Abnormal monitoring method and device for grayscale testing

Abstract

The present invention discloses a method and device for monitoring abnormalities in grayscale testing. The method includes: determining a test container corresponding to the grayscale test, obtaining at least one operating parameter of the test container when running the grayscale test; the operating parameter is data obtained by real-time collection of the operating status data of the test container; and determining whether the grayscale test is in a preset abnormal state based on a monitoring model and the operating parameter. The technical solution of the present invention, by monitoring the test container that needs to be tested, rather than detecting all containers involved in the grayscale release, can effectively determine whether the grayscale test is abnormal by monitoring the changing state of the operating parameters of the test container, thereby improving the accuracy of monitoring abnormalities during the grayscale test process.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a grayscale test abnormality monitoring method and device. Background Art

[0002] Gray release is a software release strategy that involves gradually and phasedly releasing new features or versions. Gray release is typically used for software updates or features. To prevent potential risks in new versions from impacting the majority of users, anomaly detection during gray release is crucial.

[0003] Currently, anomaly detection during the grayscale testing process usually involves obtaining the target indicators of the current version of the product in the first period, as well as the target indicators of the grayscale version of the product in the first period, and comparing the target indicators of the current version of the product in the first period with the target indicators of the grayscale version of the product in the first period to determine the comparison result, so as to determine whether there is an anomaly in the grayscale version of the product based on the comparison result. However, in the case of fewer anomalies during the grayscale testing period, the changes in the version indicator data may not be obvious, making it difficult to detect whether there are anomalies in the grayscale testing. In addition, there is no existing technology that can automatically analyze whether there are anomalies in grayscale testing parameters and trends. Therefore, there is an urgent need for a method that can accurately monitor anomalies in grayscale testing parameters. Summary of the Invention

[0004] The present invention provides a grayscale test abnormality monitoring method and device to improve the accuracy of monitoring abnormal situations during the grayscale test process.

[0005] According to one aspect of the present invention, a method for monitoring abnormalities in a grayscale test is provided, the method comprising:

[0006] Determine a test container corresponding to the grayscale test, and obtain at least one operating parameter of the test container when running the grayscale test; the operating parameter is data obtained by real-time collection of operating status data of the test container;

[0007] Based on the monitoring model and the operating parameters, it is determined whether the grayscale test is in a preset abnormal state.

[0008] According to another aspect of the present invention, there is provided a grayscale test abnormality monitoring device, the device comprising:

[0009] a parameter acquisition module, configured to determine a test container corresponding to a grayscale test and acquire at least one operating parameter of the test container when the grayscale test is being performed; the operating parameter being data acquired by real-time collection of operating status data of the test container;

[0010] The monitoring module determines whether the grayscale test is in a preset abnormal state based on the monitoring model and the operating parameters.

[0011] According to another aspect of the present invention, an electronic device is provided, comprising:

[0012] at least one processor; and

[0013] a memory communicatively connected to the at least one processor; wherein,

[0014] The memory stores a computer program that can be executed by the at least one processor. The computer program is executed by the at least one processor so that the at least one processor can execute the grayscale test abnormality monitoring method described in any embodiment of the present invention.

[0015] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the grayscale test abnormality monitoring method described in any embodiment of the present invention when executed.

[0016] The technical solution of the embodiment of the present invention determines the test container corresponding to the grayscale test, and obtains at least one operating parameter of the test container when running the grayscale test; the operating parameter is data obtained by real-time collection of the operating status data of the test container; further based on the monitoring model and the operating parameters, it is determined whether the grayscale test is in a preset abnormal state, that is, the present invention monitors the real-time parameters and trends of the test container that needs to be tested, and realizes more accurate container data monitoring based on the real-time parameters and the changing trends of the real-time parameters, so that it can effectively determine whether the grayscale test is abnormal by monitoring the changing status of the operating parameters of the test container, thereby improving the accuracy of monitoring abnormal situations during the grayscale test process.

