Mental health appointment face inquiry system and method based on multi-role cooperation

Abstract

The invention discloses a psychological health appointment face inquiry system and method based on multi-role collaboration. The system comprises a visitor module, an appointment person module, a face inquiry person module, an administrator module and a data management module. The visitor module is provided with an emotion recognition sub-module which is used for analyzing the emotion tendency and the emergency degree described by the appointment text; the face inquiry module is integrated with a cross-modal biological feedback sub-module, physiological indexes, eye movement parameters and acoustic characteristics are collected in real time through intelligent wearable equipment, an eye tracker and voice analysis, and a comprehensive emotional state index is generated through a data fusion processor. And a polling teacher carries out consultation based on the biofeedback data, and an administrator monitors system operation. According to the method, multi-role efficient collaboration is realized, and the accuracy, the response speed and the service quality of psychological health services are remarkably improved through intelligent emotion recognition, multi-modal biofeedback and adaptive process scheduling.

Description

Technical Field

[0001] The present invention relates to the field of mental health service applications, and in particular to a mental health appointment consultation system and method based on multi-role collaboration. Background Art

[0002] With the accelerating pace of society and increasing pressures of life, mental health issues are receiving increasing attention. As a crucial tool for maintaining individual mental health and preventing mental illness, demand for mental health services is rapidly growing. However, traditional mental health appointment and consultation systems often suffer from unclear role divisions, poor information sharing, and inefficient process collaboration, making them unable to meet this growing demand.

[0003] Common mental health appointment consultation systems typically only provide simple appointment functions and lack effective support for collaborative work between multiple roles, including visitors, appointment clerks, interviewers, and administrators. This leads to poor information communication between roles and inefficient process collaboration during the appointment consultation process, affecting service quality and user experience. Furthermore, the system is unable to provide personalized interviewer recommendations and appointment scheduling based on the user's mental state and needs. Users often spend a lot of time screening interviewers, and appointment times may not be in line with their actual situation. Furthermore, due to the decentralized storage of users' mental health data and the lack of effective integration and analysis, it is difficult to dynamically track and evaluate users' mental states, which fails to meet the requirements of mental health service applications. Therefore, a mental health appointment consultation system and method based on multi-role collaboration is proposed. Summary of the Invention

[0004] The present invention provides the following technical solution: a multi-role collaborative mental health appointment consultation system, comprising: Visitor module, reservation clerk module, interviewer module, administrator module and data management module. The visitor module is used for visitor registration, login, personal information management, submission of psychological counseling appointment application, viewing appointment record status, participation in interviews and feedback evaluation. The visitor module is equipped with an emotion recognition submodule, which is used to analyze the text description filled in by visitors when making an appointment. The appointment clerk module is used to conduct a preliminary review of the appointment records submitted by the visitor, coordinate the consultation resources, and communicate with the visitor and the interviewer about the specific consultation time. The interviewer module is used to receive appointment requests, view the user's psychological state data, provide consultation services and record the consultation process, and manage the interviewer's basic information and consultation arrangements. The interviewer module is internally integrated with a cross-modal biofeedback submodule, which is equipped with a smart wearable device receiving unit, an eye tracker receiving unit, and a speech analysis unit. The administrator module is used to manage the basic data of visitors, appointment makers, and interviewers, monitor the system's operating status and overall service quality, and generate data statistical reports of different dimensions. The administrator module is internally equipped with an adaptive process engine that automatically switches processing paths based on urgency. The data management module is used to collect, store, integrate, and analyze users' psychological state data, appointment records, and interview reports; The visitor module is connected to the reservation clerk module through the reservation application interface, the visitor module is connected to the interviewer module through the evaluation feedback interface, and the visitor module is connected to the data management module through the data query interface.

