Intelligent butyronitrile glove packaging system with true and false identification and source tracing functions

Abstract

The invention relates to the technical field of butyronitrile gloves, and discloses a butyronitrile glove intelligent packaging system with authenticity identification and traceability functions, comprising: a traceability network construction module for constructing an identity traceability network of each unit of butyronitrile gloves; the traceability feature analysis module is used for marking core traceability features of each unit of butyronitrile gloves; the anti-counterfeiting optimization module is used for establishing an anti-counterfeiting state switching logic library of each unit of butyronitrile gloves so as to generate a strategy updating instruction of an identity traceability network; the traceability feedback module is used for calculating a traceability credible index of the butyronitrile glove and constructing a parameter self-adaptive adjustment strategy set of the visual detection unit; and the intelligent packaging module is used for carrying out packaging treatment on the butyronitrile gloves based on the identity traceability network, the anti-counterfeiting state switching logic library, the visual detection unit and the parameter self-adaptive adjustment strategy set. According to the invention, the flow direction tracking and anti-counterfeiting verification of the butyronitrile glove can be accurately realized.

Description

Technical Field

[0001] This invention relates to an intelligent packaging system for nitrile gloves with functions of authenticity identification and traceability, belonging to the field of nitrile glove technology. Background Technology

[0002] The intelligent packaging system for nitrile gloves with authenticity verification and traceability is a packaging device that integrates radio frequency identification (RFID), QR code generation, data encryption, and information query technologies. It is used to collect nitrile glove production information in real time, assign digital identification to products, and verify product authenticity. It aims to improve the anti-counterfeiting security, supply chain transparency, and consumer trust in the circulation of nitrile gloves.

[0003] Currently, the commonly used nitrile glove packaging system mainly adopts a mechanically assisted semi-automated packaging method. The mechanical device initially shapes the nitrile gloves after production, and then the packaging equipment is driven by a preset program to complete bagging, sealing and labeling. Although this method can avoid basic glove packaging defects, the entire packaging process lacks data correlation, which can easily lead to isolated product information and make it impossible to achieve accurate flow tracking and effective anti-counterfeiting verification.

[0004] Therefore, there is an urgent need for a solution that can accurately track the flow of nitrile gloves and verify their authenticity. Summary of the Invention

[0005] This invention provides an intelligent packaging system for nitrile gloves with functions of authenticity identification and traceability. Its main purpose is to accurately track the flow of nitrile gloves and verify their authenticity.

[0006] To achieve the above objectives, the present invention provides an intelligent packaging system for nitrile gloves with authenticity verification and traceability functions, comprising: The traceability network construction module is used to acquire production line data of nitrile gloves in the production process, so as to set a digital identity for each unit of nitrile gloves. Based on the digital identity and the production line data, an identity traceability network for each unit of nitrile gloves is constructed. The production line data includes raw material batches, process parameters, quality inspection data and packaging line operation data. The traceability feature parsing module is used to set up a visual inspection unit for the nitrile gloves on the packaging line of the nitrile gloves, and to mark the core traceability features of each unit of the nitrile gloves based on the visual inspection unit and the digital identity identifier. The anti-counterfeiting optimization module is used to simulate the counterfeiting risk environment of the nitrile gloves in the subsequent circulation process based on the core traceability features, establish an anti-counterfeiting state switching logic library for each unit of the nitrile gloves based on the counterfeiting risk environment, and generate a strategy update instruction for the identity traceability network based on the anti-counterfeiting state switching logic library. The traceability feedback module is used to obtain the feedback parameters of the identity traceability network after executing the policy update instruction, so as to calculate the traceability credibility index of the nitrile gloves, and construct the parameter adaptive adjustment strategy set of the visual detection unit based on the traceability credibility index. The intelligent packaging module is used to perform the packaging process of the nitrile gloves based on the identity traceability network, the anti-counterfeiting state switching logic library, the visual detection unit, and the parameter adaptive adjustment strategy set, and obtain the packaging result.

[0007] Optionally, the step of establishing an anti-counterfeiting state switching logic library for each unit of the nitrile glove based on the counterfeiting risk environment includes: From the counterfeit risk environment, identify the risk scenario type, risk severity level, and potential counterfeit behavior path corresponding to each unit of the nitrile glove; Based on the risk scenario type, the risk severity level, and the potential counterfeiting behavior path, respectively generate scenario response instructions, level response instructions, and path blocking instructions for each unit of the nitrile glove; Label the key feature attributes corresponding to the scenario response command, the level response command, and the path blocking command; Based on the key feature attributes, establish a set of linkage rules among the scenario response instructions, the level response instructions, and the path blocking instructions; Define the execution priority of each rule in the aforementioned set of linkage rules; By utilizing the linkage between the scenario response command, the level response command, and the path blocking command, and the execution priority, an anti-counterfeiting status switching decision engine is constructed for each unit of the nitrile glove; Based on the anti-counterfeiting status switching decision engine and the linkage rule set, an anti-counterfeiting status switching logic library is established for each unit of the nitrile glove.

[0008] Optionally, the step of constructing an anti-counterfeiting status switching decision engine for each unit of the nitrile glove by utilizing the linkage relationship between the scenario response command, the level response command, and the path blocking command, and the execution priority, includes: Identify the instruction categories and triggering conditions corresponding to the scenario response instructions, the level response instructions, and the path blocking instructions; Analyze the logical relationship types in the linkage relationship and the dynamic adjustment rules of the execution priority; Based on the instruction category and the logical relationship type, construct a decision flowchart for anti-counterfeiting state switching between the scenario response instruction, the level response instruction, and the path blocking instruction; Based on the triggering conditions and the dynamic adjustment rules, calculate the state switching accuracy and risk coverage completeness index of different paths in the anti-counterfeiting state switching decision flowchart. Based on the anti-counterfeiting status switching decision flowchart, the status switching accuracy, and the risk coverage completeness index, an anti-counterfeiting response scheme set is established for each unit of the nitrile glove. Based on the anti-counterfeiting response scheme set, generate the core judgment logic and instruction sequence corresponding to each unit of the nitrile glove; By integrating the anti-counterfeiting state switching decision flowchart, the core judgment logic, and the instruction sequence, an anti-counterfeiting state switching decision engine for each unit of the nitrile glove is constructed.

[0009] Optionally, the step of constructing an identity traceability network for each unit of the nitrile glove based on the digital identity and the production line data includes: Read the unique feature code and data generation timestamp from the digital identity identifier; Analyze the production line process paths and production event sequences associated with the unique feature code in the production line data; Identify the quality status migration trajectory and process parameter compliance tags of the production event sequence; Based on the unique feature code and the production line process path, a unique positioning identifier is generated for each unit of the nitrile glove; Based on the data generation timestamp and the production event sequence, calculate the time-series compliance index for each unit of the nitrile glove; By integrating the quality status migration trajectory with the process parameter compliance label, the microscopic quality level of each unit of the nitrile glove is determined; Based on the time-series compliance index and the micro-quality level, the data archiving level and corresponding traceability information opening strategy for each unit of the nitrile glove are set; The unique positioning identifier, the time-series compliance index, the micro-quality level, the data archiving level, and the traceability information opening strategy are integrated to form the identity traceability network for each unit of nitrile gloves.

[0010] Optionally, generating the policy update instruction for the identity tracing network based on the anti-counterfeiting state switching logic library includes: The policy configuration items and data integrity rules of the identity tracing network are analyzed. Identify the risk handling level and response time threshold corresponding to the anti-counterfeiting status switching logic library; Based on the risk handling level, the handling priority of the task queue in the identity tracing network is defined; By using the response timeliness threshold, key triggering events in the identity tracing network are located; Based on the key triggering events and the handling priorities, the database operation sequence and API call steps of the strategy configuration items are generated; Based on the data integrity rules, define the transaction boundaries between the database operation sequence and the API call steps; By combining the policy configuration items, the database operation sequence, the API call steps, and the transaction boundary, a policy update instruction for the identity tracing network is generated.

[0011] Optionally, obtaining the feedback parameters of the identity tracing network after executing the policy update instruction to calculate the traceability credibility index of the nitrile gloves includes: Analyze the instruction execution timing data and the state change frequency of the digital identity in the feedback parameters; Based on the digital identity, retrieve the full lifecycle traceability record of the nitrile gloves; The health of the traceability chain for the nitrile gloves is assessed through the full lifecycle traceability records. Retrieve the historical request records of the digital identity and extract the system response stability parameters from the historical request records; Based on the instruction execution timing data and the traceability link health, the robustness of the traceability rule execution for the nitrile gloves is calculated. Based on the state change frequency and the system response stability parameters, the probability of the traceability link of the nitrile glove being interrupted is calculated; Real-time monitoring of the query and verification success rate and the frequency of abnormal alarm triggering of the digital identity identifier; The traceability reliability index of the nitrile gloves is calculated by combining the robustness of the traceability rule execution, the probability of traceability link interruption, the success rate of query verification, and the frequency of abnormal alarm triggering.

[0012] Optionally, the step of constructing the parameter adaptive adjustment strategy set of the visual detection unit based on the source tracing credibility index includes: The adjustable imaging parameter set and its image quality benchmark value of the visual detection unit are analyzed. Identify the key influencing factors of the traceability credibility index; Based on the key influencing factors, determine the priority dimensions for parameter adjustment and quality optimization of the visual detection unit; Based on the parameter priority adjustment dimension and the quality optimization priority, an imaging parameter adjustment scheme for the adjustable imaging parameter set is formulated. Based on the image quality benchmark value, the adjustable range of the imaging parameter adjustment scheme is set; By integrating the adjustable imaging parameter set, the imaging parameter adjustment scheme, and the adjustable parameter range, a parameter adaptive adjustment strategy set for the visual detection unit is generated.

[0013] Optionally, simulating the counterfeit risk environment of the nitrile gloves in subsequent distribution stages based on the core traceability features includes: The target distribution channels for the nitrile gloves are determined by the product specifications and packaging grade in the core traceability features. Extract the set of spatiotemporal elements of circulation, identity verification triggering conditions, and feature reproducibility evaluation values ​​from the core traceability features; Based on the set of spatiotemporal elements of circulation, the identity verification triggering conditions, and the feature replicability evaluation value, a deduction diagram of counterfeiting behavior of the target circulation channel is constructed. Based on the aforementioned counterfeiting behavior projection diagram, the counterfeiting risk points and regulatory weaknesses in each link of the target distribution channel are analyzed. Based on the counterfeiting behavior projection diagram, the counterfeiting risk points, and the regulatory weaknesses, a counterfeiting risk environment for the nitrile gloves in the subsequent circulation process is simulated.

[0014] Optionally, the step of annotating the core traceability features of each unit of nitrile glove based on the visual detection unit and the digital identity identifier includes: Collect the packaging visual image sequence corresponding to the visual detection unit, and read the identification data corresponding to the digital identity identifier; Synchronously align the acquisition time point and spatial work point corresponding to the packaging visual image sequence and the identification data; Based on the acquisition time point and the spatial work site, a correspondence table between the packaging visual image and the digital identity is constructed; Extract the unique associated nodes from the corresponding relationship table; The core traceability features of each unit of nitrile gloves are marked through the unique association node.

