Cabinet testing method, computer equipment, storage medium and program product

Abstract

The invention relates to the technical field of computers, and discloses a cabinet test method, computer equipment, a storage medium and a program product, comprising: acquiring configuration information of a target equipment group, the configuration information comprising at least one equipment category and an equipment number corresponding to each equipment category; obtaining a target test scene and a target test parameter value corresponding to the target test scene; and according to each equipment category and the target test scene, obtaining a sub-current function corresponding to each equipment category in the target test scene. And determining a total current function corresponding to the target device group according to the device number and the sub-current function corresponding to each device type. And sending the total current function to load simulation equipment. And according to the target test parameter value, testing the working condition of the cabinet when the target equipment group operates in the target test scene. According to the invention, the cabinet can be tested without occupying a large number of server resources.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a cabinet testing method, computer equipment, storage medium and program product. Background Art

[0002] With the continuous iteration and update of science and technology, the performance of servers has been greatly improved, and its power consumption per unit volume has also been continuously improved. In large data centers, servers are installed in cabinets, which can provide power for servers. When all servers in the cabinet are turned on, it will have a great impact on the cabinet. In order to ensure the safety of the cabinet during use, the cabinet is generally tested to identify problems with the cabinet and make improvements.

[0003] In the current cabinet testing process, it is necessary to first install servers of different models in the cabinet, and then test the servers in the cabinet in various business scenarios to determine the problems that still exist in the cabinet. In this way, the demand for the number and model of servers is high, and a large amount of server resources are required. Summary of the invention

[0004] In view of this, the present invention provides a cabinet testing method, apparatus, computer equipment, storage medium and program product to solve the problem of occupying a large amount of server resources when performing cabinet testing.

[0005] In a first aspect, the present invention provides a cabinet testing method, the method is applied to a test system, the test system includes a load simulation device, a cabinet and a target device, the cabinet is electrically connected to the load simulation device, the method is performed by the target device, and the method includes: Acquire configuration information of the target device group, wherein the configuration information includes at least one device category and the number of devices corresponding to each of the device categories; Get the target test scenario; Obtaining a target test parameter value corresponding to the target test scenario; According to each of the device categories and the target test scenario, obtaining a sub-current function corresponding to each of the device categories under the target test scenario; Determining a total current function corresponding to the target device group according to the number of devices and the sub-current functions corresponding to each of the device categories; Sending the total current function to the load simulation device, so that the load simulation device adjusts at least one circuit parameter of itself according to the total current function to simulate the load of the target device group in the target test scenario; According to the target test parameter value, the working condition of the cabinet when the target device group operates under the target test scenario is tested.

[0006] The cabinet testing method provided by the present invention has the following advantages: Traditional testing methods usually require the actual deployment and operation of the target device group, which requires a large number of physical devices. The use of load simulation devices can avoid the use of actual devices and reduce the demand for physical devices. This solution uses the configuration information of the target device group to calculate the total current function corresponding to the target device group in the target test scenario. Then, the load simulation device can adjust the circuit parameters according to the total current function to accurately simulate the load conditions of the target device group in the target test scenario. In this way, for different test scenarios, there is no need to frequently replace or reconfigure actual devices, which can improve the flexibility and efficiency of testing.

[0007] In an optional implementation, the acquiring, according to each of the device categories and the target test scenario, a sub-current function corresponding to each of the device categories in the target test scenario includes: According to the target device category, selecting sub-current functions respectively corresponding to at least one test scenario corresponding to the target device category in a pre-built target database, wherein the target device category is any one device category of at least one of the device categories; According to the target test scenario, a sub-current function corresponding to the target test scenario is selected from sub-current functions respectively corresponding to at least one test scenario corresponding to the target device category.

[0008] Specifically, through the pre-built target database, the sub-current functions that match each device category and target test scenario can be directly found. In this way, these sub-current functions can be directly used to calculate the total current function in the future, which can improve the efficiency of cabinet testing. In addition, for different device groups and different test scenarios, the sub-current functions in the target database can be reused, which is more convenient.

[0009] In an optional implementation, the acquiring, according to each of the device categories and the target test scenario, a sub-current function corresponding to each of the device categories in the target test scenario includes: According to the target device category and the target test scenario, a target current data set corresponding to the target device category under the target test scenario is acquired, wherein the target device category is any one of at least one of the device categories, and the target current data set includes coordinates of a plurality of current sampling points; According to the coordinates of each current sampling point and the pre-built target model, a sub-current function corresponding to the target device category is determined.

[0010] Specifically, the generation of the sub-current function is based on actual measurement data, which can improve the test accuracy.

[0011] In an optional implementation, determining the sub-current function corresponding to the target device category according to the coordinates of each current sampling point and a pre-built target model includes: According to the coordinates of each of the current sampling points, at least one cutting point is selected from the multiple current sampling points, wherein the cutting point is a current sampling point whose derivative is 0; According to the coordinates of each of the cutting points, the target current data set is split into a plurality of sub-data sets, and a time neighborhood corresponding to each of the sub-data sets is determined; According to the target model, coordinates of all current sampling points included in the target sub-dataset are fitted to obtain a function corresponding to the target sub-dataset, wherein the target sub-dataset is any one of the multiple sub-datasets; A sub-current function corresponding to the target device category is determined according to the time neighborhood and function corresponding to each of the sub-data sets.

[0012] Specifically, by dividing the time neighborhood, the fitting process of each sub-data set can be controlled more accurately to ensure the continuity and stability of the sub-current function.

[0013] In an optional implementation, before fitting the coordinates of all current sampling points included in the target sub-dataset according to the target model to obtain a function corresponding to the target sub-dataset, the method further includes: According to a preset coordinate conversion rule, coordinate conversion is performed on the coordinates of each current sampling point to generate a new current sampling point corresponding to each current sampling point; The newly added current sampling point corresponding to each of the current sampling points is added to the target sub-dataset to obtain the updated target sub-dataset.

