Large model dialogue control method based on role enhancement

Abstract

The invention relates to the technical field of role recognition, and discloses a large model dialogue control method based on role enhancement, which comprises the following steps: step 1, user role information is defined and configured, the user role information comprises internal roles and external roles, the internal roles comprise at least one type of enterprise internal users, and the external roles comprise at least one type of enterprise internal users; the external role comprises at least one type of enterprise external users; step 2, configuring corresponding task background and authority information for each user role, and determining accessible resources and data types according to the user roles; by accurately identifying the role identity of the user, customized dialogue and information services can be provided according to requirements and task backgrounds of different roles. For internal employees and external clients of an enterprise, the system can respectively provide in-depth technical support and basic product information query, and information overload or permission leakage possibly caused by a unified response is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of role recognition, and in particular to a large model dialogue control method based on role enhancement. Background Art

[0002] With the continuous development of artificial intelligence technology, large-model-based dialogue systems have been widely used in various industries, especially in customer service, technical support, sales consulting and other fields, greatly improving the operational efficiency and customer satisfaction of enterprises. Through natural language processing technology, large-model dialogue systems can simulate the way of dialogue with humans and automatically respond to various consultation needs of users. However, as the scale of enterprises expands, the types of user roles and needs involved are becoming increasingly complex. Users of different roles have different permissions, needs and task backgrounds when interacting with the system. The system needs to be able to accurately distinguish and personalize responses based on role identities to ensure the accuracy and efficiency of the service.

[0003] At present, although many dialogue systems based on large models can achieve a certain degree of automation and intelligence, most systems still lack the defect of effectively distinguishing user roles. Usually, these systems are relatively simple in identifying user identities, often relying on the user's login information in the system or preset role labels, and cannot be flexibly adjusted according to the specific needs of the user in the actual task. Therefore, when dealing with complex multi-role and multi-task scenarios, existing technologies are still unable to provide accurate and customized services for different user roles, nor can they effectively manage and control the permissions of different roles, which can easily lead to problems such as information confusion and permission leakage.

[0004] There is an important problem with the application of existing technologies in multi-role environments, which is the inability to achieve accurate role identification and customized response generation. Although some systems try to identify user identities through different preset rules or model fine-tuning, these methods are usually not flexible enough to adapt to the diverse needs of internal and external roles in the enterprise. At the same time, due to the imperfect design of role information injection and permission management, user information leakage or incorrect permission granting often occurs. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides a large model dialogue control method based on role enhancement, which solves the problem of how to accurately identify user roles and provide personalized responses and authority management based on role identities in a multi-role environment.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A large model dialogue control method based on role enhancement comprises the following steps: Step 1: define and configure user role information, wherein the user role information includes internal roles and external roles, wherein the internal roles include at least one type of internal enterprise users, and the external roles include at least one type of external enterprise users; Step 2: Configure the corresponding task background and permission information for each user role, and determine the resources and data types that the user can access based on the user role; Step 3: Fine-tune the large model based on the user role information to generate personalized responses that meet the needs of the role; Step 4: Based on the access rights of user roles, dynamically adjust the data query permissions in the conversation to ensure that users with different roles can only access data within their permission range.

[0007] Preferably, the role definition in step one includes roles such as senior management, technical team, business team, customers, suppliers and partners, and sub-roles are divided according to different roles.

[0008] Preferably, the permission information in step 2 includes resources such as documents, data, system interfaces, etc. that each role can access, and the permissions are dynamically adjusted according to the user's task background.

[0009] Preferably, the fine-tuning of the large model in step three is performed by injecting role-specific corpus information, including technical documents, sales materials, and product manual contents, so that the large model customizes the output response according to the role.

[0010] Preferably, the large model fine-tuning also includes multimodal enhancement of the model's input, and the multimodal enhancement includes speech recognition, image recognition and behavioral data input to enhance the large model's ability to understand different roles.

[0011] Preferably, the data query permission control in step 4 is based on the access permission and task background of the role, and the task background determines the user's current query demand through the dialogue context and dynamically adjusts the query permission.

[0012] Preferably, the data query permission in step 4 also includes adjusting the type of data accessible to the user in real time based on the context-aware mechanism and the dialogue status between the user and the system to ensure that sensitive data is exposed only when necessary.

[0013] Preferably, steps 1 to 4 are completed through a role authentication and inference mechanism, wherein the role authentication mechanism adopts SSO and two-factor authentication identity verification means, and the role inference mechanism performs role determination based on user historical behavior data and context information.

[0014] Preferably, the fine-tuning of the large model in step three is also combined with a reinforcement learning mechanism to optimize the dialogue strategy of the large model in real time according to user feedback information.

[0015] The present invention provides a large model dialogue control method based on role enhancement. It has the following beneficial effects: 1. The present invention can provide customized dialogue and information services according to the needs and task background of different roles by accurately identifying the role identity of the user. For internal employees and external customers of the enterprise, the system can provide in-depth technical support and basic product information query respectively, avoiding information overload or authority leakage that may be caused by a unified response. Through role-enhanced large model fine-tuning, the large model can understand the context of each role, thereby generating personalized responses, significantly improving the user experience.

[0016] 2. The present invention ensures that different roles can only access data related to their responsibilities through precise authority management, thereby effectively avoiding the risk of sensitive information leakage. For internal employees of the enterprise, such as technical support personnel and customer service managers, the system can authorize access to internal data, while external customers can only access public product information and FAQs. Through the meticulous management of role information and the authority verification mechanism, strict control of data query permissions is ensured, further strengthening the security and compliance of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention is a flow chart of the method. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Please refer to the attached Figure 1 The embodiment of the present invention provides a large model dialogue control method based on role enhancement, including: Step 1: Role information definition and management In order to implement the role-enhanced large-model dialogue control method, it is first necessary to accurately define and manage the role information of different users. The definition and configuration of role information is the basis of the entire method. It determines how to distinguish different roles inside and outside the enterprise, and how to perform personalized responses and data permission management based on different roles. Role information not only includes the user's basic identity information, but also covers the role's permission configuration, task background, and related access control requirements. Through the management of this role information, it can ensure that the system generates accurate dialogue responses according to the needs of different roles and implements appropriate data query permission control.

