Artwork traceability device and method based on RFID coding

Abstract

The invention relates to the field of data processing systems, in particular to an artwork traceability device and method based on RFID (Radio Frequency Identification Device) coding. An RFID tag module stores identity information and historical circulation records of an artwork; the RFID reader-writer reads the RFID tag to obtain identity information, compares the identity information with cloud data, and obtains a corresponding artwork standard image group in the database after successful comparison; the image acquisition module acquires a to-be-verified artwork image group and obtains a corresponding second environment condition; the correction module corrects the artwork image group based on the second environment condition and the first environment condition to obtain a corrected image information group; the verification module compares the corrected image information group with an artwork standard image group in a database to obtain image similarity data, and verification is passed if the image similarity data exceeds a preset value; and the uploading module uploads the traceability information to the block chain network. Errors caused by the environment during image detection verification can be corrected, so that the traceability detection accuracy is improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing systems, and in particular to an artwork tracing device and method based on RFID coding. Background Art

[0002] Artwork provenance refers to the process of meticulously tracking and documenting an artwork's history, origin, and circulation through various means and technologies. This encompasses not only the artwork's creative background and artist information, but also crucial information such as changes in ownership, exhibition records, and restoration history since its creation. Artwork provenance tracing can effectively enhance transparency in the art market, strengthen buyer confidence, prevent counterfeit and stolen goods from entering the market, and protect the rights and interests of artists and collectors.

[0003] However, in practice, factors such as shooting angle, surface reflections, and obstructions from stains can still affect image capture accuracy. For example, when photographing artwork with a smooth surface, reflections can blur the image in certain areas, causing the captured image to differ from the original image in those areas, potentially misinterpreting them as changes in physical features. Inconsistent shooting angles can also cause deviations in the shape, proportions, and other features of the artwork in the image. Summary of the Invention

[0004] The purpose of the present invention is to provide an artwork tracing device and method based on RFID coding, which aims to correct errors caused by the environment during image detection and verification, thereby improving the accuracy of traceability detection.

[0005] To achieve the above-mentioned objectives, in a first aspect, the present invention provides an artwork tracing device based on RFID coding, comprising an RFID tag module, an RFID reader / writer, an image acquisition module, a correction module, a verification module, and an upload module;

[0006] The RFID tag module is fixed on the artwork and is used to store the identity information and historical circulation records of the artwork;

[0007] The RFID reader is used to read the RFID tag to obtain identity information and compare it with cloud data. After the comparison is successful, the corresponding standard image group of artworks is obtained from the database. The standard image group of artworks is taken under the first environmental conditions;

[0008] The image acquisition module is used to acquire a group of artwork images to be verified, the group of artwork images including an overall image and local detail images, and obtain a corresponding second environmental condition;

[0009] The correction module is used to correct the artwork image group based on the second environmental condition and the first environmental condition to obtain a corrected image information group;

[0010] The verification module is used to compare the corrected image information group with the artwork standard image group in the database to obtain image similarity data. If the image similarity data exceeds a preset value, the verification is passed, and the traceability information of the current verification time point is generated based on the artwork standard image group and the second environmental condition;

[0011] The uploading module is used to upload the traceability information to the blockchain network.

[0012] Among them, the artwork tracing device based on RFID coding also includes a secondary verification module, which is used to extract the previous tracing information and perform comparative verification based on the verification time point of the tracing information.

[0013] Wherein, the RFID tag module includes a format setting unit, a database unit and a code generation unit;

[0014] The format setting unit is used to set the format of the RFID code, and the format is composed of numbers and letters;

[0015] The database unit is used to store the identity information and historical circulation records of the artwork;

[0016] The code generating unit is used to generate an RFID code using the identity information in the database and a digital signature algorithm.

