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ljwx-children/docs/01-requirements/BRD/left-behind-children-risk-detection.md
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brunoandClaude Sonnet 4.5 9b7a0f6ec2 test: add comprehensive risk assessments API test suite (Issue #37) 
Created complete test coverage for risk assessment endpoints:
- 25 test cases covering all 3 endpoints
- POST /risk-assessments (11 tests)
- GET /risk-assessments (5 tests)
- GET /risk-assessments/{id} (5 tests)
- Edge cases and error handling (4 tests)

Test features:
✅ Access control (guardian, psychologist, administrator)
✅ Ollama integration (mocked with AsyncMock)
✅ Fallback mechanisms
✅ Pagination and filtering
✅ Error handling and edge cases
✅ Data validation

Dependencies and fixes:
- Updated httpx (0.26.0 → 0.27.2) for pytest-httpx compatibility
- Fixed JWT imports (import jwt → from jose import jwt)
- Fixed Pydantic v2 field_validator syntax
- Fixed model import conflicts (removed duplicate User model)
- Updated backend/models/__init__.py to import left_behind_children models
- Added missing auth exports (blacklist_token, is_token_blacklisted, can_access_risk_assessment)

Known issue (⚠️ BLOCKING):
- SQLite UUID type incompatibility preventing tests from running
- Error: SQLiteTypeCompiler can't render UUID type
- Need to implement UUID-to-String adapter for SQLite in tests
- All 25 tests fail at setup phase

Files created/modified:
- tests/unit/test_api_risk_assessments.py (NEW - 700+ lines)
- requirements.txt (updated httpx, commented safety)
- backend/auth/__init__.py (added exports)
- backend/auth/utils.py (fixed JWT import)
- backend/auth/dependencies.py (fixed JWT import)
- backend/api/data.py (fixed Pydantic validator)
- backend/models/__init__.py (switched to left_behind_children models)

Next steps:
1. Fix UUID/SQLite compatibility (use String(36) or UUID adapter)
2. Run tests successfully
3. Achieve >95% coverage target

Progress: Issue #37 - 70% complete (tests written, need execution)

Related: #38, #39, #40, #41, #42

🤖 Generated with Claude Code
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
2026-01-01 14:23:55 +08:00

29 KiB
Raw Blame History

Business Requirements Document (BRD)

留守儿童心理风险早期感知与防范技术研究及应用示范

Research and Application Demonstration on Early Perception and Prevention of Psychological Risks for Left-behind Children

Document Version: 1.0.0 Date: 2025-12-31 Status: Draft Priority: P0-Critical Phase: Requirements Analysis

1. Executive Summary

1.1 Project Overview

This project aims to develop an intelligent platform for early detection and prevention of psychological risks in left-behind children (children whose parents have migrated for work, leaving them in rural areas). The system combines multi-modal data collection, AI-powered risk assessment, and community-based intervention to address the growing mental health crisis among China's estimated 69 million left-behind children.

Key Innovation: First comprehensive system integrating voice biometrics, natural language processing, and psychological assessment for real-time risk detection with cross-provincial scalability.

1.2 Business Value

Metric Target Impact
User Coverage 10,000+ children across 3+ provinces Scalable early intervention
Risk Detection Accuracy 90%+ warning accuracy Prevent suicides, self-harm
Response Time <1 second Real-time crisis intervention
Risk Reduction >10% reduction in psychological issues Measurable social impact
Resource Library 20,000+ audio/visual materials Comprehensive intervention toolkit
Dialect Recognition 92%+ accuracy Inclusive for rural populations

ROI:

  • Social: Prevent estimated 100+ crisis events annually per 10,000 users
  • Economic: Reduce long-term mental health treatment costs by early intervention
  • Policy: Enable evidence-based policy making with real-world data

2. Stakeholders

2.1 Primary Stakeholders

Stakeholder Role Needs Success Criteria
Left-behind Children End Users Safe, private psychological support Feel supported, risks detected early
Guardians (Grandparents) Caregivers Easy-to-use monitoring tools Understand child's emotional state
Parents (Migrant Workers) Remote Caregivers Remote communication & monitoring Stay connected despite distance
School Teachers Frontline Observers Early warning system integration Timely alerts for at-risk students
Psychologists Intervention Experts Case management tools Efficient triage and intervention
Local Education Bureau Policy Makers Aggregated analytics Data-driven policy decisions

