Automation Selector Fallback Engine Using Proof-of-Work Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional repositories for selectors in automation systems require users to manually browse and test multiple versions, leading to inefficiencies and processor-intensive tasks due to the lack of tracking and updating mechanisms for selector versions.
Innovation Solution
A fallback engine that tracks selector versions using proof of work and last-updated information to automatically update to the next best version, leveraging AI/ML capabilities for pattern recognition and processing efficiencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional repositories store multiple versions of selectors, then version control and historical tracking are improved, but manual browsing and testing of each version becomes extremely time-consuming and processor-intensive
Solution Approach 1:
The system enables self-service automation by having the automation framework automatically track selector versions, determine current states, and implement fallback operations without requiring manual user intervention. The framework monitors selector usage, identifies when selectors become invalid, and automatically tests alternative versions from the repository.
Solution Approach 2:
The system implements feedback mechanisms where the automation framework continuously monitors the state of selectors in use, detects when they become invalid or break, and uses this information to trigger automatic fallback operations to test and implement alternative versions from the repository.
2Measurement precision
If users manually test each selector version, then accuracy in selecting the correct version is improved, but productivity and processing efficiency deteriorate
Solution Approach 1:
The system replaces the mechanical process of manual user testing with an automated computational system. The framework uses machine learning models and automated testing scripts to evaluate selector versions, substituting human manual operations with automated processes that are both more accurate and faster.
Solution Approach 2:
The system performs preliminary actions by pre-testing and pre-validating selector versions from the repository before they are needed. The framework maintains a pool of pre-tested selector alternatives, so when a selector becomes invalid, a validated replacement is already available, eliminating the need for time-consuming on-demand testing.
3Adaptability or versatility
If the repository contains thousands of selector versions, then versatility and adaptability are improved, but device complexity and difficulty of managing versions increase
Solution Approach 1:
The system segments the large repository of selector versions into manageable groups or categories. The framework organizes selectors by application, function, or other logical groupings, and only actively manages a subset of relevant versions at any given time, reducing the perceived complexity while maintaining access to the full repository.
Data Source
AI summary
A method executed by a fallback engine is provided. The fallback engine is implemented as a computer program within a computing system. The method includes tracking information for versions for a selector stored in a selector repository. The information includes a proof of work defining an amount of use per version of the versions. The method includes determining a current state of an original selector version of the selector utilized by an automation and automatically implementing a fallback operation that updates a target of the selector of the automation from the original selector version to a different selector version based the proof of work for the different selector version in the selector repository.