[0015] Optionally, the step of acquiring production line data of nitrile gloves in the production process to set a digital identity for each unit of the nitrile glove includes: Based on the production line data, the raw material batch, production line vulcanization parameters, and online quality inspection results of the nitrile gloves were extracted. By integrating the raw material batches, the vulcanization parameters of the production line, and the online quality inspection results, a batch profile of the nitrile gloves is generated. Based on the batch lineage, the quality compliance of the nitrile gloves was verified, and a compliance determination conclusion was obtained. Based on the compliance determination conclusion, the core data in the batch lineage will be converted into a unique traceability code; Retrieve the packaging specifications and production date of each unit of nitrile gloves, and combine them with the unique traceability code to create a traceability file number for each unit of nitrile gloves; Based on the traceability file number, a digital identity identifier is set for each unit of the nitrile gloves.

[0016] Compared to the problems described in the background art, this embodiment of the invention constructs an identity traceability network for each unit of nitrile gloves based on the digital identity identifier and the production line data. This accurately links the digital identifier of each glove unit with the entire production process information, ensuring real-time traceability of the entire nitrile glove's trajectory from raw material input to packaging completion. Simultaneously, it provides an immutable information verification network for nitrile glove authenticity verification, improving the reliability and efficiency of the verification results, thereby enhancing the quality control capabilities and consumer trust in the nitrile glove distribution process. Furthermore, this embodiment of the invention establishes an identity traceability network for each unit of nitrile gloves based on the aforementioned counterfeit risk environment. The anti-counterfeiting status switching logic library for gloves allows the system to dynamically adjust anti-counterfeiting verification strategies based on the counterfeiting risk environment. It optimizes core traceability feature verification dimensions, identity verification trigger frequency, and counterfeiting warning thresholds in real time, improving the accuracy of responding to counterfeiting behavior in different circulation scenarios and enhancing the timeliness and reliability of authenticity assurance throughout the entire circulation process of nitrile gloves. In this embodiment, by generating strategy update instructions for the identity traceability network based on the anti-counterfeiting status switching logic library, the gradient of the open scope of traceability information can be strengthened through anti-counterfeiting status levels, improving the adaptability of instructions to different risk scenarios and enhancing the accuracy of information traceability within the identity traceability network. In conjunction with anti-counterfeiting security, this invention comprehensively enhances the dynamic control capability of identity traceability throughout the entire circulation process of nitrile gloves. Furthermore, by constructing a set of adaptive parameter adjustment strategies for the visual inspection unit based on the traceability credibility index, this embodiment of the invention clarifies the matching rules between the dynamic changes of visual inspection parameters during the nitrile glove packaging process and the correlation requirements of traceability information, as well as the impact mechanism of traceability credibility index fluctuations on the adaptability of detection accuracy. This allows for targeted optimization of the parameter adjustment logic of the visual inspection unit. Finally, this embodiment of the invention, based on the identity traceability network, the anti-counterfeiting state switching logic library, the visual inspection unit, and the... The system employs an adaptive adjustment strategy set of parameters to execute the packaging process for nitrile gloves, obtaining the packaging results. This allows for deep integration of the entire nitrile glove packaging process data, establishing a seamless product information chain. It enables precise tracking of nitrile glove distribution and efficient anti-counterfeiting verification, effectively avoiding the problems of product traceability difficulties and weak anti-counterfeiting measures caused by data isolation in traditional mechanically assisted semi-automated packaging methods. It also avoids the limitations of relying on a single mechanical operation process, which cannot meet the needs of information association and security verification. This reduces the risk of traceability failure and anti-counterfeiting gaps caused by information discontinuity in the circulation of nitrile gloves, significantly improving the adaptability of the intelligent packaging system to the entire lifecycle management and security assurance scenarios of nitrile gloves. Therefore, the intelligent packaging system for nitrile gloves with authenticity identification and traceability functions provided in this embodiment of the invention can accurately achieve the tracking of nitrile glove distribution and anti-counterfeiting verification. Attached Figure Description

[0017] Figure 1This is a functional module diagram of a nitrile glove intelligent packaging system with authenticity identification and traceability functions provided in an embodiment of the present invention; Figure 2 A system composition diagram of the visual inspection unit of a smart packaging system for nitrile gloves with authenticity identification and traceability functions, provided in an embodiment of the present invention; Figure 3 This is a flowchart illustrating an embodiment of the present invention for implementing a smart packaging method for nitrile gloves with authenticity and traceability functions.

[0018] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0021] In practice, the server-side equipment deployed in a smart packaging system for nitrile gloves with authenticity verification and traceability functions may consist of one or more devices. This smart packaging system for nitrile gloves with authenticity verification and traceability functions can be implemented as: a business instance, a virtual machine, and hardware devices. For example, this smart packaging system for nitrile gloves with authenticity verification and traceability functions can be implemented as a business instance deployed on one or more devices in a cloud node. Simply put, this smart packaging system for nitrile gloves with authenticity verification and traceability functions can be understood as software deployed on a cloud node, used to provide a smart packaging service for nitrile gloves with authenticity verification and traceability functions to various user terminals. Alternatively, this smart packaging system for nitrile gloves with authenticity verification and traceability functions can also be implemented as a virtual machine deployed on one or more devices in a cloud node. This virtual machine contains application software for managing various user terminals. Alternatively, this intelligent packaging system for nitrile gloves with authenticity verification and traceability functions can also be implemented as a server consisting of numerous identical or different types of hardware devices, with one or more hardware devices set up to provide each user with an intelligent packaging service for nitrile gloves with authenticity verification and traceability functions.

[0022] In terms of implementation, a smart packaging system for nitrile gloves with authenticity verification and traceability functions and a user terminal are mutually compatible. Specifically, if the smart packaging system for nitrile gloves with authenticity verification and traceability functions is implemented as an application installed on a cloud service platform, then the user terminal acts as a client establishing a communication connection with that application; or if the smart packaging system for nitrile gloves with authenticity verification and traceability functions is implemented as a website, then the user terminal acts as a webpage; or if the smart packaging system for nitrile gloves with authenticity verification and traceability functions is implemented as a cloud service platform, then the user terminal acts as a mini-program within an instant messaging application.

[0023] Reference Figure 1 The diagram shown is a functional block diagram of a nitrile glove intelligent packaging system with authenticity identification and traceability functions provided in an embodiment of the present invention.

[0024] The nitrile glove intelligent packaging system 100 with authenticity verification and traceability functions described in this invention can be set up in a cloud server. In terms of implementation, it can be used as one or more service devices, or as an application installed in the cloud (e.g., a server, server cluster, etc., for nitrile glove intelligent packaging with authenticity verification and traceability functions), or it can be developed as a website. Depending on the implemented functions, the nitrile glove intelligent packaging system 100 includes a traceability network construction module 101, a traceability feature analysis module 102, an anti-counterfeiting optimization module 103, a traceability feedback module 104, and an intelligent packaging module 105.

[0025] In this embodiment of the invention, in the tracking of a smart packaging system for nitrile gloves with authenticity verification and traceability functions, each of the above-mentioned modules can be implemented independently and can be called upon with other modules. Here, "calling" can be understood as one module connecting to multiple modules of another type and providing corresponding services to those connected modules. In the smart packaging system for nitrile gloves with authenticity verification and traceability functions provided by this embodiment of the invention, the applicable scope of the smart packaging architecture with authenticity verification and traceability functions can be adjusted by adding modules and directly calling them without modifying the program code, achieving cluster-based horizontal expansion. This allows for quick and flexible expansion of the smart packaging system for nitrile gloves with authenticity verification and traceability functions. In practical applications, the above modules can be set in the same device or different devices, or they can be set in a virtual device, such as a service instance in a cloud server.

[0026] The following describes, with reference to specific embodiments, the various components and specific workflows of a smart packaging system for nitrile gloves with authenticity and traceability functions.

[0027] The traceability network construction module 101 is used to acquire production line data of nitrile gloves in the production process, so as to set a digital identity identifier for each unit of nitrile gloves, and construct an identity traceability network for each unit of nitrile gloves based on the digital identity identifier and the production line data.

[0028] This invention, through obtaining production line data of nitrile gloves in the production process, sets a digital identity for each unit of nitrile gloves. This transforms the entire production process information of nitrile gloves into traceable and verifiable digital features, which helps to accurately track the source and quality trajectory of each unit of gloves. At the same time, it provides a unique and tamper-proof information basis for subsequent authenticity identification, ensuring the transparency of information and the credibility of products in the circulation process of nitrile gloves from the source.

[0029] The nitrile gloves mentioned above refer to disposable or reusable protective gloves made primarily from butadiene and acrylonitrile through processes such as polymerization, vulcanization, and molding. They can be categorized according to application scenarios, including medical nitrile gloves (such as surgical gloves and examination gloves), industrial nitrile gloves (such as dustproof gloves for electronics factories and corrosion-resistant gloves for chemicals), and civilian nitrile gloves (such as household cleaning gloves). The production line data refers to structured and unstructured information generated throughout the entire production process of nitrile gloves, which can be used to characterize the product's origin, quality, and production process, including raw material batches, process parameters, quality inspection data, and packaging line operation data. Each unit refers to a nitrile glove formed during the packaging process, possessing an independent packaging form and usable as a single unit. A traceability unit is a collection of products or a single product, including a single independent packaging unit (referring to a unit formed by individually packaging a single nitrile glove (such as sealing it in a transparent PE bag), a multi-piece combined packaging unit (referring to a unit formed by uniformly packaging a fixed number of nitrile gloves (such as 10 or 50 gloves) (such as cardboard boxes or composite film bags), and a batch packaging unit (referring to a unit formed by bulk packaging (such as corrugated cardboard boxes) of nitrile gloves of the same production batch and specification). The digital identity identifier refers to a digital information carrier generated based on the production line data to uniquely identify each unit of nitrile glove. It must be unique, tamper-proof, readable, and verifiable. Specific forms include, but are not limited to, encrypted QR codes, RFID chips, and digital serial numbers.

[0030] As an embodiment of the present invention, the step of acquiring production line data of nitrile gloves in the production process to set a digital identity for each unit of the nitrile gloves includes: Based on the production line data, the raw material batch, production line vulcanization parameters, and online quality inspection results of the nitrile gloves were extracted. By integrating the raw material batches, the vulcanization parameters of the production line, and the online quality inspection results, a batch profile of the nitrile gloves is generated. Based on the batch lineage, the quality compliance of the nitrile gloves was verified, and a compliance determination conclusion was obtained. Based on the compliance determination conclusion, the core data in the batch lineage will be converted into a unique traceability code; Retrieve the packaging specifications and production date of each unit of nitrile gloves, and combine them with the unique traceability code to create a traceability file number for each unit of nitrile gloves; Based on the traceability file number, a digital identity identifier is set for each unit of the nitrile gloves.

