An AI-based 5G mainboard fault self-diagnosis method

By constructing a three-dimensional dynamic baseline library and an adaptive threshold generation mechanism, combined with linear correlation coefficients, the problem of accurately identifying early faults in the high-speed transmission interface of 5G motherboards was solved, achieving efficient fault diagnosis and early warning.

CN122152579APending Publication Date: 2026-06-05SHENZHEN HONGXIANGYUAN TECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
SHENZHEN HONGXIANGYUAN TECH CO LTD
Filing Date
2026-03-09
Publication Date
2026-06-05

AI Technical Summary

Technical Problem

In existing technologies, early faults of high-speed transmission interfaces such as PCIe 5.0, MIPI, and SerDes on 5G motherboards are difficult to accurately identify through AI self-diagnostic models. There are problems such as missed diagnoses due to high thresholds and false alarms due to low thresholds. Furthermore, differences in baseline parameters between different production batches and operating conditions make generalization judgment difficult.

Method used

By extracting standard samples from 5G motherboards of different production batches, a full-condition gradient test environment was built, effective eye diagram data was collected, a three-dimensional dynamic baseline library was constructed, multi-dimensional fusion feature sets were integrated, an adaptive threshold generation mechanism was designed, and suspected fault interfaces were judged by combining linear correlation coefficients, and risk quantification analysis was performed.

Benefits of technology

It reduces false alarm and false negative rates in different scenarios, accurately identifies latent faults, improves the practicality of fault diagnosis and operation and maintenance efficiency, and provides differentiated early warning mechanisms and fault type determination.

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Abstract

The application relates to the field of next-generation information network industry, and specifically discloses a 5G mainboard fault self-diagnosis method based on AI, which comprises the following steps: selecting standard samples of multiple batches of 5G mainboards, building a full-working-condition gradient test environment, collecting effective eye pattern data of different high-speed serial interfaces, and performing statistical calculation to obtain a three-dimensional dynamic baseline library; collecting real-time eye pattern data of a mainboard to be diagnosed, forming a multi-dimensional fusion feature set through steady-state deviation analysis and dynamic deterioration feature extraction; generating an adaptive threshold set in combination with real-time working conditions, adopting double logic judgment of static feature over-standard and dynamic deterioration constraint to determine suspected fault interfaces; utilizing a Pearson linear correlation coefficient to identify fault deterioration interfaces, quantifying the risk of the fault deterioration interfaces, constructing a differentiated early warning mechanism and fault type judgment according to fault risk values, and realizing early identification, positioning and hierarchical disposal of latent faults of high-speed serial interfaces of the 5G mainboard.
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