AI dialogue system and method for psychological counseling

Abstract

The invention provides an AI dialogue system for psychological consultation, which relates to the technical field of AI interaction and comprises a psychological science popularization module, a personalized recommendation module, a psychological evaluation module, an emotional communication module, an early warning intervention module, a reservation management module, a natural language processing and dialogue generation module, an intelligent self-learning module and a consultation feedback module. And calling a corresponding module to carry out processing response by carrying out conversation with the AI and judging the thought trend of the user. The system has the beneficial effects that the system can analyze and process the natural language of the user, and calls the professional psychological database and the workflow to carry out natural dialogue with the user. In addition, the AI dialogue system can also provide psychological evaluation service, generate an evaluation report for reference of reservation service, judge the emotion of the user according to the content input by the user, generate early warning information when necessary and send the early warning information to a professional psychological counselor for intervention processing.

Description

Technical Field

[0001] The present invention relates to the field of AI interaction technology, and in particular to an AI dialogue system and method for psychological consultation. Background Art

[0002] As the pace of modern society accelerates, people are facing increasing psychological pressure, and the demand for psychological counseling is also increasing. However, traditional psychological counseling methods are limited by time, place and counselor resources, and cannot meet the needs of the majority of users. In the existing technology, AI software for emotional interaction include Wysa and Replika. These two AI software are personal AI companion applications that provide users with emotional support, mental health counseling and personal growth through intelligent dialogue and emotional communication. By constantly learning and adapting to user needs, they have become emotional support and companionship partners in the daily lives of many people. Wysa and Replika can be downloaded and used for free on iOS and Android platforms. Users can access them anytime, anywhere through mobile applications, and have conversations and communications with their personal AI companions, with excellent usability. However, the current psychological counseling AI automatic answering software has the following technical defects:

[0003] 1. Insufficient accuracy in sentiment understanding: Although sentiment analysis technology has made some progress, there are still cases of misjudgment of sentiment, especially for texts with complex emotional expressions or unclear contexts.

[0004] 2. Limited level of personalization: Although the software can learn user behavior and preferences, the current level of personalization is still limited, and it is difficult to provide customized services that truly meet the user's personality and needs.

[0005] 3. Lack of human expertise: Software often learns and infers based on machine learning algorithms, but they lack the expertise and experience of human psychologists or clinicians, and therefore may not be accurate or effective enough in dealing with certain complex psychological issues.

[0006] 4. Data privacy and security issues: AI psychological counseling software processes a large amount of user data, so strict data privacy and security measures are required, but sometimes these measures may not be perfect and there is a risk of data leakage or abuse.

[0007] 5. Limited ability to handle emergency situations: AI psychological counseling software are generally unable to effectively handle emergency situations or crisis interventions because they lack real-time intervention and support from human professionals. Summary of the invention

[0008] In order to solve the psychological problems of users in a timely and effective manner, the present invention provides an AI dialogue system and method for psychological consultation, an AI dialogue system for psychological consultation, mainly comprising:

[0009] Psychological science module, personalized recommendation module, psychological assessment module, emotional communication module, early warning intervention module, appointment management module, natural language processing and dialogue generation module, intelligent self-learning module, consultation feedback module; the personalized recommendation module is connected to the psychological science module and the psychological assessment module, the emotional communication module is connected to the early warning intervention module and the appointment management module, and the natural language processing and dialogue generation module is connected to the psychological science module, personalized recommendation module, psychological assessment module, emotional communication module, early warning intervention module, appointment management module, intelligent self-learning module, and consultation feedback module;

[0010] Psychological science module: Based on the preset psychological database and behavioral analysis based on user input, it provides users with a wealth of mental health knowledge and information, including an introduction to mental illness, methods for maintaining mental health, and common sense of psychological counseling;

[0011] Personalized recommendation module: used to mine users’ personal profiles and preferences and recommend relevant mental health resources to users, including psychological articles, books, and courses;

[0012] The psychological assessment module is used to call professional psychological databases to generate assessment questions, provide users with online psychological assessment services, evaluate users' psychological conditions, and generate detailed psychological assessment reports, interpret the reports, and give users suggestions so that users can better understand their psychological conditions. At the same time, the report can provide important references for professional psychological counselors; this module supports a variety of assessment scales and models to meet the assessment needs of different users, including anxiety scales, depression scales, and personality assessments;

[0013] Emotional communication module: It is used to provide users with a platform for natural dialogue and emotional communication with the AI ​​system based on natural language processing technology. Through in-depth communication with users, it provides emotional support and psychological comfort to users, and can also further understand the psychological state and needs of users, providing strong support for subsequent early warning intervention and appointment management services;

[0014] Early warning intervention module: It is used to analyze the user's current input content in real time based on the user's historical data collected in the emotional communication module, and monitor the user's emotional changes in real time. When abnormal user emotions are detected, the early warning mechanism is automatically triggered, and early warning information is sent to professional psychological counselors for timely intervention; at the same time, the module records early warning information and intervention results to provide data support for the continuous optimization and performance improvement of the system; the module also supports the customization and personalization of early warning information to meet the early warning needs of different users;

[0015] Reservation management module: used to provide users with online reservation services for professional psychological counselors, including appointment time, counselor selection, and consultation methods. This module provides users with convenient reservation services and also provides professional psychological counselors with effective customer management methods;

[0016] Natural language processing and dialogue generation module: It is used to parse the natural language input by the user through the natural language understanding model based on deep learning technology, and to generate dialogue responses that conform to the context based on the dialogue response generation model based on natural language generation technology. It supports multi-round dialogue and context understanding to achieve continuous and natural communication with users and improve user experience and satisfaction.