[0005] The present invention provides a method for psychological health appointment consultation based on multi-role collaboration, based on the above-mentioned psychological health appointment consultation system based on multi-role collaboration, comprising the following steps: S1 Visitor Appointment Application: Visitors register and log into the system through the visitor module, fill in their personal information and submit a psychological counseling appointment application. The emotion recognition submodule then analyzes the emotional tendency of the text description, identifies the emotion type, assesses the urgency, and generates an urgency score. The system automatically marks the appointment priority based on the score. S2 appointment checker review and coordination: The appointment clerk receives appointment requests through the appointment clerk module and uses the resource coordination interface to query the real-time resource status of the interviewer module. At the same time, the intelligent resource scheduling engine generates preliminary interview arrangements, and then feeds the coordination results back to the data management module through the data synchronization interface, and pushes appointment notifications to the interviewer module. S3 Interviewer Response Preparation: The interviewer receives appointment requests through the interviewer module, views the user's psychological state data and historical consultation records, and obtains the visitor's physiological indicators, eye movement parameters and voice characteristics in real time through the smart wearable device receiving unit, eye tracker receiving unit and voice analysis unit of the cross-modal biofeedback submodule. At the same time, based on the comprehensive emotional state index generated by the data fusion processor, a personalized interview plan is formulated; S4 face-to-face consultation service implementation: The consultant conducts real-time consultations through the consultation service record interface, recording key points in the consultation process; the cross-modal biofeedback submodule continuously collects the visitor's biofeedback data to assist the consultant in dynamically adjusting the consultation strategy; S5 administrator monitoring statistics: Administrators monitor the system operation status through the administrator module, use the adaptive process engine to automatically switch processing paths according to the degree of urgency, and obtain multi-dimensional statistical reports generated by the data management module through the data statistics interface to evaluate service quality and resource utilization efficiency.

[0006] Preferably, the reservation clerk module is connected to the interviewer module through a resource coordination interface, the reservation clerk module is connected to the data management module through a data synchronization interface, the interviewer module is connected to the reservation clerk module through an appointment notification interface, the interviewer module is connected to the administrator module through a consultation report interface, the administrator module is connected to the data management module through a data statistics interface, and the administrator module is connected to the visitor module, the reservation clerk module and the interviewer module through a permission management interface.

[0007] Preferably, the emotion recognition submodule is internally provided with an emotion classification engine, a semantic association analyzer and an urgency assessment unit. The emotion classification engine performs an emotion tendency analysis on the text description in the visitor's appointment application based on natural language processing technology, and divides the emotion types into three categories: positive, negative and neutral. The semantic association analyzer is used to identify mental health crisis keywords in the text and establish a mapping relationship between keywords and emotional states. The urgency assessment unit is used to automatically generate an urgency score from 1 to 5 based on the emotion type distribution and the crisis keyword density.

[0008] Preferably, the smart wearable device receiving unit of the cross-modal biofeedback submodule is used to collect in real time the visitor's physiological indicator data of heart rate variability, galvanic skin response and body temperature changes during the interview process; the eye tracker receiving unit is used to collect the visitor's eye movement parameters such as gaze point, pupil dilation and line of sight movement trajectory; and the speech analysis unit is used to extract acoustic features such as pitch, volume, speaking speed and speech tremor.

[0009] Preferably, the cross-modal biofeedback submodule further includes a data fusion processor, which is used to synchronize time and fuse features of biofeedback data of different modalities, and synchronously generate a comprehensive emotional state index.

[0010] Preferably, the adaptive process engine includes a rule configuration unit, an emergency status identification unit and a processing path scheduling unit. The rule configuration unit is used by the administrator to customize processing rules and response time limits corresponding to different levels of urgency based on the psychological counseling industry standards and the actual situation of the institution. The emergency status identification unit is used to receive the urgency score and the visitor's historical interview record analysis results from the emotion recognition sub-module, and comprehensively determine the emergency status level of the current appointment request. The processing path scheduling unit is used to automatically switch between the standard process and the emergency process according to the emergency status level.

