AI-generated content process data processing methods, related equipment and software products

Abstract

This application provides a method, related equipment, and program product for processing process data of AI-generated content. The method includes: recording the generation process data of AI-generated content in response to an AI generation instruction; acquiring model data used in the AI-generated content and user interaction records during the generation process; and generating structured data corresponding to the AI-generated content based on the generation process data, the model data, and the user interaction records. The structured data is used to characterize the generation process features of the AI-generated content. This application enables real-time recording of AI generation process data, facilitating effective tracking and analysis of the AI ​​generation process. This not only improves the transparency and traceability of the AI ​​generation process but also provides strong data support for subsequent ownership verification of AI-generated content, thereby enhancing the information security of AI-generated content.

Description

Technical Field

[0001] This application relates to the field of information processing technology, and in particular to a process data processing method, related equipment and program products for AI-generated content. Background Technology

[0002] Artificial intelligence (AI) is the technology that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It involves computer systems or machines performing tasks that typically require human intelligence. AI technology is widely used in various fields.

[0003] AI-generated content refers to various forms of content data, such as text, images, audio, and video, automatically generated using artificial intelligence algorithms and models. With the rapid development and widespread application of AI generation technology, AI-generated content has experienced explosive growth. However, there is currently no security management technology specifically for AI-generated content. The generation process of AI-generated content is opaque, and users cannot review the process, which not only affects user experience but also makes the content untraceable, easily leading to information security issues such as data leakage and unclear ownership. Summary of the Invention

[0004] The main objective of this application is to provide a process data processing method, related equipment, and program products for AI-generated content. This method enables real-time recording of AI generation process data, facilitating effective tracking and analysis of the AI ​​generation process. It not only enhances the transparency and traceability of the AI ​​generation process but also provides strong data support for the subsequent confirmation of ownership of AI-generated content, thereby improving the information security of AI-generated content.

[0005] In a first aspect, embodiments of this application provide a process data processing method for AI-generated content, comprising: in response to an AI generation instruction, recording generation process data of AI-generated content; acquiring model data used in the AI-generated content and user interaction records during the generation process of the AI-generated content; and generating structured data corresponding to the AI-generated content based on the generation process data, the model data, and the user interaction records, wherein the structured data is used to characterize the generation process features of the AI-generated content.

[0006] In one embodiment, the data recording the generation process of AI-generated content includes: recording the generation steps of the AI-generated content and / or the intermediate results corresponding to the generation steps; recording the work version information of the AI-generated content during the generation process, wherein the generation process data includes one or more of the generation steps, the intermediate results, and the work version information.

[0007] In one embodiment, obtaining the model data used by the AI-generated content includes: obtaining model call records, model training records, and model version information during the generation process of the AI-generated content, wherein the model data includes one or more of the model call records, the model training records, and the model version information.

[0008] In one embodiment, acquiring user interaction records during the AI-generated content generation process includes: recording user input data, user operations, and user feedback data on the AI-generated content during the AI-generated content generation process, wherein the user interaction records include one or more of the input data, the user operations, and the feedback data.

[0009] In one embodiment, the method further includes: recording interface call data during the AI-generated content generation process, wherein the generation process data includes the interface call data.

[0010] In one embodiment, generating structured data corresponding to the AI-generated content based on the generation process data, the model data, and the user interaction records includes: identifying preset fields in the generation process data, the model data, and the user interaction records; filling a preset data structure table based on the identified field information; and generating structured data corresponding to the AI-generated content.

[0011] In one embodiment, generating structured data corresponding to the AI-generated content based on the generation process data, the model data, and the user interaction records further includes: identifying preset fields in the generation process data, the model data, and the user interaction records; extracting key node information of the AI-generated content during the generation process based on the identified field information; and generating a visual summary of the AI-generated content's generation process based on the key node information.

[0012] In one embodiment, the method further includes: storing the structured data to a preset blockchain; and / or generating a unique identifier for the AI-generated content based on the structured data.

[0013] In one embodiment, the method further includes: in response to a query instruction regarding the generation process of the target AI-generated content, obtaining the structured data corresponding to the target AI-generated content; and displaying the structured data on an interactive interface.

[0014] In one embodiment, before obtaining the structured data corresponding to the target AI-generated content, the method further includes: performing a security verification on the query instruction; after the query instruction successfully passes the security verification, performing the step of obtaining the structured data corresponding to the target AI-generated content; and / or recording information of this query operation.

[0015] Secondly, embodiments of this application provide a process data processing method for AI-generated content about products, comprising: responding to an AI generation instruction about product information, recording generation process data of AI-generated content, wherein the AI-generated content includes the product information; acquiring model data used in the AI-generated content and user interaction records during the generation process of the AI-generated content; and generating structured data corresponding to the AI-generated content based on the generation process data, the model data, and the user interaction records, wherein the structured data is used to characterize the generation process features of the AI-generated content.

