Cabinet Testing Method, Computer Device, Storage Medium, and Program Product

Abstract

The present invention relates to the field of computer technologies, and discloses a cabinet testing method, a computer device, a storage medium, and a program product, including: obtaining configuration information of a target device group, where the configuration information includes at least one device category and the number of devices corresponding to each device category. Obtaining a target test scenario and target test parameter values corresponding to the target test scenario. According to each device category and the target test scenario, obtaining a sub-current function corresponding to each device category in the target test scenario. Determining a total current function corresponding to the target device group according to the number of devices corresponding to each device category and the sub-current function. Sending the total current function to a load simulation device. Testing the operating condition of the cabinet when the target device group operates in the target test scenario according to the target test parameter values. The present invention does not require a large amount of server resources to test the cabinet.

Description

Technical Field

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

[0002] With the continuous iterative update of science and technology, the performance of servers has been greatly improved, and the power consumption per unit volume is also continuously increasing. In large data centers, servers are assembled in cabinets, and the cabinets can provide power for the servers. When all the servers in the cabinet are powered on, it will cause a large impact on the cabinet. To ensure the safety of the cabinet during use, the cabinet is generally tested to identify and improve the problems existing in the cabinet.

[0003] During the current testing process of the cabinet, different models of servers need to be assembled in the cabinet first, and then various business scenarios of the servers in the cabinet are tested to determine the problems still existing in the cabinet. In this way, the requirements for the quantity and model of the servers are relatively high, and a large amount of server resources need to be occupied. Summary of the Invention

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

[0005] In a first aspect, the present invention provides a cabinet testing method, which is applied to a testing system. The testing 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 executed by the target device, and the method includes:

[0006] Obtain the configuration information of the target device group, where the configuration information includes at least one device category and the number of devices corresponding to each device category;

[0007] Obtain the target test scenario;

[0008] Obtain the target test parameter value corresponding to the target test scenario;

[0009] According to each device category and the target test scenario, obtain the sub-current function corresponding to each device category in the target test scenario;

[0010] Determine the total current function corresponding to the target device group according to the number of devices corresponding to each device category and the sub-current function;

[0011] Send 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;

[0012] According to the target test parameter values, test the working conditions of the cabinet when the target device group is operating in the target test scenario.

[0013] A cabinet test method provided by the present invention has the following advantages:

[0014] Traditional test methods usually require the actual deployment and operation of the target device group, which requires a large number of physical devices. By using a load simulation device, the use of actual devices can be avoided, reducing the demand for physical devices. In this solution, according to the configuration information of the target device group, the total current function corresponding to the target device group in the target test scenario is calculated. Furthermore, the load simulation device can adjust the circuit parameters according to the total current function to accurately simulate the load situation 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.

[0015] In an alternative embodiment, the obtaining of the sub-current function corresponding to each device category in the target test scenario according to each device category and the target test scenario includes:

[0016] According to the target device category, select the sub-current functions corresponding to at least one test scenario corresponding to the target device category in a pre-constructed target database, where the target device category is any one of at least one of the device categories;

[0017] According to the target test scenario, select the sub-current function corresponding to the target test scenario from the sub-current functions corresponding to at least one test scenario corresponding to the target device category.

[0018] Specifically, through the pre-constructed target database, the sub-current functions matching each device category and the 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 follow-up, which can improve the cabinet test efficiency. Moreover, for different device groups and different test scenarios, the sub-current functions in the target database can be reused, which is more convenient.

[0019] In an alternative embodiment, the obtaining of the sub-current function corresponding to each device category in the target test scenario according to each device category and the target test scenario includes:

[0020] According to the target device category and the target test scenario, obtain a target current data set corresponding to the target device category under the target test scenario, where the target device category is any one of at least one of the device categories, and the target current data set includes the coordinates of multiple current sampling points;

[0021] According to the coordinates of each of the current sampling points and a pre-constructed target model, determine a sub-current function corresponding to the target device category.

[0022] Specifically, the generation of the sub-current function is based on actual measurement data, which can improve the test accuracy.

[0023] In an optional implementation manner, the determining, according to the coordinates of each of the current sampling points and a pre-constructed target model, a sub-current function corresponding to the target device category includes:

[0024] According to the coordinates of each of the current sampling points, select at least one cut point from among the multiple current sampling points, where the cut point is a current sampling point with a derivative of 0;

[0025] According to the coordinates of each of the cut points, split the target current data set into multiple sub-data sets, and determine a time neighborhood corresponding to each of the sub-data sets;

[0026] According to the target model, fit the coordinates of all the current sampling points included in the target sub-data set to obtain a function corresponding to the target sub-data set, where the target sub-data set is any one of the multiple sub-data sets;

[0027] According to the time neighborhood and the function corresponding to each of the sub-data sets, determine a sub-current function corresponding to the target device category.

[0028] Specifically, through the division of the time neighborhood, the fitting process of each sub-data set can be more precisely controlled to ensure the continuity and stability of the sub-current function.

[0029] In an optional implementation manner, before the fitting, according to the target model, the coordinates of all the current sampling points included in the target sub-data set to obtain a function corresponding to the target sub-data set, the method further includes:

[0030] According to a preset coordinate conversion rule, perform coordinate conversion on the coordinates of each of the current sampling points to generate a new current sampling point corresponding to each of the current sampling points;

[0031] Add the new current sampling point corresponding to each of the current sampling points to the target sub-data set to obtain the updated target sub-data set.