[0014] Specifically, by generating more current sampling points through existing current sampling points, the original current characteristics can be retained and the accuracy of fitting can be improved.

[0015] In an optional implementation, when the target test scenario is a restart type test scenario, obtaining a target test parameter value corresponding to the target test scenario includes: Randomly selecting an integer value in a preset numerical interval corresponding to the target test scenario; Obtaining a target weight value corresponding to the target test scenario; The target number of tests is determined according to the integer value and the target weight value, wherein the target number of tests is the target test parameter value.

[0016] Specifically, by randomly selecting an integer value in a preset numerical range, the randomness of the test times is ensured, various situations that may be encountered in actual use are simulated, and the authenticity and representativeness of the test are improved.

[0017] In an optional implementation, when the target test scenario is a stress-type test scenario, obtaining a target test parameter value corresponding to the target test scenario includes: Obtaining a target weight value and a first test duration corresponding to the target test scenario; A second test duration is determined based on the first test duration and the target weight value, wherein the second test duration is the target test parameter value.

[0018] Specifically, the target weight value and the first test duration can be flexibly set according to different test requirements and device characteristics to adapt to the stress testing requirements of different devices and scenarios.

[0019] In an optional implementation, the acquiring, according to the target device category and the target test scenario, a target current data set corresponding to the target device category in the target test scenario includes: According to the target test scenario, obtaining a test strategy corresponding to the target test scenario; The device corresponding to the target device category is tested according to the test strategy, and the target current data set is collected.

[0020] Specifically, by acquiring the test strategy, the test can be automatically executed and the current data set can be collected according to the test strategy, which reduces the time for manual configuration and debugging and improves the test efficiency.

[0021] In an optional implementation, the target model is expressed as follows:

[0022]

[0023]

[0024] Wherein, f(t) is the current function, n is the current expansion level, t is time, and T is the time neighborhood length corresponding to the target sub-dataset. To preset the initial time, for , is the first preset constant term.

[0025] Specifically, the Fourier series can capture multiple frequency components of the current signal, ensuring that the current function can accurately express the current characteristics.

[0026] In an optional implementation, the total current function adopts the following expression:

[0027] in, is the total current function, is the second preset constant term, i is the serial number of the currently calculated device in the target device group, m is the number of devices included in the target device group, n is the current expansion level, N is the preset expansion level, j is the serial number of the currently calculated current sampling point in the currently calculated time neighborhood, P is the number of current sampling points of the currently calculated device in the currently calculated time neighborhood, is the abscissa of the jth current sampling point in the time neighborhood of the current calculation, is the ordinate of the jth current sampling point, is the difference between the abscissa of the j-th current sampling point and the abscissa of the j-1-th current sampling point, and T is the length of the time neighborhood of the current calculation.

[0028] Specifically, through the expansion of current sampling points of multiple devices and Fourier series, the fitting accuracy can be gradually improved, making the total current function closer to the actual total current change.

[0029] In an optional embodiment, the method further includes: In the process of testing the working condition of the cabinet, the working parameter values ​​corresponding to at least one component included in the cabinet are respectively monitored; When it is monitored that the working parameter value of the target component is not within the preset parameter value area, a first alarm message is generated, wherein the target component is any one of the at least one components.

[0030] Specifically, the working parameter value of at least one component included in the cabinet is monitored, and a first alarm message is generated when the working parameter value of the target component is not within the preset parameter value area, so that technical personnel can respond in time to avoid damage to the equipment and cabinet.

[0031] In an optional embodiment, the method further includes: In the process of testing the working condition of the cabinet, the power consumption value of the cabinet is monitored; When it is monitored that the power consumption value of the cabinet is not within the preset power consumption range, a second alarm information is generated.

[0032] Specifically, when the power consumption value is detected to be beyond the preset range, a second alarm message is immediately generated, so that technicians can take timely measures to avoid equipment overload or damage and reduce the waste of hardware resources. In addition, by monitoring the power consumption value, the power consumption performance of the cabinet under different load conditions can be found, helping to identify performance bottlenecks and optimization points.

[0033] In a second aspect, the present invention provides a cabinet testing device, the device comprising: An acquisition module is used to acquire configuration information of a target device group, wherein the configuration information includes at least one device category and the number of devices corresponding to each device category; acquire a target test scenario; acquire a target test parameter value corresponding to the target test scenario; and acquire a sub-current function corresponding to each device category under the target test scenario according to each device category and the target test scenario; A determination module, configured to determine a total current function corresponding to the target device group according to the number of devices and the sub-current functions corresponding to each of the device categories; A sending module, used for sending the total current function to a load simulation device, so that the load simulation device adjusts at least one circuit parameter of itself according to the total current function to simulate the load of the target device group in the target test scenario; The testing module is used to test the working condition of the cabinet when the target device group runs under the target testing scenario according to the target testing parameter value.

[0034] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the cabinet testing method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0035] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the cabinet testing method of the first aspect or any corresponding embodiment thereof.

