An AIGC content diversity and compliance collaborative management method

Abstract

The application discloses an AIGC content diversity and compliance collaborative management method, and relates to the technical field of artificial intelligence generated content management, and the specific steps of the method are as follows: collecting multi-dimensional user requirements and standardizing analysis to generate requirement data with level labels; based on the two-way matching of the compliance permission library, the permission level instruction is distributed; according to the instruction, the permission of the generation system is opened, and the authorized result is generated; the whole process is tracked and verified, and the illegal content is filtered; the consistency of the permission level is verified, the final compliant content is output to the user, and the whole process closed-loop management is completed; the application realizes accurate adaptation of requirements and permissions by analyzing user requirements, dividing levels, building a permission library and matching, balances creativity diversity and compliance, improves authorization adaptability and rationality; a whole-process tracking and verification system is constructed to realize whole-process compliance management, improve the timeliness and accuracy of verification, data is traceable, and the standardization and refinement of AIGC content management are promoted.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence-generated content management technology, specifically a method for the collaborative management of AIGC content diversity and compliance. Background Technology

[0002] With the rapid iteration of AIGC technology and its deepening application in various fields such as media, cultural and creative industries, and government and enterprise services, users' demand for personalized and diverse AIGC content has exploded, with significant differences in the level of demand across different scenarios. At the same time, content compliance has become a core constraint on the implementation of AIGC technology, with frequent occurrences of various unauthorized and excessive content generation issues. The industry's need for coordinated management of diverse AIGC content creation and compliance is becoming increasingly urgent. How to meet users' multi-dimensional personalized needs, ensure the diversity of content creation, and achieve refined compliance management throughout the entire process has become a key issue for the development of the AIGC industry.

[0003] Existing AIGC content management technologies generally suffer from an imbalance between diversity and compliance. Most solutions either prioritize personalized creation while neglecting compliance verification, easily leading to content violation risks, or excessive compliance control restricts creative boundaries, failing to meet diverse levels of personalized needs. Furthermore, existing technologies lack a compliance permission hierarchy system adapted to user needs; permission allocation is mostly a single static model, lacking precise demand-permission matching algorithms, and the correlation between permission granting and creative capabilities and material access is vague. In addition, existing compliance controls are largely focused on post-generation review, lacking real-time tracking and dual-dimensional verification of the entire AIGC generation process, and lacking end-to-end consistency verification and data traceability mechanisms, resulting in insufficient accuracy, timeliness, and traceability in control.

[0004] In summary, existing AIGC content management technologies cannot achieve closed-loop management of the entire process from demand, permissions, generation, to compliance. They struggle to balance the diversity of content creation with dynamic compliance, and are no longer suitable for application scenarios where personalized needs of different user levels coexist with strict compliance requirements. To address these technical pain points, there is an urgent need to develop an AIGC content management method that can accurately match user needs with compliant permissions, dynamically verify compliance throughout the entire process, and provide multi-dimensional collaborative control. Through standardized, algorithmic, and end-to-end control measures, this method can maximize the fulfillment of users' personalized creation needs while ensuring compliance, thereby promoting the standardized implementation of AIGC technology. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method for collaborative management of AIGC content diversity and compliance. This method collects and analyzes users' multi-dimensional needs and classifies them into levels; based on a compliance permission hierarchy library, it uses algorithms to bidirectionally match needs and permission levels to generate instructions; it grants system permissions based on the instructions; it constructs a full-process tracking and verification system to conduct dual-dimensional screening of content for both permissions and compliance, filtering out illegal content; and it verifies the consistency of permission levels before delivery, completing a closed-loop management process.

[0006] To address the aforementioned technical problems, this invention provides the following technical solution: a method for collaborative management of AIGC content diversity and compliance, the specific steps of which are as follows: S100, Demand Collection and Analysis: Collect multi-dimensional personalized user demands, standardize and analyze the original demand information, and generate standardized demand data with demand level tags. S200, Permission Level Matching: Receives standardized requirement data, and based on the compliance permission level library, performs a one-to-one bidirectional matching between the requirement level and the compliance permission level in the standardized requirement data through a requirement-permission matching degree algorithm, assigns an appropriate compliance permission level, and generates permission level instructions carrying permission level identifiers. S300, Opening Generation Permissions: Receives permission level instructions, and according to the rules corresponding to the permission level identifier, opens the matching set of creative capabilities, the scope of material calls and the content generation boundary to the AIGC generation system, completes the authorization configuration, and generates authorization result information carrying authorization scope parameters; S400, Content Compliance Control: Receive authorization result information, and based on the authorization scope parameters, use the compliance authorization verification algorithm to track and verify the entire AIGC generation process, screen the original content from both permission and compliance dimensions, filter out illegal and unauthorized content, and generate compliant content to be delivered. S500, Compliance Content Delivery: Receives compliance content to be delivered, performs permission level consistency verification, and after confirming that the compliance content to be delivered matches the standardized requirement data and compliance permission level, outputs the final compliance content to the user, completing the closed-loop management of the entire process.