[0032] Specifically, generating more current sampling points from existing current sampling points can not only retain the original current characteristics but also improve the accuracy of fitting.

[0033] In an alternative embodiment, when the target test scenario is a restart-type test scenario, the obtaining of the target test parameter value corresponding to the target test scenario includes:

[0034] Randomly selecting an integer value within a preset numerical range corresponding to the target test scenario;

[0035] Obtaining the target weight value corresponding to the target test scenario;

[0036] Determining the target number of tests according to the integer value and the target weight value, where the target number of tests is the target test parameter value.

[0037] Specifically, by randomly selecting an integer value within a preset numerical range, the randomness of the number of tests is ensured, various situations that may be encountered in actual use are simulated, and the authenticity and representativeness of the test are improved.

[0038] In an alternative embodiment, when the target test scenario is a pressure-type test scenario, the obtaining of the target test parameter value corresponding to the target test scenario includes:

[0039] Obtaining the target weight value and the first test duration corresponding to the target test scenario;

[0040] Determining the second test duration according to the first test duration and the target weight value, where the second test duration is the target test parameter value.

[0041] Specifically, the target weight value and the first test duration can be flexibly set according to different test requirements and device characteristics to meet the pressure test requirements of different devices and scenarios.

[0042] In an alternative embodiment, the obtaining of the target current dataset corresponding to the target device category in the target test scenario according to the target device category and the target test scenario includes:

[0043] According to the target test scenario, obtaining the test strategy corresponding to the target test scenario;

[0044] Testing the device corresponding to the target device category according to the test strategy and collecting the target current dataset.

[0045] Specifically, by obtaining the test strategy, the test can be automatically executed according to the test strategy and the current data set can be collected, reducing the time for manual configuration and debugging and improving the test efficiency.

[0046] In an alternative embodiment, the target model adopts the following expression:

[0047]

[0048]

[0049]

[0050] where f(t) is the current function, n is the current expansion order, t is the time, T is the length of the time neighborhood corresponding to the target sub-data set, is the preset initial time, is , is the first preset constant term.

[0051] Specifically, the Fourier series can capture multiple frequency components of the current signal, ensuring that the current function can accurately represent the current characteristics.

[0052] In an alternative embodiment, the total current function adopts the following expression:

[0053]

[0054] where is the total current function, is the second preset constant term, i is the serial number of the current calculated device in the target device group, m is the number of devices included in the target device group, n is the current expansion order, N is the preset expansion order, j is the serial number of the current calculated current sampling point in the current calculated time neighborhood, P is the number of current sampling points of the current calculated device in the current calculated time neighborhood, is the abscissa of the jth current sampling point in the current calculated time neighborhood, is the ordinate of the jth current sampling point, is the difference between the abscissa of the jth current sampling point and the abscissa of the (j - 1)th current sampling point, and T is the length of the current calculated time neighborhood.

[0055] Specifically, by expanding the current sampling points of multiple devices and the Fourier series, the fitting accuracy can be gradually improved, making the total current function closer to the actual total current change.

[0056] In an alternative embodiment, the method further includes:

[0057] During the process of testing the working condition of the cabinet, the working parameter values respectively corresponding to at least one component included in the cabinet are monitored separately.

[0058] When it is monitored that the working parameter value of the target component is not within the preset parameter value range, a first alarm message is generated, where the target component is any one of the at least one component.

[0059] Specifically, by monitoring the working parameter values of at least one component included in the cabinet and generating a first alarm message when it is monitored that the working parameter value of the target component is not within the preset parameter value range, technicians can make timely responses to avoid damage to the equipment and the cabinet.

[0060] In an alternative embodiment, the method further includes:

[0061] During the process of testing the working condition of the cabinet, the power consumption value of the cabinet is monitored.

[0062] When it is monitored that the power consumption value of the cabinet is not within the preset power consumption range, a second alarm message is generated.

[0063] Specifically, when it is monitored that the power consumption value exceeds the preset range, a second alarm message is immediately generated, which can enable technicians to take timely measures to avoid equipment overload or damage and reduce the waste of hardware resources. Moreover, 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.

[0064] In a second aspect, the present invention provides a cabinet testing device, and the device includes:

[0065] An acquisition module, configured to acquire the configuration information of the target device group, where the configuration information includes at least one device category and the number of devices corresponding to each device category; acquire the target test scenario; acquire the target test parameter value corresponding to the target test scenario; and acquire the sub-current function corresponding to each device category in the target test scenario according to each device category and the target test scenario.

[0066] A determination module, configured to determine the total current function corresponding to the target device group according to the number of devices corresponding to each device category and the sub-current function.

[0067] A sending module, configured to send 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.

[0068] A test module for testing the working condition of the cabinet when the target device group operates in the target test scenario according to the target test parameter value.

[0069] In a third aspect, the present invention provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the cabinet test method according to the first aspect or any corresponding embodiment thereof.

[0070] In a fourth aspect, the present invention provides a computer-readable storage medium, on which computer instructions are stored, and the computer instructions are used to cause a computer to execute the cabinet test method according to the first aspect or any corresponding embodiment thereof.

