Fabric line collection and anti-counterfeiting identification method based on block chain

Abstract

The invention relates to the technical field of fabric line collection and anti-counterfeiting identification, and particularly discloses a fabric line collection and anti-counterfeiting identification method based on a block chain, which comprises an anti-counterfeiting identification system, and the anti-counterfeiting identification system comprises hardware equipment and a software system; the method comprises the following steps: 1, sample preparation: placing a to-be-collected fabric sample on a sample fixing device, and ensuring that the sample is flat and free of wrinkles; step 2, collecting corners, collecting images of four corners of the fabric, and collecting at least one area at each corner; according to the invention, the fabric is collected and stored on the block chain, the authenticity of the fabric can be quickly identified only by comparing the feature codes subsequently, the anti-counterfeiting identification process is simplified, the application of the block chain technology is beneficial to fighting against counterfeit and shoddy products, protecting the rights and interests of consumers, protecting the brands and intellectual property rights of fabric manufacturers, and improving the production efficiency. The overall reputation of the fabric industry is improved, and the healthy development of the industry is promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of fabric pattern collection and anti-counterfeiting identification, and specifically relates to a fabric pattern collection and anti-counterfeiting identification method based on blockchain. Background Art

[0002] In the textile industry, anti-counterfeiting and traceability have always been an important topic. Traditional fabric anti-counterfeiting methods mainly include the following: physical labels and QR codes. Anti-counterfeiting labels, barcodes or QR codes are used to identify products. These labels can provide a certain degree of anti-counterfeiting protection, but they are easy to be copied and tampered with; fabrics are identified and tracked through radio frequency identification technology (RFID). RFID tags can be embedded in fabrics to achieve contactless identification, but their labels may also be copied; special chemical markers or inks are used on fabrics. These marks are usually visible under specific conditions (such as ultraviolet light). This method has a certain anti-counterfeiting effect, but the detection equipment is expensive and complicated.

[0003] Existing fabric anti-counterfeiting identification methods mainly rely on traditional labels, QR codes and anti-counterfeiting logos, which are easy to be copied and tampered with. Blockchain technology, with its decentralized, tamper-proof and transparent characteristics, provides a new solution for fabric anti-counterfeiting. However, there is currently a lack of an effective method that can combine the unique texture characteristics of fabrics with blockchain technology to achieve efficient anti-counterfeiting and tracking technical methods.

[0004] The present invention collects and stores fabrics on the blockchain, and the authenticity of the fabrics can be quickly identified by comparing feature codes later, which greatly simplifies the anti-counterfeiting identification process. The application of blockchain technology helps to combat counterfeit and shoddy products and protect the rights and interests of consumers. It also protects the brand and intellectual property rights of fabric manufacturers, improves the overall reputation of the fabric industry, and promotes the healthy development of the industry. Summary of the invention

[0005] The purpose of the present invention is to provide a blockchain-based fabric texture collection and anti-counterfeiting identification method to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A blockchain-based fabric pattern collection and anti-counterfeiting identification method, comprising:

[0008] An anti-counterfeiting identification system, which includes hardware equipment and software systems;

[0009] The feature collection process includes the following steps:

[0010] Step 1: Sample preparation: Place the fabric sample to be collected on the sample fixture to ensure that the sample is flat and wrinkle-free;

[0011] Step 2: Corner collection: collect images of the four corners of the fabric, collect at least one area in each corner, and collect a high-resolution image at a certain distance along the four sides of the fabric to ensure that the entire edge is covered. At the same time, collect images of the center area of ​​the fabric to capture the overall characteristics of the fabric;

[0012] Step 3: Image processing: pre-process the collected images to remove noise and impurities, extract the texture features of the fabric, and use image processing algorithms to extract fabric features;

[0013] Step 4: Feature generation, converting the extracted fabric features into a unique feature code, including but not limited to the following algorithms: extracting fiber direction angle distribution through Fourier transform, calculating density features based on gray-level co-occurrence matrix, extracting color distribution features using K-means clustering, and extracting texture feature points by combining Harris corner points and SURF algorithm;

[0014] Step 5: Data storage: Store the processed images and feature codes on the blockchain to ensure the data is tamper-proof and transparent, and record the collection time, location, and fabric information.

