Vectorized Element Processing for IT Component Location Detection
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Solution Overview
Problem
In complex IT systems, identifying the physical location of components within crowded environments is challenging, especially when components are about to fail, leading to potential catastrophic failures due to the limitations of existing system management software in tracking and monitoring components across multiple racks and machines.
Innovation Solution
A technique using a mobile computing device to process vectorized elements by analyzing a list of vectors with positional and magnitude values, determining if they form a line, and creating a report with anchor information, which improves upon traditional Hough transform methods by pre-qualifying image subsections, reducing the need for full-image processing and enhancing processing speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional Hough transform methods are used to process entire images for detecting vectors forming lines, then measurement precision is improved, but productivity deteriorates due to high computational resource requirements and slow processing speed
Solution Approach 1:
The patent divides the image processing task into two distinct passes: a first pass that processes the entire image to identify candidate regions, and a second pass that focuses only on those candidate regions. This segmentation allows the system to maintain detection accuracy while reducing overall computational load and processing time.
Solution Approach 2:
The first pass performs preliminary analysis of the entire image to identify and qualify candidate regions before the second pass conducts detailed line detection. By pre-identifying areas of interest, the system avoids performing computationally intensive operations on the entire image, thus improving processing speed without sacrificing detection accuracy.
2Measurement precision
If full-image processing is performed to ensure accurate detection of all vectors, then measurement precision is improved, but loss of time increases due to extensive computational requirements
Solution Approach 1:
The processing workflow is segmented into two phases: initial candidate identification and subsequent detailed analysis. This segmentation enables the system to spend minimal time on the entire image while concentrating computational resources only on regions likely to contain line patterns, thereby reducing total processing time while maintaining accuracy.
Solution Approach 2:
The first pass performs preliminary filtering to identify candidate regions that warrant further analysis. By performing this preliminary action, the system eliminates the need to process entire images in detail, significantly reducing processing time while ensuring that no potential line patterns are missed.
3Productivity
If computational resources are increased to process complex images rapidly, then productivity is improved, but use of energy increases due to higher processing demands
Solution Approach 1:
By segmenting the processing task into two passes with different computational intensities, the system achieves rapid processing without requiring sustained high computational resources. The first pass uses minimal resources to identify candidates, while the second pass concentrates resources only where needed, reducing overall energy consumption compared to processing the entire image at high intensity.
Solution Approach 2:
The system performs partial processing on the entire image (first pass) and excessive/detailed processing only on candidate regions (second pass). This approach achieves the necessary processing speed for productivity while avoiding the energy cost of performing full detailed processing on the entire image.
Data Source
AI summary
There is disclosed a technique for use in processing vectorized elements on a mobile computing device. A vector list is received, wherein the list comprises a set of vectors having positional and magnitude values associated with identified markers. A first test angle and stride size is determined. Vectors falling within a boundary defined by the first test angle and first stride size are analyzed and a likelihood the vectors form a line is determined. An increment size is adjusted if a positive determination result occurs. Vectors that intersect with a line are identified, wherein the vectors include vectors determined to fall with the boundary defined by the first test angle and first stride size. Based on a first policy, vectors that intersect the line are identified. A report including anchor information associated with the identified vectors is created.