CN111241864A - Code scanning-free identification analysis method and system based on 5G communication technology - Google Patents
Code scanning-free identification analysis method and system based on 5G communication technology Download PDFInfo
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- G06K—GRAPHICAL DATA READING; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K7/00—Methods or arrangements for sensing record carriers, e.g. for reading patterns
- G06K7/10—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation
- G06K7/14—Methods or arrangements for sensing record carriers, e.g. for reading patterns by electromagnetic radiation, e.g. optical sensing; by corpuscular radiation using light without selection of wavelength, e.g. sensing reflected white light
- G06K7/1404—Methods for optical code recognition
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- G—PHYSICS
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Abstract
The invention discloses a code scanning-free identification analysis method based on a 5G communication technology, which comprises the following steps: the method comprises the steps that an image acquisition device acquires an image to be analyzed of an article and sends the image to be analyzed to a server by utilizing a 5G communication technology; the server analyzes the image to be analyzed so as to obtain corresponding identification coding information; the server transmits the identification code information to an information display device by using a 5G communication technology; and the information display device displays the identification code information. The invention utilizes the high transmission speed of the 5G technology, directly collects the article image information and carries out wireless communication with the server, and the server carries out analysis by utilizing the obtained image, thereby calling the corresponding representing coding information and realizing the acquisition of the identification coding information directly through the article image. The invention also discloses an analysis system corresponding to the analysis method.
Description
Technical Field
The invention relates to the technical field of identification analysis, in particular to a code scanning-free identification analysis method and system based on a 5G communication technology.
Background
In the internet era, an analysis system becomes a central nervous system of the internet, and the ecology of the whole internet is prosperous; in the era of internet of things with interconnection of everything, the strategic need is to consider in advance and build a layout identification analysis system to construct the ecology of the internet of things with interconnection of everything.
In the prior art, when specific information of an article is obtained, a two-dimensional code or a barcode that is identified on an object is usually scanned to obtain encoded information of the article. However, in the actual production and transportation process, the two-dimensional code or the barcode of the article may be damaged, and at this time, the object information cannot be read.
With the development of the 5G technology, the data transmission speed during wireless communication is greatly improved, so that the invention discloses a code scanning-free identification analysis method and system based on the 5G communication technology, the high transmission speed of the 5G technology is utilized, the object image information is directly collected and is in wireless communication with a server, the server analyzes by utilizing the obtained image, and therefore, the corresponding representing coding information is called, and the identification coding information is directly obtained through the object image.
Disclosure of Invention
Aiming at the defects of the prior art, the invention discloses a code scanning-free identification analysis method and system based on a 5G communication technology, which directly acquire article image information and wirelessly communicate with a server by utilizing the high transmission speed of the 5G technology, and the server analyzes by utilizing the acquired image, thereby calling corresponding representing coding information and realizing the acquisition of identification coding information directly through the article image.
The invention adopts the following technical scheme:
a code scanning-free identification analysis method based on a 5G communication technology comprises the following steps:
s1, the image acquisition device acquires an image to be analyzed of the article and sends the image to be analyzed to a server by using a 5G communication technology;
s2, the server analyzes the image to be analyzed so as to obtain corresponding identification code information;
s3, the server sends the identification code information to the information display device by using 5G communication technology;
and S4, the information display device displays the identification code information.
Preferably, step S2 includes:
s201, judging whether the image to be analyzed comprises an identification coding image;
s202, when the image to be analyzed comprises an identification code image, identifying the identification code image and acquiring corresponding identification code information;
s203, when the image to be analyzed does not comprise the identification code image, comparing and matching the image to be analyzed with the stored material image, and taking the identification code information of the successfully matched material image as the identification code information of the image to be analyzed.
Preferably, step S203 includes:
s2031, when the image to be analyzed does not include the identification coding image, extracting the characteristic information of the image to be analyzed;
s2032, performing characteristic classification based on the characteristic information and outputting the classification information of the image to be analyzed;
s2033, calling a material image corresponding to the classification information;
s2034, matching the image to be analyzed with the called material image;
s2035, using the identification code information of the successfully matched material image as the identification code information of the image to be analyzed.