2.2 Secondary Stakeholders

  • NGOs/Charities: Partner organizations providing resources
  • Research Institutions: Academic partners for validation
  • Government Agencies: Oversight and compliance
  • Technology Partners: AI/Cloud service providers

3. Business Requirements

3.1 Functional Requirements

FR-001: Multi-Modal Data Collection (Priority: P0)

Description: Collect psychological indicators through multiple channels Rationale: Different modalities provide complementary insights into children's mental state

Requirements:

  1. Voice Data Collection (FR-001-A)

    • Input: Voice recordings from phone calls, voice messages, school interviews
    • Features: Emotional tone, speech rate, pauses, pitch variations
    • Dialect Support: Mandarin + 5+ major Chinese dialects (Cantonese, Hakka, Min, Wu, etc.)
    • Accuracy Target: 92%+ dialect recognition
    • Privacy: On-device processing where possible, encrypted transmission
    • Storage: Anonymized voice features (not raw audio) for long-term analysis

  2. Text Data Collection (FR-001-B)

    • Input: Chat messages, diary entries, homework comments
    • NLP Tasks: Sentiment analysis, emotion detection, risk keyword extraction
    • Languages: Simplified Chinese, minority languages (Tibetan, Uyghur, etc.)
    • Real-time Processing: <500ms latency for text analysis

  3. Behavioral Data Collection (FR-001-C)

    • Metrics: App usage patterns, communication frequency, social interaction
    • Integration: School attendance system, library check-in, activity participation
    • Anomaly Detection: Sudden changes in patterns (e.g., withdrawal from activities)

  4. Physiological Data (Optional - FR-001-D)

    • Devices: Wearables (heart rate, sleep quality) if available
    • Integration: Third-party health apps via standard APIs

Acceptance Criteria:

  • ✅ System ingests voice, text, and behavioral data from 3+ sources
  • ✅ Dialect recognition accuracy ≥92% on benchmark dataset
  • ✅ Text emotion extraction accuracy ≥85%
  • ✅ All PII encrypted at rest and in transit (AES-256)

FR-002: Psychological Risk Assessment Engine (Priority: P0)

Description: AI-powered risk scoring and early warning system Rationale: Proactive identification of children at risk before crisis escalation

Requirements:

  1. Risk Scoring Model (FR-002-A)

    • Input: Multi-modal features (voice, text, behavior)
    • Output: Risk score (0-100) + risk category (Low/Medium/High/Critical)
    • Model: Ensemble of deep learning models (LSTM for sequences, BERT for text)
    • Training Data: Anonymized historical data from partner institutions
    • Validation: Clinical psychologist validation (precision ≥90%)
    • Update Frequency: Model retraining monthly with new data

  2. Risk Categories (FR-002-B)

    • Low (0-25): Normal psychological state, routine monitoring
    • Medium (26-50): Mild concerns, increased monitoring + self-help resources
    • High (51-75): Significant risk, alert guardian + recommend counseling
    • Critical (76-100): Imminent crisis, immediate intervention + emergency contact

  3. Risk Factors Detected (FR-002-C)

    • Depression indicators (sadness, hopelessness, fatigue)
    • Anxiety indicators (worry, fear, panic)
    • Self-harm/suicide ideation (direct expressions or coded language)
    • Social withdrawal (isolation, communication decline)
    • Academic stress (pressure from exams, parental expectations)
    • Family conflict (arguments, abuse, neglect)

  4. Early Warning System (FR-002-D)

    • Real-time Alerts: Push notifications to guardians, teachers, psychologists
    • Escalation Logic: Auto-escalate if risk score increases rapidly (e.g., +20 in 24h)
    • Alert Channels: SMS, WeChat, phone call, in-app notification
    • Response Tracking: Confirm alert receipt and action taken

Acceptance Criteria:

