Control method and control device of server air-cooled test cabinet and server air-cooled test cabinet

Abstract

The application provides a control method and device of a server air-cooled test cabinet and the server air-cooled test cabinet, relates to the technical field of server testing, and the control method of the server air-cooled test cabinet comprises the following steps: acquiring server running state parameters and current environmental temperature and humidity data; determining the power consumption load level and heat dissipation demand of a server to be tested according to the server running state parameters; determining a matching heat dissipation strategy of the power consumption load level and the heat dissipation demand under the current environmental temperature and humidity data, and controlling a corresponding test channel to perform heat dissipation work. The application can dynamically adjust the heat dissipation strategy according to the real-time running state of the server and the environmental conditions, effectively avoid the problems of energy waste and insufficient heat dissipation, and improve the accuracy of heat dissipation performance verification and the test efficiency.

Description

Technical Field

[0001] This application relates to the field of server testing technology, and in particular to a control method, control device, and server air-cooled test cabinet for a server air-cooled test cabinet. Background Technology

[0002] In the field of server repair and testing, traditional repair organizations often use oil cooling or liquid cooling to dissipate heat from servers. Because these methods use oil or liquid as a medium, the operating space for repair personnel is limited when connecting test probes, network cables, power cords, or replacing circuit board components. Furthermore, the cooling medium is prone to residue on the server surface, affecting the stability of the circuit board's electrical performance. Residual media can interfere with the fault reproduction process, causing decreased test accuracy and fault location errors, while also increasing the complexity of media cleaning and maintenance. Although air-cooled test cabinets avoid the media residue problem and improve operational convenience, existing air-cooled test cabinets have significant shortcomings in their heat dissipation strategy design. Their heat dissipation mechanism typically uses a fixed pattern, unable to dynamically adjust according to the real-time operating status of the server under test and the current ambient temperature and humidity data. This manifests in the fact that all test channels use the same fan speed and airflow distribution, ignoring the differences in power consumption and load levels of different servers. In low-load testing scenarios, the fans continuously run at high speed, resulting in energy waste; in high-load testing scenarios, the cooling airflow is insufficient to match the actual cooling needs of the server, leading to distorted heat dissipation performance verification results and reduced accuracy of stability tests. Furthermore, existing air-cooled test cabinets lack independent control capabilities for each test channel. When multiple servers are tested in parallel within the same cabinet, the heat from adjacent test channels overlaps, creating thermal interference. This further weakens the reliability of fault diagnosis and the reproducibility of test results. The rigidity of this heat dissipation strategy cannot adapt to the dynamic changes in server operating parameters, making it difficult to achieve precise matching of heat dissipation, cooling time, and heat dissipation curves, thus limiting the comprehensiveness and efficiency of server heat dissipation performance verification. Summary of the Invention

[0003] The main objective of this invention is to provide a control method for a server air-cooled test cabinet, which aims to dynamically adjust the heat dissipation strategy according to the real-time operating status of the server and environmental conditions, effectively avoid energy waste and insufficient heat dissipation, and improve the accuracy and efficiency of the heat dissipation performance verification of the server under test.

[0004] To achieve the above objectives, the present invention provides a control method for a server air-cooled test cabinet, the server air-cooled test cabinet including multiple test channels, the server air-cooled test cabinet being used for verifying the heat dissipation performance and stability testing of the server under test, and the control method of the server air-cooled test cabinet including: Obtain server operating status parameters and current ambient temperature and humidity data; Based on the server operating status parameters, determine the power consumption load level and heat dissipation requirements of the server under test; Determine a matching heat dissipation strategy between the power load level and the heat dissipation requirements under the current ambient temperature and humidity data, and control the corresponding test channel to perform heat dissipation operations.

[0005] Optionally, the test channel is equipped with multiple active heat dissipation components; The step of determining a matching heat dissipation strategy between the power load level and the heat dissipation requirement under the current ambient temperature and humidity data, and controlling the corresponding test channel to perform heat dissipation operations, includes: Obtain the location distribution of key components in the server under test; Based on the location distribution of the key components, determine the thermally sensitive areas of the key components of the service under test; Calculate the current thermal load value based on the thermally sensitive area and the power load level; Based on the current heat load value and the current ambient temperature and humidity data, the target speed value and target airflow distribution ratio of each active heat dissipation component are found in the preset heat dissipation strategy lookup table. The operation of each active cooling component is controlled according to the target rotation speed value and the target airflow distribution ratio.

[0006] Optionally, the active cooling component includes an airflow fan; The step of determining the heat-sensitive area of ​​the key component of the service under test based on the location distribution of the key components includes: Based on the power consumption density and spatial location of each key component, multiple heat-sensitive sub-regions are divided, and the intersection of the coverage areas of different airflow fans is marked as the cross-heat-sensitive zone. The step of calculating the current heat load value based on the heat-sensitive area and the power load level includes: The local heat load is calculated for each of the heat-sensitive sub-regions, and the total hardware heat generation value is obtained by summing all the local heat loads. For the cross-heat-sensitive zone, the cooling contribution weight of each airflow fan to the cross-heat-sensitive zone is determined according to the effective coverage area ratio of the adjacent airflow fans in the cross-heat-sensitive zone. The step of looking up the target rotation speed and target airflow distribution ratio of each active cooling component in a preset heat dissipation strategy lookup table based on the current heat load value and the current ambient temperature and humidity data includes: Based on the total hardware heat generation value, the test pressure level parameters configured for the server under test in the current maintenance and verification phase, and the current ambient temperature and humidity data, the base rotation speed value and base airflow distribution ratio are queried from the heat dissipation strategy comparison table. Based on the cooling contribution weight of each airflow fan and the number and area of ​​the cross-thermal sensitive zone, the basic airflow distribution ratio is adjusted to determine the target speed value and target airflow distribution ratio of each airflow fan. The step of controlling the operation of each active cooling component according to the target rotation speed value and the target airflow distribution ratio includes: According to the target rotation speed value and the target air volume distribution ratio, the start-stop and speed adjustment of the airflow fan are controlled one by one.

[0007] Optionally, the control corresponding to the test channel performs a heat dissipation operation, including: Obtain the current temperature of the current test channel and determine the real-time temperature rise rate; Obtain the historical baseline curve and determine the curvature deviation value between the historical baseline curve and the real-time temperature rise rate; If the curvature deviation value exceeds the first preset curvature threshold, the corresponding test channel is controlled to increase the heat dissipation airflow, and the real-time temperature rise rate after the heat dissipation airflow is increased is obtained, and the curvature deviation value is recalculated. If the newly calculated curvature deviation value still exceeds the first preset curvature threshold, the corresponding test channel is controlled to issue a first alarm message, and if the curvature deviation value exceeds the second preset curvature threshold, the power supply of the corresponding test channel is cut off and a second alarm message is issued; the second preset curvature threshold is greater than the first preset curvature threshold.

[0008] Optionally, obtaining server operating status parameters and current ambient temperature and humidity data includes: Acquire electrical data, vibration frequency, noise level in decibels, and temperature values ​​of key components during server operation; The server's operating status parameters are determined by weighted analysis of the electrical data, vibration frequency, noise decibel value, and temperature values ​​of each key component. Collect the first temperature value of the air inlet and the second temperature value of the air outlet of the corresponding test channel in the current cabinet, as well as the first humidity data of multiple locations in the test channel; Collect the third temperature value and the second humidity data of the current external environment of the cabinet.

[0009] Optionally, determining the power consumption load level and heat dissipation requirements of the server under test based on the server operating status parameters includes: Based on the temperature values ​​and real-time power consumption values ​​of each key component in the server operating status parameters, the corresponding basic power consumption load level and basic heat dissipation requirement value are found in the preset load level lookup table. Analyze the fluctuation amplitude of electrical data, the number of abnormal frequency bands in the distribution characteristics of vibration frequency, and noise energy data in the server's operating status parameters; The fluctuation amplitude, the number of abnormal frequency bands, and the noise energy data are collected and matched with a preset correction coefficient lookup table to determine the corresponding correction coefficient. The basic heat dissipation requirement value is calculated with the correction coefficient to generate the corrected heat dissipation requirement, and the basic power load level is used as the power load level.

[0010] Optionally, the step of collecting the fluctuation amplitude, the number of abnormal frequency bands, and the noise energy data and matching them with a preset correction coefficient lookup table to determine the corresponding correction coefficient includes: Based on the fluctuation range, the corresponding first basic correction coefficient is looked up in the preset first correction coefficient lookup table; the first correction coefficient lookup table contains multiple fluctuation ranges, and each fluctuation range corresponds to a first basic correction coefficient. Based on the number of abnormal frequency bands, the corresponding second adjustment coefficient is looked up in a preset second correction coefficient lookup table; the second correction coefficient lookup table contains multiple abnormal frequency band number ranges, and each abnormal frequency band number range corresponds to a second adjustment coefficient; Multiply the first basic correction factor by the second adjustment factor to obtain the first intermediate correction factor; Based on the noise energy data, the corresponding third adjustment coefficient is looked up in a preset third correction coefficient lookup table; the third correction coefficient lookup table contains multiple noise energy ranges, and each noise energy range corresponds to a third adjustment coefficient; The correction coefficient is obtained by multiplying the first intermediate correction coefficient by the third adjustment coefficient.

[0011] Optionally, the control method for the server air-cooled test cabinet further includes: After the heat dissipation operation is completed in the corresponding test channel, the power supply of the corresponding test channel is turned off, and the test report of the temperature curve, power consumption curve and fan speed record of the corresponding test channel in each test stage is obtained. Store the test report to the remote management backend.

[0012] In addition, to achieve the above objectives, the present invention also provides a control device, the control device comprising: a memory, a processor, and a control program for a server air-cooled test cabinet stored in the memory and executable on the processor, the control program for the server air-cooled test cabinet being configured to implement the control method for the server air-cooled test cabinet as described above.

