Downtime Calculation via Morphology and Moving Average Filtering
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for calculating downtime in distributed ledger systems, such as probabilistic models, often result in false positives due to noise and temporary node failures, leading to inaccurate availability assessments.
Innovation Solution
A method involving signal processing techniques, including morphology filtering and moving average filtering, to generate smoothed signals and event labels, which are then used by a state machine to determine downtime intervals and total downtime, effectively distinguishing between system failures and noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If probabilistic models are used to calculate downtime, then system availability can be assessed, but false positives increase due to noise and temporary node failures
Solution Approach 1:
The patent introduces signal processing techniques as an intermediary layer between raw node status data and downtime calculation. Morphology filtering and moving average filtering act as mediators that process and clean the signal before downtime determination, eliminating false positives caused by noise and temporary failures while maintaining accurate downtime detection.
Solution Approach 2:
The patent applies preliminary signal processing actions before downtime calculation. By pre-filtering the data through morphology filtering (closing and opening operations) and moving average filtering, the system prepares clean signal data that accurately represents true system state, preventing false downtime declarations from noisy or temporary failures.
2Measurement precision
If signal processing filtering is applied, then false positives are reduced, but system complexity increases
Solution Approach 1:
The patent segments the signal processing into distinct functional modules: morphology filtering (closing operation with dilation and erosion), moving average filtering, and downtime detection. This segmentation allows each component to be optimized independently and simplifies implementation while maintaining high measurement precision through modular processing stages.
Data Source
AI summary
The present invention relates to methods for accurate downtime calculation. The method comprises filtering an input signal from a system by a closing process and an opening process to generate a first smoothed signal. The method may include applying moving average filtering to the first smoothed signal to generate a second smoothed signal. The method may further include generating the morphology event labels and the average event labels of the signal based on the first and second smoothed signals, and determining the downtime intervals by the event labels.