[0020] In this embodiment, the role information definition and management module is responsible for creating and managing user roles, ensuring that the information of each role is complete and accurately mapped to the actual application scenario. Role information is divided not only based on the user's identity, but also takes into account the user's specific responsibilities inside and outside the enterprise, the task background undertaken, and the permission requirements for data access. Different roles correspond to different task objectives, which in turn determines the user's behavior and permissions when interacting with the system.

[0021] Definition and division of role information In this embodiment, the role information definition first includes the division of internal and external roles of the enterprise. According to the needs of different business scenarios, the system needs to distinguish multiple roles and assign specific attributes to each role. The following is a detailed description of the division of role information: Internal roles: Internal roles include but are not limited to senior management, technical team members, business department personnel, customer service personnel, financial personnel, etc. Each role has different work content and responsibilities. For example, technical team members mainly deal with technical issues, financial personnel are responsible for fund-related inquiries and management, and senior management focuses on strategic decisions and overall business data.

[0022] External roles: External roles include customers, suppliers, partners, etc. These roles usually have little connection with the internal structure of the enterprise, but in specific business scenarios, they also need to provide specific services and data query permissions. External roles are usually further divided into different sub-roles according to different business needs. For example, customers can be divided into ordinary customers and VIP customers, and each sub-role has different query requirements and permissions.

[0023] A detailed example of role definition is as follows: Senior management: Has access to all corporate data and information.

[0024] Technical team: can access technical documentation, system logs, code libraries, etc.

[0025] Sales team: can access sales data, customer information, product information, etc.

[0026] Customers: Can only access limited product information, purchase history, support services, etc.

[0027] Configuration and management of role attributes Role information is not just a simple definition of the role name, it also includes the attribute configuration of each role. Each role will be assigned a series of permission attributes, which will affect the role's behavior and interaction in the system. Mainly including: Role ID: Assign a unique ID to each role to ensure that each role can be accurately identified in the system.

[0028] Task background: Configure specific task background information for each role. For example, the task background of the technical team includes software development, system maintenance, troubleshooting, etc.; the task background of the sales team includes customer management, marketing, etc.

[0029] Permission level: Different levels of data access permissions are granted according to the different responsibilities of the roles. Senior management usually has the highest permissions, while technical personnel and customer service personnel are granted corresponding data access permissions according to their responsibilities.

[0030] Data access control: The data access rights of each role are defined through the permission model, including the data types that the role can query, the operations that can be performed (such as query, edit, delete), etc. The permission management system dynamically adjusts the role's access rights according to the task background to meet the needs of different business scenarios.

[0031] Role information management and update mechanism Role information changes dynamically in an enterprise environment, so this embodiment provides a flexible role information management and update mechanism. The role information of a user in the system may change over time, such as when an employee changes positions or a customer changes to a VIP customer. In order to adapt to these changes, the system supports dynamic updates in the following ways: Automatic Update: The system automatically adjusts role information by monitoring changes in user behavior and background tasks. For example, when an employee changes his or her position, the system automatically adjusts the role permissions based on his or her new job functions.

[0032] Manual update: Administrators can manually adjust role configurations as needed, including modifying role attributes, adding or deleting permissions, updating the role's task background, etc.

[0033] The updated role information will take effect in real time to ensure that users can get the latest personalized services when interacting with the system.

[0034] The relationship between character information and large models The definition and management of role information is not limited to the system's permission control, but also directly affects the process of generating large model dialogues. The information of each role will be used as part of the large model input and injected into the model's input vector to help the model understand the user's background and needs. When generating dialogues, the large model will adjust the details and depth of the answer content based on the user's role information. For example, when a technician asks a question, the model will involve more technical details rather than simple business or user support content.

[0035] The role of role information in model fine-tuning: Role information is one of the important bases for fine-tuning large models. In dialogue control, the model adjusts the generated answer content according to the user role. If the role is a "technician", the system will adjust the professionalism of the dialogue content according to the background of the role, and more technical details, code examples or analysis of technical problems may be added to the answer; if the role is a "customer", the answer will be more concise and easy to understand, focusing on product features, user experience, etc.

[0036] Dynamic adaptation of role permissions and task background The role permissions in this embodiment include not only static role configuration, but also support dynamic adjustment based on the real-time conversation context. For example, if a user raises a sensitive question during a conversation, the system can automatically restrict access to sensitive data based on the permissions of the current role. Dynamic control of permissions relies on real-time task background and context information to ensure that the system can always provide appropriate data access permissions for each role.

[0037] Dynamic permission adjustment mechanism: The system can determine whether permissions need to be adjusted during a conversation through real-time analysis of role information and task background. For example, when a customer service representative is handling a user's complaint, the system can temporarily increase the representative's permissions to access more customer historical data.

[0038] Through the above detailed steps, the definition and management of role information provides a solid foundation for the role-enhanced large model dialogue control method of the present invention. Through precise role division, role attribute configuration, authority management and dynamic update mechanism, the system can generate personalized dialogue content according to the user's role and task background, and realize dynamic control of data access. This precise role management mechanism is the key technology that the present invention can successfully solve the multi-role and multi-task requirements in complex enterprise scenarios.

[0039] Step 2: Character information injection and large model fine-tuning In step 1, we have completed the definition and management of role information. Next, in order to ensure that the system can generate personalized conversation content according to the needs of different roles, step 2 involves the injection of role information and fine-tuning of the big model. In this step, we embed role information into the input data of the big model so that the big model can perform differentiated processing based on the user's role information when generating responses. In addition, the system fine-tunes the big model to meet the needs of different roles so that the model can understand and generate specific content that fits the role background. In this way, the big model can generate more accurate and personalized answers based on different user roles, and provide role-related data query permissions.

