CN114398818B - Textile jacquard detection method and system based on deep learning - Google Patents
Textile jacquard detection method and system based on deep learning Download PDFInfo
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- 239000004753 textile Substances 0.000 title claims abstract description 287
- 238000001514 detection method Methods 0.000 title claims abstract description 71
- 238000013135 deep learning Methods 0.000 title claims abstract description 21
- 230000007547 defect Effects 0.000 claims abstract description 442
- 238000000034 method Methods 0.000 claims abstract description 28
- 238000009941 weaving Methods 0.000 claims description 134
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Abstract
The embodiment of the invention provides a deep learning-based textile jacquard detection method and a deep learning-based textile jacquard detection system, which are characterized in that first textile jacquard defect information of a plurality of textile jacquard defect positioning objects with first defect type attributes is generated through a first textile jacquard detection model according to textile jacquard detection data, then second textile jacquard defect information comprising a plurality of textile jacquard defect positioning objects with second defect type attributes, which are in one-to-one correspondence with the plurality of textile jacquard defect positioning objects with first defect type attributes, is generated through a second textile jacquard detection model according to the textile jacquard detection data, and therefore third textile jacquard defect information comprising a plurality of textile jacquard defect positioning objects with target defect type attributes is generated through the first textile jacquard defect information and the second textile jacquard defect information. Therefore, the efficiency of the textile jacquard detection process can be improved, and various defect conditions can be accurately and automatically distinguished.
Description
Technical Field
The invention relates to the technical field of deep learning, in particular to a textile jacquard detection method and system based on deep learning.
Background
At present, in the process of detecting textile jacquard, the situation that various defect conditions cannot be accurately and automatically distinguished exists.
Disclosure of Invention
Accordingly, an object of the embodiments of the present invention is to provide a method and a system for detecting a textile jacquard based on deep learning, which can improve the efficiency of the textile jacquard detection process and accurately and automatically distinguish various defect situations.
According to an aspect of the embodiment of the present invention, there is provided a textile jacquard detection method based on deep learning, which is applied to a server, and the server is communicatively connected with a textile jacquard detection device, and the method includes:
obtaining textile jacquard detection data, and generating first textile jacquard defect information through a first textile jacquard detection model according to the textile jacquard detection data, wherein the first textile jacquard defect information comprises a plurality of textile jacquard defect positioning objects with first defect type attributes;
Generating second textile jacquard defect information through a second textile jacquard detection model according to the textile jacquard detection data, wherein the second textile jacquard defect information comprises a plurality of textile jacquard defect positioning objects with second defect type attributes, which are in one-to-one correspondence with the plurality of textile jacquard defect positioning objects with first defect type attributes, and the second defect type attributes are higher than the first defect type attributes;
Generating third weaving jacquard defect information through the first weaving jacquard defect information and the second weaving jacquard defect information, wherein the third weaving jacquard defect information comprises a plurality of weaving jacquard defect positioning objects with target defect type attributes, and the plurality of weaving jacquard defect positioning objects with target defect type attributes are in one-to-one correspondence with the plurality of weaving jacquard defect positioning objects contained in the first weaving jacquard defect information or the second weaving jacquard defect information.
In one possible example, the step of generating third textile jacquard defect information by the first textile jacquard defect information and the second textile jacquard defect information includes:
Establishing the weaving jacquard defect distribution of the first weaving jacquard defect information and/or the second weaving jacquard defect information;
And determining defect change information of the first textile jacquard defect information and defect change information of the second textile jacquard defect information according to the textile jacquard defect distribution and the target defect type attribute, and calculating the first textile jacquard defect information and the second textile jacquard defect information according to the defect change information of the first textile jacquard defect information and the defect change information of the second textile jacquard defect information to generate third textile jacquard defect information.
