Multilayer anti-counterfeit label identification system and method

Abstract

The invention relates to the technical field of anti-counterfeit labels, in particular to a multi-layer anti-counterfeit label identification system and method, and the system comprises an image collection module, a feature layering module, an anti-counterfeit authentication module, a logic verification module, and an anti-counterfeit evaluation module. The method comprises the following steps: acquiring an anti-counterfeit label image, and dividing the anti-counterfeit label image into a plurality of sub-images according to the communication condition of each position on the image; carrying out feature layering processing on the sub-images, extracting image features, associated with a physical layer and a logic layer, of an image feature network, and obtaining a hierarchical probability and a verification mode of the sub-images at each hierarchy; performing parallel verification and serial verification on the image features in sequence to form a logic verification relation graph of the sub-images; searching an optimal decision path of the logic verification relation graph at each level; the optimal decision path of the logic verification relation graph is used as a constraint condition, and the constraint conditions are combined and analyzed to obtain a multi-layer anti-counterfeiting verification network; the accuracy and efficiency of anti-counterfeit label identification are realized.

Description

Technical Field

[0001] The invention relates to the technical field of anti-counterfeiting labels, and in particular to a multi-layer anti-counterfeiting label identification system and method. Background Art

[0002] At present, the conventional anti-counterfeiting process is to use electronic devices to scan the anti-counterfeiting labels on the items, and then the receiving system gives the source image of the anti-counterfeiting label collected at the factory, and compares the scanned anti-counterfeiting label to see if it is consistent with the source image to identify the authenticity; however, when using electronic devices to identify a large number of anti-counterfeiting labels in different forms, it is difficult to adjust according to the verification method of the currently input anti-counterfeiting label, resulting in low label verification efficiency under multi-source integrated verification of anti-counterfeiting labels.

[0003] For example, Chinese patent publication number CN116227524A discloses a method for generating and verifying an anti-counterfeiting code and a label-based anti-counterfeiting system. The method includes: the generator of the anti-counterfeiting label obtains an initial anti-counterfeiting image containing a graphic code for anti-counterfeiting, and splits the initial anti-counterfeiting image into multiple sub-images; the multiple sub-images are divided into multiple times and printed on the label substrate to generate a printed anti-counterfeiting image corresponding to the graphic code, and there is a random offset between the printing positions of the multiple sub-images on the label substrate; the verifier of the anti-counterfeiting label obtains the anti-counterfeiting image to be verified collected by the user through the terminal; obtains the printed anti-counterfeiting image corresponding to the graphic code contained in the anti-counterfeiting image to be verified; and verifies the authenticity of the anti-counterfeiting image to be verified based on the image similarity between the anti-counterfeiting image to be verified and the printed anti-counterfeiting image.

[0004] For example, Chinese patent publication number CN113435219A discloses an anti-counterfeiting detection method, device, electronic device and storage medium. For the target image generated by the consumer scanning the anti-counterfeiting label and the source image collected when the anti-counterfeiting label leaves the factory, the similarity of the anti-counterfeiting points of the two can be compared comprehensively in terms of position coordinates and color, and the authenticity of the label scanned by the consumer can be determined based on the similarity.

[0005] The prior art describes processing anti-counterfeiting labels by generating random offsets in their positions, and using relative colors to identify anti-counterfeiting labels. However, these identification methods are mostly single methods and cannot be combined for processing based on multiple groups of anti-counterfeiting labels currently input, resulting in reduced efficiency of anti-counterfeiting labels in scenarios where a large number of anti-counterfeiting labels are to be identified, and the verification method has management difficulties. Summary of the invention

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is: a multi-layer anti-counterfeiting label recognition system, including: an image acquisition module, used to acquire the anti-counterfeiting label image, and divide the anti-counterfeiting label image into multiple sub-images in sequence according to the connectivity of each position on the image.

[0007] The feature stratification module is used to perform feature stratification processing on the sub-images to obtain an image feature network; extract image features that are feature-associated with the physical layer and the logical layer in the image feature network, set a hierarchical division standard for the image features, and determine the hierarchical probability and verification method of the sub-image at each level.

[0008] The anti-counterfeiting authentication module is used to perform parallel verification and serial verification on the image features in turn according to the verification methods of the sub-images at each level, obtain logical verification data, and form a logical verification relationship diagram of the sub-images.

[0009] The logic verification module is used to obtain multiple candidate paths based on the data of the starting and ending positions of the logic verification relationship diagram, and to search for the optimal decision path of the logic verification relationship diagram at each level with the maximum path and the minimum path of the logic verification relationship diagram as the search interval.

[0010] The anti-counterfeiting evaluation module is used to combine and analyze the constraints with the optimal decision path of the logic verification relationship diagram as the constraint condition, determine the shortest execution path after the constraints are combined in turn, and combine multiple constraints according to the shortest execution path to obtain a multi-layer anti-counterfeiting verification network.

[0011] A multi-layer anti-counterfeiting label recognition method includes: S1, inputting an anti-counterfeiting label image into an image segmentation model, generating a region prediction map, and judging whether there are connected regions in each region, and dividing to obtain sub-images corresponding to the anti-counterfeiting label image.

