Modular Alarm Interface for Cross-Subsystem Issue Origin Detection
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Solution Overview
Problem
Different departments within an organization often have limited visibility into or awareness of work and contextual information associated with other departments, leading to siloed information and a lack of comprehensive understanding of how their contributions fit into the larger production cycle.
Innovation Solution
A graphical user interface with modular alarm units that are connected via dependency relationships, allowing for rearrangement and updating based on user input, and utilizing supervised machine learning to infer and modify these relationships, thereby determining the origin of issues across multiple alarm units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If multiple separate alarm interfaces are provided for different departments, then each department can monitor its own subsystem, but visibility and awareness of work in other departments is limited
Solution Approach 1:
The patent combines multiple department-specific alarm interfaces into a single unified graphical user interface that displays alarm units from multiple departments. This consolidation allows employees to view work information from different departments simultaneously, eliminating information silos while reducing the number of separate interfaces needed.
Solution Approach 2:
The unified graphical user interface serves multiple functions: it displays alarm units from different departments, shows dependency relationships between subsystems, and provides a comprehensive view of the production cycle. This multi-functional interface replaces multiple specialized interfaces, improving information visibility without proportionally increasing complexity.
2Loss of information
If a unified interface showing all subsystems is provided, then comprehensive visibility is achieved, but the interface becomes complex and difficult to operate
Solution Approach 1:
The unified interface is segmented into multiple modular alarm units, each representing a specific subsystem or department. These alarm units can be independently configured, displayed, and manipulated. The segmentation allows comprehensive information display while maintaining operational simplicity through modular organization.
Solution Approach 2:
The interface uses spatial arrangement and visual hierarchy to organize alarm units from different departments. Dependency relationships are shown through visual connections between alarm units, creating a multi-dimensional representation that fits complex information into a manageable two-dimensional display space.
3Reliability
If dependency relationships between alarm units are manually configured, then accurate relationships are established, but time and effort are consumed
Solution Approach 1:
The system performs preliminary automatic configuration of alarm units and their dependency relationships using machine learning models trained on historical data. This preliminary action establishes accurate relationships before manual review, reducing the time and effort required for manual configuration while maintaining high accuracy.
Solution Approach 2:
The machine learning model uses feedback from labeled training data to continuously improve its accuracy in inferring dependency relationships. The system learns from correct and incorrect predictions, adjusting its inference algorithm to become more accurate over time, thereby reducing manual intervention needs.
4Loss of time
If machine learning is used to infer dependency relationships, then configuration time is reduced, but accuracy may be compromised without proper training
Solution Approach 1:
The machine learning model undergoes preliminary training on a labeled corpus of alarm unit data before deployment. This pre-training establishes the foundation for accurate relationship inference, ensuring that the model has learned correct patterns from quality training data before being used in production.
Solution Approach 2:
The machine learning model is dynamic and can be retrained or fine-tuned as new data becomes available. The system adapts to changing patterns in alarm relationships over time, maintaining high accuracy by continuously learning from new information rather than relying on static initial training.
Data Source
AI summary
A disclosed method may include (i) providing a graphical user interface that includes a plurality of modular alarm units that are each directed to a respective and different subsystem that contributes to a same production cycle of a direct broadcast satellite product or service and that are each connected to at least one other modular alarm unit in the plurality of modular alarm units via a respective dependency relationship, (ii) receiving user input indicating a rearrangement of an orientation of the plurality of modular alarm units, and (iii) based on a modified at least one dependency relationship, determining an origin of an issue causing respective alerts generated for multiple alarm units of the plurality of modular alarm units.