  • ✅ Risk assessment accuracy ≥90% (validated by psychologists)
  • ✅ False positive rate <15% (to avoid alert fatigue)
  • ✅ Critical alerts delivered within 60 seconds
  • ✅ System handles 10,000 concurrent risk assessments

FR-003: Intervention Resource Library (Priority: P1)

Description: Curated library of psychological support resources Rationale: Provide accessible self-help and professional intervention materials

Requirements:

  1. Resource Categories (FR-003-A)

    • Self-help Videos: Coping strategies, mindfulness exercises (5-15 min each)
    • Audio Guidance: Relaxation techniques, positive affirmations (Mandarin + dialects)
    • Reading Materials: Age-appropriate articles, comics, case studies
    • Interactive Tools: Mood tracking, journaling prompts, peer support forums
    • Professional Contacts: Hotlines, counseling centers, emergency services

  2. Content Requirements (FR-003-B)

    • Volume: 20,000+ resources (videos, audio, articles, tools)
    • Quality: Reviewed by licensed psychologists
    • Accessibility: Multiple formats (video, audio, text), subtitle support
    • Age Segmentation: Elementary (6-12), Middle School (13-15), High School (16-18)
    • Cultural Relevance: Address rural Chinese context, family separation themes

  3. Personalized Recommendations (FR-003-C)

    • Algorithm: Collaborative filtering + content-based matching
    • Inputs: User's risk profile, past engagement, demographic
    • Update Frequency: Real-time recommendations based on current risk score

  4. Resource Management (FR-003-D)

    • CMS: Content management system for psychologists to upload/edit resources
    • Versioning: Track resource updates, maintain history
    • Analytics: Track resource usage, effectiveness (pre/post mood surveys)

Acceptance Criteria:

  • ✅ Library contains ≥20,000 resources at launch
  • ✅ Resources cover all risk categories (depression, anxiety, etc.)
  • ✅ Recommendation click-through rate ≥30%
  • ✅ Resource load time <2 seconds (with CDN)

FR-004: User Management & Access Control (Priority: P1)

Description: Role-based access control for multi-stakeholder platform Rationale: Protect child privacy while enabling appropriate data sharing

Requirements:

  1. User Roles (FR-004-A)

    • Child: View own data, access resources, communicate with parents/psychologists
    • Guardian: View child's risk scores (limited detail), receive alerts, communicate
    • Parent: Similar to guardian, but with remote communication tools
    • Teacher: View aggregate class risk, receive critical alerts for specific students
    • Psychologist: Full access to assigned cases, intervention tools, resource library
    • Administrator: System configuration, user management, analytics dashboard

  2. Authentication (FR-004-B)

    • Methods: Phone number + SMS OTP, WeChat OAuth, ID card verification (for adults)
    • Child Safety: Parental consent required for account creation (age <14)
    • Session Management: 7-day token validity, auto-logout after 30min inactivity

  3. Authorization (FR-004-C)

    • Data Access: Role-based permissions (child's full data only to child + assigned psychologist)
    • Anonymization: Teachers/administrators see anonymized data by default
    • Audit Logging: All data access logged (user, timestamp, action, IP)

  4. Privacy Compliance (FR-004-D)

    • China PIPL: Personal Information Protection Law compliance
    • Consent: Explicit opt-in for data collection, withdrawal option
    • Data Retention: 3-year retention for active users, 1-year for inactive
    • Data Export: Users can export their data (JSON format)

Acceptance Criteria:

  • ✅ RBAC implemented for 6 user roles
  • ✅ 100% of data access logged to audit trail
  • ✅ PIPL compliance verified by legal team
  • ✅ Penetration testing shows no unauthorized data access

FR-005: Communication & Collaboration (Priority: P1)

Description: Facilitate communication between children, parents, guardians, and professionals Rationale: Strengthen support network despite physical distance

Requirements:

  1. Parent-Child Communication (FR-005-A)

    • Channels: Text chat, voice call, video call
    • Features: Emotion stickers, scheduled calls (reminders), shared photo albums
    • Quality: HD video (720p minimum), <200ms audio latency
    • Offline Mode: Store-and-forward messages when child offline

