Laser label identification device and identification method

Abstract

A laser mark identification device (5) and method (500). The illustrated identification device (5) comprises a receiving module (52) configured to receive an object image (3), the object image (3) containing a laser label image and a label region image of a laser label region (R) on an object surface; a processing module (54) configured to process the object image to obtain a texture feature of the laser label region (R) on the object surface, the texture feature being related to a material of the laser label region (R), a laser marking device used and a marking process; and an identification module (56) configured to match the object image (3) with stored standard images to identify whether the object image (3) belongs to a true class or a false class, the standard images containing a standard laser label image and a standard label region image containing a standard texture feature of a label region (R) as matching standards.

Description

Technical Field

[0001] This invention relates to a laser tag recognition device and recognition method, as well as a corresponding machine storage medium. Background Technology

[0002] In recent years, laser marking has been widely used in automobiles, consumer goods, consumer electronics, and other industries to create laser labels. Lasers are beams of light generated by stimulated emission of particles, and are hailed as "the fastest knife," "the most accurate ruler," and "the brightest light." Therefore, compared to traditional marking processes such as chemical etching, inkjet printing, and mechanical engraving, laser marking offers advantages such as high marking speed, no wear and tear, and no environmental pollution.

[0003] Laser tags on products are read and identified by a barcode reader. Existing barcode readers primarily focus on improving their ability to identify distorted tag images; however, there is still no ideal solution for verifying the authenticity of tags and products using them. This causes several problems. For example, consumers cannot accurately determine whether a product they purchased is genuine or counterfeit through laser tag verification; and on the production line, it is difficult to determine whether parts are genuine or counterfeit through laser tag verification, leading to many components having to be reworked or becoming scrap. Summary of the Invention

[0004] In view of the problems existing in the prior art, one aspect of the present invention provides a laser tag recognition device, comprising: a receiving module configured to receive an object image, the object image including a laser tag image and a tag region image of a laser tag area on the object surface; a processing module configured to process the object image to obtain texture features of the laser tag area on the object surface, the texture features being related to the material of the laser tag area, the laser marking equipment used, and the laser marking process; and a recognition module configured to match the object image with a stored standard image to identify the authenticity of the object image, the standard image including a standard laser tag image as a matching standard and a standard tag region image containing standard texture features of the tag region, wherein the matching includes matching the object's tag image and texture features with the standard tag image and standard texture features, respectively.

[0005] According to one embodiment, the factors affecting the texture features include at least one of the following:

[0006] - The process parameters in the laser marking process;

[0007] - The type and performance parameters of the laser marking equipment used; and

[0008] - The combination of the laser marking equipment used and the material of the laser label area.

[0009] According to one embodiment, the laser tag recognition device further includes an output module configured to output recognition results in order to determine the authenticity of the product corresponding to the object image.

[0010] According to one embodiment, the authenticity of the product includes:

[0011] (1) The product corresponding to the object image is genuine or counterfeit;

[0012] (2) Whether the product corresponding to the object image meets the customization requirements. Optionally, the customization requirements include one or more of the pre-defined place of origin, production institution and distribution channel.

[0013] (3) Whether the product corresponding to the object image belongs to a batch of products;

[0014] (4) The product corresponding to the object image belongs to one of multiple batches of products or does not belong to any of the multiple batches.

[0015] According to one embodiment, identifying the authenticity of the object image includes: matching the object image with a standard image representing a batch of products; if the match is successful, determining that the product corresponding to the object image belongs to the batch; and if the match fails, determining that the product corresponding to the object image does not belong to the batch.

[0016] According to one embodiment, identifying the authenticity of the object image includes: matching the object image with standard images representing each batch of products in a plurality of batches; if the object image successfully matches a standard image representing a batch of products, determining that the product corresponding to the object image belongs to that batch; and if the object image fails to match any standard image representing any batch of products, determining that the product corresponding to the object image does not belong to any batch of the plurality of batches of products.

[0017] According to one embodiment, identifying the authenticity of the object image includes: matching the object image with a standard image of a genuine product; if the match is successful, determining that the product corresponding to the object image is genuine; and if the match fails, determining that the product corresponding to the object image is counterfeit.