[0011] Preferably, when the adaptive process engine automatically switches the processing path according to the degree of urgency, the processing path includes a regular path, a priority path and an emergency intervention path. The regular path is for ordinary appointment requests with an urgency rating of 1-2, the priority path is for appointment requests with an urgency rating of 3, and the emergency intervention path is for appointment requests with an urgency rating of 4-5.

[0012] Preferably, the data management module includes a data acquisition layer, a data storage layer, a data integration layer and a data analysis layer; the appointment clerk module includes an appointment screening unit, a resource matching unit and a communication and coordination unit; the interviewer module includes an appointment management interface, a psychological status assessment interface, an interview service record interface and a personal workbench.

[0013] Preferably, the data acquisition layer collects raw data through a predefined data interface. The raw data includes personal information and appointment information filled out by the visitor, emotion recognition results, biofeedback data and interview records. The data storage adopts a hybrid database architecture. The data integration layer is used to associate the same visitor data scattered in various databases through identifiers and construct a complete visitor data portrait. The data analysis layer uses statistical analysis methods and machine learning algorithms to perform trend analysis, correlation analysis and predictive analysis on the integrated data.

[0014] In summary, compared with the prior art, the present invention provides a multi-role collaborative mental health appointment consultation system and method, which has the following beneficial effects: 1. The present invention achieves seamless information connection and efficient collaboration among various roles through the collaborative design of the visitor module, reservation clerk module, interviewer module, and administrator module. This enables the reservation clerk module to quickly review appointment applications and coordinate interview resources, reducing communication delays. The interviewer module can view user psychological state data in real time to optimize the consultation process. In combination with the administrator module, it can monitor the overall service quality and generate data reports to ensure efficient operation of the system. At the same time, the emotion recognition submodule in the visitor module can analyze the text description of the user's appointment and, combined with the psychological state data stored in the data management module, intelligently recommend matching interviewers and appropriate appointment times, reducing user screening time and improving appointment accuracy. 2. The present invention uses a data management module to centrally store and analyze users' psychological state data, appointment records, and consultation reports, supporting dynamic tracking and evaluation of changes in users' psychological states, providing more comprehensive references for counselors and improving consultation effectiveness. Furthermore, the cross-modal biofeedback submodule integrated into the counselor module can monitor the visitor's physiological and emotional state in real time, assisting counselors in more accurately grasping the user's psychological condition and improving consultation quality. 3. The present invention can automatically adjust the processing path according to the urgency of the appointment through the adaptive process engine in the administrator module, give priority to high-risk or urgent appointment requests, ensure that users receive timely psychological support, and improve the system's emergency response capabilities. At the same time, visitors can evaluate the face-to-face consultation service through the evaluation feedback interface to promote service quality optimization. The system's intelligent appointment matching and efficient collaboration mechanism reduce user waiting time and improve the overall service experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a structural schematic diagram of the present invention.

[0016] Figure 2 It is a structural schematic diagram of the present invention. DETAILED DESCRIPTION

