Protection method and system for design copyright of automobile cushion
By extracting the outline and local features of car mat designs, and combining asymmetric encryption and distributed ledger, copyright feature data is generated, solving the problems of lack of evidence and misjudgment in the copyright protection of car mat designs, and achieving highly distinguishable copyright evidence and credibility.
Patent Information
- Application Number
- CN202511459300.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-13
- Publication Date
- 2026-01-27
AI Technical Summary
Existing technologies lack reliable means of evidence preservation in the protection of copyright for car mat designs, making it difficult to provide highly distinguishable evidence. Furthermore, they cannot accurately express the originality of the design during infringement comparisons, which can easily lead to misjudgments.
By extracting the outline and local features of the car mat design drafts, an asymmetric encryption algorithm is used to generate a digital signature and record it in a distributed ledger. Combining the shape features and local feature sequences, copyright feature data is generated to ensure the credibility and immutability of the evidence.
It enables the generation of copyright feature data with high distinguishability between global and local information of car mat design, solves the problems of difficulty in providing evidence and misjudgment in copyright protection, and provides publicly verifiable and tamper-proof evidence records.
Smart Images
Figure CN121412962A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of image processing technology, and in particular to a method and system for protecting the copyright of car mat designs. Background Technology
[0002] With the booming development of the automotive consumer market, the personalization and aesthetic design of car interiors are receiving increasing attention. Car mats are a common automotive accessory, but due to the ease with which design drafts can be copied and disseminated digitally, plagiarism and infringement in the field of car mat design are commonplace. Copyright protection methods, such as work registration, can provide a legal basis, but the process is usually cumbersome and time-consuming, and it is inadequate in dealing with the high incidence and rapid pace of online infringement. When infringement disputes occur, rights holders often face difficulties in providing evidence, especially credible evidence proving that the creation time predates the infringing act.
[0003] To address the evidence collection issues in infringement disputes, existing technologies employ electronic data storage to prove the creation time. However, relying on timestamps generated by local computer file systems lacks strong legal evidentiary value due to its susceptibility to tampering, making it difficult to form a complete and valid chain of evidence for rights. In infringement comparison, existing technologies often use computer vision methods to extract image features to generate digital fingerprints representing the design. However, these methods have significant limitations when applied to car mat designs. For example, simple hash algorithms like MD5 and SHA-1 are extremely sensitive to minor changes in image content, leading to completely different hash values, thus rendering them unusable for similarity assessment. While some improved perceptual hash algorithms are robust to minor image editing, they primarily focus on the macroscopic structure of the image (such as brightness distribution), exhibiting weak ability to distinguish crucial details in car mat designs, such as complex local textures, patterns, and color layouts. Similarly, feature representation methods relying solely on contour shapes or color histograms also have shortcomings; the former ignores rich internal pattern information, while the latter loses all spatial layout features.
[0004] Therefore, existing technologies face two core problems in protecting the copyright of car mat designs. Firstly, regarding proof of rights, the lack of a reliable technical means to solidify and preserve key evidence such as creation time leads to difficulties for rights holders in providing evidence when seeking redress. Secondly, in terms of infringement comparison, existing image feature extraction schemes cannot accurately represent the originality of car mat designs. Whether it's a simple hash algorithm that is overly sensitive to minor modifications, or perceptual hashing and other feature representation methods that ignore key information such as local texture, pattern, and spatial layout, all are prone to misjudgment in similarity assessments. Summary of the Invention
[0005] Therefore, the purpose of this invention is to provide a method and system for protecting the copyright of car mat designs, in order to solve the technical problem that the existing technology cannot accurately capture the key features of the overall outline, local texture and color distribution of car mat designs, making it difficult to provide highly distinguishable and credible evidence in copyright disputes.