[0071] In a fifth aspect, the present invention provides a computer program product, including computer instructions, and the computer instructions are used to cause a computer to execute the cabinet test method according to the first aspect or any corresponding embodiment thereof. Description of the Drawings

[0072] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the related art, the following will briefly introduce the drawings required to be used in the description of the specific embodiments or the related art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0073] Figure 1 is a schematic diagram of the architecture of the test system according to an embodiment of the present invention;

[0074] Figure 2 is a schematic flowchart of the cabinet test method according to an embodiment of the present invention;

[0075] Figure 3 is a schematic flowchart of the cabinet test method according to an embodiment of the present invention;

[0076] Figure 4 is a schematic flowchart of the cabinet test method according to an embodiment of the present invention;

[0077] Figure 5 is a structural block diagram of the cabinet test device according to an embodiment of the present invention;

[0078] Figure 6 is a schematic hardware structure diagram of the computer device according to an embodiment of the present invention. Detailed Embodiments

[0079] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0080] The present invention can be implemented by a test system. As Figure 1 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 supply board of the cabinet, and the target device may be communicatively connected to 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 for testing the performance and stability of power systems, electrical equipment, or electronic products. It may include a power factor correction (PFC) rectifier voltage stabilizing circuit and a full-bridge inverter circuit. By adjusting various circuit parameters, 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 an enclosed or semi-enclosed box specially designed to install and protect network devices (such as switches), servers, and other electronic devices. The target device may be a computer device, for example, it may be a terminal, a server, etc. Hereinafter, the terminal will be taken as an example for illustration.

[0081] An embodiment of the present invention provides a cabinet testing method. By calculating the total current function according to the relevant information of the target device group and adjusting the circuit parameters of the load simulation device based on the total current function, the testing of the cabinet can be completed without using real devices, which can save device resources.

[0082] According to an embodiment of the present invention, an embodiment of a cabinet testing method 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 the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than here.

[0083] In this embodiment, a cabinet testing method is provided, which can be executed by the above-mentioned target device. Figure 2 is a flowchart of the cabinet testing method according to an embodiment of the present invention. As Figure 2 shown, the process includes the following steps:

[0084] Step S201, obtain the configuration information of the target device group.

[0085] Among them, the target device group can include multiple servers. The configuration information can include at least one device category, and the number of devices corresponding to each device category. The device category can be used to indicate the model and use of the device, for example, the model and use of the server. The model can be used to indicate the hardware architecture of the device (the model and quantity of the hardware used). The use can specifically be storing data, artificial intelligence (AI) processing, etc.

[0086] Specifically, when designing the target device group, technicians can, according to actual needs, determine the types of devices to be placed in the target device group (such as servers and switches), as well as the models and quantities of the devices. In this way, the target device can obtain the configuration information of the target device group based on the information input by the technicians.

[0087] Step S202, obtain the target test scenario.

[0088] Among them, the target test scenario can be a test scenario of the restart type or a test scenario of the stress type. For example, the test scenario of the restart type can include: Reboot test, Direct Current (DC) test, Alternating Current (AC) test. Each test can include with pressure and without pressure, and can also include Random AC test. The test scenario of the stress type can include: Central Processing Unit (CPU) stress test, memory stress test, disk stress test, Graphics Processing Unit (GPU) stress test, network card stress test, stress test on CPU / memory / disk simultaneously, etc.

[0089] Specifically, when testing the cabinet, technicians can directly input the indication information of the target test scenario on the target device. The indication information can be the identification information of the target test scenario, such as a number or a name, etc. Or, technicians can first design a complete test plan and input the test plan into the target device. The test plan can include the rules for selecting the test scenario. In this way, the target device can determine the target test scenario from a variety of preset test scenarios according to the rules for selecting the test scenario.

[0090] Step S203, obtain the target test parameter value corresponding to the target test scenario.

[0091] Among them, the target test parameter value can be used to indicate the test intensity. For example, it can be the number of tests and the test duration.

[0092] Specifically, due to the different characteristics of different test scenarios, 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 multiple situations:

[0093] First, when the target test scenario is a restart-type test scenario, the target device can first randomly select an integer value from the preset numerical range corresponding to the target test scenario. Then, obtain the target weight value corresponding to the target test scenario. Finally, determine the target test times according to the integer value and the target weight value.

[0094] Among them, the target test times are the target test parameter values.

[0095] Specifically, each test scenario can be set with a corresponding preset numerical range and weight value. After selecting the target test scenario, the corresponding preset numerical range corresponding to the target test scenario can be determined in the correspondence table between the test scenario and the preset numerical range according to the target test scenario, 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 range, and the product of the integer value and the target weight value can be determined as the target test times.

[0096] For example, the preset numerical range corresponding to the target test scenario can be [A, B], a value C is randomly selected from [A, B], the target weight value is D, and the target test times are C×D.

[0097] Second, when the target test scenario is a pressure-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 according to the first test duration and the target weight value.

[0098] Among them, the second test duration is the target test parameter value.

[0099] 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 according to the target test scenario, 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.

[0100] Step S204, according to each device category and the target test scenario, obtain the sub-current function corresponding to each device category under the target test scenario.

[0101] Specifically, step S204 can be processed in the following two ways:

[0102] Method 1

[0103] Step 1: According to the target device category and the target test scenario, obtain the target current data set corresponding to the target device category under the target test scenario.

[0104] Among them, the target device category is any one of at least one device category, and the target current data set includes the coordinates of multiple current sampling points.

