Data center liquid cooling medium performance intelligent optimization method and system

Abstract

The invention discloses a data center liquid cooling medium performance intelligent optimization method and system, and the method comprises the steps: presetting a first medium judgment standard, and carrying out the first heavy regulation and control of a current to-be-detected liquid cooling medium if the current to-be-detected liquid cooling medium is a first medium; and obtaining the cooling coefficient of the current to-be-detected liquid cooling medium after the first heavy regulation and control, comparing the cooling coefficient with a first coefficient threshold value, and analyzing the addition amount of the additive according to a comparison result. Through an intelligent means, rapid and accurate optimization of the performance of the liquid cooling medium of the data center is realized. Compared with a traditional manual test and experience judgment mode, the optimization efficiency is improved, and the accuracy and reliability of an optimization result are remarkably enhanced. Besides, through the preset first medium judgment standard and the first severe regulation and control mechanism, personalized optimization processing can be performed on the liquid cooling media with different performances, so that the heat dissipation efficiency of the data center is improved to the maximum extent, and the energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of liquid cooling performance, and particularly to an intelligent optimization method and system for the performance of liquid cooling media in a data center. Background Art

[0002] With the continuous expansion of the scale of data centers and the continuous increase in data processing volume, the energy consumption problem of data centers has become increasingly prominent. The traditional air-cooled heat dissipation method has been difficult to meet the requirements of high efficiency and energy conservation, and liquid cooling technology has emerged as the times require. Liquid cooling technology takes away the heat generated in the data center through the circulating flow of liquid media, and has the advantages of high heat dissipation efficiency, low noise, and small energy consumption. However, the performance of the liquid cooling medium directly affects the heat dissipation effect of the liquid cooling technology. Different liquid cooling media have different physical properties, such as viscosity, density, specific heat capacity, etc. These properties determine key indicators such as the flow rate and cooling capacity of the liquid cooling medium. Therefore, how to optimize the performance of the liquid cooling medium has become a key issue in improving the heat dissipation efficiency and reducing energy consumption of data centers.

[0003] However, at present, the optimization of the performance of liquid cooling media is mainly carried out through manual experiments and empirical judgments. This method is not only inefficient, but also difficult to ensure the accuracy and reliability of the optimization results. Summary of the Invention

[0004] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract, and the title. However, such simplifications or omissions shall not be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides an intelligent optimization method and system for the performance of liquid cooling media in a data center, which can solve the problems mentioned in the background art.

[0007] To solve the above technical problems, the present invention provides the following technical solutions:

[0008] In the first aspect, the present invention provides an intelligent optimization method for the performance of liquid cooling media in a data center, including:

[0009] Presetting a first medium judgment criterion, where the first medium judgment criterion includes judging the medium through a first coefficient threshold;

[0010] The first medium judgment criterion is used to judge whether the liquid cooling medium to be tested is a first medium or a second medium;

[0011] If the current liquid cooling medium to be measured is the first medium, perform first heavy regulation on the current liquid cooling medium to be measured;

[0012] The first heavy regulation is used to add an additive that meets the requirements of adaptability to the current liquid cooling medium to be measured;

[0013] Obtain the cooling coefficient of the current liquid cooling medium to be measured after the first heavy regulation, compare it with the first coefficient threshold, and analyze the addition amount of the additive according to the comparison result.

[0014] As a preferred solution of the intelligent optimization method for the performance of the liquid cooling medium in the data center according to the present invention, wherein: the preset first medium judgment criteria include:

[0015] Obtain the first parameter of all the liquid cooling media to be measured, and calculate the first cooling coefficient according to the first parameter;

[0016] Perform first classification according to the first cooling coefficient;

[0017] The first classification is used to divide the liquid cooling media to be measured into a first medium and a second medium, the first medium is an inefficient medium, and the second medium is an efficient medium.

[0018] As a preferred solution of the intelligent optimization method for the performance of the liquid cooling medium in the data center according to the present invention, wherein: the performing first classification according to the first cooling coefficient includes:

[0019] Set a first coefficient threshold. If the medium coefficient of the current liquid cooling medium to be measured is not less than the first coefficient threshold, the current liquid cooling medium to be measured is the second medium;

[0020] If the medium coefficient of the current liquid cooling medium to be measured is less than the first coefficient threshold, the current liquid cooling medium to be measured is the first medium.

[0021] As a preferred solution of the intelligent optimization method for the performance of the liquid cooling medium in the data center according to the present invention, wherein: the obtaining the first parameter of all the liquid cooling media to be measured and calculating the first cooling coefficient according to the first parameter includes:

[0022] The first parameter at least includes the viscosity and density of all the liquid cooling media to be measured;

[0023] Analyze the flow rate score of all the liquid cooling media to be measured by establishing a first model;

[0024] Collect the specific heat capacity of all the liquid cooling media to be measured by accessing the standard data manual database;

[0025] According to the flow rate scores and specific heat capacities of different liquid cooling media, obtain the first cooling coefficient of all the liquid cooling media to be measured through weighted formula comprehensive calculation.

[0026] As a preferred solution of the intelligent optimization method for the performance of the liquid cooling medium in the data center according to the present invention, wherein: the first model includes:

[0027] The first model is any model obtained through verification for the flow velocity score and viscosity and density.

