Automated Non-Synchronization Detection via Timestamp Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional synchronization management systems in complex systems, such as power grids, telecommunication networks, and aerospace systems, lack predictive capabilities and are reliant on labor-intensive manual monitoring, leading to delayed responses and compromised system integrity due to non-synchronization events.
Innovation Solution
An automated system that acquires data from multiple sources, aligns and segments it based on timestamps, extracts relevant features, and applies machine learning algorithms to detect non-synchronization events, generating alerts and initiating automatic corrective measures to prevent future disruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated detection algorithms and machine learning models are implemented, then detection precision and response speed are improved, but device complexity increases
Solution Approach 1:
The system segments the complex detection task into distinct functional modules: data acquisition module, data preparation module (alignment and segmentation), feature extraction module, and detection algorithm module. Each module handles a specific aspect of non-synchronization detection, making the overall complex system manageable and maintainable while achieving high detection precision through specialized processing at each stage.
Solution Approach 2:
The patent introduces intermediate processing layers between raw data and final detection: data alignment and segmentation serve as intermediary steps that transform raw monitoring data into structured time-window segments, and feature extraction acts as another intermediary that converts segmented data into meaningful features. These intermediaries simplify the final detection task while maintaining high precision.
2Reliability
If real-time automated corrective measures are implemented, then system stability is improved, but extent of automation increases which may lead to unintended consequences
Solution Approach 1:
The system implements a closed-loop feedback mechanism where detection algorithms continuously monitor system synchronization status, and upon detecting non-synchronization events, automatically trigger corrective measures. The system then continues to monitor to verify that corrective actions have resolved the issue, providing feedback control that enhances stability while maintaining appropriate automation levels.
Solution Approach 2:
The system performs preliminary data preparation actions (alignment and segmentation) and feature extraction before detection algorithms are applied. This preliminary processing prepares the system in advance for effective detection and corrective action, reducing response time while maintaining controlled automation through pre-configured processing pipelines.
3Measurement precision
If comprehensive feature extraction from multiple data sources is performed, then detection accuracy is improved, but loss of time in data processing increases
Solution Approach 1:
The system segments monitoring data into discrete time windows before feature extraction, allowing parallel processing of multiple segments simultaneously. This segmentation approach enables comprehensive feature extraction from multiple data sources to be performed efficiently on smaller, manageable segments rather than processing all data sequentially, reducing overall processing time while maintaining detection accuracy.
Solution Approach 2:
The system extracts features from segmented time windows rather than processing complete continuous data streams. By focusing computational resources on extracting relevant features from representative segments, the system achieves sufficient detection accuracy without the excessive time cost of comprehensive analysis of all available data from multiple sources.
Data Source
AI summary
Systems and methods are provided for detecting and resolving non-synchronization in a complex system, including acquiring monitoring data from multiple computers and devices within the complex system, preparing the acquired data by aligning data sequences from different sources based on timestamps, segmenting the prepared data into time windows, and extracting a plurality of features from the data within each of the time windows. Significant features are selected from the extracted features based on their relevance to non-synchronization detection and detection algorithms are applied to the selected features to identify non-synchronization events within the system. Alerts are generated, responsive to the detection of non-synchronization events, which trigger targeted, automatic corrective measures including adjusting particular system parameters to resolve the non-synchronization events and prevent occurrence of future non-synchronization events for enhanced stability and performance of the complex system.