[0017] Wherein, the RFID reader / writer includes a reading unit, a comparison unit and an image retrieval unit;

[0018] The reading unit is used to read the artwork information on the RFID tag corresponding to the artwork;

[0019] The comparison unit is used to compare the received artwork information with the records in the existing database to verify the identity of the artwork;

[0020] The image retrieval unit is used to retrieve a standard image group of the artwork from the database after successful identity comparison.

[0021] Wherein, the correction module includes a data acquisition unit, a pre-processing unit, a shooting environment difference unit and a correction unit;

[0022] The data acquisition unit is used to acquire the first environmental condition data and the second environmental condition data;

[0023] The pre-processing unit is used to perform preliminary processing on the first environmental condition data and the second environmental condition data;

[0024] The shooting environment difference unit is used to match and analyze the first environmental condition data with the second environmental condition data to identify difference information, where the difference information includes a light intensity difference, a color temperature difference, and an exposure time difference;

[0025] The correction unit is used to correct the artwork image group based on the difference information.

[0026] Wherein, the shooting environment difference unit includes a formatting subunit, a light intensity subunit, a color temperature subunit and an exposure subunit;

[0027] The formatting subunit is used to convert the collected first environmental condition and second environmental condition into a unified standard unit or format;

[0028] The illumination intensity subunit is used to calculate the difference between the illumination intensity in the first environment and the illumination intensity in the second shooting environment;

[0029] The color temperature subunit is used to calculate the color temperature difference between the first environmental condition and the second environmental condition;

[0030] The exposure subunit is used to calculate the exposure time difference between the first environmental condition and the second environmental condition.

[0031] The verification module includes a global feature extraction unit, a global feature matching unit, a local feature extraction unit, a local feature matching unit, a similarity calculation unit and a traceability information generation unit;

[0032] The global feature extraction unit is used to extract the global features of the entire image using a color histogram;

[0033] The global feature matching unit is used to calculate the global feature difference data between the real-time image and the database image using the cosine similarity method;

[0034] The local feature extraction unit is configured to extract local feature points in the image using a feature point detection algorithm if the global feature difference data is within a first preset range;

[0035] The local feature matching unit uses the FLANN algorithm to match feature points between the real-time image and the database image;

[0036] The similarity calculation unit calculates a structural similarity index between the real-time image and the standard image, and the verification is passed if the structural similarity index is within a second preset range;

[0037] The traceability information generating unit is used to generate traceability information at the current verification time point based on the artwork standard image group and the second environmental condition after verification.

[0038] In a second aspect, the present invention also provides a method for tracing the provenance of artworks based on RFID coding, which uses the aforementioned device for tracing the provenance of artworks based on RFID coding.

[0039] The present invention provides an RFID-based artwork tracing device and method. An RFID tag module is directly attached to the artwork and stores the artwork's unique identity information and all its movement records since its creation. This information is crucial for verifying the artwork's identity and helps prevent forgery and fraud. An RFID reader / writer reads the data from the RFID tag attached to the artwork and compares the acquired identity information with records in a cloud-based database. Once the identity information matches, the system retrieves a set of standard images of the corresponding artwork from the database. These standard images are captured under specific first environmental conditions and serve as a reference for subsequent verification. An image acquisition module captures actual images of the artwork to be verified. These images include not only an overall view of the artwork but also key local details that highlight its characteristics. The module also records the second environmental conditions under which these images were captured, which is crucial for ensuring the accuracy of the comparison results. To further improve verification accuracy, a correction module corrects the captured artwork images based on the first environmental conditions and the second environmental conditions at the time of capture. This approach reduces image distortion caused by environmental differences, resulting in a more accurate set of corrected image information. The verification module compares the corrected image information set in detail with the standard image set in the database, calculating the similarity between the two. If the calculated similarity exceeds a preset threshold, the verification is considered successful. At this point, the system generates traceability information, including the current time point, based on the standard image set and the second environmental conditions. The upload module uploads the traceability information generated by this verification to the blockchain network. Leveraging the immutable nature of blockchain technology, the security, transparency, and traceability of all traceability information can be ensured, providing strong support for the trading and collection of artworks. In this way, the system not only improves the efficiency and accuracy of artwork verification but also ensures the healthy development of the art market. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0041] Figure 1 This is a structural diagram of an artwork tracing device based on RFID coding according to the present invention.

