Universal Data Model for the next generation of AI-driven smart spacesv1.0.0

An open-plan office equipped with S2 grid sensors and actuators. The AI agent adjusts lighting, HVAC, and white noise based on occupancy and time of day to improve comfort and productivity, while respecting strict privacy by using mmWave radar instead of cameras.

A retail store uses the 6-element data model to dynamically control lighting effects, background music, and air quality for different zones and promotions. The AI agent can create immersive experiences like 'Stage rock strobe' for product launches.

A residential home leverages the Energy and Air elements to optimize HVAC and lighting based on real-time consumption and self-generation from solar panels. The AI agent schedules energy-heavy tasks during peak solar output to reduce grid dependency.

A hospital ward uses the model to maintain stringent air quality standards (GB 3095-2012) for patient recovery, while audio management provides calming background sounds and noise suppression. Non-intrusive mmWave sensing monitors patient presence without cameras.

A university classroom integrates the 6-element model to adjust lighting, sound, and display systems for different teaching modes (lecture, group work, presentation). The AI agent uses natural language parameters to set scenes like 'Cinematic dimming' for video playback.

Provide real-time aggregated spatial data (anonymized) to third-party services such as building management or energy optimization platforms. Revenue comes from per-space monthly subscription fees.

Offer pre-trained AI agents that manage a space's 6 elements autonomously. Customers pay a monthly fee based on the number of grids and complexity of scenarios.

Use accumulated spatial data to provide design recommendations for new buildings, optimizing layouts for energy efficiency and occupant comfort. Consulting fees are project-based.