ML-Based Access Component Coordination for Error Reduction
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Solution Overview
Problem
Existing access systems often have combinations of components that are not optimally coordinated, leading to increased susceptibility to errors and maintenance needs, which can be unnoticed during the planning phase, resulting in inefficiencies and higher costs.
Innovation Solution
A method using a machine learning system to analyze plan data and generate suggestions for optimizing access component combinations, including replacing individual components or adjusting parameters to extend service life and reduce maintenance efforts, by predicting potential issues and providing change or new suggestions based on architectural, security, and usage frequency information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If access components are selected during the planning phase without ML analysis, then the planning process is simple and quick, but the component combinations may not be optimally coordinated leading to increased susceptibility to errors and maintenance needs
Solution Approach 1:
The ML system performs analysis of access component combinations during the planning phase, before the system is installed. This preliminary action identifies potential coordination issues and suggests optimizations in advance, preventing problems before they occur rather than detecting them during operation or maintenance.
Solution Approach 2:
The patent replaces manual expert review of access component combinations with an automated ML system. The machine learning model analyzes plan data, component specifications, and compatibility rules to evaluate coordination quality, substituting human expertise with automated intelligent analysis that can process multiple components simultaneously.
2Reliability
If ML analysis is performed on plan data to generate optimization suggestions, then component coordination and reliability are improved, but additional time and computational resources are required during planning
Solution Approach 1:
The ML analysis is performed during the planning phase while the system is still being designed, allowing time for multiple iterations and optimizations. By conducting the analysis early, the patent avoids later time-consuming adjustments during installation or maintenance, and suggestions can be incorporated into the final plan before deployment.
Solution Approach 2:
The ML system analyzes digital plan data and component specifications rather than physical components. This virtual analysis of copied information allows rapid evaluation of multiple component combinations without physical prototyping or installation, significantly reducing the time penalty compared to traditional manual review methods.
3Productivity
If non-optimal access component combinations are installed, then initial installation is faster, but maintenance effort and costs increase over time
Solution Approach 1:
The ML system identifies and flags potential maintenance issues during the planning phase, before installation occurs. By detecting coordination problems early, the system allows planners to select alternative component combinations that are more maintainable, preventing the installation of systems that would require excessive maintenance effort later.
Solution Approach 2:
The ML analysis provides feedback on the planned component combinations, evaluating their coordination quality and predicting maintenance requirements. This feedback loop allows planners to adjust their selections based on automated assessments, ensuring that the final installed system balances installation efficiency with long-term maintainability.
Data Source
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AI summary
The invention relates to a method for generating proposals concerning one or more access components of an access system using a machine learning (ML) system, wherein the method for generating proposals comprises: --- a1) Obtaining plan data of a property by the ML system, wherein the plan data relates to the planning phase of the property and/or an access system of the property, --- a2) Analyzing the plan data using the ML system to generate at least one proposal concerning one or more access components of the access system of the property.