A dialogue method and system based on adaptive learning and situation-aware AI agent

Abstract

The present invention discloses a dialogue method and system based on adaptive learning and situation-aware AI agent, comprising the following steps: S1. collecting situation data, user interaction history data and preference data, and constructing a user portrait model and a dialogue situation model; S2. performing semantic understanding of dialogue situation data to identify key factors, and adjusting cognitive models and NLU models through adaptive learning strategies; S3. performing real-time dialogue management by tracking dialogue context and dynamically updating dialogue strategies; S4. collecting user feedback, performing feedback analysis and continuously improving adaptive learning strategies and comparing dialogue management skills; S5. performing knowledge synchronization and personalized learning at the end of the dialogue; the system comprises a user behavior modeling module, a situation-aware module, a dialogue management module and an adaptive learning module; the present invention improves the interactive quality and user satisfaction of the dialogue system by improving the adaptive learning mechanism and situation-aware technology.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence and human-computer dialogue technology, and more specifically to a dialogue method and system based on adaptive learning and situation-aware AI agents. Background Art

[0002] The development of dialogue systems (Chatbots) and natural language understanding (NLU) technology has reached a level where they can be widely used in various fields, such as customer service support, personal assistants, medical consultation, etc. These systems usually combine machine learning, deep learning and a large corpus to understand, classify and respond to user input. NLU is the core component of the dialogue system, enabling machines to understand and parse human natural language.

[0003] At present, the dialogue systems and NLU technologies in the market have achieved certain results: such as the progress of language models: Transformer-based models such as BERT and GPT have made significant progress in language understanding; the integration of multimodal capabilities: NLU systems that combine multiple information sources such as text, sound, and images can provide a richer interactive experience; industry-specific development: dialogue systems are moving towards more vertical and personalized development to meet the needs of specific fields; integration and platform trends: NLU technology is integrated into various platforms and products, such as smart homes, mobile applications, etc.

[0004] However, there are limitations and problems with adaptive learning and situational awareness in existing systems:

[0005] Challenges of adaptive learning: Current dialogue systems are limited in terms of adapting to user personalized needs and continuous learning, and often fail to fully utilize interaction history and user behavior data;

[0006] Limitations of contextual awareness: Many systems still lack the ability to deeply understand contextual information and cannot fully understand the context of the conversation, such as the user's specific environment, emotional state, etc.

[0007] Data privacy and security: Adaptive learning and situational awareness may involve the collection and analysis of users’ private data, which may lead to issues of privacy protection and data security.

[0008] Complexity of interaction logic: As the system's understanding ability improves, designing effective interaction logic and maintaining conversation state becomes more complex.

[0009] Therefore, how to improve the adaptive learning and situational awareness capabilities of AI agent dialogue is an issue that technical personnel in this field urgently need to solve. Summary of the invention

[0010] In view of this, the present invention provides a dialogue method and system based on adaptive learning and context-aware AI agent to solve some of the technical problems mentioned in the background technology.

[0011] In order to achieve the above object, the present invention adopts the following technical solution:

[0012] A conversation method based on adaptive learning and context-aware AI agent, comprising the following steps:

[0013] S1. Collect scenario data, user interaction history data and preference data, and build user portrait model and dialogue scenario model;

[0014] S2. Adaptive learning is performed through user feedback analysis, behavior adjustment, and adaptive learning strategies for the interaction between AI agents and cognitive models, and the cognitive model and NLU model are adjusted;

[0015] S3. Real-time dialogue management by tracking dialogue context and dynamically updating dialogue strategies;

[0016] S4. Collect user feedback, analyze feedback and continuously improve adaptive learning strategies and comparative conversation management skills;

[0017] S5. Knowledge synchronization and personalized learning are carried out at the end of the conversation.

[0018] Preferably, the specific content of constructing the user portrait model is: constructing a user portrait based on the user's historical data and preferences, and creating a unique tag set for each user as the basis for personalized adaptive learning;

[0019] The constructed dialogue scenario model is used to describe the user's current environment and status, including multiple dimensions, namely: time and place, user historical interactions, emotional state, and device usage.

