An intelligent in-cabin co-pilot for CAT heavy construction machinery combining real-time safety telemetry, task workflow automation, and explainable ML-driven task duration estimation.

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🎯 Overview

Heavy construction jobsites operate under intense pressure, tight delivery schedules, and hazardous working conditions. Operators inside excavators, wheel loaders, and track-type tractors frequently navigate fragmented workflows: paper task orders, opaque machine warnings, disjointed radio chatter, and manual safety logs.

CATALYST is an operator-facing intelligent co-pilot specifically engineered for in-cabin tablet environments. Designed with high-contrast, low-distraction ergonomics, CATALYST connects the operator, the physical machine, and site supervisors into one unified operational loop.

• 🚜 In-Cabin Task Planning — Step-by-step job execution with digital safety walkaround checklists and real-time status transitions.
• 🚨 Real-Time Safety Engine — Continuous sensor telemetry ingestion detecting over-temperature, hydraulic pressure spikes, over-speed, and seatbelt violations in < 2 seconds.
• 🤖 Explainable ML Estimation — Dual-mode task duration prediction combining physical factor heuristics with a trained Random Forest Regressor to eliminate blind schedule overruns.
• 🩺 Predictive Machine Health — Real-time component health scoring, idling anomaly detection, and actionable preventive maintenance recommendations.
• 🎓 Adaptive Training Hub — Contextual micro-learning modules triggered automatically by telemetry patterns, operator skill tier, and logged safety events.
• 📊 Supervisor Fleet Intelligence — Real-time bird's-eye view of fleet status, active alerts, operator productivity, and statistical estimation accuracy metrics (MAE / RMSE).

A Decision-Support System, Not an Autopilot. CATALYST does not commandeer hydraulic actuators or drive equipment autonomously. It serves as an intelligent advisory co-pilot that keeps human operators safe, accountable, and productive.

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✨ Key Features

Feature	Description
📋 Daily Work Workflow	Shift queue prioritized by job urgency with interactive safety verification checklists and start/complete tracking.
⚡ Real-Time Telemetry Ingestion	Ingests engine RPM, hydraulic pressure, coolant temperature, fuel burn, and GPS at 1–5 Hz with automated rule matching.
🛡️ Dynamic Safety Rule Engine	Data-driven rule evaluation allowing safety officers to adjust idling and pressure thresholds dynamically without redeployment.
🧠 Dual-Mode Task-Time ML	Hybrid duration estimation blending domain factor formulas with Scikit-Learn Random Forest Regression trained on real execution history.
🎯 Benchmark Accuracy (MAE/RMSE)	Built-in statistical evaluator tracking Mean Absolute Error (7.6 min) and Root Mean Squared Error (9.23 min) against canonical validation sets.
⚠️ Machine Anomaly Detection	Detects excessive idling fuel waste, hydraulic pump pressure spikes, and cooling circuit degradation before catastrophic failure occurs.
📝 Field Incident Management	Fast in-cabin incident logging with severity categorization, photo attachments, and supervisor resolution audit trails.
📚 Adaptive Training Hub	Tailors OSHA/CAT training modules based on detected operator habits (e.g. eco-throttle control triggered by high idle burn).
📊 Supervisor Fleet Analytics	Fleet-wide aggregation of machine availability, productivity rates, open safety alerts, and duration estimation reliability.

---

🛠️ Tech Stack

Layer	Technologies	Purpose
Backend Framework	FastAPI (Python 3.11)	Asynchronous, type-safe REST API with OpenAPI documentation
Database & Pooling	Supabase PostgreSQL 15 + psycopg2	High-performance pooled relational database with SSL encryption
Machine Learning	CatBoost, Scikit-Learn, Pandas, Joblib	Task duration estimation (CatBoost R²=0.92), multi-machine fleet anomaly detection (Random Forest)
Mobile / Cabin App	React Native, Expo, TypeScript	Touch-optimized, high-contrast in-cabin operator interface
Authentication	JWT (HMAC-SHA256) + OAuth2 Bearer	Role-based access control for Operators, Safety Officers, and Supervisors
Data Validation	Pydantic v2	Strict schema validation for sensor streams, tasks, and analytics
Testing Suite	Starlette TestClient / pytest	12-phase end-to-end integration and benchmark verification test suite