[0018] According to one embodiment, the matching includes: generating a multidimensional object feature vector based on the parameters and weights of the texture features of the object image; generating a multidimensional standard feature vector based on the parameters and weights of the standard texture features of the standard image; calculating the distance between the object feature vector and the standard feature vector, wherein the distance may optionally be Euclidean distance; and determining the authenticity of the object image based on whether the distance meets a distance threshold.

[0019] According to one embodiment, the recognition module uses an image recognition model to perform the recognition, taking the object image as the model input and processing it to obtain a model output representing the authenticity of the object image.

[0020] According to one embodiment, the image processing model is a machine learning model.

[0021] According to one embodiment, the machine learning model performs relearning based on information obtained during the use of the recognition device.

[0022] According to one embodiment, the identification device is disposed in a reader for capturing the object image; or the identification device is disposed in a verification device for verifying the authenticity of the product corresponding to the object image.

[0023] Another aspect of the present invention provides a laser tag identification method, optionally executed by the identification device described above, the method comprising: receiving an object image, the object image including a laser tag image and a tag region image of a laser tag area on the object surface; processing the object image to obtain texture features of the laser tag area on the object surface, the texture features being related to the material of the laser tag area, the laser marking equipment used, and the laser marking process; and matching the object image with a stored standard image to identify the authenticity of the object image, the standard image including a standard laser tag image as a matching standard and a standard tag region image containing standard texture features of the tag area, wherein the matching includes matching the object's tag image and texture features with the standard tag image and standard texture features, respectively.

[0024] Another aspect of the invention provides a machine-readable storage medium storing executable instructions that, when executed, cause one or more processors to perform the method described above.

[0025] To further understand the features and technical content of the present invention, please refer to the following detailed description and accompanying drawings. However, the drawings are provided for reference and illustration only and are not intended to limit the present invention. Attached Figure Description

[0026] Figure 1 An exemplary operating environment is shown according to some embodiments of the present invention.

[0027] Figure 2 A reader equipped with an identification device according to an embodiment of the present invention is shown.

[0028] Figure 3A schematic block diagram of an identification device according to an embodiment of the present invention is shown.

[0029] Figure 4 The identification process according to an embodiment of the present invention is illustrated.

[0030] Figure 5 A flowchart of an identification method according to an embodiment of the present invention is shown. Detailed Implementation

[0031] A key aspect of this invention lies in using the surface of the product itself for authenticity identification and verification. Specifically, each product leaves unique texture features on its surface during the manufacturing process, which can be understood as the product's "natural fingerprint." Embodiments of this invention provide an identification scheme utilizing the "natural fingerprint" of laser tags. That is, in addition to reading the laser tag itself, the texture features of the area on the product surface where the laser tag is formed are also read, and the read information is matched with stored standard information to achieve authenticity identification and verification.

[0032] The label in this invention refers to a laser label, that is, an identifier formed on the surface of a product by laser marking (e.g., laser coding, laser engraving). Laser labels can be made in different colors and / or different styles. Laser labels can be laser-engraved with one-dimensional barcodes, two-dimensional barcodes, or customized markings (e.g., letters, numbers, symbols in customized colors), etc.

[0033] In one embodiment, the laser tag and its tag area can be implemented as follows: a black block area of ​​QR code is burned on a light-colored material; a blank block area of ​​QR code is burned on a dark-colored material; a blank block area of ​​QR code is burned on a light-colored material; or a black block area of ​​QR code is burned on a dark-colored material.

[0034] In this invention, "authenticity" can include some of the situations described below.

[0035] (1) The laser label indicates whether the product is genuine or counterfeit, thus identifying counterfeit products. In this case, "genuine" corresponds to a genuine product; "counterfeit" corresponds to a counterfeit product.

[0036] (2) Whether the product corresponding to the laser label meets the customization requirements. In this case, "genuine" means the product meets the customization requirements; "fake" means the product does not meet the customization requirements.

