CPIPC Demo Project

This repository shows a small end-to-end pipeline:

1. Python program simulates sensors and exposes them through Modbus TCP.
2. PLC (Soft-PLC compiled to WASM) runs Structured-Text that calls high-performance Rust Function-Blocks for online statistics and spectrum analysis.
3. Node / Browser dashboard uses OPC UA + Socket.IO to visualise real-time data and predictions.

┌──────────────┐   Modbus TCP   ┌────────────────────┐   OPC UA   ┌─────────────────────┐
│ slave.py     │ ─────────────▶ │  PLC (Rust + ST)   │ ─────────▶ │  Web dashboard      │
│ 25 °C  etc.  │                │  running as WASM   │            │  (ECharts + Socket) │
└──────────────┘                └────────────────────┘            └─────────────────────┘

---

1. Directory layout

modbus_slave_simulator/
└─ slave.py               # synthetic sensors

st_app_with_rust_lib/
└─ MODULES/rust_computing_lib/
   ├─ implements/rust/    # you edit Rust FBs here
   └─ .MODULE/rust/wa_interface.rs  # auto-generated bridge – read-only

data_visualization_host/
└─ frontend-lite/
   ├─ public/             # static files (index.html, app.js, style.css)
   └─ server.js           # Express + Socket.IO gateway

---

2. Requirements

• Python 3.10+ – simulator
• Node 18+ – dashboard & OPC UA bridge
• Rust 1.68+ – build WASM FBs (wasm32-unknown-unknown target)

Install

pip install -r requirements.txt                 # pymodbus
cd data_visualization_host/frontend-lite
npm i                                           # express, socket.io, echarts …

---

3. Run the demo

3.1 Sensor & Modbus slave

python modbus_slave_simulator/slave.py          # listens on 0.0.0.0:5020

3.2 Compile Rust FBs (optional test)

cd st_app_with_rust_lib/MODULES/rust_computing_lib
cargo build --target wasm32-unknown-unknown --release

In a real PLC this WASM is loaded automatically; outside of PLC you may host it with Wasmtime for tests.

3.3 Web dashboard

cd data_visualization_host/frontend-lite
node server.js             # http://localhost:3000

Add a PLC endpoint (e.g. opc.tcp://localhost:4840), map variables, and start watching charts.

---

4. Rust Function-Blocks

FB	File	Purpose
simple_computing	simple_computing.rs	Incremental mean, variance, Z-score
z_anm_detc	z_anm_detc.rs	3-channel anomaly detection via Z-score
spctrm_anlys	spctrm_anlys.rs	Online spectrum analysis (freq, amplitude, phase)

Algorithms:

• Welford for variance (numerically stable).
• Zero-crossing average for ultra-low frequency estimation.
• EWMA of variance for amplitude (noise-robust).
• Phase derived from sample counter → no system-time dependency in WASM.

---

5. Frontend details (public/app.js)

• Uses ECharts v5 – no build toolchain required.
• Real-time line chart (raw values) & prediction chart:
  • Red scatter – live data
  • Blue solid – fitted past
  • Blue dashed – 60 s forecast
• Dynamic Y-range with smoothing to avoid jitter.

---

6. Extending the demo

1. Add sensors: extend slave.py, regenerate interface, implement new FB.
2. Better frequency: replace zero-cross with Goertzel or Sliding DFT.
3. Deploy: containerise python+node; run WASM on real PLC or soft-PLC (open62541).

Enjoy experimenting! 🚀