[0105] Specifically, generally, before the device leaves the factory, the manufacturer will test the device under various test scenarios and obtain the corresponding current characteristic diagram. In this way, in this solution, the current sampling points can be directly extracted from the corresponding current characteristic diagram, and the current data set can be generated. If the manufacturer does not provide it, the current data set can be obtained after the following test process.

[0106] Before collecting the target current data set, technicians can first assemble the current information collection device (for example, power instrument, circuit analysis instrument, etc.), and connect the current information collection device to the target device and the device under test respectively. In this way, during the subsequent test process, the current information collection device can collect the current data from the device under test, process it and transmit it to the target device, and the target device can obtain the current data set after processing.

[0107] The target device can obtain the 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, and collect the target current data set. Among them, the test strategy can include at least one operation type, and the test parameter indication information corresponding to each operation type (the test parameter indication information can include the preset number of tests or the preset test duration, or can also include the selection strategy of 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 can include one or more of restart operation, stress test operation, power-on operation, boot operation, shutdown operation. When the target test scenario is a stress type test scenario, the operation type can be a stress test operation.

[0108] Specific examples are as follows:

[0109] 1. Reboot test: Under the shutdown state of the server, perform multiple (for example, 2 times) reboot operations on the server, and collect the current data set of the last time.

[0110] 2. Reboot Test with Pressure: When the server is in the shutdown state, perform multiple (e.g., 2 times) reboot operations on the server. After each reboot and entering the operating system, simultaneously perform a pressure test on the CPU / memory / disk for a test duration of 30 minutes, and collect the current dataset of the last time.

[0111] 3. DC Test: When the server is in the shutdown state, perform multiple (e.g., 2 times) power-on and power-off operations on the server, and collect the current dataset of the last time.

[0112] 4. DC Test with Pressure: When the server is in the shutdown state, perform multiple (e.g., 2 times) power-on and power-off operations on the server. After each power-on and entering the operating system, simultaneously perform a pressure test on the CPU / memory / disk for a test time of 30 minutes, and collect the current dataset of the last time.

[0113] 5. AC Test: When the server is in the power-off state, perform multiple (e.g., 2 times) power-on, power-on and boot, and power-off operations on the server, and collect the current dataset of the last time.

[0114] 6. AC Test with Pressure: When the server is in the power-off state, perform multiple (e.g., 2 times) power-on, power-on and boot, and power-off operations on the server. After each power-on and entering the operating system, simultaneously perform a pressure test on the CPU / memory / disk for a test time of 30 minutes, and collect the current dataset of the last time.

[0115] 7. Random AC Test: When the server is in the power-off state, power on and boot the server. Select a random value in the range of [10s, 30s] as the target duration. After powering on and waiting for the target duration, immediately power off. Repeat multiple times (e.g., 2 times), and collect the current dataset of the last time.

[0116] 8. CPU Pressure Test: When the server is in the idle state after booting, perform the preset highest pressure test on the CPU for the first preset duration (e.g., 30 minutes), and collect the current dataset of the last second preset duration (e.g., 10 minutes).

[0117] 9. Memory Pressure Test: When the server is in the idle state after booting, perform the preset highest pressure test on the memory for the first preset duration, and collect the current dataset of the last second preset duration.

[0118] 10. Disk Pressure Test: When the server is in the idle state after booting, perform the preset highest pressure test on the disk for the first preset duration, and collect the current dataset of the last second preset duration.

[0119] 11. GPU Pressure Test: When the server is in the idle state after booting, perform the preset highest pressure test on the GPU for the first preset duration, and collect the current dataset of the last second preset duration.

[0120] 12. Network card stress test: When the server is in an idle state after startup, perform the highest preset stress test on the network card for the first preset duration, and collect the current data set for the last second preset duration.

[0121] 13. Perform stress tests on the CPU / memory / disk simultaneously: When the server is in an idle state after startup, perform the highest preset stress test on the CPU / memory / disk for the first preset duration, and collect the current data set for the last second preset duration.

[0122] During the above testing process, the target device can monitor the operation of the device under test. When it detects that the temperature value of any component of the server is greater than the preset temperature threshold, it will give an alarm and stop the test in a timely manner to avoid damage to the device under test.

[0123] Step 2: According to the coordinates of each current sampling point and the pre-constructed target model, determine the sub-current function corresponding to the target device category.

[0124] Step 1: According to the coordinates of each current sampling point, select at least one cutting point from multiple current sampling points.

[0125] Among them, the cutting point is the current sampling point where the derivative is 0.

[0126] Step 2: According to the coordinates of each cutting point, split the target current data set into multiple sub-data sets, and determine the time neighborhood corresponding to each sub-data set.

[0127] Step 3: According to the target model, fit the coordinates of all current sampling points included in the target sub-data set to obtain the function corresponding to the target sub-data set.

[0128] Among them, the target sub-data set is any one of the multiple sub-data sets. The target model can adopt the following expression:

[0129] (1)

[0130] (2)

[0131] (3)

[0132] Among them, f(t) is the current function, n is the current expansion series, t is the time, T is the length of the time neighborhood corresponding to the target sub-data set, is the preset initial time, is , is the first preset constant term.

[0133] Step 4: Determine the sub-current function corresponding to the target device category according to the time neighborhood and function corresponding to each sub-dataset.

[0134] Specifically, except for the first current sampling point in the target current dataset, 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 dataset 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. If so, the second current sampling point is determined as the cut point. In this way, at least one cut point can be determined in the target current dataset.