[0013] Furthermore, to achieve the above objectives, the present invention also provides a server air-cooled test cabinet, comprising: Test cabinet body; An air-cooled testing device is located in the main body of the testing cabinet and is used to cool down the server under test during the testing process. As described above, the control device is electrically connected to the air-cooled testing device. The control device is used to acquire server operating status parameters and current ambient temperature and humidity data, and based on the server operating status parameters, determine the power consumption load level and heat dissipation requirements of the server under test, then determine a matching heat dissipation strategy for the power consumption load level and the heat dissipation requirements under the current ambient temperature and humidity data, and control the corresponding test channel to perform heat dissipation operations.

[0014] This invention relates to a server air-cooled test cabinet, which includes multiple test channels. The cabinet is used to verify the heat dissipation performance and stability of servers under test. The control method of the server air-cooled test cabinet acquires server operating status parameters and current ambient temperature and humidity data. Based on these parameters, it determines the power consumption load level and heat dissipation requirements of the server under test. Then, it determines a matching heat dissipation strategy for the power consumption load level and heat dissipation requirements under the current ambient temperature and humidity data, and controls the corresponding test channels to perform heat dissipation operations. Thus, by acquiring server operating status parameters and ambient temperature and humidity data, dynamically determining the power consumption load level and heat dissipation requirements, and matching the optimal heat dissipation strategy to control the test channels, it achieves fine-tuning of the heat dissipation strategy. This effectively solves the energy waste and insufficient heat dissipation problems caused by the fixed heat dissipation mode in traditional air-cooled test cabinets. It can dynamically adjust the heat dissipation strategy according to the real-time operating status of the server and environmental conditions, effectively avoiding energy waste and insufficient heat dissipation, and improving the accuracy and efficiency of heat dissipation performance verification. Attached Figure Description

[0015] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0016] Figure 1 This is a schematic diagram of the control method for a server air-cooled test cabinet according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the control method for a server air-cooled test cabinet according to another embodiment of the present invention; Figure 3 This is a schematic diagram of the control method for a server air-cooled test cabinet according to another embodiment of the present invention; Figure 4 This is a schematic diagram of the control method for a server air-cooled test cabinet according to another embodiment of the present invention; Figure 5 This is a schematic diagram of the control method for a server air-cooled test cabinet according to another embodiment of the present invention; Figure 6 This is a schematic diagram of the control method for a server air-cooled test cabinet according to another embodiment of the present invention; Figure 7 This is a schematic diagram of the control method for a server air-cooled test cabinet according to another embodiment of the present invention; Figure 8 This is a schematic diagram of the control method for a server air-cooled test cabinet according to another embodiment of the present invention; Figure 9 This is a schematic diagram of the structure of a server air-cooled test cabinet according to an embodiment of the present invention.

[0017] Explanation of reference numerals in the attached figures: 10 - Test cabinet body; 20 - Air-cooled test device; 30 - Control device.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Well-known modules, units, and their connections, links, communications, or operations are not shown or described in detail. Furthermore, the described features, architectures, or functions can be combined in any way in one or more embodiments. Those skilled in the art should understand that the various embodiments described below are only for illustrative purposes and not for limiting the scope of protection of the present invention. It is also readily understood that the modules, units, or processing methods in the various embodiments described herein and shown in the accompanying drawings can be combined and designed in various different configurations. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] In the field of server repair and testing, traditional repair organizations often use oil cooling or liquid cooling to dissipate heat from servers. Because these methods use oil or liquid as a medium, the operating space for repair personnel is limited when connecting test probes, network cables, power cords, or replacing circuit board components. Furthermore, the cooling medium is prone to residue on the server surface, affecting the stability of the circuit board's electrical performance. Residual media can interfere with the fault reproduction process, causing decreased test accuracy and fault location errors, while also increasing the complexity of media cleaning and maintenance. Although air-cooled test cabinets avoid the media residue problem and improve operational convenience, existing air-cooled test cabinets have significant shortcomings in their heat dissipation strategy design. Their heat dissipation mechanism typically uses a fixed pattern, unable to dynamically adjust according to the real-time operating status of the server under test and the current ambient temperature and humidity data. This manifests in the fact that all test channels use the same fan speed and airflow distribution, ignoring the differences in power consumption and load levels of different servers. In low-load testing scenarios, the fans continuously run at high speed, resulting in energy waste; in high-load testing scenarios, the cooling airflow is insufficient to match the actual cooling needs of the server, leading to distorted heat dissipation performance verification results and reduced accuracy of stability tests. Furthermore, existing air-cooled test cabinets lack independent control capabilities for each test channel. When multiple servers are tested in parallel within the same cabinet, the heat from adjacent test channels overlaps, creating thermal interference. This further weakens the reliability of fault diagnosis and the reproducibility of test results. The rigidity of this heat dissipation strategy cannot adapt to the dynamic changes in server operating parameters, making it difficult to achieve precise matching of heat dissipation, cooling time, and heat dissipation curves, thus limiting the comprehensiveness and efficiency of server heat dissipation performance verification.

[0021] The main solution of this application embodiment is: by acquiring server operating status parameters and current ambient temperature and humidity data, and based on the server operating status parameters, determining the power consumption load level and heat dissipation requirements of the server under test, and then determining a matching heat dissipation strategy for the power consumption load level and heat dissipation requirements under the current ambient temperature and humidity data, and controlling the corresponding test channel to perform heat dissipation operations.

[0022] In this embodiment, for ease of description, the control device 30 will be used as the execution subject in the following description.

[0023] This application provides a solution that dynamically determines the power consumption load level and heat dissipation requirements by acquiring server operating status parameters and ambient temperature and humidity data, and matches the optimal heat dissipation strategy to control the test channel to perform operations. This achieves fine-tuning of the heat dissipation strategy, effectively solving the problems of energy waste and insufficient heat dissipation caused by the fixed heat dissipation mode of traditional air-cooled test cabinets. It can dynamically adjust the heat dissipation strategy according to the real-time operating status of the server and environmental conditions, effectively avoiding energy waste and insufficient heat dissipation, and improving the accuracy and testing efficiency of heat dissipation performance verification.

[0024] Therefore, the present invention proposes a control method for a server air-cooled test cabinet; it is understood that the server air-cooled test cabinet is equipped with a control device 30 for storing and executing the following method. The control device 30 can be implemented by a main controller, such as an MCU (Micro Controller Unit), a DSP (Digital Signal Processor), an FPGA (Field Programmable Gate Array), or a SOC (System On Chip).

[0025] Existing oil / liquid cooling methods have limitations in operation during server maintenance and testing, and residual heat dissipation media can affect test accuracy and fault location. Current air-cooled test cabinets employ a single cooling strategy, unable to dynamically adjust based on real-time server operating status and ambient temperature and humidity data. This results in wasted energy under low load and insufficient cooling under high load, impacting the accuracy of heat dissipation performance verification and stability testing. Furthermore, existing air-cooled test cabinets lack mechanisms for independent control of test channels. When multiple servers are tested in the same cabinet, their heat can accumulate and interfere with each other, reducing the reliability of fault diagnosis and test reproduction.

[0026] Therefore, referring to Figure 1 In one embodiment of the present invention, the server air-cooled test cabinet includes multiple test channels. The server air-cooled test cabinet is used for verifying the heat dissipation performance and stability of the server under test. The control method of the server air-cooled test cabinet includes steps S100-S300, wherein: S100: Obtain server operating status parameters and current ambient temperature and humidity data; S200. Based on the server's operating status parameters, determine the power consumption load level and heat dissipation requirements of the server under test. S300 determines the matching heat dissipation strategy between power load level and heat dissipation requirements under the current ambient temperature and humidity data, and controls the corresponding test channel to perform heat dissipation operations, including heat dissipation amount, heat dissipation time and heat dissipation curve.

[0027] The server air-cooled test cabinet is a device specifically designed for verifying the thermal performance and stability of servers. This cabinet cools the server under test using air cooling to simulate the heat dissipation conditions in a real-world operating environment and to evaluate the server's stable operation under different load conditions. Test channels are the physical spaces within the server air-cooled test cabinet used to independently house and test one or more servers under test. Each test channel typically has independent thermal control capabilities to avoid thermal interference between different servers.

[0028] Server operating status parameters refer to a series of data reflecting the current operating status of the server under test, such as power consumption, temperature of key components, fan speed, voltage, current, vibration frequency, and noise level in decibels. These parameters are used to assess the server's health and performance. Ambient temperature and humidity data refer to the temperature and humidity information of the environment inside or outside the test cabinet. This data is crucial for evaluating heat dissipation effectiveness and adjusting heat dissipation strategies, as environmental conditions directly affect heat dissipation efficiency.

[0029] In this context, power load level refers to different workload levels categorized based on the real-time power consumption of the server under test. For example, it can be divided into low load, medium load, and high load, with different load levels corresponding to different heat generation. Cooling requirement refers to the amount of heat dissipation required by the server under test to maintain its normal operating temperature under the current power load level. This requirement is dynamic and influenced by the server's own heat generation and environmental conditions. Cooling operation refers to the process by which the test channel provides cooling to the server under test, including adjusting airflow, fan speed, cooling time, and creating a specific cooling curve to achieve the desired cooling effect.

[0030] This embodiment first acquires server operating status parameters and current ambient temperature and humidity data. In one implementation, server operating status parameters can be obtained by manually observing server indicator lights, manually recording server fan speed, or through a single temperature sensor. Alternatively, they can be acquired in real-time using an embedded sensor array to collect multi-point temperature, power consumption, and vibration data. Ambient temperature and humidity data can be periodically read by placing a conventional thermometer and hygrometer within the test channel. In another implementation, power consumption data can be acquired through the server's built-in basic monitoring interface, and ambient temperature and humidity data can be acquired through a temperature and humidity sensor placed at the entrance of the test channel.

[0031] Based on the obtained server operating status parameters, the power load level and heat dissipation requirements of the server under test are determined. In one implementation, the server's current power consumption value is compared with a preset power consumption range to determine its power load level. The heat dissipation requirement can then be directly obtained from a basic lookup table based on this power load level, corresponding to a preset heat dissipation value. For example, low load corresponds to 100W heat dissipation, medium load to 300W heat dissipation, and high load to 500W heat dissipation.

