A supply chain data tracing method based on image recognition model

Abstract

The present invention discloses a supply chain data tracing method based on an image recognition model, which specifically includes the following steps: S1, image acquisition: by deploying high-definition cameras at various key nodes in the supply chain, image information of relevant products is collected in real time. The present invention relates to the field of data tracing technology. This supply chain data tracing method based on an image recognition model enables enterprises to achieve real-time monitoring and data analysis of the entire supply chain process, improve the transparency and efficiency of supply chain management, and also provide strong support for product quality control and risk management. When problems occur with products in the supply chain, an early warning mechanism is set up to warn of abnormal situations, and relevant personnel can be notified immediately for processing. Through data tracing, the links where the problems occur and the relevant time nodes can be quickly located, so that corrective measures can be taken in a timely manner to ensure product quality and safety.

Description

Technical Field

[0001] The present invention relates to the field of data tracing technology, and in particular to a supply chain data tracing method based on an image recognition model. Background Art

[0002] With the development of computer graphics technology, image verification technology has been widely used. By extracting relevant features of products in images, it can detect whether products have defects or abnormalities. Especially in the supply chain field, image verification technology is combined with supply chain products to detect and trace defective products and related product information, greatly improving efficiency.

[0003] The reference patent name is: A method for quality management of agricultural product supply chain based on big data (patent publication number: CN115965256A, patent publication date: 2023.04.14), including: generating sales orders for agricultural products; determining several logistics transfer centers from all logistics transfer centers that can meet the environmental requirements for transportation and storage of agricultural products in the sales order, and calculating the optimal transportation route of the sales order in combination with the shipping place and receiving place of the sales order; at the logistics transfer center, according to the optimal transportation routes of different sales orders, the different sales orders with the same number at the next logistics transfer center on each optimal transportation route are merged and transported, and finally the agricultural products corresponding to the sales order are transported to the logistics transfer center closest to the receiving place.

[0004] Based on the above-mentioned document: Food quality traceability is an information management method that connects various links of production, inspection, supervision and consumption, allowing consumers to understand the production and circulation processes that comply with hygiene and safety, and improving consumer confidence. Once a food quality problem occurs, food supply chain traceability can confirm the food production process, the specific cause of the accident, and promptly recall the problem food, thereby minimizing the company's economic losses and reputation losses. However, the existing technology requires a long processing time for supply chain tracing, which is not timely and accurate enough, resulting in the inability to meet real-time requirements, thereby affecting food quality and safety. To this end, the present invention provides a supply chain data tracing method based on an image recognition model. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a supply chain data tracing method based on an image recognition model, which solves the problem that the existing technology requires a long processing time when tracing the supply chain, is not timely and accurate enough, resulting in the inability to meet real-time requirements, thereby affecting food quality and safety.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a supply chain data tracing method based on an image recognition model, specifically comprising the following steps:

[0007] S1. Image acquisition: High-definition cameras are deployed at key points in the supply chain to collect image information of relevant products in real time and pre-process the collected image information.

[0008] S2. Image recognition: Use the image recognition model to extract features and classify the pre-processed image, and automatically identify key information in the image;

[0009] S3. Data association: Associating the image recognition results with the data in the central database and matching the identified key information data with the information in the central database to verify the accuracy of the data;

[0010] S4. Data storage: Store the identified key information in a central database and integrate it with the supply chain management system;

[0011] S5. Monitoring and early warning: Use high-definition cameras to monitor key links in the supply chain in real time, and set up early warning mechanisms to warn of abnormal situations;

[0012] S6. Data tracing: performing tracing operations on image information data with abnormalities;

[0013] In the S5, the image data information captured in real time by the high-definition camera is compared with the data in the central database through image recognition technology. If an abnormality is found, an early warning mechanism will be triggered and relevant personnel will be notified immediately for processing;

[0014] The traceability operation in S6 mainly includes:

[0015] S6-1. When a product quality problem occurs in the supply chain, the supply chain management system can query the image recognition results in the central database to quickly locate the link where the problem occurred;

[0016] S6-2. When it is necessary to trace a certain link in the supply chain, the image recognition results and timestamps in the central database can be queried through the supply chain management system to quickly locate the data records of the relevant time nodes.