[0040] In this embodiment, role information injection and large model fine-tuning are achieved through the following technical means. First, role information is injected into the input data of the large model as an important part of the input. The system passes role-specific data (such as role ID, role task background, etc.) together with the conversation content entered by the user to the large model. Secondly, according to the task background and data requirements of different roles, the model will be fine-tuned accordingly, so that the model can generate personalized responses based on the user role. The fine-tuning process includes annotating the data with different role labels during the model training process to help the large model better understand the needs and background of the role.

[0041] Role information injection mechanism In this embodiment, role information injection refers to embedding the user's role information into the input data of the large model as additional context information of the model, helping the model to generate more accurate answers based on different role requirements. The process of role information injection is as follows: Role information extraction: Obtain the user's role information from the system's role management module. The role information includes role ID, role task background, permission level, role-specific access data, etc. Role information is usually stored in the form of structured data, and the system obtains the current user's role characteristics through query.

[0042] Injection format: The role information is embedded into the input text of the large model in a specific format. The specific implementation method can be to insert the role information into the conversation context in the form of a label, or to provide the role features to the model together with the user input through vectorized representation. For example, the text input by the user can be concatenated with the role information to form an enhanced input string:

[0043] Among them, Role_ID represents the user's role ID. Task_Context It is the character's mission background. User_ Query is the user’s actual query or conversation content. In this way, the model can make differentiated responses based on role information.

[0044] Injection location: Role information can be injected at multiple levels of the dialogue input, usually enhanced in the input layer and hidden layer. Through this hierarchical injection, role information can effectively influence the model's understanding and generation process.

[0045] Implementation Methods for Fine-tuning Large Models After the role information is injected, the next step is to fine-tune the large model so that it can generate personalized answers that match specific contexts based on different roles. The key to fine-tuning is to let the model learn the contextual differences between different roles and generate content based on these differences. The specific steps for fine-tuning are as follows: Fine-tuning dataset construction: Build a diverse training dataset based on the specific task requirements of different roles. For each role, collect common conversation content and its corresponding answers. These data will be used together with the role labels to fine-tune the large model. For example, for technical team roles, the dataset may include a large number of technical questions and code analysis; while for marketing department roles, the dataset focuses on sales data analysis, market strategy, etc. Through this dataset construction, the model can learn how to generate relevant content based on the role background during the training process.

[0046] Fine-tuning process: Use a role label-based training method to fine-tune the large model through reinforcement learning or supervised learning methods. During the fine-tuning process, the role label is fed into the model as additional input information together with the actual conversation content, and the model fine-tunes the generated content based on the role information. For example: in, Input_Text It is a combination of role information and user input. Role_ID is the role tag, Output It is the answer generated by the model based on the role context. In this way, the model can more accurately understand the needs of different roles and generate personalized responses that fit the task context.

[0047] Optimization and update: The fine-tuned model will be further optimized based on the actual application effect. During the actual operation, the model continuously collects feedback data from users and adjusts the fine-tuning strategy based on the feedback to further improve the accuracy and response quality of the model. For example, when the technical team interacts with the model, if it finds that the model's answers are weak in certain technical areas, it will fine-tune by adding relevant technical data to optimize the model's ability to understand the technical background.

[0048] Multi-role fine-tuning strategy In this embodiment, in order to meet the needs of complex enterprise scenarios, fine-tuning is not only performed on a single role, but also needs to support fine-tuning of multiple roles. For multiple roles inside and outside the enterprise, the system can dynamically adjust the fine-tuning strategy according to the changes in the roles to ensure that each role gets the corresponding dialogue content when interacting. The specific implementation method is as follows: Role label weighting: When fine-tuning multiple roles, the system weights different role labels and adjusts the influence weight of role labels during training according to the importance of the role and task requirements. For example, the role label of senior management can be set to a higher weight to ensure that the system provides more accurate strategic information when talking to senior management; while the label weight of the technical team can be relatively low to ensure that technical issues are handled professionally.

[0049] Cross-role fine-tuning: Different roles may have overlapping task backgrounds, and the system needs to ensure information sharing and learning across roles. For example, the sales team and the customer support team may both need to query product information, so in the fine-tuning process for these two roles, the model needs to learn how to balance the needs of the two and ensure that the same content can adjust the depth and detail of the answer based on the role background.

[0050] Model performance and quality monitoring During the model fine-tuning process, the system will conduct real-time performance monitoring to ensure that the effect of the fine-tuned model meets expectations. The main monitoring indicators include the relevance, accuracy, and role suitability of the generated answers. For example, when a technician asks a question, whether the technical details of the model's answer meet actual needs; when a customer asks a question, whether the model's answer is easy to understand and meets the customer's needs.

[0051] Performance monitoring and quality assessment will be conducted as follows: Regular evaluation: After each fine-tuning, the system will regularly evaluate the model to test its performance in different roles. The evaluation can be performed through manually annotated datasets or through automated quality assessment tools.

[0052] User feedback mechanism: By collecting user feedback, the model will continue to learn and optimize during actual use to ensure that it can constantly adapt to changes in roles.

[0053] Through the above steps, the system can make full use of role information in the fine-tuning process of the large model to generate dialogue content that meets the needs of different roles. The fine-tuned large model can not only identify the user's role, but also provide personalized and accurate services based on the different needs of the role.

[0054] Step 3: Data query permission management In step 1 and step 2, we completed the definition and management of role information and the injection of role information and fine-tuning of the large model, which laid the foundation for the system to generate personalized dialogue content. In step 3, the focus is to ensure that different roles can obtain appropriate data query results according to their own permission levels when interacting with the system. Therefore, data query permission management is an important part of the role-enhanced large model dialogue control method. Through effective data query permission management, the system can ensure that different roles can only access the relevant data they need and comply with permission control, thereby avoiding data leakage and unnecessary risks.

[0055] In this embodiment, the data query permission management module dynamically manages the user's data access rights in the system by combining role information and access control policies. Specifically, the system controls and filters the user's query requests based on the user's role information, task background, and data permission level. The management of role information and query permission control complement each other to ensure that each role can only query data within the scope of its required permissions. The permission management system flexibly sets query permission policies based on role configuration and task requirements, thereby providing tailored access rights for different roles.