[0028] As a preferred solution of the intelligent optimization method for the performance of the liquid cooling medium in the data center according to the present invention, wherein: the first gravity regulation includes:

[0029] Connect to the data acquisition mechanism to obtain the bubble content of the additive and the impurity particle content of the additive;

[0030] And conduct an adaptability assessment of the additive and the liquid cooling medium;

[0031] Screen out the additive with adaptability meeting the requirements and conduct the first gravity regulation with the current liquid cooling medium;

[0032] The bubble content of the additive is obtained from the experimental data of the data acquisition mechanism. According to the bubble content of the additive and the impurity particle content of the additive, use fuzzy inference to conduct an adaptability assessment of the additive and the liquid cooling medium, and screen out the additive with adaptability meeting the requirements and conduct the first gravity regulation with the current liquid cooling medium.

[0033] As a preferred solution of the intelligent optimization method for the performance of the liquid cooling medium in the data center according to the present invention, wherein: the analysis of the addition amount of the additive according to the comparison result includes:

[0034] Conduct the first gravity regulation with the additive with adaptability meeting the requirements and the current liquid cooling medium;

[0035] Obtain the cooling coefficient of the current liquid cooling medium to be measured after the first gravity regulation, and compare it with the first coefficient threshold;

[0036] If the cooling coefficient of the liquid cooling medium is lower than the first coefficient threshold, the liquid cooling medium at this time is an inefficient medium, and it is necessary to increase the addition amount of the additive to improve the cooling performance;

[0037] If the cooling coefficient is not lower than the first coefficient threshold, the liquid cooling medium at this time is an efficient medium, then keep the current addition amount of the additive;

[0038] After adding the additive, it is necessary to re-measure the cooling coefficient to ensure the effect that the cooling coefficient is not lower than the first coefficient threshold.

[0039] In a second aspect, the present invention provides an intelligent optimization system for the performance of a liquid cooling medium in a data center, including:

[0040] A judgment model, used to preset a first medium judgment criterion, where the first medium judgment criterion includes judging the medium through a first coefficient threshold;

[0041] The first medium judgment criterion is used to judge whether the to-be-tested liquid cooling medium is the first medium or the second medium;

[0042] A regulation module, used to perform a first severe regulation on the current to-be-tested liquid cooling medium if the current to-be-tested liquid cooling medium is the first medium;

[0043] The first severe regulation is used to add an additive that meets the adaptability requirements to the current to-be-tested liquid cooling medium;

[0044] An analysis module, used to obtain the cooling coefficient of the current to-be-tested liquid cooling medium after the first severe regulation, compare it with the first coefficient threshold, and analyze the addition amount of the additive according to the comparison result.

[0045] In a third aspect, the present invention provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the method described above are implemented.

[0046] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described above are implemented.

[0047] Compared with the prior art, the beneficial effects of the present invention: The present invention proposes an intelligent optimization method and system for the performance of liquid cooling media in a data center, presets a first medium judgment criterion, where the first medium judgment criterion includes judging the medium through a first coefficient threshold; the first medium judgment criterion is used to judge whether the to-be-tested liquid cooling medium is the first medium or the second medium; if the current to-be-tested liquid cooling medium is the first medium, then perform a first severe regulation on the current to-be-tested liquid cooling medium; the first severe regulation is used to add an additive that meets the adaptability requirements to the current to-be-tested liquid cooling medium; obtain the cooling coefficient of the current to-be-tested liquid cooling medium after the first severe regulation, compare it with the first coefficient threshold, and analyze the addition amount of the additive according to the comparison result. Through intelligent means, the rapid and accurate optimization of the performance of liquid cooling media in the data center is realized. Compared with the traditional manual test and empirical judgment methods, not only the optimization efficiency is improved, but also the accuracy and reliability of the optimization results are significantly enhanced. In addition, through the preset first medium judgment criterion and the first severe regulation mechanism, the present invention can perform personalized optimization processing on liquid cooling media with different performances, thereby maximizing the heat dissipation efficiency of the data center and reducing energy consumption. This innovative technical solution undoubtedly provides strong support for the further development of liquid cooling technology in the data center. Description of the Drawings

[0048] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. Among them:

[0049] Figure 1 It is a method flow chart of a method and system for intelligent optimization of the performance of a liquid cooling medium in a data center provided by an embodiment of the present invention;

[0050] Figure 2 It is an internal structure diagram of a computer device for a method and system for intelligent optimization of the performance of a liquid cooling medium in a data center provided by an embodiment of the present invention. Specific Embodiments

[0051] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following will provide a detailed description of the specific embodiments of the present invention in conjunction with the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present invention.

[0052] Embodiment 1

[0053] Refer to Figure 1 - Figure 2 , which is the first embodiment of the present invention. This embodiment provides a method and system for intelligent optimization of the performance of a liquid cooling medium in a data center, including:

[0054] In the existing related technologies, there are some problems. For example, the process of optimizing the performance of the liquid cooling medium is cumbersome and inefficient, mainly relying on manual experiments and empirical judgments, which not only consume time and effort but also make it difficult to ensure the accuracy and reliability of the optimization results.

