Food production unique identifier management system

Abstract

The invention discloses a unique identification management system for food production, belongs to the field of food management, and constructs an innovative solution with high safety, high identification degree and high credibility in the field of food identification through a technical closed loop of artistic design, multi-source data fusion, multistage anti-counterfeiting collaboration and block chain credible evidence storage. And powerful technical support is provided for brand protection, anti-counterfeiting traceability and consumer trust establishment in the food industry.

Description

Technical Field

[0001] The present invention relates to the field of food management, and more specifically to a unique identification management system for food production. Background Art

[0002] In recent years, as consumers' demand for food safety and traceability transparency has grown, the food industry's reliance on unique product identification and anti-counterfeiting and traceability technologies has gradually deepened.

[0003] In the existing technology, food labeling systems are mostly based on traditional barcodes, QR codes or radio frequency identification (RFID) tags to achieve data storage and traceability, but their functions are concentrated on basic information recording, which has significant limitations. First, the existing labels lack artistic design capabilities, and it is difficult to dynamically integrate elements such as brand culture and regional characteristics into the labels, resulting in insufficient visual recognition of products and unable to meet the high-end market's demand for aesthetic value; secondly, at the level of technical integration, although blockchain technology has been tried for food traceability, its application mostly remains in the data storage stage, which is not convenient for deep synergy with dynamic label generation, cross-modal data conversion and multiple anti-counterfeiting mechanisms; at the same time, existing art design tools rely on manual operation, with low parameterization and automation, and it is difficult to efficiently process complex graphics, color and texture quantization encoding. Although artificial intelligence technologies such as generative adversarial networks (GAN) have made breakthroughs in the field of image generation, there is still a lack of mature solutions for cross-modal mapping with production data in industrial scenarios, which limits the personalization and non-replicability of unique labels.

[0004] In summary, the current food labeling system has significant shortcomings in terms of artistry, dynamic generation capabilities and technical integration. There is an urgent need for a new management system that integrates parametric art design, multi-source data drive, physical-digital collaborative anti-counterfeiting and blockchain trusted evidence, so as to break through the existing technical bottleneck and achieve the uniqueness, security and traceability of food labeling from production to consumption. Summary of the invention

[0005] 1. Technical issues to be solved

[0006] In view of the problems existing in the prior art, the purpose of the present invention is to provide a unique identification management system for food production. Through artistic design, multi-source data fusion, multi-level anti-counterfeiting collaboration and a technical closed loop of blockchain trusted evidence, an innovative solution with high security, high recognition and high credibility in the field of food identification is constructed, providing strong technical support for brand protection, anti-counterfeiting traceability and consumer trust building in the food industry.

[0007] 2. Technical solution

[0008] To solve the above problems, the present invention adopts the following technical solutions.

[0009] A food production unique identification management system, comprising:

[0010] An art feature parameterization module is configured to quantify the graphics, colors and textures in the preset art element library, and output a parameterized data set including a vector control point set, a color space distribution matrix and a texture feature vector, and the art element library supports importing custom vector graphics through a user interface, and automatically performs compliance checks and parameterization conversions, and calls a regional cultural feature template library according to geographical indication certification requirements;

[0011] The production data acquisition module is connected to the sensor network of the food production line to obtain structured data streams including production timestamps, raw material batch codes, and process control parameters in real time;

[0012] A cross-modal generation engine receives the parameterized data set and the structured data stream, encodes the production data into artistic style transfer parameters through a pre-trained generative adversarial network model, and fuses the parameters into the parameterized data set to generate a dynamic visual logo containing data fingerprint information;

[0013] A multiple anti-counterfeiting processing module performs multi-level anti-counterfeiting processing including random microstructure embedding, optically variable layer superposition and cryptographic hash value calculation on the dynamic visual mark;

[0014] The blockchain evidence storage module stores the characteristic hash value of the identifier and the associated production data in the blockchain node to establish an unalterable traceability chain.

[0015] Furthermore, the artistic feature parameterization module includes a graphics analysis unit, a color quantization unit and a texture analysis unit. The graphics analysis unit converts geometric patterns into editable vector data by extracting Bezier curve control points and serializing polygon vertex coordinates. The color quantization unit uses the HSV color model to perform three-dimensional discrete encoding of hue-saturation-brightness on color combinations in artistic elements. The texture analysis unit calculates the directionality, contrast and roughness indicators of the texture based on the grayscale co-occurrence matrix to generate a texture feature vector.