[0006] To solve the above-mentioned technical problems, the present invention provides a technical solution for protecting the design copyright of car mats: A method for protecting the copyright of a car mat design includes the following steps: S1, Obtain a digital image of the design draft of the car mat to be protected; Extract the outline of the design draft from the digital image, Calculate the normalized Fourier descriptor of the outline and use it as a shape feature; S2, for the design draft area defined by the contour, radial sectors are divided according to the curvature distribution of the contour, and multiple non-equidistant concentric rings are generated according to the gradient intensity distribution within the design draft area. The intersection of the radial sectors and the concentric rings forms multiple grid cells; the digital image is converted to the CIELAB color space; for each grid cell, the gradient direction histogram of the brightness L* value of the pixels inside the grid cell with gradient intensity as the weight and the quantization histogram of the a*b* two-dimensional color distribution are extracted, and the two are combined into the local feature descriptor of the grid cell; S3, following a predetermined order of scanning from the inner ring to the outer ring along a specific direction, serialize and splice the local feature descriptors of all grid cells to form a local feature sequence; combine the shape features with the local feature sequence to generate the copyright feature data of the design draft; S4. The copyright feature data is digitally signed using a private key of an asymmetric encryption algorithm, and a digital signature is generated. The digital signature is then associated with a timestamp and recorded in a distributed ledger.
[0007] Further, in step S1, the method for obtaining the shape features includes: An edge detection algorithm is used to extract the closed contour of the design draft, and a sequence of contour pixel coordinates is obtained in order. The sequence of outline pixel coordinates is converted into a sequence of complex numbers, where the coordinates of each pixel correspond to a complex number. Perform a discrete Fourier transform on the complex sequence to obtain the Fourier coefficients; The Fourier coefficients are normalized, and the amplitudes of the first N normalized Fourier coefficients are selected to form the shape feature, where N is a preset number.
[0008] Preferably, the edge detection algorithm is the Canny edge detection operator.
[0009] Further, in step S2, the method for dividing the design drawing area defined by the outline into multiple grid cells includes: Calculate the centroid of the contour as the center point of the radial sector and the concentric ring; Calculate the curvature value of each point along the contour; based on the distribution of the curvature value, set dense radial sector boundaries at locations with drastic curvature changes, dividing the design drawing area into K radial sectors with unequal angles; Calculate the gradient intensity of all pixels within the design draft area; based on the center point, statistically analyze the cumulative distribution of gradient intensity at different radii; based on the cumulative distribution, generate M non-equidistant concentric ring boundaries, such that the sum of gradient intensity within each concentric ring tends to be the same; K and M are both positive integers; The intersection of the K radial sectors and the M concentric rings constitutes the plurality of grid cells.
[0010] Preferably, the coordinates of the centroid are obtained by averaging the coordinates of all pixels inside the contour.
[0011] Preferably, the curvature values of each point are accumulated along the contour starting from a starting point, and a radial sector boundary is set whenever the accumulated value reaches a preset proportion of the total curvature value.
[0012] Further, in step S2, the method for obtaining the local feature descriptor includes: Calculate the gradient direction and gradient intensity of the L* value of each pixel in the grid cell in the CIELAB color space, as well as the a* and b* values; The gradient direction is divided into multiple preset intervals, and the gradient intensity of each pixel is accumulated into the interval corresponding to the gradient direction to obtain a gradient direction histogram with gradient intensity as the weight. The a*b* two-dimensional color plane is divided into multiple quantization units. The distribution of the a* and b* values of all pixels in the grid unit is statistically analyzed in the quantization unit to generate the quantization histogram. The gradient direction histogram and the quantization histogram are normalized respectively, and the two normalized histograms are concatenated and spliced to form the local feature descriptor of the grid cell.
[0013] Further, in step S3, the method for obtaining the local feature sequence includes: Determine an initial radial sector and a scanning direction, wherein the scanning direction is clockwise or counterclockwise; Starting from the innermost concentric ring from the center point, traverse layer by layer to the outermost concentric ring; Within each concentric ring, starting from the initial radial sector, local feature descriptors of each grid cell within the concentric ring are extracted sequentially according to the scanning direction. The extracted local feature descriptors are concatenated end to end to form a one-dimensional vector, which serves as the local feature sequence.
[0014] Further, in step S4, the step of digitally signing the copyright feature data using a private key employing an asymmetric encryption algorithm to generate a digital signature, and then recording the digital signature in a distributed ledger after associating it with a timestamp, includes: Let the copyright feature data be ,use Hash algorithms calculate hash digests , ; Let the private key of the asymmetric encryption algorithm be The hash digest is processed using the 256-bit Elliptic Curve Digital Signature Algorithm (ECDSA). Perform a signature and generate a digital signature. , ; Obtain the UTC timestamp T from a trusted timestamp issuing authority, and then use the hash digest. The generated digital signature Pack the data together with the UTC timestamp T into a single transaction data set, which is represented as follows: The transaction data is broadcast to the blockchain network for recording.
