Multi-case cascade vibration noise data acquisition clock synchronization method

By constructing a deep learning-based clock drift prediction model and a multimodal communication link, and dynamically adjusting the reference clock signal and communication mode, the clock synchronization problem of a multi-chassis cascaded vibration and noise data acquisition system was solved. This achieved high-precision and anti-interference clock synchronization, improving the stability and analysis accuracy of the data acquisition system.

CN121664341APending Publication Date: 2026-03-13杭州亿恒科技有限公司
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Patent Information

Application Number
CN202511539642.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-27
Publication Date
2026-03-13

AI Technical Summary

Technical Problem

Multi-chassis cascaded vibration and noise data acquisition systems suffer from clock frequency drift due to environmental temperature changes, clock signal phase jitter due to electromagnetic interference, and communication link fragility, all of which affect the accuracy of data analysis and fault diagnosis.

Method used

By constructing a deep learning-based clock drift prediction model, combined with a multimodal communication link and a fuzzy logic controller, the reference clock signal and communication mode are dynamically adjusted to achieve high-precision synchronization between master and slave clocks. A redundant transmission mechanism combining optical fiber and wireless radio frequency is adopted to construct a multi-level clock synchronization verification mechanism.

Benefits of technology

It achieves high-precision clock synchronization in complex industrial environments, enhances the system's anti-interference capability and fault tolerance, and improves the time consistency of data acquisition and the accuracy of analysis.

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Abstract

The invention belongs to the technical field of industrial measurement and control, and discloses a multi-case cascade vibration noise data acquisition clock synchronization method. A clock drift prediction model based on deep learning is constructed by acquiring local clock signals and environment parameters of all cases in real time, and accurate prediction of clock behaviors is achieved. A multi-mode communication strategy combining optical fibers and wireless radio frequency is innovatively adopted, reliable transmission mechanisms which are redundant to each other are formed, and the anti-interference capability is remarkably enhanced. A dynamic compensation mechanism based on the fuzzy logic controller can intelligently adjust compensation parameters according to clock states and environment changes, and accurate correction is achieved. A multi-level verification mechanism including local self-inspection, mutual inspection between cases and global optimization is established, a graph neural network technology is introduced to carry out system-level optimization, and the system has self-learning and self-optimization capabilities. According to the method, the time consistency of the vibration noise data is remarkably improved.
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