CN117152444A - Equipment data acquisition method and system for lithium battery industry - Google Patents
Equipment data acquisition method and system for lithium battery industry Download PDFInfo
- Publication number
- CN117152444A CN117152444A CN202311413037.2A CN202311413037A CN117152444A CN 117152444 A CN117152444 A CN 117152444A CN 202311413037 A CN202311413037 A CN 202311413037A CN 117152444 A CN117152444 A CN 117152444A
- Authority
- CN
- China
- Prior art keywords
- gray
- defect
- pole piece
- defect point
- image
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 44
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 title claims abstract description 27
- 229910052744 lithium Inorganic materials 0.000 title claims abstract description 27
- 230000007547 defect Effects 0.000 claims abstract description 114
- 239000011159 matrix material Substances 0.000 claims abstract description 19
- 230000011218 segmentation Effects 0.000 claims abstract description 18
- 230000008569 process Effects 0.000 claims abstract description 9
- 239000011248 coating agent Substances 0.000 claims description 18
- 238000000576 coating method Methods 0.000 claims description 18
- 230000006870 function Effects 0.000 claims description 11
- 230000002159 abnormal effect Effects 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 230000005611 electricity Effects 0.000 claims 1
- 238000013480 data collection Methods 0.000 abstract description 5
- 238000010586 diagram Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- HBBGRARXTFLTSG-UHFFFAOYSA-N Lithium ion Chemical compound [Li+] HBBGRARXTFLTSG-UHFFFAOYSA-N 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000012790 confirmation Methods 0.000 description 2
- 238000005286 illumination Methods 0.000 description 2
- 229910001416 lithium ion Inorganic materials 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 230000032798 delamination Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
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
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
-
- 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
- G06V10/762—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using clustering, e.g. of similar faces in social networks
-
- 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
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Computing Systems (AREA)
- Artificial Intelligence (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Health & Medical Sciences (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Image Processing (AREA)
- Image Analysis (AREA)
Abstract
The invention relates to the field of data acquisition of coated pole pieces, in particular to a method and a system for acquiring equipment data for the lithium battery industry, comprising the following steps: obtaining a gray set of the coated pole piece; dividing a gray level image in the gray level set of the coated pole piece; calculating the gray value of each column of pixels in the gray segmentation image to obtain basic defect points; constructing a gray gradient matrix of the basic defect points in the row direction, and dividing the defect categories of the basic defect points; calculating the average value of the gray values of each column of pixels in the gray segmentation image, and clustering the gray average values of all columns to obtain a strip-shaped defect class; a defect class database is constructed based on the defect classes. According to the invention, the defect type in the coated pole piece is found out according to the image of the coated pole piece by collecting the image of the coated pole piece, so that the defect data of the coated pole piece is obtained, the labor cost in the data collection process is saved, and the data collection efficiency is improved.
Description
Technical Field
The present invention relates generally to the field of coated pole piece data acquisition. More particularly, the invention relates to a device data acquisition method and system for the lithium battery industry.
Background
The defect detection of the coated pole piece is a ring in the processing technology of the lithium battery, is important to the quality and performance of the lithium battery, can detect delamination, stripping and other problems early, and ensures the safety and reliability of the battery. Through defect analysis, the method can help the production enterprises to improve the process and optimize equipment, and fundamentally improve the processing process quality and the production efficiency of the lithium battery.
The Chinese patent application CN114358528A discloses a method, a device and equipment for collecting work station data of non-feeding equipment in the lithium battery industry, comprising the steps of establishing a mapping table of the non-work station feeding equipment and the work station feeding equipment; when feeding the work station feeding equipment, correspondingly writing the acquired work station feeding work order number and the equipment code in the work station information table into the work station feeding equipment code according to the mapping table; when the confirmation mark in the work station information table is 0, writing the acquired work station feeding work order number into a work order number field in the work station information table; when the confirmation mark is 1, after the touch screen work order switching operation is triggered, writing information in the work order number field of the work station feeding work order in the work station information table into the work order number field; and determining a corresponding work order number based on the equipment code, and collecting data based on the work order number.
