EcoMate

EcoMate – Operational & Predictive Data Integration

This plan defines how EcoMate connects real-world system telemetry with predictive intelligence, transforming the platform into an autonomous, self-optimizing partner.

1. Real-Time IoT & Sensor Data

Purpose: Enable live performance monitoring and alerts.

Implementation: Deploy IoT devices to stream flow rate, water temperature, energy, UV intensity, pump pressure.
Ingestion: Secure endpoints (telemetry/router_ingest.py) storing data in TimescaleDB/InfluxDB.
Dashboard: Grafana or custom UI for real-time visibility.

Value: Transparent monitoring; instant critical alerts.

2. Historical & Maintenance Data

Purpose: Correlate work orders and telemetry to move from reactive to proactive.

Implementation: maintenance/history.py joins work orders with telemetry.
ML Workflow: Train models to predict MTBF and detect degradation patterns.
Output: Predictive insights stored in telemetry/predictions/.

Value: Predictive maintenance scheduling; optimized performance tuning.

3. The Digital Twin

Purpose: Create a virtual real-time representation of each system.

Components: Location, BOM, compliance, telemetry, predictive data.
Implementation: digital_twin/api.py exposes system state + simulation endpoints.
UI: Interactive schematic/3D model with overlays and what-if simulation.

Value: Shared, simulation-driven view of system health and compliance.

4. Integration Pattern

File Layout:

services/
  telemetry/
    router_ingest.py
    store.py
    predictor.py
  maintenance/
    history.py
  digital_twin/
    api.py

Workflow Hooks:

Telemetry events → store + baseline → predictor scoring.
Predictor → MaintenanceWorkflow opens PRs for preventative replacement.
Digital Twin API aggregates all data sources.

Security: TLS and token auth; device buffering via MQTT/Redis.

5. Phasing

Phase 1: Telemetry ingestion + dashboards.
Phase 2: Join with maintenance history; train predictive models.
Phase 3: Digital Twin API + interactive UI.
Phase 4: Full autonomy with optimization and orchestration.

By integrating IoT, predictive analytics, and digital twin modeling, EcoMate becomes a self-optimizing ecosystem that anticipates issues, prevents downtime, and continuously improves efficiency.

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