Wine product anti-counterfeiting traceability method and system based on RFID

Abstract

The invention relates to an RFID-based liquor product anti-counterfeiting traceability method and system, and the method comprises the steps: obtaining the label identification information and attribute information of a target liquor product, carrying out the fragmentation of the label identification information based on the attribute information, and obtaining an information fragment set; performing encryption operation on each information fragment in the information fragment set, and uploading each encrypted information fragment to a corresponding private chain node for storage; detecting real-time state information of the target wine product in real time, analyzing and judging the real-time state information, generating triple event data when a preset condition event triggering condition is met, and anchoring the triple event data to a public chain node; and when the user scans the code for verification, sequentially executing Hash consistency verification and a zero-knowledge verification process based on preset industrial standard parameters, and outputting an information abstract and a verification conclusion of the target wine product. The anti-counterfeiting method has the effect of improving the anti-counterfeiting capability of the traceability.

Description

Technical Field

[0001] This application relates to the technical field of product traceability and blockchain data security, and in particular to an RFID-based method and system for anti-counterfeiting and traceability of alcoholic beverages. Background Technology

[0002] Currently, alcoholic beverages face high risks of counterfeiting and tampering at multiple stages, including production, warehousing, transportation, and sales. Traditional barcode labels or disposable anti-counterfeiting stickers are insufficient for reliable traceability throughout the product's entire lifecycle. On one hand, counterfeiting methods are becoming increasingly sophisticated, making it difficult for consumers to distinguish genuine products from fakes visually. On the other hand, internal logistics or sales data within companies can be tampered with, resulting in insufficient credibility of traceability information.

[0003] Most existing wine traceability systems use RFID tags combined with database records to centrally store data such as product raw material information, production batches, and logistics routes on enterprise servers. However, this centralized structure suffers from problems such as data vulnerability to single-point tampering, difficulty in cross-entity sharing, and opaque consumer verification paths. Furthermore, if on-chain evidence storage is adopted, it is often difficult to balance performance and privacy, and it is impossible to differentiate processing for different risk levels, nor does it have the ability to automatically identify and warn of abnormal behavior during product circulation.

[0004] The existing technical solutions mentioned above have the following drawbacks: the existing traceability mechanisms lack the ability to manage fine-grained encryption that can be sharded, and cannot achieve on-chain hierarchical protection of key sensitive information, so there is room for improvement. Summary of the Invention

[0005] To enhance the anti-counterfeiting capabilities of traceability, this application provides an RFID-based anti-counterfeiting traceability method and system for alcoholic beverages.

[0006] The above-mentioned objective of this application is achieved through the following technical solution: An RFID-based method for anti-counterfeiting and traceability of alcoholic beverages, comprising: Obtain the label identification information and attribute information of the target alcoholic beverage product, and perform segmentation processing on the label identification information based on the attribute information to obtain a set of information fragments, which includes raw material information fragments, process information fragments, logistics information fragments and sales information fragments; Each information fragment in the information fragment set is encrypted, and the encrypted information fragments are uploaded to the corresponding private chain nodes for distributed storage. During the circulation of the target alcoholic beverage product, the real-time status information of the target alcoholic beverage product is detected in real time, and the real-time status information is analyzed and judged. When the preset event triggering conditions are met, a triplet event data is generated. The triplet event data includes coordinate information, timestamp and operator signature, and the triplet event data is anchored to the public chain node. When a user scans a code for verification, the hash consistency verification and zero-knowledge verification based on preset industry standard parameters are executed sequentially. The information summary and verification conclusion of the target alcoholic beverage product are output, and the network load is monitored in real time. The consensus strategy is dynamically switched based on the network load monitoring results.

[0007] By adopting the above technical solutions, and by acquiring label identification information and attribute information, and performing fragmentation processing based on the attribute information, a traceability data structure can be flexibly constructed according to the individual characteristics of the product, thereby improving the adaptability and interpretability of the data in the subsequent anti-counterfeiting and tracking process. By performing encryption operations on each information fragment separately and uploading it to different private chain nodes for distributed storage, the secure isolation and anti-tampering protection of multi-source heterogeneous data can be achieved, thereby improving the anti-counterfeiting system's resistance to attacks and data credibility. By detecting status information in real time during the circulation of alcoholic products and generating triplet event data and anchoring it to the public chain when preset conditions are met, it can be ensured that key circulation behaviors have on-chain traceability credentials, thereby strengthening the traceability capability of the entire product lifecycle. By executing hash consistency and zero-knowledge verification processes during code scanning verification, and combining them with dynamic switching consensus strategies based on network load, it can ensure the accuracy of verification results while taking into account system response efficiency, thereby achieving an efficient anti-counterfeiting experience for consumers.

[0008] In one example, this application can be further configured as follows: the segmentation of the tag identification information based on the attribute information to obtain a set of information fragments specifically includes: Extract the attribute information of the target alcoholic beverage product, wherein the attribute information includes at least one or more of the following: product type, packaging form, sales channel, distribution area, and retail price; Based on the attribute information, a preset risk level assessment model is invoked to determine the risk level of the target alcoholic product and obtain the corresponding risk level label. Based on the risk level label, select the corresponding segmentation weight strategy template, and split the label identification information into raw material information segments, process information segments, logistics information segments, and sales information segments according to the allocation ratio in the segmentation weight strategy template.

[0009] By adopting the above technical solutions, and extracting attribute information such as product type, packaging form, and sales channels, it is possible to perform structured modeling of the risk characteristics of alcoholic beverages in the sales chain, thereby providing a basis for personalized traceability strategies. By calling a preset risk level assessment model to generate risk level labels, differentiated encryption and on-chain strategies can be implemented for products of different levels, thereby improving the efficiency of system resource utilization. By selecting a segmentation weight template based on the risk level and splitting the label information accordingly, key data can be equipped with a stronger security protection mechanism, thereby enhancing the traceability accuracy and anti-counterfeiting and anti-diversion capabilities of high-risk products.

[0010] In one example, this application can be further configured as follows: performing encryption operations on each information fragment in the information fragment set, and uploading the encrypted information fragments to their respective private chain nodes for distributed storage, specifically includes: Each information fragment in the set of information fragments is encrypted using an encryption algorithm to generate an encrypted fragment. The encrypted fragments are sent to the corresponding private chain nodes according to the preset data ownership relationship; Write operations are performed on each private chain node to register the encrypted fragment and generate the corresponding hash digest value.

