claim 3 is characterized in that: The analysis module is used to control the overlocking module to process the overlocking fabric based on the complexity of the fabric, including: If the fabric to be sewn is of low complexity, the sewing module is controlled to continue to process the fabric to be sewn using a predetermined stitch length; If the fabric to be sewn is of a high-complexity category, the needle length of the sewing module is adjusted based on the sewing complexity parameter, the wrinkle difference is determined based on the side image information, and whether to correct the thread tension of the sewing module is determined based on the wrinkle difference.

5. The device data acquisition system for an overlock sewing machine based on the Internet of Things according to claim 4 is characterized in that: The analysis module is used to adjust the needle distance of the overlock module based on the complex parameters of the overlock, wherein: The needle distance of the overlock sewing module determined based on the overlock sewing complex parameters is proportional to the overlock sewing complex parameters.

6. The device data acquisition system for an overlock sewing machine based on the Internet of Things according to claim 5 is characterized in that: The analysis module is used to determine whether to correct the suture tension of the overlock module according to the wrinkle difference, including: If the wrinkle difference is less than or equal to the predetermined wrinkle difference, the overlock sewing module is controlled to continue to use the predetermined sewing thread tension to process the overlock sewing fabric; If the wrinkle difference amount is greater than a predetermined wrinkle difference amount, the thread tension of the overlock sewing module is corrected based on the wrinkle difference amount.

7. The device data acquisition system for an overlock sewing machine based on the Internet of Things according to claim 6 is characterized in that: The analysis module is used to correct the suture tension of the overlock module based on the wrinkle difference, wherein: The stitch tension of the overlock module determined based on the wrinkle difference is inversely proportional to the wrinkle difference.

Images