[0017] Intelligent self-learning module: Based on the automatic learning mechanism of user feedback and system operation data, it continuously optimizes and improves the system's psychological counseling service performance, including model updates and algorithm optimization. Through continuous learning and improvement, it can adapt to changes in user needs in real time and provide more accurate and personalized psychological counseling services.

[0018] Consultation feedback module: used to provide users with a way to provide feedback on psychological counseling services. By collecting users' evaluations and suggestions on service quality, it can timely understand users' needs and expectations and provide direction for subsequent improvements and optimizations. At the same time, this module is also used to provide users with post-consultation follow-up services to ensure that users can receive continuous and effective psychological support.

[0019] Furthermore, the system also includes a privacy protection and security module, a cross-language and cultural adaptation module, an emergency contact network module, and a decision support tool module. The privacy protection and security module is used to strengthen data security measures and protect user privacy, specifically including data encryption, access control, and data backup. The cross-language and cultural adaptation module is used to support multiple languages ​​and provide consulting services in multicultural backgrounds. The emergency contact network module is used to notify the user's emergency contacts or related service personnel in an emergency. The decision support tool module is used to provide data analysis for professional psychological counselors to assist in the formulation of treatment plans.

[0020] An AI dialogue method for psychological consultation is implemented based on the above system and includes the following steps:

[0021] S1: The user opens the AI ​​dialogue system through a mobile terminal or PC;

[0022] S2: The user inputs the conversation content;

[0023] S3: The system obtains the conversation content for natural language processing and determines the user's motivation. If the user's motivation is psychological evaluation, the psychological evaluation module is called for processing. If the user's motivation is appointment consultation, the appointment management module is called for processing. If the user has extreme emotions, the early warning intervention module is called for processing. If the user's motivation is emotional communication, the emotional communication module is called for processing. If the user's motivation is to learn psychological knowledge, the psychological science module is called for processing.

[0024] S4: The processing is completed and the user ends the dialogue with the system.

[0025] The technical solution provided by the present invention has the following beneficial effects: the present invention has a higher level of professionalism, pertinence and personalization in the solution of psychological diagnosis and treatment than the prior art, and can better meet the psychological health needs of users. The present invention can provide 24-hour online psychological counseling services around the clock, and users do not need to worry about time or location restrictions, and can get timely help and support at any time. Real-time ensures that users can get a quick response when they encounter psychological distress, which helps to relieve the psychological pressure and emotional problems of users in a timely manner. The present invention adopts advanced natural language processing technology, which can interact with users in a natural and smooth dialogue, and improve user experience and consulting effect. This interactive mode not only makes the consulting process more intimate and comfortable, but also helps the system to understand the user's intentions and needs more accurately, and provide more accurate psychological support. Based on a rich professional psychological database and evaluation scale, including professional psychological counseling teachers settled in the platform, the present invention provides users with professional and authoritative psychological counseling and psychological evaluation services. These resources are carefully screened and integrated to ensure that the information and suggestions obtained by users are scientific and reliable, which helps users better understand and solve their own psychological problems. The present invention has emotion recognition and early warning functions, which can automatically judge the user's emotional state and generate early warning information for intervention when necessary. This early warning mechanism can detect the user's emotional abnormalities in time, effectively prevent the deterioration of psychological problems, and provide the user with necessary support and help to ensure the user's safety and well-being. The present invention provides a flexible appointment service, and users can choose online or offline professional psychological counselors for consultation according to their own needs. This flexibility enables users to choose the most suitable consultation method according to their actual situation, which improves the convenience and personalization of the service. The present invention can provide personalized psychological counseling services based on the user's personal data and consultation history. This means that each consultation experience will be adjusted according to the user's specific needs and circumstances, thereby providing more accurate and effective support. An important advantage of the online psychological counseling of the present invention is that users can seek help while maintaining anonymity. The system provided by the present invention strictly protects user privacy, ensures that the user's personal information is not leaked, and provides users with a safe and secure consultation environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which:

[0027] Figure 1 It is a framework diagram of an AI dialogue system for psychological consultation in an embodiment of the present invention.

[0028] Figure 2 It is a flow chart of crisis warning in an embodiment of the present invention.

[0029] Figure 3It is a flow chart of an AI dialogue method for psychological consultation in an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0031] like Figure 1 As shown in the figure, an AI dialogue system for psychological counseling includes a psychology popularization module, a personalized recommendation module, a psychological assessment module, an emotional communication module, an early warning intervention module, an appointment management module, a natural language processing and dialogue generation module, an intelligent self-learning module, and a consultation feedback module. These modules are highly coupled and closely connected. Together, these modules constitute a collaborative psychological counseling AI dialogue system that provides users with a one-stop psychological service experience. Every link between the modules is closely connected and supports each other. This system is implemented using a cloud computing architecture to ensure the stability and scalability of the system. At the same time, the system ensures the security and privacy of user data, and uses technical means such as data encryption and access control to protect it.

[0032] Psychological science module: As the foundation of the system, based on the preset psychological database and behavioral analysis based on user input, it provides users with a wealth of mental health knowledge and information, including an introduction to mental illness, methods for maintaining mental health, common sense of psychological counseling, etc. This information not only provides users with a way to understand themselves and grow, but also provides necessary background knowledge for subsequent modules.

