Neural Network Inference Server for Multi-Criteria Appliance Control
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Solution Overview
Problem
Existing environment control systems fail to generate optimal commands for controlling appliances based on current environmental conditions and target conditions, as they do not adequately consider multiple, potentially complex criteria such as comfort, appliance stress, and energy consumption.
Innovation Solution
An inference server and environment controller system that utilizes a neural network to infer commands for controlling appliances by processing environmental characteristic values and set points, thereby optimizing command generation based on various criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If quick convergence from current temperature to target temperature is implemented, then comfort of people in the room is improved, but stress imposed on components of the controlled appliance and energy consumption increase
Solution Approach 1:
The system dynamically adjusts the convergence speed based on real-time conditions. When temperature difference is large, it prioritizes comfort with faster convergence; when temperature difference is small, it switches to energy-saving mode with slower convergence, making the control strategy adaptive rather than static
Solution Approach 2:
The system changes control parameters (convergence speed, heating/cooling intensity) based on the temperature difference magnitude. It uses multiple criteria including comfort level, energy consumption rate, and appliance stress to dynamically select optimal control parameters that balance competing objectives
2Reliability
If quick convergence from current temperature to target temperature is implemented, then comfort of people in the room is improved, but stress imposed on components of the controlled appliance increases
Solution Approach 1:
The control system dynamically adjusts its behavior based on operating conditions. For large temperature differences, it allows higher stress for quick comfort improvement; for small differences, it reduces stress to preserve appliance life, creating a dynamic balance between comfort and durability
Solution Approach 2:
The system changes control intensity parameters based on the magnitude of temperature difference and accumulated stress metrics, selecting from multiple control strategies that trade off between comfort improvement speed and component stress accumulation
3Reliability
If multiple complex criteria are considered for generating commands, then adequacy of commands is improved, but complexity of the control system increases
Solution Approach 1:
The patent introduces an inference server as an intermediary between the environment controller and the control appliance. This server receives environmental data and control objectives, then uses a trained neural network model to infer optimal commands, thereby externalizing the computational complexity while keeping the local controller simple
Solution Approach 2:
The system replaces traditional rule-based or algorithmic control logic with a data-driven neural network model. The neural network, trained offline on historical data, automatically learns complex relationships between environmental conditions and optimal control actions, substituting explicit mechanical control logic with intelligent inference
Data Source
AI summary
Inference server and environment controller for inferring one or more commands for controlling an appliance. The environment controller receives at least one environmental characteristic value (for example, at least one of a current temperature, current humidity level, current carbon dioxide level, and current room occupancy) and at least one set point (for example, at least one of a target temperature, target humidity level, and target carbon dioxide level); and forwards them to the inference server. The inference server executes a neural network inference engine using a predictive model (generated by a neural network training engine) for inferring the one or more commands based on the received at least one environmental characteristic value and the received at least one set point; and transmits the one or more commands to the environment controller. The environment controller forwards the one or more commands to the controlled appliance.