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 troubleshooting.
Innovation Solution
A computer-implemented method that receives load activity data and source activity data, scores similarity between the two, and automatically matches loads to sources using a matching server, which can periodically probe connections and use correlation algorithms like Spearman's rho or dynamic time warping to determine the best assignments, constructing a topology map of connections.
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are used to maintain topology information, then implementation is simple and familiar, but the information becomes outdated and requires human intervention which increases cost and reduces reliability
Solution Approach 1:
The system enables automatic self-updating of topology information by having loads and sources periodically probe connections and report status changes. The matching server automatically receives probe results and updates the topology map without human intervention, making the system self-maintaining and eliminating manual tracking requirements
Solution Approach 2:
The system implements continuous feedback loops where loads and sources periodically probe connections and report back to the matching server. This feedback mechanism ensures the topology information remains current by automatically detecting and reporting changes in connection status, physical locations, and operational states
2Loss of information
If frequent manual updates are performed to keep topology information current, then information accuracy improves, but labor cost and operational complexity increase
Solution Approach 1:
Loads and sources automatically perform connection probing and status reporting without requiring manual intervention. The system self-updates topology information by having components autonomously detect and report their connection status, physical locations, and operational changes to the matching server
Solution Approach 2:
The system performs connection probing and status reporting at periodic intervals rather than requiring continuous manual updates. Loads and sources automatically probe connections and report status changes at predetermined time intervals, ensuring information currency while minimizing operational overhead
3Measurement precision
If correlation algorithms (Spearman's rho, dynamic time warping) are used to match loads to sources, then matching accuracy improves, but computational complexity increases
Solution Approach 1:
The system transforms raw activity data into standardized parameters (Spearman's rho correlation coefficients and dynamic time warping distances) that enable accurate matching. By converting diverse activity patterns into comparable mathematical parameters, the system achieves high matching precision while managing computational complexity through parameter standardization
Data Source
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AI summary
A method of matching sources to loads includes receiving load activity data and source activity data corresponding to samples of activity over a time period at a plurality of loads and a plurality of sources, respectively. The method of matching sources to loads further includes scoring similarity between the load activity and the source activity based on the load activity data and the source activity data, and matching loads to sources based on the scoring.