First, Vooban connected all the sensors to AWS IoT Core to send data to the cloud in real time as a time series (chronologically). The data was then saved in the Amazon DynamoDB database, generating a production history at the same time. In parallel, we used machine learning models to predict anomalies in the data flow.
Amazon DynamoDB can also generate dashboards in real time to monitor and detect anomalies. For example, if a temperature sensor detects an abnormally high temperature, the user will receive an alert.
The machine learning model was trained using Amazon SageMaker, a service that enables users to build, train and deploy AI models quickly on the cloud. In this particular case, the predictive algorithm identified potentially non-compliant bags based on 50 different criteria that were analyzed in real time.