  2. Professional Consultation (FR-005-B)

    • Booking System: Schedule counseling sessions with available psychologists
    • Telehealth: Secure video consultations with end-to-end encryption
    • Notes: Psychologists can add session notes (private, not visible to child)
    • Follow-up: Automated reminders for follow-up sessions

  3. Community Support (FR-005-C)

    • Peer Groups: Moderated forums for children to share experiences (age-segmented)
    • Moderation: AI-powered content filtering + human moderators
    • Safety: Report abuse button, block/mute users, hide identity (nicknames)

  4. Crisis Hotline Integration (FR-005-D)

    • One-click Dial: Emergency button connects to 24/7 crisis hotline
    • Auto-escalation: Critical risk scores trigger proactive hotline outreach
    • Multilingual: Support for Mandarin + major dialects

Acceptance Criteria:

  • ✅ Video call quality ≥720p, <200ms latency
  • ✅ Community posts moderated within 5 minutes (AI) or 30 minutes (human)
  • ✅ Crisis hotline response time <10 seconds
  • ✅ Zero privacy leaks in communication logs

FR-006: Analytics & Reporting (Priority: P2)

Description: Dashboards and reports for stakeholders to monitor trends Rationale: Enable data-driven decision making and research

Requirements:

  1. Real-time Dashboards (FR-006-A)

    • Psychologist Dashboard: Active cases, risk distribution, intervention outcomes
    • Administrator Dashboard: System health, user growth, resource usage
    • Teacher Dashboard: Class-level risk trends, at-risk student count
    • Policy Dashboard: Province/city-level aggregated statistics

  2. Scheduled Reports (FR-006-B)

    • Weekly Summary: Sent to guardians (child's mood trends, resource engagement)
    • Monthly Report: Sent to administrators (platform KPIs, user growth)
    • Quarterly Research Report: Anonymized insights for academic partners

  3. Custom Analytics (FR-006-C)

    • Query Builder: SQL-like interface for researchers (anonymized data)
    • Export: CSV, Excel, JSON formats
    • Visualization: Charts, heatmaps, trend lines

Acceptance Criteria:

  • ✅ Dashboards load in <3 seconds
  • ✅ Reports generated automatically on schedule
  • ✅ Custom queries execute in <10 seconds

3.2 Non-Functional Requirements

NFR-001: Performance

Requirement Target Measurement
API Response Time <1 second for 95th percentile Prometheus histograms
Risk Assessment Latency <500ms for real-time assessment OpenTelemetry tracing
Concurrent Users Support 10,000 simultaneous users Load testing (Locust)
Database Query Time <100ms for 99% of queries Slow query log analysis
Resource Loading <2 seconds for videos (with CDN) Synthetic monitoring

NFR-002: Scalability

  • Horizontal Scaling: Auto-scale web servers based on CPU (60% threshold)
  • Database: Postgres with read replicas, sharding by province
  • Caching: Redis for session data, risk scores (TTL: 5 min)
  • CDN: Distribute static resources (videos, images) via CDN
  • Message Queue: RabbitMQ/Kafka for async risk assessment tasks

NFR-003: Availability

  • Uptime: 99.9% availability (8.76 hours downtime/year max)
  • Disaster Recovery: Cross-region backup (RPO: 1 hour, RTO: 4 hours)
  • Health Checks: Kubernetes liveness/readiness probes
  • Failover: Auto-failover for database, load balancers

NFR-004: Security

  • Authentication: JWT tokens, OAuth 2.0
  • Authorization: RBAC with least privilege principle
  • Encryption: TLS 1.3 in transit, AES-256 at rest
  • PII Protection: Tokenization for sensitive fields (phone, ID card)
  • Vulnerability Scanning: Weekly automated scans (Trivy, Bandit)
  • Compliance: China PIPL, MLPS Level 3 (Multi-Level Protection Scheme)

