Process data processing method of AI generation content, related equipment and program product

Abstract

The invention provides a process data processing method for AI generation content, related equipment and a program product, and the method comprises the steps: responding to an AI generation instruction, and recording the generation process data of the AI generation content; obtaining model data used by the AI generation content and a user interaction record in a generation process of the AI generation content; and according to the generation process data, the model data and the user interaction record, generating structured data corresponding to the AI generation content, the structured data being used for representing generation process features of the AI generation content. The AI generation process data can be recorded in real time, the AI generation process can be effectively tracked and analyzed, the transparency and traceability of the AI generation process can be improved, powerful data support can be provided for the right confirmation of the AI generation content in the later period, and the information security of the AI generation content can be improved.

Description

Technical Field

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

[0002] Artificial Intelligence (AI) is the study of the technology of using computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and tasks that usually require human intelligence to complete are performed by computer systems or machines. Artificial Intelligence technology is widely used in various fields.

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

[0004] The main purpose of the embodiments of the present application is to provide a process data processing method, related equipment and program products for AI-generated content, which realizes real-time recording of AI-generated process data, facilitates effective tracking and analysis of the AI ​​generation process, and not only improves the transparency and traceability of the AI ​​generation process, but also can provide 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, an embodiment of the present application provides a method for processing process data of AI-generated content, comprising: recording generation process data of AI-generated content in response to an AI generation instruction; acquiring model data used by the AI-generated content and user interaction records during the generation process of the AI-generated content; 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 generation process characteristics of the AI-generated content.

[0006] In one embodiment, the generation process data recording the 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 existing in the generation process of the AI-generated content, and 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, and 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, obtaining a user interaction record during the AI-generated content generation process includes: recording the user's input data, the user's operations, and the user's feedback data on the AI-generated content during the AI-generated content generation process, and the user interaction record includes one or more of the input data, the user's operations, and the feedback data.

[0009] In one embodiment, it also includes: recording interface call data in the process of generating the AI-generated content, and the generation process data includes the interface call data.

[0010] In one embodiment, the generating structured data corresponding to the AI ​​generated content based on the generation process data, the model data and the user interaction record includes: identifying preset fields in the generation process data, the model data and the user interaction record, filling a preset data structure table according to 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 record also includes: identifying preset fields in the generation process data, the model data and the user interaction record, and extracting key node information of the AI-generated content in the generation process based on the identified field information; and generating a visual generation process summary of the AI-generated content based on the key node information.

[0012] In one embodiment, it also includes: storing the structured data in a preset blockchain; and / or generating a unique identifier of the AI-generated content based on the structured data.

[0013] In one embodiment, it also includes: in response to a query instruction for 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, it also includes: performing a security check on the query instruction, and after the query instruction successfully passes the security check, executing the step of obtaining the structured data corresponding to the target AI generated content; and / or recording information of this query operation.

[0015] In a second aspect, an embodiment of the present application provides a method for processing process data of AI-generated content about a product, comprising: in response to an AI generation instruction about product information, recording generation process data of the AI-generated content, the AI-generated content including the product information; obtaining model data used by the AI-generated content and user interaction records during the generation process of the AI-generated content; 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 generation process characteristics of the AI-generated content.

[0016] In a third aspect, an embodiment of the present application provides a process data processing device for AI-generated content, including:

[0017] A recording module, for recording the generation process data of the AI-generated content in response to the AI-generated instruction;

[0018] An acquisition module, used to acquire model data used by the AI-generated content and user interaction records during the generation process of the AI-generated content;

[0019] A 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 record, wherein the structured data is used to characterize the generation process characteristics 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; record the work version information existing in the generation process of the AI-generated content, and the generation process data includes one or more of the generation steps, the intermediate results and the work version information.

[0021] In one embodiment, an acquisition module is used to obtain the model call record, model training record and model version information of the AI ​​generated content during the generation process, and the model data includes one or more of the model call record, the model training record and the model version information.

[0022] In one embodiment, an 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, and the user interaction record includes one or more of the input data, the user operations and the feedback data.

[0023] In one embodiment, the recording module is further used to record interface call data in the process of generating the AI-generated content, and the generation process data includes the interface call data.

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

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

[0026] In one embodiment, it also includes: a storage module, used to store the structured data in a preset blockchain; and / or, an identification module, used to generate a unique identification of the AI-generated content based on the structured data.

