Self-service psychological assessment and guidance system and method based on Internet

Abstract

The invention discloses an internet-based self-service psychological assessment and guidance system and method, and particularly relates to the field of internet, the system comprises a multi-source data acquisition module, a data preprocessing module, a layered assessment analysis module, a personalized guidance scheme generation module, a real-time feedback adjustment module and an effect tracking evaluation module; the real-time feedback adjustment module is used for collecting user feedback data and dynamically optimizing the scheme in the grooming process; the real-time feedback adjustment module is used for collecting user feedback data and dynamically optimizing the scheme in the grooming process; the effect tracking evaluation module is used for recalculating the comprehensive psychological state index after 7 days of continuous dredging, and evaluating the improvement effect; through multi-source data fusion and standardized layered evaluation, an accurate psychological state evaluation result is obtained, the problem of large result deviation caused by single evaluation dimension and dependence on a subjective questionnaire is solved, and the beneficial effects of comprehensively describing the psychological state of the user and improving the evaluation accuracy are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet, more particularly, the present application relates to an Internet-based self-help psychological evaluation and counseling system and method. BACKGROUND

[0002] The amygdala is part of the limbic system of the brain, involved in emotional regulation, learning and memory, stress response, social behavior and drug addiction, and its dysfunction may be related to a variety of psychological and neuropsychiatric disorders. In the field of mental health services, Internet-based self-help psychological evaluation and counseling technology has become an important way for the public to access psychological support. Through online questionnaires, AI conversations, pre-recorded audio, etc., it has realized the convenient supply of psychological services, allowing users to access basic psychological support at any time, and reducing the time and cost threshold of traditional psychological counseling.

[0003] Although the existing Internet-based self-help psychological evaluation and counseling technology has expanded the service coverage, it still has significant limitations. First, the evaluation dimension is single, relying only on user-submitted questionnaire data, lacking objective indicators such as physiological responses and behavioral characteristics, and unable to capture autonomic nervous responses (such as heart rate variability and skin conductance changes) caused by activation of brain emotional centers such as the amygdala, resulting in large evaluation result bias. Second, the counseling program is rigid, based on static tags to push pre-set content, unable to dynamically adjust according to user real-time state, and lacks adaptability. Third, the feedback mechanism is missing, and real-time user reactions during the counseling process are not included in the adjustment basis, making it difficult to achieve precise intervention. These problems make it difficult for existing technology to meet users' demand for personalized and precise psychological services.

[0004] Therefore, there is an urgent need for an Internet-based self-help psychological evaluation and counseling system and method that can achieve diversified evaluation dimensions, personalized counseling programs, and precise intervention processes through multi-source data collection, hierarchical evaluation analysis, dynamic program generation, real-time feedback adjustment, and effect tracking, in order to solve the problems of large evaluation bias, lack of adaptability, and lack of feedback in existing technology. SUMMARY

[0005] In order to overcome the above-mentioned defects of the prior art, the present application provides an Internet-based self-help psychological evaluation and counseling system and method, which solves the problems raised in the background art by the following scheme.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical scheme: an Internet-based self-help psychological evaluation and counseling system, comprising a user terminal, an evaluation server, a physiological sensing module, a behavior analysis module, and a counseling interaction module, and specifically further comprising: Multi-source data acquisition module: after obtaining user authorization, user data is collected through the user terminal, physiological sensing module, and behavior analysis module; Data preprocessing module: outlier rejection and standardization processing of collected data to improve data effectiveness; Hierarchical evaluation analysis module: based on the pre-processed data, single dimension evaluation and cross dimension evaluation are sequentially performed; Personalized counseling scheme generation module: determine the threshold value according to the clinical sample to divide the counseling type, and match the counseling form according to the counseling interaction frequency; Real-time feedback adjustment module: collect user feedback data during the counseling process and dynamically optimize the scheme; Effect tracking evaluation module: after 7 days of continuous counseling, the comprehensive psychological state index is recalculated to evaluate the improvement effect.