In one possible example, the step of establishing a textile jacquard defect distribution of the first textile jacquard defect information and/or the second textile jacquard defect information comprises:
Predicting the distribution of the textile jacquard defects of the first textile jacquard defect information and/or the second textile jacquard defect information through defect positioning points and defect positioning depths of the first textile jacquard defect information and/or the second textile jacquard defect information; or alternatively
And determining the weaving jacquard defect distribution of the first weaving jacquard defect information and/or the second weaving jacquard defect information according to the first weaving jacquard defect information and the second weaving jacquard defect information.
In one possible example, the step of determining a textile jacquard defect distribution of the first textile jacquard defect information and/or the second textile jacquard defect information according to the first textile jacquard defect information and the second textile jacquard defect information includes:
Acquiring the related textile jacquard defect positioning objects of the first textile jacquard defect information and the second textile jacquard defect information;
And estimating the weaving jacquard defect distribution of the first weaving jacquard defect information and/or the second weaving jacquard defect information through the related weaving jacquard defect positioning object.
In a possible example, the step of estimating the distribution of the weaving pattern defects of the first weaving pattern defect information and/or the second weaving pattern defect information by the associated weaving pattern defect localization object includes:
Constructing a long-acting textile jacquard defect positioning object as a reference textile jacquard defect positioning object;
estimating the distribution of the weaving jacquard defects of the first weaving jacquard defect information and/or the second weaving jacquard defect information according to the reference weaving jacquard defect positioning object and the related weaving jacquard defect positioning object.
According to another aspect of an embodiment of the present invention, there is provided a deep learning-based textile jacquard detection system applied to a server in communication with a textile jacquard detection device, the system comprising:
The acquisition module is used for acquiring textile jacquard detection data and generating first textile jacquard defect information through a first textile jacquard detection model according to the textile jacquard detection data, wherein the first textile jacquard defect information comprises a plurality of textile jacquard defect positioning objects with first defect type attributes;
The first generation module is used for generating second textile jacquard defect information through a second textile jacquard detection model according to the textile jacquard detection data, wherein the second textile jacquard defect information comprises a plurality of textile jacquard defect positioning objects with second defect type attributes, which are in one-to-one correspondence with the plurality of textile jacquard defect positioning objects with first defect type attributes, and the second defect type attributes are higher than the first defect type attributes;
the second generation module is used for generating third textile jacquard defect information through the first textile jacquard defect information and the second textile jacquard defect information, wherein the third textile jacquard defect information comprises a plurality of textile jacquard defect positioning objects with target defect type attributes, and the plurality of textile jacquard defect positioning objects with target defect type attributes are in one-to-one correspondence with the plurality of textile jacquard defect positioning objects contained in the first textile jacquard defect information or the second textile jacquard defect information.
According to another aspect of the embodiments of the present invention, there is provided a readable storage medium having stored thereon a computer program which, when executed by a processor, can perform the steps of the deep learning based textile jacquard detection method described above.
Compared with the prior art, the deep learning-based textile jacquard detection method and system provided by the embodiment of the invention are characterized in that the textile jacquard detection data are acquired, the first textile jacquard defect information of a plurality of textile jacquard defect positioning objects with first defect type attributes is generated through a first textile jacquard detection model according to the textile jacquard detection data, and then the second textile jacquard defect information comprising a plurality of textile jacquard defect positioning objects with second defect type attributes, which are in one-to-one correspondence with the plurality of textile jacquard defect positioning objects with first defect type attributes, is generated through a second textile jacquard detection model according to the textile jacquard detection data, so that the third textile jacquard defect information comprising a plurality of textile jacquard defect positioning objects with target defect type attributes is generated through the first textile jacquard defect information and the second textile jacquard defect information. Therefore, the efficiency of the textile jacquard detection process can be improved, and various defect conditions can be accurately and automatically distinguished.
The foregoing objects, features and advantages of embodiments of the invention will be more readily apparent from the following detailed description of the embodiments taken in conjunction with the accompanying drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows a schematic diagram of components of a server provided by an embodiment of the present invention;
Fig. 2 shows a schematic flow chart of a textile jacquard detection method based on deep learning according to an embodiment of the invention;
Fig. 3 shows a functional block diagram of a deep learning-based textile jacquard detection system according to an embodiment of the invention.
