Brew View

Brew View is a potion monitoring system that tracks cauldron fill levels and detects any suspicious courier activities to prevent theft and optimize production/routes for delivery.

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Inspiration

  • The Poyo's Factory is in need of a system to prevent cauldron overflowing and fraudulent delivery. Traditional monitoring methods weren't sufficient, leading to wasted resources and unchecked theft.

What it does

  • Brew View is a monitoring dashboard that utilizes real-time network visualization with overflow warnings, a playback system with time zone selections, fraud detection, analytics for courier performance and shows cauldron efficiency. We also implemented a route optimization that calculates the most optimal daily schedule and shows the precise timing and route the witches should take.

How we built it

  • We built Brew View by utilizing a Flask (Python) backend, paired with client-side JavaScript, for data processing and analytics. The frontend integrates Leaflet.js for interactive maps and Chart.js for time-series visuals, while a browser-based fraud detection algorithm analyzes cauldron fill trends and delivery records to instantly flag suspicious activity.

Challenges we ran into

  • The first big challenge we ran into was determining the drain rate and flow rate per cauldron. Since each cauldron was unique, we had to look at the overarching trend of the cauldron levels and create an average rate based on the distributions. The second roadblock was creating an optimal algorithm for handling witch route optimization. This took a lot of tweaking, working with different algorithms and adjusting constraints.

Accomplishments that we're proud of

  • We're proud that we were able to get the project working. It took a lot of learning and a lot of teamwork as well. We really enjoyed learning how to work with different languages and learning how to build with new frameworks. One of our team members is especially proud of his pixel drawings we embedded around the website!

What we learned

  • We learned how to utilize Flask and smaller python libraries more efficiently, due to use not having too much experience with them. We also gained hands on experience with utilizing distribution techniques such as 2 sigma and TSP algorithms. We also utilized caching data for smoother operations within our dashboard,

What's next for Brew View

  • Next, we want to be able to handle larger cauldron amounts and time lengths between nodes (cauldrons and the Enchanted Market), so the data output is smoother and faster, as well as more reliable.

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