Parameter Change Prediction for Stable Computer Operation
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Solution Overview
Problem
Existing computer systems lack a comprehensive approach to prevent operational instability caused by design parameter changes, failing to account for load fluctuations and AI determination reliability during high-speed processing.
Innovation Solution
A computer system equipped with a load prediction unit, accuracy prediction unit, stability determination unit, and warning unit, which utilize load and accuracy prediction tables and a determination model to assess stability and display instability factors, preventing operational instability by identifying and addressing potential issues before they occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If design parameters are changed to improve processing speed, then productivity is improved, but operational stability deteriorates
Solution Approach 1:
The system performs preliminary prediction of load and accuracy before design parameter changes are applied. The load prediction unit estimates future load items, the accuracy prediction unit estimates future reliability items, and the stability determination unit determines stability before actual changes occur. This allows preventive measures to be taken to maintain stability while achieving improved productivity.
Solution Approach 2:
The system establishes a feedback loop where prediction results are continuously monitored and used to adjust design parameters. The stability determination unit provides feedback on whether operational stability is maintained, allowing iterative optimization of design parameters to balance productivity and stability.
2Reliability
If design parameters are changed to enhance AI determination reliability, then reliability is improved, but device complexity increases
Solution Approach 1:
The accuracy prediction unit acts as an intermediary between design parameter changes and AI determination reliability. It predicts the impact of parameter changes on reliability without requiring complex direct manipulation of the AI system, simplifying the overall approach while maintaining reliability improvement.
Solution Approach 2:
The system focuses on changing control parameters (design parameters) that have predictable impact on AI determination reliability. By using prediction tables and determination models, the system identifies optimal parameter changes that improve reliability without unnecessarily increasing system complexity.
3Reliability
If performance tests are conducted frequently to ensure stability, then reliability is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary prediction and determination of stability before actual operation. By using prediction tables and determination models to assess stability in advance, the system eliminates the need for frequent post-implementation performance tests, reducing time loss while maintaining reliability.
Solution Approach 2:
The system replaces physical performance testing with computational prediction methods. Instead of conducting actual performance tests to verify stability, the system uses algorithms (load prediction unit, accuracy prediction unit, stability determination unit) to simulate and predict stability, significantly reducing time requirements.
Data Source
AI summary
A computer system includes processors and memory resources, comprising: a load prediction unit that estimates load items as part of a target device's performance state using a load prediction table for a design parameter of the target device; an accuracy prediction unit that estimates reliability items as part of the performance state using an accuracy prediction table for the design parameter; a stability determination unit that determines whether the target device operates in a stable or unstable state using the design parameter and a determination model based on a performance profile indicating the performance state; and a warning unit that displays instability factors, which are load or reliability items, in descending order of their contribution to operational improvement when the operation is estimated to be in an unstable state.