[0007] Preferably, the user data includes basic data, physiological data, behavior data and interaction data; the basic data is obtained through a user terminal form and specifically includes age A and gender G; the physiological data is collected through a physiological sensing module and specifically includes heart rate variability HRV, skin conductance GSR and respiratory frequency RF; the behavior data is recorded in real time through a behavior analysis module and specifically includes page stay duration PT, click interval time CI and typing speed TS; the interaction data is collected through a counseling interaction module and specifically includes questionnaire emotion score ES, stress score PS and counseling interaction frequency IF.

[0008] Preferably, the outlier rejection is based on criteria, physiological and behavior data are divided into data windows according to a single evaluation period, and in a single window: if the data satisfies , wherein represents the mean value of the data in the window, represents the standard deviation; it is determined as an outlier and is rejected, and after rejection, the missing values are supplemented by linear interpolation; the standardization processing: converts non-classification data including HRV, GSR, RF, PT, CI, TS and IF to the 0-1 interval to obtain standardized data , , , , , and , specifically represented as: standardized value , wherein represents the minimum value of the corresponding parameter in the single evaluation period, represents the maximum value of the corresponding parameter in the single evaluation period.

[0009] Preferably, the single dimension evaluation includes a physiological state index model, a behavior state index model and an emotional state index model; the physiological state index model is calculated by calculating the physiological state index to reflect the correlation between physiological indicators and emotions; the behavior state index model calculates a behavior state index to reflect the correlation between physiological indicators and emotions; the behavior state index model calculates a behavior state index to reflect the correlation between physiological indicators and emotions; the behavior state index model calculates a behavior state index

[0010] Preferably, the cross-dimension evaluation includes psychosomatic coordination evaluation and comprehensive psychological state evaluation; the psychosomatic coordination evaluation calculates a psychosomatic coordination index to reflect the correlation between physiological indicators and emotions; the behavior state index model calculates a behavior state index to reflect the correlation between physiological indicators and emotions; the behavior state index model calculates a behavior state index

[0011] Preferably, the guidance type: if PI>0.6 and SI<0.4, it is determined as a physiological dominant type, and a respiratory regulation scheme is matched; if BI>0.6 and EI<0.3, it is determined as a behavior dominant type, and an attention training scheme is matched; if EI>0.6 and SI<0.4, it is determined as an emotional dominant type, and a cognitive reconstruction scheme is matched; the guidance intensity is used to calculate a single duration and a daily frequency; the single duration ; the daily frequency ; the guidance form selection: if >0.7, real-time text interaction is adopted; if 0.3 0.7, voice guidance is adopted; <0.3, video demonstration is adopted.

[0012] Preferably, the user feedback data includes a guidance completion degree CD and an emotional fluctuation value EV; the dynamic optimization scheme includes adjustment coefficient calculation and scheme adjustment; the adjustment coefficient is used to reflect the matching degree of guidance effect and user state; the scheme adjustment: if ADJ<0.5, the single duration is shortened by 20% and the frequency is increased by 1 time / day; if 0.5 ADJ 0.8, the original scheme is maintained; if ADJ>0.8, the single duration is extended by 10% and the frequency is reduced by 0.5 times / day.

[0013] Preferably, the evaluation improvement effect is evaluated by calculating an improvement degree and scheme iteration; the improvement degree , wherein represents a comprehensive psychological state index before guidance; represents a comprehensive psychological state index after 7 days of guidance, The 7-day average dredging completion degree is represented; the scheme iteration: if IM>0.3, it represents significant improvement, maintaining the current dredging type; if 0.1<=IM<=0.3, it represents slight improvement, fusing the next adaptive type; if IM<0.1, it represents no improvement, and a warning signal is sent.