[0020] Preferably, the user feedback analysis in step S2 specifically includes: the AI ​​agent uses sentiment analysis and semantic parsing technology to identify the user's explicit and implicit feedback;

[0021] Behavior adjustment is as follows: Based on user feedback, the AI ​​agent adjusts the dialogue strategy, uses cognitive models to simulate human thinking processes and decision-making methods, and generates responses that are more adapted to user needs;

[0022] The specific content of the interaction between the AI ​​agent and the cognitive model is:

[0023] Preprocess the data before sending it to the cognitive model; retain the contextual information of the user's interaction with the AI ​​agent; regularly synchronize the new knowledge generated by the cognitive model with the AI ​​agent's knowledge base to ensure timely updating of the knowledge base; personalized adaptation: based on user feedback and behavioral data, adjust the relevant content in the knowledge base to adapt to the language habits and preferences of specific user groups.

[0024] Preferably, in step S2, the specific content of adjusting the cognitive model is:

[0025] Online fine-tuning mechanism: By collecting user feedback and behavior data and applying incremental learning algorithms, the cognitive model is continuously fine-tuned online during the interaction between the AI ​​agent and the user;

[0026] Offline optimization process: Regularly train the model offline, using larger datasets for in-depth learning and optimization to capture a wide range of user behavior patterns and language habits.

[0027] Preferably, in step S2, the specific contents of adjusting the NLU model include:

[0028] Adjustment based on intent recognition: If the scenario analysis shows that the user's intent is inclined towards a specific topic or type of query, adjust the NLU model intent recognition algorithm;

[0029] Adjustments based on sentiment analysis: If the scenario analysis identifies that the user is anxious or impatient, adjust the NLU model to increase the weight of emotion-related words to respond more sensitively to emotional expressions;

[0030] Context-based adjustment: Introducing context-aware features into the NLU model based on the user’s current context and historical conversation records;

[0031] Optimization for specific user groups: For specific user groups or fields, we introduce professional vocabulary and corpus to train and fine-tune the model for specific fields.

[0032] Preferably, the specific content of step S3, tracking the conversation context is: tracking the conversation context, integrating the historical conversation content and relevant environmental information into the decision-making process of the current conversation, so that the response of the AI ​​agent is coherent and relevant throughout the conversation process;

[0033] The specific content of the dynamic update of the dialogue strategy is: according to the user's immediate feedback and behavior, analyze the user's real-time feedback in the dialogue, such as question modifications, clear instructions or emotional changes, and dynamically adjust the dialogue strategy and the content and style of the answer based on the analysis results.

[0034] Preferably, in step S4, collecting user feedback includes collecting explicit feedback and implicit feedback;

[0035] Explicit feedback is the user's direct evaluation, and implicit feedback is the analysis of the user's behavior.

[0036] Preferably, the feedback analysis in step S4 includes:

[0037] Feedback classification: Classify the collected feedback into key areas;

[0038] Problem identification: Use data analysis methods to identify common problems in feedback and areas of user dissatisfaction;

[0039] Optimization strategy formulation: Based on the analysis results, formulate specific optimization strategies.

[0040] Preferably, the specific contents of continuously improving the adaptive learning strategy and contrast conversation management skills in step S4 are: improving the conversation management system; regularly updating and optimizing the knowledge base content based on user feedback; further refining the user portrait and improving the understanding and adaptability to individual user preferences.

[0041] Preferably, the specific content of knowledge synchronization in step S5 is: synchronously updating the new knowledge generated by the cognitive model and the user-specific data to the system knowledge base;

[0042] The specific content of personalized learning is: by analyzing the user's conversation history, extracting the user's interests, preferences and feedback trends, adjusting the relevant content in the knowledge base, and optimizing the conversation strategy.

[0043] A dialogue system based on adaptive learning and context-aware AI agent, based on the dialogue method based on adaptive learning and context-aware AI agent, including a user behavior modeling module, a context-aware module, a dialogue management module and an adaptive learning module;

[0044] User behavior modeling module, which is used to collect scenario data, user interaction history data and preference data, and build user portrait models and dialogue scenario models;

[0045] The context awareness module is used to understand the semantics of conversation context data and identify key factors;

[0046] The adaptive learning module performs adaptive learning through user feedback analysis, behavior adjustment, and adaptive learning strategies of the interaction between the AI ​​agent and the cognitive model, and adjusts the cognitive model and the NLU model. It is also used to collect user feedback, analyze feedback, and continuously improve adaptive learning strategies and comparative conversation management skills. After the conversation ends, knowledge synchronization and personalized learning are performed.

