CN113313106A - Feeding deviation rectifying method and device, computer equipment and storage medium - Google Patents
Feeding deviation rectifying method and device, computer equipment and storage medium Download PDFInfo
- Publication number
- CN113313106A CN113313106A CN202110402335.6A CN202110402335A CN113313106A CN 113313106 A CN113313106 A CN 113313106A CN 202110402335 A CN202110402335 A CN 202110402335A CN 113313106 A CN113313106 A CN 113313106A
- Authority
- CN
- China
- Prior art keywords
- deviation
- feeding
- data
- target material
- positioning data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 50
- 238000012545 processing Methods 0.000 claims abstract description 161
- 239000013077 target material Substances 0.000 claims abstract description 116
- 239000000463 material Substances 0.000 claims description 56
- 238000012937 correction Methods 0.000 claims description 11
- 238000012549 training Methods 0.000 claims description 9
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 238000004519 manufacturing process Methods 0.000 abstract description 14
- 230000005540 biological transmission Effects 0.000 abstract description 6
- 238000013473 artificial intelligence Methods 0.000 abstract description 2
- 238000005516 engineering process Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 230000003252 repetitive effect Effects 0.000 description 4
- 230000003287 optical effect Effects 0.000 description 3
- 238000013459 approach Methods 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000012790 confirmation Methods 0.000 description 2
- 238000013144 data compression Methods 0.000 description 2
- 239000008187 granular material Substances 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 238000003062 neural network model Methods 0.000 description 2
- 239000000843 powder Substances 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 230000010365 information processing Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000005055 memory storage Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/10—Office automation; Time management
- G06Q10/103—Workflow collaboration or project management
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/04—Manufacturing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
- G06V10/751—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/06—Recognition of objects for industrial automation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The embodiment of the application belongs to the technical field of automation in artificial intelligence, and relates to a feeding deviation rectifying method and device applied to automatic processing equipment, computer equipment and a storage medium. In addition, the application also relates to a block chain technology, and the initial positioning data and the deviation data of each transmission can be stored in the block chain. When the feeding device carries out continuous feeding, the position relation between the target material and the breadth of the processing equipment is obtained in a photographing and drawing mode, the feeding direction, the length and the like of the feeding device are dynamically adjusted based on the position relation, the subsequent target material to be processed can be perfectly matched with the position of the breadth for conveying, the processing continuity is guaranteed, manual participation is reduced, and the production efficiency is improved.
Description
Technical Field
The application relates to the technical field of automation in artificial intelligence, in particular to a feeding deviation rectifying method and device applied to automatic processing equipment, computer equipment and a storage medium.
Background
In the actual laser processing production, a machine manufacturer strictly controls the breadth size of a machine table when producing the machine. The production raw material is often a roll of cloth, and in automatic production, the phenomenon of more or less feeding is often caused due to feeding slipping or stretching. In this case, the processing can result in the processing pattern exceeding the machine width and the production is terminated, requiring manual intervention to readjust the material and to start the processing. Therefore, the traditional feeding deviation rectifying method needs manual work to adjust the material again and then starts processing, and therefore the problem of low production efficiency exists.
Disclosure of Invention
The embodiment of the application aims to provide a feeding deviation rectifying method and device, computer equipment and a storage medium applied to automatic processing equipment so as to solve the problem that the traditional feeding deviation rectifying method is low in production efficiency.
In order to solve the above technical problem, an embodiment of the present application provides a feeding deviation rectifying method applied to an automatic processing device, which adopts the following technical scheme:
when feeding equipment conveys a target material to a processing area according to a preset length, photographing the target material to obtain an initial material picture;
carrying out image recognition operation on the initial material picture to obtain initial positioning data;
judging whether the initial positioning data exceeds the breadth of the processing equipment or not;
if the initial positioning data do not exceed the breadth of the processing equipment, starting the processing equipment to perform material processing operation on the target material;
if the initial positioning data exceed the breadth of the processing equipment, calculating deviation data of the initial positioning data exceeding the breadth, performing deviation rectifying operation on the feeding position of the feeding equipment based on the deviation data, and starting the processing equipment to perform material processing operation on the target material until the initial positioning data do not exceed the breadth.