[0033] The psychology popular science module can display psychological knowledge, case analysis, psychology articles and other content online to help users establish a correct understanding of mental health. The implementation method is to regularly update popular science content to ensure the timeliness and accuracy of information. Users can access popular science content through the system homepage or specific entrances, and the system can recommend relevant popular science articles based on the user's interests and needs.

[0034] Personalized recommendation module: Based on the basic knowledge provided by the psychology popular science module, this module can deeply explore the user's personal information and preferences and recommend relevant mental health resources to users, such as psychological articles, books, courses, etc. This personalized recommendation not only improves user engagement and satisfaction, but also lays a solid foundation for subsequent psychological assessment and consulting services. That is, the personalized recommendation module recommends psychological counseling resources that meet the user's personalized needs based on the user's personal information, historical consultation records, and sentiment analysis results. The implementation method is to work closely with the psychology popular science module and the psychological assessment module, dynamically adjust the recommendation strategy according to the user's reading history, assessment results, and real-time emotional data, and provide accurate personalized recommendation services.

[0035] Psychological assessment module: Relying on the personalized recommendation module's preliminary understanding of user interests, it provides users with a series of online psychological assessment services. Through a variety of psychological assessment scales and models, the system can accurately assess the user's psychological condition and generate detailed reports and interpretations, providing important references for users and professional psychological counselors. That is, the psychological assessment module can provide a variety of psychological assessment tools to help users understand their own psychological state and provide references for subsequent consultations. The implementation method is that users can complete the assessment online, and the system instantly generates the assessment results and professional interpretations. The assessment results will be used as input to the personalized recommendation module to adjust the recommendation strategy.

[0036] The psychological assessment module also includes:

[0037] (a) Ability to call upon professional psychological databases to generate assessment questions and analyze results, ensuring the accuracy and professionalism of the assessment;

[0038] (b) Support a variety of assessment scales and models to meet the assessment needs of different users, including but not limited to anxiety scales, depression scales, personality assessments, etc.;

[0039] (c) It can provide users with interpretations and suggestions of assessment results to help them better understand their own psychological conditions.

[0040] Emotional communication module: Using natural language processing technology, it provides users with a platform for natural dialogue and emotional communication with the AI ​​system. Through in-depth communication with users, the system can not only provide emotional support and psychological comfort, but also further understand the user's psychological state and needs, and provide strong support for subsequent early warning intervention and appointment management services. That is, the emotional communication module uses natural language processing and dialogue generation technology to conduct natural dialogues with users and provide emotional support and suggestions. The implementation method is that this module is closely connected with the natural language processing and dialogue generation module to ensure that the system can accurately understand the user's intentions and emotions and generate appropriate responses. At the same time, the emotional communication module is also an important data source for the early warning intervention module.

[0041] Early warning intervention module: Based on the user information collected in the emotional communication module, this module can analyze the content input by the user in real time, judge the user's emotional state, and generate early warning information when necessary (for example), and send it to a professional psychological teacher for intervention in time. At the same time, the module is also responsible for recording early warning information and intervention results to provide data support for the continuous optimization and performance improvement of the system. That is, the early warning intervention module judges the emotional state of the user based on the content input by the user, and generates early warning information when necessary and sends it to a professional psychological teacher for intervention. The implementation method is that this module receives the user input data transmitted by the emotional communication module, and uses the emotional analysis algorithm to judge the user's emotional state. When abnormal emotions are detected, the early warning information is sent to a professional psychological counselor through a preset communication method (such as DingTalk workstation), and the appointment management module is triggered to arrange subsequent consultations.

[0042] The early warning intervention module also includes:

[0043] (a) Based on the user's historical data and current input content, real-time monitoring of emotional changes is performed to detect the user's psychological problems in a timely manner;

[0044] (b) When abnormal emotions of users are detected, the early warning mechanism is automatically triggered and early warning information is sent to professional psychological teachers for timely intervention;

[0045] (c) Support customization and personalization of warning information to meet the warning needs of different users, and record warning information and intervention results for subsequent analysis and improvement.

[0046] Appointment management module: After the early warning intervention module finds users who need further professional consultation, the appointment management module plays a key role. It allows users to make online appointments for professional psychological counselors, including appointment time, counselor selection, consultation method, etc. This module not only provides users with convenient appointment services, but also provides psychological counselors with effective customer management methods. That is, the appointment management module manages users' consultation service appointments to ensure smooth communication between users and counselors. The implementation method is that users can make appointments for professional psychological teachers' consultation services through the system, and the system automatically manages appointment information and reminds users and counselors at the agreed time. The appointment management module works closely with the early warning intervention module to ensure that consultation services can be arranged for users in a timely manner in emergency situations.

[0047] Natural language processing and dialogue generation module: As one of the core technologies of the system, the natural language processing and dialogue generation module runs through the entire system operation process. It is responsible for parsing the natural language input by the user and generating dialogue responses that conform to the context, supporting multiple rounds of dialogue and context understanding. This module not only ensures the interactivity and ease of use of the system, but also provides necessary technical support for other modules. That is, the natural language processing and dialogue generation module receives the user's natural language input, performs in-depth understanding and analysis, and generates responses that conform to the context. The implementation method is that this module is one of the core modules of this system. It receives user input data and performs word segmentation, part-of-speech tagging, syntactic analysis, semantic understanding and other processing. At the same time, this module works closely with the psychological assessment module, intelligent self-learning module, etc. to ensure that the generated responses are both contextual and professional.

