ZivaPulse is an AI-powered hazard prediction system tailored for mining operations, enabling real-time detection and anticipation of safety risks. By integrating environmental IoT sensors, satellite imagery and historical mining data; ZivaPulse creates a dynamic "pulse" of the mine, monitoring key parameters such as gas levels, ground movement and structural stress.
- Real-time Hazard Prediction: ML-powered analysis of past incident patterns
- Live Dashboard: Intuitive visualization with heatmaps and early warnings
- IoT Integration: Environmental sensor data collection and processing
- Edge Computing: Offline functionality for low-connectivity environments
- Mobile & Desktop Support: Responsive design for all devices
- African Mining Focus: Optimized for off-grid, solar-powered operations
ZivaPulse/
├── backend/ # FastAPI backend with ML models
├── frontend/ # React dashboard application
├── iot-gateway/ # Edge computing for sensor data
├── ml-models/ # Hazard prediction models
├── database/ # Database schemas and migrations
├── docs/ # Documentation
└── deployment/ # Docker and deployment configs
- Python 3.9+
- Node.js 16+
- Docker (optional)
- PostgreSQL
cd backend
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
uvicorn main:app --reloadcd frontend
npm install
npm startcd database
docker-compose up -dOnce the backend is running, visit:
- Swagger UI: http://localhost:8000/docs
- ReDoc: http://localhost:8000/redoc
- Fork the repository
- Create a feature branch
- Make your changes
- Add tests
- Submit a pull request
MIT License - see LICENSE file for details
For support and questions, please contact the development team and we'll be sure to get back to you or create an issue in the repository.