[0055] This application provides a method that can effectively solve the above-mentioned problems. Next, multiple embodiments will be combined to elaborate in detail how to implement the method for intelligent optimization of the performance of the liquid cooling medium in the data center;

[0056] Figure 1 It shows a method flow chart of a method and system for intelligent optimization of the performance of a liquid cooling medium in a data center, including:

[0057] S101, preset a first medium judgment criterion, and the first medium judgment criterion includes judging the medium through a first coefficient threshold;

[0058] In the embodiments of this application, the first medium judgment criterion is used to judge whether the liquid cooling medium to be tested is the first medium or the second medium;

[0059] In an optional embodiment, the first medium judgment criterion is to divide the liquid cooling medium to be tested into a high-efficiency medium and a low-efficiency medium, so as to perform targeted performance optimization subsequently.

[0060] In an optional embodiment, the first medium judgment criterion can preliminarily evaluate the cooling performance of the liquid cooling medium to be tested by setting a first coefficient threshold. This step is the starting point of the intelligent optimization method and provides a basis for subsequent first-degree regulation and determination of the additive dosage.

[0061] In an optional embodiment, the first medium judgment criterion can also comprehensively consider various physical properties of the liquid cooling medium, including but not limited to viscosity, density, specific heat capacity, etc. These properties jointly determine the cooling ability of the liquid cooling medium. By establishing a comprehensive evaluation model, the performance of the liquid cooling medium to be tested can be judged more accurately, providing more precise guidance for subsequent optimization measures.

[0062] In the embodiment of the present application, the preset first medium judgment criterion includes:

[0063] Obtain the first parameters of all the liquid cooling media to be tested, and calculate the first cooling coefficient according to the parameters;

[0064] Perform the first classification according to the first cooling coefficient;

[0065] The first classification is used to divide the liquid cooling medium to be tested into a first medium and a second medium. The first medium is a low-efficiency medium, and the second medium is a high-efficiency medium.

[0066] In the embodiment of the present application, obtaining the first parameters of all the liquid cooling media to be tested and calculating the first cooling coefficient according to the parameters includes:

[0067] The first parameters at least include the viscosity and density of all the liquid cooling media to be tested;

[0068] Analyze the flow velocity score of all the liquid cooling media to be tested by establishing a first model;

[0069] Collect the specific heat capacity of all the liquid cooling media to be tested by accessing the standard data manual database;

[0070] According to the flow velocity score and specific heat capacity of different liquid cooling media, obtain the first cooling coefficient of all the liquid cooling media to be tested through weighted formula calculation.

[0071] In the embodiment of the present application, the first model includes:

[0072] The first model is any model obtained through verification for the flow velocity score and viscosity and density.

[0073] In an optional embodiment, the first model can be a prediction model built based on a machine learning algorithm, which is trained with a large amount of experimental data and can accurately predict the flow rate score of the liquid cooling medium under different viscosities and densities. The implementation of this step not only improves the prediction accuracy of the flow rate score, but also greatly shortens the prediction time, providing strong support for subsequent optimization work. Through the use of the first model, the present application can more quickly score the flow rate of the liquid cooling medium to be tested, thereby providing a more accurate data basis for the calculation of its cooling coefficient.

[0074] In an optional embodiment, the first model can also be a regression model based on statistics, which analyzes the relationship between viscosity and density and flow rate in historical data to obtain a mathematical expression that can reflect this relationship. In this way, the present application can use the model to predict the flow rate of the liquid cooling medium to be tested, thereby providing an important basis for the calculation of its cooling coefficient. Regardless of which model is used, its core purpose is to improve the accuracy and efficiency of the liquid cooling medium performance evaluation and provide strong support for subsequent optimization measures.

[0075] In the embodiment of the present application, the viscosity and density of different liquid cooling media are collected, and the flow rate score of the liquid cooling medium is analyzed by establishing a regression model. The viscosity and density of different liquid cooling media are collected by accessing the historical experimental database. The viscosity and density of different liquid cooling media are obtained by testers in related fields based on historical experimental data, which will not be described in detail here.

[0076] Exemplarily, the viscosity and density of different liquid cooling media are collected, and the flow rate score of the liquid cooling medium is analyzed by establishing a regression model. The specific steps are as follows: Step 1: Data cleaning is performed on the viscosity and density of different liquid cooling media, and missing values, outliers and duplicate values ​​in the original healthy data set are eliminated to generate a cleaned data set; Step 2: The cleaned data set is normalized, and the data is mapped to a unified scale range to generate a standard data set; Step 3: The Pearson correlation coefficient between the viscosity, density and flow rate scores in the standard data set is calculated to quantify the linear correlation between them. According to the calculation results, the flow rate score is negatively correlated with the viscosity and positively correlated with the density; Step 4: A nonlinear model is established based on the fact that the flow rate score is negatively correlated with the viscosity and positively correlated with the density. The form of the nonlinear model is: Q = α·η b ·ρ c ; Where Q is the flow rate score; η is viscosity; ρ is density; α, b, c are the parameters to be estimated; Step 5: For nonlinear regression, use the optimization algorithm that minimizes the error to adjust the parameters, and use the cross-validation method to verify the model to ensure the predictive ability and generalization ability of the regression model; Step 6: Through verification, the regression model formula between the flow rate score and viscosity and density is obtained: Q = α·η-1 ·ρ 0.5 。

[0077] It should be explained that the Pearson correlation coefficient (abbreviated as r) is an index in statistics used to measure the degree of linear correlation between two variables. It reflects the strength and direction of the relationship between the two variables. The value range of the Pearson correlation coefficient is between -1 and 1.

