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AI System Architecture Design

AI System Architecture Design for 3D One AI

Version 1.0

1. Introduction

3D One AI is an educational platform integrating physics-based 3D simulation, robotics programming, and AI behavior modeling for K-12 STEM education. The architecture prioritizes real-time physics computation, AI inference, and multi-user scalability while adhering to China's K-12 AI curriculum standards (GB/T 29824-2023).

2. Architecture Overview

A microservices-based hybrid architecture is adopted:

  • Frontend: WebGL-based 3D editor (Browser)
  • Backend: Kubernetes-managed microservices (Cloud/On-prem)
  • Simulation Core: PhysX-based physics engine (Edge/Cloud)
  • AI Engine: Federated learning for behavior modeling
    ![Architecture Diagram](diagram-placeholder: layered with Client → API Gateway → Microservices → Physics/AI Engine → DB)

3. Technology Stack & Versions

Layer Technology Version Rationale
Frontend React + Three.js React 18, Three.js r152 GPU-accelerated WebGL rendering
API Gateway Kong 3.4.x Rate limiting, OAuth2.0 security
Physics Engine NVIDIA PhysX 5.1 Deterministic rigid-body simulation
AI Engine PyTorch + ONNX Runtime PyTorch 2.1, ONNX 1.14 Cross-platform AI model deployment
Robotics Control ROS 2 (Robot Operating System) Humble Hardware abstraction for Arduino/RPi
Database TimescaleDB + Redis TSDB 2.9, Redis 7.0 Time-series telemetry, session caching
Orchestration Kubernetes + Helm K8s 1.27, Helm 3.12 Scalable microservice deployment

4. Detailed Component Design

4.1 Physics & 3D Processing Layer

  • PhysX Engine: Handles collision detection, gravity, and material properties.
  • Three.js Pipeline: Converts CAD models (GLTF 2.0) to interactive WebGL objects.
  • Output Renderer: Generates MP4 animations via FFmpeg (v6.0).

4.2 AI Behavior Simulation

  • Training Pipeline: 
    # Federated learning for classroom privacy  
model = ResNet18()  
trainer = Flower(client=PyTorchClient(model))  # Flower 1.4
  • Inference: ONNX-optimized models for real-time path planning (A* algorithm) and object recognition (YOLOv8s).

4.3 Hardware Integration

  • Virtual Hardware SDK: Emulates sensors (e.g., ultrasonic, gyroscope) via ROS 2 topics.
  • Programming Interfaces:
    • Blockly (v9.0) for visual coding
    • Python API (3.11) with prebuilt robotics libraries (e.g., pyfirmata).

5. Implementation Roadmap

  1. Phase 1 (3 Months):
    • Deploy Kubernetes cluster (AWS EKS/On-prem) with Kong API gateway.
    • Integrate PhysX engine with Three.js frontend.
  2. Phase 2 (2 Months):
    • Train lightweight AI models (e.g., MNIST for object detection).
    • Implement ROS 2 bridge for Arduino/RPi emulation.
  3. Phase 3 (2 Months):
    • Develop curriculum-aligned templates (e.g., "Self-driving Car Simulator").
    • Stress-test with 500 concurrent users (Locust).

6. Security & Compliance

  • Authentication: OAuth2.0 via Keycloak (v22.0) for SSO with school LDAP.
  • Data Protection:
    • GDPR/CCPA-compliant anonymized telemetry storage.
    • Model training via federated learning (no raw student data).
  • Network: TLS 1.3 encryption, WAF rules (ModSecurity).

7. Scalability & Performance

  • Physics Compute: Offload intensive simulations to GPU nodes (NVIDIA A10G).
  • Auto-scaling:
    • HPA (Horizontal Pod Autoscaler) for AI inference pods (CPU/GPU metrics).
    • Redis sharding for session management.
  • Targets:
    • 50ms latency for robotics control actions.
    • 99.9% uptime (SLA for school hours).

8. Conclusion

This architecture enables 3D One AI to deliver low-latency physics simulations, privacy-preserving AI training, and scalable robotics programming for classrooms. Future extensibility includes AR/VR integration via OpenXR and LTS support for China's evolving EdTech standards.

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