[0015] The technical solution of the automotive mat design copyright protection system provided by this invention is as follows: A system for protecting the copyright of car mat designs includes a processor and a memory. The memory stores a computer program, and the processor executes the computer program to implement a method for protecting the copyright of car mat designs, as described in any of the above technical solutions.
[0016] The beneficial effects of this invention are as follows: This invention generates copyright feature data with extremely high distinguishability between global and local information by combining the shape features representing the overall outline of the car mat with the local feature sequence of the internal pattern. Furthermore, this invention employs polar coordinate grid partitioning, combined with the brightness gradient histogram and average chromaticity value in the CIELAB color space to construct local features. This allows for the simultaneous acquisition of design texture details, color distribution, and spatial layout relationships, overcoming the shortcomings of inaccurate descriptions and misjudgments caused by limited information, and resolving the difficulty of evidence collection in copyright protection. Attached Figure Description
[0017] Figure 1This is a flowchart illustrating the steps of a method for protecting the copyright of an automotive mat design according to the present invention. Detailed Implementation
[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
[0019] A specific embodiment of a method for protecting the design copyright of car mats provided by this invention: like Figure 1 As shown, the method for protecting the copyright of car mat designs includes the following steps: S1, acquire a digital image of the design draft of the car mat to be protected; extract the outline of the design draft in the digital image, calculate the normalized Fourier descriptor of the outline and use it as a shape feature.
[0020] In this step, the physical design draft or photo of the car mat is converted into a digital file in JPG or PNG format, or a vector or bitmap file exported directly from the design software is used. The file is then loaded into the computer memory and preprocessed to a uniform size, such as a 512×512 pixel color image.
[0021] The preprocessed color image is converted to a grayscale image. The Canny edge detection operator is used, with high and low thresholds set to detect all edge pixels in the grayscale image. Since the image may contain background or other interfering object edges, a contour finding algorithm, such as the findContours function, is used to identify all closed contours. These contours are then filtered based on their enclosed area or perimeter, selecting the closed contour with the largest area as the valid contour of the car mat design. This valid contour consists of a series of pixel coordinates arranged clockwise or counterclockwise. By calculating the normalized Fourier descriptor of the extracted contour pixel sequence, the contour information of the car mat design can be converted into a numerical vector, i.e., shape features. Shape features have the advantage of translation, rotation, and scaling invariance, ensuring that stable and consistent shape features can be extracted regardless of changes in the position, angle, or size of the car mat design in the image.
[0022] More specifically, the method for obtaining shape features is as follows: An edge detection algorithm is used to extract the closed contour of the design draft, and a sequence of contour pixel coordinates is obtained in order. Convert the sequence of outline pixel coordinates into a sequence of complex numbers, where the coordinates of each pixel correspond to a complex number. Perform a discrete Fourier transform on the complex sequence to obtain the Fourier coefficients; The Fourier coefficients are normalized, and the amplitudes of the first N normalized Fourier coefficients are selected to form the shape feature, where N is a preset number.
[0023] For example, the extracted contour consists of L pixels ( , Composed of, among which, Take 0, 1, ..., L-1. For each two-dimensional coordinate point ( , As a point on the complex plane, convert it to a complex number. The entire outline can be represented as a complex sequence. , ,..., Perform a discrete Fourier transform on the complex sequence to obtain the Fourier coefficients. The first Fourier coefficient To achieve translation invariance, the centroid position corresponding to the contour will be determined by... Discarded. The overall size of the contour is mainly determined by the second Fourier coefficient. amplitude Decision. To achieve scaling invariance, the magnitudes of all other Fourier coefficients are divided by... That is, the normalized amplitude is ,in, The Fourier coefficients are chosen as 2, 3, ... . In the Fourier coefficients, the low-frequency components represent the overall shape of the contour, and the high-frequency components represent the details of the contour. The amplitudes of the first N low-frequency coefficients are sufficient to represent the main shape of the contour. Preferably, N=32, and the amplitudes of the normalized first 32 Fourier coefficients are used to construct a 32-dimensional vector. In one embodiment, the first 32 Fourier coefficients do not contain... ,from At the beginning, but because Used for normalization, in practice , ...