[0047] The dialogue management module is used to perform real-time dialogue management by tracking dialogue context and dynamically updating dialogue strategies.

[0048] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the method for dialogue based on adaptive learning and context-aware AI agents is implemented.

[0049] A processing terminal includes a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and is characterized in that when the processor executes the computer program, a dialogue method based on adaptive learning and context-aware AI agent is implemented.

[0050] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a dialogue method and system based on adaptive learning and context-aware AI agents, which improves the adaptive learning mechanism and context-aware technology to enhance the interaction quality and user satisfaction of the dialogue system;

[0051] Enhanced personalization: Through deeper adaptive learning, the present invention significantly improves the ability of the dialogue system to respond to the personalized needs of users;

[0052] Improved contextual understanding: Introducing advanced situational awareness mechanisms to enable AI agents to better understand and predict user intent;

[0053] Optimization of interaction efficiency: Under the premise of ensuring user privacy, the present invention optimizes the data processing flow and improves the response speed and accuracy of the dialogue system. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0055] Figure 1 A schematic diagram of a dialogue method based on adaptive learning and context-aware AI agent provided by the present invention. DETAILED DESCRIPTION

[0056] The following will be combined with the drawings in the embodiments 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.

[0057] The embodiment of the present invention discloses a dialogue method based on adaptive learning and context-aware AI agent, such as Figure 1 , including the following steps:

[0058] S1. Collect scenario data, user interaction history data and preference data, and build user portrait model and dialogue scenario model;

[0059] S2. Adaptive learning is performed through user feedback analysis, behavior adjustment, and adaptive learning strategies for the interaction between AI agents and cognitive models, and the cognitive model and NLU model are adjusted;

[0060] S3. Real-time dialogue management by tracking dialogue context and dynamically updating dialogue strategies;

[0061] S4. Collect user feedback, analyze feedback and continuously improve adaptive learning strategies and comparative conversation management skills;

[0062] S5. Knowledge synchronization and personalized learning are carried out at the end of the conversation.

[0063] In order to further implement the above technical solution, the specific content of building a user portrait model is as follows: based on the user's historical data and preferences, a user portrait is built, and a unique set of tags is created for each user, such as "likes technology topics" and "tends to give concise answers", as the basis for personalized adaptive learning;

[0064] The dialogue scenario model is constructed to describe the user's current environment and status, including multiple dimensions, specifically:

[0065] Time and place: current date, time, geographic location, etc.

[0066] User historical interactions: previous conversation content, user behavior patterns in similar situations

[0067] Emotional state: Determine the user's emotional tendency through language analysis, such as happiness, frustration or impatience

[0068] Device usage: The type and status of the device the user is currently using, such as a mobile phone, computer, or smart home device. Including but not limited to time, location, previous conversation content, and user emotional state.

[0069] In order to further implement the above technical solution, the user feedback analysis in step S2 is specifically as follows: the AI ​​agent uses sentiment analysis and semantic parsing technology to identify the user's explicit and implicit feedback;

[0070] Behavior adjustment is as follows: Based on user feedback, the AI ​​agent adjusts the dialogue strategy, uses cognitive models to simulate human thinking processes and decision-making methods, and generates responses that are more adapted to user needs;

[0071] The specific content of the interaction between the AI ​​agent and the cognitive model is:

[0072] Preprocess the data before sending it to the cognitive model; retain the contextual information of the user's interaction with the AI ​​agent; regularly synchronize the new knowledge generated by the cognitive model with the AI ​​agent's knowledge base to ensure timely updating of the knowledge base; personalized adaptation: based on user feedback and behavioral data, adjust the relevant content in the knowledge base to adapt to the language habits and preferences of specific user groups.

[0073] In order to further implement the above technical solution, in step S2, the specific content of adjusting the cognitive model is:

[0074] Online fine-tuning mechanism: By collecting user feedback and behavior data and applying incremental learning algorithms, the cognitive model is continuously fine-tuned online during the interaction between the AI ​​agent and the user;

[0075] Offline optimization process: Regularly train the model offline, using larger datasets for in-depth learning and optimization to capture a wide range of user behavior patterns and language habits.