[0042] Figure 2 It is a structural diagram of the RFID tag module of the present invention.

[0043] Figure 3 It is a structural diagram of the RFID reader / writer of the present invention.

[0044] Figure 4 It is a structural diagram of the correction module of the present invention.

[0045] Figure 5 2 is a structural diagram of the shooting environment difference unit of the present invention.

[0046] Figure 6 It is a structural diagram of the verification module of the present invention.

[0047] RFID tag module 101, RFID reader / writer 102, image acquisition module 103, correction module 104, verification module 105, upload module 106, secondary verification module 107, format setting unit 108, database unit 109, code generation unit 110, reading unit 111, comparison unit 112, image retrieval unit 113, data acquisition unit 114, pre-processing unit 115, shooting environment difference unit 116, correction unit 117, formatting subunit 118, light intensity subunit 119, color temperature subunit 120, exposure subunit 121, global feature extraction unit 122, global feature matching unit 123, local feature extraction unit 124, local feature matching unit 125, similarity calculation unit 126, traceability information generation unit 127. DETAILED DESCRIPTION

[0048] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and are not to be construed as limiting the present invention.

[0049] First embodiment

[0050] The present invention provides an artwork tracing device based on RFID coding, comprising an RFID tag module 101, an RFID reader / writer 102, an image acquisition module 103, a correction module 104, a verification module 105, and an upload module 106; the RFID tag module 101 is fixed on the artwork and is used to store the identity information and historical circulation records of the artwork; the RFID reader / writer 102 is used to read the RFID tag to obtain the identity information and compare it with the cloud data, and after the comparison is successful, obtain the corresponding artwork standard image group in the database, the artwork standard image group is taken under the first environmental conditions; the image acquisition module 103 is used to The method comprises the following steps: collecting an artwork image group to be verified, wherein the artwork image group includes an overall image and a local detail image, and obtaining a corresponding second environmental condition; the correction module 104 is used to correct the artwork image group based on the second environmental condition and the first environmental condition to obtain a corrected image information group; the verification module 105 is used to compare the corrected image information group with the artwork standard image group in the database to obtain image similarity data. If the image similarity data exceeds a preset value, the verification is passed, and traceability information at the current verification time point is generated based on the artwork standard image group and the second environmental condition; the uploading module 106 is used to upload the traceability information to the blockchain network.

[0051] In this embodiment, the RFID tag module 101 is directly attached to the artwork and stores the artwork's unique identity information and all its movement records since its creation. This information is crucial for verifying the artwork's identity and helps prevent forgery and fraud. The RFID reader / writer 102 reads the data from the RFID tag attached to the artwork and compares the acquired identity information with records in a cloud-based database. Once the identity information matches, the system retrieves a set of standard images of the corresponding artwork from the database. These standard images are captured under specific first environmental conditions and serve as a reference for subsequent verification. The image acquisition module 103 captures actual images of the artwork to be verified. This includes not only an overall view of the artwork but also key local details that highlight its characteristics. The module also records the second environmental conditions under which these images were captured, which is crucial for ensuring the accuracy of the comparison results. To further improve verification accuracy, the correction module 104 corrects the captured artwork images based on the first environmental conditions and the second environmental conditions at the time of capture. This approach reduces image distortion caused by environmental differences, resulting in a more accurate set of corrected image information. The verification module 105 will compare the corrected image information group with the standard image group in the database in detail and calculate the similarity data between the two. If the calculated similarity exceeds the preset threshold, the verification is considered to be passed. At this time, the system will generate traceability information containing the current time point based on the standard image group and the second environmental condition. The upload module 106 will upload the traceability information generated by this verification to the blockchain network. By utilizing the tamper-proof characteristics of blockchain technology, the security, transparency and traceability of all traceability information can be ensured, providing strong support for the transaction and collection of artworks. In this way, the system not only improves the efficiency and accuracy of artwork verification, but also provides a guarantee for the healthy development of the artwork market.