[0011] By adopting the above technical solutions, and generating encrypted fragments by calling encryption algorithms on each information fragment, the privacy and security of fragmented traceability data during on-chain transmission and storage can be guaranteed, thereby effectively preventing information leakage and forgery. By distributing encrypted fragments to different private chain nodes according to preset data ownership relationships, cross-departmental or cross-organizational data control and isolation can be achieved, thereby supporting data collaboration and protection with the participation of multiple entities. By registering and storing the encrypted fragments and generating hash digest values, comparable anchor points can be formed in the subsequent verification process, thereby improving the authenticity and verifiability of traceability information.

[0012] In one example, this application can be further configured as follows: Real-time detection of the real-time status information of the target alcoholic beverage product, analysis and judgment of the real-time status information, and generation of triplet event data when preset event triggering conditions are met, specifically including: The system collects real-time status information of the target alcoholic beverage product, including logistics status, geographic location information, and storage condition data associated with the target alcoholic beverage product. Determine whether the real-time status information meets the triggering rules of preset condition events, which include product entering or leaving the warehouse, crossing geofences, changes in circulation nodes, or state transitions. When the preset event triggering conditions are met, the triplet event data is generated.

[0013] By adopting the above technical solutions, and by collecting real-time status data such as logistics status, location information, and storage conditions, it is possible to dynamically perceive changes in the behavior of alcoholic beverages during the circulation process, thereby promptly detecting potential violations or abnormal behaviors. By judging whether the status information meets the preset triggering rules for events, it is possible to automatically identify key events such as entering and leaving warehouses, geographical boundary crossings, or node jumps, thereby avoiding delays and errors caused by relying on manual reporting. By generating triple event data containing signature and coordinate information when an event is triggered, it is possible to enhance the integrity of event evidence and the ability to trace responsibility, thereby ensuring the compliance and credibility of the regulatory audit process.

[0014] In one example, this application can be further configured as follows: when the user scans the code for verification, a hash consistency verification and a zero-knowledge verification process based on preset industry standard parameters are executed sequentially, and an information digest and verification conclusion of the target alcoholic beverage product are output, specifically including: Obtain the user's query request, and based on the query request, perform a consistency comparison to obtain the consistency verification result; When the consistency verification result is that the hash consistency verification passes, the target alcoholic product is verified to see whether it meets the specified rule conditions, and a zero-knowledge verification result is obtained. The rule conditions include product circulation compliance, production area traceability matching or sales path restriction. Based on the consistency verification results and the zero-knowledge verification results, a verifiable information summary of the target alcoholic beverage product is generated, and the verification conclusion is output.

[0015] By adopting the above technical solution, and performing hash consistency comparison based on user scanning requests, it is possible to quickly verify whether off-chain product information is consistent with on-chain records, thereby initially eliminating the risk of data tampering. After the consistency verification is passed, zero-knowledge verification based on industry parameters can be performed to determine whether the product meets conditions such as circulation compliance and production area matching without exposing all traceability content, thereby enhancing the system's verification capabilities while protecting trade secrets. By outputting verification conclusions and information summaries, it is possible to provide users with clear anti-counterfeiting result feedback, thereby enhancing consumers' perception and trust in the credibility of product traceability.

[0016] In one example, this application can be further configured as follows: the real-time monitoring of network load and the dynamic switching of consensus strategies based on the network load monitoring results specifically include: Real-time collection of network status indicators of current network nodes, including communication latency, response time, and node activity; Based on a preset network load assessment model, the load level corresponding to the network status index is calculated. Based on the load level, execute the corresponding switching strategy.

[0017] By adopting the above technical solutions, and by collecting network indicators such as communication latency, response time, and node activity in real time, the system load status under the current consensus environment can be accurately assessed, thus providing a basis for decision-making for the dynamic switching of the consensus mechanism. By calculating the load level through the network load assessment model, the consensus strategy can be managed hierarchically according to the operating conditions, thereby ensuring that the system still has stability and efficiency under different load conditions. By executing the switching strategy according to the load level, a highly secure consensus protocol can be used under low load, and a fast-response protocol can be switched under high load scenarios, thereby achieving a dual balance between performance and security of the anti-counterfeiting verification system.

[0018] In one example, this application can be further configured as follows: the RFID-based anti-counterfeiting and traceability method for alcoholic beverages also includes: A behavioral trajectory chain is constructed based on the triplet event data generated during the circulation of the target alcoholic beverage product; A preset behavior trajectory rule template is invoked to perform rule comparison on the trajectory features in the behavior trajectory chain. The behavior trajectory rule template includes at least one set of legal flow path node sequences, allowed node jump ranges, and flow time interval threshold rules. When the behavior trajectory chain does not meet any rule in the rule template, it is determined to be an abnormal trajectory, and a corresponding risk warning icon is generated.

[0019] By adopting the above technical solution, and constructing a behavioral trajectory chain based on the triplet event data recorded during the circulation process, it is possible to model the circulation path of the product in the spatiotemporal dimension, thereby forming a complete behavioral chain that can be verified and traced back. By calling the preset behavioral trajectory rule template to compare the trajectory chain with the rules, it is possible to quickly identify behaviors such as boundary crossing, jumping, and abnormal intervals in the circulation path, thereby achieving a high-sensitivity early warning of abnormal behaviors in the circulation process. By determining that the non-compliance with the rules is an abnormal trajectory and generating a risk warning label, it is possible to provide system-level prompts when the product triggers a high-risk circulation mode, thereby improving the response capability to counterfeit or illegal circulation behaviors.

[0020] The second objective of this invention is achieved through the following technical solution: An RFID-based anti-counterfeiting and traceability system for alcoholic beverages, comprising: The label information segmentation module is used to obtain the label identification information and attribute information of the target alcoholic beverage product, and to segment the label identification information based on the attribute information to obtain a set of information fragments, which includes raw material information fragments, process information fragments, logistics information fragments and sales information fragments. The fragmented encryption storage module is used to perform encryption operations on each information fragment in the information fragment set, and upload the encrypted information fragments to the corresponding private chain nodes for distributed storage. The event detection module is used to detect the real-time status information of the target alcoholic beverage during its circulation process, analyze and judge the real-time status information, and generate triplet event data when the preset event triggering conditions are met. The triplet event data includes coordinate information, timestamp and operator signature, and the triplet event data is anchored to the public chain node. The QR code verification module is used to sequentially execute hash consistency verification and zero-knowledge verification based on preset industry standard parameters when a user scans a QR code for verification. It outputs an information summary and verification conclusion of the target alcoholic beverage product, monitors network load in real time, and dynamically switches consensus strategies based on the network load monitoring results.

