CN113807869A - Traceability system based on artificial intelligence optical detection - Google Patents
Traceability system based on artificial intelligence optical detection Download PDFInfo
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Abstract
The invention discloses a traceability system based on artificial intelligence optical detection, which comprises an optical data collection module, a data analysis module, a block chain coding module and a data platform, wherein the optical data collection module captures an optical image of a product and sends the image data to the data analysis module, the data analysis module comprises a preprocessing module and a neural network analysis module, the preprocessing module preprocesses the image data and sends the processed data to the neural network analysis module, and the neural network analysis module adopts a processing algorithm based on machine learning to distinguish the data of the variety, quality and component of the product and sends the data to the block chain coding module for traceability. The invention combines the graphic detection technology, the artificial intelligent edge calculation technology, the AI code technology, the multi-aperture technology and the block chain technology, thereby realizing the online nondestructive detection of the components of the detected object and tracing the products including traditional Chinese medicinal materials, cosmetics, industrial products and agricultural products.
Description
Technical Field
The invention relates to the technical field of optical detection, in particular to a traceability system based on artificial intelligence optical detection.
Background
At present, the composition of product differs, and quality can't be differentiateed once through people's eye, leads to counterfeit and shoddy commodity to seriously influence the business strategy and the channel construction of brand enterprise, and the enterprise is because there is not fine technique to lead to the enterprise to take corresponding measure cost higher, and the product needs the manual work to distinguish when classifying, and this has increased artificial cost, and the classification process is too complicated moreover, lacks convenient and reliable discernment technique, has reduced product detection's efficiency.
Disclosure of Invention
In order to solve at least or partially the above problems, an artificial intelligence optical detection-based traceability system is provided.
In order to achieve the purpose, the invention provides the following technical scheme:
the invention relates to a traceability system based on artificial intelligence optical detection, which comprises an optical data collection module, a data analysis module, a block chain coding module and a data platform, wherein the optical data collection module captures an optical image of a product, and sending the image data to a data analysis module, wherein the data analysis module comprises a preprocessing module and a neural network analysis module, the preprocessing module preprocesses the image data, and the processed data is sent to a neural network analysis module, the neural network analysis module adopts a processing algorithm based on machine learning to distinguish the data of the variety, quality and component of the product, sending the data to a block chain coding module for tracing, wherein the block chain coding module generates a unique identity for each commodity packaging device based on the variety, quality and components of the product; and code spraying is carried out on the product, and then the unique identity is uploaded to a data platform.
As a preferred technical solution of the present invention, the preprocessing module sequentially performs gray scale processing and normalization processing on the product image to obtain a preprocessed image.
As a preferred technical scheme of the invention, the neural network analysis module adopts a regional proposal network and a bounding box regression loss to realize the automatic identification of the variety, quality and components of the product.
As a preferred technical scheme of the invention, the area proposal network and the bounding box regression loss adopt a fast r-cnn model, and the fast r-cnn model comprises a network structure in the form of a convolution unit from convolution to batch normalization to activation Relu function.
As a preferred technical scheme of the invention, the data platform presents visual data to a user, the user performs query access through a unique identity, and the data of the variety, quality and composition of the product is stored by adopting a block chain technology.
Compared with the prior art, the invention has the following beneficial effects:
the invention combines the graphic detection technology, the artificial intelligence edge calculation technology, the AI code technology, the multi-aperture technology and the block chain technology to obtain the spatial morphology information of the object to be detected and the information of each pixel point to form a three-dimensional data block. Therefore, the on-line nondestructive detection of the components of the detected object can be realized, and the tracing to the products including traditional Chinese medicinal materials, cosmetics, industrial products and agricultural products can be realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic view of the overall structure of the present invention;
in the figure: 1. an optical data collection module; 2. a data analysis module; 3. a block chain encoding module; 4. a data platform; 5. a preprocessing module; 6. and a neural network analysis module.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation. Wherein like reference numerals refer to like parts throughout.
In addition, if a detailed description of the known art is not necessary to show the features of the present invention, it is omitted.
Example 1
As shown in fig. 1, the present invention provides a traceability system based on artificial intelligence optical inspection, comprising an optical data collection module 1, a data analysis module 2, a block chain coding module 3 and a data platform 4, wherein the optical data collection module 1 captures an optical image of a product, and sends the image data to a data analysis module 2, the data analysis module 2 comprises a preprocessing module 5 and a neural network analysis module 6, the preprocessing module 5 preprocesses the image data, and the processed data is sent to a neural network analysis module 6, the neural network analysis module 6 adopts a processing algorithm based on machine learning to distinguish the data of the variety, quality and component of the product, the data are sent to the block chain coding module 3 for tracing, and the block chain coding module 3 generates a unique identity for each commodity packaging device based on the variety, quality and components of the product; and code spraying is carried out on the product, and then the unique identity is uploaded to the data platform 4.
Specifically, the preprocessing module 5 sequentially performs gray processing and normalization processing on the product image to obtain a preprocessed image, the gray processing adopts three components of RGB to decompose the retina image into a red R channel, a green G channel and a blue B channel, and the images of the three channels are fused in proportion to be converted into a gray image; and the normalization processing adopts a Z-fraction normalization method to carry out dimension normalization on the image data set.
