Smart Home Control Using LLM Queries for Adaptive Device Commands
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Solution Overview
Problem
Existing smart home control systems require complex scene presetting and large amounts of training data, leading to difficulty and high costs in effectively controlling smart home devices.
Innovation Solution
A method involving determining environmental factors through sensor data and user feedback, generating queries for a large language model (LLM) to optimize control instructions for smart home devices, and adjusting based on user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex scene presetting and large amounts of training data are used, then control accuracy of smart home devices is improved, but system complexity and implementation cost increase
Solution Approach 1:
The patent introduces an intermediary component (control system with scene recognition algorithms) that mediates between user input and device control. This intermediary automatically interprets user intentions and translates them into appropriate device commands, eliminating the need for users to manually preset complex scenes while maintaining high control accuracy.
Solution Approach 2:
The patent replaces traditional mechanical scene presetting mechanisms with intelligent algorithms that dynamically recognize and respond to user needs. Instead of requiring users to manually configure scenes through complex interfaces, the system uses automated recognition and decision-making algorithms to substitute the manual configuration process, thereby reducing system complexity.
2Adaptability or versatility
If complex scene presetting is required, then control functionality is enhanced, but ease of operation deteriorates
Solution Approach 1:
The system implements self-service by automatically recognizing user intentions and autonomously generating control commands without requiring users to manually preset scenes. The control system serves itself by interpreting environmental data, user behavior patterns, and device states to automatically determine appropriate control actions, thereby enhancing functionality while maintaining ease of operation.
Solution Approach 2:
The patent applies preliminary action by pre-configuring the system with comprehensive device profiles, capability descriptions, and control parameter definitions during system initialization. This preliminary setup enables the system to quickly respond to user needs without requiring complex runtime configuration, thus providing versatile control functionality while keeping the user interface simple and easy to operate.
3Measurement precision
If extensive training data is collected and processed, then control precision is improved, but loss of time and processing resources increase
Solution Approach 1:
The patent applies partial action by selectively processing only the most relevant features and parameters from available data rather than analyzing all possible training data. The system identifies and focuses on key control parameters and device states that have the greatest impact on control precision, thereby achieving high precision while reducing processing time and computational resource requirements.
Data Source
AI summary
Embodiments of the present disclosure disclose a method and apparatus for controlling a smart home device, an electronic device, a medium, and a product. The method includes determining a smart home device that is associated with an environmental factor to be controlled; generating a query associated with the smart home device based on a current state of the environmental factor to be controlled; inputting the query into a large language model; obtaining a reply generated based on the query from the large language model; converting the reply from the large language model into a control instruction for the smart home device; and controlling the smart home device based on the control instruction.