Seed packaging bag AI intelligent anti-fake traceability management system and method

Abstract

The application provides a kind of seed packaging bag AI intelligent anti-fake traceability management system and method, relating to the field of home kitchen.The seed packaging bag AI intelligent anti-fake traceability management system includes an intelligent anti-fake packaging bag: a natural random physical texture layer, a dynamic optical structure layer overlaid thereon, and an encrypted identifier storing a digital identity DID.The microstructure of the natural random physical texture layer forms a unique feature that cannot be replicated, and the dynamic optical structure layer produces variable optical effects when physically interacting.By forming a unique biological fingerprint through the micro-random texture of the packaging substrate, and superimposing the optical variable effects of the microlens array, each packaging bag has physical-level unclonability.Combined with the dynamic comparison of real-time texture features and blockchain-anchored initial values by a multi-modal AI engine, and optical living frequency analysis, a three-dimensional verification closed loop that counterfeiters cannot easily break through is achieved.

Description

Technical Field

[0001] This invention relates to the field of data processing system technology, specifically to an AI-powered intelligent anti-counterfeiting and traceability management system and method for seed packaging bags. Background Technology

[0002] With the development of IoT and sensor technology, it has become possible to collect real-time data on seed production and transportation environments, providing a basic support for traceability. Blockchain technology, with its immutable characteristics, has built a trustworthy distributed ledger, ensuring that data from production to sales is transparent and tamper-proof. AI technology, through convolutional neural networks and multimodal anti-counterfeiting models, enables the detection of packaging bag defects, verification of authenticity, and early warning of abnormal behavior.

[0003] Currently, while existing technologies have some applications in the field of seed anti-counterfeiting and traceability, there is still room for improvement. Traditional solutions mostly rely on single static identifiers, making it difficult to achieve non-cloning at the physical anti-counterfeiting level. Furthermore, the integration of multiple technologies is not deep enough, data verification lacks dynamic multimodal collaboration, and traceability data storage and physical carrier verification are prone to disconnect in blockchain applications. Anti-fraud mechanisms are also mostly stuck in the passive response stage, and a dynamic closed-loop verification system of physical characteristics and digital identity has not yet been formed. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an AI-powered intelligent anti-counterfeiting and traceability management system and method for seed packaging bags, which solves the problem of lack of dynamic multimodal collaboration in data verification of seed anti-counterfeiting and traceability systems.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an AI-powered intelligent anti-counterfeiting and traceability management system for seed packaging bags, comprising:

[0006] Intelligent anti-counterfeiting packaging bag: It has a natural random physical texture layer, a dynamic optical structure layer covering it, and an encrypted identifier that stores digital identity (DID). The microstructure of the natural random physical texture layer forms a unique feature that cannot be copied. The dynamic optical structure layer produces a variable optical effect during physical interaction. The encrypted identifier that stores digital identity (DID) is associated with seed full-link traceability data.

[0007] User interaction terminal: Configured with an adaptive acquisition guidance program to guide users to collect physical feature data step by step. The physical feature data includes macro images of the physical texture layer, tilted video of the dynamic optical structure layer, and DID data. It integrates a lightweight edge computing module to compress the raw physical feature data and generate encrypted digests.

[0008] Multimodal AI verification engine: Receives encrypted digests and DIDs of physical feature data uploaded by the user interaction terminal, calculates the similarity between real-time features and initial features in real time through a fusion decision model, analyzes the frequency of moiré fringe changes in optical video, and outputs dynamically weighted authenticity verification results and risk level labels;

[0009] Blockchain Evidence Storage Network: Stores the initial feature hash value of physical texture and binds it with DID and seed full-chain traceability data. It manages the traceability event hashes of seed production, processing and logistics links in a hierarchical manner. The multimodal AI verification engine synchronously calls the initial physical features stored in the blockchain for comparison to realize dynamic closed-loop verification of physical carrier and digital identity.

[0010] Preferably, the natural random physical texture layer is composed of microfibers or biological particles of the packaging substrate. Its initial digital features are extracted by a convolutional neural network to generate a unique hash value. This hash value is written into the blockchain as a physical trust anchor and forms a two-way binding relationship with the DID.

[0011] Preferably, the dynamic optical structure layer includes a microlens array and a bottom random pixel matrix. The parameters of the two are optimized and matched by a genetic algorithm so that moiré fringes with spectral response are generated when the tilt angle is >15°. The dynamic optical feature of the moiré fringes is used as a liveness verification factor input into the multimodal AI verification engine.