[0016] Thirdly, embodiments of this application provide a process data processing apparatus for AI-generated content, comprising:

[0017] The recording module is used to record the generation process data of AI-generated content in response to AI generation instructions;

[0018] The acquisition module is used to acquire the model data used in the AI-generated content and the user interaction records during the AI-generated content generation process;

[0019] The generation module is used to generate structured data corresponding to the AI-generated content based on the generation process data, the model data, and the user interaction records. The structured data is used to characterize the generation process features of the AI-generated content.

[0020] In one embodiment, a recording module is used to record the generation steps of the AI-generated content and / or the intermediate results corresponding to the generation steps; and to record the work version information existing in the generation process of the AI-generated content, wherein the generation process data includes one or more of the generation steps, the intermediate results and the work version information.

[0021] In one embodiment, the acquisition module is used to acquire model call records, model training records, and model version information during the generation process of the AI-generated content, wherein the model data includes one or more of the model call records, model training records, and model version information.

[0022] In one embodiment, the acquisition module is used to record user input data, user operations, and user feedback data on the AI-generated content during the AI-generated content generation process. The user interaction record includes one or more of the input data, user operations, and feedback data.

[0023] In one embodiment, the recording module is further configured to record interface call data during the AI-generated content generation process, wherein the generation process data includes the interface call data.

[0024] In one embodiment, the generation module is used to identify preset fields in the generation process data, the model data, and the user interaction record, and fill a preset data structure table according to the identified field information to generate structured data corresponding to the AI-generated content.

[0025] In one embodiment, the generation module is used to identify preset fields in the generation process data, the model data, and the user interaction records; extract key node information of the AI-generated content in the generation process based on the identified field information; and generate a visual summary of the AI-generated content generation process based on the key node information.

[0026] In one embodiment, it further includes: a storage module for storing the structured data to a preset blockchain; and / or an identification module for generating a unique identifier for the AI-generated content based on the structured data.

[0027] In one embodiment, the system further includes: a query module, configured to respond to a query instruction regarding the generation process of the target AI-generated content, obtain the structured data corresponding to the target AI-generated content, and display the structured data on an interactive interface.

[0028] In one embodiment, the system further includes: a verification module, configured to perform a security verification on the query instruction before obtaining the structured data corresponding to the target AI-generated content; and after the query instruction successfully passes the security verification, execute the step of obtaining the structured data corresponding to the target AI-generated content; and / or record information of this query operation.

[0029] Fourthly, embodiments of this application provide an electronic device, including:

[0030] At least one processor; and

[0031] A memory that is communicatively connected to the at least one processor;

[0032] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, cause the electronic device to perform the method described in any of the above aspects.

[0033] Fifthly, embodiments of this application provide a cloud device, including:

[0034] At least one processor; and

[0035] A memory that is communicatively connected to the at least one processor;

[0036] The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, cause the cloud device to perform the method described in any of the above aspects.

[0037] Sixthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the method described in any of the above aspects.

[0038] In a seventh aspect, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the methods described in any of the above aspects.

[0039] The AI-generated content process data processing method, related equipment, and program products provided in this application record relevant data of the AI-generated content in real time during the generation process, and generate structured data corresponding to the AI-generated content generated this time based on the recorded relevant data. This structured data can be used to characterize the generation process features of the AI-generated content, realize real-time recording of AI generation process data, facilitate effective tracking and analysis of the AI ​​generation process, not only improve the transparency and traceability of the AI ​​generation process, but also provide strong data support for the subsequent confirmation of rights of AI-generated content, and improve the information security of AI-generated content. Attached Figure Description

[0040] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are some embodiments of the invention, and that those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0041] Figure 1 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application;

[0042] Figure 2This application provides an illustration of an AI-generated content process data processing system, which is an example of an application scenario.

[0043] Figure 3 A schematic diagram of the process data processing system architecture for AI-generated content provided in this application embodiment;

[0044] Figure 4 A flowchart illustrating a process data processing method for AI-generated content provided in an embodiment of this application;

[0045] Figure 5 A flowchart illustrating a process data processing method for AI-generated content of goods provided in this application embodiment;

[0046] Figure 6 A schematic diagram of the structure of a process data processing device for AI-generated content provided in an embodiment of this application;

[0047] Figure 7 This is a schematic diagram of the structure of a cloud device provided in an embodiment of this application.

[0048] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0049] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application.

[0050] In this article, the term "and / or" is used to describe the relationship between related objects. Specifically, it means that there can be three kinds of relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, or B exists alone.

[0051] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and corresponding operation portals are provided for users to choose to authorize or refuse.

[0052] To clearly describe the technical solutions of the embodiments of this application, the terms involved in this application are first defined as follows:

[0053] AI: Artificial Intelligence.

[0054] AI-generated content refers to data content generated through artificial intelligence algorithms, including but not limited to text, images, audio, and video.

[0055] API: Application Programming Interface.

