Disturbance Detection Using Probability Baselines
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Solution Overview
Problem
Current systems for detecting environmental disturbances, such as weather-related outages, often miss smaller-scale disturbances and rely on manual annotation, leading to incomplete data and limited cause-effect relationships, which can result in missed influential disturbances affecting multiple sub-regions.
Innovation Solution
A method that automatically receives service records with disturbance-related probability values, removes records associated with known global storms, generates baselines for subregions, identifies newly-discovered disturbances by analyzing probability values and features, filters out known disturbances, aggregates and scores newly-discovered disturbances, and outputs influential disturbances based on predetermined thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If manual annotation is used for disturbance detection, then data completeness is improved, but labor intensity and time consumption increase
Solution Approach 1:
The system performs self-service by automatically detecting disturbances through statistical analysis of service records without requiring manual annotation. The algorithm independently identifies spatial-temporal clusters, calculates disturbance probabilities, and generates disturbance events autonomously, eliminating the need for human labor while maintaining detection capability.
Solution Approach 2:
The patent replaces the mechanical manual annotation process with an automated computational system. Instead of human operators manually labeling disturbance records, the system uses statistical algorithms, probability calculations, and spatial-temporal cluster analysis to automatically identify and classify disturbances, substituting human effort with machine-based processing.
2Measurement precision
If manual annotation is used for disturbance detection, then detection accuracy is improved, but productivity decreases
Solution Approach 1:
The system autonomously performs disturbance detection, classification, and scoring without human intervention. It self-manages the entire workflow from receiving service records to generating disturbance events, maintaining detection accuracy through built-in statistical validation while achieving high productivity through automated processing of large datasets.
Solution Approach 2:
The system changes the detection parameters by using statistical thresholds, probability values, and spatial-temporal cluster criteria to automatically identify disturbances. Instead of relying on human judgment, the system uses quantifiable parameters such as disturbance probability scores, cluster density metrics, and temporal patterns to maintain detection accuracy at scale.
3Productivity
If automated detection is used, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms by calculating disturbance probabilities based on statistical analysis of service records and comparing them against threshold values. The algorithm continuously refines its detection by using feedback from the service record data, adjusting disturbance identification based on observed patterns and statistical significance, thereby maintaining accuracy in automated processing.
Solution Approach 2:
The system segments the detection process into distinct analytical components: spatial cluster identification, temporal pattern recognition, probability calculation, and threshold-based classification. This segmentation allows each component to be optimized independently, ensuring that automated processing maintains high detection accuracy through structured, multi-stage analysis rather than a single monolithic process.
4Device complexity
If smaller-scale disturbances are missed, then device complexity is reduced, but loss of information increases
Solution Approach 1:
The system applies local quality by detecting disturbances at multiple scales simultaneously. It identifies both large-scale global disturbances and smaller-scale local disturbances by analyzing spatial-temporal clusters at different granularities. The algorithm assigns different detection sensitivities and probability thresholds based on the local characteristics of each disturbance cluster, ensuring that smaller disturbances are not missed while maintaining manageable system complexity through localized analysis.
Data Source
AI summary
An embodiment for identifying influential disturbances is provided. The embodiment may automatically receive a set of service records including disturbance-related probability values corresponding to the disturbance-revealing events, and wherein one or more service records are mislabeled or have no label relating to an associated disturbance. The embodiment may generate baselines for a series of relevant sub-regions associated with the service records, and normalize daily summaries of disturbance probabilities for each of the relevant sub-regions. The embodiment may automatically identify subsets of service records corresponding to a series of newly-discovered disturbances by using the disturbance-related probability values and a series of associated features to identify deviations from normal non-disturbance event distributions. The embodiment may automatically identify and output a series of influential disturbances, the series of influential disturbances including newly-discovered disturbances for which a series of generated impact scores are above a predetermined threshold.