Environmental apparatus controller with learning control based on user condition
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Solution Overview
Problem
Current environmental control systems face challenges in optimizing mental and physical conditions of users due to the complexity of factors influencing arousal levels, such as illuminance, temperature, and humidity, making it difficult to control multiple environmental apparatuses effectively to achieve target conditions.
Innovation Solution
An environmental apparatus controller that includes a grasping unit for current mental and physical condition information, a learning control plan output means to generate control change plans, and a selection control unit to select and execute plans efficiently, using neural networks to update control methods based on user feedback, incorporating multiple apparatuses like air conditioners, ventilators, and aroma diffusers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If control over multiple environmental apparatuses is performed to optimize user mental and physical condition, then the quality of working environment is improved, but the computational load becomes extremely huge
Solution Approach 1:
The patent segments the control problem by separating the determination of control values into two stages: first determining a target arousal level based on user condition, then determining control values for each environmental apparatus based on the target arousal level. This segmentation reduces the computational complexity by breaking down the complex multi-factor optimization into simpler sequential steps.
Solution Approach 2:
The patent introduces an intermediary variable - the target arousal level - that mediates between the user's current condition and the control values for environmental apparatuses. This intermediary simplifies the control process by providing a single intermediate target that multiple apparatuses can work toward, rather than directly optimizing multiple factors simultaneously.
2Measurement precision
If complex simulation is performed to study influence of each factor on user condition, then the precision of control is improved, but the time required becomes extremely long
Solution Approach 1:
The patent applies preliminary action by pre-establishing the relationship between user condition and target arousal level through the arousal level determination unit. This pre-determined relationship serves as a lookup table or predefined model that can be quickly queried during operation, eliminating the need for complex real-time simulations while maintaining control precision.
Solution Approach 2:
The system implements feedback by continuously monitoring user condition information and adjusting the target arousal level and control values accordingly. This closed-loop feedback mechanism allows the system to adapt to changing conditions in real-time without requiring exhaustive simulations, as the feedback provides direct information about the current state and desired adjustments.
3Adaptability or versatility
If multiple control change plans are generated for each combination of environmental apparatuses, then the adaptability of control is improved, but the processing speed becomes slow
Solution Approach 1:
The patent applies dynamics by making the control plan selection adaptive and flexible. The selection control unit dynamically selects from multiple control change plans based on current conditions, allowing the system to adapt to different scenarios. This dynamic selection process maintains versatility while improving speed by avoiding the need to process all possible plans equally.
Solution Approach 2:
The system changes parameters by adjusting the target arousal level and control values based on different combinations of environmental apparatuses and user conditions. By parameterizing the control approach around the arousal level metric, the system can efficiently handle multiple apparatus combinations without generating exponentially more processing complexity, as all controls are coordinated through the common parameter of target arousal level.
Data Source
AI summary
An environmental apparatus controller performs control over multiple types of environmental apparatuses, and includes a grasping unit, learning control plan output means, and a selection control unit. The grasping unit grasps current mental and physical condition information of a user, environmental situation information, and target relationship information between a target mental and physical condition and a present mental and physical condition. The learning control plan output means outputs a control change plan for each combination of multiple types of the environmental apparatuses according to the current mental and physical condition information, the environmental situation information, and the target relationship information. The selection control unit selects one of a plurality of the control change plans output by the learning control plan output means and executes the plan. The learning control plan output means performs learning such that a method of determining the control change plans to be output is updated.