[0021] By adopting the above technical solutions, and by acquiring label identification information and attribute information, and performing fragmentation processing based on the attribute information, a traceability data structure can be flexibly constructed according to the individual characteristics of the product, thereby improving the adaptability and interpretability of the data in the subsequent anti-counterfeiting and tracking process. By performing encryption operations on each information fragment separately and uploading it to different private chain nodes for distributed storage, the secure isolation and anti-tampering protection of multi-source heterogeneous data can be achieved, thereby improving the anti-counterfeiting system's resistance to attacks and data credibility. By detecting status information in real time during the circulation of alcoholic products and generating triplet event data and anchoring it to the public chain when preset conditions are met, it can be ensured that key circulation behaviors have on-chain traceability credentials, thereby strengthening the traceability capability of the entire product lifecycle. By executing hash consistency and zero-knowledge verification processes during code scanning verification, and combining them with dynamic switching consensus strategies based on network load, it can ensure the accuracy of verification results while taking into account system response efficiency, thereby achieving an efficient anti-counterfeiting experience for consumers.

[0022] In summary, this application includes the following beneficial technical effects: 1. By acquiring label identification information and attribute information, and performing fragmentation processing based on the attribute information, the traceability data structure can be flexibly constructed according to the individual characteristics of the product, thereby improving the adaptability and interpretability of the data in the subsequent anti-counterfeiting and tracking process; by performing encryption operations on each information fragment separately and uploading it to different private chain nodes for distributed storage, the secure isolation and anti-tampering protection of multi-source heterogeneous data can be achieved, thereby improving the anti-counterfeiting system's anti-attack capability and data credibility. 2. By monitoring the status information in real time during the circulation of alcoholic products and generating triplet event data and anchoring it to the public blockchain when preset conditions are met, it is possible to ensure that key circulation behaviors have on-chain traceability credentials, thereby strengthening the traceability capability of the entire product lifecycle; by executing hash consistency and zero-knowledge verification processes during QR code verification and combining them with dynamic switching consensus strategies based on network load, it is possible to ensure the accuracy of verification results while taking into account system response efficiency, thereby achieving an efficient anti-counterfeiting experience for consumers. Attached Figure Description

[0023] Figure 1 This is a flowchart of an RFID-based anti-counterfeiting and traceability method for alcoholic beverages according to one embodiment of this application; Figure 2 This is a flowchart illustrating the implementation of step S10 in an RFID-based anti-counterfeiting and traceability method for alcoholic beverages according to an embodiment of this application. Figure 3 This is a flowchart illustrating the implementation of step S20 in an RFID-based anti-counterfeiting and traceability method for alcoholic beverages according to one embodiment of this application. Figure 4 This is a flowchart illustrating the implementation of step S30 in an RFID-based anti-counterfeiting and traceability method for alcoholic beverages according to one embodiment of this application. Figure 5 This is a flowchart illustrating the implementation of step S40 in an RFID-based anti-counterfeiting and traceability method for alcoholic beverages according to one embodiment of this application. Figure 6 This is a flowchart illustrating the implementation of step S40 in an RFID-based anti-counterfeiting and traceability method for alcoholic beverages according to one embodiment of this application. Figure 7 This is a flowchart of another implementation of an RFID-based anti-counterfeiting and traceability method for alcoholic beverages in one embodiment of this application; Figure 8 This is a principle block diagram of an RFID-based anti-counterfeiting and traceability system for alcoholic beverages, as described in one embodiment of this application. Detailed Implementation

[0024] The present application will be further described in detail below with reference to the accompanying drawings.

[0025] In one embodiment, such as Figure 1 As shown, this application discloses an RFID-based anti-counterfeiting and traceability method for alcoholic beverages, which specifically includes the following steps: S10: Obtain the label identification information and attribute information of the target alcoholic beverage product, and perform segmentation processing on the label identification information based on the attribute information to obtain a set of information fragments, which includes raw material information fragments, process information fragments, logistics information fragments, and sales information fragments.

[0026] Specifically, the process first collects the unique identifier of the target alcoholic beverage product and the associated attribute information from a pre-set database. The attribute information includes product type, packaging method, manufacturer, logistics channel, target sales region, etc. Then, the label parser is called to parse and initially classify the label identification information according to different business fields. For example, the origin of raw materials and ingredient ratio information can be classified into the raw material information segment, the fermentation process and storage period into the process information segment, the transportation batch and handover record into the logistics information segment, and the retail time node and sales terminal type into the sales information segment. Each type of segment is stored in an independent structure in the information segment set for subsequent processing. If a high-end alcoholic beverage product has a "limited release" attribute on its label, the segmentation processing logic can prioritize retaining the pricing information and exclusive circulation code field in the sales information segment to enhance the verification credibility and scarcity identification capability of this type of product.

[0027] S20: Perform encryption operations on each information fragment in the information fragment set, and upload the encrypted information fragments to the corresponding private chain nodes for distributed storage.

[0028] Specifically, each type of information fragment is encrypted. For raw material and process fragments, a high-strength symmetric encryption algorithm such as AES-256 is used. For logistics and sales fragments, ECC or RSA can be used to achieve asymmetric encryption to enhance distribution flexibility. After encryption, each information fragment forms an independent encrypted data packet. Then, according to the data ownership rules preset in the system, raw material and process fragments are transmitted to the private chain node on the production side, logistics fragments are managed by the logistics service node, and sales information is uploaded to the node where the retail terminal is located. Each node performs a write action and calls the on-chain evidence storage module to generate the corresponding hash digest and complete the on-chain registration. For example, in a certain encrypted distribution process of "Vintage Moutai" products, its brewing process fragment is encrypted and synchronized to the edge chain node located in the Moutai factory area, while the logistics flow fragment is transmitted to multiple warehousing and distribution nodes in Guizhou Province to form a parallel record distribution, ensuring data decentralized storage and clear responsibility boundaries.

[0029] S30: During the circulation of the target alcoholic beverage product, the real-time status information of the target alcoholic beverage product is detected in real time, and the real-time status information is analyzed and judged. When the preset event triggering conditions are met, a triplet event data is generated. The triplet event data includes coordinate information, timestamp and operator signature, and the triplet event data is anchored to the public chain node.

[0030] Specifically, after a product enters the distribution process, it continuously collects status change data through smart readers bound to RFID tags. The status includes parameters such as geographic coordinates, time information, temperature and humidity values ​​collected by sensors, and seal opening status. The system compares each piece of status data with a preset event rule base in real time. For example, it determines whether a specified geographic fence has been crossed, whether the product has been transferred from a cold chain environment to room temperature, or whether a location jump has occurred at night. When a trigger condition corresponding to a rule such as "transfer from a primary warehouse to a secondary channel" is detected, the system immediately generates a triplet event data containing the coordinate information of the action, a timestamp, and the electronic signature of the current operator. Subsequently, a hash digest is generated using the Merkle tree calculation method and uploaded to a designated public blockchain node for anchoring and writing to ensure that the event is immutable and can be publicly verified by a third-party auditing agency. For example, during a transfer from the Guangdong winery's main warehouse to a Dongguan specialty store, if the logistics route deviates from the standard path and triggers a rule, the system records the abnormal trajectory and files it on the blockchain.

