A control method and control system for a liquid cooling system in a data center

Abstract

The present invention discloses a control method and control system for a liquid cooling and heat dissipation system in a data center, which relates to the field of liquid cooling and heat dissipation technology. The system realizes real-time status monitoring of server operation through a data acquisition and monitoring module, integrates multiple data such as temperature, flow rate and pressure difference, forms a detailed comprehensive heat dissipation data set, and constructs a database for storage. At the same time, the system's coolant flow rate analysis and control module generates a server heat dissipation coefficient CN through the server operating environment parameter C1 and the server operating parameter C2, and compares and evaluates it with a preset heat dissipation threshold C, and adjusts the coolant flow rate in real time. In this way, when the server load increases, the cooling intensity can be automatically increased, the chip temperature can be reduced, and the server can be kept running stably; when the load decreases, the cooling intensity is reduced, energy consumption is reduced, and the cooling efficiency and energy utilization rate of the system are significantly improved.

Description

Technical Field

[0001] The present invention relates to the field of liquid cooling and heat dissipation technology, and in particular to a control method and control system for a liquid cooling and heat dissipation system in a data center. Background Art

[0002] As data centers continue to expand, data storage and computing demands are rising dramatically, making energy consumption in the information technology sector a critical issue that demands urgent attention. Within the efficient data processing sub-sector of information technology, heat dissipation is crucial for ensuring the proper operation of servers. In recent years, liquid cooling systems have become a key component of data center cooling technology, particularly cold plate liquid cooling systems, which are widely used in large server systems due to their superior heat dissipation efficiency and low noise levels. Cold plate liquid cooling systems, which directly contact the heat source with the coolant, more efficiently remove heat generated by the server, ensuring efficient and stable data center operation.

[0003] In a Chinese invention application with publication number CN118170226A, an immersion liquid cooling temperature control system, method, device, and medium are disclosed. These systems relate to the field of server heat dissipation technology and are used to dissipate heat from server motherboards. To address the problem that traditional solutions cannot precisely control and cool key heating components or local areas, an immersion liquid cooling temperature control system is provided. This system incorporates phased array technology, currently commonly used in antenna applications, into immersion liquid cooling for server motherboards. Phased array technology, which originally interferes with electromagnetic waves, is utilized to interfere with vibration waves. This technology then influences the flow path and flow rate of the coolant based on the vibration waves, enabling flexible and precise control of the flow path and flow rate of the coolant in the server. This system thus meets the demand for targeted cooling of key heating components in centralized server motherboards, achieving better cooling effects and ensuring normal and stable server operation.

[0004] In combination with the existing technology, the above application still has the following deficiencies:

[0005] At present, cold plate liquid cooling systems are widely used in data centers. Although they have high heat dissipation capabilities, they still have obvious control problems in actual operation. For example, when the server load fluctuates, the coolant flow rate is often difficult to adapt to the load changes in time, resulting in poor cooling efficiency. Fixed flow rate control mode is difficult to balance the relationship between temperature control and energy consumption. Especially in an environment with frequent load changes, improper coolant flow rate can easily lead to a decrease in cooling effect or an increase in energy consumption. At the same time, liquid cooling systems that lack adaptive control functions are also difficult to dynamically respond to changes in load during the heat dissipation process. These shortcomings limit the overall performance of the liquid cooling system. Summary of the Invention

[0006] In view of the deficiencies in the prior art, the present invention provides a control method and control system for a liquid cooling and heat dissipation system in a data center, which solves the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: including a data acquisition and monitoring module, a state analysis module, a coolant flow rate analysis and control module, a smooth switching control module and a feedback and optimization module;

[0008] The data acquisition and monitoring module is integrated with the integrated sensor groups installed at the liquid inlets and outlets of several servers and cold plates to collect the comprehensive heat dissipation data set of each server and cold plate in real time, and build a database to store the collected comprehensive heat dissipation data set;

[0009] The state analysis module is used to construct cold plate cooling algorithm formulas and immersion cooling algorithm formulas, input the comprehensive heat dissipation data set respectively, and calculate to obtain the server operating environment parameters C1 and server operating parameters C2;

[0010] The coolant flow rate analysis and control module is used to comprehensively calculate the server operating environment parameter C1 and the server operating parameter C2 to obtain the server heat dissipation coefficient CN, preset the heat dissipation threshold C and the server heat dissipation coefficient CN, perform preliminary comparative evaluation, and generate a coolant flow rate switching control instruction;

[0011] The smooth switching control module is used to send a control signal through the controller to adaptively adjust the start of cold plate cooling after adjusting the coolant flow rate. At the same time, it constructs a smooth control algorithm formula to calculate and output the switching smoothness adjustment parameter Stra to start the smooth cooling mode.

[0012] The feedback and optimization module is used to construct a dynamic feedback formula after the coolant flow rate is switched, calculate the output operation effect feedback parameter Cfee, and perform a secondary comparative evaluation between the preset effect threshold X and the operation effect feedback parameter Cfee. Based on the secondary comparative evaluation results, the adaptive optimization formula is executed to calculate the output adaptive feedback control factor Cada for adaptive adjustment and optimization.

[0013] Preferably, the data acquisition and monitoring module includes a data acquisition unit and a data storage unit;

[0014] The data acquisition unit is integrated with the integrated sensor group installed at the liquid inlet and outlet of several servers and cold plates through the sensor interface to collect comprehensive heat dissipation data sets in real time;

[0015] The integrated sensor set includes server pipe monitoring tools, thermocouple sensors, digital temperature and humidity sensors, conductivity sensors, flow meters, differential pressure sensors, and vibration sensors;

[0016] The comprehensive heat dissipation dataset includes server load L, chip temperature Tc, coolant inlet and outlet temperature difference △Tw, ambient temperature Ten, coolant conductivity Cco, coolant flow rate Ffl, liquid cooling system pressure difference Pdiff, and server vibration frequency Vfreq;

[0017] The data storage unit is used to build a time series database by using InfluxDB technology, and set the write port and the write port, connect the data acquisition unit to the write port, write the comprehensive heat dissipation data set collected in real time into the time series database, and arrange and store it in the order of collection time.

