Robot-based product anti-counterfeiting traceability method, system, device and medium

Abstract

This application relates to a robot-based product anti-counterfeiting and traceability method, system, equipment, and medium, belonging to the field of product anti-counterfeiting technology. The method includes: acquiring product verification challenge codes and consumer verification codes uploaded by a production-end robot, and sending the verification challenge codes to the warehousing end; responding to an inbound command, controlling a warehousing robot to perform an inbound operation according to the verification challenge codes, generating a warehousing physical challenge code; responding to an outbound command, controlling a warehousing robot to repeat the inbound operation based on the warehousing physical challenge code, generating a warehousing outbound challenge code; comparing the warehousing outbound challenge code with the warehousing physical challenge code, and outputting the warehousing verification result; responding to a consumer robot verification request, sending a consumer verification code, and receiving real-time verification data and real-time location information; comparing the real-time verification data, location information, and consumer verification code, and outputting the consumer verification result. This application can reduce the risks of product label duplication, tampering, and cross-selling, and improve the automation level and data reliability of anti-counterfeiting verification.

Description

Technical Field

[0001] This application relates to the field of product anti-counterfeiting technology, and in particular to a robot-based product anti-counterfeiting and traceability method, system, equipment and medium. Background Technology

[0002] In the fields of industrial automation and anti-counterfeiting traceability technology, with the vigorous development of the commodity economy, product varieties are becoming increasingly abundant, market size is continuously expanding, and the circulation scope of products is becoming more and more extensive. This has led consumers to pay increasing attention to the origin, quality, and safety of products, and enterprises urgently need reliable technologies to ensure product quality and maintain brand image. Anti-counterfeiting traceability technology has emerged to meet this need. It plays a crucial role in protecting consumer rights, combating counterfeit and shoddy products, and maintaining market order, greatly promoting the healthy and stable development of the market.

[0003] Previously, the industry adopted various conventional methods to solve the problem of product anti-counterfeiting and traceability. Common methods included using static tags such as QR codes and RFID to store basic product information, allowing consumers to verify authenticity by scanning the code. Linking multiple levels of packaging typically relied on manual information entry or simple barcode scanning to upload and trace product information; however, this traceability usually only extended upwards. In the warehousing stage, warehouse robots mostly served as handling tools, responsible for moving and storing goods, while consumers primarily verified products at the point of sale by scanning labels for simple authenticity checks.

[0004] However, these existing technologies have significant shortcomings. Traditional static labels are easily torn off, replaced, and copied, allowing criminals to easily bypass verification and render anti-counterfeiting functions ineffective. The hierarchical relationships between multi-level packaging are weak and unidirectional, making it difficult to meet practical needs such as logistics unpacking verification and rapid warehouse inventory checks. Moreover, most existing systems only focus on verifying the authenticity of products and lack control over cross-regional sales, making it difficult to effectively trace cross-selling. Summary of the Invention

[0005] The purpose of this application is to provide a robot-based product anti-counterfeiting and traceability method that can reduce the risks of product label duplication, replacement, and cross-selling, and improve the automation level and data credibility of anti-counterfeiting verification.

[0006] Firstly, this application provides a robot-based product anti-counterfeiting and traceability method, which adopts the following technical solution: A robot-based product anti-counterfeiting and traceability method includes: Obtain the verification challenge codes and consumption verification codes of each product uploaded by the production robot, and send the verification challenge codes to the warehouse. In response to the warehousing instruction, the warehouse robot is controlled to perform the warehousing operation on the product based on the verification challenge code, and a warehouse physical challenge code is generated; In response to the outbound command, the warehouse robot is controlled to reproduce the inbound operation based on the warehouse physical challenge code, and a warehouse outbound challenge code is generated. The warehouse outbound challenge code is compared with the warehouse physical challenge code, and the warehouse verification result is output. In response to the verification request from the consumer robot, the consumer verification code is sent to the consumer robot, and the real-time verification data and real-time location information reported by the consumer robot are received. The real-time verification data, the real-time location information, and the consumption verification code are compared, and the consumption verification result is output.

[0007] By adopting the above technical solutions, a full-chain anti-counterfeiting and traceability system is achieved, from production to warehousing to consumption, ensuring the authenticity and compliance of products at each stage and reducing the risk of labels being copied or tampered with. The operation posture of warehousing robots is integrated into anti-counterfeiting verification, deeply integrating anti-counterfeiting verification with the physical operation of robots and leveraging the advantages of embodied intelligence. Standardization and automation of anti-counterfeiting and traceability are achieved, reducing errors and risks caused by human intervention. Electronic fence technology is used to bind products to designated sales areas, achieving dual protection against counterfeiting and cross-selling. All key data are verified and compared, forming a complete, auditable, and tamper-proof traceability and anti-cross-selling chain.

[0008] In a preferred embodiment, this application can be further configured as follows: the step of obtaining the verification challenge codes and consumption verification codes of each product uploaded by the production robot, and sending the verification challenge codes to the warehouse end, includes: Obtain product verification data, which includes electronic tag data, tag location certificate, and micro-texture fingerprint data; A verification challenge code is generated for the product based on the verification data, and the verification challenge code includes the verification data and the warehouse location data. Generate a consumption verification code for the product, the consumption verification code including the verification data and the specified sales area data.

[0009] By adopting the above technical solutions, electronic tag data, tag location certificates, and micro-texture fingerprint data of products can be obtained. The inherent uniqueness of micro-texture fingerprints can be used to deeply bind with the digital identity of electronic tags, reducing the risk of tags being copied or replaced. Based on the verification data, a verification challenge code containing verification data and warehouse location data can be generated, providing a basis for warehouse entry and exit verification. A consumer verification code containing verification data and designated sales area data can be generated, facilitating consumers to verify authenticity and check for cross-selling, achieving dual protection against counterfeiting and cross-selling.

[0010] In a preferred embodiment, this application can be further configured as follows: the step of controlling the warehouse robot to perform a warehouse entry operation on the product based on the verification challenge code and generating a warehouse physical challenge code in response to the warehouse entry instruction includes: Based on the warehouse location data in the verification challenge code, the warehouse robot is controlled to perform an inbound grabbing operation on the product and obtain the verification data of the product. In response to the completion of the inbound grabbing operation, a physical challenge code for the product is generated. The physical challenge code includes the product's verification data, as well as the operation posture parameters and storage location data when the inbound grabbing operation is performed.

[0011] By adopting the above technical solution, warehouse robots can accurately perform inbound grasping operations based on verification challenge codes, obtain product verification data, and generate physical challenge codes that include product verification data, inbound operation posture parameters, and warehouse location data. This enables the recording of anti-counterfeiting and traceability information at the warehouse end, making the flow information of products in the warehousing process traceable. At the same time, by incorporating operation posture parameters into the challenge code, anti-counterfeiting verification and robot physical operation are deeply integrated, leveraging the advantages of embodied intelligence to improve the anti-counterfeiting effect.

[0012] In a preferred embodiment, this application can be further configured as follows: the step of controlling a warehouse robot to reproduce the inbound operation based on the warehouse physical challenge code in response to an outbound instruction, and generating a warehouse outbound challenge code, includes: In response to the outbound command, the warehouse robot is controlled to reproduce the operating posture during the inbound grabbing based on the operating posture parameters in the warehouse physical challenge code and the warehouse location data. When performing the outbound grabbing operation, the operation posture parameters and warehouse location data are obtained, and the verification data of the product are collected. In response to the completion of the outbound grabbing operation, a warehouse outbound challenge code is generated. The warehouse outbound challenge code includes the verification data of the product, as well as the operation posture parameters and warehouse location data when the outbound grabbing operation is performed.

[0013] By adopting the above technical solutions, the warehouse robot can reproduce the inbound grasping operation posture, which enables the anti-counterfeiting verification to be deeply integrated with the robot's physical operation, giving full play to its own intelligence advantages; it can also acquire relevant data of the outbound grasping operation and generate a warehouse outbound challenge code, which can be compared with the warehouse physical challenge code in the future to realize the anti-counterfeiting verification of the product outbound and ensure the authenticity and security of the product in the warehouse outbound process.