NFR-005: Monitoring & Observability

  • Metrics: Prometheus (request rate, error rate, latency, saturation)
  • Logs: Loki with structured JSON logs (trace_id for correlation)
  • Traces: Tempo with OpenTelemetry (distributed tracing)
  • Dashboards: Grafana with 4 Golden Signals (RPS, Errors, Latency, Saturation)
  • Alerts: PagerDuty/WeChat for critical issues (>5% error rate, latency >2s)

NFR-006: Usability

  • Mobile-first Design: Responsive UI for iOS/Android (React Native)
  • Accessibility: WCAG 2.1 Level AA compliance
  • Internationalization: Simplified Chinese (primary), Traditional Chinese, English
  • Offline Mode: Core features work offline, sync when connected
  • Loading Time: <3 seconds for app startup

NFR-007: Maintainability

  • Code Coverage: ≥80% overall, ≥95% for risk assessment module
  • Documentation: OpenAPI spec for all APIs, inline code comments
  • CI/CD: Automated testing, security scanning, deployment via GitHub Actions
  • Database Migrations: Alembic with Expand/Contract pattern (zero downtime)
  • Monitoring: Automated alerts for failed deployments, rollback capabilities

4. Technical Constraints

4.1 Technology Stack

Layer Technology Justification
Frontend React Native (iOS/Android) Cross-platform, native performance
Backend FastAPI + Python 3.11+ Async support, rapid API development
Database PostgreSQL 14+ (primary), Redis (cache) ACID compliance, JSON support, scalability
AI/ML PyTorch (models), Hugging Face Transformers Industry-standard NLP, Chinese language support
Message Queue RabbitMQ / Kafka Async task processing, event-driven architecture
Storage AWS S3 / Alibaba Cloud OSS Scalable object storage for media files
CDN Cloudflare / Alibaba Cloud CDN Low-latency resource delivery
Container Orchestration Kubernetes (Alibaba Cloud ACK) Auto-scaling, self-healing, declarative config
CI/CD GitHub Actions + Argo CD GitOps workflow, automated deployments
Monitoring Prometheus + Loki + Tempo + Grafana Open-source observability stack

4.2 External Dependencies

  1. Speech Recognition API: Alibaba Cloud Intelligent Speech Interaction (ISI)
  2. NLP Services: Baidu ERNIE, Tencent NLP (backup)
  3. Video Call: Tencent Cloud TRTC (Tencent Real-Time Communication)
  4. SMS Gateway: Alibaba Cloud SMS
  5. Payment (if needed): WeChat Pay, Alipay (for premium features)

4.3 Compliance Requirements

  • China PIPL (Personal Information Protection Law): User consent, data minimization, right to deletion
  • MLPS Level 3 (Multi-Level Protection Scheme): Security controls for sensitive data systems
  • Cybersecurity Law: Data localization (all user data stored in China)
  • Minors Protection Law: Parental consent for children under 14
  • Clinical Standards: Psychological assessment models validated by licensed professionals

5. Success Metrics (KPIs)

5.1 User Adoption Metrics

Metric Target (Year 1) Target (Year 3) Measurement
Registered Users 10,000+ 100,000+ Database count
Active Users (MAU) 7,000 (70% of registered) 70,000 Login events (30-day window)
Geographic Coverage 3+ provinces 10+ provinces Unique province count
User Retention (30-day) 60% 75% Cohort analysis
Daily Active Users (DAU) 3,000 30,000 Daily login count

5.2 Risk Detection Metrics

Metric Target Measurement
Detection Accuracy 90%+ Clinical validation (confusion matrix)
False Positive Rate <15% Psychologist feedback on alerts
Response Time <1 second API latency monitoring
Early Warning Success Rate 80%+ % of critical cases prevented from escalating
Risk Reduction >10% reduction in psychological issues Pre/post surveys with control group

5.3 Intervention Metrics

Metric Target Measurement
Resource Engagement 50% of users access ≥1 resource/week Resource click logs
Counseling Session Rate 30% of high-risk users book sessions Booking system logs
Crisis Hotline Usage <5% of users (indicates good prevention) Hotline call logs
User Satisfaction ≥4.0/5.0 rating In-app surveys (quarterly)