[0012] Preferably, the fusion decision model of the multimodal AI verification engine performs the following associated operations:

[0013] Retrieve the initial texture hash value corresponding to the DID from the blockchain;

[0014] Calculate the similarity between the initial texture hash value and the real-time texture features uploaded by the user;

[0015] Analyze the frequency of moiré fringe changes in dynamic optical videos;

[0016] If the texture feature similarity is ≥85% and the moiré fringe variation frequency is within the natural jitter range of 1-3Hz, then it is judged as true.

[0017] Preferably, the fusion decision model employs a dynamic weighting mechanism based on attention; when the texture image is clear...

[0018] When the degree is below the threshold, the weight of optical features will be increased by 40%-60%. When the DID verification is abnormal, a second verification of the blockchain traceability data will be triggered.

[0019] Preferably, the collaborative process between the user interaction terminal and the multimodal AI verification engine includes:

[0020] The terminal pre-extracts physical feature data using a lightweight MOBILENET model to encrypt digests;

[0021] Dynamically compress the optical video frame rate based on network bandwidth.

[0022] Only encrypted digests of physical feature data are sent to the cloud, while the original data is stored locally to protect privacy.

[0023] Preferably, the blockchain evidence storage network adopts a hierarchical storage strategy, with the hash values ​​of key production and quality inspection events directly recorded on the chain, and non-key data stored in IPFS. The DID is associated with the blockchain pointer, and the initial texture hash value is written as an independent data block, with modification permissions denied.

[0024] Preferably, when the multimodal AI verification engine fails to verify, the anti-fraud linkage mechanism will drive the AI ​​engine to automatically analyze the counterfeit feature patterns, upload the fraud pattern features and geographical location to the blockchain, generate a counterfeit risk heat map, and push early warning information and a list of involved DIDs to related manufacturers.

[0025] A method for AI-powered intelligent anti-counterfeiting and traceability management of seed packaging bags includes the following steps:

[0026] Step 1: Scan the natural texture of the packaging bag substrate, generate a depth feature vector and hash it, bind the hash value, dynamic optical structure parameters, and seed traceability metadata to the DID and put it on the blockchain;

[0027] Step 2: The user interaction terminal guides the collection of physical feature data, generates an encrypted digest, and transmits it to the multimodal AI verification engine for multimodal dynamic verification: The multimodal AI verification engine integrates real-time texture features, optical dynamic effects, and the initial texture hash value of the blockchain to output a trusted verification result;

[0028] Step 3: The verification results are fed back to the blockchain to update the device reputation value, and high-risk forgery characteristics are automatically entered into the anti-fraud model training library.

[0029] Preferably, the specific steps of the multimodal dynamic verification in step two include calculating the similarity between the real-time texture and the initial features of the blockchain using cosine similarity, with the calculation formula as follows:

[0030]

[0031] Where Fuser is the texture feature vector collected by the user in real time, and Finit is the initial texture stored on the blockchain.

[0032] eigenvectors;

[0033] Analyzing the frequency variation of moiré fringes in dynamic video using optical flow method;

[0034] If the similarity is ≥0.85 and the frequency of moiré fringe changes is within the range of 1-3Hz in natural human hand tremors, then the verification is successful.

[0035] This invention provides an AI-powered intelligent anti-counterfeiting and traceability management system and method for seed packaging bags. It offers the following advantages:

[0036] 1. This invention forms a unique bio-fingerprint through the microscopic random texture of the packaging substrate, superimposed with the optical variable effect of the microlens array, making each packaging bag physically unclonable. Combined with the dynamic comparison of real-time texture features with the initial value anchored to the blockchain by a multimodal AI engine, as well as optical liveness frequency analysis, a three-dimensional verification closed loop that is difficult for counterfeiters to break is realized. This anti-counterfeiting architecture, from the material source to digital intelligence, improves the problem that traditional static labels are easily copied in batches.

[0037] 2. This invention achieves the integration of anti-counterfeiting and traceability by relying on a blockchain-driven end-to-end trust mechanism. The entire lifecycle data of seeds from production to planting is strongly bound to digital identities through hierarchical on-chain recording, ensuring that traceability information is tamper-proof and can be thoroughly queried. When AI verification detects counterfeit features, it automatically analyzes the attack pattern and feeds it back to the physical anti-counterfeiting design, forming a continuously optimized positive cycle of defense. This not only establishes a credible seed quality barrier for farmers, but also reconstructs the security governance model of the agricultural input market through the proactive early warning function of risk heat map. Attached Figure Description

[0038] Figure 1 is a schematic diagram of the system flow of the present invention;

[0039] Figure 2 is a schematic diagram of the user interaction terminal data collection method of the present invention;

[0040] Figure 3 is a schematic diagram of the multimodal AI verification engine process of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, those skilled in the art will understand the following without further explanation.

