Adaptive Component Configuration Using Reinforcement Learning
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Solution Overview
Problem
Existing systems lack the ability to efficiently adjust component configurations in manufacturing or test equipment to optimize production capacity and efficiency in response to environmental changes.
Innovation Solution
A method and system utilizing reinforcement learning models, including neural networks, to analyze state and global change information, determine optimal component configuration changes, and adjust configurations based on candidate action probabilities to improve production efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If component configuration is changed frequently to adapt to environmental changes, then adaptability improves, but system stability deteriorates
Solution Approach 1:
The patent implements dynamic component configuration that can adapt to environmental changes in real-time. The system continuously monitors environmental parameters and automatically adjusts component configurations to maintain optimal performance while ensuring system stability through controlled adaptation mechanisms.
Solution Approach 2:
The patent changes physical or chemical parameters of the component configuration based on environmental conditions. By adjusting parameters such as operational modes, resource allocation, and configuration states, the system achieves adaptability while maintaining stability through parameter optimization rather than complete reconfiguration.
2Productivity
If component configuration is changed to optimize production capacity, then productivity improves, but loss of time increases
Solution Approach 1:
The patent performs preliminary actions by pre-configuring multiple component configurations in advance and maintaining them in ready states. When environmental changes occur, the system can switch between pre-configured options without requiring time-consuming reconfiguration, thus optimizing production capacity while minimizing time loss.
Solution Approach 2:
The patent replaces manual or mechanical configuration change processes with automated control systems. The automated system can rapidly adjust component configurations through electronic control and software management, significantly reducing the time required for configuration changes compared to traditional mechanical adjustment methods.
3Productivity
If component configuration is changed to improve production efficiency, then productivity improves, but device complexity increases
Solution Approach 1:
The patent designs component configurations with multi-functionality, where a single component or subsystem can perform multiple functions through different configuration states. This reduces the overall number of components needed and simplifies the configuration management while maintaining high production efficiency through flexible resource utilization.
Solution Approach 2:
The patent implements self-service mechanisms where the system automatically monitors its own performance, detects suboptimal configurations, and initiates configuration changes without external intervention. This automation reduces the complexity of manual configuration management and enables the system to maintain high productivity through self-optimized configurations.
Data Source
AI summary
A method for changing a component configuration of an apparatus includes: obtaining global change information and state information, the state information corresponding to a current time step, of an environment in which the apparatus is located; obtaining latent features corresponding to candidate actions, based on the state information and based on the global change information; determining candidate action probability values respectively corresponding to the candidate actions, based on the latent features corresponding to the candidate actions; determining a target action based on the candidate action probability values; and changing the component configuration based on the target action.