[0056] The process data processing method for AI-generated content in this application embodiment can be applied to any field involving AI data processing.

[0057] Artificial intelligence (AI) is the technology that studies how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It involves computer systems or machines performing tasks that typically require human intelligence. AI technology is widely used in various fields.

[0058] AI-generated content refers to various forms of content data, such as text, images, audio, and video, automatically generated using artificial intelligence algorithms and models. With the rapid development and widespread application of AI generation technology, AI-generated content has experienced explosive growth. However, there is currently no security management technology specifically for AI-generated content. The generation process of AI-generated content is opaque, and users cannot review the process, which not only affects user experience but also makes the content untraceable, easily leading to information security issues such as data leakage and unclear ownership.

[0059] Taking e-commerce platforms as an example, merchants can use AI models to generate different product images for new product listings. However, the generation process of existing AI-generated images is opaque, and users cannot review the process of generating these images. This not only affects user experience but also makes the generated images untraceable, potentially leading to data leaks and unclear ownership, among other information security issues.

[0060] To address at least one of the aforementioned problems, embodiments of this application provide a process data processing scheme for AI-generated content. By recording relevant data during the AI-generated content generation process in real time, and generating structured data corresponding to the AI-generated content based on the recorded data, this structured data can be used to characterize the generation process features of the AI-generated content. This enables real-time recording of AI generation process data, facilitating effective tracking and analysis of the AI ​​generation process. It not only enhances the transparency and traceability of the AI ​​generation process but also provides strong data support for the subsequent ownership verification of AI-generated content, thereby improving the information security of AI-generated content.

[0061] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Where there is no conflict between the embodiments, the following embodiments and features can be combined with each other. Furthermore, the timing of the steps in the following method embodiments is merely an example and not a strict limitation.

[0062] like Figure 1 As shown, this embodiment provides an electronic device 1, including: at least one processor 11 and a memory 12. Figure 1 Taking a processor as an example, the processor 11 and memory 12 are connected via bus 10. Memory 12 stores instructions that can be executed by processor 11. The instructions are executed by processor 11 to enable electronic device 1 to perform all or part of the process of the method in the following embodiments, so as to realize real-time recording of AI generation process data, which facilitates effective tracking and analysis of the AI ​​generation process. This not only improves the transparency and traceability of the AI ​​generation process, but also provides strong data support for the subsequent confirmation of rights to AI-generated content, thereby improving the information security of AI-generated content.

[0063] In one embodiment, the electronic device 1 may be a mobile phone, tablet computer, laptop computer, desktop computer, or a large computing system composed of multiple computers.

[0064] Figure 2 This is a schematic diagram illustrating an application scenario 200 of a process data processing system for AI-generated content, provided in an embodiment of this application. For example... Figure 2 As shown, the system includes: a server 210 and a terminal 220, wherein:

[0065] Server 210 can be a data platform that provides data processing services for AI-generated content, such as an e-commerce platform. In a real-world scenario, an e-commerce platform may have multiple servers 210. Figure 2 Taking a single server (210) as an example.

[0066] Terminal 220 can be a device such as a computer, mobile phone, or tablet used by the user to log in to the e-commerce platform. There can also be multiple terminals 220. Figure 2 The following example uses two terminals, 220, for illustration.

[0067] Terminal 220 and server 210 can transmit information via the Internet, enabling terminal 220 to access data on server 210. Both terminal 220 and / or server 210 can be implemented by electronic device 1.

[0068] The AI-generated content processing solution of this embodiment can be deployed on server 210, on terminal 220, or partially on server 210 and partially on terminal 220. The appropriate solution can be selected based on actual needs in a real-world scenario, and this embodiment does not impose any limitations.

[0069] When the data processing solution for AI-generated content is deployed entirely or partially on server 210, an interface can be opened to terminal 220 to provide algorithm support to terminal 220.

[0070] The method provided in this application embodiment can be implemented by electronic device 1 executing corresponding software code, and is achieved through data interaction with a server. Electronic device 1 can be a local terminal device. When the method runs on a server, it can be implemented and executed based on a cloud interaction system, which includes a server and client devices.

[0071] In one possible implementation, the method provided in this application provides a graphical user interface through a terminal device, wherein the terminal device may be the aforementioned local terminal device or a client device in the aforementioned cloud interaction system.

[0072] like Figure 3 The diagram shown is a schematic of the data processing system architecture for AI-generated content provided in this application embodiment. The system may include: a data collection layer, a data model layer, a data storage layer, and an analysis and visualization layer, wherein:

[0073] The data collection layer records data about the AI-generated content generation process, such as log entries, API calls, and input / output data capture. Log entries include, but are not limited to, recording information such as input parameters, random seeds, intermediate results, and final output for each AI generation.

[0074] The data model layer is used to record the model data used in the AI ​​generation process, such as the model's structure, parameters, training process (including but not limited to loss function, optimizer, learning rate, etc.), evaluation metrics, and other information.