[0056] The basic structure of data query permissions Data query permission management ensures that the system can accurately control data access permissions based on different roles. The basic components of query permission control include the following parts: Role permission level: Each role is assigned a different permission level in the system. The permission level determines the data range and operation permissions that the role can access. High-permission roles usually have wider data query permissions, while low-permission roles have limited query scopes. The configuration of permission levels is usually completed when the role information is defined, and will affect the subsequent query permission management.

[0057] Data access control rules: For each role, the system defines a set of data access control rules based on its permission level. These rules include the type of data that the role can query, the query method (such as query, update, delete, etc.), and the timeliness of data access. For example, for sales department employees, the system may only allow them to query customer information and sales data, but not access financial data or technical documents.

[0058] Dynamic permission adjustment: The system allows the query permissions of roles to be adjusted dynamically according to real-time business needs. For example, an external partner may be granted temporary query permissions in a specific project, while the permissions of internal employees may need to be adjusted after a job transfer or job change. The permission management system supports both manual and automatic methods to update the query permissions of roles.

[0059] Technical Implementation of Permission Control In this embodiment, data query permission management adopts a set of permission control mechanisms based on rule engines, and query permissions are managed and verified through the following steps: Permission verification of query requests: When a user initiates a data query request, the system first identifies the user's role identity and permission level through role information. The system will determine whether the role has the permission to perform this query based on the permission rules of the role. The permission verification process usually involves the following steps: Role information matching: By querying role information, the system identifies the current user's role and its permission level.

[0060] Permission rule application: The system matches role permission rules with query requests to determine whether the user can access relevant data.

[0061] Query permission judgment: Based on the role rules and task background, the system decides whether to allow the query request to continue. If the permission is insufficient, the corresponding prompt information is returned to reject the request.

[0062] Dynamic generation and management of permission rules: Dynamic generation of permission rules usually depends on two pieces of information: the role's permission level and the user's current task background. Based on the user's role information, the system will generate a set of specific data query rules and adjust these rules in real time. For example:

[0063] in, Role_ID Indicates the role's ID. Task_Context is the user's current task background, Data_Type The type of data being queried. The system generates query rules suitable for the current role through formula calculation, thereby accurately controlling data access.

[0064] Feedback on permission verification results: Once the query request is verified, the system responds based on the result of the permission verification. If the role has the corresponding query permission, the system will allow the query and return the data; if the role does not have sufficient permission, the request will be rejected and the user will be prompted that they do not have permission to query the data.

[0065] Rights Management Strategy and Practice In order to ensure that different roles can effectively access the required data and perform related operations, the system usually adopts a variety of strategies and practices in the permission management process, mainly including the following aspects: Role-based access control (RBAC): In this embodiment, the permission control adopts the role-based access control (RBAC) model, that is, the user's access rights are determined by the role. Each user obtains the corresponding data access rights through his or her role, and the permission rules are bound to the role. The system automatically controls the user's access to data based on the role definition and permission level.

[0066] Principle of least privilege: The system follows the principle of least privilege, that is, each role is only given the minimum privilege required to complete its tasks. In this way, the system can avoid over-authorization, thereby reducing potential security risks. For example, ordinary employees should not generally have the authority to view financial or senior management data.

[0067] Dynamic permission allocation: In some cases, the role of a user may change, or the query permissions for certain data may be temporarily adjusted. The system supports dynamic permission allocation based on user behavior or task background. For example, an external partner may temporarily obtain permission to access internal system data at a certain stage of a project, and the system will promptly revoke these permissions after the project ends.

[0068] Multi-level approval and approval flow: In some sensitive data access scenarios, data query requests may need to go through multiple levels of approval before they can be executed. The system can configure an approval flow mechanism to ensure that the query and access of sensitive data go through a compliant approval process. For example, the query of financial data may need to be approved by the manager of the finance department, while the access to technical documents may require review by the head of the technical department.

[0069] Permission management and working with large models Data query permission management and the conversation content generated by the big model complement each other. Each conversation content generated by the big model may involve different data query requests, and the permission management system is responsible for ensuring the legitimacy of these query requests. When generating personalized answers, the big model can automatically perform permission checks based on the permission level of the user role to ensure that no non-compliant query requests are generated. In addition, the combination of role information and query permission control allows the model to limit the depth and scope of the conversation based on the role, ensuring that the generated response complies with the role's permission restrictions.

[0070] For example, for a sales employee, his query permissions may be limited to customer information and product sales data. The system will limit the scope of data he can access when generating a conversation to ensure that sensitive financial or technical data is not displayed to him. For technical team members, the system will allow them to query more detailed technical documents and data support content.

[0071] Through the collaboration of this permission management and large models, the system can ensure that while providing users with personalized answers, data access rights are strictly controlled, thereby achieving efficient and secure data query functions.

[0072] Step 4: Dialogue Management and Response Generation In steps 1 to 3, we completed the definition and management of role information, the injection of role information and fine-tuning of large models, and the management of data query permissions, thus providing the system with role-customized data access control and a differentiated dialogue generation basis. In step 4, the system processes information through the dialogue management module based on the input dialogue content, role information and its permissions, and finally generates a response that meets the role background and task requirements. The core goal of this step is to ensure that the system can generate personalized and accurate dialogue content, and appropriately adjust the details of the dialogue and data query results according to the role's permission level and background.

[0073] In this embodiment, dialogue management and response generation are implemented through two key modules: dialogue context management module and response generation module. The dialogue context management module is responsible for processing and storing the dialogue content input by the user, and dynamically adjusting the flow of the dialogue in combination with the role information; the response generation module generates corresponding answers based on the adjusted dialogue context using the fine-tuned large model. Response generation should not only consider the user's query intent, but also fully consider the role's background, task requirements, and corresponding authority control. The generated response content can not only meet the user's immediate needs, but also provide a high-quality, personalized dialogue experience.

[0074] Conversation context management The dialogue context management module is responsible for maintaining and managing the dialogue state between the user and the system. The task of this module is to ensure the continuity and accuracy of the dialogue and avoid having to process the dialogue from the beginning every time the user inputs. The system will form a complete context information based on the user's input and historical dialogue content, and pass this information to the response generation module. The key steps of dialogue context management include: Conversation history: The system stores the content of each conversation between the user and the system in the session record, so that historical conversations can be retrieved based on the user's current input. These records contain questions asked by the user, the system's responses, and dynamic information related to the role.

