Protection data authentication system based on NFT digital artwork

Abstract

The invention discloses an NFT-based digital artwork protection data authentication system, and relates to the technical field of block chains, and the method comprises the steps: extracting basic information (such as a work name, a creator and the like) from a digital artwork file, optimizing and screening encrypted data, calculating a Hash value, creating an intelligent contract based on the basic information, the encrypted data and the Hash value, and verifying the intelligent contract. And decentralizing and storing the smart contract and the digital artwork file in an authentication system. The technical problems that in the prior art, vulnerabilities exist in protection data authentication of the digital artwork, and an effective means is lacked for improving authentication safety in a targeted mode are solved, the encryption screening process is optimized through accurate extraction of the basic information of the digital artwork, and the technical effect of improving the protection safety of the digital artwork is achieved.

Description

Technical Field

[0001] The present 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, the copyright and ownership protection issues of digital artworks are particularly prominent, and the contradictions in the demand for related technical means are relatively more prominent. Building a complete and efficient digital artwork protection and certification system to better meet the protection and trading needs of digital artworks has become a crucial link in solving the development of the digital artwork field. Traditional digital artwork protection and trading methods are often relatively limited and one-sided, relying only on simple encryption methods or limited copyright statements, and lacking control over the entire process of digital artwork protection and trading. There is a lack of in-depth analysis and utilization of artwork creation and circulation data, and it is difficult to fully and accurately define the ownership and value of digital artworks. There are imperfections in the protection and trading mechanisms, which have led to the damage of the rights and interests of some digital artworks, and some transactions lack credibility. The formulation of digital artwork protection and trading plans is relatively simple and fixed, and cannot well respond to the ever-changing actual needs in the field of digital art.

[0003] At the current stage, relevant technologies exist in the data authentication of digital artwork protection, 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 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 protection data authentication system based on NFT digital artworks, including: A basic information extraction module, the basic information extraction module is used to extract basic information from a digital artwork file, including the name of the work, creator information, creation date, artwork description information, and artwork category; a unique hash value generation module, the unique hash value generation module is used to optimize and screen the basic information, 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; a smart contract creation module, the smart contract creation module is used to create a smart contract based on the basic information, the encrypted data, and the hash value; a storage authentication module, the storage authentication module is used to store the smart contract and the digital artwork file in a decentralized storage authentication system.

[0006] 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, and create a smart contract based on the basic information, encrypted data and hash value. The smart contract and digital artwork files are stored 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

[0007] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0008] 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; Figure 2 A schematic diagram of the structure of the smart contract creation module of the protection data authentication system based on NFT digital artworks provided in an embodiment of the present application.

[0009] Explanation of the reference numerals: basic information extraction module 10, unique hash value generation module 20, smart contract creation module 30, storage authentication module 40. DETAILED DESCRIPTION

[0010] 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.

[0011] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0012] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is 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 to distinguish similar objects and do not represent a specific ordering of 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 that are 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 those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0013] 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: The basic information extraction module 10 is used to extract basic information from the digital artwork file, including the work name, creator information, creation date, artwork description information, and artwork category. Specifically, the basic information extraction module 10 obtains the digital artwork file from the relevant data source of the digital artwork. The digital artwork file is a digital document used to describe the basic attributes and characteristics of the digital artwork, and contains key elements such as the work name, creator information, creation date, artwork description information, and artwork category. The digital artwork file is automatically parsed using a specific data extraction technology. The technology automatically extracts basic information for subsequent processing based on the information in the file, including accurate work name, detailed creator information, precise creation date, complete artwork description information, and exact artwork category. The extracted basic information is carefully parsed, including understanding and analyzing the format, content, and meaning of the basic information. After parsing the basic information, an associated correspondence relationship between the basic information and the digital artwork file is established, that is, a one-to-one correspondence relationship between the basic information and the digital artwork file, thereby ensuring that each basic information accurately reflects the key content in the digital artwork file, thereby providing accurate and reliable data support for subsequent processing procedures.