[0014] In a preferred embodiment, this application can be further configured as follows: the step of comparing the warehouse outbound challenge code with the warehouse physical challenge code and outputting the warehouse verification result includes: The electronic tag data in the warehouse outbound challenge code and the warehouse physical challenge code are verified by hash chain, and the tag location certificate of the warehouse outbound challenge code and the warehouse physical challenge code is verified. A texture similarity comparison is performed between the micro-texture image in the warehouse outbound challenge code and the micro-texture fingerprint data in the warehouse physical challenge code; The operational posture parameters in the warehouse outbound challenge code and the warehouse physical challenge code are compared. The location information in the warehouse outbound challenge code is compared with that in the warehouse physical challenge code; When the results of the hash chain verification, the tag location credential verification, the texture similarity comparison, the operation posture parameter comparison, and the location information comparison all pass, the warehouse verification result is output.

[0015] By employing the aforementioned technical solutions, hash chain verification and tag location certificate verification are performed on the electronic tag data in the warehouse outbound challenge code and the warehouse physical challenge code. This ensures the authenticity and integrity of the electronic tag data and confirms the accuracy of the tag location. Comparison of micro-texture images and micro-texture fingerprint data leverages the inherent uniqueness of packaging micro-textures to fundamentally reduce the risk of tag duplication or tampering. Comparison of operational posture parameters deeply integrates anti-counterfeiting verification with robot physical operations, leveraging the advantages of embodied intelligence. Comparison of location information ensures the consistency of product location information during warehousing. When all comparison results pass, a warehouse verification pass result is output, enabling rigorous and accurate product verification during warehousing and ensuring the authenticity and reliability of product traceability.

[0016] In a preferred embodiment, this application can be further configured such that: the real-time verification data includes electronic tag data, tag location credentials, and micro-texture fingerprint data of the product acquired by the consumer robot; the step of comparing the real-time verification data, the real-time location information, and the consumer verification code to output the consumer verification result includes: The real-time verification data and the electronic tag data in the consumption verification code are verified by hash chain, and the real-time verification data and the tag location certificate in the consumption verification code are verified. The real-time location information of the real-time verification data is compared with the electronic fence boundary associated with the designated sales area data in the consumption verification code. The micro-texture image of the real-time verification data is compared with the micro-texture fingerprint data in the consumption verification code for texture similarity. Based on the results of the hash chain verification, the tag location credential verification, the electronic fence boundary comparison, and the texture similarity comparison, and combined with the number of verifications, the corresponding consumption verification result is output.

[0017] By adopting the above technical solutions, hash chain verification and tag location certificate verification can be performed on the electronic tag data in real-time verification data and consumer verification codes to ensure the accuracy of electronic tag data and tag location. Comparing real-time location information with the electronic fence boundary associated with designated sales area data can enable the verification of product cross-selling. Comparing the texture similarity between micro-texture images and micro-texture fingerprint data can reduce the risk of tags being copied or replaced by taking advantage of the natural uniqueness of the packaging micro-texture. Combining the verification count with the corresponding consumer verification results can realize the verification activation and deactivation mechanism, effectively preventing the tags from being copied and used.

[0018] In a preferred embodiment, this application may be further configured to include: In response to the inspection command, the warehouse physical challenge code is sent to the warehouse terminal, and the warehouse robot is controlled to obtain the product verification data; The verification data obtained by the current warehouse robot is compared with the warehouse physical challenge code, and the comparison result is output. The inspection verification result is output based on the comparison result, and an alarm message is generated when the inspection verification result is abnormal.

[0019] By adopting the above technical solutions, the products at the warehouse end are inspected, and the obtained verification data is compared with the physical challenge code of the warehouse. This allows for the timely detection of anti-counterfeiting anomalies, the generation of alarm information when anomalies occur, the supervision of products at the warehouse end to be strengthened, the anti-counterfeiting security of products to be guaranteed, and the trustworthiness of data throughout the entire chain to be achieved.

[0020] Secondly, this application provides a robot-based product anti-counterfeiting and traceability system, which adopts the following technical solution: A robot-based product anti-counterfeiting and traceability system includes: Production code module: used to obtain the verification challenge code and consumption verification code of each product uploaded by the production robot, and send the verification challenge code to the warehouse. Inbound Product Code Module: In response to an inbound command, the module controls the warehouse robot to perform an inbound operation on the product based on the verification challenge code and generates a warehouse physical challenge code. Outbound code module: In response to the outbound command, it controls the warehouse robot to reproduce the inbound operation based on the warehouse physical challenge code and generates a warehouse outbound challenge code; Outbound verification module: used to compare the warehouse outbound challenge code with the warehouse physical challenge code and output the warehouse verification result; Consumption request module: used to respond to the verification request of the consumption robot, send the consumption verification code to the consumption robot, and receive the real-time verification data and real-time location information reported by the consumption robot; Consumption verification module: used to compare the real-time verification data, the real-time location information and the consumption verification code, and output the consumption verification result.

[0021] Thirdly, this application provides an electronic device that adopts the following technical solution: An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the robot-based product anti-counterfeiting and traceability method described above.

[0022] Fourthly, this application provides a computer storage medium, as follows: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described robot-based product anti-counterfeiting and traceability method.

[0023] In summary, this application has the following beneficial technical effects: 1. This application can realize fully automated anti-counterfeiting and traceability. Robots at the production end, warehousing end and consumption end perform different operations, reducing errors and risks caused by human intervention and improving the standardization of anti-counterfeiting and traceability; 2. This application can ensure imaging consistency. The warehouse robot integrates a laser distance sensor to ensure that the imaging distance between the inbound and outbound objects is consistent, thereby improving the accuracy of micro-texture image comparison. 3. This application can achieve layered authorization and data security. Each robot only holds the corresponding operation permission, protecting core data from the hardware operation level. At the same time, the entire process of data is stored on the blockchain for evidence, forming an immutable audit chain. Attached Figure Description

[0024] Figure 1 This is a flowchart of a robot-based product anti-counterfeiting and traceability method in one embodiment of this application.

[0025] Figure 2 This is a flowchart of a sub-step of step S1 in one embodiment of this application.

[0026] Figure 3 This is a flowchart of a sub-step of step S2 in one embodiment of this application.

[0027] Figure 4 This is a flowchart of a sub-step of step S3 in one embodiment of this application.

[0028] Figure 5 This is a flowchart of a sub-step of step S4 in one embodiment of this application.

[0029] Figure 6 This is a flowchart of a sub-step of step S6 in one embodiment of this application.

[0030] Figure 7 This is a diagram showing the additional steps of a robot-based product anti-counterfeiting and traceability method in one embodiment of this application.

[0031] Figure 8 This is a schematic diagram of the structure of a robot-based product anti-counterfeiting and traceability system according to one embodiment of this application.

[0032] Figure 9 This is a schematic block diagram of an electronic device in one embodiment of this application.

[0033] Attached labels: 1. Production code module; 2. Inbound production code module; 3. Outbound production code module; 4. Outbound verification module; 5. Consumption request module; 6. Consumption verification module. Detailed Implementation

[0034] The following is in conjunction with the appendix Figure 1-9 This application will be described in further detail.

[0035] It should be noted that, in the embodiments of this invention, the data related to object information, when applied to specific products or technologies, requires the permission or consent of the object. Furthermore, the collection, use, and processing of this data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. In other words, if the embodiments of this invention involve data related to an object, it must be obtained with the object's authorization and consent, the authorization and consent of relevant departments, and in accordance with the relevant laws, regulations, and standards of the country and region. If the embodiments involve personal information, the acquisition of all personal information requires the individual's consent. If sensitive information is involved, the separate consent of the information subject is required. The embodiments also need to be implemented with the object's authorization and consent.

[0036] refer to Figure 1 A robot-based product anti-counterfeiting and traceability method, specifically including: S1. Obtain the verification challenge codes and consumption verification codes of each product uploaded by the production robot, and send the verification challenge codes to the warehouse.

[0037] Specifically, after the production robot completes the label affixing and micro-texture collection, it integrates the data including electronic tag data, tag location certificate, micro-texture fingerprint, and data of the designated sales area to generate a verification challenge code and a consumption verification code. The verification challenge code is then sent to the warehouse as the basis for subsequent verification.

[0038] In this way, physical characteristics and digital identities are bound together at the source, providing tamper-proof basic data for end-to-end anti-counterfeiting and ensuring the uniqueness and credibility of the verification basis.