[0016] Furthermore, the cross-modal generation engine includes a data-art mapping unit, a style transfer unit and a dynamic rendering unit. The data-art mapping unit maps the production timestamp, raw material batch code and process control parameters to the number of fractal iterations, polar coordinate basis function parameters and spiral phase angle respectively through a convolutional neural network. The style transfer unit uses the generator model of the generative adversarial network to fuse the fractal iteration number, polar coordinate basis function parameters and spiral phase angle into a parameterized data set to generate a unique fractal art pattern. The dynamic rendering unit performs color filling, texture overlay and dynamic light and shadow rendering on the fractal art pattern according to preset brand visual specifications, and outputs a vector graphics file of the final logo.

[0017] Furthermore, the data-art mapping unit first converts the raw material batch code into a radius increment sequence in a polar coordinate system through a modulus operation, and then maps the process control parameters to the iteration termination threshold of the Mandelbrot fractal set after normalization, and then generates the initial rotation angle of the spiral through the millisecond-level value of the production timestamp.

[0018] Furthermore, the multiple anti-counterfeiting processing module includes a physical anti-counterfeiting unit, an optical anti-counterfeiting unit and a digital anti-counterfeiting unit. The physical anti-counterfeiting unit embeds a microstructure layer containing randomly distributed nano-pits in the logo, the optical anti-counterfeiting unit superimposes an interference film color layer with angle-sensitive characteristics, and the digital anti-counterfeiting unit performs an SM hash operation on the vector graphics file of the logo to generate a digital fingerprint, and encrypts the fingerprint and writes it into the invisible coding area on the edge of the logo.

[0019] A method for generating a unique identification for food production comprises the following steps:

[0020] Step 1: Receive the production data stream and convert it into a set of control parameters through preset rules;

[0021] Step 2: calling a pre-trained style transfer model to perform cross-modal fusion of the control parameter set and the art feature parameterized dataset;

[0022] Step 3: Generate a dynamic logo graphic containing fractal geometric features, data fingerprints and brand visual elements;

[0023] Step 4: Apply physical and digital anti-counterfeiting treatment to the dynamic logo graphic;

[0024] Step 5: Write the identification feature value and associated data into the blockchain evidence storage contract;

[0025] Step 6: Connect the food label verification terminal, use the optical acquisition unit inside the food label verification terminal to capture the physical anti-counterfeiting features of the label through multi-spectral imaging, and connect it to the blockchain node to compare the matching degree between the on-site acquisition features and the data stored on the chain;

[0026] Step 7: Using the interactive display unit inside the food label verification terminal, the production traceability information is superimposed on the real-time picture of the physical label through augmented reality technology, and then the anti-counterfeiting verification unit inside the food label verification terminal is used to detect the random distribution characteristics of the microstructure layer and the angle response curve of the optically variable layer.

[0027] Furthermore, the cross-modal fusion step specifically includes:

[0028] Step 1: Convert the production data stream into a frequency domain feature vector through Fourier transform;

[0029] Step 2: Use the self-attention mechanism to calculate the correlation weight between the frequency domain feature vector and the art feature parameterized dataset;

[0030] Step 3: Dynamically weight the artistic feature parameters according to the correlation weights to generate a fused stylized parameter matrix.

[0031] 3. Beneficial effects

[0032] Compared with the prior art, the advantages of the present invention are:

[0033] (1) This solution uses an artistic feature parameterization module to quantify the graphics, colors and textures, and calls the regional cultural feature template library in combination with the geographical indication certification requirements to achieve a dynamic fusion of brand cultural elements and regional characteristics. The generative adversarial network (GAN) model based on the cross-modal generation engine maps production data (such as timestamps and raw material batch codes) into artistic style features such as fractal iteration parameters and polar coordinate basis functions to generate a dynamic visual logo with both aesthetic value and data uniqueness, solving the problem of traditional logos with single visual appearance and low recognition, and meeting the high-end market's demand for artistic logos.

[0034] (2) In this solution, the production data collection module obtains the structured data stream of the production line in real time. The cross-modal generation engine dynamically integrates the data stream with the artistic features through Fourier transform, self-attention mechanism and other technologies to generate a unique identifier containing the data fingerprint. The blockchain evidence storage module binds the identifier feature hash value with the production data to form an unalterable traceability chain, which solves the problem of the disconnection between traditional identification and production links and the low credibility of traceability information, and realizes transparent traceability of the entire chain from raw materials to finished products.