However, the method relies on operators to collect data according to the worksheet number, so that the degree of dependence on the operators is high, the workload of the operators is increased, and the data collection efficiency is low.
Disclosure of Invention
In order to solve one or more of the above technical problems, the present invention proposes to acquire defect data of point defects, crack defects and strip defects in a coated pole piece by acquiring images of a plurality of coated pole pieces, thereby reducing the dependency on operators and improving the data acquisition efficiency.
A device data acquisition method for lithium battery industry comprises the following steps:
converting the coating pole piece images acquired through different irradiation angles into gray images to obtain a coating pole piece gray set;
dividing a gray level image in the gray level set of the coated pole piece according to a gray level value dividing function to obtain a gray level divided image, wherein the gray level divided image comprises a normal area and an abnormal area;
calculating the gray value of each column of pixels in the gray segmentation image according to a difference method to obtain a basic defect point in the gray segmentation image;
constructing a gray gradient matrix of the basic defect point in the row direction, and dividing the defect category of the basic defect point based on the gray gradient matrix, wherein the defect category comprises a crack defect point category and a point defect point category;
calculating the average value of the gray values of each column of pixels in the gray segmentation image as a column gray average value, and clustering all column gray average values in the gray segmentation image to obtain a strip defect class;
and constructing a defect type database based on the crack defect point type, the point defect point type and the strip defect point type.
In one embodiment, the converting the coated pole piece image acquired through different illumination angles into a gray scale image to obtain a coated pole piece gray scale set includes:
respectively acquiring coating pole piece images along five directions which are respectively in front, back, left and right directions and above the geometric center of the coating pole piece, so as to obtain a coating pole piece image set;
and converting all the images in the coated pole piece image set into gray level images to obtain a coated pole piece gray level set.
In one embodiment, the gray value division function expression is:
in the method, in the process of the invention,the ratio of the number of the pixels in the first class to the number of all the pixels is +.>The ratio of the number of the pixels in the second class to the number of all the pixels is +.>Is the mean of the gray values in the first class, +.>Is the mean of the gray values in the second class, +.>Is the mean value of all gray values in the image, +.>For the gray value boundaries of the first and second class, ->The data is segmented for gray values.
In one embodiment, the obtained gray-value division data is traversed, and the maximum value in the gray-value division data is used as the gray-value boundary of the normal area and the abnormal area.
In one embodiment, the calculating the gray value of each column of pixels in the gray image according to the difference method to obtain the basic defect point in the gray segmented image includes:
generating a continuous gray value sequence by adopting a difference method, and calculating the derivative of the continuous gray value sequence;
and deleting the points with derivatives other than zero and the points with origin values of 255, wherein the points with derivatives of 0 are the basic defect points.
In one embodiment, the constructing the gray gradient matrix of the basic defect point in the row direction and dividing the defect categories of the basic defect point based on the gray gradient matrix includes:
the expression of the gray gradient matrix is as follows:
wherein c is the number of continuously monotonically varying gray values occurring in the left region in the 90 degree direction of the base defect point, d is the number of continuously monotonically varying gray values occurring in the right region in the 90 degree direction of the base defect point, a is the number of continuously monotonically varying gray values occurring in the left region in the 45 degree direction of the base defect point, b is the number of continuously monotonically varying gray values occurring in the right region in the 45 degree direction of the base defect point, e is the number of continuously monotonically varying gray values occurring in the left region in the 135 degree direction of the base defect point, and f is the number of continuously monotonically varying gray values occurring in the right region in the 135 degree direction of the base defect point;
if zero occurs in the gray gradient matrix in the row direction, the basic defect point is a crack defect point, and if a, b, c, d, e, f is not zero, the basic defect point is a point defect point.