[0031] S40: When a user scans a code for verification, the hash consistency verification and zero-knowledge verification based on preset industry standard parameters are executed sequentially. The information summary and verification conclusion of the target alcoholic beverage product are output, and the network load is monitored in real time. The consensus strategy is dynamically switched based on the network load monitoring results.

[0032] Specifically, after a user scans the product's RFID tag using a mobile terminal, the system automatically initiates a traceability verification process. First, it reads the tag data locally and decodes the traceability hash digest. Then, it queries and extracts the product's on-chain hash value from the public blockchain node and performs a consistency check. If the two match, it enters the next stage, the zero-knowledge verification module. This module performs matching verification based on preset rule parameters such as "whether it comes from a legal production area," "whether it has undergone cold chain transportation," and "whether it was sold in a compliant store," without exposing the original plaintext data. After completing the above two stages of verification, it outputs a structured information digest and a "verification conclusion." Simultaneously, the system dynamically monitors the communication latency, response throughput, and node activity indicators of each verification node throughout the query process. If it determines that the current network load is high, it automatically switches from the default PBFT mechanism to the RAFT mechanism to reduce the processing burden and ensure user response timeliness. For example, during a holiday when users frequently scan to verify "limited edition customized liquor," the system automatically alleviates on-chain pressure through a network switching strategy and ensures a verification success rate of over 99.9%.

[0033] By adopting the above technical solutions, and by acquiring label identification information and attribute information, and performing fragmentation processing based on the attribute information, a traceability data structure can be flexibly constructed according to the individual characteristics of the product, thereby improving the adaptability and interpretability of the data in the subsequent anti-counterfeiting and tracking process. By performing encryption operations on each information fragment separately and uploading it to different private chain nodes for distributed storage, the secure isolation and anti-tampering protection of multi-source heterogeneous data can be achieved, thereby improving the anti-counterfeiting system's resistance to attacks and data credibility. By detecting status information in real time during the circulation of alcoholic products and generating triplet event data and anchoring it to the public chain when preset conditions are met, it can be ensured that key circulation behaviors have on-chain traceability credentials, thereby strengthening the traceability capability of the entire product lifecycle. By executing hash consistency and zero-knowledge verification processes during code scanning verification, and combining them with dynamic switching consensus strategies based on network load, it can ensure the accuracy of verification results while taking into account system response efficiency, thereby achieving an efficient anti-counterfeiting experience for consumers.

[0034] In one embodiment, such as Figure 2 As shown, in step S10, the tag identification information is segmented based on attribute information to obtain a set of information fragments, specifically including: S11: Extract the attribute information of the target alcoholic beverage product. The attribute information includes at least one or more of the following: product type, packaging form, sales channel, distribution area, and retail price.

[0035] Specifically, the attribute fields of the target alcoholic beverage products are parsed from the database bound to RFID tags, and cross-validation of the fields is performed using historical data interfaces. The extracted attribute information may include the product category of the alcoholic beverage, such as baijiu, red wine, and liqueur; packaging form, such as bottled, gift box, and ceramic jar packaging; sales channels, such as direct sales, e-commerce, and distribution; circulation area, such as domestic, provincial, and limited cities; and retail price range, such as above 2,000 yuan or below 100 yuan. In actual operation, attribute priority extraction rules can be configured. For example, when a product is marked as "customized gift box + sold in a single store + priced over 5,000 yuan", its "sales channel", "retail price", and "packaging form" can be extracted first as the dominant attribute information fields, thereby providing key feature variables to support subsequent risk assessment.

[0036] S12: Based on attribute information, call the preset risk level assessment model to determine the risk level of the target alcoholic beverage product and obtain the corresponding risk level label.

[0037] Specifically, the extracted attribute information is input as an input vector into a preset risk assessment module, and the corresponding industry experience parameter matrix or rule engine is loaded to perform scoring and judgment. The risk level assessment model can perform matching and scoring based on logistic regression, fuzzy scoring tables, or decision rule sets, and set several boundary intervals to output high, medium, and low risk level labels. The high-risk label is usually assigned to scarce, high-priced, and narrowly distributed products, the medium-risk label corresponds to mass-market brands but widely distributed products, and the low-risk label covers ordinary alcoholic beverages that are low-priced, standard-packaged, and fast-moving consumer goods. In a special case, when the system detects that a product simultaneously meets the three factors of "non-standard bottle type + VIP limited sales + price higher than 3,000 yuan", it will finally output a "high-risk" label, which serves as an important reference basis for subsequent encryption strategies and shard weight allocation.

[0038] In a preferred embodiment of this application, a pre-defined risk level assessment model is constructed and trained based on the attribute information of the target alcoholic beverage product. The model is designed using a combination of rule fusion and supervised learning. First, a core indicator system for risk assessment is defined, including but not limited to five attribute dimensions: product type, packaging form, sales channel, distribution area, and retail price. Each dimension is quantitatively weighted based on industry experience and historical distribution risks, and an initial risk scoring standard is set. For example, high-end customized products, gift box packaging, targeted distribution, cross-regional distribution, or high-priced retail are all assigned higher risk factor scores. Second, a training sample set containing multiple risk level labels (such as low, medium, and high) is constructed. The sample set is derived from historical anti-counterfeiting data of alcoholic beverage brands, dealer complaint records, and regulatory penalty cases. Feature extraction and label mapping are performed on product samples with known genuine and counterfeit labels, and the model is trained using a random forest algorithm. Finally, the model structure is optimized through evaluation indicators such as cross-validation and AUC, and the model parameters are frozen before deployment to ensure the model has stability and generalization ability. In actual operation, when a new sample of the target alcoholic beverage product is input into the model, the corresponding risk level label can be output, providing key support for subsequent information segmentation and encryption strategies.

[0039] S13: Based on the risk level label, select the corresponding segmentation weight strategy template, and split the label identification information into raw material information segments, process information segments, logistics information segments, and sales information segments according to the allocation ratio in the segmentation weight strategy template.

[0040] Specifically, the system loads a segmentation weight strategy template based on the configuration table corresponding to the risk level label, and reads the recommended weight ratios for four types of segments: raw materials, process, logistics, and sales. For example, in a high-risk template, the proportion of raw materials and logistics segments is prioritized to enhance the encryption strength of scarce raw materials and distribution paths, while in a low-risk template, the weights are allocated relatively evenly to ensure the integrity of basic verification. Subsequently, the data fields in the original label identification information are mapped to different segmentation areas according to the allocation ratio, and logical splitting is performed to generate four types of data structures with independent structures: raw material information segments, process information segments, logistics information segments, and sales information segments. Each type of segment has a traceable field label and a unified segmentation number to support subsequent encrypted upload operations. For example, in the processing of a certain high-end limited edition wine, the system automatically splits 80% of the field content into the raw material and logistics segments to ensure the high immutability of the supply origin and path nodes, thereby supporting the verifiability requirements of consumers' more sensitive data.

