C2G2_APEX

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  HELIOX - DASHBOARD

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  HELIOX - Central System

Team Member Name

Tan Zu Bin(leader) Ling Chin Wei Nicole Cheng Kee Lai Wei Yang Cheong Vai Theng

Project Info

The technical implementation features a robust Flask backend serving two critical dashboards:

index.html (Unit View): Demonstrates the real-time, autonomous decision-making for a single lamp using a simulated scikit-learn ML model that processes immediate inputs (video, motion, weather).

overview.html (Network View): Provides a centralized management interface showing the status, maintenance flags, and aggregated data for the entire network of street lamps, illustrating the project's scalability and administrative functions.

Both dashboards utilize the same intelligent backend logic, demonstrating full command over a multi-unit smart grid environment.

Project Idea

The core idea is to revolutionize public infrastructure by transforming static, wasteful street lighting into a responsive, intelligent, and sustainable system. Traditional street lamps operate inefficiently, running at maximum power regardless of actual need. This project proposes an AI-driven control system that uses machine learning to make autonomous decisions. The primary goal is twofold: dramatically reduce energy consumption and operational costs by dimming or turning off lights when roads are clear, while simultaneously enhancing public safety by instantly maximizing brightness or triggering emergency protocols upon detecting vehicles, accidents, or adverse weather conditions like floods.

Built With

• anaconda
• css3
• html5
• javascript
• python
• weather.csv
• weatherapi