[0014] A unique hash value generation module 20 is used to optimize and screen the basic information, 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 features and ownership of digital artworks, including elements such as the name of the work, 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. The technology automatically obtains encrypted data for generating hash values ​​based on the contents of the basic information. Through the encrypted hash function, the digital artwork file and encrypted data are processed to generate a unique hash value, which covers key information such as the identification and authentication of the digital artwork. Parsing the hash value generated by the cryptographic hash function includes understanding and analyzing the structure, value, and digital artwork characteristics of the hash value. After parsing the hash value, establishing a correspondence between the hash value and the digital artwork file and basic information, that is, 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.

[0015] In a 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, the 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. The basic information with similar features is classified into the same category using a clustering algorithm, which involves calculating the distance, similarity and other indicators between the basic information. For example, for the name of the work, the similarity measurement will be performed based on its naming style, keywords and other features; for the creator information, it will be considered based on the region to which it belongs, the creative style and genre, etc. In 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 category meets actual needs and expectations.

[0016] A data fitting unit is used to fit the category sensitivity coefficient and imitation coefficient of the basic information through historical data based on the category of the basic information. Specifically, the data fitting unit receives the category of the basic information, collects and organizes a large amount of historical data, and the historical data covers various cases and records related to the current category of basic information, including information such as the sensitivity, imitation and corresponding influencing factors of similar digital artworks in the past, analyzes the historical data, and classifies and groups the historical data according to different categories of basic information, so as to perform fitting operations more targeted. In the fitting process, the parameters and variables of the model are continuously adjusted to ensure that the laws and trends in the historical data can be accurately captured. Through the fitting and approximation of the data points, the approximate range of the sensitivity coefficient and imitation coefficient of the basic information category is gradually determined, and cross-validation and error analysis are performed to evaluate the accuracy and reliability of the fitting results. If there is a large deviation or error in the fitting result, the data and model will be reviewed, and necessary adjustments and optimizations will be made. Finally, the sensitivity coefficient and imitation coefficient that can accurately reflect the sensitivity of the basic information category and the possibility of imitation are successfully fitted from the historical data.

[0017] The impact evaluation function construction unit is used to construct an impact evaluation function based on the category sensitivity coefficient and the imitation coefficient. Specifically, the impact 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 impact 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 , influence evaluation function The construction process of 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 0.6, then , then calculate the score of the imitation part , 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 result, providing an evaluation standard for optimal screening.

[0018] The 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, and the encrypted data is the data with the largest 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 spatial framework for iterative optimization based on the impact evaluation function. The initial spatial framework is a virtual mathematical environment used to systematically evaluate and compare basic information, and the basic information is substituted into the initial spatial framework one by one. In each iteration, the impact value of the basic information on the digital artwork authentication is calculated according to the impact evaluation function, and 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, the search direction, or introducing random factors to avoid falling into local optimal solutions, and continuously updating and optimizing the screening results. Finally, the data with the largest total impact on the digital artwork authentication in the basic information is determined, and it is selected 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.

[0019] In a possible implementation, the impact evaluation function construction unit includes: an evaluation weight subunit, and the evaluation weight subunit is used to configure sensitive and imitation evaluation weights according to the artwork category. Specifically, the evaluation weight subunit receives the input information of the artwork category. The evaluation weight subunit first analyzes the artwork category, studies the common characteristics of the artwork of this category in the market, historical sensitive situations, and the frequency and method of imitation. Based on the understanding of the characteristics of the artwork category, combined with the experience data of similar artworks in the past and industry standards, it begins to configure the corresponding evaluation weights. For factors that are considered to be particularly sensitive, prone to problems, or have a high risk of imitation in this category, higher weights are given to highlight their importance and influence. Considering the relative importance and mutual relationship between different factors, for example, if the reputation of the creator of a certain type of artwork has a decisive influence on its value and certification, then when configuring the weights, a higher weight will be given to the creator-related factors. In the process of configuring the weights, continuous adjustment and optimization are carried out, simulation calculations are performed, and the impact of different weight configurations on the evaluation results is observed to ensure that the weight setting can accurately reflect the actual importance of each factor in the artwork category. Finally, a set of reasonable and effective sensitive and imitation evaluation weights that are suitable for the artwork category is determined, which provides an important foundation and basis for the subsequent construction of the impact evaluation function and data screening.