In order to solve the above technical problem, an embodiment of the present application further provides a feeding deviation correcting device applied to an automatic processing device, which adopts the following technical scheme:
the material photographing module is used for photographing a target material to obtain an initial material picture after the feeding equipment conveys the target material to a processing area according to a preset length;
the image identification module is used for carrying out image identification operation on the initial material picture to obtain initial positioning data;
the positioning judgment module is used for judging whether the initial positioning data exceeds the breadth of the processing equipment or not;
the positioning confirming module is used for starting the processing equipment to carry out material processing operation on the target material if the initial positioning data does not exceed the breadth of the processing equipment;
and the feeding deviation rectifying module is used for calculating deviation data of the initial positioning data exceeding the breadth if the initial positioning data exceeds the breadth of the processing equipment, and rectifying the deviation operation based on the deviation data of the feeding position of the feeding equipment and the current position of the target material until the positioning data of the target material does not exceed the breadth, and starting the processing equipment to carry out material processing operation on the target material.
In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:
the method comprises a memory and a processor, wherein computer readable instructions are stored in the memory, and the processor executes the computer readable instructions to realize the steps of the feeding deviation rectifying method applied to the automatic processing equipment.
In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:
the computer readable storage medium has stored thereon computer readable instructions which, when executed by a processor, implement the steps of the feed correction method as described above for use with an automated processing equipment.
Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:
the application provides a feeding deviation rectifying method applied to automatic processing equipment, which comprises the following steps: when feeding equipment conveys a target material to a processing area according to a preset length, photographing the target material to obtain an initial material picture; carrying out image recognition operation on the initial material picture to obtain initial positioning data; judging whether the initial positioning data exceeds the breadth of the processing equipment or not; if the initial positioning data do not exceed the breadth of the processing equipment, starting the processing equipment to perform material processing operation on the target material; if the initial positioning data exceed the breadth of the processing equipment, calculating deviation data of the initial positioning data exceeding the breadth, performing deviation rectifying operation on the feeding position of the feeding equipment and the current position of the target material based on the deviation data until the positioning data of the target material do not exceed the breadth, and starting the processing equipment to perform material processing operation on the target material. When the feeding device carries out continuous feeding, the position relation between the target material and the breadth of the processing equipment is obtained in a photographing and drawing mode, the feeding direction, the length and the like of the feeding device are dynamically adjusted based on the position relation, the subsequent target material to be processed can be perfectly matched with the position of the breadth for conveying, the processing continuity is guaranteed, manual participation is reduced, and the production efficiency is improved.
Drawings
In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
FIG. 1 is a flowchart illustrating a feeding deviation rectifying method applied to an automatic processing device according to an embodiment of the present disclosure;
FIG. 2 is a flowchart illustrating an implementation of a method for predicting biased data according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a feeding deviation rectifying device applied to an automatic processing device according to a second embodiment of the present application:
FIG. 4 is a schematic structural diagram of an apparatus for predicting deviation data according to a second embodiment of the present disclosure;
FIG. 5 is a schematic block diagram of one embodiment of a computer device according to the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
Fig. 1 shows a flowchart of an implementation of a feeding deviation rectifying method applied to an automatic processing device according to an embodiment of the present application, and only a part related to the present application is shown for convenience of description.
The feeding deviation rectifying method applied to the automatic processing equipment comprises the following steps of:
in step S101, after the feeding device conveys the target material to the processing area according to the preset length, the target material is photographed to obtain an initial material picture.
In the embodiment of the application, the feeding device is mainly used for conveying the target materials to be processed, which are commonly found in the automatic, numerical control and precise conveying of materials such as granules, powder, flaky materials, strips and the like. The transmission equipment is indispensable to both light industry and heavy industry.
In the embodiment of the present application, the processing area is provided with a shooting device for shooting a picture of the processing area, the shooting device may be a camera, a monitoring camera, or the like, and when the shooting device receives a feeding stop signal (indicating that the target material has completed one feeding), the shooting device shoots the processing area, so as to obtain a picture of the target material related to the width of the processing area, that is, the above-mentioned initial material picture.
In step S102, an image recognition operation is performed on the initial material picture to obtain initial positioning data.
In the embodiment of the present application, the image recognition operation, which obtains the positioning information of the target material in the processing area, may be implemented by a conventional shape matching technique (a technique common to the image field).
In step S103, it is determined whether the initial positioning data exceeds the width of the processing apparatus.
In the embodiment of the application, because the target material to be processed can not exceed the breadth of the machine table, the initial positioning data can not exceed the breadth of the processing equipment under strict control.
In step S104, if the initial positioning data does not exceed the width of the processing equipment, the processing equipment is started to perform a material processing operation on the target material.
In the embodiment of the application, when the initial positioning data does not exceed the breadth of the processing equipment, it is indicated that the target material to be processed does not exceed the breadth of the machine, and the next operation such as laser processing can be performed.
