To achieve automated AI controlling, we collect data of different aspects and analyse them to determine what action is needed in real-time. In order to ensure the efficiency of the controlling system, our design is based on a hybrid of edge and cloud computing.
Edge computing provides a fast response control based on a limited amount of real-time data. For example, the lux value captured from the environment can be a reference to determine the ideal brightness of the light and adjust automatically. All simple data analysis and controls, which are only based on a small number of indicators, use edge computing to fit the purposes and provide real-time response.
On the other hand, cloud computing is used for control actions that may require more complicated analysis and calculations with multiple indicators or historical data patterns. Cloud computing allows the system to handle and analyse an enormous number of historical records and compute more complicated calculations. For instance, by summing up different collected data including customer traffic, temperature, indoor air quality (IAQ)and air flow density, we can determine the situation of an indoor environment and determine what action is needed for the climate system to ensure enough air flow and optimise customers’ experience.