Data processing method and system based on AIGC content

Abstract

The invention discloses a data processing method and system based on AIGC content, and the method comprises the steps: obtaining the content generated through AIGC, and distributing a unique identifier for the content; the content is subjected to metadata description, metadata of the content is generated, and the metadata comprises content types, generator IDs, generation time, content abstracts and content quality indexes; carrying out fragmentation processing on the content, and encrypting each piece of processed fragmented content to obtain each encrypted content block; and for each encrypted content block, forming an encrypted storage block by the encrypted content block, the unique identifier and the metadata, storing the encrypted storage block in a block chain, and dynamically adjusting the storage position of the encrypted storage block. By utilizing the embodiment of the invention, the efficiency and security of content management can be improved by means of unique identifier distribution, metadata description, encrypted storage, dynamic block chain management and the like of the AIGC generation content.

Description

Technical Field

[0001] The present invention belongs to the field of data processing technology, and in particular to a data processing method and system based on AIGC content. Background Art

[0002] With the rapid development of artificial intelligence (AI) technology, the application of generative artificial intelligence (AIGC) has become increasingly popular and has become an important technology in the field of content creation. AIGC can automatically generate high-quality text, images, music and other types of content through complex algorithms and models. The application of this technology not only improves the efficiency of creation, but also gives users greater creative possibilities. However, the resulting data processing and management problems are becoming increasingly prominent, and effective solutions are urgently needed. Summary of the invention

[0003] The purpose of the present invention is to provide a data processing method and system based on AIGC content to solve the deficiencies in the prior art and to improve the efficiency and security of content management by means of unique identification allocation, metadata description, encrypted storage and dynamic blockchain management of AIGC generated content.

[0004] An embodiment of the present application provides a data processing method based on AIGC content, the method comprising: Obtaining content generated by the AIGC and assigning a unique identifier to the content; Performing metadata description on the content to generate metadata of the content, wherein the metadata includes: content type, generator ID, generation time, content summary and content quality index; Slice the content, and encrypt each processed slice content to obtain each encrypted content block; For each encrypted content block, the encrypted content block, the unique identifier, and the metadata form an encrypted storage block and are stored in the blockchain, and the storage position of the encrypted storage block is dynamically adjusted.

[0005] Optionally, the unique identifier is: ;

[0006] Among them, the is a unique identifier, the is a hash function of the content, is the hash function of the generator ID, is the hash function of the timestamp, is a randomness generating function, which outputs a random bit string of length N based on the input K random factors. is a bitwise XOR operation, is a nonlinear mixing function.

[0007] Optionally, the content quality index is generated in the following manner: ;

[0008] Among them, the is the content quality index, is the quantitative score of the i-th preset content feature, is the time decay factor of the i-th preset content feature, is a nonlinear weighting parameter that presets its own content characteristics. is the nonlinear weighting parameter of the time attenuation factor, is the weight of the i-th preset content feature, is the quantitative score of the j-th preset additional content feature, Additional content features for balancing presets The balance coefficient of the influence of , n is the number of preset own content features, and m is the number of preset additional content features.

[0009] Optionally, the for: ;

[0010] Among them, the For adjustment The impact index, for The preset balance value, is the quantitative score of the k-th preset content feature, For adjustment The impact index, for The preset balance value.

[0011] Optionally, the for: ;

[0012] Among them, the is the data integrity indicator, is the current time, The last update time of the i-th preset content feature.

[0013] Another embodiment of the present application provides a data processing system based on AIGC content, the system comprising: An acquisition module, used for acquiring content generated by AIGC and assigning a unique identifier to the content; A description module, used to perform metadata description on the content and generate metadata of the content, wherein the metadata includes: content type, generator ID, generation time, content summary and content quality index; A processing module, used for processing the content in slices, and encrypting each processed slice content to obtain each encrypted content block; The storage module is used to store, for each encrypted content block, the encrypted content block, the unique identifier, and the metadata into an encrypted storage block in the blockchain, and dynamically adjust the storage position of the encrypted storage block.

[0014] Yet another embodiment of the present application provides a storage medium, wherein the storage medium stores a computer program, wherein the computer program is configured to execute any of the above methods when running.