[0052] The artwork traceability device based on RFID coding further includes a secondary verification module 107, which is used to extract the last traceability information based on the verification time point of the traceability information and perform comparative verification.

[0053] When a new verification operation is performed, the secondary verification module 107 first searches the blockchain network for the most recent traceability information of the artwork based on the time of the current verification, including the historical image of the artwork and the environmental conditions at the time.

[0054] Next, the secondary verification module 107 will conduct a detailed comparative analysis of the newly collected traceability information (including newly captured images of artworks, current environmental conditions, etc.) with the information from the previous traceability. This process not only compares the physical characteristics of the artwork, such as color and texture, but also takes into account environmental factors that may affect the display of these characteristics in different time periods, such as light changes, temperature and humidity, etc. Through this cross-time period comparative analysis, the secondary verification module 107 can identify subtle changes in the artwork over time and determine whether these changes are within the normal range of aging or wear and tear, or whether there are abnormalities, such as traces of repair, damage, etc. Finally, the secondary verification module 107 will generate a detailed report based on the comparison results. If everything is normal, the authenticity of the artwork is confirmed; if there is any doubt, the part that requires further investigation is marked, and appropriate measures are recommended, such as professional art appraisal.

[0055] The RFID tag module 101 includes a format setting unit 108, a database unit 109 and a code generation unit 110; the format setting unit 108 is used to set the format of the RFID code, which is a combination of numbers and letters; the database unit 109 is used to store the identity information and historical circulation records of the artwork; the code generation unit 110 is used to generate the RFID code using the identity information in the database and the digital signature algorithm.

[0056] The format setting unit 108 defines and sets the format standards for RFID codes. This format typically consists of a combination of numbers and letters, designed to create a unique identifier for each artwork. This code is not only machine-readable but also contains sufficient information to describe the artwork's key attributes. For example, the code might include information such as the artwork's category, creation year, and the artist's initials. Careful design of the encoding format can not only improve data processing efficiency but also enhance system compatibility and scalability. The database unit 109 stores the identity information and historical records associated with each artwork. This information covers all important aspects of the artwork's history from its creation to the present, such as ownership changes, exhibition history, and restoration records. The design of the database unit 109 must consider data security, integrity, and accessibility to ensure that all records are authentic and reliable and can be quickly provided to authorized users for query or verification when necessary. The code generation unit 110 uses the identity information in the database and advanced digital signature algorithms to generate a unique RFID code. Specifically, this unit first obtains the artwork's basic identity information from the database unit 109 and then encrypts this information using digital signature technology to generate a secure and tamper-proof RFID code.

[0057] The RFID reader / writer 102 includes a reading unit 111, a comparison unit 112, and an image retrieval unit 113; the reading unit 111 is used to read the artwork information on the RFID tag corresponding to the artwork; the comparison unit 112 is used to compare the received artwork information with the records in an existing database to verify the identity of the artwork; the image retrieval unit 113 is used to retrieve a standard image group of the artwork from the database if the identity comparison is successful.

[0058] Reading unit 111 reads stored identity information and historical records from RFID tags attached to artworks. This process utilizes radio frequency identification technology, allowing for rapid acquisition of information without direct contact. To ensure accurate data reading, this unit is typically equipped with a highly sensitive antenna and advanced signal processing algorithms that effectively filter out external interference signals, ensuring stable operation even in complex environments.

[0059] Comparison unit 112 compares the artwork information it reads with records pre-stored in the cloud or a local database. This process involves complex algorithms to match basic information such as the artwork's title, creator, and creation date, as well as more detailed historical records of its circulation. Comparison unit 112 not only verifies the consistency of this information but also checks for any signs of tampering or forgery.