[0048] The natural language processing and dialogue generation module also includes:

[0049] (a) A natural language understanding model based on deep learning technology is used to parse the natural language intent of user input and improve the accuracy and efficiency of conversations;

[0050] (b) A dialogue response generation model based on natural language generation technology, which is used to generate dialogue responses that meet the context and user needs, and improve the fluency and naturalness of the dialogue;

[0051] (c) Support multi-round dialogue and context understanding to achieve continuous and natural communication with users and improve user experience and satisfaction.

[0052] Intelligent self-learning module: The intelligent self-learning module is the key to the continuous optimization of the system. Based on user feedback and system operation data, it continuously optimizes and improves the system's psychological counseling service performance, including model updates, algorithm optimization, etc. Through continuous learning and improvement, the system can better adapt to changes in user needs and provide more accurate and personalized psychological counseling services. That is, the intelligent self-learning module continuously interacts with users, collects user feedback data, and conducts self-learning and optimization. The implementation method is that the module uses machine learning algorithms to analyze and process user feedback data, and continuously optimizes the parameters and strategies of the dialogue generation model. At the same time, the intelligent self-learning module is also combined with the personalized recommendation module to adjust the recommendation strategy according to user feedback to improve the user experience.

[0053] Intelligent self-learning modules also include:

[0054] (a) An automatic learning mechanism based on user feedback and system operation data to optimize and improve the system's psychological counseling service algorithm and improve service quality and efficiency;

[0055] (b) Support online updates and upgrades of system modules and functions to adapt to changing user needs and market environment and maintain the system's advancement and competitiveness.

[0056] Consultation feedback module: Finally, the consultation feedback module provides users with a way to provide feedback on psychological counseling services. By collecting users' evaluations, suggestions and opinions on service quality, the system can promptly understand users' needs and expectations and provide direction for subsequent improvements and optimizations. At the same time, the module also provides users with post-consultation follow-up services to ensure that users can receive continuous and effective psychological support. That is, the consultation feedback module collects users' evaluations and feedback on counseling services for improving service quality. The implementation method is that after each consultation, the system invites users to evaluate the consultation. The collected user feedback will be used as one of the input data of the intelligent self-learning module to optimize the dialogue generation model and recommendation strategy. At the same time, the consultation feedback module is also combined with other modules to jointly improve the overall performance and service quality of the system.

[0057] The system also includes a privacy protection and security module, a cross-language and cultural adaptation module, an emergency contact network module, and a decision-making support tool module. The privacy protection and security module is used to strengthen data security measures and protect user privacy, including data encryption, access control, and data backup to ensure the security and confidentiality of user data. The cross-language and cultural adaptation module is used to support multiple languages ​​and provide consulting services in a multicultural context. The emergency contact network module is used to notify the user's emergency contacts or related service personnel in an emergency. The decision-making support tool module is used to provide data analysis for professional psychological counselors to assist in the formulation of treatment plans.

[0058] After entering the system, users can select the corresponding module according to their needs. For example, when users choose the psychological assessment service, the system will guide users to complete the corresponding assessment questions and provide assessment results and professional interpretations in real time. When users choose the emotional communication service, the system will conduct natural conversations with users through the natural language processing and dialogue generation modules to provide emotional support and suggestions. If the system determines that the user's emotions are abnormal, it will trigger the early warning intervention module to make necessary interventions.

[0059] like Figure 3 As shown, an AI dialogue method for psychological consultation is implemented based on the AI ​​dialogue system, comprising the following steps:

[0060] S1: The user opens the AI ​​dialogue system through a mobile terminal or PC;

[0061] S2: The user inputs the conversation content;

[0062] S3: The system obtains the conversation content for natural language processing and determines the user's motivation. If the user's motivation is psychological evaluation, the psychological evaluation module is called to perform user evaluation processing, as described in steps S101-S107:

[0063] S101: The system obtains the conversation content, performs natural language processing, and determines whether the user has the motivation to do psychological assessment. If yes, the system sends the assessment platform link for the user to assess. If no, the system enters the emotional communication module such as S401. In addition, the user can directly enter the assessment by clicking on the assessment module;

[0064] S102: The user selects the required psychological assessment scale according to his / her needs;

[0065] S103: Outputting a corresponding psychological assessment report form according to the assessment scale completed by the user;

[0066] S104: Analyze the user's psychological assessment report data and determine whether the user needs consulting services;

[0067] S105: If necessary, a window pops up, and the user enters the consultation and appointment module after confirmation, as described in steps S201-210;

[0068] S106: If not required, enter the consultation feedback module;

[0069] S107: Analyze the correctness and completeness of the conversation content based on the user's conversation and perform self-training.