[0035] (3) In this solution, the system deeply integrates parametric design tools with artificial intelligence models to achieve automatic encoding of artistic elements (such as Bezier curve control point extraction and HSV color model quantization) and real-time generation of dynamic logos, greatly reducing manual operation costs. At the same time, technologies such as analog-to-digital conversion and fractal iterative parameter mapping of the data-art mapping unit in the cross-modal generation engine ensure the accurate conversion of production data to artistic features, thereby improving the uniqueness and efficiency of logo generation.

[0036] (4) In this solution, through the multispectral imaging and augmented reality (AR) technology of the food label verification terminal, users can capture physical anti-counterfeiting features (such as nano-pit distribution and interference film angle response) in real time, and superimpose production traceability information on the physical label image. In addition, combined with the data comparison of blockchain nodes, a "physical-digital" dual-channel verification mechanism is formed, which significantly enhances consumers' trust in product authenticity and traceability information. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the system architecture of the present invention;

[0038] Figure 2 It is a schematic diagram of the system components and functions of the present invention;

[0039] Figure 3 This is a schematic diagram of the steps of the method for generating a unique identification for food production of the present invention;

[0040] Figure 4 This is a mind map of the method for generating a unique identifier for food production according to the present invention.

[0041] Description of the numbers in the figure:

[0042] 1. Artistic feature parameterization module; 101. Graphic analysis unit; 102. Color quantization unit; 103. Texture analysis unit;

[0043] 2. Production data acquisition module;

[0044] 3. Cross-modal generation engine; 301. Data-art mapping unit; 302. Style transfer unit; 303. Dynamic rendering unit;

[0045] 4. Multiple anti-counterfeiting processing modules; 401. Physical anti-counterfeiting unit; 402. Optical anti-counterfeiting unit; 403. Digital anti-counterfeiting unit;

[0046] 5. Blockchain evidence storage module. DETAILED DESCRIPTION

[0047] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the specification of the present invention; it is obvious that the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments, and all other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making creative work are within the scope of protection of the present invention.

[0048] Example:

[0049] "Xingyao Food Group" is a large-scale comprehensive food company, whose products cover multiple categories such as snacks, condiments, and frozen foods. Its sales network covers major supermarkets and online platforms across the country. With the increasingly fierce market competition in the food industry and consumers' increasing attention to food safety and quality, companies are facing severe challenges. To this end, "Xingyao Food Group" decided to introduce this kind of food production unique identification management system. Figure 1-Figure 4 , a detailed description of the unique identification management system for food production is given:

[0050] 1. System implementation steps

[0051] 1. Artistic feature parameterization module (1):

[0052] Graphics parsing unit (101): The brand logo of "Xingyao Food Group" is composed of a unique star pattern and brand name. The graphics parsing unit uses the Bezier curve control point extraction algorithm to accurately identify the contour control points of the star pattern, and at the same time, through the polygon vertex coordinate serialization, the text contour of the brand name is converted into editable vector data. In order to highlight the regional cultural characteristics of the product, the company introduced auspicious pattern vector graphics in the local traditional paper-cutting art style. The system conducts a comprehensive inspection of the resolution, color mode, file format, size ratio, element composition, etc. of the graphics based on the pre-set strict compliance standards, and completes the parametric conversion after ensuring that it meets the requirements. In addition, according to the requirements of geographical indication certification, the system calls the regional cultural characteristics template library containing local characteristic buildings, folk customs, and natural landscapes, selects representative elements, such as the unique carved patterns of local traditional buildings, and integrates them into the art element library.

[0053] Color quantification unit (102): The company's product packaging uses bright red and yellow as the main colors, conveying a brand image of vitality and deliciousness. The color quantification unit uses the HSV color model to accurately quantify the main red color as hue 30°, saturation 90%, and lightness 85%, and quantize yellow as hue 60°, saturation 80%, and lightness 95%, and records them in detail in the color space distribution matrix. For auxiliary colors on the packaging, such as green and blue used to distinguish product series, detailed three-dimensional discrete coding is also performed.

[0054] Texture analysis unit (103): In snack packaging, imitation potato chip textures, candy particle textures, etc. are often used to increase the texture. Based on the grayscale co-occurrence matrix, the texture analysis unit accurately calculates the directional angle of the imitation potato chip texture as 30°, the contrast as 0.7, and the roughness as 0.5, and generates the corresponding texture feature vector; for the candy particle texture, the random directional, high contrast, and moderate roughness indicators are calculated to generate an accurate texture feature vector.