[0014] Preferably, an internet-based self-help psychological assessment dredging method comprises a user terminal, an assessment server, a physiological sensing module, a behavior analysis module and a dredging interaction module, and specifically further comprises the following steps: S1, after obtaining user authorization, collecting user data through the user terminal, the physiological sensing module and the behavior analysis module; S2, performing outlier rejection and standardization processing on the collected data to improve data effectiveness; S3, based on the preprocessed data, sequentially performing single-dimensional evaluation and cross-dimensional evaluation; S4, dividing the dredging type according to the threshold value determined according to the clinical sample, and matching the dredging form according to the dredging interaction frequency; S5, collecting user feedback data and dynamically optimizing the scheme during the dredging process; S6, after continuous dredging for 7 days, recalculating the comprehensive psychological state index to evaluate the improvement effect.

[0015] The technical effects and advantages of the present application are as follows: 1, the present application obtains accurate psychological state evaluation results through multi-source data fusion and standardized hierarchical evaluation, solves the problem of single evaluation dimension and large result deviation caused by dependence on subjective questionnaire, and achieves the beneficial effects of fully depicting user psychological state and improving evaluation accuracy; 2, the present application obtains a highly adaptive personalized dredging scheme through dynamic scheme generation and real-time feedback adjustment based on clinical support, solves the problem of fixed dredging scheme and insufficient adaptability caused by inability to adjust according to user real-time state, and achieves the beneficial effects of improving user acceptance and enhancing psychological intervention targeting; 3, the present application obtains a continuously optimized closed-loop intervention scheme through effect tracking and differentiated iteration system, solves the problem of missing feedback mechanism and insufficient accurate intervention caused by inability to adjust based on long-term effect, and achieves the beneficial effects of long-term improvement of user psychological state and reduction of invalid intervention cost. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is a schematic diagram of the system structure of the present application; Figure 2 is a schematic diagram of the method structure of the present application. DETAILED DESCRIPTION

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] As attached Figure 1 The system shown is an internet-based self-service psychological assessment and counseling system, which includes a user terminal, an assessment server, a physiological sensing module, a behavior analysis module, and a counseling interaction module. Specifically, it also includes a multi-source data acquisition module, a data preprocessing module, a hierarchical assessment and analysis module, a personalized counseling plan generation module, a real-time feedback and adjustment module, and an effect tracking and evaluation module.

[0019] The user terminal uses a smartphone or tablet device, supporting data collection command sending, interactive interface display, and guidance content playback. The evaluation server is deployed on an Alibaba Cloud ECS server, equipped with a MySQL 8.0 database to store user data, and runs a Python 3.9 environment to execute evaluation algorithms. The physiological sensing module is integrated into a smart bracelet, supports Bluetooth 5.3 communication with the user terminal, and has functions for collecting heart rate variability, skin conductivity, and respiratory rate. The behavior analysis module is embedded in the user terminal APP in the form of an SDK, which can capture terminal operation events in real time and filter invalid operation data according to preset rules. The guidance interaction module is a visual interactive interface within the APP, supporting text input, voice playback, and video playback, and can record the frequency and duration of user interactions.

[0020] The multi-source data acquisition module: after obtaining user authorization, collects user data through the user terminal, physiological sensing module and behavior analysis module.

