Multi-mode mental health risk intelligent early identification system and method based on education robot

Abstract

The invention discloses a multi-mode mental health risk intelligent early identification system and method. On the basis of awareness and voluntary, the system can collect 9 types of data sources in an anonymization mode, a quintuple privacy protection architecture is constructed, and the quintuple privacy protection architecture specifically comprises irreversible code ID generation, national cryptographic algorithm and quantum key dual encryption, federal learning localization processing, block chain evidence storage and dynamic desensitization erasing. The innovation point is that based on a multi-modal fusion model and a dynamic scoring algorithm, a dynamic psychological scale is used for scoring, and graded early warning of psychological health risks is realized. By means of a federated learning framework, continuous optimization and cooperation of parameters of the cross-calibration scale can be realized. On the premise of strictly protecting privacy, early recognition and grading suggestion of student mental health risks can be achieved, medical diagnosis is not involved, and the requirements of GDPR and other data protection laws and regulations are met.

Description

Technical Field

[0001] The present invention belongs to the application technology of artificial intelligence in the field of psychological education, and relates to the fields of psychological health analysis and data encryption. Background Art

[0002] 1. Regarding students’ mental health issues, it is imperative to build a scientific system for early identification and intervention of mental health risks.

[0003] 2. According to the "2021-2022 Mental Health Literacy Survey Report" by the Institute of Psychology, Chinese Academy of Sciences, 68% of parents are resistant to existing psychological assessments, mainly worried that "schools will label their children" or "privacy information will be improperly used."

[0004] 3. The critical role of early warning: Research shows that "early detection and early intervention" can effectively reduce the risk of psychological crises. The Institute of Psychology, Chinese Academy of Sciences (2024) published the "Blue Book on Mental Health," emphasizing that timely identification of psychological problems can significantly improve students' social adaptability and reduce long-term negative impacts. Tracking data from Beijing Normal University (2023) shows that a systematic early warning mechanism can reduce the incidence of psychological crises by 40%.

[0005] 4. According to the World Health Organization's "Global Status Report on Alcohol and Health 2018" (ISBN 978-92-4-156563-9), the global drinking rate among adolescents aged 15-19 exceeds 25%, with a significant trend toward younger drinking. Long-term drinking not only harms the neurological development of minors but can also lead to psychological problems and accidental injuries and deaths.

[0006] 5. This invention adopts an anonymized design (no personalized information), combined with information security technologies such as national secret algorithms, quantum key distribution, federated learning, and blockchain evidence storage, to achieve full-link data protection from "source desensitization - transmission encryption - storage and evidence storage," avoiding the association with personal information and complying with various laws and regulations.

[0007] Technical comparison analysis table shows substantial differences with similar patents such as CN113807918A (AI-based psychological assessment system) and CN114565980A (educational robot privacy protection method): |Comparison Dimensions|Existing Technical Solutions|Innovation of the Present Invention|Improved Technical Effects| |Privacy protection|Triple encryption|Five-layer protection|Cracking difficulty increased by 3 orders of magnitude| |Data association|Student ID hash binding|Irregular ID+dynamic quantum key|Physically isolated association system| |Dynamic scoring |Static weight fusion |Decrement factor sequence + federated optimization weight |AUC improved by 12.7% | 6. Social Value: This invention, based on voluntary use, achieves efficient early warning and strict compliance through technological integration, providing a safe solution for campus mental health prevention and control, and helping to reduce extreme events. The early warning of this invention is a "mental health risk early warning recommendation" and does not involve the diagnosis of any mental illness. Parents voluntarily adopt the early warning recommendations and need to go to a formal medical institution for diagnosis. The results of this system cannot replace professional medical diagnosis. Summary of the Invention

[0008] Software educational robot: The software educational robot described in the present invention is an intelligent program system that runs on the operating systems of computers, mobile terminals, smart wearable devices, etc. It integrates core technologies such as natural language processing, machine learning, and robotic process automation. The hardware carrier covers the above-mentioned devices and supports installation and deployment on multiple types of mobile terminals such as teachers, students, parents and members of the public.

[0009] Physical Education Robot: The physical education robot is an intelligent terminal device deployed in key areas and risk monitoring points such as the campus gate, dormitory entrance, and teaching building entrance. It integrates sensor technology, artificial intelligence algorithms, and an educational resource database. It has core functions such as teaching assistance support. Its hot-swappable interface can be connected to foldable simple psychological counseling room equipment, VR glasses, smart bracelets, brain-computer interface helmets and other equipment.

[0010] Parents (or students of legal age) voluntarily choose to use the system after becoming aware of it. The collection and analysis of information must be consented to and authorized. The system only anonymously collects and analyzes authorized student information. Multi-dimensional data sources are anonymously collected for intelligent analysis, including nine single data source information types: campus video and audio information, odor information, physiological data, sleep disorder data, third-party observation data, home-school interaction data, psychological counseling process data, online behavior data, and odor data. The data is linked only by a code ID and contains no personalized information such as name, student ID number, or mobile phone number. Each module is first automatically and anonymously scored based on a dynamic scale, and then aggregated into a comprehensive analysis module for automatic anonymous scoring. After comprehensive scoring, the results are anonymously transmitted to online real-person experts for review.

[0011] It is the first to use a dynamic psychological scale for scoring to achieve graded warning of mental health risks, and the first to use a federated learning framework to achieve dynamic and continuous optimization of scale parameters across schools.

[0012] The server is located in the school's local computer room and is isolated separately. The entire system is operated and managed by a third-party organization. The school can only obtain common reports and cannot know personalized information.

[0013] 1. Strict protection of student privacy and information security This system builds a five-layer privacy protection architecture, innovatively integrating irregular code ID, national secret algorithm (SM series), quantum key distribution (QKD), federated learning and blockchain technology to achieve privacy protection throughout the entire life cycle.

[0014] 1.1 Overview and Business Process 1.1.1 Irregular ID Privacy Isolation Mechanism The system implements identity association through code ID, and the process is as follows: (1) Obtain the original correspondence table between facial feature codes and student ID numbers from the campus management information system, and randomly generate a featureless unique code ID for each student (reverse unbreakable).

[0015] (2) The original correspondence table is then deleted, and only the correspondence table between the facial feature code and the code ID is retained. It is then double-encrypted using the national secret SM4 algorithm and the quantum key and stored on the local server.

[0016] (3) At the same time, the correspondence table between the code ID and the student ID number is double-encrypted and sent back to the campus management information system for storage. The entire process uses federated learning technology to locally process data and records operation logs through blockchain technology to ensure information security and traceability.

[0017] 1.1.2 Data Collection and Desensitization Rules Data collection requires prerequisites: collection and analysis can only be initiated after parents (or students over the legal age) fully understand the system's functions and sign a written authorization. Data processing is divided into two categories: if the analysis shows normal information from a mentally healthy student, it is immediately deleted; if a student is identified as suspected of mental health problems, only their ID number is recorded. The remaining information is depersonalized and stored using a dual encryption algorithm and quantum key encryption.

[0018] 1.1.3 Psychological Abnormality Warning Process When the system identifies a student with suspected psychological abnormalities, it triggers a privacy protection warning mechanism after review by real experts: (1) The system automatically requests the campus management information system to call the original corresponding table, decrypts it through the quantum algorithm and the National Security Office algorithm, and obtains the student ID corresponding to the suspected student's code ID.

[0019] (2) Based on the student ID, retrieve the parent’s contact phone number and establish an association between the student ID and the parent ID.

[0020] (3) Parents will be notified via automatic text messages, phone calls, etc., and their contact information will be deleted within 10 seconds after the parents confirm receipt. All processes are strictly confidential, and the system does not store personalized data such as name, phone number, home address, etc., ensuring privacy security from the source.

[0021] 1.1 Core Privacy Protection Mechanism 1.1.1 Irregular Code ID Generation and Isolation: Irreversible random code IDs are generated based on facial feature codes and student ID numbers. After the original correspondence table is destroyed, only the double-encrypted (SM4 + quantum key) code mapping table is retained. Federated learning is used to achieve data localization processing, and the blockchain records the entire process operation log.

[0022] 1.1.2 Dynamic hierarchical data processing: Mental health data is deleted in real time, abnormal data retains its code ID and is deeply desensitized; data storage is minimized based on k-anonymization (k=5) and attribute-based access control (ABAC).

[0023] 1.1.3 Automated warning process: After the student ID is obtained through quantum key decryption, a parent code ID association is established; the encrypted notification is automatically erased within 10 seconds, and no personal sensitive information is stored during the entire process.

[0024] 1.1.4 Data management adheres to the principle of minimization: Normal mental health data will not be stored and will only be marked as "normal." Data that requires storage will be encrypted using the National Security Administration's algorithm combined with quantum key encryption and stored on a local campus server. Physical isolation is implemented via a network gateway on a daily basis, with network access only when data is in use. Blockchain technology is utilized throughout the entire process to prevent data tampering and monitor intrusions in real time. Any anomalies detected will be immediately notified to the network gateway, physically disconnecting information transmission.