[0027] In one embodiment, it also includes: a query module, which is used to respond to a query instruction for 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, it also includes: a verification module, which is used to perform a security check 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 check, execute the step of obtaining the structured data corresponding to the target AI-generated content; and / or record information of this query operation.

[0029] In a fourth aspect, an embodiment of the present application provides an electronic device, including:

[0030] at least one processor; and

[0031] a memory communicatively coupled to the at least one processor;

[0032] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the electronic device to execute the method described in any one of the above aspects.

[0033] In a fifth aspect, an embodiment of the present application provides a cloud device, including:

[0034] at least one processor; and

[0035] a memory communicatively coupled to the at least one processor;

[0036] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the cloud device to execute the method described in any one of the above aspects.

[0037] In a sixth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions. When a processor executes the computer-executable instructions, the method described in any one of the above aspects is implemented.

[0038] In a seventh aspect, an embodiment of the present application provides a computer program product, including a computer program, which implements the method described in any of the above aspects when executed by a processor.

[0039] The process data processing method, related equipment and program product of AI-generated content provided in the embodiments of the present application record the relevant data of the AI-generated content in the generation process in real time, and generate structured data corresponding to the AI-generated content generated this time based on the recorded relevant data. The structured data can be used to characterize the generation process characteristics of the AI-generated content, realize real-time recording of the AI-generated process data, and facilitate effective tracking and analysis of the AI ​​generation process. It not only improves 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present application, and are used together with the specification to explain the principles of the present application. Obviously, the drawings described below are some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor.

[0041] Figure 1 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0042] Figure 2A schematic diagram of an application scenario of a process data processing system for AI-generated content provided in an embodiment of the present application;

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

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

[0045] Figure 5 A flowchart of a method for processing process data of a commodity AI generated content provided in an embodiment of the present application;

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

[0047] Figure 7 A schematic diagram of the structure of a cloud device provided in an embodiment of the present application.

[0048] The above drawings have shown clear embodiments of the present application, which will be described in more detail later. These drawings and text descriptions are not intended to limit the scope of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0049] Here, exemplary embodiments are described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application.

[0050] The term "and / or" in this article is used to describe the association relationship of associated objects, specifically indicating that there may be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and 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, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of the relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0052] In order to clearly describe the technical solution of the embodiment of the present application, the terms involved in the present application are first defined:

[0053] AI: Artificial Intelligence.

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

[0055] API: Application Programming Interface, application programming interface.

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

[0057] Artificial Intelligence (AI) is the study of the technology of using computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, planning, etc.), and tasks that usually require human intelligence to complete are performed by computer systems or machines. Artificial Intelligence technology is widely used in various fields.

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

[0059] Taking the e-commerce platform scenario as an example, merchants on the e-commerce platform can use AI large models to generate different product images for product updates. However, the generation process of the existing AI generation system is not transparent, and users cannot review the generation process of AI-generated product images, which not only affects the user experience, but also makes the AI-generated product images untraceable, which easily leads to information security issues such as data leakage and unclear ownership.

[0060] In order to solve at least one of the above problems, an embodiment of the present application provides a process data processing solution for AI-generated content, which records the relevant data of the AI-generated content in the generation process in real time, and generates structured data corresponding to the AI-generated content generated this time based on the recorded relevant data. The structured data can be used to characterize the generation process characteristics of the AI-generated content, realize real-time recording of AI-generated process data, and facilitate effective tracking and analysis of the AI ​​generation process. It not only improves 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.

[0061] Some embodiments of the present application are described in detail below in conjunction with the accompanying drawings. In the case where there is no conflict between the embodiments, the following embodiments and the features in the embodiments can be combined with each other. In addition, the step sequence in the following method embodiments is only 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 A processor is taken as an example. The processor 11 and the memory 12 are connected via a bus 10. The memory 12 stores instructions that can be executed by the processor 11, and the instructions are executed by the processor 11 so that the electronic device 1 can execute all or part of the process of the method in the following embodiment to realize real-time recording of AI generation process data, which is convenient for effectively tracking and analyzing the AI ​​generation process, not only improving the transparency and traceability of the AI ​​generation process, but also providing strong data support for the later confirmation of the ownership of AI-generated content, and improving the information security of AI-generated content.

