Diagnostic Data Prioritization for Bandwidth-Constrained Downloads
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Solution Overview
Problem
Large diagnostics data files lead to increased download times over restricted bandwidth networks, with much redundant data, making it inefficient to diagnose failures effectively.
Innovation Solution
Prioritize diagnostic data based on pre-defined rules to stream only the most crucial data first, allowing the diagnostic system to cancel downloads when sufficient information is received, using techniques like the Tape Archive format to efficiently transfer and reconstruct diagnostic files.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complete diagnostic data bundles are downloaded to ensure maximal failure data capture, then diagnostic completeness is improved, but download time and network bandwidth consumption increase significantly
Solution Approach 1:
The patent segments the complete diagnostic data bundle into multiple priority-based data sets. High-priority data containing critical failure information is separated from low-priority supplementary data, allowing selective transmission of only the most essential diagnostic information first through restricted bandwidth networks.
Solution Approach 2:
The patent extracts and prioritizes critical diagnostic data elements from the complete data bundle. By identifying and extracting high-value failure diagnostic information, the system transmits only the most relevant data subsets first, enabling effective diagnosis without requiring complete data download.
2Loss of time
If diagnostic data is broken down into smaller feature-centric bundles to reduce download size, then download time is reduced, but the organization complexity and manual configuration effort increase
Solution Approach 1:
The patent implements self-service through automated priority assignment mechanisms. The system automatically analyzes diagnostic data and assigns priority levels based on pre-defined rules and failure patterns, eliminating the need for manual bundle configuration and reducing organizational complexity while maintaining effective data segmentation.
Solution Approach 2:
The patent changes the parameter of data organization from manual feature-centric bundling to automated priority-based segmentation. By transforming the organizational approach and using dynamic priority parameters, the system reduces download time without increasing complexity.
3Quantity of substance
If the amount of diagnostic data produced is reduced to minimize download volume, then network bandwidth consumption is reduced, but diagnostic trace capability may be insufficient to diagnose the problem
Solution Approach 1:
The patent applies local quality by ensuring high-priority data subsets contain concentrated critical failure information with higher diagnostic value density. Rather than uniformly reducing data volume, the system maintains high diagnostic quality in essential data portions while minimizing overall transmission volume through selective prioritization.
Data Source
AI summary
Aspects are related to reducing size of diagnostic data downloads. To reduce the size, format and content are read from a diagnostic data file so that pre-defined priority rules may be applied to the diagnostic data file and/or a subset of the diagnostic data file utilizing the format or the content. Then, a priority level is assigned to the diagnostic data file or the subset based on an ability of that file or that subset to diagnose a failure as determined by the pre-defined priority rules. Next, an ordering of the diagnostic data file and/or the subset into a file stream occurs, followed by a streaming of the file stream to a remote diagnostic system. A notification can be received from the remote diagnostic system to stop the streaming if sufficient diagnostic data to diagnose the failure has been received by the remote diagnostic system.