Server test method and device, electronic equipment and storage medium

Abstract

The invention relates to a server testing method and device, electronic equipment and a storage medium, and the server testing method comprises the steps: obtaining a target temperature value of a target component injected into a baseboard management controller (BMC), the target temperature value being higher than the upper limit of the working temperature range of the target component; controlling a physical temperature control unit to adjust the surface temperature of the target component according to the target temperature value, applying a dynamic load to the target component according to a preset load parameter combination, and obtaining spontaneous heating detection data collected for the target component; and determining an actual temperature value of the target component based on the detection data and the reference temperature value. According to the embodiment of the invention, the heat dissipation performance analysis result of the server before and after self-heating compensation can be determined, the heat dissipation performance of the server before and after self-heating compensation can be accurately evaluated, the test precision of the server is improved, the interference of self-heating on the test result is eliminated, and the test efficiency is improved. And multi-dimensional and reliable data support is provided for server heat dissipation strategy optimization and performance improvement.

Description

Technical Field

[0001] This application relates to the field of server temperature testing, and more particularly to a server testing method, apparatus, electronic device, and storage medium. Background Technology

[0002] With the rapid development of information technology, servers, as the core pillar of modern data centers and network infrastructure, are crucial as their performance, operational reliability, and long-term stability directly affect the overall system's operational efficiency. Server operating temperature directly impacts its computing efficiency, performance, and lifespan. Therefore, accurate testing and scientific evaluation of server operating temperature have become a core aspect of the entire process, from server design and development to manufacturing, daily maintenance, and practical application.

[0003] However, existing server testing methods that modify temperature sensor readings through software injection cannot verify the server's true thermal response, such as fan speed adjustment and power consumption changes. The test results are not accurate enough and cannot provide reliable support for server performance optimization and thermal strategy adjustment. Summary of the Invention

[0004] In order to solve the above-mentioned technical problems, or at least partially solve the above-mentioned technical problems, this application provides a server testing method, apparatus, electronic device and storage medium.

[0005] Firstly, this application provides a server testing method, including: The target temperature value of the target component is virtually injected into the Baseboard Management Controller (BMC), and the first heat dissipation response data of the heat dissipation component in the server is obtained. The target temperature value is higher than the upper limit of the operating temperature range of the target component. The physical temperature control unit is controlled to adjust the surface temperature of the target component with the target temperature value as the target, and a dynamic load is applied to the server according to a preset load parameter combination to obtain the second heat dissipation response data of the heat dissipation component in the server; Acquire self-heating detection data for the target component when the BMC detects that the temperature of the target component has reached the target temperature; Based on the target temperature value, the first heat dissipation response data, the second heat dissipation response data, and the self-heating detection data, the analysis results of the heat dissipation performance of the server before and after self-heating compensation are determined.

[0006] This application embodiment uses a combination of virtual injection and physical temperature control to obtain heat dissipation response data under two scenarios, while simultaneously collecting self-heating detection data for self-heating compensation. This facilitates the determination of the server's heat dissipation performance analysis results before and after self-heating compensation, enabling accurate evaluation of the server's heat dissipation performance before and after self-heating compensation, improving server testing accuracy, eliminating the interference of self-heating on test results, and providing multi-dimensional and reliable data support for server heat dissipation strategy optimization and performance improvement.

[0007] Optionally, based on the target temperature value, the first heat dissipation response data, the second heat dissipation response data, and the self-heating detection data, the heat dissipation performance analysis results of the server operating temperature before and after self-heating compensation are determined, including: The real-time power consumption of the target component, the airflow rate on the surface of the target component, and the ambient infrared radiation intensity on the surface of the target component are extracted from the self-heating detection data. The actual temperature value of the target component is calculated based on the target temperature value, the real-time power consumption, the airflow rate, the ambient infrared radiation intensity, and a preset temperature correction formula. First heat dissipation performance analysis data is generated based on the target temperature value and the first heat dissipation response data; second heat dissipation performance analysis data is generated based on the actual temperature value and the second heat dissipation response data. The heat dissipation performance analysis results of the server before and after self-heating compensation are determined based on the first heat dissipation performance analysis data and the second heat dissipation performance analysis data.

[0008] This application's embodiments extract key self-heating parameters, calculate actual temperature values ​​using a preset temperature correction formula, generate heat dissipation performance analysis data for both virtual and physical scenarios, and ultimately determine the server's heat dissipation performance analysis results before and after self-heating compensation. This effectively corrects temperature detection deviations in the virtual mode, accurately distinguishes the impact of self-heating on heat dissipation performance, and solves the problems in existing technologies where temperature testing ignores the influence of self-heating and cannot accurately distinguish between virtual and real heat dissipation responses. It further improves the accuracy and comprehensiveness of test analysis data, providing a more scientific basis for server heat dissipation optimization.

[0009] Optionally, the heat dissipation performance analysis results of the server before and after self-heating compensation are determined based on the first heat dissipation performance analysis data and the second heat dissipation performance analysis data, including: During the process of virtually injecting the target temperature value of the target component into the baseboard management controller (BMC) and triggering the first heat dissipation response action corresponding to the target temperature value, the first heat dissipation strategy data of the server is obtained. In the process of controlling the physical temperature control unit to adjust the surface temperature of the target component, applying a dynamic load to the target component according to a preset load parameter combination, and triggering a second heat dissipation response action corresponding to the actual temperature value, the temperature change data of the target component and the second heat dissipation strategy data of the server are acquired. Temperature stability analysis data is determined based on the temperature change data; heat dissipation performance analysis data is determined based on the first heat dissipation strategy data and the second heat dissipation strategy data, which are used as the heat dissipation performance analysis results.

[0010] This application embodiment obtains temperature stability analysis data by analyzing temperature change data, which facilitates the analysis of whether the actual temperature value after self-heating compensation can maintain the temperature stability of the target component through the second heat dissipation response action. By comparing two sets of heat dissipation strategy data and determining heat dissipation performance analysis data, the effectiveness of heat dissipation measurement before and after self-heating compensation can be compared, providing accurate data support for optimizing the temperature threshold setting of the heat dissipation strategy and improving temperature stability, further enhancing the depth and practicality of heat dissipation performance analysis.

[0011] Optionally, before obtaining the target temperature value of the target component injected into the base plate management controller (BMC), the method further includes: In the preset correspondence between load and component, a reference component is determined that corresponds to the preset load parameter combination. The reference component is the server component with the highest operating temperature when the server is running according to the preset load parameter combination. The reference component is identified as the target component.

[0012] This application embodiment quickly identifies the reference component with the highest temperature under a specific load by establishing a preset load-component correspondence, and determines it as the target component. This avoids the waste of resources and lack of focus caused by indiscriminate testing of all components during the testing process. It enables the test to accurately focus on key heat-generating components, improving testing efficiency and relevance, while reducing testing complexity. It provides a clear test object for quickly evaluating the high-temperature tolerance and heat dissipation performance of server core components, ensuring that the test results can directly serve the optimization of server core performance.

[0013] Optionally, the method for determining the preset correspondence between loads and components includes: Obtain multiple preset load parameter combinations; For each preset load parameter combination, a dynamic load is applied to the target component according to the preset load parameter combination, and thermal imaging data inside the server is collected. The correspondence between the preset load parameter combination and the server component with the highest temperature in the thermal imaging data is determined as the correspondence between the load and the component.