[0012] S2, extracting each image factor from the sub-image, and according to the distribution of each image factor, determining the label structure corresponding to the image factor, and forming the mapping relationship of each image factor; setting the image feature network by associating the mapping relationship of the image factor with the physical layer features and the logical layer features.

[0013] S3, calculates the probability of each level in the image feature network, determines the verification method of each level, and forms a logical verification relationship diagram of the sub-image.

[0014] S4, based on the data of the starting point and end point positions of the logic verification relationship graph, multiple candidate paths are obtained, and the maximum path and the minimum path of the logic verification relationship graph are used as the search interval to search for the optimal decision path of the logic verification relationship graph at each level.

[0015] S5, taking the optimal decision path of the logic verification relationship diagram as the constraint condition, merging and analyzing each constraint condition, determining the shortest execution path after merging each constraint condition in turn, and combining multiple constraint conditions according to the shortest execution path to obtain a multi-layer anti-counterfeiting verification network.

[0016] The beneficial effects of the present invention are as follows: 1. After dividing the sub-images according to the connectivity of each position on the anti-counterfeiting label image, the present invention associates the image features on the sub-images with the physical layer and the logical layer to achieve multi-dimensional authentication, which can reduce the misjudgment rate in the anti-counterfeiting label recognition process and reduce the risk of the anti-counterfeiting label being imitated by a single feature. After that, the image features are layered to obtain the hierarchical probability and verification method of each layer of the image feature network to describe the relative situation of each layer at each position after the anti-counterfeiting label is divided, and at the same time, the verification methods are combined to facilitate the subsequent comprehensive processing of these verification methods for image processing.

[0017] 2. The present invention can ensure the consistency of image features at each divided level by verifying the parallel features and serial features in the image features, and through processing the logical verification relationship diagram, it can know the relative relationship and logical structure between multiple verification methods used at each level to adapt to the recognition of different label structures and improve the recognition accuracy of different forms of anti-counterfeiting labels.

[0018] 3. The present invention can generate optimal decision paths for different types of image features according to the different images input by the current anti-counterfeiting label by processing the optimal decision paths of image features at each higher level. The purpose of this path is to quickly identify multiple groups of anti-counterfeiting labels after subtracting some unimportant features to determine the verification status of the current anti-counterfeiting label, and then summarize all the identified situations to form a multi-layer anti-counterfeiting verification network to represent the current results identified for the anti-counterfeiting label image, so as to reduce the situation of recognition errors caused by single-mode verification and improve the speed of dynamic verification of anti-counterfeiting labels. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0020] Figure 1 It is a framework diagram of a multi-layer anti-counterfeiting label identification system.

[0021] Figure 2 It is a flow chart of a feature layering module of a multi-layer anti-counterfeiting label recognition system.

[0022] Figure 3 The invention is a flow chart of an anti-counterfeiting authentication module of a multi-layer anti-counterfeiting label recognition system.

[0023] Figure 4 The invention is a flow chart of a logic verification module of a multi-layer anti-counterfeiting label identification system.

[0024] Figure 5 It is a flow chart of a multi-layer anti-counterfeiting label identification method. DETAILED DESCRIPTION

[0025] The embodiments of the present invention are described in detail below. The embodiments described below are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. If no specific techniques or conditions are specified in the embodiments, the techniques or conditions described in the literature in the art or the product specifications are used.

[0026] See also Figure 1 A multi-layer anti-counterfeiting label identification system includes: an image acquisition module, a feature stratification module, an anti-counterfeiting authentication module, a logic verification module and an anti-counterfeiting evaluation module; wherein the output end of the image acquisition module is connected to the feature stratification module, the output end of the feature stratification module is connected to the anti-counterfeiting authentication module, the output end of the anti-counterfeiting authentication module is connected to the logic verification module, and the output end of the logic verification module is connected to the anti-counterfeiting evaluation module.

[0027] The image acquisition module is used to acquire the anti-counterfeiting label image and divide the anti-counterfeiting label image into multiple sub-images in sequence according to the connectivity of each position on the image.

[0028] The feature stratification module is used to perform feature stratification processing on the sub-images to obtain an image feature network; extract image features that are feature-associated with the physical layer and the logical layer in the image feature network, set a hierarchical division standard for the image features, and determine the hierarchical probability and verification method of the sub-image at each level.

[0029] The anti-counterfeiting authentication module is used to perform parallel verification and serial verification on the image features in turn according to the verification methods of the sub-images at each level, obtain logical verification data, and form a logical verification relationship diagram of the sub-images.

[0030] The logic verification module is used to obtain multiple candidate paths based on the data of the starting and ending positions of the logic verification relationship diagram, and to search for the optimal decision path of the logic verification relationship diagram at each level with the maximum path and the minimum path of the logic verification relationship diagram as the search interval.

[0031] The anti-counterfeiting evaluation module is used to combine and analyze the constraints with the optimal decision path of the logic verification relationship diagram as the constraint condition, determine the shortest execution path after the constraints are combined in turn, and combine multiple constraints according to the shortest execution path to obtain a multi-layer anti-counterfeiting verification network.

