Heat dissipation control method and device of storage server, equipment and storage medium

Abstract

The invention relates to a heat dissipation control method, device and equipment of a storage server and a storage medium, and aims to optimize the heat dissipation performance of the server by monitoring working characteristic parameters and power consumption data of a chip in real time. The method comprises the following steps: firstly, acquiring working load and power consumption information of each chip in a server, calculating temperature distribution of each chip, and generating an initial temperature data set; then, heat generation characteristic analysis is carried out on the set, and a heat generation model under the current load is obtained; then, a heat transfer path model is established based on the model and a server structure, and heat transfer among the chips is analyzed. And according to the heat transfer path, optimizing configuration parameters of an air cooling system and a liquid cooling system in the server, and generating optimized cooling parameter settings. And finally, the optimized parameters are applied to a heat dissipation control system of the server, dynamic adjustment is performed according to real-time load and temperature feedback, and a self-adaptive heat dissipation strategy is formed, so that the heat dissipation efficiency is improved, and stable operation of the server is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a heat dissipation control method and device for a storage server, a computer device and a storage medium. Background Art

[0002] In modern data centers, as the performance of storage servers continues to improve, heat dissipation control has become an important factor affecting system stability and energy consumption. In particular, the development of semiconductor and chip technology has significantly increased the power density of computing units inside servers, resulting in increasingly severe heat dissipation problems. In the process of efficiently processing large amounts of data, server chips often operate at higher frequencies, which inevitably increases their heat generation, and existing heat dissipation solutions are stretched to the limit in dealing with high-frequency thermal cycles and dense heat sources. In addition, traditional air cooling and liquid cooling technologies have performance bottlenecks in some high-performance storage scenarios, making it difficult to meet the requirements of heat dissipation uniformity and response speed.

[0003] As semiconductor process technology continues to shrink, more functional units are integrated inside the chip, which leads to more concentrated local heat in server chips. The increase in thermal stress not only threatens the reliability of the chip, but also may lead to performance degradation and even the risk of downtime. Current heat dissipation control strategies are mostly based on fixed parameter settings, lacking the ability to dynamically adapt to the actual workload and chip characteristics of the server, and thus unable to respond effectively to different application scenarios and temperature conditions. This limitation of heat dissipation control makes it difficult for the system to achieve efficient energy consumption management between light load and high load, resulting in low energy efficiency of the overall system.

[0004] In addition, as storage servers increase their demand for large-scale data processing, the mutual influence of heat dissipation between chips has become a new challenge. In a storage server, when multiple chips perform high-frequency calculations at the same time, due to the heat superposition effect between each other, the existing heat dissipation system is difficult to effectively identify and regulate the heat dissipation needs of each chip, resulting in some chips being overheated or even triggering thermal protection mechanisms, affecting the stability of the entire system. Therefore, an innovative heat dissipation control method based on semiconductor and chip characteristics is needed to dynamically adapt to changes in workloads, optimize heat dissipation effects, and improve the overall energy efficiency of the server. Summary of the invention

[0005] The main purpose of the present invention is to provide a heat dissipation control method, device, equipment and storage medium for a storage server to solve the problems of uneven heat dissipation and low energy efficiency caused by increased chip power density and heat accumulation in modern storage servers.

[0006] To achieve the above-mentioned purpose, the present invention provides a heat dissipation control method for a storage server, comprising the following steps: obtaining the operating characteristic parameters and real-time power consumption data of each chip in the storage server, and determining the temperature distribution of each chip in the server based on the operating characteristic parameters and the real-time power consumption data to obtain an initial temperature data set; performing heat generation characteristic analysis on the initial temperature data set to obtain a heat generation model of each chip in the storage server under the current load; establishing a heat transfer path model of each chip in the server according to the heat generation model and in combination with a preset structure of the server to obtain a heat transfer path set; optimizing the configuration parameters of the heat dissipation control system inside the server based on the heat transfer path set to obtain an optimized cooling parameter setting; applying the optimized cooling parameter setting in the storage server to control the heat dissipation control system of the storage server, wherein the heat dissipation control system includes at least: an air cooling system and a liquid cooling system.

[0007] The present invention also provides a heat dissipation control device for a storage server, comprising: a first acquisition unit, used to acquire the operating characteristic parameters and real-time power consumption data of each chip in the storage server, and based on the operating characteristic parameters and the real-time power consumption data, determine the temperature distribution of each chip in the server to obtain an initial temperature data set; a second acquisition unit, used to perform heat generation characteristic analysis on the initial temperature data set to obtain a heat generation model of each chip in the storage server under the current load; a third acquisition unit, used to establish a heat transfer path model of each chip in the server according to the heat generation model and in combination with a preset structure of the server to obtain a heat transfer path set; a fourth acquisition unit, used to optimize the configuration parameters of the heat dissipation control system inside the server based on the heat transfer path set to obtain an optimized cooling parameter setting; a control unit, used to apply the optimized cooling parameter setting in the storage server to control the heat dissipation control system of the storage server, wherein the heat dissipation control system includes at least: an air cooling system and a liquid cooling system.

[0008] The present invention also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above methods when executing the computer program.

[0009] The present invention also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0010] The heat dissipation control method, device, equipment and storage medium of the storage server provided by the present invention can analyze the heat generation of the chip in real time, build a heat generation model and establish a heat transfer path model of each chip according to the preset structure inside the server by dynamically acquiring the working characteristic parameters and real-time power consumption data of each chip in the storage server. By optimizing the configuration parameters of the air cooling and liquid cooling systems based on these models, the heat dissipation system of the server is precisely controlled, thereby effectively solving the problem of uneven heat dissipation caused by increased chip power density and heat accumulation.

[0011] In addition, the method can dynamically adjust the cooling parameters according to the actual workload and temperature feedback of the server, provide an adaptive heat dissipation control strategy, and thus improve the overall heat dissipation effect and energy efficiency of the server. Through this adaptive dynamic control method, the present invention can also ensure the stability and heat dissipation efficiency of the server under different load conditions, and avoid chip overheating or system crash caused by local heat accumulation.

[0012] Compared with the existing fixed parameter heat dissipation solutions, the heat dissipation control method of the present invention is more flexible and responsive, and can adjust the heat dissipation system according to actual working conditions to meet the high-efficiency requirements of modern data centers for heat dissipation of high-performance storage servers.

[0013] It should be noted that the present invention is applicable to a variety of high-density computing and storage scenarios, especially in chip power-intensive servers. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 1 is a schematic diagram of the steps of a heat dissipation control method for a storage server in one embodiment of the present invention; Figure 2 is a structural block diagram of a heat dissipation control device for a storage server in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0015] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] Reference Figure 1 The embodiment of the present invention provides a heat dissipation control method for a storage server, comprising the following steps: S1, obtaining operating characteristic parameters and real-time power consumption data of each chip in the storage server, and determining the temperature distribution of each chip in the server based on the operating characteristic parameters and the real-time power consumption data to obtain an initial temperature data set.