[0021] In this embodiment, it should be specifically noted that: the user data includes basic data, physiological data, behavioral data, and interaction data; the basic data is obtained through user terminal forms and specifically includes age (A) and gender (G); the physiological data is collected through a physiological sensing module and specifically includes heart rate variability (HRV), skin conductance (GSR), and respiratory rate (RF); the behavioral data is recorded in real time through a behavior analysis module and specifically includes page dwell time (PT), click interval time (CI), and typing speed (TS); the interaction data is collected through a guidance interaction module and specifically includes questionnaire emotion score (ES), stress score (PS), and guidance interaction frequency (IF). The basic data collection method is as follows: The user terminal APP displays a structured form with two required fields: age and gender. The age field only allows integers between 18 and 65 years old (if the age exceeds the range, the APP will pop up a message asking for a valid age between 18 and 65 years old). The gender field provides two radio buttons, male and female (mapped to the values ​​1 and 0 respectively). Data storage: After the user submits the data, it is stored in the evaluation server's MySQL database in the format of user ID-collection timestamp-age-gender. The storage period is consistent with the user's authorization period (maximum 2 years, after which it will be automatically de-identified and deleted). HRV Acquisition: The smart bracelet acquires heart rate signals via a PPG (photoplethysmography) sensor, calculates the RR interval (the time difference between two consecutive heartbeat peaks) at a frequency of 1 beat / second, and calculates the standard deviation of the RR interval according to the AAMI / ISO81060-2:2018 standard, which is the HRV value (unit: ms). During the acquisition process, if the bracelet detects user movement (accelerometer data > 0.5g), it pauses the acquisition and prompts the user to remain still via the app to ensure accurate HRV data. Acquisition resumes after the user remains still. GSR Acquisition: The smart bracelet acquires heart rate signals via electrodes... The sensor contacts the user's wrist skin, measures the skin's surface resistance and converts it into conductivity (unit: μS), with a measurement range of 0-100μS and a sampling frequency of 1 time / second. If poor skin contact is detected (GSR value >100μS or <0.1μS), the APP will pop up a prompt to adjust the wristband's wearing position to ensure close contact between the electrodes and the skin. RF acquisition: The smart wristband detects the frequency of chest rise and fall of the user through a thymus sensor, recording the number of breaths at 1 time / second and converting it to respiratory rate per minute (unit: breaths / minute). If the user is in a lying position (gravity sensor detects tilt angle >...), the sensor will detect the frequency of breaths. If the data is not properly stored, the data acquisition algorithm will be automatically corrected (to compensate for changes in respiratory depth while lying down). Data transmission: Physiological data is synchronized to the user's terminal APP every 5 seconds via Bluetooth in the format of User ID-Collection Timestamp-HRV-GSR-RF, and then uploaded to the evaluation server in batches every 30 seconds by the APP. Page dwell time (PT): The behavior analysis module captures the timestamp T1 of the APP page fully loading event (marked by the completion of page DOM tree loading) and the timestamp T2 of the user clicking the next page button. PT = T2 - T1 (unit: seconds). If the user causes the page to load for more than 10 seconds due to network latency, the APP automatically records the network latency label, and this PT data will not be included in subsequent analysis. Click interval (CI): The behavior analysis module only captures the user's click events on valid interactive buttons (such as the button to submit a questionnaire and play guidance audio), and records the timestamps T3 and T4 of two consecutive valid clicks. CI = T4 - T3 (unit: seconds). If the user clicks on a blank area... The click event of an invalid button will not be included in the CI calculation; Typing speed (TS): The behavior analysis module records the character input stream of the user in the questionnaire input box and counts the number of valid input characters within 1 minute (excluding the delete key, space key, and repeated input of the same character), which is TS (unit: characters / minute); If the user's input is interrupted for more than 30 seconds, the timer is reset to calculate TS; Data filtering: Behavioral data is initially filtered in real time in the user terminal APP, removing data with PT>300 seconds (user has not operated for a long time), CI>180 seconds (user has not clicked for a long time), and TS<30 characters / minute (user input is abnormally slow), and only valid data is uploaded to the server. Emotional Score (ES): The app pushes a structured emotional questionnaire, including questions about the current level of emotional fluctuation, and provides a 0-10 score slider (0 indicates no emotional fluctuation, 10 indicates extreme emotional fluctuation, difficult to control). After the user drags the slider to select, the app records the corresponding value as ES; Stress Score (PS): The current stress perception question in the questionnaire also uses a 0-10 score slider (0 indicates no stress, 10 indicates extreme stress, affecting daily life). After the user selects, it is recorded as PS; Interaction Frequency (IF): During the intervention process, the behavior analysis module counts the number of interactions between the user and the app (including clicking the pause / continue button, voice input feedback, and swiping to view intervention content), and calculates IF by interaction frequency / minute (unit: times / minute); if the user does not interact for more than 1 minute, the IF is counted as 0; Data Submission: After the user completes the questionnaire or intervention, the interaction data and behavior data are synchronously uploaded to the assessment server and stored in conjunction with the user ID.