[0032] The physical layer includes but is not limited to the following: Anti-tear layer: special glue is used to reveal hidden text after peeling off to prevent label transfer. Optical variable layer: laser engraving and optically variable ink technology are used to present dynamic colors or patterns at different angles, such as laser rain patterns and laser effects. Material composite layer: embedded with security thread paper, anti-counterfeiting film or micro-nano structure to increase the difficulty of physical imitation. RFID / NFC chip: stores unique encrypted ID and supports contactless reading, such as mobile phone NFC verification. Dynamic QR code: uses "one object, one code" technology, and displays real-time verification results and traceability information after scanning the code.

[0033] The logic layer includes but is not limited to the following: AES / RSA encryption: Encrypt label data (such as production batch, serial number) to prevent data tampering. Dynamic encoding: Make label information unpredictable through variable encoding technology such as timestamp + random number. Frequency domain watermark: Embed the brand logo into the Fourier transform domain of the label image, which is invisible to the naked eye but can be extracted through algorithms. Fluorescent / thermosensitive ink: Display hidden information under specific lighting, such as ultraviolet light or temperature.

[0034] Different anti-counterfeiting labels will have different image features in their actual physical form to identify the content, and there will also be different logical connections between the physical and logical levels. For example, the part of the ink that changes color due to different viewing angles can be identified, and the color temperature and other contents of this color change can be associated with the physical level. The verification code, serial number, etc. can be used as the logical level to verify the anti-counterfeiting label. The anti-counterfeiting label can also be described according to the content that needs to be displayed by adding water in the anti-counterfeiting label as its logical level content, and other image features as the physical level. It can be clearly found here that the content set in the same position or similar position may indicate that the content represents the verification results of different levels. At this time, multi-level verification and identification are required to identify whether the current label is normal. At the same time, the behavioral data of different labels during verification and identification can be used to describe whether the current execution process is a corresponding counterfeiting method. As shown in Table 1, whether the current anti-counterfeiting label has related problems after multi-layer verification.

[0035] Table 1. Anti-counterfeiting label identification diagram

[0036]

[0037] Table 1 illustrates the intention and related situations of label recognition in some cases. These data will be sent to the data verification terminal when the user performs label recognition. The data verification terminal here will make a comprehensive judgment on the uploaded image to determine whether the label is authentic and whether there are corresponding problems. When setting up a multi-layer anti-counterfeiting verification network in the future, the verification results of the current anti-counterfeiting label image under the corresponding verification method will be mainly marked in the form of a network diagram to determine whether the current anti-counterfeiting label can appear when it is recognized. The situation related to the problem of the anti-counterfeiting label, and finally the external staff can check the network generated by the recognition of the anti-counterfeiting label image to find out whether there are corresponding problems in the currently recognized anti-counterfeiting label.

[0038] When dividing the anti-counterfeiting label image into multiple sub-images, the anti-counterfeiting label image is divided into multiple sub-images according to the image situation described at each position, and each sub-image contains a part of its anti-counterfeiting label image, such as a logo, a verification code, a pattern, etc.

[0039] At this time, the anti-counterfeiting label image can be located based on the YOLOv5 target detection model, and then the anti-counterfeiting label image can be grid-segmented to generate a coordinate system mapping table. The coordinate system mapping table contains the positions of multiple sub-images, such as sub-images corresponding to the positioning identification area, encrypted data area, and other positions.

[0040] The implementation method of the image acquisition module includes: inputting the anti-counterfeiting label image into the image segmentation model, obtaining the regional prediction map corresponding to the anti-counterfeiting label area, and judging whether there is a connected area in each regional prediction map according to the position of each regional prediction map. If there is a connected area, each connected area is regarded as a sub-image according to the position of the connected area, and the regional prediction maps except the connected area are used as additional output sub-images; if there is no connected area, each regional prediction map is directly output as a sub-image.

[0041] The anti-counterfeiting label area describes the anti-counterfeiting type represented by the area, such as the location of the positioning identification area, the encrypted data area, etc., or describes the logo, verification code, pattern and other contents at the location, so as to divide the anti-counterfeiting label image into multiple sub-images of different styles. When these sub-images are identified, they will contain multiple binding identification features according to the different displayed contents.

[0042] Connected regions can be an important feature in security label design, as checking the presence, shape, size, and other properties of these regions can help verify the authenticity of a product. For example, a genuine security label may contain complex patterns or structures that are difficult to replicate, and these features can be identified by detecting connected regions. Connected regions can also be used to assess whether a security label is intact. If an area that should be connected is broken or missing, it may indicate that the label has been tampered with or is not original.

[0043] The connected areas in anti-counterfeiting labels usually correspond to key anti-counterfeiting features, and their contents can be divided into the following categories: microstructure, structural texture, logical anti-counterfeiting features, positioning identification area, holographic pattern area and fluorescent ink area.

[0044] After identifying the corresponding situations, these connected areas will be divided into multiple sub-images, and each image will be verified separately according to the verification method required by these images to determine the relative situation of the current anti-counterfeiting label.

[0045] Microstructures are generally represented by microtext / patterns, which are usually tiny texts that are invisible to the naked eye, such as 0.1mm characters, and require a microscope or high magnification to be identified; these features will be captured by the corresponding camera device to collect the microstructure existing on the current anti-counterfeiting label, and determine whether it is a legal microstructure based on the shape and edge sharpness of the connected area.