[0018] S2, analyzing the heat generation characteristics of the initial temperature data set to obtain a heat generation model of each chip in the storage server under the current load.

[0019] S3, establishing a heat transfer path model for each chip in the server according to the heat generation model and in combination with a preset structure of the server to obtain a heat transfer path set.

[0020] S4, based on the heat transfer path set, optimizing the configuration parameters of the heat dissipation control system inside the server to obtain optimized cooling parameter settings.

[0021] S5, applying the optimized cooling parameter settings in the storage server to control a heat dissipation control system of the storage server, wherein the heat dissipation control system includes at least: an air cooling system and a liquid cooling system.

[0022] That is, the working characteristic parameters and real-time power consumption data of each chip in the storage server are obtained. The acquisition of this data is the basis of heat dissipation control. By monitoring the working characteristic parameters of the chip, the status and performance of each chip can be mastered, and the collection of real-time power consumption data can understand the power consumption of the chip under different load conditions in real time. These data not only come from the measurement sensors of the chip itself, but may also involve environmental monitoring inside the server. For example, temperature sensors are used to capture the overall air temperature inside the server. This information provides basic support for subsequent heat dissipation strategies. After obtaining this data, based on these parameters and data, the temperature distribution of each chip inside the server can be further determined to obtain the initial temperature data set. These initial temperature data sets actually reflect the thermal distribution status of each chip under the current load.

[0023] Next, the initial temperature data set is analyzed for heat generation characteristics to generate a heat generation model for each chip in the server under the current load. The heat generation model is constructed to analyze how each chip generates heat under different operating loads. This process usually involves complex heat change trend analysis based on temperature data to generate a model that reflects the heat generation rules of the chip under various loads. Through such analysis, not only can the temperature rise rate of each chip be understood, but also chips with abnormally rapid temperature changes or those that generate a lot of heat under high load can be identified, so that targeted heat dissipation measures can be taken.

[0024] After obtaining the heat generation model, the heat transfer path model of each chip in the server is established in combination with the preset structure of the server to obtain a set of heat transfer paths. To do this, it is necessary to obtain the physical parameters of the storage server, which include not only the location of the chip, but also the geometric dimensions and material properties of the overall structure of the server. Combined with these physical parameters, the thermal physical parameters and the heat generation model can be used to establish the heat transfer equation between each chip, thereby mathematically describing the heat flow between the chips. With the help of the finite element analysis method, the heat transfer paths in the server can be simulated. These paths not only reflect the direction of the heat flow between each chip, but also show the density distribution of the heat flow. Through such simulation, the main heat transfer paths can be extracted and screened and simplified, and finally a network diagram containing the main heat transfer paths between chips is obtained. In this process, topology optimization is also used to analyze thermal resistance and thermal coupling effects, with the aim of determining the optimal heat transfer path, thereby providing an important basis for subsequent heat dissipation optimization.

[0025] Based on the heat transfer path set, the configuration parameters of the heat dissipation control system inside the server are optimized to obtain the optimized cooling parameter settings. Specifically, the heat transfer path set provides a basis for analyzing the heat transfer efficiency and heat dissipation requirements of each chip. Based on these heat transfer efficiencies and requirements, the key cooling areas can be identified and the areas where the air cooling and liquid cooling systems should focus on cooling can be determined. The fan layout in the air cooling system can be reconfigured according to these key cooling areas, including adjusting the fan speed and wind direction to optimize the heat dissipation effect; while the pipelines of the liquid cooling system are optimized according to the areas with higher heat flux density in the server, including replanning the path and flow rate of the liquid cooling pipeline, so as to provide more effective cooling in the high heat flux density areas. These optimized cooling schemes not only need to be coordinated with the parameters of the air cooling system and the liquid cooling system for configuration, but also need to monitor the temperature of each chip inside the server in real time, and dynamically adjust the working parameters of the air cooling and liquid cooling systems according to the temperature changes, so as to finally obtain the optimized cooling parameter settings.

[0026] Finally, these optimized cooling parameter settings are applied in the storage server to control the server's heat dissipation control system. The heat dissipation control system includes at least an air cooling system and a liquid cooling system. By reasonably allocating and dynamically adjusting the working parameters of air cooling and liquid cooling, it can achieve precise control of the internal temperature of the server, and through continuous temperature monitoring, it ensures that the heat dissipation system can adaptively respond to changes in the load, thereby ensuring that the system can maintain high energy efficiency and stable heat dissipation performance under various working conditions. Overall, this heat dissipation control method forms a complete and dynamic heat dissipation management mechanism from the collection of real-time chip data, the construction of a heat generation model, the analysis of the heat transfer path to the parameter optimization of the cooling system, which greatly improves the heat dissipation efficiency of each chip inside the server, thereby ensuring the stability and long-term performance of the system.

[0027] In one example, each chip is provided with a micro temperature sensor and a voltage flow sensor, the micro temperature sensor is used to measure the surface temperature parameters of the chip, the current and voltage sensors are used to measure the actual power consumption parameters of the chip, and multiple preset positions inside the server are provided with ambient temperature sensors, the ambient temperature sensors are used to capture the air temperature parameters inside the server; obtaining the operating characteristic parameters and real-time power consumption data of each chip in the storage server includes: monitoring the operating characteristic parameters of the storage server chip through the micro temperature sensor and the ambient temperature sensor, and monitoring the real-time power consumption data of the storage server chip through the current and voltage sensors; Furthermore, based on the working characteristic parameters and the real-time power consumption data, the temperature distribution of each chip in the server is determined to obtain an initial temperature data set, including: based on the real-time power consumption data and the working characteristic parameters, real-time temperature estimation is performed on each chip in the server through a preset heat dissipation model to obtain temperature estimation values ​​of each chip and module; according to the temperature estimation values, outlier removal and data interpolation are performed on the temperature of each chip to obtain a corrected temperature data set; feature mapping processing based on topological network relationships is performed on the corrected temperature data set, the temperature data is mapped into a multi-dimensional thermal coupling feature space, and a thermal coupling relationship diagram between each chip is constructed; based on the thermal coupling relationship diagram, the thermal conduction characteristics and local heat aggregation effects between each chip are extracted to generate a feature vector with complex thermal relationships to generate an initial temperature data set for each chip in the server.

[0028] In this example, the real-time operation data of the chip is obtained more accurately by setting a micro temperature sensor and a voltage flow sensor on each chip. The micro temperature sensor is mainly used to monitor the surface temperature of the chip, while the voltage flow sensor is used to measure the actual power consumption of the chip. This meticulous sensor configuration ensures that the status of each chip can be accurately grasped in real time, and ambient temperature sensors are also set at multiple preset locations inside the server to capture the air temperature data inside the server. These multi-faceted temperature and power consumption monitoring methods lay a solid data foundation for subsequent temperature distribution determination and heat dissipation control.