[0007] Furthermore, the specific process of demand collection and parsing is as follows: collect explicit demands input by users and implicit demands from the user behavior database; perform word segmentation, semantic recognition, deduplication and normalization on all demand information; extract feature information to generate demand feature vectors; divide the demands into three levels based on the demand feature vectors: ordinary personalized demands, intermediate personalized demands, and advanced personalized demands; add unique demand level labels to generate standardized demand data carrying demand level labels.

[0008] Furthermore, the compliance permission hierarchy library is a pre-built and dynamically updated structured permission database. The compliance permission hierarchy library sets three permission levels that correspond one-to-one with the S100 requirement level, namely Level 1 ordinary permission, Level 2 intermediate permission, and Level 3 advanced permission: Level 1 ordinary permission corresponds to ordinary personalized requirements, Level 2 intermediate permission corresponds to intermediate personalized requirements, and Level 3 advanced permission corresponds to advanced personalized requirements.

[0009] Furthermore, the specific process of the demand-permission matching algorithm for bidirectional one-to-one matching, permission allocation, and instruction generation is as follows: The first step is to parse the standardized requirement data, extract requirement level tags and requirement feature vectors, retrieve the compliance permission level library, and, based on the positive correspondence between requirement level tags and permission levels, lock in the compliance permission level that initially matches the requirement level. The second step is to calculate the matching degree between the demand level and the initially matched compliance permission level based on the demand feature vector: , in, This represents the requirement-permission matching degree, with a value range of [0,1]. , , The preset weighting coefficients are: compliance weight for the demand scenario, weight for the demand content type, and weight for the user's historical compliance behavior, and they satisfy the following conditions: ; This is a scenario compliance factor, with a value range of [0,1]. This is the content type factor, with a value range of [0,1]. This is the user's historical compliance factor, with a value range of [0,1]. The third step is to perform a two-way one-to-one matching verification. When M≥0.85, it is determined that the initial locked compliance permission level and the required level are mutually compatible, and this compliance permission level is locked as the final assigned compliance permission level. When M<0.85, the permission level is adjusted downward and the matching degree is recalculated until the matching is valid. The fourth step is to generate a unique permission level identifier for the final locked compliance permission level, and integrate the permission level identifier, the matching degree calculation result, and the final assigned compliance permission level information into a permission level instruction.

[0010] Furthermore, the rules corresponding to the permission level identifiers are as follows: using the permission level identifier as a unique index, each permission level identifier uniquely corresponds to a set of configuration combinations of creative capabilities, material call scope, and content generation boundaries, corresponding one-to-one with the three permission levels of the compliance permission grading library: the identifier corresponding to the first-level ordinary permission maps to basic text / image generation capabilities, a publicly available free general material library, and basic content generation boundaries, prohibiting the use of customized capabilities and exclusive materials; the identifier corresponding to the second-level intermediate permission maps to customized style generation capabilities, an industry-authorized extended material library, and intermediate content generation boundaries, opening up industry-specific creative capabilities, prohibiting the use of high-permission exclusive materials; the identifier corresponding to the third-level advanced permission maps to deeply customized creative capabilities, an exclusive authorized material library, and advanced content generation boundaries, opening up full compliance creative capabilities and authorized material call permissions.

[0011] Furthermore, the specific implementation process of granting generation permissions is as follows: parsing the permission level instruction to obtain a unique permission level identifier and permission configuration item; retrieving the rule corresponding to the permission level identifier; locking the set of creative capabilities, material call range, and content generation boundary that match the permission level identifier; sending an authorization instruction to the AIGC generation system to grant the corresponding permissions and writing the boundary restrictions that match the permission level; and generating authorization result information carrying authorization range parameters after completing the authorization configuration and verification.

[0012] Furthermore, the specific implementation process of the compliance authorization verification algorithm is as follows: The first step is to receive the authorization result information, parse the authorization scope parameters, establish a real-time tracking link for the entire AIGC generation process, and collect real-time data on the content fragments of the AIGC generation process. The second step involves semantic recognition and feature extraction of the real-time collected content fragments to generate content fragment feature vectors. Based on dual-dimensional verification requirements of permission and compliance dimensions, a compliance authorization verification algorithm is used to calculate the compliance authorization matching score of the content fragments. The formula is as follows: , in, This is the compliance authorization matching score for the content fragment, with a value range of [0, 100]. , , The preset weighting coefficients are: authorization boundary matching weight, compliance verification weight, and personalized requirement matching weight, and satisfy the following conditions: ; This is the authorization boundary matching score, with a value range of [0, 100], used to implement permission dimension verification; A content compliance score is assigned, with a value range of [0, 100], to be used for compliance dimension verification; The personalized needs matching score has a value range of [0, 100] and is used to verify the degree of fit between the content and the user's initial personalized needs; The third step involves classifying and dynamically intercepting content fragments based on the calculated compliance authorization matching score, thereby completing the verification, correction, and filtering of the content fragments. The fourth step involves processing all content fragments throughout the entire process, then performing overall compliance authorization on all retained compliant content fragments to generate complete compliant content to be delivered.

