Automatic identification and tracking method and system for lottery bundle

Abstract

The invention provides an automatic identification and tracking method and system for a lottery ticket bundle, and relates to the technical field of lottery ticket bundle identification and tracking. Multi-angle data of the lottery ticket bundle are acquired, three-dimensional contour data of a plastic package belt are synchronously acquired, and the two data are fused to generate a data set containing visual and spatial morphological characteristics; extracting the shape contour features of the edge area of the plastic packaging belt to reconstruct a three-dimensional edge path, and extracting the surface texture of the contact area of the plastic packaging belt and the lottery ticket bundle; judging the structural integrity of the plastic packaging tape according to the continuity and smoothness of the three-dimensional edge path, and evaluating the fitting tightness degree by combining the surface texture distribution uniformity of the contact area; the integrity and fitting degree result is compared with a reference model representing the standard state of the plastic packaging tape, the state of the lottery ticket bundle plastic packaging tape is marked, and finally lottery ticket bundle information with state marks and tracking codes are bound and written into a quality tracking database, so that precise recognition and information traceable management of the state of the lottery ticket bundle plastic packaging tape can be realized.

Description

An automatic identification and tracking method and system for lottery bundles Technical Field

[0001] This application relates to the field of lottery bundle identification and tracking technology, and in particular to an automatic identification and tracking method and system for lottery bundles. Background Technology

[0002] Automatic identification and tracking of lottery bundles is a key aspect of digital management in the lottery industry, enabling standardized control over the entire lottery process from issuance to sales. As the lottery industry expands and regulatory requirements increase, this technology has broad application prospects in ensuring the security of lottery circulation and improving management efficiency.

[0003] Currently, the most common automatic lottery ticket bundle identification and tracking technology is based on two-dimensional image recognition. This involves collecting a planar image of the lottery ticket bundle's surface, extracting visual information such as text and barcodes, and then associating the identification results with tracking codes to achieve tracking.

[0004] However, such recognition schemes based on two-dimensional images can only acquire planar visual information, making it difficult to accurately determine the structural integrity and adhesion status of lottery ticket bundles' plastic seals. This can easily lead to the lottery ticket bundles being swapped or damaged during circulation due to issues such as damaged seals or loose adhesion, affecting the reliability of tracking and management. Therefore, existing technologies suffer from insufficient security and reliability in lottery ticket bundle recognition and tracking. Summary of the Invention

[0005] The purpose of this application is to provide an automatic identification and tracking method and system for lottery bundles, so as to solve the problems of insufficient security and reliability in the existing lottery bundle identification and tracking technology.

[0006] To address the aforementioned technical problems, in a first aspect, this application provides an automatic identification and tracking method for lottery ticket bundles, comprising: acquiring multi-angle data of the surface of the lottery ticket bundle and simultaneously collecting three-dimensional contour data of the plastic wrap covering the lottery ticket bundle; fusing the multi-angle data with the three-dimensional contour data to construct a fused dataset, the fused dataset including the appearance features and spatial morphological features of the plastic wrap; extracting the morphological contour features corresponding to the edge region of the plastic wrap from the fused data, and reconstructing the three-dimensional edge path of the plastic wrap based on the morphological contour features, while simultaneously extracting the surface texture of the contact area between the plastic wrap and the lottery ticket bundle from the appearance features; and according to the... The continuity and smoothness of the three-dimensional edge path are used to determine the structural integrity of the plastic sealant; and the uniformity of the surface texture distribution in the contact area is combined to evaluate the tightness of the fit between the plastic sealant and the lottery ticket bundle; the structural integrity judgment result and the tightness of fit evaluation result of the plastic sealant are matched and compared with a pre-stored benchmark model; the benchmark model represents the set of appearance features and spatial morphological features corresponding to the standard state of the plastic sealant being intact and tightly fitted with the lottery ticket bundle; the plastic sealant state of the lottery ticket bundle is marked according to the matching and comparison results, and the lottery ticket bundle information with the state mark is bound with the corresponding tracking code and written into the quality tracking database of the lottery system.

[0007] Optionally, the multi-angle data and the three-dimensional contour data are fused to construct a fused dataset. The fused dataset includes the appearance features and spatial morphological features of the laminating tape, including: performing pixel-level correlation on the multi-angle data to establish a mapping relationship between each acquisition point and the corresponding spatial point in the three-dimensional contour data, generating an image contour mapping table; based on the image contour mapping table, superimposing the color and brightness information of the multi-angle data with the distance and height information of the three-dimensional contour data to form a superimposed data layer; separating the region corresponding to the laminating tape from the superimposed data layer, calculating the appearance attribute value and morphological attribute value of each point in the corresponding region, generating an initial feature set; dividing the initial feature set into regions, dividing the main region and edge region of the laminating tape, and calculating the feature statistics of each region, the feature statistics including the mean and variance; and integrating the appearance attribute value and morphological attribute value according to the feature statistics to construct a fused dataset, the fused dataset storing the appearance features and spatial morphological features of the laminating tape in a structured format.

[0008] Optionally, the initial feature set is divided into regions to define the main body region and edge region of the sealing tape, and feature statistics for each region are calculated. The feature statistics include the mean and variance, including: calculating the difference in morphological values ​​between each point and its neighboring points based on the morphological attribute values ​​of each point in the initial feature set, generating a local morphological change sequence; setting a morphological change threshold based on the local morphological change sequence, marking points with morphological changes exceeding the threshold as candidate edge points, and marking the remaining points as candidate main body points; performing connectivity analysis on the candidate edge points, merging the sets of interconnected points into continuous edge regions, and simultaneously performing connectivity analysis on the candidate main points, merging the sets of interconnected points into continuous main body regions; for the main body region and the edge region, calculating the arithmetic mean and variance of the appearance attribute values ​​and the arithmetic mean and variance of the morphological attribute values ​​of all data points in each region, respectively, to obtain two sets of feature statistics corresponding to each region.

[0009] Optionally, the morphological contour features corresponding to the edge region of the plastic wrapping tape are extracted from the fused data, and the three-dimensional edge path of the plastic wrapping tape is reconstructed based on the morphological contour features. Simultaneously, the surface texture of the contact area between the plastic wrapping tape and the lottery ticket bundle is extracted from the appearance features. This includes: scanning the edge region of the plastic wrapping tape in the fused dataset, identifying morphological change points within the edge region, and recording the three-dimensional coordinates and morphological values ​​of each morphological change point; connecting the morphological change points based on the three-dimensional coordinates to form a preliminary edge line, and calculating the morphological difference and angle difference between adjacent points on the preliminary edge line based on the morphological values; selecting a point sequence with high continuity based on the morphological difference and angle difference, fitting the point sequence into a smooth curve to generate the three-dimensional edge path of the plastic wrapping tape; locating the contact area between the plastic wrapping tape and the lottery ticket bundle in the appearance features, extracting a data block of the contact area, analyzing the pixel arrangement pattern and color changes in the data block, and extracting texture direction and texture density parameters; combining the texture direction and texture density parameters to form a surface texture descriptor.

[0010] Optionally, the structural integrity of the sealing tape is determined based on the continuity and smoothness of the three-dimensional edge path; and the adhesion between the sealing tape and the lottery ticket bundle is evaluated in conjunction with the uniformity of the surface texture distribution in the contact area. This includes: evaluating the continuity of the path by calculating the sum of the lengths of all line segments on the three-dimensional edge path and comparing it with a preset length threshold; evaluating the smoothness of the path by measuring the curvature change at each corner of the three-dimensional edge path and counting the number of curvature abrupt change points; generating a structural integrity score based on the evaluation results of continuity and smoothness; dividing the contact area into multiple sub-regions, calculating the deviation of the texture density value of each sub-region from the overall average value, counting the proportion of sub-regions with deviations exceeding the allowable range, and evaluating the distribution uniformity based on the proportion; and generating an adhesion tightness score based on the evaluation results of distribution uniformity.

[0011] Optionally, the structural integrity assessment result and the adhesion tightness evaluation result of the plastic sealing tape are compared with a pre-stored benchmark model. The benchmark model represents the set of appearance and spatial morphological features corresponding to the standard state of the plastic sealing tape being intact and tightly adhered to the lottery ticket bundle. This includes: reading the pre-stored benchmark model, which contains a standard structural integrity threshold and a standard adhesion tightness threshold; calculating the difference between the structural integrity score and the standard structural integrity threshold to obtain the integrity matching degree; calculating the ratio between the adhesion tightness score and the standard adhesion tightness threshold to obtain the adhesion matching degree; weighting the integrity matching degree and the adhesion matching degree to generate an overall matching score; and determining the matching comparison result based on the overall matching score. The matching comparison result represents the degree of closeness between the state of the plastic sealing tape and the standard state.

[0012] Optionally, the status of the plastic seal of the lottery bundle is marked according to the matching comparison result, and the lottery bundle information with the status mark is bound to the corresponding tracking code and written into the quality tracking database of the lottery system. This includes: setting a status mark value for the plastic seal of the lottery bundle according to the matching comparison result, wherein the status mark value includes normal, suspicious or abnormal categories; integrating the status mark value with the collection time and location information of the lottery bundle to form a lottery bundle information package; generating a unique tracking code by combining the production batch code, collection timestamp and serial number of the lottery bundle; associating and binding the lottery bundle information package with the unique tracking code to form a data record; and writing the data record into a designated data table of the lottery system quality tracking database in chronological order.

[0013] Secondly, this application provides an automatic identification and tracking system for lottery ticket bundles, comprising: a fusion module, used to acquire multi-angle data of the surface of the lottery ticket bundle and simultaneously collect three-dimensional contour data of the plastic wrap covering the lottery ticket bundle; to fuse the multi-angle data with the three-dimensional contour data to construct a fusion dataset, the fusion dataset including the appearance features and spatial morphological features of the plastic wrap; a reconstruction module, used to extract the morphological contour features corresponding to the edge region of the plastic wrap from the fusion data, and reconstruct the three-dimensional edge path of the plastic wrap based on the morphological contour features, while extracting the surface texture of the contact area between the plastic wrap and the lottery ticket bundle from the appearance features; and an evaluation module, used to evaluate the... The continuity and smoothness of the three-dimensional edge path are used to determine the structural integrity of the plastic sealant; and the uniformity of the surface texture distribution in the contact area is used to evaluate the tightness of the fit between the plastic sealant and the lottery ticket bundle; a matching module is used to match and compare the structural integrity judgment result and the tightness of fit evaluation result of the plastic sealant with a pre-stored benchmark model; the benchmark model represents the set of appearance and spatial morphological features corresponding to the standard state of the plastic sealant being intact and tightly fitted with the lottery ticket bundle; a binding module is used to mark the state of the plastic sealant of the lottery ticket bundle according to the matching comparison result, and bind the lottery ticket bundle information with the state mark with the corresponding tracking code and write it into the quality tracking database of the lottery system.

[0014] Thirdly, this application provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of the automatic identification and tracking method for lottery bundles as described in the first aspect above.

[0015] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, can implement the steps of the automatic identification and tracking method for lottery bundles as described in the first aspect above.

[0016] The automatic identification and tracking method for lottery ticket bundles provided in this application acquires multi-angle data of the lottery ticket bundles and simultaneously collects three-dimensional contour data of the plastic sealing tape, enabling the simultaneous acquisition of visual and spatial morphological information of the plastic sealing tape. By fusing multi-angle data with three-dimensional contour data to construct a fusion dataset containing visual and spatial morphological features, it integrates multi-dimensional features, providing comprehensive data support for subsequent analysis. By extracting the edge morphological contour features of the plastic sealing tape to reconstruct the three-dimensional edge path and extracting the surface texture of the contact area, it can accurately locate the key regional features of the plastic sealing tape. By judging the structural integrity of the plastic sealing tape through the continuity and smoothness of the three-dimensional edge path and evaluating the tightness of the fit by combining the uniformity of surface texture distribution, it can quantitatively judge the physical state and fit quality of the plastic sealing tape. By comparing the judgment and evaluation results with the benchmark model, it can accurately identify whether the plastic sealing tape meets the standard based on the standard state. By marking the state of the lottery ticket bundle plastic sealing tape and binding the information with the tracking code and writing it into the database, it can realize the quality traceability and tracking management of the lottery ticket bundles.

[0017] Furthermore, pixel-level correlation is performed on multi-angle data to establish a mapping relationship between acquisition points and spatial points of 3D contour data, generating an image contour mapping table. Image color, brightness information, and 3D distance and height information are overlaid to form an overlay data layer. Visual and morphological attribute values ​​of the laminating tape region are separated to generate an initial feature set. Feature statistics are calculated by dividing the main body and edge regions, and finally, attribute values ​​are integrated to construct a structured fusion dataset. Through pixel-level mapping and multi-dimensional information overlay, accurate correlation between images and 3D data is achieved. Through region division and feature statistics, the targeting and accuracy of feature extraction are improved. The constructed structured fusion dataset can more efficiently support subsequent laminating tape state analysis, ensuring the reliability and usability of feature data. Attached Figure Description

[0018] To more clearly illustrate the technical solutions of the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 is a flowchart illustrating an automatic identification and tracking method for lottery bundles provided in an embodiment of this application; Figure 2 is a flowchart illustrating the specific implementation of an automatic identification and tracking method for lottery bundles provided in an embodiment of this application; Figure 3 is a structural diagram illustrating an automatic identification and tracking system for lottery bundles provided in an embodiment of this application. Detailed Implementation

[0020] Existing automatic identification and tracking technologies for lottery ticket bundles mostly employ two-dimensional image recognition schemes, which can only acquire planar visual information and cannot accurately determine the structural integrity and tightness of the plastic seal of the lottery ticket bundle. This makes it difficult to detect problems such as damaged seals and loose seals in a timely manner, thus exposing lottery ticket bundles to risks such as being swapped or damaged during circulation, seriously affecting the security and reliability of identification and tracking management.

[0021] To address the aforementioned issues, this application proposes an automatic identification and tracking method for lottery ticket bundles. The core of this method lies in combining image acquisition and 3D data acquisition to achieve multi-dimensional information fusion analysis. Specifically, this method simultaneously acquires and fuses multi-angle data of the lottery ticket bundle and 3D contour data of the plastic sealing tape, accurately extracting key features of the plastic sealing tape to determine its structural integrity and assess its tightness. After comparison with a standard benchmark model, the status is marked, and finally, the status information is bound to a tracking code and stored in a database. This solution overcomes the limitations of 2D image recognition, which can only acquire planar information, and can accurately identify various problems with the plastic sealing tape, mitigating circulation risks at the source. It effectively solves the problems of insufficient security and reliability in lottery ticket bundle identification and tracking in existing technologies, ensuring the safety of lottery ticket circulation and improving management efficiency.

[0022] To enable those skilled in the art to better understand the present application, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0023] The core of this application is to provide an automatic identification and tracking method for lottery ticket bundles. A flowchart of a specific implementation is shown in Figure 1. The method includes: S101, acquiring multi-angle data of the surface of the lottery ticket bundle and simultaneously collecting three-dimensional contour data of the plastic sealing tape covering the lottery ticket bundle; fusing the multi-angle data and the three-dimensional contour data to construct a fused dataset.

[0024] The fused dataset is a structured dataset that integrates the appearance features of multi-angle data and the spatial morphological features of 3D contour data. Appearance features include color, brightness, and other appearance-related characteristics. Spatial morphological features include distance, height, and other spatial location-related characteristics. Multi-angle data refers to multiple planar images taken from different positions and angles of the lottery ticket bundle, containing information such as the appearance, shape, and color of the lottery ticket bundle and its plastic wrapping tape. 3D contour data is a dataset used to describe the spatial morphology of the plastic wrapping tape, reflecting the spatial position information of each point on the tape.

[0025] Optionally, as shown in Figure 2, step S101 may specifically include the following steps: S1011, perform pixel-level association on the multi-angle data, establish a mapping relationship between each acquisition point and the corresponding spatial point in the three-dimensional contour data, and generate an image contour mapping table.

[0026] In the above steps, pixel-level association refers to the process of establishing pixel correspondences between images from different angles, using the acquisition point as the basic unit. Mapping relationship refers to establishing a one-to-one correspondence between each pixel in the image and its unique corresponding spatial location point in the 3D contour data. The image contour mapping table is a structured table that records the above pixel-to-spatial point mapping relationship and is used for subsequent data overlay processing.

[0027] In this embodiment, firstly, when acquiring multi-angle data of the lottery ticket bundle surface, a multi-camera array or a single camera can be used for moving shooting. Key angles such as the front, left, and right sides of the lottery ticket bundle are selected to ensure that the shooting range completely covers the lottery ticket bundle and the surface plastic seal, while ensuring that there is a certain overlap area between the images of each angle to meet the subsequent matching requirements. When simultaneously acquiring the three-dimensional contour data of the plastic seal, three-dimensional measurement equipment such as laser scanning and structured light scanning can be used to scan the lottery ticket bundle in its entirety, accurately capture the spatial position information of each point of the plastic seal, form a three-dimensional contour dataset, and ensure that the spatial range of the three-dimensional data acquisition is consistent with the shooting range of the multi-angle data.

[0028] Next, feature points are extracted from each multi-angle data image and feature description information is generated. Then, by comparing the similarity of feature description information in different images, pixels corresponding to the same physical location in images from different angles are found, realizing pixel-level association between images from different angles. After completing the pixel-level association, the transformation relationship between the acquisition point coordinate system and the three-dimensional world coordinate system is established using camera calibration parameters. The core transformation relationship is shown in equation (1): In equation (1), For pixel coordinates, For the corresponding three-dimensional world coordinates, The shape values ​​of pixels in the camera coordinate system; intrinsic parameter matrix It is usually expressed as ,in For the camera Focal length in direction (unit: pixels). The coordinates of the principal point of the image (unit: pixels).

[0029] Essentially, it establishes a mapping between two-dimensional pixel coordinates and three-dimensional spatial coordinates through matrix operations. Based on this formula, a precise coordinate transformation model can be constructed, thereby locating the spatial point corresponding to each acquisition point in the three-dimensional contour data. Finally, the one-to-one correspondence between all pixels and spatial points is organized into a structured image contour mapping table containing fields such as angle information, pixel coordinates, and three-dimensional spatial coordinates, providing a basic correlation basis for the subsequent overlay of visual and spatial information.

[0030] In practical applications, assuming images of a lottery ticket bundle are taken from three angles—front, left, and right—the SIFT algorithm is used to extract the front view points. Using (100, 200) as a feature point, we match and obtain two corresponding pixels on the left side (80, 190) and the right side (120, 210), completing pixel-level association. We set the frontal shooting parameters, taking the frontal pixel (100, 200) as an example, and the intrinsic parameter matrix... ,in Pixels Pixels Pixels; the camera is parallel to the three-dimensional world coordinate system, therefore It is the identity matrix. .

[0031] The 3D contour acquisition device obtains the camera coordinate system shape value corresponding to the pixel. First calculate the left side of equation (1): Then construct the extrinsic parameter splicing matrix. To solve for 3D world coordinates Transforming equation (1) yields: Calculate the inverse matrix and substitute it into the left side: After The initial three-dimensional coordinates are obtained by transformation. Adjust the coordinate system reference plane to make Finally, the front-facing pixels were determined. Corresponding three-dimensional world coordinates .

[0032] Similarly, the left side (80, 190) corresponds to (45, 30, 20) mm, and the right side (120, 210) corresponds to (55, 30, 20) mm. These are then organized into an image contour mapping table as shown in Table 1 below:

[0033] The table above only shows the mapping relationship of some key pixels. In actual applications, information on all related pixels must be included. This example is only one example of this application. In actual applications, the table fields can be adjusted or more angle information can be added as needed. This application does not limit this.

[0034] S1012. Based on the image contour mapping table, the color information and brightness information of the multi-angle data are superimposed with the distance information and height information of the three-dimensional contour data to form a superimposed data layer.

[0035] In this step, color information refers to the RGB color value corresponding to the acquisition point, and brightness information refers to the lightness or darkness of the acquisition point. Distance information refers to the straight-line distance from the 3D spatial point to the acquisition device, and height information refers to the vertical distance of the 3D spatial point relative to a preset reference plane. The overlay data layer is a multi-layered data structure formed by superimposing the above-mentioned visual information (color and brightness) with the spatial information (distance and height).

[0036] Specifically, firstly, the color value and brightness value corresponding to each associated pixel, as well as the distance and height values ​​of the three-dimensional spatial point mapped to that pixel, are accurately extracted from the image contour mapping table. Then, using the coordinate information of the three-dimensional spatial point as a unique index, the visual information color and brightness corresponding to the same spatial point are bound one by one with the spatial information distance and height, ensuring that each set of data can correspond to a specific spatial location. Finally, according to the positional distribution rules in three-dimensional space, all the bound associated data are organized in an orderly manner to form a superimposed data layer that combines visual and spatial information. Subsequently, relevant information of the shrink wrap can be quickly located and extracted based on this data layer.

[0037] For example, taking three key pixels in the image contour mapping table as an example, the relevant data corresponding to the front pixel (100,200), the left pixel (80,190), and the right pixel (120,210) are extracted from the image contour mapping table. Among them, the front pixel (100,200) corresponds to the three-dimensional point (50,30,20) mm, the color value RGB (255,255,255), the brightness value 240, the distance value 80 mm, and the height value 50 mm; the left pixel (80,190) corresponds to the three-dimensional point (45,30,20) mm, the color value RGB (254,254,254), the brightness value 238, the distance value 81 mm, and the height value 49 mm; the right pixel (120,210) corresponds to the three-dimensional point (55,30,20) mm, the color value RGB (255,255,255), the brightness value 242, the distance value 79 mm, and the height value 51 mm.

[0038] Then, using the 3D point coordinates corresponding to each pixel as unique indexes, the visual information such as color and brightness corresponding to each 3D point is bound to spatial information such as distance and height, forming three complete sets of associated data. Finally, the three sets of associated data are sorted and arranged in ascending order of the 3D spatial X-axis coordinates (45mm, 50mm, 55mm) to complete the construction of the basic overlay data layer.

[0039] S1013. Separate the region corresponding to the plastic sealing tape from the superimposed data layer, calculate the appearance attribute value and morphology attribute value of each point in the corresponding region, and generate an initial feature set.

[0040] Among them, the appearance attribute values ​​are numerical values ​​obtained after quantifying color and brightness information, used to characterize the appearance features of the laminating tape. The morphology attribute values ​​are numerical values ​​obtained after quantizing distance and height information. The initial feature set is the original feature dataset containing the appearance and morphology attribute values ​​of all points within the laminating tape area.

[0041] In one specific implementation, a threshold segmentation algorithm is first used. Taking into account the inherent differences in color and brightness between the plastic wrap and the main body of the lottery ticket bundle, a reasonable color threshold range and a brightness threshold range are set. The region that simultaneously meets the two threshold ranges is then selected from the superimposed data layer. This region is the target region corresponding to the plastic wrap.

[0042] Subsequently, feature quantization processing was performed on each point within the target area: for visual information such as color and brightness, the min-max normalization method was used to eliminate the dimensional differences between different dimensions of data, obtaining appearance attribute values ​​of a uniform scale; for spatial morphological information such as distance and height, the z-score normalization method was used to convert the data into morphological attribute values ​​conforming to a standard normal distribution. Finally, the appearance attribute values ​​and morphological attribute values ​​of all points were summarized in order of their spatial location to form an initial feature set containing complete feature information of the laminating tape.

[0043] In practical applications, based on the overlay data layer, the color threshold range of the laminating tape is set to RGB (250-255, 250-255, 250-255), and the brightness threshold range is set to 230-255. All points are traversed, and 200 pixels that simultaneously meet both threshold conditions are selected to constitute the target area of ​​the laminating tape. Feature quantization is performed on the points within this area. Visual information is normalized using min-max, yielding a minimum brightness of 230 and a maximum brightness of 255. Points with an original brightness of 245 are used for calculation, and (245-230) ÷ (255-230) yields an appearance attribute value of 0.6. Spatial morphology information is normalized using z-score, yielding a mean distance of 80mm and a standard deviation of 2mm. Points with an original distance of 82mm are used for calculation, and (82-80) ÷ 2 yields a morphology attribute value of 1. Finally, the appearance and morphology attribute values ​​of the 200 points are arranged in three-dimensional space according to an X-axis distance of 45mm-55mm and a Y-axis distance of 30mm, forming an initial feature set.

[0044] S1014. Divide the initial feature set into regions to define the main region and edge region of the plastic sealing tape, and calculate the feature statistics of each region.

[0045] Specifically, step S1014 may include the following processes: based on the morphological attribute values ​​of each point in the initial feature set, calculate the difference in morphological values ​​between each point and its neighboring points to generate a local morphological change sequence; according to the local morphological change sequence, set a morphological change threshold, mark points whose morphological change exceeds the threshold as candidate edge points, and mark the remaining points as candidate main points; perform connectivity analysis on the candidate edge points to merge the sets of interconnected points into continuous edge regions, and simultaneously perform connectivity analysis on the candidate main points to merge the sets of interconnected points into continuous main regions; for the main regions and the edge regions, calculate the arithmetic mean and variance of the appearance attribute values ​​and the arithmetic mean and variance of the morphological attribute values ​​of all data points in each region, respectively, to obtain two sets of feature statistics corresponding to each region.

[0046] In the above steps, the main region refers to the central part of the laminating tape, a stable area with no obvious edge features. The edge region refers to the boundary of the laminating tape, the transition area between the laminating tape and the lottery ticket bundle or the external environment. Characteristic statistics include the mean and variance.

[0047] In this embodiment, firstly, based on the morphological attribute value of each point in the initial feature set, the difference between the morphological attribute values ​​of that point and multiple neighboring points is calculated. These differences are then integrated to form a local morphological change sequence, which reflects the degree of drastic morphological change at different locations of the sealing tape. Next, by statistically analyzing the distribution characteristics of the local morphological change sequence, a reasonable morphological change threshold is set. Points with morphological changes exceeding the threshold are marked as candidate edge points; these points are typically located at the sealing tape boundary and exhibit drastic morphological changes. The remaining points with gradual morphological changes are marked as candidate main points.

[0048] Next, connectivity analysis is performed on the candidate edge points and candidate main points respectively. Connected candidate edge points are merged into continuous edge regions, and connected candidate main points are merged into continuous main regions, ensuring the spatial continuity of the segmented regions. Finally, for the segmented main regions and edge regions, the arithmetic mean and variance of the appearance attribute values ​​and morphological attribute values ​​in the two regions are calculated respectively, obtaining feature statistics that can characterize the differences in features between the two regions.

[0049] For example, continuing the above example, based on an initial feature set containing 200 points, the morphological attribute values ​​of each point are extracted, and the morphological difference between each point and its four neighboring points is calculated to form a local morphological change sequence. After statistically analyzing the sequence, a morphological change threshold of 0.5 is set. 35 points with an absolute difference exceeding 0.5 are marked as candidate edge points, and the remaining 165 points are marked as candidate main points. After connectivity analysis, continuous edge regions and main regions are obtained. Feature statistics are calculated: the sum of the 165 appearance attribute values ​​in the main region is 99, the average is 99÷165=0.6, and the variance is 10.1÷(165-1)≈0.0616; the sum of the morphological attribute values ​​is 16.5, the average is 16.5÷165=0.1, and the variance is 8.2÷164≈0.05. The sum of the 35 appearance attribute values ​​in the edge area is 24.5, the average value is 24.5 ÷ 35 = 0.7, and the variance is 3.5 ÷ 34 ≈ 0.1029; the sum of the morphology attribute values ​​is 7, the average value is 7 ÷ 35 = 0.2, and the variance is 17.5 ÷ 34 ≈ 0.5147.

[0050] S1015. Based on the feature statistics, integrate the appearance attribute values ​​and morphological attribute values ​​to construct a fused dataset.

[0051] Structured format refers to a storage format with fixed data fields, data types, and organization methods.

[0052] Specifically, the validity of the feature statistics of the main and edge regions is first verified, eliminating abnormal statistical results caused by data noise or calculation errors. Then, the verified feature statistics are associated with the corresponding original appearance and morphological attribute values ​​in the initial feature set, ensuring that each original attribute value corresponds to the overall statistical characteristics of its region. Finally, according to a preset structured format, information such as region type, point coordinates, original attribute values, and region statistics are organized and stored in an orderly manner to construct a fused dataset. This dataset fully integrates the local and overall features of the laminating tape.

[0053] In practical applications, continuing the example above, the 3σ criterion is used to verify the validity of the feature statistics. The mean of all statistics is 0.43, the standard deviation is 0.25, and the reasonable range is -0.32 to 1.18. All statistics fall within this range, thus validating the statistics. Subsequently, association relationships are established, linking the original attribute values ​​of each point in the main region and the edge region with the corresponding region's statistics. A structured dataset is then constructed in tabular format, containing six fields including region type and 3D coordinates. Typical data is as follows: the main region (50, 30, 20) corresponds to the original appearance attribute value of 0.6, the original morphology attribute value of 0.0, and the main region statistics; the edge region (45, 30, 20) corresponds to the original appearance attribute value of 0.2, the original morphology attribute value of -1.2, and the edge region statistics, etc.

[0054] This application achieves efficient fusion of the appearance and spatial morphological features of plastic sealing tape through pixel-level correlation, multi-dimensional information overlay, precise region division, and feature statistical integration, solving the problem that traditional single image recognition or three-dimensional data acquisition cannot fully acquire the features of plastic sealing tape.

[0055] S102. Extract the morphological contour features corresponding to the edge region of the plastic seal from the fused data, and reconstruct the three-dimensional edge path of the plastic seal based on the morphological contour features. At the same time, extract the surface texture of the contact area between the plastic seal and the lottery ticket bundle from the appearance features.

[0056] Among them, morphological contour features refer to the set of features that can characterize the spatial morphological distribution pattern of the edge area of ​​the plastic seal, including the three-dimensional coordinates, morphological values, and morphological differences between adjacent points of the edge points; the three-dimensional edge path is a smooth curve that accurately reflects the spatial morphology of the edge of the plastic seal after connecting and fitting continuous morphological change points within the edge area; the contact area refers to the transition area where the plastic seal and the lottery ticket bundle are in contact; and the surface texture refers to the arrangement pattern and color change characteristics of pixels in the contact area image.

[0057] Optionally, step S102 may specifically include the following steps: S1021, scanning the edge region of the plastic sealing tape in the fusion dataset, identifying the morphological change points in the edge region, and recording the three-dimensional coordinates and morphological values ​​of each morphological change point.

[0058] In this step, the morphological change point refers to a spatial point in the edge region where the morphological value changes significantly relative to the surrounding adjacent points. Its core feature is the abrupt change in morphological value, which can be used to define the boundary position of the sealing tape edge.

[0059] In this embodiment, firstly, the edge regions of the plastic sealing tape that have been divided in the fusion dataset are traversed, and each point is retrieved in the order of the XY axis of the three-dimensional spatial coordinates. For each retrieved point, the difference in morphological value between it and its 8 neighboring points is calculated, and points whose absolute difference is greater than a preset morphological change threshold are determined as morphological change points. Finally, the three-dimensional coordinates (X, Y, Z) and the corresponding morphological values ​​of each morphological change point are stored through the data recording module to form a morphological change point dataset.

[0060] For example, based on the fusion dataset constructed in S101 above, 35 edge regions of the sealing tape were selected for scanning, with a preset morphological change threshold of 0.5. The scan was performed point-by-point in the order of 45mm-55mm on the X-axis and 30mm on the Y-axis, calculating the morphological value difference between each point and its 8 neighboring points. Finally, 28 morphological change points with an absolute difference greater than 0.5 were identified. Information for three typical points was recorded: Point 1: 3D coordinates (45, 30, 20) mm, morphological value 79mm; Point 2: 3D coordinates (46, 30, 20.2) mm, morphological value 79.5mm; Point 3: 3D coordinates (54, 30, 19.8) mm, morphological value 83mm. The above example is merely one example of this application; in practical applications, settings can be customized according to requirements, and this application does not limit this.

[0061] S1022. Connect the morphological change points based on the three-dimensional coordinates to form a preliminary edge line, and calculate the morphological difference and angle difference between adjacent points on the preliminary edge line based on the morphological value.

[0062] Among them, the preliminary edge line is a broken line formed by directly connecting the shape change points in the spatial position sequence of three-dimensional coordinates, which is used to initially outline the edge shape of the plastic sealing tape; the shape difference refers to the difference in shape value between two adjacent shape change points on the preliminary edge line, reflecting the degree of shape undulation of adjacent points; the angle difference refers to the difference in the angle between the line connecting two adjacent shape change points and the preset reference direction, reflecting the curvature of the edge line.

[0063] Specifically, the dataset of morphological change points obtained from S1021 is first sorted using the X-axis coordinates of the morphological change points as the primary sorting criterion and the Y-axis coordinates as the secondary criterion. Adjacent morphological change points are then connected sequentially according to the sorting results to form a preliminary edge line composed of multiple straight lines. Subsequently, the morphological values ​​and three-dimensional coordinates of adjacent points on the preliminary edge line are extracted, and the morphological difference and angular difference are calculated respectively.

[0064] In practical applications, the 28 morphological change points identified by S1021 are sorted in ascending order of their X-axis coordinates. Points 1 (45, 30, 20) to 28 (55, 30, 20) are connected sequentially to form preliminary edge lines. The morphological and angular differences between adjacent points are calculated. Taking point 1-point 2 and point 2-point 3 as examples: point 1 has a morphological value of 79mm, point 2 has a morphological value of 79.5mm, resulting in a morphological difference of 70.5mm; point 2 has a morphological value of 79.5mm, point 3 has a morphological value of 80mm, resulting in a morphological difference of 0.5mm. When calculating the angular difference, the vector between point 1 and point 2 is first constructed with an angle α1≈11.31° with the positive X-axis; the vector between point 2 and point 3 is constructed with an angle α2≈16.70°, resulting in an angular difference of 16.70°-11.31°=5.39°. This method is used to calculate all adjacent points, resulting in 27 sets of morphological and angular difference data.

[0065] S1023. Based on the shape difference and angle difference, select a point sequence with high continuity, fit the point sequence into a smooth curve, and generate a three-dimensional edge path of the sealing tape.

[0066] In this step, a point sequence with high continuity refers to an ordered sequence of morphological change points whose morphological differences and angular differences are both within a preset reasonable range.

[0067] In one specific implementation, firstly, a morphological difference threshold and an angle difference threshold are set, wherein the morphological difference threshold is set to ±1mm and the angle difference threshold is set to ±10°; the morphological difference and angle difference calculated by S1022 are compared with the thresholds, and adjacent point pairs whose differences are both within the threshold range are retained to form a point sequence with high continuity. Subsequently, the B-spline curve fitting algorithm is used to fit the point sequence to construct a B-spline curve model, the core formula of which is shown in equation (2): In equation (2), Let the coordinates of any point on the fitted curve be... The number of points in the point sequence. for The order of the spline curve, in this embodiment That is, a cubic B-spline curve. for B-order spline basis functions, For the point sequence, the first The three-dimensional coordinates of the points The parameter variable is denoted by . This formula can be used to convert a discrete sequence of points into a continuous, smooth curve, which is the three-dimensional edge path of the sealing tape.

[0068] For example, based on the 27 sets of morphological and angular difference data obtained from S1022, after filtering according to a preset threshold, 3 pairs of points with morphological differences exceeding ±1mm or angular differences exceeding ±10° are removed, resulting in a continuous high-point sequence composed of 25 morphological change points. The three-dimensional coordinates of this point sequence are substituted into equation (2) for cubic B-spline curve fitting, where =24, =3, parameter The value range is [3, 28]. The three-dimensional edge path obtained after fitting can smoothly transition between adjacent points, eliminate the broken line traces of the original preliminary edge line, and accurately reflect the spatial curvature of the plastic sealing tape edge.

[0069] S1024. In the appearance features, locate the contact area between the plastic sealing tape and the lottery ticket bundle, extract the data block of the contact area, analyze the pixel arrangement pattern and color change in the data block, and extract the texture direction and texture density parameters.

[0070] Among them, the data block refers to a local image region containing complete information of the contact area extracted from the appearance feature data; the texture direction refers to the dominant direction of pixel arrangement within the contact area, such as horizontal, vertical or oblique direction; and the texture density refers to the density of pixel arrangement per unit area, which quantitatively represents the density of texture.

[0071] Specifically, firstly, based on the 3D edge path generated by S1023, the inner boundary of the plastic sealing tape edge is located near the main body of the lottery ticket bundle in the appearance features of the fused dataset. Using this inner boundary as a reference, a preset width is extended towards the main body of the lottery ticket bundle to determine the range of the contact area. Subsequently, corresponding data blocks are extracted according to this range, and the pixel arrangement pattern and color changes in the data blocks are analyzed: by calculating the gray-level co-occurrence matrix in different directions and at different distances, the feature values ​​of the matrix are extracted; based on the feature values, the texture direction is determined, and the direction histogram statistical method is used to take the direction corresponding to the peak of the histogram as the dominant texture direction; the number of texture units in a unit pixel area is calculated to obtain the texture density parameter.

[0072] For example, in the appearance feature data, the pixel coordinate range of the inner boundary of the plastic sealing tape is determined to be (45-55, 28-30) based on the 3D edge path. Extending 5 pixels towards the main body of the lottery ticket bundle, the pixel coordinate range of the contact area is determined to be (45-55, 23-30). The size of the data block extracted from this range is 11×8 pixels. After grayscale processing of this data block, the grayscale co-occurrence matrices in four directions (0°, 45°, 90°, and 135°) are calculated, with a distance of 1. The energy, entropy, and other feature values ​​of each matrix are extracted. Through the direction histogram statistics, it is found that the feature value in the 90° direction is the largest, and the texture direction is determined to be the vertical direction. When calculating the texture density, the number of texture units in the data block is counted as 22, the area of ​​the data block is 88 pixels², and the texture density is 22÷88=0.25 units / pixel².

[0073] S1025. Combine the texture direction and the texture density parameter to form a surface texture descriptor.

[0074] In this embodiment, firstly, the texture direction and texture density parameters extracted in S1024 are standardized. The angle value of the texture direction is converted into a normalized value of 0-1, while the texture density parameter remains unchanged. Then, the two standardized parameters are combined into a two-dimensional vector using vector concatenation. This vector is the descriptor of the surface texture and can be used for subsequent identification and comparison of texture features in the contact area of ​​the sealing tape.

[0075] In practical applications, the texture direction determined by S1024 is 90°, which, after normalization, is 90°÷180°=0.5; the texture density parameter is 0.25 pixels / pixel². These two parameters are concatenated into a two-dimensional vector (0.5, 0.25), which serves as the descriptor for the surface texture of the current sealing tape contact area. This descriptor clearly quantifies the core features of the surface texture, facilitating subsequent feature matching and analysis.

[0076] This application achieves accurate reconstruction of the three-dimensional edge path of the plastic sealing tape by combining the techniques of morphological change point recognition, ordered connection, and curve fitting optimization, thus solving the problem that traditional two-dimensional edge detection cannot reflect spatial morphology. At the same time, it uses gray-level co-occurrence matrix combined with parameter combination to extract surface texture descriptors, realizing the quantitative representation of texture features in the contact area, and overcoming the defects of traditional texture extraction methods that are highly subjective and have low accuracy.

[0077] S103. Based on the continuity and smoothness of the three-dimensional edge path, determine the structural integrity of the plastic sealing tape; and in conjunction with the uniformity of the surface texture distribution in the contact area, evaluate the tightness of the fit between the plastic sealing tape and the lottery ticket bundle.

[0078] Among them, structural integrity refers to the integrity of the edge shape of the plastic sealant, reflecting whether there are structural defects such as breaks or gaps in the plastic sealant; tightness of adhesion refers to the adhesion state between the plastic sealant and the main body of the lottery ticket bundle, reflecting whether there are gaps or lifting between the two; uniformity of distribution refers to the regularity of the distribution of surface texture in the contact area, which can indirectly characterize the tightness of adhesion.

[0079] Optionally, step S103 may specifically include the following steps: S1031, evaluate the continuity of the path by calculating the sum of the lengths of all line segments on the three-dimensional edge path and comparing it with a preset length threshold.

[0080] In this step, the sum of line segment lengths refers to the cumulative value of the line segment lengths between two adjacent feature points on the 3D edge path, which is used to quantitatively characterize the overall integrity range of the 3D edge path; the preset length threshold is a reference value set according to the edge length of the standard plastic sealing tape, which is used to determine whether there are any missing parts in the actual edge path.

[0081] In this embodiment, the coordinates of all discrete feature points on the three-dimensional edge path generated in S1023 are first extracted. The line segment lengths between adjacent feature points are calculated sequentially using the spatial distance formula, and then all line segment lengths are summed to obtain a total. This total is then compared with a preset length threshold. If the total is greater than or equal to the preset length threshold, the path is considered to have good continuity; if the total is less than the preset length threshold, the path is considered to have discontinuous regions and poor continuity. The spatial distance formula is shown in equation (3): (3) In equation (1), For the first The length of the line segment, ( )and( ( ) represent the 3D coordinates of two adjacent feature points on the 3D edge path. The sum of the lengths of all line segments is the length of each segment. The cumulative value, that is, from the first line segment to the second line segment. The lengths of the line segments are added together sequentially, where n is the total number of feature points on the three-dimensional edge path.

[0082] For example, based on the three-dimensional edge path of the plastic sealing tape obtained in S1023, 25 feature points are extracted, and the line segment lengths of adjacent feature points are calculated sequentially according to formula (3). An example calculation is performed using three line segments: the coordinates of the first adjacent point are... and Calculated The coordinates of the adjacent points in the second segment are: and Calculated The coordinates of the adjacent points in the third segment are: and Calculated After calculating in sequence, the sum of all line segment lengths is obtained. The preset length threshold is set to 24mm based on the edge length of the standard lottery ticket wrapping tape. The continuity of the three-dimensional edge path was determined to be good.

[0083] S1032. By measuring the curvature change at each corner of the three-dimensional edge path, the number of curvature abrupt change points is counted, and the smoothness of the path is evaluated.

[0084] Curvature change refers to the change in the curvature of the curve at the corner on the three-dimensional edge path, which is used to characterize the smoothness of the corner transition; curvature abrupt change point refers to the point where the curvature change exceeds the preset abrupt change threshold. The existence of such points will cause sharp protrusions or depressions in the path, affecting the smoothness.

[0085] Specifically, firstly, on the three-dimensional edge path processed by S1031, feature points corresponding to all corner positions are identified, and the curvature value of each corner point is calculated using the curvature calculation formula. Subsequently, the curvature difference between adjacent corner points is calculated, and this difference is used as the curvature change amount, which is compared with the preset mutation threshold. The number of points where the curvature change amount exceeds the preset mutation threshold is counted. The fewer the mutation points, the better the path smoothness; the more mutation points, the worse the path smoothness. The curvature calculation formula for the three-dimensional curve is shown in Equation (4).

[0086] In equation (4), K is the curvature value at the corner point. The parametric equations for the three-dimensional edge path are as follows: for The first derivative, for The second derivative, × denotes the vector cross product operation. Represents the magnitude of a vector.

[0087] In practical applications, based on the three-dimensional edge path composed of the 25 feature points mentioned above, 8 corner points are identified, and the curvature value of each corner point is calculated according to equation (4). An example calculation is performed using 3 corner points: Corner point 1 parametric equation first derivative modulus The modulus of the cross product of the second derivative and the first derivative Calculated Corner Calculated Corner Calculated Set the preset mutation threshold to 0.01, and calculate the curvature difference between adjacent corner points: absolute value Non-mutation point; absolute value Non-abrupt point. After calculating the curvature difference of all adjacent corner points, the number of curvature abrupt change points was found to be 1. Based on practical testing experience, when the number of abrupt change points... At time 2, the path smoothness is deemed good, therefore the smoothness of the three-dimensional edge path meets the requirements.

[0088] S1033. Based on the evaluation results of continuity and smoothness, generate a structural integrity score.

[0089] In this embodiment, a structural integrity scoring system is first established, with both continuity and smoothness weighted at 50%. The continuity assessment result for S1031 is assigned a score of 100 for good continuity and below 60 for poor continuity; similarly, the smoothness assessment result for S1032 is assigned a score of 100 for good smoothness and below 60 for poor smoothness. Subsequently, a weighted summation formula is used to calculate the total structural integrity score: Structural Integrity Score = Continuity Score × 50% + Smoothness Score × 50%. Based on the total score, a grade is determined: a total score ≥ 90 indicates structural integrity, 80-89 indicates basic integrity, and < 80 indicates incompleteness.

[0090] For example, S1031 was judged to have good continuity, and was assigned a score of 100; S1032 was judged to have good smoothness, and was assigned a score of 100. Substituting into the formula, the structural integrity score is calculated as 100 × 50% + 100 × 50% = 100 points. According to the grading standard, the plastic sealing tape is judged to have structural integrity.

[0091] S1034. Divide the contact area into multiple sub-regions, calculate the deviation of the texture density value of each sub-region from the overall average value, count the proportion of sub-regions whose deviation exceeds the allowable range, and evaluate the distribution uniformity based on the proportion.

[0092] Among them, a sub-region refers to a small local area that is uniformly divided into contact areas of a fixed size, which is used to refine the detection accuracy of texture distribution; deviation refers to the difference between the texture density value of each sub-region and the average texture density of the entire contact area, reflecting the degree of difference between local texture and overall texture; allowable range is a reference range set according to the texture distribution difference under standard bonding conditions; sub-region ratio refers to the ratio of the number of sub-regions with deviations exceeding the allowable range to the total number of sub-regions.

[0093] Specifically, firstly, the contact area between the plastic sealing tape and the lottery ticket bundle, as determined by S1024, is selected and evenly divided into four equal-sized sub-regions using a 2×2 grid. The texture density value of each sub-region is extracted, and the arithmetic mean of all sub-region texture density values ​​is calculated as the overall average. Then, the deviation of each sub-region's texture density value from the overall average is calculated, and this deviation is compared to a preset allowable range. The number of sub-regions with deviations exceeding the allowable range is counted. Finally, the ratio of this number to the total number of sub-regions is calculated; a smaller ratio indicates better distribution uniformity, while a larger ratio indicates poorer distribution uniformity.

[0094] For example, the contact area data block size determined in S1024 above is 11×8 pixels, divided into 4 sub-regions using a 2×2 grid, each sub-region being approximately 5×4 pixels in size. The extracted texture density values ​​for each sub-region are 0.26, 0.24, 0.27, and 0.23 per pixel². The overall average value is calculated as (0.26 + 0.24 + 0.27 + 0.23) ÷ 4 = 0.25 per pixel². A preset allowable range is set to ±0.02, and the deviations for each sub-region are calculated as follows: Sub-region 1 deviation = 0.01; Sub-region 2 deviation = -0.01; Sub-region 3 deviation = 0.02; Sub-region 4 deviation = -0.02. The number of sub-regions with deviations exceeding the allowable range is 0, and the sub-region ratio is 0 ÷ 4 = 0. Based on empirical criteria, a ratio ≤ 10% indicates good distribution uniformity; therefore, the surface texture distribution of this contact area is uniform.

[0095] S1035. Based on the evaluation results of the uniformity of distribution, generate a score for the tightness of fit.

[0096] In this step, the bonding tightness score is a quantitative characterization of the bonding state between the plastic sealing tape and the lottery ticket bundle, based on the uniformity of surface texture distribution. The higher the score, the tighter the bonding.

[0097] In this embodiment, a fitting tightness scoring standard is established, and a corresponding score is assigned based on the sub-region proportions obtained in S1034: 100 points are assigned when the proportion is 0%, 90-99 points are assigned when the proportion is between 0% and 10%, 80-89 points are assigned when the proportion is between 10% and 20%, and below 80 points are assigned when the proportion exceeds 20%. The proportions corresponding to the distribution uniformity assessment are directly converted into fitting tightness scores based on this standard.

[0098] In practical applications, the sub-region ratio obtained in S1034 above is 0, and it is assigned a score of 100 according to the scoring criteria. That is, the tightness score of the adhesion between the plastic wrap and the lottery ticket bundle is 100, indicating that the two are tightly adhered. The above example is only one example of this application. In practical applications, it can be set according to needs, and this application does not limit it.

[0099] This application solves the practical dilemma of difficulty in quantifying and judging structural defects and bonding problems in traditional plastic sealing testing, and realizes a leap from qualitative testing to quantitative evaluation, thereby improving the objectivity and reliability of the test results.

[0100] S104. The structural integrity judgment result and the tightness evaluation result of the plastic sealing tape are matched and compared with the pre-stored benchmark model.

[0101] The baseline model represents the set of appearance and spatial morphological features corresponding to the standard state of intact plastic wrapping tape and tight adhesion to the lottery ticket bundle. This model was constructed by collecting a large number of standard qualified lottery ticket bundle plastic wrapping tape samples, extracting features related to the structural integrity and tightness of adhesion of the samples, and then conducting statistical analysis and optimization.

[0102] Optionally, step S104 may specifically include the following steps: S1041, reading a pre-stored benchmark model, wherein the benchmark model includes a standard structural integrity threshold and a standard fitting tightness threshold.

[0103] In this step, the standard structural integrity threshold is the structural integrity qualification scoring standard set in the benchmark model, used to determine whether the actual structural integrity score meets the standard requirements; the standard fit tightness threshold is the fit tightness qualification scoring standard set in the benchmark model, used to determine whether the actual fit tightness score meets the standard.

[0104] In this embodiment, the benchmark model stored in a local database or cloud server is called by the data reading module. The model is a preset structured data file containing multiple parameters related to the qualified status of the sealing tape. The core parameters are the standard structural integrity threshold and the standard bonding tightness threshold.

[0105] For example, for the lottery ticket bundle specifications currently being tested, a pre-stored benchmark model is read, from which a standard structural integrity threshold of 90 points and a standard fitting tightness threshold of 90 points are extracted. These two thresholds are the lowest acceptable scores obtained based on the structural integrity scores and fitting tightness scores of 1000 lottery ticket bundle sealing tape samples.

[0106] S1042. Calculate the difference between the structural integrity score and the standard structural integrity threshold to obtain the integrity matching degree.

[0107] Among them, the integrity matching degree is a parameter used to quantitatively characterize how close the actual structural integrity score is to the standard threshold. The smaller the difference, the closer the actual structural integrity is to the standard state, and the higher the matching degree.

[0108] Specifically, first, the structural integrity score of the plastic sealing tape generated by S1033 is obtained. Then, the standard structural integrity threshold is subtracted from the actual structural integrity score to obtain the integrity matching degree. If the calculation result is non-negative, it means that the actual structural integrity meets or exceeds the standard requirements; if it is negative, it means that the actual structural integrity does not meet the standard requirements, and the larger the absolute value of the negative value, the greater the gap with the standard state.

[0109] In practical applications, the structural integrity score obtained in S1033 is 100 points, while the standard structural integrity threshold read in S1041 is 90 points. The difference is calculated as follows: Integrity matching degree = 100 points - 90 points = 10 points. This result is positive, indicating that the actual structural integrity of the sealing tape meets the standard requirements and is highly close to the standard state.

[0110] S1043. Calculate the ratio of the fit tightness score to the standard fit tightness threshold to obtain the fit matching degree.

[0111] Among them, the fit matching degree is a parameter used to quantify how close the actual fit tightness score is to the standard threshold. The closer the ratio is to 1, the closer the actual fit tightness is to the standard state, and the higher the matching degree.

[0112] In this embodiment, the bonding tightness score of the sealing tape generated in S1035 is first obtained. Then, a ratio calculation method is used to divide the actual bonding tightness score by the standard bonding tightness threshold to obtain the bonding matching degree. To avoid parameter imbalance caused by an excessively large ratio, the calculation result is limited to between 0 and 1.2. If the result is greater than 1.2, it is taken as 1.2. When the ratio is ≥1, it indicates that the actual bonding tightness meets or exceeds the standard requirements; when the ratio is <1, it indicates that the standard requirements are not met.

[0113] For example, the adhesion tightness score obtained in S1035 is 100 points, and the standard adhesion tightness threshold read in S1041 is 90 points. The ratio is calculated as: Adhesion matching degree = 100 points ÷ 90 points ≈ 1.11. This result is between 0 and 1.2 and greater than 1, indicating that the actual adhesion tightness between the plastic sealing tape and the lottery ticket bundle meets the standard requirements and is highly close to the standard state.

[0114] S1044. The integrity matching degree and the fit matching degree are weighted and combined to generate an overall matching score.

[0115] In this step, weighted combination refers to assigning different weights to integrity matching degree and fitting matching degree according to the importance of the impact of structural integrity and tightness on the overall quality of the sealing tape, and then obtaining the overall matching score by weighted summation, so as to comprehensively reflect the overall closeness between the actual sealing tape state and the standard state.

[0116] Specifically, based on the quality inspection requirements of the plastic sealing tape for lottery tickets, the weights of integrity matching degree and fit matching degree are set, and the sum of the two weights is 1. In this embodiment, considering the core role of structural integrity in the plastic sealing protection effect, the weight of integrity matching degree is set to 0.6 and the weight of fit matching degree is set to 0.4. Then, the weighted summation formula is used to calculate the overall matching score, as shown in equation (5).

[0117] In equation (5), The overall matching score, The weight of the completeness matching degree, For completeness matching degree, To align with the weighting of the matching degree, To ensure a good fit.

[0118] In practical applications, the integrity matching degree obtained above =10 points, fit and match ≈1.11, substitute into equation (5) to calculate: =0.6×10+0.4×1.11=6+0.444=6.444 points. To facilitate subsequent judgment, the overall matching score is normalized to the range of 0-10 points. The result of this calculation is already within this range and no further processing is required.

[0119] S1045. Based on the overall matching score, determine the matching comparison result, which characterizes the degree of closeness between the state of the sealing tape and the standard state.

[0120] In this embodiment, the correspondence between the overall matching score and the matching comparison result is first established: an overall matching score ≥ 8 points indicates "high matching," meaning the sealing tape is very close to the standard state and the quality is qualified; a score 5 ≤ overall matching score < 8 points indicates "basic matching," meaning the sealing tape basically meets the standard requirements, with minor defects that do not affect its use; an overall matching score < 5 points indicates "mismatch," meaning the sealing tape differs significantly from the standard state, has obvious defects, and the quality is unqualified. Then, the overall matching score generated in S1044 is compared with the above correspondence to determine the final matching comparison result.

[0121] For example, the overall matching score obtained in S1044 above is 6.444. According to the established correspondence, 6.444 is between 5 and 8. Therefore, the matching comparison result is determined to be "basic match", indicating that the condition of the plastic sealing tape basically meets the standard requirements, and there are minor defects but they do not affect its use.

[0122] This application achieves a comprehensive matching evaluation of the structural integrity and tightness of the sealing tape, solving the practical problems of inconsistent standards and strong subjectivity in traditional testing. It realizes a precise quantitative characterization of the degree to which the state of the sealing tape is close to the standard state, improving the consistency and reliability of the test results.

[0123] S105. Mark the status of the plastic seal of the lottery bundle according to the matching comparison result, and write the lottery bundle information with status mark and corresponding tracking code into the quality tracking database of the lottery system.

[0124] Among them, the status marker is an identifier used to intuitively represent the quality status of the plastic sealing tape of the lottery ticket bundle; the tracking code is a unique identifier assigned to each lottery ticket bundle to achieve full-process quality traceability; the quality tracking database is a structured database used to store lottery ticket bundle quality-related information, supporting information query and traceability by code, time and other conditions.

[0125] Optionally, step S105 may specifically include the following steps: S1051, setting the status flag value of the lottery ticket bundle sealing tape according to the matching comparison result.

[0126] The status marker values ​​include normal, suspicious, or abnormal categories. The normal category corresponds to a state where the plastic seal is structurally intact and tightly adhered to the lottery ticket bundle, fully meeting the standard requirements; the suspicious category corresponds to a state where the plastic seal is close to the standard threshold, with minor defects that do not currently affect use, but require close monitoring; the abnormal category corresponds to a state where the plastic seal has obvious structural defects or is not tightly adhered, failing to meet the usage requirements.

[0127] In this embodiment, a correspondence rule between the matching comparison result and the status flag value is first established: when the matching comparison result is "highly matched", the corresponding status flag value is set to "normal"; when the matching comparison result is "basically matched", the corresponding status flag value is set to "suspicious"; and when the matching comparison result is "not matched", the corresponding status flag value is set to "abnormal". Subsequently, the matching comparison result obtained in S1045 is acquired, and the corresponding status flag value is automatically matched and generated according to the above rules.

[0128] For example, if the matching comparison result obtained in S1045 above is "basic match", then according to the corresponding rules, the status flag value of the plastic seal of the lottery ticket bundle is set to "suspicious". This flag value will be directly associated with the subsequent lottery ticket bundle information to quickly identify its quality status.

[0129] S1052. Integrate the status flag value with the collection time and location information of the lottery bundle to form a lottery bundle information package.

[0130] Among them, the collection time refers to the specific time when the test data of the lottery ticket bundle sealing tape is collected, which is used to record the time node of quality inspection; the location information refers to the specific inspection station or production area information of the lottery ticket bundle during inspection, which is used to locate the inspection process; the lottery ticket bundle information package is a structured data set that integrates the quality status and inspection-related basic information of the lottery ticket bundle, which is convenient for subsequent unified processing and storage.

[0131] Specifically, the system first obtains the precise time of data collection through the system time module and the location information of the current detection station through the station positioning module. Then, the data integration algorithm is called to integrate the status flag value, collection time, and location information generated by S1051 according to the preset structured format to ensure that each information field is clear and accurately associated, and finally form a lottery bundle information package containing complete basic information.

[0132] In practical application, the status flag value obtained by S1051 is "suspicious", the collection time obtained by the system time module is "2026-01-14 10:30:25", and the location information obtained by the workstation positioning module is "Lottery Production Workshop No. 3 Inspection Station". The above information is integrated according to a preset format to form a lottery bundle information package: {Status Flag: Suspicious, Collection Time: 2026-01-14 10:30:25, Location Information: Lottery Production Workshop No. 3 Inspection Station}.

[0133] S1053. Combine the production batch code, collection timestamp, and serial number of the lottery bundle to generate a unique tracking code, and associate and bind the lottery bundle information package with the unique tracking code to form a data record.

[0134] The production batch code is an encoding used to identify the production batch of the lottery bundle, containing information such as the production year, month, and batch number; the collection timestamp is a digital format encoding converted from the collection time, which is convenient for computer systems to identify and sort; the serial number is a unique sequential number assigned to each lottery bundle within the same production batch; the unique tracking code is an exclusive code generated by integrating multiple unique identification information to ensure that the code of each lottery bundle is not repeated; and the data record is a complete data unit that links the unique tracking code with the lottery bundle information package.

[0135] In this embodiment, the production batch code, collection timestamp, and serial number of the lottery ticket bundle are first extracted. The collection timestamp is converted into a 13-digit numeric code using a time format conversion algorithm. Then, a unique tracking code is generated using a concatenation encryption algorithm. The code generation rule is "production batch code + collection timestamp + serial number," and the concatenated string is MD5 encrypted to ensure the uniqueness and security of the code. Finally, the lottery ticket bundle information package is bound to the generated unique tracking code through a data association module, forming a complete data record containing the code and the information package.

[0136] For example, the production batch code of this lottery bundle is "CP20260105", representing a lottery batch produced on January 5, 2026. The 13-digit timestamp converted from the collection time "2026-01-14 10:30:25" is "1736836225000", and the serial number within the same batch is "00123". The string "CP20260105173683622500000123" is obtained by concatenating these characters according to the rules. After MD5 encryption of this string, a unique tracking code "E8D4F2A9C7B3E1F0A2D4C6E8F0A1B2C3D" is generated. Associate and bind this code with the lottery bundle information package formed above to form a data record: {Unique tracking code: E8D4F2A9C7B3E1F0A2D4C6E8F0A1B2C3D, Status marker: Suspicious, Collection time: 2026-01-14 10:30:25, Location information: Lottery production workshop No. 3 inspection station}.

[0137] S1054. Write the data records into the designated data table of the quality tracking database in chronological order.

[0138] In this step, the specified data table is a table in the quality tracking database specifically used to store data related to the quality inspection of lottery ticket wrapping tape. Its structure corresponds to the fields of the data records, which facilitates the orderly storage and efficient querying of data.

[0139] Specifically, firstly, a stable connection is established with the quality tracking database through the database connection module. After verifying connection permissions, the system locates the designated data table storing lottery ticket bundle quality data. Then, the data writing algorithm is invoked to read the collection time field from the data records and write the data records into the data table in chronological order of collection time. Data validation is performed synchronously during the writing process to ensure the integrity and correct format of the data records' fields. If validation fails, an error message is returned and the writing attempt is retried; if validation succeeds, data storage is completed, and a successful write log record is generated.

[0140] In practical applications, a connection to the MySQL quality tracking database is established via JDBC, locating the specified data table "lottery_seal_quality". This table contains four fields: "tracking_code", "status_mark", "collect_time", and "location_info", corresponding to the unique tracking code, status mark, collection time, and location information of the data record, respectively. The collection time of the data record, "2026-01-14 10:30:25", is read, and the data record is written to the table in chronological order. Data verification shows that the fields are complete and the format is correct, indicating successful writing and generating the log "2026-01-14 10:30:30 Data record written successfully, tracking code: E8D4F2A9C7B3E1F0A2D4C6E8F0A1B2C3D".

[0141] This application utilizes a combination of technologies, including status label classification, multi-dimensional information integration, unique tracking code generation, and time-series data storage, to achieve precise identification and full-process traceability of the quality status of lottery ticket packaging tape. This technological combination solves the practical problems of ambiguous status distinctions, scattered information, and difficulty in traceability in traditional quality inspection, enabling structured management and unique traceability of quality information, and improving the standardization and efficiency of quality control.

[0142] Figure 3 is a schematic diagram of a specific embodiment of an automatic identification and tracking system for lottery tickets provided in this application. Referring to Figure 3, the system may include: a fusion module 31, used to acquire multi-angle data of the surface of the lottery ticket bundle and simultaneously collect three-dimensional contour data of the plastic wrap covering the lottery ticket bundle; to fuse the multi-angle data with the three-dimensional contour data to construct a fusion dataset, the fusion dataset including the appearance features and spatial morphological features of the plastic wrap; and a reconstruction module 32, used to extract the morphological contour features corresponding to the edge region of the plastic wrap from the fusion data, and reconstruct the three-dimensional edge path of the plastic wrap based on the morphological contour features, while extracting the surface texture of the contact area between the plastic wrap and the lottery ticket bundle from the appearance features. The evaluation module 33 is used to determine the structural integrity of the plastic sealant based on the continuity and smoothness of the three-dimensional edge path; and to evaluate the tightness of the fit between the plastic sealant and the lottery ticket bundle by combining the uniformity of the surface texture distribution in the contact area; the matching module 34 is used to match and compare the structural integrity judgment result and the tightness of fit evaluation result of the plastic sealant with a pre-stored benchmark model; the benchmark model represents the set of appearance features and spatial morphological features corresponding to the standard state of the plastic sealant being intact and tightly fitted with the lottery ticket bundle; the binding module 35 is used to mark the state of the plastic sealant of the lottery ticket bundle according to the matching comparison result, and to bind the lottery ticket bundle information with the state mark with the corresponding tracking code and write it into the quality tracking database of the lottery system.

[0143] The automatic lottery bundle identification and tracking system of this application embodiment is used to implement the aforementioned automatic lottery bundle identification and tracking method. Therefore, the specific implementation of the automatic lottery bundle identification and tracking system can be found in the embodiment section of the automatic lottery bundle identification and tracking method above. The specific implementation can be referred to the description of the corresponding embodiments, which will not be repeated here.

