Data Handling Facility Ontology for Power-Monitor Connection Discovery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing data handling facilities face challenges in accurately monitoring and managing space, power, and cooling capacities due to incomplete, outdated, or inaccurate engineering drawings, leading to inefficient resource utilization and difficulty in forecasting operational impacts from hypothetical scenarios.
Innovation Solution
An ontology system is employed to automatically discover physical connections between components using electrical power monitors, forming a physical ontology layer, and a logical ontology layer, which can forecast operational changes based on user queries, integrating data handling facility management with predefined taxonomies and query tools.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If engineering drawings are used to monitor and manage data handling facilities, then a structured framework is provided, but the data becomes incomplete, outdated, or inaccurate
Solution Approach 1:
The system performs preliminary actions by continuously discovering and updating physical connections between components before engineering drawings become outdated. The auto-discovery tool proactively monitors the facility and updates the ontology layer in advance, preventing data obsolescence rather than reacting to it.
Solution Approach 2:
The system implements feedback mechanisms where the ontology engine continuously compares discovered physical connections with existing engineering drawings, automatically updating the drawings based on real-time facility status. This closed-loop feedback ensures data remains current and accurate.
2Productivity
If traditional monitoring methods are used, then implementation is simple, but resource utilization efficiency is poor
Solution Approach 1:
The ontology engine serves multiple functions simultaneously: it discovers physical connections, validates engineering drawings, forecasts operational impacts, and manages facility data. This multi-functionality improves resource utilization efficiency without proportionally increasing system complexity.
Solution Approach 2:
The physical ontology layer acts as an intermediary between raw facility data and engineering drawings, automatically reconciling discrepancies and providing an accurate representation of the facility. This intermediary layer simplifies the overall system by centralizing the complexity in a manageable component.
3Adaptability or versatility
If engineering drawings are relied upon, then a baseline framework exists, but forecasting operational impacts becomes difficult
Solution Approach 1:
The system performs preliminary forecasting actions by using the ontology engine to predict operational impacts before hypothetical modifications are implemented. This allows stakeholders to assess potential consequences in advance, improving adaptability while maintaining data reliability through the accurate physical ontology layer.
Data Source
AI summary
A method for managing an ontology of a data handling facility of a communication system. The method includes discovering connections between some of a plurality of electrically powered components of the data handling facility based on time-series data obtained from a plurality of electrical power monitors, and forming a physical ontology layer of the data handling facility and a logical ontology layer of the data handling facility. In addition, the method includes receiving a query from a user concerning a hypothetical modification to the operation of the data handling facility, and forecasting change in the operation of the data handling facility based on the query received from the user.