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0018] See also Figure 1 The present invention provides a technical solution, a multi-role collaborative mental health appointment consultation system, comprising: Visitor module, appointment clerk module, interviewer module, administrator module and data management module. The visitor module is used for visitor registration, login, personal information management, submission of psychological counseling appointment application, viewing appointment record status, participation in interview and feedback evaluation. The visitor module is equipped with an emotion recognition submodule. The emotion recognition submodule is used to analyze the text description filled in by the visitor when making an appointment. The emotion recognition submodule is equipped with an emotion classification engine, a semantic association analyzer and an urgency assessment unit. The emotion classification engine performs emotion tendency analysis on the text description in the visitor's appointment application based on natural language processing technology, and divides the emotion type into three categories: positive, negative and neutral. The semantic association analyzer is used to identify mental health crisis keywords in the text and establish a mapping relationship between keywords and emotional states. The urgency assessment unit is used to automatically generate an urgency score from 1 to 5 based on the distribution of emotion types and the density of crisis keywords. The specific implementation steps of the above functions are as follows; First, text preprocessing and feature extraction are performed: After a visitor submits a text description through the reservation interface, the system first standardizes the text. This process includes removing irrelevant symbols and special characters, converting between traditional and simplified Chinese, segmenting words, and annotating parts of speech. Text features are then extracted, using natural language processing techniques to identify sentence structure, extract emotion-carrying words such as adjectives and adverbs, and annotate modifying elements such as negation and degree adverbs, thus constructing a structured text framework for subsequent analysis. Three-dimensional analysis of the sentiment classification engine: Based on the pre-trained Chinese sentiment dictionary, it matches the sentiment polarity words in the text. By counting the frequency of positive words (such as "relaxed" and "hopeful") and negative words (such as "anxious" and "painful"), an initial sentiment tendency distribution is formed. Combined with dependency syntax analysis technology, it identifies transitional conjunctions (such as "but" and "actually") and negative structures (such as "did not feel" and "not"), and dynamically adjusts the sentiment polarity judgment. For example, "I am not depressed" is corrected to a neutral expression. For mixed expressions containing both positive and negative words, the core appeal is identified through the topic model. For example, "Although nervous, I look forward to change" will be judged as neutral and positive, and marked with "looking forward to improvement" as the core appeal label; Crisis word activation in the semantic association analyzer: A built-in professional vocabulary library in the field of mental health includes eight categories of crisis keywords, such as suicidal tendencies (such as "wanting to end my life") and traumatic stress (such as "unable to sleep"). When a complete phrase or synonymous expression in the vocabulary is detected, the crisis flag is immediately triggered, and the metaphorical expression is identified through the word vector model. For example, the metaphor "life is like a heavy shackle" is parsed as a strong psychological burden and associated with the "depressive tendency" crisis category. The system records the frequency and contextual intensity of such indirect expressions; Emotion-Crisis Correlation Mapping: We preset a baseline emotional value for each crisis keyword and adjust the crisis weight based on the contextual sentiment. For example, the word "pain" in a positive context will lower the crisis level. Repeated crisis words in consecutive appointment requests are processed with attenuated memory to avoid assessment bias caused by repeated short-term consultations. Urgency Assessment Model: A basic level is assigned based on the density of negative emotional vocabulary (percentage of negative words). Those with a density ≥30% are directly promoted to the third level assessment. The frequency of crisis keywords per 100 words is counted. A high frequency (≥5 times) triggers an emergency intervention path. Time-oriented expressions and action-oriented vocabulary are identified, and the assessment level is upgraded. Past consultation records are accessed, and a weighted assessment is enabled for users with a history of crisis intervention, increasing the existing score by 1-2 levels. Graded response mechanism: perform differential processing based on the final score; Level 1-2: Included in the regular appointment queue, prompting the appointment staff to complete the first response within 24 hours; Level 3: Activate the priority processing mark and push it to a prominent position on the appointment agent's workbench, requiring resource coordination to be completed within 12 hours; Level 4-5: The crisis intervention process is automatically triggered and pushed directly to the emergency processing unit of the administrator module. The emergency duty mechanism for interviewers is simultaneously activated, and the first contact is required to be completed within 2 hours. The appointment clerk module is used to conduct a preliminary review of the appointment records submitted by the visitor, coordinate face-to-face consultation resources, and communicate with the visitor and the interviewer about the specific face-to-face consultation time. The interviewer module is used to receive appointment requests, view the user's psychological state data, provide face-to-face consultation services and record the consultation process, and manage the interviewer's basic information and face-to-face consultation arrangements. The interviewer module is internally integrated with a cross-modal biofeedback sub-module, which is equipped with an intelligent wearable device receiving unit, an eye tracker receiving unit, and a speech analysis unit. The intelligent wearable device receiving unit of the cross-modal biofeedback sub-module is used to collect real-time physiological indicator data such as heart rate variability, galvanic skin response, and body temperature changes of the visitor during the face-to-face consultation. The eye tracker receiving unit is used to collect eye movement parameters such as the visitor's gaze point, pupil dilation, and gaze movement trajectory. The speech analysis unit is used to extract acoustic