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

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

[0065] Server 210 may be a data platform that provides data processing services for AI-generated content, such as an e-commerce platform. In actual scenarios, an e-commerce platform may have multiple servers 210. Figure 2 In the figure, one server 210 is taken as an example.

[0066] The terminal 220 may be a computer, mobile phone, tablet or other device used by the user to log in to the e-commerce platform. There may also be multiple terminals 220. Figure 2 Two terminals 220 are taken as an example for illustration.

[0067] The terminal 220 and the server 210 can transmit information via the Internet, so that the terminal 220 can access the data on the server 210. The terminal 220 and / or the server 210 can be implemented by the electronic device 1.

[0068] The process data processing scheme of AI-generated content in the embodiment of the present application can be deployed on the server 210, or on the terminal 220, or partially on the server 210 and partially on the terminal 220. In actual scenarios, it can be selected based on actual needs, and this embodiment does not limit it.

[0069] When the process data processing solution of AI generated content is fully or partially deployed on the server 210, a calling interface can be opened to the terminal 220 to provide algorithm support for the terminal 220.

[0070] The method provided in the embodiment of the present application can be implemented by executing the corresponding software code by the electronic device 1, and can be implemented by exchanging data with the server. The electronic device 1 can be a local terminal device. When the method is run on the server, the method can be implemented and executed based on the cloud interaction system, wherein the cloud interaction system includes a server and a client device.

[0071] In a possible implementation, the method provided in the embodiment of the present application provides a graphical user interface through a terminal device, wherein the terminal device can be the local terminal device mentioned above, or can be the client device in the cloud interaction system mentioned above.

[0072] like Figure 3 As shown, it is a schematic diagram of the architecture of the process data processing system for AI-generated content provided in an embodiment of the present application. 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 is used to record the data of the AI-generated content generation process, such as logging, API calls, and input and output data capture. Logging includes but is not limited to recording the input parameters, random seeds, intermediate results, final output, and other information generated by each AI.

[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 indicators, and other information.

[0075] The data storage layer is used to store logs and user interaction records using a database or file system. The log content includes but is not limited to the source, scale, preprocessing method, 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 instructions, feedback, modifications, and other information.

[0076] The analysis and visualization layer is used to use 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. The structured data is displayed using preset visualization tools to realize the generation process of the AI-generated content.

[0077] The process data processing system for the above-mentioned AI-generated content can be integrated into the AI ​​application, or it can be used as a third-party application to monitor the AI ​​application to achieve real-time recording and analysis of the generation process of the AI ​​application.

[0078] Please see Figure 4 , which is a process data processing method for AI-generated content in one embodiment of the present application, which can be Figure 1 The electronic device 1 shown is used to perform and can be applied to Figure 2-Figure 3 In the process data processing application scenario of AI-generated content shown in , in order to realize the real-time recording of AI-generated process data, it is convenient to effectively track and analyze the AI ​​generation process, which not only improves the transparency and traceability of the AI ​​generation process, but also provides strong data support for the later confirmation of the ownership of AI-generated content, and improves the information security of AI-generated content. In this embodiment, taking terminal 220 as the execution end as an example, the method includes the following steps:

[0079] Step 401: In response to an AI generation instruction, record generation process data of AI generated content.

[0080] In this step, by responding to AI generation instructions and recording the generation process data of AI-generated content, the operation steps and decision paths of AI when generating content can be fully captured. AI-generated content includes but is not limited to content in the form of images, text, audio and video. AI generation instructions can be instructions triggered by users through AI large models, such as merchant users triggering generation instructions for making product images through AI, so that AI can generate product images that meet user needs according to generation instructions. By recording the relevant data of AI in the process of generating product images in real time, the operation steps and decision paths of AI when generating product images can be fully captured.

[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 generation process of the AI-generated content, where the generation process data includes one or more of the generation steps, the intermediate results, and the work version information.

[0082] In this embodiment, a method for recording and tracking the generation process of AI-generated content in detail is provided. By recording the generation steps of AI-generated content and the corresponding intermediate results in detail, the process of AI-generated content is made more transparent, and an in-depth understanding of the AI ​​generation process can be achieved. This method allows for fine-grained analysis of each generation step, which helps to understand the decision logic and operation path of AI when generating content. The generation steps of AI-generated content include but are not limited to the various steps in the AI ​​generation process, such as text generation, picture generation, audio generation and other steps. The intermediate results include but are not limited to the intermediate results of each generation step, such as text fragments, picture sketches, audio fragments, etc.