[0042] All other embodiments obtained under the premise of creative labor are within the scope of protection of this invention.

[0043] Example:

[0044] As shown in Figure 1- Figure 3As shown, this embodiment of the invention provides an AI-powered intelligent anti-counterfeiting and traceability management system for seed packaging bags, including an intelligent anti-counterfeiting packaging bag: having a natural random physical texture layer, a dynamic optical structure layer covering it, and an encrypted identifier storing a digital identity (DID). The microstructure of the natural random physical texture layer forms a unique and uncopyable feature. The dynamic optical structure layer generates variable optical effects during physical interaction. The encrypted identifier storing the digital identity (DID) is associated with the seed's full-chain traceability data. The natural random physical texture layer is composed of microfibers or biological particles from the packaging substrate. Its initial digital features are extracted and a unique hash value is generated through a convolutional neural network. This hash value is written into the blockchain as a physical trust anchor, forming a two-way binding relationship with the DID. The dynamic optical structure layer includes a microlens array and a bottom-layer random pixel matrix. The parameters of the two are optimized and matched by a genetic algorithm, so that moiré fringes with a spectral response are generated when the tilt angle is >15°. The dynamic optical feature of these moiré fringes is input into the multimodal AI verification engine as a liveness verification factor.

[0045] User interaction terminal: Configured with an adaptive acquisition guide program to guide users to collect physical feature data step by step. The physical feature data includes macro images of the physical texture layer, tilted video of the dynamic optical structure layer, and DID data. It integrates a lightweight edge computing module to compress the raw physical feature data and generate encrypted digests.

[0046] Multimodal AI Verification Engine: Receives encrypted digests and DIDs of physical feature data uploaded from user interaction terminals. Through a fusion decision model, it calculates the similarity between real-time features and initial features in real time, analyzes the frequency of moiré fringe changes in optical video, and outputs dynamically weighted verification results and risk level labels. The fusion decision model of the multimodal AI verification engine performs the following related operations:

[0047] Retrieve the initial texture hash value corresponding to the DID from the blockchain;

[0048] Calculate the similarity between the initial texture hash value and the real-time texture features uploaded by the user;

[0049] Analyze the frequency of moiré fringe changes in dynamic optical videos;

[0050] The fusion decision model uses an attention mechanism for dynamic weighting. When the texture image sharpness is below a threshold,

[0051] The weight of optical features is increased by 40%-60%, triggering secondary verification of blockchain traceability data when DID verification fails; the collaborative process between the user interaction terminal and the AI ​​engine includes:

[0052] The terminal pre-extracts physical feature data using a lightweight MOBILENET model to encrypt digests;

[0053] Dynamically compress the optical video frame rate based on network bandwidth.

[0054] Only encrypted digests of physical characteristic data are transmitted to the cloud, while the original data remains locally to protect privacy.

[0055] The blockchain-based evidence storage network stores the initial feature hash value of the physical texture and binds it to the DID and seed end-to-end traceability data. It hierarchically manages the traceability event hashes of seed production, processing, and logistics. The multimodal AI verification engine synchronously calls the initial physical features stored on the blockchain for comparison, realizing dynamic closed-loop verification between the physical carrier and the digital identity. The blockchain-based evidence storage network adopts a hierarchical storage strategy. The hash values ​​of key production and quality inspection events are directly recorded on the chain, while non-key data is stored in IPFS and linked to the DID through on-chain pointers. The initial texture hash value is written as an independent data block, and modification permissions are denied.

[0056] When the multimodal AI verification engine fails to verify, the anti-fraud linkage mechanism will drive the multimodal AI verification engine to automatically analyze the counterfeit feature patterns, upload the fraud pattern characteristics and geographical location to the blockchain, generate a counterfeit risk heat map, and push early warning information and a list of involved DIDs to related manufacturers.