[0020] The influence proportion allocation subunit is used to add the evaluation weight to the influence evaluation function to allocate the influence proportion to 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, allocates the influence proportion to the category sensitivity coefficient and the imitation coefficient, and a certain artwork category has a sensitive evaluation weight of , 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 the imitation part 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 allocated, so as to construct 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 the subsequent screening of encrypted data that has a great impact on authentication, ensuring the effectiveness and accuracy of the authentication system. 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 the 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 optimizations, find the proportion allocation scheme that can most reasonably reflect 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 the imitation coefficient, providing a powerful tool and basis for subsequent analysis and decision-making.

[0021] In a possible implementation, the unique hash value generation module further includes: a digital artwork file reconstruction unit, the digital artwork file reconstruction unit is used to add the encrypted data as an insertion authentication file into the digital artwork file to reconstruct 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, determines a suitable insertion position, and the selection of the position takes into account the security of the file, the reading efficiency, and the compatibility with relevant standards and specifications. After the insertion position is determined, the encrypted data is accurately added to the digital artwork file in the form of 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 damage. In order to ensure the accuracy and completeness of the reconstruction, a series of verification and verification operations are performed, for example, checking whether the size of the inserted file meets expectations, whether the data is read normally, and whether the position and status of the encrypted data in the file are correct. The overall performance and security evaluation of the reconstructed digital artwork file is performed. If any problems or potential risks are found, adjustments and optimizations will be made in a timely manner, and finally the encrypted data is successfully integrated into the digital artwork file to complete the reconstruction. A hash value calculation unit, the hash value calculation unit is used to calculate the hash value of the reconstructed digital artwork file through the encrypted hash function to generate a unique hash value. Specifically, the hash value calculation unit obtains the reconstructed digital artwork file, starts the encrypted hash function, which has a high degree of randomness and irreversibility, and takes all the data of the reconstructed digital artwork file as input and passes it into the encrypted hash function. The function will calculate and process every byte and every bit in the file. During the calculation process, any slight change in the file, even if it is only a byte change, will cause a huge change in the output hash value. The encrypted hash function will gradually calculate a fixed-length hash value through a series of complex logical and mathematical operations, such as bit operations, hash collision processing, etc. 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.

[0022] 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 the encrypted data into the digital artwork file based on the insertion position to reconstruct the digital artwork file. Specifically, the data insertion subunit obtains the encrypted data and the digital artwork file. If the encrypted data is inserted to the end of the digital artwork file, the subunit 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 subunit 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 subunit 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 subunit 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 properly 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 subunit 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 is read smoothly, and whether the encrypted data is accurately inserted into the specified position. The data insertion subunit successfully inserts the encrypted data into the digital artwork file and completes the file reconstruction.

[0023] 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. 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 the 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 many times to ensure that the logic of the contract is 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, which provides a legally effective and technically guaranteed contractual framework for the transaction, management and protection of digital artworks.