[0055] 2. Production data collection module (2):

[0056] In the production workshop of "Xingyao Food Group", a comprehensive and high-precision sensor network is deployed. The time sensor uses atomic clock calibration technology to accurately record the production timestamp of each batch of products, accurate to milliseconds; the raw material batch sensor obtains the raw material batch code by scanning the QR code on the raw material packaging or reading the RFID tag to ensure that the source of each batch of raw materials can be traced; the process parameter sensor uses high-precision temperature sensors, pressure sensors, flow sensors, etc. to monitor key process control parameters such as frying temperature, seasoning ratio, and packaging weight in real time. For example, when producing a batch of potato chips, the sensor records the production timestamp as 15:45:10 300 milliseconds on May 20, 2023, and the raw material batch code is "POTATO20250701008" (representing a specific batch of potatoes), "OIL20250701003" (representing a specific batch of edible oil), etc. The process control parameters are frying temperature 180℃, seasoning ratio of salt 0.5%, spice 0.3%, and packaging weight 100g ± 2g.

[0057] 3. Cross-modal generation engine (3):

[0058] Data-art mapping unit (301): uniquely maps the production timestamp, raw material batch code and process control parameters. Taking the production timestamp as an example, the last three digits of its millisecond value are converted into the initial rotation angle of the spiral through a specific formula. Assuming that the last three digits are 250, the initial rotation angle generated is 25°; for the raw material batch code, it is converted into a radius increment sequence in the polar coordinate system through complex modulus operations and code mapping algorithms. For example, the raw material batch code "POTATO20250701008" is converted into a set of radius increment values ​​[0.15, 0.25, 0.1] after operation; the frying temperature in the process control parameter is mapped to the iteration termination threshold of the Mandelbrot fractal set after normalization. If the frying temperature is 180°C, the iteration termination threshold obtained after normalization is 0.85.

[0059] Style transfer unit (302): using the generator model of the generative adversarial network, the number of fractal iterations, polar coordinate basis function parameters, spiral phase angle, etc. are integrated with the parameterized data set of artistic features. For example, the parameters obtained by the above mapping are combined with parameterized data such as brand logo, color, texture, etc. to generate a logo with a unique fractal art pattern, which contains both the information of the production data and a unique artistic style.

[0060] Dynamic rendering unit (303): According to the preset brand visual specifications of the enterprise, the fractal art pattern is carefully rendered. First, the brand's main colors of red and yellow are filled, and then the imitation potato chip texture is superimposed to make it more suitable for the product characteristics. Finally, dynamic light and shadow effects are added to simulate the gloss of potato chips under the light, and the vector graphics file of the final logo is output.

[0061] 4. Multiple anti-counterfeiting processing modules (4):

[0062] Physical anti-counterfeiting unit (401): During the logo production process, an anti-counterfeiting layer containing randomly distributed nano-pits and micro-structure lines is embedded on the logo surface through advanced nano-imprinting and photolithography technology. The diameter of these nano-pits is between 30-80 nanometers, with a distribution density of 1200-1500 per square millimeter, and the width of the micro-structure lines is between 50-100 nanometers, and the distribution is completely random, making it difficult to copy.

[0063] Optical anti-counterfeiting unit (402): Multi-layer vacuum coating and interference technology are used to superimpose an interference film color layer with angle-sensitive characteristics and a diffractive optical element on the surface of the logo. When the logo is observed from different angles, the interference film will show a color gradient from red to yellow, and the diffractive optical element will produce a unique pattern change. The angle response curves of the color change and pattern change are precisely designed and unique.

[0064] Digital anti-counterfeiting unit (403): performs SM3 hash operation on the vector graphics file of the logo to generate a digital fingerprint, and obtains a digital fingerprint with a length of 256 bits. Then, the digital fingerprint is encrypted using the AES encryption algorithm, and the encrypted fingerprint is written into the invisible coding area at the edge of the logo. The invisible coding area uses special fluorescent ink and micro-printing technology, and can only be revealed under ultraviolet irradiation of a specific wavelength and a high-power microscope.

[0065] 5. Blockchain Evidence Storage Module (5):

[0066] The characteristic hash value of the identification and the associated production data, such as production timestamp, raw material batch code, process control parameters, etc., are stored in the blockchain node through the blockchain dedicated interface. The blockchain adopts the form of a consortium chain, and the nodes include the production department, quality inspection department, logistics department within the enterprise, and the cooperating third-party regulatory agencies, financial institutions and industry associations. Each node saves the complete traceability chain information to ensure the data cannot be tampered with and is traceable.