[0039] S2. In response to the inbound instruction, control the warehouse robot to perform the inbound operation on the product based on the verification challenge code, and generate a warehouse physical challenge code.

[0040] Specifically, the warehouse robot accurately locates and grasps products based on the warehouse location data in the verification challenge code. During the grasping process, it triggers the capture of micro-texture images through distance sensors, recording complete operational posture parameters such as the joint angle of the robotic arm, the coordinates of the gripper, the rotation angle, the opening and closing degree, and the grasping force. These parameters are then combined with the verification data to generate a warehouse physical challenge code.

[0041] The above deeply binds the robot's physical operation parameters with the product's anti-counterfeiting data, forming a unique basis for warehouse verification and establishing an unreplicable physical constraint for subsequent outbound verification.

[0042] S3. In response to the outbound command, control the warehouse robot to reproduce the inbound operation based on the warehouse physical challenge code, and generate the warehouse outbound challenge code.

[0043] Specifically, the warehouse robot analyzes the operation posture parameters and warehouse location data in the warehouse physical challenge code, accurately reproduces the grasping posture when entering the warehouse, triggers the capture of micro-texture images at the same distance, and simultaneously collects electronic tag data, tag location vouchers, and current operation posture parameters and location information to generate a warehouse outbound challenge code.

[0044] In summary, by replicating the robot's posture, the conditions for inbound and outbound operations are kept consistent, thus deeply integrating anti-counterfeiting verification with the robot's physical operation, leveraging the advantages of embodied intelligence, and reducing verification errors caused by grasping deviations.

[0045] S4. Compare the warehouse outbound challenge code with the warehouse physical challenge code and output the warehouse verification result.

[0046] Specifically, the system compares the electronic tag data of the two devices to perform hash chain verification and location credential verification, compares the microscopic texture image and fingerprint to perform texture similarity analysis, compares the consistency of operation posture parameters, compares the matching degree of location information, and outputs a verification conclusion based on the comprehensive comparison results.

[0047] The above integrates the robot's operational precision into the comparison logic, forming a standardized automated verification process that reduces errors and risks caused by human intervention.

[0048] S5. In response to the verification request from the consumer robot, send the consumer verification code to the consumer robot and receive the real-time verification data and real-time location information reported by the consumer robot.

[0049] Specifically, after the consumer robot autonomously scans the product label, it initiates a verification request to the cloud. The cloud retrieves the product's verification code from the blockchain and issues it. The consumer robot autonomously adjusts its angle to capture microscopic texture images, obtains electronic tag data and its own real-time location information, and then reports it to the cloud.

[0050] The above enables consumers to independently verify data acquisition, providing real-time and reliable comparison data for terminal authenticity verification and cross-selling determination, thereby improving the convenience and accuracy of consumer verification.

[0051] S6. Compare the real-time verification data, real-time location information, and consumption verification code, and output the consumption verification result.

[0052] Specifically, hash chain verification and location credential verification are performed by comparing electronic tag data. The real-time location is compared with the electronic fence boundary associated with the designated sales area in the consumption verification code. The similarity between the micro-texture image and the fingerprint is compared. Based on the number of verifications, conclusions such as first-time verification activation or non-first-time failure are output.

[0053] The above achieves dual protection against counterfeiting and cross-selling, effectively preventing labels from being copied and used through verification activation and deactivation mechanisms, forming a complete and auditable terminal traceability closed loop.

[0054] refer to Figure 2 Furthermore, in one embodiment, step S1 is refined into the following sub-steps: S10. Obtain product verification data, including electronic tag data, tag location certificate, and micro-texture fingerprint data.

[0055] Specifically, after the electronic tags are affixed at the production end, a visual positioning robot precisely measures the center and four corner coordinates of the tag in the three-dimensional coordinate system of the packaging, forming a tag location certificate. At the same time, a data acquisition robot equipped with a high-resolution microscope camera automatically takes multi-angle photos of the tag body area, the edge transition area where the tag meets the packaging, and the surrounding packaging background texture area. Using image feature extraction algorithms, unique texture feature vectors are extracted from these microscopic images to generate microscopic texture fingerprint data.

[0056] In summary, by deeply binding the physically unclonable feature of micro-texture fingerprints with the digital identity of electronic tags, each product possesses a unique dual identity—both physical and digital. The micro-texture is determined by random physical factors such as the natural fiber structure of the packaging material, the random distribution of printing dots, and the adhesion marks on the label edges. This makes it impossible to counterfeit by copying label information and can be identified using AI technology. Simultaneously, the automated collection process by robots avoids positional, angular, and environmental interference caused by manual operation, ensuring the standardization and accuracy of the collected data. This reduces the risk of labels being removed and pasted onto counterfeit products, establishing an unforgeable foundation of trust for end-to-end anti-counterfeiting verification.

[0057] S11. Generate a verification challenge code for the product based on the verification data. The verification challenge code includes the verification data and the warehouse location data.

[0058] Specifically, the production-end robot control system encrypts and integrates the acquired verification data with the designated warehouse location data for product entry, forming a verification challenge code. The warehouse location data includes information such as the warehouse area number, shelf level, and specific coordinates where the product should be stored upon entry, and is associated with the product's batch number, production time, and logistics route. The verification challenge code, packaged using an encryption algorithm, is written to the internal storage area of ​​an electronic tag via a built-in RFID reader / writer module. Simultaneously, the data is uploaded to the blockchain for distributed notarization, ensuring that any subsequent reading of the verification challenge code is traceable and tamper-proof.

[0059] The above method binds the product's digital and physical identities with its warehouse and logistics location information in a three-element manner, forming a unique verification challenge code for the warehouse. This code not only carries the core data required for anti-counterfeiting verification but also includes the product's expected location within the warehouse system, providing prior information for the subsequent precise positioning of the inbound robot. Furthermore, through a blockchain-based notarization mechanism, the verification challenge code is ensured to be immutable throughout its entire lifecycle. Any unauthorized modification to the label data will be detected by the hash value recorded on the blockchain, enabling the warehouse robot to pre-position the product based on the warehouse location data in the challenge code before scanning the label, significantly improving the efficiency and accuracy of the inbound operation.

[0060] S12. Generate a consumption verification code for the product. The consumption verification code includes verification data and data for the designated sales area.

[0061] Specifically, based on the verification data obtained above, the production-side robot control system further associates it with the product's designated sales area data to generate a consumer verification code. The designated sales area data includes the geographical range where the product is authorized for sale, stored as the latitude and longitude coordinates of the electronic fence boundary. The consumer verification code uses a hash algorithm to de-identify sensitive information in the verification data, such as micro-texture fingerprints and tag location credentials, retaining only the hash value instead of the original data. Simultaneously, the designated sales area data is encapsulated in hash value form. The consumer verification code is stored independently of the verification challenge code on the blockchain and mapped to the product's unique identification code for use by consumers during verification.

[0062] By binding verification data with designated sales area data, the system simultaneously achieves dual functions of authenticity verification and cross-regional sales investigation at the consumer end. Specifically, the consumer verification code undergoes hash-based anonymization to ensure that consumer devices can only access the minimum dataset required for verification, preventing the reverse engineering of sensitive information such as core microscopic fingerprints or tag location credentials, thus protecting core anti-counterfeiting assets at the data level. Furthermore, the embedding of designated sales area data allows the consumer robot to determine whether a product is being sold across regions based on a comparison of its real-time location with the electronic fence boundary while verifying authenticity, achieving integrated control over anti-counterfeiting and anti-cross-regional sales.

[0063] In addition, refer to Figure 3 Furthermore, in one embodiment, step S2 is refined into the following sub-steps: S20. Based on the warehouse location data in the verification challenge code, control the warehouse robot to perform an inbound grabbing operation on the product and obtain the product's verification data.

[0064] Specifically, after the warehouse robot obtains the verification challenge code of the product from the warehouse operation cloud platform, it parses the warehouse location data contained therein. This data includes three-dimensional spatial positioning parameters such as the warehouse area number where the product is expected to be stored, the row and column coordinates of the shelf, and the floor height information.

