Protection data authentication system based on NFT digital artwork

Abstract

This invention discloses a data authentication system for protecting digital artworks based on NFTs, relating to the field of blockchain technology. The method includes extracting basic information (such as the work title and creator) from digital artwork files, optimizing and screening encrypted data, calculating a hash value, creating a smart contract based on the basic information, encrypted data, and hash value, and decentrally storing the smart contract and digital artwork files in an authentication system. This method addresses the technical issues of existing technologies, such as vulnerabilities in digital artwork protection data authentication and the lack of effective means to specifically enhance authentication security. By accurately extracting basic digital artwork information, the encryption and screening process is optimized, achieving the technical effect of enhancing the security of digital artwork protection.

Description

Technical Field

[0001] This application relates to the field of blockchain technology, and in particular to a protection data authentication system based on NFT digital artworks. Background Art

[0002] In the context of digital artwork protection and trading, copyright and ownership protection are particularly prominent, and the conflicting demands for related technical means are even more pronounced. Building a comprehensive and efficient digital artwork protection and certification system to better meet the protection and trading needs of digital artworks has become a crucial step in addressing the development of the digital artwork sector. Traditional digital artwork protection and trading methods are often limited and one-sided, relying solely on simple encryption methods or limited copyright declarations. They lack control over the entire digital artwork protection and trading process, lack in-depth analysis and utilization of artwork creation and circulation data, and struggle to fully and accurately define the ownership and value of digital artworks. Imperfections in protection and trading mechanisms have resulted in the infringement of the rights and interests of some digital artworks and a lack of credibility in some transactions. The development of digital artwork protection and trading plans is relatively simple and fixed, failing to effectively address the ever-changing needs of the digital art sector.

[0003] At the current stage, relevant technologies exist in the protection data authentication of digital artworks, and there is a lack of effective means to specifically improve the technical problem of authentication security. Summary of the Invention

[0004] This application provides a protection data authentication system based on NFT digital artworks. It extracts basic information (such as the name of the work, creator, etc.) from the digital artwork files, optimizes and screens the encrypted data, calculates the hash value, creates a smart contract based on the basic information, encrypted data and hash value, and stores the smart contract and digital artwork files in a decentralized authentication system, thereby achieving the technical effect of improving the security of digital artwork protection.

[0005] This application provides a data authentication system for protecting NFT digital artworks, including:

[0006] A basic information extraction module is used to extract basic information from a digital artwork file, including the work name, creator information, creation date, artwork description information, and artwork category; a unique hash value generation module is used to optimize and screen the basic information to obtain encrypted data, and perform hash value calculation on the digital artwork file and the encrypted data through the encrypted hash function to generate a unique hash value; a smart contract creation module is used to create a smart contract based on the basic information, the encrypted data, and the hash value; and a storage authentication module is used to decentralize the storage authentication system of the smart contract and the digital artwork file.

[0007] The protection data authentication system based on NFT digital artworks proposed in this application will first extract basic information (such as the name of the work, creator, etc.) from the digital artwork file, optimize the encrypted data, calculate the hash value, create a smart contract based on the basic information, encrypted data and hash value, and store the smart contract and digital artwork files in a decentralized manner in the authentication system. By accurately extracting the basic information of digital artworks, the encryption screening process is optimized, achieving the technical effect of improving the security of digital artwork protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings of the embodiments of the present invention are briefly introduced below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in precise order. Instead, various steps may be processed in reverse order or simultaneously as needed. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0009] Figure 1 A schematic diagram of the structure of a protection data authentication system based on NFT digital artworks provided in an embodiment of the present application;

[0010] Figure 2 Schematic diagram of the smart contract creation module structure of the protection data authentication system based on NFT digital artwork provided in the embodiment of the present application.

[0011] Description of the accompanying drawings: basic information extraction module 10, unique hash value generation module 20, smart contract creation module 30, storage authentication module 40. DETAILED DESCRIPTION

[0012] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0013] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0014] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict, and the terms “first\second” involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. The terms “including” and “having” and any variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs. The terms used herein are for the purpose of describing the embodiments of this application only.

[0015] The embodiment of the present application provides a protection data authentication system based on NFT digital artworks, such as Figure 1 As shown, the method includes:

[0016] Basic information extraction module 10 is used to extract basic information from digital artwork files, including the artwork title, creator information, creation date, artwork description information, and artwork category. Specifically, basic information extraction module 10 obtains digital artwork files from relevant digital artwork data sources. Digital artwork files are digitized documents that describe the basic attributes and characteristics of digital artworks, including key elements such as the artwork title, creator information, creation date, artwork description information, and artwork category. Using specific data extraction technology, digital artwork files are automatically parsed. Based on the information in the files, this technology automatically extracts basic information for subsequent processing, including the accurate artwork title, detailed creator information, precise creation date, complete artwork description information, and precise artwork category. The extracted basic information is then carefully parsed, including understanding and analyzing the format, content, and meaning of the basic information. After parsing the basic information, a correlation and correspondence relationship is established between the basic information and the digital artwork file, i.e., a one-to-one correspondence between the basic information and the digital artwork file. This ensures that each basic information item accurately reflects the key content in the digital artwork file, thereby providing accurate and reliable data support for subsequent processing.