[0015] Preferably, the anti-counterfeiting identification system also includes a data acquisition module, which is responsible for image acquisition and preliminary processing; an image processing module, which performs in-depth processing and feature extraction on the acquired images; a blockchain node module, which stores and verifies the image and feature data of the fabric, and supports fast query and comparison; a matching and verification module, which performs feature matching based on the acquired new image; verifies the identity of the fabric, and a user interface module, which provides a user operation interface and supports data entry, query and management functions.

[0016] Through the setting of the above technical scheme, the corner acquisition and image acquisition at a certain distance on the edge in step 2 ensure the comprehensive capture of the fabric texture and avoid missing key features, and the image acquisition in the center area helps to capture the overall characteristics of the fabric and improve the accuracy of texture recognition; the image processing process in step 3, including removing noise and impurities and extracting texture features, ensures the consistency and standardization of the data, facilitates subsequent feature comparison and identification, and uses image processing algorithms to extract fabric features, thereby improving processing efficiency and accuracy.

[0017] The feature code generated in step 4 is the unique identifier of the fabric. Each fabric has its own unique feature code, which greatly enhances the anti-counterfeiting identification capability. In step 5, the processed image and feature code are stored on the blockchain, which utilizes the immutability of the blockchain to ensure the security and authenticity of the data, record the collection time, location and fabric information, increase the transparency of the data, and facilitate tracking and verification.

[0018] The fabrics are collected and stored on the blockchain. Subsequently, the authenticity of the fabrics can be quickly identified by comparing the feature codes, which greatly simplifies the anti-counterfeiting identification process. The application of blockchain technology helps to combat counterfeit and shoddy products and protect the rights and interests of consumers. It also protects the brand and intellectual property rights of fabric manufacturers, improves the overall credibility of the fabric industry, and promotes the healthy development of the industry.

[0019] Preferably, the distance between the four sides of the fabric is 10 cm.

[0020] Preferably, the feature extraction includes extraction of fiber direction, density and color distribution.

[0021] Preferably, the feature code is a hash value generated based on texture, color and shape.

[0022] Preferably, the blockchain node is used to store and verify the image and feature data of the fabric, ensuring the immutability and transparency of the data.

[0023] Preferably, the user interface is used for data entry, query and management, and the fabric information includes production batch and supplier.

[0024] Preferably, the hardware equipment includes a high-resolution camera, a microscope, a stable light source and a sample fixing device, and the software system includes image acquisition software, image processing software, blockchain node software, a database management system, a matching and verification system and a user interface.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] The present invention ensures the comprehensive capture of fabric texture by the cooperation between the above steps, and the corner capture and edge image capture at certain intervals in step 2 avoid missing key features, and the image capture of the central area helps to capture the overall features of the fabric, thereby improving the accuracy of texture recognition; the image processing process in step 3, including removing noise and impurities and extracting texture features, ensures the consistency and standardization of data, facilitates subsequent feature comparison and recognition, and uses image processing algorithms to extract fabric features, thereby improving processing efficiency and accuracy.

[0027] The feature code generated in step 4 is the unique identifier of the fabric. Each fabric has its own unique feature code, which greatly enhances the anti-counterfeiting identification capability. In step 5, the processed image and feature code are stored on the blockchain, which utilizes the immutability of the blockchain to ensure the security and authenticity of the data, record the collection time, location and fabric information, increase the transparency of the data, and facilitate tracking and verification.