[0022] The data preprocessing module performs outlier removal and standardization on the collected data to improve data validity.

[0023] In this embodiment, it should be specifically noted that: the outlier removal is based on... The criteria divide physiological and behavioral data into data windows based on a single assessment cycle. Within a single window: if data satisfy ,in This represents the mean of the data within the window. The standard deviation is used to identify outliers and remove them. Missing values ​​are then filled in using linear interpolation. The standardization process involves transforming non-categorical data, including HRV, GSR, RF, PT, CI, TS, and IF, to the 0-1 range to obtain standardized data. , , , , , as well as Specifically, it is represented as: standardized value ,in This represents the minimum value of the corresponding parameter within a single evaluation period. This indicates the maximum value of the corresponding parameter within a single evaluation period.

[0024] The hierarchical evaluation and analysis module performs single-dimensional evaluation and cross-dimensional evaluation sequentially based on the preprocessed data.

[0025] In this embodiment, it should be specifically noted that the single-dimensional assessment includes a physiological state index model, a behavioral state index model, and an emotional state index model; the physiological state index model calculates the physiological state index. This reflects the correlation between physiological indicators and emotions; the behavioral state index model calculates the behavioral state index. This reflects the correlation between behavioral characteristics and psychological states; the emotional state index model calculates an emotional state index. To integrate subjective ratings and interactive behaviors; the cross-dimensional assessment includes mind-body synergy assessment and comprehensive psychological state assessment; the mind-body synergy assessment calculates the mind-body synergy index. To reflect the synergy between physiology and behavior; the comprehensive psychological state assessment calculates a comprehensive psychological state index. This index reflects the impact of age and gender on psychological state. The physiological state index, based on clinical research, shows a negative correlation between HRV and anxiety level (higher HRV indicates less anxiety), a positive correlation between GSR and stress level (higher GSR indicates more stress), and a negative correlation between RF and emotional stability (higher RF indicates more unstable emotions). The behavioral state index uses a correlation analysis between behavioral characteristics and psychological activity; faster TS (typing speed), shorter PT (page dwell time), and shorter CI (click interval) reflect higher user psychological activity; a higher BI value indicates a more active user behavior. The emotional state index is based on a fusion assessment model of subjective ratings and interactive behavior in the *Psychological Assessment Manual*; higher ES (emotional score) and PS (stress score) indicate a greater emotional load; higher IF (interaction frequency) indicates a higher emotional load. The higher the user's participation in guidance, the more it can help correct the bias of subjective ratings; the higher the EI value, the more attention the user's emotional state needs; the cross-dimensional index cites research on the correlation between mind-body synergy and psychological state, the higher the synergy between physiological state (PI) and behavioral state (BI), the more coordinated the user's overall psychological state; emotional state (EI) can correct the assessment bias of mind-body synergy; the higher the SI value, the more coordinated the user's mind-body state; the comprehensive psychological state index cites research on the impact of age and gender on psychological state assessment, the older the user, the lower the amplitude of psychological fluctuations (for every year of age increase, the amplitude of psychological fluctuations decreases by 1%), and women are more sensitive to emotions than men (women are 20% more sensitive than men); the higher the MSI value, the more intervention the user's psychological state needs.

