SCADA-project/docs/architecture/overview.md

Architecture Overview

Hybrid AI + Edge SCADA for Small Water Systems

Core Principles

• Local First: As much processing as possible happens on the edge device.
• Low Egress: Healthy state = highly compressed summaries. Only alerts carry rich context.
• Hybrid Intelligence: Small models on edge for real-time decisions + larger models for complex reasoning and onboarding.
• Monitored Objects as the central abstraction.
• Multi-Tenancy with clear RBAC (Owner, Operator, User, Field Tech).

High-Level Components

1. Edge Device (Raspberry Pi + Cellular)

   • Local real-time signal processing
   • Local web UI for setup (hotspot mode)
   • Camera support for analog sensors
   • eSIM / cellular connectivity

2. Cloud / Server Layer

   • Multi-tenant web application
   • Dashboard (Basic + Advanced modes)
   • Alert management and history
   • Operator multi-system view
   • Strong model for complex tasks (onboarding interview, anomaly detection, recommendations)

3. AI Model Strategy

   • Small specialized models on edge
   • 9B-class model for personality/continuity and triage decisions
   • Larger models (30B+) for complex reasoning and onboarding

Data Flow

• Normal operation: Edge summarizes and uploads compressed data every 30-60 min
• Alert condition: Edge sends rich payload immediately + triggers notifications

See: specs/ folder for detailed specifications.