Detailed Description
In order to enable those skilled in the art to better understand the present invention, a technical solution of the present embodiment of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiment of the present invention, and it is apparent that the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are intended to be within the scope of the present disclosure.
The terms first, second, third and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented, for example, in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 shows an exemplary component diagram of a server 100. The server 100 may include one or more processors 104, such as one or more Central Processing Units (CPUs), each of which may implement one or more hardware threads. The server 100 may also include any storage medium 106 for storing any kind of information such as code, settings, data, etc. For example, and without limitation, storage medium 106 may include any one or more of the following combinations: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any storage medium may store information using any technique. Further, any storage medium may provide volatile or non-volatile retention of information. Further, any storage medium may represent fixed or removable components of server 100. In one case, the server 100 may perform any of the operations of the associated instructions when the processor 104 executes the associated instructions stored in any storage medium or combination of storage media. The server 100 also includes one or more drive units 108, such as a hard disk drive unit, an optical disk drive unit, etc., for interacting with any storage media.
The server 100 also includes input/output 110 (I/O) for receiving various inputs (via input unit 112) and for providing various outputs (via output unit 114). One particular output mechanism may include a presentation device 116 and an associated Graphical User Interface (GUI) 118. The server 100 may also include one or more network interfaces 120 for exchanging data with other devices via one or more communication units 122. One or more communication buses 124 couple the components described above together.
The communication unit 122 may be implemented in any manner, for example, via a local area network, a wide area network (e.g., the internet), a point-to-point connection, etc., or any combination thereof. The communication unit 122 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers 100, etc., governed by any protocol or combination of protocols.
Fig. 2 is a schematic flow chart of a deep learning-based textile jacquard detection method according to an embodiment of the present invention, which may be executed by the server 100 shown in fig. 1, and detailed steps of the deep learning-based textile jacquard detection method are described as follows.
Step S110, obtaining textile jacquard detection data, and generating first textile jacquard defect information through a first textile jacquard detection model according to the textile jacquard detection data, wherein the first textile jacquard defect information comprises a plurality of textile jacquard defect positioning objects with first defect type attributes.
Step S120, generating second textile jacquard defect information according to the textile jacquard detection data through a second textile jacquard detection model, where the second textile jacquard defect information includes a plurality of textile jacquard defect positioning objects having second defect type attributes corresponding to the plurality of textile jacquard defect positioning objects having first defect type attributes one by one, and the second defect type attributes are higher than the first defect type attributes.
Step S130, generating third weaving jacquard defect information through the first weaving jacquard defect information and the second weaving jacquard defect information, wherein the third weaving jacquard defect information comprises a plurality of weaving jacquard defect positioning objects with target defect type attributes, and the plurality of weaving jacquard defect positioning objects with target defect type attributes are in one-to-one correspondence with the plurality of weaving jacquard defect positioning objects contained in the first weaving jacquard defect information or the second weaving jacquard defect information.
Based on the above steps, the present embodiment generates first textile jacquard defect information of a plurality of textile jacquard defect positioning objects having a first defect type attribute by acquiring textile jacquard detection data and passing through a first textile jacquard detection model according to the textile jacquard detection data, and then generates second textile jacquard defect information including a plurality of textile jacquard defect positioning objects having a second defect type attribute in one-to-one correspondence with the plurality of textile jacquard defect positioning objects having a first defect type attribute according to the textile jacquard detection data by passing through a second textile jacquard detection model, thereby generating third textile jacquard defect information including a plurality of textile jacquard defect positioning objects having a target defect type attribute by passing through the first textile jacquard defect information and the second textile jacquard defect information. Therefore, the efficiency of the textile jacquard detection process can be improved, and various defect conditions can be accurately and automatically distinguished.