[0041] In one embodiment, such as Figure 3 As shown, in step S20, each information fragment in the information fragment set is encrypted, and the encrypted information fragments are uploaded to their respective private chain nodes for distributed storage. Specifically, this includes: S21: Encrypt each information fragment in the information fragment set by calling the encryption algorithm to generate encrypted fragments.

[0042] Specifically, for each information fragment in the information fragment set, encryption processing logic is executed, a preset encryption algorithm interface is called and the original fragment data and corresponding encryption parameters are passed in. Different types of fragments can be adapted to symmetric or asymmetric encryption algorithms of different strengths. For example, AES-256 algorithm is used to encrypt raw material information fragments to ensure processing efficiency, and RSA algorithm is used to encrypt logistics and sales information fragments to enhance the decryption security on the node side. After each encryption, a standardized encrypted fragment data structure is returned, which includes the ciphertext body, encryption algorithm identifier and encryption timestamp fields to ensure the verifiability of the subsequent on-chain evidence storage process. For example, when the input information fragment is "place of origin: Yibin, Sichuan", after encryption processing, the ciphertext "R#dsf9k38q82==" is generated and the encryption algorithm identifier "AES256-v2" is output. Then, this structure is written as an encrypted fragment into the queue to wait for upload processing.

[0043] S22: Send the encrypted fragments to the corresponding private chain nodes according to the preset data ownership relationship.

[0044] Specifically, for each encrypted fragment, its corresponding data ownership tag is read and matched with the preset private chain node distribution rules. A mapping relationship is established based on the information type and node permission model. For example, encrypted fragments containing raw material and process information are sent to the production supervision chain node, and encrypted fragments containing logistics and sales information are distributed to the circulation chain and sales chain nodes. At the same time, according to the node receiving strategy, metadata including node identifier, encryption digest and encryption timestamp are uploaded. Asynchronous batch push operation is performed through the API channel of the link encryption, and the push status and receipt result are recorded after successful sending for subsequent verification. For example, when the ownership chain identifier of a "logistics information fragment" is "Chain-B2", the encrypted fragment will be pushed to the private chain storage node identified as B2, and a corresponding upload transaction number will be generated for subsequent traceability.

[0045] S23: Perform write operations on each private chain node to register the encrypted fragment and generate the corresponding hash digest value.

[0046] Specifically, after receiving the encrypted fragment, each target private chain node performs an on-chain write operation. First, it parses the received encrypted fragment and performs a legality check, including whether the encryption algorithm identifier is valid, whether the ciphertext format is complete, and whether the timestamp is within the validity period. After the check passes, the fragment is written to the node's local ledger, and a hash calculation is performed on the ciphertext content to generate a unique hash digest value. This digest value will be used as a global index identifier for the fragment on the chain and is bound to the evidence storage time, source node information, and original upload transaction number. For example, the hash value "bf7a29b6…d832" is calculated for the encrypted fragment "R#dsf9k38q82==" and registered in the April 2025 ledger partition under the "Production Supervision Chain" node. Any subsequent access request to this fragment can be quickly located and verified through this hash digest value, thereby ensuring the immutability of the on-chain data and inter-chain mutual trust.

[0047] In one embodiment, such as Figure 4 As shown, in step S30, the real-time status information of the target alcoholic beverage product is detected and analyzed. When the preset event triggering conditions are met, a triplet event data is generated, specifically including: S31: Collect real-time status information of the target alcoholic beverage products. The real-time status information includes the logistics status, geographical location information and storage condition data associated with the target alcoholic beverage products.

[0048] Specifically, the system continuously collects and analyzes RFID reading event streams bound to the labels of target alcoholic beverage products. Status data obtained from edge gateways, logistics terminals, or warehouse sensor nodes is converted into structured status information. This status information includes the current logistics status code, geographic coordinates, timestamps, and storage condition parameters such as temperature and humidity. This information is then aggregated according to a preset sampling frequency and time window to form a multi-dimensional status record stream covering the product's lifecycle. For example, during transportation, when the RFID reader detects a bottle labeled "SPC202504-A" entering the GPS coordinate range "114.3055, 30.5928" and detects that the temperature of the vehicle's storage environment exceeds 30°C, a status data record with the status code "TRANS-ENTRY," location information, and temperature and humidity fields will be generated in real time for subsequent comparison and analysis.

[0049] S32: Determine whether the real-time status information meets the triggering rules of preset condition events. Preset condition events include product entering or leaving the warehouse, crossing geofences, changes in circulation nodes, or state transitions.

[0050] Specifically, based on the collected real-time status information, the current logistics node, historical trajectory fragments, and key field values ​​are extracted. An event rule parser is used to compare each rule against the configured preset event triggering conditions. The triggering rules can be based on regional boundaries to determine whether the product crosses a set geofence area, or to determine whether there is a transition between consecutive status codes, such as from "IN-STOCK" to "OUT-BOUND", or to determine whether the change in location point conforms to the node change logic, such as from "winery warehouse" to "distribution center". Each judgment logic generates a Boolean flag based on the matching result of the current status information and the rule template, and writes it into the event candidate buffer. For example, when a wine product with the label number "WZ-00012" is shipped from the North China warehouse and its location coordinates jump to outside the Beijing-Tianjin area, it is immediately identified as "cross-regional behavior" and a trigger flag is added to start the subsequent triple generation process.

[0051] S33: When the preset event triggering conditions are met, generate triplet event data.

[0052] Specifically, after determining that the current state information has triggered a preset event, the current timestamp, the latest geographical location information, and the signature credential for executing the node's operation are packaged into a structured triple format. The triple structure encapsulates the event coordinate field, the UTC time field, and the operator's signature field, respectively. The signature field supports digital signatures or key-derived digests from trusted nodes, used for on-chain anchoring and subsequent auditing. To ensure the integrity of the time sequence, the hash of the previous event can also be introduced as a forward index. For example, if a triggering event is "database entry", the generated triple would be: {location:'113.27,23.13', timestamp:'2025-12-17T10:32:25Z', signer:'0xA97F…D4'}. Finally, this structure is output for subsequent block records to call, thereby ensuring that the event process is complete, trustworthy, and traceable.

[0053] In one embodiment, such as Figure 5 As shown, in step S40, when the user scans the code for verification, the hash consistency verification and the zero-knowledge verification process based on preset industry standard parameters are executed sequentially, outputting the information digest and verification conclusion of the target alcoholic beverage product, specifically including: S41: Obtain the user's query request, perform a consistency comparison based on the query request, and obtain the consistency verification result.