[0060] Image retrieval unit 113 retrieves a set of standard images corresponding to the artwork from the database. These standard images are captured under specific first environmental conditions and capture the artwork's overall appearance and important details. Image retrieval unit 113 must not only rapidly locate and extract relevant images but also adjust the output format based on current usage requirements to facilitate smooth use by subsequent modules (such as correction module 104 and verification module 105).

[0061] The correction module 104 includes a data acquisition unit 114, a preprocessing unit 115, a shooting environment difference unit 116 and a correction unit 117; the data acquisition unit 114 is used to acquire first environmental condition data and second environmental condition data; the preprocessing unit 115 is used to perform preliminary processing on the first environmental condition data and the second environmental condition data; the shooting environment difference unit 116 is used to match and analyze the first environmental condition data with the second environmental condition data to identify difference information, the difference information including light intensity difference, color temperature difference and exposure time difference; the correction unit 117 is used to correct the artwork image group based on the difference information.

[0062] Data acquisition unit 114 collects first and second environmental condition data from various sources. The first environmental condition data refers to the ideal environmental parameters, such as light intensity, color temperature, and exposure time, used when creating the standard set of artwork images. The second environmental condition data, on the other hand, refers to the actual environmental parameters when capturing the artwork images during the actual verification process. This data is crucial for subsequent calibration work, as it directly impacts the faithful reproduction of the artwork images.

[0063] The preprocessing unit 115 performs preliminary processing on the first and second environmental condition data. This process includes data cleaning, format conversion, and necessary information extraction to ensure that all data is in an analyzable state. For example, preprocessing may involve removing outliers, standardizing data in different formats to facilitate comparison, or extracting feature information that is particularly important for the calibration process.

[0064] The shooting environment difference unit 116 matches and analyzes the pre-processed first and second environmental condition data, identifying differences between them. This difference information primarily includes, but is not limited to, differences in light intensity, color temperature, and exposure time. By accurately measuring and comparing these key parameters, the unit can quantify the specific differences between the two environments, providing the necessary adjustment basis for the correction unit 117.

[0065] Correction unit 117 meticulously corrects the artwork image set based on the difference information obtained from shooting environment difference unit 116. This step involves complex image processing techniques, such as brightness and contrast adjustment, color balance correction, and detail enhancement. Based on the specific differences, correction unit 117 automatically adjusts various image parameters to minimize image distortion caused by environmental changes, ensuring that the final corrected image output is as close as possible to the artwork's true appearance under ideal conditions.

[0066] The shooting environment difference unit 116 includes a formatting subunit 118, a light intensity subunit 119, a color temperature subunit 120 and an exposure subunit 121; the formatting subunit 118 is used to convert the collected first environmental conditions and the second environmental conditions into a unified standard unit or format; the light intensity subunit 119 is used to calculate the difference between the light intensity in the first environment and the light intensity in the second shooting environment; the color temperature subunit 120 is used to calculate the color temperature difference between the first environmental condition and the second environmental condition; the exposure subunit 121 is used to calculate the exposure time difference between the first environmental condition and the second environmental condition.

[0067] The formatting subunit 118 converts the collected first and second environmental conditions into a unified standard unit or format. This ensures data consistency and comparability in subsequent calculations. For example, light intensity may need to be converted from different units of measurement (such as lux and lumens) to a common standard unit; color temperature may need to be converted to Kelvin (K); and exposure time should also be standardized to a standard time unit such as seconds (s) or milliseconds (ms).

[0068] The illumination intensity subunit 119 calculates the difference between the illumination intensity in the first environment and the illumination intensity in the second shooting environment. This process relies on standardized data provided by the formatting subunit 118. It then evaluates the impact of illumination changes on the image quality of the artwork by calculating the specific numerical difference in illumination intensity between the two environments. Variations in illumination intensity can directly affect image brightness and contrast, making accurately identifying and quantifying these differences crucial for subsequent image correction. This subunit may apply specialized algorithms to compensate for image distortion caused by varying illumination intensities.

