CN114253199A - Weaving control method and system - Google Patents
Weaving control method and system Download PDFInfo
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- CN114253199A CN114253199A CN202210188788.8A CN202210188788A CN114253199A CN 114253199 A CN114253199 A CN 114253199A CN 202210188788 A CN202210188788 A CN 202210188788A CN 114253199 A CN114253199 A CN 114253199A
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
- G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
- G05B19/0423—Input/output
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- D—TEXTILES; PAPER
- D04—BRAIDING; LACE-MAKING; KNITTING; TRIMMINGS; NON-WOVEN FABRICS
- D04C—BRAIDING OR MANUFACTURE OF LACE, INCLUDING BOBBIN-NET OR CARBONISED LACE; BRAIDING MACHINES; BRAID; LACE
- D04C3/00—Braiding or lacing machines
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/20—Pc systems
- G05B2219/25—Pc structure of the system
- G05B2219/25257—Microcontroller
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- Automation & Control Theory (AREA)
- Textile Engineering (AREA)
- Sewing Machines And Sewing (AREA)
- Knitting Machines (AREA)
Abstract
The application discloses a weaving control method and a system, wherein the method comprises the following steps: photographing a first fabric to obtain a picture of the first fabric; judging whether the braided wire of the first fabric in the picture can be identified or not, and if so, acquiring the crossing rule of the braided wire in the first fabric; determining knitting parameters of a knitting machine according to the crossing rules; sending the knitting parameters to a knitting machine to control the knitting machine to knit so as to obtain a second fabric; and photographing the second fabric to obtain a picture of the second fabric, comparing whether the intersection rules of the braided wires in the picture of the first fabric and the picture of the second fabric are the same, and if so, determining that the braiding parameters are correct braiding parameters. Through the method and the device, the problem that cloth imitation is carried out by means of manual analysis of weaving parameters in the prior art is solved, so that efficiency is improved, and error probability is reduced to a certain extent.
Description
Technical Field
The present application relates to the field of image processing, and in particular, to a knitting control method and system.
Background
In the prior art, there is a need to produce a fabric for use in a garment or home textile sample from a customer-supplied garment or home textile sample.
In the prior art, the cloth is analyzed manually, and then control parameters are calculated according to the cloth analysis result to control a knitting machine to knit the cloth.
Disclosure of Invention
The embodiment of the application provides a weaving control method and a weaving control system, which are used for at least solving the problem that cloth imitation is carried out by manually analyzing weaving parameters in the prior art.
According to an aspect of the present application, there is provided a knitting control method including: the method comprises the steps of taking a picture of a first fabric to obtain a picture of the first fabric, wherein the picture of the first fabric is obtained by taking a picture after the first fabric is amplified by a first preset multiple; judging whether the braided wire of the first fabric in the picture can be identified or not, and if so, acquiring the crossing rule of the braided wire in the first fabric; determining knitting parameters of a knitting machine according to the crossing rules, wherein the knitting parameters are used for indicating that knitting lines in the knitted fabric conform to the crossing rules; sending the knitting parameters to a knitting machine to control the knitting machine to knit so as to obtain a second fabric; and photographing the second fabric to obtain a picture of the second fabric, comparing whether the intersection rules of the braided wires in the picture of the first fabric and the picture of the second fabric are the same, and if so, determining that the braiding parameters are correct braiding parameters.
Further, whether the intersection rules of the braided wires in the picture of the first fabric and the picture of the second fabric are the same or not is compared: inputting the picture of the first cloth and the picture of the second cloth into a first machine learning model, wherein the first machine learning model is obtained by training through multiple groups of training data, and each group of training data in the multiple groups of training data comprises the pictures of two cloths and a label used for identifying whether the cross rules of the cloths in the two cloth pictures are the same or not; and acquiring an output label from the first machine learning model, wherein the label is used for indicating whether the intersection rules of the braided wires in the picture of the first fabric and the picture of the second fabric are the same.
Further, obtaining the crossing rule of the braided wires in the first fabric includes: comparing the picture of the first cloth with a picture stored in advance, and finding out a picture with similarity exceeding a threshold value; and taking the intersection rule corresponding to the picture with the similarity exceeding the threshold value as the intersection rule of the braided wire of the first fabric.
