Parameter search device and parameter search method
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Solution Overview
Problem
Existing parameter search systems for mechanical devices, such as air conditioners and information processing devices, face inefficiencies due to a fixed number of search times or search time, leading to either insufficient or unnecessary searches.
Innovation Solution
A parameter search device utilizing processing circuitry to collect operation results, acquire evaluation values, and predict the relationship between parameters and evaluation values using a machine learning model. This device compares prediction results with the best search outcome and determines whether to continue searching based on the number of search candidates exceeding the best evaluation value and a predefined threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed number of search times or search time is predetermined, then the search process becomes simple to control, but the search efficiency deteriorates due to either insufficient or unnecessary searches
Solution Approach 1:
The patent implements feedback by continuously monitoring the evaluation values of search candidates and using this information to dynamically adjust the search termination decision. The determination unit receives feedback from the evaluation results and decides whether to continue or terminate the search, replacing fixed predetermined controls with adaptive feedback-based control.
Solution Approach 2:
The patent transforms the static, predetermined search control into a dynamic process where the search termination condition changes based on real-time evaluation results. The determination unit adaptively adjusts the search continuation decision according to the observed evaluation values, making the control system flexible and responsive rather than rigid and fixed.
2Reliability
If the search time is extended to ensure sufficient parameter exploration, then the search thoroughness is improved, but the time consumption increases
Solution Approach 1:
The determination unit uses feedback from evaluation values to assess whether the search has sufficiently explored the parameter space. By monitoring whether subsequent search candidates improve upon the best evaluation value found so far, the system can reliably determine when to terminate the search, ensuring thoroughness without unnecessary extension.
Solution Approach 2:
The search system performs self-assessment through the determination unit, which automatically evaluates whether the search should continue based on the observed performance of search candidates. This self-service mechanism eliminates the need for externally predetermined search times and allows the system to autonomously decide when sufficient exploration has been achieved.
3Productivity
If the search is terminated early to save time, then the time efficiency is improved, but the parameter search completeness deteriorates
Solution Approach 1:
The determination unit continuously monitors evaluation values and uses this feedback to make informed termination decisions. By comparing the evaluation value of the current best candidate with subsequent candidates, the system can confidently terminate the search when no improving candidates are found, ensuring completeness while maintaining time efficiency.
Solution Approach 2:
The patent replaces the mechanical, fixed-time search termination mechanism with an intelligent, evaluation-based termination mechanism. Instead of using predetermined time limits that may cut off useful searches or extend unnecessary ones, the system uses the determination unit to substitute mechanical timing with intelligent assessment of search quality.
4Reliability
If unnecessary searches are continued after finding the target parameter, then the search robustness is maintained, but the computational resources are wasted
Solution Approach 1:
The determination unit uses feedback from evaluation values to detect when the target parameter has been found and to decide whether continued searching is worthwhile. When the evaluation value of the best candidate stabilizes and subsequent candidates do not improve it, the feedback mechanism triggers termination, preventing wasteful continuation while maintaining robustness through careful monitoring.
Solution Approach 2:
The search system autonomously monitors its own performance through the determination unit and self-regulates the continuation or termination of searches. This self-service capability allows the system to recognize when the target parameter has been sufficiently found and to stop computational waste without external intervention, balancing robustness with resource efficiency.
Data Source
AI summary
A parameter search device includes an operation result collection unit to collect an operation result including parameters indicating an operation condition of a mechanical device, an evaluation value acquisition unit to acquire evaluation values of the parameters obtained using the operation result, and a parameter search unit to search for a target parameter from the parameters indicating the operation condition of the mechanical device. The parameter search unit predicts a relationship between parameters and evaluation values using a machine learning model, uses the number of search candidates whose evaluation value exceeds the search best as a search end index, and determines whether or not to continue parameter search on the basis of a comparison result between the search end index and a threshold.