[0054] Specifically, the system receives query request data packets triggered by the user's scanning action, parses out the unique identification information corresponding to the scanned RFID tag, reads the hash digest value associated with the identifier, retrieves the sharded data record on the private chain of the wine product from the local cache, extracts the encrypted digest, reassembles the hash to generate a comparison value, and then performs a consistency comparison with the hash value recorded in the triple event data anchored in the current public chain node. The comparison method uses the standard SHA256 algorithm combined with Merkle tree path for fast verification. When the locally generated hash value is completely consistent with the hash value recorded on the chain, the consistency verification is determined to be successful. For example, when a user scans a wine bottle with the number "GF20251217-002", the system automatically extracts its bound digest "0xA91F..." and matches it with the record on the chain. If the values ​​are found to be completely consistent, the verification status is recorded as successful for the next step of the zero-knowledge verification module to call.

[0055] S42: When the consistency verification result is that the hash consistency verification is passed, the target wine product is verified to see if it meets the specified rule conditions, and the zero-knowledge verification result is obtained. The rule conditions include product circulation compliance, production area traceability matching or sales path restriction.

[0056] Specifically, based on the successful hash consistency verification, the associated status records and event trajectory information of the target wine product are extracted. The zero-knowledge verification engine is invoked, and constraint verification is performed based on the industry's preset rule parameter template. The rule template includes restrictions such as compliance judgment of circulation path, consistency comparison between production area and label information, whether it is through blacklisted channels or abnormal sales jumps. The verification process does not expose the original on-chain data content, but only returns a boolean value indicating whether the rule verification is successful. For example, it checks whether the wine bottle "GF20251217-002" has a record of being sold directly from the secondary wholesale node without going through the retail channel. If it is found that its event trajectory does not conform to the allowed path, the zero-knowledge verification returns a "failed" flag for subsequent generation of abnormal prompts or traceability warnings.

[0057] S43: Based on the consistency verification results and zero-knowledge verification results, generate a verifiable information summary of the target alcoholic beverage product and output the verification conclusion.

[0058] Specifically, the consistency verification results and zero-knowledge verification Boolean judgment results from the previous stage are summarized to construct a standardized verification summary text. The summary content includes product identifier, verification time, verification source, hash verification status, and rule verification conclusion. It also includes a simplified verification path and some publicly available field values ​​for user display. Finally, the verification summary is generated and transmitted to the user terminal for display or recording, and stored in the log area for audit traceability. For example, for a record that passes QR code verification, the output summary format is as follows: "ID GF20251217-002, Verification time 2025-12-17 16:15:22, Verification status: Passed, Zero-knowledge verification result: Complies with circulation rules". If the verification fails, "Data suspected of being tampered with or sales path non-compliant" is output to help users judge the authenticity and guide them to the right to claim their rights.

[0059] In one embodiment, such as Figure 6 As shown, in step S40, which involves real-time monitoring of network load and dynamic switching of consensus strategies based on the network load monitoring results, the specific steps include: S44: Real-time collection of network status indicators of current network nodes, including communication latency, response time, and node activity.

[0060] Specifically, the communication channels of each participating node are accessed periodically to collect key network status indicators such as average communication latency, round-trip response time of data packets, and node signal holding frequency per unit time. The raw data collected each time is recorded and stored in the monitoring buffer. At the same time, the node activity score is calculated based on the number of active responses of the nodes to reflect the computational collaboration stability of the current participating nodes. In actual deployment, the nodes are polled every 30 seconds. When a node experiences multiple consecutive communication timeouts or a sudden drop in activity, the node is marked as a low-performance node example. For example, "Node A-12 has a response latency of more than 800ms 3 times within 5 minutes" is recorded as a potential load bottleneck node.

[0061] S45: Calculate the load level corresponding to the network status index based on the preset network load assessment model.

[0062] Specifically, based on the collected network status indicators, they are normalized and input into a preset network load assessment model for calculation. This assessment model uses a weighted aggregation strategy combined with an adaptive interval judgment threshold to divide the current network status into three levels: low load, medium load, and high load. The weights for communication latency and response time are set to 0.4, and the weight for node activity is set to 0.2. The final load level serves as an important basis for judging the switching consensus mechanism. For example, when the comprehensive score exceeds 0.75 and the node response rate is below 70%, it is judged as a "high load" level and recorded in the status update module for the next switching strategy judgment.

[0063] Furthermore, the pre-defined network load assessment model is constructed using a multi-index fusion mechanism. The model uses communication latency, response time, and node activity as core input features, and combines historical monitoring data to complete parameter optimization through supervised training. First, raw index data with a 5-second cycle is extracted from the distributed node operation logs of 90 consecutive days in the production environment. After normalization, it forms a training sample set, and the output label is set as a manually labeled load level label. A multilayer perceptron is used as the basic assessment structure, with an input layer dimension of 3 and two hidden layers containing 64 and 32 neurons respectively. The activation function is ReLU, and the output layer uses softmax to generate a three-class probability distribution. During training, the cross-entropy loss function is used and iterative updates are performed through the Adam optimizer, which has good generalization ability. After the model is trained, it is deployed to the running module in the form of a model parameter file. During system operation, the current load level score result can be output by loading the input index in real time, and the consensus strategy switching logic can be executed.

[0064] S46: Execute the corresponding switching strategy based on the load level.

[0065] Specifically, based on the calculated network load level value, the system calls the corresponding policy configuration from the consensus policy rule set. If the current load level is low, the original high-performance consensus mechanism such as PBFT is maintained to ensure verification efficiency and security strength. If the load is medium, the system switches to a lightweight Raft or PoA-type consensus method to reduce communication resource consumption. If the load is high, the system further switches to a fault-tolerant policy based on an asynchronous communication mechanism, such as using a fast election Byzantine protocol to avoid global blocking caused by node bottlenecks. The switching operation is dynamically completed by calling the consensus engine reconfiguration interface and the policy switching log is recorded in real time. For example, when the detection result is "high load", the policy switching instruction from BFT to asynchronous HotStuff is triggered, and "consensus mechanism switching completed on 2025-12-17 16:22:15" is written in the log.

[0066] In one embodiment, such as Figure 7 As shown, this RFID-based anti-counterfeiting and traceability method for alcoholic beverages also includes: S50: Construct a behavioral trajectory chain based on the triplet event data generated during the circulation of the target alcoholic beverage product.

