Automatic Load-Source Matching via Activity Correlation
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Solution Overview
Problem
As systems with increasing numbers of loads and sources become more complex, tracking which loads connect to which sources and maintaining up-to-date physical location information becomes difficult, especially in dynamic environments like data centers, leading to inefficiencies in power management and reliability monitoring.
Innovation Solution
A method and system that utilize activity-aware loads and sources to report their activity data, which is then processed by a matching server to automatically match loads to sources based on correlation analysis and optimization algorithms, providing real-time topology information without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual tracking methods (spreadsheets, databases, bar codes, RFID tags) are used to maintain topology information, then human intervention is required which increases maintenance cost and potential unreliability, but these methods can provide topology information updates
Solution Approach 1:
The system enables self-service by having loads and sources automatically report their own activity data to the topology discovery server. The matching engine automatically correlates this data to determine connectivity relationships without human intervention, eliminating manual tracking while maintaining continuous accuracy
Solution Approach 2:
The patent replaces manual mechanical tracking methods (spreadsheets, physical tagging) with an automated electronic system that uses activity data correlation and pattern matching algorithms to dynamically discover and maintain topology information
2Loss of information
If traditional tracking methods are used in dynamic environments where loads and sources are continuously added, removed, relocated, and reconnected, then topology information becomes outdated, but updating these methods frequently increases maintenance effort
Solution Approach 1:
The system maintains continuous topology information by continuously collecting activity data from loads and sources and continuously running the matching engine to update connectivity relationships in real-time, ensuring information remains current without periodic manual updates
Solution Approach 2:
The system uses feedback from activity data changes to automatically detect topology modifications. When activity patterns change, the matching engine re-evaluates connectivity relationships and updates the topology map accordingly, maintaining accuracy in dynamic environments
3Productivity
If activity monitoring and correlation analysis are implemented to automatically match loads to sources, then real-time topology information is obtained, but data processing complexity increases
Solution Approach 1:
The system segments the topology discovery process into distinct functional modules: activity data collection from loads and sources, data normalization and preprocessing, pattern matching and correlation analysis, and results aggregation. This modular approach manages complexity while maintaining real-time discovery capability
Data Source
AI summary
A method of matching sources to loads includes affecting load activity at a load with a pattern, receiving source activity data representing source activity at a plurality of sources, and matching the load to at least one source based on the source activity data. A system for matching sources to loads includes a load activity manipulator operably connected to a load and configured to affect load activity of the load with a pattern. The system for matching sources to loads further includes an activity monitor configured to receive source activity data representing source activity at the sources. The system for matching sources to loads also includes a matching engine operably connected to the activity monitor and configured to match the load to at least one source based on the source activity data.