[0028] The fabrics are collected and stored on the blockchain. Subsequently, the authenticity of the fabrics can be quickly identified by comparing the feature codes, which greatly simplifies the anti-counterfeiting identification process. The application of blockchain technology helps to combat counterfeit and shoddy products and protect the rights and interests of consumers. It also protects the brand and intellectual property rights of fabric manufacturers, improves the overall credibility of the fabric industry, and promotes the healthy development of the industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of the steps of the present invention. DETAILED DESCRIPTION

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0031] A blockchain-based fabric pattern collection and anti-counterfeiting identification method, comprising:

[0032] An anti-counterfeiting identification system, which includes hardware equipment and software systems;

[0033] The feature collection process includes the following steps:

[0034] Step 1: Sample preparation: Place the fabric sample to be collected on the sample fixture to ensure that the sample is flat and wrinkle-free;

[0035] Step 2: corner collection: collect images of the four corners of the fabric, collect at least one area in each corner, and collect a high-resolution image at a certain distance along the four sides of the fabric to ensure that the entire edge is covered. At the same time, collect images of the center area of ​​the fabric to capture the overall characteristics of the fabric. The distance between the four sides of the fabric is 10 cm;

[0036] Step 3: Image processing, pre-processing the collected images, removing noise and impurities, and extracting the texture features of the fabric, the texture features of the fabric include fiber direction, density and color distribution, and using an image processing algorithm to extract fabric features, the image processing algorithm is edge detection and feature point extraction;

[0037] Step 4: Feature generation, converting the extracted fabric features into a unique feature code, including but not limited to the following algorithms: extracting fiber direction angle distribution through Fourier transform, calculating density features based on gray-level co-occurrence matrix, extracting color distribution features using K-means clustering, extracting texture feature points by combining Harris corner points and SURF algorithm, the feature is a hash value generated based on texture, color, shape, etc.;

[0038] Step 5: Data storage: The processed images and feature codes are stored on the blockchain to ensure the data is tamper-proof and transparent, and the collection time, location and fabric information are recorded. The fabric information includes production batch and supplier.

[0039] The present invention further specifically describes that the anti-counterfeiting identification system also includes a data acquisition module, which is responsible for the acquisition and preliminary processing of images; an image processing module, which performs in-depth processing and feature extraction on the acquired images; a blockchain node module, which stores and verifies the image and feature data of the fabric, and supports fast query and comparison; a matching and verification module, which performs feature matching according to the acquired new image; verifies the identity of the fabric, and a user interface module, which provides a user operation interface and supports data entry, query and management functions;

[0040] The present invention further specifically details that the blockchain node is used to store and verify the image and feature data of the fabric to ensure the immutability and transparency of the data;

[0041] The present invention further specifically details that the hardware device includes a high-resolution camera, a microscope, a stable light source and a sample fixing device, and the software system includes image acquisition software, image processing software, blockchain node software, database management system, matching and verification system and user interface;

[0042] The present invention further specifically describes that the anti-counterfeiting identification method also includes:

[0043] Verification and application: collect images of the fabric to be verified, and use the matching system to compare the newly collected image features with the feature codes in the blockchain. At the same time, based on the matching results, confirm the identity of the fabric;

[0044] Application scenarios: Identify counterfeit fabrics, ensure product authenticity, collect and verify fabric features at all stages of the supply chain, achieve full-process tracking, track quality issues in fabric production through feature codes, and locate problem batches;

[0045] Security and privacy: ensure the security of data transmission and storage, prevent data leakage and tampering, and use encryption technology to protect the privacy of fabric feature data;

[0046] Optimization and improvement: Continuously optimize image acquisition and processing algorithms, improve the accuracy and efficiency of feature extraction, adjust and improve system architecture and processes based on actual application feedback, and enhance user experience and system performance;

[0047] Through the setting of the above technical scheme, the corner acquisition and image acquisition at a certain distance on the edge in step 2 ensure the comprehensive capture of the fabric texture and avoid missing key features, and the image acquisition in the center area helps to capture the overall characteristics of the fabric and improve the accuracy of texture recognition; the image processing process in step 3, including removing noise and impurities and extracting texture features, ensures the consistency and standardization of the data, facilitates subsequent feature comparison and identification, and uses image processing algorithms to extract fabric features, thereby improving processing efficiency and accuracy.