[0014] This application embodiment acquires multiple preset load parameter combinations and collects internal thermal imaging data of the server under different load conditions. It accurately identifies the component with the highest temperature corresponding to each load parameter combination, thereby establishing a correspondence between load combination parameters and the component with the highest temperature. This solves the problem that the prior art cannot accurately locate key heat-generating components under specific loads, providing a reliable basis for quickly determining target components in subsequent tests. It ensures the comprehensiveness and accuracy of the correspondence between load and components, and further improves the pertinence and practicality of the entire temperature testing system.

[0015] Optionally, the method further includes: Obtain multiple preset load parameter combinations; For each preset load parameter combination, a dynamic load is applied to the target component according to the preset load parameter combination, and the component temperature and performance parameters read by BMC for multiple server components are obtained; Among the performance parameters collected under multiple preset load parameter combinations, the optimal performance parameter range for each server component is determined; Obtain the optimal temperature range corresponding to the server component operating within the optimal performance parameter range.

[0016] This application embodiment synchronously collects temperature and performance data of server components under multiple preset load parameter combinations, accurately filters out the optimal component temperature corresponding to the best performance, solves the problem that the prior art cannot determine the optimal operating temperature of the server's core components, helps to control the operating temperature of the server's core components within the optimal range, thereby continuously exerting the server's optimal performance, improving operational stability and service life.

[0017] Optionally, the method further includes: For any server component, the optimal component temperature value of the server component is injected into the BMC, and a third heat dissipation response action corresponding to the optimal component temperature value is triggered. Obtain the preset load parameters of the server components; Apply dynamic load to the server component according to the preset load parameters, and obtain the current performance parameters of the server component read by the BMC; Verify whether the current performance parameters are within the optimal performance parameter range; If the current performance parameters are within the optimal performance parameter range, the verification is considered successful.

[0018] This application embodiment injects the optimal component temperature value into the BMC and applies dynamic load to the server component according to the preset load parameter combination to verify whether the optimal temperature range corresponding to the server component working within the optimal performance parameter range is reliable. This provides a basis for configuring heat dissipation parameters in the actual application of the server and helps to improve the stability and overall performance of the server.

[0019] Secondly, this application provides a server testing apparatus, comprising: The virtual injection module is used to virtually inject the target temperature value of the target component into the baseboard management controller (BMC) and obtain the first heat dissipation response data of the heat dissipation component in the server. The target temperature value is higher than the upper limit of the operating temperature range of the target component. The physical adjustment module is used to control the physical temperature control unit to adjust the surface temperature of the target component to the target temperature value, and to apply dynamic load to the server according to a preset load parameter combination, and to obtain the second heat dissipation response data of the heat dissipation component in the server. The acquisition module is used to acquire self-heating detection data collected on the target component when the BMC detects that the temperature of the target component has reached the target temperature; The determination module is used to determine the heat dissipation performance analysis results of the server operating temperature before and after self-heating compensation based on the target temperature value, the first heat dissipation response data, the second heat dissipation response data, and the self-heating detection data.

[0020] Thirdly, this application provides an electronic device, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements the server testing method described in any of the first aspects.

[0021] Fourthly, this application provides a computer-readable storage medium storing a program for a server testing method, wherein when the program for the server testing method is executed by a processor, it implements the steps of the server testing method described in any of the first aspects.

[0022] The technical solutions provided in this application have the following advantages compared with the prior art: This application embodiment uses a combination of virtual injection and physical temperature control to obtain heat dissipation response data under two scenarios, while simultaneously collecting self-heating detection data for self-heating compensation. This facilitates the determination of the server's heat dissipation performance analysis results before and after self-heating compensation, enabling accurate evaluation of the server's heat dissipation performance before and after self-heating compensation, improving server testing accuracy, eliminating the interference of self-heating on test results, and providing multi-dimensional and reliable data support for server heat dissipation strategy optimization and performance improvement. Attached Figure Description

[0023] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating a server testing method provided in this application embodiment; Figure 2 A structural diagram of a server testing device provided in an embodiment of this application; Figure 3 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] Existing server testing methods that modify temperature sensor readings via software injection cannot verify the server's true thermal response, such as fan speed adjustments and power consumption changes. The test results lack accuracy and cannot provide reliable support for server performance optimization and thermal management adjustments. Therefore, this application provides a server testing method, apparatus, electronic device, and storage medium.

[0028] First, this application provides a server testing system, including: The dual-mode temperature control module includes a physical temperature control unit and a virtual injection unit, and the working mode is selected through a mode switcher; The physical temperature control unit includes: The system utilizes a thermoelectric cooling element (TEC), an infrared heating array, and a PID controller to achieve a temperature control range of -50℃ to 100℃ with a fluctuation of ≤±0.5℃. The TEC and infrared heating array are combined, and the PID controller enables precise temperature control. In practical deployments, the TEC and infrared heating array are installed inside the server chassis or test chamber, positioned around key server components to effectively alter the surrounding temperature environment. The PID controller dynamically adjusts the operating power of the TEC and infrared heating array based on temperature sensor feedback, maintaining a temperature control range of -50℃ to 100℃ with fluctuations within ≤±0.5℃. For example, when performing low-temperature testing on the server CPU, the PID controller drives the TEC to begin cooling, gradually adjusting the cooling power based on real-time temperature data until the set low temperature value is reached and maintained stably.

[0029] The virtual injection unit modifies the raw data of the temperature sensor through the BMC debugging interface, supports the IPMI / SMBIOS protocol, and has an injection latency of <100ms. It connects to the server via the BMC debugging interface and uses software tools that support the IPMI / SMBIOS protocol to modify the raw temperature sensor data. Before testing, the virtual injection unit is configured to familiarize itself with the server's BMC protocol type, ensuring the accuracy of the injected data and maintaining an injection latency of <100ms to guarantee the real-time performance and effectiveness of the test. For example, when testing the server's high-temperature protection mechanism, a temperature value higher than the server's normal operating temperature limit is injected into the BMC through the virtual injection unit to simulate an overheating scenario. The BMC's response is then observed to see if it meets expectations, such as whether alarms are issued promptly or whether frequency reduction strategies are initiated.

[0030] The extreme start-up test chamber provides a precise temperature-controlled environment for cold start testing of servers from -40℃ to 65℃. The chamber is made of vacuum insulation material with a thermal conductivity of <0.03W / m. K features excellent thermal insulation performance, effectively isolating external temperature interference. It has at least three built-in temperature sensors with an accuracy of ±0.1℃, which are used to monitor temperature changes at different locations within the test chamber, ensuring accurate control and recording of the test environment temperature. After the server is placed inside, it stabilizes at the target temperature for 30 minutes and can record parameters such as power-on delay, BMC initialization time, OS startup time, power consumption, and current during the power-on process.