[0070] If the user's motivation is to make an appointment for consultation, the appointment management module is called to process the appointment. The specific implementation process of the mechanism for analyzing and processing the user input content is as follows:

[0071] S201: The system obtains the conversation content, performs natural language processing, and determines whether the user has the motivation to do psychological counseling. If yes, the system sends a reservation system link for the user to make an appointment for counseling. If no, the system enters the emotional communication module such as S401. In addition, the user can directly make a consultation appointment by clicking on the appointment module;

[0072] S202: The user selects an appointment time according to his / her own situation, such as his / her own free time, and simultaneously selects the psychological counseling service he / she needs;

[0073] S203: The system pops up the must-read notice for appointment consultation, and the user confirms to proceed to the next step after reading;

[0074] S204: The user fills in his / her own consultation information and selects the consultation method, and enters the payment system after submission;

[0075] S205: If the user has successfully paid, a prompt will be displayed indicating that the reservation is successful and a reservation voucher and order will be popped up;

[0076] S206: If the user cancels the payment, return to step S202;

[0077] S207: If the user chooses online consultation, the user is reminded to log into the system and check online at the scheduled time to start the consultation service;

[0078] S208: If the user chooses offline consultation, a period of time before the appointment time, such as one day before the appointment time, or one hour before the appointment time, the appointment information is sent through a mobile terminal (such as SMS or WeChat) to remind the user to arrive on time and verify offline, and the consultation service starts after verification;

[0079] S209: If the user consultation service is finished, enter the consultation feedback module;

[0080] S210: Analyze the correctness and completeness of the conversation content based on the user's conversation and perform system self-training.

[0081] like Figure 2 As shown, if the user has extreme emotions, the early warning intervention module is called for processing. The specific implementation process of the mechanism for analyzing and processing the user input content is as follows:

[0082] S301: The system monitors the conversation content input by the user in real time through natural language processing technology;

[0083] S302: Using a sentiment analysis algorithm to perform sentiment recognition on the user input to determine the user's emotional state;

[0084] S303: The system determines whether a crisis warning needs to be triggered according to preset crisis warning triggering conditions (such as specific keywords, emotion intensity thresholds, etc.);

[0085] S304: If it is determined that a crisis warning is needed, the system first attempts to soothe the person's emotions through gentle and encouraging language, such as: "I noticed that you are very emotional now, you can calm down first, and we will face the problem together"; ask for the person's and parents' contact information, notify family members, ask about the school and city where the person is located, and notify the school's psychological counseling center to conduct crisis intervention;

[0086] S305: Generate detailed crisis warning information based on the user's specific situation and system analysis, including the user's basic information, emotional state, possible risk factors, etc.;

[0087] S306: If the crisis warning information is distributed to relevant personnel, including system administrators, psychological counselors, emergency contacts, etc., it is determined whether the psychological counselor responds. If so, the family and the school psychological counseling center are notified to conduct crisis intervention. If not, the 24-hour public welfare psychological hotline or other institutional platforms are connected;

[0088] S307: The system continuously follows up on the processing of early warning information to ensure timely and effective response; at the same time, it collects and analyzes feedback information during the processing to optimize the performance and effectiveness of the crisis early warning system;

[0089] S308: After the crisis event is properly handled, the system re-evaluates the user to determine whether his or her emotional state has returned to normal, and adjusts subsequent psychological counseling and support strategies based on the evaluation results to effectively train the AI.

[0090] If the user's motivation is emotional communication, the emotional communication module is called for processing, and the specific implementation process of the analysis and processing mechanism based on the user input content is as follows:

[0091] S401: The user inputs text or voice through the AI ​​psychological counseling dialogue system on the mobile terminal or PC to express his or her feelings, questions or concerns and conduct emotional communication;

[0092] S402: The system receives the user's input, analyzes the user's text or voice through the conversation, and extracts key information and emotional tendencies;

[0093] S403: The system uses a sentiment analysis algorithm to determine the user's emotional state, such as positive, negative, anxious, depressed, etc. This is usually based on information such as vocabulary, syntax, and context in the text;

[0094] S404: the system searches for relevant emotion regulation strategies, suggestions or resources in a pre-established psychological database according to the user's emotional state, where the psychological database includes psychological expertise, case studies, suggestion lists, etc.;

[0095] S405: The system generates a personalized emotion regulation response based on the query results of the psychological database and in combination with the user's personal information and historical records, including emotional support, advice, guidance, or related psychological article recommendations;

[0096] S406: The system sends the generated emotion regulation response to the user in the form of text, voice or other forms to help the user understand and process his or her emotions;

[0097] S407: The system may ask the user for feedback on the emotion regulation response to evaluate its effectiveness and use it for subsequent system optimization and improvement.

[0098] If the user needs personalized recommendations, the specific implementation process of the analysis and processing mechanism based on the user input content is as follows:

[0099] S501: The user inputs personal questions or emotional appeals in the dialogue interface and sends the dialogue content to the system;

[0100] S502: The system uses natural language processing technology to analyze the user's conversation content and uses a text analysis algorithm to determine whether the user has a potential motivation to conduct a psychological assessment;

[0101] S503: The system records and analyzes the user's behavior data in the dialogue system, such as dialogue content, dialogue duration, interaction frequency, etc., to construct a user behavior profile;

[0102] S504: Based on behavioral analysis, the system identifies the user's personalized needs, such as emotional support, psychological counseling, specific topic discussion, etc.;

[0103] S505: The system generates tweet content that meets the user's personalized needs based on the user's needs and behavior characteristics, combined with a preset tweet library and a machine learning algorithm;

[0104] S506: The system pushes the generated personalized tweets to the user through the dialogue interface to provide targeted psychological support and suggestions.