[0032] Therefore, a matching cooling strategy is determined for the power load level and the heat dissipation requirement under the current ambient temperature and humidity data, and the corresponding test channel is controlled to perform heat dissipation operations. The heat dissipation operation includes heat dissipation amount, heat dissipation time, and heat dissipation curve. In one implementation, a preset cooling scheme can be selected from a preset strategy library based on the determined power load level and heat dissipation requirement, combined with the current ambient temperature (e.g., considering only temperature, not humidity). This scheme may include a preset fan speed and duration to provide a expected heat dissipation amount. As another implementation, a preset heat dissipation curve template, such as a linear temperature rise / fall curve, can be selected based on the power load level and heat dissipation requirement, and the fan at a specific location within the test channel can be controlled to adjust its speed according to the curve template to achieve the expected heat dissipation amount and heat dissipation time.

[0033] This embodiment avoids the operational inconvenience and media residue problems of traditional oil / liquid cooling methods, while overcoming the limitations of the single heat dissipation strategy in existing air-cooled test cabinets. By dynamically adjusting the heat dissipation volume, heat dissipation time, and heat dissipation curve, this method can provide adaptive heat dissipation for servers with different power consumption load levels, thereby improving the accuracy of heat dissipation performance verification and stability testing, reducing energy consumption, and improving the convenience of test operations.

[0034] This embodiment of the server air-cooled test cabinet includes multiple test channels. The server air-cooled test cabinet is used for verifying the heat dissipation performance and stability of the server under test. The control method of the server air-cooled test cabinet acquires server operating status parameters and current ambient temperature and humidity data. Based on the server operating status parameters, it determines the power consumption load level and heat dissipation requirements of the server under test, and then determines a matching heat dissipation strategy for the power consumption load level and heat dissipation requirements under the current ambient temperature and humidity data, controlling the corresponding test channels to perform heat dissipation operations. In this way, by acquiring server operating status parameters and ambient temperature and humidity data, dynamically determining the power consumption load level and heat dissipation requirements, and matching the optimal heat dissipation strategy to control the test channels to perform operations, it achieves fine-grained adjustment of the heat dissipation strategy. This effectively solves the energy waste and insufficient heat dissipation problems caused by the fixed heat dissipation mode of traditional air-cooled test cabinets. It can dynamically adjust the heat dissipation strategy according to the real-time operating status of the server and environmental conditions, effectively avoiding energy waste and insufficient heat dissipation problems, and improving the accuracy and testing efficiency of heat dissipation performance verification.

[0035] In practice, the heat distribution inside the server under test is often uneven, and the execution of heat dissipation work lacks refined control methods, which may lead to insufficient local heat dissipation or excessive overall energy consumption, making it difficult to achieve efficient and precise heat dissipation.

[0036] Therefore, referring to Figure 2 Another embodiment of the present invention provides a control method for a server air-cooled test cabinet, based on the above. Figure 1In the embodiment shown, the test channel is equipped with multiple active heat dissipation components.

[0037] Determine a matching heat dissipation strategy between the power load level and heat dissipation requirements under the current ambient temperature and humidity data, and control the corresponding test channel to perform heat dissipation operations, including steps S310-S350, wherein: S310. Obtain the location distribution of key components in the server under test; S320. Based on the location distribution of key components, determine the heat-sensitive areas of the key components to be tested. S330. Calculate the current heat load value based on the heat-sensitive area and power load level; S340. Based on the current heat load value and the current ambient temperature and humidity data, find the target speed value and target air volume distribution ratio of each active heat dissipation component in the preset heat dissipation strategy comparison table. S350 controls the operation of each active cooling component according to the target speed value and the target air volume distribution ratio.

[0038] The test chamber is equipped with multiple active cooling components designed to provide diverse and independently controllable localized heat dissipation capabilities. These components can be vortex tubes, which generate hot and cold airflows using compressed air, suitable for precise cooling of specific points; air amplifiers, which use a small amount of compressed air to drive surrounding air into a high-speed airflow, achieving air circulation and cooling in a localized area; bladeless fans, which generate stable and uniform airflow through their internal structure, suitable for areas requiring high airflow uniformity; synthetic jet radiators, which generate periodic jets through vibrating diaphragms, achieving forced convection heat transfer in a localized area; and common airflow fans, providing wide-area or directional airflow. The combined use of these components allows the test cabinet to perform regional and differentiated heat dissipation control based on the actual heat distribution inside the server.

[0039] This embodiment obtains the location distribution of key components in the server under test by acquiring the precise physical coordinate information of high-heat-generating key components such as the central processing unit (CPU), graphics processing unit (GPU), memory module, and power supply module through pre-loaded server design drawings, CAD model data, or by scanning the server and locating sensors before testing. This provides the basis for refined heat dissipation and data support for subsequent thermal sensitive area division.

[0040] Identifying the thermally sensitive areas of critical components in the service under test, based on their location, involves dividing the server's internal space into several areas requiring focused attention. This is done by using algorithms or pre-defined rules, based on the acquired location information of the critical components, their estimated heat generation, size, and the thermal impact range between them. For example, high-power CPUs and GPUs and their surrounding areas can be defined as core thermally sensitive areas, while other components such as memory and hard drives can be defined as secondary thermally sensitive areas. This division helps to concentrate heat dissipation resources on the areas most in need of cooling.

[0041] Calculating the current thermal load value based on heat-sensitive areas and power load levels involves calculating the local thermal load for each identified heat-sensitive area, taking into account the power density and quantity of its key components and the current power load level of the server under test. Then, all local thermal load values ​​are weighted and summed to obtain the overall thermal load value of the server under test. The weights can be set according to the importance of the area, the difficulty of heat dissipation, or the degree of impact on server performance, thus more accurately reflecting the server's current cooling needs.

[0042] Based on the current heat load and ambient temperature and humidity data, the target rotational speed and target airflow distribution ratio for each active cooling component are looked up in a pre-established multidimensional lookup table or database. The input parameters of this lookup table include the calculated current heat load, ambient temperature and humidity within the test channel, while the output parameters are the specific control commands for each active cooling component, such as its target rotational speed (or power level) and airflow distribution ratio within the entire test channel. This lookup table can be obtained through extensive experimental data, computational fluid dynamics (CFD) simulation results, or machine learning model training to ensure optimal heat dissipation solutions are provided under various operating conditions.

[0043] Controlling the operation of each active cooling component according to the target speed and target airflow distribution ratio means that the control system sends precise control signals to each active cooling component based on the target parameters obtained from the cooling strategy lookup table. For example, the fan motor speed can be adjusted using pulse width modulation (PWM) signals or analog voltage signals, or the airflow distribution can be changed by adjusting mechanical structures such as guide vanes and valves. The control system monitors the actual operating status of each component in real time and performs closed-loop feedback adjustment to ensure that the cooling operation is executed accurately according to the preset strategy.

[0044] Through the above technical solution, this embodiment can accurately locate and target heat sources based on the actual location distribution and heat-sensitive areas of key components inside the server under test, effectively avoiding localized overheating. Simultaneously, by assigning refined target rotation speeds and airflow distribution ratios to each active cooling component, overall overheating is avoided, significantly reducing energy consumption during testing. This refined, regionalized heat dissipation control strategy ensures that the server under test maintains its optimal operating temperature under various power loads and environmental conditions, greatly improving the accuracy and reliability of heat dissipation performance verification and stability testing. Furthermore, this method can dynamically adjust the heat dissipation strategy based on real-time thermal load and ambient temperature and humidity data, enhancing the test cabinet's adaptability to different server models and complex testing scenarios.

[0045] In practical applications, when there are multiple active heat dissipation components, especially airflow fans, in the test channel and their coverage may overlap, simply using an overall heat load search strategy may lead to over-cooling or under-cooling of local areas, and may also cause unnecessary energy consumption, making it difficult to achieve the best balance between heat dissipation efficiency and energy consumption.

[0046] Therefore, referring to Figure 3 Another embodiment of the present invention provides a control method for a server air-cooled test cabinet, based on the above. Figure 2 In the embodiment shown, the active cooling component includes an airflow fan.

[0047] An airflow fan is a device that generates airflow through rotating blades to facilitate heat transfer. Its key feature is its ability to provide directional and adjustable airflow and speed, making it suitable for precise cooling of specific areas. In a server air-cooled test cabinet, airflow fans can be strategically deployed around the server under test to specifically dissipate heat from heat-generating components. Their operating parameters, such as rotation speed, directly affect the airflow and air pressure they generate, thus influencing the cooling effect. This embodiment uses monitoring data to adjust each airflow fan to achieve optimal operating performance while minimizing power consumption.

[0048] Based on the location distribution of key components, the thermally sensitive areas of the key components to be tested are determined, including step S321, wherein: S321. Based on the power consumption density and spatial location of each key component, multiple heat-sensitive sub-regions are divided, and the intersection of the coverage areas of different airflow fans is marked as the cross-heat-sensitive zone.

[0049] Power density refers to the power consumed by critical components per unit volume or area, reflecting their heat generation intensity; spatial location refers to the precise coordinates of critical components within the server. This power density and spatial location data can be obtained through the server's hardware monitoring interface, design drawings, or pre-calibration. A heat-sensitive sub-region refers to dividing the server's interior into several local areas with different heat generation characteristics and cooling requirements, based on the power density and spatial location of critical components. For example, high-power components such as the CPU, GPU, and memory modules, and their surrounding areas, are designated as independent heat-sensitive sub-regions. Airflow fan coverage refers to the area that a single airflow fan can cover and provide cooling under normal operating conditions. This can be determined through the fan's physical characteristics, installation location, and airflow simulation analysis. A cross-heat-sensitive zone refers to an area where the effective coverage areas of at least two different airflow fans overlap, and this overlapping area contains one or more heat-sensitive sub-regions. Marking these areas as cross-heat-sensitive zones is to identify areas that may be affected by multiple fans simultaneously, allowing for special consideration in cooling strategy development and avoiding redundant cooling or resource waste. The airflow speed in some overlapping areas needs to be adjusted, otherwise it will cause the airflow fan to consume too much energy or the overlapping areas to be not cooled properly.

