Method and system for monitoring connection reliability of optical fiber connector based on AI

By using AI technology to collect and analyze multimodal data from fiber optic connectors, the timeliness and accuracy issues of existing monitoring methods have been resolved, enabling intelligent identification and real-time early warning of the status of fiber optic connectors.

CN121121631APending Publication Date: 2025-12-12CHINA ELECTRONICS RELIABILITY AND ENVIRONMENTAL TESTING INSTITUTE ((THE FIFTH INSTITUTE OF ELECTRONICS MINISTRY OF INDUSTRY AND INFORMATION TECHNOLOGY) (CHINA SAIBAO LABORATORY)
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Patent Information

Application Number
CN202511219217.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-08-28
Publication Date
2025-12-12

AI Technical Summary

Technical Problem

The current connection status monitoring of fiber optic connectors relies on manual inspection and physical quantity alarms based on threshold rules, which has problems such as poor timeliness, high cost, high false alarm and false alarm rates, and inability to identify parameter change trends.

Method used

An AI-based approach is adopted to collect multimodal monitoring data of fiber optic connectors, including time-series parameters of optical power, insertion loss, and return loss, as well as connector end-face images. Spatial features are extracted using a convolutional neural network, and temporal trend features are mined using a long short-term memory network. Finally, feature fusion and discrimination are performed through an attention mechanism and a residual classification network to generate connection reliability judgment results.

Benefits of technology

It enables intelligent identification and real-time sensing of the status of fiber optic connectors, improving the accuracy and robustness of monitoring. It can identify chronic degradation and periodic jitter in advance and generate traceable monitoring alarm information.

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Abstract

The invention relates to the technical field of optical fiber connector connection monitoring, in particular to an AI-based optical fiber connector connection reliability monitoring method and system, and the method comprises the steps: constructing a multi-mode monitoring data source through synchronously collecting time sequence parameters including optical power, insertion loss, return loss and other physical quantities and a connector end face image, the image is subjected to feature mapping through the convolutional neural network, spatial features of pollution, scratches and alignment deviation are automatically extracted, manual inspection is replaced, and the recognition efficiency and objectivity are improved. Time sequence parameters are input into the modeling performance evolution trend of the long-short-term memory network, and weak dynamic characteristics such as chronic degradation are effectively captured. And through fusing image space features and parameter trend features, strengthening key factor characterization, and outputting a reliability determination result of the connector through a residual error classification network, accurate classification of normal, to-be-maintained and fault states is realized, traceability alarm information can be generated, and intelligent monitoring and early warning of the operation state of the connector are realized.
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