Preferably, a feature classification model is used to classify the feature information, and the training method of the feature classification model includes:
s301, obtaining an image set and a classification label set corresponding to the image set, and randomly dividing the image set and the classification label set into a training set and a test set according to a preset proportion;
s302, extracting the feature information of the images in the training set, training the initialized BP neural network by using the feature information of the images in the training set and the corresponding classification labels, and finishing after updating the iteration preset times;
s303, extracting characteristic information of the images in the test set, inputting the characteristic information into the BP neural network after iteration is finished, and outputting a classification result;
and S304, matching the classification result with the classification label corresponding to the test set to judge the classification accuracy, finishing the training of the feature classification model when the classification accuracy is greater than or equal to a preset classification accuracy threshold, and otherwise, randomly dividing the image set and the classification label set into a training set and a test set according to a preset proportion and then executing the step S302.
Preferably, the identification code information includes an identification code and identification code reading information corresponding to the identification code.
The utility model provides a exempt from to sweep code mark analytic system based on 5G communication technology, includes the server and utilizes image acquisition device and the information display device that 5G carries out wireless communication with the server respectively, wherein:
the image acquisition device is used for acquiring an image to be analyzed of an article and sending the image to be analyzed to the server;
the server is used for analyzing the image to be analyzed so as to obtain corresponding identification coding information;
the server is also used for sending the identification code information to an information display device;
the information display device is used for displaying the identification code information.
Preferably, the server includes a judgment module, an identification code information acquisition module and a matching module, wherein:
the judging module is used for judging whether the image to be analyzed comprises an identification coding image;
the identification code information acquisition module is used for identifying the identification code image and acquiring corresponding identification code information when the image to be analyzed comprises the identification code image;
the matching module is used for comparing and matching the image to be analyzed with the stored material image when the image to be analyzed does not comprise the identification code image, and the identification code information acquisition module is also used for taking the identification code information of the successfully matched material image as the identification code information of the image to be analyzed.
Preferably, the matching module comprises a feature extraction unit, a classification unit, an image calling unit and a matching unit, wherein:
the characteristic extraction unit is used for extracting the characteristic information of the image to be analyzed when the image to be analyzed does not comprise the identification coding image;
the classification unit is used for performing characteristic classification based on the characteristic information and outputting the classification information of the image to be analyzed;
the image calling unit is used for calling the material image corresponding to the classification information;
the matching unit is used for matching the image to be analyzed with the called material image;
the identification code information acquisition module is used for taking the identification code information of the successfully matched material image as the identification code information of the image to be analyzed.
Preferably, the classification unit classifies the feature information by using a feature classification model, and the training method of the feature classification model includes:
s301, obtaining an image set and a classification label set corresponding to the image set, and randomly dividing the image set and the classification label set into a training set and a test set according to a preset proportion;
s302, extracting the feature information of the images in the training set, training the initialized BP neural network by using the feature information of the images in the training set and the corresponding classification labels, and finishing after updating the iteration preset times;
s303, extracting characteristic information of the images in the test set, inputting the characteristic information into the BP neural network after iteration is finished, and outputting a classification result;
and S304, matching the classification result with the classification label corresponding to the test set to judge the classification accuracy, finishing the training of the feature classification model when the classification accuracy is greater than or equal to a preset classification accuracy threshold, and otherwise, randomly dividing the image set and the classification label set into a training set and a test set according to a preset proportion and then executing the step S302.
Preferably, the identification code information includes an identification code and identification code reading information corresponding to the identification code.
In summary, the present invention discloses a scanning-free code identifier parsing method based on 5G communication technology, including: the method comprises the steps that an image acquisition device acquires an image to be analyzed of an article and sends the image to be analyzed to a server by utilizing a 5G communication technology; the server analyzes the image to be analyzed so as to obtain corresponding identification coding information; the server transmits the identification code information to an information display device by using a 5G communication technology; and the information display device displays the identification code information. The invention utilizes the high transmission speed of the 5G technology, directly collects the article image information and carries out wireless communication with the server, and the server carries out analysis by utilizing the obtained image, thereby calling the corresponding representing coding information and realizing the acquisition of the identification coding information directly through the article image. The invention also discloses an analysis system corresponding to the analysis method.