[0050] Calculate the current heat load value based on the heat-sensitive area and power load level, including step S331, where: S331. Calculate the local heat load for each heat-sensitive sub-region, and then sum all the local heat loads to obtain the total hardware heat generation value. For cross-heat-sensitive areas, determine the cooling contribution weight that each airflow fan should bear for the cross-heat-sensitive area based on the effective coverage area ratio of the adjacent airflow fans in the cross-heat-sensitive area.

[0051] Local heat load refers to the heat generated by each heat-sensitive sub-region under its current operating state. Its calculation can be based on parameters such as the power density, operating temperature, and thermal conductivity of key components within that region. The total hardware heat generation is the sum of the heat generated by the hardware in all heat-sensitive sub-regions, representing the overall cooling requirements of the server under test. Given that cross-thermal-sensitive areas may be cooled by multiple airflow fans, the local heat load of this area needs to be recalculated to avoid double counting or overestimating cooling requirements. The recalculation method can be based on a preset weighting model, such as allocating weights according to the distance between adjacent airflow fans and the cross-thermal-sensitive area, the rated airflow of the fans, or the cooling contribution obtained through experimental calibration. For example, if a cross-thermal-sensitive area is covered by two fans, and their cooling contributions to the area are 60% and 40% respectively, then the local heat load of this area can be weighted and averaged or proportionally distributed according to its weight with each fan when calculating the total heat load, to more accurately reflect the actual cooling requirements.

[0052] Based on the current heat load value and the current ambient temperature and humidity data, the target rotation speed value and target airflow distribution ratio of each active cooling component are found in the preset heat dissipation strategy lookup table, including steps S341-S342, wherein: S341. Based on the total hardware heat generation value, the test pressure level parameters configured for the server under test in the current maintenance and verification phase, and the current ambient temperature and humidity data, query the basic rotation speed value and basic air volume distribution ratio in the heat dissipation strategy comparison table. S342. Based on the cooling contribution weight of each airflow fan and the number and area of ​​the cross-thermal sensitive zone, the basic airflow distribution ratio is corrected to determine the target speed value and target airflow distribution ratio of each airflow fan.

[0053] The heat dissipation strategy reference table is a pre-established database that stores recommended operating parameters for each active cooling component (including airflow fans) under different total hardware heat generation values ​​and ambient temperature and humidity conditions. By querying this heat dissipation strategy reference table, a preliminary, general base speed value and base airflow allocation ratio can be quickly obtained. Based on this, this embodiment adjusts the base airflow allocation ratio according to the cooling contribution weight of each airflow fan, the number and area of ​​cross-thermal sensitive zones, and determines the target speed value and target airflow allocation ratio for each airflow fan. When cross-thermal sensitive zones are identified, special processing is required. The adjustment process may include: if there are many cross-thermal sensitive zones, it indicates a complex distribution of hotspots inside the server and a high degree of fan overlap, which may require more precise adjustment of the contribution of each fan; the size of the cross-thermal sensitive zone directly affects its heat dissipation requirements and dependence on adjacent fans, and larger cross-thermal sensitive zones may require more careful airflow allocation. For example, for multiple airflow fans covering the same cross-thermal sensitive zone, the base airflow allocation ratio of some fans can be appropriately reduced or adjusted according to the actual heat load of the area and the relative position of each fan to avoid over-cooling and energy waste. Conversely, if the cross-thermal sensitive area is a critical hotspot with high cooling requirements, it may be necessary to fine-tune the fan allocation ratio to ensure adequate cooling. This adjustment allows for a more precise assignment of cooling tasks to each airflow fan.

[0054] According to the target speed value and target airflow distribution ratio, the operation of each active cooling component is controlled separately, including step S351, wherein: S351. Control the start / stop and speed adjustment of the airflow fans one by one according to the target speed value and the target air volume distribution ratio.

[0055] The control device 30 precisely controls the start-up, stop, and operating speed of each airflow fan based on the corrected target speed value and target airflow distribution ratio of each airflow fan, using independent control signals. For example, the fan speed can be adjusted using a pulse width modulation (PWM) signal to achieve stepless or multi-level adjustment of airflow. This individual control method ensures that each fan can provide the most appropriate heat dissipation according to the specific needs of its assigned heat-sensitive area and cross-heat-sensitive areas.

[0056] Through the above technical solution, this embodiment effectively solves the problem of achieving the optimal balance between heat dissipation efficiency and energy consumption when multiple active heat dissipation components (especially airflow fans) overlap in the coverage area of ​​a server air-cooled test cabinet. By finely dividing the heat-sensitive sub-regions according to the power consumption density and spatial location of key components, and identifying the overlapping heat-sensitive zones covered by different airflow fans, this embodiment can more accurately assess local heat load. In particular, when calculating the total heat load, the local heat load of the overlapping heat-sensitive zones is weighted according to the weight shared by adjacent airflow fans, avoiding the repeated calculation of the heat dissipation requirements of overlapping areas, making the heat load assessment more accurate. On this basis, this embodiment corrects the basic airflow allocation ratio according to the number and area of ​​the overlapping heat-sensitive zones, thereby determining a more reasonable target speed value and target airflow allocation ratio for each airflow fan. This control strategy ensures that each airflow fan provides only the optimal heat dissipation it needs, avoiding overcooling and unnecessary energy consumption in overlapping areas, and also ensuring effective heat dissipation of key hot spots. Ultimately, by controlling the start, stop, and speed adjustment of each airflow fan, precise, efficient, and energy-saving heat dissipation management of hot spots inside the server was achieved, significantly improving the accuracy and reliability of server air-cooling testing and optimizing energy utilization efficiency during the testing process.

[0057] In this embodiment, the control method for the server air-cooled test cabinet is executed cyclically at preset time intervals (e.g., 5 to 30 seconds). After a complete heat dissipation strategy matching and fan control cycle is completed, the control device re-acquires the current server operating status parameters and ambient temperature and humidity data, re-determines the power consumption load level and heat dissipation requirements, and returns to execute the heat dissipation strategy matching and fan control steps. Through the above cyclic execution method, a closed-loop feedback control process of "monitoring-decision-control-re-monitoring" is formed, enabling the heat dissipation strategy to continuously and adaptively adjust according to the dynamic changes in the server operating status, achieving the technical effect of "dynamically adjusting the heat dissipation strategy according to the real-time operating status and environmental conditions of the server".

[0058] Furthermore, during the process of controlling the start / stop and speed adjustment of the airflow fans one by one according to the target speed value and target airflow distribution ratio, a gradual adjustment method is adopted to avoid instantaneous large fluctuations in local airflow static pressure. Specifically, for each airflow fan, the target speed value is approached step by step according to a preset increment (e.g., adjusting from 50 RPM to 100 RPM each time); a preset delay (e.g., 100 milliseconds to 500 milliseconds) is inserted between each two adjacent fan adjustments to allow the local airflow field caused by the adjustment of that fan to stabilize before issuing an adjustment command for the next fan. Through the above-mentioned gradual interval adjustment method, the instability of the flow field caused by instantaneous changes in static pressure during the process of controlling multiple fans one by one is reduced, ensuring that the heat dissipation airflow distribution in the air-cooled test cabinet is achieved in the expected proportion.

[0059] In actual heat dissipation operations, the operating status of the server under test may change dynamically, or the test environment may experience unpredictable fluctuations, causing the preset heat dissipation strategy to be unable to fully adapt, which may lead to the risk of local overheating, affecting the accuracy and safety of the test. There is a lack of dynamic monitoring and response mechanisms for real-time abnormal situations during the heat dissipation operation.

[0060] Therefore, referring to Figure 4 Another embodiment of the present invention provides a control method for a server air-cooled test cabinet, based on the above. Figure 1 The embodiment shown controls the corresponding test channel to perform heat dissipation operations, including steps S360-S390, wherein: S360: Obtain the current temperature of the current test channel and determine the real-time temperature rise rate; S370. Obtain the historical reference curve and determine the curvature deviation value between the historical reference curve and the real-time temperature rise rate. S380. If the curvature deviation value exceeds the first preset curvature threshold, control the corresponding test channel to increase the heat dissipation airflow, obtain the real-time temperature rise rate after the heat dissipation airflow is increased, and recalculate the curvature deviation value. S390. If the newly calculated curvature deviation value still exceeds the first preset curvature threshold, control the corresponding test channel to issue a first alarm message, and if the monitored curvature deviation value exceeds the second preset curvature threshold, cut off the power supply of the corresponding test channel and issue a second alarm message; the second preset curvature threshold is greater than the first preset curvature threshold.

[0061] The current temperature of the test channel can be acquired in real time by a temperature sensor installed within the channel, such as a thermocouple, thermistor, or infrared temperature sensor. The real-time temperature rise rate refers to the ratio of the temperature change within the test channel to the time interval over a certain time interval. This can be calculated using continuously acquired temperature data; for example, acquiring the temperature every second and then calculating the ratio of the difference between two adjacent temperature acquisitions to the time interval. The historical baseline curve is a pre-stored reference curve showing the temperature rise rate of the server under test at a specific power load level under standard or ideal test conditions. This historical baseline curve can be obtained through multiple experimental tests, simulations, or manually imported empirical data, and can be optimized and adjusted in subsequent tests based on actual conditions. The historical baseline curve is the temperature rise rate curve obtained from each test. Initially, some manually imported test data is used; subsequent curves are optimized and adjusted based on actual conditions. The curvature deviation value is used to quantify the difference in shape between the real-time temperature rise rate curve and the historical baseline curve. For example, it can be obtained by comparing the slope, second derivative, or calculating the difference using a curve fitting algorithm.