[0069] The color temperature subunit 120 is responsible for calculating the difference in color temperature between the first and second environmental conditions. Color temperature refers to the color characteristics of a light source and is typically expressed in Kelvin (K). Different color temperatures can cause the image to exhibit varying shades of cool and warm, affecting the true reproduction of color. The color temperature subunit 120 compares the color temperature values ​​under the two environments and determines the specific difference between them. This information is critical for adjusting the white balance of the image, as correct white balance ensures the faithful reproduction of the colors of the artwork image and avoids color deviation caused by changes in ambient color temperature.

[0070] Exposure subunit 121 calculates the difference in exposure time between the first and second environmental conditions. Exposure time refers to the length of time the camera shutter is open, which directly affects the image brightness and dynamic range. Different exposure times can result in overexposure or underexposure, affecting the rendering of details. Exposure subunit 121 compares the exposure times under the two environments and adjusts the image brightness and detail accordingly. This step helps ensure that images of artworks are well-exposed, even under varying shooting conditions.

[0071] The verification module 105 includes a global feature extraction unit 122, a global feature matching unit 123, a local feature extraction unit 124, a local feature matching unit 125, a similarity calculation unit 126 and a traceability information generation unit 127; the global feature extraction unit 122 is used to extract global features of the entire image using a color histogram; the global feature matching unit 123 is used to calculate global feature difference data between the real-time image and the database image using a cosine similarity method; the local feature extraction unit 124 is used to extract local feature points in the image using a feature point detection algorithm if the global feature difference data is within a first preset range; the local feature matching unit 125 is used to match feature points between the real-time image and the database image using a FLANN algorithm; the similarity calculation unit 126 calculates a structural similarity index between the real-time image and the standard image, and verification is passed if the structural similarity index is within a second preset range; the traceability information generation unit 127 is used to generate traceability information for the current verification time point based on the artwork standard image group and the second environmental condition after verification is passed.

[0072] Global feature extraction unit 122 uses color histogram technology to extract global features from the entire image. Color histograms are a method for describing the color distribution of an image, representing the overall color characteristics of the image by counting the frequency of occurrence of different colors in the image. This method effectively captures the color composition of an image and is very useful for identifying and distinguishing artworks with distinct color differences. Global feature extraction is not only fast but also relatively simple, providing a basis for preliminary screening.

[0073] The global feature matching unit 123 uses the cosine similarity method to calculate global feature difference data between the real-time captured artwork image and the standard image in the database. Cosine similarity is a metric that measures the angle between two vector directions. Its value ranges from -1 to 1, with 1 indicating identical directions and 0 indicating orthogonality. During this process, if the cosine similarity between the color histograms (i.e., global features) of two images is close to 1, it indicates that their color distributions are very similar. This matching method can help quickly eliminate images that are clearly mismatched, reducing the burden of subsequent processing.

[0074] If the global feature difference data falls within a preset first range, indicating a high degree of similarity between the two images, the local feature extraction unit 124 intervenes. It uses a feature point detection algorithm (such as SIFT or SURF) to extract local feature points from the image. These feature points are typically located at key locations in the image, such as edges and corners, and are crucial for describing the image's details. Extracting local features facilitates a deeper analysis of image content, particularly when two artworks are similar in overall appearance but differ in details.

[0075] The local feature matching unit 125 uses the FLANN (Fast Library for Approximate Nearest Neighbors) algorithm to match feature points between the live image and the database image. FLANN is an efficient approximate nearest neighbor search algorithm, particularly suitable for fast retrieval of high-dimensional datasets. This process finds as many matching feature points as possible between the two images, further confirming their similarity. This step significantly improves verification accuracy, especially when dealing with artwork with complex patterns and textures.