[0015] Yet another embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the methods described above.

[0016] Compared with the prior art, the present invention provides a data processing method based on AIGC content, which obtains content generated by AIGC and assigns a unique identifier to the content; performs metadata description on the content and generates metadata of the content, wherein the metadata includes: content type, generator ID, generation time, content summary and content quality index; performs fragmentation processing on the content, encrypts each processed fragmented content to obtain each encrypted content block; for each encrypted content block, the encrypted content block, the unique identifier and the metadata form an encrypted storage block and store it in the blockchain, and dynamically adjusts the storage position of the encrypted storage block, so as to improve the efficiency and security of content management by means of unique identification assignment, metadata description, encrypted storage and dynamic blockchain management of AIGC generated content. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A hardware structure block diagram of a computer terminal for a data processing method based on AIGC content provided by an embodiment of the present invention; Figure 2 A flowchart of a data processing method based on AIGC content provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a data processing system based on AIGC content provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, but should not be construed as limiting the present invention.

[0019] The embodiment of the present invention first provides a data processing method based on AIGC content, which can be applied to electronic devices such as computer terminals, specifically ordinary computers, etc.

[0020] The following describes it in detail by taking running on a computer terminal as an example. Figure 1 The hardware structure block diagram of a computer terminal of a data processing method based on AIGC content provided by an embodiment of the present invention. Figure 1 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus, wherein the memory may include a non-volatile storage medium and an internal memory.

[0021] The non-volatile storage medium can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor can execute any data processing method based on AIGC content.

[0022] The processor is used to provide computing and control capabilities and support the operation of the entire computer equipment.

[0023] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor, the processor can execute any data processing method based on the AIGC content.

[0024] The network interface is used for network communication, such as sending assigned tasks, etc. Those skilled in the art will appreciate that Figure 1 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0025] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0026] See also Figure 2 , an embodiment of the present invention provides a data processing method based on AIGC content, which may include the following steps: S201, acquiring content generated by AIGC, and assigning a unique identifier to the content; In the current digital content creation environment, the content generated based on AIGC is often huge and diverse, and how to effectively manage and identify this content has become an important challenge. To solve this problem, this step ensures that each content is unique in the entire digital ecosystem by assigning a unique identifier (UID) to each generated content, which facilitates subsequent tracking, management, and verification. UID not only enables content to be accurately identified and referenced, but also helps protect the copyright and ownership of the content to prevent plagiarism or misuse.

[0027] Specifically, a unique identifier may be: ;

[0028] The formula is designed to generate a unique and unpredictable unique identifier (UID) through multiple inputs to ensure the uniqueness and traceability of the content generated by AIGC. Combining multiple parameters together helps to resist conventional collision attacks (i.e. different content generates the same UID) and forgery attacks, enhancing data security.

[0029] Among them, the is a unique identifier, the A hash function (such as SHA-3 or other strong cryptographic hash functions, etc.) for the content generates a secure hash value for the content, which represents the unique characteristics of the content. The hash function should ensure that different content generates different hash values, and even small changes in the content can cause the hash value to change significantly. Example: If the content is an article, the hash value obtained using SHA-256 is different from the hash value of another article, ensuring the uniqueness of each article. For an article titled "Application of AI in Healthcare", the hash value generated using SHA-256 may be "6c3b7e71e272b1e281d3d03b2cdd36c5c0f8aa34e2c4d8e3e3a9b8b8e78989f0", which completely represents the content. If a sentence in the article is modified, the hash value will change significantly.

[0030] Said A hash function (such as SHA-256 or other hash functions, etc.) for the generator ID ensures that the identity of the user who generated the content is effectively encoded to prevent multiple contents of a certain user from being confused with other user contents. Example: If the generator ID is "User123", if it is hashed with SHA-1, a hash result of a fixed length can be obtained, which is bound to a specific user ID. If the generator ID is "User123", after SHA-1 hashing, its hash result may be "1f3a9779c8e1d033cc0c7773b4c1882d2f8ecb6e", which will correspond one-to-one to all the generated content of the user.