In one embodiment, clustering all column gray scale averages in the gray scale division image to obtain a strip defect class includes:
clustering all the column gray average values to obtain cluster clusters, wherein each cluster corresponds to one strip defect;
and outputting a clustering result when the triggering condition is met in the clustering process of all the column gray average values, and increasing the minimum searching radius until the triggering condition is met at the same time if the triggering condition is not met, wherein the triggering condition expression is as follows:
wherein,for the gray average value of all columns in any one cluster, n and m are columns, and +.>For the edge gray level whole value within the cluster, < >>For standard deviation calculation mode, exp is a normalization function,>、/>is a preset threshold.
An equipment data acquisition system for lithium battery industry, comprising:
a processor; and
and the memory is used for storing computer instructions of the equipment data acquisition method facing the lithium battery industry, and when the computer instructions are run by the processor, the equipment executes the equipment data acquisition method facing the lithium battery industry.
The invention has the beneficial effects that:
1. by collecting the image of the coated pole piece, point defects, crack defects and strip defects in the coated pole piece are found out according to the image of the coated pole piece, so that defect data of the coated pole piece are obtained, dependence on operators is reduced, labor cost is saved, and data collection efficiency is improved.
2. The images of the coated pole piece are respectively acquired through different angles, a plurality of images are obtained by photographing at one time, defects of the coated pole piece are analyzed based on the images, the characteristic identification precision is high, and the accuracy of data is improved.
Drawings
The above, as well as additional purposes, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description when read in conjunction with the accompanying drawings. In the drawings, embodiments of the invention are illustrated by way of example and not by way of limitation, and like reference numerals refer to similar or corresponding parts and in which:
FIG. 1 is a flow diagram schematically illustrating an embodiment of the present invention;
fig. 2 is a system architecture diagram schematically illustrating an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.
Referring to fig. 1, a device data acquisition method for lithium battery industry includes the following steps:
converting the coating pole piece images acquired through different irradiation angles into gray images to obtain a coating pole piece gray set;
dividing a gray level image in the gray level set of the coated pole piece according to a gray level value dividing function to obtain a gray level divided image, wherein the gray level divided image comprises a normal area and an abnormal area;
calculating the gray value of each column of pixels in the gray segmentation image according to a difference method to obtain basic defect points in the gray segmentation image;
constructing a gray gradient matrix of the basic defect point in the row direction, and dividing the defect categories of the basic defect point based on the gray gradient matrix, wherein the defect categories comprise crack defect point categories and point defect point categories;
calculating the average value of the gray values of each column of pixels in the gray segmentation image as a column gray average value, and clustering all column gray average values in the gray segmentation image to obtain a strip defect class;
and constructing a defect type database based on the crack defect point type, the point defect point type and the strip defect point type.
Through the steps, the equipment data acquisition method for the lithium battery industry is characterized in that the point-like defects, the crack defects and the strip-like defects in the coated pole piece are found out according to the image of the coated pole piece by acquiring the image of the coated pole piece, so that the defect data of the coated pole piece are obtained, the dependence on operators is reduced, the labor cost is saved, and the data acquisition efficiency is improved. And finally, establishing a defect type database, judging the working condition of the lithium-ion coating machine according to the data in the database, providing an evaluation basis for the working condition of the lithium-ion coating machine, and facilitating the improvement of the manufacturing process of the coated pole piece.
In one embodiment, converting the coated pole piece images acquired through different illumination angles into gray scale images to obtain a coated pole piece gray scale set includes:
respectively acquiring coating pole piece images along five directions which are respectively in front, back, left and right directions and above the geometric center of the coating pole piece, so as to obtain a coating pole piece image set;
and converting all the images in the coated pole piece image set into gray level images to obtain a coated pole piece gray level set.
The method comprises the steps of adopting light sources in different directions to irradiate a coated pole piece, installing irradiation lamps in five directions of front, back, left and right and upper part of the geometric center of the coated pole piece, installing one irradiation lamp in each direction, arranging a camera above a lithium battery coating machine, shooting the coated pole piece perpendicularly by the camera, shooting the coated pole piece once by each lamp, obtaining five RGB images by each coated pole piece once, converting the obtained RGB images into gray images by one light source direction, and obtaining five gray images of the coated pole piece.