[0076] In order to further implement the above technical solution, in step S2, the specific content of adjusting the NLU model includes:

[0077] Adjustment based on intent recognition: If the scenario analysis shows that the user's intent is inclined towards a specific topic or type of query, adjust the NLU model intent recognition algorithm;

[0078] For example, increasing the number of training samples related to a specific topic, or adjusting the weights of specific features in the intent recognition algorithm;

[0079] Adjustments based on sentiment analysis: If the scenario analysis identifies that the user is anxious or impatient, adjust the NLU model to increase the weight of emotion-related words to respond more sensitively to emotional expressions;

[0080] Modify the sentiment analysis component of the NLU model to give more weight to words and language patterns associated with anxiety or urgency in its analysis;

[0081] Context-based adjustment: Introducing context-aware features into the NLU model based on the user’s current context and historical conversation records;

[0082] Introducing context-aware features into the NLU model, such as time tags, location tags, or reference information based on previous conversation content;

[0083] Optimization for specific user groups: For specific user groups or fields, we introduce professional vocabulary and corpus to train and fine-tune the model for specific fields.

[0084] In practical applications, choose a suitable small model, such as MobileBERT or ALBERT, which are more compact and computationally efficient.

[0085] The online fine-tuning mechanism of the model is to realize an online learning system, allowing the AI ​​agent to continuously fine-tune the cognitive model during the interaction with the user, by collecting user feedback and behavior data and applying incremental learning algorithms;

[0086] Offline optimization process: Regularly train the model offline and use larger datasets for in-depth learning and optimization, which helps the model capture a wider range of user behavior patterns and language habits.

[0087] In order to further implement the above technical solution, the specific content of step S3, tracking the conversation context is: tracking the conversation context, integrating the historical conversation content and relevant environmental information into the decision-making process of the current conversation, so that the response of the AI ​​agent is coherent and relevant throughout the conversation process;

[0088] The specific content of the dynamic update of the dialogue strategy is: according to the user's immediate feedback and behavior, analyze the user's real-time feedback in the dialogue, such as question modifications, clear instructions or emotional changes, and dynamically adjust the dialogue strategy and the content and style of the answer based on the analysis results.

[0089] For example, if a user displays anxiety, the AI ​​agent might adopt a more soothing and supportive communication style. Conversation strategies can also be adjusted based on the user’s location and time, such as providing shorter responses at night.

[0090] During the conversation, the AI ​​agent will continuously monitor the user's response, such as language changes, response speed, and the type of questions asked. Based on this real-time data, the AI ​​agent adjusts the content and style of the answer. For example, if the user is confused about a certain answer, the AI ​​agent may provide a more detailed explanation or use a different expression, thereby adjusting the response in real time during the conversation to better adapt to changes in the situation.

[0091] In order to further implement the above technical solution, in step S4, collecting user feedback includes collecting explicit feedback and implicit feedback;

[0092] Explicit feedback is direct evaluation by users; implicit feedback is the analysis of user behavior, such as conversation duration, interruption frequency, etc. By analyzing conversation duration and interruption frequency, the AI ​​system can understand the fluency and satisfaction of user interaction; and user behavior patterns (such as query frequency, attention to specific topics) help the system identify user interests and preferences, thereby personalizing the service.

[0093] In order to further implement the above technical solution, the feedback analysis in step S4 includes:

[0094] Feedback classification: Classify the collected feedback into key areas such as conversation comprehension, relevance and accuracy of answers;

[0095] Problem identification: Use data analysis methods to identify common problems in feedback and areas of user dissatisfaction;

[0096] Optimization strategy formulation: Based on the analysis results, formulate specific optimization strategies, such as improving the accuracy of NLU, improving the dialogue process, adjusting the answer strategy, etc.

[0097] In order to further implement the above technical solution, the specific contents of continuously improving the adaptive learning strategy and contrastive conversation management skills in step S4 are as follows:

[0098] Improve the dialogue management system, such as by enhancing context tracking capabilities to make the dialogue more coherent in multiple rounds of interaction;

[0099] Based on user feedback, regularly update and optimize the knowledge base content to ensure the accuracy and timeliness of information;

[0100] Further refine user portraits and improve understanding and adaptability to individual user preferences.