[0065] Then, during the receiving process, the robot uses a visual recognition module to identify the position calibration label on the packaging, establish a real-time packaging coordinate system, map the theoretical storage location data to the actual physical space, and autonomously plan the robotic arm's motion path. As the gripper approaches the packaging, a laser distance sensor integrated into the gripper provides real-time feedback on the vertical distance between the gripper and the packaging surface. When the distance reaches a preset shooting trigger value, the control system automatically triggers a miniature camera to capture microscopic texture images of the label body area, edge transition area, and background texture area. Simultaneously, the RFID reader / writer module reads the electronic tag data and tag location certificate from the electronic tag. After the gripper continues to close until it fully contacts the packaging, a multi-array pressure sensor records the gripping force and pressure distribution data to ensure stable gripping posture and prevent damage to the packaging.

[0066] By using the warehouse location data in the verification challenge code as the navigation basis for the robot's inbound operation, the warehouse robot can accurately locate the gripping area before touching the product, significantly improving the efficiency and accuracy of the inbound operation. The linkage triggering mechanism of the laser distance sensor and the miniature camera ensures the consistency of the imaging distance and angle during micro-texture image acquisition, laying a comparable foundation for image comparison during subsequent outbound verification. The multi-array pressure sensor enables the robot to sense the gripping force in real time, avoiding packaging deformation or detachment due to excessively tight or loose gripping, ensuring the stability of verification data collection. The entire process is completed autonomously by the robot, replacing traditional manual barcode scanning and manual data entry, reducing human error, and achieving standardization and automation of anti-counterfeiting data collection at the warehouse end.

[0067] S21. In response to the completion of the inbound grabbing operation, generate the product's physical challenge code. The physical challenge code includes the product's verification data, as well as the operation posture parameters and storage location data when the inbound grabbing operation was performed.

[0068] Specifically, after completing the inbound grasping operation, the warehouse robot automatically records all kinematic and dynamic parameters of the grasping process through its built-in high-precision encoder. This includes the joint angles of the six degrees of freedom of the robotic arm, the three-dimensional spatial coordinates of the gripper center in the warehouse coordinate system, the rotation angles of the gripper around each axis, the gripper opening and closing angles, the real-time grasping force curve, the pressure distribution cloud map, and the distance value at which the image is triggered. These operational posture parameters are encrypted and integrated with the product verification data obtained from the verification challenge code and the actual storage location data for this inbound operation to form a unique physical challenge code for the product. This physical challenge code is synchronously stored on a blockchain network via a cloud platform, establishing a mapping relationship with the product's unique identification code. It also integrates the robot's physical operational posture as an integral part of anti-counterfeiting verification, ensuring that any subsequent outbound operation must reproduce this specific posture to pass verification. This upgrades anti-counterfeiting verification from simple data comparison to a dual constraint of data and physical operation.

[0069] The above makes the physical challenge code in the warehouse an insurmountable physical anti-counterfeiting barrier. When leaving the warehouse, the robot must accurately reproduce the grasping posture when entering the warehouse so that the activated sensor array can be aligned with the effective recognition area to complete the verification.

[0070] Meanwhile, the challenge code binds product verification data with warehouse location data, enabling full traceability of product flow information throughout the warehousing process. Any unauthorized movement of the product's storage location will be detected by subsequent verification. By incorporating robot operating parameters into the anti-counterfeiting chain, the robot's inherent intelligence is fully utilized, transforming it from a passive handling tool into a core executor actively participating in anti-counterfeiting verification.

[0071] In addition, refer to Figure 4 Furthermore, in one embodiment, step S3 is refined into the following sub-steps: S30. In response to the outbound command, based on the operation posture parameters in the warehouse physical challenge code and the warehouse location data, control the warehouse robot to reproduce the operation posture during the inbound grabbing.

[0072] Specifically, when the warehouse operation cloud platform receives an outbound instruction, it first retrieves the corresponding warehouse physical challenge code for the product. From this code, it parses all the operational posture parameters recorded during the inbound grasping process, including the precise angle values ​​of the six joints of the robotic arm, the three-dimensional spatial coordinates of the gripper center in the warehouse coordinate system, the rotation angle of the gripper around each coordinate axis, the gripper opening and closing degree, the real-time grasping force curve, and the shooting trigger distance. Subsequently, the cloud platform sends these operational posture parameters to the warehouse robot control system. The robot reverse-engineers the motion trajectory based on the parameters and drives the servo motors of each joint in chronological order, so that the gripper at the end of the robotic arm moves precisely to the position coordinates of the grasping position during inbound, adjusts to the same spatial rotation angle, and approaches the product packaging with a completely consistent approach angle and speed.

[0073] During the reproduction process, the robot synchronously monitors the deviation between the encoder feedback values ​​of each joint and the target parameters, and corrects the motion trajectory in real time through closed-loop control to ensure that the final grasping posture and the posture error during storage are controlled within the robot's repeatability accuracy range, thereby achieving a precise mapping from digital instructions to physical actions.

[0074] In summary, by requiring warehouse robots to accurately reproduce the operational postures during the inbound grabbing process, anti-counterfeiting verification is upgraded from simple data comparison to a dual constraint of data and physical operation. The operational posture parameters are unique and cannot be copied. Even if an attacker completely copies the digital content of the electronic tag, they cannot know the specific posture parameters of the robot grabbing the product during inbound. This makes the warehouse physical challenge code an insurmountable physical anti-counterfeiting barrier.

[0075] Meanwhile, the posture reproduction mechanism ensures that the sensor array activated on the gripper can be accurately aligned with the effective identification area on the packaging when the product is shipped out. This makes the robot no longer a passive handling tool, but a core execution subject that actively participates in anti-counterfeiting verification.

[0076] S31. When performing the outbound grabbing operation, obtain the operation posture parameters and warehouse location data during the outbound grabbing operation, and collect the product verification data.

[0077] Specifically, during the outbound grasping process, which replicates the inbound posture of the warehouse robot, the robot control system records the operational posture parameters of the grasping operation in real time through a built-in high-precision encoder. These parameters include the angles of each joint of the robotic arm, the spatial coordinates of the gripper center, rotation angle, opening and closing degree, grasping force curve, and the shooting distance value triggered by the laser distance sensor. Simultaneously, as the gripper approaches the package, when the distance sensor detects the same trigger distance as the inbound record, it automatically triggers a miniature camera to re-capture microscopic texture images of the label body area, edge transition area, and background texture area. The RFID reading and writing module simultaneously reads the electronic tag data and tag location certificate from the electronic tag. Furthermore, the robot records the actual position coordinates of the gripper during this grasping operation as warehouse location data, creating a comparison benchmark with the warehouse location data recorded during inbound. All these real-time data from the outbound grasping operation constitute the raw data acquisition layer for outbound verification. Through multi-sensor fusion acquisition by the robot, the integrity and comparability of the outbound data are ensured.

[0078] The above-mentioned approach, by simultaneously collecting posture parameters and product verification data during the outbound grasping operation, provides a complete and accurate basis for subsequent comparison with inbound data. The collection of posture parameters allows the system to quantitatively assess the consistency between outbound and inbound operations, identifying any grasping deviations caused by robot malfunctions, human intervention, or malicious damage. The secondary collection of microscopic texture images and electronic tag data enables effective verification of whether products have been tampered with or tags have been replaced during warehousing. The entire collection process is completed autonomously by the robot without human intervention, avoiding positional deviations, angular deviations, and environmental interference caused by manual operation. This ensures the standardization and accuracy of outbound verification data, establishing a reliable real-time data foundation for anti-counterfeiting verification in the warehousing process.

[0079] S32. In response to the completion of the outbound grabbing operation, generate a warehouse outbound challenge code. The warehouse outbound challenge code includes the product's verification data, as well as the operation posture parameters and warehouse location data when the outbound grabbing operation was performed.

[0080] Specifically, after completing the outbound grabbing operation, the warehouse robot encrypts and integrates the acquired outbound grabbing posture parameters, product verification data collected during the grabbing process, and real-time warehouse location data to generate a warehouse outbound challenge code. This challenge code uses the same encryption algorithm and data structure as the physical warehouse challenge code, ensuring comparability between the two.

[0081] The generated warehouse outbound challenge code is uploaded by the robot to the warehouse operation cloud platform. The cloud platform compares and associates it with the corresponding product's warehouse physical challenge code stored in the blockchain, and records and stores information such as the timestamp of this outbound operation, the robot number, and the outbound destination.