[0024] 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 tests on the smart contract based on the artwork category to obtain security test results of 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 for defining the scope of application and characteristics of the smart contract, and includes characteristics of different art forms, styles and values. The smart contract is deeply analyzed by using 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 functions, performance and security). The technology automatically conducts security tests of various dimensions based on specific requirements and constraints in the artwork category, and obtains detailed results including functional testing, performance testing and security testing. In the functional test, unit functional testing and integrated functional testing are covered, and each functional unit in the smart contract is individually inspected to ensure that it can complete specific operations as expected, and then the coordinated operation of multiple functional units is detected to verify their stability and accuracy in the integrated state. The performance test focuses on load testing and response time testing. By simulating various load conditions, such as a large number of transaction requests or concurrent operations, the performance of smart contracts in high-pressure environments is tested, and the response time for various operations is measured to evaluate its efficiency and timeliness. Security testing includes static risk testing and dynamic risk testing. Static risk testing reviews the syntax, logic and potential security vulnerabilities of the contract code, while dynamic risk testing monitors the security status of smart contracts in actual operation scenarios, such as the ability to resist external attacks and prevent data leakage. For each dimension of testing, special testing tools and techniques are used to carefully design detailed test cases and scenarios, and operations are carried out in strict accordance with the pre-set test process. During the testing process, the results and generated data of each test step are carefully recorded, covering whether the execution is successful, errors or abnormal situations that occur, the specific values ​​of performance indicators, etc. After comprehensive and in-depth multi-dimensional testing, detailed and accurate security test results in each dimension are finally obtained.

[0025] The result screening unit is used to screen according to the security test results and determine the risk dimension. 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 the comprehensive testing of smart contracts and contain 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 the possible dimensions of risk based on the 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 high-risk vulnerabilities found in the security test. The complex and diverse test results are checked one by one, focusing on those data performances that exceed the preset threshold or deviate from the normal range. For example, in the performance test, if the response time is far beyond 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. No. Through comprehensive and integrated comparison and measurement of test results in different dimensions, 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 in 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 lead to a decline in performance or increase the degree of exposure to security risks. Ultimately, 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.

[0026] A risk feature acquisition unit, wherein the risk feature acquisition unit is used to acquire risk features of the decentralized storage. Specifically, the risk feature acquisition unit determines the scope and objectives of acquiring risk features, including comprehensive consideration of various components of the decentralized storage system, such as storage nodes, data transmission channels, storage protocols, etc., and uses means and methods such as monitoring the operating status of the system, collecting user feedback and error information, and analyzing the system's log files to collect relevant data. The data will be preliminarily screened and classified to remove irrelevant or redundant information and only retain key data related to risk features. The screened data will be analyzed using data mining algorithms, machine learning models, or statistical analysis methods to discover potential patterns and laws in the data. Through analysis, features that may indicate the existence of risks are identified, such as frequent failures of storage nodes, abnormal delays in data transmission, abnormal fluctuations in storage capacity, etc., and compared with industry standards and best practices to determine whether the acquired features are abnormal or risky situations. In the process of acquiring risk features, verification and correction will be continuously carried out. By repeatedly collecting and analyzing data, or introducing external expert opinions and evaluations, the accuracy and reliability of the acquired risk features are ensured. The risk feature acquisition unit finally successfully acquires the risk features of decentralized storage, providing an important foundation and basis for subsequent risk assessment and response.

[0027] A collaborative authentication means matching unit, the 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 of decentralized storage, 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 the 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 to be further strengthened and supplemented. According to the searched missing situations, matching is performed from the 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, and 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.

[0028] An authentication means adding unit, 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, according to the architecture and rules of the authentication system, the collaborative authentication means is adjusted and adapted as necessary, 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 investigated 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 guarantees and enhancements for the security and reliability of the system.

[0029] In a possible implementation, the security test result acquisition unit includes: a risk dimension determination subunit, which is used to obtain the security test results of each dimension based on the multi-dimensional security test, arrange them in positive order according to the security test results, filter the risk test results in the latter position, and determine the risk dimension. Specifically, after performing the multi-dimensional security test, the security test results of each dimension are obtained respectively, and they are arranged in positive order according to the results, that is, they are arranged in order from the best to the worst security, and the risk test results in the latter position are filtered out. By comprehensively evaluating these results, the risk dimension is determined.

[0030] The patch feature acquisition subunit is used to acquire 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 acquires 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 acquiring 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, solutions, and scope of application, and accurately matches them with the specific risk types, impact levels, and related features exposed by the risk dimensions. For 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 the contract patch to ensure that the addition of the patch will not have an adverse impact on the normal functions, and at the same time be able to effectively deal with risks. After the configuration is completed, verification and testing will be carried out to simulate the occurrence of risk situations, 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.

