HELIOS-NODE

Predictive DC-microgrid controller for solar arrays. Features an MPPT loop in Rust, CNN-LSTM cloud forecasting in PyTorch, real PVGIS data integration, an egui dashboard, and post-quantum trust anchors over local IPC. Fully functional end-to-end.

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what exists

component	path	status
MPPT controller (P&O + AI bias)	rust/src/controller.rs	real — runs (100 ms tick)
async telemetry & UI loop	rust/src/main.rs	real — runs
egui real-time dashboard	rust/src/ui.rs	real — runs (cargo run -- --ui)
SHA-256 chained audit log (SQLite)	rust/src/db.rs	real — active
DB schema + migrations	migrations/0001_initial_schema.sql	real — applied
CNN-LSTM irradiance predictor	ai/agent.py	real — active IPC bridge via SQLite
PVGIS TMY data client	ai/pvgis_client.py	real — fetches data for Murcia
training dataset generator	ai/dataset_generator.py	real — adds cloud perturbations
model training pipeline	ai/train.py	real — generates helios_predictor.pt
helios-sentinel PQC daemon	helios-sentinel/src/	framework real

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architecture

PVGIS Data (Murcia)
    │  8760 hours of real irradiance
    ▼
ai/dataset_generator.py    ← adds cloud drops and sensor noise
    │  train_sequences.pt
    ▼
ai/train.py                ← trains LSTM → helios_predictor.pt
    │
    ▼
ai/agent.py (--serve)      ← AI Agent (Python)
    │  reads telemetry from SQLite
    │  writes forecast [0,1] to ai_forecasts table
    ▼
energy_bus.sqlite          ← IPC Bridge (SQLite WAL)
    ▲
    │  reads forecast every 1s
    │  writes telemetry every 100ms
helios-core (Rust)         ← MPPT Controller
  controller.rs             ← injects forecast as predictive_bias into P&O duty-cycle step
  main.rs                   ← control loop + simulated physics
  ui.rs                     ← egui dashboard (Power Gauge, Timeline, AI Status)

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stack

Rust 1.78 · tokio · egui · sqlx / SQLite WAL · sha2 · ml-kem 0.2 · ml-dsa 0.1 · PyTorch 2.x

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run

1. Prepare Data & Train Model

# Fetch real PVGIS data for Aljucer, Murcia
python ai/pvgis_client.py

# Generate training dataset with cloud perturbations
python ai/dataset_generator.py

# Train the LSTM model
python ai/train.py --epochs 50

2. Run the System

Open two terminals.

Terminal 1 (AI Agent):

python ai/agent.py --serve

Terminal 2 (Rust Core + Dashboard):

cd rust/
cargo run --release -- --ui

The system will start simulating days using the real PVGIS irradiance data, and you'll see the dashboard rendering the power telemetry and the AI forecasts in real-time.

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what's missing

• real ADC/GPIO reads replacing the PVGIS-simulated V/I sensor in main.rs (can be easily wired to an INA219).
• actual ML-DSA signature in helios-sentinel: sign_checkpoint() (mock returns 64 zero bytes).
• end-to-end integration test / CI.

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license

unlicensed / private research. contact: Antonio Rodríguez (QuantumDrizzy)