[0024] S2, for the design draft area defined by the contour, radial sectors are divided according to the curvature distribution of the contour, and multiple non-equally spaced concentric rings are generated according to the gradient intensity distribution within the design draft area. The intersection of the radial sectors and the concentric rings forms multiple grid cells; the digital image is converted to the CIELAB color space; for each grid cell, the gradient direction histogram of the brightness L* value of the pixels inside the grid cell with gradient intensity as the weight and the quantization histogram of the a*b* two-dimensional color distribution are extracted, and the two are combined into the local feature descriptor of the grid cell.
[0025] The purpose of this step is to refine and quantify the detailed information within the design artwork, compensating for the inadequacy of outline features alone. By dividing the artwork into non-uniform grid cells based on the curvature of the outline and the intensity of its internal gradient, the focus of the analysis can be concentrated on key areas of the design artwork that contain more visual information (such as corners and complex patterns). Converting to the CIELAB color space makes the color analysis more consistent with human visual perception.
[0026] Specifically, the method for dividing the design drawing area defined by the outline into multiple grid units is as follows: Calculate the centroid of the contour as the center point of the radial sector and the concentric ring; Calculate the curvature value of each point along the contour; based on the distribution of curvature values, set dense radial sector boundaries at locations with drastic curvature changes, dividing the design drawing area into K radial sectors with unequal angles; Calculate the gradient intensity of all pixels within the design draft area; based on the center point, statistically analyze the cumulative distribution of gradient intensity at different radii; based on the cumulative distribution, generate M non-equidistant concentric ring boundaries, such that the sum of gradient intensity within each concentric ring tends to be the same; where K and M are both positive integers; The intersection of the K radial sectors and the M concentric rings constitutes the plurality of grid cells.
[0027] More specifically, the coordinates of all pixels within the contour are averaged to obtain the coordinates of the centroid, which will serve as the common center of all radial sectors and concentric rings. The curvature of each pixel is calculated along the contour boundary. Curvature can be approximated by calculating the rate of change of the tangent direction between adjacent points on the contour. At sharp turns in the car mat contour, the curvature value will be high; at straight edges, the curvature value will be close to zero. The sum of the absolute values of curvature for the entire contour is then calculated. Starting from a point such as the apex of the contour, the curvature value is accumulated along the contour. Each time the accumulated value reaches a certain value... When the value is an integer multiple of the curve, a dividing line is drawn from the centroid to the contour point. In regions with complex contour shapes and high curvature, the sector will be very narrow, while in regions with gentle shapes, the sector will be wide, thus achieving dense sampling of key shape regions.
[0028] When dividing concentric rings based on gradient intensity, the gradient intensity of all pixels within the design drawing area (i.e., inside the outline) is calculated using the Sobel operator, where the gradient intensity is high at textures and edges. The sum of the gradient intensities of all pixels within the calculated area is then used. Each concentric ring should contain approximately The gradient energy. Starting from the center of mass, with increasing radii... Draw a circle and calculate the radius less than 1 / 3. The cumulative gradient intensity of all pixels within the circular region Find the radius. , ,..., , making , And so on. M-1 radii define the boundaries of M concentric rings. If the center of the artwork has a complex pattern, the inner rings will be narrow; if the edges are a solid color, the outer rings will be wide. The geometric intersection of K radial sectors and M concentric rings forms K×M grid cells.
[0029] In addition, specific methods for obtaining local feature descriptors include: Calculate the gradient direction and gradient intensity of the L* value of each pixel in the grid cell in the CIELAB color space, as well as the a* and b* values; The gradient direction is divided into multiple preset intervals, and the gradient intensity of each pixel is accumulated into the interval corresponding to the gradient direction to obtain a gradient direction histogram with gradient intensity as the weight. The a*b* two-dimensional color plane is divided into multiple quantization units. The distribution of the a* and b* values of all pixels in the grid unit is statistically analyzed in the quantization unit to generate the quantization histogram. The gradient direction histogram and the quantization histogram are normalized respectively. The two normalized histograms are then concatenated and spliced to form the local feature descriptor of the grid cell.