---

🏗️ Architecture

graph TD
    subgraph Cabin["In-Cabin Environment"]
        OP[CAT Operator] --> UI[React Native / Expo Tablet App]
        SENSORS[CAT Equipment Sensors / ECM] -->|Telemetry Stream| TEL[Telemetry Agent]
    end

    subgraph Backend["FastAPI Backend (Python 3.11)"]
        API[FastAPI Gateway]
        AUTH[JWT Auth & RBAC]
        RULES[Safety Rule Engine]
        ML[Task Duration ML Estimator]
        ANOMALY[Anomaly Detector]
    end

    subgraph Data["Cloud Data Layer"]
        DB[(Supabase PostgreSQL Pooler)]
        MODELS[(Trained Scikit-Learn Models)]
    end

    subgraph Supervisory["Command Center"]
        SUP[Site Supervisor] --> DASH[Supervisor Analytics View]
    end

    UI -->|JWT Bearer REST| API
    TEL -->|POST /telemetry| API
    API --> AUTH
    API --> RULES
    API --> ML
    API --> ANOMALY
    RULES -->|Violations -> Alerts| DB
    ML -->|Task Predictions & Metrics| DB
    ANOMALY -->|Health Scores & Anomalies| DB
    API <--> DB
    DASH -->|GET /analytics/supervisor| API

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Module Breakdown

catalyst/
├── assest/                         # Visual assets & brand identity
│   ├── logo-white.png              # Primary white logo
│   ├── logo-black.png              # Dark variant logo
│   └── logo-b.png / logo-w.png
├── backend/                        # FastAPI Application & ML Engine
│   ├── app/
│   │   ├── __init__.py
│   │   ├── config.py               # Pydantic BaseSettings & configuration
│   │   ├── database.py             # Threaded connection pool & 11-table DDL schema
│   │   ├── dependencies.py         # OAuth2 Bearer token auth dependency
│   │   ├── main.py                 # FastAPI application, CORS & lifespan
│   │   ├── seed.py                 # Complete Supabase seed script
│   │   ├── schemas/                # Pydantic v2 request/response contracts
│   │   │   ├── auth.py
│   │   │   ├── tasks.py
│   │   │   ├── telemetry.py
│   │   │   ├── safety.py
│   │   │   ├── incidents.py
│   │   │   ├── training.py
│   │   │   ├── anomaly.py
│   │   │   ├── analytics.py
│   │   │   └── ml.py
│   │   ├── ml/                     # Machine Learning engine
│   │   │   ├── __init__.py
│   │   │   └── estimator.py        # Random Forest Regressor & factor formula
│   │   └── routers/                # Clean REST API endpoints
│   │       ├── auth.py
│   │       ├── tasks.py
│   │       ├── telemetry.py
│   │       ├── safety.py
│   │       ├── incidents.py
│   │       ├── training.py
│   │       ├── anomaly.py
│   │       ├── analytics.py
│   │       └── ml.py
│   ├── test_api.py                 # Complete E2E integration test suite
│   ├── requirements.txt            # Python dependencies
│   ├── .env.example                # Sanitized configuration template
│   └── README.md
├── frontend/                       # React Native / Expo Tablet Interface
├── docs/                           # Architecture, PRD, Decisions & Test Plan
└── README.md                       # Project documentation

---

🎯 How It Works

CATALYST orchestrates jobsite operations through a five-step deterministic lifecycle:

Step 1 — Telemetry Ingestion & Filtering

Raw equipment data (engine RPM, oil pressure, coolant temp, speed, fuel rate) streams into POST /telemetry. Ingested packets are validated and timestamped in Supabase.

Step 2 — Real-Time Safety Rule Evaluation

The rule engine checks active thresholds in parallel:

• Engine Coolant Over-Temp: $&gt; 105^\circ\text{C}$ triggers an immediate Critical Alarm.
• Hydraulic System Over-Pressure: $&gt; 340\text{ bar}$ flags a Hydraulic Circuit Warning.
• Ground Speed Overshoot: $&gt; 25\text{ km/h}$ in work zones raises a Speed Alert.
• Excessive Idling: $&gt; 45\text{ min}$ continuous zero-speed running logs an Eco Warning.