[0013] Furthermore, the content fragments are processed in a tiered manner and dynamically intercepted throughout the entire process: when S≥90, the content fragment is determined to meet the authorization and compliance requirements and is retained; when 60≤S<90, the content fragment is determined to have local defects, and the content fragment is targeted for correction and optimization, and the compliance authorization matching score is recalculated until it meets the retention requirements; when S<60, the content fragment is determined to be in violation of regulations or not in compliance with permission requirements and is immediately filtered and intercepted.

[0014] Furthermore, the specific implementation process for delivering compliant content is as follows: receiving the compliant content to be delivered, simultaneously retrieving standardized requirement data, permission level instructions, and authorization result information, and performing end-to-end consistency verification on the compliant content to be delivered; after the verification is passed, generating the final compliant content and completing the delivery to the user according to the content format and delivery channel specified by the user collected by S100; at the same time, storing the requirement data, permission matching data, authorization configuration data, compliance verification data, and delivered content data of the entire process into the compliance traceability database to complete the data closure and traceability management of the entire process.

[0015] Compared with existing technologies, this method for collaborative management of AIGC content diversity and compliance has the following beneficial effects: I. This invention standardizes and analyzes multi-dimensional user needs, classifies them into levels, and builds a compliance permission hierarchy library corresponding to each level. It utilizes a need-permission matching mechanism to achieve precise matching between needs and permissions. Based on the matching results, it opens corresponding creative capabilities, material access scope, and content generation boundaries to the AIGC generation system. This achieves layered satisfaction of users' personalized needs, releasing matching creative capabilities according to different need levels, ensuring that the diversity of content creation aligns with users' actual needs. Simultaneously, by defining compliance boundaries from the generation front-end through permission hierarchy, it prevents unauthorized creative behavior, effectively balancing the relationship between content creation diversity and compliance. This breaks the imbalance in existing control methods where the two are difficult to balance, improving the adaptability and rationality of AIGC creation authorization.

[0016] Second, this invention constructs a tracking and verification system for the entire AIGC generation process, conducting dual-dimensional screening of generated content based on permissions and compliance. Combined with dynamic hierarchical processing and interception correction mechanisms, it achieves precise filtering of content that violates permissions or exceeds authorization limits. Simultaneously, it completes full-link verification of permission level consistency before content delivery, establishing a full-process data traceability management system. This enables compliance control across all stages from requirement analysis to content delivery, improving the timeliness and accuracy of compliance verification. Furthermore, the closed-loop storage of data throughout the process ensures traceability of the control process, guaranteeing the standardization of AIGC content control. This allows compliance control to extend beyond content filtering, better adapting to the personalized content delivery needs of different scenarios, and promoting the standardization and refinement of AIGC content control.

[0017] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0019] Figure 1 A flowchart illustrating the steps of a collaborative management method for AIGC content diversity and compliance; Figure 2 A flowchart illustrating the requirements gathering, analysis, and permission level matching process for a collaborative management method of AIGC content diversity and compliance; Figure 3 This is a flowchart illustrating the content compliance management and delivery process for an AIGC content diversity and compliance collaborative management method. Detailed Implementation

[0020] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below. Example

[0021] Demand Collection and Analysis: This process collects explicit user requirements for general corporate new media promotional content, covering core information such as basic content types, new media publishing platforms, core promotional themes, and basic content length requirements. Simultaneously, it retrieves implicit requirements from the user's past creations of corporate promotional content from the user behavior database, including historical content expression styles, commonly used content presentation formats, and the dissemination direction of past promotional content. This comprehensive demand collection ensures complete coverage of the company's daily promotional creative needs, preventing a disconnect between generated content and actual usage requirements due to missing requirements. The collected explicit and implicit demand information undergoes word segmentation, semantic recognition, deduplication, and normalization. Core feature information is extracted from the processed information to generate a demand feature vector. Based on the generated demand feature vector, the company's new media promotional needs are categorized into general personalized needs. A unique demand level label is added to each demand level. This unique label allows for rapid identification of the demand level in subsequent permission matching processes, ultimately generating standardized demand data carrying that demand level label.