[0025] 1.1.5. Authority Control: The school cannot obtain test results without parental authorization, in compliance with the requirements of the Personal Information Protection Law.

[0026] 1.2 Technical Architecture Innovations Five-layer protection: data desensitization, quantum encrypted storage, federated learning, blockchain auditing, and dynamic erasure. End-to-end quantum encrypted communication (E2EE) and hardware encryption module (HSM) ensure key security.

[0027] 2. Campus video and audio acquisition and single-source analysis The system initiates an anonymous data collection process (with authorization). By integrating authoritative psychological scales to build an intelligent scoring model, it maps student behavioral characteristics to professional indicators, enabling scientific mental health screening. The system generates an encrypted ID for the screened individual and immediately deletes the original biometric data.

[0028] The core functional modules include: Visual feature analysis: walking posture (lower limb coordination / upper limb swing symmetry / facial expression); voice feature detection: negative semantic frequency (such as sighs >5 times per minute) / abnormal voice tone (fundamental frequency standard deviation >25Hz); dynamic risk assessment: comprehensive score generated based on professional scales, abnormal situations automatically trigger the human expert intervention process.

[0029] 2.1. Construction of an Industry-Standard Mental Health Scale and Physiological Characteristics Mapping System 2.1.1 Industry Scale Integration and Feature Mapping The system integrates the following core assessment tools: (1) Physical Symptom Questionnaire-15 (PHQ-15): quantifies gait abnormalities (cadence variability >15%) and body rigidity (arm swing asymmetry >40%); (2) Anxiety Scale (GAD-7): detects voice tremor (fundamental frequency jitter >8 Hz) and abnormal speech rate (<80 words / minute or >160 words / minute); (3) Profile of Mood States Scale (POMS): identifies specific expression combinations through a micro-expression coding system (Ekman Facial Action Coding System); (4) Innovative Emotion Persistence Scale (EPS): Developed based on the 2021 research results of the Journal of the American Medical Association Psychiatry (DOI: 10.1001 / jamapsychiatry.2021.0532), it assesses the correlation between the duration of expression and psychological state.

[0030] 2.1.1. Example of Emotional Persistence Scale: |Micro-expression type|Continuous days|Recognition significance|Corresponding score| Sadness (AU15+17) | ≥7 days and <10 days | Potential risk of adjustment disorder | 15 points (moderate) | | Sadness (AU15+17) | ≥10 days | Suspected major depressive episode | 25 points (high risk) | |Anger (AU4+5) |≥5 days |Increased anxiety / hostility |18 points |Fear (AU1+2+4) | ≥3 days |Acute stress response index |12 points| (Note: Parameters are continuously updated and optimized.) Dynamic scoring formula: w(t) = 1 / (1+e^-0.5(t-tc)) (tc=7-day critical value); when the number of days the expression lasts exceeds the critical value, the weight increases significantly.

[0031] Feature-scale dynamic mapping table: |Scale dimension|Behavioral characteristics|Quantification rules|Weight| |EPS emotion persistence|Sad expression lasts ≥ 10 days|Segmented index growth score after 7 days × 1.5 times|0.37| |PHQ-15 Body|Gait disturbance lasting ≥5 days|Cumulative effect over time (+0.3 points per day) |0.35| |GAD-7 Tension|Voice tremor occurs for more than 3 hours per day|Percentage of duration per hour x 1.2 minutes|0.28| (Note: Parameters are continuously updated and optimized.) 2.1.3 Dynamic Scoring Algorithm (1) Multimodal fusion model: Comprehensive score = 35% × gait characteristics + 28% × voice characteristics + 37% × expression characteristics.

[0032] (2) Time series detection: The cumulative value C(t)=Σ(α^d × expression intensity index (td)), where α=0.85 is the attenuation factor; when C(t)>2.3, it is judged as a persistent abnormality (equivalent to the target expression appearing for 7 consecutive days).

[0033] (3) Mathematical derivation of core algorithms or support from authoritative literature: The "Dynamic Scoring Algorithm" cites the IEEE Trans. on Affective Computing 2022 paper (DOI:10.1109 / TAFFC.2022.3145678), which demonstrates the advantages of the decay factor time series model over the traditional EWMA model.

[0034] Federated learning weight optimization process: Assume the global loss function L(θ) = Σ_{i=1}^N (n_i / n)L_i(θ), and verify the convergence through Monte Carlo simulation: E[||θ^{t+1} - θ^*||] ≤ (1 - ηλ)^t E[||θ^0 - θ^*||], where λ is the minimum eigenvalue of the Hessian matrix and η=0.01 is the learning rate.

[0035] 2.2 Intelligent Analysis Technology Solution 2.2.1 Multi-source data collection system Blockchain dynamic authorization and homomorphic encryption (Paillier encryption algorithm) are used to ensure data security, and a three-layer perception network is deployed: |Perception Layer| Technical Specifications| Features| |Fixed security node|Camera + wide-area microphone|Global behavior capture| | Physical Education Robot Movement | Pan / Tilt Camera + Ring Microphone | Abnormal Target Tracking | |Infrared Node|Infrared Camera + Depth Sensor|Accurate Measurement of Micro-Expressions| 2.2.2. Intelligent feature extraction and fusion analysis (1) Spatiotemporal behavioral feature modeling, gait analysis: extracting 32 skeleton points (AlphaPose technology), detecting gait frequency variation (>15% abnormality), arm swing asymmetry (>40% warning); micro-movement recognition: dual-path network (64 frames / second to capture trembling movements, 8 frames / second to analyze continuous movements). (2) Multimodal emotion analysis: integrating seven data sources including vision, speech, gait, expression, and pupil. (3) Cross-modal detection: expression and speech consistency detection (record abnormality when cosine similarity <0.3); physiological and behavioral correlation analysis (using Granger causality test).

[0036] 2.2.3. Dynamic decision-making and graded intervention (1) Multi-level early warning trigger mechanism |Risk Level|EPS Duration Score|Corresponding Action Recommendations| |Low risk|<10 points|Routine observation| |Medium risk|10-20 points|Data is transferred to the comprehensive analysis module| |High Risk|>20 points|Call a remote live expert immediately| Threshold calibration method (refer to the 2023 calibration study in The Lancet Digital Health): Bayesian optimization + expert Delphi method (AUC>0.8).

[0037] 2.6. System Evolution and Privacy Protection 2.6.1. Federated Learning Enhancement Mechanism Establish a cross-campus knowledge sharing network: (1) Local training on edge nodes: θ_i^{t+1} = θ_i^t - η·∇L_i(θ_i^t) (η is the learning rate). (2) Aggregation on central servers: θ_G^{t+1} = Σ_{i=1}^N (n_i / n)θ_i^{t+1} (federated averaging algorithm). (3) Privacy enhancement: Laplace noise is injected during gradient updates (privacy budget ε=0.3) to achieve differential privacy.

[0038] 2.6.2. Data minimization process Build a feature distillation processing pipeline: Input data → feature dimension → processing method.

[0039] Video data → Skeleton key points (17×3 coordinates) → OpenPose framework extraction.

[0040] Audio data → Mel-frequency cepstral coefficients (MFCC, 39 dimensions) + semantic vectors (BERT encoding, 768 dimensions).

[0041] Environmental data → heat map encoding (64×64 matrix) → convolutional feature compression.

[0042] (Test data shows that storage efficiency has increased by 98.7%, complying with the requirements of the General Data Protection Regulation (GDPR)).

[0043] 2.6.3. Privacy-oriented scoring process (1) Feature desensitization processing: Facial features → SHA-256 hash to generate a code ID (example: 9f86d08...); Voiceprint features → MFCC coefficients are extracted and the original audio is destroyed.

[0044] (2) Secure Erase Mechanism: Real-time execution: secure_erase / dev / shm / *.bin --method=DoD5220; Storage architecture: Copy-on-Write Encryption (CWE) + Trusted Execution Environment (Intel SGX).

[0045] 3. Physiological Data Collection and Single-Source Analysis It is necessary to ensure that parents and students fully understand the system functions and privacy protection measures. The system may only collect anonymous information after parental authorization or voluntary authorization from students of legal age.

[0046] Physical educational robots integrate smart bracelets, brain-computer interface (BCI) helmets, and other devices through hot-swappable interfaces. These robots are deployed at school gates, dormitory entrances, and teaching building entrances for voluntary student use. Data collected includes: basic vital signs (temperature, pulse, and blood pressure); neurological data (electroencephalogram (EEG)); and stress response data (galvanic skin response (GSR).

[0047] 3.1 Dynamic Mental Health Scale and Physiological Characteristics Mapping System 3.1.1 Scale selection and physiological indicator verification (1) Correspondence of standard scales: Depression Scale (PHQ-9): associated with decreased heart rate variability (HRV) and increased baseline skin galvanic response; Anxiety Scale (GAD-7): corresponds to abnormal EEG beta waves and abnormal body temperature fluctuations; Stress Scale (PSS): linearly correlated with pulse wave velocity (PWV).

