feat(examples): pumpingstation-3pumps-dashboard end-to-end demo + bump generalFunctions

New top-level examples/ folder for end-to-end demos that show how multiple
EVOLV nodes work together (complementing the per-node example flows under
nodes/<name>/examples/). Future end-to-end demos will live as siblings.

First demo: pumpingstation-3pumps-dashboard
- 1 pumpingStation (basin model, manual mode for the demo so it observes
  rather than auto-shutting pumps; safety guards disabled — see README)
- 1 machineGroupControl (optimalcontrol mode, absolute scaling)
- 3 rotatingMachine pumps (hidrostal-H05K-S03R curve)
- 6 measurement nodes (per pump: upstream + downstream pressure mbar,
  simulator mode for continuous activity)
- Process demand input via dashboard slider (0-300 m3/h) AND auto random
  generator (3s tick, [40, 240] m3/h) — both feed PS q_in + MGC Qd
- Auto/Manual mode toggle (broadcasts setMode to all 3 pumps)
- Station-wide Start / Stop / Emergency-Stop buttons
- Per-pump setpoint slider, individual buttons, full status text
- Two trend charts (flow per pump, power per pump)
- FlowFuse dashboard at /dashboard/pumping-station-demo

build_flow.py is the source of truth — it generates flow.json
deterministically and is the right place to extend the demo.

Bumps:
  nodes/generalFunctions  43f6906 -> 29b78a3
    Fix: childRegistrationUtils now aliases the production
    softwareType values (rotatingmachine, machinegroupcontrol) to the
    dispatch keys parent nodes check for (machine, machinegroup). Without
    this, MGC <-> rotatingMachine and pumpingStation <-> MGC wiring
    silently never matched in production even though tests passed.
    Demo confirms: MGC reports '3 machine(s) connected'.

Verified end-to-end on Dockerized Node-RED 2026-04-13: pumps reach
operational ~5s after deploy, MGC distributes random demand across them,
basin tracks net flow direction, all dashboard widgets update each second.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

examples/README.md

@@ -0,0 +1,42 @@
+# EVOLV — End-to-End Example Flows
+
+Demo flows that show how multiple EVOLV nodes work together in a realistic wastewater-automation scenario. Each example is self-contained: its folder has a `flow.json` you can import directly into Node-RED plus a `README.md` that walks through the topology, control modes, and dashboard layout.
+
+These flows complement the per-node example flows under `nodes/<name>/examples/` (which exercise a single node in isolation). Use the per-node flows for smoke tests during development; use the flows here when you want to see how a real plant section behaves end-to-end.
+
+## Catalogue
+
+| Folder | What it shows |
+|---|---|
+| [`pumpingstation-3pumps-dashboard/`](pumpingstation-3pumps-dashboard/) | Wet-well basin + machineGroupControl orchestrating 3 pumps (each with up/downstream pressure measurements), individual + auto control, process-demand input via dashboard slider or random generator, full FlowFuse dashboard. |
+
+## How to import
+
+1. Bring up the EVOLV stack: `docker compose up -d` from the superproject root.
+2. Open Node-RED at `http://localhost:1880`.
+3. Menu → **Import** → drop in the example's `flow.json` (or paste the contents).
+4. Open the FlowFuse dashboard at `http://localhost:1880/dashboard`.
+
+Each example uses a unique dashboard `path` so they can coexist in the same Node-RED runtime.
+
+## Adding new examples
+
+When you create a new end-to-end example:
+
+1. Make a subfolder under `examples/` named `<scenario>-<focus>`.
+2. Include `flow.json` (Node-RED export) and `README.md` (topology, control modes, dashboard map, things to try).
+3. Test it on a fresh Dockerized Node-RED — clean import, no errors, dashboard loads.
+4. Add a row to the catalogue table above.
+
+## Wishlist for future examples
+
+These are scenarios worth building when there's a session for it:
+
+- **Pump failure + MGC re-routing** — kill pump 2 mid-run, watch MGC redistribute to pumps 1 and 3.
+- **Energy-optimal vs equal-flow control** — same demand profile run through `optimalcontrol` and `prioritycontrol` modes side-by-side, energy comparison chart.
+- **Schedule-driven demand** — diurnal flow pattern (low at night, peak at 7 am), MGC auto-tuning over 24 simulated hours.
+- **Reactor + clarifier loop** — `reactor` upstream feeding `settler`, return sludge controlled by a small `pumpingStation`.
+- **Diffuser + DO control** — aeration grid driven by a PID controller from a dissolved-oxygen sensor.
+- **Digital sensor bundle** — MQTT-style sensor (BME280, ATAS, etc.) feeding a `measurement` node in digital mode + parent equipment node.
+- **Maintenance window** — entermaintenance / exitmaintenance cycle with operator handover dashboard.
+- **Calibration walk-through** — measurement node calibrate cycle with stable / unstable input demonstrations.