[0031] Said A hash function for a timestamp (such as RIPEMD-160, a hash function based on Unix timestamps such as BLAKE3, etc.) provides a unique identifier for the time when the content was generated, so that even if the same user generates similar content at a similar time, it can still be distinguished by the timestamp. Example: If the content was generated at "October 1, 2023 12:00", the processed hash value will be associated with the generation time, ensuring the uniqueness of this time point. If the content was generated at "2023-10-01 12:00:00", after hashing, the hash value "d4f2db2c0a15d1cf8b0c8e4b615b828c7e2fbc12" may be obtained, and this hash is bound to a specific time point.

[0032] Said It is a randomness generation function that outputs a random bit string of length N based on the input K random factors. The introduction of randomness enhances the unpredictability of the UID and ensures that even if the content and generator information are exactly the same, different UIDs will be generated due to different random factors. Example: Input K random factors (such as UUID) and output a random bit string of length N to increase the complexity of the UID. If K random factors (such as "RandomFactor1" and "RandomFactor2") are input, the output random bit string may be "1010101010101010", and this random string further increases the complexity of the UID.

[0033] Said is a bitwise XOR operation, It is a nonlinear mixing function that processes the combined hash result, making the UID complex in structure and difficult to reverse, thereby improving security. Example: Through nonlinear transformation (such as using the sigmoid function or other complex functions such as high-order polynomial functions, etc.), the generated UID is unpredictable in form. Using a complex nonlinear function, such as the sigmoid function, to process the combined result, a completely different UID structure may be generated, such as "qzZ9F3kUjvR7". This generation process ensures the complexity and security of the UID.

[0034] By assigning unique identifiers (UIDs) to content generated by AIGC, the uniqueness and traceability of content can be effectively achieved, ensuring that each content has a clear identity in the digital ecosystem. This not only simplifies the management and retrieval process of content and improves the response speed of information systems, but also enhances data security and reduces the risk of illegal tampering or theft. In addition, UIDs also facilitate the execution of smart contracts, ensure the legality of content use, and thus safeguard the rights and interests of creators. In an environment where content generation is increasingly diverse, the introduction of UIDs is a necessary measure to protect copyright, improve content management efficiency, and ensure data security.

[0035] S202, performing metadata description on the content to generate metadata of the content, wherein the metadata includes: content type, generator ID, generation time, content summary and content quality index; In modern digital content management, in order to achieve effective management and utilization of AIGC (artificial intelligence generated content), the generation of metadata has become a key link. Metadata can not only help users quickly understand the basic information of the content, but also play an important role in content retrieval, classification and analysis. In this method, the generated metadata contains a number of key information, including content type, generator ID, generation time, content summary and content quality index.

[0036] Content type: Indicates the type of content, such as articles, images, videos, etc., so that the system can classify and process different types of content; Generator ID: Identifies the user who generated the content, which helps to track the identity of the creator and maintain copyright; Generation time: Records the time when the content was generated, which helps with version management and time-related analysis of the content; Content summary: Briefly summarizes the core information of the content, providing users with a way to quickly understand the content; Content quality index: Reflects the quality of the content through quantitative indicators, providing a basis for subsequent content evaluation and recommendation.

[0037] First, the generated content can be analyzed and the content type can be automatically identified using natural language processing (NLP) technology or image recognition technology. The system can assign corresponding content type identifiers to the content based on its characteristics (such as text, image, video, etc.), such as marking text content as "Article", images as "Image", and videos as "Video".

[0038] The generator ID is extracted from the user information of the generated content. This information is usually recorded when the user creates the content. The system should ensure the user permission setting to prevent the forgery of the generator identity. While the content is being generated, the system automatically records the current timestamp as an identifier of the content generation time. The timestamp can use a standard date and time format to ensure the accuracy of its time information.

[0039] In order to generate content summaries, text summarization techniques (such as extraction or generation) can be used. The system analyzes the core themes and important information of the content, and generates concise content summaries by setting keyword extraction, sentence compression, or using pre-trained models, usually limited to a certain number of words to ensure clarity of information.

[0040] The generation of content quality index is relatively complex, and multiple indicators can be combined to evaluate the overall quality of content. For example, it can be quantified based on user interaction (such as likes, comments), content originality, and content integrity. By setting specific quality standards and calculation formulas, the final content quality index is obtained. Ultimately, these metadata will be integrated into a structured format (such as JSON or XML) for easy storage, retrieval, and transmission, making subsequent content management and utilization more efficient.