[0036] In a fifth aspect, the present invention provides a computer program product, including computer instructions, where the computer instructions are used to enable a computer to execute the cabinet testing method of the above-mentioned first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related technologies, the drawings required for use in the specific embodiments or the related technical descriptions will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1 is a schematic diagram of the architecture of a test system according to an embodiment of the present invention; Figure 2 is a schematic diagram of a process flow of a cabinet testing method according to an embodiment of the present invention; Figure 3 is a schematic diagram of a process flow of a cabinet testing method according to an embodiment of the present invention; Figure 4 is a schematic diagram of a process flow of a cabinet testing method according to an embodiment of the present invention; Figure 5 is a structural block diagram of a cabinet testing device according to an embodiment of the present invention; Figure 6 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0040] The present invention can be implemented by a test system, such as Figure 1As shown, the test system may include a load simulation device, a cabinet and a target device. The load simulation device may be electrically connected to the power board of the cabinet, and the target device may be communicated with the cabinet and the load simulation device respectively. Among them, the load simulation device is a device used to simulate various load conditions in actual use, mainly used to test the performance and stability of power supply systems, electrical equipment or electronic products, which may include a power factor correction (PFC) rectifier voltage regulator circuit and a full-bridge inverter circuit. By adjusting the parameters of each circuit, different working states can be simulated to help users understand the performance of the device under various conditions. Among them, the circuit parameters may be resistance parameters, capacitance parameters, and inductance parameters. The cabinet is a closed or semi-closed box specially designed to install and protect network equipment (e.g., switches), servers and other electronic equipment. The target device may be a computer device, for example, a terminal, a server, etc., and the following description will take the terminal as an example.

[0041] An embodiment of the present invention provides a cabinet testing method, which calculates a total current function according to relevant information of a target device group and adjusts circuit parameters of a load simulation device based on the total current function. The cabinet test can be completed without using real devices, thereby saving equipment resources.

[0042] According to an embodiment of the present invention, a cabinet testing method embodiment is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0043] In this embodiment, a cabinet testing method is provided, which can be executed by the above-mentioned target device. Figure 2 is a flow chart of a cabinet testing method according to an embodiment of the present invention. Figure 2 As shown, the process includes the following steps: Step S201: Obtain configuration information of a target device group.

[0044] The target device group may include multiple servers. The configuration information may include at least one device category and the number of devices corresponding to each device category. The device category may be used to indicate the model and purpose of the device, for example, the model and purpose of the server. The model may be used to indicate the hardware architecture of the device (the model and quantity of the hardware used). The purpose may be data storage, artificial intelligence (AI) processing, etc.

[0045] Specifically, when designing a target device group, the technician can determine the types of devices (e.g., servers and switches) that need to be placed in the target device group, as well as the models and quantities of the devices according to actual needs. In this way, the target device can obtain the configuration information of the target device group based on the information input by the technician.

[0046] Step S202, obtaining a target test scenario.

[0047] Among them, the target test scenario can be a restart type test scenario or a stress type test scenario. For example, the restart type test scenario may include: restart (Reboot) test, direct current (DC) test, alternating current (AC) test, each test may include with stress and without stress, and may also include random AC (Random AC) test. Stress type test scenarios may include: central processing unit (CPU) stress test, memory stress test, disk stress test, graphics processing unit (GPU) stress test, network card stress test, simultaneous stress test of CPU / memory / disk, etc.

[0048] Specifically, when testing the cabinet, the technician can directly input the indication information of the target test scenario on the target device, and the indication information can be the identification information of the target test scenario, such as a number or name. Alternatively, the technician can first design a complete test plan and input the test plan into the target device. The test plan may include rules for selecting a test scenario. In this way, the target device can select the rules of the test scenario and determine the target test scenario from a plurality of preset test scenarios.

[0049] Step S203, obtaining target test parameter values ​​corresponding to the target test scenario.

[0050] The target test parameter value may be used to indicate the test intensity, for example, the number of tests and the test duration.

[0051] Specifically, since different test scenarios have different characteristics, corresponding types of test parameter values ​​can be used for testing different test scenarios. Accordingly, the target device can first determine the type of the target test scenario, and then determine the target test parameter value according to the type of the target test scenario, which can specifically include the following situations: First, when the target test scenario is a restart type test scenario, the target device can first randomly select an integer value in the preset value interval corresponding to the target test scenario, and then obtain the target weight value corresponding to the target test scenario. Finally, the target number of tests is determined based on the integer value and the target weight value.

[0052] Among them, the target test number is the target test parameter value.

[0053] Specifically, each test scenario can be provided with a corresponding preset numerical interval and weight value. After selecting the target test scenario, the preset numerical interval corresponding to the target test scenario can be determined in the correspondence table between the test scenario and the preset numerical interval, and the target weight value corresponding to the target test scenario can be determined in the correspondence table between the test scenario and the weight value. Then, an integer value can be randomly selected from the determined preset numerical interval, and the product of the integer value and the target weight value can be determined as the target number of tests.

[0054] For example, the preset numerical interval corresponding to the target test scenario may be [A, B], a numerical value C is randomly selected from [A, B], the target weight value is D, and the target number of tests is C×D.

[0055] Second, when the target test scenario is a stress-type test scenario, the target device can first obtain the target weight value and the first test duration corresponding to the target test scenario, and then determine the second test duration based on the first test duration and the target weight value.

[0056] Among them, the second test duration is the target test parameter value.

[0057] Specifically, each test scenario can be set with a corresponding test duration and weight value. After selecting the target test scenario, the target device can determine the first test duration corresponding to the target test scenario in the correspondence table between the test scenario and the test duration, and determine the target weight value corresponding to the target test scenario in the correspondence table between the test scenario and the weight value. Then, the product of the first test duration and the target weight value can be determined as the second test duration.

[0058] Step S204: according to each device category and the target test scenario, a sub-current function corresponding to each device category in the target test scenario is obtained.

[0059] Specifically, step S204 can be processed in the following two ways: Method 1 Step 1: According to the target device category and the target test scenario, a target current data set corresponding to the target device category in the target test scenario is obtained.

[0060] The target device category is any one of at least one device category, and the target current data set includes coordinates of multiple current sampling points.