[0017] The unique hash value generation module 20 is used to optimize and screen the basic information to obtain encrypted data, and calculate the hash value of the digital artwork file and the encrypted data through the encrypted hash function to generate a unique hash value. Specifically, the unique hash value generation module 20 obtains basic information from the basic information extraction module. The basic information is important data used to describe the key characteristics and ownership of the digital artwork, including elements such as the work name, creator information, creation date, artwork description information, and artwork category. Advanced screening algorithms and encryption technologies are used to automatically optimize and screen the basic information. This technology automatically obtains encrypted data for generating hash values ​​based on the various contents in the basic information. The digital artwork file and encrypted data are processed through the encrypted hash function to generate a unique hash value. This hash value covers key information such as the identification and authentication of the digital artwork. Parsing the hash value generated by the cryptographic hash function involves understanding and analyzing the structure, value, and characteristics of the digital artwork it represents. After parsing the hash value, a correspondence is established between the hash value and the digital artwork file and basic information, i.e., a one-to-one correspondence between the hash value and the digital artwork, thereby ensuring that each hash value accurately reflects the unique attributes of the digital artwork, thereby providing strong guarantees for the authentication and protection of digital artworks.

[0018] In one possible implementation, the unique hash value generation module 20 includes: a basic information category determination unit, which is used to perform cluster analysis on the basic information to determine the category of the basic information. Specifically, when the basic information is input into the basic information category determination unit, cluster analysis is started, and the various features in the basic information are quantified and standardized so that different features can be compared and analyzed on a unified scale. A clustering algorithm is used to classify basic information with similar features into the same category, which involves calculating indicators such as distance and similarity between basic information. For example, for the name of a work, similarity measurement will be performed based on its naming style, keywords and other features; for the creator information, consideration will be given to the region to which it belongs, the creative style and genre, etc. During the clustering process, the clustering parameters are continuously adjusted and optimized to ensure the accuracy and rationality of the clustering results. Multiple iterations are performed to try different clustering methods and parameter combinations until a satisfactory classification result is obtained. The clustering results are evaluated and verified, and external standard data sets or expert opinions are introduced to verify whether the determined basic information categories meet actual needs and expectations.

[0019] A data fitting unit is configured to fit the category sensitivity coefficient and imitation coefficient of the basic information using historical data based on the category of the basic information. Specifically, the data fitting unit receives the category of the basic information and collects and organizes a large amount of historical data. The historical data covers various cases and records related to the current category of the basic information, including information such as the sensitivity level, imitation status, and corresponding influencing factors of similar digital artworks in the past. The historical data is analyzed and classified and grouped according to the different categories of the basic information to facilitate more targeted fitting operations. During the fitting process, the model parameters and variables are continuously adjusted to ensure that the patterns and trends in the historical data are accurately captured. By fitting and approximating the data points, the approximate range of the basic information category sensitivity coefficient and imitation coefficient is gradually determined. Cross-validation and error analysis are then performed to evaluate the accuracy and reliability of the fitting results. If there are significant deviations or errors in the fitting results, the data and model are reviewed and necessary adjustments and optimizations are performed. Ultimately, a sensitivity coefficient and imitation coefficient that accurately reflect the sensitivity level and imitation likelihood of the basic information category are successfully fitted from the historical data.

[0020] The influence evaluation function construction unit is used to construct an influence evaluation function based on the category sensitivity coefficient and the imitation coefficient. Specifically, the influence evaluation function construction unit receives the category sensitivity coefficient and the imitation coefficient, analyzes the coefficients, and clarifies the role and weight of each in reflecting the influence of basic information on digital artwork authentication. The basic information is divided into Category, The sensitivity coefficient of the basic information of each category is , the replication coefficient is , the evaluation weight is and , impact evaluation function The construction process is as follows: First, for each category, calculate the sensitive part score This part reflects the degree of influence of the sensitivity of the information in the authentication. For example, if the sensitivity coefficient of a certain information is is 0.8, and its sensitive evaluation weight is is 0.6, then , then calculate the imitation score , reflects the impact of the imitation of this category of information on authentication, for example, The imitation coefficient is 0.7, and the imitation evaluation weight is 0.5, then Finally, the impact evaluation function That is, the scores of the sensitive part and the imitation part of all categories of basic information are summed up. The function comprehensively considers the sensitivity and possibility of imitation of each category of basic information in the authentication process, and can accurately reflect the impact of each category of basic information on the authentication results, providing an evaluation standard for optimal screening.

[0021] An optimization screening unit is used to construct an iterative optimization space according to the impact evaluation function to perform optimization screening on the basic information to obtain the encrypted data, wherein the encrypted data is the data with the greatest total impact on the digital artwork authentication in the basic information. Specifically, after receiving the impact evaluation function, the optimization and screening unit builds an initial space framework for iterative optimization based on the impact evaluation function. The initial space framework is a virtual mathematical environment used to systematically evaluate and compare basic information, and the basic information is substituted into the initial space framework one by one. In each iteration, the impact value of the basic information on the authentication of digital artworks is calculated according to the impact evaluation function. This calculation process is repeated continuously to comprehensively evaluate the importance and influence of each basic information, pay close attention to those data with a large total impact, and conduct more in-depth analysis and comparison of the data to determine whether they meet the encryption standards and requirements to ensure the accuracy and effectiveness of the screening. A variety of optimization algorithms and strategies will be adopted, including adjusting the iteration step size, search direction, or introducing random factors to avoid falling into local optimal solutions, continuously updating and optimizing the screening results, and finally determining the data with the largest total impact on the authentication of digital artworks in the basic information, and selecting it as the encrypted data. The optimization and screening unit successfully and accurately selects the most critical and influential data from a large amount of basic information for encryption, providing strong protection and support for the authentication of digital artworks.