[0082] In summary, by generating a warehouse outbound challenge code that includes outbound operation posture parameters and verification data, a standardized comparison carrier is provided for anti-counterfeiting verification in the warehouse outbound process. The warehouse outbound challenge code and the warehouse physical challenge code generated during warehousing form a pair of comparable data sets, and the comparison result directly reflects whether the product has remained authentic and intact during warehousing. The inclusion of outbound operation posture parameters allows the system to trace the specific execution status of outbound capture; any non-standard operation or malicious damage will leave identifiable abnormal traces in the posture parameters.

[0083] Meanwhile, the generation of warehouse outbound challenge codes enables the on-chain storage of data in the outbound process, making every warehouse transfer of products traceable and tamper-proof. This provides a complete and reliable chain of evidence for subsequent logistics traceability and liability determination, further enhancing the security and reliability of the entire chain of anti-counterfeiting and traceability.

[0084] In addition, refer to Figure 5 Furthermore, in one embodiment, step S4 is refined into the following sub-steps: S40. Perform hash chain verification on the electronic tag data in the warehouse outbound challenge code and the warehouse physical challenge code, and verify the tag location certificate of the warehouse outbound challenge code and the warehouse physical challenge code.

[0085] Specifically, the warehouse operation cloud platform extracts electronic tag data from the warehouse physical challenge code and the warehouse outbound challenge code. The electronic tag data contains hierarchical hash chain information; that is, the hash value of the current packaging layer is calculated based on the hash value of the previous packaging layer, the identifier of the current packaging layer, and the layer index. The platform obtains the verified layer hash value corresponding to the current packaging layer from the blockchain network and compares the recalculated current hash value in the outbound challenge code with the hash value stored on the chain bit by bit to confirm that the integrity of the hierarchical association has not been compromised. Simultaneously, the platform verifies the tag position credentials of the warehouse outbound challenge code and the warehouse physical challenge code. The tag position credentials contain the theoretical coordinate values ​​of the tag in the packaging's three-dimensional coordinate system. These coordinate values ​​are precisely measured by the vision system and stored on the chain during labeling at the production end. The platform compares the tag position coordinates carried in the outbound challenge code with the theoretical coordinates stored on the chain to confirm that the tag's attachment position on the packaging has not shifted or changed, thereby verifying whether the physical binding relationship between the tag and the packaging remains unchanged.

[0086] The above verification of the label location certificate further confirms the physical binding relationship between the label and the packaging. Even if the electronic label data itself is not tampered with, if the label is torn off and re-pasted onto other packaging, its location coordinates will inevitably deviate from the theoretical coordinates of the original certificate, thus being identified as abnormal by the system.

[0087] S41. Compare the texture similarity between the micro-texture image in the warehouse outbound challenge code and the micro-texture fingerprint data in the warehouse physical challenge code.

[0088] Specifically, the cloud platform extracts micro-texture fingerprint data collected during warehousing from the physical challenge code. This fingerprint data is a texture feature vector extracted from an effective recognition area centered on the label. The effective recognition area includes three sub-regions: the label body area, the edge transition area, and the background texture area. From the outbound challenge code, the platform extracts the current micro-texture image captured during outbound. First, the label position is located in the image based on the label location certificate. An effective recognition area identical to the one taken during warehousing is delineated using the label as an anchor point. Then, the texture feature vectors of each sub-region are extracted. The cloud platform uses a feature point matching algorithm to calculate the similarity between the current feature vector of each sub-region and the warehousing fingerprint. Weighted weights are assigned to each sub-region based on the repeatability accuracy of the warehousing robot. Regions closer to the label center, such as the label body area, have higher weights, followed by the edge transition area, with the background texture area having the lowest weight. Finally, a weighted comprehensive similarity is calculated as the texture similarity comparison result.

[0089] In summary, by dividing the texture acquisition area into three sub-regions and assigning different weights, the verification process becomes more accurate. The edge transition area, as a key region where the label and packaging physically combine, directly reflects whether the label has been tampered with due to subtle changes; assigning it a higher weight improves the detection sensitivity for tampering. Weighting based on the robot's positioning accuracy adapts the comparison process to the robot's own characteristics, reducing the risk of misjudgment due to grasping deviations.

[0090] S42. Compare the operational posture parameters in the warehouse outbound challenge code and the warehouse physical challenge code.

[0091] Specifically, the cloud platform extracts the operational posture parameters recorded during inbound grasping from the warehouse physical challenge code, including the joint angle sequence of the six degrees of freedom of the robotic arm, the three-dimensional spatial coordinates of the gripper center in the warehouse coordinate system, the rotation angle of the gripper around each coordinate axis, the gripper opening and closing degree value, the real-time grasping force curve, and the distance value when the laser distance sensor triggers the shooting. At the same time, it extracts the operational posture parameters recorded during outbound grasping from the warehouse outbound challenge code.

[0092] The platform then compares the two sets of parameters item by item, calculating the deviation values ​​between each parameter, including the difference in joint angles, the Euclidean distance of spatial coordinates, the difference in rotation angles, the percentage deviation of opening and closing degrees, the correlation coefficient of the gripping force curve, and the absolute difference in shooting trigger distance. All deviation values ​​must be less than a preset tolerance threshold, which is set based on the repeatability accuracy of the warehouse robot and the measurement accuracy of the sensors, ensuring that the allowable deviation range is within normal mechanical errors, while also being able to identify posture changes caused by abnormal or malicious operations.

[0093] In summary, by comparing operational posture parameters, anti-counterfeiting verification is deeply integrated with robot physical operations, leveraging the unique advantages of embodied intelligence. Operational posture parameters are highly unique and unpredictable; even if an attacker completely copies the data content of the electronic tag, they cannot know the specific posture parameters of the robot grasping the product upon entry into the warehouse. The robot posture reproduction verification mechanism upgrades anti-counterfeiting verification from simple data comparison to a dual constraint of data and physical operation. Any unauthorized operation or human intervention causing changes in the grasping posture will be identified through parameter comparison. Simultaneously, the precise comparison of posture parameters ensures that the imaging conditions during outbound verification are completely consistent with those during inbound verification, providing physical assurance for the comparability of microscopic texture images.

[0094] S43. Compare the location information in the warehouse outbound challenge code and the warehouse physical challenge code.

[0095] Specifically, the cloud platform extracts the warehouse location data recorded at the time of product receipt from the warehouse physical challenge code. This data includes the actual storage area number where the product was stored, the row and column coordinates of the shelf, the shelf height information, and the three-dimensional spatial coordinates of the gripper center in the warehouse coordinate system. It also extracts the real-time warehouse location data at the time of outbound grabbing from the warehouse outbound challenge code, including the product's current storage location information and the actual coordinates of the gripper center at the time of outbound grabbing.

[0096] Then, the platform performs a multi-dimensional comparison of the two sets of location information. First, it compares whether the warehouse area number and shelf coordinates are consistent with the inbound record to confirm that the product has not been moved to other storage locations during storage. Second, it compares the actual coordinates of the gripper center with the coordinates at the time of inbound to confirm whether the robot's positioning is accurate during outbound grabbing. At the same time, based on the product's flow record during storage, it checks whether there are any illegal outbound records or transfer operations for the product.

[0097] S44. When the results of hash chain verification, tag location certificate verification, texture similarity comparison, operation posture parameter comparison and location information comparison are all passed, output the warehouse verification result.

[0098] Specifically, after completing all the aforementioned verification and comparison steps, the cloud platform makes a comprehensive judgment on the results. Hash chain verification and tag location certificate verification constitute a dual verification at the digital identity layer, confirming that the electronic tag data has not been tampered with and that the physical binding relationship between the tag and the packaging is intact; texture similarity comparison constitutes verification at the physical feature layer, confirming that the microscopic texture of the packaging is consistent with that at the time of warehousing, and that no tag has been torn or goods have been swapped; operation posture parameter comparison constitutes verification at the robot operation layer, confirming that the robot's posture during outbound grabbing is precisely consistent with that at the time of warehousing; location information comparison constitutes verification at the warehousing and logistics layer, confirming that the product's location information during warehousing is complete and has not been illegally moved. Only when all these verifications pass will the platform output a warehousing verification pass result, and simultaneously record the timestamp of this verification, the operating robot number, verification result, and other information on the blockchain for evidence storage, and issue a verification pass instruction to the warehousing robot, allowing it to move the product to the outbound port to complete the outbound operation.