[0135] For each cut point, the target device can split the target current dataset into multiple sub-datasets according to the coordinates of each cut point and determine the time neighborhood corresponding to each sub-dataset. In this way, after splitting, it can be ensured that each sub-dataset satisfies the following conditions: within the time neighborhood D = {t∣a ≤ t ≤ b} corresponding to the sub-dataset, , and t ≠ t0, f(t0) > f(t), Dirichlet condition (there are only a finite number of extreme points in the neighborhood), and ensure that the finally fitted function satisfies .

[0136] For any sub-dataset, the target device can use the target model to fit the coordinates of all current sampling points included in the sub-dataset to obtain the function corresponding to the sub-dataset.

[0137] In this way, the target device can splice the functions corresponding to each sub-dataset according to the time neighborhood corresponding to each sub-dataset 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:

[0138] g(t) =

[0139] In some alternative embodiments, before step three, in order to make the fitted sub-current function more accurate, the target device can generate the coordinates of more current sampling points based on the coordinates of the original current sampling points in the sub-dataset. Specifically, the following processing can be performed:

[0140] According to the preset coordinate conversion rule, convert the coordinates of each current sampling point to generate new current sampling points corresponding to each current sampling point. Add the new current sampling points corresponding to each current sampling point to the target subset of data to obtain an updated target subset of data.

[0141] Specifically, for the target subset of data, the target device can determine the abscissa movement amount and the ordinate movement amount based on the coordinates of the first current sampling point in the target subset of data and the origin coordinates. Further, the target device can convert the abscissa of each current sampling point in the target subset of data according to the abscissa movement amount, and convert the ordinate of each current sampling point in the target subset of data according to the ordinate movement amount to obtain the new coordinates of each current sampling point. Finally, construct new current sampling points based on the negative value of the abscissa and the negative value of the ordinate of the current sampling point, and add the new current sampling points to the target subset of data. In this way, the number of current sampling points in the target subset of data increases, and a more accurate function can be obtained by performing a fitting operation on the resampled target model.

[0142] Method 2

[0143] The target device can first select 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 according to the target device category. Then, select the sub-current function corresponding to the target test scenario from the sub-current functions corresponding to at least one test scenario according to the target test scenario. Alternatively, the target device can first select the sub-current functions corresponding to at least one device category in the target test scenario from the pre-constructed target database according to the target test scenario. Then, select the sub-current function corresponding to the target device category from the sub-current functions corresponding to at least one device category according to the target device category.

[0144] Among them, the target device category is any one of at least one device category. The sub-current functions in the target database can be obtained by testing and collecting according to the steps of Method 1 and stored in the target database.

[0145] Step S205, determine the total current function corresponding to the target device group according to the number of devices corresponding to each device category and the sub-current function.

[0146] Specifically, the target device can determine the superposition times of the sub-current functions corresponding to each device category according to the number of devices corresponding to each device category. Furthermore, according to each sub-current function and its corresponding superposition times, perform superposition on the sub-current functions corresponding to each device category respectively to obtain the total current function corresponding to the target device group. For example, the total current function can adopt the following expression:

[0147] (4)

[0148] Among them, 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 currently expanded series number, N is the preset expanded series number, 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 j-th current sampling point in the currently calculated time neighborhood, 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 currently calculated time neighborhood.

[0149] Step S206: Send the total current function to the load simulation device so that the load simulation device adjusts at least one of its circuit parameters according to the total current function to simulate the load of the target device group in the target test scenario.

[0150] Among them, the circuit parameters include one or more of resistance parameters, capacitance parameters, and inductance parameters.

[0151] Specifically, the target device can send the total current function to the load simulation device. The load simulation device can calculate the numerical values of the circuit parameters according to its own set rules and the total current function, and adjust its circuit parameters according to the numerical values of the circuit parameters, 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.

[0152] Step S207: Test the working condition of the cabinet when the target device group operates in the target test scenario according to the target test parameter value.

[0153] Specifically, when the target test parameter value is the target test duration, the target device can first control the cabinet to supply power to the load simulation device and start timing. The target device can monitor the working conditions of the cabinet during the power supply process. Among them, the working conditions can include the power consumption values of the cabinet at multiple time points, and the working parameter values (such as temperature values) of each component included in the cabinet at multiple time points. When the timing duration reaches the target test duration, the power supply to the load simulation device can be stopped by controlling the cabinet, and the test of the target test scenario can be completed. Or, when the target test parameter value is the target test number, the target device can control the cabinet to supply power to the load simulation device according to the rules set in the test software at the start of the test and monitor the working conditions of the cabinet during the power supply process. After completing one test, the target device can control the cabinet to stop supplying power to the load simulation device.

[0154] During the process of testing the working conditions of the cabinet, the target device determines in real time whether the cabinet is operating safely. Accordingly, the target device can perform the following operations:

[0155] First, monitor the working parameter values (such as temperature values) respectively corresponding to at least one component included in the cabinet. When it is detected that the working parameter value of the target component is not within the preset parameter value range, a first alarm message is generated, where the target component is any one of the at least one component.

[0156] Second, monitor the power consumption value of the cabinet. When it is detected that the power consumption value of the cabinet is not within the preset power consumption range, a second alarm message is generated. And the target device can record the current test information (the working parameter values of each component, fault information), so that technicians can analyze the reasons, adjust the test strategy in time, and repair the faults to ensure the reliability of the test data.