[0037] The customization requirements may include one or more of the following: place of origin (e.g., in some cases, identifying the product's place of origin is used to identify the product's grade and specifications), production organization (e.g., in some cases, identifying the product's production organization is used to identify the product's grade and specifications), and distribution channel (in some cases, identifying the product's distribution channel is used to identify whether the product's source is compliant).

[0038] (3) Whether the product corresponding to the laser label belongs to the same batch of products. In this case, "genuine" means the product belongs to the same batch of products; "fake" means the product does not belong to the same batch of products.

[0039] (4) In the case of multiple batches of products, it can identify which batch the product corresponding to the laser label belongs to, or whether it does not belong to any of the multiple batches. In this case, "true" corresponds to the product belonging to one of the multiple batches of products; "false" corresponds to the product not belonging to any of the multiple batches.

[0040] "A batch of products" or "a batch of products" refers to products corresponding to a batch of labels printed on the same material by the same laser marking equipment.

[0041] For example, the products corresponding to a batch of laser labels printed on material B by laser marking equipment A are designated as batch I; the products corresponding to a batch of laser labels printed on material C by laser marking equipment A are designated as batch II; the products corresponding to a batch of laser labels printed on material A by another laser marking equipment D are designated as batch III; and the products corresponding to a batch of laser labels printed on material C by the other laser marking equipment D are designated as batch IV.

[0042] In addition to limitations on printing equipment and printing materials, "a batch of products" or "a single batch of products" may also include limitations on other factors, such as limitations on the printing period.

[0043] The identification scheme according to embodiments of the present invention can be applied in many fields such as industry, consumer goods, retail, buildings, agriculture, and transportation. For example, the identification scheme according to embodiments of the present invention can be used to: (1) identify the authenticity of products, for example, reduce the number of counterfeit products that imitate brands and minimize reputational damage by providing identification schemes; (2) identify whether products belong to the same batch of products; (3) in the process of authenticity identification and verification, by transmitting product information to the network, it has the function of tracking and tracing, which helps to improve the transparency of distribution channels; (4) in the process of customers verifying authenticity through client applications, it can establish direct contact with customers, thereby improving customer relationship management.

[0044] The specific embodiments of the present invention will now be described with reference to the accompanying drawings.

[0045] Figure 1 An exemplary operating environment 100 according to some embodiments of the present invention is shown. In environment 100, object 1 (object 1 is a product to be identified and verified, for example, a screw) has a laser tag, which is, for example, the letters "SN". Reader 2 is configured to align with the laser tag area R of object 1, capture the texture features of the laser tag area while reading the laser tag, and generate an object image 3 containing a tag area image of the tag image. A database 4 stores standard images used as comparison criteria, which include standard tag images and standard tag area images containing standard texture features of the standard tag areas. Laser tag recognition device (hereinafter referred to as "recognition device") 5 includes a recognition strategy, namely, matching the object image and the standard image, for example, comparing the object's tag image and the standard tag image; after the tag comparison is successful, matching the texture features of the tag area with the standard texture features; if the match is successful, determining that the object image belongs to the true category; if the match is unsuccessful, determining that the object image belongs to the false category.

[0046] The identification device 5 can be implemented in hardware, software, or a combination of both. For the hardware implementation, it can be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), data signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic units designed to perform their functions, or combinations thereof. For the software implementation, it can be implemented using microcode, program code, or code segments, and can also be stored in machine-readable storage media such as storage components.

[0047] In one implementation, the identification device 5 includes a memory and a processor. The memory contains instructions that, when executed by the processor, cause the processor to perform an identification strategy / method according to an embodiment of the present invention.

[0048] Figure 2 A reader 200 equipped with an identification device 5 according to an embodiment of the present invention is shown. The identification device 5 can be implemented as identification software provided in the reader 200. The reader 200 can be a fixed reader, a handheld reader, etc.

[0049] Understandably, when the identification device 5 is implemented in software form, the software can be applied to the code reading program or product verification program, thereby optimizing the authenticity identification function of the code reading program or product verification program.