[0046] Structural texture is represented by a complex texture formed by randomly distributed fibers, light-variable particles, etc. The texture characteristics of the current structure are identified by analyzing the shape complexity and distribution density of the connected area.

[0047] Logical anti-counterfeiting features are generally encrypted data areas, which contain connected areas of encrypted QR codes or barcodes and store the product's unique ID or hash value. By verifying the digital signature after decoding, such as the SM2 / SM3 national secret algorithm, the corresponding logical anti-counterfeiting features can be obtained.

[0048] The positioning identification area contains special geometric figures, such as concentric circles and crosshairs, which are used for coordinate system alignment. The content of the identification area is determined by verifying the consistency of the geometric properties of the connected area, such as the coordinates of the center of the circle and the angle of the line segment.

[0049] The holographic pattern area contains dynamic light-changing patterns, such as different texts displayed at different viewing angles; this part analyzes the color change patterns of connected areas when shooting from multiple angles; and uses multiple images to determine the features acquired by the sub-image.

[0050] The fluorescent ink area represents the hidden pattern developed under ultraviolet light, which mainly detects the brightness distribution of the connected area under a light source of a specific wavelength; this part can also be represented by the color temperature difference under characteristic light, which ultimately indicates whether the sub-image of this part is displayed normally.

[0051] Then, the output sub-images are layered to determine the image features of each sub-image for verification, and the recognition of the anti-counterfeiting label is explained based on the differences between each image feature and the preset image.

[0052] In one embodiment of the present invention, Figure 2As shown, the implementation method of the feature hierarchical module includes: extracting each image factor from the sub-image, determining the label structure corresponding to each image factor in turn according to the distribution of each image factor, and forming a mapping relationship between each image factor according to the label structure.

[0053] The mapping relationship of each image factor is associated with the physical layer feature and the logical layer feature, and the image feature corresponding to each image factor is selected.

[0054] Image feature networks are constructed using image features to determine the hierarchical probabilities of image features at each level under the mapping relationship of the label structure, and based on the probabilities of each level, hierarchical division criteria for image features are set.

[0055] When the image feature is used to construct an image feature network, the implementation method also includes: using the image factor type of each image feature as a standard, using each image feature as a node, using the Pearson correlation coefficient between each image feature as a weight, connecting each image feature to obtain an image feature network, calculating the conditional probability distribution of image features at each level of the image feature network, setting the probability threshold of each level according to the conditional probability distribution of image features at each level, and obtaining the hierarchical structure of the current image feature division. The conditional probability here is the above-mentioned hierarchical probability, and the image feature network finally divided will be expressed as a binary tree number, etc., multiple levels divided by the probability threshold of each level as an interval, and the details of the conditional probability described at each level, such as the conditional probability of the physical layer recognition being effective and the high-frequency component accounting for a large proportion, which will indicate the probability value of the current anti-counterfeiting label under different recognition conditions, and whether the recognition method is effective; the Pearson correlation coefficient between image features will be calculated using the extracted image features after preprocessing to eliminate the dimension data to determine whether there is a correlation between multiple image features.

[0056] Each image factor described here represents the distribution position of the corresponding connected area in the sub-image, and then the image corresponding to each image factor is labeled. After describing the content corresponding to these images, if a certain image factor has a clear corresponding relationship with the label structure, the mapping relationship is regarded as a direct mapping, that is, a certain factor corresponds to a specific pattern in the anti-counterfeiting label and the corresponding feature can be directly extracted. For example, if the image factor represents the content of the positioning mark area, this content can be directly recognized by a single image, then there is a direct mapping relationship. If the image factor represents the content on the holographic pattern area, when multiple image factors need to be used for comprehensive consideration, then it needs to be set as an indirect mapping relationship; then these mapping relationships are associated with the required physical layer features and logical layer features to obtain image features at all levels. For example, the form of Table 2 can be used to classify the image factors and identify the various sub-images existing in the current anti-counterfeiting label image.

[0057] Table 2. Image factor definitions

[0058]

[0059] Table 2 describes some of the image factor types that can be described. These image factors will be identified using corresponding technologies to determine how many sub-images of different forms each anti-counterfeiting label should be divided into.

[0060] For example, after the mapping relationship is established, if three approximately circular contours are detected, they are mapped to the positioning area; if a square modular distribution is detected, it is mapped to the QR code in the data area; if the high-frequency component ratio is >25%, it is mapped to the micro-text area. For other recognized contents, they will be mapped according to the type corresponding to their image factors to determine the mapping relationship of each image factor, as shown in Tables 3 and 4. When there are images related to micro-text and fluorescent fibers, their association methods can be described in the following table.

[0061] Table 3. Physical layer feature associations

[0062]

[0063] Table 4. Logical layer feature associations

[0064]

[0065] Tables 3 and 4 mainly explain how the image factors can be associated, and then what features and contents of the associated contents need to be verified, and finally form a hierarchical network, and then calculate the conditional probability of each level to obtain the corresponding hierarchical probability. As shown in Table 5, when verifying the image features of each level, the corresponding image factors will be combined, and after calculating the relative hierarchical probability, the value will be compared to see if it is greater than the probability threshold, so as to divide multiple identification levels to determine which method to verify the authenticity of the anti-counterfeiting label.