[0157] Third, during the process of testing the working conditions of the cabinet, the target device can also monitor the working conditions 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, through the alarm, technicians can immediately take corresponding measures to prevent equipment damage. The test system can also include a heat dissipation device (such as a fan). Therefore, at the same time, the target device can also monitor the rotation speed of the fan to ensure the effective operation of the fan and maintain the equipment operating within a suitable temperature range.

[0158] Fourth, during the process of testing the working conditions of the test cabinet, the target device can analyze each generated alarm message and / or the load change situation to obtain a test result, and the test result is used to indicate whether the cabinet passes the test of the target test scenario. For example, when there is an alarm message, it is determined that the test fails; when the load increase in any unit time is greater than a preset threshold, it is determined that the test fails, etc. When there is no alarm message and the load increase in all unit times is less than or equal to the preset threshold, it is determined that the test passes, etc.

[0159] In some alternative embodiments, 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, a real device cannot be provided currently. 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 under various test scenarios, obtain the current data set corresponding to each hardware model under various scenarios respectively, and then determine the current function corresponding to each hardware model according to 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 according to the quantity corresponding to each hardware model and the current function (specifically, the method of calculating the total current function of the target device group can be referred to above). 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 it is not necessary to occupy a large amount of device resources.

[0160] For the cabinet test method provided in this embodiment, traditional test methods usually require actual deployment and operation of the target device group, which requires a large number of physical devices. However, by using the load simulation device, the use of real devices can be avoided, and the demand for physical devices is reduced. In this solution, the total current function corresponding to the target device group in the target test scenario is calculated through the configuration information of the target device group. Furthermore, the load simulation device can adjust the circuit parameters according to the total current function to accurately simulate the load situation of the target device group in the target test scenario. In this way, for different test scenarios, it is not necessary to frequently replace or reconfigure real devices, which can improve the flexibility and efficiency of the test.

[0161] The following uses a specific example to illustrate the cabinet test process in detail.

[0162] For each test scenario, a technician can pre-construct a test model corresponding to the test scenario and add the test model to the test model library. Before the test, the technician can also adjust each parameter in the test model according to the test requirements.

[0163] For example, the test model for the restart type of test scenario can be as follows:

[0164] Test Case name: model1

[0165] Run Type:Reboot

[0166] Fail_Continue: true

[0167] Mode_Times: 1

[0168] Counts_Range: [10,30]

[0169] Running: true

[0170] 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 testing other random models after the test of the test model fails, "Mode_Times" is the weight value, "Counts_Range" is the range of the number of test reboots (that is, the above preset numerical range), and "Running" is used to indicate whether the test model participates in the test. When "Running" in this test model is "true", it indicates that the test model is participating in the test currently. When "Running" in this test model is "false", it indicates that the test model does not participate in the test currently.

[0171] The test models corresponding to the test scenarios of the stress type can be as follows:

[0172] Test Casename: model2

[0173] Run Type: CPU stress

[0174] Fail_Continue: true

[0175] Run_times: 20min

[0176] Mode_Times: 1

[0177] Running: true

[0178] Among them, "Run_times" is the test duration.

[0179] Such as Figure 3As shown, when performing a restart type of test, the target device can first obtain the total target number of tests, and randomly select a test model from the test model library of the restart type (i.e., the process of obtaining the target test scenario mentioned above). Then, the target device can determine the corresponding total current function according to the selected test model and the configuration information of the target device group, and transmit it to the load simulation device. Moreover, the target device can randomly select an integer value from the test restart times range 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 test on the cabinet for the same number of times as the number of tests. After completing the test process of this test model, a test model can be randomly selected again from the test model library of the restart type, and so on, until the number of tests on the cabinet reaches the total target number of tests, then stop the test.

[0180] As Figure 4 shown, when performing a restart type of test, the target device can first obtain the total target test duration, and randomly select a test model from the test model library of the pressure type. Then, the target device can determine the corresponding total current function according to the selected test model and the configuration information of the target device group, and transmit it to the load simulation device. Moreover, the target device can determine the product of the test duration included in the test model and the weight value as the test duration corresponding to the test model. Finally, the target device can perform the test on the cabinet for the test duration. After completing the test process of this test model, a test model can be randomly selected again from the test model library of the pressure type, and so on, until the test duration on the cabinet reaches the total target test duration, then stop the test.

[0181] In this embodiment, a cabinet test device is also provided. This device is used to implement the above embodiments and preferred implementation manners, and those that have been described will not be repeated. As used hereinafter, the term "module" can be a combination of software and / or hardware that can achieve a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware is also possible and contemplated.

[0182] This embodiment provides a cabinet test device, as Figure 5 shown, including:

[0183] An acquisition module 501, configured to acquire the configuration information of the target device group, where the configuration information includes at least one device category and the number of devices corresponding to each device category; acquire the target test scenario; acquire the target test parameter value corresponding to the target test scenario; and acquire the sub-current function corresponding to each device category in the target test scenario according to each device category and the target test scenario.

[0184] A determination module 502, configured to determine a total current function corresponding to a target device group according to the number of devices corresponding to each device category and a sub-current function.

[0185] 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 to simulate the load of the target device group in a target test scenario.