[0029] Specifically, the process of obtaining the operating characteristic parameters and real-time power consumption data of each chip is completed through the collaboration of these sensors. The micro temperature sensor and the ambient temperature sensor work together to monitor the temperature changes of the chip and its surrounding environment, thereby building a comprehensive temperature data set, while the current and voltage sensors continuously monitor the real-time power consumption of the chip. This multiple monitoring not only makes the temperature and power consumption data complement each other, but also enables the chip behavior under various operating load conditions to be accurately recorded, thereby providing more comprehensive information support for heat generation analysis and optimization of heat dissipation strategies.

[0030] Based on these collected operating characteristic parameters and real-time power consumption data, the temperature distribution of each chip inside the server can be further determined to obtain an initial temperature data set. In order to accurately determine these temperature distributions, firstly, based on the real-time power consumption data and the operating characteristic parameters of the chip, the real-time temperature of each chip is estimated through a pre-set heat dissipation model. In this way, a temperature estimate can be generated for each chip to understand its thermal state under the current load. However, since there may be some outliers in the actual monitoring data, in order to improve the accuracy of the temperature data, these estimates need to be processed by outlier removal and data interpolation, and finally a corrected temperature data set is obtained.

[0031] After obtaining the corrected temperature data, it is necessary to perform feature mapping processing on these data based on the topological network relationship, and map the temperature data into a multi-dimensional thermal coupling feature space. Through this processing, a thermal coupling relationship diagram between each chip is constructed to clarify the interaction and thermal coupling effect between each chip in heat transfer. Based on this thermal coupling relationship diagram, the thermal conduction characteristics and local heat aggregation effects between each chip are further extracted to generate a feature vector with a complex thermal relationship in order to generate an initial temperature data set for each chip inside the server. This process converts temperature data into thermal coupling features, which can not only help identify which chips have strong heat transfer, but also reveal the local heat accumulation phenomenon of the chip, which is crucial for further optimizing the heat dissipation path and cooling strategy inside the server.

[0032] By conducting such detailed monitoring and data analysis of the temperature of each chip, we can better understand the thermal behavior and heat distribution of the chip during operation, especially the temperature changes and heat accumulation under high load conditions. This understanding provides a key reference for the establishment of subsequent heat dissipation models and the optimization of heat dissipation control.

[0033] In one example, the heat generation characteristic analysis is performed on the initial temperature data set to obtain a heat generation model of each chip in the storage server under the current load, including: based on the initial temperature data set, performing a temperature change trend analysis on each chip in the storage server to obtain the temperature rise rate of each chip and generate a temperature change rate set; performing a regional clustering analysis on the temperature change rate set to identify the key heat generation area of ​​each chip and obtain a key heat generation area set; for the key heat generation area set, applying the energy conservation analysis based on the first law of thermodynamics to quantitatively calculate the heat generation of each chip in the key area and obtain a heat generation rate set of each chip under different loads; constructing a dynamic thermal model based on the heat generation rate set, performing a time series prediction on the heat generation change of each chip under the current load and obtaining a dynamic feature set of the heat generation model; performing a multi-dimensional correlation analysis on the dynamic feature set to determine the thermal coupling relationship and mutual influence degree between chips and obtain a global feature set of heat generation inside the server; and generating a heat generation model of each chip in the server under the current load based on the global feature set.

[0034] In this example, we mainly describe how to determine the heat generation of each chip under different load conditions, so as to effectively guide the heat dissipation optimization of the server. Specifically, based on the initial temperature data set, the temperature change trend of each chip in the storage server is analyzed. By conducting in-depth research on these temperature data, the temperature rise rate of each chip can be calculated, and finally a set of temperature change rates can be generated. The temperature rise rate is an important indicator of the chip heat generation process. It reflects the heat generation of the chip under the current load, especially the speed of its heat generation. Based on this rate set, we can further determine which chips generate the most heat under the current environment and load conditions, so as to identify the key areas of high heat generation.

[0035] Next, we perform regional cluster analysis on the temperature change rate set to identify the key heat generation area of ​​each chip and obtain the key heat generation area set. The goal of regional cluster analysis is to classify chips that produce similar heat changes into one category in order to identify the key heat dissipation areas inside the server. These key heat generation areas represent the parts that need the most cooling and heat dissipation under a specific load. By accurately identifying these areas, more targeted information can be provided for the design and optimization of the cooling system.

[0036] Then, for these key heat generation areas, we further apply energy conservation analysis based on the first law of thermodynamics to quantitatively calculate the heat generation of each chip in the key area, and obtain the heat generation rate set of each chip under different loads. The application of the first law of thermodynamics means that precise heat analysis is performed on the basis of energy conservation, so that the heat generation rate of each chip can be accurately quantified. This quantitative analysis makes the heat generation behavior of each chip in the server clearer, especially under high load conditions, you can see which chips generate the most heat and their heat generation patterns under different load conditions.

[0037] Based on the heat generation rate set, a dynamic thermal model is constructed to perform time series prediction on the heat generation changes of each chip under the current load, and obtain the dynamic feature set of the heat generation model. Through time series prediction, the future heat generation of each chip can be estimated, so that necessary heat dissipation measures can be taken in advance to ensure that the system can maintain good heat dissipation performance and temperature control when the load changes. The dynamic feature set reflects the evolution of heat generation in the time dimension and is an important basis for predicting and managing chip heat.

[0038] In order to better understand the thermal relationship between chips, a multidimensional correlation analysis was performed on the dynamic feature set to determine the thermal coupling relationship and the degree of mutual influence between chips, thereby obtaining a global feature set for heat generation within the server. The purpose of multidimensional correlation analysis is to study the mutual relationship between the thermal generation behaviors of each chip. This coupling relationship may include direct heat conduction or indirect effects caused by heat accumulation in adjacent chips. Through this analysis, it is possible to understand how the heat between chips affects each other and how these thermal couplings affect the temperature state of the entire server. This global feature set is crucial to understanding the overall behavior of heat generation within the server, and it can provide guidance for system-level heat dissipation optimization.

[0039] Finally, based on the global feature set, a heat generation model of each chip in the server under the current load is generated. This heat generation model is a comprehensive description of the heat generation behavior in the server. It not only reflects the temperature rise of each chip, but also reveals the heat transfer and coupling relationship between chips.