In one embodiment, the gray value division function expression is:
in the method, in the process of the invention,the ratio of the number of the pixels in the first class to the number of all the pixels is +.>The ratio of the number of the pixels in the second class to the number of all the pixels is +.>Is the mean of the gray values in the first class, +.>Is the mean of the gray values in the second class, +.>Is the mean value of all gray values in the image, +.>For the gray value boundaries of the first and second class, ->The data is segmented for gray values.
In one embodiment, the obtained gradation value divided data is traversed with the maximum value in the gradation value divided data as the gradation value boundary of the normal region and the abnormal region. All gray values in the image are sequenced from big to small to obtain a gray value setWherein the aggregate range of gray values of the abnormal region is +.>The range of the set of gray values of the normal region is +.>Then, the normal region gray value in the five gray images is recorded as 255, and the abnormal region gray value is kept unchanged.
In one embodiment, calculating gray values of each column of pixels in a gray scale image according to a difference method to obtain a basic defect point in a gray scale divided image includes:
generating a continuous gray value sequence by adopting a difference method, and calculating the derivative of the continuous gray value sequence;
and deleting the points with derivatives other than zero and the points with origin values of 255, wherein the points with derivatives of 0 are the basic defect points.
In one embodiment, constructing a gray gradient matrix of the basic defect point in the row direction, and dividing the defect categories of the basic defect point based on the gray gradient matrix, including:
the expression of the gray gradient matrix is:
wherein c is the number of continuously monotonically varying gray values occurring in the left region in the 90 degree direction of the base defect point, d is the number of continuously monotonically varying gray values occurring in the right region in the 90 degree direction of the base defect point, a is the number of continuously monotonically varying gray values occurring in the left region in the 45 degree direction of the base defect point, b is the number of continuously monotonically varying gray values occurring in the right region in the 45 degree direction of the base defect point, e is the number of continuously monotonically varying gray values occurring in the left region in the 135 degree direction of the base defect point, and f is the number of continuously monotonically varying gray values occurring in the right region in the 135 degree direction of the base defect point;
if zero occurs in the gray gradient matrix in the row direction, the basic defect point is a crack defect point, and if a, b, c, d, e, f is not zero, the basic defect point is a point defect point. And a defect point is operated once in each gray level image to obtain 5 gray level gradient matrixes, so that the accuracy of data is improved.
In one embodiment, the average value of the gray values of each column of pixels in the gray-scale divided image is calculated as a column gray-scale average value, and all column gray-scale average values form a column gray-scale average value sequence, which is recorded asSubscript i expresses column i.
In one embodiment, clustering all column gray averages in a gray scale segmented image to obtain a bar defect class includes: clustering all the column gray average values to obtain cluster clusters, wherein each cluster corresponds to one strip defect;
clustering the column gray average value sequences according to the increasing direction of the column gray average value sequence subscript by using a DBSCAN clustering mode to obtain a plurality of cluster clusters, wherein each cluster comprises a plurality of column gray average values and corresponds to a plurality of columns. Clustering according to the increasing direction of the gray average value sequence, wherein each cluster corresponds to one strip defect. The parameter setting of DBSCAN may be a minimum cluster density of 1, the distance function may be euclidean distance, the minimum search radius of 1, and in other embodiments, the minimum cluster density may be 2, 3, 4, and other values, and the minimum search radius of 2, 3, 4, and other values.
And outputting a clustering result when the triggering condition is met in the clustering process of the column gray average value sequence, and increasing the minimum searching radius if the triggering condition is not met until the triggering condition is met at the same time, wherein the triggering condition is as follows:
wherein,for the gray average value of all columns in any one cluster, n and m are columns, and +.>For the edge gray level whole value within the cluster, < >>For standard deviation calculation mode, exp is a normalization function,>、/>is a preset threshold. />0.9%>Is 0.2The formula (1) is more than 0.9, which indicates that the gray scale integral value in the cluster is more stable, and the formula (2) is less than or equal to 0.2, which indicates that obvious demarcations appear between the front cluster and the rear cluster. In other embodiments, ->May also be 0.8, 0.7, ">The specific values of 0.3 and 0.4 can be set according to practical situations.