[0061] Specifically, generally, before the device leaves the factory, the manufacturer will test the device in various test scenarios and obtain the corresponding current characteristic diagram. In this way, in this solution, the current sampling points can be directly extracted according to the corresponding current characteristic diagram, and a current data set can be generated. If the manufacturer does not provide it, the current data set can be obtained through the following test process.

[0062] Before collecting the target current data set, the technician can first assemble the current information collection equipment (for example, power instrument, circuit analysis instrument, etc.), and connect the current information collection equipment to the target device and the device under test respectively. In this way, in the subsequent test process, the current information collection equipment can collect current data from the device under test, process it, and then transmit it to the target device, and the target device can obtain the current data set after processing.

[0063] The target device can obtain a test strategy corresponding to the target test scenario according to the target test scenario, and test the device corresponding to the target device category according to the test strategy to collect the target current data set. Among them, the test strategy may include at least one operation type, and test parameter indication information corresponding to each operation type (the test parameter indication information may include a preset number of tests or a preset test duration, or may also include a strategy for selecting the number of tests or the test duration), the timing of collecting the current data set, etc. When the target test scenario is a restart type test scenario, the operation type may include one or more of a restart operation, a stress test operation, a power-on operation, a startup operation, and a shutdown operation. When the target test scenario is a stress type test scenario, the operation type may be a stress test operation.

[0064] The following are some examples: 1. Restart test: When the server is turned off, restart the server multiple times (for example, 2 times) and collect the last current data set.

[0065] 2. Stress restart test: When the server is shut down, restart the server multiple times (for example, 2 times). After each restart to enter the operating system, stress test the CPU / memory / disk at the same time. The test lasts 30 minutes and the last current data set is collected.

[0066] 3. DC test: When the server is turned off, power it on and off multiple times (for example, twice) and collect the last current data set.

[0067] 4. DC test with stress: When the server is turned off, power it on and off multiple times (for example, twice). After each startup and entering the operating system, stress test the CPU / memory / disk at the same time. The test time is 30 minutes, and the last current data set is collected.

[0068] 5. AC test: When the server is powered off, power on, start up, and shut down the server multiple times (for example, twice) and collect the last current data set.

[0069] 6. Stress AC test: When the server is powered off, power on, start up, and shut down the server multiple times (for example, twice). After each startup and entering the operating system, stress test the CPU / memory / disk at the same time. The test time is 30 minutes, and the last current data set is collected.

[0070] 7. Random AC test: When the server is powered off, power on and start the server. A random value in [10s, 30s] is selected as the target duration. After the server is turned on, the power is immediately turned off after waiting for the target duration. This cycle is repeated multiple times (for example, 2 times) to collect the last current data set.

[0071] 8. CPU stress test: When the server is in idle state, the CPU is subjected to a preset maximum stress test for the first preset duration (for example, 30 minutes), and a current data set is collected for the last second preset duration (for example, 10 minutes).

[0072] 9. Memory stress test: When the server is powered on and idle, perform the preset maximum stress test on the memory for the first preset duration, and collect the current data set for the last second preset duration.

[0073] 10. Disk stress test: When the server is powered on and idle, the disk is subjected to a preset maximum stress test for the first preset duration, and a current data set is collected for the last second preset duration.

[0074] 11. GPU stress test: When the server is powered on and in idle state, the GPU is subjected to the preset maximum stress test for the first preset duration, and the current data set for the last second preset duration is collected.

[0075] 12. Network card stress test: When the server is powered on and in idle state, the network card is subjected to the preset highest stress test for the first preset duration, and the current data set for the last second preset duration is collected.

[0076] 13. Simultaneously perform stress tests on the CPU / memory / disk: When the server is powered on and idle, perform the preset maximum stress test on the CPU / memory / disk for the first preset duration, and collect the current data set for the last second preset duration.

[0077] During the above test process, the target device can monitor the operation of the device under test. When it is detected that the temperature value of any component of the server is greater than the preset temperature threshold, an alarm is issued and the test is stopped in time to avoid damage to the device under test.

[0078] Step 2: Determine the sub-current function corresponding to the target device category according to the coordinates of each current sampling point and the pre-built target model.

[0079] Step 1: Select at least one cutting point from a plurality of current sampling points according to the coordinates of each current sampling point.

[0080] The cut point is the current sampling point where the derivative is 0.

[0081] Step 2: split the target current data set into multiple sub-data sets according to the coordinates of each cutting point, and determine the time neighborhood corresponding to each sub-data set.

[0082] Step 3: Fit the coordinates of all current sampling points included in the target sub-dataset according to the target model to obtain a function corresponding to the target sub-dataset.

[0083] The target sub-dataset is any sub-dataset among multiple sub-datasets. The target model can be expressed as follows: (1) (2) (3) Where f(t) is the current function, n is the current expansion level, t is time, and T is the length of the time neighborhood corresponding to the target sub-dataset. To preset the initial time, for , is the first preset constant term.

[0084] Step 4: Determine the sub-current function corresponding to the target device category according to the time neighborhood and function corresponding to each sub-data set.

[0085] Specifically, except for the first current sampling point in the target current data set, the target device can calculate the derivative of the second current sampling point according to the coordinates of the first current sampling point and the coordinates of the second current sampling point, the second current sampling point is any current sampling point in the target current data set except the first current sampling point, and the first current sampling point is the previous current sampling point between the second current sampling points. Then, the target device can determine whether the derivative of the second current sampling point is 0, and if so, the second current sampling point is determined as a cutting point. In this way, at least one cutting point can be determined in the target current data set.