[0022] In a possible implementation, the impact evaluation function construction unit includes: an evaluation weight sub-unit, and the evaluation weight sub-unit is used to configure sensitive and imitation evaluation weights according to the artwork category. Specifically, upon receiving input information about the artwork category, the evaluation weight subunit first analyzes the artwork category, studying common market characteristics, historical sensitivities, and the frequency and methods of imitation. Based on this understanding of the characteristics of the artwork category and in combination with empirical data from past similar artworks and industry standards, it begins to configure corresponding evaluation weights. Factors considered particularly sensitive, prone to problems, or with a high risk of imitation within the category are assigned higher weights to highlight their importance and influence. The relative importance and interrelationships between different factors are taken into account. For example, if the reputation of the creator of a certain type of artwork has a decisive influence on its value and certification, then factors related to the creator will be given a higher weight when configuring the weights. During the weight configuration process, the subunit continuously adjusts and optimizes the weights, performs simulation calculations, and observes the impact of different weight configurations on the evaluation results to ensure that the weight settings accurately reflect the actual importance of each factor in the artwork category. Ultimately, a set of reasonable and effective sensitivity and imitation evaluation weights appropriate to the artwork category is determined, providing an important foundation and basis for the subsequent construction of the impact evaluation function and data screening.

[0023] The influence proportion allocation subunit is used to add the evaluation weight to the influence evaluation function to distribute the influence proportion of the category sensitivity coefficient and the imitation coefficient, and construct the influence evaluation function. Specifically, the influence proportion allocation subunit adds the evaluation weight to the influence evaluation function to distribute the influence proportion of the category sensitivity coefficient and the imitation coefficient. For a certain artwork category, its sensitive evaluation weight is , the imitation evaluation weight is , the category sensitivity coefficient is , the replication coefficient is When constructing the impact evaluation function, the impact ratio of the sensitive part is calculated as , indicating the actual impact of sensitive information in the authentication of artworks of this category, and the impact of imitation accounts for , taking multiple categories as an example, suppose there are Category, The sensitivity coefficient of each category is , the replication coefficient is , the sensitive evaluation weight is , the imitation evaluation weight is . Then the impact evaluation function The construction is: For each category , calculate its sensitive part score and imitation part score , then, the impact evaluation function In this way, the evaluation weights are integrated into the function, and the sensitivity and imitation of different categories of basic information are reasonably distributed, thus constructing an evaluation function that can accurately reflect the comprehensive impact of each category of basic information in digital artwork authentication. Such a function provides a basis for subsequent screening of encrypted data that has a great impact on authentication, ensuring the effectiveness and accuracy of the authentication system.

[0024] The impact proportion allocation subunit first obtains the evaluation weight and the existing category sensitivity coefficient and imitation coefficient, introduces the evaluation weight into the construction of the impact evaluation function, analyzes the relationship between the evaluation weight and the category sensitivity coefficient and imitation coefficient, and determines the proportion of each coefficient in the impact evaluation function. If the evaluation weight indicates that a factor is extremely important, then when allocating the impact proportion, the weight of the coefficient corresponding to the factor in the function will be increased accordingly. In order to achieve accurate impact proportion allocation, the subunit may use a variety of mathematical methods and models, perform multiple trial calculations and simulations, and adjust the weight ratio of the coefficient to observe the impact on the function output results. In continuous attempts and optimization, find the proportion allocation scheme that most reasonably reflects the overall impact of each coefficient. Taking into account the interaction and synergy between different coefficients, ensure the scientificity and rationality of the allocation scheme, and finally successfully construct an impact evaluation function that can accurately reflect the impact proportion of the category sensitivity coefficient and imitation coefficient, providing a powerful tool and basis for subsequent analysis and decision-making.

[0025] In one possible implementation, the unique hash value generation module further includes a digital artwork file reconstruction unit configured to insert the encrypted data into the digital artwork file as an insertion authentication file, thereby reconstructing the digital artwork file. Specifically, the digital artwork file reconstruction unit obtains the encrypted data and the original digital artwork file, analyzes the structure and format of the digital artwork file, and determines an appropriate insertion location. The location is selected based on file security, reading efficiency, and compatibility with relevant standards and specifications. Once the insertion location is determined, the encrypted data is accurately added to the digital artwork file as an insertion authentication file. During the insertion process, it is necessary to ensure that the encrypted data matches the data format and encoding method of the original file to avoid data conflicts or corruption. To ensure the accuracy and integrity of the reconstruction, a series of checksums and verification operations are performed, such as checking whether the size of the inserted file meets expectations, whether data reading is normal, and whether the position and status of the encrypted data in the file are correct. The reconstructed digital artwork file is then evaluated for overall performance and security. If any problems or potential risks are found, adjustments and optimizations are made promptly, ultimately successfully integrating the encrypted data into the digital artwork file and completing the reconstruction. A hash value calculation unit is configured to perform hash value calculation on the reconstructed digital artwork file using the cryptographic hash function to generate a unique hash value. Specifically, the hash value calculation unit obtains the reconstructed digital artwork file, activates the cryptographic hash function, which is highly random and irreversible, and uses all the data of the reconstructed digital artwork file as input and passes it into the cryptographic hash function. The function calculates and processes every byte and every bit in the file. During the calculation process, any slight change in the file, even a change of just one byte, will cause a huge change in the output hash value. The cryptographic hash function gradually calculates a hash value of a fixed length through a series of complex logical and mathematical operations, such as bit operations and hash collision processing. After the calculation is completed, the generated unique hash value is output as a unique identifier and verification basis for the digital artwork file. The hash value calculation unit successfully generates a unique and deterministic hash value for the reconstructed digital artwork file, providing key technical support for the authentication and protection of digital artworks.

