Control methods and electronic equipment for liquid cooling systems

By intelligent monitoring and active control of the liquid cooling system, and by utilizing linear predictive coding and a health benchmark library, the problem of failing to identify early performance degradation in existing technologies has been solved, thus achieving efficient and reliable operation of the liquid cooling system.

CN121578867BActive Publication Date: 2026-05-26INSPUR SUZHOU INTELLIGENT TECH CO LTD
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Patent Information

Application Number
CN202610102917.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-01-26
Publication Date
2026-05-26
Estimated Expiration
2046-01-26

AI Technical Summary

Technical Problem

Existing liquid cooling systems employ a passive control strategy with a fixed threshold, which fails to identify early performance degradation, resulting in control lag, low energy efficiency, and reliability risks.

Method used

By monitoring the performance indicators of electronic equipment, determining the load level information, preprocessing acoustic and vibration data, extracting sub-linear prediction coefficients using linear predictive coding, matching health benchmark database data based on load level information, calculating residual energy and coefficient distance, and generating early warning information to achieve intelligent monitoring and proactive control.

Benefits of technology

It enables intelligent monitoring and active control of the liquid cooling system, significantly improving operational reliability and energy efficiency, and can promptly identify potential faults and execute corresponding control operations.

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Abstract

This application provides a control method and electronic device for a liquid cooling system, which can be applied to the field of heat dissipation technology. The control method includes: determining the load level information of the electronic device within a target time period based on performance index data; preprocessing the target data to obtain multiple sub-target data frames; performing linear prediction encoding on each of the multiple sub-target data frames to obtain multiple sub-linear prediction coefficients; matching benchmark data from a preset health benchmark library based on the load level information; comparing the multiple sub-target data frames and multiple sub-linear prediction coefficients with the matched benchmark data to determine the residual energy and coefficient distance corresponding to the target data; determining health indicators based on the residual energy and coefficient distance corresponding to the target data; generating early warning information based on the health indicators and a preset health threshold; and using the early warning information to prompt control operations corresponding to the early warning information on the liquid cooling system.
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