[0026] The personalized guidance plan generation module: classifies guidance types based on thresholds determined from clinical samples, and matches guidance forms according to the frequency of guidance interactions; In this embodiment, it should be specifically noted that: the guidance type is determined as follows: if PI > 0.6 and SI < 0.4, it is determined to be physiologically dominant and a breathing regulation program is matched; if BI > 0.6 and EI < 0.3, it is determined to be behaviorally dominant and an attention training program is matched; if EI > 0.6 and SI < 0.4, it is determined to be emotionally dominant and a cognitive restructuring program is matched; the guidance intensity is used to calculate the single session duration and daily frequency; the single session duration... The daily frequency The selection of the diversion method: if If the value is >0.7, real-time text interaction is used; if it is 0.3... Version 0.7 uses voice guidance. <0.3, if video demonstration is used. The classification of intervention types is based on: ROC curve analysis of 80 clinical samples to determine the judgment threshold for each type (sensitivity ≥85%, specificity ≥80%); judgment rules: if PI>0.6 and SI<0.4: judged as physiologically dominant type (e.g., anxiety caused by rapid heart rate or abnormal skin conductance), matched with the 4-7-8 breathing method intervention program (specific steps: inhale for 4 seconds, hold your breath for 7 seconds, exhale for 8 seconds, repeat 10 times as one set); if BI>0.6 and EI<0.3: judged as behaviorally dominant type (e.g., stress caused by inattention or slow behavior), matched with the Stroop task attention training program (specific steps: the APP displays words with inconsistent colors and text, such as red words displayed in blue font, and the user needs to quickly say the font color, 10 sets per training session, 15 words per set). If EI > 0.6 and SI < 0.4: This indicates an emotion-driven problem (e.g., psychological issues caused by cognitive biases or emotional dysregulation), and the ABC theory cognitive restructuring scheme is applied (specific steps: the app guides the user to record event A - thought B - emotion C, then analyzes the rationality of thought B, generates alternative thoughts, and completes one event analysis per training session); the guidance method is selected based on the user's... (Standardized Interaction Frequency) determines user interaction activity. The higher the level, the more suitable the user is for highly interactive formats; selection rules: if >0.7 (High Interaction): Employing real-time text interaction, the app pops up a guiding question every 2 minutes. After the user inputs text feedback, the app adjusts the next guidance content based on the feedback; if 0.3 ≤ ≤0.7 (Interactive): Uses voice guidance with pre-recorded audio from a professional psychological counselor (holding a national level-two psychological counselor certificate), inserting a 10-second pause every 3 minutes to allow the user to adjust their state; if <0.3 (Low Interaction): The video demonstration format is used to show the steps of the guidance (such as the animation demonstration of the 4-7-8 breathing method). There is no interaction required, and users only need to watch and learn. Format switching mechanism: If the user actively switches the format during the guidance process (such as clicking the switch to voice button), the APP records the switching operation, and the next guidance will use the format selected by the user by default.

[0027] The real-time feedback adjustment module collects user feedback data and dynamically optimizes the plan during the diversion process.

[0028] In this embodiment, it should be specifically noted that: the user feedback data includes the completion rate (CD) and the emotional fluctuation value (EV); the dynamic optimization scheme includes the calculation of adjustment coefficients and scheme adjustment; the adjustment coefficients... This is used to reflect the matching degree between the guidance effect and the user's state; the scheme adjustment: if ADJ < 0.5, then the duration of each session will be shortened by 20% and the frequency will be increased by 1 time / day; if 0.5 ADJ If ADJ > 0.8, maintain the original plan; if ADJ > 0.8, extend the duration of each session by 10% and reduce the frequency by 0.5 times / day. The completion rate of the intervention is calculated as follows: the intervention plan is broken down into several steps (e.g., a physiologically driven plan is broken down into three steps: breathing training preparation, cyclical training, and relaxation closing). The app records the number of steps N completed by the user and the total number of steps M. CD = (N / M) × 100% (unit: %). If the user exits the intervention midway (clicks the exit button), CD is calculated based on the number of completed steps (e.g., if 1 out of 3 steps is completed, CD = 33.3%). Emotional fluctuation value: During the counseling process, the smart bracelet collects GSR data at a frequency of 30 seconds / time, calculates the standard deviation σGSR and mean μGSR of GSR during the counseling process, and EV=σGSR / μGSR (the value range is 0-1 after standardization); the higher the EV value, the greater the emotional fluctuation of the user during the counseling process; Data collection frequency: CD is updated once after each step is completed, and EV is calculated once every 30 seconds, and synchronized to the evaluation server in real time.