[0105] If the user's motivation is to learn psychological knowledge, the psychology popular science module is called for processing; the specific implementation process of the mechanism for analyzing and processing the user input content is as follows:

[0106] S601: The user inputs questions, points of interest or topics related to psychology on the system interface as demand input, and the user's demand input is sent to the system via text, voice or other supported input methods;

[0107] S602: After receiving the demand content input by the user, the system performs natural language processing, including text segmentation, part-of-speech tagging, syntactic analysis and other steps;

[0108] S603: The system identifies the user's core needs, interests or topics of concern by analyzing the processed text, and determines the scope of the psychology popular science knowledge base that needs to be called according to the user's needs;

[0109] S604: The system retrieves relevant information from a psychology popular science knowledge base based on the results of the needs analysis and understanding. The knowledge base includes psychology theories, case studies, answers to common psychological problems, etc.;

[0110] S605: The system generates corresponding psychology popular science content based on the information retrieved from the knowledge base and the personalized needs of the user. Content generation involves steps such as text editing, information integration, and language optimization to ensure the professionalism and readability of the content;

[0111] S606: The system sends the generated psychology popular science content to the user through a dialogue interface. The content can be displayed in various forms such as text, graphics, and video to meet the needs and preferences of different users.

[0112] S4: The processing is completed and the user ends the dialogue with the system.

[0113] The present invention has made great improvements and upgrades, improving the accuracy, safety, timeliness and efficiency of various aspects. The main innovative improvements are as follows:

[0114] 1. Professional psychological database and workflow integration:

[0115] Implementation method: We collected and organized a large number of professional psychological databases, including psychotherapy manuals, psychological assessment tools, etc. Based on these resources, we designed a set of standardized psychological counseling service workflows. We implemented automated workflow management in the system to ensure that each consultation can be conducted in accordance with professional procedures.

[0116] Technical details: Use database management system (MySQL) to store psychological database resources; use workflow engine (Camunda) to automate and standardize the consultation process.

[0117] 2. Psychological assessment services and report generation:

[0118] Implementation: A variety of psychological assessment scales and MBTI dialogue test models have been developed and integrated into the system. Users can enter the psychological assessment platform through the system interface to select the assessment scale. After completing the assessment, the system will automatically generate a report based on the assessment results. Users can also complete the assessment scale by talking to AI, and the MBTI dialogue test model will generate a test report.

[0119] Technical details: The evaluation interface is developed using HTML, CSS, and JavaScript; the backend uses Python to process the evaluation data and generate reports in HTML format.

[0120] 3. Early warning and intervention mechanism:

[0121] Implementation method: Analyze user input using natural language processing (NLP) technology and judge the user's emotional state through sentiment analysis algorithms. When the system detects that the user's emotions are abnormal or there is a risk, it will automatically trigger the early warning mechanism and send the early warning information to professional psychological counselors.

[0122] Technical details: The sentiment analysis model is trained using the NLP framework (PyTorch); during the model training phase, appropriate optimizers and loss functions are used. For example, the present invention uses the Adam optimizer, which has good performance and fast convergence speed. For sentiment analysis tasks, the present invention uses the cross entropy loss function to apply to the sentiment analysis model to make the obtained sentiment information more accurate. Hyperparameter adjustment: The choice of hyperparameters has a great impact on model performance, and the optimal hyperparameter combination can be found through grid search, random search, or Bayesian optimization. It can reach a higher level of sentiment analysis accuracy, and realize the real-time sending of warning information through HTTPAPI after accurate analysis.

[0123] 4. Advances in Natural Language Processing and Dialogue Generation:

[0124] Implementation: Use deep learning technology (Transformer) to train the dialogue generation model. By increasing the number of layers of the Transformer model and adjusting the self-attention mechanism to enhance the model's ability to capture long-distance dependencies, it helps the model better understand the complex history of psychological counseling conversations. The present invention incorporates psychological theories and terminology into model training to help the system better understand and generate professional psychological counseling answers. Personalization and emotional modeling: Each consultant has a unique background and emotional state, and the model should be able to respond personalized based on the conversation history. The model's ability to recognize and respond to emotions can be improved by introducing a sentiment analysis module.

[0125] Technical details: Use GPU-accelerated deep learning framework for model training; fine-tune pre-trained models to adapt to the specific needs of the psychological counseling field; use Beam Search technology to generate diverse responses.

[0126] 5. Intelligent self-learning module:

[0127] Implementation: We designed an intelligent self-learning module based on reinforcement learning, which can adjust its answering strategy and service mode according to user feedback and behavior data.

[0128] Technical details: The psychological counseling AI dialogue system is trained using a reinforcement learning algorithm (Policy Gradient); the effectiveness of different strategies is evaluated through methods such as A / B testing; user feedback and behavior data are used as reward signals to guide the model's learning process.

[0129] 6. Consultation feedback module:

[0130] Implementation method: After each consultation, the system will send a satisfaction questionnaire to the user. The user can submit feedback through the system interface or email. At the same time, it also provides functions such as message board and online customer service, so that users can contact at any time.

[0131] Technical details: Use forms and database technology to store user feedback data; use data analysis tools (Excel, R) to count and analyze feedback data; use CRM systems or email servers to automatically process and respond to user feedback.

[0132] 7. Stricter data privacy and security measures:

[0133] Implementation: We use a variety of security measures such as data encryption, access control, and security auditing to protect the security and confidentiality of user data. At the same time, we also cooperate with professional data security agencies to regularly conduct security checks and vulnerability repairs on the system.