[0101] In this embodiment, adaptive learning post-processing is performed after the conversation ends:

[0102] Processing analysis: After the conversation is over, the performance of the AI ​​agent is evaluated using indicators such as satisfaction score and conversation success rate. The conversation log is analyzed through cognitive models to identify common problems and changes in user preferences.

[0103] Long-term optimization: Based on the analysis results, the behavioral strategies of the AI ​​agent and the relevant parameters of the cognitive model are optimized in the long term. In terms of behavioral strategy optimization, through the above analysis, behavioral strategies such as questioning skills and information presentation methods are adjusted.

[0104] In order to further implement the above technical solution, the specific content of knowledge synchronization in step S5 is: synchronously updating the new knowledge generated by the cognitive model and the user-specific data to the system knowledge base;

[0105] The specific content of personalized learning is: by analyzing the user's conversation history, extracting the user's interests, preferences and feedback trends, adjusting the relevant content in the knowledge base, and optimizing the conversation strategy; for example, adjusting the topic recommendation algorithm to better adapt to the user's personal preferences. For example, if the analysis finds that users often ask questions about a specific field (such as photography), the AI ​​agent will enhance its knowledge base content in this field and be more inclined to introduce photography-related topics and suggestions in future conversations.

[0106] A dialogue system based on adaptive learning and context-aware AI agent, based on a dialogue method based on adaptive learning and context-aware AI agent, including a user behavior modeling module, a context-aware module, a dialogue management module and an adaptive learning module;

[0107] User behavior modeling module, which is used to collect scenario data, user interaction history data and preference data, and build user portrait models and dialogue scenario models;

[0108] The context awareness module is used to understand the semantics of conversation context data and identify key factors;

[0109] The adaptive learning module performs adaptive learning through user feedback analysis, behavior adjustment, and adaptive learning strategies of the interaction between the AI ​​agent and the cognitive model, and adjusts the cognitive model and the NLU model. It is also used to collect user feedback, analyze feedback, and continuously improve adaptive learning strategies and comparative conversation management skills. After the conversation ends, knowledge synchronization and personalized learning are performed.

[0110] The dialogue management module is used to perform real-time dialogue management by tracking dialogue context and dynamically updating dialogue strategies.

[0111] In this embodiment, the system is equipped with a data collection module to obtain data from multiple sources (such as social media, IoT devices, user devices, etc.) through APIs; a data preprocessing module uses natural language processing (NLP) technology to clean, normalize and extract features from the collected data, and a batch processing system is used to process batch data, such as historical interaction records.

[0112] This application proposes a number of innovations to address the limitations of existing technologies, mainly focusing on improvements in adaptive learning mechanisms and context-aware technologies to enhance the interaction quality and user satisfaction of the dialogue system:

[0113] (1) Detailed description of the adaptive learning mechanism:

[0114] In the present invention, the adaptive learning mechanism of the AI ​​agent includes the following key elements:

[0115] User behavior model: By analyzing the user's historical interaction data, a dynamic user behavior model is constructed to update the user's preferences and needs in real time;

[0116] Context-awareness: AI agents can adaptively adjust their response strategies based on conversation history and external context information to provide more personalized services;

[0117] Continuous learning framework: Continuously learn from user feedback and behavior, using online learning and incremental learning techniques, so that the AI ​​agent can gradually improve its understanding of user intent;

[0118] (2) Detailed description of situational awareness technology:

[0119] Contextual awareness enables AI agents to recognize and adapt to the user’s current environment and state, including:

[0120] Environmental recognition: using sensor data or integrated services such as location, time, weather, etc. to understand the user's physical environment;

[0121] Emotional perception: Analyze the emotional color of user language and identify the user's emotional state to provide a more considerate response;

[0122] Multi-turn dialogue management: Through dialogue context management, topic coherence and context consistency can be maintained in a series of interactions.

[0123] Embodiment 1

[0124] In e-commerce platforms, the dialogue system based on adaptive learning and context-aware AI agents integrates AI agents as personalized shopping assistants.