In step S105, if the initial positioning data exceeds the breadth of the processing device, calculating deviation data of the initial positioning data exceeding the breadth, and performing deviation rectification operation on the feeding position of the feeding device and the current position of the target material based on the deviation data until the positioning data of the target material does not exceed the breadth, and starting the processing device to perform material processing operation on the target material.
In the embodiment of the application, when the initial positioning data exceeds the breadth of the processing equipment, it is indicated that the target material to be processed exceeds the breadth of the machine, the next operation such as laser processing cannot be performed, and the deviation correction needs to be performed on the feeding position of the feeding equipment and the current position of the target material, so that the position of the target material which is conveyed by the subsequent material to be processed is corrected, and the processing continuity is further ensured.
In the embodiment of the application, after the target material is conveyed to a processing area, after the positioning data (curve track points) of the target material are obtained by a traditional template matching method, the external rectangle of the data to be processed is calculated, whether the whole external rectangle exceeds the breadth of a machine or not is judged, if the external rectangle exceeds the breadth, the exceeding length is calculated, then the length is fed back to a feeding shaft according to the calculated length, the adjustment is carried out forwards or backwards, the picture is taken after the adjustment, and the curve data are obtained after the matching. And calculating a circumscribed rectangle. If the breadth is exceeded, the adjustment is continued. And if the width is not exceeded, the material processing head starts to process.
In an embodiment of the present application, a feeding deviation rectifying method applied to an automatic processing device is provided, including: when feeding equipment conveys a target material to a processing area according to a preset length, photographing the target material to obtain an initial material picture; carrying out image recognition operation on the initial material picture to obtain initial positioning data; judging whether the initial positioning data exceeds the breadth of the processing equipment or not; if the initial positioning data do not exceed the breadth of the processing equipment, starting the processing equipment to perform material processing operation on the target material; if the initial positioning data exceed the breadth of the processing equipment, calculating deviation data of the initial positioning data exceeding the breadth, and performing deviation rectifying operation on the feeding position of the feeding equipment based on the deviation data until the initial positioning data do not exceed the breadth, and starting the processing equipment to perform material processing operation on the target material. When the feeding device carries out continuous feeding, the position relation between the target material and the breadth of the processing equipment is obtained in a photographing and drawing mode, the feeding direction, the length and the like of the feeding device are dynamically adjusted based on the position relation, the subsequent target material to be processed can be perfectly matched with the position of the breadth for conveying, the processing continuity is guaranteed, manual participation is reduced, and the production efficiency is improved.
With continued reference to fig. 2, a flowchart of an implementation of the method for predicting the bias data provided in the embodiment of the present application is shown, and for convenience of explanation, only the portion related to the present application is shown.
In some optional implementations of this embodiment, after step S105, the method further includes:
in step S201, after the feeding device meets the preset number of times of conveying, initial positioning data and deviation data of each conveying are collected in historical conveying data.
In the embodiment of the application, after the feeding device transmits the target material each time, the complex operations such as photographing operation, image recognition operation, breadth judgment and the like are performed, so that the situation of too much repetitive work exists in the original scheme, and unnecessary energy is wasted, and therefore, a technical scheme for the repetitive work needs to be designed, so that the method for predicting the deviation data provided by the embodiment of the application is extended.
In step S202, the initial positioning data and the bias data are input to the prediction model to be trained to perform model training operation, so as to obtain a trained prediction model.
In step S203, after the feeding device delivers the target material to the processing area, current positioning data corresponding to the current target material is collected.
In step S204, the current positioning data is input to the trained prediction model to perform a deviation prediction operation, so as to obtain predicted deviation data.
In step S205, deviation correction is performed on the feeding position of the feeding device and the current position of the target material based on the predicted deviation data.
In the embodiment of the application, the prediction model can learn historical deviation rectifying operation data, so that the initial positioning data and the deviation data in the historical deviation rectifying operation data learn implicit association characteristics, and the data needing deviation rectifying at present is predicted based on the association characteristics and the current positioning data. After the feeding equipment transmits the target material with the same specification for a certain number of times, the deviation rectifying operation on the feeding position of the feeding equipment and the current position of the target material has certain regularity, and the next deviation rectifying operation can be predicted according to the regularity, so that the complex operations of photographing operation, image recognition operation, breadth judgment and the like are effectively reduced, and certain energy is saved.
In some optional implementations of this embodiment, the prediction model is a BP neural network prediction model.
In the embodiment of the application, a BP Network (Back-ProPagation Network), also called a Back ProPagation neural Network, continuously corrects a Network weight and a threshold value to enable an error function to descend along a negative gradient direction through training of sample data, and approaches to expected output. The neural network model is widely applied and is mainly used for function approximation, model identification and classification, data compression, time series prediction and the like.