[0018] Preferably, the status analysis module includes an operating environment analysis unit and a server operation analysis unit;

[0019] The operating environment analysis unit is used to construct a cold plate cooling algorithm formula. It extracts a comprehensive heat dissipation data set through the write port of the time series database and inputs it into the cold plate cooling algorithm formula to calculate and output the server operating environment parameter C1.

[0020] The server operating environment parameter C1 is calculated and output by the following cold plate cooling algorithm formula;

[0021] C1=α1·L+β1·(Tc-Ten)+γ1·ΔTw+δ1·Cco+ε1·Pdiff;

[0022] Where (Tc-Ten) represents the temperature difference between the chip temperature and the ambient temperature, α1 represents the server load control coefficient, β1 represents the temperature difference control coefficient between the chip temperature and the ambient temperature, γ1 represents the coolant inlet and outlet temperature difference control coefficient, δ1 represents the coolant conductivity control coefficient, and ε1 represents the liquid cooling system pressure difference control coefficient. The specific values ​​of α1, β1, γ1, δ1, and ε1 are set by the user.

[0023] Preferably, the server operation analysis unit is used to construct an immersion cooling algorithm formula, extract a comprehensive heat dissipation data set through the write port of the time series database and input it into the server operation algorithm formula to calculate and output the server operation parameter C2;

[0024] The server operating parameter C2 is calculated and output by the following immersion cooling algorithm formula;

[0025] C2=α2·(L-Lth)+β2·(Tc-Tmax)+δ2·Ffl+ε2·Vfreq;

[0026] Where Lth is the load threshold, which is set based on server design and historical heat dissipation data. Tmax is the upper limit of the chip temperature. α2 is the control coefficient for the difference between the server load and the load threshold Lth. β2 is the control coefficient for the difference between the chip temperature and the upper limit of the chip temperature. δ2 is the control coefficient for the coolant flow rate. ε2 is the control coefficient for the server vibration frequency. The specific values ​​of α2, β2, δ2, and ε2 are set by the user.

[0027] Preferably, the coolant flow rate analysis and control module includes an adaptive analysis unit and a cooling flow rate control unit;

[0028] The adaptive analysis unit is used to perform a comprehensive calculation based on the obtained server operating environment parameter C1 and server operating parameter C2 to obtain the server heat dissipation coefficient CN, and comprehensively analyze the operating status of the cold plate server at the current time t;

[0029] The server heat dissipation coefficient CN is calculated using the following algorithm formula:

[0030] CN(t)=[(C1·A1)+(C2·A2)]+A;

[0031] Where CN(t) represents the server heat dissipation coefficient CN at the current time t, A1 and A2 represent the preset weight values ​​of the server operating environment parameter C1 and the server operating parameter C2, respectively, and A1 + A2 = 1. The specific values ​​are set by the user.

[0032] The cooling flow rate control unit performs a preliminary comparative evaluation of the preset heat dissipation threshold C and the obtained server heat dissipation coefficient CN based on the standard value of the cold plate server operating power, and generates a coolant flow rate switching control instruction based on the preliminary comparative evaluation results. The specific evaluation contents are as follows;

[0033] When the server heat dissipation coefficient CN is greater than or equal to twice the heat dissipation threshold C, it indicates that the cold plate server is in a load state. At this time, a first coolant flow rate switching control instruction is sent to adjust the cold plate cooling liquid flow rate to 100%;

[0034] When the server heat dissipation coefficient CN ≥ the heat dissipation threshold C, it indicates that the cold plate server is in a load state. At this time, a second coolant flow rate switching control instruction is sent to adjust the cold plate cooling liquid flow rate to 65%;

[0035] When the server heat dissipation coefficient CN is less than the heat dissipation threshold C, it indicates that the cold plate server is in a load state. At this time, a third coolant flow rate switching control instruction is sent to reduce the cold plate cooling coolant flow rate to below 45%.

[0036] Preferably, the smooth switching control module is used to, when receiving the first switching logic and the second switching logic, send a command to the cooling management module to switch the coolant flow rate mode, perform the coolant flow rate switching, construct a smooth control algorithm formula during the switching process, calculate the output smoothness adjustment parameter Stra, and smoothly control the switching process;

[0037] The smoothness adjustment parameter Stra is calculated using the following smoothing control algorithm formula:

[0038]

[0039] Where △t represents the time change, △Pdiff represents the pressure difference change of the liquid cooling system, △Ffl represents the coolant flow rate change, η represents the weight coefficient of the pressure difference change of the liquid cooling system, and λ represents the weight coefficient of the coolant flow rate change.

[0040] Preferably, the feedback and optimization module includes a dynamic feedback unit, a feedback analysis unit and an adaptive adjustment unit;

[0041] The dynamic feedback unit is used to continuously monitor the operating effect of the server after the cooling mode is smoothly switched. By constructing a dynamic feedback formula, it calculates and outputs the operating effect feedback parameter Cfee to feedback the operating effect after cooling;

[0042] The operation effect feedback parameter Cfee is calculated using the following dynamic feedback formula:

[0043]

[0044] Where Ttarget represents the target chip temperature set by the system, Ptarget represents the target pressure of the cooling system, a1, a2, and a3 represent the chip temperature feedback adjustment coefficient, the inlet and outlet water temperature difference feedback adjustment coefficient, and the pressure difference feedback adjustment coefficient, respectively. Their specific values ​​are set and obtained by the user.