[0066] Table 5. Level classification criteria

[0067]

[0068] Under the hierarchical division corresponding to Table 5, if the L1 level detects that the coding factor confidence is 0.7 → triggering L1 verification, the texture factor energy value is 0.28 (within [0.12, 0.40]), and it is found that the high frequency accounts for 24%; the conditional probability of the texture factor and the spectrum factor in the L2 level rises to 0.88, and the conditional probability is greater than 0.85, and the microscopic verification is started. Finally, the combined conditional probability calculated in L3 is 0.93, which is greater than the probability of entering the L3 level; the verification actions used in the corresponding level are then regarded as the verification methods of the subsequent sub-images at each level.

[0069] That is, the implementation method of determining the verification method of the sub-image at each level also includes: extracting the label structure of the image features at each level, based on the label structure, using the image factor type described by the image features to perform a joint search, comparing the search results with the image features at each level, and selecting the largest data set after comparison as the verification method of the sub-image at each level.

[0070] Here, the image features at each level are verified twice to determine whether the retrieved data is the same as the current image features. If they are the same, the retrieved verification method is used as the verification method for the sub-image in the current anti-counterfeiting label. The label structure will indicate the areas where the image features are located at each level, such as the positioning area, data area, background area, micro-text area, etc., to indicate the relative description of the image features.

[0071] In one embodiment of the present invention, Figure 3 As shown, the implementation method of the anti-counterfeiting authentication module also includes: applying a proportional factor to the hierarchical probability of each image feature to obtain the adjusted probability threshold of each level of the image feature, and setting the hierarchical relative position information of each image feature according to the feature area corresponding to the adjusted probability threshold; the hierarchical relative position information represents the area containing the corresponding image feature in the current sub-image divided by the adjusted probability threshold, and then records the center point coordinates of this feature area, the distance and direction between the area and other areas, and the dependency relationship between the image feature and other image features, and uses these contents as the hierarchical relative position information of each image feature.

[0072] For example, the above-mentioned proportional factors will set different weights according to the different levels of each image feature. For example, the probability thresholds on the L1, L2 and L3 layers mentioned above are used for schematic illustration. At this time, the weights of each level can be adjusted, such as L1 probability 0.70, after adjustment: 0.70×1.2=0.84; L2 probability 0.85, after adjustment: 0.85×1.0=0.85; L3 probability 0.90, after adjustment: 0.90×0.8=0.72; At this time, it is explained that the currently used probability threshold will be adjusted according to each image feature under normal recognition conditions. At this time, the first layer threshold is increased and the third layer threshold is reduced to determine whether the divided image features will change after the current method is adjusted, and these changed information will be used as the main content of subsequent processing.

[0073] Using the hierarchical relative position information of each image feature, the image features are verified in parallel and serially in turn to determine the independent features and dependent features in the image features, and the verification results of the independent features and dependent features are regarded as logical verification data.

[0074] Parallel features mainly verify whether image features are independent features, while serial features verify whether image features are dependent features; independent features mean that the recognition result of a single image can represent the recognition result of the current anti-counterfeiting label in a certain aspect. When there is a problem with this recognition, it can be directly considered that there is a problem with the anti-counterfeiting label, or that the anti-counterfeiting label is fake; dependent features mean that it is necessary to combine and comprehensively judge the corresponding contents of multiple image features to determine the problem with the current anti-counterfeiting label, or to use this to determine whether the anti-counterfeiting label is authentic.

[0075] Simultaneous parallel processing utilizes multi-threaded or distributed computing resources to verify each independent feature at the same time, which can greatly improve efficiency, especially when processing high-resolution images; if any independent feature fails to pass the verification, the anti-counterfeiting label is immediately marked as suspicious.

[0076] Serial processing tends to verify in sequence, and the next step is performed only when each dependent feature is successful. Only when all dependent features are verified can the anti-counterfeiting label be preliminarily considered to be authentic; if there is any discrepancy in the middle, further investigation is required. By dividing the anti-counterfeiting labels into these two forms, the image data input by the current anti-counterfeiting label can be adjusted to achieve the common processing of multiple different types of anti-counterfeiting labels as much as possible.

[0077] Subsequently, the implementation methods of performing parallel verification and serial verification on the image features in sequence include: verifying the fields of each node in the image feature network, determining the basic verification data, and configuring the basic verification data with an asynchronous message queue mechanism; the field verification is to describe whether the text description used for the verification method and the image features is normal, and the content of this part of the text description is executed using an asynchronous message queue mechanism to determine whether the verification of the current anti-counterfeiting label is in the synchronous processing scope or the asynchronous processing scope.

[0078] If the asynchronous message queue mechanism is configured, and the node in the current image feature network points to the next node, the image feature corresponding to the current node is regarded as a dependent feature, otherwise it is regarded as an independent feature; at this time, it is determined whether the current asynchronously executed node in the divided image feature network depends on the execution results of other nodes. If it is dependent, it means that sequential inspection is required, otherwise it can be implemented asynchronously.