[0076] The similarity calculation unit 126 calculates the structural similarity index (SSIM) between the real-time image and the standard image. SSIM is an indicator that measures the visual quality similarity between two images, taking into account three factors: brightness, contrast, and structure. If the calculated SSIM value falls within the second preset range, the two images are considered to be structurally similar enough, and thus the verification is passed. This method not only considers the differences at the pixel level, but also pays attention to the characteristics of the human visual system's image perception, providing a more comprehensive similarity assessment standard.

[0077] The traceability information generation unit 127 generates traceability information based on the current verification time, the artwork's standard image set, and the second environmental condition. This traceability information details all relevant data for the verification, including but not limited to the artwork's identity, environmental conditions, and verification time. It is then uploaded to the blockchain network to ensure security and immutability. This not only helps track the artwork's historical circulation but also provides reliable data support for future authentication work.

[0078] In summary, Verification Module 105 implements a multi-level verification of artwork images through a series of carefully designed sub-units. From initial color matching to final structural similarity assessment, each step is designed to ensure the accuracy of the final result. This meticulous approach not only elevates the professional level of artwork authentication but also contributes to the healthy development of the art market.

[0079] Second embodiment

[0080] The present invention also provides an artwork tracing method based on RFID coding, which adopts the artwork tracing device based on RFID coding.

[0081] Each artwork is equipped with an RFID tag module. The formatting unit in this module sets the RFID code format, and the database unit stores the artwork's identity information and historical transaction records. The code generation unit uses the identity information in the database and a digital signature algorithm to generate a unique RFID code for each artwork.

[0082] When an artwork needs to be authenticated, an RFID reader is used to read the information on the artwork's corresponding RFID tag. The reading unit retrieves this information, and the comparison unit compares it with existing records in the cloud or local database to verify the artwork's identity. Once the identity is confirmed, the image retrieval unit retrieves a standard set of images of the artwork from the database for subsequent use.

[0083] Before capturing an actual image of the artwork to be verified, the calibration module's data acquisition unit collects data from the first environmental condition (for capturing the standard image) and the second environmental condition (for the actual verification). After preliminary processing of this data by the preprocessing unit, the shooting environment difference unit further analyzes and identifies differences between the two environments, such as differences in light intensity, color temperature, and exposure time. Finally, the calibration unit applies the necessary corrections to the artwork image based on this difference information to ensure image consistency and accuracy.

[0084] After image correction is complete, the verification phase begins. The global feature extraction unit uses color histogram technology to extract global features from the entire image. The global feature matching unit then uses cosine similarity to calculate the global feature difference data between the live image and the database image. If the results meet expectations, the local feature extraction unit uses a feature point detection algorithm to extract local feature points in the image. The local feature matching unit then uses the FLANN algorithm to match feature points between the live image and the database image.

[0085] The similarity calculation unit calculates the structural similarity index (SSIM) between the real-time image and the reference image. If the SSIM meets a preset standard, the verification is considered successful. At this point, the traceability information generation unit generates traceability information based on the current verification time, the reference image set, and the second environmental conditions, recording all relevant information for this verification.

[0086] All traceability information generated will be securely uploaded to the blockchain network through the upload module. Leveraging the immutable nature of blockchain technology, the security, transparency, and traceability of artwork traceability information are guaranteed, providing a solid foundation of trust for the art market.