[0031] The raw material batch refers to the unique identification number generated during the production and procurement stages of the core raw materials (such as butadiene, acrylonitrile, vulcanizing agents, etc.) used in the production of nitrile gloves. This number is assigned by the raw material supplier or manufacturer according to preset rules (such as "raw material type-production year-month-batch number") and is used to associate the procurement information, quality inspection reports, storage conditions, and usage records of the same batch of raw materials to ensure raw material traceability. The production line vulcanization parameters refer to the set of key process parameters executed by the vulcanization equipment after the nitrile gloves are formed on the production line and enter the vulcanization process. These parameters are used to control the rubber crosslinking reaction process to ensure the mechanical properties (such as tensile strength and elasticity) and safety of the gloves. The online quality inspection results refer to the nitrile... After the gloves complete key processes such as molding and vulcanization on the production line, real-time testing data and judgments are collected by online inspection equipment (such as vision inspection systems, leakage testing machines, and tensile strength testers) to assess whether the glove quality meets preset standards. The batch spectrum refers to a structured information set built according to the "raw material-production-inspection" logic, based on the raw material batches of nitrile gloves, production line vulcanization parameters, and online quality inspection results. This set is presented in a tree or table format, clearly linking the raw material source, key production process parameters, and real-time quality status of each batch of nitrile gloves, forming a complete information chain from raw material input to semi-finished product inspection. The quality compliance verification refers to verifying the relevant information in the batch spectrum according to the corresponding industry standards for nitrile gloves. The process involves comparing key indicators (such as raw material quality, vulcanization parameters, and online quality testing results) with standard requirements to determine whether a batch of gloves meets production compliance and quality safety standards. The core data refers to crucial information in the batch lineage for identifying the authenticity and traceability of nitrile gloves, and must be included in the digital identity identifier. This includes raw material batch number, production line number, key vulcanization parameters (temperature, time), comprehensive online quality testing conclusions, and production period. The unique traceability code is a globally unique string or code generated by encrypting the core data using a hash algorithm (such as SHA-256) or a symmetric encryption algorithm (such as AES-256). This code corresponds one-to-one with the corresponding batch of gloves and cannot be repeated. It cannot be reverse-engineered; the packaging specifications refer to the parameters determined for each unit of nitrile gloves during the packaging process, used to characterize the packaging form and the quantity contained. These parameters need to match market circulation needs and usage scenarios, specifically including packaging form (such as individual PE bags, composite film boxes, corrugated cartons), the number of gloves contained in each unit, packaging dimensions (length × width × height), etc.; the production date refers to the specific date on which each unit of nitrile gloves completes the production process (molding, vulcanization, online testing) and enters the packaging process, accurate to year-month-day; the traceability file number refers to the number generated by combining the unique traceability code, packaging specifications, and production date according to preset coding rules (such as production date-packaging specification code-unique traceability code fragment), used to uniquely identify each unit of nitrile gloves.

[0032] Furthermore, this embodiment of the invention constructs an identity traceability network for each unit of nitrile glove based on the digital identity identifier and the production line data. This network can accurately link the digital identifier of each glove unit with the entire production process information, ensuring real-time traceability of the entire nitrile glove's trajectory from raw material input to packaging completion. Simultaneously, it provides an immutable information verification network for authenticating nitrile gloves, improving the reliability and efficiency of the identification results. This, in turn, enhances the quality control capabilities and consumer trust in the nitrile glove distribution process. The identity traceability network refers to a digital information network with the digital identity identifier of each unit of nitrile glove as the core node, supported by production line data. It is constructed through data association, encrypted storage, and access control, possessing full-link traceability and authenticity verification functions. Its core is to structurally associate dispersed digital identity identifiers (such as encrypted QR codes, RFID chip information, and traceability file numbers) with corresponding production line data (raw material batches, vulcanization parameters, online quality inspection results, etc.) to form a "one identifier, one file" mapping relationship.

[0033] As an embodiment of the present invention, the step of constructing an identity traceability network for each unit of the nitrile glove based on the digital identity identifier and the production line data includes: Read the unique feature code and data generation timestamp from the digital identity identifier; Analyze the production line process paths and production event sequences associated with the unique feature code in the production line data; Identify the quality status migration trajectory and process parameter compliance tags of the production event sequence; Based on the unique feature code and the production line process path, a unique positioning identifier is generated for each unit of the nitrile glove; Based on the data generation timestamp and the production event sequence, calculate the time-series compliance index for each unit of the nitrile glove; By integrating the quality status migration trajectory with the process parameter compliance label, the microscopic quality level of each unit of the nitrile glove is determined; Based on the time-series compliance index and the micro-quality level, the data archiving level and corresponding traceability information opening strategy for each unit of the nitrile glove are set; The unique positioning identifier, the time-series compliance index, the micro-quality level, the data archiving level, and the traceability information opening strategy are integrated to form the identity traceability network for each unit of nitrile gloves.

[0034] The unique feature code refers to a fixed-length, unique, and irreversible string code generated after processing the core production line data of nitrile gloves using a cryptographic hash algorithm (such as SHA-256); the data generation timestamp refers to the system UTC time at which the unique feature code is generated for each unit of nitrile glove and associated with the production line data; the production event sequence refers to a set of records of state change events experienced by each unit of glove at each key workstation on the production line, arranged in chronological order; and the quality state migration trajectory refers to the trajectory of each unit of nitrile glove from its original state to its current state, constructed based on the quality inspection results and process execution status in the production event sequence. The quality change path from raw material state to semi-finished product state to finished product state is presented in the form of state nodes + state description + judgment result; the process parameter compliance label refers to the compliance or non-compliance classification identifier generated after comparing key process parameters (such as vulcanization temperature, vulcanization time, molding pressure) in the production event sequence with preset industry standards or enterprise internal control standards; the unique positioning identifier refers to the code generated by the algorithm through combining unique feature code and production line process path, used to accurately locate the production position and process flow of each unit of nitrile glove; the temporal compliance index refers to the index used to measure the compliance of adjacent events in the production event sequence. The quantitative assessment value for whether the actual time interval meets the preset standard time interval requirement can be calculated by multiplying the actual number of compliant items by the total number of time-series items by 100 (value range 0-100); the micro-quality level refers to the level calculated based on the quality state migration trajectory and compliance labels of all process parameters, using preset evaluation rules (such as weighted scoring method), used to finely distinguish the product quality differences within the same qualified batch. For example, a pair of gloves with all process parameters compliant and passing quality inspection on the first try can be rated as A++; another pair of gloves with a few parameters at the critical value but passing quality inspection can be rated as A; the data archiving level refers to Based on the aforementioned time-series compliance index and micro-quality level, a storage period and access permission level are set for the complete traceability data of each unit of glove. Among them, the data retention period for high-quality products is longer and the access permission is higher. The traceability information opening strategy refers to the range and level of information that can be accessed after scanning the QR code for different user roles (such as end users, distributors, and quality supervision departments), based on the aforementioned data archiving level. For example, for end users, scanning A++ grade gloves only displays genuine products, high-quality products, and production dates; while quality supervision departments, after scanning the same QR code and being authorized, can view complete raw material reports, process parameter curves, and quality inspection records.

[0035] Optionally, the unique location identifier for each unit of nitrile glove can be generated using a key-value hash algorithm based on the process path. For example, the key node identifiers in the production line process path (such as production line number, vulcanizing furnace number, quality inspection station number, and packaging station number) and the unique feature code are used as ordered input tuples. A key-value hash algorithm (such as the HMAC algorithm based on SHA-256) is used, with the production line node identifier as the key and the unique feature code as the value, to generate the final composite hash value as the unique location identifier. The micro-quality grade of each unit of nitrile glove can be determined using a weighted quantization statistical algorithm based on fuzzy comprehensive evaluation. The data archiving level and corresponding traceability information access strategy for the nitrile gloves can be set based on a strategy mapping algorithm using a multidimensional decision table. For example, a multidimensional decision table can be constructed with the time-series compliance index and micro-quality level as input dimensions and the data archiving level and traceability information access strategy as output. This table defines clear mapping rules. For example, when the time-series compliance index > threshold T1 and the micro-quality level = "A++", it is mapped to the archiving level: permanent and the access strategy: open all data to the quality supervision department. When the index and level fall into other ranges, it is mapped to different archiving levels and strategy combinations. The system can achieve automatic and rapid matching and setting of strategies by querying this decision table.

[0036] The traceability feature parsing module 102 is used to set up a visual inspection unit for the nitrile gloves on the packaging line of the nitrile gloves, and to mark the core traceability features of each unit of the nitrile gloves based on the visual inspection unit and the digital identity identifier.

[0037] This invention, through the installation of a visual inspection unit on the nitrile glove packaging line, can capture in real time the appearance quality issues of the nitrile gloves before packaging and operational deviations during the packaging process. At the same time, it provides a visual status benchmark for the accurate attachment of subsequent digital identification marks (such as QR code labeling position calibration), thereby enhancing the control over the packaging quality of nitrile gloves and the compliance of the product appearance.

[0038] The packaging line refers to an automated or semi-automated production line for nitrile gloves after they have completed the production process (molding, vulcanization, and online quality inspection) and enter the packaging stage. It consists of a conveying device, packaging equipment, auxiliary modules, and a control system connected in series according to the process sequence. It is used to realize the entire process of nitrile gloves from the temporary storage of finished products to the final packaged finished products, including key processes such as glove straightening, sorting, bagging / boxing, sealing, and labeling (attaching digital identification tags). The visual inspection unit refers to an intelligent inspection device integrated into the nitrile glove packaging line for real-time detection of the appearance quality of gloves and the standardization of packaging operations.

[0039] To clearly illustrate the architecture of the visual inspection unit, please refer to... Figure 2 The diagram shows the system composition of a visual inspection unit provided in an embodiment of the present invention. The transmission module provides a stable transport guarantee for the subsequent image acquisition and analysis module to obtain images of the appearance of nitrile gloves, which is the basis for ensuring that each unit of nitrile glove can accurately enter the inspection field of view. The image acquisition and analysis module intuitively presents the logic of image acquisition, feature extraction and analysis using LED light source, camera and computer, providing a key image data processing path for the identification and labeling of the core traceability features (combined with digital identity) of each unit of nitrile glove. The control feedback module relies on Raspberry Pi single board computer to realize the linkage control between the inspection results and the subsequent traceability labeling link. It should be noted that the linear presentation of each module in the diagram is essentially a concise display of the core association of "inspection object transport - image acquisition and analysis - traceability labeling linkage" in the nitrile glove packaging line. In the actual scenario, the complexity of the coordination of each module is far greater than that shown in the diagram.

[0040] Furthermore, this embodiment of the invention, by labeling the core traceability features of each unit of nitrile gloves based on the visual detection unit and the digital identity identifier, can enhance the comprehensive perception capability of the physical state and digital information of nitrile gloves across all dimensions. Simultaneously, it lays a concrete feature foundation for subsequent information verification in the identity traceability network, enhancing the accuracy of quality traceability and the efficiency of authenticity identification in the entire circulation process of nitrile gloves. The core traceability features refer to the set of key features extracted after data fusion based on the physical state data of nitrile gloves obtained by the visual detection unit (including inherent physical features and packaging state features) and the digital information carried by the digital identity identifier, used to uniquely associate the physical entity and digital file of each unit of nitrile gloves. The physical state data includes the inherent visual uniqueness features of nitrile gloves (such as fingertip texture, edge contour, and surface micro-texture) and packaging standardization features (such as sealing flatness and label position deviation); the digital information includes the unique traceability code and data generation timestamp in the digital identity identifier.