features such as pitch, volume, speaking rate, and voice tremor. The cross-modal biofeedback sub-module also includes a data fusion processor, which is used to synchronize the time and feature fusion of biofeedback data of different modalities and simultaneously generate a comprehensive emotional state index. The specific implementation process of the above functions is as follows; Real-time collection of multi-source data: Visitors wear devices such as smart bracelets or chest straps to collect the following physiological indicators in real time; Heart rate variability: Analyzes changes in the intervals between heartbeats to reflect the state of the autonomic nervous system (such as stress level); Galvanic skin response: Detects emotional arousal (such as tension or fear) through changes in skin conductivity; Body temperature changes: monitor body surface temperature fluctuations to assist in determining emotional stress responses; Capture visitors' eye behavior through infrared cameras or head-mounted devices; Recording where the gaze is focused (e.g., avoiding eye contact may indicate anxiety), changes in pupil diameter are positively correlated with emotional intensity (e.g., pupils dilate with excitement or fear), and analyzing scanning patterns (e.g., frequent and rapid movements may be associated with a state of agitation); Speech analysis unit: collects speech signals through a microphone and extracts the following acoustic features; Pitch and volume: unusually elevated voices may reflect anger or anxiety; Speech speed: Rapid speech is often accompanied by nervousness; Voice tremor: detects the frequency of voice shaking and assesses emotional stability; Data synchronization and preprocessing: Data from each unit is synchronized at the millisecond level through a unified timestamp to ensure temporal consistency of multimodal data. Interference signals such as motion artifacts (such as wearable device displacement) and environmental noise (such as background noise) are eliminated, and data of different dimensions (such as heart rate beats per minute and electrodermal micro-Siemens) are normalized to a comparable value range. Multimodal data fusion and analysis: Physiological indicators, eye movement parameters, and voice features are integrated according to time windows to construct a joint feature matrix. When the client's voice tremors intensify, heart rate increases and pupil dilation occurs simultaneously, which may indicate an emotional crisis. In a calm state, each indicator tends to the baseline level. The weight of each modality is adjusted according to the consultation scenario (for example, focusing on voice analysis in the early stage and combining eye movement data later). Based on psychological clinical data, the fused features are mapped to emotional dimensions (such as pleasure, excitement, and stress index). The index curve and early warning prompts (such as "Current stress level: high") are output to the consultant's dashboard. The administrator module is used to manage the basic data of visitors, appointment makers and interviewers, as well as monitor the system operation status and overall service quality, and generate data statistical reports of different dimensions. The administrator module is internally equipped with an adaptive process engine, which is used to automatically switch processing paths according to the degree of urgency. The adaptive process engine includes a rule configuration unit, an emergency status identification unit and a processing path scheduling unit. The rule configuration unit allows the administrator to customize processing rules and response time limits corresponding to different degrees of urgency based on the psychological counseling industry standards and the actual situation of the institution. The emergency status identification unit is used to receive the urgency score from the emotion recognition submodule and the analysis results of the visitor's historical interview records, and comprehensively determine the urgency level of the current appointment request. The processing path scheduling unit is used to automatically switch between standard processes and emergency processes according to the urgency level. When the adaptive process engine automatically switches processing paths according to the degree of urgency, the processing paths include conventional paths, priority paths and emergency intervention paths. The conventional path is for ordinary appointment requests with an urgency rating of 1-2, the priority path is for appointment requests with an urgency rating of 3, and the emergency intervention path is for appointment requests with an urgency rating of 4-5; The data management module is used to collect, store, integrate and analyze users' psychological state data, appointment records and interview reports. The data management module includes a data collection layer, a data storage layer, a data integration layer and a data analysis layer. The appointment clerk module includes an appointment screening unit, a resource matching unit and a communication and coordination unit. The interviewer module includes an appointment management interface, a psychological status assessment interface, an interview service record interface and a personal workbench. The data collection layer collects raw data through predefined data interfaces. The raw data includes personal information and appointment information filled in by visitors, emotion recognition results, biofeedback data and interview records. The data storage adopts a hybrid database architecture. The data integration layer is used to associate the same visitor data scattered in various databases through identifiers and build a complete visitor data portrait. The data analysis layer uses statistical analysis methods and machine learning algorithms to perform trend analysis, correlation analysis and predictive analysis on the integrated data. The visitor module is connected to the reservationist module through the appointment application interface, the visitor module is connected to the interviewer module through the evaluation feedback interface, the visitor module is connected to the data management module through the data query interface, the reservationist module is connected to the interviewer module through the resource coordination interface, the reservationist module is connected to the data management module through the data synchronization interface, the interviewer module is connected to the reservationist module through the appointment notification interface, the interviewer module is connected to the administrator module through the consultation report interface, the administrator module is connected to the data management module through the data statistics interface, and the administrator module is connected to the visitor module, the reservationist module and the interviewer module through the authority management interface.