[0026] In one possible implementation, the digital artwork file reconstruction unit includes: a data insertion subunit, which is used to insert the encrypted data to the end of the digital artwork file, or determine the insertion position based on a random number function, and insert it into the digital artwork file based on the insertion position to reconstruct the digital artwork file. Specifically, the data insertion sub-unit obtains the encrypted data and the digital artwork file. If the encrypted data is chosen to be inserted at the end of the digital artwork file, the sub-unit will directly add the encrypted data to the end of the file. The process is relatively simple and direct, ensuring that the encrypted data is connected to the main body of the file. When the insertion position is determined based on the random number function, the sub-unit will start the random number generation mechanism. The random number function will generate a random value according to a specific algorithm and a random seed. The sub-unit will determine the specific insertion position based on this random value and the structure and size of the digital artwork file. After determining the insertion position, the sub-unit will carefully insert the encrypted data into the specified position. During the insertion process, the migration and rearrangement of the data need to be handled to ensure that the insertion operation does not destroy the original structure and data integrity of the digital artwork file. After the insertion is completed, the sub-unit will check the integrity and accuracy of the reconstructed digital artwork file, for example, check whether the file format is still correct, whether the data reading is smooth, and whether the encrypted data is accurately inserted into the specified position. The data insertion sub-unit successfully inserts the encrypted data into the digital artwork file and completes the file reconstruction.

[0027] The smart contract creation module 30 is used to create a smart contract based on the basic information, the encrypted data, and the hash value. Specifically, after the smart contract creation module 30 is started, it first obtains the basic information, the encrypted data, and the hash value, analyzes and organizes the basic information obtained, extracts key elements therein, such as the name of the work, the creator information, the creation date, etc., converts these elements into clear terms in the contract, stipulates the basic attributes of the digital artwork and the ownership of related rights, processes the encrypted data, and integrates the encrypted data into the smart contract, which may be used as a special verification mark or encrypted permission control part to enhance the security and confidentiality of the contract. As for the hash value, it is embedded in the contract as the unique identifier of the digital artwork, and the tamper-proof nature of the hash value ensures the contract is intact. The smart contract can be accurately associated with a specific digital artwork and its integrity and authenticity can be verified. In the process of creating a contract, the specific terms and conditions of the smart contract are generated according to the preset rules and templates and combined with the above-mentioned processed information. The terms include the transaction rules, usage rights, copyright statements, verification mechanisms, etc. of the digital artwork. The generated contract is checked and verified multiple times to ensure that the contract is logically rigorous, the terms are clear, and there are no legal loopholes or technical defects. The smart contract creation module 30 successfully creates a complete, accurate and effective smart contract, providing a legally effective and technically guaranteed contract framework for the transaction, management and protection of digital artworks.

[0028] In one possible implementation, Figure 2As shown, the smart contract creation module 30 includes a security test result acquisition unit, which is used to perform multi-dimensional security testing on the smart contract based on the artwork category and obtain security test results for each dimension. Specifically, the security test result acquisition unit starts with the artwork category associated with the smart contract. The artwork category is a key identifier used to define the scope of application and characteristics of the smart contract, including characteristics of different art forms, styles, and values. It uses multi-dimensional security testing technology (a series of methods and means for comprehensively evaluating the security of smart contracts, covering multiple key areas such as functionality, performance, and security) to conduct an in-depth analysis of the smart contract. This technology automatically conducts security testing in various dimensions based on the specific requirements and constraints of the artwork category, obtaining detailed results including functional testing, performance testing, and security testing. Functional testing includes unit functional testing and integrated functional testing, individually verifying each functional unit in the smart contract to ensure that it can complete specific operations as expected. It then tests the coordinated operation of multiple functional units to verify their stability and accuracy in an integrated state. Performance testing focuses on load testing and response time testing. By simulating diverse load conditions, such as large numbers of transaction requests or concurrent operations, the performance of smart contracts in high-stress environments is tested. The response time for various operations is also measured to assess their efficiency and timeliness. Security testing includes static risk testing and dynamic risk testing. Static risk testing examines the syntax, logic, and potential security vulnerabilities of the contract code, while dynamic risk testing monitors the security status of smart contracts in actual operational scenarios, such as the ability to resist external attacks and prevent data leaks. For each dimension of testing, specialized testing tools and techniques are used, and detailed test cases and scenarios are carefully designed. Pre-defined test procedures are strictly followed during testing. During the testing process, the results and generated data of each test step are carefully recorded, covering successful execution, errors or abnormal situations, and the specific values ​​of performance indicators. After comprehensive and in-depth multi-dimensional testing, detailed and accurate security test results are ultimately obtained for each dimension.

[0029] The result screening unit is used to screen the security test results and determine the risk dimensions. Specifically, the result screening unit starts working by obtaining the security test results. The security test results are a detailed data set covering multiple dimensions such as function, performance, and security. These data are derived from a comprehensive test of the smart contract and include key information such as the accuracy of function execution, the level of performance load, and the presence or absence of security vulnerabilities. Using advanced screening and analysis technology (professional methods and tools for accurately locating and evaluating potential risks in test results), these security test results are deeply analyzed. This technology automatically screens out possible risk dimensions based on specific indicators and preset standards in each test result, such as the failure of a key function in the functional test, the serious excess of response time in the performance test, and the discovery of high-risk vulnerabilities in the security test. The complex and diverse test results are checked one by one, focusing on data performance that exceeds the preset threshold or deviates from the normal range. For example, in the performance test, if the response time far exceeds the expected acceptable limit, or in the security test, a major security vulnerability that may lead to serious consequences is detected, these will be regarded as significant risk warning signals. By comprehensively and comprehensively comparing and measuring the test results of different dimensions, the specific dimensions with potential risks can be accurately located. In-depth correlation analysis of multiple interrelated data points is required to determine that the problems presented by a certain dimension are not isolated individual phenomena, but rather present a risk situation with systematic and trend characteristics. The possible interactions and influence relationships between different dimensions should be fully considered. For example, a defect in a certain function may indirectly cause a decline in performance or increase the degree of security risk exposure. Ultimately, the specific dimensions with risks are successfully identified, which provides clear direction guidance and key areas of focus for subsequent risk disposal measures and optimization and improvement of smart contracts, thereby ensuring that the security and reliability of smart contracts are effectively improved.