[0057] A method for AI-powered intelligent anti-counterfeiting and traceability management of seed packaging bags includes the following steps:

[0058] Step 1: Scan the natural texture of the packaging bag substrate, generate a depth feature vector and hash it, bind the hash value, dynamic optical structure parameters, and seed traceability metadata to the DID and put it on the blockchain;

[0059] Step 2: The user interaction terminal guides the collection of physical feature data, generates an encrypted digest, and transmits it to the multimodal AI verification engine for multimodal dynamic verification: The multimodal AI verification engine integrates real-time texture features, optical dynamic effects, and the initial texture hash value of the blockchain to output a trusted verification result;

[0060] Step 3: The verification results are fed back to the blockchain to update the device reputation value, and high-risk forgery characteristics are automatically entered into the anti-fraud model training library.

[0061] The specific steps of multimodal dynamic verification in step two include calculating the similarity between the real-time texture and the initial features of the blockchain using cosine similarity. The calculation formula is as follows:

[0062]

[0063] Where Fuser is the texture feature vector collected by the user in real time, and Finit is the initial texture feature vector stored in the blockchain;

[0064] Analyzing the frequency variation of moiré fringes in dynamic video using optical flow method;

[0065] If the similarity is ≥0.85 and the frequency of moiré fringe changes is within the range of 1-3Hz in natural human hand tremors, then the verification is successful.

[0066] Intelligent anti-counterfeiting and traceability of hybrid rice seeds:

[0067] The packaging bag uses a biodegradable hemp fiber substrate. The natural fiber distribution on its surface is imaged using a 1200dpi industrial scanner, and a 1024-dimensional feature vector FIN ITF INIT is extracted using ResNet-50 to generate the SHA-256 hash value HINITH INIT.

[0068] The microlens array covers an AI-generated random pixel matrix, optimized by a genetic algorithm to ensure the generation of green spectral moiré fringes when tilted at 18°;

[0069] Bind the HINITH INIT to the DID and write the associated seed production data to the blockchain;

[0070] Farmers verify the purchase: Farmers use a mobile app to scan the packaging bag, take macro photos of the hemp fiber texture, tilt the phone to shoot a 5-second video to capture the dynamic changes of the moiré stripes, and scan the code to obtain the DID;

[0071] MobileNetV3 compresses texture features into a 182KB summary, adaptively reduces the video frame rate to 3fps in weak network environments, and encrypts the transmission to the cloud.

[0072] AI dynamically verifies cosine similarity calculation:

[0073] Similarity ≥ 0.85 and 1Hz ≤ frequency ≤ 3Hz, verification passed;

[0074] The frequency is compliant, but the Similarity is less than 0.85, suggesting possible repackaging and posing a Level 1 risk.

[0075] Similarity ≥0.85 but frequency is abnormal, indicating high-level counterfeiting attacks, thus classifying it as a high-risk product;

[0076] The two anomalies prompted us to proactively pinpoint the source of the counterfeit goods and report the issue to users for emergency response.

[0077] Blockchain traceability display: Farmers' mobile phones display a visual timeline, recording the temperature and humidity throughout the product's cold chain logistics process;

[0078] Anti-fraud proactive defense: Counterfeiters imitate moiré patterns but fix the frequency at 0.5Hz, attempting to enter the market with high-quality counterfeit packaging. The system responds with a closed-loop mechanism.

[0079] The AI ​​engine identifies abnormal frequencies, marks high-risk patterns, and analyzes forgery signatures.

[0080] Generate a heatmap of counterfeit goods and upload it to the blockchain;

[0081] Push parameter optimization instructions to packaging manufacturers;

[0082] Manufacturers are producing the next generation of packaging bags with new optical structures based on new parameters, making counterfeiting much more difficult.