[0099] In addition, refer to Figure 6 Furthermore, in one embodiment, step S6 is refined into the following sub-steps: S60. Perform hash chain verification on the electronic tag data in the real-time verification data and the consumption verification code, and verify the tag location certificate in the real-time verification data and the consumption verification code.

[0100] Specifically, after receiving the real-time verification data reported by the consumer robot, the cloud platform extracts the electronic tag data from it. The electronic tag data contains the hash chain information of the hierarchical association, that is, the hash value of the current packaging level is calculated and generated based on the hash value of the previous packaging level, the identifier of the current packaging level and the hierarchical index.

[0101] Then, the platform retrieves the verified hierarchical hash value corresponding to the current product from the blockchain network. It compares the current hash value recalculated from the real-time verification data with the hash value stored on the chain bit by bit to confirm that the integrity of the hierarchical association has not been compromised. At the same time, the platform compares and verifies the label location certificate in the real-time verification data with the label location certificate stored in the consumption verification code. The label location certificate contains the theoretical coordinate value of the label in the three-dimensional coordinate system of the packaging. This coordinate value is accurately measured by the vision system and stored on the chain during labeling at the production end.

[0102] Finally, the platform compares the tag position coordinates carried in the real-time verification data with the theoretical coordinates stored on the blockchain to confirm that the tag's attachment position on the packaging has not shifted or changed, and to verify whether the physical binding relationship between the tag and the packaging remains in its original state.

[0103] S61. Compare the real-time location information of the real-time verification data with the electronic fence boundary associated with the specified sales area data in the consumption verification code.

[0104] Specifically, the cloud platform extracts the robot's real-time location information from the real-time verification data reported by the consumer robot. This location information, obtained by the robot's built-in GPS or BeiDou positioning module, includes the latitude and longitude coordinates of its current location. Next, the platform parses the product's designated sales area data from the consumer verification code. This data is stored as a set of latitude and longitude coordinates of the electronic fence boundary, defining the geographical area within which the product is authorized for sale. The platform calculates the spatial relationship between the real-time location coordinates and the electronic fence boundary to determine whether the location is inside the electronic fence boundary. If the real-time location is inside the electronic fence boundary, the product is determined to be circulating within the designated sales area, and the cross-selling verification passes. If the real-time location is outside the electronic fence boundary, the product is determined to be outside the designated sales area, and cross-selling is suspected. This comparison result, as an important component of consumer verification, together with the authenticity verification result, constitutes a complete consumer verification conclusion.

[0105] By comparing real-time location information with the boundaries of electronic fences, precise verification of product cross-regional sales is achieved. The association between electronic fence boundaries and designated sales area data clearly defines the legal circulation range of each product at its source. Automatic cross-regional sales verification during consumer verification effectively prevents such sales. The cross-regional sales verification results are output simultaneously with the authenticity verification results, allowing consumers to confirm product authenticity while also understanding whether the product is circulating within the designated area, providing technical support for brands to combat cross-regional sales.

[0106] S62. Compare the texture similarity between the micro-texture image of the real-time verification data and the micro-texture fingerprint data in the consumer verification code.

[0107] Specifically, the cloud platform extracts the currently captured micro-texture image from the real-time verification data reported by the consumer robot. This image is acquired by the consumer robot after autonomously adjusting the shooting angle and distance, covering the tag body area, edge transition area, and background texture area. The cloud platform locates the tag position in the image based on the tag location certificate, using the tag as an anchor point to delineate the same effective recognition area as when it was collected at the production end. Then, the platform extracts the product's micro-texture fingerprint data from the consumer verification code; this fingerprint data is the micro-texture feature vector collected at the production end.

[0108] Next, the platform uses a lightweight feature matching AI algorithm to calculate the similarity between the current micro-texture image and the micro-texture fingerprint data. Since consumer robots do not have the high-precision positioning capabilities of warehouse robots, a relatively lenient threshold setting is used for this similarity comparison, allowing for certain shooting angles and lighting deviations. Finally, the platform compares the calculated similarity with the preset threshold. If it is higher than the threshold, the micro-texture verification is deemed successful; otherwise, it is deemed a suspected forgery.

[0109] In summary, texture similarity comparison and electronic tag data verification complement each other. Even if the electronic tag data is completely copied, the physical characteristics of the micro-texture cannot be forged, fundamentally reducing the risk of the tag being copied or replaced.

[0110] S63. Based on the results of hash chain verification, tag location certificate verification, electronic fence boundary comparison and texture similarity comparison, and combined with the number of verifications, output the corresponding consumption verification result.

[0111] Specifically, after completing all the aforementioned verifications and comparisons, the cloud platform comprehensively judges the results and outputs the corresponding consumption verification result based on the number of product verifications recorded in the blockchain. If the hash chain verification, tag location certificate verification, electronic fence boundary comparison, and texture similarity comparison all pass, and the number of verifications found in the blockchain is zero, meaning the product is being verified for the first time, then the result of verification passed and verification activated is output. At the same time, the verification information is recorded on the blockchain, the verification status is marked as activated, and the first verification date, verification device de-identification information, and verification location are recorded.

[0112] If all verifications pass but the number of verifications is greater than zero, indicating that the product is not being verified for the first time, the system will output a result indicating that the verification has expired, along with the number of queries and the date of the first verification, advising consumers to use the product with caution. If the authenticity verification passes but the electronic fence boundary comparison fails, the system will output a result indicating that the product is genuine but suspected of being a counterfeit. If any authenticity verification fails, the system will output a result indicating that the product is suspected of being counterfeit.

[0113] The above-mentioned activation and deactivation mechanism effectively prevents the risk of label duplication and misuse. After the initial verification, the verification is marked as activated and the verification information is recorded. Any subsequent verification of the same label will trigger a notification that the verification has expired, allowing consumers to identify whether the label has been used multiple times. Even if an attacker successfully copies the label information, they cannot obtain a complete verification result through secondary verification because the label becomes invalid after the initial verification. Furthermore, the independent output of the cross-selling verification results allows consumers to identify whether the product is circulating within the designated sales area, providing brands with consumer-level oversight to combat cross-selling activities.

[0114] In addition, refer to Figure 7 Furthermore, in one embodiment, the following steps are also included: S7. In response to the inspection command, the warehouse physical challenge code is sent to the warehouse terminal, and the warehouse robot is controlled to obtain the product verification data.

[0115] Specifically, the warehouse operation cloud platform generates inspection tasks for specific products or randomly selected products according to a preset inspection plan or in response to proactive inspection instructions from the management end. The cloud platform retrieves the warehouse physical challenge code corresponding to the target product. This challenge code contains verification data recorded at the time of entry, including electronic tag data, tag location vouchers, micro-texture fingerprint data, and operational posture parameters and warehouse location data during entry and retrieval.

[0116] The cloud platform distributes physical challenge codes to designated inspection robots at the warehouse. The inspection robots autonomously navigate to the target storage location based on the storage location data in the challenge code, reproduce the inbound grasping posture according to the operation posture parameters in the challenge code, trigger a miniature camera to capture microscopic texture images through the laser distance sensor integrated on the gripper, and read the electronic tag data and tag location certificate from the electronic tag through the RFID reading and writing module. At the same time, it records the operation posture parameters and real-time location information during this inspection and grasping. All the collected data are integrated to form inspection verification data, which is then uploaded to the cloud platform along with the product's unique identification code.

[0117] The above describes how the proactive inspection and verification of warehouse robots, triggered by inspection commands, achieves routine and automated anti-counterfeiting monitoring in the warehousing process. The inspection robots can conduct random or planned spot checks on inventory products without disrupting normal warehousing operations, promptly identifying potential anti-counterfeiting anomalies.

[0118] S8. Compare the verification data obtained by the current warehouse robot with the warehouse physical challenge code, and output the comparison result.