[0040] In one example, according to the heat generation model, in combination with the preset structure of the server, a heat transfer path model of each chip in the server is established to obtain a heat transfer path set, including: obtaining physical parameters of the storage server, and determining a spatial geometric relationship set in the storage server based on the physical parameters of the storage server, wherein the physical parameters at least include chip position, server structure size and material properties; extracting thermophysical parameters of the spatial geometric relationship set, and establishing heat transfer equations between chips in combination with the heat generation model to obtain a mathematical description set of heat transfer between chips; based on the mathematical description set, using a finite element analysis method to simulate the heat transfer paths between chips in the storage server to obtain the heat transfer paths and heat flux density distribution between chips, and generate a heat flow path set; according to the heat flow path set, extracting the main heat transfer paths, performing path screening and simplification, establishing a heat transfer path network diagram containing each chip, and obtaining a heat transfer path set; topologically optimizing the heat transfer path set, determining the optimal path of heat transfer through thermal resistance and thermal coupling effect analysis, and generating a heat transfer path set for subsequent heat dissipation optimization.

[0041] In this example, the heat transfer path model of each chip inside the server is established mainly based on the heat generation model and combined with the preset structure of the server, thereby obtaining a set of heat transfer paths, so as to clarify the way heat is transferred inside the server, and then provide a basis for the optimization of the cooling system.

[0042] Specifically, we first obtain the physical parameters of the storage server, which include the position of the chip, the geometric dimensions of the server structure, and its material properties. These parameters are the basis for building a heat transfer path model, because the relative position of the chip, the overall structure of the server, and the thermal conductivity of the used materials will directly affect the effect of heat transfer. Based on these physical parameters, we can determine the set of spatial geometric relationships inside the server. These spatial geometric relationships reflect the layout of each chip inside the server, thus providing a geometric basis for subsequent heat transfer analysis.

[0043] After understanding the spatial geometric relationship, the next step is to extract the thermophysical parameters of these sets, combine the heat generation model established in the previous step, and establish the heat transfer equation between the chips, so as to obtain a set of mathematical descriptions of heat transfer between chips. This heat transfer equation is established to describe the flow of heat between chips in a mathematical way. The heat transfer equation usually considers parameters such as thermal conductivity, distance, and temperature gradient between chips. Through these mathematical descriptions, we can initially understand how heat is transferred between chips and which chips have the most important heat transfer.

[0044] After obtaining the mathematical description of heat transfer, the finite element analysis method is used to simulate the heat transfer path between the chips inside the storage server. Finite element analysis is a sophisticated numerical simulation method that divides the complex structure inside the server into many small units in order to accurately simulate the transfer of heat between these units. Through finite element analysis, the heat transfer path and heat flux density distribution between the chips can be obtained, thereby generating a set of heat flow paths. These heat flow paths reflect how heat flows inside the server, especially indicating which paths have more concentrated heat flow. This information is crucial for understanding the distribution of heat inside the system.

[0045] Based on the set of heat flow paths, these paths are then extracted and simplified, and finally a network diagram of the heat transfer paths of each chip is established. The purpose of constructing this network diagram is to identify and retain the most important heat transfer paths, thereby simplifying the complex heat transfer network into a key heat transfer model. In this process, some unimportant or less contributing heat transfer paths can be deleted through path screening, making the model more concise and easier to analyze and optimize.

[0046] In order to further optimize the heat transfer path, the heat transfer path set was topologically optimized. This step is to determine the optimal heat transfer path by analyzing thermal resistance and thermal coupling effects. The size of the thermal resistance determines the difficulty of heat transfer, while the thermal coupling effect reflects the thermal interaction between different chips. By combining the analysis of these two aspects, the optimal heat transfer path inside the server can be obtained, thereby providing guidance for the subsequent cooling system design. Through the optimized heat transfer path set, we can have a clearer understanding of how heat should flow inside the server under different load conditions, so that these paths can be given priority in the design and configuration of the cooling system, thereby achieving more effective heat dissipation control.

[0047] In one example, based on the heat transfer path set, the configuration parameters of the heat dissipation control system inside the server are optimized to obtain optimized cooling parameter settings, including: based on the heat transfer path set, the heat transfer efficiency and heat dissipation requirements of each chip are analyzed to obtain a heat dissipation requirement set for each chip; the cooling key areas of the heat dissipation requirement set are identified to determine the cooling key areas of the air cooling system and the liquid cooling system to obtain a cooling key area set; based on the cooling key area set, the fan layout in the air cooling system is reconfigured, the speed and wind direction of each fan are adjusted to obtain fan configuration parameters set; according to the fan configuration parameter set, the pipeline of the liquid cooling system is optimized and designed, and the path and flow rate of the liquid cooling pipeline are re-planned in combination with the high heat flux density area in the heat transfer path to obtain a liquid cooling pipeline optimization scheme; the fan configuration parameter set is combined with the liquid cooling pipeline optimization scheme, and adaptive cooling control is performed by real-time monitoring of the temperature changes of each chip to dynamically adjust the working parameters of the air cooling and liquid cooling systems to obtain a dynamic cooling parameter setting; based on the dynamic cooling parameter setting, the energy efficiency ratio of the overall cooling system is calculated, and the cooling parameters are adjusted secondary based on the energy efficiency ratio to obtain an optimized cooling parameter setting.

[0048] In this example, based on the heat transfer path set, the heat transfer efficiency and heat dissipation requirements of each chip in the server are analyzed in detail to obtain the heat dissipation requirement set of each chip. The heat transfer efficiency is obtained through the heat transfer path model mentioned above. Combining these models can quantify the heat flow and heat accumulation of each chip. By analyzing these data, it can be determined which chips require more cooling and which areas have more urgent heat dissipation needs, thereby obtaining the heat dissipation requirement set of each chip. These requirement sets provide direction for the optimization of the cooling system and determine which parts need to focus on heat dissipation design.

[0049] Next, the cooling key areas are identified for the heat dissipation demand set, and the cooling key areas of the air cooling system and the liquid cooling system are determined to obtain a set of cooling key areas. This means that the thermal demand of each chip inside the server needs to be analyzed and the key areas that require additional heat dissipation need to be divided. Based on these cooling key areas, the resource allocation of the air cooling system and the liquid cooling system can be determined, for example, which areas need to be equipped with more fans and which areas need to increase the density of liquid cooling pipes. The goal of this process is to ensure that cooling resources can be distributed where they are most needed, avoiding waste of resources and uneven cooling effects.

[0050] After identifying the key cooling areas, the fan layout in the air cooling system needs to be reconfigured based on this information. The fan layout is an important factor in the heat dissipation efficiency of the air cooling system. The fan position, speed and wind direction need to be adjusted to ensure that the cooling airflow can cover the key cooling areas to the maximum extent. In order to implement this part, simulation software can be used to simulate the airflow path of the fan to determine the air cooling effect under different configurations. In this way, the optimal configuration parameter set of the fan can be obtained, including the position, power setting, speed, and wind direction angle of each fan, to ensure that the cooling airflow can effectively cover the key cooling areas of the chip.