[0079] After the cross-execution of the independent features and dependent features, the verification methods of each level are compared to see whether they are consistent with the independent features and dependent features at the intersection position. If they are consistent, the data corresponding to the image features are output as logical verification data.

[0080] If they are inconsistent, the hierarchical relative position information of the independent features and the dependent features is recorded, and the independent features and the dependent features are mapped to each other according to the positions recorded in the hierarchical relative position information, and output as logic verification data.

[0081] At this time, it is required that after the execution of independent features and dependent features, the results of independent verification and dependent verification at the intersection position can be consistent. If they are consistent, the verified data will be output. When they are inconsistent, these data will be marked in a mutual mapping manner, and then output as logic verification data. After that, these output data will be combined into a logic verification relationship diagram. The logic verification relationship diagram mainly records these relatively independent and interdependent image features, and connects the logical relationships between these features.

[0082] According to the cross-execution order of independent features and dependent features in the logic verification data, the nodes of the image feature network corresponding to the independent features and the dependent features are connected and combined into a logic verification relationship diagram of the sub-image.

[0083] At this time, the connection is mainly based on the features contained in the logical verification data, and then the data corresponding to the two features are connected according to the cross-execution order of these features under the asynchronous mechanism to obtain a logic diagram about the current execution order. This logic diagram is then used to complete the processing of subsequent decision paths.

[0084] In one embodiment of the present invention, a search mechanism is mainly introduced in the logic verification module to determine the relative dependencies and possible paths between the verification methods of each image feature in the logic verification relationship diagram; for example, depth-first search (DFS) or breadth-first search (BFS) is used to traverse to obtain the paths processed by these verification methods and image features.

[0085] Then, the verification status of each node in the logical verification relationship network is determined, such as the verification status of passing, failing, or feasible or infeasible, and the confidence score or other indicators of each node are added to calculate a comprehensive weight for each path; thereby obtaining a path with the highest weight and a path with the lowest weight, which represent the maximum path and the minimum path. Then, a search interval is obtained according to the weights of the maximum path and the minimum path, and an optimal decision path is found for the paths existing in these intervals.

[0086] like Figure 4 As shown, the implementation method of the logic verification module includes: identifying image features of each path node on the candidate path, and determining that the image features include at least one of a shape factor, a texture factor, a spectrum factor, and a coding factor.

[0087] Based on the types of image features contained in each path node, the maximum path and the minimum path containing different numbers of image features in the candidate path are identified; at this time, what is identified is the processing method of the candidate path when different image features need to be identified, and the paths with the largest weights and the smallest weights contained in these processing methods. These paths will then obtain multiple maximum paths and minimum paths according to the different types of image features contained.

[0088] The number of image feature types corresponding to the maximum weight value in the maximum path and the minimum path is used as the search interval, and each candidate path is retrieved, and the shortest path connecting the path nodes in each candidate path is extracted to obtain the optimal decision path. At this time, the shortest path algorithm is used for each path node in the candidate path in the search interval to obtain a shortest path from the starting point to other points, and the optimal decision path to be output is obtained in turn.

[0089] For example, at this time, the four factors corresponding to the image features are used to identify the optimal decision path in a group of candidate paths, among which candidate path 1 has the largest weight and candidate path 2 has the smallest weight. At this time, the number of image feature types for candidate path 1 is 3, and the number of image feature types for candidate path 2 is 2. At this time, the main search is for candidate paths containing 2 to 3 types of image features, and then the shortest path at this time is found. This usage method will retain as many features as possible, and can retain the complete topological structure. It can retain key features while avoiding redundant features; at the same time, it can reduce the problem of anti-counterfeiting label recognition errors caused by missing features, and ultimately improve the overall recognition efficiency and speed.

[0090] In one embodiment of the present invention, when constraints are merged, the contents corresponding to the optimal decision paths are merged, and the merged contents are compared to see whether the anti-counterfeiting label can be verified after the verification method is combined. If it can be verified, the shortest execution path of each constraint after the merger is output. The shortest execution path output at this time represents the minimum data nodes required to implement the corresponding verification method or image feature processing after satisfying the data represented by each constraint, and is a node that can complete the verification of the method. These shortest execution paths are then combined using constraints to obtain a multi-layer anti-counterfeiting verification network that can meet the current anti-counterfeiting label recognition.

[0091] At the same time, when generating a logical verification relationship diagram, if multiple different anti-counterfeiting label images are input, then multiple logical verification relationship diagrams will be generated, and there will be multiple sets of data for the optimal decision path. Merging these data can reduce the processing efficiency under the automatic identification processing of multiple anti-counterfeiting labels, and reduce the problem of identification errors caused by damage to some labels.

[0092] Therefore, the implementation method of the anti-counterfeiting evaluation module includes: using the logical relationship of each constraint condition to group, constructing a constraint relationship matrix, calculating the similarity of each element in the constraint relationship matrix, and merging elements greater than the similarity threshold; at this time, the constraint relationship matrix will contain the path nodes in the optimal decision path, and each element is represented as a path node of the optimal decision path. After that, the node is grouped, and then these nodes are compared for similarity to complete the merger. When calculating the similarity, the Pearson correlation coefficient is used to calculate, so that the path nodes with a similarity greater than 0.7 are merged to realize the merger of each element in the constraint relationship matrix.