[0030] A risk feature acquisition unit, wherein the risk feature acquisition unit is used to acquire the risk features of the decentralized storage. Specifically, the risk signature acquisition unit determines the scope and objectives of acquiring risk signatures, including a comprehensive consideration of all components of the decentralized storage system, such as storage nodes, data transmission channels, and storage protocols. It uses means and methods such as monitoring the system's operating status, collecting user feedback and error information, and analyzing system log files to collect relevant data. It performs preliminary screening and classification of the data to remove irrelevant or redundant information and retain only key data related to risk signatures. It then analyzes the screened data using data mining algorithms, machine learning models, or statistical analysis methods to discover potential patterns and regularities in the data. Through analysis, it identifies signatures that may indicate the presence of risks, such as frequent failures of storage nodes, abnormal data transmission delays, and abnormal fluctuations in storage capacity. These signatures are then compared with industry standards and best practices to determine whether the acquired signatures constitute an anomaly or risk. During the process of acquiring risk signatures, it conducts continuous verification and revisions, ensuring the accuracy and reliability of the acquired risk signatures through repeated data collection and analysis, or by incorporating external expert opinions and assessments. The risk signature acquisition unit ultimately successfully acquires risk signatures for decentralized storage, providing an important foundation and basis for subsequent risk assessment and response.

[0031] A collaborative authentication means matching unit is used to perform risk correlation analysis based on the risk characteristics of the decentralized storage and the risk dimensions, perform missing searches based on the risk correlations, and match collaborative authentication means. Specifically, the collaborative authentication means matching unit obtains the risk characteristics and determined risk dimensions stored in a decentralized manner, and conducts a detailed risk correlation analysis on the risk characteristics and risk dimensions, which involves comparing various indicators, data patterns and potential correlation factors of the two. By establishing a complex mathematical model or using machine learning technology, the degree of correlation and impact weight between the risk characteristics and risk dimensions are calculated. Based on the analysis results of the risk correlation, a missing search is performed, which means finding the missing parts of the risk response measures that may exist in the current system to determine which aspects need further strengthening and supplementation. According to the missing situations found, matching is performed from a pre-established collaborative authentication means library, which stores various authentication means and strategies for dealing with different risk situations. In the matching process, a variety of factors are comprehensively considered, such as the severity of the risk, the scope of impact, the possible losses, and the cost and feasibility of implementing the authentication means, etc., and finally the collaborative authentication means that best suits the current risk situation is determined. The matching results are re-evaluated and verified to ensure that the selected collaborative authentication means can effectively deal with risks and are compatible with the operation and management requirements of the entire system.

[0032] The authentication means adding unit is used to add the collaborative authentication means to the authentication system. Specifically, the authentication means adding unit obtains the collaborative authentication means determined by the collaborative authentication means matching unit, analyzes the acquired collaborative authentication means, clarifies its specific functions, application scenarios and key points of integration with the existing authentication system, connects and interacts with the authentication system, obtains the relevant interfaces and permissions of the authentication system to ensure that the collaborative authentication means can be smoothly added thereto. During the adding process, the collaborative authentication means is adjusted and adapted as necessary according to the architecture and rules of the authentication system, for example, parameter settings are modified, data formats are adjusted or compatibility processing is performed to ensure that the collaborative authentication means can be seamlessly connected with the authentication system. The collaborative authentication means is integrated through specific technical means and operating procedures. The relevant codes, modules or configuration information are gradually imported into the authentication system. During the import process, the feedback and operation status of the system are monitored in real time to ensure that the joining process does not cause system errors or exceptions. The collaborative authentication means after joining are preliminarily tested and verified, and some common authentication scenarios are simulated to check whether the collaborative authentication means can work normally and work well with other parts of the authentication system. If problems are found during the test, they are promptly checked and repaired, and the joining and testing operations are performed again until the collaborative authentication means runs stably in the authentication system. The authentication means joining unit successfully joins the collaborative authentication means into the authentication system, providing new protection and enhancement for the security and reliability of the system.

[0033] In one possible implementation, the security test result acquisition unit includes a risk dimension determination subunit, configured to obtain security test results for each dimension based on the multi-dimensional security test, arrange the security test results in ascending order, filter the risk test results at the lowest risk level, and determine the risk dimension. Specifically, after performing the multi-dimensional security test, the security test results for each dimension are obtained, arranged in ascending order (i.e., from most secure to least secure), filter the risk test results at the lowest risk level, and determine the risk dimension by comprehensively evaluating these results.

[0034] The patch feature acquisition subunit is used to obtain the patch features of the smart contract, use the patch features to match the risk dimensions, and configure the contract patch. Specifically, the patch feature acquisition subunit obtains the patch features of the smart contract through a variety of channels, including retrieval from a dedicated patch database, extraction from documents or instructions provided by the development team, or inference of possible patch features by analyzing the code and structure of the smart contract. After obtaining the patch features, it compares and matches them one by one with the previously determined risk dimensions, analyzes the problems targeted by the patch features, the solutions and the scope of application, and accurately matches them with the specific risk types, impact levels and related features exposed by the risk dimensions. For the successfully matched patch features, corresponding configurations are performed according to the actual situation of the risk dimension, which involves adjusting the parameter settings of the patch and determining its role in the smart contract. The application location and method in the smart contract, as well as the integration and coordination with other existing security measures, will fully consider the overall architecture and operation logic of the smart contract when configuring contract patches to ensure that the addition of patches will not have an adverse impact on normal functions and can effectively deal with risks. After the configuration is completed, verification and testing will be carried out to simulate the occurrence of risk situations to check whether the patch can function as expected and whether it works well with other parts of the smart contract. If problems are found in the patch configuration or the effect is not ideal during the test, the matching process and configuration method will be reviewed, and necessary adjustments and optimizations will be made until the patch can effectively match the risk dimension and provide reliable protection for the smart contract.