[0086] For each cutting point, the target device can split the target current data set into multiple sub-data sets according to the coordinates of each cutting point, and determine the time neighborhood corresponding to each sub-data set. In this way, after splitting, it can be ensured that each sub-data set meets the following conditions: within the time neighborhood D={t|a≤t≤b} corresponding to the sub-data set, ∃t 0 ∈D, ∀t∈D and t≠t 0 , f(t 0 )>f(t), Dirichlet condition (there are only a finite number of extreme points in the neighborhood), and ensure that the final fitted function satisfies .

[0087] For any sub-data set, the target device can use the target model to fit the coordinates of all current sampling points included in the sub-data set to obtain a function corresponding to the sub-data set.

[0088] In this way, the target device can splice the functions corresponding to each sub-data set according to the time neighborhood corresponding to each sub-data set to obtain the sub-current function corresponding to the target device category. For example, multiple time neighborhoods include [a, b], (b, c], (c, d], the function corresponding to [a, b] is function 1, the function corresponding to (b, c] is function 2, and the function corresponding to (c, d] is function 3. The spliced ​​sub-current function can be as follows: g(t)=

[0089] In some optional implementations, before step 3, in order to make the fitted sub-current function more accurate, the target device may generate coordinates of more current sampling points based on the coordinates of the original current sampling points in the sub-data set, and specifically may perform the following processing: According to the preset coordinate transformation rule, the coordinates of each current sampling point are transformed to generate a newly added current sampling point corresponding to each current sampling point. The newly added current sampling point corresponding to each current sampling point is added to the target sub-dataset to obtain an updated target sub-dataset.

[0090] Specifically, for the target sub-dataset, the target device can determine the horizontal coordinate movement amount and the vertical coordinate movement amount according to the coordinates of the first current sampling point in the target sub-dataset and the origin coordinates. Further, the target device can convert the horizontal coordinate of each current sampling point in the target sub-dataset according to the horizontal coordinate movement amount, and convert the vertical coordinate of each current sampling point in the target sub-dataset according to the vertical coordinate movement amount to obtain the new coordinates of each current sampling point. Finally, according to the negative value of the horizontal coordinate and the negative value of the vertical coordinate of the current sampling point, a new current sampling point is constructed and added to the target sub-dataset. In this way, the number of current sampling points in the target sub-dataset increases, and the target model is sampled for fitting operations to obtain a more accurate function.

[0091] Method 2 The target device may first select, according to the target device category, the sub-current functions corresponding to the devices of the target device category in at least one test scenario from the pre-constructed target database. Then, according to the target test scenario, the sub-current function corresponding to the target test scenario is selected from the sub-current functions corresponding to at least one test scenario. Alternatively, the target device may first select, according to the target test scenario, the sub-current functions corresponding to at least one device category under the target test scenario from the pre-constructed target database. Then, according to the target device category, the sub-current function corresponding to the target device category is selected from the sub-current functions corresponding to at least one device category.

[0092] The target device category is any device category of at least one device category. The sub-current function in the target database can be collected and tested according to the steps of the first method and stored in the target database.

[0093] Step S205 : determining a total current function corresponding to the target device group according to the number of devices and the sub-current functions corresponding to each device category.

[0094] Specifically, the target device can determine the number of superpositions of the sub-current functions corresponding to each device category according to the number of devices corresponding to each device category, and then, according to each sub-current function and its corresponding number of superpositions, the sub-current functions corresponding to each device category are superimposed to obtain the total current function corresponding to the target device group. For example, the total current function can be expressed as follows: (4) in, is the total current function, is the second preset constant term, i is the serial number of the currently calculated device in the target device group, m is the number of devices included in the target device group, n is the current expansion level, N is the preset expansion level, j is the serial number of the currently calculated current sampling point in the currently calculated time neighborhood, P is the number of current sampling points of the currently calculated device in the currently calculated time neighborhood, is the horizontal coordinate of the jth current sampling point in the time neighborhood of the current calculation, is the ordinate of the jth current sampling point, is the difference between the abscissa of the j-th current sampling point and the abscissa of the j-1-th current sampling point, and T is the length of the time neighborhood of the current calculation.

[0095] Step S206 , sending the total current function to the load simulation device, so that the load simulation device adjusts at least one circuit parameter of itself according to the total current function, and simulates the load of the target device group in the target test scenario.

[0096] The circuit parameters include one or more of resistance parameters, capacitance parameters and inductance parameters.

[0097] Specifically, the target device can send the total current function to the load simulation device. The load simulation device can calculate the circuit parameter values ​​according to the rules and total current function set by itself, and adjust its own circuit parameters according to the circuit parameter values, so that the load simulation device after adjusting the circuit parameters can simulate the load of the target device group in the target test scenario.

[0098] Step S207: testing the working condition of the cabinet when the target device group is running in the target test scenario according to the target test parameter value.

[0099] Specifically, when the target test parameter value is the target test duration, the target device can first control the cabinet to power the load simulation device and start timing. The target device can monitor the working condition of the cabinet during the power supply process, wherein the working condition may include the power consumption value of the cabinet at multiple time points, and the working parameter values ​​of the various components included in the cabinet at multiple time points (for example, it can be a temperature value). When the timing duration reaches the target test duration, the cabinet can be controlled to stop powering the load simulation device to complete the test of the target test scenario. Alternatively, when the target test parameter value is the target number of tests, the target device can control the cabinet to power the load simulation device at the beginning of the test according to the rules set in the test software, and monitor the working condition of the cabinet during the power supply process. After completing a test, the target device can control the cabinet to stop powering the load simulation device.

[0100] In the process of testing the working condition of the cabinet, the target device determines in real time whether the cabinet is working safely. Accordingly, the target device can perform the following operations: First, the operating parameter values ​​(for example, temperature values) corresponding to at least one component included in the cabinet are monitored respectively. When the operating parameter value of the target component is not within the preset parameter value range, a first alarm message is generated, wherein the target component is any one of the at least one component.