[0067] 2. Generation method implementation steps

[0068] Step 1:

[0069] Receive production data streams from the sensor network of the production workshop and perform preliminary cleaning and sorting of the data. Convert the production timestamp to a unified time format, such as "YYYY-MM-DD HH:MM:SS.fff"; parse the raw material batch code according to the preset rules to extract key information such as raw material type, batch number, supplier, etc.; normalize the process control parameters so that their value range is between 0-1 to facilitate subsequent processing.

[0070] Step 2:

[0071] The pre-trained style transfer model is called to convert the production data stream into a frequency domain feature vector through Fourier transform. Then, the self-attention mechanism is used to calculate the correlation weight between the frequency domain feature vector and the art feature parameterized dataset. For example, for the frequency domain feature vector corresponding to the production timestamp, the correlation weight with the color feature vector is calculated to be 0.35, and the correlation weight with the texture feature vector is calculated to be 0.25, etc. Finally, the art feature parameters are dynamically weighted according to the correlation weight to generate the fused stylized parameter matrix.

[0072] Step 3:

[0073] Based on the fused stylized parameter matrix, a dynamic logo graphic containing fractal geometric features, data fingerprints and brand visual elements is generated. Using a professional graphics rendering engine, the number of fractal iterations, polar coordinate basis function parameters, etc. are converted into specific graphic shapes and lines, combined with the brand's color and texture elements to generate a logo graphic with a unique visual effect.

[0074] Step 4:

[0075] Physical and digital anti-counterfeiting treatments are applied to the dynamic logo graphics in sequence. First, a nano-concave and micro-structure line anti-counterfeiting layer is embedded on the graphic surface, then an interference film color layer and a diffraction optical element are superimposed, and finally a digital fingerprint is generated and encrypted and written into the invisible coding area.

[0076] Step 5:

[0077] The identification feature value and associated data are written into the blockchain evidence storage contract according to the format requirements of the blockchain. The contract clearly stipulates the data storage format, access rights, update mechanism, data encryption method, etc. to ensure the secure storage and effective management of data.

[0078] Step 6:

[0079] In the sales terminal and consumer verification scenarios, the food label verification terminal is connected. The optical acquisition unit inside the verification terminal is used to capture the physical anti-counterfeiting features of the label, such as the distribution of nano-pits, the color change of the interference film, the pattern of the diffraction optical element, etc., through high-resolution multi-spectral imaging and microstructure detection technology. The collected feature data is then connected to the blockchain node through a secure network and compared with the data stored on the chain to verify the authenticity of the label.

[0080] Step 7:

[0081] The interactive display unit inside the food label verification terminal is used to overlay the production traceability information onto the real-time image of the physical label through augmented reality technology. When consumers use the verification terminal to scan the product label, the screen will display the product's production process, raw material source, quality inspection report, nutritional ingredients and other information in the form of a 3D model. At the same time, the anti-counterfeiting verification unit is used to detect the random distribution characteristics of the microstructure layer and the angle response curve of the optically variable layer to further verify the anti-counterfeiting performance of the label.

[0082] By implementing the unique identification management system for food production, "Xingyao Food Group" has successfully solved the problems of product anti-counterfeiting and quality traceability. It uses the parameterized module of artistic features to quantify the encoding of graphics, colors and textures, and combines the requirements of geographical indication certification to call the regional cultural feature template library to achieve a dynamic fusion of brand cultural elements and regional characteristics, significantly improving the brand image and market competitiveness. Consumers can easily obtain detailed information about the product through the verification terminal, and are more assured of the product quality. The company can also quickly respond to market feedback, optimize production management processes, and improve production efficiency and product quality.

[0083] The above is only a preferred specific implementation of the present invention; however, the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical solution and its improved conception within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A food production unique identification management system, characterized in that: include: An art feature parameterization module (1) is configured to perform quantifiable encoding on graphics, colors and textures in a preset art element library, and output a parameterized data set including a vector control point set, a color space distribution matrix and a texture feature vector, wherein the art element library supports importing custom vector graphics through a user interface, and automatically performs compliance checks and parameterization conversions, and calls a regional cultural feature template library according to geographical indication certification requirements; A production data acquisition module (2) is connected to a sensor network of a food production line to acquire a structured data stream including production timestamps, raw material batch codes, and process control parameters in real time; A cross-modal generation engine (3) receives the parameterized data set and the structured data stream, encodes the production data into artistic style transfer parameters through a pre-trained generative adversarial network model, and fuses the parameters into the parameterized data set to generate a dynamic visual logo containing data fingerprint information; A multiple anti-counterfeiting processing module (4) performs a multi-level anti-counterfeiting processing including random microstructure embedding, optically variable layer superposition, and cryptographic hash value calculation on the dynamic visual mark; The blockchain evidence storage module (5) stores the characteristic hash value of the identifier and the associated production data in the blockchain node to establish an unalterable traceability chain.