[0031] The dynamic load simulation module generates programmable loads and links them with temperature control to simulate actual server operating conditions. Load types include 100% CPU load, continuous disk read / write, and 90% network bandwidth utilization. It can automatically switch load modes based on the real-time component temperature read by the BMC. In physical temperature control mode, the dynamic load simulation module can work in conjunction with the physical temperature control unit to apply dynamic loads to server components and simultaneously monitor fan speed, power consumption fluctuations, and component performance changes. It can also provide load support for performance and temperature correlation analysis in multi-load parameter testing scenarios. The self-heating compensation engine dynamically corrects the test temperature based on the collected data, eliminating the impact of server self-heating on test accuracy. The collected data includes real-time power consumption of the component under test obtained through the PMBus interface, airflow rate at the component location obtained through the anemometer, ambient infrared radiation intensity, and temperature data collected by the BMC software sensors. The actual temperature is calculated using the calculation model T_actual=T_sensor+k×P_power / v_airflow+m×I_ambient (where k is the component thermal resistance coefficient, P_power is the real-time power consumption obtained through the PMBus interface, v_airflow is the airflow rate obtained through the anemometer, m is the environmental interference coefficient, and I_ambient is the ambient infrared radiation intensity) and temperature correction is performed to ensure that the test temperature is consistent with the actual application environment.

[0032] A cross-platform analysis and control terminal that automatically adapts to different BMC brands, enabling control, data processing, report generation, and remote collaboration throughout the entire testing process. Specifically, the cross-platform analysis and control terminal includes: The BMC brand recognition module automatically identifies the server's BMC brand before testing begins, currently supporting mainstream brands such as Huawei, iDRAC, and BMC. This module sends a specific recognition command to the server, retrieves the returned BMC information, and compares it with a built-in BMC brand database to accurately identify the BMC brand and model used by the server. Upon successful identification, the system automatically loads the test parameter configuration and operation command set compatible with the BMC brand, ensuring smooth subsequent testing and avoiding test anomalies or inaccurate data due to BMC brand differences. The integrated test script engine supports both Python and Lua, two mainstream scripting languages, allowing users to write or select existing test scripts based on their specific testing needs. The script engine provides users with richer test functionality, extensibility, and flexibility, facilitating customized testing for different server models, testing scenarios, and testing objectives. For example, users can write a test script to automatically perform performance tests on the server under different temperature points and load modes, recording the results according to a preset data format, thus improving testing efficiency and automation.

[0033] The infrared thermal imaging data processor is responsible for processing the server surface temperature distribution image data acquired by the infrared thermal imager. Through professional image processing algorithms and temperature analysis models, it converts the raw infrared thermal imaging images into intuitive temperature cloud maps, which are then displayed on the terminal's visual interface. Users can clearly understand the temperature distribution of various server components during operation by observing the temperature cloud map, quickly locate abnormal temperature areas or high-temperature hotspots, and provide intuitive visual evidence for further analysis of the server's heat dissipation performance.

[0034] During the test execution, the cross-platform analysis and control terminal collects various types of data in real time, including server component temperature data, fan speed data, power consumption data, dynamic load data, and BMC alarm logs. This data is organized according to the preset data format and storage structure and stored in the terminal's database. The database adopts an efficient data storage and management mechanism to ensure data integrity and security, while supporting fast data query and retrieval functions to facilitate subsequent data analysis and automatic generation of test reports.

[0035] The test report includes, but is not limited to, temperature stability curves proving the authenticity of the physical injection, stable operation of key components at corrected temperatures, cold start time comparison tables for low and normal temperatures, heat dissipation strategy scoring reports, and detailed descriptions of the overall server performance evaluation. The report is presented in an intuitive combination of charts and text, allowing users to quickly and clearly understand the test results and evaluation conclusions. The system also supports exporting the test report in various common formats (such as PDF, Word, and Excel) for easy sharing, archiving, and integration with other relevant documents. Furthermore, the intelligent report interpretation function provides users with a concise and clear report summary based on the test results and evaluation conclusions, highlighting key test findings and critical evaluation indicators, helping users quickly grasp the core points of the test results and improving the practicality and readability of the test report.

[0036] Based on the aforementioned server testing system, this application provides a server testing method, such as... Figure 1 As shown, it includes: Step S101: Virtually inject the target temperature value of the target component into the base plate management controller (BMC) to obtain the first heat dissipation response data of the heat dissipation component in the server; In this embodiment, BMC is a baseboard management controller responsible for monitoring and managing the server hardware status; the target component can be a specified component, such as a CPU; the target temperature value is a temperature parameter set for simulating high-temperature test scenarios that is higher than the upper limit of the normal operating temperature of the target component; the reference operating temperature upper limit is the highest temperature value allowed for the stable operation of the target component; virtual injection is an operation that modifies the original data of the temperature sensor through the BMC debugging interface; the first heat dissipation response data is the heat dissipation-related response data generated by the server heat dissipation component after the target temperature value is virtually injected, including fan speed changes, CPU frequency reduction, BMC alarm logs, etc.

[0037] For example, if the target component is a CPU with a reference operating temperature limit of 85°C, a target temperature value of 95°C is injected into the BMC through a virtual injection unit that supports the IPMI / SMBIOS protocol. The injection delay is controlled within 80ms, and the first heat dissipation response data of the heat dissipation component is obtained in real time, including data such as the fan starting speed increasing to 80%, the CPU not triggering frequency reduction, and the BMC not generating a high temperature alarm.

[0038] In this step, the heat dissipation response of the heat dissipation components of the server under high temperature operating conditions can be simulated by virtual injection, and the first heat dissipation response data of the heat dissipation components can be obtained.

[0039] Step S102: Control the physical temperature control unit to adjust the surface temperature of the target component with the target temperature value, and apply dynamic load to the server according to the preset load parameter combination to obtain the second heat dissipation response data of the heat dissipation component in the server. In this embodiment, the physical temperature control unit includes a thermoelectric cooling element (TEC), an infrared heating array, and a PID controller. The temperature control range of the physical temperature control unit is 50°C to 100°C, with a fluctuation of ≤±0.5°C. The preset load parameter combination is a pre-set parameter combination that simulates the actual operating load of the server. The preset load parameter combination includes CPU 100% load, continuous disk read / write, network bandwidth occupancy of 90%, etc. The dynamic load can be automatically switched according to the real-time temperature of the target component surface. The second heat dissipation response data is the heat dissipation response data of the target heat dissipation component under physical high temperature environment and dynamic load.

[0040] In this step, in order to avoid the virtual injection test process interfering with the physical temperature control test process, after a period of time after step S101, the surface temperature of the target component can be adjusted by physical means to the target temperature value to construct a real high temperature test environment. According to the preset load parameter combination corresponding to the surface temperature of the target component, dynamic load is applied to the server to simulate the actual operating conditions and obtain heat dissipation response data that is close to the actual application scenario.

[0041] For example, the PID controller of the physical temperature control unit can drive the TEC and / or infrared heating array to work, precisely adjust the CPU surface temperature to 95℃, and control the fluctuation within ±0.3℃. Apply a dynamic load of 100% CPU load and continuous disk read / write according to the preset load parameter combination, monitor and obtain the second heat dissipation response data in real time. The second heat dissipation response data includes: fan speed gradually increases to 90%, CPU triggers frequency reduction to 80% of the original frequency after 30 seconds, and BMC generates high temperature warning logs, etc.

[0042] Step S103: Obtain self-heating detection data for the target component when the BMC detects that the temperature of the target component has reached the target temperature; Self-heating detection data reflects the self-heating status of the target component, including: the real-time power consumption of the target component, the airflow rate on the surface of the target component, and the ambient infrared radiation intensity on the surface of the target component. The real-time power consumption can be obtained through the PMBus interface, the airflow rate can be obtained through the wind speed sensor, and the ambient infrared radiation intensity can be collected through the infrared radiation sensor.

