Intelligent Cable Patching for Datacenter Racks
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Solution Overview
Problem
Current cable installation in datacenters is time-consuming and prone to human error due to manual planning and configuration, particularly in large-scale environments with tens of thousands of racks requiring power, networking, and other connections.
Innovation Solution
The use of automatic mapping processes and data structure generation to determine optimal cable placement and rack connections, reducing manual input errors and improving accuracy through the generation of data structures that guide cable routing and patching systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data entry and manual cable configuration methods are used, then flexibility and adaptability are maintained, but input errors increase and efficiency decreases
Solution Approach 1:
The system enables automatic self-service through machine learning models that autonomously generate cable patching schemes without requiring manual data entry or configuration. The ML model processes infrastructure data structures to automatically determine optimal cable connections, eliminating human intervention in the patching decision-making process while maintaining high accuracy.
Solution Approach 2:
The patent replaces manual mechanical processes (hand-writing cable paths, physical cable tracing, manual configuration) with automated computational systems. Machine learning algorithms substitute for human operators in generating patching schemes, while automated documentation systems replace manual record-keeping, significantly improving both speed and accuracy.
2Loss of time
If manual cable routing planning is performed, then complex cable paths can be handled, but design and engineering time increases significantly
Solution Approach 1:
The system performs preliminary action by pre-generating cable patching schemes using machine learning models before actual cable installation begins. The ML model analyzes infrastructure data structures in advance to determine optimal connections, allowing cable teams to prepare routing plans automatically without time-consuming manual design during the installation phase.
Solution Approach 2:
The patent uses copying by creating digital replicas of cable paths and connections through automated documentation systems. Once the ML model determines optimal routes, the system automatically generates and copies routing information into documentation systems, eliminating the need for manual recreation of cable path records and ensuring consistency across multiple installations.
3Ease of operation
If automated systems are introduced to improve efficiency, then installation speed increases, but system complexity increases
Solution Approach 1:
The system achieves universality by creating a multi-functional machine learning platform that handles multiple cable installation tasks simultaneously. The same ML model infrastructure generates patching schemes, determines routing paths, validates connections, and produces documentation across different cable types and infrastructure configurations, reducing the need for separate specialized systems for each function.
Data Source
AI summary
In certain embodiments, cable patching may be facilitated. For example, identifiers corresponding to available ports for devices or objects within racks and required ports for patching devices into the racks may be determined. Cable patching schemes for the racks may be retrieved from sensors. The available ports, the required ports, and the cable patching schemes may be input into a prediction model, the prediction model being configured based on the inputs. New identifiers corresponding to new available ports for a new rack and new required ports for patching new devices into the new rack may be determined. A predicted cable patching scheme for patching the new rack may be obtained from the prediction model. In some embodiments, the predicted cable patching scheme may be transmitted to a user device.