[0017] Preferably, the key nodes of the supply chain in S1 mainly include: production, packaging, warehousing and transportation.

[0018] Preferably, the preprocessing process in S1 mainly includes: denoising, contrast enhancement, color correction and segmentation operations to improve image quality and ensure the accuracy of subsequent image recognition.

[0019] Preferably, the image recognition model in S2 is based on a deep learning algorithm to identify key information in the image, and the deep learning algorithm includes a convolutional neural network and a recurrent neural network.

[0020] Preferably, the key information in the image in S2 mainly includes: product barcode, QR code, batch number and production date.

[0021] Preferably, the S4 ensures data security and traceability through data storage integration, and supports long-term storage and query of data.

[0022] Beneficial effects

[0023] The present invention provides a supply chain data tracing method based on an image recognition model. Compared with the existing technology, it has the following advantages:

[0024] (1) The supply chain data traceability method based on the image recognition model enables enterprises to realize real-time monitoring and data analysis of the entire supply chain process, improve the transparency and efficiency of supply chain management, and also provide strong support for product quality control and risk management. When problems occur in products in the supply chain, the abnormal situation can be warned through the set early warning mechanism, and the relevant personnel can be notified immediately to handle it. Through data tracing, the link where the problem occurred and the relevant time node can be quickly located, so that corrective measures can be taken in time to ensure the quality and safety of the product. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is an operational flow chart of the supply chain data tracing method of the present invention;

[0026] Figure 2 This is a technical principle diagram of the supply chain data tracing method of the present invention. DETAILED DESCRIPTION

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0028] See also Figure 1-2 The present invention provides a technical solution: a supply chain data tracing method based on an image recognition model, which specifically includes the following steps:

[0029] S1. Image acquisition: High-definition cameras are deployed at key points in the supply chain to collect image information of relevant products in real time and pre-process the collected image information.

[0030] S2. Image recognition: Use the image recognition model to extract features and classify the pre-processed image, and automatically identify key information in the image;

[0031] The image recognition model is trained using existing supply chain data (such as product labels, batch numbers, production locations, and production dates) to ensure that the model can accurately identify key products and status in the supply chain.

[0032] S3. Data association: Associating the image recognition results with the data in the central database and matching the identified key information data with the information in the central database to verify the accuracy of the data;

[0033] S4. Data storage: Store the identified key information in a central database and integrate it with the supply chain management system;

[0034] S5. Monitoring and early warning: Use high-definition cameras to monitor key links in the supply chain in real time, and set up early warning mechanisms to warn of abnormal situations;

[0035] S6. Data tracing: performing tracing operations on image information data with abnormalities;

[0036] S5 uses image recognition technology to compare the image data captured by the HD camera in real time with the data in the central database. If any abnormality is found, an early warning mechanism will be triggered and relevant personnel will be notified immediately for processing.

[0037] The traceability operations in S6 mainly include:

[0038] S6-1. When a product quality problem occurs in the supply chain, the supply chain management system can query the image recognition results in the central database to quickly locate the link where the problem occurred;

[0039] S6-2. When tracing a certain link in the supply chain is required, the supply chain management system can query the image recognition results and timestamps in the central database to quickly locate the data records at the relevant time nodes;

[0040] Early warning mechanism: By real-time monitoring of key links in the supply chain, if any abnormal situation is found during the identification process, such as missing or damaged products, the system can promptly issue an alarm and immediately notify relevant personnel to handle the situation;

[0041] System optimization and update: By regularly updating the image recognition model to adapt to changes such as new products and new packaging that may appear in the supply chain, the system is continuously optimized based on actual operation to improve the accuracy and efficiency of traceability;

[0042] System protection: Encrypt image data and application systems to ensure data security and user privacy;

[0043] The present invention: The key nodes of the supply chain in S1 mainly include: production, packaging, warehousing and transportation;

[0044] The present invention: The preprocessing process in S1 mainly includes: denoising, contrast enhancement, color correction and segmentation operations to improve image quality and ensure the accuracy of subsequent image recognition;

[0045] The present invention: The image recognition model in S2 is based on a deep learning algorithm to identify key information in the image, and the deep learning algorithm includes a convolutional neural network and a recurrent neural network;

[0046] CNN: Convolutional neural network is a type of feedforward neural network with a deep structure that includes convolution calculations. It is one of the representative algorithms of deep learning.