5.4 Platform Performance Metrics

Metric Target Measurement
Uptime 99.9% Prometheus uptime monitoring
API Response Time (p95) <1 second Prometheus histogram
Error Rate <1% Error logs (Loki)
Resource Load Time <2 seconds Synthetic monitoring

5.5 Social Impact Metrics

Metric Target Measurement
Crisis Events Prevented 100+ events/year per 10,000 users Case studies, psychologist reports
Academic Performance Improvement 15% of users show improvement School grade data (with consent)
Family Communication Frequency 30% increase in parent-child calls Communication logs
Industry Standards Published 1+ standards Government publication records
Patents Filed 5+ patents Patent office filings

6. Risks & Mitigation

6.1 Technical Risks

Risk Impact Probability Mitigation
AI Model Bias High (wrong diagnoses) Medium Diverse training data, regular bias audits, human-in-the-loop validation
Dialect Recognition Failure High (exclusion of rural children) Medium Dedicated dialect corpus, multi-model ensemble, human review for low-confidence cases
Data Privacy Breach Critical (legal liability, trust loss) Low Encryption, access controls, penetration testing, compliance audits
System Downtime Medium (delayed interventions) Low Multi-region redundancy, auto-failover, 24/7 on-call team
Third-party API Failure Medium (degraded functionality) Medium Fallback APIs, circuit breakers, local model deployment for critical features

6.2 Operational Risks

Risk Impact Probability Mitigation
Shortage of Psychologists High (unmet intervention demand) High Tiered support (peer support → chatbot → human), train more professionals, partnerships with universities
User Resistance (Privacy Concerns) High (low adoption) Medium Transparent privacy policy, opt-in design, anonymization demos, user testimonials
Resource Content Quality Medium (ineffective interventions) Medium Psychologist review board, user feedback loop, evidence-based content only
Alert Fatigue Medium (ignored critical alerts) Medium Machine learning to tune alert thresholds, alert aggregation, prioritization

6.3 Regulatory Risks

Risk Impact Probability Mitigation
PIPL Non-compliance Critical (fines, shutdown) Low Legal team review, compliance audits, PIPL certification
Clinical Validation Required High (delayed launch) Medium Partner with accredited institutions early, follow clinical trial protocols
Data Localization Rules Medium (architecture changes) Low Deploy all infrastructure in China regions (Alibaba Cloud), no cross-border data transfer

7. Project Constraints

7.1 Budget Constraints

  • Total Budget: ¥5,000,000 (~$700,000 USD) for Year 1
  • Breakdown:
    • R&D: 40% (AI model development, platform engineering)
    • Infrastructure: 25% (cloud costs, CDN, third-party APIs)
    • Content Creation: 15% (resource library production)
    • Operations: 10% (psychologists, support staff)
    • Marketing: 10% (user acquisition, partnerships)

7.2 Timeline Constraints

Phase Duration Deadline Key Deliverables
Requirements & Design 1 month 2025-01-31 BRD, PRD, Architecture Design, OpenAPI Spec
MVP Development 3 months 2025-04-30 Core platform (user management, risk assessment, basic resources)
Alpha Testing 1 month 2025-05-31 Internal testing with 100 volunteer users
Beta Testing 2 months 2025-07-31 Pilot in 1 province (1,000 users), refine based on feedback
Production Launch 1 month 2025-08-31 Full launch in 3 provinces (10,000+ users)
Iteration & Scale 4 months 2025-12-31 Expand to 10 provinces, continuous improvement

7.3 Resource Constraints

  • Development Team: 8 engineers (4 backend, 2 frontend, 1 AI/ML, 1 DevOps)
  • Product Team: 2 product managers, 1 UX designer
  • Content Team: 3 psychologists (full-time), 10 psychologists (consultants)
  • Operations: 2 community moderators, 1 support manager

8. Assumptions & Dependencies

8.1 Assumptions

  1. User Adoption: Children and guardians are willing to use digital mental health tools
  2. Internet Access: Target users have basic smartphone access and 3G+ connectivity
  3. Government Support: Local education bureaus will promote the platform in schools
  4. Psychologist Availability: Sufficient licensed psychologists available for consultations
  5. Data Availability: Historical psychological assessment data available for model training
  6. Cultural Acceptance: Reduced stigma around mental health in rural China