[0119] Specifically, after receiving the inspection verification data uploaded by the inspection robot, the cloud platform retrieves the corresponding warehouse physical challenge code for the product from the blockchain and performs multi-dimensional comparison and verification. First, it verifies the electronic tag data using hash chain verification and tag location certificate verification to confirm the authenticity and integrity of the electronic tag data and that the physical binding relationship between the tag and the packaging has not changed. Then, it compares the texture similarity between the micro-texture image and the micro-texture fingerprint data in the warehouse physical challenge code, using a feature matching algorithm to calculate the similarity value and determine whether the micro-texture of the packaging is consistent with that at the time of warehousing, detecting whether there has been tag tampering or goods substitution. Next, it compares the operation posture parameters during inspection and grasping with those in the warehouse physical challenge code, calculating the deviation value to confirm whether the inspection and grasping posture is consistent with the warehousing posture. Finally, it compares the real-time location information during inspection with the warehouse location data in the warehouse physical challenge code to confirm whether the product is still in its original storage location. The cloud platform integrates all verification and comparison results and outputs the inspection comparison result.

[0120] In summary, by comparing the verification data obtained from inspections with the physical challenge codes in the warehouse from multiple dimensions, a comprehensive inspection of the product status in the warehousing process is achieved. Hash chain verification and tag location certificate verification ensure that the electronic tag data has not been tampered with and that the tags have not been physically moved; texture similarity comparison utilizes the natural uniqueness of micro-textures to identify tag tampering or goods swapping; operation posture parameter comparison ensures the consistency of inspection operations and avoids misjudgments caused by operational deviations; and location information comparison confirms the accuracy of the product storage location. This verification mechanism enables the inspection system to promptly detect anti-counterfeiting anomalies in the inventory, including various abnormal situations such as damaged tags, tampered tags, goods swapping, and illegal product movement, providing comprehensive anti-counterfeiting monitoring methods for warehouse management.

[0121] S9. Output the inspection verification results based on the comparison results, and generate alarm information when the inspection verification results are abnormal.

[0122] Specifically, the cloud platform makes a comprehensive judgment based on the results of the above verifications and comparisons. If the results of electronic tag data verification, tag location certificate verification, micro-texture similarity comparison, operation posture parameter comparison, and location information comparison all pass the preset thresholds, the inspection verification result is determined to be normal. The cloud platform records the timestamp of this inspection, the inspection robot number, the inspected product information, and the verification result, and stores this information on the blockchain as a record of the product's warehousing and circulation. If any of the above verifications or comparisons fails, the inspection verification result is determined to be abnormal. The cloud platform immediately generates an alarm message, which includes the unique identifier of the abnormal product, its current storage location, the type of abnormality (electronic tag abnormality, tag location abnormality, texture abnormality, posture abnormality, or location abnormality), the inspection time, and the inspection robot number. The cloud platform pushes the alarm message to the warehouse management end and records the abnormal information on the blockchain, triggering subsequent processing procedures. Managers can locate the abnormal product based on the alarm message and perform manual review or handling.

[0123] In summary, the inspection mechanism and alarm system together constitute a proactive defense system in the warehousing process, enabling problematic products to be detected and dealt with before they leave the warehouse, effectively ensuring the anti-counterfeiting security of products and achieving reliable and controllable data throughout the entire chain.

[0124] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0125] This application also provides a robot-based product anti-counterfeiting and traceability system, which corresponds one-to-one with the robot-based product anti-counterfeiting and traceability method in the embodiments.

[0126] refer to Figure 8 A robot-based product anti-counterfeiting and traceability system includes: a production coding module 1, an inbound production coding module 2, an outbound production coding module 3, an outbound verification module 4, a consumption request module 5, and a consumption verification module 6. Detailed descriptions of each functional module are as follows: Production Code Module 1: Used to obtain the verification challenge codes and consumption verification codes of each product uploaded by the production robot, and to send the verification challenge codes to the warehouse. Inbound Product Code Module 2: In response to inbound instructions, it controls the warehouse robot to perform inbound operations on products based on verification challenge codes and generates warehouse physical challenge codes; Outbound code module 3: In response to outbound instructions, it controls the warehouse robot to reproduce the inbound operation based on the warehouse physical challenge code and generates a warehouse outbound challenge code; Outbound verification module 4: Used to compare the warehouse outbound challenge code with the warehouse physical challenge code and output the warehouse verification result; Consumption Request Module 5: This module is used to respond to the verification request from the consumption robot, send the consumption verification code to the consumption robot, and receive the real-time verification data and real-time location information reported by the consumption robot. Consumption verification module 6: This module compares real-time verification data and real-time location information with the consumption verification code and outputs the consumption verification result.

[0127] The system comprises the following modules: Production Code Module 1 acquires and issues verification challenge codes and consumption verification codes, enabling centralized data collection and distribution; Inbound Code Module 2 controls a warehousing robot to perform inbound operations based on the verification challenge codes and generates a warehousing physical challenge code, recording product inbound information; Outbound Code Module 3 controls a warehousing robot to reproduce the inbound operation based on the warehousing physical challenge code and generates a warehousing outbound challenge code, ensuring consistency between inbound and outbound operations; Outbound Verification Module 4 compares the warehousing outbound challenge code with the warehousing physical challenge code, outputs the warehousing verification result, and ensures the authenticity of products in the warehousing process; Consumption Request Module 5 responds to the verification request from the consumption robot, issues a consumption verification code, and receives real-time verification data and real-time location information; Consumption Verification Module 6 compares the data received by the consumption robot and outputs the consumption verification result, achieving product verification at the consumer end.

[0128] Specific limitations regarding the robot-based product anti-counterfeiting and traceability system can be found in the context of the limitations on robot-based product anti-counterfeiting and traceability methods, and will not be repeated here. The modules in the aforementioned robot-based product anti-counterfeiting and traceability system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in an electronic device, or stored in the memory of the electronic device as software, so that the processor can call and execute the corresponding operations of each module. In one embodiment, an electronic device is provided, which is a user terminal. (Reference) Figure 9 The electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores detection data tables. The network interface allows communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a robot-based product anti-counterfeiting and traceability method. Through the combined efforts of various modules, a reliable traceability system can be achieved across the entire product chain, from production source, warehousing and distribution to end consumer. This reduces the risk of labels being copied or tampered with, deeply integrates anti-counterfeiting verification with robot physical operations, leverages the advantages of embodied intelligence, and achieves standardization and automation of anti-counterfeiting and traceability. It reduces errors and risks caused by human intervention, protects core data at the hardware operation level, and achieves dual protection against counterfeiting and cross-selling. This effectively prevents labels from being copied and used, eliminates image comparison errors, improves the targeting and efficiency of data collection, and adapts the comparison process to the characteristics of robots, forming a complete, auditable, and tamper-proof traceability and anti-cross-selling chain.

[0129] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: S1. Obtain the verification challenge codes and consumption verification codes of each product uploaded by the production robot, and send the verification challenge codes to the warehouse.

[0130] S2. In response to the inbound instruction, control the warehouse robot to perform the inbound operation on the product based on the verification challenge code, and generate a warehouse physical challenge code.

[0131] S3. In response to the outbound command, control the warehouse robot to reproduce the inbound operation based on the warehouse physical challenge code, and generate the warehouse outbound challenge code.

[0132] S4. Compare the warehouse outbound challenge code with the warehouse physical challenge code and output the warehouse verification result.

[0133] S5. In response to the verification request from the consumer robot, send the consumer verification code to the consumer robot and receive the real-time verification data and real-time location information reported by the consumer robot.

[0134] S6. Compare the real-time verification data, real-time location information, and consumption verification code, and output the consumption verification result.

[0135] In one embodiment, the sub-steps of step S1 refinement include: S10. Obtain product verification data, including electronic tag data, tag location certificate, and micro-texture fingerprint data.

[0136] S11. Generate a verification challenge code for the product based on the verification data. The verification challenge code includes the verification data and the warehouse location data.

[0137] S12. Generate a consumption verification code for the product. The consumption verification code includes verification data and data for the designated sales area.

[0138] In one embodiment, the sub-steps of step S2 refinement include: S20. Based on the warehouse location data in the verification challenge code, control the warehouse robot to perform an inbound grabbing operation on the product and obtain the product's verification data.

[0139] S21. In response to the completion of the inbound grabbing operation, generate the product's physical challenge code. The physical challenge code includes the product's verification data, as well as the operation posture parameters and storage location data when the inbound grabbing operation was performed.

[0140] In one embodiment, the refined sub-steps of step S3 include: S30. In response to the outbound command, based on the operation posture parameters in the warehouse physical challenge code and the warehouse location data, control the warehouse robot to reproduce the operation posture during the inbound grabbing.