[0017] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1This is a flow chart of a grayscale test abnormality monitoring method provided by an embodiment of the present invention;

[0020] Figure 2 is a flow chart of another abnormality monitoring method for grayscale testing provided by an embodiment of the present invention;

[0021] Figure 3 is a schematic diagram of an operating parameter trend graph applicable to an embodiment of the present invention;

[0022] Figure 4 is a flow chart of another abnormality monitoring method for grayscale testing provided by an embodiment of the present invention;

[0023] Figure 5 1 is a schematic structural diagram of an abnormality monitoring device for grayscale testing provided by an embodiment of the present invention;

[0024] Figure 6 3 is a schematic structural diagram of an electronic device for implementing the abnormality monitoring method of the grayscale test according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions 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 embodiments described are only 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 ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0026] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0027] Example 1

[0028] Figure 1This is a flowchart of a grayscale test abnormality monitoring method provided by an embodiment of the present invention. This embodiment is applicable to the case of grayscale test abnormality monitoring. The method can be executed by a grayscale test abnormality monitoring device. The grayscale test abnormality monitoring device can be implemented in the form of hardware and / or software. The grayscale test abnormality monitoring device can be configured in any electronic device with network communication function. Figure 1 As shown, the abnormality monitoring method of the grayscale test of the present invention includes the following process:

[0029] S110: Determine a test container corresponding to the grayscale test, and obtain at least one operating parameter of the test container when running the grayscale test; the operating parameter is data obtained by real-time collection of operating status data of the test container.

[0030] The test container can be a container for storing updated data of the current version or current function compared to the previous version or previous function, and each grayscale test tests at least one container in the test container, that is, runs the test container until all test containers can run stably, that is, it is determined that the current version can be stably launched.

[0031] Specifically, during a grayscale test, the number of associated containers is actually very large, but the proportion of test containers undergoing grayscale testing is relatively small, perhaps even less than 1% of the total number of service containers corresponding to the grayscale test. To accurately detect whether a grayscale test has experienced anomalies, it is necessary to precisely monitor the test containers corresponding to the grayscale test. Specifically, during each grayscale test, the test container corresponding to the grayscale test must be identified first, allowing for real-time acquisition of at least one operating parameter of the test container during the grayscale test. By monitoring changes in these operating parameters, it is possible to determine whether a grayscale test has experienced anomalies. This allows for better identification of test containers undergoing grayscale testing, improving testing efficiency and avoiding anomalies and omissions.

[0032] For example, if the target application update needs to be applied to a first number of containers, but the target application update needs to be gradually tested before going online, that is, the updated content is gradually released for testing rather than all at once. If the current grayscale test involves the second number of test containers and no anomalies are found, the next grayscale test will be increased to the third number of test containers, and grayscale testing will be repeated in this manner until all the updated target applications are released to users. The first number is much larger than the second and third numbers; the second number is smaller than the third number.

[0033] Optionally, obtaining at least one operating parameter of the test container during the grayscale test includes: Each operating parameter corresponds to a different sampling interval and sampling frequency; the sampling interval can be the time range for data collection. The sampling frequency can be understood as collecting the operating status data of the test container once every preset time period. The time interval between the start and end of each collection is the sampling interval.

[0034] Specifically, the sampling interval and sampling frequency corresponding to each operating parameter can be determined based on the importance and fluctuation sensitivity of each operating parameter. For operating parameters with high importance, the corresponding sampling interval is longer and the sampling frequency is higher, so as to facilitate real-time monitoring of the grayscale test status. For operating parameters with low fluctuation sensitivity, the corresponding sampling interval becomes longer and the sampling frequency decreases. Conversely, for operating parameters with high fluctuation sensitivity, the corresponding sampling interval becomes shorter and the sampling frequency increases. For example, for operating parameters with high importance and high fluctuation sensitivity, the corresponding sampling frequency can be 15-45s; for operating parameters with low importance and low fluctuation sensitivity, the corresponding sampling frequency can be 60s-90s.

[0035] Furthermore, the sampling interval and sampling frequency corresponding to each operating parameter can be given by the monitoring model as the preset sampling interval and preset sampling frequency corresponding to each operating parameter. Before the current grayscale test begins, the sampling interval and sampling frequency corresponding to each current operating parameter are dynamically adjusted based on the preset sampling interval and preset sampling frequency corresponding to each operating parameter to obtain the sampling interval and sampling frequency corresponding to each operating parameter of the current grayscale test.

[0036] The technical solution of this embodiment can improve the effectiveness and pertinence of test data while reducing overhead by setting different sampling intervals and sampling frequencies for each operating parameter.