Fig. 2 is a schematic frame diagram of a device data acquisition system for the lithium battery industry according to an embodiment of the present invention. The apparatus 40 comprises a processor and a memory storing computer program instructions which, when executed by the processor, implement a method for device data collection for the lithium battery industry according to the first aspect of the present invention. The device also includes other components, such as a communication bus and a communication interface, which are well known to those skilled in the art, and the arrangement and function of which are known in the art and therefore not described in detail herein.
In the context of this patent, the foregoing memory may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, the computer readable storage medium may be any suitable magnetic or magneto-optical storage medium, such as, for example, resistance change Memory RRAM (Resistive Random Access Memory), dynamic Random Access Memory DRAM (Dynamic Random Access Memory), static Random Access Memory SRAM (Static Random-Access Memory), enhanced dynamic Random Access Memory EDRAM (Enhanced Dynamic Random Access Memory), high-Bandwidth Memory HBM (High-Bandwidth Memory), hybrid storage cube HMC (Hybrid Memory Cube), etc., or any other medium that may be used to store the desired information and that may be accessed by an application, a module, or both. Any such computer storage media may be part of, or accessible by, or connectable to, the device. Any of the applications or modules described herein may be implemented using computer-readable/executable instructions that may be stored or otherwise maintained by such computer-readable media.
In the description of the present specification, the meaning of "a plurality", "a number" or "a plurality" is at least two, for example, two, three or more, etc., unless explicitly defined otherwise.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
Claims (8)
1. The equipment data acquisition method for the lithium battery industry is characterized by comprising the following steps of:
converting the coating pole piece images acquired through different irradiation angles into gray images to obtain a coating pole piece gray set;
dividing a gray level image in the gray level set of the coated pole piece according to a gray level value dividing function to obtain a gray level divided image, wherein the gray level divided image comprises a normal area and an abnormal area;
calculating the gray value of each column of pixels in the gray segmentation image according to a difference method to obtain a basic defect point in the gray segmentation image;
constructing a gray gradient matrix of the basic defect point in the row direction, and dividing the defect category of the basic defect point based on the gray gradient matrix, wherein the defect category comprises a crack defect point category and a point defect point category;
calculating the average value of the gray values of each column of pixels in the gray segmentation image as a column gray average value, and clustering all column gray average values in the gray segmentation image to obtain a strip defect class;
and constructing a defect type database based on the crack defect point type, the point defect point type and the strip defect point type.
2. The method for acquiring equipment data for the lithium battery industry according to claim 1, wherein the step of converting the coated pole piece images acquired by different irradiation angles into gray level images to obtain a coated pole piece gray level set comprises the following steps:
respectively acquiring coating pole piece images along five directions which are respectively in front, back, left and right directions and above the geometric center of the coating pole piece, so as to obtain a coating pole piece image set;
and converting all the images in the coated pole piece image set into gray level images to obtain a coated pole piece gray level set.
3. The lithium battery industry-oriented equipment data acquisition method according to claim 1, wherein the gray value segmentation function expression is:
in the method, in the process of the invention,the ratio of the number of the pixels in the first class to the number of all the pixels is +.>The ratio of the number of the pixels in the second class to the number of all the pixels is +.>Is the mean of the gray values in the first class, +.>Is the mean of the gray values in the second class,is the mean value of all gray values in the image, +.>For the gray value boundaries of the first and second class, ->The data is segmented for gray values.
4. The lithium battery industry-oriented device data acquisition method according to claim 3, wherein the obtained gray value segmentation data is traversed, and the maximum value in the gray value segmentation data is used as a gray value boundary of a normal area and an abnormal area.