[0035] A missing risk dimension acquisition subunit is used to acquire a missing risk dimension, where the missing risk dimension is a risk dimension of missing patches and is different from the risk characteristics of the decentralized storage. Specifically, the missing risk dimension acquisition subunit sorts out the existing risk dimensions and configured patches, and compares and checks whether each risk dimension has a corresponding valid patch. For risk dimensions that are not covered by patches, they are preliminarily marked as potential missing risk dimensions. The potential missing risk dimensions are studied and compared with the risk characteristics of decentralized storage. During the comparison process, multiple aspects will be considered, including the manifestation of risks, the scope of impact, the root causes, etc. If a risk dimension not only has no patch, but also its characteristics are significantly different from the common risk characteristics of known decentralized storage, then it can be determined to be a missing risk dimension. In order to ensure accuracy, a variety of analysis methods and tools may be used. For example, data mining technology can be used to explore hidden patterns and associations, or expert experience and industry standards can be used for judgment. Relevant data and information are repeatedly verified and cross-checked to avoid misjudgment and omissions. After obtaining the confirmed missing risk dimensions, they will be recorded and classified in detail to provide a clear and accurate basis for subsequent processing and response.

[0036] The compensation target determination subunit is configured to analyze the missing risk dimensions and determine compensation targets. Specifically, the compensation target determination subunit obtains the missing risk dimensions identified by the missing risk dimension acquisition subunit and analyzes the missing risk dimensions. This involves breaking down and studying various aspects of the risk dimensions, including but not limited to analyzing the direct losses that may result from the risk, potential chain reactions, and long-term impacts on system stability and security. The potential impact paths and possible consequences of the missing risk dimensions are simulated and predicted. During the analysis process, the overall system architecture, business processes, and interactions with other relevant components are considered to comprehensively assess the scope and severity of the risk. Based on the detailed analysis results of the missing risk dimensions, the specific direction and focus of compensation are identified, including setting goals for reducing losses, restoring normal system operation, and enhancing risk resilience. Specific quantitative indicators and measurable standards are set when determining compensation targets. For example, a requirement to reduce losses caused by the risk to a specific level within a certain period of time or to improve system stability to a specific numerical range is specified. The rationality and feasibility of the compensation targets are evaluated to ensure that they can effectively address the challenges posed by the missing risk dimensions while meeting the system's resource and capacity constraints.

[0037] The collaborative authentication means acquisition subunit is used to perform correlation calculation with the authentication means in the authentication means library according to the compensation target to obtain the collaborative authentication means. Specifically, the collaborative authentication method acquisition subunit obtains a pre-built authentication method library, which includes various authentication methods such as multi-signature, multi-factor authentication, combined on-chain and off-chain storage, multi-level encryption, and decentralized identity (DID). For each authentication method, it extracts its key features and applicable scenarios, and calculates the correlation between the compensation target and each authentication method in the authentication method library. During the calculation process, the required functions, performance, security, and other requirements of the compensation target are compared with the characteristics and effects provided by each authentication method. For example, if the compensation goal is to enhance the access control security of specific data, the strength and flexibility of each authentication method in access control are evaluated. For each comparison and calculation, a correlation score is given to indicate the degree of fit between the authentication method and the compensation goal. By calculating the correlation and ranking the scores of all authentication methods, the authentication methods with higher scores and high correlation with the compensation goal are screened out. These selected authentication methods are identified as collaborative authentication methods, which can provide effective support and guarantee for achieving the compensation goal. Data verification and algorithm optimization are continuously performed to ensure that the acquired collaborative authentication methods are accurate and effective.

[0038] In one possible implementation, the collaborative authentication means acquisition subunit includes a screening probability constraint configuration microunit, which is used to configure screening probability constraints, where the screening probability constraints are determined based on a historical test database. Specifically, the screening probability constraint configuration microunit is used to configure screening probability constraints, where the constraints are determined based on a historical test database. The historical test database contains a large amount of test data and results from similar past situations. By analyzing and summarizing this data, screening probability constraints applicable to the current situation are derived.

[0039] The value threshold determination micro-unit is used to determine the value threshold according to the screening probability constraint, screen the correlation calculation results, and generate an initial authentication means combination. Specifically, the value threshold determination micro-unit obtains the previously configured screening probability constraint, analyzes and processes the screening probability constraint, and considers multiple factors, such as the characteristics of the probability distribution, the central tendency and discreteness of the data, etc., and determines a specific value threshold according to the screening probability constraint. The threshold will serve as a standard for screening the correlation calculation results, obtain a data set of correlation calculation results, and compare each correlation calculation result in the data set with the determined value threshold. Results greater than or equal to the value threshold are retained; results less than the value threshold are excluded. After the screening process, the authentication means corresponding to the retained results are combined together to generate an initial authentication means combination. The accuracy of the calculation and the rationality of the screening will be continuously checked to ensure that the generated initial authentication means combination has certain rationality and potential effectiveness.