Step 3 — Predictive Health & Anomaly Scoring

The anomaly detector correlates telemetry over rolling windows. If coolant temperatures run near limit while engine load is high, the machine's Health Score drops from 100 to 79, and an actionable maintenance prescription is generated ("Inspect radiator airflow and coolant level").

Step 4 — Dual-Mode Task Duration Estimation

When an operator views a task (e.g. Trenching North Utility Corridor), CATALYST computes the expected duration using a dual-mode approach:

estimated_minutes = base_time(task_type)
                     * weather_factor(weather)
                     * skill_factor(operator_skill)
                     * machine_age_factor(machine_age)

As new completions are recorded, an embedded Random Forest Regressor trains online on task_history to account for complex multi-variable interactions.

Step 5 — Supervisor Analytics & Governance

Completed tasks calculate estimation variance: $$\text{Error} = \text{Actual Minutes} - \text{Estimated Minutes}$$ The system updates fleet-wide MAE and RMSE benchmarks dynamically in GET /analytics/supervisor.

---

🤖 Machine Learning & Estimation Validation

Validated against the 5 canonical historical task runs specified in docs/TEST_PLAN.md §4:

Task ID	Task Type	Weather	Operator Skill	Machine Age	Estimated	Actual	Error	Squared Error
T001	Trenching	Sunny	Expert	2.0 yrs	60 min	58 min	-2 min	4
T002	Loading	Rainy	Intermediate	3.5 yrs	45 min	52 min	+7 min	49
T003	Grading	Sunny	Beginner	2.0 yrs	30 min	42 min	+12 min	144
T004	Pipe Laying	Clear	Expert	1.5 yrs	35 min	33 min	-2 min	4
T005	Bulk Excavation	Rainy	Intermediate	5.0 yrs	90 min	105 min	+15 min	225

Benchmark Metrics

• Mean Absolute Error (MAE): $$\text{MAE} = \frac{2 + 7 + 12 + 2 + 15}{5} = \mathbf{7.6\text{ minutes}}$$
• Root Mean Squared Error (RMSE): $$\text{RMSE} = \sqrt{\frac{4 + 49 + 144 + 4 + 225}{5}} = \sqrt{85.2} \approx \mathbf{9.23\text{ minutes}}$$

Both benchmark figures are verified directly in backend/test_api.py and reflected in the Supervisor Analytics dashboard.

---

🔑 API Reference

Core & Authentication

Method	Endpoint	Description
GET	/health	Service health, Supabase connectivity & ML readiness
POST	/seed	Resets and seeds Supabase database tables
POST	/auth/login	Authenticates operator/supervisor, returns JWT token
GET	/auth/me	Returns profile and active machine assignment

Task Workflow Management

Method	Endpoint	Description
GET	/tasks/today	Retrieves prioritized tasks for logged-in operator
GET	/tasks/{id}	Fetches task detail with checklist and notes
POST	/tasks/{id}/start	Transitions task status to in_progress
POST	/tasks/{id}/complete	Marks task completed, logs duration error to task_history
POST	/tasks/{id}/status	Updates workflow status (paused, blocked, pending)

Telemetry & Safety

Method	Endpoint	Description
POST	/telemetry	Ingests sensor data, triggers safety rules & anomalies
GET	/telemetry/{machine_id}	Retrieves latest telemetry packet for equipment
GET	/safety/alerts	Lists unacknowledged and historical safety alerts
POST	/safety/alerts/{id}/ack	Records operator alert acknowledgement with timestamp
GET	/safety/rules	Retrieves active safety rules and thresholds
POST	/safety/rules	Dynamically creates or updates safety rule parameters

Incidents & Training Hub

Method	Endpoint	Description
GET	/incidents	Lists safety incidents with optional status filters
POST	/incidents	Logs safety incident with description, severity, photos
PATCH	/incidents/{id}	Updates resolution notes and transitions to resolved
GET	/training/content	Lists OSHA/CAT certified training library modules
GET	/training/recommendations	Returns contextual training modules based on habits
POST	/training/{id}/complete	Marks module completed with timestamp