[0047] RNN: A recurrent neural network (RNN) is a type of recursive neural network that takes sequence data as input and recursively evolves in the direction of the sequence, with all nodes (recurrent units) connected in a chain. RNNs have memory, parameter sharing, and are Turing complete, making them advantageous in learning nonlinear features of sequences. They are used in natural language processing, such as speech recognition, language modeling, and machine translation, and are also used for various time series forecasting applications.

[0048] The present invention: The key information in the image in S2 mainly includes: product barcode, QR code, batch number and production date;

[0049] The present invention: S4 ensures data security and traceability through data storage integration and supports long-term storage and query of data.

[0050] To sum up: This supply chain data traceability method based on the image recognition model enables enterprises to achieve real-time monitoring and data analysis of the entire supply chain process, improve the transparency and efficiency of supply chain management, and also provide strong support for product quality control and risk management. When problems occur with products in the supply chain, the abnormal situation can be warned through the set early warning mechanism, and the relevant personnel can be notified immediately for processing. Through data tracing, the link where the problem occurred and the relevant time node can be quickly located, so that corrective measures can be taken in time to ensure product quality and safety.

[0051] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0052] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0053] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A supply chain data tracing method based on an image recognition model, characterized in that: The specific steps include: S1. Image acquisition: High-definition cameras are deployed at key points in the supply chain to collect image information of relevant products in real time and pre-process the collected image information. S2. Image recognition: Use the image recognition model to extract features and classify the pre-processed image, and automatically identify key information in the image; S3. Data association: Associating the image recognition results with the data in the central database and matching the identified key information data with the information in the central database to verify the accuracy of the data; S4. Data storage: Store the identified key information in a central database and integrate it with the supply chain management system; S5. Monitoring and early warning: Use high-definition cameras to monitor key links in the supply chain in real time, and set up early warning mechanisms to warn of abnormal situations; S6. Data tracing: performing tracing operations on image information data with abnormalities; In the S5, the image data information captured in real time by the high-definition camera is compared with the data in the central database through image recognition technology. If an abnormality is found, an early warning mechanism will be triggered and relevant personnel will be notified immediately for processing; The traceability operation in S6 mainly includes: S6-1. When a product quality problem occurs in the supply chain, the supply chain management system can query the image recognition results in the central database to quickly locate the link where the problem occurred; S6-2. When it is necessary to trace a certain link in the supply chain, the image recognition results and timestamps in the central database can be queried through the supply chain management system to quickly locate the data records of the relevant time nodes.

2. The supply chain data tracing method based on an image recognition model according to claim 1, characterized in that: The key nodes of the supply chain in S1 mainly include: production, packaging, warehousing and transportation.

3. The supply chain data tracing method based on an image recognition model according to claim 1, characterized in that: The preprocessing process in S1 mainly includes: denoising, contrast enhancement, color correction and segmentation operations to improve image quality and ensure the accuracy of subsequent image recognition.

4. The supply chain data tracing method based on an image recognition model according to claim 1, characterized in that: The image recognition model in S2 is based on a deep learning algorithm to identify key information in an image, and the deep learning algorithm includes a convolutional neural network and a recurrent neural network.

5. The supply chain data tracing method based on an image recognition model according to claim 1, characterized in that: The key information in the image in S2 mainly includes: product barcode, QR code, batch number and production date.

6. The supply chain data tracing method based on an image recognition model according to claim 1, characterized in that: The S4 ensures data security and traceability through data storage integration and supports long-term storage and query of data.

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