[0045] Preferably, the feedback analysis unit obtains the optimal operating conditions and temperature range of the equipment from the supplier of the server or cooling system. Based on this data, combined with the heat dissipation capacity of the system, the preset effect threshold X and the obtained operating effect feedback parameter Cfee are used to perform a secondary comparative evaluation to determine the current cooling effect. The specific evaluation content is as follows;

[0046] When the running effect feedback parameter Cfee> effect threshold X, it means that the current cooling effect is abnormal, and the unit is adaptively adjusted at this time;

[0047] When the running effect feedback parameter Cfee≤effect threshold X, it indicates that the cooling effect is normal. At this time, the current cooling mode is continued to be executed and continuously monitored.

[0048] Preferably, the adaptive adjustment unit is used to calculate and output the adaptive feedback control factor Cada through the constructed adaptive optimization formula when the secondary comparative evaluation shows that the cooling effect is abnormal, so as to dynamically adjust the cooling mode and cooling intensity;

[0049] The adaptive feedback control factor Cada is calculated by the following adaptive optimization formula;

[0050]

[0051] Where A adaptove represents the weight factor, represents the weighted average value of the historical operation effect feedback parameters, Cfee(i) represents the operation effect feedback parameter at the i-th moment, wi represents the weight value of the operation effect feedback parameter at the i-th moment, and n represents the selected historical period.

[0052] A control method for a liquid cooling system in a data center comprises the following steps:

[0053] S1. By integrating with the integrated sensor groups installed at the liquid inlets and outlets of several servers and cold plates, a comprehensive heat dissipation data set of each server and cold plate is collected in real time, and a database is built to store the collected comprehensive heat dissipation data set;

[0054] S2. Construct a cold plate cooling algorithm formula and an immersion cooling algorithm formula, input the comprehensive heat dissipation data set respectively, and calculate to obtain the server operating environment parameter C1 and the server operating parameter C2;

[0055] S3, comprehensively calculating the server operating environment parameter C1 and the server operating parameter C2 to obtain the server heat dissipation coefficient CN, performing a preliminary comparative evaluation of the preset heat dissipation threshold C and the server heat dissipation coefficient CN, and generating a coolant flow rate switching control instruction;

[0056] S4. After the coolant flow rate is adjusted and switched, a control signal is sent through the controller to adaptively adjust the start of the cold plate cooling. At the same time, a smooth control algorithm formula is constructed to calculate and output the switching smoothness adjustment parameter Stra to start the smooth cooling mode;

[0057] S5. Construct a dynamic feedback formula, calculate and output the operation effect feedback parameter Cfee, and preset the effect threshold X and the operation effect feedback parameter Cfee for secondary comparative evaluation. Based on the secondary comparative evaluation result, execute the adaptive optimization formula to calculate the output adaptive feedback control factor Cada for adaptive adjustment optimization.

[0058] The present invention provides a control method and control system for a data center liquid cooling and heat dissipation system. This method has the following beneficial effects:

[0059] (1) The system monitors the server's operation status in real time through a data acquisition and monitoring module, integrating various sensors such as temperature, flow rate, and pressure difference to form a detailed comprehensive heat dissipation data set. Simultaneously, the system's coolant flow rate analysis and control module generates a server heat dissipation coefficient CN based on the server's operating environment parameters C1 and C2, and compares and evaluates this with the preset heat dissipation threshold C, adjusting the coolant flow rate in real time. This allows the system to automatically increase cooling intensity, lower chip temperature, and maintain stable server operation when the server load increases; and reduce cooling intensity and energy consumption when the load decreases, significantly improving the system's cooling efficiency and energy utilization.

[0060] (2) The system's smooth switching control module effectively mitigates the impact of rapid changes in coolant flow rate on the server by dynamically calculating the smoothness adjustment parameter Stra, allowing the coolant flow rate pattern to remain stable during the switching process. This design allows for smooth coolant management during flow rate adjustment, avoiding system instability or hardware loss caused by coolant flow rate and pressure fluctuations. Especially in cold plate liquid cooling systems, smooth coolant flow rate switching ensures the reliability and operating life of the equipment, meeting the data center's demand for a highly stable cooling system.

[0061] (3) The system's feedback and optimization module outputs the operating effect feedback parameter Cfee through a dynamic feedback formula, performs a secondary comparative evaluation with the effect threshold X, automatically determines the cooling effect, and adaptively adjusts the cooling state. If an abnormal cooling effect is detected, the system can calculate the adaptive feedback control factor Cada through the adaptive adjustment unit and perform optimization operations to improve the cooling system's self-regulation ability under load fluctuations and maintain a stable cooling effect. This module enables the cooling system to have intelligent feedback and self-optimization functions, can adapt to complex operating environments, and significantly enhance the reliability and intelligence of the data center liquid cooling system. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 This is a schematic diagram of the control system flow of a data center liquid cooling and heat dissipation system according to the present invention;

[0063] Figure 2 The figure is a schematic diagram of the steps of a control method for a liquid cooling and heat dissipation system of a data center according to the present invention. DETAILED DESCRIPTION

[0064] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0065] Example 1

[0066] See also Figure 1 The present invention provides a control system for a liquid cooling and heat dissipation system in a data center. To achieve the above objectives, the present invention is implemented through the following technical solutions: including a data acquisition and monitoring module, a state analysis module, a coolant flow rate analysis and control module, a smooth switching control module, and a feedback and optimization module;