[0093] The elements in the merged constraint relationship matrix are connected, and the path length and verification cost of the constraint conditions corresponding to each element are used to perform path retrieval to obtain the shortest execution path. The constraints are summarized using the shortest execution path to obtain a multi-layer anti-counterfeiting verification network.

[0094] When performing path retrieval, the goal is to combine multiple constraints to find a path that is convenient for implementation. These paths represent multiple optimal decision paths that summarize the decision content after making decisions based on different label images. Then, multiple decision paths can be combined based on this path to understand the processing process under multi-type label recognition.

[0095] When connecting the elements in the merged constraint relationship matrix, the path length in the constraint condition corresponding to the element is used as the weight of the element, and the verification cost of the two connected elements is used as the weight of the edge. This path length will represent the number of nodes in the optimal decision path represented by the constraint condition, and the verification cost will use the time required to complete the recognition and processing of the image features corresponding to the current element as the verification cost. Then, the shortest path that these elements can connect is obtained, and this path is regarded as the shortest execution path output. Finally, the corresponding elements on the shortest execution path are used to combine multiple optimal decision paths to complete the multi-layer anti-counterfeiting verification network for the current anti-counterfeiting label recognition. The multi-layer anti-counterfeiting verification network will represent the models required for the recognition of multiple anti-counterfeiting labels to improve the integration processing of various anti-counterfeiting labels when dividing sub-images of anti-counterfeiting labels or inputting various forms of anti-counterfeiting labels.

[0096] like Figure 5 As shown, the present invention also provides a multi-layer anti-counterfeiting label recognition method, including: S1, inputting the anti-counterfeiting label image into the image segmentation model, generating a region prediction map, and judging whether there are connected regions in each region, and dividing the anti-counterfeiting label image to obtain sub-images corresponding to the anti-counterfeiting label image.

[0097] S2, extracting each image factor from the sub-image, and according to the distribution of each image factor, determining the label structure corresponding to the image factor, and forming the mapping relationship of each image factor; setting the image feature network by associating the mapping relationship of the image factor with the physical layer features and the logical layer features.

[0098] S3, calculates the probability of each level in the image feature network, determines the verification method of each level, and forms a logical verification relationship diagram of the sub-image.

[0099] S4, based on the data of the starting point and end point positions of the logic verification relationship graph, multiple candidate paths are obtained, and the maximum path and the minimum path of the logic verification relationship graph are used as the search interval to search for the optimal decision path of the logic verification relationship graph at each level.

[0100] S5, taking the optimal decision path of the logic verification relationship diagram as the constraint condition, merging and analyzing each constraint condition, determining the shortest execution path after merging each constraint condition in turn, and combining multiple constraint conditions according to the shortest execution path to obtain a multi-layer anti-counterfeiting verification network.

[0101] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations on the present invention. A person skilled in the art may make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention and they are still covered by the protection scope of the present invention.

Claims

1. A multi-layer anti-counterfeiting label identification system, characterized in that: include: An image acquisition module is used to acquire an anti-counterfeiting label image and divide the anti-counterfeiting label image into a plurality of sub-images in sequence according to the connectivity of each position on the image; The feature stratification module is used to perform feature stratification processing on the sub-images to obtain an image feature network; extract image features that are feature-associated with the physical layer and the logical layer in the image feature network, set a hierarchical division standard for the image features, and determine the hierarchical probability and verification method of the sub-images at each level; The anti-counterfeiting authentication module is used to perform parallel verification and serial verification on the image features in turn according to the verification methods of the sub-images at each level, obtain logical verification data, and form a logical verification relationship diagram of the sub-images; A logic verification module is used to obtain multiple candidate paths based on the data of the starting point and end point positions of the logic verification relationship graph, and to search for the optimal decision path at each level of the logic verification relationship graph using the maximum path and the minimum path of the logic verification relationship graph as the search interval; The anti-counterfeiting evaluation module is used to combine and analyze the constraints with the optimal decision path of the logic verification relationship diagram as the constraint condition, determine the shortest execution path after the constraints are combined in turn, and combine multiple constraints according to the shortest execution path to obtain a multi-layer anti-counterfeiting verification network.

2. A multi-layer anti-counterfeiting label identification system according to claim 1, characterized in that: The implementation methods of the image acquisition module include: The anti-counterfeiting label image is input into the image segmentation model to obtain the regional prediction map corresponding to the anti-counterfeiting label area. According to the position of each regional prediction map, it is determined whether there is a connected area in each regional prediction map. If there is a connected area, each connected area is regarded as a sub-image according to the position of the connected area, and the regional prediction maps except the connected area are used as additional output sub-images; if there is no connected area, each regional prediction map is directly output as a sub-image.