[0062] The first preset curvature threshold is an empirical value used to determine whether the real-time temperature rise trend begins to deviate from the normal range, indicating a potential risk of insufficient heat dissipation. When the curvature deviation of the real-time temperature rise rate exceeds this threshold, the control device 30 determines that the current heat dissipation capacity may be insufficient to cope with the heat load. At this time, the control device 30 instructs the air-cooled testing device 20 (e.g., fan, blower, etc.) in the test channel to increase its speed or adjust the air duct opening to increase the heat dissipation airflow, thereby enhancing the heat dissipation capacity. After increasing the heat dissipation airflow, the control device 30 continues to monitor the temperature in real time and recalculates the temperature rise rate and its curvature deviation value from the historical baseline curve to evaluate the adjustment effect. If, after one adjustment of the heat dissipation airflow, the curvature deviation value remains high and exceeds the first preset curvature threshold again, it indicates that the problem of insufficient heat dissipation has not been effectively alleviated. At this time, the system will issue a first alarm message, such as through audible and visual alarms, interface prompts, or remote notifications, to remind the operator to pay attention. The second preset curvature threshold is a higher safety limit value than the first preset curvature threshold, indicating that the temperature rise trend has reached a dangerous level. When the curvature deviation value further deteriorates and exceeds the second preset curvature threshold, to prevent the server under test from being damaged due to overheating or causing a safety accident, the control system will immediately cut off the power supply to the corresponding test channel and issue a second alarm message, such as an emergency stop signal or a higher-level alarm notification, to ensure the safety of equipment and personnel. This embodiment dynamically adjusts the fan speed and duct opening based on preset thresholds; when an abnormal temperature rise trend is detected, a graded alarm is automatically triggered and redundant heat dissipation modules are activated.

[0063] Through the above technical solution, this embodiment can achieve real-time dynamic monitoring and intelligent response to the heat dissipation operation in the server air-cooled test cabinet. By continuously acquiring the current temperature of the test channel and calculating the real-time temperature rise rate, and comparing it with the historical benchmark curve, abnormal deviations in the temperature rise trend can be detected in a timely manner. When the temperature rise trend is detected to deviate from the normal range, the control device 30 can automatically increase the heat dissipation airflow for initial intervention, effectively avoiding the risk of local overheating caused by insufficient preset strategies or sudden situations. If the abnormal situation continues to worsen, the system will trigger a tiered alarm mechanism, from issuing the first alarm message to remind attention, to cutting off the power supply and issuing the second alarm message in extreme cases, thereby ensuring the safety and stability of the server under test during the test process, avoiding equipment damage and test data distortion, and significantly improving the reliability and intelligence level of the test process.

[0064] In practical applications, relying on only a limited or single data source may not fully and accurately reflect the actual operating status and environmental conditions of the server under test, thus affecting the accurate judgment of subsequent power load levels and heat dissipation requirements, resulting in an inadequate heat dissipation strategy, or even missing potential anomalies.

[0065] Optionally, refer to Figure 5 The present invention also provides a control method for a server air-cooled test cabinet, based on the above. Figure 1 The embodiment shown obtains server operating status parameters and current ambient temperature and humidity data, including steps S110-S140, wherein: S110: Acquire electrical data, vibration frequency, noise decibel value, and temperature values ​​of key components during server operation. S120, weighted analysis of electrical data, vibration frequency, noise decibel value and temperature values ​​of each key component, to determine the server's operating status parameters; S130. Collect the first temperature value of the air inlet and the second temperature value of the air outlet of the corresponding test channel in the current cabinet, as well as the first humidity data of multiple locations in the test channel. S140: Collect the third temperature value and second humidity data of the current external environment of the cabinet.

[0066] The test involves acquiring electrical data, vibration frequency, noise levels (decibels), and temperatures of key components during server operation to comprehensively and multidimensionally capture the real-time operating status of the server under test. Electrical data, such as current and voltage values, are key indicators reflecting the server's real-time power consumption and load, and are typically acquired in real-time through the server's built-in monitoring interfaces (such as BMC and IPMI). Monitoring this electrical data allows for accurate assessment of the server's energy consumption and workload. Vibration frequency reflects the operational stability of internal mechanical components (such as fans and hard drives). Abnormal vibration frequencies may indicate mechanical failures or imbalances; these can be collected by accelerometers installed at the server's mounting location, and abnormal patterns can be identified through methods such as spectrum analysis. Noise levels (decibels) are an important parameter for assessing the server's operating noise level; high noise may indicate excessive fan operation or mechanical friction. This can be monitored in real-time using a microphone array located within the test channel. Temperature values ​​of key components, such as the CPU, GPU, memory, and hard drives, directly reflect the server's heat dissipation efficiency and hotspot distribution. This data is usually acquired through temperature sensors integrated within the server.

[0067] After acquiring the aforementioned multi-dimensional data, this embodiment uses weighted analysis of electrical data, vibration frequency, noise decibel levels, and temperature values ​​of key components to determine server operating status parameters. The purpose of weighted analysis is to comprehensively evaluate the overall operating status of the server and assign different importance weights to different types of data. For example, based on experience, historical data, or preset models, higher weights can be assigned to the temperature of key components, as it directly relates to heat dissipation performance; medium weights can be assigned to electrical data to reflect power consumption load; and appropriate weights can be assigned to vibration frequency and noise decibel levels to reflect mechanical health and potential anomalies. By comprehensively calculating these weighted data, such as using weighted averaging, weighted summation, or more complex machine learning algorithms (of course, simplified methods are preferred for weighted calculations; model optimization is only introduced when simplified methods cannot meet accuracy requirements, in order to simplify the complexity and maintenance costs of the server test cabinet), a comprehensive server operating status parameter can be obtained. This parameter can more comprehensively and accurately characterize the server's current health status, performance load, and potential abnormal trends, thus providing a reliable basis for subsequent judgments on power consumption load levels and heat dissipation requirements. For example, a comprehensive state index S = w1*T_avg + w2*P_total + w3*V_score + w4*N_score can be set, where T_avg is the average temperature of key components, P_total is the total power consumption, V_score is the vibration score, N_score is the noise score, and w1-w4 are the corresponding weights. These weights can be adjusted according to actual testing requirements and server characteristics.

[0068] In addition, to comprehensively understand the test environment conditions, this embodiment also collects the first temperature value of the air inlet, the second temperature value of the air outlet, and the first humidity data at multiple locations within the test channel of the current rack. These data are acquired using temperature and humidity sensors installed within the test channel. The first temperature value of the air inlet and the second temperature value of the air outlet directly reflect the temperature rise within the test channel, and the heat dissipation efficiency can be evaluated by calculating the temperature difference. Collecting first humidity data at multiple locations within the test channel helps to understand the distribution of moisture within the channel, which is crucial for evaluating the reliability and heat dissipation performance of electronic components. Furthermore, this embodiment also collects the third temperature value and second humidity data of the external environment of the current rack. These external environmental data are acquired using external temperature and humidity sensors and serve as benchmark environmental conditions to evaluate the test rack's isolation and adaptability to changes in the external environment, ensuring the accuracy and repeatability of the test results. The current ambient temperature and humidity data are acquired using temperature and humidity sensors installed within the test channel.

[0069] Through the above technical solution, this embodiment can acquire the operating status parameters of the server under test and the temperature and humidity data of the test environment from multiple dimensions and in a more refined manner. This comprehensive data acquisition and weighted analysis method overcomes the inaccurate evaluation problems that may be caused by a single data source or coarse data acquisition, making the determination of server operating status parameters more accurate and reliable. In addition, detailed monitoring of the temperature and humidity inside and outside the test channel ensures accurate control of the test conditions. Therefore, based on this high-precision, multi-dimensional data, the control device 30 can more accurately determine the power consumption load level and heat dissipation requirements of the server under test, and formulate a more matched and optimized heat dissipation strategy accordingly. This significantly improves the accuracy, effectiveness, and reliability of the server air-cooled test cabinet in heat dissipation performance verification and stability testing, and can promptly detect potential operational anomalies, ensuring the safety and efficiency of the testing process.

[0070] In practical applications, relying solely on basic parameters such as the temperature and real-time power consumption of key components to assess the heat dissipation requirements of a server may not be sufficient to capture subtle changes and potential anomalies in the server's internal operation, such as power fluctuations, mechanical vibrations, or abnormal noises. These factors can all cause dynamic changes in heat dissipation requirements, thereby affecting the accuracy of the heat dissipation strategy and the reliability of the test results.

[0071] Therefore, referring to Figure 6 Another embodiment of the present invention provides a control method for a server air-cooled test cabinet, based on the above. Figure 1 The illustrated embodiment determines the power consumption load level and heat dissipation requirements of the server under test based on server operating status parameters, including steps S210-S240, wherein: S210. Based on the temperature values ​​and real-time power consumption values ​​of each key component in the server's operating status parameters, find the corresponding basic power consumption load level and basic heat dissipation requirement value in the preset load level comparison table.

[0072] This embodiment aims to provide a preliminary, baseline assessment of the power load level and heat dissipation requirements for the server under test. The control device 30 continuously monitors the temperature values ​​of key components within the server, such as the CPU, GPU, and memory modules, as well as the overall real-time power consumption of the server. This data is used as input to look up values ​​in a pre-established load level lookup table. This load level lookup table is typically constructed based on extensive experimental data, simulation models, or industry standards, and includes basic power load levels (e.g., divided into low, medium, and high levels) and corresponding initial heat dissipation requirements (e.g., recommended fan speed percentage or required airflow level) for different combinations of key component temperatures and power consumption levels. In this way, a preliminary heat dissipation strategy can be quickly obtained.

[0073] S220. Analyze the fluctuation amplitude of electrical data in the server's operating status parameters, the number of abnormal frequency bands in the distribution characteristics of vibration frequency, and noise energy data.