[0041] Specifically, a method for generating a content quality index may be: ;

[0042] This formula is used to calculate the content quality index (MCQI), which is designed to comprehensively evaluate the quality of generated content by integrating influencing factors from multiple dimensions. The formula combines the quantitative scoring of content features with time decay, weights, and additional features to establish an accurate and dynamic quality assessment system. This assessment method not only considers the intrinsic quality of the content, but also introduces the influence of external factors (such as time and additional features) to ensure the comprehensiveness and accuracy of the MCQI.

[0043] Among them, the It is the content quality index, which indicates the final quality index of the content. It can be used to quantitatively evaluate the content for comparison and analysis. Assuming that after generating a piece of content, the MCQI is calculated to be 4.5, indicating that the quality of the content is at a good level. is the quantitative score of the i-th preset content feature, reflecting the contribution of this feature to the content quality. Assume that S_1 represents the originality score of the content, and the score is 0.9, indicating that the originality of the content is high. is the time attenuation factor of the i-th preset content feature, which adjusts the influence of the feature over time. is a nonlinear weighting parameter that presets its own content characteristics. It is a nonlinear weighting parameter of the time decay factor, which controls the relative influence of S_i and T_i in quality assessment. Example: Setting p = 2 and q = 1 means that the influence on content characteristics is stronger, while the influence on time decay is relatively weaker.

[0044] Said is the weight of the i-th preset content feature, reflecting the importance of each feature in quality assessment. If the readability of the content is considered the most important, w_1 can be set to 0.4, and the weights of other features can be adjusted accordingly. is the quantitative score of the jth preset additional content feature, which is used to supplement content evaluation and cover additional influencing factors. Assume that C_1 is the user interaction score (such as the number of comments and likes). If its quantitative score is 3, it means that the content has received good user feedback. Additional content features for balancing presets The balance coefficient of the influence of If it is set to 0.2, the influence of the additional features will account for a certain proportion in the overall quality calculation, but will not dominate the final result. The n is the number of preset own content features, and the m is the number of preset additional content features.

[0045] Preset content features refer to characteristic indicators that can directly reflect the quality and value of the content itself. These features are usually related to the content structure, language expression, information richness, etc., and can quantitatively show the intrinsic quality of the content. By evaluating these features, the overall level of the content can be more effectively judged, thereby providing users with better content recommendations. Specific examples: 1. Originality: reflects the uniqueness and novelty of the content. The quantitative score can be based on content similarity analysis and plagiarism detection results. The score range is usually 0 to 1. Example: The originality score of a newly published research paper is 0.95, indicating that its content is highly original.

[0046] 2. Readability: A measure of how easy it is to read a text, usually scored based on sentence length, vocabulary complexity, structural clarity, etc. Example: An article has a readability score of 0.85, indicating that its text is simple and easy to understand, suitable for a wide range of readers.

[0047] 3. Information Density: refers to the amount of information contained in the content, usually measuring the density of key information and the comprehensiveness of the description. Example: An in-depth market analysis report is rated 0.90, indicating that the information is detailed and has reference value.

[0048] 4. Logical Structure: reflects the organization and logical coherence of the content. The score can be based on the cohesion between paragraphs and the clarity of the arguments. Example: The logical structure score of a research paper is 0.88, indicating that its ideas are clear and the arguments are reasonable.

[0049] 5. Diversity: This measures the diversity of viewpoints, examples, or data sources in the content, reflecting the comprehensiveness of the content. Example: A review article involving multi-perspective discussions has a diversity score of 0.92, indicating that it covers a variety of viewpoints.

[0050] Preset additional content features are features that can affect content evaluation but are not directly reflected in the content text. They are usually related to user interaction, content dissemination, and external environment. By evaluating these features, we can have a more comprehensive understanding of the performance and influence of the content, and also help to judge user acceptance and market performance of the content. Specific examples: 1. User Engagement: This measures the user interaction with the content, including likes, comments, shares, etc., reflecting the popularity and influence of the content. Example: A social media article has a user engagement score of 4.5 (out of 5), indicating that it has received a large number of likes and comments.