[0022] Permission Level Matching: This step receives standardized requirement data with requirement level tags generated in the previous steps. Based on a pre-built and dynamically updated compliance permission level library, permission matching is performed. This dynamically updated library adapts in real-time to changes in compliance requirements and requirement types for enterprise new media promotion scenarios. A requirement-permission matching algorithm is used to perform a one-to-one bidirectional match between the general personalized requirement levels in the standardized requirement data and the compliance permission levels in the compliance permission level library, assigning appropriate compliance permission levels to the generation of enterprise new media promotion content. First, the standardized requirement data is parsed to accurately extract requirement level tags and requirement feature vectors. Then, complete information from the compliance permission level library is retrieved. Based on the positive correspondence between requirement level tags and permission levels, the first-level general permission is quickly identified as the compliance permission level that initially matches the requirement level. The application of the positive correspondence improves the efficiency of initial permission locking. Then, based on the extracted requirement feature vector, the requirement-permission matching degree algorithm is used to calculate the requirement-permission matching degree between the general personalized requirement level and the locked first-level general permission. The formula is: ,in, Demand-permission matching degree; , , The preset weighting coefficients are: compliance weight for the demand scenario, weight for the demand content type, and weight for the user's historical compliance behavior. For scenario compliance factors; Content type factor; The system uses historical compliance factors for users; quantitative matching calculations objectively determine the degree of fit between requirements and permissions. After the matching calculation is completed, a two-way one-to-one matching verification is performed. If the calculated matching score meets the verification standard of greater than or equal to 0.85, it is determined that the Level 1 ordinary permission and the ordinary personalized requirement level are bidirectionally compatible, and the Level 1 ordinary permission is determined as the final assigned compliance permission level. A unique permission level identifier is generated for the finally locked Level 1 ordinary permission. This unique permission level identifier can serve as a unique index in the subsequent authorization configuration process, allowing authorization configuration to quickly and accurately locate the corresponding permission. Then, the permission level identifier, the requirement-permission matching score calculation result, and the complete information of the Level 1 ordinary permission are integrated to generate a permission level instruction carrying the permission level identifier.

[0023] Generation Permission Opening: Receive permission level instructions carrying permission level identifiers generated in the above steps. Perform authorization configuration work according to the rules corresponding to the permission level identifier. Authorizing according to the rules corresponding to the identifier makes the authorization configuration more targeted. Parse the permission level instructions to accurately obtain the unique permission level identifier and corresponding permission configuration items. Then, retrieve the rule system corresponding to the permission level identifier. Based on the rules, lock the basic text / image generation capabilities, publicly available free general-purpose material libraries, and basic content generation boundaries that match the level 1 ordinary permissions. Precise rule retrieval and locking ensure that the authorized creative capabilities, material scope, and generation boundaries perfectly align with the company's daily promotional needs, avoiding the opening of irrelevant creative capabilities and material resources. Send authorization instructions to the AIGC generation system, opening the corresponding basic text / image generation capabilities according to the locked content, granting full access to the publicly available free general-purpose material library, and strictly writing the basic content generation boundary restrictions that match the level 1 ordinary permissions. Writing boundary restrictions defines the creative scope of the AIGC generation system from the source, ensuring that content generation always stays within the compliance framework and avoiding the generation of content beyond the boundaries. After completing the authorization configuration, a comprehensive authorization verification process is conducted. This verification process can identify errors and deviations in the authorization configuration, ensuring that the authorized creative capabilities, material access scope, and boundary restrictions are accurate. Once the verification is successful, authorization result information carrying authorization scope parameters is generated.