[0078] It should be noted that the regression model formula is obtained based on the statistical analysis of a large amount of experimental data and has high accuracy and reliability. Among them, α is a constant that needs to be obtained by fitting the experimental data. Through this formula, this application can quickly calculate the flow rate scores of the liquid cooling medium under different viscosities and densities, providing an important reference basis for subsequent optimization work. At the same time, the application of this formula also greatly improves the efficiency of the liquid cooling medium performance evaluation, reduces the evaluation cost, and provides strong support for the optimization of the liquid cooling medium performance in the data center.

[0079] In an optional embodiment, in addition to collecting the specific heat capacity of all the liquid cooling media to be tested by accessing the standard data handbook database, it can also be obtained by experimental determination. Experimental determination can ensure the accuracy and authenticity of the specific heat capacity data, providing a more reliable basis for the subsequent calculation of the cooling coefficient. During the experimental determination process, it is necessary to strictly control the experimental conditions to ensure that all the liquid cooling media to be tested are determined under the same conditions to obtain comparable specific heat capacity data. Through the implementation of this step, this application can more comprehensively master the physical properties of the liquid cooling media to be tested, providing more accurate guidance for subsequent performance optimization.

[0080] However, if it is obtained by experimental determination, it will lead to an increase in the amount of calculation and an extension of the optimization process. Therefore, in a preferred embodiment of this application, by accessing the standard data handbook database to collect the specific heat capacity of all the liquid cooling media to be tested, accurate specific heat capacity data can be quickly obtained, while avoiding the additional costs and time consumption brought by experimental determination. In this way, this application can more efficiently perform the intelligent optimization of the liquid cooling medium performance, improving the optimization efficiency and reducing the optimization cost.

[0081] After obtaining the flow rate scores and specific heat capacities of all the liquid cooling media to be tested, this application will obtain the first cooling coefficient of all the liquid cooling media to be tested through weighted calculation according to the flow rate scores and specific heat capacities of different liquid cooling media. The implementation of this step comprehensively considers various physical properties of the liquid cooling medium, can more accurately reflect its cooling performance, and provides strong support for subsequent classification and regulation.

[0082] Specifically, the first cooling coefficient of all the liquid cooling media to be measured is obtained through a weighted formula, and the specific formula is: K = ω1·Q + ω2·C; where K is the cooling coefficient; Q is the flow rate score; C is the specific heat capacity; ω1 and ω2 are the weights of the flow rate score and the specific heat capacity respectively.

[0083] It should be noted that the values of the weights ω1 and ω2 can be adjusted according to actual needs to reflect the importance of the flow rate score and the specific heat capacity in the cooling performance of the liquid cooling media. For example, in some application scenarios, the flow rate may play a dominant role in the cooling effect. In this case, the weight ω1 of the flow rate score can be appropriately increased; while in other scenarios, if the influence of the specific heat capacity is more significant, the weight ω2 of the specific heat capacity can be correspondingly increased. This flexible weight setting enables the method of this application to adapt to different data center environments and types of liquid cooling media, ensuring the accuracy and practicality of the optimization results.

[0084] In the embodiment of this application, the first classification according to the first cooling coefficient includes:

[0085] Set a first coefficient threshold. If the medium coefficient of the current liquid cooling medium to be measured is not less than the first coefficient threshold, the current liquid cooling medium to be measured is the second medium;

[0086] If the medium coefficient of the current liquid cooling medium to be measured is less than the first coefficient threshold, the current liquid cooling medium to be measured is the first medium.

[0087] In an alternative embodiment, setting the first coefficient threshold is to quantitatively classify the cooling performance of the liquid cooling media to be measured, so as to take targeted optimization measures subsequently. The implementation of this step enables the method of this application to more accurately identify the liquid cooling media with low performance, thereby improving the pertinence and efficiency of the optimization work. At the same time, the setting of the first coefficient threshold also takes into account the actual operation requirements and environmental factors of the data center, ensuring the practicality and reliability of the optimization results. In specific operations, the value of the first coefficient threshold can be adjusted according to the actual situation of the data center to achieve the best optimization effect.

[0088] In an alternative embodiment, the first coefficient threshold can be set to a specific value, such as 0.8. The determination of this value is based on a large amount of experimental data and analysis, and can better reflect the cooling performance of the liquid cooling media in the data center. When the cooling coefficient of the liquid cooling medium to be measured is greater than or equal to 0.8, it is determined as an efficient medium (the second medium) and no further performance optimization is required; while when its cooling coefficient is less than 0.8, it is determined as an inefficient medium (the first medium), and targeted optimization measures need to be taken to improve its cooling performance.

[0089] In an alternative embodiment, the first coefficient threshold can also be dynamically adjusted according to the operating historical data of the data center and the performance of the liquid cooling medium. This dynamic adjustment strategy enables the method of the present application to continuously adapt to the changes in the operating environment of the data center, ensuring the persistence and stability of the optimization effect. In specific operations, the first coefficient threshold can be updated and optimized in real time by monitoring the operating parameters of the data center and the performance data of the liquid cooling medium, combined with machine learning algorithms, so as to achieve more accurate performance classification and optimization decisions.

[0090] In the embodiment of the present application, the cooling coefficients of different liquid cooling media are calculated, and the system obtains the first coefficient threshold by comprehensively averaging the cooling coefficients of each liquid cooling medium in a weighted manner. The specific formula for the first coefficient threshold is: where K i is the cooling coefficient of the i-th liquid cooling medium; θ i is the weight of the i-th liquid cooling medium; n is the number of types of liquid cooling media; K avg is the first coefficient threshold. The liquid cooling media with a cooling coefficient greater than or equal to the first coefficient threshold are classified as high-efficiency media, and the liquid cooling media with a cooling coefficient less than the first coefficient threshold are classified as low-efficiency media.