2. A food production unique identification management system according to claim 1, characterized in that: The art feature parameterization module (1) comprises a graphics analysis unit (101), a color quantization unit (102) and a texture analysis unit (103). The graphics analysis unit (101) converts a geometric pattern into editable vector data by extracting Bezier curve control points and serializing polygon vertex coordinates. The color quantization unit (102) uses the HSV color model to perform hue-saturation-lightness three-dimensional discrete encoding on the color combination in the art element. The texture analysis unit (103) calculates the directionality, contrast and roughness index of the texture based on the grayscale co-occurrence matrix to generate a texture feature vector.

3. A food production unique identification management system according to claim 1, characterized in that: The cross-modal generation engine (3) comprises a data-art mapping unit (301), a style transfer unit (302) and a dynamic rendering unit (303). The data-art mapping unit (301) maps the production timestamp, the raw material batch code and the process control parameters into the number of fractal iterations, the polar coordinate basis function parameters and the spiral phase angle respectively through a convolutional neural network. The style transfer unit (302) utilizes a generator model of a generative adversarial network to fuse the number of fractal iterations, the polar coordinate basis function parameters and the spiral phase angle into a parameterized data set to generate a unique fractal art pattern. The dynamic rendering unit (303) performs color filling, texture overlay and dynamic light and shadow rendering on the fractal art pattern according to a preset brand visual specification, and outputs a vector graphics file of a final logo.

4. A food production unique identification management system according to claim 3, characterized in that: The data-art mapping unit (301) first converts the raw material batch code into a radius increment sequence in a polar coordinate system through a modulus operation, then maps the process control parameter into an iteration termination threshold of the Mandelbrot fractal set after normalization, and then generates an initial rotation angle of the spiral line through the millisecond value of the production timestamp.

5. A food production unique identification management system according to claim 1, characterized in that: The multiple anti-counterfeiting processing module (4) comprises a physical anti-counterfeiting unit (401), an optical anti-counterfeiting unit (402) and a digital anti-counterfeiting unit (403); the physical anti-counterfeiting unit (401) embeds a microstructure layer containing randomly distributed nano-pits in the logo; the optical anti-counterfeiting unit (402) superimposes an interference film color layer with angle-sensitive characteristics; and the digital anti-counterfeiting unit (403) performs an SM3 hash operation on a vector graphics file of the logo to generate a digital fingerprint, and writes the encrypted fingerprint into an invisible coding area at the edge of the logo.

6. A method for generating a unique identification for food production, comprising a unique identification management system for food production according to any one of claims 1 to 5, characterized in that: The following steps are involved: Step 1: Receive the production data stream and convert it into a set of control parameters through preset rules; Step 2: calling a pre-trained style transfer model to perform cross-modal fusion of the control parameter set and the art feature parameterized dataset; Step 3: Generate a dynamic logo graphic containing fractal geometric features, data fingerprints and brand visual elements; Step 4: Apply physical and digital anti-counterfeiting treatment to the dynamic logo graphic; Step 5: Write the identification feature value and associated data into the blockchain evidence storage contract; Step 6: Connect the food label verification terminal, use the optical acquisition unit inside the food label verification terminal to capture the physical anti-counterfeiting features of the label through multi-spectral imaging, and connect it to the blockchain node to compare the matching degree between the on-site acquisition features and the data stored on the chain; Step 7: Using the interactive display unit inside the food label verification terminal, the production traceability information is superimposed on the real-time picture of the physical label through augmented reality technology, and then the anti-counterfeiting verification unit inside the food label verification terminal is used to detect the random distribution characteristics of the microstructure layer and the angle response curve of the optically variable layer.

7. A method for generating a unique identification for food production according to claim 6, characterized in that: The cross-modal fusion step specifically includes: Step 1: Convert the production data stream into a frequency domain feature vector through Fourier transform; Step 2: Use the self-attention mechanism to calculate the correlation weight between the frequency domain feature vector and the art feature parameterized dataset; Step 3: Dynamically weight the artistic feature parameters according to the correlation weights to generate a fused stylized parameter matrix.

Images