[0037] In this embodiment, it is optional that serious anomalies are more likely to occur at the beginning of the grayscale test, so it is necessary to monitor certain operating parameters and issue early warnings. In the event of a serious anomaly, the grayscale test may be unable to continue running, or may require the grayscale test to be interrupted. Specifically, after obtaining at least one operating parameter of the test container, the method further includes: if the running time of the grayscale test is within a preset monitoring interval and at least one operating parameter meets a first alarm condition, a first alarm message is issued; the preset monitoring interval is the time from the start of the grayscale test and lasts for a preset period of time; the first alarm message includes at least the operating parameters that meet the first alarm condition and the first alarm condition corresponding to the operating parameters that meet the first alarm condition; wherein, in response to at least one operating parameter meeting the first alarm condition, the grayscale test is interrupted.

[0038] When a serious anomaly occurs and the grayscale test is still running, the first alarm condition is used to instruct the grayscale test to be interrupted. At least one operating parameter satisfies the first alarm condition, including: if a preset parameter exists in the at least one operating parameter and / or the fluctuation amplitude of the operating parameter is greater than or equal to the fluctuation abnormality threshold, then the at least one operating parameter is determined to meet the first alarm condition; wherein, when the fluctuation amplitude of the operating parameter is greater than or equal to the fluctuation abnormality threshold, the grayscale test is instructed to be interrupted.

[0039] The preset parameters are operating data that instructs the grayscale test to be interrupted. The preset parameters may include a first parameter, a second parameter, and a third parameter. The first parameter may indicate that the service associated with the grayscale test is not started. The second parameter is the detection of a core dump file associated with the grayscale test, that is, when the program under test crashes, the operating system will generate a core dump file that records the memory status, stack information, etc. at the time of the program crash. The third parameter is the data generated by a kernel panic associated with the grayscale test, that is, when the operating system kernel encounters a serious error that cannot be handled, it will trigger a kernel panic, causing the system to crash and force a restart or enter debug mode.

[0040] If the fluctuation amplitude of an operating parameter is greater than or equal to the abnormal fluctuation threshold, the operating parameter also meets the first alarm condition. For example, when the operating parameter is the gateway success rate associated with the grayscale test, the first alarm condition is met if the gateway success rate associated with the grayscale test decreases by at least a preset threshold; when the operating parameter is the error log volume associated with the grayscale test, the first alarm condition is met if the error log volume associated with the grayscale test increases sharply by a preset percentage and the error log volume exceeds the preset amount.

[0041] In addition, it should be noted that the monitoring model can monitor the operating parameters of the grayscale test within the preset monitoring interval to determine whether at least one operating parameter meets the first alarm condition. The monitoring model is an artificial intelligence model.

[0042] The technical solution of this embodiment monitors the operating parameters within a preset monitoring range to promptly detect whether the operating parameters meet the first alarm condition, thereby avoiding the occurrence of serious anomalies that are not discovered in time and affecting the efficiency of grayscale testing.

[0043] S120: Determine whether the grayscale test is in a preset abnormal state based on the monitoring model and the operating parameters.

[0044] Among them, the monitoring model is an artificial intelligence model, which is equipped with the function of identifying the change state of the operating parameters; the change state can be the normal change state and the abnormal change state of the operating parameters; when the data of the operating parameters appear in the preset operating state data, it is determined that the change state of the operating parameters has an abnormal change state. The monitoring model can be used in advance

[0045] Specifically, the operating parameters are input into the monitoring model, and the monitoring model can analyze the operating parameters to determine whether the preset operating status data appears in the operating parameters, so as to output whether the change state of the operating parameters appears abnormal, thereby accurately determining whether the grayscale test is in a preset abnormal state.

[0046] As an optional embodiment, determining whether the grayscale test is in a preset abnormal state based on the monitoring model and operating parameters includes: determining a data change trend of each operating parameter based on the operating parameters; and identifying the data change trend of each operating parameter based on the monitoring model to determine whether the grayscale test is in the preset abnormal state. The monitoring model is configured with a function for identifying the data change trend of the operating parameters.

[0047] The data change trend may be a change trend of an operating parameter over time.