[0051] 2. Reach: This measures how widely the content has spread across the web, including how many times it has been viewed and shared. Example: A video has a reach score of 300,000 views on YouTube, meaning it has been seen by a large audience.

[0052] 3. Social Shares: This reflects the number of times content is shared on social platforms and is often used to assess the viral potential of content. Example: A popular blog post is shared 2,000 times on Facebook, demonstrating its broad audience appeal.

[0053] 4. User Feedback: User feedback rating on the content, including the positivity and negativity of the comments. Example: The user feedback rating of an article is 0.9, indicating that most comments are positive.

[0054] 5. Update Frequency: This measures the frequency and timeliness of content updates, usually used for news or dynamically changing content. Example: A financial report has an update frequency score of 0.8, indicating that its content updates are relatively timely.

[0055] By combining the preset own content features and the preset additional content features, the quality of the generated content can be evaluated more comprehensively and accurately, helping users make better choices in an environment with complex information.

[0056] Specifically, a Can be: ;

[0057] This formula is used to calculate the weight w_i of each preset content feature. By combining the feature score with the preset balance value, the relative influence of each feature in the final content quality evaluation is ensured. The calculation of weights can reflect the importance of different features to the overall quality evaluation, ensuring the fairness and accuracy of the evaluation.

[0058] Among them, the For adjustment The impact index, for The preset balance value is used to adjust the base value of the feature score to prevent features with a score of zero from affecting the overall calculation. is the quantitative score of the k-th preset content feature, For adjustment The impact index, for The preset balance value.

[0059] Specifically, a Can be: ;

[0060] This formula is used to calculate the time decay factor T_i of the i-th preset content feature. The design of time decay takes into account the changes of content features over time. As time goes by, the influence of features will weaken. This method ensures that when evaluating content quality, the importance of features can be dynamically adjusted to maintain the timeliness of the evaluation.

[0061] Among them, the It is a data integrity indicator that determines the rate of time decay. The larger its value, the faster the effect of time on the feature decays. For example, set gamma = 0.1. is the current time, The last update time of the i-th preset content feature.

[0062] Generating detailed metadata is crucial for managing and using content generated by AIGC. First, the clarification of content types helps the system quickly identify and process different forms of content, thereby optimizing the classification and retrieval mechanism. Secondly, the record of the generator ID not only improves the ability to track content, but also effectively protects the copyright of the creator and ensures the legal use of the content. The generation time helps to analyze the timeliness of the content and ensure that users get the latest information. In addition, the content summary provides a way for users to quickly understand the content and improves the user experience, while the content quality index, as a quantitative indicator, standardizes the quality of the content and facilitates the system's evaluation and comparison of the content. The integration of these metadata is of great practical significance for building an intelligent content management system, improving user experience, and ensuring content security.

[0063] S203, processing the content in slices, and encrypting each processed slice content to obtain each encrypted content block; In the data processing method based on AIGC-generated content, sharding the generated content and encrypting each shard is a key step to ensure content security and privacy protection. Sharding is to break down larger content into several small blocks to facilitate subsequent encryption and storage. After each shard is encrypted independently, these encrypted content blocks can be securely stored in the blockchain in the form of encrypted storage blocks. This processing method not only enhances the security of the data, but also improves the robustness and recoverability of the system. The sharding process ensures that even if one or several shards are destroyed or lost, other shards can still maintain integrity, which can effectively reduce the potential risk of data leakage. In addition, through shard encryption, the system can implement independent access control for each shard, further enhancing the level of data protection.

[0064] First, the generated content can be received and processed in segments according to a specific strategy. The size of the segments can be adjusted according to the nature of the content and the subsequent processing requirements. For example, a long article can be divided into several paragraphs, video content can be divided into multiple frames, and images can be divided into multiple blocks. After segmentation, each content segment should have a clear identification for subsequent tracking and management.

