Algorithm Replacement Selection Using Execution Context Matching
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Solution Overview
Problem
Selecting a suitable replacement algorithm for industrial assets is a time-consuming, expensive, and error-prone process, especially when dealing with complex algorithms that perform differently under various operating conditions, and there are numerous algorithms to evaluate.
Innovation Solution
A system that includes an algorithm selection platform accessing information from an available algorithm data store, comparing each algorithm with the current algorithm's requirements, determining algorithm execution context information, and automatically selecting a potential replacement algorithm based on this comparison.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual testing and evaluation of algorithms is performed, then algorithm performance improvement can be achieved, but the process becomes time-consuming and expensive
Solution Approach 1:
The system enables algorithms to automatically evaluate and replace themselves through self-diagnosis and self-optimization mechanisms. The algorithm selection platform autonomously compares available algorithms against current performance requirements and executes replacements without manual intervention, allowing the system to service its own optimization needs.
Solution Approach 2:
The system performs preliminary evaluation and comparison of algorithms before actual deployment or replacement. By pre-assessing algorithm performance against defined requirements and context information, the system prepares replacement candidates in advance, reducing the time needed for actual algorithm updates when performance improvements are needed.
2Reliability
If manual algorithm replacement is performed, then algorithm updates can be implemented, but human errors increase
Solution Approach 1:
The system eliminates human intervention in the algorithm replacement process by enabling algorithms to self-evaluate and self-replace based on automated performance assessment. This self-service mechanism removes human errors from the equation while maintaining reliable algorithm updates through systematic comparison and evaluation.
Solution Approach 2:
The system implements automated feedback loops where algorithm performance is continuously monitored, compared against requirements, and used to trigger replacement decisions. This closed-loop feedback system ensures accurate and consistent algorithm updates without human intervention, reducing errors while maintaining reliability.
3Reliability
If a substantial number of algorithms are evaluated, then better replacement options can be found, but the complexity of selection increases
Solution Approach 1:
The system segments the algorithm evaluation process into distinct components: context information gathering, requirement definition, algorithm comparison, and selection execution. This segmentation allows the system to manage complex algorithm selection by breaking it into manageable steps, enabling thorough evaluation of many algorithms without overwhelming system complexity.
Solution Approach 2:
The algorithm selection platform acts as an intermediary between the pool of available algorithms and the deployment environment. It mediates the complex evaluation process by systematically comparing algorithms against requirements and context information, simplifying the selection of numerous algorithms through structured intermediate assessment steps.
4Productivity
If algorithms are selected without considering execution context, then selection speed increases, but algorithm performance under various operating conditions deteriorates
Solution Approach 1:
The system performs preliminary gathering and analysis of execution context information before algorithm selection. By pre-collecting data about operating conditions, environment, and requirements, the system can quickly match algorithms to appropriate contexts without sacrificing performance, as the context assessment is already completed beforehand.
Solution Approach 2:
The system considers changes in execution context parameters when selecting algorithms. By monitoring and responding to parameter changes in the operating environment, the system ensures algorithms are selected based on current conditions, maintaining performance reliability across varying conditions while keeping the selection process efficient through parameter-based matching.
Data Source
AI summary
According to some embodiments, an available algorithm data store may contain information about a pool of available algorithms. An algorithm selection platform coupled to the available algorithm data store may access the information about the pool of available algorithms and compare the information about each of the pool of available algorithms with at least one requirement associated with the current algorithm executing in the real environment. The algorithm selection platform may then automatically determine algorithm execution context information and, based on said comparison and the algorithm execution context information, select at least one of the pool of available algorithms as a potential replacement algorithm. An indication of the selected at least one potential replacement algorithm may then be transmitted (e.g., to be evaluated in a shadow environment by an algorithm evaluation platform).