[0041] As an embodiment of the present invention, the step of marking the core traceability features of each unit of nitrile glove based on the visual detection unit and the digital identity identifier includes: Collect the packaging visual image sequence corresponding to the visual detection unit, and read the identification data corresponding to the digital identity identifier; Synchronously align the acquisition time point and spatial work point corresponding to the packaging visual image sequence and the identification data; Based on the acquisition time point and the spatial work site, a correspondence table between the packaging visual image and the digital identity is constructed; Extract the unique associated nodes from the corresponding relationship table; The core traceability features of each unit of nitrile gloves are marked through the unique association node.

[0042] The packaging visual image sequence refers to a series of digital images continuously captured at fixed time intervals or triggered conditions at the final packaging station of each unit of nitrile gloves, after sealing and before leaving the production line. For example, for a box of gloves, the industrial camera continuously captures three images on the conveyor belt: the first frame is a panoramic view of the front of the packaging bag, the second frame is a close-up of the sealing, and the third frame is a side image containing quantity information. These three images constitute a packaging visual image sequence in chronological order. The identification data refers to the structured data block parsed after reading the digital identity identifier (such as a QR code) by a barcode scanning device. This data block contains at least a traceability code that uniquely identifies each unit of gloves and can be expanded to include key information such as production batch and specifications. The synchronization alignment refers to the process of establishing a precise spatiotemporal correlation between each frame of the packaging visual image sequence and each reading operation of the identification data during data processing. Specifically, it is necessary to stamp each frame of the image and each barcode scanning record with the same timestamp in the same time system (such as UTC) and the same physical location identifier (station number). The spatial workstation is a unique location code on the packaging production line that is responsible for performing specific operations (such as barcode scanning and image acquisition). For example, the spatial workstation could be production line 2 - final inspection station - barcode scanner 01. The correspondence table is a two-dimensional relationship table stored in the database. Each record establishes a one-to-one mapping relationship between the identification data and a specific frame in the packaging visual image sequence. The table contains at least a unique index field (such as glove number), image sequence information (image number, acquisition timestamp, spatial workstation), and identification data information (unique feature code, traceability file number, data generation timestamp), presented in a row-column correspondence format. The unique association node is a key record selected from the correspondence table that can most strongly and uniquely prove the binding relationship between the physical packaging of a unit glove and its digital identity. This node is usually the record generated when the identification data of the unit glove is successfully read for the first time and the image quality is qualified.

[0043] Optionally, spatiotemporal calibration algorithms (such as timestamp interpolation calibration and spatial coordinate matching algorithms) can be used to synchronously align the acquisition time point and spatial work point corresponding to the packaging visual image sequence and the identification data. The correspondence table between the packaging visual image and the digital identity can be constructed using a key-value pair mapping table generation method. For example, using the unique number of each unit of nitrile glove as the core index key, the image number, acquisition timestamp, and spatial work point information of the packaging visual image sequence are mapped one-to-one with the unique feature code, traceability file number, and data generation timestamp information of the digital identity, establishing a structured table with corresponding rows and columns to achieve accurate association between the packaging visual image and the digital identity.

[0044] The anti-counterfeiting optimization module 103 is used to simulate the counterfeiting risk environment of the nitrile gloves in the subsequent circulation process based on the core traceability features, establish an anti-counterfeiting state switching logic library for each unit of the nitrile gloves based on the counterfeiting risk environment, and generate a strategy update instruction for the identity traceability network based on the anti-counterfeiting state switching logic library.

[0045] This invention, by simulating the counterfeiting risk environment of nitrile gloves in subsequent circulation stages based on the core traceability features, extends static traceability feature verification to dynamic counterfeiting risk simulation, exposing potential counterfeiting risks in circulation in advance (such as the replacement of digital identifiers, mismatch between physical features and digital information, etc.), helping to improve the resilience of nitrile gloves to complex counterfeiting behaviors, and strengthening the whole-process guarantee of authenticity identification and quality safety of nitrile gloves in the entire circulation process.

[0046] The subsequent circulation links refer to the entire flow of nitrile gloves from the manufacturer to the end user after the gloves are smart-packaged, including key nodes such as warehousing, transportation, distribution, and retail. The counterfeit risk environment refers to a virtual environment constructed by the system based on core traceability features and a counterfeit rule base. This environment simulates counterfeit scenarios at different nodes in the subsequent circulation links (such as label tampering, physical replacement, and information disconnection) to predict the counterfeit risk of nitrile gloves. The environment needs to restore the actual working conditions of each circulation node (such as label wear caused by transportation bumps and the impact of warehousing temperature and humidity on packaging) and inject abnormal features from the counterfeit behavior prediction graph (such as tampered digital labels and counterfeit physical textures). Finally, the prediction results of node-counterfeit type-risk level-response strategy are output.

[0047] As an embodiment of the present invention, the step of simulating the counterfeit risk environment of the nitrile gloves in the subsequent circulation process based on the core traceability features includes: The target distribution channels for the nitrile gloves are determined by the product specifications and packaging grade in the core traceability features. Extract the set of spatiotemporal elements of circulation, identity verification triggering conditions, and feature reproducibility evaluation values ​​from the core traceability features; Based on the set of spatiotemporal elements of circulation, the identity verification triggering conditions, and the feature replicability evaluation value, a deduction diagram of counterfeiting behavior of the target circulation channel is constructed. Based on the aforementioned counterfeiting behavior projection diagram, the counterfeiting risk points and regulatory weaknesses in each link of the target distribution channel are analyzed. Based on the counterfeiting behavior projection diagram, the counterfeiting risk points, and the regulatory weaknesses, a counterfeiting risk environment for the nitrile gloves in the subsequent circulation process is simulated.

[0048] The product specifications refer to structured information extracted from core traceability features to characterize the physical parameters and application scenarios of nitrile gloves, including size (e.g., S / M / L / XL), thickness, application type (e.g., medical examination / surgical / industrial corrosion protection / civilian cleaning), and protective performance indicators (e.g., puncture resistance rating, chemical corrosion resistance type). The packaging level refers to the complexity of the product packaging, the content of anti-counterfeiting technology, and the brand value conveyed, including basic packaging (e.g., simple plastic bags without anti-counterfeiting labels), intermediate packaging (e.g., color box packaging with fragile labels), and advanced packaging (e.g., holographic anti-counterfeiting boxes, disposable tear strips, and internal independent seals). Packaging), for example, branded gloves targeting high-end hospitals typically use premium packaging. It should be noted that the packaging grade is an important external clue for determining product authenticity and is also one of the indicators of the difficulty of counterfeiting. The target distribution channel refers to the specific circulation path of nitrile gloves from the manufacturer to the end user, determined by combining product specifications and packaging grade. This includes hospital direct sales channels, pharmacy retail channels, online e-commerce platforms, and industrial supplies wholesale markets. For example, high-specification medical sterile gloves typically target hospital direct sales channels or professional medical device distributor channels. The distribution spatiotemporal element set refers to the set of elements used to describe the product's distribution process. The key data set with time and space attributes includes planned sales areas (e.g., East China), typical logistics routes (e.g., "Shanghai warehouse -> Hangzhou distribution center -> a hospital"), and estimated turnover time (e.g., 3 months from factory to use). The feature replicability assessment value refers to a quantitative or graded evaluation of the ease with which anti-counterfeiting features (especially digital identity identifiers) used in a unit of gloves can be successfully counterfeited or copied by criminals. The counterfeiting behavior projection diagram refers to a graph based on the set of spatiotemporal elements in circulation, identity verification triggering conditions (e.g., QR code verification at transportation nodes, feature comparison at retail terminals), and the feature replicability assessment value, using a "node-time-counterfeiting type-probability" approach. A visual flowchart constructed using the dimension "" needs to be marked with each link of the target distribution channel (such as warehousing, transportation, distribution, and retail), the estimated time of each link, possible types of counterfeiting (such as label tampering and physical replacement), and the probability of counterfeiting (calculated based on the feature replicability assessment value) to intuitively present the potential path of counterfeiting behavior; the counterfeiting risk points refer to the specific links or locations where counterfeiting behavior may occur in the path revealed by the counterfeiting behavior projection diagram. For example, in online e-commerce platform channels, the main counterfeiting risk points include the authenticity verification link when goods enter the platform warehouse, the goods transfer link between different merchants within the platform, and the terminal delivery link;The aforementioned regulatory weaknesses refer to areas in the corresponding distribution channels where existing management measures, technical means, or laws and regulations are inadequate or have blind spots, making it difficult to detect, prevent, and punish counterfeit activities. For example, regulatory weaknesses in online e-commerce platforms include lax verification of third-party merchant qualifications, lack of verification interfaces for real-time integration with brand traceability systems, and insufficient monitoring capabilities for cross-regional sales.

[0049] Optionally, the replicability assessment value of the core traceability features can be extracted using the Analytic Hierarchy Process (AHP) combined with the fuzzy comprehensive evaluation method; the counterfeit behavior projection diagram of the target distribution channel can be constructed using Markov Chain combined with directed graph modeling. For example, each link of the target distribution channel (warehousing, transportation, distribution, and retail) can be regarded as a state node of the Markov chain. Based on the distribution spatiotemporal element set, the state transition probability (such as the probability of transitioning from warehousing to transportation) can be determined. The counterfeit probability of each node can be calculated by combining the replicability assessment value of the features. Then, the node-transfer path-counterfeit probability-counterfeit type can be visualized by the directed graph modeling method to form the counterfeit behavior projection diagram.

[0050] Furthermore, this embodiment of the invention establishes an anti-counterfeiting status switching logic library for each unit of nitrile gloves based on the aforementioned counterfeiting risk environment. This allows the system to dynamically adjust anti-counterfeiting verification strategies according to the counterfeiting risk environment, and optimize functions such as core traceability feature verification dimensions, identity verification trigger frequency, and counterfeiting warning threshold in real time. This improves the accuracy of responding to counterfeiting behavior in different circulation scenarios and enhances the timeliness and reliability of authenticity assurance during the entire circulation process of nitrile gloves. The anti-counterfeiting status switching logic library refers to a set of structured rules built based on the counterfeiting risk environment to define the correspondence between anti-counterfeiting status, trigger conditions, and switching strategies for each unit of nitrile gloves in different circulation scenarios.

[0051] As an embodiment of the present invention, the step of establishing an anti-counterfeiting state switching logic library for each unit of the nitrile glove based on the counterfeiting risk environment includes: From the counterfeit risk environment, identify the risk scenario type, risk severity level, and potential counterfeit behavior path corresponding to each unit of the nitrile glove; Based on the risk scenario type, the risk severity level, and the potential counterfeiting behavior path, respectively generate scenario response instructions, level response instructions, and path blocking instructions for each unit of the nitrile glove; Label the key feature attributes corresponding to the scenario response command, the level response command, and the path blocking command; Based on the key feature attributes, establish a set of linkage rules among the scenario response instructions, the level response instructions, and the path blocking instructions; Define the execution priority of each rule in the aforementioned set of linkage rules; By utilizing the linkage between the scenario response command, the level response command, and the path blocking command, and the execution priority, an anti-counterfeiting status switching decision engine is constructed for each unit of the nitrile glove; Based on the anti-counterfeiting status switching decision engine and the linkage rule set, an anti-counterfeiting status switching logic library is established for each unit of the nitrile glove.