[0067] The data acquisition and monitoring module is integrated with the integrated sensor groups installed at the liquid inlets and outlets of several servers and cold plates to collect the comprehensive heat dissipation data set of each server and cold plate in real time, and build a database to store the collected comprehensive heat dissipation data set;

[0068] The state analysis module is used to construct cold plate cooling algorithm formulas and immersion cooling algorithm formulas, input the comprehensive heat dissipation data set respectively, and calculate to obtain the server operating environment parameters C1 and server operating parameters C2;

[0069] The coolant flow rate analysis and control module is used to comprehensively calculate the server operating environment parameter C1 and the server operating parameter C2 to obtain the server heat dissipation coefficient CN, preset the heat dissipation threshold C and the server heat dissipation coefficient CN, perform preliminary comparative evaluation, and generate a coolant flow rate switching control instruction;

[0070] The smooth switching control module is used to trigger when it is determined that the coolant flow rate needs to be switched, send a control signal through the controller to switch the coolant flow rate, and at the same time build a smooth control algorithm formula to calculate and output the switching smoothness adjustment parameter Stra to perform smooth coolant flow rate switching;

[0071] The feedback and optimization module is used to construct a dynamic feedback formula after the coolant flow rate is switched, calculate the output operation effect feedback parameter Cfee, and perform a secondary comparative evaluation between the preset effect threshold X and the operation effect feedback parameter Cfee. Based on the secondary comparative evaluation results, the adaptive optimization formula is executed to calculate the output adaptive feedback control factor Cada for adaptive adjustment and optimization.

[0072] In this embodiment, the system uses the data acquisition and monitoring module to obtain real-time heat dissipation data for each server and cold plate through integrated sensors, ensuring the comprehensiveness and accuracy of data collection and providing reliable support for subsequent analysis. The state analysis module constructs cold plate and immersion cooling algorithm formulas, analyzes and generates server operating environment parameters C1 and server operating parameters C2, and then comprehensively calculates server operating environment parameters C1 and server operating parameters C2 to obtain the server heat dissipation coefficient CN. The preset heat dissipation threshold C and server heat dissipation coefficient CN are used for preliminary comparative evaluation to generate the coolant flow rate switching control instruction. When the coolant flow rate mode needs to be switched, the smooth switching control module constructs a smooth control algorithm and calculates the switching smoothness adjustment parameter Stra to achieve smooth flow rate switching, ensuring that temperature fluctuations are minimized and avoiding system instability caused by sudden flow rate changes. Finally, the feedback and optimization module performs dynamic feedback evaluation after switching. By comparing the set effect threshold X with the operating feedback parameter Cfee, it analyzes the cooling effect and adaptively optimizes the dynamic adjustment of the control factor Cada, thereby achieving continuous optimization of the cooling process. Compared with existing technologies, the innovation of this system lies in the intelligent decision-making of coolant flow rate control in an adaptive manner, which greatly improves the system's response speed to server load fluctuations and energy efficiency management level, reduces the energy consumption of the cooling system, and achieves a more energy-saving and stable liquid cooling effect, effectively extending the service life of server equipment and further improving the operating efficiency and reliability of the data center.

[0073] Example 2

[0074] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the data acquisition and monitoring module includes a data acquisition unit and a data storage unit;

[0075] The data acquisition unit is integrated with the integrated sensor group installed at the liquid inlet and outlet of several servers and cold plates through the sensor interface to collect comprehensive heat dissipation data sets in real time;

[0076] The integrated sensor set includes server pipe monitoring tools, thermocouple sensors, digital temperature and humidity sensors, conductivity sensors, flow meters, differential pressure sensors, and vibration sensors;

[0077] The comprehensive heat dissipation dataset includes server load L, chip temperature Tc, coolant inlet and outlet temperature difference △Tw, ambient temperature Ten, coolant conductivity Cco, coolant flow rate Ffl, liquid cooling system pressure difference Pdiff, and server vibration frequency Vfreq;

[0078] The data storage unit is used to build a time series database by using InfluxDB technology, and set the write port and the write port, connect the data acquisition unit to the write port, write the comprehensive heat dissipation data set collected in real time into the time series database, and arrange and store it in the order of collection time.

[0079] In this embodiment, the data acquisition and monitoring module in the system collects and processes key heat dissipation data of the server and cold plate in real time through the cooperation of the data acquisition unit and the data storage unit. Through the integrated sensor group, the system can comprehensively monitor multiple key parameters such as server load, chip temperature, inlet and outlet temperature difference of the coolant, ambient temperature, etc., to form a comprehensive heat dissipation data set. These data are stored in a time series database through InfluxDB technology to ensure that they are recorded and managed in chronological order, making the system more accurate in tracking, analyzing and optimizing the data. This module achieves higher real-time performance and data accuracy, and can capture more detailed heat dissipation parameters. Through this efficient data acquisition and storage mechanism, the system can accurately identify problems of insufficient or excessive heat dissipation, optimize the performance of the cooling system, and significantly improve the energy efficiency of the data center and the stability of the system.

[0080] Example 3

[0081] This embodiment is explained in Example 2, please refer to Figure 1 ,Specifically: the state analysis module includes an operating environment analysis unit and a server ,operation analysis unit;

[0082] The operating environment analysis unit is used to construct a cold plate cooling algorithm formula. It extracts a comprehensive heat dissipation data set through the write port of the time series database and inputs it into the cold plate cooling algorithm formula to calculate and output the server operating environment parameter C1.