[0048] The feature code generated in step 4 is the unique identifier of the fabric. Each fabric has its own unique feature code, which greatly enhances the anti-counterfeiting identification capability. In step 5, the processed image and feature code are stored on the blockchain, which utilizes the immutability of the blockchain to ensure the security and authenticity of the data, record the collection time, location and fabric information, increase the transparency of the data, and facilitate tracking and verification.

[0049] The fabrics are collected and stored on the blockchain. Subsequently, the authenticity of the fabrics can be quickly identified by comparing the feature codes, which greatly simplifies the anti-counterfeiting identification process. The application of blockchain technology helps to combat counterfeit and shoddy products and protect the rights and interests of consumers. It also protects the brand and intellectual property rights of fabric manufacturers, improves the overall credibility of the fabric industry, and promotes the healthy development of the industry.

[0050] Furthermore, this design application is applied to the fabric pattern collection and anti-counterfeiting identification of the blockchain. The fabric is collected and stored on the blockchain. The authenticity of the fabric can be quickly identified by comparing the feature code, which greatly simplifies the anti-counterfeiting identification process. The application of blockchain technology helps to combat counterfeit and shoddy products and protect the rights and interests of consumers. At the same time, it also protects the brand and intellectual property rights of fabric manufacturers, improves the overall reputation of the fabric industry, and promotes the healthy development of the industry.

[0051] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for collecting fabric patterns and identifying anti-counterfeiting based on blockchain, characterized in that: include: An anti-counterfeiting identification system, which includes hardware equipment and software systems; The feature collection process includes the following steps: Step 1: Sample preparation: Place the fabric sample to be collected on the sample fixture to ensure that the sample is flat and wrinkle-free; Step 2: Corner collection: collect images of the four corners of the fabric, collect at least one area in each corner, and collect a high-resolution image at a certain distance along the four sides of the fabric to ensure that the entire edge is covered. At the same time, collect images of the center area of ​​the fabric to capture the overall characteristics of the fabric; Step 3: Image processing: pre-process the collected images to remove noise and impurities, and extract the texture features of the fabric, including fiber direction, density and color distribution; Step 4: Feature generation, converting the extracted fabric features into a unique feature code; Step 5: Data storage: Store the processed image and feature code hash on the blockchain to ensure the data is tamper-proof and transparent, and record the collection time, location, and fabric information.

2. According to the blockchain-based fabric texture collection and anti-counterfeiting identification method of claim 1, it is characterized by: The anti-counterfeiting identification system also includes a data acquisition module, an image processing module, a blockchain node module, a matching and verification module and a user interface module.

3. According to the blockchain-based fabric pattern collection and anti-counterfeiting identification method of claim 1, it is characterized by: The distance between the four sides of the fabric is 10 cm.

4. According to a blockchain-based fabric pattern collection and anti-counterfeiting identification method according to claim 1, it is characterized by: The feature extraction includes extraction of fiber direction, density and color distribution.

5. According to a blockchain-based fabric pattern collection and anti-counterfeiting identification method according to claim 1, it is characterized by: The feature code is a hash value generated based on texture, color and shape.

6. The method for collecting fabric patterns and identifying anti-counterfeiting based on blockchain according to claim 1 is characterized in that: The blockchain nodes are used to store and verify the image and feature data of the fabric, ensuring the data is tamper-proof and transparent.

7. According to a blockchain-based fabric pattern collection and anti-counterfeiting identification method according to claim 1, it is characterized by: The user interface is used for data entry, query and management, and the fabric information includes production batch and supplier.

8. According to a blockchain-based fabric pattern collection and anti-counterfeiting identification method according to claim 1, it is characterized by: The hardware equipment includes a high-resolution camera, a microscope, a stable light source and a sample fixing device, and the software system includes image acquisition software, image processing software, blockchain node software, a database management system, a matching and verification system and a user interface.

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