[0051] At the same time, according to the fan configuration parameter set, the pipeline of the liquid cooling system is optimized. The liquid cooling system is an efficient heat dissipation method, which is particularly suitable for handling the heat dissipation requirements of high heat density areas in the server. In order to optimize the design of the liquid cooling pipeline, it is necessary to re-plan the path of the liquid cooling pipeline and the flow rate of the coolant in combination with the high heat flux density area in the heat transfer path. When implementing this part, you can judge which areas have the highest heat flux density based on the analysis results of the heat transfer path, and increase the density of the liquid cooling pipeline in these areas. The optimization of the flow rate is also key, and hydraulic calculations are required to ensure that the coolant has sufficient flow rate in key areas to quickly take away the heat from the chip surface. These optimized designs can be simulated by computer-aided design (CAD) software to simulate the layout of the liquid cooling pipeline, determine the diameter, bending radius, flow rate and other parameters of the pipeline, and finally obtain the optimization plan for the liquid cooling pipeline.

[0052] After that, the fan configuration parameter set and the liquid cooling pipeline optimization solution are combined, and adaptive cooling control is implemented by real-time monitoring of the temperature changes of each chip to dynamically adjust the working parameters of the air cooling and liquid cooling systems, thereby obtaining dynamic cooling parameter settings. This means that during the actual operation of the server, it is necessary to collect the temperature data of each chip in real time through sensors to determine the current heat dissipation effect. If the temperature rise in a certain area exceeds the set threshold, the speed of the fan in that area can be increased immediately or the flow rate of the liquid cooling pipeline can be increased. This adaptive cooling control requires an intelligent control system to manage all cooling equipment. It can dynamically adjust the working status of each fan and liquid cooling pipeline through an integrated data analysis algorithm to ensure that the server can maintain an appropriate temperature under any operating conditions.

[0053] Finally, based on the dynamic cooling parameter settings, the energy efficiency ratio of the overall cooling system is calculated, and the cooling parameters are adjusted secondary based on the energy efficiency ratio to obtain the optimized cooling parameter settings. The energy efficiency ratio is an important indicator for evaluating the performance of the heat dissipation system, which reflects the cooling effect brought about by the unit energy consumption. At this stage, the energy efficiency ratio of the cooling system under the current configuration is calculated by analyzing the real-time data of the energy consumption of the entire system and the temperature of each chip, and judging whether there is a waste of cooling resources or insufficient heat dissipation based on the calculation results. If the energy efficiency ratio is low, it means that the cooling efficiency is not high, and the working parameters of air cooling and liquid cooling need to be optimized secondary to improve the overall heat dissipation effect and reduce energy consumption. In specific implementation, the heat dissipation effects under different parameter settings can be compared through performance verification in a short period of time, so as to select the optimal cooling parameters.

[0054] Applying the optimized cooling parameters to the server's heat dissipation control system can achieve precise control of the server's internal temperature and maximize overall energy efficiency. The entire heat dissipation control process forms a dynamic adaptive closed-loop control mechanism through continuous data collection, analysis and optimization, which can not only improve the efficiency of the heat dissipation system, but also reduce energy consumption and achieve stable and efficient operation of the server.

[0055] In one example, a fan configuration parameter set is combined with a liquid cooling pipeline optimization scheme, and adaptive cooling control is performed by real-time monitoring of the temperature changes of each chip to dynamically adjust the working parameters of the air cooling and liquid cooling systems to obtain dynamic cooling parameter settings, including: combining the fan configuration parameter set with the liquid cooling pipeline optimization scheme to obtain a preliminary cooling parameter set, and real-time monitoring of the temperature data of each chip inside the server to obtain a real-time temperature data set; adaptive cooling control is performed based on the preliminary cooling parameter set and the real-time temperature data set to dynamically adjust the fan speed of the air cooling system and the coolant flow of the liquid cooling system to obtain a first round of dynamic cooling parameters; the first round of dynamic cooling parameters are verified, and the cooling effect and energy efficiency ratio are evaluated by analyzing the temperature response of each chip to obtain a cooling effect evaluation result; according to the cooling effect evaluation result, the first round of dynamic cooling parameters are secondary optimized and adjusted to obtain the cooling parameter settings after the secondary optimization; the cooling parameter settings after the secondary optimization are applied to the internal cooling system of the server, and the final dynamic cooling parameter settings are obtained through continuous temperature monitoring and cooling parameter adjustment.

[0056] That is, the fan configuration parameter set and the liquid cooling pipeline optimization scheme are combined to form a preliminary cooling parameter set. This means that the configuration scheme of the fan position, speed and wind direction, as well as the optimization scheme of the path and flow rate of the liquid cooling pipeline have been obtained through the previous optimization process. After combining these two parts, an overall preliminary cooling parameter set is formed, which is used to preliminarily control the cooling system of the entire server. At the same time, the temperature of each chip inside the server needs to be monitored in real time to obtain a real-time temperature data set. Through the micro temperature sensors installed on each chip and the ambient temperature sensors in the server, the chip surface temperature and the temperature of the air inside the server can be captured in real time. The collection of these temperature data is the basis for dynamically adjusting the cooling system because it reflects the heat dissipation effect of each chip under the current cooling conditions.

[0057] After obtaining the preliminary cooling parameter set and real-time temperature data set, the system begins to implement adaptive cooling control. The core of this control process is to adjust the working status of the air cooling system and the liquid cooling system according to the changes in temperature data. For example, when the temperature of a chip rises to the set upper limit, the system will automatically increase the speed of the fan in that area or increase the coolant flow rate of the liquid cooling system to ensure that the heat of the chip can be quickly taken away. At the same time, for areas with lower temperatures or low heat demand, the fan speed can be appropriately reduced or the coolant flow rate can be reduced to save energy. The key to this adaptive cooling lies in the real-time nature of the temperature monitoring data and the flexibility of adjustment. It requires the intelligent control system to dynamically adjust the parameters of the fan and liquid cooling system through complex data processing algorithms to obtain the first round of dynamic cooling parameters.

[0058] After the first round of dynamic cooling parameters are implemented, they need to be verified to ensure that these parameters can effectively reduce the temperature and improve the energy efficiency ratio in actual operation. Specifically, the cooling effect and energy efficiency ratio can be evaluated by analyzing the temperature response of each chip to obtain the cooling effect evaluation result. The evaluation of the cooling effect is to determine whether the current cooling configuration is reasonable and whether it can effectively cope with the heat generated by the server. If the temperature of some chips drops significantly, it means that the cooling effect is significant; if the temperature in some areas drops slowly or is still above the safe level, the cooling configuration may need to be further optimized. The evaluation of the energy efficiency ratio is to ensure the energy efficiency of the cooling system, that is, to achieve the best cooling effect with the lowest energy consumption.