[0075] Context update and management: Whenever the system generates a response, the conversation context is updated based on the user's input and the generated response. The user's input and the system-generated response are automatically recorded and matched with the task context information of the current role. For example, for the same technical support role, after the user asks a question, the system not only needs to generate an accurate technical response based on the question, but also needs to ensure the consistency of the previous and subsequent conversations.

[0076] Context enhancement: After the role information is injected, the system passes the role's specific task background, authority level and other information to the response generation module together with the historical conversation content. This additional information can help the big model better understand the user's needs and generate more relevant and accurate responses.

[0077] In this embodiment, the key formula for updating the conversation context is as follows:

[0078] in, Context new Represents the updated conversation context, Context old Indicates historical conversation records. User_ Query is the current user's input. Role_ID and Task_Context They respectively represent the role's identity and mission background.

[0079] Response generation module After the conversation context is fully updated, the system passes the context information to the response generation module. This module generates the final conversation response through reasoning and generation after fine-tuning the large model. The generated response not only needs to provide answers based on user input, but also considers the task background, permission restrictions and other relevant information of the user role. The response generation process includes the following key steps: Model input construction: The response generation module constructs an input format suitable for large model processing based on the dialogue context, role information and user input. Specifically, the system combines the current dialogue history, role task background and user query information to form a complete input sequence.

[0080] Large model reasoning and generation: After the input data is constructed, the system passes the data to the fine-tuned large model for reasoning and generation. The fine-tuned model generates personalized responses based on the injected role information. For example, for technical roles, the system will prioritize technical answers; for sales roles, it will generate responses based on market analysis. The large model generates responses in the following ways: in, Response is the resulting system response, Context new is the updated conversation context, Role_ ID It is the role information. User_Query is the current user input.

[0081] Response optimization and output: After generating the initial response, the system may further optimize the generated content based on the context and permission control. For example, for queries from high-level roles, the system may return more detailed and complex answers, while for ordinary employees, the response will remain concise and clear. In addition, the system will limit the scope of the response content based on permission management rules to ensure that users can only access information that matches their permissions.

[0082] Multi-turn dialogue management and response generation Since most interactions between users and the system are multi-round conversations, the system needs to support multi-round conversation management. Multi-round conversations not only require the system to remember the previous conversation content, but also to update the context in real time based on new input. In the scenario of multi-round conversations, the response generation module also needs to consider the following aspects: Dynamic context adjustment: In a multi-round conversation, the system dynamically updates the context based on the progress of each round of conversation, and the response of each round must take into account the content of the previous round. For example, when a user asks a question to the technical support department, the system needs to adjust the subsequent response based on the technical details mentioned earlier to maintain the consistency of the conversation.

[0083] Response generation for complex tasks: For complex tasks (such as cross-departmental collaboration), the system needs to generate collaborative responses based on the input of multiple roles. At this time, the system needs to generate appropriate responses based on the authority information and task background of each role, and ensure that the conversation content between different roles is non-conflicting and in line with their responsibilities and authorities.

[0084] Combining response content with permission management In the response generation process, the system must not only generate personalized responses based on the role task background, but also follow the data query permission management rules to ensure that different roles can only access the data they have permission to access. This process is achieved in the following ways: Data access restrictions: When generating responses, if sensitive data is involved, the system will check the permission level of the current role to ensure that it can only access queryable data. If the role permissions are insufficient, the system will filter out sensitive information or provide appropriate prompt information.

[0085] Permission judgment mechanism: The response generation module will dynamically adjust the response content in combination with the permission judgment mechanism. For example, when an external customer inquires about the details of an order, the system will only return the customer's related order data without involving the company's internal financial data.

[0086] Through the above methods, the dialogue management and response generation module in this embodiment can provide accurate and personalized dialogue content for different roles, and maintain the fluency and consistency of the dialogue in multi-round dialogue scenarios, while strictly complying with data access permission management to ensure the security and rationality of the system.

[0087] Step 5: System optimization and feedback mechanism After completing the definition and management of role information, role information injection and large model fine-tuning, data query permission management, and dialogue management and response generation, the system's continuous optimization and feedback mechanism become key steps to ensure the long-term effectiveness and efficiency of the system. The core goal of step 5 is to continuously improve the dialogue generation quality of the large model, the adaptability of role information, the accuracy of permission management, and the relevance of dialogue content by collecting user feedback and system operation data. The role of system optimization and feedback mechanism is not only reflected in the improvement of dialogue quality, but also includes improving the accuracy of responses and the intelligence level of the system through optimized training and model tuning.

[0088] In this embodiment, the system optimization and feedback mechanism is implemented through multiple links such as automated feedback collection, data analysis, and model iteration. Based on the actual operation of the dialogue and user feedback, the system will dynamically adjust the model generation logic and the configuration of role information to optimize the dialogue capabilities of the system. The system optimization and feedback mechanism includes two main parts: a user feedback collection and analysis module, and a model optimization and adjustment module.

[0089] User feedback collection and analysis User feedback is an important part of the system optimization process. By timely collecting and analyzing user feedback on the system, the system can discover potential problems and perform targeted optimization. Specifically, user feedback is collected mainly through the following methods: User evaluation: Every time a user completes a conversation with the system, the system will prompt the user to evaluate the satisfaction of the conversation. The evaluation content usually includes scores on response accuracy, response speed, and the degree of personalization of the answer.

[0090] User behavior analysis: The system monitors the interaction records between users and the system to analyze possible problems that users may encounter during use. For example, users may repeatedly query certain tasks or express dissatisfaction with certain answers. By analyzing these behaviors, the system can identify potential deficiencies.

[0091] Real-time feedback: During multiple rounds of conversations, the differences between the user's input and the system's output are also collected as real-time feedback. The system will analyze whether it needs to adjust the logic of response generation or the accuracy of data query based on these differences.