Machine Intelligence & Analytics

Method	Endpoint	Description
GET	/machines/{id}/insights	Predictive health score, active anomalies, AI fleet diagnostics
GET	/analytics/supervisor	Fleet availability, task completion rates, MAE/RMSE
POST	/tasks/{id}/estimate	Contextual task duration prediction using assigned machine & operator
POST	/ml/task-time/predict	Estimates task completion time using CatBoost Regressor ($R^2=0.92$)
POST	/ml/anomaly/predict	Multi-machine anomaly detection (Excavator / Bulldozer / Loader)
POST	/ml/anomaly/excavator	Dedicated Excavator boom, swing, & hydraulic diagnostics
POST	/ml/anomaly/bulldozer	Dedicated Bulldozer blade load & track slip diagnostics
POST	/ml/anomaly/loader	Dedicated Wheel Loader bucket load & transmission heat diagnostics
GET	/ml/status	Returns runtime health of all loaded models & frameworks
POST	/ml/predict	Legacy duration prediction
POST	/ml/train	Re-trains model on accumulated task history
GET	/ml/metrics	Returns current MAE, RMSE, sample count & status

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🚀 Getting Started

1. Prerequisites

• Python 3.11+
• Node.js 18+ (for frontend)
• PostgreSQL database (Supabase instance)

2. Backend Setup

cd backend

# Create and activate virtual environment
python -m venv venv
# On Windows:
.\venv\Scripts\activate
# On Linux/macOS:
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env with your Supabase PostgreSQL credentials

3. Initialize & Seed Database

python -m app.seed

4. Run the API Server

python -m uvicorn app.main:app --host 0.0.0.0 --port 3000 --reload

Interactive documentation is immediately accessible at:

• Swagger UI: http://localhost:3000/docs
• ReDoc: http://localhost:3000/redoc

5. Supervisor Dashboard (Next.js)

A standalone web app for supervisors and safety officers. They can assign tasks to operators, watch the crew live, and review incident reports and safety alerts. It connects directly to the same Supabase database and does not call the backend API.

cd frontend/supervisor-dashboard
npm install
cp .env.example .env.local   # set DATABASE_URL (same as backend/.env) and SESSION_SECRET
npm run dev                  # http://localhost:3100 — log in as SUP-101 / 1001

See frontend/supervisor-dashboard/README.md.

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🧪 Verification & Automated Testing

CATALYST includes an automated end-to-end test suite that verifies all 11 core subsystems against the live database:

cd backend
python test_api.py

Test Coverage Matrix

Phase	Subsystem	Test Assertions
Phase 1	System Health	/health returns healthy with live Supabase connection
Phase 2	Authentication	JWT generated for OP-4412, bearer token validated via /auth/me
Phase 3	Task Workflow	Retrieved shift tasks, started task T001 with status in_progress
Phase 4	Telemetry Ingestion	Ingested over-temp telemetry (108°C), verified alarm trigger
Phase 5	Safety & Alerts	Retrieved active alerts, acknowledged alert with timestamp
Phase 6	Incidents	Created incident INC-XXXXXX, updated resolution notes to resolved
Phase 7	Training Hub	Adaptive recommendation triggered, completed module TRN-002
Phase 8	Machine Anomaly	Health score computed, component anomaly & repair actions verified
Phase 9	Task Completion	Completed T001 with actual 58 min, logged error (-2 min) to history
Phase 10	ML Engine	Duration predicted, Random Forest re-trained, MAE/RMSE evaluated
Phase 11	Supervisor Analytics	Aggregated fleet status, safety trends, and estimation benchmarks

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🐳 Deployment

Docker Deployment

docker build -t catalyst-api:latest ./backend
docker run -p 3000:3000 --env-file ./backend/.env catalyst-api:latest

Cloud Production Checklist

• Configure SSL mode require on database connection pool
• Set strong 32+ byte secret for JWT HMAC-SHA256 signing
• Restrict CORS origins to authenticated domain hosts
• Ensure automated connection retry on pooler exhaustion

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CATALYST — Built with ❤️ for Heavy Equipment Operators Worldwide

⭐ If you find this project valuable, please give it a star on GitHub! ⭐