[0043] In this step, when the BMC reads that the temperature of the target component has reached the target temperature value, it can acquire detection data such as the real-time power consumption of the target component, the airflow rate on the surface of the target component, and the ambient infrared radiation intensity collected by multiple sensors, as self-heating detection data collected for the target component.

[0044] Step S104: Based on the target temperature value, the first heat dissipation response data, the second heat dissipation response data, and the self-heating detection data, determine the heat dissipation performance analysis results of the server before and after self-heating compensation.

[0045] In this embodiment of the application, the heat dissipation performance analysis results before and after self-heating compensation are determined based on the first heat dissipation response data, the second heat dissipation response data and the self-heating detection data. The heat dissipation performance analysis results may include: heat dissipation response accuracy, heat dissipation strategy effectiveness, temperature deviation impact, etc.

[0046] In this step, the computer can compare the first heat dissipation response data, the second heat dissipation response data, and the self-heating detection data to comprehensively evaluate the impact of self-heating on heat dissipation performance and the actual performance of the heat dissipation system.

[0047] For example, by integrating the target temperature value of 95℃, the first heat dissipation response data in the virtual scene, the second heat dissipation response data in the physical scene, and the self-heating detection data, the analysis concludes that the heat dissipation response in the virtual scene before self-heating compensation is too ideal, and that the heat dissipation system needs to withstand a higher load in the real scene after compensation. The fan speed adjustment and frequency reduction strategies are more in line with actual needs, thus forming a complete heat dissipation performance analysis result.

[0048] This application embodiment uses a combination of virtual injection and physical temperature control to obtain heat dissipation response data under two scenarios, while simultaneously collecting self-heating detection data for self-heating compensation. This facilitates the determination of the server's heat dissipation performance analysis results before and after self-heating compensation, enabling accurate evaluation of the server's heat dissipation performance before and after self-heating compensation, improving server testing accuracy, eliminating the interference of self-heating on test results, and providing multi-dimensional and reliable data support for server heat dissipation strategy optimization and performance improvement.

[0049] In another embodiment of this application, step S104 determines the heat dissipation performance analysis results of the server before and after self-heating compensation based on the target temperature value, the first heat dissipation response data, the second heat dissipation response data, and the self-heating detection data, including: Step S201: Extract the real-time power consumption of the target component, the airflow rate on the surface of the target component, and the ambient infrared radiation intensity on the surface of the target component from the self-heating detection data. Real-time power consumption is the power consumption of a target component when it reaches the target temperature, directly reflecting the component's heat generation level. Airflow rate is the speed at which air flows over the surface of the target component, affecting its heat dissipation efficiency. Ambient infrared radiation intensity is the strength of infrared radiation energy in the environment surrounding the target component, which can interfere with component temperature detection. For example, from self-heating detection data, the CPU's real-time power consumption is extracted to be 150W, the airflow rate on the CPU surface is 0.8m / s, and the ambient infrared radiation intensity on the CPU surface is 200W / m².

[0050] In this step, key influencing parameters are extracted from the collected self-heating detection data to provide data support for accurately calculating the actual temperature value.

[0051] Step S202: Calculate the actual temperature value of the target component based on the target temperature value, the real-time power consumption, the airflow rate, the ambient infrared radiation intensity, and the preset temperature correction formula; The preset temperature correction formula is: T_actual=T_sensor +k×P_power / v_airflow +m×I_ambient Where T_actual is the actual temperature value, T_sensor is the real-time temperature value of the target component acquired by the BMC software sensor, and k is the component's thermal resistance coefficient (preset to 0.02℃). m² s / W), P_power is the real-time power consumption, v_airflow is the airflow rate, and m is the environmental interference coefficient (default is 0.01℃). (m² / W), where I_ambient is the ambient infrared radiation intensity.

[0052] In this step, the deviation of the temperature collected by the BMC sensor can be corrected by the above formula, the influence of self-heating and environmental factors on temperature detection can be eliminated, and the true temperature of the target component can be obtained.

[0053] For example, if the target temperature is known to be 93℃, and the T_sensor value collected by the BMC sensor is 93℃, then by substituting the real-time power consumption of 150W, the airflow rate of 0.8m / s, the ambient infrared radiation intensity of 200W / m², and the preset k and m values ​​into the formula, the actual temperature value is calculated to be 93 + 0.02 × 150 / 0.8 + 0.01 × 200 = 97.75℃.

[0054] Step S203: Generate first heat dissipation performance analysis data based on the target temperature value and the first heat dissipation response data; generate second heat dissipation performance analysis data based on the actual temperature value and the second heat dissipation response data; The first heat dissipation performance analysis data is the result of heat dissipation performance analysis under the scenario of virtual injection of target temperature value, reflecting the logic response characteristics of heat dissipation components. The second heat dissipation performance analysis data is the result of heat dissipation performance analysis after physical temperature control actually reaches the target temperature value and the influence of self-heating is taken into account, reflecting the heat dissipation capability in real scenario.

[0055] In this step, the thermal response data of both virtual and physical test scenarios can be analyzed separately to generate targeted analysis results.

[0056] For example: Based on the target temperature of 95℃ and the first heat dissipation response data, the first heat dissipation performance analysis data is generated: When the virtual temperature is 95℃, the fan speed increases from 50% to 80% within 10 seconds, the CPU does not reduce the frequency, the BMC does not alarm, and the heat dissipation response logic conforms to the preset strategy. Based on the actual temperature value of 97.75℃ and the second heat dissipation response data, the second heat dissipation performance analysis data is generated: Under physical conditions, BMC detects that the temperature of the target component reaches 95℃. When the actual temperature of the target component reaches 97.75℃, the fan speed increases from 50% to 90% within 8 seconds, and the CPU frequency drops to 75% of the original frequency after 20 seconds. BMC generates a high temperature warning, indicating that the actual heat dissipation pressure is higher than that in the virtual scenario.

[0057] Step S204: Determine the heat dissipation performance analysis results of the server before and after self-heating compensation based on the first heat dissipation performance analysis data and the second heat dissipation performance analysis data.

[0058] This step integrates the heat dissipation performance analysis results from both scenarios to form comprehensive test analysis data, providing a complete basis for evaluating server heat dissipation performance. For example, by integrating the first and second heat dissipation performance analysis data mentioned above, the differences in heat dissipation response between virtual and real scenarios, the degree of impact of self-heating on heat dissipation performance, etc., are clarified, and the final results are determined as the server's heat dissipation performance analysis results before and after self-heating compensation.

[0059] In practical applications, data analysis through testing can accurately recreate the server's actual heat dissipation pressure, avoiding misjudgments of heat dissipation capacity due to temperature distortion caused by virtual simulation. Because virtual injection only modifies sensor data and does not consider the increased self-heating under high CPU load (the extra heat from 160W power consumption) and the impact of insufficient airflow rate on heat dissipation, the 95℃ sensed by the BMC does not reflect the actual heating state of the components, leading to a misjudgment that the cooling system has met the cooling requirements. The corrected 97.75℃, however, is the actual temperature the components withstand. The actual response of the cooling components at this point (fans need to increase to 90% speed, CPU frequency drops to 80% of its original frequency after 30 seconds, and the BMC generates a high-temperature warning) is the effective feedback from the server under real high-temperature scenarios, completely solving the pain point of existing technologies where software injection cannot verify the actual heat dissipation response.