[0019] The present invention provides a method for psychological health appointment consultation based on multi-role collaboration, based on the above-mentioned psychological health appointment consultation system based on multi-role collaboration, comprising the following steps: S1 Visitor Appointment Application: Visitors register and log into the system through the visitor module, fill in their personal information and submit a psychological counseling appointment application. The emotion recognition submodule then analyzes the emotional tendency of the text description, identifies the emotion type, assesses the urgency, and generates an urgency score. The system automatically marks the appointment priority based on the score. S2 appointment checker review and coordination: The appointment clerk receives appointment requests through the appointment clerk module and uses the resource coordination interface to query the real-time resource status of the interviewer module. At the same time, the intelligent resource scheduling engine generates preliminary interview arrangements, and then feeds the coordination results back to the data management module through the data synchronization interface, and pushes appointment notifications to the interviewer module. S3 Interviewer Response Preparation: The interviewer receives appointment requests through the interviewer module, views the user's psychological state data and historical consultation records, and obtains the visitor's physiological indicators, eye movement parameters and voice characteristics in real time through the smart wearable device receiving unit, eye tracker receiving unit and voice analysis unit of the cross-modal biofeedback submodule. At the same time, based on the comprehensive emotional state index generated by the data fusion processor, a personalized interview plan is formulated; S4 face-to-face consultation service implementation: The consultant conducts real-time consultations through the consultation service record interface, recording key points in the consultation process; the cross-modal biofeedback submodule continuously collects the visitor's biofeedback data to assist the consultant in dynamically adjusting the consultation strategy; S5 administrator monitoring statistics: Administrators monitor the system operation status through the administrator module, use the adaptive process engine to automatically switch processing paths according to the degree of urgency, and obtain multi-dimensional statistical reports generated by the data management module through the data statistics interface to evaluate service quality and resource utilization efficiency.

