User-relocatable self-learning environmental control device capable of adapting previous learnings to current location in controlled environment
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Solution Overview
Problem
Existing boiler-based heating systems lack efficient and user-friendly control mechanisms, particularly in adapting to new locations and optimizing energy usage, as they often require manual adjustments and rely on outdated programming methods.
Innovation Solution
A smart thermostat system with a processor, memory, and wireless communication capabilities that learns heating schedules through automated algorithms, detects location changes, and adjusts parameters to optimize heating operations, including the use of a boiler control device and user interface for seamless integration with boiler-based heating systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a thermostat is relocated to a new location, then it can control heating for a different room or area, but it loses the previously learned heating schedule and thermal characteristics that were adapted to the old location
Solution Approach 1:
The patent implements a copying mechanism where the thermostat stores learned heating schedules and thermal characteristics in memory, then copies and transfers this learned information when relocated to a new location. This allows the thermostat to retain valuable adaptive data across moves, resolving the contradiction by preserving information that would otherwise be lost during relocation.
Solution Approach 2:
The patent utilizes parameter changes by detecting relocation through sensor data (accelerometer, gyroscope, magnetometer) and environmental parameter comparisons. When relocation is detected, the system modifies its operational parameters by applying previously learned heating schedules to the new location, effectively adapting the stored information to the new context while preserving the learned patterns.
2Loss of energy
If a thermostat uses automated schedule learning algorithms to adapt to local conditions, then heating efficiency improves, but the device complexity increases due to additional sensors and processing requirements
Solution Approach 1:
The patent applies multi-functionality by integrating multiple sensor types (accelerometer, gyroscope, magnetometer, temperature sensor) into a single thermostat device that performs both relocation detection and environmental monitoring functions. This universal approach allows the device to achieve improved heating efficiency through automated learning while consolidating complexity into a unified multi-functional platform rather than separate devices.
Solution Approach 2:
The thermostat implements self-service through automated schedule learning algorithms that continuously monitor environmental parameters and occupancy patterns, then automatically adjust heating schedules without user intervention. This self-learning capability improves energy efficiency by adapting to local conditions while reducing the need for manual programming and user interaction.
3Measurement precision
If a thermostat continuously monitors environmental parameters and occupancy to learn heating schedules, then heating control accuracy improves, but the energy consumption of the thermostat itself increases
Solution Approach 1:
The patent implements periodic action by having the thermostat monitor environmental parameters and occupancy at scheduled intervals rather than continuously. The automated learning algorithm processes data periodically to update heating schedules, allowing the thermostat to achieve accurate environmental monitoring and adaptive control while consuming energy only during measurement and processing cycles, thus reducing overall power consumption.
Data Source
AI summary
A thermostat device may include a processing system configured to learn a heating schedule at a first location according to an automated schedule learning algorithm that processes inputs including user inputs and occupancy sensing inputs and derives schedule-affecting parameters therefrom that are processed to compute the heating schedule. The processing system may also be configured to determine whether the thermostat has been moved to a new location, and if it is determined that the thermostat has been moved to the new location, then determine one or more parameters associated with the new location and establish a new heating schedule for the new location, and where zero or more of the previously measured schedule-affecting parameters are re-used based on the one or more parameters associated with the new location.