[0065] For each shard, encryption is performed using a secure encryption algorithm (such as AES, RSA, etc.). Encryption parameters (such as keys) should be generated and managed by the system to ensure security. The encrypted content block will become an unreadable ciphertext, and can only be decrypted back to the original content under appropriate conditions (for example, using the corresponding key or permission). Each encrypted content block is organized into an encrypted storage block together with its unique identifier and related metadata (such as content type, generation time, etc.). These storage blocks can include a complete description of the content to ensure that necessary information can be quickly obtained later. All encrypted storage blocks are stored in the blockchain, and the immutability and transparency of the blockchain ensure the security and integrity of the data. Storage operations can be selected based on different strategies, such as distributed storage or centralized management, and can be flexibly configured according to system needs.

[0066] The main purpose of implementing content sharding and encryption is to enhance security and improve data management efficiency. By breaking down the generated content into multiple small blocks and encrypting them separately, even if some shards are accessed or damaged without authorization, it is difficult for attackers to recover the complete content, thereby effectively reducing the risk of information leakage. In addition, sharding simplifies the storage and retrieval process, allowing the system to flexibly operate specific content blocks without processing the entire data set, while improving performance and reliability in high-load environments, complying with modern data protection regulations, and ensuring the privacy and security of user data.

[0067] S204: For each encrypted content block, the encrypted content block, the unique identifier, and the metadata form an encrypted storage block and store it in the blockchain, and dynamically adjust the storage location of the encrypted storage block.

[0068] In the data processing method based on AIGC content, combining each encrypted content block with a unique identifier and related metadata into an encrypted storage block and storing it in the blockchain is a key step to ensure data integrity, security and traceability. Each encrypted storage block contains not only the encrypted content, but also the identification information and descriptive data of the content, forming a complete data unit. This combination ensures that during the storage process, users can clearly identify the source, characteristics and encryption status of the content. At the same time, the use of blockchain technology for storage can provide additional security for data with its decentralized and tamper-resistant nature. Dynamic adjustment of the storage location ensures storage flexibility and efficient operation of the system, reduces the risk of single point failures, and improves the robustness of the system.

[0069] Specifically, every time an encrypted content block is generated, the system will extract the unique identifier and related metadata of the content. The unique identifier is generated by hashing the content, generator ID and timestamp to ensure its uniqueness in the entire system. The metadata includes information such as content type, generation time, content summary and content quality index, which provides the necessary context for data management.

[0070] Combine the encrypted content block, unique identifier, and metadata into a complete encrypted storage block. The data structure formed by this storage block can contain fields such as encrypted content, identifier, timestamp, and other metadata to ensure that the data is clearly organized and easy to retrieve and manage later.

[0071] The constructed encrypted storage blocks are stored through the blockchain network. The specific method is to package the storage blocks into transactions and add them to the blockchain through a consensus mechanism to ensure the transparency and immutability of the storage process. In order to ensure data security and privacy protection, transactions will be digitally signed before being uploaded to the chain to verify the legitimacy of the initiator.

[0072] During the storage process, the system dynamically adjusts the storage location of encrypted storage blocks in the blockchain based on factors such as data access frequency, system load, and storage efficiency. For example, blocks with higher frequency access can be accessed first and stored at the front end of the chain, while blocks that are less frequently accessed can be stored at the back end of the chain. This strategy helps optimize the storage performance of the blockchain, increase retrieval speed, and reduce latency.

[0073] The status of storage blocks can be continuously monitored, and storage policies can be adjusted by analyzing storage usage and access patterns to ensure that the system always runs in the best state. If necessary, the system can migrate storage blocks to cope with changes in the storage environment or technology updates.

[0074] Through the above steps, the system can effectively combine encrypted content blocks with unique identifiers and metadata into encrypted storage blocks and store them in the blockchain. At the same time, dynamically adjusting the storage location not only ensures efficient and secure data management, but also facilitates subsequent data access, ultimately achieving full life cycle management of AIGC-generated content.

[0075] The main purpose of this process is to ensure the security, integrity and accessibility of data. By combining encrypted content blocks, unique identifiers and metadata into an encrypted storage block, the system can effectively maintain the integrity and traceability of data, thereby increasing users' trust in the content. At the same time, the blockchain-based storage method ensures that the data cannot be tampered with, so that each storage operation is transparently recorded, enhancing data security. Dynamic adjustment of storage locations further improves the efficiency and flexibility of the system, ensuring that content can still be quickly accessed and managed under high load conditions.