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For purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made in detail to the present invention as illustrated in the accompanying drawings, in which:
fig. 1 is a flowchart of a scanning-free code identifier parsing method based on a 5G communication technology disclosed in the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, the present invention discloses a code scanning free identifier parsing method based on 5G communication technology, which includes:
s1, the image acquisition device acquires an image to be analyzed of the article and sends the image to be analyzed to a server by using a 5G communication technology;
s2, the server analyzes the image to be analyzed so as to obtain corresponding identification code information;
s3, the server sends the identification code information to the information display device by using 5G communication technology;
and S4, the information display device displays the identification code information.
In the invention, the image acquisition device and the information display device can be designed integrally or respectively and independently, and the image acquisition device can select cameras with different specifications and resolutions according to actual needs.
In the prior art, because of the influence of the transmission speed of wireless communication data, a two-dimensional code or barcode mode is usually adopted for coding identification, and the two-dimensional code and barcode have a uniform regular structure and have small data volume, which is beneficial to wireless transmission of information. However, most of the two-dimensional codes and the bar codes are attached to the articles through the paper identifiers, and the paper identifiers are easily damaged or polluted in the using process, so that information cannot be read normally.
In specific implementation, step S2 includes:
s201, judging whether the image to be analyzed comprises an identification coding image;
s202, when the image to be analyzed comprises an identification code image, identifying the identification code image and acquiring corresponding identification code information;
s203, when the image to be analyzed does not comprise the identification code image, comparing and matching the image to be analyzed with the stored material image, and taking the identification code information of the successfully matched material image as the identification code information of the image to be analyzed.
In the invention, after the image is obtained, the image is preprocessed firstly, namely whether the image comprises a clearly visible identification code image (two-dimensional code, bar code image and the like) is judged, if the image comprises the identification code image, the identification code information corresponding to the identification code image is directly read, so that the subsequent calculation can be avoided, and the obtaining efficiency of the identification code information is improved.
In specific implementation, step S203 includes:
s2031, when the image to be analyzed does not include the identification coding image, extracting the characteristic information of the image to be analyzed;
s2032, performing characteristic classification based on the characteristic information and outputting the classification information of the image to be analyzed;
s2033, calling a material image corresponding to the classification information;
s2034, matching the image to be analyzed with the called material image;
s2035, using the identification code information of the successfully matched material image as the identification code information of the image to be analyzed.
Because a large number of material images are stored in the server, if the images to be analyzed are matched with all the images, the calculation amount is large, and the matching time is too long, therefore, in the invention, the neural network technology can be utilized to extract the characteristics of the images to be analyzed, then the images to be analyzed are classified, and the material images of the corresponding classes are called for matching, so that the number of the material images to be matched can be greatly reduced, the matching speed is accelerated, and the matching efficiency is improved.
In specific implementation, a feature classification model is adopted to classify the feature information, and the training method of the feature classification model comprises the following steps:
s301, obtaining an image set and a classification label set corresponding to the image set, and randomly dividing the image set and the classification label set into a training set and a test set according to a preset proportion;
s302, extracting the feature information of the images in the training set, training the initialized BP neural network by using the feature information of the images in the training set and the corresponding classification labels, and finishing after updating the iteration preset times;
s303, extracting characteristic information of the images in the test set, inputting the characteristic information into the BP neural network after iteration is finished, and outputting a classification result;
and S304, matching the classification result with the classification label corresponding to the test set to judge the classification accuracy, finishing the training of the feature classification model when the classification accuracy is greater than or equal to a preset classification accuracy threshold, and otherwise, randomly dividing the image set and the classification label set into a training set and a test set according to a preset proportion and then executing the step S302.
In the invention, the BP neural network can be trained, and when the classification accuracy reaches the requirement, the BP neural network is used as a characteristic classification model.
In specific implementation, the identification code information includes an identification code and identification code interpretation information corresponding to the identification code.