[0083] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An AI-powered intelligent anti-counterfeiting and traceability management system for seed packaging bags, characterized in that: include: The intelligent anti-counterfeiting packaging bag features a natural random physical texture layer, a dynamic optical structure layer covering it, and an encrypted identifier storing a digital identity (DID). The microstructure of the natural random physical texture layer forms a unique and uncopyable feature. The dynamic optical structure layer generates variable optical effects during physical interaction. The encrypted identifier storing the DID is associated with seed-based full-chain traceability data. The natural random physical texture layer is composed of microfibers or biological particles from the packaging substrate. Its initial digital features are extracted and a unique hash value is generated through a convolutional neural network. This hash value is written into the blockchain as a physical trust anchor, forming a two-way binding relationship with the DID. The dynamic optical structure layer includes a microlens array and a bottom-layer random pixel matrix. The parameters of the two are optimized and matched using a genetic algorithm, resulting in moiré fringes with a spectral response when the tilt angle is >15°. This dynamic optical feature of the moiré fringes is used as a liveness verification factor input into a multimodal AI verification engine. User interaction terminal: Configured with an adaptive acquisition guidance program to guide users to collect physical feature data step by step. The physical feature data includes macro images of the physical texture layer, tilted video of the dynamic optical structure layer, and DID data. It integrates a lightweight edge computing module to compress the raw physical feature data and generate encrypted digests. Multimodal AI verification engine: Receives encrypted digests and DIDs of physical feature data uploaded by the user interaction terminal, calculates the similarity between real-time features and initial features in real time through a fusion decision model, analyzes the frequency of moiré fringe changes in optical video, and outputs dynamically weighted authenticity verification results and risk level labels. The fusion decision model of the multimodal AI verification engine performs the following correlation operations: Retrieve the initial texture hash value corresponding to the DID from the blockchain; Calculate the similarity between the initial texture hash value and the real-time texture features uploaded by the user; Analyze the frequency of moiré fringe changes in dynamic optical videos; If the texture feature similarity is ≥85% and the moiré fringe variation frequency is within the natural jitter range of 1-3Hz, then it is judged as true; Blockchain-based evidence storage network: Stores the initial feature hash value of physical textures and binds it to DID and seed end-to-end traceability data, hierarchically managing the traceability event hashes of seed production, processing, and logistics. The multimodal AI verification engine synchronously calls the initial texture hash value features stored in the blockchain for comparison, realizing dynamic closed-loop verification of physical carrier and digital identity; The fusion decision model uses an attention mechanism for dynamic weighting. When the clarity of the texture image is below the threshold, the weight of the optical features is increased by 40%-60%. When the DID verification is abnormal, a secondary verification of the blockchain traceability data is triggered.

2. The seed packaging bag AI intelligent anti-counterfeiting and traceability management system according to claim 1, characterized in that: The collaborative process between the user interaction terminal and the multimodal AI verification engine includes: The terminal pre-extracts physical feature data using a lightweight MOBILENET model to encrypt digests; Dynamically compress the optical video frame rate based on network bandwidth. Only encrypted digests of physical characteristic data are transmitted to the cloud, while the original data remains locally to protect privacy.

3. The seed packaging bag AI intelligent anti-counterfeiting and traceability management system according to claim 1, characterized in that: The blockchain evidence storage network adopts a hierarchical storage strategy. The hash values ​​of key production and quality inspection events are directly recorded on the chain, while non-key data is stored in IPFS. The DID is associated with the chain pointer, and the initial texture hash value is written as an independent data block, with modification permissions denied.

4. The seed packaging bag AI intelligent anti-counterfeiting and traceability management system according to claim 1, characterized in that: When the multimodal AI verification engine fails to verify, the anti-fraud linkage mechanism will drive the AI ​​engine to automatically analyze the counterfeit feature patterns, upload the fraud pattern features and geographical location to the blockchain, generate a counterfeit risk heat map, and push early warning information and a list of involved DIDs to related manufacturers.

5. A seed packaging bag AI intelligent anti-counterfeiting and traceability management method, using the seed packaging bag AI intelligent anti-counterfeiting and traceability management system according to any one of claims 1-4, characterized in that, Includes the following steps: Step 1: Scan the natural texture of the packaging bag substrate, generate a depth feature vector and hash it, bind the hash value, dynamic optical structure parameters, and seed traceability metadata to the DID and put it on the blockchain; Step 2: The user interaction terminal guides the collection of physical feature data, generates an encrypted digest, and transmits it to the multimodal AI verification engine for multimodal dynamic verification: The multimodal AI verification engine integrates real-time texture features, optical dynamic effects, and the initial texture hash value of the blockchain to output a trusted verification result; Step 3: The verification results are fed back to the blockchain to update the device reputation value, and high-risk forgery characteristics are automatically entered into the anti-fraud model training library.

6. The AI-powered intelligent anti-counterfeiting and traceability management method for seed packaging bags according to claim 5, characterized in that: The specific steps of multimodal dynamic verification in step two include calculating the similarity between real-time texture features and initial blockchain texture features using cosine similarity. The calculation formula is as follows: Where Fuser is the texture feature vector collected by the user in real time, and Finit is the initial texture feature vector stored in the blockchain; Analyzing the frequency variation of moiré fringes in dynamic video using optical flow method; If the similarity is ≥0.85 and the frequency of moiré fringe changes is within the range of 1-3Hz in natural human hand tremors, then the verification is successful.

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