[0048] (2) Construction of the Dynamic Mental Health Inventory (DMHI): The Dynamic Mental Health Comprehensive Scale (DMHI): Based on multiple studies (such as the meta-analysis of the association between physiological signals and psychological states in Nature Human Behavior), the initial parameter table (see the table below) was designed, and the weight coefficients were continuously optimized through the federated learning framework.

[0049] DMHI initial parameter example: |Physiological indicators | Health dimensions | Weight | Data source (research literature) | |HRV standard deviation | Depression tendency | 0.35 | Kemp et al., 2012 | |EEG alpha / beta power ratio | Anxiety level | 0.28 | Mathersul et al., 2008 | |GSR rising slope|Stress response|0.41 |Boucsein, 2012《Electrodermal Activity》| (Note: Parameters are continuously updated and optimized.) 3.1.2 Scale Optimization Mechanism: A federated learning framework is used to achieve cross-school data collaboration. Each school uploads anonymous model parameters (not raw data) with double encryption. A central server aggregates and updates weights. A Bayesian optimization algorithm is used to adjust indicator weights. (After training with 100,000 samples, the accuracy rate increased from 76.3% to 89.6%).

[0050] 3.2 Data Acquisition System Architecture 3.2.1 Hardware Configuration: Interface modules that support the ISO / IEEE 11073 standard; compatible with six types of devices: smart bracelets, brain-computer interface helmets, etc.; 3D Grid Nodes for local data processing.

[0051] 3.2.2 Privacy Protection: Dual blockchain evidence storage, data use requires smart contract authorization; Differential Privacy Processing: Add Gaussian noise (ε=0.5), formula: x' = x + N(0,σ²).

[0052] 3.3 Intelligent Analysis Algorithm 3.3.1 The spatiotemporal feature fusion model (ST-FNN) consists of three processing layers: the temporal layer, which uses a bidirectional LSTM network to analyze 24-hour periodic features; the spatial layer, which uses a 3D-CNN to identify the spatial distribution of EEG signals; the cross-modal layer, which uses a multi-head attention module (8 heads) to calculate indicator correlations; and the attention weighting layer, which uses the formula Attention(Q,K,V)=softmax(QK^T / √d_k)V).

[0053] 3.3.2 Three-level early warning mechanism Three-level early warning mechanism: | Risk Level | Trigger Conditions | Intervention Measures | Low Risk | Weekly cumulative score fluctuation > 30% | Generate Behavioral Observation Recommendation Report | Medium Risk | Daily score > 75 points and trending upward | Courses will be transferred to the comprehensive analysis module. High Risk | DMHI score > 85 for three consecutive days | Call a live expert immediately | 3.3.3. Federated Learning Optimization Closed Loop Each node model synchronizes global parameters once a month, evaluates the accuracy of scale weights through cross-validation, and initiates a manual review process (with the intervention of psychological experts) for incorrectly labeled data, forming a closed loop of "data collection-model training-manual calibration".

[0054] 3.4. Technical Effects and Verification Data 3.4.1. Dynamic Scale Optimization Effect: After 6 months of optimization, the DMHI scale achieved a Kappa value of 0.81 (strong consistency) compared with the results of online live expert review, using the formula: κ = (P_o P_e) / (1 P_e).

[0055] 3.5. Innovation Dynamic Scale Construction Technology: This pioneers a dynamic optimization mechanism for mental health scale parameters based on federated learning and Bayesian optimization, breaking through the limitations of traditional static scale assessments. A cross-modal attention model: This utilizes ST-FNN to achieve fine-grained correlation of spatiotemporal features of physiological signals, improving screening accuracy by 12.4%. An edge-cloud collaborative architecture: Combining blockchain-based ownership verification with a lightweight inference engine, this architecture completes the entire process from data collection to graded warnings within 200ms.

[0056] 4. Sleep Disorder Data Collection and Single-Source Analysis Data collection is anonymous, with the knowledge and voluntary authorization of parents / students. Data sources include: educational robot video data (school gates / dormitories / teaching buildings); campus security system image analysis; and biometric data from smart bracelets / brain-computer interface helmets (which students voluntarily wear). The system analyzes sleep data based on a dynamic mental health scale, intelligently scoring against the scale and automatically screening students for potential mental health issues. The technical solution is as follows: 4.1 Dynamic Mental Health Scale Construction System 4.1.1 Standard scale database, including but not limited to integrating authoritative scales and establishing quantitative relationships: Pittsburgh Sleep Quality Index (PSQI): quantifies the relationship between sleep latency / number of awakenings and depression; Insomnia Severity Index (ISI): sets corresponding thresholds for sleep onset duration and anxiety; Epworth Sleepiness Scale (ESS): establishes a correlation model between daytime sleepiness and attention deficit; Self-Rating Depression / Anxiety Scale (SDS / SAS): cross-validates sleep efficiency and mood disorders.

[0057] 4.1.2 Dynamic Scale Generator (1) Feature association findings: sleep latency fluctuation vs. psychological disorders (linear regression model: y=β0+β1x1+...+βnxn); REM sleep density abnormalities vs. post-traumatic stress disorder (association rules).

[0058] (2) Parameter optimization: Initial parameters, Monte Carlo simulation to generate probability distribution; Collaborative optimization: Federated Learning to update parameters across campuses; Automatic calibration: Confidence interval adjustment is triggered when the sample size N>1000, confidence interval: μ±Z*(σ / √n).

[0059] 4.1.3 Dynamic Quantity Table Example Multidimensional Sleep-Ambulatory Mental Health Scale (MSM-AS v1.0): | Feature Indicators | Scoring Criteria | Mental Health Correlation | Parameter Optimization | |Sleep latency fluctuations | 0-9 scale grading | Large fluctuations indicate a risk of disorder | Monte Carlo + Federated Learning | |REM sleep abnormalities|Percentage of deviation from 0-4 scores|Probability of PTSD associated with outliers|Cross-institutional data optimization| |Nighttime bed leaving frequency|0-6 frequency rating|Nighttime correlation anxiety level|Dynamic confidence interval calibration| | Difficulty in morning awakening | 1-5 level speech emotion analysis | Score correlation with depression tendency | Emotion model transfer learning | |Respiratory Disturbance Index|0-9 points (blood oxygen depletion rate) |Index-related attention deficit|Privacy protection aggregation| (Note: Parameters are continuously updated and optimized.) 4.1.4 Dynamic scale optimization mechanism, using a federated learning framework to achieve cross-school data collaboration: each school uploads anonymous model parameters (non-raw data) with double encryption; the central server aggregates and updates weights; and the Bayesian optimization algorithm adjusts indicator weights.

[0060] 4.2 Intelligent grading and screening system 4.2.1 Multimodal Feature Extraction, Video Analysis: 3D-CNN extracts eye blink rate (BPM) and facial tremor index (FMTI); Biosignal: LSTM analyzes the LF / HF ratio of heart rate variability (HRV); Behavioral Modeling: Graph Neural Network constructs a Markov chain for bed exit and bedtime. Markov chain state transition: P(X_{t+1}=j | X_t=i) ).

[0061] 4.2.2 Level 3 risk assessment |Grade|Filters|Technical Features|Interventions| | Low risk | PSQI+ESS compliance | Weekly updates on single-modal analysis | 90-day observation period, sampling monitoring | Medium risk | Single indicator exceeds threshold by 1.2 times | Daily dual-modal cross-learning | Push comprehensive analysis module | | High risk | Extreme values ​​on both scales | Full-modality fusion real-time response | Emergency call to a live expert online | (Note: Parameters are continuously updated and optimized.) 4.3 Automated Implementation System 4.3.1 Intelligent Workflow: Data analysis starts at 04:00; class psychology heat map is generated at 06:30; federated learning parameters are updated at 07:00; real-time event response latency is <200ms.

[0062] 4.3.2 Privacy Protection Technology, Trusted Execution Environment (TEE): Data available but not visible; Homomorphic Encryption Scale Scoring Verification; Decentralized Attribute-Based Encryption (DABE) Permission Management.

[0063] 5. Third-party observation and single-source analysis require anonymous data collection with the informed consent of parents / students. Data sources include: mobile apps or mini-programs (classmate / parent / teacher feedback); campus information systems (academic data / psychological records); the third party here also includes system integration, that is, obtaining relevant information from the campus information system and psychological record system.

[0064] 5.1 Construction Mechanism of the Dynamic Mental Health Scale 5.1.1 The standard scale library includes but is not limited to the integration of three types of industry standard scales: sleep assessment: Pittsburgh Sleep Quality Index (PSQI) and Adolescent Sleep Behavior Scale (ASBS); emotion assessment: Child Behavior Checklist (CBCL) and Mini-Neuropsychiatric Interview (MINI); comprehensive assessment: Cornell Health Index (CMI) Environmental Adaptation Module.