[0031] 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, and 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 patches, but also its characteristics are obviously 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 judgments can be made with the help of expert experience and industry standards. 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.

[0032] The compensation target determination subunit is used to analyze the missing risk dimension and determine the compensation target. Specifically, the compensation target determination subunit obtains the missing risk dimension identified by the missing risk dimension acquisition subunit, analyzes the missing risk dimension, and involves disassembling and studying various aspects of the risk dimension, including but not limited to analyzing the direct losses that may be caused by the risk, potential chain reactions, and long-term impacts on system stability and security, simulating and predicting the potential impact paths and possible consequences of the missing risk dimension. During the analysis process, the overall architecture of the system, business processes, and interactions with other related parts are considered to comprehensively assess the scope and severity of the risk. Based on the detailed analysis results of the missing risk dimension, the specific direction and focus of compensation are clarified, including the formulation of goals for reducing losses, restoring the normal operation of the system, and enhancing the risk resistance capability. When determining the compensation target, specific quantitative indicators and measurable standards are set, for example, it is stipulated that the losses caused by the risk will be reduced to a specific level within a certain period of time, or the stability of the system will be improved to a specific numerical range. The rationality and feasibility of the compensation target are evaluated to ensure that it can effectively cope with the challenges brought by the missing risk dimension and meet the resource and capacity constraints of the system.

[0033] The collaborative authentication means acquisition subunit is used to calculate the correlation between the authentication means in the authentication means library and the compensation target to obtain the collaborative authentication means. Specifically, the collaborative authentication means acquisition subunit acquires a pre-built authentication means library, which contains various authentication means such as multi-signature, multi-factor authentication, on-chain and off-chain combined storage, multi-level encryption, decentralized identity authentication (DID), etc. For each authentication means, its key features and applicable scenarios and other related information are extracted, and the compensation target is correlated with each authentication means in the authentication means library. During the calculation process, the requirements of the functions, performance, security, etc. required by the compensation target are compared with the characteristics and effects that each authentication means can provide. For example, if the compensation goal is to enhance the access control security of specific data, then the strength and flexibility of each authentication means in access control will be evaluated. For each comparison and calculation, a correlation score will be given to indicate the degree of fit between the authentication means and the compensation goal. By calculating the correlation and ranking the scores of all authentication means, the authentication means with higher scores and high correlation with the compensation target are screened out. The screened out authentication means are determined as collaborative authentication means, 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 means are accurate and effective.

[0034] In a possible implementation, the collaborative authentication means acquisition subunit includes: a screening probability constraint configuration microunit, the screening probability constraint configuration microunit is used to configure the screening probability constraint, and the screening probability constraint is determined according to the historical test database. Specifically, the screening probability constraint configuration microunit is used to configure the screening probability constraint, and the constraint is determined according to the historical test database, which contains a large amount of test data and results in similar situations in the past. By analyzing and summarizing these data, the screening probability constraint conditions applicable to the current situation are obtained.

[0035] 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, 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 be used 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. For results greater than or equal to the value threshold, they are retained; and for results less than the value threshold, they are excluded. After the screening process, the authentication means corresponding to the retained results are combined together to generate an initial authentication means combination, and 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.

[0036] 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. A series of new candidate groups are generated by adding or reducing operations. Each new candidate group 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 effects on compensation 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 modifying the scope, generating candidate groups, evaluating and determining the optimal solution are repeated continuously to gradually optimize and find the combination of authentication means that best meets the compensation goal.

[0037] 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 the 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 the key information such as the identification, characteristics or related parameters of the authentication means combination currently visited 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 created and maintained a valid access taboo table, providing important support and guarantee for the optimization process of the entire authentication means combination.

[0038] An iterative optimization micro-unit is used to filter the authentication means combinations 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 combinations 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 to the current optimal solution but are different, 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 them according to 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, and the above steps of filtering, determining directions, generating candidate combinations, evaluating and updating optimal solutions are repeated. In each iteration, the optimal collaborative authentication means is closer. The process continues 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 is 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.