In summary, the present application provides a feeding deviation rectifying method applied to an automatic processing device, including: when the feeding equipment conveys a target material to a processing area according to a preset length, photographing the target material to obtain an initial material picture; carrying out image recognition operation on the initial material picture to obtain initial positioning data; judging whether the initial positioning data exceeds the breadth of the processing equipment or not; if the initial positioning data do not exceed the breadth of the processing equipment, starting the processing equipment to perform material processing operation on the target material; and if the initial positioning data exceeds the breadth of the processing equipment, calculating deviation data of the initial positioning data exceeding the breadth, performing deviation rectifying operation on the feeding position of the feeding equipment and the current position of the target material based on the deviation data until the positioning data of the target material does not exceed the breadth, and starting the processing equipment to perform material processing operation on the target material. When the feeding device carries out continuous feeding, the position relation between the target material and the breadth of the processing equipment is obtained in a photographing and drawing mode, the feeding direction, the length and the like of the feeding device are dynamically adjusted based on the position relation, the subsequent target material to be processed can be perfectly matched with the position of the breadth for conveying, the processing continuity is guaranteed, manual participation is reduced, and the production efficiency is improved. Meanwhile, the prediction model can learn historical deviation rectifying operation data, so that the initial positioning data and the deviation data in the historical deviation rectifying operation data learn implicit association characteristics, and the data needing deviation rectifying at present are predicted based on the association characteristics and the current positioning data. After the feeding equipment transmits the target material with the same specification for a certain number of times, the deviation rectifying operation on the feeding position of the feeding equipment and the current position of the target material has certain regularity, and the next deviation rectifying operation can be predicted according to the regularity, so that the complex operations of photographing operation, image recognition operation, breadth judgment and the like are effectively reduced, and certain energy is saved.
It is emphasized that, in order to further ensure the privacy and security of the initial positioning data and the biased data for each transmission, the initial positioning data and the biased data for each transmission may also be stored in a node of a block chain.
The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
The application is operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet-type devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware associated with computer readable instructions, which can be stored in a computer readable storage medium, and when executed, can include processes of the embodiments of the methods described above. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
Example two
Referring further to fig. 3, as an implementation of the method shown in fig. 1, the present application provides an embodiment of a feeding deviation rectifying device applied to an automatic processing device, where the embodiment of the device corresponds to the embodiment of the method shown in fig. 1, and the device may be applied to various electronic devices.
As shown in fig. 3, the feeding deviation rectifying device 100 applied to the automatic processing equipment of the embodiment includes: the device comprises a material photographing module 101, an image recognition module 102, a positioning judgment module 103, a positioning confirmation module 104 and a feeding deviation rectifying module 105. Wherein:
the material photographing module 101 is used for photographing a target material to obtain an initial material picture after the feeding equipment conveys the target material to a processing area according to a preset length;
the image identification module 102 is configured to perform image identification operation on the initial material picture to obtain initial positioning data;
the positioning judgment module 103 is used for judging whether the initial positioning data exceeds the breadth of the processing equipment;
a positioning confirmation module 104, configured to start the processing equipment to perform a material processing operation on the target material if the initial positioning data does not exceed the breadth of the processing equipment;
and the feeding deviation rectifying module 105 is used for calculating deviation data of the initial positioning data exceeding the breadth if the initial positioning data exceeds the breadth of the processing equipment, rectifying deviation operation on the feeding position of the feeding equipment and the current position of the target material based on the deviation data until the positioning data of the target material does not exceed the breadth, and starting the processing equipment to perform material processing operation on the target material.
In the embodiment of the application, the feeding device is mainly used for conveying the target materials to be processed, which are commonly found in the automatic, numerical control and precise conveying of materials such as granules, powder, flaky materials, strips and the like. The transmission equipment is indispensable to both light industry and heavy industry.
In the embodiment of the present application, the processing area is provided with a shooting device for shooting a picture of the processing area, the shooting device may be a camera, a monitoring camera, or the like, and when the shooting device receives a feeding stop signal (indicating that the target material has completed one feeding), the shooting device shoots the processing area, so as to obtain a picture of the target material related to the width of the processing area, that is, the above-mentioned initial material picture.
In the embodiment of the present application, the image recognition operation, which obtains the positioning information of the target material in the processing area, may be implemented by a conventional shape matching technique (a technique common to the image field).
In the embodiment of the application, because the target material to be processed can not exceed the breadth of the machine table, the initial positioning data can not exceed the breadth of the processing equipment under strict control.