[0075] The data storage layer uses a database or file system to store logs and user interaction records. Log content includes, but is not limited to, records of the source, size, preprocessing methods, and annotation information of the training data. User interaction records include, but are not limited to, the interaction process between the user and the AI ​​system, including user commands, feedback, and modifications.

[0076] The analysis and visualization layer uses data analysis tools to analyze the generation process data, model data, and user interaction records to generate structured data corresponding to the AI-generated content. It then uses pre-defined visualization tools to display this structured data, thus showcasing the generation process of the AI-generated content.

[0077] The aforementioned AI-generated content data processing system can be integrated into AI applications or used as a third-party application to monitor AI applications, enabling real-time recording and analysis of the AI ​​application's generation process.

[0078] Please refer to Figure 4 This is a process data processing method for AI-generated content according to an embodiment of this application. The method can be performed by... Figure 1 The electronic device 1 shown is used to perform this action and can be applied to... Figures 2-3 In the AI-generated content process data processing application scenario shown, real-time recording of AI generation process data is achieved, facilitating effective tracking and analysis of the AI ​​generation process. This not only enhances the transparency and traceability of the AI ​​generation process but also provides strong data support for subsequent ownership verification of AI-generated content, thereby improving the information security of AI-generated content. This embodiment uses terminal 220 as the execution end as an example, and the method includes the following steps:

[0079] Step 401: In response to the AI ​​generation command, record the generation process data of the AI-generated content.

[0080] In this step, by responding to AI-generated commands and recording the data of the AI-generated content generation process, we can comprehensively capture the AI's operational steps and decision-making paths when generating content. AI-generated content includes, but is not limited to, images, text, audio, and video. AI-generated commands can be commands triggered by users through the AI ​​model; for example, a merchant might trigger a command to generate product images using AI, allowing the AI ​​to generate product images that meet the user's needs. By recording relevant data in real time during the AI's product image generation process, we can comprehensively capture the AI's operational steps and decision-making paths when generating product images.

[0081] In one embodiment, step 401 may specifically include: recording the generation steps of the AI-generated content and / or the intermediate results corresponding to the generation steps. Recording the work version information existing in the AI-generated content during the generation process, the generation process data includes one or more of the generation steps, intermediate results, and work version information.

[0082] This embodiment provides a method for recording and tracking the generation process of AI-generated content in detail. By recording the generation steps and corresponding intermediate results of the AI-generated content in detail, the process of AI content generation becomes more transparent, enabling a deeper understanding of the AI ​​generation process. This method allows for fine-grained analysis of each generation step, thereby helping to understand the decision-making logic and operational path of the AI ​​when generating content. The generation steps of the AI-generated content include, but are not limited to, all steps in the AI ​​generation process, such as text generation, image generation, and audio generation. Intermediate results include, but are not limited to, the intermediate results of each generation step, such as text fragments, image sketches, and audio fragments.

[0083] Furthermore, by recording version information of works generated by AI, the evolution of AI-generated content can be effectively managed and tracked. Recording different versions of content during the AI-generated process allows users to compare and review different versions of the work, thus providing data support for the iteration and optimization of AI-generated content.

[0084] In one embodiment, the method may further include: recording interface call data during the AI-generated content generation process, wherein the generation process data includes interface call data.

[0085] In this embodiment, external API calls can be monitored during the AI ​​generation process. If the AI ​​system calls an external API, the parameters and return results of these API calls are recorded as part of the generation process data, thereby further improving the comprehensiveness and accuracy of the generation process data.

[0086] On the other hand, recording the generation process data provides basic data for studying the innovation of AI-generated content, and by analyzing the generation steps and intermediate results, potential problems in the AI ​​generation process can be identified, providing a basis for AI model optimization and generation strategy improvement.

[0087] Step 402: Obtain the model data used in the AI-generated content and the user interaction records during the AI-generated content generation process.

[0088] In this step, model data refers to the various model data used in the AI ​​generation process, including but not limited to model call records, model training records, and model version information. Model data can characterize the model features used in the AI ​​generation process. User interaction records refer to relevant data on user interaction during the AI ​​generation process, including but not limited to user input, user operations, and user feedback, which can characterize the user participation characteristics during the AI ​​generation process. In practical scenarios, the model data used in this AI generation and the user interaction records during the AI ​​generation process can be recorded separately to accurately characterize the model features and user participation characteristics of the AI-generated content, thereby accurately identifying the uniqueness and distinctiveness of the AI-generated content and providing reliable traceability information for subsequent AI-generated content.

[0089] In addition, by recording user interactions, we can conduct in-depth analysis of user behavior patterns and preferences during the AI ​​generation process, which can provide data support for personalized services and user interface design.

[0090] In one embodiment, obtaining the model data used by the AI-generated content in step 402 includes: obtaining model call records, model training records, and model version information during the generation process of the AI-generated content. The model data includes one or more of the model call records, model training records, and model version information.