[0067] Specifically, based on the unique label of the target alcoholic beverage product, the triplet event data triggered throughout its entire lifecycle are sorted by time series and added to the behavior trajectory chain in chronological order, forming a data linked list with continuous nodes and a closed-loop structure. Each triplet event data contains a complete description of the operational behavior scenario, including specific geographical coordinates, timestamp of occurrence, and signature information of the actual operator. For example, when the status changes at any stage of the product's circulation, such as production, factory exit, warehousing, distribution, and terminal sales, the corresponding event node is automatically generated and included in the behavior trajectory chain, thereby ensuring the reproducibility and reliable traceability of the product's circulation path.

[0068] S60: Call the preset behavior trajectory rule template to perform rule comparison on the trajectory features in the behavior trajectory chain. The behavior trajectory rule template includes at least one set of legal flow path node sequences, allowed node jump ranges, and flow time interval threshold rules.

[0069] Specifically, the structural features of the generated behavioral trajectory chain are matched with the pre-configured behavioral trajectory rule template. The occurrence time, geographical location information, and node type identifier of adjacent nodes in the trajectory chain are extracted. Each item is compared to see if it meets the legal node sequence, regional jump logic, and time interval threshold requirements in the preset rule template. The rule template can be set by industry regulatory departments, brand operators, or platform institutions according to the circulation characteristics of different categories of wine products. For example, for high-value customized wines, the path "factory → primary warehouse → flagship store → terminal scanning" can be set as the only legal sequence. If the trajectory chain shows a jump from "factory → terminal scanning", it is identified as an illegal path feature, thereby supporting the accurate identification of potential abnormal behaviors such as bypassing channels, cross-selling, and cross-regional sales.

[0070] S70: When the behavior trajectory chain does not meet any rule in the rule template, it is judged as an abnormal trajectory and a corresponding risk warning sign is generated.

[0071] Specifically, when any indicator in the trajectory comparison results does not meet the requirements of the preset behavior rule template, the trajectory chain is marked as abnormal and a risk warning label containing the abnormality type, trigger location, trigger time and corresponding tag is automatically generated. This warning label will be recorded in the traceability platform log and can be retrieved and verified by user scanning or enterprise supervision backend. For example, if it is detected that a product jumps from Guangzhou to Beijing on the same day without the support of intermediate warehousing nodes, exceeding the allowed jump range and time interval threshold, the "abnormal jump" risk label will be triggered, thereby significantly improving the accuracy and response efficiency of identifying malicious circulation paths.

[0072] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0073] In one embodiment, an RFID-based anti-counterfeiting and traceability system for alcoholic beverages is provided, which corresponds one-to-one with the RFID-based anti-counterfeiting and traceability method for alcoholic beverages described in the above embodiments. For example... Figure 8 As shown, this RFID-based anti-counterfeiting and traceability system for alcoholic beverages includes a label information fragmentation module, a fragmented encrypted storage module, and an event detection and barcode verification module. Detailed descriptions of each functional module are as follows: The label information segmentation module is used to obtain the label identification information and attribute information of the target alcoholic beverage product, and to segment the label identification information based on the attribute information to obtain a set of information fragments. The set of information fragments includes raw material information fragments, process information fragments, logistics information fragments, and sales information fragments. The fragmented encryption storage module is used to perform encryption operations on each information fragment in the information fragment set, and upload the encrypted information fragments to the corresponding private chain nodes for distributed storage. The event detection module is used to detect the real-time status information of the target alcoholic beverage during the circulation process, analyze and judge the real-time status information, and generate triple event data when the preset event trigger conditions are met. The triple event data includes coordinate information, timestamp and operator signature, and anchors the triple event data to the public chain node. The QR code verification module is used to sequentially perform hash consistency verification and zero-knowledge verification based on preset industry standard parameters when a user scans a QR code for verification. It outputs an information summary and verification conclusion of the target alcoholic beverage product, monitors network load in real time, and dynamically switches consensus strategies based on the network load monitoring results.

[0074] Optionally, the tag information fragmentation module includes: The attribute information extraction submodule is used to extract the attribute information of the target alcoholic beverage product. The attribute information includes at least one or more of the following: product type, packaging form, sales channel, distribution area, and retail price. The risk level assessment submodule is used to determine the risk level of the target alcoholic beverage product based on attribute information and by calling a preset risk level assessment model to obtain the corresponding risk level label. The weighted segmentation strategy submodule is used to select the corresponding segmentation weight strategy template based on the risk level label, and to split the label identification information into raw material information segments, process information segments, logistics information segments and sales information segments according to the allocation ratio in the segmentation weight strategy template.

[0075] Optional, the fragmented encrypted storage module includes: The information fragment encryption submodule is used to call the encryption algorithm to encrypt each information fragment in the information fragment set and generate encrypted fragments. The sharding node distribution submodule is used to send encrypted fragments to the corresponding private chain nodes according to the preset data ownership relationship; The on-chain evidence storage submodule is used to perform write operations in each private chain node, register the encrypted fragments for evidence storage, and generate the corresponding hash digest value.

[0076] Optionally, the event detection module includes: The status information acquisition submodule is used to collect real-time status information of the target alcoholic beverage products. The real-time status information includes the logistics status, geographical location information and storage condition data associated with the target alcoholic beverage products. The event trigger determination submodule is used to determine whether the real-time status information meets the triggering rules of the preset condition event. The preset condition events include product entering or leaving the warehouse, crossing the geofence, changes in circulation nodes, or state transitions. The triplet generation submodule is used to generate triplet event data when the preset event trigger conditions are met.

[0077] Optionally, the QR code verification module includes: The user request retrieval submodule is used to retrieve user query requests, perform consistency comparisons based on the query requests, and obtain consistency verification results. The zero-knowledge rule verification submodule is used to verify whether the target alcoholic beverage product meets the specified rule conditions when the consistency verification result is that the hash consistency verification is passed, and to obtain the zero-knowledge verification result. The rule conditions include product circulation compliance, production area traceability matching or sales path restrictions. The verification result generation submodule is used to generate a verifiable information summary of the target alcoholic beverage product based on the consistency verification results and the zero-knowledge verification results, and output the verification conclusion.

[0078] The network status acquisition submodule is used to collect network status indicators of the current network nodes in real time. These indicators include communication latency, response time, and node activity. The load level assessment submodule is used to calculate the load level corresponding to the network status indicators based on a preset network load assessment model. The consensus strategy switching submodule is used to execute the corresponding switching strategy based on the load level.

[0079] Optionally, this RFID-based anti-counterfeiting and traceability system for alcoholic beverages also includes: The behavior trajectory construction module is used to construct a behavior trajectory chain based on the triplet event data generated during the circulation process of the target alcoholic beverage product. The trajectory rule comparison module is used to call the preset behavior trajectory rule template to compare the trajectory features in the behavior trajectory chain. The behavior trajectory rule template includes at least one set of legal flow path node sequences, allowed node jump ranges, and flow time interval threshold rules. The risk warning generation module is used to determine an abnormal trajectory and generate a corresponding risk warning identifier when the behavior trajectory chain does not meet any rule in the rule template.