[0076] It can be seen that the content generated by AIGC is obtained, and a unique identifier is assigned to the content; metadata is described for the content to generate metadata for the content, wherein the metadata includes: content type, generator ID, generation time, content summary and content quality index; the content is fragmented, and each fragmented content after processing is encrypted to obtain each encrypted content block; for each encrypted content block, the encrypted content block, the unique identifier and the metadata form an encrypted storage block and are stored in the blockchain, and the storage position of the encrypted storage block is dynamically adjusted, so that the efficiency and security of content management can be improved by means of unique identification allocation, metadata description, encrypted storage and dynamic blockchain management of AIGC generated content.

[0077] Another embodiment of the present invention provides a data processing system based on AIGC content, see Figure 3 , the system may include: An acquisition module 301 is used to acquire content generated by AIGC and assign a unique identifier to the content; A description module 302 is used to perform metadata description on the content and generate metadata of the content, wherein the metadata includes: content type, generator ID, generation time, content summary and content quality index; The processing module 303 is used to process the content in slices, and encrypt each processed slice content to obtain each encrypted content block; The storage module 304 is used to store, for each encrypted content block, the encrypted content block, the unique identifier, and the metadata into an encrypted storage block in the blockchain, and dynamically adjust the storage location of the encrypted storage block.

[0078] It can be seen that the content generated by AIGC is obtained, and a unique identifier is assigned to the content; metadata is described for the content to generate metadata for the content, wherein the metadata includes: content type, generator ID, generation time, content summary and content quality index; the content is fragmented, and each fragmented content after processing is encrypted to obtain each encrypted content block; for each encrypted content block, the encrypted content block, the unique identifier and the metadata form an encrypted storage block and are stored in the blockchain, and the storage position of the encrypted storage block is dynamically adjusted, so that the efficiency and security of content management can be improved by means of unique identification allocation, metadata description, encrypted storage and dynamic blockchain management of AIGC generated content.

[0079] An embodiment of the present invention further provides a storage medium, in which a computer program is stored, wherein the computer program is configured to execute the steps of any of the above method embodiments when running.

[0080] Specifically, in this embodiment, the above storage medium may be configured to store a computer program for performing the following steps: S201, acquiring content generated by AIGC, and assigning a unique identifier to the content; S202, performing metadata description on the content to generate metadata of the content, wherein the metadata includes: content type, generator ID, generation time, content summary and content quality index; S203, processing the content in slices, and encrypting each processed slice content to obtain each encrypted content block; S204: For each encrypted content block, the encrypted content block, the unique identifier, and the metadata form an encrypted storage block and store it in the blockchain, and dynamically adjust the storage location of the encrypted storage block.

[0081] It can be seen that the content generated by AIGC is obtained, and a unique identifier is assigned to the content; metadata is described for the content to generate metadata for the content, wherein the metadata includes: content type, generator ID, generation time, content summary and content quality index; the content is fragmented, and each fragmented content after processing is encrypted to obtain each encrypted content block; for each encrypted content block, the encrypted content block, the unique identifier and the metadata form an encrypted storage block and are stored in the blockchain, and the storage position of the encrypted storage block is dynamically adjusted, so that the efficiency and security of content management can be improved by means of unique identification allocation, metadata description, encrypted storage and dynamic blockchain management of AIGC generated content.

[0082] An embodiment of the present invention further provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0083] Specifically, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0084] Specifically, in this embodiment, the processor may be configured to perform the following steps through a computer program: S201, acquiring content generated by AIGC, and assigning a unique identifier to the content; S202, performing metadata description on the content to generate metadata of the content, wherein the metadata includes: content type, generator ID, generation time, content summary and content quality index; S203, processing the content in slices, and encrypting each processed slice content to obtain each encrypted content block; S204: For each encrypted content block, the encrypted content block, the unique identifier, and the metadata form an encrypted storage block and store it in the blockchain, and dynamically adjust the storage location of the encrypted storage block.