[0060] Analyzing test data can also expose potential flaws in the cooling strategy, providing precise guidance for optimization. In a virtual injection scenario, 80% fan speed seems to match the set temperature of 95℃, but the corrected actual temperature shows that this speed cannot offset the additional heat generated by the components' self-heating, resulting in a consistently higher actual temperature. This discrepancy directly reveals the shortcomings of the original cooling strategy: the fan speed threshold is based on an ideal simulated temperature setting, without considering the increase in self-heating under actual operating conditions. If cooling is optimized based on virtual data, the server will experience insufficient cooling in real high-load scenarios, leading to prolonged high-temperature operation of components, accelerated aging, or even failure. Based on the actual temperature of 97.75℃, the optimization direction is clear: increase the fan speed threshold when the CPU temperature reaches 95℃ to 90%, and trigger frequency throttling protection in advance, making the cooling strategy more aligned with real-world application scenarios.

[0061] The test data analysis also verifies the accuracy of the temperature sensor and heat dissipation linkage logic, ensuring the reliability of the monitoring system. The difference between the corrected actual temperature and the virtual injection temperature (2.75℃) reflects the deviation of the sensor reading not offsetting self-heating and environmental interference. This data can be used to calibrate the linkage algorithm between the sensor and the BMC. Under the original logic, a sensor reading of 95℃ did not trigger a strong heat dissipation response, but an actual temperature of 97.75℃ already required emergency cooling, indicating that the linkage threshold was too lenient. Based on the corrected actual temperature, the temperature judgment logic of the BMC can be adjusted to take into account the deviation between the sensor reading and the actual temperature, making the triggering timing and intensity of the heat dissipation response more accurate and avoiding heat dissipation lag caused by the sensor underestimating the temperature.

[0062] This application's embodiments extract key self-heating parameters, calculate actual temperature values ​​using a preset temperature correction formula, generate heat dissipation performance analysis data for both virtual and physical scenarios, and ultimately determine the server's heat dissipation performance analysis results before and after self-heating compensation. This effectively corrects temperature detection deviations, clearly reveals the impact of self-heating on heat dissipation performance, and solves the problems in existing technologies where temperature testing ignores the influence of self-heating and cannot accurately distinguish between virtual and real heat dissipation responses. It further improves the accuracy and comprehensiveness of test analysis data, providing a more scientific basis for server heat dissipation optimization.

[0063] In another embodiment of this application, step S204 determines the heat dissipation performance analysis results of the server before and after self-heating compensation based on the first heat dissipation performance analysis data and the second heat dissipation performance analysis data, including: Step S301: During the process of virtually injecting the target temperature value of the target component into the baseboard management controller (BMC) and triggering the first heat dissipation response action corresponding to the target temperature value, the first heat dissipation strategy data of the server is obtained. In this embodiment, the first heat dissipation response action is a heat dissipation operation initiated by the BMC after receiving the target temperature value injected virtually, and the first heat dissipation strategy data are heat dissipation control parameters executed by the BMC in the virtual scene, including fan speed control curve, frequency reduction trigger threshold, alarm trigger conditions, etc.

[0064] For example: Inject a target temperature value of 95℃ into the BMC to trigger the first heat dissipation response action. During the 10-minute test, acquire the first heat dissipation strategy data (fan speed threshold is 80% speed when starting at 95℃, frequency reduction trigger threshold is 100℃, alarm trigger threshold is 105℃).

[0065] Step S302: During the process of controlling the physical temperature control unit to adjust the surface temperature of the target component, applying a dynamic load to the target component according to a preset load parameter combination, and triggering a second heat dissipation response action corresponding to the actual temperature value, the temperature change data of the target component and the second heat dissipation strategy data of the server are acquired. In this embodiment, the second heat dissipation response action is a heat dissipation operation initiated by the BMC when the physical temperature control reaches the actual temperature value and is subjected to dynamic load. The temperature change data is the temperature data recorded by the target component during the heat dissipation and cooling process, which changes over time. The second heat dissipation strategy data is the heat dissipation control parameters actually executed by the BMC in a real scenario.

[0066] In this step, the surface temperature of the target component can be obtained as the actual temperature value. A dynamic load is applied according to the preset load parameter combination corresponding to the target temperature, and the second heat dissipation response action corresponding to the actual temperature value is triggered. During this process, the temperature change data of the target component at multiple times is obtained.

[0067] Step S303: Determine temperature stability analysis data based on the temperature change data; determine heat dissipation performance analysis data based on the first heat dissipation strategy data and the second heat dissipation strategy data, as the heat dissipation performance analysis result.

[0068] Temperature stability analysis data is the result obtained by performing temperature change data temperature stability analysis, and is used to reflect whether the temperature of the target component can remain stable after self-heating compensation; heat dissipation performance analysis data is the evaluation result obtained by comparing the response timeliness and intensity rationality of two sets of heat dissipation strategy data.

[0069] In this step, it can be determined whether the temperature change data changes within the preset temperature threshold range. If so, it can be determined that the temperature change data meets the stability requirements. The two sets of heat dissipation strategy data can be compared to determine the heat dissipation performance analysis data. For example, the fan speed adjustment response in the virtual scene is 1℃ lagging, while the actual speed adjustment in the real scene is more timely. The frequency reduction and alarm thresholds in the virtual scene are too lenient, while the protection mechanism needs to be triggered in advance due to the influence of self-heating in the real scene. Finally, the data are integrated to form the heat dissipation performance analysis results.

[0070] This application embodiment obtains temperature stability analysis data by analyzing temperature change data, which facilitates the analysis of whether the actual temperature value after self-heating compensation can maintain the temperature stability of the target component through the second heat dissipation response action. By comparing two sets of heat dissipation strategy data and determining heat dissipation performance analysis data, the effectiveness of heat dissipation measurement before and after self-heating compensation can be compared, providing accurate data support for optimizing the temperature threshold setting of the heat dissipation strategy and improving temperature stability, further enhancing the depth and practicality of heat dissipation performance analysis.

[0071] In another embodiment of this application, before obtaining the target temperature value of the target component injected into the base plate management controller (BMC), the method further includes: Step S401: In the preset correspondence between load and component, determine the reference component corresponding to the preset load parameter combination. The reference component is the server component with the highest operating temperature when the server is running according to the preset load parameter combination. In this embodiment, the pre-defined correspondence between load and component is a pre-established set of associations between different combinations of load parameters and corresponding high-temperature components in the server. This set is constructed based on a large amount of test data. The reference component is the component in the server that generates the most heat and has the highest temperature under a specific load parameter combination, and its heat dissipation status plays a key role in the overall operational stability of the server. For example, the pre-defined load and component correspondence clearly states that: the reference component corresponding to 100% CPU load is the CPU, the reference component corresponding to continuous disk read / write is the disk, and the reference component corresponding to 90% network bandwidth usage is the network card.

[0072] In this step, by querying the preset correspondence, the key test object under a specific load can be quickly located, improving the test's relevance. For example, the preset load parameter combination for this test is 100% CPU load; by querying the correspondence, the reference component is determined to be the CPU.

[0073] Step S402: The reference component is determined as the target component.

[0074] The target component is the core object of this server test, and the reference component is used as the target component.