[0074] The analysis aims to delve into subtle features in the server's operational status that may indicate potential risks or anomalies. Specifically, for fluctuations in electrical data, the control device 30 collects real-time data on the server's power supply current and voltage, calculating their fluctuation amplitude within a specific time window, for example, by calculating standard deviation, peak-to-peak value, or coefficient of variation. Large fluctuations may indicate instability in the power module or drastic load changes on internal server components, potentially leading to additional heat generation or decreased heat dissipation efficiency. Regarding the number of anomalous frequency bands in the vibration frequency distribution characteristics, vibration data from the server is acquired using an accelerometer installed at the server's mounting location, and this data undergoes spectral analysis (e.g., through Fast Fourier Transform). The control device 30 identifies anomalous frequency components inconsistent with the server's normal operating mode, such as vibrations caused by fan bearing wear, loose mechanical parts, or airflow imbalances, and counts the number of these anomalous frequency bands. For noise energy data, noise data during server operation is acquired using a microphone array located in the test channel, and its sound pressure level or energy value within a specific frequency band is calculated. Abnormally high noise energy may indicate fan failure, airflow turbulence, or mechanical component malfunction, all of which can affect heat dissipation.

[0075] S230. After collecting the data on fluctuation amplitude, number of abnormal frequency bands, and noise energy, match them with the preset correction coefficient comparison table to determine the corresponding correction coefficient.

[0076] The data matching process aims to quantify the abnormal indicators obtained from the above analysis into correction factors for heat dissipation requirements. Specifically, the fluctuation amplitude of electrical data, the number of abnormal vibration frequency bands, and noise energy data are used as inputs and matched against a pre-defined correction coefficient lookup table. This lookup table can be a multi-dimensional lookup table or it can be obtained through training a machine learning model (again, machine learning models are not the first choice; simplification is preferred, and machine learning is only used when simplification is not possible). For example, when the fluctuation amplitude is larger, the number of abnormal frequency bands is more numerous, and the noise energy is higher, the lookup table will output a larger correction coefficient. This correction coefficient reflects the additional heat dissipation required beyond the basic heat dissipation requirements under the current server operating conditions.

[0077] S240. Calculate the basic heat dissipation requirement value and the correction factor to generate the corrected heat dissipation requirement, and use the basic power load level as the power load level.

[0078] This implementation aims to refine the initially determined base cooling requirements based on the aforementioned correction coefficients, resulting in more accurate and robust cooling needs. The calculation can employ multiplication (e.g., corrected cooling requirement = base cooling requirement × correction coefficient), addition, or other preset functional relationships. For example, if the correction coefficient is 1.2, the corrected cooling requirement will be 1.2 times the base cooling requirement. This ensures that the cooling strategy can adequately handle potential anomalies during server operation. Furthermore, since power load levels are typically discrete classifications, the previously identified base power load level is directly used as the final power load level.

[0079] Through the above technical solution, this embodiment, in determining the power load level and heat dissipation requirements of the server under test, not only considers basic parameters such as the temperature and real-time power consumption of key components, but also identifies potential abnormal or unstable factors in server operation by analyzing the fluctuation amplitude of electrical data, the number of abnormal frequency bands in the distribution characteristics of vibration frequency, and noise energy data. These subtle changes in operating status are quantified as correction coefficients and used to correct the basic heat dissipation requirement value, thereby generating a more accurate and dynamic corrected heat dissipation requirement. This method can more comprehensively and precisely assess the actual heat dissipation requirements of the server, avoiding insufficient or excessive heat dissipation problems caused by relying solely on surface temperature or power consumption data. By identifying potential abnormal conditions in advance and correcting the heat dissipation strategy, the risk of overheating of the server during testing can be effectively prevented, improving the accuracy and foresight of heat dissipation operations, thereby enhancing the accuracy and reliability of server heat dissipation performance verification and stability testing. It also helps optimize energy consumption and extend the service life of test equipment and the server under test.

[0080] To more accurately assess the cooling requirements of the server under test, factors such as the fluctuation amplitude of electrical data, the number of abnormal frequency bands of vibration, and noise energy data are comprehensively considered. These factors are then aggregated and matched against a pre-defined correction coefficient table to determine the correction coefficients. However, these parameters each reflect different dimensions of the server's operating state. Simply aggregating them and performing a one-time match may not fully capture the complex interactions between them, resulting in insufficiently refined determination of the correction coefficients. This, in turn, affects the accuracy and optimization of the cooling strategy.

[0081] Therefore, referring to Figure 7 Another embodiment of the present invention provides a control method for a server air-cooled test cabinet, based on the above. Figure 6 The embodiment shown collects data on fluctuation amplitude, the number of abnormal frequency bands, and noise energy, and matches it with a preset correction coefficient lookup table to determine the corresponding correction coefficient, including steps S231-S235, wherein: S231. Based on the fluctuation range, look up the corresponding first basic correction coefficient in the preset first correction coefficient lookup table; the first correction coefficient lookup table contains multiple fluctuation ranges, and each fluctuation range corresponds to a first basic correction coefficient. S232. Based on the number of abnormal frequency bands, look up the corresponding second adjustment coefficient in the preset second correction coefficient lookup table; the second correction coefficient lookup table contains multiple abnormal frequency band quantity ranges, and each abnormal frequency band quantity range corresponds to a second adjustment coefficient. S233. Multiply the first basic correction factor by the second adjustment factor to obtain the first intermediate correction factor; S234. Based on the noise energy data, look up the corresponding third adjustment coefficient in the preset third correction coefficient lookup table; the third correction coefficient lookup table contains multiple noise energy ranges, and each noise energy range corresponds to a third adjustment coefficient. S235. Multiply the first intermediate correction factor by the third adjustment factor to obtain the correction factor.

[0082] The fluctuation range reflects the stability of the server's electrical data (such as current and voltage values). Large fluctuations may indicate unstable power supply or drastic changes in the load on internal components, both of which can lead to additional heat generation or decreased heat dissipation efficiency. The first correction factor lookup table predefines the mapping relationship between different fluctuation ranges and their corresponding first basic correction factors. For example, when the fluctuation range is small, the first basic correction factor may be close to 1, indicating a minor impact; when the fluctuation range is large, the first basic correction factor may be greater than 1, indicating a need for increased heat dissipation. By consulting this table, the impact of electrical fluctuations on heat dissipation requirements can be preliminarily quantified.

[0083] The number of abnormal frequency bands reflects abnormal mechanical vibrations or fan vibrations within the server. Abnormal vibrations can lead to component wear, poor contact, and consequently, localized overheating or impaired heat dissipation. The second correction factor table provides a corresponding second adjustment factor based on the number of detected abnormal frequency bands. For example, the more abnormal frequency bands detected, the larger the second adjustment factor may be, indicating a more significant impact of vibration abnormalities on heat dissipation requirements. This helps to incorporate mechanical stability factors into the assessment of heat dissipation requirements.

[0084] Multiplying the first basic correction factor by the second adjustment factor yields the first intermediate correction factor. This step aims to provide a preliminary comprehensive consideration of two independent but potentially interdependent factors: electrical fluctuations and mechanical vibrations. Through multiplication, the potential impact of their combined or synergistic effects on heat dissipation requirements can be revealed. For example, if both electrical fluctuations and mechanical vibrations are present and of a high degree, the resulting first intermediate correction factor will be larger than if either factor were considered individually, thus more comprehensively reflecting the additional heat dissipation requirements imposed by the overall instability of the server.

[0085] Noise energy data reflects the operating status of internal server components such as fans, hard drives, or other mechanical parts. Abnormal noise energy may indicate fan malfunction, bearing wear, or airflow obstruction, all of which directly affect heat dissipation efficiency. The third correction factor table provides a corresponding third adjustment factor based on the noise energy level. For example, higher noise energy may require a larger third adjustment factor, meaning the cooling system may experience reduced efficiency or require additional compensation.

[0086] The first intermediate correction factor is multiplied by the third adjustment factor to obtain the final correction factor. This final multiplication step comprehensively integrates the effects of the three key dimensions: electrical fluctuations, mechanical vibrations, and noise energy. By multiplying step by step, it is ensured that the contribution of each factor to the final correction factor is accurately quantified and superimposed, resulting in a correction factor that comprehensively reflects the complexity of the server's operating state. This correction factor will be used to precisely adjust the basic heat dissipation requirements, making them more closely match the actual heat dissipation load of the server under its current non-ideal operating conditions.

[0087] Through the above technical solution, this embodiment calculates correction coefficients stepwise and in multiple dimensions for fluctuation amplitude, number of abnormal frequency bands, and noise energy data in the server's operating status parameters. This layered, multiplicative correction mechanism can more precisely and comprehensively capture the changes in heat dissipation requirements caused by multiple factors such as electrical fluctuations, mechanical vibrations, and abnormal noise under non-ideal operating conditions. Compared to simple one-time matching, this solution can more accurately quantify the impact of these complex factors on heat dissipation requirements, thereby making the final determined heat dissipation requirement value more precise. This avoids insufficient or excessive heat dissipation due to inaccurate correction coefficients, improves the adaptability and optimization of the heat dissipation strategy, and thus ensures the stability of the server under test and the reliability of the test results during the testing process.

[0088] If key data from the testing process is not recorded, analyzed, and archived promptly and accurately after the cooling operation is completed, the test results may be incomplete, making it difficult to comprehensively evaluate the server's cooling performance and providing a valid basis for subsequent optimization. Furthermore, keeping the test channel powered on after the operation is complete will result in unnecessary energy waste.

[0089] Therefore, referring to Figure 8 Another embodiment of the present invention provides a control method for a server air-cooled test cabinet, based on the above. Figure 1 The embodiment shown further includes steps S400-S500 in the control method of the server air-cooled test cabinet, wherein: S400: After the heat dissipation operation is completed in the corresponding test channel, turn off the power supply of the corresponding test channel and obtain the test report of the temperature curve, power consumption curve and fan speed record of the corresponding test channel in each test stage. S500 stores test reports to the remote management backend.

[0090] The process involves shutting off the power supply to the corresponding test channel after the cooling operation is completed. This ensures that the server under test (DUT) and related equipment within the test channel cease operation after testing, thus avoiding energy consumption and equipment damage caused by idle operation. This is achieved by the control module sending a shutdown command to the power management unit of the test channel. For example, a programmable logic controller (PLC) or microcontroller can be integrated into the power distribution unit of the server air-cooled test cabinet. Upon receiving a signal indicating that the cooling operation is complete, the PLC or microcontroller controls the corresponding power switch module to disconnect the power output of the test channel. This not only helps save energy but also provides necessary safety assurance for subsequent server replacement or maintenance.