[0125] (1) Operation details:

[0126] The AI ​​agent analyzes the user's browsing and purchasing history, and uses cognitive models and data analysis techniques to build personalized user behavior models.

[0127] In the process of interacting with users, AI agents recommend products based on user behavior models, provide customized discount information, and guide users to complete the purchase process.

[0128] (2) Interaction:

[0129] Every user interaction is used to update the user model in real time, affecting subsequent recommendation algorithms.

[0130] The AI ​​agent dynamically adjusts the conversation strategy based on user input and current shopping scenarios (such as holidays and promotions) to provide more accurate shopping recommendations.

[0131] Embodiment 2

[0132] The dialogue system based on adaptive learning and situation-aware AI agent of the present invention is used for smart home controller.

[0133] (1) Operation details:

[0134] In a smart home system, AI agents control smart devices in the home, such as lights and temperature control devices.

[0135] The AI ​​agent interprets the user's verbal commands and automatically adjusts device settings based on the usage habits of family members and environmental information (such as time and weather).

[0136] (2) Interaction:

[0137] Sensors throughout the home provide real-time data that AI agents use to anticipate users’ needs, such as automatically increasing the heating temperature in cold weather.

[0138] The AI ​​agent adjusts the control strategy according to the user's living habits and preferences to achieve more intelligent and personalized home management.

[0139] Embodiment 3

[0140] The dialogue system based on adaptive learning and situation-aware AI agent of the present invention is used for health consultation chatbot.

[0141] (1) Operation details:

[0142] In healthcare applications, AI agents act as personal health advisors for users.

[0143] AI agents analyze users’ health data (e.g., exercise, diet, sleep patterns) and real-time medical information (e.g., health news) to provide customized health advice and reminders.

[0144] (2) Interaction:

[0145] The AI ​​agent tracks the health progress of users based on changes in their health data and provides dynamically adjusted recommendations as needed, such as adjusting eating habits or modifying exercise plans.

[0146] User feedback is used to optimize the AI ​​agent’s health recommendations, ensuring they are relevant and effective.

[0147] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, a dialogue method based on adaptive learning and context-aware AI agents is implemented.

[0148] A processing terminal includes a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and is characterized in that when the processor executes the computer program, a dialogue method based on adaptive learning and situation-aware AI agents is implemented.

[0149] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0150] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A conversation method based on adaptive learning and context-aware AI agent, characterized in that: The following steps are involved: S1. Collect scenario data, user interaction history data and preference data, and build user portrait model and dialogue scenario model; S2. Adaptive learning is performed through user feedback analysis, behavior adjustment, and adaptive learning strategies for the interaction between AI agents and cognitive models, and the cognitive model and NLU model are adjusted; S3. Real-time dialogue management by tracking dialogue context and dynamically updating dialogue strategies; S4. Collect user feedback, analyze feedback and continuously improve adaptive learning strategies and comparative conversation management skills; S5. Knowledge synchronization and personalized learning are carried out at the end of the conversation; The user feedback analysis in step S2 is specifically as follows: the AI ​​agent uses sentiment analysis and semantic parsing techniques to identify the user's explicit and implicit feedback; Behavior adjustment is as follows: Based on user feedback, the AI ​​agent adjusts the dialogue strategy, uses cognitive models to simulate human thinking processes and decision-making methods, and generates responses that are more adapted to user needs; The specific content of the interaction between the AI ​​agent and the cognitive model is: Preprocess the data before sending it to the cognitive model; retain the contextual information of the user's interaction with the AI ​​agent; regularly synchronize the new knowledge generated by the cognitive model with the AI ​​agent's knowledge base to ensure timely updating of the knowledge base; personalized adaptation: based on user feedback and behavior data, adjust the relevant content in the knowledge base to adapt to the language habits and preferences of specific user groups; In step S2, the specific content of adjusting the cognitive model is: Online fine-tuning mechanism: By collecting user feedback and behavior data and applying incremental learning algorithms, the cognitive model is continuously fine-tuned online during the interaction between the AI ​​agent and the user; Offline optimization process: Regularly train the model offline, use larger data sets for in-depth learning and optimization, and capture a wide range of user behavior patterns and language habits; The specific contents of adjusting the NLU model include: Adjustment based on intent recognition: If the scenario analysis shows that the user's intent is inclined towards a specific topic or type of query, adjust the NLU model intent recognition algorithm; Adjustments based on sentiment analysis: If the scenario analysis identifies that the user is anxious or impatient, adjust the NLU model to increase the weight of emotion-related words to respond more sensitively to emotional expressions; Context-based adjustment: Introducing context-aware features into the NLU model based on the user’s current context and historical conversation records; Optimization for specific user groups: For specific user groups or fields, we introduce professional vocabulary and corpus to train and fine-tune the model for specific fields.