[0083] The server operating environment parameter C1 is calculated and output by the following cold plate cooling algorithm formula;

[0084] C1=α1·L+β1·(Tc-Ten)+γ1·ΔTw+δ1·Cco+ε1·Pdiff;

[0085] Where (Tc-Ten) represents the temperature difference between the chip temperature and the ambient temperature, α1 represents the server load control coefficient, β1 represents the temperature difference control coefficient between the chip temperature and the ambient temperature, γ1 represents the coolant inlet and outlet temperature difference control coefficient, δ1 represents the coolant conductivity control coefficient, and ε1 represents the liquid cooling system pressure difference control coefficient. The specific values ​​of α1, β1, γ1, δ1, and ε1 are set by the user.

[0086] The server operation analysis unit is used to construct an immersion cooling algorithm formula, extract a comprehensive heat dissipation data set through the write port of the time series database, input it into the server operation algorithm formula, and calculate and output the server operation parameter C2;

[0087] The server operating parameter C2 is calculated and output by the following immersion cooling algorithm formula;

[0088] C2=α2·(L-Lth)+β2·(Tc-Tmax)+δ2·Ffl+ε2·Vfreq;

[0089] Where Lth is the load threshold, which is set based on server design and historical heat dissipation data. Tmax is the upper limit of the chip temperature. α2 is the control coefficient for the difference between the server load and the load threshold Lth. β2 is the control coefficient for the difference between the chip temperature and the upper limit of the chip temperature. δ2 is the control coefficient for the coolant flow rate. ε2 is the control coefficient for the server vibration frequency. The specific values ​​of α2, β2, δ2, and ε2 are set by the user.

[0090] In this embodiment, the system's operating environment analysis unit, based on a cold plate cooling algorithm, extracts comprehensive heat dissipation data from a time-series database in real time to calculate server operating environment parameter C1, accurately reflecting the server's operating environment and enabling the cold plate cooling mode to adapt to dynamically changing load demands. The server operation analysis unit, based on an immersion cooling algorithm, calculates server operating parameter C2, accurately determining load levels, temperature differences, and coolant flow rates to accommodate the server's heat dissipation needs under high loads. By separately calculating server operating environment parameter C1 and server operating parameter C2, the system can flexibly select the appropriate cooling mode and intelligently switch coolant flow rates in the event of sudden load changes or temperature violations, ensuring a balance between cooling efficiency and energy consumption.

[0091] Example 4

[0092] This embodiment is explained in Example 3, please refer to Figure 1 ,Specifically: the coolant flow rate analysis and control module includes an adaptive ,analysis unit and a cooling flow rate control unit;

[0093] The adaptive analysis unit is used to perform a comprehensive calculation based on the obtained server operating environment parameter C1 and server operating parameter C2 to obtain the server heat dissipation coefficient CN, and comprehensively analyze the operating status of the cold plate server at the current time t;

[0094] The server heat dissipation coefficient CN is calculated using the following algorithm formula:

[0095] CN(t)=[(C1·A1)+(C2·A2)]+A;

[0096] Where CN(t) represents the server heat dissipation coefficient CN at the current time t, A1 and A2 represent the preset weight values ​​of the server operating environment parameter C1 and the server operating parameter C2, respectively, and A1 + A2 = 1. The specific values ​​are set by the user.

[0097] The cooling flow rate control unit performs a preliminary comparative evaluation of the preset heat dissipation threshold C and the obtained server heat dissipation coefficient CN based on the standard value of the cold plate server operating power, and generates a coolant flow rate switching control instruction based on the preliminary comparative evaluation results. The specific evaluation contents are as follows;

[0098] When the server heat dissipation coefficient CN is greater than or equal to twice the heat dissipation threshold C, it indicates that the cold plate server is in a load state. At this time, a first coolant flow rate switching control instruction is sent to adjust the cold plate cooling liquid flow rate to 100%;

[0099] When the server heat dissipation coefficient CN ≥ the heat dissipation threshold C, it indicates that the cold plate server is in a load state. At this time, a second coolant flow rate switching control instruction is sent to adjust the cold plate cooling liquid flow rate to 65%;

[0100] When the server heat dissipation coefficient CN is less than the heat dissipation threshold C, it indicates that the cold plate server is in a load state. At this time, a third coolant flow rate switching control instruction is sent to reduce the cold plate cooling coolant flow rate to below 45%.

[0101] In this embodiment, the system calculates the server heat dissipation coefficient CN based on the server operating environment parameters C1 and server operating parameters C2 through an adaptive analysis unit, thereby comprehensively evaluating the current operating status of the cold plate server. The cooling flow rate control unit, based on the server heat dissipation coefficient CN and a preset heat dissipation threshold C, performs three-level flow rate switching control: automatically increasing the coolant flow rate to 100% under high load conditions to ensure sufficient heat dissipation capacity; adjusting the flow rate to 65% under medium load conditions to balance energy consumption and heat dissipation requirements; and reducing the flow rate to below 45% under low load conditions to achieve higher energy utilization. Compared to traditional constant flow rate cooling solutions, this module introduces an adaptive control mechanism that enables the cooling system to flexibly adjust the flow rate as the server load changes, thereby achieving precise cooling and minimal energy consumption even in load fluctuations.

[0102] Example 5

[0103] This embodiment is explained in Example 4. Please refer to Figure 1Specifically, the smooth switching control module is used to, upon receiving the first switching logic and the second switching logic, cause the control system to send a coolant flow rate mode switching instruction to the cooling management module to switch the coolant flow rate. During the switching process, a smooth control algorithm formula is constructed to calculate the output smoothness adjustment parameter Stra to smoothly control the switching process.

[0104] The smoothness adjustment parameter Stra is calculated using the following smoothing control algorithm formula:

[0105]

[0106] Where △t represents the time change, △Pdiff represents the pressure difference change of the liquid cooling system, △Ffl represents the coolant flow rate change, η represents the weight coefficient of the pressure difference change of the liquid cooling system, and λ represents the weight coefficient of the coolant flow rate change.