[0020] This solution achieves seamless information connection and efficient collaboration among various roles through the collaborative design of the visitor module, appointment clerk module, interviewer module and administrator module. The appointment clerk module can quickly review appointment applications and coordinate interview resources to reduce communication delays, while the interviewer module can view user psychological state data in real time to optimize the consultation process. Combined with the administrator module, it can monitor the overall service quality and generate data reports to ensure the efficient operation of the system. At the same time, the emotion recognition sub-module in the visitor module can analyze the text description of the user's appointment, and combined with the psychological state data stored in the data management module, intelligently recommend matching interviewers and appropriate appointment times, reducing user screening time and improving appointment accuracy.

[0021] This solution uses the data management module to centrally store and analyze users' psychological state data, appointment records and consultation reports, supports dynamic tracking and evaluation of changes in users' psychological states, provides more comprehensive reference basis for counselors, and improves consultation effects. In addition, the cross-modal biofeedback submodule integrated in the counselor module can monitor the physiological and emotional state of visitors in real time, assisting counselors to grasp users' psychological conditions more accurately and improve consultation quality.

[0022] This solution also implements the adaptive process engine in the administrator module to automatically adjust the processing path according to the urgency of the appointment, giving priority to high-risk or urgent appointment requests, ensuring that users receive timely psychological support, and improving the system's emergency response capabilities. At the same time, visitors can evaluate face-to-face consultation services through the evaluation feedback interface to promote service quality optimization. The system's intelligent appointment matching and efficient collaboration mechanism reduce user waiting time and improve the overall service experience.

[0023] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0024] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A multi-role collaborative mental health appointment consultation system, characterized by: include: Visitor module, reservation clerk module, interviewer module, administrator module and data management module. The visitor module is used for visitor registration, login, personal information management, submission of psychological counseling appointment application, viewing appointment record status, participation in interviews and feedback evaluation. The visitor module is equipped with an emotion recognition submodule, which is used to analyze the text description filled in by visitors when making an appointment. The appointment clerk module is used to conduct a preliminary review of the appointment records submitted by the visitor, coordinate the consultation resources, and communicate with the visitor and the interviewer about the specific consultation time. The interviewer module is used to receive appointment requests, view the user's psychological state data, provide consultation services and record the consultation process, and manage the interviewer's basic information and consultation arrangements. The interviewer module is internally integrated with a cross-modal biofeedback submodule, which is equipped with a smart wearable device receiving unit, an eye tracker receiving unit, and a speech analysis unit. The administrator module is used to manage the basic data of visitors, appointment makers, and interviewers, monitor the system's operating status and overall service quality, and generate data statistical reports of different dimensions. The administrator module is internally equipped with an adaptive process engine that automatically switches processing paths based on urgency. The data management module is used to collect, store, integrate, and analyze users' psychological state data, appointment records, and interview reports; The visitor module is connected to the reservation clerk module through the reservation application interface, the visitor module is connected to the interviewer module through the evaluation feedback interface, and the visitor module is connected to the data management module through the data query interface.

2. The multi-role collaborative mental health appointment consultation system according to claim 1 is characterized by: The reservation clerk module is connected to the interviewer module through the resource coordination interface, the reservation clerk module is connected to the data management module through the data synchronization interface, the interviewer module is connected to the reservation clerk module through the appointment notification interface, the interviewer module is connected to the administrator module through the consultation report interface, the administrator module is connected to the data management module through the data statistics interface, and the administrator module is connected to the visitor module, the reservation clerk module and the interviewer module through the authority management interface.

3. The multi-role collaborative mental health appointment consultation system according to claim 1 is characterized by: The emotion recognition submodule is internally equipped with an emotion classification engine, a semantic association analyzer and an urgency assessment unit. The emotion classification engine performs an emotional tendency analysis on the text description in the visitor's appointment application based on natural language processing technology, and divides the emotional types into three categories: positive, negative and neutral. The semantic association analyzer is used to identify mental health crisis keywords in the text and establish a mapping relationship between keywords and emotional states. The urgency assessment unit is used to automatically generate an urgency score from 1 to 5 based on the distribution of emotion types and the density of crisis keywords.

4. The multi-role collaborative mental health appointment consultation system according to claim 1 is characterized by: The smart wearable device receiving unit of the cross-modal biofeedback submodule is used to collect in real time the visitor's physiological indicator data such as heart rate variability, galvanic skin response and body temperature changes during the interview process; the eye tracker receiving unit is used to collect the visitor's eye movement parameters such as gaze point, pupil dilation and gaze movement trajectory; and the speech analysis unit is used to extract acoustic features such as pitch, volume, speaking speed and speech tremor.