[0030] For example, the outline of the car mat design has multiple arc-shaped indentations and protrusions. In areas with dramatic curvature changes, such as corners, the visual focus of the design is more concentrated. This invention uses denser radial dividing lines in areas of high curvature and sparser dividing lines in areas of low curvature, such as smooth edges, dividing the entire design area into 12 radial sectors of varying angles. A complex brand logo is printed in the center of the design, while the edge areas are solid colors. The pixel color and brightness of the logo area change dramatically, resulting in high gradient intensity values. Concentric rings are divided based on the cumulative distribution of gradient intensity. The sum of the gradient intensity of all pixels within the included area is calculated as it expands outward from the centroid. Ring boundaries are set such that the sum of the gradient intensity within each ring is approximately equal. In this embodiment, it is divided into 5 concentric rings. The ring in the central area containing the logo is very narrow, while the ring in the solid-color edge area is very wide. The intersection of the aforementioned 12 radial sectors and 5 concentric rings forms 60 grid cells of varying shapes and sizes, corresponding to the geometrically and textually significant areas of the design artwork. For each of these 60 grid cells, the following operations are performed: Convert the original RGB image to the CIELAB color space, where L* represents luminance and a* and b* represent color.
[0031] Within this grid cell, the gradient direction and gradient intensity are calculated for the L* channel value of each pixel. The gradient direction is divided into 8 intervals, and the gradient intensity value of each pixel is accumulated into the counter of the corresponding interval according to the gradient direction. Pixels with larger gradient intensities, such as edges or textures, contribute more to the histogram, ultimately resulting in an 8-dimensional texture feature vector.
[0032] Within this grid cell, the a*b* two-dimensional color plane is divided into an 8×8 grid. The frequency of (a*, b*) values falling into these 64 color intervals for all pixels within this grid cell is counted, generating a 64-dimensional color feature vector. The 8-dimensional texture feature vector and the 64-dimensional color feature vector are concatenated to form a 72-dimensional vector. This vector is normalized to a length of 1; the normalized 72-dimensional vector serves as the local feature descriptor for this grid cell.
[0033] S3. Following a predetermined scanning order from the inner ring to the outer ring along a specific direction, the local feature descriptors of all grid cells are serialized and spliced together to form a local feature sequence; the shape features are combined with the local feature sequence to generate the copyright feature data of the design draft.
[0034] Specifically, in this step, a starting radial sector and a scanning direction are determined, which can be clockwise or counterclockwise; starting from the innermost concentric ring from the aforementioned center point, the process is traversed layer by layer to the outermost concentric ring; within each concentric ring, starting from the starting radial sector, local feature descriptors of each grid cell within the concentric ring are extracted sequentially according to the scanning direction; the sequentially extracted local feature descriptors are concatenated end to end to form a one-dimensional vector, which serves as the local feature sequence.
[0035] Taking the 60 grid cells generated in step S2 as an example, a scanning order is defined to ensure that the same sequence is obtained each time the same design is processed. The starting radial sector is set to the sector directly above, and the scanning direction is clockwise. Starting from the innermost first concentric ring, the local feature descriptors of the 12 grid cells within this ring are extracted sequentially in a clockwise direction; each is a 72-dimensional vector. Next, the process moves to the second concentric ring, and the local feature descriptors of its 12 internal grid cells are extracted in the same clockwise direction and concatenated to the end of the result from the previous step. This process is repeated until the outermost fifth concentric ring is scanned. After the above scanning and concatenation, the 60 72-dimensional local feature descriptors are connected into a 4320-dimensional one-dimensional vector. The 32-dimensional shape feature vector calculated in S1 is concatenated with the generated 4320-dimensional local feature sequence vector to generate a 4352-dimensional vector. This vector is a comprehensive vector that integrates global shape and local texture color information, and is the copyright feature data of the design artwork.
[0036] S4. The copyright feature data is digitally signed using a private key of an asymmetric encryption algorithm to generate a digital signature. The digital signature is then associated with a timestamp and recorded in a distributed ledger.