[0134] Technical details: Use HTTPS protocol to encrypt data transmission; encrypt and store user sensitive information (AES encryption); set strict user permissions and access control policies; monitor and trace system operations through logging and auditing tools.

[0135] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An AI dialogue system for psychological consultation, characterized by: include: Psychological science module, personalized recommendation module, psychological assessment module, emotional communication module, early warning intervention module, appointment management module, natural language processing and dialogue generation module, intelligent self-learning module, consultation feedback module; the personalized recommendation module is connected to the psychological science module and the psychological assessment module, the emotional communication module is connected to the early warning intervention module and the appointment management module, and the natural language processing and dialogue generation module is connected to the psychological science module, personalized recommendation module, psychological assessment module, emotional communication module, early warning intervention module, appointment management module, intelligent self-learning module, and consultation feedback module; Psychological science module: Based on the preset psychological database and behavioral analysis based on user input, it provides users with a wealth of mental health knowledge and information, including an introduction to mental illness, methods for maintaining mental health, and common sense of psychological counseling; Personalized recommendation module: used to mine users’ personal profiles and preferences and recommend relevant mental health resources to users, including psychological articles, books, and courses; The psychological assessment module is used to call professional psychological databases to generate assessment questions, provide users with online psychological assessment services, evaluate users' psychological conditions, and generate detailed psychological assessment reports, interpret the reports, and give users suggestions so that users can better understand their psychological conditions. At the same time, the report can provide important references for professional psychological counselors; this module supports a variety of assessment scales and models to meet the assessment needs of different users, including anxiety scales, depression scales, and personality assessments; Emotional communication module: It is used to provide users with a platform for natural dialogue and emotional communication with the AI ​​system based on natural language processing technology. Through in-depth communication with users, it provides emotional support and psychological comfort to users, and can also further understand the psychological state and needs of users, providing strong support for subsequent early warning intervention and appointment management services; Early warning and intervention module: It is used to analyze the user's current input content in real time based on the user's historical data collected in the emotional communication module, and monitor the user's emotional changes in real time. When abnormal user emotions are detected, the early warning mechanism is automatically triggered and early warning information is sent to professional psychological counselors for timely intervention. At the same time, the module records early warning information and intervention results to provide data support for the continuous optimization and performance improvement of the system; The module also supports customization and personalization of warning information to meet the warning needs of different users; Reservation management module: used to provide users with online reservation services for professional psychological counselors, including appointment time, counselor selection, and consultation methods. This module provides users with convenient reservation services and also provides professional psychological counselors with effective customer management methods; Natural language processing and dialogue generation module: It is used to parse the natural language input by the user through the natural language understanding model based on deep learning technology, and to generate dialogue responses that conform to the context based on the dialogue response generation model based on natural language generation technology. It supports multi-round dialogue and context understanding to achieve continuous and natural communication with users and improve user experience and satisfaction. Intelligent self-learning module: Based on the automatic learning mechanism of user feedback and system operation data, it continuously optimizes and improves the system's psychological counseling service performance, including model updates and algorithm optimization. Through continuous learning and improvement, it can adapt to changes in user needs in real time and provide more accurate and personalized psychological counseling services. Consultation feedback module: used to provide users with a way to provide feedback on psychological counseling services. By collecting users' evaluations and suggestions on service quality, it can timely understand users' needs and expectations and provide direction for subsequent improvements and optimizations. At the same time, this module is also used to provide users with post-consultation follow-up services to ensure that users can receive continuous and effective psychological support.

2. The AI ​​dialogue system for psychological consultation as claimed in claim 1, characterized in that: The system also includes a privacy protection and security module, a cross-language and cultural adaptation module, an emergency contact network module, and a decision support tool module. The privacy protection and security module is used to strengthen data security measures and protect user privacy, including data encryption, access control, and data backup. The cross-language and cultural adaptation module is used to support multiple languages ​​and provide consulting services in multicultural contexts. The emergency contact network module is used to notify the user's emergency contacts or related service personnel in an emergency. The decision support tool module is used to provide data analysis for professional psychological counselors to assist in the formulation of treatment plans.

3. An AI dialogue method for psychological consultation, implemented based on the system according to any one of claims 1-2, characterized in that: The steps include: S1: The user opens the AI ​​dialogue system through a mobile terminal or PC; S2: The user inputs the conversation content; S3: The system obtains the conversation content for natural language processing and determines the user's motivation. If the user's motivation is psychological evaluation, the psychological evaluation module is called for processing. If the user's motivation is appointment consultation, the appointment management module is called for processing. If the user has extreme emotions, the early warning intervention module is called for processing. If the user's motivation is emotional communication, the emotional communication module is called for processing. If the user's motivation is to learn psychological knowledge, the psychological science module is called for processing. S4: The processing is completed and the user ends the dialogue with the system.

4. An AI dialogue method as claimed in claim 3, characterized in that: When the user motivation is psychological assessment, the following steps are included: S101: Send a psychological assessment link or directly conduct a psychological assessment by clicking on the psychological assessment module; S102: The user selects the required psychological assessment scale according to his / her needs; S103: Generate a corresponding psychological assessment report form according to the psychological assessment scale completed by the user; S104: Analyze the user's psychological assessment report data and determine whether the user needs consulting services. If so, go to step S105; if not, go to step S106; S105: After the user confirms that he needs consulting services, a window pops up to enter the reservation management module; S106: Enter the consultation feedback module; S107: Analyze the correctness and completeness of the conversation content according to the user's conversation, and call the intelligent self-learning module to perform system self-training to improve the accuracy of system processing.