[0059] According to the cooling effect evaluation results, the first round of dynamic cooling parameters are optimized and adjusted for the second time to obtain the cooling parameter settings after the second optimization. This second optimization process mainly focuses on optimizing those areas that performed poorly in the first round of cooling, and improves the heat dissipation effect of these areas by further adjusting the fan layout, speed, or flow rate and path of the liquid cooling system. The purpose of the second optimization is to refine the configuration of the cooling system so that the heat dissipation of each area is more balanced, and to avoid the situation where the performance of the entire server is reduced due to excessive temperature in a certain area. When implementing this step, the temperature data under different load conditions may be combined for comparative analysis to find the optimal cooling solution.

[0060] The cooling parameter settings after secondary optimization are applied to the cooling system inside the server, and through continuous temperature monitoring and dynamic adjustment of cooling parameters, the optimal dynamic cooling parameter settings are finally obtained. This means that during the actual operation of the server, the system will continuously collect temperature data and make corresponding adjustments based on this data. Even when the workload changes, such as during peak load periods or low load conditions, the cooling system can automatically adjust the working status of air cooling and liquid cooling according to real-time temperature changes to ensure that the temperature inside the server is always within a safe and efficient range.

[0061] The implementation of this dynamic cooling control enables the entire heat dissipation system to respond to heat generation problems under different load conditions in an adaptive manner. It not only ensures precise control of chip temperature, but also achieves efficient energy utilization by dynamically adjusting the operating parameters of the cooling system.

[0062] In one example, based on the dynamic cooling parameter setting, the energy efficiency ratio of the overall cooling system is calculated, and based on the energy efficiency ratio, the cooling parameters are adjusted for the second time to obtain an optimized cooling parameter setting, including: based on the dynamic cooling parameter setting, real-time collection of energy consumption data of the air cooling and liquid cooling systems and temperature data of each chip, calculating the energy efficiency ratio of the overall cooling system, and obtaining an energy efficiency ratio evaluation result; based on the energy efficiency ratio evaluation result, multi-objective optimization is performed on the dynamic cooling parameter setting to determine the final adjustment direction of the cooling parameters to obtain a final adjusted cooling parameter set; performance verification is performed on the final adjusted cooling parameter set in a short period of time to ensure the stability and high energy efficiency of the cooling system under different load conditions; the final adjusted cooling parameter set is applied to the heat dissipation control system of the server to obtain an optimized cooling parameter setting to achieve precise control of the internal temperature of the server and maximize the overall energy efficiency.

[0063] That is, based on the dynamic cooling parameter setting, the energy consumption data of the air cooling and liquid cooling systems and the temperature data of each chip are collected in real time to calculate the energy efficiency ratio of the overall cooling system and obtain the energy efficiency ratio evaluation result. The dynamic cooling parameter setting is a cooling solution obtained through the preliminary optimization and adaptive adjustment in the previous steps. Therefore, these parameters need to be actually applied to the server, and the energy consumption data and the temperature changes of each chip are collected during operation. Specifically, the energy consumption data of the air cooling system is usually obtained by monitoring the power consumption of the fan, while the energy consumption data of the liquid cooling system is obtained by measuring the power consumption of the pump and the flow state of the coolant. At the same time, the temperature data of each chip is obtained in real time through temperature sensors installed on the chip surface and inside the server. Based on these data, the energy efficiency ratio of the entire cooling system can be calculated. The energy efficiency ratio is a key indicator to measure the cooling effect of the heat dissipation system under unit energy consumption.

[0064] After obtaining the energy efficiency ratio evaluation results, it is necessary to perform multi-objective optimization on the dynamic cooling parameter settings based on this result to determine the final adjustment direction of the cooling parameters in order to obtain the final adjusted cooling parameter set. The core of multi-objective optimization is to comprehensively consider multiple factors, including cooling effect, energy consumption, temperature stability, etc. If the energy efficiency ratio is low, it means that the system consumes too much energy but the cooling effect is not ideal. At this time, the cooling system needs to be reconfigured. The specific implementation of multi-objective optimization may include further adjusting the speed of each fan in the air cooling system, and even adjusting the working period of the fan to increase the working frequency when the temperature rises faster and reduce energy consumption when the temperature is lower. At the same time, for the liquid cooling system, the flow rate of the coolant can be optimized to ensure that the flow rate reaches a suitable level in the key hot area, and appropriately reduce the flow rate in the area with lower heat demand to reduce the power consumption of the pump. Through the process of multi-objective optimization, the cooling parameter combination with the highest energy efficiency ratio can be found, thereby maximizing the cooling effect while minimizing energy consumption.

[0065] The final adjusted cooling parameter set is subjected to performance verification in a short period of time to ensure the stability and high energy efficiency of the cooling system under different load conditions. This means that after multi-objective optimization, the newly obtained cooling parameter settings need to be actually verified for performance. The focus of performance verification is to check the performance of the system under different load conditions, including the heat dissipation capacity under high load and the energy consumption performance under low load. By placing the server under simulated high load and low load conditions, the cooling effect and energy efficiency of these parameter settings are verified respectively, ensuring that the cooling system can maintain effective heat dissipation performance and maintain energy efficiency optimization regardless of the system state. When implementing performance verification, environmental simulation tools can be used to simulate the load conditions of the server in the real operating environment, and by real-time monitoring of the temperature of each chip and the energy consumption of the cooling system, it is ensured that the final adjusted cooling parameter set is the best.

[0066] The final adjusted cooling parameter set is applied to the server's heat dissipation control system to obtain optimized cooling parameter settings to achieve precise control of the server's internal temperature and maximize overall energy efficiency. This means that the optimized cooling parameters will eventually become the system's regular operating parameters and will continue to be used to control the air cooling and liquid cooling systems inside the server. After applying these parameters to the actual cooling system, the server can maintain a stable chip temperature under different workloads and environmental conditions through intelligent control of the cooling system, thereby reducing performance degradation or hardware damage caused by overheating. At the same time, through these optimized cooling parameter settings, the cooling system can minimize energy consumption while ensuring the heat dissipation effect, so that the energy efficiency of the entire server can be maximized, which is very important for the economy and environmental friendliness of the server.

[0067] The whole process is an iterative optimization process that continuously improves cooling parameters through energy efficiency evaluation. After each parameter adjustment, the optimal settings of the cooling system are gradually found through energy efficiency evaluation, performance verification, multi-objective optimization and other steps to ensure that the heat dissipation performance and energy efficiency of the server are both optimal. Through such design and implementation, the cooling system of the entire server can not only effectively cope with heat generation under high load conditions, but also achieve high energy efficiency by continuously adjusting cooling parameters, which is of great significance to the stable operation and energy management of server groups in modern data centers.