[0091] Obtaining test reports containing temperature curves, power consumption curves, and fan speed records for each test channel at each test stage is crucial for systematically collecting and organizing key performance data during the testing process. This results in structured reports to facilitate the evaluation of the heat dissipation performance and stability of the server under test. Temperature curves are obtained by deploying multiple temperature sensors (e.g., thermocouples, thermistors) within the test channel to monitor the real-time temperatures of key components (such as the CPU, GPU, memory, and hard drive) and air inlet / outlet temperatures, recording this data in time-series format. Power consumption curves are obtained by integrating power meters or current / voltage sensors into the power circuit of the test channel to collect real-time electrical data and calculate real-time power consumption. For fan speed records, if the active cooling components (e.g., airflow fans) within the test channel have speed feedback functionality, their speed data is recorded in real-time. All this data is timestamped and ultimately integrated and analyzed by the control system to generate a test report according to a preset report template. The report may include test start / end times, ambient temperature and humidity, alarm information (if any), and power-off time (if any).

[0092] Storing test reports to a remote management backend aims to achieve centralized management, long-term storage, and remote access to test data, thereby facilitating data analysis, historical tracing, and multi-user collaboration. The test cabinet's control system can connect to the remote management backend server via wired (e.g., Ethernet) or wireless (e.g., Wi-Fi, 5G) networks. Once a test report is generated, the control device 30 uploads the report file to the remote management backend's database or file storage system via standard protocols (e.g., FTP, HTTP / HTTPS) or message queuing services (e.g., MQTT). The remote management backend can be a cloud platform, an internal enterprise server, or a data center, providing data storage, querying, analysis, and visualization interfaces for authorized users to view and manage test data anytime, anywhere.

[0093] After completing the heat dissipation operation, all active cooling components are automatically stopped, and a test report is generated containing the temperature curve, power consumption curve, and fan speed records of the server under test at each test stage. If an alarm or power cut-off is triggered during the test, the corresponding timestamp and the cause of the abnormality are recorded in the test report.

[0094] The above technical solution automatically shuts off the power supply to the test channel after the heat dissipation operation is completed, effectively avoiding energy waste, reducing operating costs, and improving the safety of the test environment. Simultaneously, the systematic acquisition and generation of test reports containing temperature curves, power consumption curves, and fan speed records ensures the integrity, accuracy, and traceability of test data. This detailed data provides a solid foundation for evaluating the heat dissipation performance of the server under test, enabling R&D personnel to comprehensively analyze the server's performance under different loads and environmental conditions. Furthermore, storing the test reports in a remote management backend enables centralized management and remote access to test data, greatly facilitating data sharing, historical data comparison, and long-term trend analysis. This significantly improves testing efficiency and data utilization value, providing valuable data support for server optimization design and performance improvement.

[0095] The present invention also proposes a control device 30, which includes: a memory, a processor, and a control program for a server air-cooled test cabinet stored in the memory and executable on the processor. The control program for the server air-cooled test cabinet is configured to implement the control method for the server air-cooled test cabinet as described above.

[0096] It is worth noting that since the control device 30 of the present invention is based on the control method of the server air-cooled test cabinet described above, the embodiments of the control device 30 of the present invention include all the technical solutions of all embodiments of the control method of the server air-cooled test cabinet described above, and the technical effects achieved are exactly the same, so they will not be repeated here.

[0097] Existing oil / liquid cooling methods have limitations in operation during server maintenance and testing, and residual heat dissipation media can affect test accuracy and fault location. Current air-cooled test cabinets employ a single cooling strategy, unable to dynamically adjust based on real-time server operating status and ambient temperature and humidity data. This results in wasted energy under low load and insufficient cooling under high load, impacting the accuracy of heat dissipation performance verification and stability testing. Furthermore, existing air-cooled test cabinets lack mechanisms for independent control of test channels. When multiple servers are tested in the same cabinet, their heat can accumulate and interfere with each other, reducing the reliability of fault diagnosis and test reproduction.

[0098] The present invention also proposes a server air-cooled test cabinet, which includes a test cabinet body 10, an air-cooled test device 20, and a control device 30 as described in the above embodiments, wherein: The air-cooled testing device 20 is mounted on the main body 10 of the testing cabinet. The air-cooled testing device 20 is used to cool down the server under test during the testing process. The control device 30 is electrically connected to the air-cooled testing device 20. The control device 30 is used to acquire the server's operating status parameters and the current ambient temperature and humidity data. Based on the server's operating status parameters, it determines the power consumption load level and heat dissipation requirements of the server under test. Then, it determines the matching heat dissipation strategy for the power consumption load level and heat dissipation requirements under the current ambient temperature and humidity data, and controls the corresponding test channel to perform heat dissipation operations.

[0099] The core innovation of this embodiment lies in the fact that by electrically connecting the control device 30 to the air-cooled testing device 20, the control device 30 can dynamically match the heat dissipation strategy, thereby solving the problems of single heat dissipation strategy and heat interference between test channels in the prior art. Specifically, the control device 30 determines the power load level and heat dissipation requirements in real time based on server operating status parameters, and intelligently selects a matching heat dissipation strategy based on current ambient temperature and humidity data, providing appropriate heat dissipation volume, heat dissipation time, and heat dissipation curves for different loads. Simultaneously, by controlling the corresponding test channels to independently perform heat dissipation operations, heat superposition interference during multi-server testing is avoided. Since the control device 30 can dynamically adjust the heat dissipation intensity according to real-time parameters, the heat dissipation airflow can be reduced to decrease energy consumption in low-load scenarios, while heat dissipation is enhanced to ensure sufficient cooling in high-load scenarios. Through the above technical solutions, this embodiment significantly improves the accuracy of heat dissipation performance verification and stability testing, effectively reduces energy consumption during the testing process, and improves the convenience of testing operations.

[0100] It is worth noting that since the server air-cooled test cabinet of the present invention is based on the control device 30 described above, the embodiments of the server air-cooled test cabinet of the present invention include all the technical solutions of all embodiments of the control device 30 described above, and the technical effects achieved are exactly the same, so they will not be repeated here.

[0101] The principles of all the above embodiments will be explained through a specific implementation method. It should be noted that this implementation method is not a limitation on the above embodiments. The implementation scheme is as follows: In a server air-cooling testing center, User A needs to verify the thermal performance and stability of a batch of new servers. The center is equipped with a server air-cooling testing cabinet, which has multiple independent testing channels, each capable of accommodating one server under test.

[0102] When a server under test is placed in one of the test channels and the test is started, the control system first acquires the server's operating status parameters in real time. This includes electrical data such as current and voltage values ​​obtained through the server's built-in monitoring interface, vibration frequency obtained through an accelerometer installed at the server's installation location, noise decibel values ​​obtained through a microphone array within the test channel, and temperature values ​​of key components (such as the CPU, GPU, memory module, and power supply module). Simultaneously, the system also collects the first temperature value at the air inlet of the current test channel, the second temperature value at the air outlet, the first humidity data at multiple locations within the channel, and the third temperature value and second humidity data of the external environment of the server rack, to comprehensively understand the current ambient temperature and humidity data.

[0103] The control device 30 performs a weighted analysis on the acquired server operating status parameters. For example, it multiplies the current and voltage values ​​in the electrical data to obtain the real-time power consumption, and combines this with the temperature values ​​of each key component to look up the corresponding basic power consumption load level and basic heat dissipation requirement value in a preset load level lookup table. Simultaneously, the control device 30 also analyzes the fluctuation amplitude of the electrical data, the number of abnormal frequency bands in the vibration frequency distribution characteristics, and noise energy data. After these data are collected, they are matched with a preset correction coefficient lookup table to determine a correction coefficient. For example, based on the fluctuation amplitude, a first basic correction coefficient is looked up in the first correction coefficient lookup table; based on the number of abnormal frequency bands, a second adjustment coefficient is looked up in the second correction coefficient lookup table; and the two are multiplied to obtain a first intermediate correction coefficient. Then, based on the noise energy data, a third adjustment coefficient is looked up in the third correction coefficient lookup table; the first intermediate correction coefficient is multiplied by the third adjustment coefficient to obtain the final correction coefficient. Finally, the basic heat dissipation requirement value is calculated with this correction coefficient to generate the corrected heat dissipation requirement, and the basic power consumption load level is used as the power consumption load level of the server.

[0104] After determining the power consumption load level and heat dissipation requirements of the server under test, the control device 30 determines a matching heat dissipation strategy based on this information and the current ambient temperature and humidity data. The test channel is equipped with multiple active cooling components, such as airflow fans. For finer control, the control device 30 first acquires the location distribution of key components (such as the CPU and GPU) in the server under test, and divides multiple heat-sensitive sub-regions based on the power consumption density and spatial location of each key component. Furthermore, the control device 30 identifies the overlapping areas covered by different airflow fans and marks them as cross-heat-sensitive zones.

[0105] Next, the control device 30 calculates the current heat load value based on these heat-sensitive areas and power consumption load levels. Specifically, it calculates the local heat load for each heat-sensitive sub-region separately, and then sums all the local heat loads to obtain the total heat load value. For the local heat load of overlapping heat-sensitive areas, the system will recalculate it according to the weight shared by adjacent airflow fans.

[0106] Based on the calculated total heat load and the current ambient temperature and humidity data, the control device 30 will look up the base speed value and base airflow distribution ratio of each active heat dissipation component (such as an airflow fan) in a preset heat dissipation strategy lookup table. Considering the special characteristics of the cross-thermal sensitive areas, the control device 30 will adjust the base airflow distribution ratio according to the number and area of ​​the cross-thermal sensitive areas, thereby determining the target speed value and target airflow distribution ratio of each airflow fan.

