Dynamic 3D Data Center Mapping via Optical and Beacon Fusion
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Solution Overview
Problem
Current data center mapping methods are error-prone, labor-intensive, and unable to accurately track ongoing changes in physical assets, leading to operational inefficiencies and downtime due to reliance on manual methods and limited automation capabilities.
Innovation Solution
A method using a smart device to capture images and receive positioning beacon signals, leveraging peer-to-peer networks and indoor positioning technologies to generate a dynamic 3D map of data centers by determining physical characteristics and asset locations, enabling continuous tracking and updating of asset positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual mapping methods are used to track data center assets, then implementation simplicity is maintained, but mapping accuracy and reliability deteriorate due to human error and labor-intensive processes
Solution Approach 1:
The patent replaces manual mechanical mapping processes with an automated optical and computational system. A camera captures images of data center racks, and image processing algorithms automatically identify assets, determine their positions, and generate 3D maps. This substitution eliminates human error in manual tracking while achieving high mapping accuracy through computer vision and peer-to-peer positioning beacon signals.
Solution Approach 2:
The patent creates digital copies of the physical data center environment through 3D mapping. By capturing images and processing them to generate virtual representations of racks and assets, the system maintains an accurate digital twin that can be updated continuously. This copying approach enables precise tracking without requiring physical intervention in the data center.
2Productivity
If manual mapping methods are used, then initial setup cost is reduced, but ongoing labor costs and time consumption increase significantly
Solution Approach 1:
The patent implements continuous automated mapping by having the system continuously capture images, process them, and update the 3D map without interruption. The automated image processing and peer-to-peer beacon signal reception enable the system to maintain current maps in real-time, eliminating the periodic manual re-mapping that would otherwise be required to track asset movements and changes.
Solution Approach 2:
The system performs self-updating through automated image capture and processing. The camera continuously monitors the data center environment, and the image processing algorithms automatically detect changes in asset positions or additions/removals of equipment. This self-service capability eliminates the need for external manual intervention to maintain map accuracy.
3Reliability
If automated image processing is implemented, then mapping accuracy improves, but computational complexity and processing requirements increase
Solution Approach 1:
The patent divides the complex mapping task into separate functional modules: image capture by camera, image processing to identify racks and assets, peer-to-peer beacon signal reception for positioning, and 3D map generation. Each module handles a specific aspect of the mapping process, making the overall system more manageable and reliable while maintaining high accuracy through specialized processing at each stage.
Data Source
AI summary
In a computing environment comprising a plurality of equipment racks wherein each equipment rack comprises one or more of compute, storage and network assets, the method captures an image of at least one equipment rack in the computing environment. The method identifies a known object in the image to determine physical characteristics of the equipment rack. The method receives, from a peer-to-peer network, positioning beacon signals respectively associated with at least a portion of the compute, storage and network assets in and around the equipment rack. The method determines a closest asset among the portion of the compute, storage and network assets for which positioning beacon signals are received. The method obtains data indicative of physical characteristics associated with the closest asset, obtains a model of the computing environment based on the obtained data, and then obtains a three-dimensional map of the computing environment based on the model.