[0068] It is worth emphasizing that for large chip servers, maintenance costs are extremely high, especially when there are problems with the cooling system. The impact is not only the maintenance cost of the equipment, but also the huge economic losses and operational risks caused by long-term downtime. Since these servers generate a lot of heat during continuous operation, the cooling requirements are very strict. Any poor cooling may cause the system to overheat, resulting in a sharp drop in performance or even a complete shutdown. Therefore, keeping the temperature of the server within a safe range is crucial to the stability of the equipment and extending its service life. For this reason, the application of this advanced adaptive cooling control method is particularly necessary to ensure the efficient and safe operation of the server! In order to facilitate the understanding of the present invention, an embodiment is now provided for understanding: The experimental object is a large storage server at the data center level. The server is equipped with 48 XYZ-1000 high-performance processing chips. The maximum power consumption of a single chip is 120W. The surface temperature of the chip can exceed 90℃ under full load. The temperature is required to be controlled below 75℃. The server is equipped with two cooling systems: air cooling and liquid cooling to meet the stringent heat dissipation requirements.

[0069] At the initial stage of the experiment, micro temperature sensors and current and voltage sensors were used to monitor the real-time temperature and power consumption of the chips. Complete operating characteristic parameters and power consumption data were obtained. Based on these data, the temperature distribution was estimated using a preset heat dissipation model. The results showed that the temperature of the 16 chips in the middle of the server quickly rose to 85°C under high load (within the initial 5 minutes), becoming a key heat generation area.

[0070] After the key heat generation area is determined, the energy conservation analysis of these chips is performed based on the first law of thermodynamics. The heat generation rate set under full load is calculated. About 720 joules of heat are generated per minute. Finite element simulation is performed using the heat transfer path model to identify the heat flux concentration area. The liquid cooling system pipeline is optimized in combination with the high heat flux density. The pipeline density in the key hot area is increased, and the coolant flow rate is increased to 3 liters per minute.

[0071] The air cooling and liquid cooling synergistic adaptive control strategy is applied. The cooling system is dynamically optimized and adjusted. Based on the preliminary cooling parameters, the temperature of each chip is monitored in real time, and dynamic cooling control is implemented. The fan speed and coolant flow rate are adjusted through the intelligent control system. After running for 3 hours, the monitoring data shows that the chip temperature in the central high-temperature area drops from 85°C to 70°C. The overall energy efficiency ratio of the system is improved by 15%.

[0072] The performance verification after multi-objective optimization shows that the energy consumption of the air cooling system is too high under low load. The fan speed is further optimized under low load. The speed is reduced by 20%. It does not affect the efficient cooling effect of liquid cooling. The short-term performance verification results show that the cooling parameters finally adjusted have good adaptability under different load conditions.

[0073] Finally, the optimized cooling parameters are applied to the heat dissipation control system. The cooling system is finely managed through real-time monitoring and adaptive adjustment. The chip temperature is maintained within a safe range. The energy consumption of the cooling system is significantly reduced. The energy efficiency ratio is further improved. The risk of downtime due to overheating is avoided. The maintenance cost is greatly reduced. The overall energy efficiency and operational reliability are improved.

[0074] The heat dissipation control method of this project has significantly improved the heat dissipation efficiency of the server through experiments. It reduces the energy consumption and maintenance cost of system operation. For large servers with high maintenance costs and huge downtime costs, the solution of the present invention is indispensable. Its effectiveness and necessity have been fully verified.

[0075] like Figure 2 As shown, an embodiment of the present invention further provides a heat dissipation control device for a storage server, comprising: The first acquisition unit 1 is used to acquire the operating characteristic parameters and real-time power consumption data of each chip in the storage server, and determine the temperature distribution of each chip in the server based on the operating characteristic parameters and the real-time power consumption data to obtain an initial temperature data set.

[0076] The second acquisition unit 2 is used to analyze the heat generation characteristics of the initial temperature data set to obtain a heat generation model of each chip in the storage server under the current load.

[0077] The third acquisition unit 3 is used to establish a heat transfer path model of each chip in the server according to the heat generation model and in combination with a preset structure of the server to obtain a heat transfer path set.

[0078] The fourth acquisition unit 4 is used to optimize the configuration parameters of the heat dissipation control system inside the server based on the heat transfer path set to obtain optimized cooling parameter settings.

[0079] The control unit 5 is used to apply the optimized cooling parameter settings in the storage server to control the heat dissipation control system of the storage server, wherein the heat dissipation control system at least includes: an air cooling system and a liquid cooling system.

[0080] In this embodiment, for the specific implementation of each unit in the above device embodiment, please refer to the above method embodiment, which will not be repeated here.

[0081] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer 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, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0082] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0083] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0084] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0085] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the presence of other identical elements in the process, device, article or method including the element.

[0086] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A heat dissipation control method for a storage server, characterized in that: The following steps are involved: Acquire operating characteristic parameters and real-time power consumption data of each chip in the storage server, and determine the temperature distribution of each chip in the server based on the operating characteristic parameters and the real-time power consumption data to obtain an initial temperature data set; Performing heat generation characteristic analysis on the initial temperature data set to obtain a heat generation model of each chip in the storage server under the current load; According to the heat generation model, in combination with the preset structure of the server, a heat transfer path model of each chip in the server is established to obtain a heat transfer path set; Based on the heat transfer path set, optimizing the configuration parameters of the heat dissipation control system inside the server to obtain optimized cooling parameter settings; The optimized cooling parameter settings are applied in the storage server to control a heat dissipation control system of the storage server, wherein the heat dissipation control system includes at least: an air cooling system and a liquid cooling system.

2. The heat dissipation control method according to claim 1, characterized in that: Each chip is provided with a micro temperature sensor and a voltage flow sensor, the micro temperature sensor is used to measure the surface temperature parameter of the chip, the current and voltage sensor is used to measure the actual power consumption parameter of the chip, and multiple preset positions inside the server are provided with ambient temperature sensors, the ambient temperature sensor is used to capture the air temperature parameter inside the server; Acquiring the operating characteristic parameters and real-time power consumption data of each chip in the storage server, including: monitoring the operating characteristic parameters of the storage server chip through the micro temperature sensor and the ambient temperature sensor, and monitoring the real-time power consumption data of the storage server chip through the current and voltage sensor; and / or, Based on the operating characteristic parameters and the real-time power consumption data, the temperature distribution of each chip in the server is determined to obtain an initial temperature data set, including: Based on the real-time power consumption data and the operating characteristic parameters, a preset heat dissipation model is used to perform real-time temperature estimation on each chip in the server to obtain a temperature estimation value of each chip and module; According to the temperature estimation value, outlier removal and data interpolation are performed on the temperature of each chip to obtain a corrected temperature data set; Performing feature mapping processing based on topological network relationship on the corrected temperature data set, mapping the temperature data into a multi-dimensional thermal coupling feature space, and constructing a thermal coupling relationship diagram between each chip; Based on the thermal coupling relationship diagram, the heat conduction characteristics and local heat concentration effects between the chips are extracted to generate a feature vector with complex thermal relationships, so as to generate an initial temperature data set of each chip in the server.