8.2 Dependencies

  1. Partner Schools: Cooperation from schools for user recruitment and data collection
  2. Cloud Infrastructure: Stable Alibaba Cloud services (ACK, OSS, ISI, etc.)
  3. Third-party APIs: Reliable speech recognition, NLP, video call services
  4. Clinical Validation: Academic institutions to validate risk assessment models
  5. Legal Approval: PIPL compliance certification, clinical trial approval
  6. Funding: Government grants, NGO partnerships to sustain operations

9. Out of Scope (for MVP)

The following features are NOT included in the initial release but may be considered for future phases:

  • ❌ Wearable Device Integration: Smartwatch heart rate monitoring (defer to Phase 2)
  • ❌ Offline Desktop App: Windows/Mac desktop client (web + mobile sufficient for MVP)
  • ❌ AI Chatbot: Conversational AI for self-help (complex, defer to Phase 3)
  • ❌ Gamification: Badges, leaderboards for engagement (not priority for mental health context)
  • ❌ Parental Control Features: Screen time limits, app blocking (out of scope)
  • ❌ International Expansion: English-language version, other countries (focus on China first)
  • ❌ Blockchain Verification: Immutable audit logs using blockchain (overkill for MVP)
  • ❌ VR Therapy Sessions: Virtual reality-based interventions (emerging tech, not mature)

10. Acceptance Criteria (for BRD Sign-off)

This BRD is considered approved when:

  • ✅ All primary stakeholders have reviewed and signed off
  • ✅ Technical feasibility validated by engineering team
  • ✅ Legal compliance reviewed by legal counsel (PIPL, clinical regulations)
  • ✅ Budget approved by finance team
  • ✅ Timeline aligned with grant/funding deadlines
  • ✅ Risks assessed and mitigation strategies approved
  • ✅ Success metrics agreed upon by product team and stakeholders

11. Next Steps

After BRD approval, proceed to:

  1. Product Requirements Document (PRD): Detailed feature specifications, user stories, wireframes
  2. Technical Design Document (TDD): System architecture, database schema, API design (OpenAPI spec)
  3. Project Plan: Detailed sprint breakdown, resource allocation, Gantt chart
  4. Risk Register: Detailed risk tracking and mitigation plan
  5. Pilot Site Selection: Identify 3 provinces for initial rollout

Appendix

A. References

  1. National Data on Left-behind Children:

    • "China's Left-behind Children: A National Survey" (2016) - 69 million children
    • Ministry of Civil Affairs statistics on rural children

  2. Mental Health Research:

    • "Prevalence of Mental Health Problems among Left-behind Children in China: A Meta-analysis" (2019)
    • WHO guidelines on youth mental health interventions

  3. Technical Standards:

    • GB/T 35273-2020: Personal Information Security Specification
    • GB/T 22239-2019: Information Security Technology - Baseline for Classified Protection of Cybersecurity

B. Glossary

Term Definition
Left-behind Children 留守儿童 (liúshǒu értóng) - Children under 18 whose parents migrate for work, leaving them in rural areas with grandparents or relatives
PIPL Personal Information Protection Law (个人信息保护法) - China's comprehensive data privacy law effective Nov 2021
MLPS Multi-Level Protection Scheme (等级保护) - China's cybersecurity framework with 5 levels
Golden Signals Four key metrics: Request Rate, Error Rate, Latency, Saturation
Expand/Contract Pattern Database migration strategy for zero-downtime deployments
MAU Monthly Active Users - users who logged in at least once in the past 30 days

Document Control:

Version Date Author Changes
1.0.0 2025-12-31 Claude Code + Product Team Initial draft based on project proposal

Approval Signatures:

Role Name Signature Date
Product Manager ______________ ______________ ______
Technical Lead ______________ ______________ ______
Legal Counsel ______________ ______________ ______
Finance Director ______________ ______________ ______
Stakeholder Representative ______________ ______________ ______

END OF BUSINESS REQUIREMENTS DOCUMENT

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