[0107] Subsequently, the control device 30 will control the start / stop and speed adjustment of the airflow fans in the test channel one by one according to these target speed values ​​and target airflow distribution ratios to perform heat dissipation operations. This dynamic and precise control method contrasts with the single strategy of uniform fan speed and airflow distribution in existing air-cooled test cabinets. It can avoid the problems of energy waste at low loads and insufficient heat dissipation at high loads, and improve the accuracy of heat dissipation performance verification and stability testing.

[0108] During the heat dissipation process, the control device 30 continuously acquires the real-time temperature of the current test channel and determines the real-time temperature rise rate. The control device 30 compares this real-time temperature rise rate with a pre-stored historical baseline curve and calculates the curvature deviation value between the two. The historical baseline curve is an ideal temperature rise rate curve optimized and adjusted based on multiple test data. If the current curvature deviation value exceeds a first preset curvature threshold, it indicates that the heat dissipation effect may be poor or the server load has changed. The control device 30 will control the corresponding test channel to increase the airflow and recalculate the curvature deviation value. If the newly calculated curvature deviation value still exceeds the first preset curvature threshold, the control device 30 will issue a first alarm message. If the detected curvature deviation value exceeds a second preset curvature threshold (which is greater than the first preset curvature threshold), the control device 30 will automatically cut off the power supply to the corresponding test channel and issue a second alarm message to prevent server damage or test data distortion. This dynamic adjustment and graded alarm mechanism based on the real-time temperature rise rate compensates for the shortcomings of existing technologies in responding to abnormal situations.

[0109] When the corresponding test channel completes its heat dissipation operation, the control system automatically shuts off the power supply to that test channel and acquires the temperature curve, power consumption curve, and fan speed records for that channel at each test stage, generating a detailed test report. If an alarm or power cut-off is triggered during the test, the report will also record the corresponding timestamp and the cause of the anomaly. This test report will then be stored in the remote management backend for user A to perform subsequent data analysis and fault diagnosis.

[0110] Using the above method, the server air-cooled test cabinet can dynamically match and execute heat dissipation strategies based on the real-time operating status of the server under test and the current ambient temperature and humidity data. This solves the problems of existing air-cooled test cabinets, such as single heat dissipation strategies, energy waste, insufficient heat dissipation, and affected test accuracy. At the same time, it avoids the drawbacks of oil cooling / liquid cooling methods, such as inconvenience in operation and media residue, thus improving test efficiency and reliability.

[0111] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A control method for a server air-cooled test cabinet, the server air-cooled test cabinet comprising multiple test channels, the server air-cooled test cabinet being used for verifying the heat dissipation performance and stability testing of a server under test, characterized in that... The control method for the server air-cooled test cabinet includes: Obtain server operating status parameters and current ambient temperature and humidity data; Based on the server operating status parameters, determine the power consumption load level and heat dissipation requirements of the server under test; Determine a matching heat dissipation strategy between the power load level and the heat dissipation requirements under the current ambient temperature and humidity data, and control the corresponding test channel to perform heat dissipation operations.

2. The control method for the server air-cooled test cabinet as described in claim 1, characterized in that, The test channel is equipped with multiple active heat dissipation components; The step of determining a matching heat dissipation strategy between the power load level and the heat dissipation requirement under the current ambient temperature and humidity data, and controlling the corresponding test channel to perform heat dissipation operations, includes: Obtain the location distribution of key components in the server under test; Based on the location distribution of the key components, determine the thermally sensitive areas of the key components of the service under test; Calculate the current thermal load value based on the thermally sensitive area and the power load level; Based on the current heat load value and the current ambient temperature and humidity data, the target speed value and target airflow distribution ratio of each active heat dissipation component are found in the preset heat dissipation strategy lookup table. The operation of each active cooling component is controlled according to the target rotation speed value and the target airflow distribution ratio.

3. The control method for the server air-cooled test cabinet as described in claim 2, characterized in that, The active cooling component includes an airflow fan; The step of determining the heat-sensitive area of ​​the key component of the service under test based on the location distribution of the key components includes: Based on the power consumption density and spatial location of each key component, multiple heat-sensitive sub-regions are divided, and the intersection of the coverage areas of different airflow fans is marked as the cross-heat-sensitive zone. The step of calculating the current heat load value based on the heat-sensitive area and the power load level includes: The local heat load is calculated for each of the heat-sensitive sub-regions, and the total hardware heat generation value is obtained by summing all the local heat loads. For the cross-heat-sensitive zone, the cooling contribution weight of each airflow fan to the cross-heat-sensitive zone is determined according to the effective coverage area ratio of the adjacent airflow fans in the cross-heat-sensitive zone. The step of looking up the target rotation speed and target airflow distribution ratio of each active cooling component in a preset heat dissipation strategy lookup table based on the current heat load value and the current ambient temperature and humidity data includes: Based on the total hardware heat generation value, the test pressure level parameters configured for the server under test in the current maintenance and verification phase, and the current ambient temperature and humidity data, the base rotation speed value and base airflow distribution ratio are queried from the heat dissipation strategy comparison table. Based on the cooling contribution weight of each airflow fan and the number and area of ​​the cross-thermal sensitive zone, the basic airflow distribution ratio is adjusted to determine the target speed value and target airflow distribution ratio of each airflow fan. The step of controlling the operation of each active cooling component according to the target rotation speed value and the target airflow distribution ratio includes: According to the target rotation speed value and the target air volume distribution ratio, the start-stop and speed adjustment of the airflow fan are controlled one by one.

4. The control method for the server air-cooled test cabinet as described in claim 1, characterized in that, The control corresponding to the test channel performs a heat dissipation operation, including: Obtain the current temperature of the current test channel and determine the real-time temperature rise rate; Obtain the historical baseline curve and determine the curvature deviation value between the historical baseline curve and the real-time temperature rise rate; If the curvature deviation value exceeds the first preset curvature threshold, the corresponding test channel is controlled to increase the heat dissipation airflow, and the real-time temperature rise rate after the heat dissipation airflow is increased is obtained, and the curvature deviation value is recalculated. If the newly calculated curvature deviation value still exceeds the first preset curvature threshold, the corresponding test channel is controlled to issue a first alarm message, and if the curvature deviation value exceeds the second preset curvature threshold, the power supply of the corresponding test channel is cut off and a second alarm message is issued; the second preset curvature threshold is greater than the first preset curvature threshold.

5. The control method for the server air-cooled test cabinet as described in claim 1, characterized in that, The acquisition of server operating status parameters and current ambient temperature and humidity data includes: Acquire electrical data, vibration frequency, noise level in decibels, and temperature values ​​of key components during server operation; The server's operating status parameters are determined by weighted analysis of the electrical data, vibration frequency, noise decibel value, and temperature values ​​of each key component. Collect the first temperature value of the air inlet and the second temperature value of the air outlet of the corresponding test channel in the current cabinet, as well as the first humidity data of multiple locations in the test channel; Collect the third temperature value and the second humidity data of the current external environment of the cabinet.

6. The control method for the server air-cooled test cabinet as described in claim 1, characterized in that, The step of determining the power consumption load level and heat dissipation requirements of the server under test based on the server operating status parameters includes: Based on the temperature values ​​and real-time power consumption values ​​of each key component in the server operating status parameters, the corresponding basic power consumption load level and basic heat dissipation requirement value are found in the preset load level lookup table. Analyze the fluctuation amplitude of electrical data, the number of abnormal frequency bands in the distribution characteristics of vibration frequency, and noise energy data in the server's operating status parameters; The fluctuation amplitude, the number of abnormal frequency bands, and the noise energy data are collected and matched with a preset correction coefficient lookup table to determine the corresponding correction coefficient. The basic heat dissipation requirement value is calculated with the correction coefficient to generate the corrected heat dissipation requirement, and the basic power load level is used as the power load level.

7. The control method for the server air-cooled test cabinet as described in claim 6, characterized in that, The step of collecting the fluctuation amplitude, the number of abnormal frequency bands, and the noise energy data and matching them with a preset correction coefficient lookup table to determine the corresponding correction coefficient includes: Based on the fluctuation range, the corresponding first basic correction coefficient is looked up in the preset first correction coefficient lookup table; the first correction coefficient lookup table contains multiple fluctuation ranges, and each fluctuation range corresponds to a first basic correction coefficient. Based on the number of abnormal frequency bands, the corresponding second adjustment coefficient is looked up in a preset second correction coefficient lookup table; the second correction coefficient lookup table contains multiple abnormal frequency band number ranges, and each abnormal frequency band number range corresponds to a second adjustment coefficient; Multiply the first basic correction factor by the second adjustment factor to obtain the first intermediate correction factor; Based on the noise energy data, the corresponding third adjustment coefficient is looked up in a preset third correction coefficient lookup table; the third correction coefficient lookup table contains multiple noise energy ranges, and each noise energy range corresponds to a third adjustment coefficient; The correction coefficient is obtained by multiplying the first intermediate correction coefficient by the third adjustment coefficient.

8. The control method for the server air-cooled test cabinet as described in claim 1, characterized in that, The control method for the server air-cooled test cabinet also includes: After the heat dissipation operation is completed in the corresponding test channel, the power supply of the corresponding test channel is turned off, and the test report of the temperature curve, power consumption curve and fan speed record of the corresponding test channel in each test stage is obtained. Store the test report to the remote management backend.

9. A control device, characterized in that, The control device includes: a memory, a processor, and a control program for a server air-cooled test cabinet stored in the memory and executable on the processor, wherein the control program for the server air-cooled test cabinet is configured to implement the control method for the server air-cooled test cabinet as described in any one of claims 1 to 8.

10. A server air-cooled test cabinet, characterized in that, include: Test cabinet body; An air-cooled testing device is located in the main body of the testing cabinet and is used to cool down the server under test during the testing process. The control device as described in claim 9 is electrically connected to the air-cooled testing device. The control device is used to acquire server operating status parameters and current ambient temperature and humidity data, and determine the power consumption load level and heat dissipation requirements of the server under test based on the server operating status parameters. Then, it determines a matching heat dissipation strategy for the power consumption load level and the heat dissipation requirements under the current ambient temperature and humidity data, and controls the corresponding test channel to perform heat dissipation operations.

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