In order to meet various different requirements and facilitate the interpretation of workers, in the invention, the identification code information not only comprises an original identification code, but also explains each code segment in the identification code, thereby obtaining corresponding identification code interpretation information.
The invention also discloses a code scanning-free identification analysis system based on the 5G communication technology, which comprises a server, and an image acquisition device and an information display device which are respectively in wireless communication with the server by using 5G, wherein:
the image acquisition device is used for acquiring an image to be analyzed of an article and sending the image to be analyzed to the server;
the server is used for analyzing the image to be analyzed so as to obtain corresponding identification coding information;
the server is also used for sending the identification code information to an information display device;
the information display device is used for displaying the identification code information.
When the method is implemented specifically, the server comprises a judging module, an identification code information obtaining module and a matching module, wherein:
the judging module is used for judging whether the image to be analyzed comprises an identification coding image;
the identification code information acquisition module is used for identifying the identification code image and acquiring corresponding identification code information when the image to be analyzed comprises the identification code image;
the matching module is used for comparing and matching the image to be analyzed with the stored material image when the image to be analyzed does not comprise the identification code image, and the identification code information acquisition module is also used for taking the identification code information of the successfully matched material image as the identification code information of the image to be analyzed.
When the method is specifically implemented, the matching module comprises a feature extraction unit, a classification unit, an image calling unit and a matching unit, wherein:
the characteristic extraction unit is used for extracting the characteristic information of the image to be analyzed when the image to be analyzed does not comprise the identification coding image;
the classification unit is used for performing characteristic classification based on the characteristic information and outputting the classification information of the image to be analyzed;
the image calling unit is used for calling the material image corresponding to the classification information;
the matching unit is used for matching the image to be analyzed with the called material image;
the identification code information acquisition module is used for taking the identification code information of the successfully matched material image as the identification code information of the image to be analyzed.
In specific implementation, the classification unit classifies the feature information by using a feature classification model, and the training method of the feature classification model comprises the following steps:
s301, obtaining an image set and a classification label set corresponding to the image set, and randomly dividing the image set and the classification label set into a training set and a test set according to a preset proportion;
s302, extracting the feature information of the images in the training set, training the initialized BP neural network by using the feature information of the images in the training set and the corresponding classification labels, and finishing after updating the iteration preset times;
s303, extracting characteristic information of the images in the test set, inputting the characteristic information into the BP neural network after iteration is finished, and outputting a classification result;
and S304, matching the classification result with the classification label corresponding to the test set to judge the classification accuracy, finishing the training of the feature classification model when the classification accuracy is greater than or equal to a preset classification accuracy threshold, and otherwise, randomly dividing the image set and the classification label set into a training set and a test set according to a preset proportion and then executing the step S302.
In specific implementation, the identification code information includes an identification code and identification code interpretation information corresponding to the identification code.
Finally, it is noted that the above-mentioned embodiments illustrate rather than limit the invention, and that, while the invention has been described with reference to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A code scanning-free identification analysis method based on a 5G communication technology is characterized by comprising the following steps:
s1, the image acquisition device acquires an image to be analyzed of the article and sends the image to be analyzed to a server by using a 5G communication technology;
s2, the server analyzes the image to be analyzed so as to obtain corresponding identification code information;
s3, the server sends the identification code information to the information display device by using 5G communication technology;
and S4, the information display device displays the identification code information.
2. The method for parsing a scan-free code label based on 5G communication technology as claimed in claim 1, wherein step S2 comprises:
s201, judging whether the image to be analyzed comprises an identification coding image;
s202, when the image to be analyzed comprises an identification code image, identifying the identification code image and acquiring corresponding identification code information;
s203, when the image to be analyzed does not comprise the identification code image, comparing and matching the image to be analyzed with the stored material image, and taking the identification code information of the successfully matched material image as the identification code information of the image to be analyzed.