[0186] A testing module 504, configured to test the operating condition of a cabinet when a target device group operates in a target test scenario according to a target test parameter value.

[0187] In some alternative embodiments, the obtaining module 501 is specifically configured to:

[0188] Select sub-current functions corresponding to at least one test scenario corresponding to a target device category from a pre-constructed target database according to the target device category, where the target device category is any one of at least one device category;

[0189] Select a sub-current function corresponding to the target test scenario from the sub-current functions corresponding to at least one test scenario corresponding to the target device category according to the target test scenario.

[0190] In some alternative embodiments, the obtaining module 501 is specifically configured to:

[0191] Obtain a target current data set corresponding to a target device category in a target test scenario according to the target device category and the target test scenario, where 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;

[0192] Determine a sub-current function corresponding to the target device category according to the coordinates of each current sampling point and a pre-constructed target model.

[0193] In some alternative embodiments, the determination module 502 is configured to:

[0194] Select at least one cut point from multiple current sampling points according to the coordinates of each current sampling point, where the cut point is a current sampling point with a derivative of 0;

[0195] Split the target current data set into multiple sub-data sets according to the coordinates of each cut point, and determine a time neighborhood corresponding to each sub-data set;

[0196] Perform fitting on the coordinates of all current sampling points included in a target sub-data set according to the target model to obtain a function corresponding to the target sub-data set, where the target sub-data set is any one of the multiple sub-data sets.

[0197] Determine a sub-current function corresponding to the target device category according to the time neighborhood and function corresponding to each sub-dataset.

[0198] In some alternative embodiments, the determining module 502 is further configured to:

[0199] Perform coordinate transformation on the coordinates of each current sampling point according to a preset coordinate transformation rule to generate a new current sampling point corresponding to each current sampling point;

[0200] Add the new current sampling point corresponding to each current sampling point to the target sub-dataset to obtain an updated target sub-dataset.

[0201] In some alternative embodiments, when the target test scenario is a restart-type test scenario, the obtaining module 501 is specifically configured to:

[0202] Randomly select an integer value within a preset numerical range corresponding to the target test scenario;

[0203] Obtain a target weight value corresponding to the target test scenario;

[0204] Determine a target number of tests according to the integer value and the target weight value, where the target number of tests is the target test parameter value.

[0205] In some alternative embodiments, when the target test scenario is a pressure-type test scenario, the obtaining module 501 is specifically configured to:

[0206] Obtain a target weight value and a first test duration corresponding to the target test scenario;

[0207] Determine a second test duration according to the first test duration and the target weight value, where the second test duration is the target test parameter value.

[0208] In some alternative embodiments, the obtaining module 501 is specifically configured to:

[0209] Obtain a test strategy corresponding to the target test scenario according to the target test scenario;

[0210] Test the device corresponding to the target device category according to the test strategy, and collect a target current dataset.

[0211] In some alternative embodiments, the target model adopts the following expression:

[0212]

[0213]

[0214]

[0215] Among them, f(t) is the current function, n is the current expansion order, t is the time, T is the time neighborhood length corresponding to the target sub-dataset, is the preset initial time, is , is the first preset constant term.

[0216] In some alternative embodiments, the total current function adopts the following expression:

[0217]

[0218] Among them, is the total current function, is the second preset constant term, i is the serial number of the current calculated device in the target device group, m is the number of devices included in the target device group, n is the current expansion order, N is the preset expansion order, j is the serial number of the current calculated current sampling point within the current calculated time neighborhood, P is the number of current sampling points of the current calculated device within the current calculated time neighborhood, is the abscissa of the j-th current sampling point within the current calculated time neighborhood, 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 current calculated time neighborhood.

[0219] In some alternative embodiments, the device further includes a monitoring module 505 for:

[0220] During the process of testing the working condition of the test cabinet, respectively monitor the working parameter values corresponding to at least one component included in the cabinet;

[0221] When it is monitored that the working parameter value of the target component is not within the preset parameter value range, generate a first alarm message, where the target component is any one of the at least one component.

[0222] In some alternative embodiments, the monitoring module 505 is further used for:

[0223] During the process of testing the working condition of the test cabinet, monitor the power consumption value of the cabinet;

[0224] When it is monitored that the power consumption value of the cabinet is not within the preset power consumption range, generate a second warning message.

[0225] The further function descriptions of the above-mentioned various modules and units are the same as those in the corresponding above embodiments, and will not be elaborated here.

[0226] The cabinet device in this embodiment is presented in the form of functional units. Here, the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and a memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0227] An embodiment of the present invention further provides a computer device having the above Figure 5 shown cabinet device.

[0228] Please refer to Figure 6 , Figure 6 which is a schematic structural diagram of a computer device provided by an alternative embodiment of the present invention. As Figure 6 shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including a high-speed interface and a low-speed interface. Each component communicates with each other using different buses and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed within the computer device, including instructions stored in the memory or on the memory to display graphical information of the GUI on an external input / output device (such as a display device coupled to the interface). In some alternative 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 (such as a server array, a set of blade servers, or a multi-processor system). Figure 6 In

[0229] FIG., a single processor 10 is taken as an example.

[0230] 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.

[0231] The memory 20 may include a program storage area and a data storage area. Among them, the program storage area may store an operating system and application programs 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-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some alternative embodiments, the memory 20 may optionally include a memory remotely provided with respect to the processor 10, and these remote memories may be connected to the computer device through 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.