[0052] The risk scenario types refer to classifications of counterfeit threats based on factors such as distribution channels and product characteristics, sharing common characteristics. These include, but are not limited to, online platform cross-selling, counterfeit goods mixed into offline wholesale markets, recycling genuine packaging for counterfeiting, and cross-regional counterfeiting. The risk severity level refers to a quantitative assessment of the potential brand damage, economic loss, or security threat caused by a specific risk scenario, which can be divided into high-risk (potentially causing major safety accidents or huge compensation), medium-risk (causing significant economic losses), and low-risk (minor impact, such as small-scale cross-selling). For example, recycling medical sterile glove packaging for counterfeiting is considered a high-risk level. The potential counterfeiting behavior path refers to the counterfeiters' actions under specific risk scenarios. A series of specific and orderly action steps that may be taken to achieve the objective. For example, in the scenario of counterfeiting through the recycling of genuine packaging, the potential counterfeiting path is: Step 1: Illegally recycling discarded genuine packaging; Step 2: Cleaning and processing it before filling it with inferior counterfeit gloves; Step 3: Resealing and putting it into the market. The scenario response instructions refer to basic operational commands designed for specific risk scenarios to adjust the anti-counterfeiting status. For example, in the scenario of cross-selling on online platforms, one scenario response instruction is to enable the initial scan geolocation verification. The level response instructions refer to anti-counterfeiting strength adjustment instructions generated based on the severity level of the risk, corresponding to the risk level. For example, for high-risk situations, the level response instructions... This could involve marking the product status as abnormal and immediately sending an alert to the regulatory platform; for medium-risk situations, the instruction could be marking the product status as suspicious and logging it; the path-blocking instruction refers to a targeted instruction generated based on potential counterfeit behavior paths to cut off the counterfeit behavior chain. For example, for the re-entry into the market step in the path of recycling genuine packaging, the path-blocking instruction would return a warning message indicating that the product has been consumed after the initial scan activation; the key feature attributes refer to metadata used to accurately describe the applicable conditions, execution parameters, or target of an instruction, including the instruction's associated scenario ID, the risk level corresponding to the instruction, and the resources required for instruction execution. The set of linkage rules refers to the set of scenarios, levels, and paths that are executed in a specific order under certain conditions, based on key feature attributes. For example, a linkage rule could be: IF trigger 'transportation stop scenario response instruction' (associated scenario ID: SC-001, medium risk) AND trigger 'medium risk level response instruction' (associated level: medium risk), THEN prioritize the execution of 'dynamic timeliness code verification' in the scenario response instruction, simultaneously execute 'dual verification' in the level response instruction, and associate the execution of 'vibration sensor alarm' in the path blocking instruction.If the 'High-Risk Level Response Instruction' is triggered, and the 'Retail Terminal Scenario Response Instruction' is triggered, then the routine verification in the scenario response instruction is paused, and the 'Full-Dimensional Verification' in the high-risk response instruction and the 'Pre-Shelf Locking' in the path blocking instruction are executed first. The execution priority refers to the numerical weight used by the system to determine the order of rule execution when the conditions of multiple rules are met simultaneously; rules with higher priority are executed first. The linkage relationship refers to the logical relationship of collaboration, dependency, or mutual exclusion between different instructions in the linkage rule set. For example, the geolocation verification instruction and the alarm sending instruction work collaboratively in one rule, and their dependency relationship is: the instruction marking an anomaly will only be executed if the geolocation verification fails. The anti-counterfeiting status switching decision engine is an intelligent decision-making module built based on the linkage rule set, execution priority, and linkage relationship. Its core is to receive real-time query requests (such as QR code scanning events) and their context (such as IP addresses), load the corresponding linkage rule set, perform logical reasoning based on the conditions and priorities of the rules, and finally output the anti-counterfeiting status switching operation to be executed (such as changing the status from inactive to active or to abnormal).

[0053] Optionally, scenario response instructions, level response instructions, and path blocking instructions corresponding to each unit of nitrile gloves can be generated using case-based reasoning technology. For example, a case library for responding to counterfeit nitrile gloves (including historical risk scenarios, corresponding instructions, and execution effects) can be constructed. Using the "case retrieval-case adaptation-case learning" process of the CBR system, the most similar historical best instructions to the current risk scenario can be retrieved. After parameter fine-tuning, scenario response instructions, level response instructions, and path blocking instructions are obtained respectively. The set of linkage rules between the scenario response instructions, the level response instructions, and the path blocking instructions can be established using fuzzy logic reasoning algorithms, such as: identifying the key feature attributes of the instructions... Defined as fuzzy input variables and instruction coordination relationships as fuzzy output variables, a fuzzy rule base is constructed. Through fuzzification, fuzzy inference, and defuzzification processes, the fuzzy rules are transformed into standardized "IF-THEN" structured linkage rules, forming a linkage rule set. The execution priority of each rule in the linkage rule set can be defined using the analytic hierarchy process (AHP), such as constructing an execution priority evaluation system (target layer: execution priority; criterion layer: risk severity level weight, instruction impact scope weight, execution cost weight; scheme layer: each linkage rule). A criterion layer judgment matrix is ​​constructed through expert scoring, and the score of each linkage rule under the criterion layer is calculated, and the rules are sorted from high to low based on the total score.

[0054] As another embodiment of the present invention, the step of constructing an anti-counterfeiting status switching decision engine for each unit of the nitrile glove by utilizing the linkage relationship between the scenario response command, the level response command, and the path blocking command, and the execution priority, includes: Identify the instruction categories and triggering conditions corresponding to the scenario response instructions, the level response instructions, and the path blocking instructions; Analyze the logical relationship types in the linkage relationship and the dynamic adjustment rules of the execution priority; Based on the instruction category and the logical relationship type, construct a decision flowchart for anti-counterfeiting state switching between the scenario response instruction, the level response instruction, and the path blocking instruction; Based on the triggering conditions and the dynamic adjustment rules, calculate the state switching accuracy and risk coverage completeness index of different paths in the anti-counterfeiting state switching decision flowchart. Based on the anti-counterfeiting status switching decision flowchart, the status switching accuracy, and the risk coverage completeness index, an anti-counterfeiting response scheme set is established for each unit of the nitrile glove. Based on the anti-counterfeiting response scheme set, generate the core judgment logic and instruction sequence corresponding to each unit of the nitrile glove; By integrating the anti-counterfeiting state switching decision flowchart, the core judgment logic, and the instruction sequence, an anti-counterfeiting state switching decision engine for each unit of the nitrile glove is constructed.

[0055] The instruction category refers to the functional classification of scenario response instructions, level response instructions, and path blocking instructions based on their essential roles in anti-counterfeiting. This includes verification instructions (such as geolocation verification), control instructions (such as switching the product status to "abnormal"), and alarm instructions (such as sending warning information to the administrator). For example, the first-time scan geolocation verification instruction is classified as a verification instruction. The trigger condition refers to one or more prerequisite events or data states that must be met to initiate or execute an instruction. For example, for a control instruction that marks the status as suspicious, the trigger condition could be a scan count > 1 and the scanned IP address not matching the shipping location. The logical relationship type refers to... The attribute types extracted from the linkage relationships, representing the collaborative logic between scenario response instructions, level response instructions, and path blocking instructions, include sequential execution (instruction B must be executed after instruction A), conditional branching (instruction D is executed if condition C is met, otherwise instruction E is executed), and parallel execution (instructions F and G can be triggered simultaneously). The dynamic adjustment rule refers to the rule that dynamically adjusts the execution priority based on real-time changes in the counterfeiting risk environment (such as updates to counterfeiting probability, changes in distribution channels, and adjustments to external regulatory policies). The anti-counterfeiting state switching decision flowchart is a visual graphical model that clearly depicts the process from receiving a query, based on the instruction category and relational pattern attributes. The term "state switching accuracy" refers to the percentage of times, in historical data or simulation tests, that a state switching result caused by a certain decision path (e.g., "normal" or "abnormal") matches the actual situation (e.g., whether the product is genuine or counterfeit). The "risk coverage completeness index" refers to the proportion of counterfeit risk scenarios that a certain decision path can effectively address, relative to all risk scenarios required to be covered by its design objective. Its calculation dimensions include scenario coverage (the proportion of covered risk scenario types to the total number of scenarios), path coverage (the proportion of covered potential counterfeit behavior paths to the total number of paths), and measures. The adaptability (whether the instructions for covering risks are suitable) is calculated using the following formula: Risk Coverage Completeness Index = Scenario Coverage × 40% + Path Coverage × 40% + Measure Adaptability × 20%; The anti-counterfeiting response scheme set refers to a collection of specific anti-counterfeiting action plans that have been evaluated and optimized for each unit of glove; The core judgment logic refers to the core logic rules extracted from the anti-counterfeiting response scheme set and used to guide the decision engine in risk identification → instruction selection → state switching, presented in IF-THEN-ELSE structured statements; The instruction sequence refers to the combination of instructions extracted from the anti-counterfeiting response scheme set and sorted by execution priority + relational pattern attributes for specific risk scenarios.

[0056] Optionally, the anti-counterfeiting state switching decision flowchart between the scenario response command, the level response command, and the path blocking command can be constructed using a visualization modeling tool combined with a directed graph algorithm; a set of anti-counterfeiting response schemes for each unit of the nitrile glove can be established using a multi-objective optimization algorithm combined with a risk scenario enumeration method.

[0057] This invention generates policy update instructions for the identity traceability network based on the anti-counterfeiting status switching logic library. This strengthens the gradient of the open scope of traceability information through anti-counterfeiting status levels, improves the adaptability of instructions to different risk scenarios, and enables the identity traceability network to achieve synergy in information traceability accuracy and anti-counterfeiting security. This comprehensively enhances the dynamic control capability of identity traceability throughout the entire circulation process of nitrile gloves. The policy update instructions refer to a set of instructions generated based on the anti-counterfeiting status levels, triggering conditions, and switching strategies in the anti-counterfeiting status switching logic library, used to dynamically adjust the core operating rules of the identity traceability network. The core function of these instructions is to drive the identity traceability network to update the status or behavioral rules of specific digital identity identifiers.

[0058] As an embodiment of the present invention, the step of generating a policy update instruction for the identity tracing network based on the anti-counterfeiting state switching logic library includes: The policy configuration items and data integrity rules of the identity tracing network are analyzed. Identify the risk handling level and response time threshold corresponding to the anti-counterfeiting status switching logic library; Based on the risk handling level, the handling priority of the task queue in the identity tracing network is defined; By using the response timeliness threshold, key triggering events in the identity tracing network are located; Based on the key triggering events and the handling priorities, the database operation sequence and API call steps of the strategy configuration items are generated; Based on the data integrity rules, the transaction boundary between the database operation sequence and the API call steps is defined; By combining the policy configuration items, the database operation sequence, the API call steps, and the transaction boundary, a policy update instruction for the identity tracing network is generated.