In a possible example, for step S130, the present embodiment may establish a distribution of the first and/or second textile jacquard defect information, and then determine, according to the distribution of the first and second textile jacquard defect information and the target defect type attribute, defect variation information of the first and second textile jacquard defect information, where the first and second textile jacquard defect information are calculated according to the defect variation information of the first and second textile jacquard defect information to generate the third textile jacquard defect information.
In one possible example, in the process of establishing the weaving pattern defect distribution of the first weaving pattern defect information and/or the second weaving pattern defect information, the weaving pattern defect distribution of the first weaving pattern defect information and/or the second weaving pattern defect information can be predicted according to the defect positioning points and the defect positioning depths of the first weaving pattern defect information and/or the second weaving pattern defect information. Or determining the first textile jacquard defect information and/or the textile jacquard defect distribution of the second textile jacquard defect information according to the first textile jacquard defect information and the second textile jacquard defect information.
In a possible example, in determining the distribution of the weaving jacquard defects of the first weaving jacquard defect information and/or the second weaving jacquard defect information according to the first weaving jacquard defect information and the second weaving jacquard defect information, the relevant weaving jacquard defect positioning object of the first weaving jacquard defect information and the second weaving jacquard defect information can be obtained, and then the distribution of the weaving jacquard defects of the first weaving jacquard defect information and/or the second weaving jacquard defect information is estimated through the relevant weaving jacquard defect positioning object.
In a possible example, in the process of estimating the distribution of the weaving jacquard defects of the first weaving jacquard defect information and/or the second weaving jacquard defect information through the relevant weaving jacquard defect positioning object, a long-acting weaving jacquard defect positioning object can be specifically constructed as a reference weaving jacquard defect positioning object, and the distribution of the weaving jacquard defects of the first weaving jacquard defect information and/or the second weaving jacquard defect information can be estimated according to the reference weaving jacquard defect positioning object and the relevant weaving jacquard defect positioning object.
Fig. 3 shows a functional block diagram of a deep learning-based textile jacquard detection system 200 according to an embodiment of the present invention, where functions implemented by the deep learning-based textile jacquard detection system 200 may correspond to steps performed by the above-described method. The deep learning-based textile jacquard detection system 200 may be understood as the above-mentioned server 100, or a processor of the server 100, or may be understood as a component which is independent from the above-mentioned server 100 or a processor and performs the functions of the present invention under the control of the server 100, as shown in fig. 3, and the functions of the functional modules of the deep learning-based textile jacquard detection system 200 will be described in detail.
The obtaining module 210 is configured to obtain textile jacquard detection data, and generate first textile jacquard defect information according to the textile jacquard detection data through a first textile jacquard detection model, where the first textile jacquard defect information includes a plurality of textile jacquard defect positioning objects having a first defect type attribute.
The first generating module 220 is configured to generate second textile jacquard defect information according to the textile jacquard detection data through a second textile jacquard detection model, where the second textile jacquard defect information includes a plurality of textile jacquard defect positioning objects having second defect type attributes corresponding to the plurality of textile jacquard defect positioning objects having first defect type attributes one to one, and the second defect type attributes are higher than the first defect type attributes.
The second generating module 230 is configured to generate third textile jacquard defect information according to the first textile jacquard defect information and the second textile jacquard defect information, where the third textile jacquard defect information includes a plurality of textile jacquard defect positioning objects having a target defect type attribute, and the plurality of textile jacquard defect positioning objects having the target defect type attribute are in one-to-one correspondence with the plurality of textile jacquard defect positioning objects included in the first textile jacquard defect information or the second textile jacquard defect information.
In one possible example, the second generating module 230 generates the third textile jacquard defect information by:
Establishing the weaving jacquard defect distribution of the first weaving jacquard defect information and/or the second weaving jacquard defect information;
And determining defect change information of the first textile jacquard defect information and defect change information of the second textile jacquard defect information according to the textile jacquard defect distribution and the target defect type attribute, and calculating the first textile jacquard defect information and the second textile jacquard defect information according to the defect change information of the first textile jacquard defect information and the defect change information of the second textile jacquard defect information to generate third textile jacquard defect information.