5. The method for collecting equipment data for lithium battery industry according to claim 1, wherein the calculating the gray value of each column of pixels in the gray image according to the difference method to obtain the basic defect point in the gray segmentation image comprises:
generating a continuous gray value sequence by adopting a difference method, and calculating the derivative of the continuous gray value sequence;
and deleting the points with derivatives other than zero and the points with origin values of 255, wherein the points with derivatives of 0 are the basic defect points.
6. The method for collecting device data for lithium battery industry according to claim 1, wherein the constructing a gray gradient matrix of the basic defect point in the row direction and dividing the defect categories of the basic defect point based on the gray gradient matrix comprises:
the expression of the gray gradient matrix is as follows:
wherein c is the number of continuously monotonically varying gray values occurring in the left region in the 90 degree direction of the base defect point, d is the number of continuously monotonically varying gray values occurring in the right region in the 90 degree direction of the base defect point, a is the number of continuously monotonically varying gray values occurring in the left region in the 45 degree direction of the base defect point, b is the number of continuously monotonically varying gray values occurring in the right region in the 45 degree direction of the base defect point, e is the number of continuously monotonically varying gray values occurring in the left region in the 135 degree direction of the base defect point, and f is the number of continuously monotonically varying gray values occurring in the right region in the 135 degree direction of the base defect point;
if zero occurs in the gray gradient matrix in the row direction, the basic defect point is a crack defect point, and if a, b, c, d, e, f is not zero, the basic defect point is a point defect point.
7. The method for collecting device data for lithium battery industry according to claim 1, wherein clustering all column gray average values in the gray segmented image to obtain a strip defect class comprises:
clustering all the column gray average values to obtain cluster clusters, wherein each cluster corresponds to one strip defect;
and outputting a clustering result when the triggering condition is met in the clustering process of all the column gray average values, and increasing the minimum searching radius until the triggering condition is met at the same time if the triggering condition is not met, wherein the triggering condition expression is as follows:
wherein,for the gray average value of all columns in any one cluster, n and m are columns, and +.>For the edge gray level whole value within the cluster, < >>For standard deviation calculation mode, exp is a normalization function,>、/>is a preset threshold.
8. The utility model provides a lithium electricity industry-oriented equipment data acquisition system which characterized in that includes:
a processor; and
a memory storing computer instructions of a lithium battery industry oriented device data acquisition method, which when executed by the processor, cause a device to perform the lithium battery industry oriented device data acquisition method according to any one of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311413037.2A CN117152444B (en) | 2023-10-30 | 2023-10-30 | Equipment data acquisition method and system for lithium battery industry |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311413037.2A CN117152444B (en) | 2023-10-30 | 2023-10-30 | Equipment data acquisition method and system for lithium battery industry |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117152444A true CN117152444A (en) | 2023-12-01 |
| CN117152444B CN117152444B (en) | 2024-01-26 |
Family
ID=88906434