[0075] In this step, the component that generates the most heat and has the highest temperature under the preset load parameter combination can be identified as the test object. This allows the test to focus on the component with the highest temperature under a specific load, avoiding the omission of high temperature risk points.

[0076] For example, if the reference component determined in step 1 is the CPU, then the CPU is directly identified as the target component for this temperature test, and subsequent operations such as virtual injection, physical temperature control, and load application are all carried out around the CPU.

[0077] This application embodiment quickly identifies the reference component with the highest temperature under a specific load by establishing a preset load-component correspondence, and determines it as the target component. This avoids the waste of resources and lack of focus caused by indiscriminate testing of all components during the testing process. It enables the test to accurately focus on key heat-generating components, improving testing efficiency and relevance, while reducing testing complexity. It provides a clear test object for quickly evaluating the high-temperature tolerance and heat dissipation performance of server core components, ensuring that the test results can directly serve the optimization of server core performance.

[0078] In another embodiment of this application, the method for determining the correspondence between the preset load and the component includes: Step S501: Obtain multiple preset load parameter combinations; In this embodiment, the multiple preset load parameter combinations are a set of load parameters covering common server operating conditions, including but not limited to different proportions of CPU load, disk read / write load, network bandwidth usage load, and mixed load, which can comprehensively simulate diverse operating scenarios in actual server applications.

[0079] Step S502: For each preset load parameter combination, apply dynamic load to the target component according to the preset load parameter combination, and collect thermal imaging data inside the server. Dynamic load is a switchable load form generated based on a combination of preset load parameters. Thermal imaging data is image data collected by an infrared thermal imager that reflects the temperature distribution of various components inside the server, and can intuitively show the temperature of each component.

[0080] In this step, thermal imaging data is collected under different loads to provide an intuitive basis for identifying the high-temperature components corresponding to each load. For example, for a CPU load parameter of 90%, the load is applied through a dynamic load simulation module, and the infrared thermal imager corresponding to the infrared thermal imaging data processor is activated to collect temperature distribution images of components such as the CPU, memory, disk, motherboard, and network card inside the server. The same operation is performed sequentially for other preset load parameter combinations to collect the corresponding thermal imaging data.

[0081] Step S503: Determine the correspondence between the preset load parameter combination and the server component with the highest temperature in the thermal imaging data as the correspondence between the load and the component.

[0082] In this step, the thermal imaging data corresponding to each preset load parameter combination can be analyzed and processed, converted into a temperature cloud map, the component with the highest temperature under each load parameter can be identified, and the component with the highest temperature can be associated with the corresponding load parameter combination to form a preset correspondence between load and component.

[0083] This application embodiment acquires multiple preset load parameter combinations and collects internal thermal imaging data of the server under different load conditions. It accurately identifies the component with the highest temperature corresponding to each load parameter combination, thereby establishing a correspondence between load combination parameters and the component with the highest temperature. This solves the problem that the prior art cannot accurately locate key heat-generating components under specific loads, providing a reliable basis for quickly determining target components in subsequent tests. It ensures the comprehensiveness and accuracy of the correspondence between load and components, and further improves the pertinence and practicality of the entire temperature testing system.

[0084] In another embodiment of this application, the method further includes: Step S601: Obtain multiple preset load parameter combinations; Step S602: For each preset load parameter combination, apply dynamic load to the target component according to the preset load parameter combination, and obtain the component temperature and performance parameters read by BMC for multiple server components; Component temperature is real-time temperature data of server components (such as CPU, memory, disk, etc.) read by BMC. Performance parameters are indicators that reflect the operating performance of server components, including CPU processing speed, memory read and write speed, disk I / O speed, network transmission speed, etc.

[0085] Step S603: Determine the optimal performance parameter range for each server component from the performance parameters collected under multiple preset load parameter combinations. The optimal performance parameter range is the range of parameters that best represent the overall performance of each server component under different preset load parameter combinations.

[0086] In this step, the optimal performance parameter range can be determined by statistically analyzing the peak distribution and stability fluctuation range of performance parameters, and the optimal performance parameter range for each server component can be selected.

[0087] Step S604: Obtain the optimal temperature range corresponding to the server component operating within the optimal performance parameter range.

[0088] The optimal temperature range is the temperature range of each server component when it operates within its optimal performance parameter range.

[0089] In this step, by correlating all temperature data within the optimal performance parameter range, the temperature distribution range can be statistically analyzed, and the optimal temperature range for each component to perform at its best can be extracted.

[0090] This application embodiment synchronously collects temperature and performance data of server components under multiple preset load parameter combinations, accurately filters out the optimal component temperature corresponding to the best performance, solves the problem that the prior art cannot determine the optimal operating temperature of the server's core components, helps to control the operating temperature of the server's core components within the optimal range, thereby continuously exerting the server's optimal performance, improving operational stability and service life.

[0091] In another embodiment of this application, the method further includes: Step S701: For any server component, inject the optimal component temperature value of the server component into the BMC, and trigger the third heat dissipation response action corresponding to the optimal component temperature value. The optimal component temperature value is any value within the optimal temperature range in the aforementioned embodiments. The third heat dissipation response action is a targeted heat dissipation operation corresponding to the optimal temperature value, which aims to stabilize the temperature of the server components within the optimal temperature range.

[0092] Step S702: Obtain the preset load parameters of the server components; The preset load parameter is the preset load parameter corresponding to the server component in the preset load parameter combination corresponding to the optimal performance parameter range of each server component in step S603.

[0093] Step S703: Apply dynamic load to the server component according to the preset load parameters, and obtain the current performance parameters of the server component read by the BMC; The current performance parameters are the real-time performance parameters after injecting the optimal component temperature value of the server component into the BMC and applying dynamic load to the server component according to preset load parameters.

[0094] Step S704: Verify whether the current performance parameter is within the optimal performance parameter range; This step verifies the validity of the optimal temperature range determined in the aforementioned embodiments when the server component operates within the optimal performance parameter range by comparing the current performance parameters with the optimal performance parameter range.

[0095] Step S705: If the current performance parameter is within the optimal performance parameter range, the verification is confirmed to be successful.

[0096] In this step, when the current performance parameters are within the optimal performance parameter range, it indicates that the optimal temperature range corresponding to the server component operating within the optimal performance parameter range determined in the aforementioned embodiments is accurate and can be used for guidance.

[0097] This application embodiment injects the optimal component temperature value into the BMC and applies dynamic load to the server component according to the preset load parameter combination to verify whether the optimal temperature range corresponding to the server component working within the optimal performance parameter range is reliable. This provides a basis for configuring heat dissipation parameters in the actual application of the server and helps to improve the stability and overall performance of the server.

[0098] For ease of understanding, this application also provides a complete implementation of a server testing process, as follows: I. Preliminary Preparations: Establishing the Correspondence Between Load and Components Multiple preset load parameter combinations are obtained from the test system parameter library, covering various combinations of CPU load at different percentages, disk read / write load, and network bandwidth usage load, such as 60% CPU load + intermittent disk read / write, 90% CPU load + 80% network bandwidth usage, 50% CPU load + continuous disk read / write + 70% network bandwidth usage, etc., comprehensively covering complex server operating conditions.

[0099] For each preset load parameter combination, a corresponding dynamic load is applied to the server through the dynamic load simulation module. At the same time, the infrared thermal imager is activated to collect thermal imaging data of the CPU, memory, disk, motherboard, network card and other components inside the server, which are then converted into temperature cloud maps by the infrared thermal imaging data processor.