3. The method for parsing scan-free code identifiers based on 5G communication technology as claimed in claim 2, wherein step S203 comprises:
s2031, when the image to be analyzed does not include the identification coding image, extracting the characteristic information of the image to be analyzed;
s2032, performing characteristic classification based on the characteristic information and outputting the classification information of the image to be analyzed;
s2033, calling a material image corresponding to the classification information;
s2034, matching the image to be analyzed with the called material image;
s2035, using the identification code information of the successfully matched material image as the identification code information of the image to be analyzed.
4. The scanning-free code identification parsing method based on 5G communication technology as claimed in claim 3, wherein the feature information is classified by using a feature classification model, and the training method of the feature classification model comprises:
s301, obtaining an image set and a classification label set corresponding to the image set, and randomly dividing the image set and the classification label set into a training set and a test set according to a preset proportion;
s302, extracting the feature information of the images in the training set, training the initialized BP neural network by using the feature information of the images in the training set and the corresponding classification labels, and finishing after updating the iteration preset times;
s303, extracting characteristic information of the images in the test set, inputting the characteristic information into the BP neural network after iteration is finished, and outputting a classification result;
and S304, matching the classification result with the classification label corresponding to the test set to judge the classification accuracy, finishing the training of the feature classification model when the classification accuracy is greater than or equal to a preset classification accuracy threshold, and otherwise, randomly dividing the image set and the classification label set into a training set and a test set according to a preset proportion and then executing the step S302.
5. The method as claimed in any of claims 1 to 4, wherein the id code information includes an id code and id code interpretation information corresponding to the id code.
6. The utility model provides a exempt from to sweep code mark analytic system based on 5G communication technology, its characterized in that includes the server and utilizes image acquisition device and information display device that 5G carries out wireless communication with the server respectively, wherein:
the image acquisition device is used for acquiring an image to be analyzed of an article and sending the image to be analyzed to the server;
the server is used for analyzing the image to be analyzed so as to obtain corresponding identification coding information;
the server is also used for sending the identification code information to an information display device;
the information display device is used for displaying the identification code information.
7. The system of claim 6, wherein the server comprises a determining module, an id code information obtaining module, and a matching module, and wherein:
the judging module is used for judging whether the image to be analyzed comprises an identification coding image;
the identification code information acquisition module is used for identifying the identification code image and acquiring corresponding identification code information when the image to be analyzed comprises the identification code image;
the matching module is used for comparing and matching the image to be analyzed with the stored material image when the image to be analyzed does not comprise the identification code image, and the identification code information acquisition module is also used for taking the identification code information of the successfully matched material image as the identification code information of the image to be analyzed.
8. The system of claim 7, wherein the matching module comprises a feature extraction unit, a classification unit, an image calling unit, and a matching unit, and wherein:
the characteristic extraction unit is used for extracting the characteristic information of the image to be analyzed when the image to be analyzed does not comprise the identification coding image;
the classification unit is used for performing characteristic classification based on the characteristic information and outputting the classification information of the image to be analyzed;
the image calling unit is used for calling the material image corresponding to the classification information;
the matching unit is used for matching the image to be analyzed with the called material image;
the identification code information acquisition module is used for taking the identification code information of the successfully matched material image as the identification code information of the image to be analyzed.
9. The system according to claim 8, wherein the classification unit classifies the feature information by using a feature classification model, and the training method of the feature classification model comprises:
s301, obtaining an image set and a classification label set corresponding to the image set, and randomly dividing the image set and the classification label set into a training set and a test set according to a preset proportion;
s302, extracting the feature information of the images in the training set, training the initialized BP neural network by using the feature information of the images in the training set and the corresponding classification labels, and finishing after updating the iteration preset times;
s303, extracting characteristic information of the images in the test set, inputting the characteristic information into the BP neural network after iteration is finished, and outputting a classification result;
and S304, matching the classification result with the classification label corresponding to the test set to judge the classification accuracy, finishing the training of the feature classification model when the classification accuracy is greater than or equal to a preset classification accuracy threshold, and otherwise, randomly dividing the image set and the classification label set into a training set and a test set according to a preset proportion and then executing the step S302.
10. The system according to any of claims 6-9, wherein the id code information comprises an id code and id code interpretation information corresponding to the id code.