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311413037.2A Active CN117152444B (en) | 2023-10-30 | 2023-10-30 | Equipment data acquisition method and system for lithium battery industry |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117152444B (en) |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007078572A (en) * | 2005-09-15 | 2007-03-29 | Tokyo Seimitsu Co Ltd | Flaw inspection device of image, and flaw inspection method of image |
| CN103593670A (en) * | 2013-10-14 | 2014-02-19 | 浙江工业大学 | Copper sheet and strip surface defect detection method based on-line sequential extreme learning machine |
| WO2015055060A1 (en) * | 2013-10-16 | 2015-04-23 | 湖南镭目科技有限公司 | Online detecting method for continuous casting slab surface quality |
| CN111179225A (en) * | 2019-12-14 | 2020-05-19 | 西安交通大学 | Test paper surface texture defect detection method based on gray gradient clustering |
| US20210174489A1 (en) * | 2019-12-04 | 2021-06-10 | Beijing Boe Optoelectronics Technology Co., Ltd. | Method and apparatus for detecting a screen, and electronic device |
| WO2021168733A1 (en) * | 2020-02-27 | 2021-09-02 | 京东方科技集团股份有限公司 | Defect detection method and apparatus for defect image, and computer-readable storage medium |
| CN113929118A (en) * | 2021-11-19 | 2022-01-14 | 山东泰普锂业科技有限公司 | Process for preparing lithium fluoride from lepidolite |
| CN114358528A (en) * | 2021-12-18 | 2022-04-15 | 青岛奥利普自动化控制系统有限公司 | Lithium battery industry no-loading equipment work order data acquisition method, device and equipment |
| CN114749342A (en) * | 2022-04-20 | 2022-07-15 | 华南理工大学 | Method, device and medium for identifying coating defects of lithium battery pole piece |
| CN114862862A (en) * | 2022-07-11 | 2022-08-05 | 江苏大田阀门制造有限公司 | Pump body cold shut defect identification method and system based on image processing |
| CN114926436A (en) * | 2022-05-20 | 2022-08-19 | 南通沐沐兴晨纺织品有限公司 | Defect detection method for periodic pattern fabric |
| CN114972329A (en) * | 2022-07-13 | 2022-08-30 | 江苏裕荣光电科技有限公司 | Image enhancement method and system of surface defect detector based on image processing |
| CN114972356A (en) * | 2022-08-03 | 2022-08-30 | 海门市腾飞橡塑厂 | Plastic product surface defect detection and identification method and system |
| CN115082483A (en) * | 2022-08-23 | 2022-09-20 | 启东金耀億华玻纤材料有限公司 | Glass fiber board surface defect identification method based on optical camera |
| CN115311292A (en) * | 2022-10-12 | 2022-11-08 | 南通创铭伊诺机械有限公司 | Strip steel surface defect detection method and system based on image processing |
| CN115311265A (en) * | 2022-10-10 | 2022-11-08 | 南通惠罗家用纺织品有限公司 | Loom intelligence control system based on weaving quality |
| WO2023134792A2 (en) * | 2022-12-15 | 2023-07-20 | 苏州迈创信息技术有限公司 | Led lamp wick defect detection method |
-
2023
- 2023-10-30 CN CN202311413037.2A patent/CN117152444B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007078572A (en) * | 2005-09-15 | 2007-03-29 | Tokyo Seimitsu Co Ltd | Flaw inspection device of image, and flaw inspection method of image |
| CN103593670A (en) * | 2013-10-14 | 2014-02-19 | 浙江工业大学 | Copper sheet and strip surface defect detection method based on-line sequential extreme learning machine |
| WO2015055060A1 (en) * | 2013-10-16 | 2015-04-23 | 湖南镭目科技有限公司 | Online detecting method for continuous casting slab surface quality |
| US20210174489A1 (en) * | 2019-12-04 | 2021-06-10 | Beijing Boe Optoelectronics Technology Co., Ltd. | Method and apparatus for detecting a screen, and electronic device |
| CN111179225A (en) * | 2019-12-14 | 2020-05-19 | 西安交通大学 | Test paper surface texture defect detection method based on gray gradient clustering |
| WO2021168733A1 (en) * | 2020-02-27 | 2021-09-02 | 京东方科技集团股份有限公司 | Defect detection method and apparatus for defect image, and computer-readable storage medium |
| CN113929118A (en) * | 2021-11-19 | 2022-01-14 | 山东泰普锂业科技有限公司 | Process for preparing lithium fluoride from lepidolite |
| CN114358528A (en) * | 2021-12-18 | 2022-04-15 | 青岛奥利普自动化控制系统有限公司 | Lithium battery industry no-loading equipment work order data acquisition method, device and equipment |
| CN114749342A (en) * | 2022-04-20 | 2022-07-15 | 华南理工大学 | Method, device and medium for identifying coating defects of lithium battery pole piece |
| CN114926436A (en) * | 2022-05-20 | 2022-08-19 | 南通沐沐兴晨纺织品有限公司 | Defect detection method for periodic pattern fabric |