[0037] The purpose of this step is to address the difficulties in proving copyright infringement, the susceptibility of electronic evidence to tampering, and the lack of credibility in traditional copyright protection. Using a private key for digital signatures can prove the source (identity authentication) and integrity (tamper prevention) of the copyright data. Recording the digital signature and authoritative timestamp on a distributed ledger such as a blockchain creates a publicly verifiable, immutable, and traceable record. This provides copyright owners with strong legal evidence, effectively proving that their design existed at a specific point in time.
[0038] Specifically, in this step, a hash function is used to calculate and generate a fixed-length hash value, also known as a digest. The hashing process is one-way, meaning the original feature data cannot be deduced from the hash value, thus ensuring the security of copyright features. The designer or rights holder uses their private key to encrypt the generated hash value using an asymmetric encryption algorithm. The transaction data, including signatures and timestamps, is then broadcast to a distributed ledger network to ensure immutability.
[0039] More specifically, let the copyright feature data be ,use Hash algorithms calculate hash digests , ; Let the private key of the asymmetric encryption algorithm be The hash digest is processed using the 256-bit Elliptic Curve Digital Signature Algorithm (ECDSA). Perform a signature and generate a digital signature. , ; Obtain the UTC timestamp T from a trusted timestamp issuing authority, and then use the hash digest. The generated digital signature Pack the data together with the UTC timestamp T into a single transaction data set, which is represented as follows: The transaction data is broadcast to the blockchain network for recording.
[0040] To ensure the integrity and non-repudiation of copyright signature data, the SHA-256 algorithm is used to compress the complete copyright signature data into a unique hash digest. The copyright holder uses their personal ECC private key to sign the hash digest using an elliptic curve digital signature algorithm, generating a unique digital signature. This hash digest, digital signature, and current timestamp obtained from an authoritative institution are packaged together into a transaction and submitted to a distributed ledger such as a blockchain, creating a publicly verifiable and tamper-proof copyright record.
[0041] A specific embodiment of the copyright protection system for car mat designs provided by this invention: The car mat design copyright protection system includes a processor and a memory. The memory stores a computer program, which, when executed by the processor, enables the car mat design copyright protection method described in the above embodiments.
[0042] The copyright protection system for automotive mat designs also includes other components well known to those skilled in the art, such as communication buses and communication interfaces, the setup and functions of which are known in the art and will not be described further here.
[0043] In this invention, the aforementioned memory can be any tangible medium containing or storing a program that can be used or combined with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium can be any suitable magnetic or magneto-optical storage medium, such as resistive random access memory (RRAM), dynamic random access memory (DRAM), static random access memory (SRAM), enhanced dynamic random access memory (EDRAM), high-bandwidth memory (HBM), hybrid memory cube (HMC), etc., or any other medium that can be used to store desired information and can be accessed by an application, module, or both. Any such computer storage medium can be part of a device or accessible to or connected to a device. Any application or module described in this invention can be implemented by computer-readable / executable instructions stored or otherwise maintained on such a computer-readable medium.
[0044] While various embodiments of the invention have been shown and described in this specification, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Many modifications, alterations, and alternatives will occur to those skilled in the art without departing from the spirit and essence of the invention.
Claims
1. A method for protecting the copyright of an automotive mat design, characterized in that, Includes the following steps: S1, Obtain a digital image of the design draft of the car mat to be protected; Extract the outline of the design draft from the digital image, Calculate the normalized Fourier descriptor of the outline and use it as a shape feature; S2, for the design draft area defined by the contour, radial sectors are divided according to the curvature distribution of the contour, and multiple non-equidistant concentric rings are generated according to the gradient intensity distribution within the design draft area. The intersection of the radial sectors and the concentric rings forms multiple grid cells; the digital image is converted to the CIELAB color space; for each grid cell, the gradient direction histogram of the brightness L* value of the pixels inside the grid cell with gradient intensity as the weight and the quantization histogram of the a*b* two-dimensional color distribution are extracted, and the two are combined into the local feature descriptor of the grid cell; S3, following a predetermined order of scanning from the inner ring to the outer ring along a specific direction, serialize and splice the local feature descriptors of all grid cells to form a local feature sequence; combine the shape features with the local feature sequence to generate the copyright feature data of the design draft; S4. The copyright feature data is digitally signed using a private key of an asymmetric encryption algorithm, and a digital signature is generated. The digital signature is then associated with a timestamp and recorded in a distributed ledger.