[0083] In addition, by recording the version information of the works in the AI ​​generation process, the evolution of AI-generated content can be effectively managed and tracked. By recording different versions of the content in the AI ​​generation process, it is convenient for users to compare and trace back different versions of works in the AI ​​generation process, thereby 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, where the generation process data includes the 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 calling interfaces 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 innovativeness of AI-generated content, and by analyzing the generation steps and intermediate results, it can identify potential problems in the AI ​​generation process, providing a basis for AI model optimization and generation strategy improvement.

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

[0088] In this step, model data refers to various model data used in this 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 based on the AI ​​generation process. User interaction records refer to relevant data on user participation in the AI ​​generation process, including but not limited to user input, user operation, and user feedback data, which can characterize the user participation characteristics in the AI ​​generation process. In actual scenarios, the model data used in this AI generation and the user interaction records in the AI ​​generation process can be recorded separately, so as to accurately characterize the model features and user participation features of the AI-generated content, and then accurately identify the uniqueness and distinctiveness of the AI-generated content, and provide reliable traceability information for subsequent AI-generated content.

[0089] In addition, by recording user interactions, we can deeply analyze the user's behavior patterns and preferences during the AI ​​generation process, and provide data support for personalized services and user interface design.

[0090] In one embodiment, obtaining model data used by 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, and 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 by AI-generated content includes but is not limited to model call records, model training records, and model version information, among which the model call records may include the time, parameters, and input content of each call to the AI ​​model during the generation process of the AI-generated content, as well as the output content returned by the AI ​​model. If model training is involved, the model training record may include information such as training data sets and training parameters. For example, by recording the database used in the AI ​​generation process and subsequent operation steps, such as subsequent operations including model training or model fine-tuning steps, the training process of the AI ​​model can be identified. At this time, the database used in the process can be recorded as a training data set, and the relevant parameters of the training steps can be recorded as training parameters. Model version information includes but is not limited to the training details, performance indicators, and usage of different versions of models of the AI ​​system.

[0092] By obtaining the model call records, model training records, and model version information used by AI-generated content, it is possible to comprehensively track and analyze the model data used in the generation process of AI-generated content, which not only improves the transparency of model usage, but also helps identify and solve potential problems in the AI ​​generation process and improve the reliability and quality of 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 and user experience of AI-generated content.

[0094] In one embodiment, the user interaction record obtained in the AI-generated content generation process in step 402 includes: recording the user's input data, the user's operations, and the user's feedback data on the AI-generated content during the AI-generated content generation process, and the user interaction record includes one or more of the input data, the user's operations, and the feedback data.

[0095] In this embodiment, the user interaction record includes but is not limited to the user's input data, user operations, and user feedback data on the AI-generated content during the AI-generated content generation process. The user input data may include all information input by the user during the AI ​​generation process, such as prompt text, pictures, audio, etc. input by the user. The user's operation may include various operations of the user on the AI ​​system, such as selecting a model, adjusting parameters, modifying generated content, etc. The user's feedback data may include the user's feedback data on the AI ​​generation results, such as the user's satisfaction with the AI-generated content, suggestions, etc. By obtaining the user interaction records in the process of AI-generated content generation, an in-depth analysis of user behavior and preferences can be achieved, thereby comprehensively and accurately characterizing the user participation characteristics involved in the generation process of AI-generated content, and accurately identifying the distinctiveness of AI-generated content.

[0096] Optionally, the accumulated user interaction records provide a rich basis for data analysis and decision-making. Through long-term analysis of user interaction records, user behavior models can be established to predict users' future behaviors and preferences, thereby meeting user needs in advance. And by monitoring user operations and feedback, anomalies and errors in the system can be discovered in a timely manner, enhancing the robustness and stability of the AI ​​generation system.

[0097] Step 403: Generate structured data corresponding to the AI-generated content based on the generation process data, model data, and user interaction records. The structured data is used to characterize the generation process characteristics 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 trust of the AI ​​system. In scenarios involving copyright and responsibility division, structured data can be used as evidence to help determine the ownership and responsibility of AI-generated content and improve the information security of AI-generated content.