2. According to claim 1, a dialogue method based on adaptive learning and context-aware AI agent is characterized in that: The specific content of building a user portrait model is as follows: based on the user's historical data and preferences, build a user portrait and create a unique tag set for each user as the basis for personalized adaptive learning; The constructed dialogue scenario model is used to describe the user's current environment and status, including multiple dimensions, namely: time and place, user historical interactions, emotional state, and device usage.

3. According to claim 1, a dialogue method based on adaptive learning and context-aware AI agent is characterized in that: In step S3, the specific content of tracking the conversation context is: tracking the conversation context, integrating the historical conversation content and relevant environmental information into the decision-making process of the current conversation, so that the response of the AI ​​agent is coherent and relevant throughout the conversation process; The specific content of the dynamic update of the dialogue strategy is: according to the user's immediate feedback and behavior, analyze the user's real-time feedback in the dialogue, such as question modifications, clear instructions or emotional changes, and dynamically adjust the dialogue strategy and the content and style of the answer based on the analysis results.

4. The method for dialogue based on adaptive learning and context-aware AI agent according to claim 1, characterized in that: In step S4, collecting user feedback includes collecting explicit feedback and implicit feedback; Explicit feedback is the user's direct evaluation, and implicit feedback is the analysis of the user's behavior.

5. The method for dialogue based on adaptive learning and context-aware AI agent according to claim 1, characterized in that: The feedback analysis in step S4 includes: Feedback classification: Classify the collected feedback into key areas; Problem identification: Use data analysis methods to identify common problems in feedback and areas of user dissatisfaction; Optimization strategy formulation: Based on the analysis results, formulate specific optimization strategies.

6. The method for dialogue based on adaptive learning and context-aware AI agent according to claim 1, characterized in that: The specific contents of continuously improving the adaptive learning strategy and contrasting conversation management skills in step S4 are: improving the conversation management system; Based on user feedback, the knowledge base content is regularly updated and optimized; user portraits are further refined to improve the understanding and adaptation capabilities to individual user preferences.

7. The method for dialogue based on adaptive learning and context-aware AI agent according to claim 1, characterized in that: The specific contents of knowledge synchronization in step S5 are: synchronously updating the new knowledge generated by the cognitive model and the user-specific data to the system knowledge base; The specific content of personalized learning is: by analyzing the user's conversation history, extracting the user's interests, preferences and feedback trends, adjusting the relevant content in the knowledge base, and optimizing the conversation strategy.

8. A dialogue system based on adaptive learning and context-aware AI agent, characterized in that: A conversation method based on adaptive learning and context-aware AI agent according to any one of claims 1 to 7, comprising a user behavior modeling module, a context-aware module, a conversation management module and an adaptive learning module; User behavior modeling module, which is used to collect scenario data, user interaction history data and preference data, and build user portrait models and dialogue scenario models; The context awareness module is used to understand the semantics of conversation context data and identify key factors; The adaptive learning module performs adaptive learning through user feedback analysis, behavior adjustment, and adaptive learning strategies of the interaction between the AI ​​agent and the cognitive model, and adjusts the cognitive model and the NLU model. It is also used to collect user feedback, analyze feedback, and continuously improve adaptive learning strategies and comparison management skills. The conversation ends with knowledge synchronization and personalized learning; The dialogue management module is used to perform real-time dialogue management by tracking dialogue context and dynamically updating dialogue strategies.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, it implements a dialogue method based on adaptive learning and context-aware AI agent as described in any one of claims 1 to 7.

10. A processing terminal, comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, it implements a dialogue method based on adaptive learning and context-aware AI agent as described in any one of claims 1 to 7.

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