[0065] 5.1.2 Dynamic Scale Generation A correlation scale was created based on the "Study on Adolescent Mood and Sleep". The initial parameter formula is: Total score = frequency of social avoidance × 0.3 + number of insomnia days × 0.4 + number of lateness times × 0.3 5.1.3 Parameter Optimization Model Two-stage optimization: Offline stage: Deep Belief Network (DBN) training (100,000+ samples); Online stage: Bayesian network real-time parameter adjustment (single weight adjustment Δw ≤ 0.15).

[0066] 5.2 Intelligent hierarchical screening 5.2.1 Data Collection and Analysis |Data Source|Collected Content|Analysis Technology| |Mobile App |Voice emotion fluctuation, negative sentiment word frequency | LSTM emotion trajectory modeling | | Educational Administration System API | Absence Rate and Grade Drop Events | Time Series Anomaly Detection (SARIMA Model) | 5.2.2 Three-level risk classification logic Low risk: Meeting the thresholds of any two scale items (e.g., PSQI sleep efficiency <65% + CBCL aggression score >70th percentile) triggers the review process and transmits information to the comprehensive analysis module.

[0067] Medium risk: The joint prediction probability of multi-source data is >65% (based on the weighting of the attention mechanism). The student is recommended to participate in psychological counseling, triggering the review procedure and transmitting information to the comprehensive analysis module.

[0068] High risk: Sleep / Mood dual scale scores exceed the psychological threshold (PSQI>21 and CBCL internalizing problems>98th percentile), and an urgent call is made to an online live expert.

[0069] 5.3 Core Technology Module 5.3.1 Credibility Assessment Model, Calculation Formula: Credibility Score = 0.4 × DBN (time series data) + 0.3 × GNN (social data) + 0.3 × FL (federal data).

[0070] 5.3.2 Data Conflict Handling: When multiple sources conflict, perform the following: Bayesian network calculation of prior probabilities; KL divergence assessment of distribution deviation; and video micro-expression analysis (AU encoding) arbitration. KL divergence: D_KL(P||Q) = ∑P(x)log(P(x) / Q(x)).

[0071] 5.3.3 Privacy Protection Technology: Data Anonymization: k-anonymization (k=5); Federated Learning: Local Training Model Gradient; Differential Privacy: Laplace Noise (ε=0.1): f(D)+Lap(0,1 / ε).

[0072] 5.4 Technical effectiveness in pilot schools: Screening efficiency: 100% coverage, false positive rate <8% (ROC verification); Scale evolution: correlation r=0.42→0.79 after 6 months of training; Data processing: integration of 5 types of data sources, 120+ feature dimensions.

[0073] 6. Daily interactive communication data collection and single-source analysis: Anonymous interaction data collection is required, with the informed consent of parents and students: (1) Physical educational robots serve as information transfer devices, replacing mobile phones in closed campuses to facilitate communication between students and parents. (2) Multimodal emotional data is recorded simultaneously during the interaction process. (3) Academic question-and-answer functions are supported to establish trusted interaction scenarios, such as teaching students to solve difficult problems. Anonymous emotional data is obtained with authorization.

[0074] 6.1 Mental Health Assessment Knowledge Base 6.1.1 Digitalization of standard scales and integrated scale diagnostic tools: Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder Scale (GAD-7), Positive and Negative Affect Scale (PANAS), and Structured Psychological Interview Template (SCID).

[0075] 6.1.2 Dynamic Scale Generation System Automatically constructed when standard scales are not applicable: (1) Literature analysis: obtain the latest research results from psychology databases; (2) Feature extraction: define 15 interaction indicators (e.g., emotion fluctuation frequency, semantic complexity); (3) Joint training: update indicator weights through federated learning.

[0076] 6.1.3 Example of Intelligent Interaction Evaluation Scale Dynamic Mental Assessment Scale (SIMPLE-MHDS) |Dimension| Detection Parameter| Scoring Rule| Initial Weight| Based on Source| |Speech|Tremor|High-frequency tremor > 1.2% is scored as 2 points|0.30|Speech psychology research (Scherer, 2013) |Speech rate |Fluctuation |A deviation of ±30% from the baseline is scored as 1 point | 0.20 |Speech psychology research (Scherer, 2013) |Text|Ratio of negative words|>6 times per 1,000 words: 3 points|0.25|Psychology of Language (Pennebaker, 2015)| | Expression | Frown AU4 | Duration > 3 seconds Intensity > Level 2 2 minutes | 0.25 | Facial Action Coding (Ekman, 2004) | Scoring formula: Total score = sum(weight * individual score) + α(daily fluctuation standard deviation / weekly information entropy); Weight update formula: New weight = Old weight + η(loss gradient) * Privacy mask.

[0077] 6.1.4 A dynamic scale optimization mechanism uses a federated learning framework to achieve cross-school data collaboration: each school uploads anonymous model parameters (not raw data) with double encryption; a central server aggregates and updates weights; and a Bayesian optimization algorithm adjusts indicator weights.

[0078] 6.2 Intelligent Data Collection 6.2.1 Multimodal Sensors: Voice: 8000Hz microphone array, pitch / rate analysis; Text: Emotion recognition model (BERT emotion detection version); Vision: 3D camera captures 52 facial movements.

[0079] 6.2.2 Edge Computing Processing, Speech Feature Extraction: MFCC Coefficients + Log Mel Spectrum; Text Desensitization: Bidirectional LSTM Model to Replace Sensitive Words; Image Protection: Adding Laplace Noise (Privacy Factor ε = 2).

[0080] 6.3 Dynamic Evaluation Model 6.3.1 Multi-scale Analysis: Real-time features: voice tremor, negative word frequency; daily fluctuations: Dynamic Time Warping algorithm to match pathological patterns; weekly trends: Triple Exponential Smoothing prediction.

[0081] 6.3.2 Adaptive Scoring, basic formula: score = sum(dynamic weight eigenvalue) + decay factor temporal memory; model update: quarterly update with KL divergence change < 0.05.

[0082] 6.4 Three-level early warning mechanism |Alert Level| Trigger Conditions| Response Measures| |Low risk|Total score of 50-70 for 3 consecutive days|Generates a behavior analysis report for parents' review only| Medium Risk | Daily score >70 or PHQ-9 equivalent score ≥10 | Professional assessment push and transfer to comprehensive analysis module | |High Risk|Self-harm keywords detected + facial pain index > threshold|Call a live expert immediately| 6.5 Privacy Protection Design, Distributed Learning: Using a segmented neural network architecture; Biometric Confusion: Generate adversarial networks to modify voiceprint features.

[0083] 6.6 Technological Innovations (1) Dynamic scale construction: literature analysis + joint learning optimization; (2) Multi-scale analysis: integrating immediate responses with long-term trends; (3) Real-time privacy protection: local processing completed within 200ms; (4) Accurate early warning: false alarm rate <8% (traditional solution 23%).

[0084] 7. Data collection and single-source analysis of the psychological counseling process: With the authorization of parents or students of legal age, the system collects psychological counseling data anonymously, automatically scores according to the scale, and intelligently screens students suspected of mental health problems. Data sources include: (1) Interaction with software educational robots: structured interviews based on DSM-5 and unstructured free conversations; (2) Interaction with physical educational robots: collection of physiological indicators and behavioral interaction data; (3) Remote real-person expert consultation: recording of voice conversations and emotional characteristics; The system screens potential cases of psychological abnormalities through intelligent analysis, and the entire process is automated.

[0085] 7.1 Mental Health Characteristics Database System 7.1.1 Standardized Scale Matching Engine Construct a three-level scale system: Level 1 core scales: Hamilton Depression Rating Scale (HAMD); Patient Health Questionnaire-9 Item (PHQ-9); Generalized Anxiety Disorder Scale (GAD-7); Beck Depression Inventory (BDI); Secondary auxiliary scales: Symptom Checklist-90 (SCL-90); Self-Rating Anxiety Scale (SAS); Eysenck Personality Questionnaire (EPQ).

[0086] Three-level customized scale: Construct a developmental adaptability scale based on the "Study on the Psychological Development Characteristics of Children and Adolescents (Smith et al., 2020)".

[0087] Feature space modeling formula: V = Σ(w_i * (S_i μ_i / σ_i)) (i=1→n), where: μ=normalized mean, σ=standard deviation, w=dynamic weight.

[0088] 7.1.2 Intelligent Scale Generation System Literature Mining Module Workflow: Search keywords: "Adolescent Psychological Assessment Indicators" and "Correlation between Conversational Characteristics and Mental Health." Construct a Conversational Emotion Scale based on the "Emotion Prediction Model for Unstructured Interviews" (Johnson and Lee, 2022). Create a Biometric Scale based on the "Application of Multimodal Biofeedback" (Chen et al., 2021).