[0091] Exemplarily, the specific heat capacity of different liquid cooling media is collected by accessing the standard data handbook database. It should be noted that the specific heat capacity of the liquid cooling medium refers to the heat absorbed or released by a unit mass of the liquid cooling medium when the temperature changes by 1°C. The standard data handbook database contains the thermophysical properties of various liquids, including specific heat capacity, density, thermal conductivity, etc.;

[0092] Furthermore, according to the flow rate score and specific heat capacity of different liquid cooling media, the cooling coefficient of the liquid cooling medium is obtained through comprehensive calculation using a weighted formula. The specific formula is: K = ω1·Q + ω2·C; where K is the cooling coefficient; Q is the flow rate score; C is the specific heat capacity; ω1 and ω2 are the weights of the flow rate score and specific heat capacity respectively;

[0093] Furthermore, the cooling coefficients of different liquid cooling media are calculated, and the system obtains the first coefficient threshold by comprehensively averaging the cooling coefficients of each liquid cooling medium in a weighted manner. The specific formula for the first coefficient threshold is: where K i is the cooling coefficient of the i-th liquid cooling medium; θ i is the weight of the i-th liquid cooling medium; n is the number of types of liquid cooling media; K avg is the first coefficient threshold. The liquid cooling media with a cooling coefficient greater than or equal to the first coefficient threshold are classified as high-efficiency media, and the liquid cooling media with a cooling coefficient less than the first coefficient threshold are classified as low-efficiency media;

[0094] Suppose there are three liquid cooling media, labeled 1, 2, and 3 respectively; the flow rate scores are labeled as Q1 = 80, Q2 = 60, Q3 = 90 respectively; the specific heat capacities are labeled as C1 = 4 kJ, C2 = 3.5 kJ, C3 = 4.5 kJ respectively; the weight of the flow rate score ω1 = 0.6, the weight of the specific heat capacity ω2 = 0.4, and the weight of each liquid cooling media θ1 = 0.4, θ2 = 0.3, θ3 = 0.3; First, calculate the cooling coefficient of each liquid cooling media: K1 = ω1·Q1 + ω2·C1 = 0.6×80 + 0.4×4 = 48 + 1.6 = 49.6; K2 = ω1·Q2 + ω2·C2 = 0.6×60 + 0.4×3.5 = 36 + 1.4 = 37.4; K3 = ω1·Q3 + ω2·C3 = 0.6×90 + 0.4×4.5 = 54 + 1.8 = 55.8; Then, calculate the first coefficient threshold: Therefore, the cooling coefficient K1 of the liquid cooling media 1 = 49.6, which is greater than the first coefficient threshold K avg = 47.8. Therefore, the liquid cooling media 1 is a high-efficiency media; the cooling coefficient K2 of the liquid cooling media 2 = 37.4, which is less than the first coefficient threshold K avg = 47.8. Therefore, the liquid cooling media 2 is a low-efficiency media; the cooling coefficient K3 of the liquid cooling media 3 = 55.8, which is greater than the first coefficient threshold K avg = 47.8. Therefore, the liquid cooling media 3 is a high-efficiency media.

[0095] It should be noted that the first media judgment criterion is preset. The first media judgment criterion includes that judging the media through the first coefficient threshold can automatically classify the liquid cooling media as high-efficiency or low-efficiency, providing a clear guiding direction for subsequent first severity regulation and additive adjustment. This classification not only improves the efficiency of the optimization process but also ensures that the optimization measures can accurately target the liquid cooling media with lower performance, avoiding unnecessary resource waste. At the same time, through the preset first media judgment criterion, the present invention can quickly identify the objects to be optimized among a large number of liquid cooling media to be tested, laying a solid foundation for subsequent performance improvement work.

[0096] S102, if the current liquid cooling media to be tested is the first media, then perform the first severity regulation on the current liquid cooling media to be tested;

[0097] In the embodiments of the present application, the first severity regulation is used to add additives that meet the requirements of adaptability to the current liquid cooling media to be tested;

[0098] In an optional embodiment, the additive whose adaptability meets the requirements is the additive with high adaptability during the adaptability evaluation. The adaptability evaluation criteria can be set to evaluate the adaptability of various additives, and the additives with high adaptability are selected for addition. The adaptability evaluation can be comprehensively considered based on multiple dimensions such as the chemical compatibility, physical stability, and cooling performance improvement effect of the additive and the liquid cooling medium. In this way, this application can ensure that the selected additive can not only effectively improve the cooling performance of the liquid cooling medium, but also maintain good compatibility with the liquid cooling medium, avoiding damage to the data center equipment.

[0099] In the embodiment of this application, the first severe regulation includes:

[0100] Connect to the data acquisition mechanism to obtain the bubble content of the additive and the impurity particle content of the additive;

[0101] And conduct an adaptability evaluation on the additive and the liquid cooling medium;

[0102] Screen out the additives whose adaptability meets the requirements and conduct the first severe regulation on the current liquid cooling medium;

[0103] The bubble content of the additive is obtained from the experimental data of the data acquisition mechanism. According to the bubble content of the additive and the impurity particle content of the additive, use fuzzy inference to conduct an adaptability evaluation on the additive and the liquid cooling medium, and screen out the additives whose adaptability meets the requirements and conduct the first severe regulation on the current liquid cooling medium.