Further, in the case that the braided wire of the first fabric is not identified in the photograph, the method further includes: the first cloth is magnified by a second preset multiple and then photographed to obtain a picture of the first cloth, and the braided wires in the picture obtained by photographing again are identified until the crossing rules of the braided wires can be identified.
Further, the second predetermined multiple is greater than the first predetermined multiple, and the second predetermined multiple and the first predetermined multiple are preconfigured.
There is also provided in this embodiment a knitting control system, comprising: the shooting module is used for shooting a first fabric to obtain a picture of the first fabric, wherein the picture of the first fabric is obtained by shooting the first fabric after the first fabric is amplified by a first preset multiple; the identification module is used for judging whether the braided wire of the first fabric in the picture can be identified or not, and if so, acquiring the crossing rule of the braided wire of the first fabric; the first determining module is used for determining knitting parameters of a knitting machine according to the crossing rules, wherein the knitting parameters are used for indicating that knitting lines in the knitted fabric conform to the crossing rules; the sending module is used for sending the knitting parameters to a knitting machine so as to control the knitting machine to knit to obtain a second fabric; and the second determining module is used for photographing the second fabric to obtain a picture of the second fabric, comparing whether the intersection rules of the braided wires in the picture of the first fabric and the picture of the second fabric are the same or not, and if so, determining that the braiding parameters are correct braiding parameters.
Further, the second determination module is configured to: inputting the picture of the first cloth and the picture of the second cloth into a first machine learning model, wherein the first machine learning model is obtained by training through multiple groups of training data, and each group of training data in the multiple groups of training data comprises the pictures of two cloths and a label used for identifying whether the cross rules of the cloths in the two cloth pictures are the same or not; and acquiring an output label from the first machine learning model, wherein the label is used for indicating whether the intersection rules of the braided wires in the picture of the first fabric and the picture of the second fabric are the same.
Further, the identification module is configured to: comparing the picture of the first cloth with a picture stored in advance, and finding out a picture with similarity exceeding a threshold value; and taking the intersection rule corresponding to the picture with the similarity exceeding the threshold value as the intersection rule of the braided wire of the first fabric.
Further, in the case that the braided wire of the first fabric in the photo cannot be identified, the identification module is further configured to: the first cloth is magnified by a second preset multiple and then photographed to obtain a picture of the first cloth, and the braided wires in the picture obtained by photographing again are identified until the crossing rules of the braided wires can be identified.
Further, the second predetermined multiple is greater than the first predetermined multiple, and the second predetermined multiple and the first predetermined multiple are preconfigured.
In the embodiment of the application, a first fabric is photographed to obtain a photo of the first fabric, wherein the photo of the first fabric is obtained by photographing the first fabric after the first fabric is magnified by a first preset multiple; judging whether the braided wire of the first fabric in the picture can be identified or not, and if so, acquiring the crossing rule of the braided wire in the first fabric; determining knitting parameters of a knitting machine according to the crossing rules, wherein the knitting parameters are used for indicating that knitting lines in the knitted fabric conform to the crossing rules; sending the knitting parameters to a knitting machine to control the knitting machine to knit so as to obtain a second fabric; and photographing the second fabric to obtain a picture of the second fabric, comparing whether the intersection rules of the braided wires in the picture of the first fabric and the picture of the second fabric are the same, and if so, determining that the braiding parameters are correct braiding parameters. Through the method and the device, the problem that cloth imitation is carried out by means of manual analysis of weaving parameters in the prior art is solved, so that efficiency is improved, and error probability is reduced to a certain extent.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the application and, together with the description, serve to explain the application and are not intended to limit the application. In the drawings:
fig. 1 is a flowchart of a weaving control method according to an embodiment of the present application.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowcharts, in some cases, the steps illustrated or described may be performed in an order different than presented herein.
The method in the embodiment can be applied to various weaving devices, for example, the warp knitting shogging device comprises a flower disc and a guide bar, wherein the cross section of the flower disc is in a ring shape with unequal outer diameters, the ring shape is divided into x rows along the circumferential direction, and x is more than or equal to 2 and less than or equal to 8; at least two slopes are formed between the adjacent transverse rows on the outer surface of the flower disc.
This embodiment adopts this kind of structural design, has designed a gradual gentle slope for the buffering, lets the sley bar remove step by step, can avoid the unstability that this kind of large-span brought, is favorable to realizing high accuracy and the steadily of tricot machine in the production surface fabric in-process, is favorable to protecting the machine to carry out serialization production, also is favorable to realizing the high-speed of machine.