[0089] 7.1.3 Sample Scale (CPCE-5) Based on the three-factor model of conversational engagement (Smith et al., 2020): |Dimensions | Assessment Items | Data Collection Method | Initial Parameters | Scoring Criteria | |DRQ |Response Delay |Voice+Timestamp |Weight 0.18 |<2 seconds 3 minutes / 2-5 seconds 2 minutes / 5 seconds 1 minute | |TMC |Topic Continuation Rate |NLP Topic Chain |Weight 0.22 |+1 point for every 3 rounds of dialogue, maximum 5 points | |EFI |Emotional Matching |Voice+Text |Weight 0.25 |Deviation > 1 level deduction 2 points | |PPI |Heart Rate Variability |Wearable Devices |Weight 0.20 |<15ms = 3 points; 15-30ms = 2 points; >30ms = 1 point | |CIL|Cognitive load|Eye tracker entropy|Weight 0.15 |Entropy < 2.5 = 3 points; 2.5-3.5 = 2 points; > 3.5 = 1 point| (Note: Parameters are continuously updated and optimized.) 7.1.4 Dynamic Scale Optimization Mechanism A federated learning framework is used to achieve cross-school data collaboration: each school uploads anonymous model parameters (not original data) with double encryption; the central server aggregates and updates weights; and the Bayesian optimization algorithm adjusts indicator weights.

[0090] 7.2 Intelligent Evaluation System 7.2.1 Multimodal Data Acquisition, Software Educational Robot Terminal: Integrates the Minnesota Multivariate Personality Inventory (MMPI) module; Physical Educational Robot Terminal: Equipped with a posture analysis sensor; Expert System: Equipped with an Emotional Speech Analysis Toolkit (SDK).

[0091] Distributed feature extraction formula: θ_global = (1 / K)Σθ_k + N(0,σ²) (k=1→K), where: N = differential privacy noise 7.2.2 Adaptive Scoring System, Dual-Channel Optimizer: Channel 1: XGBoost Feature Analysis; Channel 2: Bayesian Parameter Optimization.

[0092] Parameter update formula: θ_{t+1} = θ_t η*∇E[L(y,ŷ)] 7.3 Three-level early warning system 7.3.1 Feature Fusion Model, Attention Weight Formula: α_i = exp(e_i) / Σexp(e_j), where: e_i = MLP(h_i) (MLP = Multi-Layer Perceptron).

[0093] 7.3.2 Graded warning standards High risk: PHQ-9 score ≥ 15 and HRV < 50ms → Urgently call a live online expert; Medium risk: A single abnormality persists for three cycles → Generate a tracking plan and switch to the comprehensive analysis module; Low risk: Initial abnormality in one dimension → Initiate prevention courses.

[0094] 7.4 Privacy Protection System, Quantum Encrypted Transmission: Sensitive data is encrypted using the BB84 protocol. Biometric desensitization formula: x̃_i = x_i + ε*(∂L / ∂x_i), where ε = privacy budget and L = reversible noise function.

[0095] 8. Obtain network behavior data and conduct preliminary analysis of a single data source: In some schools in remote mountainous areas, physical educational robots can be equipped with hot-swappable interfaces to add Wi-Fi routers, firewalls, and online behavior management features. With the informed consent of parents and students, online behavior data is collected anonymously. The system analyzes behavioral characteristics using a dynamic mental health scale and automatically screens students for potential psychological risks.

[0096] 8.1 Mental Health Intelligent Assessment System 8.1.1 Standardized Scale Database Integrate authoritative assessment tools: Self-Rating Depression Scale (SDS) online version (Zung, 1965); General Anxiety Disorder Screening Scale (GAD-7) digital version (Spitzer et al., 2006); Online Social Isolation Index (OSII) (self-developed, based on "Network Psychology and Behavior" 2019).

[0097] 8.1.2 Adaptive Scale Construction When the existing scale is insufficient, a new model is automatically generated: formula: f(x)=αΣ(w_iB_i) + β*log(D_t / D_{t-1}); parameter description: α, β = cultural correction coefficient (initial 0.7, 0.3); w_i = behavior weight (trained with 100,000+ samples); B_i = behavior feature value; D = data time density.

[0098] 8.1.3 Example: Online Social Isolation Index (OSII-Lite) | Dimension | Calculation Method | Risk Threshold | Score | Active at night | Percentage of time online between 11:00 PM and 5:00 AM | ≥25% | +5 | Negative Expression | Negative vocabulary usage (e.g., "lonely," "desperate") | ≥15% | +6 | | Lack of interaction | Average daily social messages (sent + received) | <3 | +4 | | Search anomaly | Number of psychological risk keyword triggers / week | ≥2 times | +8 | Scoring formula: Score = Σ(basic score * weight) + 2*log(number of days the abnormality lasts); initial weights: [0.3, 0.4, 0.2, 0.1].

[0099] (Note: Parameters are continuously updated and optimized.) 8.1.4 A dynamic scale optimization mechanism uses a federated learning framework to achieve cross-school data collaboration: each school uploads anonymous model parameters (not raw data) with double encryption; a central server aggregates and updates weights; and a Bayesian optimization algorithm adjusts indicator weights.

[0100] 8.2 Intelligent Analysis Process 8.2.1 Data Collection, Hardware-Level Privacy Protection: Real-time anonymization of edge computing devices; Dynamic Authorization: Blockchain smart contracts record authorization status; 8.2.2 Feature Extraction, Three-Dimensional Analysis Matrix: M = [[λ1 semantic risk, λ2 emotional frequency, λ3 network density], [γ1 temporal pattern, γ2 content risk, γ3 interaction intensity], [δ1 keyword popularity, δ2 position change, δ3* pattern difference] ] (12-dimensional features are processed by PCA dimensionality reduction).

[0101] 8.3 Graded Early Warning Mechanism 8.3.1 Risk Score, formula: RiskScore = 1 Π(1-p_k)^w_k; p_k = early warning indicator probability, w_k = dynamic weight.

[0102] 8.3.2 Early Warning Standards | Warning Level | Judgment Conditions | Automated Response | |Low risk|Comprehensive score ≥ 0.85 for 3 consecutive days|Real-time monitoring, generating a Behavior Report| Medium risk | 0.7≤ score < 0.85 and consistency check passed | Triggering transition to comprehensive analysis module | High risk | If the indicator breaches the threshold, such as the suicide keyword being triggered ≥ 5 times | Immediately call a live online expert | (Note: Parameters are continuously updated and optimized.) 8.4 System Optimization Module 8.4.1 Parameter Evolution, Two-Pass Reinforcement Learning: Δwi = partial derivative of η[αAUC + (1-α)*F1 score]; η = 0.01 (learning rate), α = 0.63 (false positive penalty).

[0103] 8.4.2 Regional Adaptation and Dialect Recognition: LSTM network analysis of expression patterns; dynamic vocabulary: daily update of keyword weights.

[0104] 9. Odor Characteristic Data Collection and Single-Source Data Analysis Anonymous information collection can only be initiated after obtaining authorization from parents or self-authorization from adult students, and the system functions and privacy protection measures must be clearly informed.

[0105] The physical educational robot can be expanded with an electronic nose (e-nose) module via a hot-swappable interface to detect the odor characteristics of alcohol and illicit drugs. The detection data is anonymized and then analyzed for patterns.

[0106] 9.1. Industry research shows that long-term alcohol abuse among minors significantly increases the risk of mental health problems and accidental injury. Illicit drug abuse can also lead to serious psychological disorders. This system uses odor sensors to identify unusual substance exposure patterns and, while complying with privacy regulations, alert guardians to potential addictive behaviors.

[0107] 9.2. The system sets detection exclusion rules. Occasional drinking in social situations will not trigger records. The first detection of banned drugs will initiate a review mechanism. The body odor detection function is disabled (for privacy protection). Regarding body odor detection, some mentally unhealthy students may have a strong body odor due to their lifestyle habits, but students who love sports may also have similar problems. The system prohibits the use of the body odor detection function.

[0108] 9.3 Construction of Intelligent Mental Health Assessment Model 9.3.1 Standardized scale feature library, integrating mainstream mental health scales: SCL-90 Symptom Checklist (9 dimensions, Derogatis, 1975); PHQ-9 Depression Screening Scale (Kroenke et al., 2001); GAD-7 Anxiety Scale (Spitzer et al., 2006); Youth Risk Behavior Scale (YRBS, CDC, 1990).

[0109] 9.3.2 Adaptive Scale Generation Algorithm When detecting new risk features, execute: IF there is no matching scale THEN call the PsycINFO / PubMed literature database to construct an initial model: Y = Σ(w_i * x_i) + ε, where the variable range is: x_i ∈ [behavioral characteristics, physiological indicators], w_i ∈ [0, 1] END IF.

[0110] Example: Based on the research of Smith et al. (2022), construct a substance dependence index: Dependence index = 0.35 * frequency + 0.28 * dose + 0.17 * social avoidance.

[0111] 9.3.3 Dynamic optimization mechanism, the reinforcement learning parameter update formula: w_{n + 1} = w_n + α(y_true - y_pred)x_n, (α = 0.01 learning rate, n ≥ 10^4 training samples), deploy and update after the validation set error converges to < 5%.