[0024] Content Compliance Control: Receive authorization result information containing authorization scope parameters generated in the above steps. Based on these parameters, use a compliance authorization verification algorithm to perform real-time tracking and verification of the entire process of AIGC-generated general new media daily promotional content for enterprises. First, establish a real-time tracking link for the entire AIGC generation process based on the authorization scope parameters to accurately capture each content segment during the generation process. Then, use this link to collect real-time data on all content segments generated during AIGC generation. Real-time collection ensures the integrity and timeliness of content segment information, allowing subsequent verification work to be based on the latest content information. Perform semantic recognition and feature extraction on all real-time collected content segments to generate corresponding content segment feature vectors. Semantic recognition and feature extraction accurately capture the core information and creative characteristics of each content segment. Based on the dual-dimensional verification requirements of permission and compliance dimensions, calculate the compliance authorization matching score for each content segment using a compliance authorization verification algorithm. The formula is: ,in, Scoring is assigned to content fragments based on their compliant authorization. , , The preset weighting coefficients are: authorization boundary matching weight, compliance verification weight, and personalized requirement matching weight. For authorization boundary matching; Rate the content for compliance; For authorization boundary matching; Content compliance is scored to ensure that generated content is both within acceptable limits and compliant with regulations. Based on the calculated compliance authorization matching score, all content fragments are categorized and dynamically blocked throughout the entire process. This approach addresses content fragments with varying levels of compliance, making compliance control more flexible and precise. Content fragments with a compliance authorization matching score of 90 or higher are deemed to meet authorization and compliance requirements and are retained; these fragments can directly become part of the final promotional content. Content fragments with a compliance authorization matching score between 60 and 90 are identified as having minor flaws. These flaws are addressed through targeted correction and optimization. After correction, the compliance authorization matching score is recalculated using the compliance authorization verification algorithm until it meets the retention requirements. Targeted correction and optimization ensures that content fragments with minor issues meet compliance and authorization requirements, reducing content waste. Content fragments with a compliance authorization matching score below 60 are deemed to be in violation of regulations or not meeting permission requirements and are filtered and blocked. Non-compliant and over-authorized content fragments are removed to ensure the compliance of generated content. After performing hierarchical processing, dynamic interception, and correction optimization on all content fragments generated throughout the AIGC process, a comprehensive compliance authorization verification is performed on all retained compliant content fragments. Once the verification is passed, all retained compliant content fragments are integrated to generate complete compliant content to be delivered.

[0025] Compliance Content Delivery: Receive the complete compliance content to be delivered generated in the above steps. Simultaneously retrieve standardized requirement data, permission level instructions, and authorization result information from the entire AIGC content generation process. Based on this core information, conduct end-to-end permission level consistency verification on the compliance content to be delivered. This end-to-end consistency verification ensures that the compliance content to be delivered maintains a high degree of consistency with the initial personalized requirements, allocated compliance permission levels, and authorized creative scope, ensuring that the final delivered content fully meets the company's actual usage needs. After verifying that the compliance content to be delivered fully matches the standardized requirement data and compliance permission levels, perform final format optimization and channel adaptation according to the content format and new media delivery channels specified by the user collected during the requirement collection and analysis. Subsequently, generate the final general-purpose new media daily promotional compliance content for the company and complete the delivery to the user, such as... Figure 1 As shown. Simultaneously, all data related to the AIGC content generation process, including requirement data, permission matching data, authorization configuration data, compliance verification data, and delivered content data, were comprehensively organized and synchronously stored in the compliance traceability database. This end-to-end data storage ensures that every step of the content generation process is searchable and traceable, providing data support for the subsequent generation and optimization of corporate new media promotional content. It also provides a complete basis for subsequent compliance verification work, completing the closed-loop and traceable management of the entire AIGC content generation process. This ensures that the entire AIGC generation process for corporate new media promotional content is under a standardized and traceable control system. Example

[0026] Demand Collection and Analysis: This process involves collecting explicit user requirements for customized research report support content in the high-end financial industry. This includes customized analytical dimensions, professional data presentation formats, specific content creation requirements for the financial industry, professional depth standards for the research report content, and core financial sectors and targets analyzed. Simultaneously, implicit requirements from the user's past financial research report content are retrieved from the user behavior database. These implicit requirements include the professional analytical framework of historical research report support content, commonly used financial industry analysis models, specific data retrieval habits, and requirements for report content layout and data visualization. This comprehensive collection of explicit and implicit requirements fully covers the in-depth customization needs of high-end financial industry research report support content, accurately identifying the professional creation needs of the financial industry and avoiding a disconnect between the generated content and the actual usage requirements of the financial research report due to incomplete requirements collection. The collected high-end financial industry research report support requirements are then processed through word segmentation, semantic recognition, deduplication, and normalization. The core financial professional features of the processed information are extracted and a requirement feature vector is generated. This feature vector quantitatively reflects the core in-depth customization requirements of this high-end financial research report support content. Based on the generated demand feature vector, the auxiliary demand for this high-end financial industry research report is divided into advanced personalized demand. A unique demand level label is added to each demand level. The unique demand level label enables the subsequent permission matching process to quickly and accurately identify the high-end demand level in the financial industry, and finally generate standardized demand data with demand level labels.