[0085] It can be seen that the content generated by AIGC is obtained, and a unique identifier is assigned to the content; metadata is described for the content to generate metadata for the content, wherein the metadata includes: content type, generator ID, generation time, content summary and content quality index; the content is fragmented, and each fragmented content after processing is encrypted to obtain each encrypted content block; for each encrypted content block, the encrypted content block, the unique identifier and the metadata form an encrypted storage block and are stored in the blockchain, and the storage position of the encrypted storage block is dynamically adjusted, so that the efficiency and security of content management can be improved by means of unique identification allocation, metadata description, encrypted storage and dynamic blockchain management of AIGC generated content.

[0086] The above describes in detail the structure, features and effects of the present invention based on the embodiments shown in the drawings. The above is only a preferred embodiment of the present invention, but the present invention is not limited to the scope of implementation shown in the drawings. Any changes made according to the concept of the present invention, or modifications to equivalent embodiments with equivalent changes, which still do not exceed the spirit covered by the description and drawings, should be within the protection scope of the present invention.

Claims

1. A data processing method based on AIGC content, characterized in that: The method comprises: Obtaining content generated by the AIGC and assigning a unique identifier to the content; Performing metadata description on the content to generate metadata of the content, wherein the metadata includes: content type, generator ID, generation time, content summary and content quality index; Slice the content, and encrypt each processed slice content to obtain each encrypted content block; For each encrypted content block, the encrypted content block, the unique identifier, and the metadata form an encrypted storage block and are stored in the blockchain, and the storage position of the encrypted storage block is dynamically adjusted.

2. The method according to claim 1, characterized in that The unique identifier is: ； Among them, the is a unique identifier, the is a hash function of the content, is the hash function of the generator ID, is the hash function of the timestamp, is a randomness generating function, which outputs a random bit string of length N based on the input K random factors. is a bitwise XOR operation, is a nonlinear mixing function.

3. The method according to claim 2, characterized in that The content quality index is generated in the following manner: ； Among them, the is the content quality index, is the quantitative score of the i-th preset content feature, is the time decay factor of the i-th preset content feature, is a nonlinear weighting parameter that presets its own content characteristics. is the nonlinear weighting parameter of the time attenuation factor, is the weight of the i-th preset content feature, is the quantitative score of the j-th preset additional content feature, Additional content features for balancing presets The balance coefficient of the influence of , n is the number of preset own content features, and m is the number of preset additional content features.

4. The method according to claim 3, characterized in that Said for: ； Among them, the For adjustment The impact index, for The preset balance value, is the quantitative score of the k-th preset content feature, For adjustment The impact index, for The preset balance value.

5. The method according to claim 4, characterized in that Said for: ； Among them, the is the data integrity indicator, is the current time, The last update time of the i-th preset content feature.

6. A data processing system based on AIGC content, characterized in that: The system comprises: An acquisition module, used for acquiring content generated by AIGC and assigning a unique identifier to the content; A description module, used to perform metadata description on the content and generate metadata of the content, wherein the metadata includes: content type, generator ID, generation time, content summary and content quality index; A processing module, used for processing the content in slices, and encrypting each processed slice content to obtain each encrypted content block; The storage module is used to store, for each encrypted content block, the encrypted content block, the unique identifier, and the metadata into an encrypted storage block in the blockchain, and dynamically adjust the storage position of the encrypted storage block.

7. The system according to claim 6, characterized in that The unique identifier is: ； Among them, the is a unique identifier, the is a hash function of the content, is the hash function of the generator ID, is the hash function of the timestamp, is a randomness generating function, which outputs a random bit string of length N based on the input K random factors. is a bitwise XOR operation, is a nonlinear mixing function.

8. The system according to claim 7, characterized in that The content quality index is generated in the following manner: ； Among them, the is the content quality index, is the quantitative score of the i-th preset content feature, is the time decay factor of the i-th preset content feature, is a nonlinear weighting parameter that presets its own content characteristics. is the nonlinear weighting parameter of the time attenuation factor, is the weight of the i-th preset content feature, is the quantitative score of the j-th preset additional content feature, Additional content features for balancing presets The balance coefficient of the influence of , n is the number of preset own content features, and m is the number of preset additional content features.

9. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the method according to any one of claims 1 to 5 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to run the computer program to perform the method according to any one of claims 1 to 5.

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