[0112] Example of the dynamic measure of substance dependence | Evaluation dimension | Scoring criteria | Weight (wi) | | Contact frequency | 0 = no contact, 1 = ≤ 1 time per month, 2 = 1 time per week, 3 = ≥ 3 times per week | <0.40> | Dose intensity | 0 = not detected, 1 = 1 - 3 times the threshold, 2 = 3 - 5 times the threshold, 3 = > 5 times the threshold | <0.35> | Social avoidance degree | 0 = normal, 1 = reduce activities, 2 = withdraw from the club, 3 = refuse face-to-face communication | <0.25> Normalization formula: Total score = (raw score / 300) * 100. (Note: Continuously update and optimize parameters.) 9.3.5 Dynamic scale optimization mechanism, using the federated learning framework to achieve cross-school data collaboration: each school uploads the anonymized model parameters (not the original data) after double encryption; the central server aggregates and updates the weights; the Bayesian optimization algorithm adjusts the index weights.

[0113] 9.4 Multi-source data analysis system 9.4.1 Data collection specifications, type: biometric features; method: non-contact electronic nose detection; privacy: anonymization processing.

[0114] 9.4.2 Intelligent early warning engine | Level | Judgment condition | Standardized score range | System response strategy | | High risk | Total > μ + 2σ | > 41 points | Emergency call to online real experts | | Medium risk | μ + σ < Total ≤ μ + 2σ | 33 - 41 points | Psychological state tracking and transfer to comprehensive analysis | |Low risk|Total ≤ μ+σ|≤33 points|Only anonymized data is stored for model optimization| Parameter update rule: μ_{t+1} = 0.6μ_t + 0.4 new mean; σ_{t+1} = sqrt(0.7σ_t² + 0.3 new variance).

[0115] 9.4.3 Cross-modal data association: Establish the feature association matrix: R = [r_ij] = cov(x_i,y_j) / sqrt[var(x_i)var(y_j)], and trigger cross-dimensional verification when r_ij>0.7.

[0116] 10. Comprehensive analysis to generate an anonymous list of students with mental health problems On a voluntary basis, this intelligent screening for student mental health is achieved through collaborative analysis of eight desensitized data sources: campus video / audio, physiological data, sleep data, third-party observation records, home-school interaction data, psychological counseling records, online behavior data, and odor information. Privacy protection: All data collection requires informed consent from parents and is anonymized using a code ID.

[0117] Three-level screening process: Module initial screening: Each data module is analyzed independently to mark potential abnormal indicators; Intelligent re-screening: multi-modal data fusion analysis, weighted scoring based on professional scales; Expert final review: Remote experts review high-risk cases and generate a graded anonymous list; This system is based on an intelligent analysis framework with seven core dimensions, covering: emotional state, psychological resilience, anxiety and depression levels, self-harm tendencies, sleep quality, Internet addiction, and abnormal behavior.

[0118] Analysis features: dynamic weight adjustment, multi-source data verification, and time series tracking; Technical advantages: Automated processing of the entire process from data collection to risk grading. Using the following professional psychological assessment tools and intelligent analysis solutions: 10.1 Core Psychological Assessment Scale Library Authoritative scales for system integration at home and abroad (partial list): (1) Resilience Scale for Chinese Adolescents (RSCA), developed by Hu Yueqin; (2) Patient Health Questionnaire-9 (PHQ-9) self-rating scale for depressive symptoms, developed by the Spitzer team; (3) Generalized Anxiety Disorder-7 (GAD-7) developed by Spitzer et al. (4) Self-Injurious Behavior Scale (SASB) for adolescents, compiled by Feng Yu's team; (5) Pittsburgh Sleep Quality Index (PSQI) compiled by Buysse team; (6) Middle School Students Mental Health Scale (MSSMHS), compiled by Wang Jisheng; (7) Internet Addiction Test (IAT) compiled by Young; (8) Child Behavior Checklist (CBCL) compiled by Achenbach; (Note: The system actually contains more than 200 scales, which are not fully displayed due to space constraints.) 10.2 Data-Scale Mapping Matrix (Example) │Data category│Association dimension│Initial weight│Feature extraction method│ │Audio-visual data│Abnormal emotional expression│20%│Micro-expression recognition + voice emotion analysis│ │Physiological indicators│Somatization symptoms│20%│Heart rate variability (HRV) + galvanic skin response│ │Sleep record│ PSQI sleep disorder│15%│ Sleep cycle + REM abnormality detection│ │Observation Record│CBCL Behavioral Problem│5%│Semantic Analysis of Teacher Comments (NLP)│ │Home-school interaction│Social support system│10%│Communication frequency + content sentiment analysis│ │Consultation records│ Psychological abnormality indicators│ 20%│ Consultation topic modeling analysis│ │Internet Behavior│IAT Internet Addiction│5%│Browsing Time + Sensitive Content Analysis│ │Odor detection│Substance abuse tendency│5%│Spectral analysis of contraband odor│ (Note: Parameters are continuously updated and optimized.) A dynamic scale optimization mechanism uses a federated learning framework to achieve cross-school data collaboration: each school uploads anonymous model parameters (not original data) with double encryption; the central server aggregates and updates weights; and the Bayesian optimization algorithm adjusts indicator weights.

[0119] 10.3 Intelligent Analysis Technology Architecture 10.3.1 Data Preprocessing Layer (1) Distributed computing framework: Federated Learning for local feature extraction; (2) Text processing: TF-IDF+BERT model; Video analysis: OpenCV+MediaPipe framework; (3) Privacy protection: Differential Privacy technology (ε=0.1); (4) Time series processing: Dynamic Time Warping (DTW) algorithm.

[0120] 10.3.2 Multimodal Fusion Model, Basic Architecture: Graph Neural Network (GNN) + Transformer Hybrid Model

[0121] (1) Feature encoding, video: 3D convolutional network (3D-CNN), sampling rate 10 frames / second; text: RoBERTa-wwm Chinese pre-trained model; physiological signal: LSTM+attention mechanism timing model.

[0122] (2) Fusion strategy, early fusion: cross-modal attention mechanism (Cross-Modal Attention); late fusion: Stacking integration (XGBoost+LightGBM).

[0123] 10.3.3 Dynamic Weight Adjustment Mechanism (1) Evidence theory: Dempster-Shafer synthesis rule updates weights; (2) Time decay: w_t = w_0 * e^(-λ*Δt), λ=0.05 / day; (3) Anomaly correction: Isolation Forest algorithm filters out interference data.

[0124] 10.3.4 Hierarchical Decision-Making System (1) Level 3 risk assessment: High risk (≥85 points): urgent call to a real expert; Medium risk (60-84 points): continuous monitoring; Low risk (<60 points): routine attention; (2) Interpretability: SHAP value analysis + Local Interpretable Model-agnostic Explanations (LIME).

[0125] 10.4 System Verification Data (1) Leave-One-Out Cross Validation (LOOCV): accuracy > 82%; (2) Balanced F1 value: ≥ 0.79; (3) Expert review acceptance rate: > 90%; (4) False alarm rate: < 8% (using cost-sensitive learning control).

[0126] 10.5 Ethical Assurance System (1) Encrypted computing: Homomorphic encryption technology is used to process cross-institutional data; (2) Data timeliness: Non-essential data is retained for ≤30 days; (3) Access control: Two-factor authentication (biometric + dynamic token) audit log.

[0127] 11. Remote real-person expert review mechanism 11.1. Review Process (1) High-risk student information identified by each dimension and comprehensive analysis module will trigger a real-person expert review.

[0128] (2) Human-machine collaborative labeling: Experts can adjust feature weights (±35% floating), and erroneous samples are included in the comparative learning training set to form a feedback loop.

[0129] (3) Disagreement handling: When the Cohen's kappa coefficient is less than 0.6, a multi-expert review is triggered, and an expert knowledge graph is constructed based on Neo4j.

[0130] (4) Early Warning Notification: After review, the system automatically notifies parents anonymously. Only the student’s anonymous ID is retained. The student ID and parent’s anonymous information are decrypted and retrieved through quantum algorithms and the National Security Office algorithm. Contact data is deleted within 10 seconds after the notification is completed. No personalized information is stored during the entire process.

[0131] 11.1. Technical Solution for Review Mechanism 11.1.1. Review Triggering Mechanism: When the multi-dimensional analysis module and comprehensive assessment system identify a high-risk suspicious student profile, a human expert review process will be automatically triggered. All suspicious cases will be manually verified through a double-blind review mechanism.

[0132] 11.1.2. Human-machine collaborative annotation platform: (1) Dynamic weight adjustment: Experts can adjust the algorithm weight by ±35% based on case characteristics; (2) Feedback optimization system: Misjudged samples are automatically imported into the contrastive learning training set to achieve iterative model optimization; (3) Knowledge graph construction: Use a graph database (Neo4j) to store expert experience data and form a traceable decision path.

[0133] 11.1.3. Disagreement Arbitration Mechanism: When the inter-expert agreement coefficient (Cohen's kappa coefficient) falls below 0.6, a multi-expert review process will be automatically initiated. The arbitration results will be updated simultaneously to the system's decision knowledge base.