[0092] The collected user feedback data will be fed into the analysis module. The analysis module is mainly responsible for classifying, aggregating and quantitatively analyzing the feedback. By analyzing user feedback, the system can identify potential problems in the current model when answering certain questions, such as insufficient accuracy of answers, insufficient understanding of role identities, or improper handling of permission management. The results of the feedback analysis will be fed back to the model optimization module.

[0093] Model optimization and adjustment Based on the collected user feedback data, the system enters the model optimization and adjustment stage. The core of this stage is to improve the dialogue generation capability of the large model and the accuracy of understanding the character information by adjusting the model parameters, enhancing the data set, and optimizing the fine-tuning algorithm. The main methods of system optimization include: Model fine-tuning: Through user feedback data, the system can fine-tune the large model in a targeted manner. For example, if users complain that the responses of certain roles are not accurate enough, the system can fine-tune the relevant role information based on these specific feedback to optimize the dialogue generation effect under that role. In addition, the system can also adjust the model's response strategy for roles with different permissions based on user feedback, such as strengthening the accurate response to high-authority roles.

[0094] Dataset expansion and diversification: In order to enhance the adaptability and generalization ability of the model, the system will expand the training dataset based on user feedback data. By adding more dialogue instances, role information, and task background, the model can adapt to more diverse scenarios and user needs, and avoid generating inaccurate or irrelevant responses.

[0095] Feedback-driven iterative optimization: The system will regularly update and optimize the weight parameters during the training process based on the feedback analysis results. For example, the model may further enhance the response quality of the role-related dialogue and the accuracy of data query based on the specific needs of certain roles (such as the frequent query of specific data by the technical support role).

[0096] System automatic adjustment and self-learning In order to improve the adaptive ability of the system, the system optimization and feedback mechanism in this embodiment also includes an automatic adjustment and self-learning mechanism. The system can automatically adjust the dialogue generation strategy, role information fine-tuning and permission management rules based on the continuously collected user feedback data without manual intervention. For example, when the system recognizes that the query permissions of a certain role are too broad, or certain types of questions frequently appear in the dialogue, the system can automatically adjust the role permissions or increase the dialogue training data of a specific type.

[0097] Automatic optimization feedback loop: The system continuously processes feedback data through a closed-loop mechanism and feeds the optimization results back to the model fine-tuning stage. This process enables the system to self-learn and gradually optimize.

[0098] Intelligent adjustment rules: The system can automatically adjust the query permissions and the level of detail of the response content according to the task background of different roles. For example, based on user feedback, the system will automatically optimize the permission control rules to avoid over-authorization or insufficient permissions.

[0099] System performance monitoring and improvement In addition to user feedback and model optimization, system performance monitoring is also an important part of the optimization and feedback mechanism. By monitoring the system's response speed, accuracy, and user satisfaction in real time, the system can promptly detect problems and make adjustments when performance degrades. For example, the system can monitor the inference speed and response delay of a large model and optimize the inference process based on the monitoring data.

[0100] Performance monitoring: The system will track various performance indicators through real-time data monitoring, such as response time, error rate, frequency of user feedback, etc.

[0101] Performance adjustment: Based on the performance monitoring results, the system can adjust itself, for example, by reducing unnecessary calculation steps, optimizing data query efficiency, etc. to improve response speed.

[0102] Through the above optimization and feedback mechanism, the system can continuously improve its dialogue generation quality, role recognition accuracy, and the effectiveness of query permission control, and ultimately achieve more intelligent and efficient dialogue management and response generation. Example

[0103] Assume that an enterprise is deploying an intelligent technical support system that interacts with employees through a large model to help them solve technical problems related to products. The users of the system include technical support engineers within the enterprise (role A) and external customers (role B). Technical support engineers usually have access to higher-level technical documents and internal company data, while external customers have more limited permissions and can only obtain basic product usage information and FAQs.

[0104] In order to ensure that the system can accurately distinguish and dynamically respond between these roles, a large model dialogue control method based on role enhancement is adopted. This method enables the system to provide personalized responses based on the identity and authority of the role, while ensuring the security and accuracy of sensitive information.

[0105] Step 1: Role information definition and management Before implementation, the system first defines and manages role information. The internal technical support engineer (role A) and the external customer (role B) are defined as two main roles, and are given different permissions and task backgrounds.

[0106] Role A (Technical Support Engineer): Role permissions: Ability to access the company's internal technical documentation, detailed product maintenance manuals, developer tools, and internal databases.

[0107] Task background: Answer technical questions from internal employees or customers and provide in-depth technical support.

[0108] Role B (external customer): Role permissions: Can only access public product documentation, FAQs, and basic technical support content.

[0109] Task background: Solve common problems and obtain basic operating instructions for the product.

[0110] By defining these roles and their permission information in the system's role management module, the system can accurately identify users and provide appropriate responses based on their roles.

[0111] Step 2: Character information injection and large model fine-tuning Once the role information is defined, the system will fine-tune the big model according to the needs of each role. To ensure that the big model can understand and respond to the needs of different roles, the system will combine the role information with the corresponding task background data and inject this information into the training process of the big model.

[0112] Fine-tuning for Role A (Technical Support Engineer): In the fine-tuning of role A, the system focuses on a deep understanding of technical issues. The large model will be trained to recognize and respond to technical questions about complex product functions, system configuration, troubleshooting, etc.

[0113] For example, when a technical support engineer asks about a complex error log for a product, the system can quickly query the company's internal database and provide a detailed error analysis report and solution.

[0114] Fine-tuning for Role B (external customer): For the fine-tuning of role B, the system will focus more on basic product information and answers to frequently asked questions.

[0115] For example, when an external customer asks how to perform basic operations of a product, the system will only return concise and clear operating steps without involving the company's internal technical documentation.

[0116] The process of injecting role information and fine-tuning the large model ensures that each role receives a response tailored to its identity and needs when talking to the system.

[0117] Step 3: Data query permission management After defining the role information and fine-tuning the large model, the next task is to set corresponding permissions for each role to ensure that users can only obtain content related to their role when querying data.

