PON Data Collection Device Change Detection Threshold
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Solution Overview
Problem
Current data collection methods in passive optical networks (PON) face challenges in collecting highly precise and valid data from a large number of optical network units (ONUs), as existing methods either generate excessive data when reporting frequency is high, overwhelming storage and transmission channels, or fail to capture sudden changes when reporting frequency is low, leading to inaccurate analysis.
Innovation Solution
A data collection method that detects change amplitude in performance indicator data within a change detection window and sends data to a data analysis device only when the change exceeds a threshold, allowing for finer-grained data collection, detection of sudden changes, and reduced reporting volume, using a change detection window and threshold configured by the data analysis device.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data collection and reporting periodicity is shortened to capture sudden changes, then measurement precision is improved, but the quantity of data to be reported increases excessively
Solution Approach 1:
The patent changes the parameter of data reporting by introducing a change detection threshold. Instead of reporting all collected data, the system only reports data when the change amplitude exceeds the threshold, thus reducing reporting volume while maintaining detection precision for significant changes.
Solution Approach 2:
The patent applies different quality standards to different data points by using change detection thresholds. Data points with significant changes (exceeding threshold) are reported with high priority, while data points with minor changes are filtered out, creating a non-uniform reporting strategy that optimizes both precision and volume.
2Quantity of substance
If data collection and reporting periodicity is lengthened to reduce data volume, then the quantity of data to be reported is reduced, but measurement precision deteriorates and sudden changes are missed
Solution Approach 1:
The system changes the reporting parameter from fixed periodicity to event-driven reporting based on change detection. This allows the system to maintain long periodicity for volume reduction while still capturing sudden changes when they exceed the detection threshold.
Solution Approach 2:
The change detection threshold acts as an intermediary mechanism between data collection and data reporting. It filters and selects which data points should be reported, enabling the system to achieve both volume reduction and precision maintenance simultaneously.
3Measurement precision
If high frequency data reporting is implemented to ensure data accuracy, then measurement precision is improved, but transmission channel capacity is overwhelmed
Solution Approach 1:
The patent changes the transmission parameter from continuous high-frequency reporting to threshold-based reporting. This reduces the number of transmission events while maintaining accuracy for significant changes, thereby improving transmission efficiency without sacrificing data accuracy.
Solution Approach 2:
The system extracts only the essential data points that exceed the change detection threshold from the complete data set. By taking out only the significant changes for reporting, the system maintains data accuracy while reducing transmission load and improving efficiency.
4Quantity of substance
If low frequency data reporting is implemented to reduce storage requirements, then the quantity of data to be stored is reduced, but data analysis accuracy deteriorates
Solution Approach 1:
The patent changes the storage parameter by implementing selective storage based on change detection thresholds. Only data points that exceed the threshold are stored for analysis, reducing storage volume while preserving the accuracy needed for detecting significant changes and restoring network topology.
Solution Approach 2:
The system applies different storage quality standards to different data points. Significant changes (exceeding threshold) are stored with high priority for accurate analysis, while minor changes are excluded from storage, optimizing both storage volume and analysis accuracy.
Data Source
AI summary
In an embodiment, a data collection method includes: collecting, by a data collection device, performance indicator data of a target device based on a collection periodicity; detecting change amplitude of the collected performance indicator data that is in a change detection window, where the change detection window includes multiple collection periodicities; and when it is detected that change amplitude of the performance indicator data that is in the change detection window is greater than or equal to a change detection threshold, sending the performance indicator data that is in the change detection window to a data analysis device.