[0048] Specifically, determining the data change trend of each operating parameter based on the operating parameters includes: fitting the operating parameters using a preset data processing method to determine the data change trend of the operating parameters. The preset data processing method may be a method for determining the data change trend of the operating parameters using a mathematical method, including but not limited to linear regression, moving average, and exponential smoothing. Alternatively, the acquired operating parameter values ​​at different times may be plotted into a data trend graph, and the change trend of each operating parameter may be determined from the graph.

[0049] Furthermore, the monitoring model can analyze the data change trend of each operating parameter to determine whether the data change trend of the operating parameter meets the preset data change trend. If the data change trend of the operating parameter meets the preset data change trend, the monitoring model will output that the grayscale test is in a preset abnormal state. The preset data change trend can be understood as the data change trend of the operating parameter when the grayscale test is in a preset abnormal state.

[0050] The technical solution of this embodiment first determines the data change trend of each operating parameter, and further identifies the data change trend of each operating parameter based on the monitoring model to determine whether the grayscale test is in a preset abnormal state, so as to avoid directly processing a large number of operating parameters due to the insensitivity of the monitoring model to a large number of operating parameters, resulting in inaccurate output results. By first determining the data change trend of each operating parameter and then using the monitoring model to identify the data change trend, the accuracy of the output results can be greatly improved, and the efficiency of the model for data analysis can also be improved.

[0051] The technical solution of the embodiment of the present invention determines the test container corresponding to the grayscale test, and obtains at least one operating parameter of the test container when running the grayscale test; the operating parameter is data obtained by real-time collection of the operating status data of the test container; further based on the monitoring model and the operating parameters, it is determined whether the grayscale test is in a preset abnormal state, that is, the present invention monitors the real-time parameters and trends of the test container that needs to be tested, and realizes more accurate container data monitoring based on the real-time parameters and the changing trends of the real-time parameters, so that it can effectively determine whether the grayscale test is abnormal by monitoring the changing status of the operating parameters of the test container, thereby improving the accuracy of monitoring abnormal situations during the grayscale test process.

[0052] Example 2

[0053] Figure 2 This is a flowchart of another abnormal monitoring method for grayscale testing provided by an embodiment of the present invention. The technical solution of this embodiment further describes in detail "determining the data change trend of each operating parameter based on the operating parameters; identifying the data change trend of each operating parameter based on the monitoring model, and determining whether the grayscale test is in a preset abnormal state" on the basis of the above embodiment. This embodiment can be combined with various optional solutions in one or more of the above embodiments. Figure 2 As shown in the figure, the abnormal monitoring methods of grayscale testing include:

[0054] S210: Determine a test container corresponding to the grayscale test, and obtain at least one operating parameter of the test container when running the grayscale test; the operating parameter is data obtained by real-time collection of operating status data of the test container.

[0055] S220 , plotting each operating parameter into an operating parameter trend graph corresponding to each operating parameter; the operating parameter trend graph is used to represent a data change trend of the operating parameter over time.

[0056] Operational parameters may include at least one of the following: concurrency, memory usage, CPU usage, number of threads, success rate, latency, and error log volume. Concurrency can be the number of requests per second (QPS); success rate can include, but is not limited to, program execution success rate, gateway success rate, and call success rate; call success rate can include, but is not limited to, interface call success rate and service call success rate; latency can be latency parameters associated with grayscale testing, including, but not limited to, P50, P90, P99, and average latency. P99 and P90 results can reveal occasional issues such as resource contention, slow queries, and network jitter.

[0057] The operating parameter trend chart can be understood as a visual chart that can reflect the trend of data changes. The operating parameter trend chart can be displayed in the form of a line chart, a scatter chart, or an area chart. For example, Figure 3 The line chart shown.

[0058] S230. Determine, based on the monitoring model, whether the operating parameter trend graph corresponding to the operating parameter satisfies a second alarm condition; the second alarm condition is used to indicate that the grayscale test is in a preset abnormal state.

[0059] The preset abnormal state can be understood as a state when the running state data of the grayscale container is abnormal to varying degrees.

[0060] Specifically, the operating parameter trend graph is identified based on the monitoring model to obtain first trend data corresponding to the operating parameter. When the first trend data satisfies a preset data trend change range, a determination is made as to whether the operating parameter trend graph corresponding to the operating parameter satisfies a second alarm condition. The preset data trend change range can be understood as data trend change information when the operating parameter is in a preset abnormal state.