In the embodiment of the application, when the initial positioning data does not exceed the breadth of the processing equipment, it is indicated that the target material to be processed does not exceed the breadth of the machine, and the next operation such as laser processing can be performed.
In the embodiment of the application, when the initial positioning data exceeds the breadth of the processing equipment, it is indicated that the target material to be processed exceeds the breadth of the machine, the next operation such as laser processing cannot be performed, and the deviation correction needs to be performed on the feeding position of the feeding equipment and the current position of the target material, so that the position of the target material which is conveyed by the subsequent material to be processed is corrected, and the processing continuity is further ensured.
In the embodiment of the application, after the target material is conveyed to a processing area, after the positioning data (curve track points) of the target material are obtained by a traditional template matching method, the external rectangle of the data to be processed is calculated, whether the whole external rectangle exceeds the breadth of a machine or not is judged, if the external rectangle exceeds the breadth, the exceeding length is calculated, then the length is fed back to a feeding shaft according to the calculated length, the adjustment is carried out forwards or backwards, the picture is taken after the adjustment, and the curve data are obtained after the matching. And calculating a circumscribed rectangle. If the breadth is exceeded, the adjustment is continued. And if the width is not exceeded, the material processing head starts to process.
In the embodiment of the application, a feeding deviation correcting device applied to automatic processing equipment is provided, when feeding is carried out continuously, the position relation of the target material and the breadth of the processing equipment is obtained in a photographing and drawing mode, the feeding direction, the feeding length and the like of the feeding device are dynamically adjusted based on the position relation, the subsequent target material to be processed can perfectly fit with the position conveying of the breadth, the processing continuity is guaranteed, manual participation is reduced, and the production efficiency is improved.
Continuing to refer to fig. 4, a schematic structural diagram of an apparatus for predicting deviation data provided in the second embodiment of the present application is shown, and for convenience of illustration, only the relevant portions of the present application are shown.
In some optional implementations of the present embodiment, the feeding deviation rectifying device 100 applied to the automatic processing equipment further includes: a data acquisition module 106, a model training module 107, a current positioning data acquisition module 108, a deviation prediction module 109, and a deviation correction operation module 110. Wherein:
the data acquisition module 106 is used for acquiring initial positioning data and deviation data of each conveying in historical conveying data after the feeding equipment meets the preset conveying times;
the model training module 107 is configured to input the initial positioning data and the deviation data to a prediction model to be trained to perform model training operation, so as to obtain a trained prediction model;
the current positioning data acquisition module 108 is used for acquiring current positioning data corresponding to the current target material after the feeding equipment conveys the target material to the processing area;
the deviation prediction module 109 is configured to input the current positioning data to a trained prediction model to perform deviation prediction operation, so as to obtain predicted deviation data;
and the deviation rectifying operation module 110 is used for rectifying the feeding position of the feeding equipment and the current position of the target material based on the predicted deviation data.
In the embodiment of the application, after the feeding device transmits the target material each time, the complex operations such as photographing operation, image recognition operation, breadth judgment and the like are performed, so that the situation of too much repetitive work exists in the original scheme, and unnecessary energy is wasted, and therefore, a technical scheme for the repetitive work needs to be designed, so that the method for predicting the deviation data provided by the embodiment of the application is extended.
In the embodiment of the application, the prediction model can learn historical deviation rectifying operation data, so that the initial positioning data and the deviation data in the historical deviation rectifying operation data learn implicit association characteristics, and the data needing deviation rectifying at present is predicted based on the association characteristics and the current positioning data. After the feeding equipment transmits the target material with the same specification for a certain number of times, the deviation rectifying operation on the feeding position of the feeding equipment and the current position of the target material has certain regularity, and the next deviation rectifying operation can be predicted according to the regularity, so that the complex operations of photographing operation, image recognition operation, breadth judgment and the like are effectively reduced, and certain energy is saved.
In some optional implementations of this embodiment, the prediction model is a BP neural network prediction model.
In the embodiment of the application, a BP Network (Back-ProPagation Network), also called a Back ProPagation neural Network, continuously corrects a Network weight and a threshold value to enable an error function to descend along a negative gradient direction through training of sample data, and approaches to expected output. The neural network model is widely applied and is mainly used for function approximation, model identification and classification, data compression, time series prediction and the like.