[0232] 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 further include a combination of the above types of memories.

[0233] The computer device further 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 through a bus or other means. Figure 6 Taking connection through a bus as an example.

[0234] The input device 30 may receive input digital or character information, and generate key signal inputs related to the user settings and function control of the computer device, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointing stick, one or more mouse buttons, a trackball, a joystick, etc. The output device 40 may include a display device, an auxiliary lighting device (such as an LED), and a haptic feedback device (such as 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 alternative embodiments, the display device may be a touch screen.

[0235] Embodiments of the present invention also provide a computer-readable storage medium. The method according to the embodiments of the present invention can be implemented in hardware, firmware, or be implemented as computer code that can be recorded on a storage medium, or be implemented as computer code that is originally stored in a remote storage medium or a non-transitory machine-readable storage medium and downloaded through a network and will be stored in a local storage medium, so that the method described herein can be stored as 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 disc, a read-only memory, a random access memory, a flash memory, a hard disk, or a solid-state drive, 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 the computer, the processor, or the hardware, the methods shown in the above embodiments are implemented.

[0236] A part of the present invention can be applied as a computer program product, such as computer program instructions. When executed by a computer, through the operation of the computer, the methods and / or technical solutions according to the present invention can be invoked or provided. Those skilled in the art should be able to understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are 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. Herein, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to the computer.

[0237] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall 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, which includes a load simulation device, a cabinet, and a target device. The cabinet is electrically connected to the load simulation device. The method is executed by the target device and includes: Obtain the configuration information of the target device group. The configuration information includes at least one device category and the number of devices corresponding to each device category. Obtain the target test scenario. Obtain the target test parameter values corresponding to the target test scenario. According to each device category and the target test scenario, obtain the sub-current function corresponding to each device category in the target test scenario. According to the number of devices corresponding to each device category and the sub-current function, determine the total current function corresponding to the target device group. Send 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 values, test the working condition of the cabinet when the target device group operates in the target test scenario.

2. The method according to claim 1, wherein The step of obtaining the sub-current function corresponding to each device category in the target test scenario according to each device category and the target test scenario includes: According to the target device category, select at least one sub-current function corresponding to the target device category from a pre-constructed target database. The target device category is any one of the at least one device category, and each sub-current function in the at least one sub-current function corresponding to the target device category corresponds to a test scenario. According to the target test scenario, select the sub-current function corresponding to the target test scenario from the at least one sub-current function corresponding to the target device category.

3. The method according to claim 1, wherein The step of obtaining the sub-current function corresponding to each device category in the target test scenario according to each device category and the target test scenario includes: According to the target device category and the target test scenario, obtain the target current data set corresponding to the target device category in the target test scenario. The target device category is any one of the at least one device category, and the target current data set includes the coordinates of multiple current sampling points. According to the coordinates of each current sampling point and a pre-constructed target model, determine the sub-current function corresponding to the target device category.

4. The method according to claim 3, characterized in that, The step of determining the sub-current function corresponding to the target device category according to the coordinates of each current sampling point and a pre-constructed target model includes: According to the coordinates of each current sampling point, select at least one cut-off point from the multiple current sampling points. The cut-off point is the current sampling point where the derivative is 0. According to the coordinates of each cut-off point, split the target current data set into multiple sub-data sets and determine the time neighborhood corresponding to each sub-data set. According to the target model, fit the coordinates of all current sampling points included in the target sub-dataset to obtain a function corresponding to the target sub-dataset, where the target sub-dataset is any one of the multiple sub-datasets; Determine a sub-current function corresponding to the target device category according to the time neighborhood and function corresponding to each sub-dataset.

5. The method according to claim 4, wherein Before the step of 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, perform coordinate conversion on the coordinates of each current sampling point to generate a new current sampling point corresponding to each current sampling point; Add the new current sampling point corresponding to each current sampling point 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, the obtaining of the target test parameter value corresponding to the target test scenario includes: Randomly select an integer value in a preset numerical range corresponding to the target test scenario; Obtain a target weight value corresponding to the target test scenario; Determine a target test number according to the integer value and the target weight value, where the target test number 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 pressure-type test scenario, the obtaining of the target test parameter value corresponding to the target test scenario includes: Obtain a target weight value and a first test duration corresponding to the target test scenario; Determine a second test duration according to the first test duration and the target weight value, where the second test duration is the target test parameter value.

8. The method according to claim 3, wherein The obtaining of the target current dataset corresponding to the target device category in the target test scenario according to the target device category and the target test scenario includes: According to the target test scenario, obtain a test strategy corresponding to the target test scenario; Test the device corresponding to the target device category according to the test strategy, and collect the target current dataset.

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

10. The method according to claim 9, wherein The total current function adopts the following expression: Among them, 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 currently expanded series number, N is the preset expanded series number, 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 j-th current sampling point in the currently calculated time neighborhood, 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 currently calculated time neighborhood.

11. The method according to any one of claims 1 to 5, characterized in that The method further includes: During the process of testing the working condition of the cabinet, monitor the working parameter values corresponding to at least one component included in the cabinet respectively; When it is monitored that the working parameter value of the target component is not within the preset parameter value area, generate a first alarm message, where the target component is any one of the at least one component.

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

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

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing 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, It includes computer instructions for causing a computer to execute the cabinet testing method according to any one of claims 1 to 12.

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