Optionally, the range of angular range of motion of the push stroke between adjacent courses is 7.5 ° to 82.5 °. Optionally, the pressure angle of the slope surface ranges from 1 to 39 degrees. When two slopes are formed between adjacent rows, the pressure angle of any slope ranges from 15 degrees to 35 degrees; when three slopes are formed between adjacent rows, the pressure angle of any slope ranges from 10 degrees to 15 degrees; when four slopes are formed between adjacent rows, the pressure angle of any slope ranges from 5 degrees to 10 degrees; when five slopes are formed between adjacent transverse rows, the pressure angle range of any slope is 1-5 degrees.
Optionally, the transition of the slope surface is arc-shaped to form an overall precise surface curve, so that the production stability is further improved. The guide bars are i, i is more than or equal to 5 and less than or equal to 8.
In the embodiment, a new stroke process can be adopted to replace the traditional four-stroke process to produce the warp-knitted double-needle-bed plush fabric, so that the number N of core yarns which are distributed and arranged in parallel along the same direction and cover a coil formed by the surface yarns or the core yarns in a rectangular area formed by any continuous 2 (transverse rows) x 2 (longitudinal rows) in the fabric is more than or equal to 9, the limitation that the number of the core yarns which are distributed and arranged in parallel along the same direction cannot exceed 5 or 6 because the maximum transverse movement of a single time can only reach 4 or 5 needles in the traditional process is broken through, the number of the core yarns in a unit area is increased, and the problems that the core yarn distribution density of the fabric produced by the traditional process is insufficient, the coverage rate is not high, and the occupied proportion is low are solved. Meanwhile, the production precision can be improved, stable production is realized, and through testing, the production efficiency can be greatly improved, so that the production efficiency is improved by 30-50%. Certainly, the structure improvement of this application not only is applicable to the production of plush surface fabric, can also use in other knitting fields, not only limits to the plush surface fabric that this application said.
When in weaving, the guide bars are i, i is more than or equal to 5 and less than or equal to 8; at least four guide bars are used for arranging the core yarn, and at least one guide bar is used for arranging the face yarn. Further, guide bars GB 1-GB 3 and GBi-2-GBi are used for arranging the core yarn. Further, guide bars GB1, GB2 and GBi-1, GBi are used for arranging the core yarn.
Further, the knitting process of a certain part may be:
N-N-N-N-N-N/0-0-0-0-0-0/(N-1)-(N-1)-(N-1)-(N-1)-(N-1)-(N-1)/0-0-0-0-0-0//
or 0-0-0-N-N-N/N-N-N-0-0-0/0-0-0- (N-1) - (N-1) - (N-1)/(N-1) - (N-1) -0-0-0/H
Or N-N-N-N-N-N-N-N/0-0-0-0-0-0-0-0/(N-1) - (N-1) - (N-1) - (N-1) - (N-1) - (N-1) - (N-1) - (N-0-0-0-0/ion/membrane
Or 0-0-0-0-N-N-N-N/N-N-N-N-0-0-0-0-
0-0-0-0-(N-1)-(N-1)-(N-1)-(N-1)/ (N-1)-(N-1)-(N-1)-(N-1)-0-0-0-0//
Or N- (N-1) - (N-1) - (N-1) - (N-1) - (N-1)/0-1-1-1-1-1/H/R
Or 1-1-1-N- (N-1) - (N-1)/(N-1) - (N-1) - (N-1) -0-1-1/H/R
Or N- (N-1) - (N-1) - (N-1) - (N-1) - (N-1) - (N-1) - (N-1)/0-1-1-1-1-1-1-1/H/cell
Or 1-1-1-1-N- (N-1) - (N-1) - (N-1)/(N-1) - (N-1) - (N-1) -0-1-1-1// ion
Wherein N is more than or equal to 9 and is a positive integer.