5. The multi-role collaborative mental health appointment consultation system according to claim 1 is characterized by: The cross-modal biofeedback submodule further includes a data fusion processor, which is used to synchronize the time and feature of biofeedback data of different modalities and synchronously generate a comprehensive emotional state index.

6. The multi-role collaborative mental health appointment consultation system according to claim 1 is characterized by: The adaptive process engine includes a rule configuration unit, an emergency status identification unit and a processing path scheduling unit. The rule configuration unit is used by the administrator to customize processing rules and response time limits corresponding to different levels of urgency based on the psychological counseling industry standards and the actual situation of the institution. The emergency status identification unit is used to receive the urgency score and the visitor's historical interview record analysis results from the emotion recognition sub-module, and comprehensively determine the emergency status level of the current appointment request. The processing path scheduling unit is used to automatically switch between the standard process and the emergency process according to the emergency status level.

7. The multi-role collaborative mental health appointment consultation system according to claim 6 is characterized by: When the adaptive process engine automatically switches the processing path according to the degree of urgency, the processing path includes a regular path, a priority path and an emergency intervention path. The regular path is for ordinary appointment requests with an urgency rating of 1-2, the priority path is for appointment requests with an urgency rating of 3, and the emergency intervention path is for appointment requests with an urgency rating of 4-5.

8. The multi-role collaborative mental health appointment consultation system according to claim 1 is characterized by: The data management module includes a data collection layer, a data storage layer, a data integration layer and a data analysis layer; the appointment clerk module includes an appointment screening unit, a resource matching unit and a communication and coordination unit; the interviewer module includes an appointment management interface, a psychological status assessment interface, an interview service record interface and a personal workbench.

9. The multi-role collaborative mental health appointment consultation system according to claim 8 is characterized by: The data acquisition layer collects raw data through a predefined data interface. The raw data includes personal information and appointment information filled out by visitors, emotion recognition results, biofeedback data and interview records. The data storage adopts a hybrid database architecture. The data integration layer is used to associate the same visitor data scattered in various databases through identifiers and build a complete visitor data portrait. The data analysis layer uses statistical analysis methods and machine learning algorithms to perform trend analysis, correlation analysis and predictive analysis on the integrated data.

10. A method for mental health appointment consultation based on multi-role collaboration, based on the mental health appointment consultation system based on multi-role collaboration according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1 Visitor Appointment Application: Visitors register and log into the system through the visitor module, fill in their personal information and submit a psychological counseling appointment application. The emotion recognition submodule then analyzes the emotional tendency of the text description, identifies the emotion type, assesses the urgency, and generates an urgency score. The system automatically marks the appointment priority based on the score. S2 appointment checker review and coordination: The appointment clerk receives appointment requests through the appointment clerk module and uses the resource coordination interface to query the real-time resource status of the interviewer module. At the same time, the intelligent resource scheduling engine generates preliminary interview arrangements, and then feeds the coordination results back to the data management module through the data synchronization interface, and pushes appointment notifications to the interviewer module. S3 Interviewer Response Preparation: The interviewer receives appointment requests through the interviewer module, views the user's psychological state data and historical consultation records, and obtains the visitor's physiological indicators, eye movement parameters and voice characteristics in real time through the smart wearable device receiving unit, eye tracker receiving unit and voice analysis unit of the cross-modal biofeedback submodule. At the same time, based on the comprehensive emotional state index generated by the data fusion processor, a personalized interview plan is formulated; S4 face-to-face consultation service implementation: The consultant conducts real-time consultation through the consultation service record interface and records key points of the consultation process; The cross-modal biofeedback submodule continuously collects biofeedback data from clients, assisting counselors in dynamically adjusting counseling strategies; S5 administrator monitoring statistics: Administrators monitor the system operation status through the administrator module, use the adaptive process engine to automatically switch processing paths according to the degree of urgency, and obtain multi-dimensional statistical reports generated by the data management module through the data statistics interface to evaluate service quality and resource utilization efficiency.

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