[0141] S31. When performing the outbound grabbing operation, obtain the operation posture parameters and warehouse location data during the outbound grabbing operation, and collect the product verification data.

[0142] S32. In response to the completion of the outbound grabbing operation, generate a warehouse outbound challenge code. The warehouse outbound challenge code includes the product's verification data, as well as the operation posture parameters and warehouse location data when the outbound grabbing operation was performed.

[0143] In one embodiment, the refined sub-steps of step S4 include: S40. Perform hash chain verification on the electronic tag data in the warehouse outbound challenge code and the warehouse physical challenge code, and verify the tag location certificate of the warehouse outbound challenge code and the warehouse physical challenge code.

[0144] S41. Compare the texture similarity between the micro-texture image in the warehouse outbound challenge code and the micro-texture fingerprint data in the warehouse physical challenge code.

[0145] S42. Compare the operational posture parameters in the warehouse outbound challenge code and the warehouse physical challenge code.

[0146] S43. Compare the location information in the warehouse outbound challenge code and the warehouse physical challenge code.

[0147] S44. When the results of hash chain verification, tag location certificate verification, texture similarity comparison, operation posture parameter comparison and location information comparison are all passed, output the warehouse verification result.

[0148] In one embodiment, the sub-steps of step S6 are further refined as follows: S60. Perform hash chain verification on the electronic tag data in the real-time verification data and the consumption verification code, and verify the tag location certificate in the real-time verification data and the consumption verification code.

[0149] S61. Compare the real-time location information of the real-time verification data with the electronic fence boundary associated with the specified sales area data in the consumption verification code.

[0150] S62. Compare the texture similarity between the micro-texture image of the real-time verification data and the micro-texture fingerprint data in the consumer verification code.

[0151] S63. Based on the results of hash chain verification, tag location certificate verification, electronic fence boundary comparison and texture similarity comparison, and combined with the number of verifications, output the corresponding consumption verification result.

[0152] In one embodiment, the refined steps further include: S7. In response to the inspection command, the warehouse physical challenge code is sent to the warehouse terminal, and the warehouse robot is controlled to obtain the product verification data.

[0153] S8. Compare the verification data obtained by the current warehouse robot with the warehouse physical challenge code, and output the comparison result.

[0154] S9. Output the inspection verification results based on the comparison results, and generate alarm information when the inspection verification results are abnormal.

[0155] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0156] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

Claims

1. A robot-based product anti-counterfeiting traceability method, characterized in that, include: Obtain the verification challenge codes and consumption verification codes of each product uploaded by the production robot, and send the verification challenge codes to the warehouse. In response to the warehousing instruction, the warehouse robot is controlled to perform the warehousing operation on the product based on the verification challenge code, and a warehouse physical challenge code is generated; In response to the outbound command, the warehouse robot is controlled to reproduce the inbound operation based on the warehouse physical challenge code, and a warehouse outbound challenge code is generated. The warehouse outbound challenge code is compared with the warehouse physical challenge code, and the warehouse verification result is output. In response to the verification request from the consumer robot, the consumer verification code is sent to the consumer robot, and the real-time verification data and real-time location information reported by the consumer robot are received. The real-time verification data, the real-time location information, and the consumption verification code are compared, and the consumption verification result is output.

2. The method of claim 1, wherein, The step of obtaining the verification challenge codes and consumption verification codes of each product uploaded by the production robot, and sending the verification challenge codes to the warehouse, includes: Obtain product verification data, which includes electronic tag data, tag location certificate, and micro-texture fingerprint data; A verification challenge code is generated for the product based on the verification data, and the verification challenge code includes the verification data and the warehouse location data. Generate a consumption verification code for the product, the consumption verification code including the verification data and the specified sales area data.

3. The method of claim 2, wherein, The step of controlling the warehouse robot to perform a warehouse entry operation on the product based on the verification challenge code in response to the warehouse entry command, and generating a warehouse physical challenge code, includes: Based on the warehouse location data in the verification challenge code, the warehouse robot is controlled to perform an inbound grabbing operation on the product and obtain the verification data of the product. In response to the completion of the inbound grabbing operation, a physical challenge code for the product is generated. The physical challenge code includes the product's verification data, as well as the operation posture parameters and storage location data when the inbound grabbing operation is performed.

4. The method of claim 3, wherein, The step of responding to an outbound instruction, controlling a warehouse robot to reproduce the inbound operation based on the warehouse physical challenge code, and generating a warehouse outbound challenge code includes: In response to the outbound command, the warehouse robot is controlled to reproduce the operating posture during the inbound grabbing based on the operating posture parameters in the warehouse physical challenge code and the warehouse location data. When performing the outbound grabbing operation, the operation posture parameters and warehouse location data are obtained, and the verification data of the product are collected. In response to the completion of the outbound grabbing operation, a warehouse outbound challenge code is generated. The warehouse outbound challenge code includes the verification data of the product, as well as the operation posture parameters and warehouse location data when the outbound grabbing operation is performed.

5. The method of claim 4, wherein, The step of comparing the warehouse outbound challenge code with the warehouse physical challenge code and outputting the warehouse verification result includes: The electronic tag data in the warehouse outbound challenge code and the warehouse physical challenge code are verified by hash chain, and the tag location certificate of the warehouse outbound challenge code and the warehouse physical challenge code is verified. A texture similarity comparison is performed between the micro-texture image in the warehouse outbound challenge code and the micro-texture fingerprint data in the warehouse physical challenge code; The operational posture parameters in the warehouse outbound challenge code and the warehouse physical challenge code are compared. The location information in the warehouse outbound challenge code is compared with that in the warehouse physical challenge code; When the results of the hash chain verification, the tag location credential verification, the texture similarity comparison, the operation posture parameter comparison, and the location information comparison all pass, the warehouse verification result is output.

6. The method according to claim 2 or 3, characterized in that, The real-time verification data includes electronic tag data, tag location credentials, and micro-texture fingerprint data of the product acquired by the consumer robot. The step of comparing the real-time verification data, the real-time location information, and the consumer verification code to output the consumer verification result includes: The real-time verification data and the electronic tag data in the consumption verification code are verified by hash chain, and the real-time verification data and the tag location certificate in the consumption verification code are verified. The real-time location information of the real-time verification data is compared with the electronic fence boundary associated with the designated sales area data in the consumption verification code. The micro-texture image of the real-time verification data is compared with the micro-texture fingerprint data in the consumption verification code for texture similarity. Based on the results of the hash chain verification, the tag location credential verification, the electronic fence boundary comparison, and the texture similarity comparison, and combined with the number of verifications, the corresponding consumption verification result is output.

7. The method of claim 1, wherein, Also includes: In response to the inspection command, the warehouse physical challenge code is sent to the warehouse terminal, and the warehouse robot is controlled to obtain the product verification data; The verification data obtained by the current warehouse robot is compared with the warehouse physical challenge code, and the comparison result is output. The inspection verification result is output based on the comparison result, and an alarm message is generated when the inspection verification result is abnormal.

8. A robot-based product anti-counterfeiting traceability system, characterized in that, include: Production code module (1): used to obtain the verification challenge code and consumption verification code of each product uploaded by the production robot, and send the verification challenge code to the warehouse end; Inbound Product Code Module (2): In response to the inbound instruction, the module controls the warehouse robot to perform inbound operation on the product based on the verification challenge code and generates a warehouse physical challenge code; Outbound code module (3): In response to the outbound instruction, it controls the warehouse robot to reproduce the inbound operation based on the warehouse physical challenge code and generates a warehouse outbound challenge code; Outbound verification module (4): used to compare the warehouse outbound challenge code with the warehouse physical challenge code and output the warehouse verification result; Consumption request module (5): In response to the verification request of the consumption robot, the consumption verification code is sent to the consumption robot, and the real-time verification data and real-time location information reported by the consumption robot are received; Consumption verification module (6): used to compare the real-time verification data, the real-time location information and the consumption verification code, and output the consumption verification result.

9. An electronic device, comprising: It includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as any one of the robot-based product anti-counterfeiting and traceability methods as claimed in claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer program is stored and can be loaded by a processor and executed as any one of the robot-based product anti-counterfeiting and traceability methods as described in claims 1 to 7.

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