| CN114862862A (en) * | 2022-07-11 | 2022-08-05 | 江苏大田阀门制造有限公司 | Pump body cold shut defect identification method and system based on image processing |
| CN114972329A (en) * | 2022-07-13 | 2022-08-30 | 江苏裕荣光电科技有限公司 | Image enhancement method and system of surface defect detector based on image processing |
| CN114972356A (en) * | 2022-08-03 | 2022-08-30 | 海门市腾飞橡塑厂 | Plastic product surface defect detection and identification method and system |
| CN115082483A (en) * | 2022-08-23 | 2022-09-20 | 启东金耀億华玻纤材料有限公司 | Glass fiber board surface defect identification method based on optical camera |
| CN115311265A (en) * | 2022-10-10 | 2022-11-08 | 南通惠罗家用纺织品有限公司 | Loom intelligence control system based on weaving quality |
| CN115311292A (en) * | 2022-10-12 | 2022-11-08 | 南通创铭伊诺机械有限公司 | Strip steel surface defect detection method and system based on image processing |
| WO2023134792A2 (en) * | 2022-12-15 | 2023-07-20 | 苏州迈创信息技术有限公司 | Led lamp wick defect detection method |
Non-Patent Citations (3)
| Title |
|---|
| 戴光;崔巍;张颖;王学增;: "基于灰度-梯度共生矩阵的焊缝缺陷聚类分析", 中国安全科学学报, no. 03, pages 81 - 87 * |
| 蔡晓军;: "基于嵌入式机器视觉的多瓶口缺陷检测系统设计", 包装工程, no. 23, pages 174 - 179 * |
| 郭慧;王霄;刘传泽;周玉成;: "基于灰度共生矩阵和分层聚类的刨花板表面图像缺陷提取方法", 林业科学, no. 11, pages 114 - 123 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117152444B (en) | 2024-01-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110175507B (en) | Model evaluation method, device, computer equipment and storage medium | |
| CN113129335B (en) | Visual tracking algorithm and multi-template updating strategy based on twin network | |
| CN111783844A (en) | Target detection model training method and device based on deep learning and storage medium | |
| CN112528845B (en) | Physical circuit diagram identification method based on deep learning and application thereof | |
| CN110909804B (en) | Method, device, server and storage medium for detecting abnormal data of base station | |
| CN117495891B (en) | Point cloud edge detection method and device and electronic equipment | |
| CN115861738A (en) | Category semantic information guided remote sensing target detection active sampling method | |
| CN109800477B (en) | Lithium ion battery three-factor inconsistency prediction method and device | |
| CN117152444B (en) | Equipment data acquisition method and system for lithium battery industry | |
| WO2021227295A1 (en) | Cnn-based cancer cell multi-scale scaling positioning detection method | |
| CN112668365A (en) | Material warehousing identification method, device, equipment and storage medium | |
| CN115001394B (en) | Solar cell state monitoring method and system based on artificial intelligence | |
| CN116188374A (en) | Socket detection method, device, computer equipment and storage medium | |
| CN113554079B (en) | Power load abnormal data detection method and system based on secondary detection method | |
| Li et al. | A novel approach to remote sensing image retrieval with multi-feature VP-tree indexing and online feature selection | |
| Xu et al. | Research on improved residual network classification method for defect recognition of thermal battery | |
| CN111984812B (en) | Feature extraction model generation method, image retrieval method, device and equipment | |
| CN117649660B (en) | Global information fusion-based cell division equilibrium degree evaluation method and terminal | |
| CN115858895B (en) | Multi-source heterogeneous data processing method and system for smart city | |
| CN110096529B (en) | Network data mining method and system based on multidimensional vector data | |
| CN117931954A (en) | Database data interface management method based on flexible configuration | |
| CN116704218A (en) | Power industry target detection method and device | |
| CN116705158A (en) | Clustering method, device, equipment and storage medium for space transcriptome data | |
| CN116894637A (en) | Form processing method, form processing device, computer equipment and storage medium | |
| CN117953415A (en) | Article placement detection method, device, computer equipment and storage medium |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |