Logo graph and two-dimensional code dot matrix identification method and system

Abstract

The invention relates to the technical field of graph identification, and provides a logo graph and two-dimensional code dot matrix identification method and system, and the method comprises the steps: carrying out the partitioning processing of a logo graph, obtaining a logo core recognition region and an auxiliary decoration region, and marking the coordinates of feature key points of the core recognition region; generating an original two-dimensional code dot matrix binary array according to the two-dimensional code dot matrix information; performing density adjustment on the binary array of the original two-dimensional code dot matrix based on the logo partitioning result and the carrier material parameters to generate an adaptive array; extracting edge contour features of the logo graph, generating a feature hash value, and embedding the feature hash value as dynamic verification information into the adaptive array to obtain a graph code fusion binary array; and gray scale gradient rendering is carried out on the image code fusion binary array, and a final logo and two-dimensional code dot matrix fusion identification image is generated. According to the invention, the three-in-one design of logo feature protection, material adaptation and dynamic anti-counterfeiting is realized for the first time, and the integration of visual beauty, information bearing and anti-counterfeiting traceability can be realized.

Description

Technical Field

[0001] This invention relates to the field of graphic identification technology, and more specifically, to a method and system for identifying logo graphics and QR code dot matrix. Background Technology

[0002] With the rapid development of the Internet of Things, industrial traceability, and brand anti-counterfeiting, QR code dot matrix labels have been widely used in product packaging, production line management, and logistics tracking due to their advantages such as large information storage capacity, fast recognition, and low cost. Meanwhile, brand logos, as the core of corporate visual identity, are key elements for enhancing brand recognition and strengthening brand awareness. Integrating logo graphics with QR code dot matrix labels to achieve a unified "brand display + information delivery" has become an important development direction in the current field of labeling technology.

[0003] Currently, existing logo-QR code fusion technologies are mainly divided into two categories: basic overlay and simple embedded. However, these technologies still have many technical shortcomings in practical applications, making it difficult to balance visual integration, recognition stability, anti-counterfeiting security, and cross-scenario adaptability. For example, Chinese invention patent application number CN107239814A discloses a stacked QR code that integrates logo icons. It forms a region by stacking multiple code areas, allowing users to easily select any area for scanning, achieving convenient scanning. It can also distinguish modules belonging to the QR code and non-coded graphics through statistical methods. However, the aforementioned stacked QR code that integrates logo icons requires layered design, which can easily lead to visual fragmentation and blurred edges. The core recognition area of ​​the logo can be obscured by the dot matrix, compromising the integrity and visual recognizability of the logo. Summary of the Invention

[0004] Therefore, in order to solve the problems of visual fragmentation and blurred edges caused by layering in existing technologies, which compromise the integrity and visual recognizability of the logo, this invention provides a method and system for logo graphic and QR code dot matrix marking, the specific technical solution of which is as follows: A method for identifying a logo graphic and a QR code dot matrix includes the following steps: Obtain the logo graphic to be merged, QR code dot matrix information, and carrier material parameters; The logo graphic is subjected to feature extraction and partitioning to obtain the core recognition area and auxiliary decoration area of ​​the logo, and the coordinates of the feature key points of the core recognition area are marked. Generate the original binary array of QR code dot matrix based on the QR code dot matrix information; Based on the logo partitioning results and carrier material parameters, the density of the original QR code dot matrix binary array is adjusted to generate an adaptation array; in the core recognition area, only dot matrix display units are retained at the coordinates of non-feature key points, and the dot diameter and contrast of the display units are adjusted according to the carrier material parameters. Extract the edge contour features of the logo graphic, generate feature hash values, embed the feature hash values ​​as dynamic verification information into the adaptation array, and obtain the image-code fusion binary array. Based on the grayscale distribution of the logo graphic, the binary array of the logo and QR code fusion is rendered with grayscale gradient to generate the final logo and QR code dot matrix fusion identification image.

[0005] The logo graphic and QR code dot matrix identification method described in this invention achieves deep symbiosis between the dot matrix and the logo through logo feature partitioning and density adaptive adjustment, avoiding visual fragmentation and edge blurring caused by layering. At the same time, the core area protection design improves the stability of logo recognition. Furthermore, by integrating carrier material parameters and embedding dynamic verification information, it also solves the defects of poor stability of multi-material adaptation and insufficient anti-counterfeiting in existing technologies. In addition, grayscale gradient rendering makes the fusion effect more natural.

[0006] In summary, the proposed logo graphic and QR code dot matrix marking method is the first to achieve a three-in-one design of logo feature protection, material adaptation, and dynamic anti-counterfeiting. It can achieve the integration of visual aesthetics, information carrying, and anti-counterfeiting traceability, and solve the problems of visual fragmentation and edge blurring caused by layering in existing technologies, which damage the integrity and visual recognizability of the logo.

[0007] Preferably, the specific method for feature extraction and partitioning of the logo graphic includes the following steps: Obtain the edge density features, Fourier energy frequency domain energy features, and color difference contrast of the logo graphic; The edge density features, Fourier energy frequency domain energy features, and color difference contrast are normalized, and the visual saliency weight values ​​are obtained based on the normalized edge density features, Fourier energy frequency domain energy features, and color difference contrast. Obtain the logo partitioning threshold, normalize the visual salience weight values, and then partition the logo graphic based on the normalized visual salience weight values ​​and the logo partitioning threshold.

[0008] Preferably, the specific method for adjusting the density of the original QR code dot matrix binary array includes the following steps: Obtain the surface refractive index of the carrier material, the ambient light absorption rate, and the wavelength of the light source. Based on the surface refractive index of the carrier material, the ambient light absorption rate, and the wavelength of the light source, obtain the fundamental optical physics terms used to characterize the optical transmission efficiency of the carrier medium. Obtain the background texture gradient norm, and based on the background texture gradient norm, obtain a background texture suppression term used to quantify the interference intensity of the carrier background on dot matrix recognition. Obtain the device decoding coefficients used to represent the overall error correction capability of the barcode scanning hardware system; The minimum recognizable dot diameter is obtained based on optical physics fundamentals, background texture suppression, and device decoding coefficients. The dot matrix density of the auxiliary decorative area is obtained based on the minimum identifiable dot diameter.

[0009] Preferably, the specific method for adjusting the density of the original QR code dot matrix binary array includes the following steps: The dot density of the core recognition area is obtained based on the normalized visual saliency weight values.

[0010] Preferably, the specific method for embedding the feature hash value as dynamic verification information into the adaptation array includes the following steps: Extract key feature points from the core recognition area of ​​the logo and generate a 256-bit SHA-256 feature hash value; In the auxiliary decoration area, redundant dot matrix units are selected as embedding bits, and the hash values ​​are encoded and embedded using a binary replacement method. Add an 8-bit CRC-8 checksum to bind the original dot matrix information.

[0011] Preferably, the specific method for grayscale gradient rendering of the image-code fused binary array includes the following steps: Convert the logo graphic into a smoothed 8-bit grayscale image and establish a coordinate mapping relationship between the image-code fusion binary array and the logo grayscale image; A dynamic grayscale mapping function is constructed to calculate the grayscale of the dot matrix display unit by superimposing the average grayscale value of the logo in the corresponding area with the grayscale compensation value. The grayscale of the blank unit inherits the average grayscale value of the logo in the corresponding area. The dot matrix unit with embedded verification information superimposes the grayscale value according to the preset rules. The rendering parameters were adjusted according to the differences in the logo partitioning results, the edges were smoothed and the contrast was globally calibrated to ensure that the merged logo image looks natural and meets the requirements for QR code recognition.

[0012] A logo graphic and QR code dot matrix identification system is provided for implementing the aforementioned logo graphic and QR code dot matrix identification method, comprising: The information acquisition module is used to acquire the logo graphic to be merged, the QR code dot matrix information, and the carrier material parameters; The logo processing module is used to extract features and partition the logo graphic to obtain the core recognition area and auxiliary decoration area of ​​the logo, and mark the coordinates of the feature key points of the core recognition area. The dot matrix generation module is used to generate a binary array of the original QR code dot matrix based on the QR code dot matrix information. The density adjustment module is used to adjust the density of the original QR code dot matrix binary array based on the logo partitioning results and carrier material parameters to generate an adaptation array. In the core recognition area, only dot matrix display units are retained at the coordinates of non-feature key points, and the dot diameter and contrast of the display units are adjusted according to the carrier material parameters. The dynamic verification module is used to extract the edge contour features of the logo graphic, generate feature hash values, and embed the feature hash values ​​as dynamic verification information into the adaptation array to obtain a binary array of graphic-code fusion. The fusion rendering module is used to perform grayscale gradient rendering on the binary array of the logo and QR code fusion based on the grayscale distribution of the logo graphic, and generate the final logo and QR code dot matrix fusion identification image.

[0013] Preferably, the logo processing module includes: The logo feature acquisition unit is used to acquire the edge density features, Fourier energy frequency domain energy features, and color difference contrast of the logo graphic. The normalization processing unit is used to normalize the edge density features, Fourier energy frequency domain energy features, and color difference contrast. The saliency acquisition unit is used to obtain visual saliency weight values ​​based on the normalized edge density features, Fourier energy frequency domain energy features, and color difference contrast. The partitioning unit is used to obtain the logo partitioning threshold, normalize the visual salience weight value, and then partition the logo graphic according to the normalized visual salience weight value and the logo partitioning threshold.

[0014] Preferably, the density adjustment module includes: The optical term acquisition unit is used to acquire the surface refractive index of the carrier material, the ambient light absorption rate, and the wavelength of the light source. Based on the surface refractive index of the carrier material, the ambient light absorption rate, and the wavelength of the light source, it acquires the fundamental optical physics terms used to characterize the optical transmission efficiency of the carrier medium. The background term acquisition unit is used to acquire the background texture gradient norm and, based on the background texture gradient norm, acquires a background texture suppression term used to quantify the interference intensity of the carrier background on dot matrix recognition. Decoding coefficient acquisition unit, used to acquire device decoding coefficients that represent the comprehensive error correction capability of the barcode scanning hardware system; The identification point diameter acquisition unit is used to obtain the minimum recognizable point diameter based on optical physics fundamentals, background texture suppression, and device decoding coefficients. The dot density acquisition unit is used to acquire the dot density of the auxiliary decorative area based on the minimum identifiable dot diameter.

[0015] Preferably, the dynamic verification module includes: The hash value acquisition unit is used to extract key feature points of the core recognition area of ​​the logo and generate a 256-bit SHA-256 feature hash value. The encoding embedding unit is used to select redundant dot matrix units in the auxiliary decoration area as embedding bits, and embeds the hash value using binary replacement after encoding the hash value. The verification binding unit is used to add an 8-bit CRC-8 checksum to the original dot matrix information. Attached Figure Description

[0016] The invention will be further understood from the following description taken in conjunction with the accompanying drawings. The components in the drawings are not necessarily drawn to scale, but rather the emphasis is on illustrating the principles of the embodiments. In different views, the same reference numerals designate corresponding parts.

[0017] Figure 1 This is a schematic diagram of the overall process of a method for identifying a logo graphic and a QR code dot matrix in one embodiment of the present invention; Figure 2 This is a flowchart illustrating a specific method for feature extraction and partitioning of a logo graphic in one embodiment of the present invention. Figure 3 This is a flowchart illustrating a specific method for adjusting the density of an original QR code dot matrix binary array in one embodiment of the present invention. Figure 4 This is a flowchart illustrating a specific method for embedding feature hash values ​​as dynamic verification information into an adaptation array in one embodiment of the present invention. Figure 5 This is a flowchart illustrating a specific method for grayscale gradient rendering of a binary array fused with image and code, according to an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to its embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and do not limit the scope of protection of the invention.

[0019] It should be noted that when an element is referred to as being "fixed to" another element, it can be directly attached to the other element or there may be an intervening element. When an element is referred to as being "connected to" another element, it can be directly connected to the other element or there may be an intervening element. The terms "vertical," "horizontal," "left," "right," and similar expressions used herein are for illustrative purposes only and do not represent the only possible implementation.

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0021] In this invention, "first" and "second" do not represent a specific quantity or order, but are merely used to distinguish names.

[0022] Before describing the embodiments of the present invention, a brief introduction to the prior art will be given.

[0023] With the rapid development of the Internet of Things, industrial traceability, and brand anti-counterfeiting, QR code dot matrix labels have been widely used in product packaging, production line management, and logistics tracking due to their advantages such as large information storage capacity, fast recognition, and low cost. Meanwhile, brand logos, as the core of corporate visual identity, are key elements for enhancing brand recognition and strengthening brand awareness. Integrating logo graphics with QR code dot matrix labels to achieve a unified "brand display + information delivery" has become an important development direction in the current field of labeling technology.

[0024] Currently, existing logo and QR code dot matrix fusion technologies are mainly divided into two categories: basic overlay and simple embedded. However, they still have many technical shortcomings in practical applications, making it difficult to balance visual fusion, recognition stability, anti-counterfeiting security, and cross-scene adaptability. Specific problems are as follows: Firstly, existing fusion technologies mostly adopt a "layered overlay" mode, which directly overlays the QR code dot matrix onto the logo graphic surface, or simply embeds dot matrix units in the blank areas of the logo, without differentiating and adapting them to the logo's characteristic areas. This approach easily leads to visual fragmentation and blurred edges, with the core recognition area of ​​the logo being obscured by the dot matrix, thus compromising the logo's integrity and visual recognizability. For example, Chinese invention patent application number CN107239814A discloses a stacked QR code that integrates logo icons. Furthermore, the use of a uniform dot matrix density and arrangement rule between the core recognition area and the auxiliary decorative area fails to balance the logo's visual effect with the information storage and recognition requirements of the QR code dot matrix. This is especially problematic for logos with complex outlines and rich details, easily leading to dot matrix recognition failure or blurred logo features.

[0025] Secondly, existing technologies lack an adaptive adjustment mechanism for carrier materials. Different carrier materials (such as paper, metal, plastic, and glass) have significantly different optical properties (refractive index, absorptivity, and reflectivity), which significantly affects the display effect and recognition stability of the QR code dot matrix. For example, metal surfaces have high reflectivity, which can easily cause glare and interfere with QR code recognition; transparent plastic materials have high transmittance, which can easily lead to inconsistent dot matrix contrast. Existing technologies mostly use fixed dot matrix diameter and contrast parameters without dynamic optimization based on the characteristics of the carrier material. This results in large fluctuations in the recognition rate of fused logos on different carrier materials, poor cross-material adaptability, and difficulty in meeting the needs of multi-scenario industrial applications.

[0026] Third, the anti-counterfeiting security is insufficient. Existing integrated logo QR code dot matrix information is mostly stored in plaintext or simply encrypted, lacking a dynamic verification mechanism deeply bound to the logo graphic. Criminals can easily copy the logo graphic and forge the QR code dot matrix, leading to counterfeit and substandard products entering the market and harming the rights of businesses and consumers. While some technologies attempt to embed verification information, this information is unrelated to logo features, and the embedding method easily interferes with the normal decoding of the QR code dot matrix, failing to achieve dual verification of "logo authenticity + dot matrix information validity."

[0027] Fourth, the visual fusion effect is abrupt. Existing QR code dot matrix technologies mostly use a black-and-white binary display mode, lacking a natural transition with the grayscale or color distribution of the logo graphic. There are obvious grayscale breaks and boundaries between the dot matrix units and logo pixels, resulting in poor visual aesthetics of the fused logo and affecting the brand display effect. Although some improved technologies introduce grayscale fusion, they only use a fixed grayscale mapping ratio and do not dynamically adapt to the grayscale distribution characteristics of the logo. This makes it impossible to achieve seamless visual fusion between the dot matrix and the logo, and the dot matrix recognition rate is prone to decrease due to too small a grayscale difference.

[0028] In summary, current logo graphic and QR code dot matrix fusion technologies generally suffer from low visual integration, poor cross-material adaptability, and insufficient anti-counterfeiting security, failing to fully meet the practical application needs of various scenarios such as brand display, information traceability, and anti-counterfeiting. Therefore, developing a logo graphic and QR code dot matrix identification method that can achieve precise logo feature partitioning, adaptive adjustment to carrier materials, dynamic verification for anti-counterfeiting, seamless grayscale fusion, and balance visual effects and recognition stability has become an urgent technical problem to be solved in this field.

[0029] One of the objectives of this invention is to solve the problem of visual fragmentation and blurred edges caused by layering in the prior art, which undermines the integrity and visual recognizability of the logo.

[0030] Therefore, such as Figure 1 As shown, one embodiment of the present invention provides a method for identifying a logo graphic and a QR code dot matrix, comprising the following steps: S1: Obtain the logo graphic to be merged, QR code dot matrix information, and carrier material parameters.

[0031] The logo images here include, but are not limited to, text-based, graphic-based, color, and grayscale images. The carrier materials include metal, glass, paper, and plastic. Carrier material parameters include optical properties such as refractive index, absorptivity, and reflectivity.

[0032] S2 performs feature extraction and partitioning on the logo graphic to obtain the core recognition area and auxiliary decoration area of ​​the logo, and marks the coordinates of the key feature points in the core recognition area.

[0033] As a preferred technical solution, such as Figure 2 As shown, the specific method for feature extraction and partitioning of the logo graphic includes the following steps: S21, obtain the edge density features, Fourier energy frequency domain energy features, and color difference contrast of the logo graphic.

[0034] S22, normalize the edge density features, Fourier energy frequency domain energy features, and color difference contrast, and obtain the visual saliency weight value based on the normalized edge density features, Fourier energy frequency domain energy features, and color difference contrast.

[0035] S23, obtain the logo partition threshold, normalize the visual salience weight value, and then partition the logo graphic according to the normalized visual salience weight value and the logo partition threshold.

[0036] Visual salience weight value .in, These represent the normalized edge density features, Fourier energy frequency domain energy features, and color difference contrast, respectively. These are the weighting coefficients.

[0037] Specifically, edge density features are used to characterize the intensity of human eye attention capture by the contour structure, denoted as: . This represents an adaptive local window k×k, which is a local window centered at (x,y). , These represent the Gaussian filter and the input logo graphic, respectively. This represents the Sobel gradient operator, and NMS represents non-maximum suppression of edges, removing stray edges. Here, NMS is used for non-maximum suppression and noise suppression to avoid printing noise and decorative fine edges being misclassified as highly significant.

[0038] Fourier energy frequency domain energy features are used to reflect the visual information content of texture details, where high frequencies represent rich details and low frequencies represent smooth regions. A Discrete Fourier Transform (DFT) can be performed on a local region first, and then the Fourier energy frequency domain energy features can be obtained by extracting the proportion of high-frequency energy. However, since window-by-window DFT has extremely high computational cost and suffers from spectral leakage and picket-fence effects, Difference of Gaussians (DoG) can be used to replace local DFT.

[0039] Color contrast ratio represents the salience of colors based on the color sensitivity characteristics of the human eye. It can be determined by converting RGB to the CIELab color space and then calculating the local contrast ratio.

[0040] For weighting coefficients It can be adaptively adjusted according to different application scenarios. Please refer to the table below for details:

[0041] As a preferred technical solution, the fixed weight defect can be solved by adaptively allocating edge entropy (i.e., contour complexity) and saturation mean. For example, α = (0.5 + 0.3 × edge entropy) / (1 + 0.3 × edge entropy + 0.2 × saturation mean), β = 0.2 / (1 + 0.3 × edge entropy + 0.2 × saturation mean), γ = 0.3 × saturation mean / (1 + 0.3 × edge entropy + 0.2 × saturation mean).

[0042] To avoid decoding failure due to excessive weakening of highly saliency regions, the error correction capacity can be correlated to obtain the QR code scenario adaptation factor. EC represents the QR code error correction capacity, which is between 0 and 1 and is determined by the version / error correction level. It can be set by technical personnel based on experience.

[0043] After introducing the QR code scene adaptation factor, the visual saliency weight value is represented as follows: .

[0044] Preferably, a power-law fitting of human visual nonlinear perception can be used to obtain the visual saliency weight value. .

[0045] After normalizing the visual saliency weight values, the results are based on the normalized visual saliency weight values. Divide the logo area to guide the dot matrix layout. 1. Core recognition area: 1. The QR code dot matrix density is reduced to 30%~50% in the auxiliary area to avoid key feature points; 2. Auxiliary decoration area: Normal arrangement of QR code dots; low visual area: It can encrypt the dot matrix to increase information capacity.

[0046] In summary, by fusing multi-channel visual features, the visual appeal intensity of each area in the logo image is quantified. This embodiment can automatically divide the core recognition area (high saliency) and the auxiliary decoration area (low saliency), avoiding the subjectivity of manual marking, while ensuring that the QR code dot matrix is ​​weakened in the high saliency area to maintain the recognizability of the logo.

[0047] S3 generates the original binary array of QR code dots based on the QR code dot matrix information.

[0048] S4, based on the logo partitioning results and carrier material parameters, adjusts the density of the original QR code dot matrix binary array to generate an adaptation array; in the core recognition area, only dot matrix display units are retained at the coordinates of non-feature key points, and the dot diameter and contrast of the display units are adjusted according to the carrier material parameters.

[0049] As a preferred technical solution, such as Figure 3 As shown, the specific method for adjusting the density of the original QR code dot matrix binary array includes the following steps: S41, obtain the surface refractive index of the carrier material, the ambient light absorption rate, and the wavelength of the light source, and obtain the optical physics fundamentals used to characterize the optical transmission efficiency of the carrier medium based on the surface refractive index of the carrier material, the ambient light absorption rate, and the wavelength of the light source.

[0050] S42, obtain the background texture gradient norm, and obtain the background texture suppression term used to quantify the interference intensity of the carrier background on the dot matrix recognition based on the background texture gradient norm.

[0051] S43, obtain the device decoding coefficients used to represent the overall error correction capability of the barcode scanning hardware system.

[0052] S44, the minimum recognizable dot diameter is obtained based on optical physics fundamentals, background texture suppression, and device decoding coefficients.

[0053] S45, obtain the dot matrix density of the auxiliary decorative area based on the minimum identifiable dot diameter.

[0054] For example, minimum identifiable point diameter .in, These represent the device decoding coefficients, optical physics fundamentals, and background texture suppression, respectively.

[0055] The device decoding coefficient can be understood as a calibration coefficient determined by the optical lens and CMOS resolution of the barcode scanner. It is generally set based on experience, for example, device decoding coefficient = 0.8 × camera pixel density / minimum recognizable pixel density supported by the device + 0.2 × lens modulation transfer function.

[0056] Preferably, the device decoding coefficient is set based on the device's basic decoding coefficient, which is between 0.6 and 1.2. For example, an industrial barcode scanner is set to 0.8, and a mobile phone scanner is set to 1.0. Here, scanning distance and incident angle are introduced to dynamically correct the device decoding coefficient, generating an engineering-calibrable device coefficient, thus obtaining the device decoding coefficient. . These represent, in order, the device's basic decoding coefficient, the actual scanning distance, the device's standard calibration scanning distance (default is 100mm), and the actual scanning incident angle (the angle between the ray and the carrier normal). This represents the scanning distance correction index, used to fit the attenuation of the device's resolution, and is generally between 0.8 and 1.0. Thus, the farther the scanning distance, the larger the device's decoding coefficient, and the larger the corresponding dot diameter needs to be; the larger the incident angle, the larger the device's decoding coefficient, the greater the loss of reflected light, and the larger the corresponding dot diameter needs to be. This effectively matches the actual scanning patterns.

[0057] Fundamentals of Optical Physics middle, These represent, in order, the center wavelength of the light source of the barcode scanning device, the wavelength-dependent ambient light absorptivity, and the wavelength-dependent surface refractive index of the carrier material. Background texture suppression item. middle, These represent the background gradient calibration coefficient and the background texture correction index, respectively. The background gradient calibration coefficient is used for division by zero protection and amplitude constraint, i.e., balancing the pixel diameter variation in smooth / complex scenes. It is generally between 40 and 60, with a default value of 50. The background texture correction index is used to suppress the amplitude of the lg term, avoiding excessively large pixel diameters when the background is smooth, while ensuring the monotonicity of larger pixel diameters as the texture becomes more complex. It is generally between 0.3 and 0.5.

[0058] This represents the normalized background texture gradient norm. The background texture gradient norm, to some extent, indicates the complexity of the carrier surface texture; a larger value indicates a more complex texture. Before normalizing the background texture gradient norm, Gaussian noise reduction can be performed on the grayscale image of the carrier background to suppress industrial noise.

[0059] Of course, the normalized background texture gradient norm generally needs to be limited to a lower limit, such as 0.01, for division by zero protection. The calculated minimum identifiable dot diameter is limited to the minimum dot diameter of the carrier processing technology and the maximum effective dot diameter of the dot matrix by a numerical clamping function, then unit conversion is performed, and finally the dot matrix density of the auxiliary decorative area is obtained based on the minimum identifiable dot diameter.

[0060] For example, the dot density of the auxiliary decorative area is set to .in, This represents the Euclidean distance from the current dot diameter to the core area boundary, and the constant 0.8 represents the baseline density of the auxiliary area, i.e., the maximum tolerable density. Here, exponential decay is used to simulate the spatial continuity requirement of dot matrix recognition, ensuring scanning stability and avoiding artificial boundary effects.

[0061] The specific method for adjusting the density of the original QR code dot matrix binary array includes the following steps: obtaining the dot matrix density of the core recognition area based on the normalized visual saliency weight values. For example, the dot matrix density of the core recognition area... Here, the constants 0.5 and 0.3 represent the basic distribution probability and the saliency modulation intensity, respectively. The saliency modulation intensity can be calibrated by visual experiments based on the logic of the nonlinear response threshold of the human eye to changes in saliency (Weber-Fechner law). In this type, the dot density of the core area is negatively correlated with visual saliency, which can protect the logo's recognizability.

[0062] In summary, based on the normalized visual saliency weights and the minimum identifiable point diameter, and guided by visual saliency and constrained by spatial continuity, intelligent optimization of dot matrix density can be achieved, eliminating the jagged edge effect caused by traditional binarization.

[0063] S5. Extract the edge contour features of the logo graphic, generate feature hash values, embed the feature hash values ​​as dynamic verification information into the adaptation array, and obtain the image-code fusion binary array.

[0064] As a preferred technical solution, such as Figure 4 As shown, the specific method for embedding the feature hash value as dynamic verification information into the adaptation array includes the following steps: S51 extracts key feature points from the core recognition area of ​​the logo and generates a 256-bit SHA-256 feature hash value.

[0065] S52, redundant dot matrix units are selected as embedding bits in the auxiliary decoration area, and the hash value is encoded and embedded using a binary replacement method.

[0066] S53 adds an 8-bit CRC-8 checksum to bind the original dot matrix information, and finally outputs a binary array of image and code fusion.

[0067] Specifically, the dynamic embedding method for logo feature hash values ​​employs a four-step process: hash generation, position adaptation, encoding embedding, and verification binding, to accurately embed the logo feature hash values ​​into an adaptation array. The first step, feature extraction and hash generation, extracts key feature points such as corners and inflection points of the logo's core recognition area edge contour, generates a 256-bit fixed-length feature hash value using the SHA-256 algorithm, converts it to a 64-bit hexadecimal string, and then to a 256-bit binary hash string. The second step, dynamic embedding position adaptation, based on the logo's partitioning results, selects redundant dot matrix units (i.e., non-critical units that do not affect QR code dot matrix decoding) only in the auxiliary decorative area as embedding bits. The number of embedding bits is ≥256 bits + 8 check bits, and the distance between adjacent embedding bits is ≥3 points. The first step is to embed redundant dot matrix units (to avoid interference with decoding); the second step is to encode and embed the 256-bit binary hash string into groups of 4 bits each, with each group corresponding to one hexadecimal code element. Redundant dot matrix units are embedded using binary substitution and bit flipping error-tolerant methods (the original "1" corresponds to the dot matrix display unit; the grayscale of the unit is finely adjusted according to the binary value of the code element during embedding, without changing the display / blank attributes); the third step is to verify and bind the original dot matrix information in the adaptation array. An 8-bit CRC-8 check code (calculated based on the 256-bit hash string) is added to the end of the embedding bits to form complete embedded data of the hash string and check code.

[0068] S6. Based on the grayscale distribution of the logo graphic, perform grayscale gradient rendering on the binary array of the logo and QR code fusion to generate the final logo and QR code dot matrix fusion identification image.

[0069] As a preferred technical solution, such as Figure 5 As shown, the specific method for grayscale gradient rendering of the image-code fused binary array includes the following steps: S61, convert the logo graphic into a smoothed 8-bit grayscale image, and establish the coordinate mapping relationship between the image-code fusion binary array and the logo grayscale image.

[0070] S62, construct a dynamic grayscale mapping function, calculate the grayscale of the dot matrix display unit by superimposing the grayscale average value of the logo in the corresponding area with the grayscale compensation value, and the grayscale of the blank unit inherits the grayscale average value of the logo in the corresponding area. The dot matrix unit with embedded verification information superimposes the grayscale value according to the preset rules.

[0071] S63 adjusts rendering parameters differently based on the logo partitioning results, smooths edges and globally calibrates contrast to ensure that the fused logo image looks natural and meets the requirements for barcode recognition.

[0072] Specifically, gradient rendering can be achieved by combining the binary array of the image and code (including dot matrix display unit "1", blank unit "0" and embedded dynamic verification information) with the grayscale distribution of the logo graphic and through pixel-level grayscale adaptation.

[0073] The first step is logo grayscale preprocessing: convert the original logo graphic into an 8-bit grayscale image, denoted as I_gray(x,y), where (x,y) are pixel coordinates; perform a 3×3 Gaussian smoothing filter on the grayscale image to eliminate noise interference from the logo itself, and obtain a smoothed grayscale image I_smooth(x,y), avoiding rendering breaks caused by sudden changes in local grayscale.

[0074] The second step is to align the coordinates of the image-code fusion array: map the image-code fusion binary array A(x',y') to the logo grayscale image pixel coordinate system to ensure that the dot matrix unit corresponds one-to-one with the logo pixel (coordinate mapping relationship: x'=x×s, y'=y×s, s is the dot matrix unit scaling factor, which adapts to the logo size and dot matrix density. The core is to ensure that each dot matrix unit covers 1-4 logo grayscale pixels, balancing rendering accuracy and computing cost); define the logo pixel area corresponding to the dot matrix display unit A(x',y')=1 as R_dot(x,y), and the logo pixel area corresponding to the blank unit A(x',y')=0 as R_blank(x,y).

[0075] The third step is to establish grayscale dynamic mapping rules: Based on the smoothed grayscale distribution of the logo, construct a grayscale mapping function for dot matrix units. The core principle is that the grayscale of the dot matrix display unit matches the grayscale of the surrounding logo, the grayscale of the blank unit inherits the grayscale of the surrounding logo, and the grayscale of the dot matrix unit with embedded verification information is slightly marked.

[0076] For grayscale calculation of the dot matrix display unit: Take the mean value μ_dot of the smooth grayscale of the logo within the coverage area R_dot(x,y) of the unit, and combine it with the core requirements of QR code recognition (black and white contrast ≥30), calculate the grayscale value G_dot=μ_dot-ΔG, where ΔG is the grayscale compensation value, and the dynamic adjustment range can be set to 15-40; when μ_dot≤50 (dark area of ​​logo), ΔG=15-20 to avoid excessive blackness causing dot matrix blurring; when 50<μ_dot<200 (middle grayscale area of ​​logo), ΔG=25-30 to ensure the grayscale gradient between the dot matrix and the surrounding area; when μ_dot≥200 (light area of ​​logo), ΔG=35-40 to improve the contrast between the dot matrix and the light background to ensure QR code recognition; finally, G_dot needs to be cropped to the range of 0-255, denoted as G_dot_final.

[0077] For calculating the grayscale of the blank unit: take the mean value μ_blank of the smooth grayscale of the logo within the coverage area R_blank(x,y) of the unit, and directly use it as the grayscale of the blank unit G_blank=μ_blank, to ensure that the blank unit is completely integrated into the surrounding logo area without obvious boundary.

[0078] For grayscale adjustment of dot matrix units embedding verification information: For dot matrix display units embedding dynamic verification information, a grayscale fine adjustment of ±5 is superimposed on G_dot_final. For example, if the verification bit is "1", then +5 is applied, and if it is "0", then -5 is applied. This fine adjustment range does not affect the visual fusion degree and can be used as an auxiliary identifier for the extraction of verification information during decoding, thereby improving the reliability of dynamic verification.

[0079] The fourth step is to optimize the rendering of different areas: combine the partitioning results of the logo's core recognition area and the auxiliary decoration area, and adjust the rendering parameters differently to avoid interference with logo recognition by rendering the core area.

[0080] For the core recognition area: the ΔG of the dot matrix display unit is reduced by 5-10 to reduce grayscale differences and weaken the presence of the dot matrix. The grayscale of the blank unit completely inherits the grayscale of the surrounding logo. The edge feathering value is increased to 2 pixels to smooth the boundary between the dot matrix and the logo.

[0081] For auxiliary decorative areas: render according to standard mapping rules, take the median value of ΔG, and feather the edge by 1 pixel to balance visual appeal and recognition.

[0082] For low visual areas: the dot matrix display unit ΔG is increased by 5, enhancing the dot matrix contrast and increasing the information storage density. The grayscale of blank units can be appropriately reduced to create a slight difference with the dot matrix units without affecting the fusion.

[0083] Step 5, Edge Smoothing and Global Calibration: At the junction of the rendered dot matrix units and logo pixels, a bilateral filtering algorithm is used for edge smoothing to eliminate grayscale steps. The average grayscale value μ_global and contrast C_global of the entire image are calculated (C_global = μ_max - μ_min, μ_max / μ_min are the maximum / minimum grayscale values ​​of the entire image). If C_global < 30, the contrast is deemed insufficient, and the grayscale of all dot matrix display units is uniformly reduced by 10-15 to ensure that the scanning device can stably capture the dot matrix signal. Finally, an 8-bit grayscale format fused logo image I_fusion(x,y) is generated, with a grayscale value range of 0-255, which can be directly used for printing / engraving output.

[0084] The logo graphic and QR code dot matrix identification method described in this invention achieves deep symbiosis between the dot matrix and the logo through logo feature partitioning and density adaptive adjustment, avoiding visual fragmentation and edge blurring caused by layering. At the same time, the core area protection design improves the stability of logo recognition. Furthermore, by integrating carrier material parameters and embedding dynamic verification information, it also solves the defects of poor stability of multi-material adaptation and insufficient anti-counterfeiting in existing technologies. In addition, grayscale gradient rendering makes the fusion effect more natural.

[0085] In summary, the proposed logo graphic and QR code dot matrix marking method is the first to achieve a three-in-one design of logo feature protection, material adaptation, and dynamic anti-counterfeiting. It can achieve the integration of visual aesthetics, information carrying, and anti-counterfeiting traceability, and solve the problems of visual fragmentation and edge blurring caused by layering in existing technologies, which damage the integrity and visual recognizability of the logo.

[0086] An embodiment of the present invention also provides a logo graphic and QR code dot matrix identification system for implementing the aforementioned logo graphic and QR code dot matrix identification method, which includes an information acquisition module, a logo processing module, a dot matrix generation module, a density adjustment module, a dynamic verification module, and a fusion rendering module.

[0087] The information acquisition module is used to acquire the logo graphic to be fused, the QR code dot matrix information, and the carrier material parameters; the logo processing module is used to extract features and partition the logo graphic to obtain the core recognition area and auxiliary decoration area of ​​the logo, and mark the coordinates of the feature key points of the core recognition area.

[0088] As a preferred technical solution, the logo processing module includes: a logo feature acquisition unit, used to acquire the edge density features, Fourier energy frequency domain energy features, and color difference contrast of the logo graphic; a normalization processing unit, used to normalize the edge density features, Fourier energy frequency domain energy features, and color difference contrast; and a saliency acquisition unit, used to acquire visual saliency weight values ​​based on the normalized edge density features, Fourier energy frequency domain energy features, and color difference contrast.

[0089] Visual salience weight value .in, These represent the normalized edge density features, Fourier energy frequency domain energy features, and color difference contrast, respectively. These are the weighting coefficients.

[0090] The partitioning unit is used to obtain the logo partitioning threshold. After normalizing the visual saliency weight values, it partitions the logo graphic based on the normalized visual saliency weight values ​​and the logo partitioning threshold. The dot matrix generation module is used to generate the original QR code dot matrix binary array based on the QR code dot matrix information.

[0091] The density adjustment module is used to adjust the density of the original QR code dot matrix binary array based on the logo partitioning results and carrier material parameters to generate an adaptation array. In the core recognition area, only dot matrix display units are retained at the coordinates of non-feature key points, and the dot diameter and contrast of the display units are adjusted according to the carrier material parameters.

[0092] Specifically, within the core identification area, dot matrix display units are retained only at non-feature key point coordinates, and the density of these display units does not exceed 50% of the density of the auxiliary decorative area. The core identification area is the visual core of the logo graphic and the key to brand recognition, including trademark text, core graphic outlines, and iconic patterns. The core purpose of the dot matrix adaptation design in this area is to maximize the integrity and recognizability of the logo's core features, while appropriately retaining dot matrix display units to carry basic information without interfering with logo recognition. By retaining dot matrix display units only at non-feature key point coordinates, the logo's key feature points, such as the intersections of text strokes, the inflection points of graphic outlines, and the core pixels of iconic patterns, can be precisely avoided. This prevents dot matrix units from covering the logo's core visual elements, solving the core defect of existing technologies where dot matrix overlay / embedding leads to blurred logo features and reduced recognizability. This ensures that the human eye can quickly recognize the logo's brand information, while also fulfilling the brand display function. Meanwhile, strictly controlling the density of dot matrix display units in the core recognition area to within 50% of that in the auxiliary decoration area can significantly reduce the visual presence of dot matrix units in the core recognition area, reduce visual interference of the dot matrix on the core area of ​​the logo, and make the core features of the logo the visual focus; the appropriately retained dot matrix display units can carry basic QR code positioning information such as positioning patterns and timing information, providing basic support for subsequent dot matrix decoding, and avoiding the problem of failure of dot matrix decoding and positioning around the core recognition area due to the complete absence of dot matrix.

[0093] The auxiliary decorative area surrounds the core logo recognition area, including the logo border, background texture, and decorative patterns. This area has minimal impact on brand recognition. Its dot matrix adaptation design aims to maximize the carrying capacity of QR code dot matrix information while optimizing dot matrix display parameters based on the carrier material characteristics, ensuring stable dot matrix recognition across materials and scenarios. Since the auxiliary decorative area does not prioritize displaying the core logo features, it can employ a high-density dot matrix arrangement, fully carrying the complete information of the QR code dot matrix (including code value data and dynamic verification information). This solves the defect in existing technologies where dot matrix information capacity is compressed to protect the logo, ensuring the QR code dot matrix has complete information storage and traceability functions, meeting the core application needs of industrial traceability and anti-counterfeiting verification. Furthermore, different carrier materials, such as paper, metal, plastic, and glass, have significantly different optical properties and surface textures, directly affecting the imaging effect of the dot matrix display unit, such as the high reflectivity of metal and the low contrast of transparent plastic. By adjusting the dot diameter and contrast according to the carrier material parameters, the optical and surface characteristics of different materials can be dynamically adapted; for example, increasing the dot diameter and improving the contrast for metal materials can combat glare interference.

[0094] The normal-density dot matrix in the auxiliary decorative area provides a stable QR code positioning reference and complete code value information, which works in conjunction with the low-density positioning dot matrix in the core recognition area to ensure that scanning devices such as mobile phones and industrial barcode readers can quickly locate the dot matrix area and completely parse the dot matrix information. The optimization of the dot matrix parameters in this area (such as adjusting the dot diameter based on the minimum recognizable dot diameter function d) can ensure that the dot matrix unit meets the optical imaging and decoding algorithm requirements of the scanning device, avoid decoding failure caused by too small a dot diameter and insufficient contrast, and improve the overall decoding reliability of the fused mark.

[0095] As a preferred technical solution, the density adjustment module includes an optical item acquisition unit, a background item acquisition unit, a decoding coefficient acquisition unit, a recognition dot diameter acquisition unit, and a dot matrix density acquisition unit.

[0096] The optical term acquisition unit is used to acquire the surface refractive index of the carrier material, the ambient light absorption rate, and the wavelength of the light source. Based on the surface refractive index of the carrier material, the ambient light absorption rate, and the wavelength of the light source, it acquires the fundamental optical physics terms used to characterize the optical transmission efficiency of the carrier medium.

[0097] The background item acquisition unit is used to acquire the background texture gradient norm, and based on the background texture gradient norm, to acquire the background texture suppression term used to quantify the interference intensity of the carrier background on the dot matrix recognition; the decoding coefficient acquisition unit is used to acquire the device decoding coefficient used to represent the comprehensive error correction capability of the barcode scanning hardware system.

[0098] The identification dot diameter acquisition unit is used to obtain the minimum recognizable dot diameter based on optical physics fundamentals, background texture suppression, and device decoding coefficients; the dot density acquisition unit is used to obtain the dot density of the auxiliary decorative area based on the minimum recognizable dot diameter.

[0099] For example, minimum identifiable point diameter .in, These represent the device decoding coefficients, optical physics fundamentals, and background texture suppression, respectively.

[0100] The dynamic verification module is used to extract the edge contour features of the logo graphic, generate feature hash values, and embed the feature hash values ​​as dynamic verification information into the adaptation array to obtain the image-code fusion binary array; the fusion rendering module is used to perform grayscale gradient rendering on the image-code fusion binary array according to the grayscale distribution of the logo graphic to generate the final logo and QR code dot matrix fusion identification image.

[0101] In the fused logo image, the difference between the grayscale value of the dot matrix display unit and the grayscale value of the corresponding logo pixel does not exceed 20.

[0102] The dynamic verification module includes a hash value acquisition unit, an encoding embedding unit, and a verification binding unit.

[0103] The hash value acquisition unit is used to extract key feature points of the logo core recognition area and generate a 256-bit SHA-256 feature hash value; the encoding embedding unit is used to select redundant dot matrix units in the auxiliary decoration area as embedding bits, encode the hash value and embed it using binary replacement; the verification binding unit is used to add an 8-bit CRC-8 check code to bind the original dot matrix information.

[0104] In summary, the logo graphic and QR code dot matrix identification system has the following advantages: 1. Logo feature partitioning adaptation: Automatically extract the core recognition area of ​​the logo (such as trademark text and core graphics) and auxiliary decorative area. The dot matrix density of the core area is reduced by 30%-50% and avoids key feature points, while the dot matrix density of the auxiliary area is increased as needed to balance visual recognition and information storage.

[0105] 2. Multi-material parameter self-calibration: Based on the carrier material such as paper, aluminum, plastic, etc., it automatically adjusts the dot matrix diameter and contrast to ensure recognition stability in different scenarios.

[0106] 3. Dynamic verification information embedding: The logo feature hash value is bound to the dot matrix code information, and a dynamic verification bit is embedded. During decoding, the consistency between the dot matrix information and the logo feature must be verified at the same time to improve the anti-counterfeiting level.

[0107] 4. Grayscale gradient blending rendering: Abandoning the traditional black and white dot matrix, the grayscale information of the logo is mapped and blended with the binary value of the dot matrix code to achieve a natural visual transition between the dot matrix and the logo without obvious disjointedness.

[0108] In other words, this invention is the first to achieve a three-in-one design of logo feature protection, material adaptation, and dynamic anti-counterfeiting, which can realize the integration of visual aesthetics, information carrying, and anti-counterfeiting traceability. It solves the problem of visual fragmentation and edge blurring caused by layering in the existing technology, which damages the integrity and visual recognizability of the logo.

[0109] The technical features of the embodiments described can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0110] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for identifying a logo graphic and a QR code dot matrix, characterized in that, The method for combining the logo graphic with the QR code dot matrix identifier includes the following steps: Obtain the logo graphic to be merged, QR code dot matrix information, and carrier material parameters; The logo graphic is subjected to feature extraction and partitioning to obtain the core recognition area and auxiliary decoration area of ​​the logo, and the coordinates of the feature key points of the core recognition area are marked. Generate the original binary array of QR code dot matrix based on the QR code dot matrix information; Based on the logo partitioning results and carrier material parameters, the density of the original QR code dot matrix binary array is adjusted to generate an adaptation array; in the core recognition area, only dot matrix display units are retained at the coordinates of non-feature key points, and the dot diameter and contrast of the display units are adjusted according to the carrier material parameters. Extract the edge contour features of the logo graphic, generate feature hash values, embed the feature hash values ​​as dynamic verification information into the adaptation array, and obtain the image-code fusion binary array. Based on the grayscale distribution of the logo graphic, the binary array of the logo and QR code fusion is rendered with grayscale gradient to generate the final logo and QR code dot matrix fusion identification image.

2. The method for identifying a logo graphic and a QR code dot matrix as described in claim 1, characterized in that, The specific methods for feature extraction and partitioning of logo graphics include the following steps: Obtain the edge density features, Fourier energy frequency domain energy features, and color difference contrast of the logo graphic; The edge density features, Fourier energy frequency domain energy features, and color difference contrast are normalized, and the visual saliency weight values ​​are obtained based on the normalized edge density features, Fourier energy frequency domain energy features, and color difference contrast. Obtain the logo partitioning threshold, normalize the visual salience weight values, and then partition the logo graphic based on the normalized visual salience weight values ​​and the logo partitioning threshold.

3. The method for identifying a logo graphic and a QR code dot matrix as described in claim 2, characterized in that, The specific method for adjusting the density of the original QR code dot matrix binary array includes the following steps: Obtain the surface refractive index of the carrier material, the ambient light absorption rate, and the wavelength of the light source. Based on the surface refractive index of the carrier material, the ambient light absorption rate, and the wavelength of the light source, obtain the fundamental optical physics terms used to characterize the optical transmission efficiency of the carrier medium. Obtain the background texture gradient norm, and based on the background texture gradient norm, obtain a background texture suppression term used to quantify the interference intensity of the carrier background on dot matrix recognition. Obtain the device decoding coefficients used to represent the overall error correction capability of the barcode scanning hardware system; The minimum recognizable dot diameter is obtained based on optical physics fundamentals, background texture suppression, and device decoding coefficients. The dot matrix density of the auxiliary decorative area is obtained based on the minimum identifiable dot diameter.

4. The method for identifying a logo graphic and a QR code dot matrix as described in claim 3, characterized in that, The specific method for adjusting the density of the original QR code dot matrix binary array includes the following steps: The dot density of the core recognition area is obtained based on the normalized visual saliency weight values.

5. The method for identifying a logo graphic and a QR code dot matrix as described in claim 4, characterized in that, The specific method for embedding the feature hash value as dynamic verification information into the adaptation array includes the following steps: Extract key feature points from the core recognition area of ​​the logo and generate a 256-bit SHA-256 feature hash value; In the auxiliary decoration area, redundant dot matrix units are selected as embedding bits, and the hash values ​​are encoded and embedded using a binary replacement method. Add an 8-bit CRC-8 checksum to bind the original dot matrix information.

6. The method for identifying a logo graphic and a QR code dot matrix as described in claim 5, characterized in that, The specific method for grayscale gradient rendering of a binary array fused with an image and code includes the following steps: Convert the logo graphic into a smoothed 8-bit grayscale image and establish a coordinate mapping relationship between the image-code fusion binary array and the logo grayscale image; A dynamic grayscale mapping function is constructed to calculate the grayscale of the dot matrix display unit by superimposing the average grayscale value of the logo in the corresponding area with the grayscale compensation value. The grayscale of the blank unit inherits the average grayscale value of the logo in the corresponding area. The dot matrix unit with embedded verification information superimposes the grayscale value according to the preset rules. The rendering parameters were adjusted according to the differences in the logo partitioning results, the edges were smoothed and the contrast was globally calibrated to ensure that the merged logo image looks natural and meets the requirements for QR code recognition.

7. A logo graphic and QR code dot matrix identification system, used to implement the logo graphic and QR code dot matrix identification method as described in any one of claims 1-6, characterized in that, The logo graphic and QR code dot matrix identification system includes: The information acquisition module is used to acquire the logo graphic to be merged, the QR code dot matrix information, and the carrier material parameters; The logo processing module is used to extract features and partition the logo graphic to obtain the core recognition area and auxiliary decoration area of ​​the logo, and mark the coordinates of the feature key points of the core recognition area. The dot matrix generation module is used to generate a binary array of the original QR code dot matrix based on the QR code dot matrix information. The density adjustment module is used to adjust the density of the original QR code dot matrix binary array based on the logo partitioning results and carrier material parameters to generate an adaptation array. In the core recognition area, only dot matrix display units are retained at the coordinates of non-feature key points, and the dot diameter and contrast of the display units are adjusted according to the carrier material parameters. The dynamic verification module is used to extract the edge contour features of the logo graphic, generate feature hash values, and embed the feature hash values ​​as dynamic verification information into the adaptation array to obtain a binary array of graphic-code fusion. The fusion rendering module is used to perform grayscale gradient rendering on the binary array of the logo and QR code fusion based on the grayscale distribution of the logo graphic, and generate the final logo and QR code dot matrix fusion identification image.

8. The logo graphic and QR code dot matrix identification system as described in claim 7, characterized in that, The logo processing module includes: The logo feature acquisition unit is used to acquire the edge density features, Fourier energy frequency domain energy features, and color difference contrast of the logo graphic. The normalization processing unit is used to normalize the edge density features, Fourier energy frequency domain energy features, and color difference contrast. The saliency acquisition unit is used to obtain visual saliency weight values ​​based on the normalized edge density features, Fourier energy frequency domain energy features, and color difference contrast. The partitioning unit is used to obtain the logo partitioning threshold, normalize the visual salience weight value, and then partition the logo graphic according to the normalized visual salience weight value and the logo partitioning threshold.

9. A logo graphic and QR code dot matrix identification system as described in claim 8, characterized in that, The density adjustment module includes: The optical term acquisition unit is used to acquire the surface refractive index of the carrier material, the ambient light absorption rate, and the wavelength of the light source. Based on the surface refractive index of the carrier material, the ambient light absorption rate, and the wavelength of the light source, it acquires the fundamental optical physics terms used to characterize the optical transmission efficiency of the carrier medium. The background term acquisition unit is used to acquire the background texture gradient norm and, based on the background texture gradient norm, acquires a background texture suppression term used to quantify the interference intensity of the carrier background on dot matrix recognition. Decoding coefficient acquisition unit, used to acquire device decoding coefficients that represent the comprehensive error correction capability of the barcode scanning hardware system; The identification point diameter acquisition unit is used to obtain the minimum recognizable point diameter based on optical physics fundamentals, background texture suppression, and device decoding coefficients. The dot density acquisition unit is used to acquire the dot density of the auxiliary decorative area based on the minimum identifiable dot diameter.

10. A logo graphic and QR code dot matrix identification system as described in claim 9, characterized in that, The dynamic verification module includes: The hash value acquisition unit is used to extract key feature points of the core recognition area of ​​the logo and generate a 256-bit SHA-256 feature hash value. The encoding embedding unit is used to select redundant dot matrix units in the auxiliary decoration area as embedding bits, and embeds the hash value using binary replacement after encoding the hash value. The verification binding unit is used to add an 8-bit CRC-8 checksum to the original dot matrix information.

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