In one possible example, the second generation module 230 establishes the textile jacquard defect distribution of the first textile jacquard defect information and/or the second textile jacquard defect information by:
Predicting the distribution of the textile jacquard defects of the first textile jacquard defect information and/or the second textile jacquard defect information through defect positioning points and defect positioning depths of the first textile jacquard defect information and/or the second textile jacquard defect information; or alternatively
And determining the weaving jacquard defect distribution of the first weaving jacquard defect information and/or the second weaving jacquard defect information according to the first weaving jacquard defect information and the second weaving jacquard defect information.
In one possible example, the second generating module 230 determines the first and/or second Jacquard defect information by:
Acquiring the related textile jacquard defect positioning objects of the first textile jacquard defect information and the second textile jacquard defect information;
And estimating the weaving jacquard defect distribution of the first weaving jacquard defect information and/or the second weaving jacquard defect information through the related weaving jacquard defect positioning object.
In one possible example, the second generation module 230 estimates the textile jacquard defect distribution of the first textile jacquard defect information and/or the second textile jacquard defect information by:
Constructing a long-acting textile jacquard defect positioning object as a reference textile jacquard defect positioning object;
estimating the distribution of the weaving jacquard defects of the first weaving jacquard defect information and/or the second weaving jacquard defect information according to the reference weaving jacquard defect positioning object and the related weaving jacquard defect positioning object.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other manners. The above-described apparatus and method embodiments are merely illustrative, for example, flow diagrams and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present invention may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
Alternatively, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present invention, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk Solid STATE DISK (SSD)), etc.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any accompanying drawings in the claims should not be taken as limiting the claim concerned.
Claims (2)
1. A textile jacquard detection method based on deep learning, which is characterized by being applied to a server, wherein the server is in communication connection with a textile jacquard detection device, and the method comprises the following steps:
obtaining textile jacquard detection data, and generating first textile jacquard defect information through a first textile jacquard detection model according to the textile jacquard detection data, wherein the first textile jacquard defect information comprises a plurality of textile jacquard defect positioning objects with first defect type attributes;
Generating second textile jacquard defect information through a second textile jacquard detection model according to the textile jacquard detection data, wherein the second textile jacquard defect information comprises a plurality of textile jacquard defect positioning objects with second defect type attributes, which are in one-to-one correspondence with the plurality of textile jacquard defect positioning objects with first defect type attributes, and the second defect type attributes are higher than the first defect type attributes;
Generating third weaving jacquard defect information through the first weaving jacquard defect information and the second weaving jacquard defect information, wherein the third weaving jacquard defect information comprises a plurality of weaving jacquard defect positioning objects with target defect type attributes, and the plurality of weaving jacquard defect positioning objects with target defect type attributes are in one-to-one correspondence with the plurality of weaving jacquard defect positioning objects contained in the first weaving jacquard defect information or the second weaving jacquard defect information;
the step of generating third textile jacquard defect information by the first textile jacquard defect information and the second textile jacquard defect information comprises the following steps:
Establishing the weaving jacquard defect distribution of the first weaving jacquard defect information and/or the second weaving jacquard defect information;
Determining defect change information of the first textile jacquard defect information and defect change information of the second textile jacquard defect information according to the textile jacquard defect distribution and the target defect type attribute, and calculating the first textile jacquard defect information and the second textile jacquard defect information according to the defect change information of the first textile jacquard defect information and the defect change information of the second textile jacquard defect information to generate third textile jacquard defect information;
The step of establishing the textile jacquard defect distribution of the first textile jacquard defect information and/or the second textile jacquard defect information comprises the following steps:
Predicting the distribution of the textile jacquard defects of the first textile jacquard defect information and/or the second textile jacquard defect information through defect positioning points and defect positioning depths of the first textile jacquard defect information and/or the second textile jacquard defect information; or alternatively
Determining the weaving jacquard defect distribution of the first weaving jacquard defect information and/or the second weaving jacquard defect information according to the first weaving jacquard defect information and the second weaving jacquard defect information;
the step of determining the first textile jacquard defect information and/or the second textile jacquard defect information according to the first textile jacquard defect information and the second textile jacquard defect information comprises the following steps:
Acquiring the related textile jacquard defect positioning objects of the first textile jacquard defect information and the second textile jacquard defect information;
Estimating the weaving jacquard defect distribution of the first weaving jacquard defect information and/or the second weaving jacquard defect information through the related weaving jacquard defect positioning object;
the step of estimating the distribution of the weaving jacquard defects of the first weaving jacquard defect information and/or the second weaving jacquard defect information through the related weaving jacquard defect positioning object comprises the following steps:
Constructing a long-acting textile jacquard defect positioning object as a reference textile jacquard defect positioning object;
estimating the distribution of the weaving jacquard defects of the first weaving jacquard defect information and/or the second weaving jacquard defect information according to the reference weaving jacquard defect positioning object and the related weaving jacquard defect positioning object.