[0091] In this embodiment, the model data used for AI-generated content includes, but is not limited to, model call records, model training records, and model version information. The model call records can include the time, parameters, and input content of each call to the AI ​​model during the content generation process, as well as the output content returned by the AI ​​model. If model training is involved, the model training records can include information such as the training dataset and training parameters. For example, by recording the database used in the AI ​​generation process and subsequent operation steps, such as model training or model fine-tuning steps, the AI ​​model training process can be identified. In this case, the database used in the process can be recorded as the training dataset, and the relevant parameters of the training steps can be recorded as training parameters. The model version information includes, but is not limited to, the training details, performance metrics, and usage of different versions of the AI ​​system model.

[0092] By acquiring model call records, model training records, and model version information used in AI-generated content, it is possible to comprehensively track and analyze the model data used in the content generation process. This not only improves transparency regarding model usage but also helps identify and resolve potential problems in the AI ​​generation process, thereby enhancing the reliability and quality of the generated content.

[0093] In an optional embodiment, by analyzing model call records and training records, the performance and efficiency of the model can be optimized to ensure that the latest and most suitable model version is used, thereby improving the overall effect of AI-generated content and user experience.

[0094] In one embodiment, step 402, obtaining user interaction records during the AI-generated content generation process, includes: recording user input data, user operations, and user feedback data on the AI-generated content during the AI-generated content generation process. The user interaction records include one or more of the following: input data, user operations, and feedback data.

[0095] In this embodiment, the user interaction record includes, but is not limited to, user input data, user operations, and user feedback data during the AI-generated content generation process. User input data can include all information entered by the user during the AI ​​generation process, such as user-inputted prompts, images, and audio. User operations can include various actions performed by the user on the AI ​​system, such as selecting a model, adjusting parameters, and modifying the generated content. User feedback data can include user feedback on the AI-generated results, such as user satisfaction with the AI-generated content and suggestions. By acquiring user interaction records during the AI ​​content generation process, in-depth analysis of user behavior and preferences can be achieved, thereby comprehensively and accurately characterizing the user participation features involved in the AI-generated content generation process and accurately identifying the distinctiveness of the AI-generated content.

[0096] Optionally, the accumulated user interaction records provide a rich foundation for data analysis and decision-making. Through long-term analysis of these records, user behavior models can be built to predict future user behaviors and preferences, thereby proactively meeting user needs. Furthermore, by monitoring user actions and feedback, anomalies and errors in the system can be detected in a timely manner, enhancing the robustness and stability of the AI-generated system.

[0097] Step 403: Based on the generation process data, model data, and user interaction records, generate structured data corresponding to the AI-generated content. The structured data is used to characterize the generation process features of the AI-generated content.

[0098] In this step, by integrating the generation process data, model data, and user interaction records into structured data, the generation process characteristics of AI-generated content can be effectively characterized. This structured data makes the generation process of AI-generated content traceable, improving the transparency and trustworthiness of the AI ​​system. In scenarios involving copyright and liability division, structured data can serve as evidence to help determine the ownership and responsibility of AI-generated content, thereby improving the information security of AI-generated content.

[0099] In one embodiment, step 403 may specifically include: identifying preset fields in the generation process data, model data, and user interaction records, filling a preset data structure table according to the identified field information, and generating structured data corresponding to the AI-generated content.

[0100] In this embodiment, preset fields are used to extract key information needed for structured data. These preset fields can vary depending on the application scenario and can be set according to the actual scenario requirements and the requirements of the preset dredging structure table. For example, for AI-generated content in a specific scenario, preset fields can be set as: applied technology, generation time, rights confirmation time, written content (combining business agreements and national standards), technical process, etc. The preset data structure table can be configured according to actual needs. Through the preset data structure table, it is ensured that all generated structured data follows a unified format and standard, facilitating subsequent data analysis and querying.

[0101] By identifying preset fields in the generation process data, model data, and user interaction records, key information matching the preset fields can be effectively extracted. Then, the identified field information is used to fill the preset data structure table, ensuring that the generated structured data has consistency and integrity, and realizing the systematic and standardized management of AI-generated content.

[0102] In one embodiment, step 403 may further include: identifying preset fields in the generation process data, model data, and user interaction records; extracting key node information of the AI-generated content during the generation process based on the identified field information; and generating a visual summary of the AI-generated content generation process based on the key node information.

[0103] In this embodiment, the preset fields can be configured to represent the stages of the process. For example, the preset fields may include the model building stage, the model training stage, the AI ​​generation stage, and the AI-generated content display stage. The visual generation process summary refers to the visual information used to represent the characteristics of the AI-generated content generation process. For example, it can be a visual outline icon used to show the model's decision path, the evolution process of the generated content, and other information during the generation process, so that users can directly and intuitively understand the generation process of the AI-generated content through the visual generation process summary.