[0029] The effect tracking and evaluation module recalculates the comprehensive psychological state index after 7 consecutive days of intervention to assess the improvement effect.

[0030] In this embodiment, it should be specifically noted that: the evaluation of the improvement effect is completed by calculating the degree of improvement and iterating the solution; the degree of improvement ,in This represents the overall psychological state index prior to intervention; This indicates the overall psychological state index 7 days after intervention. This represents the average completion rate of traffic management over 7 days. The proposed solution iterates as follows: if IM > 0.3, it indicates significant improvement, and the current traffic management type is maintained; if 0.1 <= IM <= 0.3, it indicates slight improvement, and the sub-adaptive type is integrated; if IM < 0.1, it indicates no improvement, and an early warning signal is issued.

[0031] In another aspect, in some embodiments, this application provides as appended Figure 2 The illustrated method is a self-service psychological assessment and guidance method based on the Internet, which includes a user terminal, an assessment server, a physiological sensing module, a behavior analysis module, and a guidance and interaction module. Specifically, it also includes the following steps: S1. After obtaining user authorization, collect user data through the user terminal, physiological sensing module and behavior analysis module; S2. Perform outlier removal and standardization on the collected data to improve data validity; S3. Based on the preprocessed data, perform single-dimensional evaluation and cross-dimensional evaluation in sequence; S4. Classify the counseling type based on the threshold determined by clinical samples, and match the counseling form according to the frequency of counseling interaction; S5. Collect user feedback data and dynamically optimize the plan during the diversion process; S6. After 7 consecutive days of counseling, recalculate the comprehensive psychological state index and evaluate the improvement effect.

[0032] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other. In conclusion, 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 principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An internet-based self-help psychological assessment and counseling system, characterized in that, The user terminal, the evaluation server, the physiological sensing module, the behavior analysis module, and the counseling interaction module are included, and the specific steps further include: The multi-source data acquisition module: after obtaining the user's authorization, the user data is collected through the user terminal, the physiological sensing module, and the behavior analysis module; The data preprocessing module: the collected data is subjected to outlier rejection and standardization processing to improve data effectiveness; The hierarchical evaluation analysis module: based on the preprocessed data, single-dimensional evaluation and cross-dimensional evaluation are sequentially performed; The individualized counseling scheme generation module: the threshold value determined according to the clinical sample is used to divide the counseling type, and the counseling form is matched according to the counseling interaction frequency; The real-time feedback adjustment module: user feedback data is collected during the counseling process and the scheme is dynamically optimized; The effect tracking evaluation module: after continuous counseling for 7 days, the comprehensive psychological state index is recalculated to evaluate the improvement effect. 2.The Internet-based self-help psychological assessment and counseling system according to claim 1, characterized in that: The user data includes basic data, physiological data, behavior data, and interaction data; the basic data is obtained through a user terminal form and specifically includes age A and gender G; the physiological data is collected by a physiological sensing module and specifically includes heart rate variability HRV, skin conductance GSR, and respiratory frequency RF; the behavior data is recorded in real time by a behavior analysis module and specifically includes page stay duration PT, click interval time CI, and typing speed TS; the interaction data is collected by a counseling interaction module and specifically includes questionnaire emotion score ES, stress score PS, and counseling interaction frequency IF. 3.The Internet-based self-help psychological assessment and counseling system according to claim 2, characterized in that: The outlier rejection: based on criteria, physiological and behavioral data are divided into data windows by single evaluation period, within a single window: if the data satisfies , where represents the mean of the data within the window, represents the standard deviation; it is determined as an outlier and is rejected, after the rejection, the missing values are supplemented by linear interpolation; the standardization processing: the non-classification data including HRV, GSR, RF, PT, CI, TS and IF are converted to 0-1 interval to obtain standardized data , , , , , and , specifically represented as: standardized value , where represents the minimum value of the corresponding parameter within the single evaluation period, represents the maximum value of the corresponding parameter within the single evaluation period.