[0027] Permission Level Matching: This process receives standardized requirement data with requirement level tags generated in the previous steps. Permission matching is then performed based on a pre-built, dynamically updated compliance permission level library. This dynamically updated library adapts in real-time to changes in compliance requirements within the high-end financial industry, updates to financial regulatory policies, and professional changes in research report creation needs, ensuring that permission matching always aligns with the latest financial industry compliance standards and professional creation requirements. A requirement-permission matching algorithm is used to perform a one-to-one bidirectional match between the advanced personalized requirement level in the standardized requirement data and the compliance permission levels in the compliance permission level library. First, the standardized requirement data is parsed to accurately extract requirement level tags and requirement feature vectors that match financial expertise. Then, complete information from the compliance permission level library is retrieved. Based on the positive correspondence between requirement level tags and permission levels, Level 3 advanced permissions are quickly identified as the initial compliance permission level that matches the current advanced personalized requirement level in the financial industry. Next, based on the extracted financial professional requirement feature vectors, the requirement-permission matching algorithm calculates the requirement-permission matching degree between the current advanced personalized requirement level and the identified Level 3 advanced permissions. This quantitative matching degree calculation objectively determines the degree of fit between the high-end requirements of the financial industry and Level 3 advanced permissions. After calculating the matching degree, a two-way one-to-one matching verification is performed. The calculated matching degree value meets the verification standard of greater than or equal to 0.85, determining that the Level 3 advanced permissions and the advanced personalized requirement level are mutually compatible. Therefore, Level 3 advanced permissions are determined as the final compliant permission level. This clear verification standard provides a unified basis for determining the compatibility of high-end permissions in the financial industry, ensuring the rationality, rigor, and professionalism of permission allocation, while avoiding financial compliance risks arising from the abuse of Level 3 advanced permissions. A unique permission level identifier is generated for the finally locked Level 3 advanced permissions. This unique permission level identifier serves as a unique professional index in subsequent authorization configuration stages, allowing for quick and accurate location of the corresponding advanced permissions. Then, this permission level identifier, the requirement-permission matching degree calculation result, and the complete professional information of the Level 3 advanced permissions are integrated to generate permission level instructions carrying the permission level identifier, such as... Figure 2 As shown, the integrated permission level instructions can completely transmit the core information of high-end permissions in the financial industry to the next stage, ensuring that the authorization configuration stage can obtain comprehensive, accurate and financially-compliant permission information.

[0028] Generation Permission Opening: Receives permission level instructions carrying permission level identifiers generated in the above steps, and performs authorization configuration work according to the rules corresponding to the permission level identifiers. Parses the permission level instructions to accurately obtain the unique permission level identifier and the corresponding financial professional permission configuration item. Then, it retrieves the rule system corresponding to the permission level identifier. Based on rule locking, it provides deeply customized creation capabilities, a dedicated authorized material library, and advanced content generation boundaries that match the Level 3 advanced permissions. Precise rule retrieval and locking ensure that the authorized creation capabilities, the scope of financial-specific materials, and generation boundaries perfectly align with the in-depth customization needs of high-end financial research report auxiliary content. The in-depth customization creation capabilities can adapt to the professional depth requirements of financial research reports, and the dedicated authorized material library can provide research reports with professional data and material support specific to the financial industry. Sends authorization instructions to the AIGC generation system, opening the corresponding in-depth customized creation capabilities according to the locked content, granting full access to the financial industry-specific authorized material library, and simultaneously writing advanced content generation boundary restrictions that match the Level 3 advanced permissions, ensuring that content generation always remains within the financial compliance framework. After completing the authorization configuration, conducts comprehensive authorization verification. Upon successful verification, it generates authorization result information carrying authorization scope parameters.

[0029] Content Compliance Control: The system receives authorization result information containing authorization scope parameters generated in the above stages. Based on these parameters, a compliance authorization verification algorithm is used to track and verify the entire process of AIGC generating customized research report support content for the high-end financial industry in real time. First, a real-time tracking link for the entire AIGC generation process is established based on the authorization scope parameters. Then, all content fragments generated during the AIGC generation process are collected in real time through this link. Real-time collection ensures the integrity and timeliness of financial professional content fragment information. Semantic recognition and feature extraction are performed on all collected financial professional content fragments to generate corresponding content fragment feature vectors. Semantic recognition and feature extraction accurately capture the core information such as financial professional data, analytical logic, and professional conclusions in each content fragment, allowing subsequent compliance verification to accurately pinpoint the professionalism and compliance issues of the content fragments. Based on dual-dimensional verification requirements of permission and compliance dimensions, combined with professional compliance standards in the financial industry, a compliance authorization verification algorithm is used to calculate the compliance authorization matching score for each content fragment. Based on the calculated compliance authorization matching score, all financial professional content fragments are graded and dynamically intercepted throughout the entire process. This approach can specifically address content fragments with different levels of compliance and professionalism, making compliance control in financial scenarios more flexible and precise. Content fragments with a compliance authorization matching score of 90 or higher are deemed to meet authorization, compliance, and financial professionalism requirements and are retained. These content fragments can directly become part of the final financial research report's supporting content. Content fragments with a compliance authorization matching score between 60 and 90 are identified as having local professional or compliance flaws. For these flaws, the content fragments are targeted for correction and optimization. After correction, the compliance authorization matching score is recalculated using the compliance authorization verification algorithm until the value meets the retention requirements. Content fragments with a compliance authorization matching score less than 60 are deemed to be in violation of regulations and do not meet authorization requirements, and are filtered and intercepted to ensure the compliance and professionalism of the generated financial research report's supporting content. After grading, dynamically intercepting, and correcting and optimizing all content fragments generated throughout the AIGC process, a comprehensive compliance authorization verification is performed on all retained compliant and professional content fragments. After passing the verification, all retained compliant and professional content fragments are integrated to generate complete compliant content to be delivered.