[0061] Optionally, the monitoring model may be capable of analyzing operating parameter trend graphs. Generating the monitoring model may include: obtaining at least one historical operating parameter from different grayscale test phases, plotting the historical operating parameter into a historical operating parameter trend graph, wherein different historical operating parameter trend graphs correspond to different trend graph analysis results, using the historical operating parameter trend graph and the trend graph analysis results corresponding to the historical operating parameter trend graph as training data, and training the monitoring model to be trained based on the training data to obtain the monitoring model. The trend graph analysis result may be an analysis result of the historical operating parameter trend graph corresponding to whether the grayscale test is in a preset abnormal state.

[0062] Furthermore, based on the data to be trained, a monitoring model to be trained is trained to obtain a monitoring model, including: dividing the data to be trained into training data, test data, and verification data; training the monitoring model to be trained in chart analysis capabilities based on the training data to obtain a trained monitoring model; and verifying whether the chart analysis capabilities of the trained monitoring model meet the standards based on the test data and verification data. If so, the trained monitoring model is used as a monitoring model for grayscale testing. The present invention ensures the accuracy of the monitoring model's ability to analyze operating parameter trend graphs during training.

[0063] S240: If the operating parameter trend graph corresponding to the operating parameter meets the second alarm condition, it is determined that the grayscale test is in a preset abnormal state, and a second alarm message is issued.

[0064] Among them, the second alarm information may include but is not limited to the operating parameters corresponding to the second alarm condition, the corresponding change trend data, the number of test containers corresponding to the grayscale test, the environment configuration information corresponding to the grayscale test, and the grayscale test items; the change trend data can be the trend data obtained after the monitoring model analyzes the operating parameter trend chart corresponding to the operating parameters.

[0065] The present invention can prompt the issuance of the second alert information in various ways, including but not limited to common alert methods such as pop-up windows, new message prompts, and sound effect prompts. The present invention can display the second alert information, and the platform for displaying the second alert information can be a web page, an application, a mobile app that is in synchronous communication with the above terminals, a short message, a server, etc., which is not specifically limited by the present invention.

[0066] In this embodiment, optionally, the second alarm condition may include a stability alarm condition, a first abnormal alarm condition, and a second abnormal alarm condition; the stability alarm condition is used to indicate that the grayscale test has a stability problem; the first abnormal alarm condition is used to indicate that the grayscale test is in a first abnormal state; the second abnormal alarm condition is used to indicate that the grayscale test is in a second abnormal state; the severity of the first abnormal state is greater than the severity of the second abnormal state;

[0067] Correspondingly, if the operating parameter trend graph corresponding to the operating parameter meets the second alarm condition, it is determined that the grayscale test is in a preset abnormal state, including steps A1-A3:

[0068] Step A1. If the operating parameter trend graph corresponding to the operating parameter meets the stability alarm condition, the control monitoring model outputs a stability problem in the grayscale test and outputs the second alarm information corresponding to the stability alarm condition; the second alarm information corresponding to the stability alarm condition may include but is not limited to the operating parameters that meet the stability alarm condition and the change trend data corresponding to the operating parameters that meet the stability alarm condition.

[0069] Step A2: If the operating parameter trend graph corresponding to the operating parameter meets the first abnormal alarm condition, the control monitoring model outputs that the grayscale test is in the first abnormal state, and outputs the second alarm information corresponding to the first abnormal alarm condition; the second alarm information corresponding to the first abnormal alarm condition may include but is not limited to the operating parameters that meet the first abnormal alarm condition and the change trend data corresponding to the operating parameters that meet the first abnormal alarm condition.

[0070] Step A3: If the operating parameter trend graph corresponding to the operating parameter meets the second abnormal alarm condition, the control monitoring model outputs the grayscale test in the second abnormal state, and outputs the second alarm information corresponding to the second abnormal alarm condition; the second alarm information corresponding to the second abnormal alarm condition may include but is not limited to the operating parameters that meet the second abnormal alarm condition and the change trend data corresponding to the operating parameters that meet the second abnormal alarm condition.

[0071] The technical solution of this embodiment divides the second alarm condition into alarm conditions of different degrees, so that when an abnormality occurs in the grayscale test, it can more accurately determine what type of abnormal state the grayscale test currently corresponds to. That is, targeted abnormality checks can be performed, and the problems corresponding to the grayscale test can be quickly adjusted, thereby improving the efficiency of the grayscale test.