[0087] The above disclosure is only a preferred embodiment of the present invention, and certainly cannot be used to limit the scope of the rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. An artwork tracing device based on RFID coding, characterized in that: It includes RFID tag module, RFID reader / writer, image acquisition module, correction module, verification module and upload module; The RFID tag module is fixed on the artwork and is used to store the identity information and historical circulation records of the artwork; The RFID reader is used to read the RFID tag to obtain identity information and compare it with cloud data. After the comparison is successful, the corresponding standard image group of artworks is obtained from the database. The standard image group of artworks is taken under the first environmental conditions; The image acquisition module is used to acquire a group of artwork images to be verified, the group of artwork images including an overall image and local detail images, and obtain a corresponding second environmental condition; The correction module is used to correct the artwork image group based on the second environmental condition and the first environmental condition to obtain a corrected image information group; The verification module is used to compare the corrected image information group with the artwork standard image group in the database to obtain image similarity data. If the image similarity data exceeds a preset value, the verification is passed, and the traceability information of the current verification time point is generated based on the artwork standard image group and the second environmental condition; The uploading module is used to upload the traceability information to the blockchain network.

2. The artwork tracing device based on RFID coding according to claim 1, characterized in that: The artwork traceability device based on RFID coding also includes a secondary verification module, which is used to extract the previous traceability information and perform comparative verification based on the verification time point of the traceability information.

3. The artwork tracing device based on RFID coding according to claim 2, characterized in that: The RFID tag module includes a format setting unit, a database unit and a code generation unit; The format setting unit is used to set the format of the RFID code, and the format is composed of numbers and letters; The database unit is used to store the identity information and historical circulation records of artworks; The code generating unit is used to generate an RFID code using the identity information in the database and a digital signature algorithm.

4. The artwork tracing device based on RFID coding according to claim 3, characterized in that: The RFID reader / writer includes a reading unit, a comparison unit and an image retrieval unit; The reading unit is used to read the artwork information on the RFID tag corresponding to the artwork; The comparison unit is used to compare the received artwork information with the records in the existing database to verify the identity of the artwork; The image retrieval unit is used to retrieve a standard image group of the artwork from the database after successful identity comparison.

5. The artwork tracing device based on RFID coding according to claim 4, characterized in that: The correction module includes a data acquisition unit, a pre-processing unit, a shooting environment difference unit and a correction unit; The data acquisition unit is used to acquire the first environmental condition data and the second environmental condition data; The pre-processing unit is used to perform preliminary processing on the first environmental condition data and the second environmental condition data; The shooting environment difference unit is used to match and analyze the first environmental condition data with the second environmental condition data to identify difference information, where the difference information includes a light intensity difference, a color temperature difference, and an exposure time difference; The correction unit is used to correct the artwork image group based on the difference information.

6. The artwork tracing device based on RFID coding according to claim 5, characterized in that: The shooting environment difference unit includes a formatting subunit, a light intensity subunit, a color temperature subunit and an exposure subunit; The formatting subunit is used to convert the collected first environmental condition and second environmental condition into a unified standard unit or format; The illumination intensity subunit is used to calculate the difference between the illumination intensity in the first environment and the illumination intensity in the second shooting environment; The color temperature subunit is used to calculate the color temperature difference between the first environmental condition and the second environmental condition; The exposure subunit is used to calculate the exposure time difference between the first environmental condition and the second environmental condition.

7. The artwork tracing device based on RFID coding according to claim 6, characterized in that: The verification module includes a global feature extraction unit, a global feature matching unit, a local feature extraction unit, a local feature matching unit, a similarity calculation unit and a traceability information generation unit; The global feature extraction unit is used to extract the global features of the entire image using a color histogram; The global feature matching unit is used to calculate the global feature difference data between the real-time image and the database image using the cosine similarity method; The local feature extraction unit is configured to extract local feature points in the image using a feature point detection algorithm if the global feature difference data is within a first preset range; The local feature matching unit uses the FLANN algorithm to match feature points between the real-time image and the database image; The similarity calculation unit calculates a structural similarity index between the real-time image and the standard image, and the verification is passed if the structural similarity index is within a second preset range; The traceability information generating unit is used to generate traceability information at the current verification time point based on the artwork standard image group and the second environmental condition after verification.

8. A method for tracing the provenance of artworks based on RFID coding, characterized in that: An artwork tracing device based on RFID coding is used as described in any one of claims 1 to 7.

Images