[0030] Compliance Content Delivery: Receive the complete compliance content to be delivered generated in the above stages, and simultaneously retrieve standardized requirement data, permission level instructions, and authorization result information from the entire AIGC content generation process. Based on this core information, conduct end-to-end permission level consistency verification of the compliance content to be delivered. After verification confirms that the compliance content to be delivered fully matches the standardized requirement data and compliance permission level, optimize the compliance content for the final financial professional format and channel adaptation according to the user-specified financial research report format and dedicated delivery channel collected during the requirement collection and analysis. Subsequently, generate the final high-end financial industry customized research report auxiliary compliance content and complete delivery to the user. Simultaneously, comprehensively organize the financial requirement data, permission matching data, authorization configuration data, compliance verification data, and delivered content data from the entire AIGC content generation process, and synchronously store them in the compliance traceability database, such as... Figure 3 As shown, the storage of financial data throughout the entire process enables data to be searchable and traceable at every stage of this content generation. It also provides professional data support for the customized creation and optimization of subsequent high-end financial research report auxiliary content, completing the closed-loop and traceable management of the entire process of AIGC content generation. This ensures that the AIGC generation of customized research report auxiliary content for the high-end financial industry is under a standardized, professional, and traceable control system.

[0031] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for collaborative management of AIGC content diversity and compliance, characterized in that, The specific steps of this method are as follows: S100, Demand Collection and Analysis: Collect multi-dimensional personalized user demands, standardize and analyze the original demand information, and generate standardized demand data with demand level tags. S200, Permission Level Matching: Receives standardized requirement data, and based on the compliance permission level library, performs a one-to-one bidirectional matching between the requirement level and the compliance permission level in the standardized requirement data through a requirement-permission matching degree algorithm, assigns an appropriate compliance permission level, and generates permission level instructions carrying permission level identifiers. S300, Opening Generation Permissions: Receives permission level instructions, and according to the rules corresponding to the permission level identifier, opens the matching set of creative capabilities, the scope of material calls and the content generation boundary to the AIGC generation system, completes the authorization configuration, and generates authorization result information carrying authorization scope parameters; S400, Content Compliance Control: Receive authorization result information, and based on the authorization scope parameters, use the compliance authorization verification algorithm to track and verify the entire AIGC generation process, screen the original content from both permission and compliance dimensions, filter out illegal and unauthorized content, and generate compliant content to be delivered. S500, Compliance Content Delivery: Receives compliance content to be delivered, performs permission level consistency verification, and after confirming that the compliance content to be delivered matches the standardized requirement data and compliance permission level, outputs the final compliance content to the user, completing the closed-loop management of the entire process.

2. The method for collaborative management of AIGC content diversity and compliance according to claim 1, characterized in that, In step S100, the specific process of demand collection and parsing is as follows: collecting explicit demands input by users and implicit demands in the user behavior database, performing word segmentation, semantic recognition, deduplication and normalization on the full demand information, and extracting feature information to generate demand feature vectors. Based on the demand feature vector, the demand is divided into three levels: ordinary personalized demand, intermediate personalized demand, and advanced personalized demand, and a unique demand level label is added to generate standardized demand data with demand level labels.

3. The method for collaborative management of AIGC content diversity and compliance according to claim 1, characterized in that, In step S200, the compliance permission hierarchy library is a pre-built and dynamically updated structured permission database. The compliance permission hierarchy library is set with three permission levels that correspond one-to-one with the requirement level in S100: Level 1 ordinary permission, Level 2 intermediate permission, and Level 3 advanced permission. Level 1 ordinary permission corresponds to ordinary personalized requirements, Level 2 intermediate permission corresponds to intermediate personalized requirements, and Level 3 advanced permission corresponds to advanced personalized requirements.