In summary, the application provides a feeding deviation correcting device applied to automatic processing equipment, when a feeding device carries out continuous feeding, the position relation of a target material and the breadth of the processing equipment is obtained through a photographing and drawing mode, the feeding direction, the length and the like of the feeding device are dynamically adjusted based on the position relation, the subsequent target material to be processed can perfectly fit with the position conveying of the breadth, the processing continuity is guaranteed, manual participation is reduced, and the production efficiency is improved. Meanwhile, the prediction model can learn historical deviation rectifying operation data, so that the initial positioning data and the deviation data in the historical deviation rectifying operation data learn implicit association characteristics, and the data needing deviation rectifying at present are predicted based on the association characteristics and the current positioning data. After the feeding equipment transmits the target material with the same specification for a certain number of times, the deviation rectifying operation on the feeding position of the feeding equipment and the current position of the target material has certain regularity, and the next deviation rectifying operation can be predicted according to the regularity, so that the complex operations of photographing operation, image recognition operation, breadth judgment and the like are effectively reduced, and certain energy is saved.
In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 5, fig. 5 is a block diagram of a basic structure of a computer device according to the present embodiment.
The computer device 200 includes a memory 210, a processor 220, and a network interface 230 communicatively coupled to each other via a system bus. It is noted that only computer device 200 having components 210 and 230 is shown, but it is understood that not all of the illustrated components are required and that more or fewer components may alternatively be implemented. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 210 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the storage 210 may be an internal storage unit of the computer device 200, such as a hard disk or a memory of the computer device 200. In other embodiments, the memory 210 may also be an external storage device of the computer device 200, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the computer device 200. Of course, the memory 210 may also include both internal and external storage devices of the computer device 200. In this embodiment, the memory 210 is generally used for storing an operating system installed in the computer device 200 and various types of application software, such as computer readable instructions for a feeding deviation correction method applied to an automatic processing device. In addition, the memory 210 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 220 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 220 is generally operative to control overall operation of the computer device 200. In this embodiment, the processor 220 is configured to execute the computer readable instructions or the processing data stored in the memory 210, for example, the computer readable instructions of the feeding deviation rectifying method applied to the automatic processing equipment.
The network interface 230 may include a wireless network interface or a wired network interface, and the network interface 230 is generally used to establish a communication connection between the computer device 200 and other electronic devices.
The application provides a computer equipment, when material feeding unit carries out the continuity pay-off, through the mode of taking pictures and gathering the picture obtain the position relation of the breadth of target material and processing equipment to based on this position relation dynamic adjustment material feeding unit's pay-off direction and length etc. make follow-up target material of treating processing can perfectly agree with the position of this breadth and carry, thereby guaranteed the continuity of processing, and reduce artifical participation, improve production efficiency.
The present application provides yet another embodiment that provides a computer-readable storage medium having computer-readable instructions stored thereon that are executable by at least one processor to cause the at least one processor to perform the steps of the feed correction method as described above for an automated processing apparatus.
The computer-readable storage medium provided by the application can be used for acquiring the position relation between a target material and the breadth of processing equipment in a photographing and drawing mode when a feeding device carries out continuous feeding, and dynamically adjusting the feeding direction, the feeding length and the like of the feeding device based on the position relation, so that the subsequent target material to be processed can perfectly fit with the position of the breadth for conveying, the processing continuity is guaranteed, manual participation is reduced, and the production efficiency is improved.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.
Claims (10)
1. A feeding deviation rectifying method applied to automatic processing equipment is characterized by comprising the following steps:
when feeding equipment conveys a target material to a processing area according to a preset length, photographing the target material to obtain an initial material picture;
carrying out image recognition operation on the initial material picture to obtain initial positioning data;
judging whether the initial positioning data exceeds the breadth of the processing equipment or not;
if the initial positioning data do not exceed the breadth of the processing equipment, starting the processing equipment to perform material processing operation on the target material;
if the initial positioning data exceed the breadth of the processing equipment, calculating deviation data of the initial positioning data exceeding the breadth, performing deviation rectifying operation on the feeding position of the feeding equipment and the current position of the target material based on the deviation data until the positioning data of the target material do not exceed the breadth, and starting the processing equipment to perform material processing operation on the target material.
2. The method as claimed in claim 1, wherein after the steps of calculating deviation data of the initial positioning data exceeding the web surface, performing deviation correction operation on the feeding position of the feeding device and the current position of the target material based on the deviation data, and starting the processing device to perform material processing operation on the target material until the positioning data of the target material does not exceed the web surface, the method further comprises:
after the feeding equipment meets the preset conveying times, acquiring initial positioning data and deviation data of each conveying in historical conveying data;
inputting the initial positioning data and the deviation data into a prediction model to be trained to perform model training operation to obtain a trained prediction model;
after the feeding equipment conveys the target material to the processing area, acquiring current positioning data corresponding to the current target material;
inputting the current positioning data into the trained prediction model to perform deviation prediction operation to obtain prediction deviation data;
and performing the deviation rectifying operation on the feeding position of the feeding equipment and the current position of the target material based on the predicted deviation data.