[0050] Figure 3A schematic block diagram of an identification device 5 according to an embodiment of the present invention is shown. Figure 3 As shown, the identification device 5 mainly includes a receiving module 52, a processing module 54, an identification module 56, and an output module 58.

[0051] It should be noted that the naming of the modules of the identification device 5 should be understood as logical descriptions, rather than limitations on their physical form or configuration. For example, one or more of the receiving module 52, processing module 54, identification module 56, and output module 58 can be implemented in the same chip or circuit, or they can be respectively disposed in different chips or circuits; this invention does not limit this. The modules of the identification device 5 can also be further divided into multiple sub-modules, each sub-module being implemented as a subroutine.

[0052] Figure 4 An identification process 400 according to an embodiment of the present invention is shown. This identification process 400 can be performed in the identification device 5 described above.

[0053] In block 402, receiving module 52 receives an object image captured by the reader. The object image includes a laser tag image and a tag region image, which is an image of the tag region on the object surface. For example, Figure 1 An image of the surface region R where the laser label "SN" is located.

[0054] In box 404, the processing module 54 processes the captured object image to obtain the texture features of a specified area on the surface of the object (i.e., the area on the product label that is laser-etched to form a laser label).

[0055] Texture features include natural fingerprint features of labeled areas on the surface of an object, and the natural fingerprint features are associated with at least one of the following.

[0056] (1) The material of the label area on the object's surface.

[0057] (2) Process parameters in the laser marking process that forms the laser label, such as one or more of the focused spot size, laser power and beam quality.

[0058] (3) The type of laser marking equipment used (e.g., fiber laser marking machine, end-pumped infrared / green / ultraviolet laser marking machine, laser engraving machine, CO2 laser marking machine) and its performance parameters (e.g., output power, beam quality, pulse width, pulse repetition frequency).

[0059] (4) The combination of laser marking equipment and printing materials used. Considering that different printing equipment will produce different texture features when printing on the same material, and the same printing equipment will produce different texture features when printing on different materials, the combination of printing equipment and printing materials is a crucial factor affecting texture features.

[0060] In box 406, the recognition module 56 matches the object image with a stored standard image (e.g., the recognition module 56 retrieves a corresponding standard image from database 4 for identifying the object / product) to determine the authenticity of the object image. The standard image includes a laser-marked standard label image as the matching criterion and a standard label region image containing standard texture features of the standard label region.

[0061] In this matching process, the object's label image and texture features can be matched against the standard label image and standard texture features, respectively. For example, first, the object's label image is matched against the standard label image. After this match is successful, the texture features of the object's label region are then matched against the standard texture features. It is understandable that matching of the label image and matching of the texture features can be performed simultaneously.

[0062] Regarding the matching method between the object's label image and the standard label image, the recognition module 56 may adopt a suitable image processing and matching scheme, and this invention does not impose any limitations.

[0063] Regarding the comparison between the texture features of an object and the standard texture features, the recognition module 56 can achieve this by extracting multi-dimensional feature vectors and calculating the vector difference (see box 4603).

[0064] In one embodiment, taking a standard image as sample A and an object image as sample B as an example, the recognition module 56 calculates the multidimensional feature vector Y abstracted from sample A. A The multidimensional feature vector Y abstracted from sample B B The similarity between the two (e.g., represented by the vector difference between them) is used to determine whether the object image belongs to a true category or a false category. Specifically, the recognition module 56 generates a multidimensional feature vector Y of sample A based on multiple parameters representing the microtextural features of sample A and the weights of each parameter. A (For example, a standard feature vector); a multidimensional feature vector Y of sample B is generated based on multiple parameters characterizing the microtextural features of sample B and the weights of each parameter. B (For example, the object's feature vector). Next, the standard feature vector Y is calculated. A With the object feature vector Y BThe distance between the objects is calculated, for example, the Euclidean distance. Next, it is determined whether the calculated distance is greater than a distance threshold. If the calculated distance is greater than the distance threshold, the object image is determined to belong to the pseudo-class; if the calculated distance is less than or equal to the distance threshold, the object image is determined to belong to the true class.