4. The method for collaborative management of AIGC content diversity and compliance according to claim 1, characterized in that, In step S200, the specific process of the demand-permission matching algorithm performing bidirectional one-to-one matching, allocating permissions, and generating instructions is as follows: The first step is to parse the standardized requirement data, extract requirement level tags and requirement feature vectors, retrieve the compliance permission level library, and, based on the positive correspondence between requirement level tags and permission levels, lock in the compliance permission level that initially matches the requirement level. The second step is to calculate the matching degree between the demand level and the initially matched compliance permission level based on the demand feature vector: ， in, Demand-permission matching degree; , , The preset weighting coefficients are: compliance weight for the demand scenario, weight for the demand content type, and weight for the user's historical compliance behavior. For scenario compliance factors; Content type factor; For users' historical compliance factors; The third step is to perform a two-way one-to-one matching verification. When M≥0.85, it is determined that the initial locked compliance permission level and the required level are mutually compatible, and this compliance permission level is locked as the final assigned compliance permission level. When M<0.85, the permission level is adjusted downward and the matching degree is recalculated until the matching is valid. The fourth step is to generate a unique permission level identifier for the final locked compliance permission level, and integrate the permission level identifier, the matching degree calculation result, and the final assigned compliance permission level information into a permission level instruction.

5. The method for collaborative management of AIGC content diversity and compliance according to claim 1, characterized in that, In step S300, the rules corresponding to the permission level identifiers are as follows: each permission level identifier uniquely corresponds to a set of configuration combinations of creative capabilities, material call scope, and content generation boundaries, and corresponds one-to-one with the three permission levels of the compliance permission grading library: the identifier corresponding to the first-level ordinary permission maps to basic text / image generation capabilities, public free general material library, and basic content generation boundaries, and prohibits the call to customized capabilities and exclusive materials; the identifier corresponding to the second-level intermediate permission maps to customized style generation capabilities, industry authorized extended material library, and intermediate content generation boundaries, opens industry-specific creative capabilities, and prohibits the call to high-permission exclusive materials; the identifier corresponding to the third-level advanced permission maps to deep customized creative capabilities, exclusive authorized material library, and advanced content generation boundaries, and opens full compliance creative capabilities and authorized material call permissions.

6. The method for collaborative management of AIGC content diversity and compliance according to claim 1, characterized in that, In step S300, the specific implementation process of granting generation permissions is as follows: parsing the permission level instruction to obtain a unique permission level identifier and permission configuration item; retrieving the rule corresponding to the permission level identifier; locking the set of creative capabilities, material call range, and content generation boundary that match the permission level identifier; sending an authorization instruction to the AIGC generation system to grant the corresponding permissions; and writing the boundary restrictions that match the permission level; and after completing the authorization configuration and verification, generating authorization result information carrying authorization range parameters.

7. The method for collaborative management of AIGC content diversity and compliance according to claim 1, characterized in that, In step S400, the specific implementation process of the compliance authorization verification algorithm is as follows: The first step is to receive the authorization result information, parse the authorization scope parameters, establish a real-time tracking link for the entire AIGC generation process, and collect real-time data on the content fragments of the AIGC generation process. The second step involves semantic recognition and feature extraction of the real-time collected content fragments to generate content fragment feature vectors. Based on dual-dimensional verification requirements of permission and compliance dimensions, a compliance authorization verification algorithm is used to calculate the compliance authorization matching score of the content fragments. The formula is as follows: ， in, Scoring is assigned to content fragments based on their compliant authorization. , , The preset weighting coefficients are: authorization boundary matching weight, compliance verification weight, and personalized requirement matching weight. For authorization boundary matching; Rate the content for compliance; Matching scores to individual needs; The third step involves classifying and dynamically intercepting content fragments based on the calculated compliance authorization matching score, thereby completing the verification, correction, and filtering of the content fragments. The fourth step involves processing all content fragments throughout the entire process, then performing overall compliance authorization on all retained compliant content fragments to generate complete compliant content to be delivered.

8. A method for collaborative management of AIGC content diversity and compliance according to claim 1 or 7, characterized in that, In step S400, the content fragments are graded and dynamically intercepted throughout the entire process: when S≥90, the content fragment is determined to meet the authorization and compliance requirements and is retained; when 60≤S<90, the content fragment is determined to have local defects, the content fragment is targeted for correction and optimization, and the compliance authorization matching score is recalculated until it meets the retention requirements; when S<60, the content fragment is determined to be in violation or not in compliance with the permission requirements and is immediately filtered and intercepted.

9. The method for collaborative management of AIGC content diversity and compliance according to claim 1, characterized in that, In step S500, the specific implementation process of the compliant content delivery is as follows: receiving the compliant content to be delivered, synchronously retrieving standardized requirement data, permission level instructions, and authorization result information, and performing end-to-end consistency verification on the compliant content to be delivered; after the verification is passed, generating the final compliant content and completing the delivery to the user according to the content format and delivery channel specified by the user collected in S100; at the same time, storing the requirement data, permission matching data, authorization configuration data, compliance verification data, and delivery content data of the entire process into the compliance traceability database to complete the data closure and traceability management of the entire process.

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