[0118] Permission management for role A (technical support engineer): Role A has full access rights and can query detailed technical documentation, historical fault reports, and other confidential information of the product. The system uses a permission control mechanism to ensure that technical support engineers can access internal databases and other advanced data when solving customer problems.

[0119] Permission management for role B (external customer): Role B's permissions are strictly limited and can only access public documents and FAQs. For example, when an external customer asks a common problem about a product, the system will only return public solutions without providing any internal technical documents or sensitive data.

[0120] Through this permission management mechanism, the system ensures that users with different roles will not access sensitive information that does not belong to them when querying data, thereby improving the system's security and data protection capabilities.

[0121] Step 4: Dialogue Management and Response Generation In the dialogue management and response generation steps, the system generates personalized responses based on the content and role information input by the user.

[0122] Dialogue management for role A (technical support engineer): For example, role A asks: "I need to view the detailed error log of a certain product." The system generates the following response based on role A's permissions and task background: "Please provide the serial number of your product and I will retrieve the appropriate error log for you." The system can access the company's internal database based on the permissions of role A and return specific error logs and related solutions.

[0123] Dialogue management for role B (external customer): For example, role B asks: "How do I set up my product?" The system generates the following response based on role B's permissions: "Please refer to our user manual which contains detailed setup steps." The system only returns public, basic operating instructions without any advanced configuration or internal documentation.

[0124] During this process, the system ensures that each role receives a response appropriate to their identity and authority through precise role identification and conversation context management.

[0125] Step 5: System optimization and feedback mechanism In order to continuously optimize the system's conversational capabilities, the system will continuously improve response generation and permission management strategies based on user feedback.

[0126] User feedback collection: After each conversation, the system automatically collects user satisfaction scores and feedback information. For technical support engineers, the system may collect feedback on the effectiveness of the solution, the speed of problem resolution, etc. For external customers, the system will collect feedback on whether the response is clear and whether the problem has been solved.

[0127] Data analysis and model optimization: Based on the collected feedback data, the system analyzes which characters have problems in the conversation. For example, character A may need a more accurate error log analysis function, and character B may need more basic function introduction. Then, the system will optimize the model in a targeted manner to improve the conversation quality.

[0128] Adaptive Adjustment: Based on feedback, the system can automatically adjust the training of large models to enhance responsiveness to specific areas and tasks, such as improving the quality of answers to technical support questions or optimizing query efficiency for common questions. Example

[0129] In a globally operated e-commerce platform, the company hopes to improve the user experience through an intelligent customer service system. The system interacts with customers in real time through a large model to help customers solve problems related to products, orders, payments, etc. The user roles of the system are mainly divided into internal staff (such as customer service managers, warehouse managers, etc.) and external customers. External customers need to obtain product-related information, order inquiries, and problem answers, while internal staff can access customer order details, inventory information, and backend system data.

[0130] In order to ensure that the system can provide accurate and customized services according to different roles, a large model dialogue control method based on role enhancement is used. This method can accurately distinguish the identities of different roles in a multi-role environment and provide targeted responses and permission control.

[0131] Step 1: Role information definition and management Before the system is implemented, the role information needs to be defined and managed. The system defines different roles for internal staff and external customers, and classifies them according to the responsibilities and permissions of each role.

[0132] Role A (Customer Service Manager): Permissions: Access a customer's order history, payment records, refund requests, product inventory data, and more.

[0133] Task Background: Resolve customer complaints, coordinate order issues, process refund requests and manage the customer service team.

[0134] Role B (Warehouse Manager): Permissions: Can access inventory data, shipping information, and product receipt records, but cannot view customers' private order information or payment information.

[0135] Task background: Manage product inventory, monitor product shipments, and ensure smooth order processing.

[0136] Role C (external customer): Permissions: Access only public product information, order status, and FAQs.

[0137] Task background: Query product information, place orders, pay and track order status.

[0138] The system uses the role management module to ensure that the permissions and task backgrounds of different roles are clearly defined, and provides a basis for subsequent fine-tuning and permission control.

[0139] Step 2: Character information injection and large model fine-tuning Based on the role definition, the system injects role information into the big model and fine-tunes the big model to ensure that it can provide personalized responses for different roles.

[0140] Fine-tuning of Role A (Customer Service Manager): The fine-tuning provided for role A focuses on understanding customer service related tasks. The large model will be trained to analyze and solve complex customer problems based on the needs of role A. The training process will include a large amount of historical data on customer complaints, refund processing procedures, etc.

[0141] For example, when a customer service manager inquires about the status of an order, the system can provide detailed customer information and order history, and even analyze whether a return is required.

[0142] Minor adjustments for role B (warehouse keeper): The fine-tuning provided for role B focuses on tasks related to inventory and shipment. The system is able to identify role B's needs for product inventory quantity and shipment status. The system trains the large model by adding specific data sets about inventory and shipment, allowing warehouse managers to accurately understand inventory levels, shipment delays, and other key logistics information.

[0143] For example, when a warehouse manager asks about the inventory of a certain product, the system can quickly return the latest inventory information and estimated delivery time.

[0144] Fine-tuning of Role C (external customer): For external customers, the system fine-tunes the large model so that it can handle tasks such as answering common questions, product inquiries, and order status tracking. The fine-tuning data includes common product information, FAQs (Frequently Asked Questions), and simple order inquiry processes.

[0145] For example, when an external customer asks whether a certain product is in stock, the system will query the public product information database and return a concise answer based on the inventory status.

[0146] In this way, the system customizes the training of the large model according to the specific needs and task background of each role, so that users of each role can get accurate and personalized responses when communicating with the system.

[0147] Step 3: Data query permission management Based on the role information definition and large model fine-tuning, the system will then perform strict permission management to ensure that each role can only access its corresponding permission data. The core of this step is to ensure that the system can strictly follow the role permission control rules when responding.

[0148] Permission management for role A (customer service manager): Role A can access sensitive data such as customer order history, payment records, refund information, etc. When querying an order, the customer service manager can see the customer's detailed order information, including product name, order status, payment method, etc.