[0099] In one embodiment, step 403 may specifically include: identifying preset fields in 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, the preset fields are used to extract the key information required for structured data. With different application scenarios, the preset fields may be different. The preset fields can be set according to the actual scenario requirements and the requirements of the preset dredging structure table. For example, the preset fields for AI-generated content for a specific scenario can be set as: applied technology, generation time, confirmation time, written content (combination of business agreement and national standards), technical process and other fields. 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 follow a unified format and standard, which is convenient for subsequent data analysis and query.

[0101] By identifying preset fields in generation process data, model data, and user interaction records, we can effectively extract key information that matches the preset fields, and then fill in the preset data structure table based on the identified field information to ensure the consistency and integrity of the generated structured data, thereby achieving systematic and standardized management of AI-generated content.

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

[0103] In this embodiment, the preset fields can be configured as fields that characterize the process stages, for example, the preset fields can 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 characterize the generation process characteristics of the AI ​​generated content, such as a visual outline icon, which is used to display the decision path of the model during the generation process of the AI ​​generated content, the evolution process of the generated content, and other information, so that users can directly understand the generation process of the AI ​​generated content intuitively through the visual generation process summary.

[0104] Specifically, data analysis tools are used to identify preset fields in the generation process data, model data, and user interaction records, and key node information corresponding to each preset field in the generation process of AI-generated content is effectively extracted. The key node information is used as the display information of the summary node to generate the corresponding visual generation process summary. For example, the model establishment phase corresponds to the display of graphic information involved in the model establishment process, the model training phase corresponds to the display of graphic information involved in the model training process, the AI ​​generation phase corresponds to the display of graphic information involved in the AI ​​generation process, and the AI-generated content display phase corresponds to the display of graphic information of AI works.

[0105] In one embodiment, after step 403, the method further includes: in response to a query instruction for 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 the structured data at any time. For example, if the user wants to view the previously completed AI work and view its generation process to see if it is helpful to him, the user can trigger the query instruction. The system obtains the structured data corresponding to the generated content by responding to the query instruction for the generation process of the target AI-generated content, and displays the data on the interactive interface, such as displaying the visual generation process summary of the target AI-generated content in the form of a chart. This process enables users to more intuitively understand and analyze the generation process and results of AI-generated content. By providing a display of structured data, users can more easily identify the key features and patterns of the generated content, thereby improving the transparency and explainability of AI-generated content. It can also enhance the interactive experience between users and AI systems, enabling users to more effectively use AI-generated content for decision-making and further operations.

[0107] In one embodiment, the method further includes: storing the structured data in 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 aggregated to save the structured data on the blockchain, which facilitates retrieval and analysis and improves 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 the structured data.

[0110] In this embodiment, structured data can characterize the generation process characteristics of AI-generated content and is unique. A unique identifier of the AI-generated content can be generated based on the structured data, and the structured data can be marked with the unique identifier to facilitate subsequent query and tracing, thereby improving the information security of the AI-generated content during transmission and use.

[0111] Optionally, the structured data may be configured with an export function so that the user can export the recorded structured data into different formats to improve the interactive experience.

[0112] In one embodiment, before obtaining the structured data corresponding to the target AI generated content, it also includes: performing a security check on the query instruction, and after the query instruction successfully passes the security check, executing the step of obtaining the structured data corresponding to the target AI generated content.

[0113] In this embodiment, by performing security verification on the query instruction, it is ensured that only verified query instructions can perform subsequent acquisition operations. It effectively prevents unauthorized access and potential security threats, and improves the security and reliability of the system. After the query instruction successfully passes the security verification, the system will execute the step of obtaining the structured data corresponding to the target AI-generated content, thereby ensuring the legitimacy and accuracy of data access. It not only protects sensitive data, but also optimizes the use of system resources, avoiding resource waste due to invalid or malicious requests.

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

[0115] Optionally, after each query, the information of the query operation can be recorded, for example, a traceability log can be generated to record all operation behaviors for the convenience of subsequent information tracing.

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

[0117] The above-mentioned process data processing method for AI-generated content provides a systematic method to record and analyze the generation process of AI-generated content. By responding to AI-generated instructions and recording the generation process data of AI-generated content, the operation steps and decision paths of AI when generating content can be fully captured. It also records the model data used by AI and the user interaction records during the generation process, thereby providing an in-depth understanding of the interaction between AI and users. By adopting a multi-level and multi-dimensional recording method, combined with blockchain, hash value, visualization and other technologies, the generation process of AI-generated content can be effectively tracked and analyzed, the transparency and traceability of AI-generated content can be improved, and strong data support can be provided for the later confirmation of the rights of AI-generated content.