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| Publication number | Priority date | Publication date | Assignee | Title |
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101393634A (en) * | 2008-07-09 | 2009-03-25 | 中国物品编码中心 | Article code parsing system, device and method |
| CN102517842A (en) * | 2011-08-25 | 2012-06-27 | 海尔集团公司 | Washing machine capable of choosing washing procedure by automatically recognizing material information of clothes and method therefor |
| CN102591994A (en) * | 2012-03-07 | 2012-07-18 | 我查查信息技术(上海)有限公司 | Information acquisition method and device based on image acquisition device and mobile communication device |
| US20140043497A1 (en) * | 2012-08-08 | 2014-02-13 | Sony Corporation | Image sensor, imaging apparatus, and apparatus and method for manufacturing image sensor |
| CN103647954A (en) * | 2013-12-27 | 2014-03-19 | 成都三零凯天通信实业有限公司 | Mobile video monitoring system based on 3G channel and intelligent terminal |
| KR20140073197A (en) * | 2012-12-06 | 2014-06-16 | 삼성전기주식회사 | Washing machine using image-detection and driving method thereof |
| CN107354662A (en) * | 2017-08-18 | 2017-11-17 | 无锡小天鹅股份有限公司 | Device for clothing processing and its control method, device and computer-readable recording medium |
| CN108664872A (en) * | 2017-04-02 | 2018-10-16 | 田雪松 | Information authentication system based on dot matrix identification |
| CN109753581A (en) * | 2018-11-30 | 2019-05-14 | 北京拓尔思信息技术股份有限公司 | Image processing method, device, electronic equipment and storage medium |
| US20190377972A1 (en) * | 2018-06-08 | 2019-12-12 | Guangdong Oppo Mobile Telecommunications Corp., Ltd. | Method and apparatus for training, classification model, mobile terminal, and readable storage medium |
| CN110766096A (en) * | 2019-10-31 | 2020-02-07 | 北京金山云网络技术有限公司 | Video classification method and device and electronic equipment |
-
2020
- 2020-02-17 CN CN202010096255.8A patent/CN111241864A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101393634A (en) * | 2008-07-09 | 2009-03-25 | 中国物品编码中心 | Article code parsing system, device and method |
| CN102517842A (en) * | 2011-08-25 | 2012-06-27 | 海尔集团公司 | Washing machine capable of choosing washing procedure by automatically recognizing material information of clothes and method therefor |
| CN102591994A (en) * | 2012-03-07 | 2012-07-18 | 我查查信息技术(上海)有限公司 | Information acquisition method and device based on image acquisition device and mobile communication device |
| US20140043497A1 (en) * | 2012-08-08 | 2014-02-13 | Sony Corporation | Image sensor, imaging apparatus, and apparatus and method for manufacturing image sensor |
| KR20140073197A (en) * | 2012-12-06 | 2014-06-16 | 삼성전기주식회사 | Washing machine using image-detection and driving method thereof |
| CN103647954A (en) * | 2013-12-27 | 2014-03-19 | 成都三零凯天通信实业有限公司 | Mobile video monitoring system based on 3G channel and intelligent terminal |
| CN108664872A (en) * | 2017-04-02 | 2018-10-16 | 田雪松 | Information authentication system based on dot matrix identification |
| CN107354662A (en) * | 2017-08-18 | 2017-11-17 | 无锡小天鹅股份有限公司 | Device for clothing processing and its control method, device and computer-readable recording medium |
| US20190377972A1 (en) * | 2018-06-08 | 2019-12-12 | Guangdong Oppo Mobile Telecommunications Corp., Ltd. | Method and apparatus for training, classification model, mobile terminal, and readable storage medium |
| CN109753581A (en) * | 2018-11-30 | 2019-05-14 | 北京拓尔思信息技术股份有限公司 | Image processing method, device, electronic equipment and storage medium |
| CN110766096A (en) * | 2019-10-31 | 2020-02-07 | 北京金山云网络技术有限公司 | Video classification method and device and electronic equipment |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113284200A (en) * | 2021-05-19 | 2021-08-20 | 电子科技大学成都学院 | Identification code generation system and method based on 5G communication and image recognition |
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