[0059] The policy configuration items refer to software parameters or data fields used to control anti-counterfeiting and traceability behaviors in the identity traceability network, which can be dynamically modified during system operation. These include, but are not limited to: product status fields (e.g., inactive, activated, abnormal), query response templates (e.g., different prompt information templates returned to the user based on different statuses), and alarm rule switches (e.g., a Boolean configuration item that controls whether to send a high-risk alarm to the administrator). The data integrity rules refer to the constraints that must be followed when modifying the policy configuration items to prevent data logic errors and ensure the correctness of business logic. For example, a data integrity rule can be defined as: every traceability... The source record must include three mandatory fields: identity ID, circulation timestamp, and operator's digital signature. Data for traceability storage must pass SHA-256 hash verification before storage; failure to verify results in rejection. The risk handling level refers to the classification of the severity of counterfeiting risk associated with the current event (such as a single QR code scan) based on the analysis results of the anti-counterfeiting state switching logic library. This includes high risk (e.g., a significant discrepancy in geographical location detected on the first scan, suggesting counterfeit goods), medium risk (e.g., abnormal frequency of scans, not the first scan), and low risk (e.g., normal first-time QR code verification). The response time threshold refers to the requirement for the identity traceability network to update its strategy and risk management mechanisms in response to risks identified by the anti-counterfeiting state switching logic library. The maximum time threshold for risk handling; the task queue refers to the ordered data structure in the identity tracing network used to temporarily store tasks to be executed (such as policy update tasks, risk handling tasks, and tracing query tasks); the handling priority refers to the execution order set for tasks in the task queue in the identity tracing network based on the risk handling level, with higher priority tasks occupying network resources (such as CPU, memory, and database connections) first and being scheduled for execution; the key triggering event refers to a specific event in the identity tracing network that meets the response time threshold and triggers the policy update task, usually directly related to high-urgency risks, and is the triggering condition for the generation of policy update instructions; the database operation sequence refers to... To enable modifications to identity tracing network policy configuration items (such as updating verification rules and adjusting permission thresholds), a set of database operation instructions arranged in a preset logical order is provided, including operations such as data query (SELECT), insert (INSERT), update (UPDATE), and delete (DELETE). The API call steps refer to the ordered steps for calling external system API interfaces, set according to policy update requirements, to achieve collaboration between the identity tracing network and external systems (such as risk control systems, regulatory platforms, and terminal query systems). These steps include determining the interface address, parameter encapsulation (such as identity authentication tokens and policy update data), request sending, response receiving, and verification.The transaction boundary refers to the scope within which one or more database operation sequences and API call steps are combined into an indivisible transaction. Within this boundary, all operations either execute successfully, or if even one operation fails, all operations are rolled back (e.g., undoing executed database updates or notifying the API caller to cancel the operation), and the system state is restored to its state before the transaction began.

[0060] Optionally, the policy configuration items and data integrity rules of the identity tracing network can be parsed based on the automated parsing algorithm of the configuration metadata and business rule engine. Specifically, firstly, a configuration metadata list is established, which defines all configurable items in key-value pair form. At the same time, the data integrity rules are written as business rule scripts. When the system starts, a configuration parser loads and parses this metadata list and rule scripts, converting them into structured configuration objects and rule objects in memory for the decision engine to query and call. The risk handling level and response time threshold corresponding to the anti-counterfeiting state switching logic library can be identified by an intelligent recognition algorithm based on multi-dimensional feature input and fuzzy reasoning. For example, the multi-dimensional features of a query event (such as the query source IP risk score and query time anomaly degree) can be used as input. A fuzzy inference system is constructed, which internally defines the membership functions of these features to risk treatment levels and response timeliness, as well as fuzzy inference rules. The system calculates the values ​​of input features under each membership function, applies fuzzy rules for inference, and finally outputs the determined level and threshold after defuzzification. The database operation sequence and API call steps of the policy configuration items can be generated using a code automatic generation algorithm based on template engine and context binding. For example, an operation template is preset for each type of key triggering event. Placeholders are used in the template. When the event is triggered, the algorithm selects the corresponding template according to the event type, and then binds the current context information (such as the specific traceability code, target status, etc.) to the template, replacing all placeholders, thereby generating specific, immediately executable SQL statements and API call requests.

[0061] The traceability feedback module 104 is used to obtain the feedback parameters of the identity traceability network after executing the policy update instruction, so as to calculate the traceability credibility index of the nitrile glove, and construct the parameter adaptive adjustment strategy set of the visual detection unit based on the traceability credibility index.

[0062] This invention, through obtaining feedback parameters from the identity traceability network after executing the policy update instruction, calculates the traceability credibility index of the nitrile gloves. This directly correlates the actual execution effect of the policy update with the credibility of the nitrile glove traceability data. The credibility index quantitative evaluation mechanism dynamically reflects the operational status of the traceability network, offsetting potential parameter adaptation biases after the policy update and improving the reliability of nitrile glove authenticity identification and traceability results. The feedback parameters refer to a set of quantitative data collected in real time after the nitrile glove identity traceability network executes the policy update instruction, used to intuitively reflect the implementation effect of the policy update and the network's operational status. The traceability credibility index is a comprehensive indicator used to quantitatively evaluate the credibility of the nitrile glove traceability data and the operational reliability of the identity traceability network.

[0063] As an embodiment of the present invention, obtaining the feedback parameters of the identity tracing network after executing the policy update instruction, in order to calculate the traceability credibility index of the nitrile gloves, includes: Analyze the instruction execution timing data and the state change frequency of the digital identity in the feedback parameters; Based on the digital identity, retrieve the full lifecycle traceability record of the nitrile gloves; The health of the traceability chain for the nitrile gloves is assessed through the full lifecycle traceability records. Retrieve the historical request records of the digital identity and extract the system response stability parameters from the historical request records; Based on the instruction execution timing data and the traceability link health, the robustness of the traceability rule execution for the nitrile gloves is calculated. Based on the state change frequency and the system response stability parameters, the probability of the traceability link of the nitrile glove being interrupted is calculated; Real-time monitoring of the query and verification success rate and the frequency of abnormal alarm triggering of the digital identity identifier; The traceability reliability index of the nitrile gloves is calculated by combining the robustness of the traceability rule execution, the probability of traceability link interruption, the success rate of query verification, and the frequency of abnormal alarm triggering.

[0064] The instruction execution time sequence data refers to a structured data set recorded chronologically by the identity traceability network during the execution of the nitrile glove strategy update instruction, reflecting the execution status and time nodes of each stage of the instruction. The status change frequency refers to the total number of times the status of the nitrile glove digital identity changes within a preset statistical period (e.g., 24 hours), and the average number of changes per unit time. The full lifecycle traceability record refers to a structured data set bound to the digital identity and recording key information at each stage of the entire process from the start of nitrile glove production to final use / disposal, including the nitrile glove production stage (production work orders, raw material batches, quality inspectors). The data set includes information from various stages: warehousing (inbound slips, outbound slips, storage temperature and humidity), transportation (logistics providers, transportation routes, and delivery records), and sales (distributor information, sales order numbers, and consumer query records). The traceability chain health refers to a quantitative indicator, calculated through weighted calculation based on the entire lifecycle traceability records of nitrile gloves, assessing the degree to which the traceability chain is free of missing information, contradictions, and tampering, considering three dimensions: chain integrity, data consistency, and anti-tampering effectiveness. The historical request records refer to detailed records of all user requests for traceability queries, authenticity verification, and status retrieval related to the digital identity of nitrile gloves within a preset time frame (e.g., the past 7 days). The system response stability parameter refers to a set of quantitative data extracted from the historical request records of the nitrile glove's digital identity, reflecting the performance stability of the traceability system when processing requests. This includes parameters such as response time fluctuation, request success rate, and timeout retry frequency. The robustness of traceability rule execution refers to the probability coefficient, calculated using a probability model based on the nitrile glove's instruction execution timing data and the health of the traceability link, indicating the likelihood that traceability rules (such as data verification rules and state transition rules) will execute according to preset requirements. The value range is 0-1. The probability of traceability link interruption refers to the probability calculated using a risk assessment model based on the nitrile glove's state change frequency index and the system response stability parameter. The probability coefficient of failures such as data transmission interruption or record loss in the traceability link, ranging from 0 to 1; the query verification success rate refers to the ratio of the number of traceability query and authenticity verification requests initiated for nitrile glove digital identity to the total number of requests within the real-time monitoring period (e.g., 1 hour), in which the system successfully returns valid results (e.g., complete traceability records, authenticity determination results), ranging from 0 to 1; the abnormal alarm trigger frequency refers to the total number of alarms triggered by the traceability network for nitrile glove digital identity within the real-time monitoring period, such as data abnormalities (e.g., record tampering), request abnormalities (e.g., high-frequency unauthorized queries), and status abnormalities (e.g., abnormal flow).

[0065] Optionally, the robustness of the traceability rules for the nitrile gloves can be calculated using a weighted logistic regression model; the probability of the traceability link being interrupted for the nitrile gloves can be calculated using a combination of fault tree analysis (FTA) and Bayesian network model; and the success rate of the query verification of the digital identity can be monitored in real time using real-time data stream processing tools, such as Flink.

[0066] As another embodiment of the present invention, the calculation method of this embodiment does not affect the implementation of the above basic scheme, but is merely a further explanation. The reliability index can also be obtained in other ways. The traceability reliability index of the nitrile gloves is calculated using the following formula:

[0067] in, Indicates the traceability credibility index. This indicates the baseline credibility of the identity traceability network corresponding to nitrile gloves. This represents the coupling coefficient between query validation success rate and rule execution robustness. This indicates the success rate of the query verification. Indicates the robustness of rule enforcement. The suppression weight represents the frequency of abnormal alarm triggering. This represents the attenuation coefficient indicating the frequency of abnormal alarm triggering. This indicates the frequency of abnormal alarm triggering. This indicates the probability of a broken trace link. This represents the link integrity sensitivity coefficient.

[0068] In detail, the coupling coefficient between the query verification success rate and the rule execution robustness ( This is used to balance the joint contribution weight of market validation behavior and the reliability of system rule execution to credibility. Its value ranges from (0,2). The specific value can be determined by analyzing the strength of the positive correlation between successful market validation and robust rule execution in historical data. For example, for medical glove products with complex distribution channels and high risk of cross-selling, both market validation and rule execution are crucial. It can be set to 1.5; however, for industrial gloves with a single distribution channel and low risk, It can be set to 0.8. The suppression weight for the frequency of abnormal alarm triggering ( This is used to control the magnitude of the negative impact of abnormal alarms on overall reliability, and its value ranges from (0,1]. For example, when... Setting it to 0.5 indicates that the potential negative impact of abnormal alarms on the credibility index is reduced to 50% of its original level. This avoids excessive impact of alarms on the credibility assessment while retaining a certain risk warning effect. The suppression weight of abnormal alarm trigger frequency can be determined by combining the Analytic Hierarchy Process (AHP) with an expert scoring matrix. The attenuation coefficient of the abnormal alarm trigger frequency ( This is used to adjust the decay rate of the impact of a single alarm on reliability, that is, the deceleration rate of the marginal impact of alarm frequency. Its value ranges from (0,1]. The specific value can be determined by analyzing the correlation curve between the historical distribution of alarm frequency and the actual failure rate of the system. For example, in the initial deployment stage when alarms are dense but the false alarm rate is also high, It can be set to 0.2 to smooth out the impact of a single alarm; while in a stable system and accurate alarm phase, It can be set to 0.05 to ensure that each alarm causes a sufficient decrease in credibility, thereby improving the sensitivity of risk response. The link integrity sensitivity coefficient ( ) is used to amplify or reduce the probability of interruption in the tracing link ( The penalty for credibility ranges from 0 to 3. The specific value can be determined by analyzing the impact of missing data at different stages on the overall reliability of the traceability conclusion. For example, for data interruptions in critical stages (such as production and warehousing), It can be set to 2.0 for severe penalties; for data delays in non-critical processes (such as secondary distributor inventory), It can be set to 0.5 for a mild penalty.