[0118] Please see Figure 5 , which is a process data processing method for AI-generated content of a product in one embodiment of the present application, which can be Figure 1 The electronic device 1 shown is used to perform and can be applied to Figure 2-3 In the process data processing application scenario of AI generating content shown in , this embodiment takes the terminal 220 as an execution end as an example. Compared with the previous embodiment, this embodiment takes the scenario in which a user uses 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 an AI generation instruction regarding product information, record generation process data of AI generated content, where the AI ​​generated content includes product information.

[0120] In this step, product information includes but is not limited to product-related knowledge information, appearance information, product promotion information, product-related order information, product-related service information, etc. AI-generated content about products includes but is not limited to product images, product introduction text, product introduction audio and video, etc., such as the AI ​​main picture of the product, the AI ​​details page of the product, etc.

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

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

[0123] The above-mentioned process data processing method of the AI-generated content of goods realizes the recording and analysis of the AI ​​generation process in the e-commerce scenario, improves the transparency and traceability of the generation process of the AI-generated content containing product information, and improves the information security of the AI-generated content in the e-commerce scenario.

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

[0125] Please see Figure 6 , which is a process data processing device 600 of AI generated content according to an embodiment of the present application, which can be applied to Figure 1 The electronic device 1 shown can be applied to Figure 2-Figure 3In the process data processing application scenario of AI-generated content shown in , the real-time recording of AI-generated process data is realized, which is convenient for effectively tracking and analyzing the AI ​​generation process, not only improving the transparency and traceability of the AI ​​generation process, but also providing strong data support for the later confirmation of the ownership of AI-generated content, and improving the information security of AI-generated content. The device includes: a recording module 601, an acquisition module 602 and a generation module 603, and the functional principles of each module are as follows:

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

[0127] The acquisition module 602 is used to obtain the model data used by the 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 the AI-generated content based on the generation process data, model data and user interaction records, and the structured data is used to characterize the generation process characteristics of the AI-generated content.

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

[0130] In one embodiment, the acquisition module 602 is used to obtain model call records, model training records and model version information during the generation process of AI-generated content, and 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 the user's input data, user operations and user feedback data on the AI-generated content during the AI-generated content generation process, and the user interaction record includes one or more of the input data, user operations and feedback data.

[0132] In one embodiment, the recording module 601 is further used to record the interface call data in the process of generating AI-generated content, and the generation process data includes the 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 in 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 the preset fields in the generation process data, model data and user interaction records, and extract the key node information of the AI-generated content in the generation process according to the identified field information. A visual generation process summary of the AI-generated content is generated according to the key node information.

[0135] In one embodiment, it further includes: a storage module for storing structured data in a preset blockchain. And / or, an identification module for generating a unique identification of AI-generated content based on the structured data.

[0136] In one embodiment, the system further includes: a query module for obtaining structured data corresponding to the target AI-generated content in response to a query instruction for the generation process of the target AI-generated content, and displaying the structured data in the interactive interface.

[0137] In one embodiment, it further includes: a verification module, which is used 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 the information of this query operation.

[0138] For a detailed description of the process data processing device 600 of the above-mentioned AI generated content, please refer to the description of the relevant method steps in the above-mentioned embodiment. Its implementation principle and technical effects are similar, and will not be repeated here in this embodiment.

[0139] Figure 7 The following is a schematic diagram of the structure of a cloud device 70 provided in an exemplary embodiment of the present application. The cloud device 70 can be used to run the method 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 A processor is taken as an example.

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

[0141] 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 memory, flash memory, magnetic disk or optical disk.

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

[0143] Furthermore, if Figure 7 The cloud device also includes: a firewall 701, a load balancer 702, a communication component 706, a power supply component 703 and other components. Figure 7 Only some components are shown schematically, which does not mean that the cloud device only includes Figure 7 Components shown.

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

[0145] In one embodiment, the above Figure 7 The power supply component 703 provides power to various components of the device where the power supply component 703 is located. 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 where the power supply component is located.