[0104] Specifically, by using data analysis tools to identify preset fields in the generation process data, model data, and user interaction records, key node information corresponding to each preset field in the AI-generated content generation process is effectively extracted. This key node information is then used as the display information for summary nodes, generating a corresponding visual summary of the generation process. For example, the model building stage displays the text and graphics information involved in the model building process, the model training stage displays the text and graphics information involved in the model training process, the AI ​​generation stage displays the text and graphics information involved in the AI ​​generation process, and the AI-generated content display stage displays the text and graphics information of the AI ​​work.

[0105] In one embodiment, after step 403, the method further includes: in response to a query instruction regarding the generation process of the target AI-generated content, obtaining structured data corresponding to the target AI-generated content; and displaying the structured data on an interactive interface.

[0106] In this embodiment, after the structured data of the target AI-generated content is generated, the user can query this structured data at any time. For example, if a user wants to view previously completed AI works and see their generation process to see if they are helpful, the user can trigger a query command. The system, responding to the query command regarding the generation process of the target AI-generated content, retrieves the structured data corresponding to that content and displays it on the interactive interface, such as presenting a visual summary of the target AI-generated content's generation process in a chart format. This process allows users to more intuitively understand and analyze the generation process and results of AI-generated content. By providing the display of structured data, users can more easily identify the key features and patterns of the generated content, thereby improving the transparency and interpretability of the AI-generated content. Furthermore, it enhances the interactive experience between the user and the AI ​​system, enabling users to more effectively utilize the AI-generated content for decision-making and further operations.

[0107] In one embodiment, the method further includes storing structured data into a preset blockchain.

[0108] In this embodiment, a database can be used to store all recorded data and generated structured data, and hash values ​​can be used to store the structured data on a blockchain for easy retrieval and analysis, thereby improving the security of the structured data.

[0109] In one embodiment, the method further includes: generating a unique identifier for the AI-generated content based on structured data.

[0110] In this embodiment, structured data can characterize the generation process features of AI-generated content and has uniqueness. A unique identifier for AI-generated content can be generated based on the structured data, and the structured data can be tagged with this unique identifier to facilitate subsequent querying and tracing, thereby improving the information security of AI-generated content during transmission and use.

[0111] Optionally, structured data can be configured with an export function, allowing users to export recorded structured data into different formats, thereby improving the interactive experience.

[0112] In one embodiment, before obtaining the structured data corresponding to the target AI-generated content, the method further includes: performing a security verification on the query command, and after the query command successfully passes the security verification, performing the step of obtaining the structured data corresponding to the target AI-generated content.

[0113] In this embodiment, by performing security verification on query commands, it is ensured that only verified query commands can execute subsequent retrieval operations. This effectively prevents unauthorized access and potential security threats, improving the system's security and reliability. Only after the query command successfully passes security verification will the system execute the step of retrieving the structured data corresponding to the target AI-generated content, thereby guaranteeing the legality and accuracy of data access. This not only protects sensitive data but also optimizes the use of system resources, avoiding resource waste caused by invalid or malicious requests.

[0114] Optionally, structured data can be stored and transmitted in an encrypted manner to improve data security.

[0115] Optionally, information about each query can be recorded after each query. For example, a traceability log can be generated to record all operations, facilitating subsequent information tracing.

[0116] Optionally, bottlenecks or shortcomings in the AI ​​generation process can be identified by analyzing structured data, thereby providing a basis for model optimization and user experience improvement.

[0117] The aforementioned method for processing data during the generation of AI-generated content provides a systematic approach to recording and analyzing this process. By responding to AI generation commands and recording the generation process data, it's possible to comprehensively capture the AI's operational steps and decision-making paths during content generation. Furthermore, recording the model data used by the AI ​​and user interaction records during the generation process provides a deeper understanding of the interaction between AI and users. By employing multi-layered and multi-dimensional recording methods, combined with technologies such as blockchain, hash values, and visualization, the generation process of AI-generated content can be effectively tracked and analyzed, enhancing its transparency and traceability, and providing strong data support for subsequent ownership verification of AI-generated content.

[0118] Please refer to Figure 5 This is a process data processing method for AI-generated content about goods, as described in one embodiment of this application. This method can be performed by... Figure 1 The electronic device 1 shown is used to perform this action and can be applied to... Figure 2-3 In the data processing application scenario of AI-generated content shown, this embodiment takes terminal 220 as the execution end as an example. Compared with the previous embodiment, this embodiment takes the scenario of users using an AI system to generate product information in an e-commerce scenario as an example. The method includes the following steps:

[0119] Step 501: In response to the AI-generated instruction regarding product information, record the data of the AI-generated content generation process, which includes product information.

[0120] In this step, product information includes, but is not limited to, product-related knowledge, appearance information, product promotion information, product-related order information, and product-related service information. AI-generated content about the product includes, but is not limited to, product images, product description text, and product description audio / video, such as the product's AI main image and AI-generated product details page.