2. A deep learning-based textile jacquard detection system, characterized by being applied to a server, which is in communication connection with a textile jacquard detection device, the system comprising:
The acquisition module is used for acquiring textile jacquard detection data and generating first textile jacquard defect information through a first textile jacquard detection model according to the textile jacquard detection data, wherein the first textile jacquard defect information comprises a plurality of textile jacquard defect positioning objects with first defect type attributes;
The first generation module is used for generating second textile jacquard defect information through a second textile jacquard detection model according to the textile jacquard detection data, wherein the second textile jacquard defect information comprises a plurality of textile jacquard defect positioning objects with second defect type attributes, which are in one-to-one correspondence with the plurality of textile jacquard defect positioning objects with first defect type attributes, and the second defect type attributes are higher than the first defect type attributes;
The second generation module is used for generating third textile jacquard defect information through the first textile jacquard defect information and the second textile jacquard defect information, wherein the third textile jacquard defect information comprises a plurality of textile jacquard defect positioning objects with target defect type attributes, and the plurality of textile jacquard defect positioning objects with target defect type attributes are in one-to-one correspondence with the plurality of textile jacquard defect positioning objects contained in the first textile jacquard defect information or the second textile jacquard defect information;
The second generation module generates third textile jacquard defect information by:
Establishing the weaving jacquard defect distribution of the first weaving jacquard defect information and/or the second weaving jacquard defect information;
Determining defect change information of the first textile jacquard defect information and defect change information of the second textile jacquard defect information according to the textile jacquard defect distribution and the target defect type attribute, and calculating the first textile jacquard defect information and the second textile jacquard defect information according to the defect change information of the first textile jacquard defect information and the defect change information of the second textile jacquard defect information to generate third textile jacquard defect information;
The second generation module establishes a textile jacquard defect distribution of the first textile jacquard defect information and/or the second textile jacquard defect information by:
Predicting the distribution of the textile jacquard defects of the first textile jacquard defect information and/or the second textile jacquard defect information through defect positioning points and defect positioning depths of the first textile jacquard defect information and/or the second textile jacquard defect information; or alternatively
Determining the weaving jacquard defect distribution of the first weaving jacquard defect information and/or the second weaving jacquard defect information according to the first weaving jacquard defect information and the second weaving jacquard defect information;
The second generation module determines the first and/or second jacquard weave defect information by:
Acquiring the related textile jacquard defect positioning objects of the first textile jacquard defect information and the second textile jacquard defect information;
Estimating the weaving jacquard defect distribution of the first weaving jacquard defect information and/or the second weaving jacquard defect information through the related weaving jacquard defect positioning object;
the second generation module estimates the textile jacquard defect distribution of the first textile jacquard defect information and/or the second textile jacquard defect information by:
Constructing a long-acting textile jacquard defect positioning object as a reference textile jacquard defect positioning object;
estimating the distribution of the weaving jacquard defects of the first weaving jacquard defect information and/or the second weaving jacquard defect information according to the reference weaving jacquard defect positioning object and the related weaving jacquard defect positioning object.
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