[0101] Second, monitor the power consumption of the cabinet. When the power consumption of the cabinet is not within the preset power consumption range, a second alarm message is generated. In addition, the target device can record the current test information (operating parameter values ​​of each component, fault information), so that technicians can analyze the cause, adjust the test strategy in time, repair the fault, and ensure the reliability of the test data.

[0102] Third, in the process of testing the working condition of the cabinet, the target device can also monitor the working condition of the load simulation device. When it is detected that the working parameter value of any component of the load simulation device is not within the preset parameter value range, a third alarm message is generated. In this way, the alarm allows the technicians to take corresponding measures immediately to prevent damage to the equipment. The test system can also include a heat dissipation device (for example, a fan). Therefore, at the same time, the target device can also monitor the speed of the fan to ensure that the fan works effectively and maintains the device running within a suitable temperature range.

[0103] Fourth, in the process of testing the working condition of the cabinet, the target device can analyze the generated alarm information and / or load changes to obtain the test results, which are used to indicate whether the cabinet has passed the test of the target test scenario. For example, when there is an alarm message, it is determined that the test has not passed; when the load increase in any unit time is greater than a preset threshold, it is determined that the test has not passed, etc. When there is no alarm information and the load increase in all unit times is less than or equal to the preset threshold, it is determined that the test has passed, etc.

[0104] In some optional implementations, when collecting the current data set corresponding to each device category, since in the case of designing a new type of device, the device has not been actually produced, that is, the real device cannot be provided at present, therefore, for such a device, its configuration information (at least one hardware model, and the quantity corresponding to each hardware model) can be obtained first. In this way, the target device can test the hardware corresponding to each hardware model in various test scenarios, obtain the current data set corresponding to the hardware model in various scenarios, and then determine the current function corresponding to each hardware model based on the current data set and the above-mentioned target model. Further, for any test scenario, the target device can determine the current function of the device based on the quantity and current function corresponding to each hardware model (specifically, refer to the above-mentioned method of calculating the total current function of the target device group). In this way, for any device, the above method can be used to determine the current function of the device without providing a real device and without occupying more device resources.

[0105] The cabinet testing method provided in this embodiment is different from the traditional testing method which usually requires the actual deployment and operation of the target device group, which requires a large number of physical devices. The use of load simulation equipment can avoid the use of actual equipment and reduce the demand for physical equipment. This solution calculates the total current function corresponding to the target device group in the target test scenario through the configuration information of the target device group. Then, the load simulation equipment can adjust the circuit parameters according to the total current function to accurately simulate the load conditions of the target device group in the target test scenario. In this way, for different test scenarios, there is no need to frequently replace or reconfigure the actual equipment, which can improve the flexibility and efficiency of the test.

[0106] The following is a detailed description of the cabinet testing process using a specific example.

[0107] For each test scenario, the technician can pre-build a test model corresponding to the test scenario and add the test model to the test model library. Before testing, the technician can also adjust various parameters in the test model according to the test requirements.

[0108] For example, the test model for a restart type test scenario can be as follows: “Test Case name: model1 Run Type: Reboot Fail_Continue: true Mode_Times: 1 Counts_Range: [10,30] Running: true Among them, "Test Case name" is the name of the test model, "Run Type" is the "type of the test model" (used to indicate the test scenario), "Fail_Continue" is used to indicate whether to continue other random model tests after the test model fails, "Mode_Times" is the weight value, "Counts_Range" is the test restart count interval (that is, the above-mentioned preset value interval), and "Running" is used to indicate whether the test model participates in the test. When "Running" in the test model is "true", it indicates that the test model is currently participating in the test, and when "Running" in the test model is "false", it indicates that the test model is not currently participating in the test.

[0109] The test model corresponding to the stress type test scenario can be as follows: Test Casename: model2 Run Type: CPU stress Fail_Continue: true Run_times: 20min Mode_Times: 1 Running: true Among them, "Run_times" is the test duration.

[0110] like Figure 3 As shown, when performing a restart type test, the target device can first obtain the target total number of tests, and randomly select a test model from the restart type test model library (that is, the above-mentioned process of obtaining the target test scenario). Then, the target device can determine the corresponding total current function based on the selected test model and the configuration information of the target device group, and transmit it to the load simulation device. In addition, the target device can randomly select an integer value from the test restart number interval included in the test model, and determine the product of the selected integer value and the weight value included in the test model as the number of tests corresponding to the test model. Perform the same number of tests on the cabinet as the number of tests. After executing the test process of the test model, a test model can be randomly selected again from the restart type test model library, and so on, until the number of tests on the cabinet reaches the target total number of tests, and then stop the test.

[0111] like Figure 4As shown, when performing a restart type test, the target device can first obtain the target total test duration, and randomly select a test model from the stress type test model library. Then, the target device can determine the corresponding total current function based on the selected test model and the configuration information of the target device group, and transmit it to the load simulation device. In addition, the target device can determine the product of the test duration and the weight value included in the test model as the test duration corresponding to the test model. Finally, the target device can perform a test on the cabinet with the test duration. After executing the test process of the test model, a test model can be randomly selected again from the stress type test model library, and so on, until the test duration of the cabinet reaches the target total test duration, and then the test is stopped.

[0112] In this embodiment, a cabinet testing device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0113] This embodiment provides a cabinet testing device, such as Figure 5 As shown, including: The acquisition module 501 is used to acquire configuration information of the target device group, wherein the configuration information includes at least one device category and the number of devices corresponding to each device category; acquire a target test scenario; acquire a target test parameter value corresponding to the target test scenario; and acquire a sub-current function corresponding to each device category under the target test scenario according to each device category and the target test scenario; A determination module 502, configured to determine a total current function corresponding to a target device group according to the number of devices and the sub-current functions corresponding to each device category; A sending module 503, configured to send the total current function to a load simulation device, so that the load simulation device adjusts at least one circuit parameter of itself according to the total current function, and simulates the load of the target device group in a target test scenario; The testing module 504 is used to test the working condition of the cabinet when the target device group runs in the target test scenario according to the target test parameter value.