3. The method as claimed in claim 1, wherein the prediction model is a BP neural network prediction model.
4. The method as claimed in claim 1, wherein after the steps of calculating deviation data of the initial positioning data exceeding the web surface, and performing deviation correction operation on the feeding position of the feeding device and the current position of the target material based on the deviation data until the positioning data of the target material does not exceed the web surface, starting the processing device to perform material processing operation on the target material, the method further comprises:
storing the initial positioning data and the deviation data for each delivery into a blockchain.
5. The utility model provides a be applied to pay-off deviation correcting device of automatic processing equipment which characterized in that includes:
the material photographing module is used for photographing a target material to obtain an initial material picture after the feeding equipment conveys the target material to a processing area according to a preset length;
the image identification module is used for carrying out image identification operation on the initial material picture to obtain initial positioning data;
the positioning judgment module is used for judging whether the initial positioning data exceeds the breadth of the processing equipment or not;
the positioning confirming module is used for starting the processing equipment to carry out material processing operation on the target material if the initial positioning data does not exceed the breadth of the processing equipment;
and the feeding deviation rectifying module is used for calculating deviation data of the initial positioning data exceeding the breadth if the initial positioning data exceeds the breadth of the processing equipment, and rectifying the deviation operation based on the deviation data of the feeding position of the feeding equipment and the current position of the target material until the positioning data of the target material does not exceed the breadth, and starting the processing equipment to carry out material processing operation on the target material.
6. The feeding deviation correcting device applied to the automatic processing equipment as claimed in claim 5, wherein the device further comprises:
the data acquisition module is used for acquiring the initial positioning data and the deviation data of each conveying in historical conveying data after the feeding equipment meets the preset conveying times;
the model training module is used for inputting the initial positioning data and the deviation data into a prediction model to be trained to perform model training operation to obtain a trained prediction model;
the current positioning data acquisition module is used for acquiring current positioning data corresponding to the current target material after the feeding equipment conveys the target material to the processing area;
the deviation prediction module is used for inputting the current positioning data into the trained prediction model to perform deviation prediction operation to obtain predicted deviation data;
and the deviation rectifying operation module is used for carrying out deviation rectifying operation on the feeding position of the feeding equipment and the current position of the target material based on the predicted deviation data.
7. The feeding deviation correcting device applied to the automatic processing equipment as claimed in claim 6, wherein the prediction model is a BP neural network prediction model.
8. The feeding deviation correcting device applied to the automatic processing equipment as claimed in claim 5, wherein the device further comprises:
and the block chain storage module is used for storing the initial positioning data and the deviation data which are conveyed each time into a block chain.
9. A computer apparatus comprising a memory having computer readable instructions stored therein and a processor which when executed implements the steps of a feed correction method for an automated processing apparatus as claimed in any one of claims 1 to 4.
10. A computer readable storage medium, wherein computer readable instructions are stored thereon, which when executed by a processor implement the steps of the method of any one of claims 1 to 4 applied to a feed correction of an automated processing equipment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110402335.6A CN113313106A (en) | 2021-04-14 | 2021-04-14 | Feeding deviation rectifying method and device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110402335.6A CN113313106A (en) | 2021-04-14 | 2021-04-14 | Feeding deviation rectifying method and device, computer equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113313106A true CN113313106A (en) | 2021-08-27 |
Family
ID=77372307
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110402335.6A Pending CN113313106A (en) | 2021-04-14 | 2021-04-14 | Feeding deviation rectifying method and device, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113313106A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114226469A (en) * | 2021-11-29 | 2022-03-25 | 首钢集团有限公司 | Slab position measuring device, rolling system, control method, and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108323797A (en) * | 2018-02-09 | 2018-07-27 | 杭州安脉盛智能技术有限公司 | Cigarette Weight Control System based on GPR models starts position predicting method and system |