[0144] This application also provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the steps of any of the above-described methods for automatic identification and tracking of lottery bundles.

[0145] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of any of the above-described methods for the automatic identification and tracking of lottery bundles.

[0146] In one exemplary embodiment, the aforementioned computer-readable storage medium may include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, random access memory, portable hard drives, magnetic disks, or optical disks.

[0147] Embodiments of the present invention also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps in any of the above embodiments of the automatic identification and tracking method for lottery bundles.

[0148] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0149] The above provides a detailed description of the automatic identification and tracking method and system for lottery bundles provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the embodiments above are merely for the purpose of helping to understand the method and its core ideas. It should be noted that those skilled in the art can make various improvements and modifications to this application without departing from its principles, and these improvements and modifications also fall within the protection scope of this application.

Claims

1. A method for automatic identification and tracking of lottery ticket bundles, characterized in that, include: Acquire multi-angle data of the surface of the lottery ticket bundle, and simultaneously collect three-dimensional contour data of the plastic sealing tape covering the lottery ticket bundle; The multi-angle data and the three-dimensional contour data are fused to construct a fused dataset, which includes the appearance and spatial morphological features of the plastic seal. Morphological contour features corresponding to the edge regions of the plastic seal are extracted from the fused data, and the three-dimensional edge path of the plastic seal is reconstructed based on these features. Simultaneously, the surface texture of the contact area between the plastic seal and the lottery ticket bundle is extracted from the appearance features. The structural integrity of the plastic seal is determined based on the continuity and smoothness of the three-dimensional edge path. Furthermore, the tightness of the fit between the plastic seal and the lottery ticket bundle is evaluated by combining the uniformity of the surface texture distribution in the contact area. The structural integrity assessment results and the adhesion tightness assessment results of the plastic sealing tape are matched and compared with the pre-stored benchmark model; the benchmark model represents the set of appearance and spatial morphological features corresponding to the standard state of the plastic sealing tape being intact and tightly adhered to the lottery ticket bundle. The status of the plastic seal of the lottery bundle is marked according to the matching comparison results, and the information of the lottery bundle with the status mark is bound with the corresponding tracking code and written into the quality tracking database of the lottery system.

2. The method according to claim 1, characterized in that, The multi-angle data and the three-dimensional contour data are fused to construct a fused dataset. The fused dataset contains the appearance and spatial morphological features of the laminating tape, including: pixel-level association of the multi-angle data to establish a mapping relationship between each acquisition point and the corresponding spatial point in the three-dimensional contour data, generating an image contour mapping table; based on the image contour mapping table, superimposing the color and brightness information of the multi-angle data with the distance and height information of the three-dimensional contour data to form a superimposed data layer; separating the region corresponding to the laminating tape from the superimposed data layer, calculating the appearance and morphological attribute values ​​of each point in the corresponding region, generating an initial feature set; dividing the initial feature set into regions, dividing the main region and edge region of the laminating tape, and calculating the feature statistics of each region, including the mean and variance; and integrating the appearance and morphological attribute values ​​according to the feature statistics to construct a fused dataset, which stores the appearance and spatial morphological features of the laminating tape in a structured format.

3. The method according to claim 2, characterized in that, The initial feature set is divided into regions to define the main body region and edge region of the sealing tape. Feature statistics for each region are calculated, including mean and variance. The process includes: calculating the difference in morphological values ​​between each point and its neighbors based on the morphological attribute values ​​of each point in the initial feature set, generating a local morphological change sequence; setting a morphological change threshold based on the local morphological change sequence, marking points with morphological changes exceeding the threshold as candidate edge points, and marking the remaining points as candidate main body points; performing connectivity analysis on the candidate edge points to merge connected point sets into continuous edge regions, and simultaneously performing connectivity analysis on the candidate main points to merge connected point sets into continuous main body regions; and calculating the arithmetic mean and variance of the appearance attribute values ​​and the arithmetic mean and variance of the morphological attribute values ​​for all data points in each main body region and edge region, respectively, to obtain two sets of feature statistics for each region.

4. The method according to claim 1, characterized in that, The process involves extracting morphological contour features corresponding to the edge region of the plastic wrapping tape from the fused data, reconstructing the three-dimensional edge path of the plastic wrapping tape based on these features, and simultaneously extracting the surface texture of the contact area between the plastic wrapping tape and the lottery ticket bundle from the appearance features. This includes: scanning the edge region of the plastic wrapping tape in the fused data set, identifying morphological change points within the edge region, and recording the three-dimensional coordinates and morphological values ​​of each morphological change point; connecting the morphological change points based on the three-dimensional coordinates to form a preliminary edge line, and calculating the morphological difference and angle difference between adjacent points on the preliminary edge line based on the morphological values; selecting a point sequence with high continuity based on the morphological difference and angle difference, fitting the point sequence into a smooth curve to generate the three-dimensional edge path of the plastic wrapping tape; locating the contact area between the plastic wrapping tape and the lottery ticket bundle in the appearance features, extracting a data block of the contact area, analyzing the pixel arrangement pattern and color changes in the data block, and extracting texture direction and texture density parameters; and combining the texture direction and texture density parameters to form a surface texture descriptor.

5. The method according to claim 1, characterized in that, The structural integrity of the sealing tape is determined based on the continuity and smoothness of the three-dimensional edge path; and the adhesion between the sealing tape and the lottery ticket bundle is evaluated by combining the uniformity of the surface texture distribution in the contact area. This includes: evaluating the continuity of the path by calculating the sum of the lengths of all line segments on the three-dimensional edge path and comparing it with a preset length threshold; evaluating the smoothness of the path by measuring the curvature change at each corner of the three-dimensional edge path and counting the number of curvature abrupt change points; generating a structural integrity score based on the evaluation results of continuity and smoothness; dividing the contact area into multiple sub-regions, calculating the deviation of the texture density value of each sub-region from the overall average value, counting the proportion of sub-regions with deviations exceeding the allowable range, and evaluating the distribution uniformity based on the proportion; and generating an adhesion tightness score based on the evaluation results of distribution uniformity.

6. The method according to claim 1, characterized in that, The structural integrity assessment results and the adhesion tightness assessment results of the plastic sealing tape are matched and compared with the pre-stored benchmark model; The benchmark model characterizes the set of appearance and spatial morphological features corresponding to the standard state of the plastic sealant being intact and tightly bonded to the lottery ticket bundle. This includes: reading a pre-stored benchmark model, which contains a standard structural integrity threshold and a standard bonding tightness threshold; calculating the difference between the structural integrity score and the standard structural integrity threshold to obtain an integrity matching degree; calculating the ratio between the bonding tightness score and the standard bonding tightness threshold to obtain a bonding matching degree; weighting the integrity matching degree and the bonding matching degree to generate an overall matching score; and determining a matching comparison result based on the overall matching score, whereby the matching comparison result characterizes the degree of closeness between the plastic sealant state and the standard state.

7. The method according to claim 1, characterized in that, The process involves marking the status of the plastic seal of the lottery bundle based on the matching comparison results, and then binding the information of the lottery bundle with the status mark to the corresponding tracking code and writing it into the quality tracking database of the lottery system. This includes: setting a status mark value for the plastic seal of the lottery bundle according to the matching comparison results, wherein the status mark value includes categories such as normal, suspicious, or abnormal; integrating the status mark value with the collection time and location information of the lottery bundle to form a lottery bundle information package; generating a unique tracking code by combining the production batch code, collection timestamp, and serial number of the lottery bundle; associating and binding the lottery bundle information package with the unique tracking code to form a data record; and writing the data record into a designated data table in the quality tracking database of the lottery system in chronological order.

8. An automatic identification and tracking system for lottery ticket bundles, characterized in that, include: The fusion module is used to acquire multi-angle data of the surface of the lottery ticket bundle and simultaneously collect three-dimensional contour data of the plastic sealing tape covering the lottery ticket bundle. The multi-angle data and the three-dimensional contour data are fused to construct a fused dataset, which includes the appearance features and spatial morphological features of the plastic seal. A reconstruction module extracts the morphological contour features corresponding to the edge regions of the plastic seal from the fused data, reconstructs the three-dimensional edge path of the plastic seal based on the morphological contour features, and extracts the surface texture of the contact area between the plastic seal and the lottery ticket bundle from the appearance features. An evaluation module determines the structural integrity of the plastic seal based on the continuity and smoothness of the three-dimensional edge path, and evaluates the tightness of the fit between the plastic seal and the lottery ticket bundle based on the uniformity of the surface texture distribution in the contact area. The matching module is used to match and compare the structural integrity judgment result and the bonding tightness evaluation result of the plastic sealing tape with the pre-stored benchmark model; The benchmark model represents the set of appearance and spatial morphological features corresponding to the standard state of the plastic seal tape being intact and tightly attached to the lottery ticket bundle; The binding module is used to mark the status of the plastic seal of the lottery bundle based on the matching comparison results, and then bind the lottery bundle information with the status mark to the corresponding tracking code and write it into the quality tracking database of the lottery system.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor, configured to execute the computer program to implement the steps of the automatic identification and tracking method for lottery bundles as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, enables the automatic identification and tracking method for lottery bundles as described in any one of claims 1 to 7.

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