[0104] Exemplarily, comprehensively analyze the cooling coefficient of the current liquid cooling medium. If the current liquid cooling medium is an inefficient medium, additives are added for the first heavy regulation to improve the cooling capacity of the current liquid cooling medium. The bubble content of the additives is obtained from the experimental data of the data acquisition mechanism. The specific process of the experimental data is as follows: Step 1: Prepare various additive samples to be tested, mix the additive samples to be tested evenly with different liquid cooling media, and maintain a constant temperature; Step 2: Use a stirrer to make the evenly mixed mixture generate bubbles, and use a bubble counter to count the generated bubbles, record the number of bubbles per unit time, and mark it as the bubble content of the additives; Step 3: To ensure the accuracy of the bubble content of the additives obtained from the experimental results, multiple repeated experiments should be carried out, and the average value and standard deviation should be calculated, and the experimental conditions, such as temperature, stirring speed, etc., should be strictly controlled to reduce experimental errors; Hypothesis: Quantitatively compare three different additives. Assume that the additives are A, B, and C. Under the same conditions, the bubble contents generated are weighed for a certain amount of additives A, B, and C respectively, dissolved in the same volume of liquid cooling medium to ensure complete dissolution. The dissolved mixtures are poured into three containers respectively, placed in a constant temperature bath, and the solutions are stirred at the same speed and time using a stirrer to generate bubbles. The bubble counter is used to record the number of bubbles generated by each additive solution in the same time. The bubble content of additive type A is 120 per minute, the bubble content of additive type B is 80 per minute, and the bubble content of additive type C is 180 per minute.

[0105] Furthermore, the impurity particle content of the additives is obtained by spectroscopic analysis. It should be explained that the impurity particle content of the additives is the metal element content in the additives. Through this method, the metal element content in the liquid cooling medium can be accurately measured. For example, ICP (Inductively Coupled Plasma Spectroscopy) can be used to detect the concentrations of multiple metal elements in the liquid.

[0106] It should be noted that if the current liquid cooling medium to be tested is the first medium, performing the first heavy regulation on the current liquid cooling medium to be tested can significantly improve the cooling performance of the liquid cooling medium and ensure that it meets the requirements of the efficient operation of the data center. Through the first heavy regulation, the performance bottleneck of the inefficient medium can be targeted to be improved, the risk of equipment overheating caused by insufficient cooling can be reduced, thereby improving the stability and reliability of the data center. In addition, the implementation of the first heavy regulation can also extend the service life of the liquid cooling medium, reduce the replacement frequency, and further reduce the operating cost of the data center. In summary, performing the first heavy regulation on the current liquid cooling medium to be tested is a key step in optimizing the performance of the liquid cooling medium in the data center and is of great significance for improving the overall operation efficiency and energy conservation and emission reduction.

[0107] S103. Obtain the cooling coefficient of the current liquid cooling medium after the first heavy regulation, compare it with the first coefficient threshold, and analyze the addition amount of the additive according to the comparison result.

[0108] In the embodiment of the present application, analyzing the addition amount of the additive according to the comparison result includes:

[0109] Perform the first heavy regulation on the additive whose adaptability meets the requirements and the current liquid cooling medium;

[0110] Obtain the cooling coefficient of the current liquid cooling medium to be measured after the first heavy regulation, and compare it with the first coefficient threshold;

[0111] If the cooling coefficient of the liquid cooling medium is lower than the first coefficient threshold, the liquid cooling medium at this time is an inefficient medium, and it is necessary to increase the addition amount of the additive to improve the cooling performance;

[0112] If the cooling coefficient is not lower than the first coefficient threshold, the liquid cooling medium at this time is an efficient medium, then keep the current addition amount of the additive;

[0113] After adding the additive, it is necessary to re-measure the cooling coefficient to ensure that the cooling coefficient is not lower than the effect of the first coefficient threshold.

[0114] Specifically, perform secondary calculation on the liquid cooling medium after the first heavy regulation to obtain the cooling coefficient, compare it with the preset cooling coefficient threshold, and analyze the addition amount of the additive according to the comparison result;

[0115] Perform the first heavy regulation on the additive with high adaptability and the current liquid cooling medium, perform secondary calculation on the liquid cooling medium after the first heavy regulation to obtain the cooling coefficient, and compare it with the preset cooling coefficient threshold. If the cooling coefficient of the liquid cooling medium is lower than the first coefficient threshold K avg , the liquid cooling medium at this time is an inefficient medium, and it is necessary to increase the addition amount of the additive to improve the cooling performance; if the cooling coefficient is higher than or equal to the first coefficient threshold K avg , the liquid cooling medium at this time is an efficient medium, then keep the current addition amount of the additive; after adding the additive, it is necessary to re-measure the cooling coefficient to ensure that the cooling coefficient is higher than the effect of the first coefficient threshold K avg If there is a deviation between the actual cooling coefficient and the first coefficient threshold K avg , it is necessary to continue to finely adjust the addition amount of the additive and repeat the above steps.