3. The heat dissipation control method according to claim 1, characterized in that: Performing heat generation characteristic analysis on the initial temperature data set to obtain a heat generation model of each chip in the storage server under the current load includes: Based on the initial temperature data set, performing temperature change trend analysis on each chip in the storage server, obtaining the temperature rise rate of each chip, and generating a temperature change rate set; Performing regional cluster analysis on the temperature change rate set, identifying the key heat generation area of ​​each chip, and obtaining a key heat generation area set; For the key heat generation area set, energy conservation analysis based on the first law of thermodynamics is applied to quantitatively calculate the heat generation of each chip in the key area, and obtain a set of heat generation rates of each chip under different loads; Building a dynamic thermal model based on the heat generation rate set, performing time series prediction on the heat generation change of each chip under the current load, and obtaining a dynamic feature set of the heat generation model; Performing multi-dimensional correlation analysis on the dynamic feature set to determine the thermal coupling relationship and mutual influence degree between the chips, and obtaining a global feature set of heat generation inside the server; A heat generation model of each chip in the server under the current load is generated according to the global characteristic set.

4. The heat dissipation control method according to claim 3, characterized in that: According to the heat generation model, in combination with the preset structure of the server, a heat transfer path model of each chip in the server is established to obtain a heat transfer path set, including: Acquire physical parameters of the storage server, and determine a set of spatial geometric relationships within the storage server based on the physical parameters of the storage server, wherein the physical parameters at least include chip positions, server structure dimensions, and material properties; Extracting thermal physical property parameters from the spatial geometric relationship set, combining the thermal generation model, establishing heat transfer equations between chips, and obtaining a mathematical description set of heat transfer between chips; Based on the mathematical description set, a finite element analysis method is used to simulate the heat transfer path between the chips in the storage server, so as to obtain the heat transfer path and heat flux density distribution between the chips and generate a heat flow path set; According to the heat flow path set, the main heat transfer paths are extracted, the paths are screened and simplified, a heat transfer path network diagram including each chip is established, and a heat transfer path set is obtained; The heat transfer path set is topologically optimized, and the optimal path of heat transfer is determined through thermal resistance and thermal coupling effect analysis, so as to generate a heat transfer path set for subsequent heat dissipation optimization.

5. The heat dissipation control method according to claim 4, characterized in that: Based on the heat transfer path set, the configuration parameters of the heat dissipation control system inside the server are optimized to obtain optimized cooling parameter settings, including: Based on the heat transfer path set, the heat transfer efficiency and heat dissipation requirements of each chip are analyzed to obtain the heat dissipation requirement set of each chip; Identify cooling key areas for the heat dissipation demand set, determine cooling key areas for the air cooling system and the liquid cooling system, and obtain a cooling key area set; Based on the set of cooling key areas, the fan layout in the air cooling system is reconfigured, and the speed and wind direction of each fan are adjusted to obtain a set of fan configuration parameters; According to the fan configuration parameter set, the pipeline of the liquid cooling system is optimized and designed, and the path and flow rate of the liquid cooling pipeline are replanned in combination with the high heat flux density area in the heat transfer path to obtain an optimization solution for the liquid cooling pipeline; Combine the fan configuration parameter set with the liquid cooling pipeline optimization scheme, and perform adaptive cooling control by real-time monitoring of the temperature changes of each chip to dynamically adjust the working parameters of the air cooling and liquid cooling systems to obtain dynamic cooling parameter settings; Based on the dynamic cooling parameter setting, the energy efficiency ratio of the overall cooling system is calculated, and the cooling parameters are adjusted secondary based on the energy efficiency ratio to obtain optimized cooling parameter settings.

6. The heat dissipation control method according to claim 5, characterized in that: Combine the fan configuration parameter set with the liquid cooling pipeline optimization solution, and perform adaptive cooling control by real-time monitoring of the temperature changes of each chip to dynamically adjust the working parameters of the air cooling and liquid cooling systems to obtain dynamic cooling parameter settings, including: Combine the fan configuration parameter set with the liquid cooling pipeline optimization solution to obtain a preliminary cooling parameter set, and monitor the temperature data of each chip inside the server in real time to obtain a real-time temperature data set; Performing adaptive cooling control based on the preliminary cooling parameter set and the real-time temperature data set to dynamically adjust the fan speed of the air cooling system and the coolant flow of the liquid cooling system to obtain a first round of dynamic cooling parameters; Verify the first round of dynamic cooling parameters, analyze the temperature response of each chip, evaluate the cooling effect and energy efficiency ratio, and obtain the cooling effect evaluation results; According to the cooling effect evaluation results, the first round of dynamic cooling parameters are adjusted for secondary optimization to obtain the cooling parameter settings after secondary optimization; The cooling parameter settings after the secondary optimization are applied to the internal cooling system of the server, and the final dynamic cooling parameter settings are obtained through continuous temperature monitoring and cooling parameter adjustment.

7. The heat dissipation control method according to claim 5, characterized in that: Based on the dynamic cooling parameter setting, the energy efficiency ratio of the overall cooling system is calculated, and the cooling parameters are adjusted secondary based on the energy efficiency ratio to obtain an optimized cooling parameter setting, including: Based on dynamic cooling parameter settings, the energy consumption data of the air cooling and liquid cooling systems and the temperature data of each chip are collected in real time to calculate the energy efficiency ratio of the overall cooling system and obtain the energy efficiency ratio evaluation results; According to the results of the energy efficiency ratio evaluation, multi-objective optimization is performed on the dynamic cooling parameter settings to determine the final adjustment direction of the cooling parameters and obtain the final adjusted cooling parameter set; Perform performance verification of the final adjusted cooling parameter set in a short period of time to ensure the stability and high energy efficiency of the cooling system under different load conditions; The final adjusted cooling parameter set is applied to the heat dissipation control system of the server to obtain optimized cooling parameter settings, so as to achieve accurate control of the internal temperature of the server and maximize the overall energy efficiency.

8. A heat dissipation control device for a storage server, characterized in that: include: a first acquisition unit, configured to acquire operating characteristic parameters and real-time power consumption data of each chip in the storage server, and determine the temperature distribution of each chip in the server based on the operating characteristic parameters and the real-time power consumption data to obtain an initial temperature data set; A second acquisition unit is used to analyze the heat generation characteristics of the initial temperature data set to obtain a heat generation model of each chip in the storage server under the current load; A third acquisition unit is used to establish a heat transfer path model of each chip in the server according to the heat generation model and in combination with a preset structure of the server to obtain a heat transfer path set; A fourth acquisition unit, configured to optimize the configuration parameters of the heat dissipation control system inside the server based on the heat transfer path set to obtain optimized cooling parameter settings; A control unit is used to apply the optimized cooling parameter settings in the storage server to control the heat dissipation control system of the storage server, wherein the heat dissipation control system includes at least: an air cooling system and a liquid cooling system.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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