Order Placement
Front EndThe place order app contains 6 views:
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Back End
After customer order a meal, the detail of the order is sent to the table Order.
Then the lambda will extract features for the machine learning to predict the time to place order to the restaurant and the estimate food arrival time, and store in the table order_stroage. The python server on EC2 based on the table order_stroage to place order to restaurant.
Then the lambda will extract features for the machine learning to predict the time to place order to the restaurant and the estimate food arrival time, and store in the table order_stroage. The python server on EC2 based on the table order_stroage to place order to restaurant.
Delivery Process
The back end will send order request to restaurant app, at the evaluated best order placed time, by SNS and APNs. The notification received by restaurant includes:
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Feedback Process
Customer can make feedback for the ordered food. When the rating is provided, the lambda will calculate the rate for the restaurant and store in the table SmartFoodDelivery.
Spill Detection
We made huge effort to try a machine learning model to detect spill by humidity data.
After customer pick up the food, the sensors measure the difference inside and outside the box and send to table HumidityExperiment, which trigger the lambda function to call prediction whether the cleaner is needed to clean the box.
After customer pick up the food, the sensors measure the difference inside and outside the box and send to table HumidityExperiment, which trigger the lambda function to call prediction whether the cleaner is needed to clean the box.