[0116] In summary, the present invention proposes an intelligent optimization method for the performance of the liquid cooling medium in a data center, presetting a first medium judgment criterion, where the first medium judgment criterion includes judging the medium through a first coefficient threshold; the first medium judgment criterion is used to judge whether the liquid cooling medium to be tested is a first medium or a second medium; if the current liquid cooling medium to be tested is the first medium, then perform a first severity regulation on the current liquid cooling medium to be tested; the first severity regulation is used to add an additive that meets the requirements of adaptability to the current liquid cooling medium to be tested; obtain the cooling coefficient of the current liquid cooling medium to be tested after the first severity regulation, compare it with the first coefficient threshold, and analyze the addition amount of the additive according to the comparison result. Through intelligent means, the rapid and accurate optimization of the performance of the liquid cooling medium in the data center is realized. Compared with the traditional manual test and empirical judgment methods, it not only improves the optimization efficiency, but also significantly enhances the accuracy and reliability of the optimization result. In addition, through the preset first medium judgment criterion and the first severity regulation mechanism, the present invention can perform personalized optimization processing on liquid cooling media with different performances, thereby maximizing the heat dissipation efficiency of the data center and reducing energy consumption. This innovative technical solution undoubtedly provides strong support for the further development of the liquid cooling technology in the data center.

[0117] Example 2

[0118] In a preferred embodiment, in the embodiment of the present application, the specific working process for evaluating the adaptability between the additive and the liquid cooling medium is as follows:

[0119] Step C1, after obtaining the bubble content of the additive and the impurity particle content of the additive through the data acquisition mechanism, define the bubble content of the additive and the impurity particle content of the additive as input variables, and divide them into different fuzzy sets respectively.

[0120] For example, "Low", "Medium", "High" for the bubble content of the additive, "Few", "Moderate", "Many".

[0121] Step C2, define the adaptability between the additive and the liquid cooling medium as the output variable, and divide it into a fuzzy set. For example, "Low", "Medium", "High" for the adaptability.

[0122] Step C3, formulate a set of fuzzy rules to describe the influence of different input variables on the output variable. The definition of the rules can be based on professional knowledge or obtained through data analysis and experiments. For example:

[0123] Mark the bubble content of the additive as T, the impurity particle content of the additive as B, and the adaptability as P, then it can be defined

[0124] Rule 1: IF (T is Low) AND (B is Few) THEN (P is High)

[0125] Rule 2: IF (T is High) AND (B is Many) THEN (P is Low) ...

[0127] Step C4, perform fuzzy inference according to the fuzzy rules to determine the solution for the compatibility between the additive and the liquid cooling medium.

[0128] It should be noted that the division of the fuzzy set can be adjusted according to the actual situation. For example, although three fuzzy sets are taken as an example in this embodiment, in fact, the bubble content of the additive, the impurity particle content of the additive, and the compatibility can be divided into more than three sets to facilitate more accurate adjustment according to different liquid cooling media.

[0129] Furthermore, for the judgment of high, medium, and low of the bubble content and impurity particle content of the additive, thresholds can be set according to the actual situation for judgment. For example, when the bubble content of the additive exceeds 120 per minute, it is calibrated as "Hot", and when the impurity particle content of the additive is higher than 80%, it is calibrated as "High", etc., which will not be elaborated here.

[0130] It should be noted that through the above fuzzy inference method, the present invention can comprehensively consider the bubble content and impurity particle content of the additive and accurately evaluate the compatibility between the additive and the liquid cooling medium. This method not only improves the accuracy and objectivity of the compatibility evaluation, but also avoids the subjectivity and uncertainty brought by human judgment. In practical applications, by adjusting the fuzzy rules and set division, personalized compatibility evaluations can be carried out for different types and liquid cooling media in data centers, so as to ensure that the selected additive can effectively improve the cooling performance of the liquid cooling medium while maintaining good compatibility.

[0131] Embodiment 3

[0132] In this embodiment, an intelligent optimization system for the performance of the liquid cooling medium in a data center is further provided, including:

[0133] A judgment model for presetting a first medium judgment criterion, where the first medium judgment criterion includes judging the medium through a first coefficient threshold;

[0134] The first medium judgment criterion is used to judge whether the liquid cooling medium to be tested is a first medium or a second medium;

[0135] A regulation module for, if the current liquid cooling medium to be tested is a first medium, performing a first heavy regulation on the current liquid cooling medium to be tested;

[0136] The first heavy regulation is used to add additives that meet the adaptability requirements to the current liquid cooling medium to be tested;

[0137] An analysis module is used to obtain the cooling coefficient of the current liquid cooling medium to be tested after the first heavy regulation, compare it with the first coefficient threshold, and analyze the addition amount of the additive according to the comparison result.

[0138] The above-mentioned unit modules can be embedded in the processor in the computer device in hardware form or independent of it, or stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to each module above.

[0139] This embodiment also provides a computer device, which can be a terminal, and its internal structure diagram can be as Figure 2 shown. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it realizes an intelligent optimization method for the performance of the liquid cooling medium in a data center. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad set on the shell of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0140] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by the processor, the following steps are realized:

[0141] Preset a first medium judgment criterion, and the first medium judgment criterion includes judging the medium through the first coefficient threshold;

[0142] The first medium judgment criterion is used to judge whether the liquid cooling medium to be tested is a first medium or a second medium;

[0143] If the current liquid cooling medium to be tested is the first medium, perform the first heavy regulation on the current liquid cooling medium to be tested;

[0144] The first heavy regulation is used to add additives that meet the adaptability requirements to the current liquid cooling medium to be tested;

[0145] Obtain the cooling coefficient of the current liquid-cooled medium to be measured after the first heavy regulation, compare it with the first coefficient threshold, and analyze the addition amount of the additive according to the comparison result.