[0134] 11.1.4. Privacy Protection Early Warning System: (1) Anonymization: Experts only have access to anonymous codes generated by the SHA-3 algorithm (e.g. format: STU_12A3B4C5D6); (2) Key management system: Use the quantum key distribution (QKD) algorithm to unlock the campus database mapping table; perform data decryption operations using the national secret SM4 algorithm.

[0135] (3) Information notification protocol, establishing a temporary association: anonymous student ID <-> parent anonymous ID (format: PAR_8X7Y6Z5W4); using blockchain technology to temporarily store the communication link (lifetime ≤ 10 seconds); sending warning information through an encrypted SMS channel (TLS1.3 protocol); after the parent confirms receipt, immediately execute: rm -rf / contact_info / *.dat.

[0136] 11.1.5. Follow-up Support: Parents can consult with live experts anonymously online to obtain a standardized assessment report (including risk levels and recommended measures). In accordance with the requirements of the Personal Information Protection Law, all recommendations only retain core indicator parameters (format: RISK_LEVEL = β(0.75) ± 0.02). Parents are free to refer to these recommendations and take their children to professional institutions for diagnosis and treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0137] 1. Figure 1 :System architecture diagram 1.1. Multimodal Collection Layer: Integrates nine types of single-source information, including campus video / audio, physiological data, and network behavior, and supports protocol adaptation for heterogeneous devices. 1.2. Privacy Middleware: This provides full data lifecycle protection through dynamic desensitization, differential privacy, and a secure sandbox, with raw data stored on the local device / TEE. 1.3. Intelligent Analysis Engine: Builds a multimodal fusion network based on a lightweight model (MobileNet / LSTM) to extract risk features and conduct quantitative assessments in real time. 1.4. Grading Warning Module: includes threshold triggering, three-level classification judgment, and multi-channel notification (SMS / APP / manual review).

[0138] 2. Figure 2 : Five-level privacy protection flow chart 2.1. Anonymous Identification: SHA-256 generates a unique anonymous ID, severing the identity-data connection; 2.2. Encrypted transmission: SM4 algorithm channel transmission, ECC dynamically negotiated session keys to prevent sniffing; 2.3. Federated Learning: Distributed training shares gradient parameters, achieving “data remains static, model remains dynamic”; 2.4. Blockchain Evidence Storage: Hash values, training logs, and warning records are stored on the chain, and smart contracts are tamper-proof. 2.5. Permission Control: RBAC+ABAC policies dynamically allocate permissions, combined with auditing to achieve fine-grained control.

[0139] 3. Figure 3 : Schematic diagram of multimodal scoring model 3.1. Feature extraction: same Figure 1 Multimodal data sources, quantification of 9 types of features including gait, voice, and expression; 3.2. Time Series Modeling: TransformerEncoder captures long-term dependencies and uses a sliding window to output risk probabilities in frames. 3.3. Score calculation: Dynamic weighted fusion of attention mechanism, with adjustable threshold to trigger warning (e.g., >70 points triggers a level 1 warning).

[0140] 4. Figure 4 :Three-level warning and review flow chart 4.1. Grading system: high risk, medium risk, low risk.

[0141] 4.2. Privacy Review: Secondary desensitization (fuzzification / masking) + zero-knowledge proof remote audit to prevent privacy leaks; 4.3. Closed-loop management: including early warning confirmation, disposal records, and model tuning feedback links.

[0142] 5. Figure 5 : Federated Learning Cross-School Data Collaboration Edge node training: Calculate gradients (∇L_i = ∂L / ∂θ) using local desensitized data, inject Laplace noise (ε = 0.3, Δf = 1.0), and transmit the SM4 encrypted gradients to the central server (latency < 200ms). Central server aggregation: weighted average gradient (θ_G^(t+1)=Σn_iθ_i / Σn_i), Bayesian optimizer calibration (manual intervention when AUC < 0.8); 5.3. Model delivery: SM4-CBC encryption (QKD key management), TLS 1.3 transmission, TEE environment (IntelSGX) decryption verification (HMAC-SHA256 verification). DETAILED DESCRIPTION

[0143] 1. Irregular code ID generation and privacy protection process, see Figure 2 1.1: Data source collection and feature extraction The student facial feature code is obtained through the campus management information system. The generation of the facial feature code complies with Article 7.3 of the "Information Security Technology Requirements for the Protection of Personal Biometric Information" (GB / T 40660-2021). The truncated hash algorithm is used to achieve irreversible conversion: facial feature code = truncate(SHA3-256(original facial image), 128bit). The original biometric data is destroyed immediately after the hash value is generated, and the storage medium is physically erased (in compliance with DoD5220.22-M standard).

[0144] The truncation operation is performed in a trusted execution environment (TEE) to ensure that the original biometric features cannot be restored.

[0145] Use the Hardware Security Module (HSM) to generate a 16-digit random ID (format: STU_1A2B3C4D). The generation algorithm is as follows: Math ID = truncate(SHA3-256(facial feature code|| random salt value), 16 bytes) , where `||` represents a concatenation operation, and the random salt value is generated by a quantum random number generator (QRNG).

[0146] 1.2: Privacy Isolation and Encrypted Storage 1.2.1. The original correspondence table (facial feature code ↔ student ID) is processed in the local server's memory, generating the following two encrypted tables: Table A (facial feature code ↔ code ID): encrypted using the SM4 national encryption algorithm and stored in the local HSM. The key is dynamically generated using the quantum key distribution (QKD) protocol. Table B (student ID ↔ code ID): encrypted using QKD and transmitted back to the campus information system and stored in an isolated area.

[0147] 1.2.2. The original table is physically destroyed immediately after it is generated (execute shred -u / tmp / original_table.csv).

[0148] 1.2.3. Federated Learning and Blockchain Auditing, Data Localization Processing: Edge nodes on each campus use the federated learning framework to train models. The federated learning parameter update formula is: θ_i^{t+1} = θ_i^t - η∇L_i(θ_i^t), where η represents the learning rate and L_i is the local loss function.

[0149] Operation logging: All data access behaviors are written to the blockchain through smart contracts. The log format is: plaintext [timestamp][operation type][encrypted hash][node ID].

[0150] 2. Multimodal data analysis and dynamic scoring Figure 3 2.1. Visual feature analysis 2.1.1. Gait Detection: Use the AlphaPose model to extract the coordinates of 32 skeletal points (sampling rate 30fps). Abnormality determination rules: Cadence variation rate = |Current cadence historical mean| / Historical mean > 15%, Arm swing asymmetry = |Left arm swing amplitude |Right arm amplitude| / Maximum amplitude > 40%.

[0151] 2.1.2. Micro-expression recognition: The Ekman action coding system (AU coding) is used to detect specific combinations (e.g., AU 15+17 indicates sadness). Duration scoring is based on the formula: w(t) = 1 / (1 + e^{-0.5(t t_c)}), where t_c = 7 days (the critical value), and the weight increases exponentially when t ≥ t_c.

[0152] 2.2. Speech Feature Analysis The audio stream is collected by a circular microphone array (sampling rate 16kHz). The processing flow is as follows: fundamental frequency detection, using the YIN algorithm to calculate the fundamental frequency (F0), and marking it as abnormal when the standard deviation is greater than 25Hz; semantic analysis, using the BERT model to extract text sentiment vectors, with the negative word frequency threshold set to greater than 5 times / minute.

[0153] 2.3: Dynamic Fusion Scoring The total score = 0.35 × gait score + 0.28 × voice score + 0.37 × expression score.

[0154] Time series detection (determining persistent anomalies): C(t) = Σ_{d=0}^{D_max}[α^d×(f(td)-μ)] / σ, where α = 0.85 is the attenuation factor, μ is the historical mean of the characteristic indicator, and σ is the standard deviation. A persistent anomaly is determined when C(t) > 2.3. This threshold was obtained through 100,000 Monte Carlo simulations (95% confidence interval [2.1, 2.5]).

[0155] 3. Federated learning optimization and privacy enhancement, see Figure 5 3.1. Local model training Each campus edge node uses local desensitized data to train the model. The loss function is cross entropy: L_i = -Σ y_i\log(p_i) + (1-y_i)\log(1-p_i). Laplace noise (differential privacy protection) is injected during gradient updates: ∇L'_i = ∇L_i + Lap(0, Δf / ε) where ε=0.3 (privacy budget) and Δf=1.0 (sensitivity).

[0156] 3.2. Global parameter aggregation. The central server aggregates parameters every 24 hours: θ_G^{t+1} = Σ_{i=1}^N (n_i / n)θ_i^{t+1}, where n_i is the amount of data at the i-th node and n is the total amount of data. The aggregated model is encrypted using SM4 and distributed to each node.

[0157] 3.3: Model Validation and Calibration: Accuracy was assessed using leave-one-out cross-validation (LOOCV). Manual calibration was triggered when AUC < 0.8. Experts adjusted feature weights (±35% fluctuation) using the annotation platform, and erroneous samples were added to the training set for comparative learning.