2. The method for protecting the copyright of a car mat design according to claim 1, characterized in that, In step S1, the method for obtaining the shape features includes: An edge detection algorithm is used to extract the closed contour of the design draft, and a sequence of contour pixel coordinates is obtained in order. The sequence of outline pixel coordinates is converted into a sequence of complex numbers, where the coordinates of each pixel correspond to a complex number. Perform a discrete Fourier transform on the complex sequence to obtain the Fourier coefficients; The Fourier coefficients are normalized, and the amplitudes of the first N normalized Fourier coefficients are selected to form the shape feature, where N is a preset number.
3. The method for protecting the copyright of a car mat design according to claim 2, characterized in that, The edge detection algorithm is the Canny edge detection operator.
4. The method for protecting the copyright of a car mat design according to claim 1, characterized in that, In step S2, the method for dividing the design drawing area defined by the outline into multiple grid units includes: Calculate the centroid of the contour as the center point of the radial sector and the concentric ring; Calculate the curvature value of each point along the contour; based on the distribution of the curvature value, set dense radial sector boundaries at locations with drastic curvature changes, dividing the design drawing area into K radial sectors with unequal angles; Calculate the gradient intensity of all pixels within the design draft area; based on the center point, statistically analyze the cumulative distribution of gradient intensity at different radii; based on the cumulative distribution, generate M non-equidistant concentric ring boundaries, such that the sum of gradient intensity within each concentric ring tends to be the same; K and M are both positive integers; The intersection of the K radial sectors and the M concentric rings constitutes the plurality of grid cells.
5. The method for protecting the copyright of a car mat design according to claim 4, characterized in that, The coordinates of the centroid are obtained by averaging the coordinates of all pixels inside the contour.
6. The method for protecting the copyright of a car mat design according to claim 4, characterized in that, The curvature values of each point along the contour are accumulated starting from a starting point. Whenever the accumulated value reaches a preset proportion of the total curvature value, a radial sector boundary is set.
7. The method for protecting the copyright of a car mat design according to claim 4, characterized in that, In step S2, the method for obtaining the local feature descriptor includes: Calculate the gradient direction and gradient intensity of the L* value of each pixel in the grid cell in the CIELAB color space, as well as the a* and b* values; The gradient direction is divided into multiple preset intervals, and the gradient intensity of each pixel is accumulated into the interval corresponding to the gradient direction to obtain a gradient direction histogram with gradient intensity as the weight. The a*b* two-dimensional color plane is divided into multiple quantization units. The distribution of the a* and b* values of all pixels in the grid unit is statistically analyzed in the quantization unit to generate the quantization histogram. The gradient direction histogram and the quantization histogram are normalized respectively, and the two normalized histograms are concatenated and spliced to form the local feature descriptor of the grid cell.
8. The method for protecting the copyright of a car mat design according to claim 7, characterized in that, In step S3, the method for obtaining the local feature sequence includes: Determine an initial radial sector and a scanning direction, wherein the scanning direction is clockwise or counterclockwise; Starting from the innermost concentric ring from the center point, traverse layer by layer to the outermost concentric ring; Within each concentric ring, starting from the initial radial sector, local feature descriptors of each grid cell within the concentric ring are extracted sequentially according to the scanning direction. The extracted local feature descriptors are concatenated end to end to form a one-dimensional vector, which serves as the local feature sequence.
9. A method for protecting the copyright of an automotive mat design according to claim 1, characterized in that, In step S4, the copyright feature data is digitally signed using a private key employing an asymmetric encryption algorithm to generate a digital signature. This digital signature is then associated with a timestamp and recorded in a distributed ledger, including: Let the copyright feature data be ,use Hash algorithms calculate hash digests , ; Let the private key of the asymmetric encryption algorithm be The hash digest is processed using the 256-bit Elliptic Curve Digital Signature Algorithm (ECDSA). Perform a signature and generate a digital signature. , ; Obtain the UTC timestamp T from a trusted timestamp issuing authority, and then use the hash digest. The generated digital signature Pack the data together with the UTC timestamp T into a single transaction data set, which is represented as follows: The transaction data is broadcast to the blockchain network for recording.
10. A system for protecting the design copyright of car mats, characterized in that, It includes a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement a method for protecting the copyright of a car mat design as described in any one of claims 1 to 9.