| CN110385720A (en) * | 2019-07-26 | 2019-10-29 | 南京航空航天大学 | A kind of robot localization error compensating method based on deep neural network |
| CN111097664A (en) * | 2019-12-20 | 2020-05-05 | 广西柳州联耕科技有限公司 | Real-time deviation rectifying method based on robot gluing |
| WO2020196338A1 (en) * | 2019-03-27 | 2020-10-01 | Sony Corporation | Medical arm system, control device, and control method |
| CN112001478A (en) * | 2020-05-11 | 2020-11-27 | 浙江蓝卓工业互联网信息技术有限公司 | Rubber blanking amount prediction method based on machine learning dual model |
| CN112029990A (en) * | 2020-07-16 | 2020-12-04 | 宝钢湛江钢铁有限公司 | Automatic deviation rectifying control method for cold-rolled strip steel continuous annealing heating furnace |
| CN112172319A (en) * | 2020-10-12 | 2021-01-05 | 苏州怡拓生物传感技术有限公司 | Deviation correcting device and method for flat printing of coil stock |
| CN112589232A (en) * | 2020-12-15 | 2021-04-02 | 广东工业大学 | Weld joint tracking method and device based on independent deviation correction type deep learning |
-
2021
- 2021-04-14 CN CN202110402335.6A patent/CN113313106A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108323797A (en) * | 2018-02-09 | 2018-07-27 | 杭州安脉盛智能技术有限公司 | Cigarette Weight Control System based on GPR models starts position predicting method and system |
| WO2020196338A1 (en) * | 2019-03-27 | 2020-10-01 | Sony Corporation | Medical arm system, control device, and control method |
| CN110385720A (en) * | 2019-07-26 | 2019-10-29 | 南京航空航天大学 | A kind of robot localization error compensating method based on deep neural network |
| CN111097664A (en) * | 2019-12-20 | 2020-05-05 | 广西柳州联耕科技有限公司 | Real-time deviation rectifying method based on robot gluing |
| CN112001478A (en) * | 2020-05-11 | 2020-11-27 | 浙江蓝卓工业互联网信息技术有限公司 | Rubber blanking amount prediction method based on machine learning dual model |
| CN112029990A (en) * | 2020-07-16 | 2020-12-04 | 宝钢湛江钢铁有限公司 | Automatic deviation rectifying control method for cold-rolled strip steel continuous annealing heating furnace |
| CN112172319A (en) * | 2020-10-12 | 2021-01-05 | 苏州怡拓生物传感技术有限公司 | Deviation correcting device and method for flat printing of coil stock |
| CN112589232A (en) * | 2020-12-15 | 2021-04-02 | 广东工业大学 | Weld joint tracking method and device based on independent deviation correction type deep learning |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114226469A (en) * | 2021-11-29 | 2022-03-25 | 首钢集团有限公司 | Slab position measuring device, rolling system, control method, and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US9256946B2 (en) | Accuracy compensation method, system, and device | |
| CN101363188A (en) | Cutting method along boundary of laser cutting equipment | |
| CN111680551A (en) | Method and device for monitoring livestock quantity, computer equipment and storage medium | |
| CN112668629A (en) | Intelligent warehousing method, system, equipment and storage medium based on picture identification | |
| CN111813624A (en) | Robot execution time length estimation method based on time length analysis and related equipment thereof | |
| CN113313106A (en) | Feeding deviation rectifying method and device, computer equipment and storage medium | |
| CN111353489A (en) | Text image processing method and device, computer equipment and storage medium | |
| CN110814529A (en) | Flexible circuit board laser cutting method | |
| CN107240105A (en) | A kind of image cropping method and device | |
| CN112288025A (en) | Abnormal case identification method, device and equipment based on tree structure and storage medium | |
| CN115249315A (en) | Heterogeneous computing device-oriented deep learning image classification method and device | |
| CN114218174B (en) | Industrial internet data storage method, system and storage medium based on block chain | |
| CN113673519A (en) | Character recognition method based on character detection model and related equipment thereof | |
| CN110826606B (en) | Element matching method, device, server and readable storage medium | |
| CN114638501A (en) | Business data processing method and device, computer equipment and storage medium | |
| WO2021174688A1 (en) | Facial detection method and system | |
| CN117260035A (en) | Power control method and system of handheld laser welding machine | |
| CN116899834A (en) | Safety monitoring method, system and storage medium for coil coating production line | |
| CN116765640A (en) | Marking cutting control method, device, equipment and computer readable storage medium | |
| CN116168404A (en) | Intelligent document processing method and system based on space transformation | |
| CN116385422A (en) | Hidden crack detection method and device, computer equipment and storage medium | |
| CN113901768A (en) | Standard file generation method, device, equipment and storage medium | |
| WO2021022712A1 (en) | Method and device for image recognition model training, and computer device | |
| CN113128168A (en) | Pad parameter checking and correcting method and device, computer equipment and storage medium | |
| US11092948B1 (en) | Systems and methods of determining a difference of position between a malleable object and a target shape |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210827 |