The acquired target spectrogram is analyzed and identified to distinguish the differences of different product spectrograms of varieties, quality, components and the like, so that the target is accurately analyzed. Meanwhile, the edge computing technology discriminates and judges visual behaviors, environments and flaws in different states in the detection field, intelligent recognition and analysis of behaviors of target people are met, the neural network analysis module 6 is applied to the multi-industry field, and automatic recognition of varieties, quality and components of products is achieved by adopting a regional proposal network and a bounding box regression loss. The regression loss of the regional proposal network and the bounding box adopts a faster-cnn model, the faster-cnn model comprises a network structure in a convolution unit form from convolution to batch standardization to activation of the Relu function, and the neural network comprises a small sample fast adaptation sensing system based on meta-learning, an excess sensing system based on a multi-branch convolution network and an object level sensing system based on a three-dimensional convolution neural network.
The AI blockchain can identify nuances that are not discernible by the naked eye, and the composition of the product can be identified by multi-aperture techniques without assay. Analyzing and detecting components of fruits, vegetables, medicinal materials, wine and the like, such as sweetness detection of the fruits, pesticide residue detection of the vegetables, heavy metal indexes of crops, authenticity and year detection of white spirit, year and origin detection of dried orange peel, gastrodine content and gastrodine polysaccharide content detection of gastrodia elata, and the like. And a detection result is immediately generated by multi-dimensional imaging photographing without sampling and testing.
The data platform 4 presents visual data to a user, the user inquires and visits through the unique identity, the data of the product in terms of variety, quality and composition adopts a block chain technology to store certificates, a code spray printing technology is applied through special printing ink tracing, the product quality true and false detection and circulation management and control are improved, an application applet is traced through one unique code of one object, and a product safety tracing platform is built. The source of the product can be checked, the destination can be traced, and the responsibility can be researched. The management of expanding to each product from the category of the product is realized, and the foundation of communicating with the terminal consumer is laid. Through automatic product transformation and the installation of special printing ink spouts a yard system, can trace back the sign of "AI sign indicating number" and endow on each product, realize that "an thing is only one sign indicating number", the consumer scans out relevant information through cell-phone applet, carries out the inquiry of true and false, and is simple directly perceived. The traceability field is improved, digitization, automation, standardization and informatization are developed, a block chain technology is applied, big data are mined, better service is improved, and product quality safety is guaranteed. The application of the block chain technology enables the production and sale links to be more transparent, and ensures that the tracing authentication of the product is more comprehensively brought into market supervision. Therefore, fake goods, low-price goods and goods left-right boards are prevented, the safety of consumers is maintained, and the health and the benefits of people are guaranteed.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A traceability system based on artificial intelligence optical detection comprises an optical data collection module (1), a data analysis module (2), a block chain coding module (3) and a data platform (4), and is characterized in that the optical data collection module (1) captures an optical image of a product and sends the image data to the data analysis module (2), the data analysis module (2) comprises a preprocessing module (5) and a neural network analysis module (6), the preprocessing module (5) preprocesses the image data and sends the processed data to the neural network analysis module (6), the neural network analysis module (6) adopts a processing algorithm based on machine learning to distinguish the data of the product, such as variety, quality and component, and sends the data to the block chain coding module (3) for traceability, the block chain coding module (3) generates a unique identity for each commodity packaging device based on the variety, quality and components of the product; and code spraying is carried out on the product, and the unique identity is uploaded to the data platform (4).
2. The tracing system based on artificial intelligence optical detection as claimed in claim 1, wherein said preprocessing module (5) sequentially performs gray processing and normalization processing on the product image to obtain a preprocessed image.
3. The traceability system based on artificial intelligence optical detection as claimed in claim 1, wherein the neural network analysis module (6) adopts a regional proposal network and a bounding box regression loss to realize automatic identification of product varieties, quality and components.
4. The traceability system based on artificial intelligence optical detection, as claimed in claim 3, wherein the area proposal network and bounding box regression loss employs a faster-cnn model comprising network structure in the form of convolution units normalized from convolution to batch to activation Relu function.
5. The traceability system based on artificial intelligence optical detection as claimed in claim 1, wherein the data platform (4) presents visual data to a user, the user performs inquiry access through a unique identity, and the data of the product variety, quality and composition are stored by using a block chain technology.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111429162A (en) * | 2020-04-16 | 2020-07-17 | 汪金小 | Energized block chain agricultural product quality credible traceability system based on nondestructive detection technology |
| CN111968080A (en) * | 2020-07-21 | 2020-11-20 | 山东农业大学 | Hyperspectrum and deep learning-based method for detecting internal and external quality of Feicheng peaches |
| CN112464762A (en) * | 2020-11-16 | 2021-03-09 | 中国科学院合肥物质科学研究院 | Agricultural product screening system and method based on image processing |
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- 2021-09-18 CN CN202111112518.0A patent/CN113807869A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111429162A (en) * | 2020-04-16 | 2020-07-17 | 汪金小 | Energized block chain agricultural product quality credible traceability system based on nondestructive detection technology |
| CN111968080A (en) * | 2020-07-21 | 2020-11-20 | 山东农业大学 | Hyperspectrum and deep learning-based method for detecting internal and external quality of Feicheng peaches |
| CN112464762A (en) * | 2020-11-16 | 2021-03-09 | 中国科学院合肥物质科学研究院 | Agricultural product screening system and method based on image processing |
Non-Patent Citations (1)
| Title |
|---|
| 朱旭;马?;姬江涛;金鑫;赵凯旋;张开;: "基于Faster R-CNN的蓝莓冠层果实检测识别分析", 南方农业学报, no. 06, pages 253 - 261 * |
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