[0114] In some optional implementations, the acquisition module 501 is specifically configured to: According to the target device category, selecting sub-current functions corresponding to at least one test scenario corresponding to the target device category in a pre-built target database, wherein the target device category is any one device category of the at least one device category; According to the target test scenario, a sub-current function corresponding to the target test scenario is selected from the sub-current functions respectively corresponding to at least one test scenario corresponding to the target device category.

[0115] In some optional implementations, the acquisition module 501 is specifically configured to: According to the target device category and the target test scenario, a target current data set corresponding to the target device category in the target test scenario is acquired, wherein the target device category is any one of at least one device category, and the target current data set includes coordinates of a plurality of current sampling points; According to the coordinates of each current sampling point and the pre-built target model, a sub-current function corresponding to the target device category is determined.

[0116] In some optional implementations, the determination module 502 is configured to: According to the coordinates of each current sampling point, at least one cutting point is selected from the multiple current sampling points, wherein the cutting point is a current sampling point whose derivative is 0; According to the coordinates of each cutting point, the target current data set is split into multiple sub-data sets, and the time neighborhood corresponding to each sub-data set is determined; According to the target model, coordinates of all current sampling points included in the target sub-dataset are fitted to obtain a function corresponding to the target sub-dataset, wherein the target sub-dataset is any one of the multiple sub-datasets; A sub-current function corresponding to the target device category is determined based on the time neighborhood and function corresponding to each sub-data set.

[0117] In some optional implementations, the determination module 502 is further configured to: According to a preset coordinate transformation rule, coordinate transformation is performed on the coordinates of each current sampling point to generate a new current sampling point corresponding to each current sampling point; The newly added current sampling points corresponding to each current sampling point are added to the target sub-dataset to obtain an updated target sub-dataset.

[0118] In some optional implementations, when the target test scenario is a restart type test scenario, the acquisition module 501 is specifically used to: Randomly select an integer value in a preset numerical range corresponding to the target test scenario; Obtain the target weight value corresponding to the target test scenario; The target number of tests is determined based on the integer value and the target weight value, wherein the target number of tests is the target test parameter value.

[0119] In some optional implementations, when the target test scenario is a stress-type test scenario, the acquisition module 501 is specifically configured to: Obtaining a target weight value and a first test duration corresponding to a target test scenario; The second test duration is determined based on the first test duration and the target weight value, wherein the second test duration is the target test parameter value.

[0120] In some optional implementations, the acquisition module 501 is specifically configured to: According to the target test scenario, obtain the test strategy corresponding to the target test scenario; The device corresponding to the target device category is tested according to the test strategy, and the target current data set is collected.

[0121] In some optional implementations, the target model is expressed as follows:

[0122]

[0123]

[0124] Where f(t) is the current function, n is the current expansion level, t is time, and T is the length of the time neighborhood corresponding to the target sub-dataset. To preset the initial time, for , is the first preset constant term.

[0125] In some optional implementations, the total current function is expressed as follows:

[0126] in, is the total current function, is the second preset constant term, i is the serial number of the currently calculated device in the target device group, m is the number of devices included in the target device group, n is the current expansion level, N is the preset expansion level, j is the serial number of the currently calculated current sampling point in the currently calculated time neighborhood, P is the number of current sampling points of the currently calculated device in the currently calculated time neighborhood, is the horizontal coordinate of the jth current sampling point in the time neighborhood of the current calculation, is the ordinate of the jth current sampling point, is the difference between the abscissa of the j-th current sampling point and the abscissa of the j-1-th current sampling point, and T is the length of the time neighborhood of the current calculation.

[0127] In some optional embodiments, the device further includes a monitoring module 505, which is used to: In the process of testing the working condition of the cabinet, the working parameter values ​​corresponding to at least one component included in the cabinet are respectively monitored; When it is monitored that the working parameter value of the target component is not within the preset parameter value area, a first alarm message is generated, wherein the target component is any one of the at least one component.

[0128] In some optional implementations, the monitoring module 505 is further configured to: In the process of testing the working condition of the cabinet, the power consumption value of the cabinet is monitored; When it is monitored that the power consumption value of the cabinet is not within the preset power consumption range, a second alarm information is generated.

[0129] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0130] The cabinet device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0131] The embodiment of the present invention also provides a computer device having the above Figure 5 Cabinet assembly shown.

[0132] See also Figure 6 , Figure 6 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 6 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 6 A processor 10 is taken as an example.

[0133] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware integrated circuit. The hardware integrated circuit may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable logic gate array, a general purpose array logic or any combination thereof.

[0134] The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

[0135] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0136] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0137] The computer device also includes an input device 30 and an output device 40. The processor 10, the memory 20, the input device 30 and the output device 40 may be connected via a bus or other means. Figure 6 The example of connecting through bus is taken in the following.

[0138] The input device 30 can receive input digital or character information, and generate key signal input related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, an indicator bar, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (e.g., an LED) and a tactile feedback device (e.g., a vibration motor), etc. The above-mentioned display device includes but is not limited to a liquid crystal display, a light emitting diode, a display and a plasma display. In some optional embodiments, the display device may be a touch screen.