[0065] For example, based on the surface material of the label area of ​​sample A, the process parameters in the laser marking process, and the model and setting parameters of the laser marking equipment, an N-dimensional feature matrix X of sample A can be abstracted. A X A ∈R N Generate a weight matrix W representing the weights of the factors considered in sample A. A W A ∈R M,N+1 ; and based on the N-dimensional feature matrix X of sample A A and weight matrix W A (For example, X) A and W A Multiplying these two matrices generates a multidimensional feature vector Y from sample A. A (For example, a standard feature vector). Similarly, based on the surface material of the label area of ​​sample B, the process parameters during laser marking, and the model and settings of the laser marking equipment, an N-dimensional feature matrix X of sample B is abstracted. B X B ∈R N Generate a weight matrix W representing the weights of the factors considered in sample B. B W B ∈R M,N+1 ; and based on the N-dimensional feature matrix X of sample B B and weight matrix W B (For example, X) B and W B Multiplying these two matrices generates a multidimensional feature vector Y for sample B. B (For example, the object's feature vector). Next, the standard feature vector Y is calculated. A With the object feature vector Y B Similarity between them, for example, by calculating their Euclidean distance. Standard feature vector Y A With the object feature vector Y B The similarity between them can be calculated using the following formula:

[0066]

[0067] When the standard eigenvector Y A With the object feature vector Y BWhen the similarity (Similarity(A,B)) between samples is less than a predetermined threshold, sample B is determined to meet the standard (i.e., the determination result is "true"). For example, sample B is a genuine product or sample B and sample A belong to the same batch of products.

[0068] The recognition module 56 can use an image processing model to complete the above matching (i.e., matching between the object image and the standard image).

[0069] This image processing model can be implemented using artificial intelligence techniques, for example, as a trained machine learning model. The robustness of its discrimination ability can be improved by using a suitable neural network model.

[0070] In one embodiment, a large number of counterfeit images and standard images are used as samples to train the image processing model so that when a new image is received, the model can determine whether the new image belongs to the true category (i.e., the matching result is that it can match the standard image) or the pseudo category (i.e., the matching result is that it cannot match the standard image).

[0071] In this embodiment, the image processing model can also relearn based on information from the usage process in order to periodically obtain updated parameters, thereby improving the model's intelligence and processing speed.

[0072] The following describes the matching process in some scenarios.

[0073] In one scenario, referring to box 4061, the recognition module 56 determines whether the product corresponding to the object image belongs to the same batch of products through matching. In this scenario, it is only necessary to determine whether the object belongs to the same batch of products, without needing to identify a specific laser tag or product. In other words, in this scenario, as long as it is determined that the product corresponding to the object image belongs to the same batch of products, the recognition result is true; otherwise, the recognition result is false.

[0074] In this scenario, the recognition module 56 can compare the object image with a standard image representing a batch of products to determine the authenticity of the object image, thereby determining whether the product corresponding to the object image belongs to one of the batch of products.

[0075] For example, multiple standard images (each standard image contains a standard label image and a standard label area image) and multiple counterfeit images (each counterfeit image contains a counterfeit laser label image and a label area image) of multiple products from the same batch are used to train an image processing model so that when a new image is input, the model can determine whether the product corresponding to the new image belongs to the batch and output the judgment result.

[0076] It's understandable that even images of products from the same batch may have subtle differences and won't be completely identical. By training with a large number of batch samples, the model can identify whether a new input image corresponds to a product from the same batch.

[0077] In another scenario, the identification device can determine which batch of multiple batches the product corresponding to the object image belongs to, or whether it does not belong to any of these batches.

[0078] For example, the recognition module 56 matches the object image with standard images representing each batch of products in multiple batches; if the object image successfully matches the standard image representing a batch of products, it determines that the product corresponding to the object image belongs to that batch; if the object image fails to match the standard image representing any batch of products, it determines that the product corresponding to the object image does not belong to any batch of multiple batches of products.