[0149] When the customer service manager asks for order details, the system will return complete order information, including whether it has been paid, whether it has been shipped, and the estimated delivery date.

[0150] Permission management for role B (warehouse administrator): Role B can only access inventory information and shipping-related data, but cannot access customers' private information. Warehouse managers can view the current inventory, warehousing records, and shipping status of products, but cannot see customers' payment information.

[0151] For example, when a warehouse manager asks about the inventory of a certain product, the system will return the inventory information based on the permissions without involving the order or payment information of the product.

[0152] Permission management for role C (external customer): Role C's permissions are limited to querying public product information, order status, and FAQs. Customers cannot access any sensitive data related to order processing, payment information, refunds, etc.

[0153] For example, when a customer asks about the status of an order, the system only returns the current status of the order and the estimated delivery date, without involving any payment details or other sensitive information of the order.

[0154] Through this permission management mechanism, the system ensures that each role can access the corresponding data according to its permissions, avoiding the risk of data leakage or permission abuse.

[0155] Step 4: Dialogue Management and Response Generation In the dialogue management and response generation steps, the system generates personalized responses based on the content and role information input by the user.

[0156] Dialogue management for role A (customer service manager): For example, if role A asks, "Has the customer's order #12345 been paid?" the system will return the following response based on the customer service manager's permissions: "Customer order #12345 has been paid for and is expected to be shipped within 24 hours." The system will access the backend payment records based on the permissions of role A and return detailed payment information.

[0157] Dialogue management for role B (warehouse manager): For example, if persona B asks, "What is the inventory status of product X?", the system will return the following response: "The current inventory of product X is 150 pieces, and the supply is expected to be replenished within two days." The system queries inventory data based on the warehouse administrator's permissions and provides relevant delivery time information.

[0158] Dialogue management for role C (external customer): For example, if role C asks, "Is product X still in stock?" the system will return the following response: “Product X is currently in stock and available for immediate purchase.” The system returns public product inventory information based on the customer's query permissions without involving any sensitive data.

[0159] In this step, the system dynamically generates an appropriate response based on the user's role, ensuring that the response content meets the role's task requirements and authority requirements.

[0160] Step 5: System optimization and feedback mechanism During the implementation process, the system will continue to collect user feedback, analyze the quality of conversations and the accuracy of character responses, and continuously optimize the system's performance. By regularly updating the training data of the large model, adjusting the rights management rules, and optimizing the conversation generation algorithm, the system can gradually improve the accuracy, response speed, and personalization of the conversation.

[0161] User feedback collection and analysis: The system evaluates the quality of responses by collecting real-time user feedback (such as conversation satisfaction ratings, customer behavior analysis, etc.). The feedback information is transmitted to the feedback analysis module, which analyzes user evaluations and behavior patterns to identify potential conversation problems and mismatches in role information.

[0162] Model optimization and iteration: Based on user feedback data, the system will regularly fine-tune the large model to optimize the effect of dialogue generation and enhance the accuracy of character information recognition. The system will also adjust the dialogue strategy based on feedback, such as adjusting the question and answer template for external customers to improve the user experience.

[0163] Through the system's optimization and feedback mechanism, the system can continuously improve the quality of conversations, and self-adjust and improve in practical applications to adapt to changing user needs.

[0164] This example demonstrates the application of a large model dialogue control method based on role enhancement in an enterprise customer service system. Through the combination of precise role definition and management, large model fine-tuning, authority management, dialogue generation, and feedback mechanism, this method can effectively improve the personalization and accuracy of system responses, ensure that users with different roles receive appropriate services, and at the same time ensure data security.

[0165] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A large model dialogue control method based on role enhancement, characterized in that: The following steps are involved: Step 1: define and configure user role information, wherein the user role information includes internal roles and external roles, wherein the internal roles include at least one type of internal enterprise users, and the external roles include at least one type of external enterprise users; Step 2: Configure the corresponding task background and permission information for each user role, and determine the resources and data types that the user can access based on the user role; Step 3: Fine-tune the large model based on the user role information to generate personalized responses that meet the needs of the role; Step 4: Based on the access rights of user roles, dynamically adjust the data query permissions in the conversation to ensure that users with different roles can only access data within their permission range.

2. The large model dialogue control method based on role enhancement according to claim 1 is characterized in that: The role definition in step 1 includes roles such as senior management, technical team, business team, customers, suppliers and partners, and sub-roles are divided according to different roles.

3. The large model dialogue control method based on role enhancement according to claim 1 is characterized in that: The permission information in step 2 includes the documents, data, system interfaces and other resources that each role can access, and the permissions are dynamically adjusted according to the user's task background.

4. The large model dialogue control method based on role enhancement according to claim 1 is characterized in that: The large model fine-tuning in step three is performed by injecting role-specific corpus information, including technical documents, sales materials, and product manuals, so that the large model can customize the output response according to the role.

5. The large model dialogue control method based on role enhancement according to claim 1 is characterized in that: The large model fine-tuning also includes multimodal enhancement of the model's input, which includes speech recognition, image recognition, and behavioral data input to enhance the large model's ability to understand different roles.

6. The large model dialogue control method based on role enhancement according to claim 1 is characterized in that: The data query permission control in step 4 is based on the access rights and task background of the role. The task background determines the user's current query needs through the conversation context and dynamically adjusts the query permission.

7. The large model dialogue control method based on role enhancement according to claim 1 is characterized in that: The data query permission in step 4 also includes adjusting the type of data that the user can access in real time based on the context-aware mechanism and the dialogue status between the user and the system to ensure that sensitive data is exposed only when necessary.

8. The large model dialogue control method based on role enhancement according to claim 1 is characterized in that: The steps 1 to 4 are completed through a role authentication and inference mechanism. The role authentication mechanism adopts SSO and two-factor authentication identity verification means, while the role inference mechanism performs role determination based on user historical behavior data and context information.

9. The large model dialogue control method based on role enhancement according to claim 1 is characterized in that: The large model fine-tuning in step three is also combined with a reinforcement learning mechanism to optimize the dialogue strategy of the large model in real time based on user feedback information.

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