[0139] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0140] A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and / or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

[0141] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A cabinet testing method, characterized in that: The method is applied to a test system, the test system includes a load simulation device, a cabinet and a target device, the cabinet is electrically connected to the load simulation device, the method is performed by the target device, and the method includes: Acquire configuration information of the target device group, wherein the configuration information includes at least one device category and the number of devices corresponding to each of the device categories; Get the target test scenario; Obtaining a target test parameter value corresponding to the target test scenario; According to each of the device categories and the target test scenario, obtaining a sub-current function corresponding to each of the device categories under the target test scenario; Determining a total current function corresponding to the target device group according to the number of devices and the sub-current functions corresponding to each of the device categories; Sending the total current function to the load simulation device, so that the load simulation device adjusts at least one circuit parameter of itself according to the total current function to simulate the load of the target device group in the target test scenario; According to the target test parameter value, the working condition of the cabinet when the target device group operates under the target test scenario is tested.

2. The method according to claim 1, characterized in that: The acquiring, according to each of the device categories and the target test scenario, a sub-current function corresponding to each of the device categories in the target test scenario includes: According to the target device category, selecting sub-current functions respectively corresponding to at least one test scenario corresponding to the target device category in a pre-built target database, wherein the target device category is any one device category of at least one of the device categories; According to the target test scenario, a sub-current function corresponding to the target test scenario is selected from sub-current functions respectively corresponding to at least one test scenario corresponding to the target device category.

3. The method according to claim 1, characterized in that The acquiring, according to each of the device categories and the target test scenario, a sub-current function corresponding to each of the device categories in the target test scenario includes: According to the target device category and the target test scenario, a target current data set corresponding to the target device category under the target test scenario is acquired, wherein the target device category is any one of at least one of the device categories, and the target current data set includes coordinates of a plurality of current sampling points; According to the coordinates of each current sampling point and the pre-built target model, a sub-current function corresponding to the target device category is determined.

4. The method according to claim 3, characterized in that The determining, according to the coordinates of each current sampling point and the pre-built target model, a sub-current function corresponding to the target device category comprises: According to the coordinates of each of the current sampling points, at least one cutting point is selected from the multiple current sampling points, wherein the cutting point is a current sampling point whose derivative is 0; According to the coordinates of each of the cutting points, the target current data set is split into a plurality of sub-data sets, and a time neighborhood corresponding to each of the sub-data sets is determined; According to the target model, coordinates of all current sampling points included in the target sub-dataset are fitted to obtain a function corresponding to the target sub-dataset, wherein the target sub-dataset is any one of the multiple sub-datasets; A sub-current function corresponding to the target device category is determined according to the time neighborhood and function corresponding to each of the sub-data sets.

5. The method according to claim 4, characterized in that Before fitting the coordinates of all current sampling points included in the target sub-dataset according to the target model to obtain a function corresponding to the target sub-dataset, the method further includes: According to a preset coordinate conversion rule, coordinate conversion is performed on the coordinates of each current sampling point to generate a new current sampling point corresponding to each current sampling point; The newly added current sampling point corresponding to each of the current sampling points is added to the target sub-dataset to obtain the updated target sub-dataset.

6. The method according to any one of claims 1 to 5, characterized in that: When the target test scenario is a restart type test scenario, obtaining a target test parameter value corresponding to the target test scenario includes: Randomly selecting an integer value in a preset numerical interval corresponding to the target test scenario; Obtaining a target weight value corresponding to the target test scenario; The target number of tests is determined according to the integer value and the target weight value, wherein the target number of tests is the target test parameter value.

7. The method according to any one of claims 1 to 5, characterized in that: When the target test scenario is a stress-type test scenario, obtaining a target test parameter value corresponding to the target test scenario includes: Obtaining a target weight value and a first test duration corresponding to the target test scenario; A second test duration is determined based on the first test duration and the target weight value, wherein the second test duration is the target test parameter value.

8. The method according to claim 3, characterized in that The step of acquiring, according to the target device category and the target test scenario, a target current data set corresponding to the target device category under the target test scenario includes: According to the target test scenario, obtaining a test strategy corresponding to the target test scenario; The device corresponding to the target device category is tested according to the test strategy, and the target current data set is collected.

9. The method according to claim 4 or 5, characterized in that: The target model uses the following expression: Wherein, f(t) is the current function, n is the current expansion level, t is time, and T is the time neighborhood length corresponding to the target sub-dataset. To preset the initial time, for , is the first preset constant term.

10. The method according to claim 9, characterized in that The total current function is expressed as follows: in, is the total current function, is a second preset constant term, i is the serial number of the device currently being calculated in the target device group, m is the number of devices included in the target device group, n is the current expansion level, N is the preset expansion level, j is the serial number of the current sampling point currently being calculated in the time neighborhood of the current calculation, P is the number of current sampling points of the device currently being calculated in the time neighborhood of the current calculation, is the abscissa of the jth current sampling point in the time neighborhood of the current calculation, is the ordinate of the j-th current sampling point, is the difference between the abscissa of the j-th current sampling point and the abscissa of the j-1-th current sampling point, and T is the length of the time neighborhood of the current calculation.

11. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: In the process of testing the working condition of the cabinet, the working parameter values ​​corresponding to at least one component included in the cabinet are respectively monitored; When it is monitored that the working parameter value of the target component is not within the preset parameter value area, a first alarm message is generated, wherein the target component is any one of the at least one components.

12. The method according to any one of claims 1 to 5, characterized in that: The method further comprises: In the process of testing the working condition of the cabinet, the power consumption value of the cabinet is monitored; When it is monitored that the power consumption value of the cabinet is not within the preset power consumption range, a second alarm information is generated.

13. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the cabinet testing method according to any one of claims 1 to 12 by executing the computer instructions.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the cabinet testing method according to any one of claims 1 to 12.

15. A computer program product, characterized in that The method comprises computer instructions, wherein the computer instructions are used to cause a computer to execute the cabinet testing method according to any one of claims 1 to 12.

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