[0069] It should be noted that the source tracing credibility index formula proposed in this application breaks through the limitations of traditional methods that rely solely on a single dimension (such as query success rate) for static evaluation, by using core credibility factor items. Comprehensive quantification system rule execution reliability Effectiveness of market validation behavior To improve the effective execution rate of traceability rules for nitrile gloves and the effectiveness of market query verification; and through anomaly attenuation items Characterizing the frequency of abnormal alarms using a non-linear decay method The inhibitory effect on credibility reflects the diminishing marginal return on credibility under continuous risk accumulation; further, this is further addressed by introducing a link integrity penalty term. The integrity of the data link in the traceability system is used as a fundamental constraint for credibility assessment. This highlights the core principle that a broken traceability link in the nitrile glove system leads to zero credibility. Ultimately, the system's baseline credibility is used to determine the credibility. The overall results are calibrated based on the inherent reliability of the system to improve the credibility of the traceability credibility index.

[0070] Furthermore, by constructing a set of adaptive parameter adjustment strategies for the visual inspection unit based on the traceability credibility index, this embodiment of the invention can clarify the matching rules between the dynamic changes of visual inspection parameters and the requirements for the correlation of traceability information during the packaging process of nitrile gloves, as well as the impact mechanism of the fluctuation of the traceability credibility index on the adaptability of inspection accuracy. This allows for targeted optimization of the parameter adjustment logic of the visual inspection unit. The set of adaptive parameter adjustment strategies refers to a set of rules and schemes for real-time and precise adjustment of the core operating parameters of the visual inspection unit based on the dynamic feedback of the traceability credibility index of each unit of nitrile glove.

[0071] As an embodiment of the present invention, the step of constructing the parameter adaptive adjustment strategy set of the visual detection unit based on the source tracing credibility index includes: The adjustable imaging parameter set and its image quality benchmark value of the visual detection unit are analyzed. Identify the key influencing factors of the traceability credibility index; Based on the key influencing factors, determine the priority dimensions for parameter adjustment and quality optimization of the visual detection unit; Based on the parameter priority adjustment dimension and the quality optimization priority, an imaging parameter adjustment scheme for the adjustable imaging parameter set is formulated. Based on the image quality benchmark value, the adjustable range of the imaging parameter adjustment scheme is set; By integrating the adjustable imaging parameter set, the imaging parameter adjustment scheme, and the adjustable parameter range, a parameter adaptive adjustment strategy set for the visual detection unit is generated.

[0072] The adjustable imaging parameter set refers to the set of hardware and software parameters in the visual inspection unit that can be dynamically adjusted by the control system to change the image acquisition effect, including but not limited to camera exposure time, light source brightness, and image acquisition resolution. The image quality benchmark value refers to the minimum technical threshold that the image quality must meet in advance to ensure that the visual inspection unit can accurately extract the core traceability features of nitrile gloves (such as digital identification and appearance defects), such as minimum image sharpness and maximum allowable noise level. The key influencing factors refer to variables that play a dominant role in the traceability credibility index calculation process and are directly related to the imaging quality of the visual inspection unit, such as rule execution reliability component and abnormal alarm frequency component. The quality optimization priority refers to the ranking of the quality optimization objectives of the visual inspection unit according to the degree of influence of the key influencing factors on the traceability credibility index. The imaging parameter adjustment scheme refers to the operation scheme with a clear adjustment direction and numerical range formulated for the adjustable imaging parameter set of the visual inspection unit based on the parameter priority adjustment dimension and quality optimization priority. The adjustable parameter range refers to a safe and effective numerical range set for each imaging parameter when setting an adjustment scheme. This range must ensure that the adjusted parameters are both technically feasible and meet the requirements of the image quality benchmark.

[0073] Optionally, the key influencing factors of the traceability credibility index can be identified using the Pearson correlation coefficient method. For example, Pearson correlation analysis can be used to calculate the correlation strength between visual detection data (feature recognition rate, anomaly marking rate) and the traceability credibility index, and variables with significant correlation can be screened as key influencing factors. The imaging parameter adjustment scheme of the adjustable imaging parameter set can be formulated using a multi-objective optimization algorithm. For example, a genetic algorithm can be used to construct an optimization model with the objective function of maximizing the improvement of key influencing factors and minimizing parameter adjustment costs. The parameter adjustment scheme that meets the priority of quality optimization can be generated through iterative optimization.

[0074] The intelligent packaging module 105 is used to perform the packaging process of the nitrile gloves based on the identity traceability network, the anti-counterfeiting state switching logic library, the visual detection unit and the parameter adaptive adjustment strategy set, and obtain the packaging result.

[0075] This invention, through its embodiment, utilizes the identity traceability network, the anti-counterfeiting state switching logic library, the visual detection unit, and the parameter adaptive adjustment strategy set to perform the packaging process of nitrile gloves, obtaining the packaging result. It can deeply correlate the entire nitrile glove packaging process data, connect product information links, accurately track the flow of nitrile gloves, and efficiently verify anti-counterfeiting. This effectively avoids the problems of difficult product traceability and weak anti-counterfeiting caused by data isolation in traditional mechanically assisted semi-automated packaging methods. It also avoids the limitations of relying on a single mechanical operation process that cannot meet the needs of information association and security verification, reducing the risk of traceability failure and anti-counterfeiting deficiencies caused by information gaps in the circulation of nitrile gloves. This significantly improves the adaptability of the intelligent packaging system to the entire lifecycle management and security assurance scenarios of nitrile gloves.

[0076] The packaging results refer to a structured set of results that comprehensively reflects the integrity of traceability information, the effectiveness of anti-counterfeiting strategies, the accuracy of visual inspection, and the adaptability of parameters in the packaging process. This is achieved by associating, verifying features, controlling risks, and adapting parameters of production data throughout the entire nitrile glove packaging process, based on an identity traceability network, an anti-counterfeiting state switching logic library, a visual inspection unit, and a parameter adaptive adjustment strategy set. For example, the packaging results of a certain batch of nitrile gloves (production batch BN20240610) may include the digital identity of each glove and production line data, the recognition accuracy of the visual inspection unit for core traceability features (fingerprint texture, QR code), the compliance rate of the "medium-risk" anti-counterfeiting strategy configured by the anti-counterfeiting state switching logic library for this batch, the adaptation rate of visual inspection parameters after optimization by the parameter adaptive adjustment strategy set, and quantitative data on dimensions such as the traceability query response time and the frequency of abnormal alarm triggering for each glove after packaging.

[0077] Compared to the problems described in the background art, this embodiment of the invention constructs an identity traceability network for each unit of nitrile gloves based on the digital identity identifier and the production line data. This accurately links the digital identifier of each glove unit with the entire production process information, ensuring real-time traceability of the entire nitrile glove's trajectory from raw material input to packaging completion. Simultaneously, it provides an immutable information verification network for nitrile glove authenticity verification, improving the reliability and efficiency of the verification results, thereby enhancing the quality control capabilities and consumer trust in the nitrile glove distribution process. Furthermore, this embodiment of the invention establishes an identity traceability network for each unit of nitrile gloves based on the aforementioned counterfeit risk environment. The anti-counterfeiting status switching logic library for gloves allows the system to dynamically adjust anti-counterfeiting verification strategies based on the counterfeiting risk environment. It optimizes core traceability feature verification dimensions, identity verification trigger frequency, and counterfeiting warning thresholds in real time, improving the accuracy of responding to counterfeiting behavior in different circulation scenarios and enhancing the timeliness and reliability of authenticity assurance throughout the entire circulation process of nitrile gloves. In this embodiment, by generating strategy update instructions for the identity traceability network based on the anti-counterfeiting status switching logic library, the gradient of the open scope of traceability information can be strengthened through anti-counterfeiting status levels, improving the adaptability of instructions to different risk scenarios and enhancing the accuracy of information traceability within the identity traceability network. In conjunction with anti-counterfeiting security, this invention comprehensively enhances the dynamic control capability of identity traceability throughout the entire circulation process of nitrile gloves. Furthermore, by constructing a set of adaptive parameter adjustment strategies for the visual inspection unit based on the traceability credibility index, this embodiment of the invention clarifies the matching rules between the dynamic changes of visual inspection parameters during the nitrile glove packaging process and the correlation requirements of traceability information, as well as the impact mechanism of traceability credibility index fluctuations on the adaptability of detection accuracy. This allows for targeted optimization of the parameter adjustment logic of the visual inspection unit. Finally, this embodiment of the invention, based on the identity traceability network, the anti-counterfeiting state switching logic library, the visual inspection unit, and the... The system employs an adaptive adjustment strategy set of parameters to execute the packaging process for nitrile gloves, obtaining the packaging results. This allows for deep integration of the entire nitrile glove packaging process data, establishing a seamless product information chain. It enables precise tracking of nitrile glove distribution and efficient anti-counterfeiting verification, effectively avoiding the problems of product traceability difficulties and weak anti-counterfeiting measures caused by data isolation in traditional mechanically assisted semi-automated packaging methods. It also avoids the limitations of relying on a single mechanical operation process, which cannot meet the needs of information association and security verification. This reduces the risk of traceability failure and anti-counterfeiting gaps caused by information discontinuity in the circulation of nitrile gloves, significantly improving the adaptability of the intelligent packaging system to the entire lifecycle management and security assurance scenarios of nitrile gloves. Therefore, the intelligent packaging system for nitrile gloves with authenticity identification and traceability functions provided in this embodiment of the invention can accurately achieve the tracking of nitrile glove distribution and anti-counterfeiting verification.

[0078] like Figure 3The diagram shown is a flowchart illustrating a smart packaging method for nitrile gloves with authenticity verification and traceability functions according to the present invention. In this embodiment, the smart packaging method for nitrile gloves with authenticity verification and traceability functions includes: S1. Obtain production line data of nitrile gloves in the production process to set a digital identity for each unit of nitrile gloves. Based on the digital identity and the production line data, construct an identity traceability network for each unit of nitrile gloves. The production line data includes raw material batches, process parameters, quality inspection data, and packaging line operation data. S2. Set up a visual inspection unit for the nitrile gloves on the packaging line of the nitrile gloves, and mark the core traceability features of each unit of the nitrile gloves based on the visual inspection unit and the digital identity mark. S3. Based on the core traceability features, simulate the counterfeit risk environment of the nitrile gloves in the subsequent circulation process. Based on the counterfeit risk environment, establish an anti-counterfeiting state switching logic library for each unit of the nitrile gloves. Based on the anti-counterfeiting state switching logic library, generate the strategy update instruction for the identity traceability network. S4. Obtain the feedback parameters of the identity tracing network after executing the policy update instruction, so as to calculate the traceability credibility index of the nitrile glove, and construct the parameter adaptive adjustment strategy set of the visual detection unit based on the traceability credibility index. S5. Based on the identity traceability network, the anti-counterfeiting state switching logic library, the visual detection unit, and the parameter adaptive adjustment strategy set, perform the packaging process of the nitrile gloves to obtain the packaging result.