[0080] For specific limitations regarding an RFID-based anti-counterfeiting and traceability system for alcoholic beverages, please refer to the limitations of an RFID-based anti-counterfeiting and traceability method for alcoholic beverages mentioned above, which will not be repeated here. Each module in the aforementioned RFID-based anti-counterfeiting and traceability system for alcoholic beverages can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0081] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0082] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for anti-counterfeiting and traceability of alcoholic beverages based on RFID, characterized in that, The aforementioned RFID-based anti-counterfeiting and traceability method for alcoholic beverages includes: Obtain the label identification information and attribute information of the target alcoholic beverage product, and perform segmentation processing on the label identification information based on the attribute information to obtain a set of information fragments, which includes raw material information fragments, process information fragments, logistics information fragments and sales information fragments; Each information fragment in the information fragment set is encrypted, and the encrypted information fragments are uploaded to the corresponding private chain nodes for distributed storage. During the circulation of the target alcoholic beverage product, the real-time status information of the target alcoholic beverage product is detected in real time, and the real-time status information is analyzed and judged. When the preset event triggering conditions are met, a triplet event data is generated. The triplet event data includes coordinate information, timestamp and operator signature, and the triplet event data is anchored to the public chain node. When a user scans a code for verification, the hash consistency verification and zero-knowledge verification based on preset industry standard parameters are executed sequentially. The information summary and verification conclusion of the target alcoholic beverage product are output, and the network load is monitored in real time. The consensus strategy is dynamically switched based on the network load monitoring results.

2. The RFID-based anti-counterfeiting and traceability method for alcoholic beverages according to claim 1, characterized in that, The step of segmenting the tag identification information based on the attribute information to obtain a set of information fragments specifically includes: Extract the attribute information of the target alcoholic beverage product, wherein the attribute information includes at least one or more of the following: product type, packaging form, sales channel, distribution area, and retail price; Based on the attribute information, a preset risk level assessment model is invoked to determine the risk level of the target alcoholic product and obtain the corresponding risk level label. Based on the risk level label, select the corresponding segmentation weight strategy template, and split the label identification information into raw material information segments, process information segments, logistics information segments, and sales information segments according to the allocation ratio in the segmentation weight strategy template.

3. The RFID-based anti-counterfeiting and traceability method for alcoholic beverages according to claim 1, characterized in that, The step of performing encryption operations on each information fragment in the information fragment set and uploading the encrypted information fragments to the corresponding private chain nodes for distributed storage specifically includes: Each information fragment in the set of information fragments is encrypted using an encryption algorithm to generate an encrypted fragment. The encrypted fragments are sent to the corresponding private chain nodes according to the preset data ownership relationship; Write operations are performed on each private chain node to register the encrypted fragment and generate the corresponding hash digest value.

4. The RFID-based anti-counterfeiting and traceability method for alcoholic beverages according to claim 1, characterized in that, The process involves real-time detection of the target alcoholic beverage product's real-time status information, analysis and judgment of this information, and generation of triplet event data when preset event triggering conditions are met. Specifically, this includes: The system collects real-time status information of the target alcoholic beverage product, including logistics status, geographic location information, and storage condition data associated with the target alcoholic beverage product. Determine whether the real-time status information meets the triggering rules of preset condition events, which include product entering or leaving the warehouse, crossing geofences, changes in circulation nodes, or state transitions. When the preset event triggering conditions are met, the triplet event data is generated.

5. The RFID-based anti-counterfeiting and traceability method for alcoholic beverages according to claim 3, characterized in that, When a user scans a code for verification, a hash consistency verification and a zero-knowledge verification process based on preset industry standard parameters are executed sequentially. The process outputs an information digest and verification conclusion for the target alcoholic beverage product, specifically including: Obtain the user's query request, and based on the query request, perform a consistency comparison to obtain the consistency verification result; When the consistency verification result is that the hash consistency verification passes, the target alcoholic product is verified to see whether it meets the specified rule conditions, and a zero-knowledge verification result is obtained. The rule conditions include product circulation compliance, production area traceability matching or sales path restriction. Based on the consistency verification results and the zero-knowledge verification results, a verifiable information summary of the target alcoholic beverage product is generated, and the verification conclusion is output.

6. The RFID-based anti-counterfeiting and traceability method for alcoholic beverages according to claim 1, characterized in that, The real-time monitoring of network load and the dynamic switching of consensus strategies based on network load monitoring results specifically include: Real-time collection of network status indicators of current network nodes, including communication latency, response time, and node activity; Based on a preset network load assessment model, the load level corresponding to the network status index is calculated. Based on the load level, execute the corresponding switching strategy.

7. The RFID-based anti-counterfeiting and traceability method for alcoholic beverages according to claim 1, characterized in that, The aforementioned RFID-based anti-counterfeiting and traceability method for alcoholic beverages also includes: A behavioral trajectory chain is constructed based on the triplet event data generated during the circulation of the target alcoholic beverage product; A preset behavior trajectory rule template is invoked to perform rule comparison on the trajectory features in the behavior trajectory chain. The behavior trajectory rule template includes at least one set of legal flow path node sequences, allowed node jump ranges, and flow time interval threshold rules. When the behavior trajectory chain does not meet any rule in the rule template, it is determined to be an abnormal trajectory, and a corresponding risk warning icon is generated.

8. An RFID-based anti-counterfeiting and traceability system for alcoholic beverages, characterized in that, The aforementioned RFID-based anti-counterfeiting and traceability system for alcoholic beverages includes: The label information segmentation module is used to obtain the label identification information and attribute information of the target alcoholic beverage product, and to segment the label identification information based on the attribute information to obtain a set of information fragments, which includes raw material information fragments, process information fragments, logistics information fragments and sales information fragments. The fragmented encryption storage module is used to perform encryption operations on each information fragment in the information fragment set, and upload the encrypted information fragments to the corresponding private chain nodes for distributed storage. The event detection module is used to detect the real-time status information of the target alcoholic beverage during its circulation process, analyze and judge the real-time status information, and generate triplet event data when the preset event triggering conditions are met. The triplet event data includes coordinate information, timestamp and operator signature, and the triplet event data is anchored to the public chain node. The QR code verification module is used to sequentially execute hash consistency verification and zero-knowledge verification based on preset industry standard parameters when a user scans a QR code for verification. It outputs an information summary and verification conclusion of the target alcoholic beverage product, monitors network load in real time, and dynamically switches consensus strategies based on the network load monitoring results.

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