38 lines
1.4 KiB
Markdown
38 lines
1.4 KiB
Markdown
# Architecture Overview
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**Hybrid AI + Edge SCADA for Small Water Systems**
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## Core Principles
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- **Local First**: As much processing as possible happens on the edge device.
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- **Low Egress**: Healthy state = highly compressed summaries. Only alerts carry rich context.
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- **Hybrid Intelligence**: Small models on edge for real-time decisions + larger models for complex reasoning and onboarding.
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- **Monitored Objects** as the central abstraction.
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- **Multi-Tenancy** with clear RBAC (Owner, Operator, User, Field Tech).
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## High-Level Components
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1. **Edge Device** (Raspberry Pi + Cellular)
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- Local real-time signal processing
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- Local web UI for setup (hotspot mode)
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- Camera support for analog sensors
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- eSIM / cellular connectivity
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2. **Cloud / Server Layer**
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- Multi-tenant web application
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- Dashboard (Basic + Advanced modes)
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- Alert management and history
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- Operator multi-system view
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- Strong model for complex tasks (onboarding interview, anomaly detection, recommendations)
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3. **AI Model Strategy**
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- Small specialized models on edge
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- 9B-class model for personality/continuity and triage decisions
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- Larger models (30B+) for complex reasoning and onboarding
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## Data Flow
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- Normal operation: Edge summarizes and uploads compressed data every 30-60 min
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- Alert condition: Edge sends rich payload immediately + triggers notifications
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**See**: `specs/` folder for detailed specifications. |