Inspiration
Signal interruptions due to external factors can cause trains to stop abruptly. We aim to minimize such events.
What it does
The incoming signal data is analyzed and potential future anomalies are identified. It publishes this event to a convenient PubSub server that can be subscribed by existing applications at siemens (e.g. for alerting). We also provide a simple sample application that shows those anomalous events on a map
How we built it
The analysis is done in python. The pubsub server is provided by pypubsub on github. The front-end is boilerplate code from google. We decided on this architecture as it loosely couples the publisher (the analysis application) and any number of potential subscribers. Additional subscribers can be registered easily. This is great for extensibility when the application is integrated into an existing application landscape.
Challenges we ran into
Data analysis part Integrating the node pubsub listener into the front-end
Accomplishments that we're proud of
Using the pubsub server as intended
What we learned
pubsub architecture, a bit of typescript and how to use docker to deploy the three components
What's next for SubSiemens
Integrating in an existing application landscape where your signal data is to be provided to our analysis app. Also adding further subscribers to the pubsub server such that the anomaly events can be read by your existing applications.
Log in or sign up for Devpost to join the conversation.