Spacecrafts generate huge amounts of data. A significant challenge for autonomous control systems and human operators is ensuring that the right data (and combinations of data) are available at the right time for control and decision-making and ensuring that the data is at the right abstraction level. In this paper, we describe a data abstraction architecture that provides a canonical way to assemble and interact with data abstractions. We have developed an open, flexible toolkit that allows end users to build data abstraction networks. Two use cases were successfully tested against a Lunar habitat simulation, demonstrating that high level state information can be generated by the data abstraction architecture and used by a high level controller. Our approach improves the process of both control and monitoring of space systems by separating controls and displays from data abstraction.

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A Lunar habitat will be highly sensored and generate large amounts of data or telemetry. For this data to be useful to humans monitoring these systems and to automated algorithms controlling these systems it will need to be converted into more abstract data. This abstracted data will reflect the trends, states and characteristics of the systems and their environments. Currently this data abstraction process is manual and ad hoc. We are developing a Data Abstraction Architecture (DAA) that allows engineers to design software processes that iteratively convert habitat data into higher and higher levels of abstraction. The DAA is a series of mathematical or logical transformations of telemetry data to provide appropriate inputs from a hardware system to a hardware system controller, system engineer, or crew. The DAA also formalizes the relationships between data and control and the relationships between the data themselves. We have connected our Data Abstraction Architecture to a simulation of a Lunar habitat in order to test its ability to aid in the monitoring and control functions.