In the present embodiment, a partial knitting control method is provided, and fig. 1 is a flowchart of a knitting control method according to an embodiment of the present application, and as shown in fig. 1, the method includes the following steps:
step S102, a first cloth is photographed to obtain a photo of the first cloth, wherein the photo of the first cloth is obtained by photographing the first cloth after the first cloth is magnified by a first preset multiple;
step S104, judging whether the braided wire of the first cloth in the picture can be identified, and if so, acquiring the crossing rule of the braided wire of the first cloth;
in the step, the photo of the first cloth is compared with a pre-stored photo, and the photo with the similarity exceeding a threshold value is found; and taking the intersection rule corresponding to the picture with the similarity exceeding the threshold value as the intersection rule of the braided wire of the first fabric. In the case that the braided wire of the first fabric in the picture cannot be identified, the method further comprises the following steps: the first cloth is magnified by a second preset multiple and then photographed to obtain a picture of the first cloth, and braided wires in the picture obtained by photographing again are identified until the crossing rule of the braided wires can be identified; wherein the second predetermined multiple is greater than the first predetermined multiple, the second predetermined multiple and the first predetermined multiple being preconfigured.
As an optional implementation manner, when a picture of the first fabric is taken, a ruler is arranged on the first fabric, the density of the knitting lines of the first fabric is obtained according to the size of the ruler in the picture, and after the density is obtained, the density is used as a parameter carried in a crossing rule and is used as a knitting parameter for determining knitting performed by the knitting machine.
As another optional embodiment, after the scale is set, the thickness of the knitting line of the first fabric may be identified, and the identified thickness of the knitting line is also carried in the crossing rule as a knitting parameter for determining the knitting performed by the knitting machine.
Optionally, the knitting machine compares the size of the knitting wire with the knitting wire used by the knitting machine after receiving the size of the knitting wire, and if the knitting wire used by the knitting machine is thicker than the identified knitting wire, the knitting machine sends a prompt message, wherein the prompt message is used for indicating that the knitting wire needs to be replaced, and if the knitting wire used by the knitting machine is thinner than the identified knitting wire, the knitting machine notifies the size of the knitting wire used by the knitting machine; and after receiving the thickness of the knitting wire used by the knitting machine, increasing the knitting density according to the ratio of the identified thickness of the knitting wire to the thickness of the knitting wire used by the knitting machine, and sending the increased knitting density to the knitting machine for knitting.
Step S106, determining knitting parameters of a knitting machine according to the crossing rules, wherein the knitting parameters are used for indicating that knitting lines in the knitted fabric conform to the crossing rules;
step S108, sending the knitting parameters to a knitting machine to control the knitting machine to knit so as to obtain a second fabric;
step S110, the second cloth is photographed to obtain a picture of the second cloth, whether the intersection rules of the knitting lines in the picture of the first cloth and the picture of the second cloth are the same or not is compared, and if the intersection rules are the same, the knitting parameters are determined to be correct knitting parameters.
The method for implementing this step may be various, for example, the photo of the first fabric and the photo of the second fabric are input into a first machine learning model, where the first machine learning model is obtained by training using multiple sets of training data, and each set of training data in the multiple sets of training data includes two fabric photos and a label for identifying whether the cross rules of the fabrics in the two fabric photos are the same; and acquiring an output label from the first machine learning model, wherein the label is used for indicating whether the intersection rules of the braided wires in the picture of the first fabric and the picture of the second fabric are the same.
As an optional implementation manner, after this step, the picture of the second fabric, the correct knitting parameters and the model of the knitting machine are saved as a set of data, the saved data is referred to as second training data, whether the number of the sets saved by the second training data exceeds a predetermined number is judged, and if the number exceeds the predetermined number, multiple sets of the second training data are sent to a machine learning training server for training a second machine learning model. The second machine learning model is used for outputting the model of the knitting machine and the parameters used for programming after inputting the picture of the preset cloth.
Through the steps, the problem that cloth imitation is carried out by manually analyzing knitting parameters in the prior art is solved, so that the efficiency is improved, and the error probability is reduced to a certain extent.
Further, whether the intersection rules of the braided wires in the picture of the first fabric and the picture of the second fabric are the same or not is compared: inputting the picture of the first cloth and the picture of the second cloth into a first machine learning model, wherein the first machine learning model is obtained by training through multiple groups of training data, and each group of training data in the multiple groups of training data comprises the pictures of two cloths and a label used for identifying whether the cross rules of the cloths in the two cloth pictures are the same or not; and acquiring an output label from the first machine learning model, wherein the label is used for indicating whether the intersection rules of the braided wires in the picture of the first fabric and the picture of the second fabric are the same.