[0107] In this embodiment, the system's smooth switching control module ensures smooth coolant flow rate conversion by intelligently controlling the coolant flow rate switching process. Upon receiving the first or second switching logic, the module immediately sends a switching instruction to the cooling management module and calculates the smoothness adjustment parameter Stra based on a constructed smooth control algorithm formula. This formula combines the time change Δt, the liquid cooling system pressure difference change ΔPdiff, the coolant flow rate change ΔFfl, and their corresponding weighting coefficients to ensure a gradual transition in the system's pressure and flow rate adjustments during the switching process, thereby avoiding sudden fluctuations in temperature and pressure. The smooth switching control module significantly reduces system instability during the coolant flow rate switching process, preventing hardware failures or performance degradation caused by sudden pressure changes or abnormal flow rates. Through smooth control, the switching process is safer, heat dissipation efficiency is maximized, and system energy efficiency and equipment lifespan are significantly improved. This improvement enables the system to seamlessly switch coolant flow rate modes under complex load conditions, ensuring the continued efficient operation of the data center while reducing energy consumption and maintenance costs.

[0108] Example 6

[0109] This embodiment is explained in Example 5, please refer to Figure 1 ,Specifically: the feedback and optimization module includes a dynamic feedback unit, a feedback ,analysis unit, and an adaptive adjustment unit;

[0110] The dynamic feedback unit is used to continuously monitor the operating effect of the server after the cooling mode is smoothly switched. By constructing a dynamic feedback formula, it calculates and outputs the operating effect feedback parameter Cfee to feedback the operating effect after cooling;

[0111] The operation effect feedback parameter Cfee is calculated using the following dynamic feedback formula:

[0112]

[0113] Where Ttarget represents the target chip temperature set by the system, Ptarget represents the target pressure of the cooling system, a1, a2, and a3 represent the chip temperature feedback adjustment coefficient, the inlet and outlet water temperature difference feedback adjustment coefficient, and the pressure difference feedback adjustment coefficient, respectively. Their specific values ​​are set and obtained by the user.

[0114] The feedback analysis unit typically obtains the optimal operating conditions and temperature ranges for the equipment from the server or cooling system supplier. Based on this data, combined with the system's heat dissipation capabilities, the preset effect threshold X and the obtained operating effect feedback parameter Cfee, a secondary comparative evaluation is performed to determine the current cooling effect. The specific evaluation contents are as follows:

[0115] When the running effect feedback parameter Cfee> effect threshold X, it means that the current cooling effect is abnormal, and the unit is adaptively adjusted at this time;

[0116] When the running effect feedback parameter Cfee≤effect threshold X, it indicates that the cooling effect is normal. At this time, the current cooling mode is continued to be executed and continuously monitored.

[0117] The adaptive adjustment unit is used to calculate and output the adaptive feedback control factor Cada through the constructed adaptive optimization formula when the secondary comparative evaluation shows that the cooling effect is abnormal, and dynamically adjust the cooling mode and cooling intensity;

[0118] The adaptive feedback control factor Cada is calculated by the following adaptive optimization formula;

[0119]

[0120] Where A adaptove represents the weight factor, represents the weighted average value of the historical operation effect feedback parameters, Cfee(i) represents the operation effect feedback parameter at the i-th moment, wi represents the weight value of the operation effect feedback parameter at the i-th moment, and n represents the selected historical period.

[0121] In this embodiment, the system's feedback and optimization module, through a dynamic feedback unit, a feedback analysis unit, and an adaptive adjustment unit, enables real-time monitoring, performance evaluation, and automatic optimization of the cooling system. First, after a smooth cooling mode switch, the dynamic feedback unit continuously monitors the server's operating performance and uses a dynamic feedback formula to calculate the operating performance feedback parameter Cfee to evaluate the system's cooling performance. Next, the feedback analysis unit compares the feedback result with the system's set target chip temperature Ttarget, target pressure Ptarget, and a preset performance threshold X to determine whether the current cooling is operating normally. If Cfee exceeds the performance threshold X, the system initiates adaptive optimization through the adaptive adjustment unit, constructing an adaptive feedback control factor Cada. Based on a combination of historical and current data, the system dynamically adjusts the cooling mode and intensity to ensure continuous optimization of the cooling effect. The introduction of this module significantly enhances the cooling system's intelligence and adaptability. Compared to the passive adjustment of traditional cooling systems, the feedback and optimization module actively monitors the system's operating status and promptly performs optimization adjustments when anomalies are detected, ensuring optimal cooling performance at all times. This adaptive optimization mechanism, based on a combination of historical data and real-time feedback, not only effectively improves cooling efficiency but also reduces energy waste and hardware loss caused by overcooling or insufficient heat dissipation.

[0122] Example 7

[0123] See also Figure 1 and Figure 2 A control method for a liquid cooling system in a data center includes the following steps:

[0124] S1. By integrating with the integrated sensor groups installed at the liquid inlets and outlets of several servers and cold plates, a comprehensive heat dissipation data set of each server and cold plate is collected in real time, and a database is built to store the collected comprehensive heat dissipation data set;

[0125] S2. Construct a cold plate cooling algorithm formula and an immersion cooling algorithm formula, input the comprehensive heat dissipation data set respectively, and calculate to obtain the server operating environment parameter C1 and the server operating parameter C2;

[0126] S3, comprehensively calculating the server operating environment parameter C1 and the server operating parameter C2 to obtain the server heat dissipation coefficient CN, performing a preliminary comparative evaluation of the preset heat dissipation threshold C and the server heat dissipation coefficient CN, and generating a coolant flow rate switching control instruction;

[0127] S4. After the coolant flow rate is adjusted and switched, a control signal is sent through the controller to adaptively adjust the start of the cold plate cooling. At the same time, a smooth control algorithm formula is constructed to calculate and output the switching smoothness adjustment parameter Stra to start the smooth cooling mode;

[0128] S5. Construct a dynamic feedback formula, calculate and output the operation effect feedback parameter Cfee, and preset the effect threshold X and the operation effect feedback parameter Cfee for secondary comparative evaluation. Based on the secondary comparative evaluation result, execute the adaptive optimization formula to calculate the output adaptive feedback control factor Cada for adaptive adjustment optimization.