[0146] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

[0147] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0148] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate a device for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0149] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including an instruction device, and the instruction device implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0150] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus, so that a series of operation steps are performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions for implementing the process Figure 1 in one process or multiple processes and / or boxes Figure 1 steps for the functions specified in one box or multiple boxes.

[0151] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0152] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. An intelligent optimization method for the performance of liquid cooling media in a data center, characterized in that, Including: A preset first medium judgment criterion, which includes judging the medium through a first coefficient threshold; The first medium judgment criterion is used to judge whether the to-be-tested liquid cooling medium is a first medium or a second medium; If the current to-be-tested liquid cooling medium is a first medium, perform a first severity regulation on the current to-be-tested liquid cooling medium; The first severity regulation is used to add an additive that meets the adaptability requirements to the current to-be-tested liquid cooling medium; Obtain the cooling coefficient of the current to-be-tested liquid cooling medium after the first severity regulation, compare it with the first coefficient threshold, and analyze the addition amount of the additive according to the comparison result.

2. The intelligent optimization method for the performance of the liquid cooling medium in the data center according to claim 1, wherein, The preset first medium judgment criterion includes: Obtain the first parameters of all to-be-tested liquid cooling media, and calculate the first cooling coefficient according to the first parameter; Perform a first classification according to the first cooling coefficient; The first classification is used to classify the to-be-tested liquid cooling medium into a first medium and a second medium. The first medium is an inefficient medium, and the second medium is an efficient medium.

3. The intelligent optimization method for the performance of the liquid cooling medium in the data center according to claim 2, wherein, The performing a first classification according to the first cooling coefficient includes: Set a first coefficient threshold. If the medium coefficient of the current to-be-tested liquid cooling medium is not less than the first coefficient threshold, the current to-be-tested liquid cooling medium is a second medium; If the medium coefficient of the current to-be-tested liquid cooling medium is less than the first coefficient threshold, the current to-be-tested liquid cooling medium is a first medium.

4. The intelligent optimization method for the performance of the liquid cooling medium in the data center according to claim 3, wherein, The obtaining the first parameters of all to-be-tested liquid cooling media and calculating the first cooling coefficient according to the first parameter includes: The first parameter at least includes the viscosity and density of all to-be-tested liquid cooling media; Analyze the flow rate score of all to-be-tested liquid cooling media by establishing a first model; Collect the specific heat capacity of all to-be-tested liquid cooling media by accessing the standard data manual database; According to the flow rate scores and specific heat capacities of different liquid cooling media, obtain the first cooling coefficient of all to-be-tested liquid cooling media through weighted calculation.

5. The intelligent optimization method for the performance of the liquid cooling medium in the data center according to claim 4, wherein, The first model includes: The first model is any model obtained by verification of the relationship between the flow rate score and viscosity and density.

6. The intelligent optimization method for the performance of the liquid cooling medium in the data center according to claim 5, wherein, The first severity regulation includes: Connect to a data acquisition mechanism to obtain the bubble content of the additive and the impurity particle content of the additive; And evaluate the adaptability of the additive and the liquid cooling medium; Screen out the additive that meets the adaptability requirements and perform a first severity regulation with the current liquid cooling medium; The bubble content of the additive is obtained from the experimental data of the data acquisition mechanism. According to the bubble content of the additive and the impurity particle content of the additive, use fuzzy inference to evaluate the adaptability of the additive and the liquid cooling medium, and screen out the additive that meets the adaptability requirements and perform a first severity regulation with the current liquid cooling medium.

7. The intelligent optimization method for the performance of the liquid cooling medium in the data center according to claim 6, characterized in that, The analyzing the addition amount of the additive according to the comparison result includes: Perform a first severity regulation on the additive that meets the adaptability requirements and the current liquid cooling medium; Obtain the cooling coefficient of the current to-be-tested liquid cooling medium after the first severity regulation, and compare it with the first coefficient threshold; If the cooling coefficient of the liquid cooling medium is lower than the first coefficient threshold, the liquid cooling medium at this time is an inefficient medium, and the addition amount of the additive needs to be increased to improve the cooling performance; If the cooling coefficient is not less than the first coefficient threshold, the liquid cooling medium at this time is an efficient medium, then keep the current additive addition amount; After adding the additive, it is necessary to re-measure the cooling coefficient to ensure that the cooling coefficient is not lower than the effect of the first coefficient threshold.

8. An intelligent optimization system for the performance of liquid cooling media in a data center, characterized in that, Including: A judgment model for presetting a first medium judgment criterion, the first medium judgment criterion including medium judgment through a first coefficient threshold; The first medium judgment criterion is used to judge whether the liquid-cooled medium to be measured is a first medium or a second medium; A regulation module for, if the current liquid-cooled medium to be measured is a first medium, performing a first severity regulation on the current liquid-cooled medium to be measured; The first severity regulation is used to add an additive with satisfactory adaptability to the current liquid-cooled medium to be measured; An analysis module for obtaining the cooling coefficient of the current liquid-cooled medium to be measured after the first severity regulation, comparing it with the first coefficient threshold, and analyzing the addition amount of the additive according to the comparison result.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method described in any one of claims 1 to 7.

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