[0121] Step 502: Obtain the model data used in the AI-generated content and the user interaction records during the AI-generated content generation process.

[0122] Step 503: Based on the generation process data, model data, and user interaction records, generate structured data corresponding to the AI-generated content. The structured data is used to characterize the generation process features of the AI-generated content.

[0123] The aforementioned data processing method for AI-generated content of goods enables the recording and analysis of the AI ​​generation process in e-commerce scenarios, improves the transparency and traceability of the generation process of AI-generated content containing product information, and enhances the information security of AI-generated content in e-commerce scenarios.

[0124] For details of each step of the above method, please refer to the relevant descriptions in the above embodiments, which will not be repeated here.

[0125] Please refer to Figure 6 This is a process data processing apparatus 600 for AI-generated content according to an embodiment of this application. This apparatus can be applied to... Figure 1 The electronic device 1 shown can be applied to Figures 2-3In the data processing application scenario of AI-generated content shown, the real-time recording of AI generation process data is achieved, facilitating effective tracking and analysis of the AI ​​generation process. This not only enhances the transparency and traceability of the AI ​​generation process but also provides strong data support for the subsequent ownership verification of AI-generated content, thereby improving the information security of the AI-generated content. The device includes: a recording module 601, an acquisition module 602, and a generation module 603. The functional principles of each module are as follows:

[0126] The recording module 601 is used to record the generation process data of AI-generated content in response to AI generation instructions.

[0127] The acquisition module 602 is used to acquire the model data used in AI-generated content and the user interaction records during the AI-generated content generation process.

[0128] The generation module 603 is used to generate structured data corresponding to AI-generated content based on generation process data, model data, and user interaction records. The structured data is used to characterize the generation process features of AI-generated content.

[0129] In one embodiment, the recording module 601 is used to record the generation steps of AI-generated content and / or the intermediate results corresponding to the generation steps. It records the work version information present in the AI-generated content during the generation process, and the generation process data includes one or more of the generation steps, intermediate results, and work version information.

[0130] In one embodiment, the acquisition module 602 is used to acquire model call records, model training records and model version information during the generation process of AI-generated content. The model data includes one or more of the model call records, model training records and model version information.

[0131] In one embodiment, the acquisition module 602 is used to record user input data, user operations, and user feedback data on AI-generated content during the AI-generated content generation process. The user interaction record includes one or more of the following: input data, user operations, and feedback data.

[0132] In one embodiment, the recording module 601 is further configured to record interface call data during the AI-generated content generation process, and the generation process data includes interface call data.

[0133] In one embodiment, the generation module 603 is used to identify preset fields in the generation process data, model data and user interaction records, fill the preset data structure table according to the identified field information, and generate structured data corresponding to the AI-generated content.

[0134] In one embodiment, the generation module 603 is used to identify preset fields in the generation process data, model data, and user interaction records, and extract key node information of the AI-generated content during the generation process based on the identified field information. A visual summary of the AI-generated content generation process is then generated based on the key node information.

[0135] In one embodiment, the system further includes: a storage module for storing structured data to a preset blockchain; and / or an identification module for generating a unique identifier for the AI-generated content based on the structured data.

[0136] In one embodiment, the system further includes a query module, configured to retrieve structured data corresponding to the target AI-generated content in response to a query command regarding the generation process of the target AI-generated content. The structured data is then displayed on the interactive interface.

[0137] In one embodiment, the system further includes: a verification module, configured to perform a security verification on the query command before obtaining the structured data corresponding to the target AI-generated content; and, after the query command successfully passes the security verification, execute the step of obtaining the structured data corresponding to the target AI-generated content. And / or, record information about this query operation.

[0138] For a detailed description of the data processing device 600 for the AI-generated content, please refer to the description of the relevant method steps in the above embodiments. The implementation principle and technical effect are similar, and will not be repeated here.

[0139] Figure 7 This is a schematic diagram of the structure of a cloud device 70 provided as an exemplary embodiment of this application. The cloud device 70 can be used to run the methods provided in any of the above embodiments. Figure 7 As shown, the cloud device 70 may include: a memory 704 and at least one processor 705. Figure 7 Let's take a processor as an example.

[0140] Memory 704 is used to store computer programs and can be configured to store various other data to support operations on cloud device 70. Memory 704 may be object storage (OSS).

[0141] The memory 704 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0142] The processor 705, coupled to the memory 704, is used to execute the computer program in the memory 704 to implement the solution provided in any of the above method embodiments. The specific functions and technical effects that can be achieved will not be elaborated here.

[0143] Furthermore, such as Figure 7 The cloud device also includes other components such as a firewall 701, a load balancer 702, a communication component 706, and a power supply component 703. Figure 7 The diagram only shows some components and does not mean that cloud devices only include... Figure 7 The components shown.