[0039] The storage authentication module 40 is used for the storage authentication module to store the smart contract and the digital artwork file in a decentralized storage authentication system. Specifically, the storage authentication module 40 obtains the smart contract and the digital artwork file from the relevant data source. The smart contract and the digital artwork file are key data for secure storage and authentication in the decentralized storage authentication system, and contain various important information and parameters. The advanced storage authentication technology (a series of methods and means for ensuring reliable storage and authentication of data in a decentralized environment) is used to prepare these data comprehensively. The technology encrypts the data, adds digital signatures, and generates unique identifiers to improve the security and traceability of the data. The prepared data is transmitted, including sending data to each node or storage location of the decentralized storage authentication system. The transmission process covers key parameters such as the transmission speed and integrity of the data. When the data transmission is completed, the storage authentication module 40 will send a confirmation request to the system to obtain feedback information on the successful data storage of the system. These feedbacks may include detailed data such as storage location and storage time. The key links in the entire storage process are recorded, including transmission time, data size, storage location, etc., to form a detailed storage log for subsequent auditing and tracking. In subsequent operations, 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. Once any abnormality is found, the corresponding recovery or error correction process is immediately initiated to ensure the security and accuracy of the data, thereby realizing the reliable storage and authentication of smart contracts and digital artwork files in the decentralized storage authentication system.

[0040] 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 manner in the 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.

[0041] 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.

[0042] The above specific implementation manner does not constitute a limitation to the protection scope of the present 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 substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does 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 in that: The protection data authentication system based on NFT digital artworks includes: A basic information extraction module, which 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, the 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, wherein the smart contract creation module is used to create a smart contract based on the basic information, the encrypted data, and the hash value; A storage authentication module is used to store the smart contract and the digital artwork file in a decentralized storage authentication system.

2. The protection data authentication system based on NFT digital artwork as claimed in claim 1 is characterized in that: The unique hash value generation module includes: A basic information category determination unit, the basic information category determination unit is used to perform cluster analysis on the basic information to determine the category of the basic information; A data fitting unit, the data fitting unit is used to fit the category sensitivity coefficient and the 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; The 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, and the encrypted data is the data with the largest total impact on the digital artwork authentication in the basic information.

3. The protection data authentication system based on NFT digital artwork as claimed in claim 2 is characterized in that: The impact evaluation function construction unit includes: An evaluation weight subunit, the evaluation weight subunit is used 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 allocate the influence proportion of the category sensitivity coefficient and the imitation coefficient, and construct the influence evaluation function.

4. The protection data authentication system based on NFT digital artwork as claimed in claim 1 is characterized in that: The unique hash value generation module includes: A digital artwork file reconstruction unit, the digital artwork file reconstruction unit is used to add the encrypted data as an insertion authentication file into the digital artwork 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 through the encrypted hash function to generate a unique hash value.

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

6. The protection data authentication system based on NFT digital artwork as claimed in claim 1 is characterized in that: The smart contract creation module includes: A security test result acquisition unit, the security test result acquisition unit is used to perform a multi-dimensional security test on the smart contract based on the artwork category, and obtain security test results of each dimension; A result screening unit, the result screening unit is used to screen according to the safety test results to determine the risk dimension; A risk feature acquisition unit, the risk feature acquisition unit is used to acquire the risk features of the decentralized storage; A collaborative authentication means matching unit, the 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 search based on the risk correlation, 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.

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

8. The protection data authentication system based on NFT digital artwork as claimed in claim 7 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 value threshold determination micro-unit, the value threshold determination micro-unit is used to determine the value threshold according to the screening probability constraint, screen the correlation calculation result, and generate an initial authentication means combination; A range modification micro-unit, wherein the range modification micro-unit is used to modify the range 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; An access taboo table setting micro-unit, the access taboo table setting micro-unit is used to set an access taboo table, the access taboo table is used to taboo the authentication means combination that has been visited; An 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 to obtain the collaborative authentication means.

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