[0040] The range modification micro-unit is used to modify the scope of the current initial authentication means combination, generate a new candidate group, evaluate the new candidate group through the compensation effect of the compensation target, and determine the current optimal solution. Specifically, the range modification micro-unit obtains the current initial authentication means combination, and performs range modification on the initial authentication means combination. The modification method includes adding new authentication means or reducing existing authentication means. When adding authentication means, appropriate means are selected from the set of available authentication means for addition; when reducing authentication means, parts that have little impact on the compensation target or overlap with other means functions are removed according to certain rules and strategies. By adding or reducing operations, a series of new candidate groups are generated. For each new candidate group, it is evaluated according to the compensation effect of the compensation target. It is necessary to simulate the performance of the candidate group in actual applications and measure their compensation effects through a series of indicators and methods. The degree of achievement of the goal. For example, if the compensation goal is to improve data security to a certain level, then the effectiveness of each candidate group in preventing various attacks and protecting data confidentiality and integrity will be evaluated; if the compensation goal is to improve system efficiency, then the performance of the candidate group in terms of processing speed, resource consumption, etc. will be examined. During the evaluation process, a comprehensive score is calculated for each newly created candidate group to reflect its compensation effect on the compensation goal. By comparing the scores of each newly created candidate group, the candidate group with the highest score is determined as the current optimal solution. The above steps of scope modification, candidate group generation, evaluation and determination of the optimal solution are repeated continuously to gradually optimize and find the combination of authentication methods that best meets the compensation goal.

[0041] An access taboo table setting micro-unit is used to set an access taboo table, and the access taboo table is used to set taboos for the authentication means combinations that have been visited. Specifically, the access taboo table setting micro-unit clarifies that its main task is to create and maintain an access taboo table for recording the authentication means combinations that have been visited, and initialize a data structure, such as an array, a linked list or a hash table, etc., for storing relevant information of the authentication means combinations that have been visited and processed. Each time the authentication means combination is evaluated and processed, the access taboo table setting micro-unit will add key information such as the identification, characteristics or related parameters of the currently visited authentication means combination to the access taboo table. The added information should have sufficient uniqueness and recognition so as to be able to accurately identify and distinguish different authentication means combinations. The access taboo table setting micro-unit is responsible for access taboos. The table is cleaned and optimized regularly to prevent the performance degradation or data confusion caused by the table being too large. The cleaning strategy may include deleting too old access records or merging similar records to save storage space. In the subsequent authentication means combination screening and optimization process, whenever a new candidate combination needs to be generated, the access taboo table setting micro unit checks whether the combination already exists in the access taboo table. If so, it will avoid repeated processing and evaluation, thereby improving the efficiency of the entire solution and avoiding invalid calculations. The access taboo table setting micro unit successfully creates and maintains a valid access taboo table, providing important support and guarantee for the optimization process of the entire authentication means combination.

[0042] Iterative optimization micro-unit, the iterative optimization micro-unit is used to filter the authentication means combination based on the access taboo table and determine the search direction, iteratively optimize the current optimal solution until all authentication means combinations are completed, and the collaborative authentication means are obtained. Specifically, the iterative optimization micro-unit obtains the access taboo table, filters the currently generated authentication means combination according to the access taboo table, and excludes combinations that have been marked as taboo and are not suitable for further consideration, thereby reducing unnecessary calculations and evaluations. Next, the search direction is determined. Based on the characteristics of the current optimal solution, the authentication means combination space that has not yet been explored, and further analysis of the compensation target, the search direction can be determined by exploring in multiple directions that have similar characteristics but are different from the current optimal solution, or giving priority to authentication means combinations that have potential high value but have not been fully studied. After determining the search direction, iterative optimization of the current optimal solution begins, including generating new candidate authentication means combinations and performing compensation based on the compensation effect of the compensation target. These candidate combinations are evaluated, and similar to the previous process, new optimal solutions are determined by comparing and calculating scores. The above steps of filtering, determining direction, generating candidate combinations, evaluating and updating optimal solutions are repeated continuously. In each iteration, the optimal collaborative authentication means is closer. The process will continue until the exploration and evaluation of all possible authentication means combinations are completed. When there are no more unprocessed authentication means combinations, the optimal solution obtained at this time is the final collaborative authentication means. During the entire iterative optimization process, the strategy will be continuously monitored and adjusted to ensure that the optimal solution can be found efficiently while avoiding falling into the dilemma of local optimality. The iterative optimization micro-unit finally successfully obtained the collaborative authentication means that can meet the compensation goals to the greatest extent.

[0043] Storage authentication module 40 is used to store the smart contract and digital artwork file in a decentralized storage authentication system. Specifically, storage authentication module 40 retrieves the smart contract and digital artwork file from relevant data sources. These smart contract and digital artwork files are key data for secure storage and authentication in the decentralized storage authentication system, and contain various important information and parameters. This data is comprehensively prepared using advanced storage authentication technology (a series of methods and means for ensuring reliable data storage and authentication in a decentralized environment). This technology encrypts the data, adds digital signatures, and generates unique identifiers to enhance data security and traceability. The prepared data is then transmitted to various nodes or storage locations in the decentralized storage authentication system. The transmission process considers key parameters such as data transmission speed and integrity. Once the data transmission is complete, storage authentication module 40 sends a confirmation request to the system to obtain feedback on the successful data storage. This feedback may include detailed information such as the storage location and storage time. Key aspects of the entire storage process are recorded, including transmission time, data size, storage location, and so on, forming a detailed storage log for subsequent auditing and tracking. During subsequent operation, the storage authentication module 40 will also regularly check and verify the stored data to ensure that the data has not been tampered with or damaged during storage. If any anomaly is discovered, the corresponding recovery or error correction process is immediately initiated, thereby ensuring the security and accuracy of the data, thereby achieving reliable storage and authentication of smart contracts and digital artwork files in the decentralized storage authentication system.