[0079] In the embodiment concerning "batch", since it is not necessary to compare the object image with multiple images corresponding to a batch of products separately, a significant amount of computing power can be saved, improving recognition efficiency. In another scenario, referring to box 4062, the recognition module 56 determines the authenticity of the product corresponding to the object image through matching.

[0080] In this scenario, the recognition module 56 matches the object image with a standard image representing a single genuine product to determine whether the object image belongs to the genuine or fake category, thereby determining the authenticity of the product corresponding to the object image. For some possible examples of product authenticity, please refer to the relevant descriptions above, which will not be repeated here.

[0081] For example, an image processing model can be trained using standard images and a large number of images of counterfeit or non-compliant products, so that when a new image is input, the model can determine the authenticity of the product corresponding to that new image.

[0082] Figure 5 A flowchart of an identification method 500 according to an embodiment of the present invention is shown. This identification method 500 can be performed by the identification device 5 described above. Therefore, the above description also applies here.

[0083] In step S502, an object image is received, the object image comprising a laser tag image and a tag area image of a tag area on the object surface.

[0084] In step S504, the object image is processed to obtain the texture features of the label area on the object surface. The texture features are related to the material of the laser label area, the laser marking equipment used, and the marking process.

[0085] In step S506, the object image is matched with a stored standard image to distinguish the authenticity of the object image. The standard image includes a standard laser tag image as a matching standard and a standard tag region image containing standard texture features of the tag region. The matching includes matching the object's tag image and texture features with the standard tag image and standard texture features, respectively.

[0086] The present invention also provides a machine-readable storage medium storing executable instructions that, when executed, cause a machine to perform the identification method or identification process as described above.

[0087] It is understood that all operations in the methods or processes described above are exemplary, and the present invention is not limited to any operation in the methods or processes or the order of such operations, but should be covered by all other equivalent variations under the same or similar concept.

[0088] It is understood that the identification device described above can be implemented in various ways. For example, it can be implemented as hardware, software, or a combination thereof.

[0089] Identification devices may include one or more processors. These processors may be implemented using electronic hardware, computer software, or any combination thereof. Whether these processors are implemented as hardware or software will depend on the specific application and the overall design constraints imposed on the system. As an example, the processors, any portions of processors, or any combination of processors provided in this invention may be implemented as microprocessors, microcontrollers, digital signal processors (DSPs), field-programmable gate arrays (FPGAs), programmable logic devices (PLDs), state machines, gate logic, discrete hardware circuits, and other suitable processing units configured to perform the various functions described in this invention. The functionality of the processors, any portions of processors, or any combination of processors provided in this invention may be implemented as software executed by a microprocessor, microcontroller, DSP, or other suitable platform.

[0090] Software can be broadly considered as representing instructions, instruction sets, code, code segments, program code, programs, subroutines, software modules, applications, software applications, software packages, routines, subroutines, objects, running threads, procedures, functions, etc. Software can reside on a computer-readable medium. Computer-readable media can include, for example, memory, which can be, for example, magnetic storage devices (e.g., hard disks, floppy disks, magnetic stripes), optical disks, smart cards, flash memory devices, random access memory (RAM), read-only memory (ROM), programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), registers, or removable disks. Although memory is shown as separate from the processor in several aspects of the invention, memory can also reside within the processor (e.g., in caches or registers).

[0091] The above description is provided to enable any person skilled in the art to implement the various aspects described herein. Various modifications to these aspects will be apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects. Therefore, the claims are not intended to be limited to the aspects shown herein. All structural and functional equivalents of the elements of the various aspects described herein that are known or would be apparent to those skilled in the art are expressly incorporated herein by reference and are intended to be covered by the claims.

Claims

1. A laser tag recognition device, comprising: A receiving module configured to receive an object image, the object image comprising a laser tag image and a tag region image of a laser tag region on the object surface; A processing module configured to process the object image to obtain texture features on the object surface, including a laser-marked area comprising a laser-marked label formed by laser marking, wherein the texture features include natural fingerprint features of the laser-marked area and are related to the material of the laser-marked area, the laser marking equipment used, and the laser marking process; and The recognition module is configured to match the object image with a stored standard image to determine the authenticity of the object image. The standard image includes a standard laser tag image as a matching standard and a standard tag region image containing standard texture features of the tag region. The matching process includes matching the laser tag image and texture features of the object with the standard laser tag image and the standard texture features, respectively.