[0129] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A control system for a data center liquid cooling system, characterized by: It includes data acquisition and monitoring module, state analysis module, coolant flow rate analysis and control module, smooth switching control module and feedback and optimization module; The data acquisition and monitoring module is integrated with the integrated sensor group installed at the liquid inlet and outlet of several servers and cold plates to collect the comprehensive heat dissipation data set of each server and cold plate in real time, and build a database to store the collected comprehensive heat dissipation data set; The state analysis module is used to construct a cold plate cooling algorithm formula and an immersion cooling algorithm formula, input the comprehensive heat dissipation data set respectively, and calculate to obtain the server operating environment parameter C1 and the server operating parameter C2; The state analysis module includes an operating environment analysis unit and a server operation analysis unit; The operating environment analysis unit is used to construct a cold plate cooling algorithm formula, extract a comprehensive heat dissipation data set through the write port of the time series database and input it into the cold plate cooling algorithm formula to calculate and output the server operating environment parameter C1; The server operating environment parameter C1 is calculated and output by the following cold plate cooling algorithm formula; C1=α1·L+β1·(Tc-Ten)+γ1·ΔTw+δ1·Cco+ε1·Pdiff; Where (Tc-Ten) represents the temperature difference between the chip temperature and the ambient temperature, α1 represents the server load control coefficient, β1 represents the temperature difference control coefficient between the chip temperature and the ambient temperature, γ1 represents the coolant inlet and outlet temperature difference control coefficient, δ1 represents the coolant conductivity control coefficient, and ε1 represents the liquid cooling system pressure difference control coefficient. The specific values ​​of α1, β1, γ1, δ1, and ε1 are set by the user. The server operation analysis unit is used to construct an immersion cooling algorithm formula, extract a comprehensive heat dissipation data set through the write port of the time series database, input it into the server operation algorithm formula, and calculate and output the server operation parameter C2; The server operating parameter C2 is calculated and output by the following immersion cooling algorithm formula; C2=α2·(L-Lth)+β2·(Tc-Tmax)+δ2·Ffl+ε2·Vfreq; Where Lth is the load threshold, which is set based on server design and historical heat dissipation data. Tmax is the upper limit of the chip temperature. α2 is the control coefficient for the difference between the server load and the load threshold Lth. β2 is the control coefficient for the difference between the chip temperature and the upper limit of the chip temperature. δ2 is the control coefficient for the coolant flow rate. ε2 is the control coefficient for the server vibration frequency. The specific values ​​of α2, β2, δ2, and ε2 are set by the user. The coolant flow rate analysis and control module is used to comprehensively calculate the server operating environment parameter C1 and the server operating parameter C2 to obtain the server heat dissipation coefficient CN, preset the heat dissipation threshold C and the server heat dissipation coefficient CN, perform preliminary comparative evaluation, and generate a coolant flow rate switching control instruction; The coolant flow rate analysis and control module includes an adaptive analysis unit and a cooling flow rate control unit; The adaptive analysis unit is used to perform a comprehensive calculation based on the obtained server operating environment parameter C1 and server operating parameter C2 to obtain the server heat dissipation coefficient CN, and comprehensively analyze the operating status of the cold plate server at the current time t; The server heat dissipation coefficient CN is calculated by the following algorithm formula: CN(t)=[(C1·A1)+(C2·A2)]+A; Where CN(t) represents the server heat dissipation coefficient CN at the current time t, A1 and A2 represent the preset weight values ​​of the server operating environment parameter C1 and the server operating parameter C2, respectively, and A1 + A2 = 1. The specific values ​​are set by the user. The smooth switching control module is used to send a control signal through the controller after adjusting the coolant flow rate to adaptively adjust the start of the cold plate cooling, and at the same time construct a smooth control algorithm formula to calculate and output the switching smoothness adjustment parameter Stra to start the smooth cooling mode; The feedback and optimization module is used to construct a dynamic feedback formula after the coolant flow rate is switched, calculate and output the operating effect feedback parameter Cfee, and preset the effect threshold X and the operating effect feedback parameter Cfee for secondary comparative evaluation. Based on the secondary comparative evaluation results, the adaptive optimization formula is executed to calculate the output adaptive feedback control factor Cada for adaptive adjustment optimization.

2. The control system of a data center liquid cooling and heat dissipation system according to claim 1, characterized in that: The data acquisition and monitoring module includes a data acquisition unit and a data storage unit; The data acquisition unit is integratedly connected to the integrated sensor group installed at the liquid inlet and outlet of several servers and cold plates through a sensor interface to collect a comprehensive heat dissipation data set in real time; The integrated sensor set includes a server pipe monitoring tool, a thermocouple sensor, a digital temperature and humidity sensor, a conductivity sensor, a flow meter, a differential pressure sensor, and a vibration sensor; The comprehensive heat dissipation data set includes server load L, chip temperature Tc, coolant inlet and outlet temperature difference ΔTw, ambient temperature Ten, coolant conductivity Cco, coolant flow rate Ffl, liquid cooling system pressure difference Pdiff and server vibration frequency Vfreq; The data storage unit is used to build a time series database by using InfluxDB technology, and set a write port and a write port, connect the data acquisition unit to the write port, write the comprehensive heat dissipation data set collected in real time into the time series database, and arrange and store it in the order of collection time.