Further, obtaining the crossing rule of the braided wires in the first fabric includes: comparing the picture of the first cloth with a picture stored in advance, and finding out a picture with similarity exceeding a threshold value; and taking the intersection rule corresponding to the picture with the similarity exceeding the threshold value as the intersection rule of the braided wire of the first fabric.
Further, in the case that the braided wire of the first fabric is not identified in the photograph, the method further includes: the first cloth is magnified by a second preset multiple and then photographed to obtain a picture of the first cloth, and the braided wires in the picture obtained by photographing again are identified until the crossing rules of the braided wires can be identified.
Further, the second predetermined multiple is greater than the first predetermined multiple, and the second predetermined multiple and the first predetermined multiple are preconfigured.
Through the method and the device, the problem that cloth imitation is carried out by means of manual analysis of weaving parameters in the prior art is solved, so that efficiency is improved, and error probability is reduced to a certain extent.
In this embodiment, an electronic device is provided, comprising a memory in which a computer program is stored and a processor configured to run the computer program to perform the method in the above embodiments.
The programs described above may be run on a processor or may also be stored in memory (or referred to as computer-readable media), which includes both non-transitory and non-transitory, removable and non-removable media, that implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
These computer programs may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks, and corresponding steps may be implemented by different modules.
This embodiment provides a device or system, referred to as a weave control system, comprising: the shooting module is used for shooting a first fabric to obtain a picture of the first fabric, wherein the picture of the first fabric is obtained by shooting the first fabric after the first fabric is amplified by a first preset multiple; the identification module is used for judging whether the braided wire of the first fabric in the picture can be identified or not, and if so, acquiring the crossing rule of the braided wire of the first fabric; the first determining module is used for determining knitting parameters of a knitting machine according to the crossing rules, wherein the knitting parameters are used for indicating that knitting lines in the knitted fabric conform to the crossing rules; the sending module is used for sending the knitting parameters to a knitting machine so as to control the knitting machine to knit to obtain a second fabric; and the second determining module is used for photographing the second fabric to obtain a picture of the second fabric, comparing whether the intersection rules of the braided wires in the picture of the first fabric and the picture of the second fabric are the same or not, and if so, determining that the braiding parameters are correct braiding parameters.
The system or the apparatus is used for implementing the functions of the method in the foregoing embodiments, and each module in the system or the apparatus corresponds to each step in the method, which has been described in the method and is not described herein again.
For example, the second determining module is configured to: inputting the picture of the first cloth and the picture of the second cloth into a first machine learning model, wherein the first machine learning model is obtained by training through multiple groups of training data, and each group of training data in the multiple groups of training data comprises the pictures of two cloths and a label used for identifying whether the cross rules of the cloths in the two cloth pictures are the same or not; and acquiring an output label from the first machine learning model, wherein the label is used for indicating whether the intersection rules of the braided wires in the picture of the first fabric and the picture of the second fabric are the same.
For another example, the identification module is configured to: comparing the picture of the first cloth with a picture stored in advance, and finding out a picture with similarity exceeding a threshold value; and taking the intersection rule corresponding to the picture with the similarity exceeding the threshold value as the intersection rule of the braided wire of the first fabric. Optionally, in the case that the braided wire of the first fabric in the photo is not identified, the identification module is further configured to: the first cloth is magnified by a second preset multiple and then photographed to obtain a picture of the first cloth, and the braided wires in the picture obtained by photographing again are identified until the crossing rules of the braided wires can be identified. Optionally, the second predetermined multiple is greater than the first predetermined multiple, and the second predetermined multiple and the first predetermined multiple are preconfigured.
The problem of relying on manual analysis to weave the parameter and carrying out cloth imitation and exist among the prior art has been solved through this embodiment to improve efficiency, and reduced the probability of makeing mistakes to a certain extent.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A knitting control method characterized by comprising:
the method comprises the steps of taking a picture of a first fabric to obtain a picture of the first fabric, wherein the picture of the first fabric is obtained by taking a picture after the first fabric is amplified by a first preset multiple;
judging whether the braided wire of the first fabric in the picture can be identified or not, and if so, acquiring the crossing rule of the braided wire in the first fabric;
determining knitting parameters of a knitting machine according to the crossing rules, wherein the knitting parameters are used for indicating that knitting lines in the knitted fabric conform to the crossing rules;
sending the knitting parameters to a knitting machine to control the knitting machine to knit so as to obtain a second fabric;
and photographing the second fabric to obtain a picture of the second fabric, comparing whether the intersection rules of the braided wires in the picture of the first fabric and the picture of the second fabric are the same, and if so, determining that the braiding parameters are correct braiding parameters.