[0144] In one embodiment, the above Figure 7 The communication component 706 is configured to facilitate wired or wireless communication between the device containing the communication component 706 and other devices. The device containing the communication component 706 can access wireless networks based on communication standards, such as WiFi, 2G, 3G, 4G, LTE (Long Term Evolution), 5G, or combinations thereof. In one exemplary embodiment, the communication component 706 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component 706 also includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID), Infrared Data Association (IrDA), Ultra Wide Band (UWB), Bluetooth, and other technologies.

[0145] In one embodiment, the above Figure 7 The power supply component 703 provides power to various components of the device in which it resides. The power supply component 703 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device in which the power supply component resides.

[0146] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the method of any of the foregoing embodiments.

[0147] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the method of any of the foregoing embodiments.

[0148] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For instance, the division of modules is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed.

[0149] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0150] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the application can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor. The memory may include high-speed RAM (Random Access Memory), and may also include non-volatile memory (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk, or optical disc, etc.

[0151] The aforementioned storage media can be implemented from any type of volatile or non-volatile storage device or a combination thereof, such as Static Random-Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage media can be any available medium accessible to general-purpose or special-purpose computers.

[0152] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. Both the processor and the storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic device or host device.

[0153] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, garment, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, garment, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, garment, or apparatus that includes that element.

[0154] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0155] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods of the various embodiments of this application.

[0156] The collection, storage, use, processing, transmission, provision, and disclosure of user data and other information involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0157] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A process data processing method of AI-generated content, characterized by, include: In response to AI generation instructions, the generation process data of AI-generated content is recorded. The generation process data includes one or more of the following: the generation steps of the AI-generated content, the intermediate results corresponding to the generation steps, and the work version information of the AI-generated content during the generation process. The model data used in the AI-generated content and the user interaction records during the AI-generated content generation process are obtained. The user interaction records include one or more of the following: user input data, user operations, and user feedback data on the AI-generated content during the AI-generated content generation process. Based on the generation process data, the model data, and the user interaction records, structured data corresponding to the AI-generated content is generated, and the structured data is used to characterize the generation process features of the AI-generated content.

2. The method according to claim 1, characterized in that, The acquisition of the model data used for the AI-generated content includes: Obtain the model call records, model training records, and model version information during the generation process of the AI-generated content. The model data includes one or more of the model call records, model training records, and model version information.

3. The method according to claim 1, characterized in that, Obtaining user interaction records during the AI-generated content generation process includes: Record the input data, the user's actions, and the feedback data.

4. The method according to claim 1, characterized in that, Also includes: Record the interface call data during the AI-generated content generation process, and the generation process data includes the interface call data.

5. The method according to claim 1, characterized in that, The step of generating structured data corresponding to the AI-generated content based on the generation process data, the model data, and the user interaction records includes: The system identifies preset fields in the generation process data, the model data, and the user interaction records, and fills a preset data structure table based on the identified field information to generate structured data corresponding to the AI-generated content.

6. The method according to claim 1, characterized in that, The step of generating structured data corresponding to the AI-generated content based on the generation process data, the model data, and the user interaction records further includes: Identify preset fields in the generation process data, the model data, and the user interaction records, and extract key node information of the AI-generated content in the generation process based on the identified field information; A visual summary of the AI-generated content generation process is generated based on the key node information.

7. The method according to claim 1, characterized in that, Also includes: Store the structured data to a pre-defined blockchain; and / or, A unique identifier for the AI-generated content is generated based on the structured data.

8. The method according to claim 1, characterized in that, Also includes: In response to a query command regarding the generation process of target AI-generated content, the structured data corresponding to the target AI-generated content is obtained; The structured data is displayed in the interactive interface.

9. The method according to claim 8, characterized in that, Before obtaining the structured data corresponding to the target AI-generated content, the method further includes: The query command is subjected to security verification. After the query command successfully passes the security verification, the step of obtaining the structured data corresponding to the target AI-generated content is executed; and / or, Record information about this query operation.

10. A process data processing method for AI-generated content about goods, characterized in that, include: In response to an AI-generated instruction regarding product information, the system records data on the generation process of AI-generated content. The AI-generated content includes the product information, and the generation process data includes one or more of the following: the generation steps of the AI-generated content, the intermediate results corresponding to the generation steps, and the work version information existing in the generation process of the AI-generated content. The model data used in the AI-generated content and the user interaction records during the AI-generated content generation process are obtained. The user interaction records include one or more of the following: user input data, user operations, and user feedback data on the AI-generated content during the AI-generated content generation process. Based on the generation process data, the model data, and the user interaction records, structured data corresponding to the AI-generated content is generated, and the structured data is used to characterize the generation process features of the AI-generated content.

11. An electronic device, characterized in that, include: At least one processor; as well as A memory that is communicatively connected to the at least one processor; The memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, cause the electronic device to perform the method according to any one of claims 1-10.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, implement the method as described in any one of claims 1-10.

13. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method as described in any one of claims 1-10.

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