Optical Path Dirt Detection Using Performance Data Models
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Solution Overview
Problem
Existing methods for identifying optical path dirt in high-bandwidth optical networks, such as those using PAM4 modulation, are inefficient and costly, leading to high maintenance costs and service interruptions due to the inability to accurately detect and locate dirt on optical paths.
Innovation Solution
An optical path dirt identification method and apparatus that uses performance data and a dirt identification model to determine if dirt is present on an optical path's end face, enabling timely detection and prevention of service interruptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional manual inspection methods are used to detect optical path dirt, then device complexity and cost are reduced, but measurement precision and detection accuracy deteriorate
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated detection system that uses performance data analysis and machine learning models to identify dirt conditions, thereby improving detection accuracy while reducing manual intervention complexity
Solution Approach 2:
The patent introduces performance data as an intermediary between the physical dirt condition and the detection system. By monitoring parameters like bit error rates, signal quality, and other performance indicators, the system indirectly detects dirt presence without requiring direct physical inspection
2Productivity
If manual inspection methods are used, then operation and maintenance costs are reduced, but productivity and maintenance efficiency deteriorate
Solution Approach 1:
The patent implements continuous monitoring and early warning capabilities that detect dirt conditions before they cause service interruptions. By performing preliminary detection and alerting operations, the system enables proactive maintenance scheduling, reducing the time needed to locate and address root causes
Solution Approach 2:
The patent establishes a feedback loop where performance data is continuously collected, analyzed by machine learning models, and used to generate alerts or recommendations. This automated feedback mechanism significantly improves maintenance efficiency by providing real-time insights into optical path conditions
3Reliability
If dirt is not detected accurately, then service interruption risk increases, but the cost of implementing advanced detection systems increases
Solution Approach 1:
The patent performs preliminary detection of dirt conditions through continuous performance monitoring, enabling early intervention before service interruptions occur. This approach improves reliability by preventing failures rather than responding to them after they occur
Solution Approach 2:
The patent implements self-diagnostic capabilities where the system automatically monitors its own performance, detects anomalies, and generates maintenance recommendations without requiring external intervention. This self-service approach improves reliability while keeping the detection system manageable
Data Source
AI summary
Embodiments of this application provide an optical path dirt identification method and apparatus. The method includes: obtaining first data, where the first data is performance data that is of a port of an optical path and that is collected in a cycle; and determine, based on the first data and a dirt identification model, whether dirt occurs on the optical path, where the dirt identification model is determined by using historical performance data of the port of the first optical path. It may be determined, by using the performance data of the port of the optical path and the dirt identification model, whether degradation of the optical path is caused by dirt on an end face of the optical path.