Sensor-Based Spatial Zone Definition for Building Automation
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Solution Overview
Problem
Existing smart home and building management systems lack the ability to accurately define and manage spatial zones based on environmental data, leading to inefficient operation of systems like HVAC, lighting, and security, as they often rely on blanket settings rather than zone-specific customization.
Innovation Solution
A zoning system that uses network-connected sensors to capture and analyze data, automatically identifying trends and patterns to define spatial zones, which are then used to customize the operation of other systems, such as HVAC, lighting, and security, by configuring them based on zone definitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If blanket settings are used for system operation, then device complexity is reduced, but measurement precision of spatial zones deteriorates
Solution Approach 1:
The system divides the premises into multiple spatial zones based on sensor data analysis, allowing each zone to have independent operational settings. This segmentation enables precise spatial zone definition without requiring complex manual configuration of each individual zone, as the zones are automatically created and managed by the system.
Solution Approach 2:
The system automatically defines spatial zones and configures system operations based on sensor data patterns without requiring manual user intervention. The processor autonomously analyzes sensor data, identifies trends, creates zone definitions, and adjusts HVAC, lighting, and security settings, eliminating the need for complex user setup while maintaining high measurement precision.
2Productivity
If zone-specific customization is implemented, then productivity of system operation is improved, but device complexity increases
Solution Approach 1:
The system automatically performs zone definition and configuration tasks without manual user input. The processor analyzes sensor data, identifies occupancy patterns, and autonomously creates and manages zone-specific settings for HVAC, lighting, and security systems, achieving high operational efficiency while keeping the user interface simple.
Solution Approach 2:
The system pre-configures zone definitions and operational parameters based on historical sensor data and occupancy patterns before user needs arise. By continuously learning and updating zone configurations in advance, the system is ready to optimize operations immediately when conditions change, improving productivity without requiring complex real-time user configuration.
3Ease of operation
If manual zone configuration is used, then ease of operation is maintained, but adaptability to changing zone usage deteriorates
Solution Approach 1:
The system automatically adapts to changing zone usage by continuously analyzing sensor data and updating zone definitions without requiring manual reconfiguration. When occupancy patterns change, the processor detects these changes and automatically adjusts zone boundaries and operational settings, maintaining ease of operation while achieving high adaptability.
Solution Approach 2:
The system implements dynamic zone definitions that automatically adjust based on real-time and historical sensor data. Zone boundaries, occupancy patterns, and operational parameters are continuously updated to reflect changing usage conditions, allowing the system to adapt seamlessly to new situations without manual intervention while keeping the user interface simple and static.
4Measurement precision
If sensor data analysis is performed to define zones, then measurement precision of spatial zones is improved, but use of energy increases
Solution Approach 1:
The system performs sensor data analysis at optimized intervals and only processes data necessary for zone definition and validation. Rather than continuously analyzing all sensor data, the processor focuses on key occupancy patterns and environmental changes, achieving high measurement precision while minimizing energy consumption through selective and interval-based data processing.
Data Source
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AI summary
A method includes capturing data with a plurality of network connected sensors installed in or around a premises, storing at least a sampling of the data in an electronic storage device, analyzing the stored data with a processor to automatically identify one or more types of trends or patterns, creating, based on the analysis, a zone definition for a first zone that corresponds to an area of the premises from which data was captured by one or more sensors selected from among the plurality of sensors, and configuring at least one system operating in or around the premises automatically based on the first zone definition.