2. Method according to claim 1, characterized in that it is compared whether the crossing rules of the weaving lines are the same in the photograph of the first cloth and in the photograph of the second cloth:
inputting the picture of the first cloth and the picture of the second cloth into a first machine learning model, wherein the first machine learning model is obtained by training through multiple groups of training data, and each group of training data in the multiple groups of training data comprises the pictures of two cloths and a label used for identifying whether the cross rules of the cloths in the two cloth pictures are the same or not;
and acquiring an output label from the first machine learning model, wherein the label is used for indicating whether the intersection rules of the braided wires in the picture of the first fabric and the picture of the second fabric are the same.
3. The method of claim 1, wherein obtaining a crossing rule of the braided wires in the first fabric comprises:
comparing the picture of the first cloth with a picture stored in advance, and finding out a picture with similarity exceeding a threshold value;
and taking the intersection rule corresponding to the picture with the similarity exceeding the threshold value as the intersection rule of the braided wire of the first fabric.
4. The method according to any one of claims 1 to 3, wherein in the case that the braided wire of the first fabric is not identified in the photograph, further comprising:
the first cloth is magnified by a second preset multiple and then photographed to obtain a picture of the first cloth, and the braided wires in the picture obtained by photographing again are identified until the crossing rules of the braided wires can be identified.
5. The method of claim 4, wherein the second predetermined multiple is greater than the first predetermined multiple, and wherein the second predetermined multiple and the first predetermined multiple are preconfigured.
6. A weave control system, comprising:
the shooting module is used for shooting a first fabric to obtain a picture of the first fabric, wherein the picture of the first fabric is obtained by shooting the first fabric after the first fabric is amplified by a first preset multiple;
the identification module is used for judging whether the braided wire of the first fabric in the picture can be identified or not, and if so, acquiring the crossing rule of the braided wire of the first fabric;
the first determining module is used for determining knitting parameters of a knitting machine according to the crossing rules, wherein the knitting parameters are used for indicating that knitting lines in the knitted fabric conform to the crossing rules;
the sending module is used for sending the knitting parameters to a knitting machine so as to control the knitting machine to knit to obtain a second fabric;
and the second determining module is used for photographing the second fabric to obtain a picture of the second fabric, comparing whether the intersection rules of the braided wires in the picture of the first fabric and the picture of the second fabric are the same or not, and if so, determining that the braiding parameters are correct braiding parameters.
7. The system of claim 6, wherein the second determination module is configured to:
inputting the picture of the first cloth and the picture of the second cloth into a first machine learning model, wherein the first machine learning model is obtained by training through multiple groups of training data, and each group of training data in the multiple groups of training data comprises the pictures of two cloths and a label used for identifying whether the cross rules of the cloths in the two cloth pictures are the same or not;
and acquiring an output label from the first machine learning model, wherein the label is used for indicating whether the intersection rules of the braided wires in the picture of the first fabric and the picture of the second fabric are the same.
8. The system of claim 6, wherein the identification module is configured to:
comparing the picture of the first cloth with a picture stored in advance, and finding out a picture with similarity exceeding a threshold value;
and taking the intersection rule corresponding to the picture with the similarity exceeding the threshold value as the intersection rule of the braided wire of the first fabric.
9. The system of any one of claims 6 to 8, wherein in the event that the braided wire of the first fabric in the photograph is not identified, the identification module is further configured to:
the first cloth is magnified by a second preset multiple and then photographed to obtain a picture of the first cloth, and the braided wires in the picture obtained by photographing again are identified until the crossing rules of the braided wires can be identified.
10. The system of claim 9, wherein the second predetermined multiple is greater than the first predetermined multiple, the second predetermined multiple and the first predetermined multiple being preconfigured.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202210188788.8A CN114253199B (en) | 2022-03-01 | 2022-03-01 | Weaving control method and system |
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| CN202210188788.8A CN114253199B (en) | 2022-03-01 | 2022-03-01 | Weaving control method and system |
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| CN114253199B CN114253199B (en) | 2022-05-06 |
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| CN214193630U (en) * | 2020-11-25 | 2021-09-14 | 南旋实业有限公司 | Knitting device |
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