3. The control system of a data center liquid cooling and heat dissipation system according to claim 1, characterized in that: The cooling flow rate control unit performs a preliminary comparative evaluation of a preset heat dissipation threshold C and the obtained server heat dissipation coefficient CN based on the standard value of the cold plate server operating power, and generates a coolant flow rate switching control instruction based on the preliminary comparative evaluation result. The specific evaluation content is as follows; When the server heat dissipation coefficient CN is greater than or equal to twice the heat dissipation threshold C, it indicates that the cold plate server is in a load state. At this time, a first coolant flow rate switching control instruction is sent to adjust the cold plate cooling liquid flow rate to 100%; When the server heat dissipation coefficient CN ≥ the heat dissipation threshold C, it indicates that the cold plate server is in a load state. At this time, a second coolant flow rate switching control instruction is sent to adjust the cold plate cooling liquid flow rate to 65%; When the server heat dissipation coefficient CN is less than the heat dissipation threshold C, it indicates that the cold plate server is in a load state. At this time, a third coolant flow rate switching control instruction is sent to reduce the cold plate cooling coolant flow rate to below 45%.

4. The control system of a data center liquid cooling and heat dissipation system according to claim 1, characterized in that: The smooth switching control module is used to, after adjusting and switching the coolant flow rate, send a coolant flow rate switching control instruction to the coolant management module to switch the coolant flow rate. During the switching process, a smooth control algorithm formula is constructed to calculate and output a smoothness adjustment parameter Stra to smoothly control the switching process. The smoothness adjustment parameter Stra is obtained by calculating the following smoothing control algorithm formula: Where △t represents the time change, △Pdiff represents the pressure difference change of the liquid cooling system, △Ffl represents the coolant flow rate change, η represents the weight coefficient of the pressure difference change of the liquid cooling system, and λ represents the weight coefficient of the coolant flow rate change.

5. The control system of a data center liquid cooling and heat dissipation system according to claim 4, characterized in that: The feedback and optimization module includes a dynamic feedback unit, a feedback analysis unit and an adaptive adjustment unit; The dynamic feedback unit is used to continuously monitor the operating effect of the server after the cooling mode is smoothly switched, and calculate and output the operating effect feedback parameter Cfee by constructing a dynamic feedback formula to feedback the operating effect after cooling; The operation effect feedback parameter Cfee is calculated and obtained by the following dynamic feedback formula: Where Ttarget represents the target chip temperature set by the system, Ptarget represents the target pressure of the cooling system, a1, a2, and a3 represent the chip temperature feedback adjustment coefficient, the inlet and outlet water temperature difference feedback adjustment coefficient, and the pressure difference feedback adjustment coefficient, respectively. Their specific values ​​are set and obtained by the user.

6. The control system of a data center liquid cooling and heat dissipation system according to claim 5, characterized in that: The feedback analysis unit performs a secondary comparative evaluation by comparing the preset effect threshold X with the obtained operation effect feedback parameter Cfee to determine the current cooling effect. The specific evaluation content is as follows: When the running effect feedback parameter Cfee> effect threshold X, it means that the current cooling effect is abnormal, and the unit is adaptively adjusted at this time; When the running effect feedback parameter Cfee≤effect threshold X, it indicates that the cooling effect is normal. At this time, the current cooling mode is continued to be executed and continuously monitored.

7. The control system of a data center liquid cooling and heat dissipation system according to claim 6, characterized in that: The adaptive adjustment unit is used to calculate and output the adaptive feedback control factor Cada through the constructed adaptive optimization formula when the cooling effect of the secondary comparison evaluation is abnormal, and dynamically adjust the coolant heat dissipation control; The adaptive feedback control factor Cada is obtained by calculating the following adaptive optimization formula: Where A adaptove represents the weight factor, represents the weighted average value of the historical operation effect feedback parameters, Cfee(i) represents the operation effect feedback parameter at the i-th moment, wi represents the weight value of the operation effect feedback parameter at the i-th moment, and n represents the selected historical period.

8. A control method for a data center liquid cooling and heat dissipation system, applied to a control system of a data center liquid cooling and heat dissipation system according to any one of claims 1 to 7, characterized in that: The following steps are involved: S1. By integrating with the integrated sensor groups installed at the liquid inlets and outlets of several servers and cold plates, a comprehensive heat dissipation data set of each server and cold plate is collected in real time, and a database is built to store the collected comprehensive heat dissipation data set; S2. Construct a cold plate cooling algorithm formula and an immersion cooling algorithm formula, input the comprehensive heat dissipation data set respectively, and calculate to obtain the server operating environment parameter C1 and the server operating parameter C2; S3, comprehensively calculating the server operating environment parameter C1 and the server operating parameter C2 to obtain the server heat dissipation coefficient CN, performing a preliminary comparative evaluation of the preset heat dissipation threshold C and the server heat dissipation coefficient CN, and generating a coolant flow rate switching control instruction; S4. After the coolant flow rate is adjusted and switched, a control signal is sent through the controller to adaptively adjust the start of the cold plate cooling. At the same time, a smooth control algorithm formula is constructed to calculate and output the switching smoothness adjustment parameter Stra to start the smooth cooling mode; S5. Construct a dynamic feedback formula, calculate and output the operation effect feedback parameter Cfee, and preset the effect threshold X and the operation effect feedback parameter Cfee for secondary comparative evaluation. Based on the secondary comparative evaluation result, execute the adaptive optimization formula to calculate the output adaptive feedback control factor Cada for adaptive adjustment optimization.

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