2. The laser tag recognition device as described in claim 1, wherein, Factors affecting the texture features include at least one of the following: - The process parameters in the laser marking process; - The type and performance parameters of the laser marking equipment used; and - The combination of the laser marking equipment used and the material of the laser label area.

3. The laser tag recognition device as described in claim 1, wherein, The laser tag recognition device also includes an output module configured to output recognition results in order to determine the authenticity of the product corresponding to the object image.

4. The laser tag recognition device as described in claim 3, wherein, The authenticity of the product includes: (1) The product corresponding to the object image is genuine or counterfeit; (2) Whether the product corresponding to the object image meets the customization requirements; (3) Whether the product corresponding to the object image belongs to a batch of products; (4) The product corresponding to the object image belongs to one of the multiple batches of products or does not belong to any of the multiple batches.

5. The laser tag recognition device as described in claim 4, wherein, The customization requirements include one or more of the pre-specified place of origin, production facility, and distribution channel.

6. The laser tag recognition device as described in any one of claims 1-5, wherein, Identifying the authenticity of the object image includes: The object image is matched with a standard image representing a batch of products; If a match is successful, it is determined that the product corresponding to the object image belongs to that batch; and If a match fails, it is determined that the product corresponding to the object image does not belong to that batch.

7. The laser tag recognition device as described in any one of claims 1-5, wherein, Identifying the authenticity of the object image includes: The object image is matched with standard images representing each batch of products from multiple batches. If the object image successfully matches a standard image representing a batch of products, it is determined that the product corresponding to the object image belongs to that batch. If the object image fails to match any standard image representing any batch of products, it is determined that the product corresponding to the object image does not belong to any batch of the multiple batches of products.

8. The laser tag recognition device as described in claim 1, wherein, Identifying the authenticity of the object image includes: The object image is matched with a standard image of a product of type real; If a match is successful, the product corresponding to the object image is determined to be genuine; and If a match fails, the product corresponding to the object image is determined to be fake.

9. The laser tag recognition device as described in claim 1, wherein, The matching includes: A multidimensional object feature vector is generated based on the parameters and weights of the texture features of the object image. A multidimensional standard feature vector is generated based on the parameters and weights of the standard texture features of the standard image. Calculate the distance between the object's feature vector and the standard feature vector; and The authenticity of the object image is determined based on whether the distance meets a distance threshold.

10. The laser tag recognition device as described in claim 9, wherein, The distance mentioned is a Euclidean distance.

11. The laser tag recognition device as described in claim 1, wherein, The recognition module uses an image recognition model to perform the recognition. The object image is used as the model input, and after model processing, a model output representing the authenticity of the object image is obtained.

12. The laser tag recognition device as described in claim 11, wherein, The image recognition model is a machine learning model, and the machine learning model performs relearning based on the information obtained by the recognition device during use.

13. The laser tag recognition device as described in claim 1, wherein, The recognition device is installed in a reader for capturing images of the object; or The identification device is installed in a verification device used to verify the authenticity of the product corresponding to the object image.

14. A laser tag identification method, performed by the identification device as described in any one of claims 1-13, the method comprising: Receive an object image, the object image comprising a laser tag image and a tag area image of the laser tag area on the object surface; The object image is processed to obtain texture features on the object surface, including a laser-marked area containing a laser label formed by laser marking. These texture features include natural fingerprint features of the laser-marked area and are related to the material of the laser-marked area, the laser marking equipment used, and the laser marking process. The object image is matched with a stored standard image to determine its authenticity. The standard image includes a standard laser tag image used as a matching standard and a standard tag region image containing standard texture features of the tag region. The matching process includes matching the laser tag image and texture features of the object with the standard laser tag image and standard texture features, respectively.

15. A machine-readable storage medium storing executable instructions that, when executed, cause one or more processors to perform the method of claim 14.

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