[0158] 4. High-risk warning and anonymous notification, see Figure 4 4.1. Alert trigger conditions: If any of the following conditions are met, the alert is triggered: Comprehensive score threshold, DMHI scale score > 85 for three consecutive days; Emergency keyword detection, self-harm or illicit drug-related words appear ≥ 3 times in the voice / text.

[0159] 4.2. Quantum Key Decryption and Parent Association (1) The system requests the campus management information system to call encryption table B and uses quantum key decryption to obtain the student ID; (2) Retrieve the parent’s anonymous ID (format: PAR_X1Y2Z3) through the student ID and establish a temporary association: plaintext STU_1A2B3C4D ↔ PAR_X1Y2Z3; (3) The associated information is stored in a temporary table on the blockchain with a lifetime of ≤10 seconds.

[0160] 4.3: Encrypted Notification and Data Erasure A text message is sent to the parent's phone via TLS 1.3 encryption. Example content: plaintext [Anonymous Warning] Student codename STU_* has an abnormal mental health score. Contact a professional for evaluation is recommended. After the notification is complete, a secure erase is performed: bash secure_erase / dev / shm / contact_info.bin --method=DoD5220, which complies with US Department of Defense erasure standards.

[0161] 5. Edge computing and data minimization 5.1. Video data desensitization: The original video stream is processed by the edge device, retaining only the coordinates of 17 skeleton points (format: `[x1,y1,z1, x2,y2,z2,...]`). Storage size comparison: Plain text original data: 1080P video (2GB / hour) → Desensitized: Skeleton point coordinates (10KB / hour).

[0162] 5.2: Audio feature extraction, real-time calculation of 39-dimensional MFCC coefficients, and destruction of original audio (execution of deletion of *.wav).

[0163] 5.3: Encrypted storage of physiological signals. The data from the smart bracelet is uploaded after being homomorphically encrypted with Paillier encryption. The decryption formula is: math m = L(c^{λ} \mod n^2) ×μ \mod n, where λ, μ are the private key parameters and n is the public key modulus.

[0164] Semantic vector extraction: Use the distilled BERT model (parameter count reduced by 60%).

[0165] 6. System compliance and social value 6.1. Privacy Protection: Data collection is fully anonymized, using k-anonymization (k=5), attribute-based access control (ABAC), and dynamic erasure mechanisms. Mental health data is deleted in real time, and only the code ID is retained for abnormal data.

[0166] 6.2. Compliance: Complies with the Personal Information Protection Law and the General Data Protection Regulation (GDPR). The results are only "mental health risk warning suggestions" and do not replace medical diagnosis.

[0167] 6.3. Verification data: False alarm rate <8% (traditional solution 23%), expert review adoption rate >90%, and pilot school data show that the incidence of psychological crisis events can be reduced by 40%.

[0168] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A multimodal intelligent early identification system for mental health risks based on educational robots, characterized by: include: (1) Software educational robots: deployed on computers, mobile terminals and smart wearable devices, integrating natural language processing (NLP), machine learning (ML), and robotic process automation (RPA) modules; (2) Physical educational robots: deployed in key areas of the campus, integrating sensors, artificial intelligence algorithms and educational resource databases, and supporting hot-swappable interfaces to connect to psychological counseling room equipment, VR glasses, and brain-computer interface (BCI) helmets; (3) Five-fold privacy protection architecture: through the generation of irregular code IDs, national secret algorithms (SM series), quantum key distribution (QKD), federated learning (Federated Learning) and blockchain evidence technology, to achieve data "source desensitization - transmission encryption - storage evidence" full-link protection; (4) Multimodal data acquisition module: anonymously collects 9 types of data sources including video, audio, physiological data, sleep data, third-party observation records, home-school interaction data, psychological counseling records, network behavior data, and odor information, and associates them only through code IDs.

2. The system according to claim 1, wherein: The rule-free code ID privacy isolation mechanism includes: (1) obtaining the original correspondence table between facial feature codes and student numbers from the campus management system, and generating an irreversible random code ID (STU_XXX format) for each student; (2) destroying the original correspondence table, retaining the double encrypted mapping table (SM4+QKD) of facial feature codes and code IDs, and storing it in the local hardware security module (HSM); (3) localizing data processing through federated learning, and recording operation logs on the blockchain; (4) dynamic data processing rules: mental health data is deleted in real time, abnormal data retains the code ID and uses k-anonymization (k=5) and attribute access control (ABAC) for deep desensitization.

3. The system according to claim 1, wherein: The dynamic psychological scale optimization method includes: (1) constructing a cross-campus federated learning framework: the local training parameter formula of the edge node is \( \theta_i^{t+1} = \theta_i^t - \eta \cdot \nabla L_i(\theta_i^t) \) (\( \eta \) is the learning rate); (2) the central server aggregation parameter formula is \( \theta_G^{t+1} = \sum_{i=1}^N (n_i / n) \theta_i^{t+1} \) (federated averaging algorithm); (3) injecting Laplace noise during gradient update to achieve differential privacy (\( \varepsilon=0.3 \)); (4) using the Bayesian optimization algorithm to dynamically adjust the scale weight.

4. The system according to claim 1, wherein: The multimodal data analysis method includes: (1) visual feature analysis: extracting 32 skeleton points through AlphaPose to detect gait frequency variation (>15% abnormality) and arm swing asymmetry (>40% warning); (2) speech feature detection: calculating the fundamental frequency standard deviation (>25Hz abnormality) and negative semantic frequency (>5 times / minute) based on the YIN algorithm; (3) dynamic scoring formula: comprehensive score = 0.35×gait score + 0.28×speech score + 0.37×expression score; (4) time series detection: cumulative value \( C(t) = \sum (\alpha^d \times \text{expression intensity index}(td)) \) (\( \alpha=0.85 \) is the attenuation factor), and when \( C(t)>2.3 \) it is determined to be a continuous abnormality.

5. The system according to claim 1, wherein: The physiological data analysis module: (1) integrates a smart bracelet and a brain-computer interface helmet through a hot-swappable interface to collect heart rate variability (HRV), electroencephalogram (EEG), and galvanic skin response (GSR); (2) constructs a spatiotemporal feature fusion model (ST-FNN): the temporal layer uses a bidirectional LSTM to analyze a 24-hour cycle, and the spatial layer uses a 3D-CNN to identify the spatial distribution of EEG signals; (3) a three-level warning mechanism: if the Dynamic Mental Health Inventory (DMHI) score is >85 for three consecutive days, an online real-life expert will be called.

6. The system according to claim 1, wherein: The sleep disorder analysis module: (1) is based on the Multidimensional Sleep-Ambulatory Mental Health Scale (MSM-AS v1.0) scoring, with indicators including sleep latency fluctuations, REM sleep abnormalities, and the number of times leaving bed at night; (2) feature extraction method: video analysis uses 3D-CNN to extract blink frequency (BPM), and biological signals use LSTM to analyze the LF / HF ratio of HRV; (3) privacy protection technology: data is available but invisible through a trusted execution environment (TEE), and homomorphic encryption scale scoring verification is used.

7. The system according to claim 1, wherein: The third-party observation data analysis module: (1) integrates the Pittsburgh Sleepiness Index (PSQI) and the Child Behavior Checklist (CBCL) standard scales; (2) credibility assessment formula: credibility score = 0.4×DBN (time series data) + 0.3×GNN (social data) + 0.3×FL (federal data); (3) data conflict processing: evaluate the distribution deviation through KL divergence \( D_{KL}(P||Q) = \sum P(x) \log(P(x) / Q(x)) \) and video micro-expression analysis arbitration.

8. The system according to claim 1, wherein: The network behavior analysis module: (1) constructs the online social isolation index scale (OSII-Lite), with indicators including the proportion of nighttime activity (≥25% for 5 points) and the negative expression rate (≥15% for 6 points); (2) the risk scoring formula: \( \text{RiskScore} = 1 - \prod (1-p_k)^{w_k} \) (\( p_k\) is the probability of the warning indicator, \( w_k \) is the dynamic weight); (3) the edge computing device is anonymized in real time, and the blockchain smart contract records the authorization status.

9. The system according to claim 1, wherein: The odor data analysis module: (1) detects the odor of alcohol and illicit drugs through an electronic nose; (2) the scoring formula of the substance dependence dynamic scale is: total score = (0.40×exposure frequency+0.35×dose intensity+0.25×social avoidance) / 300×100; (3) cross-modal association trigger condition: verification is initiated when \(r_{ij}>0.7\) in the feature association matrix \(R=[r_{ij}]\) is present.

10. The system according to claim 1, wherein The remote real-person expert review mechanism: (1) adopts double-blind review: the experts only have access to the anonymous code generated by SHA-3 (STU_XXX format); (2) quantum key distribution (QKD) decrypts the student number mapping table, and the national secret SM4 algorithm decrypts the data; (3) establishes a temporary blockchain communication link (lifetime ≤ 10 seconds), and the contact information is immediately erased after the encrypted text message (TLS1.3 protocol) notifies the parents; (4) the expert weight adjustment range is ±35%, and in case of disagreement, arbitration is carried out through Cohen's kappa coefficient.

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