[0100] Analyze the temperature cloud map corresponding to each load parameter combination, identify the server component with the highest temperature, and establish a correlation between the load parameter combination and the high-temperature component to form a preset correspondence between load and component. For example, CPU 100% load + continuous disk read / write corresponds to CPU, and continuous disk read / write + network bandwidth usage of 90% corresponds to disk.

[0101] II. Determine the target component for testing The preset load parameter combination for this test is defined as 100% CPU load + continuous disk read / write + 90% network bandwidth usage. The load and component correspondence established in the previous steps is queried to determine that the reference component for this combination is the CPU (the component with the highest temperature when the server is running with this combination).

[0102] The CPU, which is the reference component, was selected as the target component for this test, and all subsequent test operations will revolve around the CPU.

[0103] III. Core Tests: Dual-Mode Temperature Control and Heat Dissipation Response Data Acquisition The virtual injection unit is activated, and the target temperature value of the CPU is virtually injected into the BMC (Body Management Controller) via the BMC debugging interface. This target temperature value is higher than the upper limit of the CPU's operating temperature range (assuming the upper limit of the CPU's operating temperature range is 85℃, the injected target temperature value is 95℃). The injection latency is controlled within 80ms (meeting the <100ms requirement). During the injection process, the first heat dissipation response action corresponding to the target temperature value is triggered, and the first heat dissipation response data of the heat dissipation components in the server is acquired in real time (fan speed increases to 80%, CPU does not throttle, BMC does not generate a high temperature alarm) and the first heat dissipation strategy data (fan speed starts at 80% when the fan speed adjustment threshold is 95℃, throttling trigger threshold is 100℃, alarm trigger threshold is 105℃).

[0104] After a period of time, the system switches to physical temperature control mode, controlling the physical temperature control unit (including thermoelectric cooler TEC, infrared heating array, and PID controller) to adjust the CPU surface temperature to a target of 95℃. The PID controller precisely controls the temperature, keeping the temperature fluctuation within ±0.3℃ (meeting the ≤±0.5℃ requirement). Simultaneously, a dynamic load is applied to the server according to a preset load parameter combination: 100% CPU load + continuous disk read / write + 90% network bandwidth usage. Real-time secondary heat dissipation response data of the heat dissipation components is obtained (fan speed gradually increases to 90%, CPU frequency drops to 80% of original frequency after 3 seconds, and the BMC generates a high-temperature warning log).

[0105] When the BMC detects that the CPU temperature has reached the target temperature of 95℃, it simultaneously collects self-heating detection data for the CPU, including the CPU real-time power consumption of 160W obtained through the PMBus interface, the airflow rate on the CPU surface of 0.9m / s obtained through the wind speed sensor, and the ambient infrared radiation intensity of 220W / m² obtained through a dedicated sensor.

[0106] IV. Data Processing: Calculation of Actual Temperature and Analysis of Generated Heat Dissipation Performance Key parameters were extracted from the collected self-heating detection data: CPU real-time power consumption 160W, CPU surface airflow rate 0.9m / s, and ambient infrared radiation intensity 220W / m².

[0107] Substituting into the preset temperature correction formula T_actual=T_sensor +k×P_power / v_airflow +m×I_ambient (where T_sensor is the temperature value collected by the BMC software sensor, 93℃, and k is the component's thermal resistance coefficient, 0.02℃) m² s / W, m is the environmental interference factor 0.01℃ The actual temperature of the CPU, calculated using m² / W, is 93 + 0.02 × 160 / 0.9 + 0.01 × 220 ≈ 97.98℃.

[0108] Based on the target temperature of 95℃ and the first heat dissipation response data, the first heat dissipation performance analysis data is generated: In the virtual injection scenario, the fan speed increases from 50% to 80% within 10 seconds, the CPU does not throttle, the BMC does not issue an alarm, and the heat dissipation response meets the theoretical expectations; Based on the actual temperature of 97.98℃ and the second heat dissipation response data, the second heat dissipation performance analysis data is generated: Under physical conditions, the fan speed increases from 50% to 90% within 8 seconds, the CPU throttles back to 80% of its original frequency after 20 seconds, the BMC generates a high temperature warning, and the actual heat dissipation pressure is higher than the theoretical expectations.

[0109] During the physical temperature control test, the CPU temperature change data was continuously recorded (gradually decreasing from 97.98℃ to 95.7℃ within 10 minutes). This temperature change data was used to determine the temperature stability analysis data (temperature fluctuation range of 2.28℃). The data from the first and second heat dissipation strategies were compared to determine the heat dissipation performance analysis data (the fan speed adjustment response was 1℃ lagging in the virtual scenario, while the speed adjustment was more timely in the real scenario; the frequency reduction and alarm thresholds in the virtual scenario were more lenient, while the protection mechanism needed to be triggered in advance in the real scenario). These results were then integrated to form the heat dissipation performance analysis results of the server before and after self-heating compensation.

[0110] V. Extended Testing: Determining the Optimal Temperature Range of Components Obtain multiple preset load parameter combinations, including 50% CPU load + intermittent disk read / write, 70% CPU load + 70% network bandwidth usage, 80% CPU load + intermittent disk read / write + 90% network bandwidth usage, etc.

[0111] For each combination of load parameters, a dynamic load is applied to the target component CPU according to the combination. The component temperature and performance parameters of multiple server components (CPU, memory, disk) are read through BMC. For example, when the CPU is at 70% load and the network bandwidth is at 70%, the CPU temperature is 72℃ and the computing speed is 3.5GHz, the memory temperature is 65℃ and the read / write speed is 1600MHz / s, and the disk temperature is 60℃ and the IO rate is 180MB / s.

[0112] Statistical analysis of performance parameters under multiple load combinations determined the optimal performance parameter range for each server component: CPU 3.5GHz-3.8GHz, memory 1600MHz / s-1800MHz / s, and disk 180MB / s-200MB / s.

[0113] Associate all temperature data corresponding to the optimal performance parameter range of each component to determine the optimal temperature range for each component to operate within the optimal performance parameter range: CPU 70℃-78℃, memory 62℃-68℃, disk 58℃-65℃.

[0114] VI. Verification Test: Validation of the Optimal Temperature Range For the server component CPU, a typical value of 75℃ within its optimal temperature range of 70℃-78℃ is selected as the optimal component temperature value. This temperature value is injected into the BMC through the virtual injection unit, triggering the third heat dissipation response action corresponding to 75℃ (fan speed is adjusted to 60%, and CPU temperature is monitored in real time).

[0115] Obtain multiple preset load parameters for the CPU, including CPU 60% load, CPU 80% load, etc.

[0116] For each preset load parameter, a corresponding dynamic load is applied to the CPU, and the current performance parameters of the CPU are read through the BMC, such as the CPU operating speed of 3.6GHz at 60% load and 3.7GHz at 80% load.

[0117] Verify whether the current performance parameters are within the CPU's optimal performance parameter range (3.5GHz-3.8GHz). After comparing the current performance parameters under all load parameters, they are all within this range, confirming that the optimal temperature range verification is successful.