[0079] In the several embodiments provided by this invention, it should be understood that the provided systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0080] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0081] Finally, it should be noted that in the above embodiments, each embodiment can be combined with each other or independent. Deleting any one of them will not affect the technical implementation of other embodiments. The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A smart packaging system for nitrile gloves with authenticity verification and traceability functions, characterized in that, The system includes: The traceability network construction module is used to acquire production line data of nitrile gloves in the production process, so as to set a digital identity for each unit of nitrile gloves. Based on the digital identity and the production line data, an identity traceability network for each unit of nitrile gloves is constructed. The production line data includes raw material batches, process parameters, quality inspection data and packaging line operation data. The traceability feature parsing module is used to set up a visual inspection unit for the nitrile gloves on the packaging line of the nitrile gloves, and to mark the core traceability features of each unit of the nitrile gloves based on the visual inspection unit and the digital identity identifier. The anti-counterfeiting optimization module is used to simulate the counterfeiting risk environment of the nitrile gloves in the subsequent circulation process based on the core traceability features, establish an anti-counterfeiting state switching logic library for each unit of the nitrile gloves based on the counterfeiting risk environment, and generate a strategy update instruction for the identity traceability network based on the anti-counterfeiting state switching logic library. The traceability feedback module is used to obtain the feedback parameters of the identity traceability network after executing the policy update instruction, so as to calculate the traceability credibility index of the nitrile gloves, and construct the parameter adaptive adjustment strategy set of the visual detection unit based on the traceability credibility index. The intelligent packaging module is used to perform the packaging process of the nitrile gloves based on the identity traceability network, the anti-counterfeiting state switching logic library, the visual detection unit, and the parameter adaptive adjustment strategy set, and obtain the packaging result.

2. The intelligent packaging system for nitrile gloves with authenticity verification and traceability functions as described in claim 1, characterized in that, Based on the aforementioned counterfeit risk environment, the establishment of an anti-counterfeiting status switching logic library for each unit of the nitrile glove includes: From the counterfeit risk environment, identify the risk scenario type, risk severity level, and potential counterfeit behavior path corresponding to each unit of the nitrile glove; Based on the risk scenario type, the risk severity level, and the potential counterfeiting behavior path, respectively generate scenario response instructions, level response instructions, and path blocking instructions for each unit of the nitrile glove; Label the key feature attributes corresponding to the scenario response command, the level response command, and the path blocking command; Based on the key feature attributes, establish a set of linkage rules among the scenario response instructions, the level response instructions, and the path blocking instructions; Define the execution priority of each rule in the aforementioned set of linkage rules; By utilizing the linkage between the scenario response command, the level response command, and the path blocking command, and the execution priority, an anti-counterfeiting status switching decision engine is constructed for each unit of the nitrile glove; Based on the anti-counterfeiting status switching decision engine and the linkage rule set, an anti-counterfeiting status switching logic library is established for each unit of the nitrile glove.

3. The intelligent packaging system for nitrile gloves with authenticity verification and traceability functions as described in claim 2, characterized in that, The process of constructing an anti-counterfeiting status switching decision engine for each unit of nitrile glove by utilizing the linkage relationship between the scenario response command, the level response command, and the path blocking command, and the execution priority, includes: Identify the instruction categories and triggering conditions corresponding to the scenario response instructions, the level response instructions, and the path blocking instructions; Analyze the logical relationship types in the linkage relationship and the dynamic adjustment rules of the execution priority; Based on the instruction category and the logical relationship type, construct a decision flowchart for anti-counterfeiting state switching between the scenario response instruction, the level response instruction, and the path blocking instruction; Based on the triggering conditions and the dynamic adjustment rules, calculate the state switching accuracy and risk coverage completeness index of different paths in the anti-counterfeiting state switching decision flowchart. Based on the anti-counterfeiting status switching decision flowchart, the status switching accuracy, and the risk coverage completeness index, an anti-counterfeiting response scheme set is established for each unit of the nitrile glove. Based on the anti-counterfeiting response scheme set, generate the core judgment logic and instruction sequence corresponding to each unit of the nitrile glove; By integrating the anti-counterfeiting state switching decision flowchart, the core judgment logic, and the instruction sequence, an anti-counterfeiting state switching decision engine for each unit of the nitrile glove is constructed.

4. The intelligent packaging system for nitrile gloves with authenticity verification and traceability functions as described in claim 1, characterized in that, The step of constructing an identity traceability network for each unit of nitrile glove based on the digital identity and the production line data includes: Read the unique feature code and data generation timestamp from the digital identity identifier; Analyze the production line process paths and production event sequences associated with the unique feature code in the production line data; Identify the quality status migration trajectory and process parameter compliance tags of the production event sequence; Based on the unique feature code and the production line process path, a unique positioning identifier is generated for each unit of the nitrile glove; Based on the data generation timestamp and the production event sequence, calculate the time-series compliance index for each unit of the nitrile glove; By integrating the quality status migration trajectory with the process parameter compliance label, the microscopic quality level of each unit of the nitrile glove is determined; Based on the time-series compliance index and the micro-quality level, the data archiving level and corresponding traceability information opening strategy for each unit of the nitrile glove are set; The unique positioning identifier, the time-series compliance index, the micro-quality level, the data archiving level, and the traceability information opening strategy are integrated to form the identity traceability network for each unit of nitrile gloves.

5. The intelligent packaging system for nitrile gloves with authenticity verification and traceability functions as described in claim 1, characterized in that, The step of generating the policy update instruction for the identity tracing network based on the anti-counterfeiting state switching logic library includes: The policy configuration items and data integrity rules of the identity tracing network are analyzed. Identify the risk handling level and response time threshold corresponding to the anti-counterfeiting status switching logic library; Based on the risk handling level, the handling priority of the task queue in the identity tracing network is defined; By using the response timeliness threshold, key triggering events in the identity tracing network are located; Based on the key triggering events and the handling priorities, the database operation sequence and API call steps of the strategy configuration items are generated; Based on the data integrity rules, the transaction boundary between the database operation sequence and the API call steps is defined; By combining the policy configuration items, the database operation sequence, the API call steps, and the transaction boundary, a policy update instruction for the identity tracing network is generated.

6. The intelligent packaging system for nitrile gloves with authenticity verification and traceability functions as described in claim 1, characterized in that, The step of obtaining the feedback parameters of the identity tracing network after executing the policy update instruction, in order to calculate the traceability credibility index of the nitrile gloves, includes: Analyze the instruction execution timing data and the state change frequency of the digital identity in the feedback parameters; Based on the digital identity, retrieve the full lifecycle traceability record of the nitrile gloves; The health of the traceability chain for the nitrile gloves is assessed through the full lifecycle traceability records. Retrieve the historical request records of the digital identity and extract the system response stability parameters from the historical request records; Based on the instruction execution timing data and the traceability link health, the robustness of the traceability rule execution for the nitrile gloves is calculated. Based on the state change frequency and the system response stability parameters, the probability of the traceability link of the nitrile glove being interrupted is calculated; Real-time monitoring of the query and verification success rate and the frequency of abnormal alarm triggering of the digital identity identifier; The traceability reliability index of the nitrile gloves is calculated by combining the robustness of the traceability rule execution, the probability of traceability link interruption, the success rate of query verification, and the frequency of abnormal alarm triggering.

7. The intelligent packaging system for nitrile gloves with authenticity verification and traceability functions as described in claim 1, characterized in that, The step of constructing a set of adaptive parameter adjustment strategies for the visual detection unit based on the source tracing credibility index includes: The adjustable imaging parameter set and its image quality benchmark value of the visual detection unit are analyzed. Identify the key influencing factors of the traceability credibility index; Based on the key influencing factors, determine the priority dimensions for parameter adjustment and quality optimization of the visual detection unit; Based on the parameter priority adjustment dimension and the quality optimization priority, an imaging parameter adjustment scheme for the adjustable imaging parameter set is formulated. Based on the image quality benchmark value, the adjustable range of the imaging parameter adjustment scheme is set; By integrating the adjustable imaging parameter set, the imaging parameter adjustment scheme, and the adjustable parameter range, a parameter adaptive adjustment strategy set for the visual detection unit is generated.

8. The intelligent packaging system for nitrile gloves with authenticity verification and traceability functions as described in claim 1, characterized in that, The step of simulating the counterfeit risk environment of the nitrile gloves in subsequent distribution stages based on the core traceability features includes: The target distribution channels for the nitrile gloves are determined by the product specifications and packaging grade in the core traceability features. Extract the set of spatiotemporal elements of circulation, identity verification triggering conditions, and feature reproducibility evaluation values ​​from the core traceability features; Based on the set of spatiotemporal elements of circulation, the identity verification triggering conditions, and the feature replicability evaluation value, a deduction diagram of counterfeiting behavior of the target circulation channel is constructed. Based on the aforementioned counterfeiting behavior projection diagram, the counterfeiting risk points and regulatory weaknesses in each link of the target distribution channel are analyzed. Based on the counterfeiting behavior projection diagram, the counterfeiting risk points, and the regulatory weaknesses, a counterfeiting risk environment for the nitrile gloves in the subsequent circulation process is simulated.

9. The intelligent packaging system for nitrile gloves with authenticity verification and traceability functions as described in claim 1, characterized in that, The core traceability features of each unit of nitrile glove, based on the visual detection unit and the digital identity identifier, include: Collect the packaging visual image sequence corresponding to the visual detection unit, and read the identification data corresponding to the digital identity identifier; Synchronously align the acquisition time point and spatial work point corresponding to the packaging visual image sequence and the identification data; Based on the acquisition time point and the spatial work site, a correspondence table between the packaging visual image and the digital identity is constructed; Extract the unique associated nodes from the corresponding relationship table; The core traceability features of each unit of nitrile gloves are marked through the unique association node.

10. The intelligent packaging system for nitrile gloves with authenticity verification and traceability functions as described in claim 1, characterized in that, The step of acquiring production line data for nitrile gloves in the production process to set a digital identity for each unit of the nitrile glove includes: Based on the production line data, the raw material batch, production line vulcanization parameters, and online quality inspection results of the nitrile gloves were extracted. By integrating the raw material batches, the vulcanization parameters of the production line, and the online quality inspection results, a batch profile of the nitrile gloves is generated. Based on the batch lineage, the quality compliance of the nitrile gloves was verified, and a compliance determination conclusion was obtained. Based on the compliance determination conclusion, the core data in the batch lineage will be converted into a unique traceability code; Retrieve the packaging specifications and production date of each unit of nitrile gloves, and combine them with the unique traceability code to create a traceability file number for each unit of nitrile gloves; Based on the traceability file number, a digital identity identifier is set for each unit of the nitrile gloves.

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