Single-phase immersion liquid cooling data center cabinet intelligent liquid sending control method and system

By constructing a thermal adaptation evaluation model and using reinforcement learning algorithms to optimize pump frequency and valve opening, the shortcomings of liquid cooling systems in matching cooling supply and demand were solved, achieving efficient and stable liquid cooling control and reducing thermal risks and energy consumption.

CN121586244BActive Publication Date: 2026-05-08TIANJIN TIER TECHNOLOGY CO LTD
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Patent Information

Application Number
CN202610106744.4
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2026-01-27
Publication Date
2026-05-08
Estimated Expiration
2046-01-27

AI Technical Summary

Technical Problem

Existing liquid cooling systems lack a real-time response mechanism for matching cooling supply and demand, resulting in insufficient heat dissipation or low energy efficiency, which poses safety hazards, especially in thermal risk scenarios.

Method used

By collecting remote sensing data of liquid cooling in the cabinet, performing validity verification, smoothing and standardization, a cold and hot adaptation evaluation model is constructed. Reinforcement learning algorithms are used to optimize pump frequency and valve opening to achieve flow regulation and resource reallocation, and an emergency protection mechanism is set up.

Benefits of technology

It achieves precise matching of liquid cooling supply and demand, improves energy efficiency ratio, enhances the system's adaptability to complex load changes, reduces thermal risks, and ensures stable equipment operation.

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Abstract

The application discloses a single-phase immersion liquid cooling data center cabinet intelligent liquid sending control method and system, and relates to the technical field of cabinet liquid cooling. The single-phase immersion liquid cooling data center cabinet intelligent liquid sending control method and system comprises the following steps: S1, collecting cabinet liquid cooling telemetry data, and performing preprocessing on the cabinet liquid cooling telemetry data; S2, evaluating the cabinet liquid cooling supply-demand matching degree, judging the cooling supply-demand state and generating a flow regulation instruction, triggering cooling resource redistribution when the cooling insufficient state and thermal imbalance condition are met; S3, analyzing the flow regulation instruction, executing pump valve regulation of the liquid sending branch, collecting feedback data after execution for deviation evaluation, and performing compensation regulation when the evaluation result does not meet the expectation; and S4, after a fault event is triggered, evaluating the overall liquid cooling operation risk. The problems that liquid cooling supply cannot be matched in real time due to the severe change of heat load between cabinets, and then high-load cabinet temperature rise exceeds the limit and liquid flow is wasted in the low-load area are solved.
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