[0146] An embodiment of the present application further provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the method of any of the aforementioned embodiments is implemented.

[0147] An embodiment of the present application also provides a computer program product, including a computer program, which implements the method of any of the aforementioned embodiments when executed by a processor.

[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 only schematic, for example, the division of modules is only a logical function division, and there may be other division methods in actual implementation, such as multiple modules can be combined or integrated into another system, or some features can be ignored or not executed.

[0149] The above-mentioned integrated module implemented in the form of a software function module can be stored in a computer-readable storage medium. The above-mentioned software function module is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to perform some steps of the methods of various embodiments of the present application.

[0150] It should be understood that the above-mentioned processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The memory may include a high-speed RAM (Random Access Memory) memory, and may also include non-volatile storage NVM (Nonvolatile memory, NVM for short), such as at least one disk storage, and can also be a USB flash drive, a mobile hard disk, a read-only memory, a disk or an optical disk, etc.

[0151] The above storage medium 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 memory, flash memory, magnetic disk or optical disk. The storage medium can be any available medium that can be accessed by a general or special computer.

[0152] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the storage medium can also exist as discrete components in an electronic device or a main control device.

[0153] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, clothing or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, clothing or device. In the absence of more restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, clothing or device including the element.

[0154] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0155] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to execute the methods of each embodiment of the present application.

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

[0157] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for processing process data of AI generated content, characterized in that: include: In response to the AI ​​generation instruction, record the generation process data of the AI ​​generated content; Obtaining model data used by the AI-generated content and user interaction records during the generation of the AI-generated content; According to the generation process data, the model data and the user interaction record, structured data corresponding to the AI-generated content is generated, and the structured data is used to characterize generation process characteristics of the AI-generated content.

2. The method according to claim 1, characterized in that: The data recording the generation process of AI-generated content includes: Recording the steps of generating the AI-generated content and / or the intermediate results corresponding to the steps of generating the content; 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.

3. The method according to claim 1, characterized in that The obtaining of model data used by the AI ​​to generate content includes: The model call record, model training record and model version information of the AI ​​generated content during the generation process are obtained, and the model data includes one or more of the model call record, the model training record and the model version information.

4. The method according to claim 1, characterized in that: Obtaining user interaction records during the AI-generated content generation process, including: Record the user's input data, user operations, and user feedback data on the AI-generated content during the AI-generated content generation process, wherein the user interaction record includes one or more of the input data, the user's operations, and the feedback data.

5. The method according to claim 1, characterized in that Also includes: The interface call data in the process of generating the AI-generated content is recorded, and the generation process data includes the interface call data.

6. The method according to claim 1, characterized in that Generating structured data corresponding to the AI-generated content according to the generation process data, the model data, and the user interaction record includes: Identify preset fields in the generation process data, the model data, and the user interaction record, fill in a preset data structure table according to the identified field information, and generate structured data corresponding to the AI-generated content.

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

8. The method according to claim 1, characterized in that Also includes: Storing the structured data in a preset blockchain; and / or, Generate a unique identifier for the AI-generated content based on the structured data.

9. The method according to claim 1, characterized in that: Also includes: In response to a query instruction for a generation process of target AI-generated content, acquiring the structured data corresponding to the target AI-generated content; The structured data is displayed on an interactive interface.

10. The method according to claim 9, characterized in that Before obtaining the structured data corresponding to the target AI generated content, the method further includes: Performing a security check on the query instruction, and after the query instruction successfully passes the security check, executing the step of obtaining the structured data corresponding to the target AI-generated content; and / or, Records the information of this query operation.

11. A method for processing process data of AI-generated content about a product, characterized in that: include: In response to an AI generation instruction regarding product information, recording generation process data of AI generated content, the AI ​​generated content including the product information; Obtaining model data used by the AI-generated content and user interaction records during the generation of the AI-generated content; According to the generation process data, the model data and the user interaction record, structured data corresponding to the AI-generated content is generated, and the structured data is used to characterize generation process characteristics of the AI-generated content.

12. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively coupled to the at least one processor; The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the electronic device to perform the method described in any one of claims 1-11.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and when the processor executes the computer-executable instructions, the method according to any one of claims 1 to 11 is implemented.

14. A computer program product, characterized in that The method comprises a computer program, which implements the method according to any one of claims 1 to 11 when being executed by a processor.

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