5. An AI dialogue method as claimed in claim 3, characterized in that: When the user's motivation is to make an appointment for consultation, the following steps are included: S201: Send an appointment link or directly make a consultation appointment by clicking on the appointment management module; S202: The user selects an appointment time according to his / her own situation and simultaneously selects the psychological counseling service he / she needs; S203: The system pops up the must-read notice for appointment consultation, and the user confirms to proceed to the next step after reading; S204: The user fills in his / her own consultation information and selects the consultation method, and enters the payment system after submission; S205: If the user has successfully paid, a prompt will be displayed indicating that the reservation is successful and a reservation voucher and order will be popped up; S206: If the user cancels the payment or the payment fails, return to step S202; S207: If the user chooses online consultation, the user is reminded to log into the system and check online at the scheduled time to start the consultation service; S208: If the user chooses offline consultation, the appointment information is sent through the mobile terminal before the appointment time to remind the user to arrive on time and check offline, and the consultation service starts after the check-in; S209: If the user consultation service is finished, enter the consultation feedback module; S210: Analyze the correctness and completeness of the conversation content according to the user's conversation, and call the intelligent self-learning module to perform system self-training to improve the accuracy of system processing.

6. An AI dialogue method as claimed in claim 3, characterized in that: When a user has extreme emotions, the process of calling the early warning intervention module to handle the situation is as follows: S301: The system monitors the conversation content input by the user in real time through natural language processing technology; S302: Using a sentiment analysis algorithm to perform sentiment recognition on the content input by the user to determine the user's emotional state; S303: If the preset crisis warning triggering conditions are met, including specific keywords and emotion intensity thresholds, the crisis warning is triggered; S304: Generate detailed crisis warning information based on the user's specific situation and system analysis, including the user's basic information, emotional state, and possible risk factors; S305: Distribute crisis warning information to relevant personnel, including system administrators, professional psychological counselors, and emergency contacts; at the same time, the system calms the user's emotions through gentle and encouraging language; S306: The system continuously follows up on the processing of crisis warning information to ensure timely and effective response; at the same time, it collects and analyzes feedback information during the crisis warning processing process to optimize the performance and effect of the crisis warning module; S307: After the crisis warning is processed, the system re-evaluates the user to determine whether his or her emotional state has returned to normal, and adjusts subsequent psychological counseling and support strategies based on the evaluation results to effectively train the system.

7. An AI dialogue method as claimed in claim 3, characterized in that: When the user's motivation is emotional communication, the specific steps include: S401: receiving user input, parsing the user's text or voice through the conversation, and extracting key information and emotional tendencies; S402: using a sentiment analysis algorithm to determine the user's emotional state, including positive, negative, anxious, and depressed; S403: According to the user's emotional state, query relevant emotion regulation strategies, suggestions or resources in a pre-established psychological database, where the psychological database includes psychological expertise, case studies, and suggestion columns; S404: Based on the query results of the psychological database, combined with the user's personal information and historical records, generate a personalized emotion regulation response, including emotional support, advice, guidance or related psychological article recommendations; S405: Sending the generated emotion regulation response to the user in the form of text, voice or other forms to help the user understand and process his or her emotions; S406: Evaluate the system effect based on the user's feedback on the emotion regulation response for subsequent system optimization and improvement.

8. An AI dialogue method as claimed in claim 3, characterized in that: When a user needs personalized recommendation service, the personalized recommendation module is called, which includes the following steps: S501: The user inputs personal questions or emotional appeals in the dialogue interface and sends the dialogue content to the system; S502: Analyze the user's conversation content using natural language processing technology, and determine whether the user has potential motivation to conduct psychological assessment through text analysis algorithm; S503: The system records and analyzes the user's behavior data in the system, including conversation content, conversation duration, and interaction frequency, to construct a user behavior profile; S504: Based on behavioral analysis, identify the user's personalized needs, including emotional support, psychological counseling, and specific topic discussions; S505: Based on user needs and behavior characteristics, combined with a preset tweet library and a machine learning algorithm, the system generates tweet content that meets the user's personalized needs; S506: The system pushes the generated personalized tweets to the user through the dialogue interface to provide targeted psychological support and suggestions.

9. An AI dialogue method as claimed in claim 3, characterized in that: When the user's motivation is to learn psychological knowledge, the following steps are included: S601: The user inputs questions, points of interest or topics related to psychology on the system interface as demand input, and the demand input is sent via text, voice or other supported input methods; S602: Analyze the demand input based on natural language processing technology, including text segmentation, part-of-speech tagging, and syntactic analysis; S603: Identify the user's core needs, interests or topics of concern by analyzing the processed text; determine the scope of psychology popular science knowledge that needs to be called according to the user's needs; S604: According to the results of the needs analysis and understanding, relevant information is retrieved from a psychology popular science knowledge base, where the psychology popular science knowledge base includes psychology theories, case studies, and answers to common psychological problems. S605: Generate corresponding psychology popular science content based on the information retrieved from the psychology popular science knowledge base and in combination with the user's personalized needs; S606: Send the generated psychology popular science content to the user through the dialogue interface. The psychology popular science content can be displayed in various forms, including text, graphics, and video, to meet the needs and preferences of different users.

Images