[0118] In another embodiment of this application, a server testing apparatus is also provided, such as... Figure 2 As shown, it includes: The virtual injection module 11 is used to virtually inject the target temperature value of the target component into the baseboard management controller (BMC) and obtain the first heat dissipation response data of the heat dissipation component in the server. The target temperature value is higher than the upper limit of the operating temperature range of the target component. The physical adjustment module 12 is used to control the physical temperature control unit to adjust the surface temperature of the target component with the target temperature value as the target, and to apply dynamic load to the server according to the preset load parameter combination, and to obtain the second heat dissipation response data of the heat dissipation component in the server; The acquisition module 13 is used to acquire the self-heating detection data collected on the target component when the BMC detects that the temperature of the target component has reached the target temperature; The determination module 14 is used to determine the heat dissipation performance analysis results of the server operating temperature before and after self-heating compensation based on the target temperature value, the first heat dissipation response data, the second heat dissipation response data and the self-heating detection data.

[0119] In another embodiment of this application, an electronic device is also provided, including a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus. Memory, used to store computer programs; When the processor executes a program stored in memory, it implements the server testing method described in any of the foregoing method embodiments.

[0120] The electronic device provided in this invention combines virtual injection with physical temperature control by executing a program stored in the memory. It acquires heat dissipation response data under two scenarios and collects self-heating detection data for subsequent analysis. This facilitates the verification of the server's true heat dissipation response, such as fan speed adjustment and power consumption changes, thereby improving the accuracy of test results. It solves the problem that software injection in the prior art cannot verify the true heat dissipation response. It can comprehensively and accurately evaluate the server's operating status and heat dissipation performance in high-temperature environments, providing multi-dimensional and reliable data support for server heat dissipation strategy optimization and performance improvement.

[0121] The communication bus 1140 mentioned in the above-mentioned electronic device can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus 1140 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0122] The communication interface 1120 is used for communication between the above-mentioned electronic device and other devices.

[0123] The memory 1130 may include random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor.

[0124] The processor 1110 mentioned above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0125] In another embodiment of this application, a computer-readable storage medium is also provided, on which a program for a server testing method is stored, wherein when the program for the server testing method is executed by a processor, it implements the steps of the server testing method described in any of the foregoing method embodiments.

[0126] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0127] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A server testing method, characterized in that, include: The target temperature value of the target component is virtually injected into the Baseboard Management Controller (BMC), and the first heat dissipation response data of the heat dissipation component in the server is obtained. The target temperature value is higher than the upper limit of the operating temperature range of the target component. The physical temperature control unit is controlled to adjust the surface temperature of the target component with the target temperature value as the target, and a dynamic load is applied to the server according to a preset load parameter combination to obtain the second heat dissipation response data of the heat dissipation component in the server; Acquire self-heating detection data for the target component when the BMC detects that the temperature of the target component has reached the target temperature; Based on the target temperature value, the first heat dissipation response data, the second heat dissipation response data, and the self-heating detection data, the heat dissipation performance analysis results of the server before and after self-heating compensation are determined.

2. The server testing method according to claim 1, characterized in that, Based on the target temperature value, the first heat dissipation response data, the second heat dissipation response data, and the self-heating detection data, the analysis results of the server's heat dissipation performance before and after self-heating compensation are determined, including: The real-time power consumption of the target component, the airflow rate on the surface of the target component, and the ambient infrared radiation intensity on the surface of the target component are extracted from the self-heating detection data. The actual temperature value of the target component is calculated based on the target temperature value, the real-time power consumption, the airflow rate, the ambient infrared radiation intensity, and a preset temperature correction formula. First heat dissipation performance analysis data is generated based on the target temperature value and the first heat dissipation response data; second heat dissipation performance analysis data is generated based on the actual temperature value and the second heat dissipation response data. The heat dissipation performance analysis results of the server before and after self-heating compensation are determined based on the first heat dissipation performance analysis data and the second heat dissipation performance analysis data.

3. The server testing method according to claim 2, characterized in that, Based on the first heat dissipation performance analysis data and the second heat dissipation performance analysis data, the heat dissipation performance analysis results of the server before and after self-heating compensation are determined, including: During the process of virtually injecting the target temperature value of the target component into the baseboard management controller (BMC) and triggering the first heat dissipation response action corresponding to the target temperature value, the first heat dissipation strategy data of the server is obtained. In the process of controlling the physical temperature control unit to adjust the surface temperature of the target component, applying a dynamic load to the target component according to a preset load parameter combination, and triggering a second heat dissipation response action corresponding to the actual temperature value, the temperature change data of the target component and the second heat dissipation strategy data of the server are acquired. Temperature stability analysis data is determined based on the temperature change data; heat dissipation performance analysis data is determined based on the first heat dissipation strategy data and the second heat dissipation strategy data, which are used as the heat dissipation performance analysis results.

4. The server testing method according to claim 1, characterized in that, Before obtaining the target temperature value of the target component injected into the base plate management controller (BMC), the method further includes: In the preset correspondence between load and component, a reference component is determined that corresponds to the preset load parameter combination. The reference component is the server component with the highest operating temperature when the server is running according to the preset load parameter combination. The reference component is identified as the target component.

5. The server testing method according to claim 4, characterized in that, The method for determining the preset correspondence between loads and components includes: Obtain multiple preset load parameter combinations; For each preset load parameter combination, a dynamic load is applied to the target component according to the preset load parameter combination, and thermal imaging data inside the server is collected. The correspondence between the preset load parameter combination and the server component with the highest temperature in the thermal imaging data is determined as the correspondence between the load and the component.

6. The server testing method according to claim 1, characterized in that, The method further includes: Obtain multiple preset load parameter combinations; For each preset load parameter combination, a dynamic load is applied to the target component according to the preset load parameter combination, and the component temperature and performance parameters read by BMC for multiple server components are obtained; Among the performance parameters collected under multiple preset load parameter combinations, the optimal performance parameter range for each server component is determined; Obtain the optimal temperature range corresponding to the server component operating within the optimal performance parameter range.

7. The server testing method according to claim 1, characterized in that, The method further includes: For any server component, the optimal component temperature value of the server component is injected into the BMC, and a third heat dissipation response action corresponding to the optimal component temperature value is triggered. Obtain the preset load parameters of the server components; Apply dynamic load to the server component according to the preset load parameters, and obtain the current performance parameters of the server component read by the BMC; Verify whether the current performance parameters are within the optimal performance parameter range; If the current performance parameters are within the optimal performance parameter range, the verification is considered successful.

8. A server testing device, characterized in that, include: The virtual injection module is used to virtually inject the target temperature value of the target component into the baseboard management controller (BMC) and obtain the first heat dissipation response data of the heat dissipation component in the server. The target temperature value is higher than the upper limit of the operating temperature range of the target component. The physical adjustment module is used to control the physical temperature control unit to adjust the surface temperature of the target component to the target temperature value, and to apply dynamic load to the server according to a preset load parameter combination, and to obtain the second heat dissipation response data of the heat dissipation component in the server. The acquisition module is used to acquire self-heating detection data collected on the target component when the BMC detects that the temperature of the target component has reached the target temperature; The determination module is used to determine the heat dissipation performance analysis results of the server operating temperature before and after self-heating compensation based on the target temperature value, the first heat dissipation response data, the second heat dissipation response data, and the self-heating detection data.

9. An electronic device, characterized in that, It includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; A processor, when executing a program stored in memory, implements the server testing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program for a server testing method, which, when executed by a processor, implements the steps of the server testing method according to any one of claims 1-7.

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