Multi-sensor platform for a building
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Solution Overview
Problem
There is a need for improved sensor assemblies that can efficiently sense various conditions in building management systems, including environmental and occupancy parameters, while also supporting advanced data processing and communication to enhance building operations.
Innovation Solution
A multiple sensor sensing assembly is proposed, featuring a baseboard with a microcontroller unit (MCU) and universal sensors, coupled with a daughterboard containing application-specific sensors, which uses embedded AI to process and synthesize data from multiple sensors, and communicates output parameters to a remote device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple sensors are integrated into a single sensing assembly, then the ability to sense various building conditions is improved, but the device complexity increases
Solution Approach 1:
The sensing assembly is divided into a baseboard containing universal sensors (temperature, humidity, light, microphone) and optional daughterboards containing application-specific sensors (IR, TOF). This segmentation allows the system to sense various building conditions while managing complexity through modular architecture, where only required daughterboards are integrated.
Solution Approach 2:
The baseboard is designed as a universal platform that can support multiple types of sensors and multiple daughterboards. The baseboard's MCU and communication infrastructure provide multi-functional capabilities, allowing the same baseboard to work with different sensor combinations depending on the building management needs.
2Productivity
If embedded AI is used to process sensor data locally, then the effectiveness of building management is improved, but the processing requirements and energy consumption increase
Solution Approach 1:
The embedded AI processes only the necessary sensor data locally to generate output parameters, rather than processing all raw sensor data. This partial processing approach improves building management effectiveness by providing synthesized information while reducing energy consumption compared to full data processing.
3Loss of information
If sensor data is transmitted to remote devices, then the communication capability is improved, but the bandwidth requirements increase
Solution Approach 1:
The embedded AI extracts only the essential output parameters from the raw sensor data before transmission to remote devices. This extraction process improves communication capability by providing meaningful information while reducing bandwidth consumption by transmitting only synthesized parameters rather than complete raw datasets.
Data Source
AI summary
A sensing assembly includes a baseboard and a daughterboard operatively coupled to the baseboard. The baseboard includes a microcontroller unit (MCU) mounted to the baseboard, the MCU executing a Real Time Operating System (RTOS) and embedded Artificial Intelligence (AI) code, and two or more sensors that are mounted to the baseboard and operatively coupled to the MCU. The daughterboard includes two or more sensors that are mounted to the daughterboard. The MCU is configured to receive an output signal from each of the two or more sensors mounted to the daughterboard and the two or more sensors mounted to the baseboard and to process two or more of the output signals using the embedded AI code to produce one or more output parameters. The baseboard includes communication circuitry for communicating one or more of the output parameters to a remote device such as a remote server.