4. The internet-based self-help psychological assessment and counseling system of claim 3, wherein: The single-dimension evaluation includes a physiological state index model, a behavior state index model and an emotion state index model; the physiological state index model reflects the correlation between physiological indexes and emotions by calculating a physiological state index ; the behavior state index model embodies the correlation between behavior characteristics and psychological states by calculating a behavior state index ; and the emotion state index model fuses subjective scores and interactive behaviors by calculating an emotion state index .

5. The Internet-based self-help psychological assessment and counseling system according to claim 4, wherein: The cross-dimension evaluation includes a psychosomatic coordination evaluation and a comprehensive psychological state evaluation; the psychosomatic coordination evaluation reflects the coordination of physiology and behavior by calculating a psychosomatic coordination index The comprehensive psychological state evaluation reflects the influence of age and gender on the psychological state by calculating a comprehensive psychological state index . 6.The Internet-based self-help psychological assessment and counseling system according to claim 5, characterized in that: The type of the dredging: if PI>0.6 and SI<0.4, it is determined as a physiological dominant type, and a respiratory regulation scheme is matched; if BI>0.6 and EI<0.3, it is determined as a behavior dominant type, and an attention training scheme is matched; if EI>0.6 and SI<0.4, it is determined as an emotion dominant type, and a cognitive reconstruction scheme is matched; the dredging intensity is used to calculate a single time length and a daily frequency; the single time length ; the daily frequency ; the dredging form selection: if >0.7, real-time text interaction is adopted; if 0.3 0.7, voice guidance is adopted; <0.3, video demonstration is adopted. 7.The Internet-based self-help psychological assessment and counseling system according to claim 1, wherein: The user feedback data includes a counseling completion degree CD and an emotional fluctuation value EV; the dynamic optimization scheme includes adjustment coefficient calculation and scheme adjustment; the adjustment coefficient is used to reflect the matching degree of the counseling effect and the user state; the scheme adjustment: if ADJ<0.5, the single time length is shortened by 20% and the frequency is increased by 1 time / day; if 0.5 ADJ 0.8, the original scheme is maintained; if ADJ>0.8, the single time length is extended by 10% and the frequency is reduced by 0.5 times / day. 8.The Internet-based self-help psychological assessment and counseling system according to claim 1, wherein: The evaluation of the improvement effect is completed by calculating an improvement degree and a scheme iteration; the improvement degree wherein represents the comprehensive psychological state index before the dredging; represents the comprehensive psychological state index after the dredging for 7 days, represents the average dredging completion degree for 7 days; the scheme iteration: if IM>0.3, it represents significant improvement, and the current dredging type is maintained; if 0.1<=IM<=0.3, it represents slight improvement, and the next adaptive type is fused; and if IM<0.1, it represents no improvement, and a warning signal is sent.

9. An internet-based self-help psychological assessment and counseling method for implementing an internet-based self-help psychological assessment and counseling system according to any one of claims 1 to 8, characterized in that, The user terminal, the evaluation server, the physiological sensing module, the behavior analysis module, and the counseling interaction module are included, and the specific steps further include the following steps: S1, after obtaining the user's authorization, the user data is collected through the user terminal, the physiological sensing module, and the behavior analysis module; S2, the collected data is subjected to outlier rejection and standardization processing to improve data effectiveness; S3, based on the preprocessed data, single-dimensional evaluation and cross-dimensional evaluation are sequentially performed; S4, the threshold value determined according to the clinical sample is used to divide the counseling type, and the counseling form is matched according to the counseling interaction frequency; S5, user feedback data is collected during the counseling process and the scheme is dynamically optimized; S6, after continuous counseling for 7 days, the comprehensive psychological state index is recalculated to evaluate the improvement effect.

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