3. A multi-layer anti-counterfeiting label identification system according to claim 1, characterized in that: The implementation methods of the feature layering module include: Extract each image factor from the sub-image, determine the label structure corresponding to each image factor in turn according to the distribution of each image factor, and form a mapping relationship of each image factor according to the label structure; Associating the mapping relationship of each image factor with the physical layer feature and the logical layer feature, and selecting the image feature corresponding to each image factor; Image feature networks are constructed using image features to determine the hierarchical probabilities of image features at each level under the mapping relationship of the label structure, and based on the probabilities of each level, hierarchical division criteria for image features are set.

4. A multi-layer anti-counterfeiting label identification system according to claim 3, characterized in that: When the image features are used to construct the image feature network, the implementation method also includes: Taking the image factor type of each image feature as the standard, each image feature is taken as a node, and the Pearson correlation coefficient between each image feature is used as the weight, each image feature is connected to obtain an image feature network, and the conditional probability distribution of image features at each level of the image feature network is calculated. According to the conditional probability distribution of image features at each level, the probability threshold of each level is set to obtain the hierarchical structure of the current image feature division.

5. A multi-layer anti-counterfeiting label identification system according to claim 3, characterized in that: The implementation method of determining the verification method of the sub-image at each level also includes: The label structure of image features at each level is extracted. Based on the label structure, the image factor type described by the image features is used for joint retrieval. The retrieval results are compared with the image features at each level. The largest data set after comparison is selected as the verification method for sub-images at each level.

6. A multi-layer anti-counterfeiting label identification system according to claim 1, characterized in that: The implementation of the anti-counterfeiting authentication module also includes: Applying a proportional factor to the hierarchical probability of each image feature to obtain an adjusted probability threshold of each level of the image feature, and setting the hierarchical relative position information of each image feature according to the feature area corresponding to the adjusted probability threshold; Using the hierarchical relative position information of each image feature, the image features are sequentially verified in parallel and serially, the independent features and dependent features existing in the image features are determined, and the verification results of the independent features and dependent features are regarded as logical verification data; According to the cross-execution order of independent features and dependent features in the logic verification data, the nodes of the image feature network corresponding to the independent features and the dependent features are connected and combined into a logic verification relationship diagram of the sub-image.

7. A multi-layer anti-counterfeiting label identification system according to claim 6, characterized in that: The implementation methods of performing parallel verification and serial verification on image features in sequence include: Verify the fields of each node in the image feature network, determine the basic verification data, and configure the asynchronous message queue mechanism for the basic verification data; If the asynchronous message queue mechanism is configured, and the node in the current image feature network points to the next node, the image feature corresponding to the current node is regarded as a dependent feature, otherwise it is regarded as an independent feature; After the cross-execution of independent features and dependent features, the verification methods of each level are compared to see whether they are consistent with the independent features and dependent features at the intersection position. If they are consistent, the data corresponding to the image features are output as logical verification data; If they are inconsistent, the hierarchical relative position information of the independent features and the dependent features is recorded, and the independent features and the dependent features are mapped to each other according to the positions recorded in the hierarchical relative position information, and output as logic verification data.

8. The multi-layer anti-counterfeiting label identification system according to claim 1, characterized in that: The implementation of the logic verification module includes: Identify image features of each path node on the candidate path, and determine that the image features include at least one of a shape factor, a texture factor, a spectrum factor, and a coding factor; Based on the types of image features contained in each path node, identify the maximum and minimum paths containing different types of image features in the candidate paths; The number of image feature types corresponding to the maximum weight value in the maximum path and the minimum path is used as the search interval, each candidate path is retrieved, and the shortest path connecting the path nodes in each candidate path is extracted to obtain the optimal decision path.

9. A multi-layer anti-counterfeiting label identification system according to claim 1, characterized in that: The implementation methods of the anti-counterfeiting assessment module include: Use the logical relationship of each constraint condition to group, construct a constraint relationship matrix, calculate the similarity of each element in the constraint relationship matrix, and merge the elements that are greater than the similarity threshold; The elements in the merged constraint relationship matrix are connected, and the path length and verification cost of the constraint conditions corresponding to each element are used to perform path retrieval to obtain the shortest execution path. The constraints are summarized using the shortest execution path to obtain a multi-layer anti-counterfeiting verification network.

10. A multi-layer anti-counterfeiting label identification method, characterized in that: include: S1, input the anti-counterfeiting label image into the image segmentation model, generate a region prediction map, and determine whether there are connected regions in each region, and divide it to obtain sub-images corresponding to the anti-counterfeiting label image; S2, extracting each image factor from the sub-image, and determining the label structure corresponding to the image factor according to the distribution of each image factor, and forming a mapping relationship between each image factor; setting an image feature network based on the association between the mapping relationship of the image factor and the physical layer features and the logical layer features; S3, calculate the probability of each level in the image feature network, determine the verification method of each level, and form a logical verification relationship diagram of the sub-image; S4, obtaining multiple candidate paths based on the data of the starting point and the end point of the logic verification relationship graph, using the maximum path and the minimum path of the logic verification relationship graph as the search interval, and searching for the optimal decision path of the logic verification relationship graph at each level; S5, taking the optimal decision path of the logic verification relationship diagram as the constraint condition, merging and analyzing each constraint condition, determining the shortest execution path after merging each constraint condition in turn, and combining multiple constraint conditions according to the shortest execution path to obtain a multi-layer anti-counterfeiting verification network.

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