[0044] The embodiment of the present application extracts basic information (such as the name of the work, creator, etc.) from the digital artwork file, optimizes the encrypted data, calculates the hash value, creates a smart contract based on the basic information, encrypted data and hash value, and stores the smart contract and digital artwork file in a decentralized authentication system. By accurately extracting the basic information of the digital artwork, the encryption screening process is optimized, achieving the technical effect of improving the security of digital artwork protection.

[0045] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other and are not used to limit the scope of protection of the present invention.

[0046] The above specific embodiments do not constitute a limitation to the scope of protection of this application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of this application should be included in the scope of protection of this application. In some cases, the actions or steps recorded in this application can be performed in an order different from that in the embodiments and can still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A protection data authentication system based on NFT digital artworks, characterized by: The protection data authentication system based on NFT digital artworks includes: A basic information extraction module is used to extract basic information from digital artwork files, including the work title, creator information, creation date, artwork description information, and artwork category; a unique hash value generation module, the unique hash value generation module being configured to optimize and screen the basic information to obtain encrypted data, and perform hash value calculation on the digital artwork file and the encrypted data using a cryptographic hash function to generate a unique hash value; A smart contract creation module, configured to create a smart contract based on the basic information, the encrypted data, and the hash value; A storage authentication module, configured to decentralize the storage authentication system for the smart contract and the digital artwork file; Wherein, the unique hash value generation module includes: a basic information category determination unit, configured to perform cluster analysis on the basic information to determine the category of the basic information; a data fitting unit configured to fit a category sensitivity coefficient and an imitation coefficient of the basic information through historical data based on the category of the basic information; An impact evaluation function construction unit, wherein the impact evaluation function construction unit is used to construct an impact evaluation function based on the category sensitivity coefficient and the imitation coefficient; An optimization screening unit is used to construct an iterative optimization space according to the impact evaluation function to perform optimization screening on the basic information to obtain the encrypted data, wherein the encrypted data is the data with the greatest total impact on the digital artwork authentication in the basic information.

2. The protection data authentication system based on NFT digital artwork according to claim 1 is characterized in that: The impact evaluation function construction unit includes: An evaluation weight subunit, configured to configure sensitive and imitation evaluation weights according to the artwork category; The influence proportion allocation subunit is used to add the evaluation weight to the influence evaluation function to distribute the influence proportion of the category sensitivity coefficient and the imitation coefficient, and construct the influence evaluation function.

3. The protection data authentication system based on NFT digital artwork according to claim 1 is characterized in that: The unique hash value generation module includes: a digital artwork file reconstruction unit, configured to insert the encrypted data into the digital artwork file as an insertion authentication file to reconstruct the digital artwork file; A hash value calculation unit is used to perform hash value calculation on the reconstructed digital artwork file using the cryptographic hash function to generate a unique hash value.

4. The protection data authentication system based on NFT digital artwork according to claim 3 is characterized in that: The digital artwork file reconstruction unit includes: A data insertion subunit is used to insert the encrypted data into the end of the digital artwork file, or to determine an insertion position based on a random number function, and insert the encrypted data into the digital artwork file based on the insertion position to reconstruct the digital artwork file.

5. The protection data authentication system based on NFT digital artwork according to claim 1 is characterized in that: The smart contract creation module includes: A security test result acquisition unit, configured to perform a multi-dimensional security test on the smart contract based on the artwork category, and obtain security test results for each dimension; A result screening unit, configured to screen the safety test results and determine risk dimensions; a risk feature acquisition unit, configured to acquire risk features of the decentralized storage; A collaborative authentication means matching unit, the collaborative authentication means matching unit being used to perform risk correlation analysis based on the risk characteristics of the decentralized storage and the risk dimensions, perform missing searches based on the risk correlations, and match collaborative authentication means; An authentication means adding unit, wherein the authentication means adding unit is used to add the collaborative authentication means to the authentication system.

6. The protection data authentication system based on NFT digital artwork according to claim 5 is characterized in that: The security test result acquisition unit includes: a risk dimension determination subunit, the risk dimension determination subunit being configured to obtain safety test results of each dimension based on the multi-dimensional safety test, arrange the safety test results in positive order, filter the post-risk test results, and determine the risk dimension; A patch feature acquisition subunit, which is used to acquire patch features of the smart contract, match the patch features with the risk dimensions, and configure the contract patch; a missing risk dimension acquisition subunit, the missing risk dimension acquisition subunit being used to acquire a missing risk dimension, the missing risk dimension being a risk dimension of missing patches and being different from the risk characteristics of the decentralized storage; a compensation target determination subunit, the compensation target determination subunit being configured to analyze the missing risk dimension and determine a compensation target; The collaborative authentication means acquisition subunit is used to perform correlation calculation with the authentication means in the authentication means library according to the compensation target to obtain the collaborative authentication means.

7. The protection data authentication system based on NFT digital artwork according to claim 6 is characterized in that: The collaborative authentication means acquisition subunit includes: A screening probability constraint configuration micro unit, wherein the screening probability constraint configuration micro unit is used to configure a screening probability constraint, wherein the screening probability constraint is determined based on a historical test database; A threshold value determination micro-unit, the threshold value determination micro-unit is used to determine the threshold value according to the screening probability constraint, screen the correlation calculation results, and generate an initial authentication means combination; A range modification micro-unit is used to modify the range of the current initial authentication means combination to generate a new candidate group, evaluate the new candidate group based on the compensation effect of the compensation target, and determine the current optimal solution; An access taboo table setting micro unit, wherein the access taboo table setting micro unit is used to set an access taboo table, wherein the access taboo table is used to set taboos on the authentication means combinations that have been accessed; An iterative optimization micro-unit is used to filter authentication means combinations based on the access taboo table and determine a search direction, iteratively optimize the current optimal solution until all authentication means combinations are completed, and obtain the collaborative authentication means.

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