[0072] In this embodiment, optionally, when certain specific change trends appear in the operating parameters, they may be mistaken for abnormal data and an alarm may be issued because they are different from the normal data change trends. However, in actual situations, the data with certain specific change trends are not abnormal data. Therefore, in order to avoid the false alarm principle, it is necessary to monitor certain specific change trends, specifically: based on the monitoring model, the data change trend of each operating parameter is identified, and whether the grayscale test is in a preset abnormal state is determined. It also includes: based on the monitoring model, determining whether the operating parameter trend graph corresponding to the operating parameter meets the false alarm condition; the false alarm condition is a condition indicating that the operating parameter matches the preset operating data; if the operating parameter trend graph corresponding to the operating parameter meets the false alarm condition, the grayscale test is not in the preset abnormal state, and no alarm information is output.

[0073] The preset operating data can be understood as data that does not match the normal data change trend, but is still normal data change trend information. The preset operating data is determined based on the characteristics of the actual business.

[0074] Specifically, the operating parameter trend graph is identified based on the monitoring model to obtain second trend data corresponding to the operating parameter. If the second trend data matches the preset operating data, it is determined that the operating parameter trend graph corresponding to the operating parameter meets the false alarm condition.

[0075] For example, the preset operating data corresponding to the operating parameter may be trend information that the operating parameter returns to normal after continuously increasing or decreasing at a first preset change rate for a first period of time.

[0076] If the operating parameter is a QPS value, the preset operating data may be a preset number of QPS values ​​as a preset value; the preset value may be zero.

[0077] If the operating parameter is delay, especially P90 and P99, the preset operating data may be that the increase of the delay parameter is less than a preset amplitude; for example, the increase of P90 and / or P99 is less than 50%.

[0078] If the operating parameter is one of memory usage, CPU usage, and latency, the preset operating data corresponding to the operating parameter can be trend information of a situation in which the test program corresponding to the grayscale test is restarted and the operating parameter decreases in the second time period after the restart.

[0079] If the operating parameter is memory usage and / or CPU usage, the preset operating data may be that the memory usage and / or CPU usage is less than the preset usage; for example, the memory usage and / or CPU usage is less than 0.4.

[0080] The technical solution of this embodiment is based on the monitoring model to determine whether the operating parameter trend graph corresponding to the operating parameter meets the false alarm condition, so as to accurately judge whether there are certain specific changing trends in the changing trend of the operating parameters, thereby avoiding misreporting normal situations as abnormalities and realizing accurate monitoring of the operating status of the grayscale test.

[0081] The technical solution of the embodiment of the present invention determines the test container corresponding to the grayscale test and obtains at least one operating parameter of the test container when running the grayscale test; the operating parameter is data obtained by real-time collection of the operating status data of the test container. Each operating parameter is plotted into an operating parameter trend graph corresponding to each operating parameter; the operating parameter trend graph is used to represent the data change trend of the operating parameter over time. The operating parameter trend graph can effectively reflect the change trend of the data, so that it can be quickly and accurately determined based on the monitoring model whether the operating parameter trend graph corresponding to the operating parameter meets the second alarm condition. This effectively avoids the situation where the monitoring model is insensitive to a large number of operating parameters and directly processes a large number of operating parameters, resulting in inaccurate output results, greatly improving the accuracy of the output results and the efficiency of the model for data analysis; further, if the operating parameter trend graph corresponding to the operating parameter meets the second alarm condition, it is determined that the grayscale test is in a preset abnormal state and a second alarm information is issued, thereby accurately determining whether the grayscale test is abnormal and improving the accuracy of monitoring abnormal situations during the grayscale test.

[0082] Example 3

[0083] Figure 4 This is a flowchart of another abnormal monitoring method for grayscale testing provided by an embodiment of the present invention. The technical solution of this embodiment further describes in detail "determining whether the operating parameter trend graph corresponding to the operating parameter meets the second alarm condition based on the monitoring model" on the basis of the above embodiment. This embodiment can be combined with various optional solutions in one or more of the above embodiments. Figure 4 As shown in the figure, the abnormal monitoring methods of grayscale testing include:

[0084] S310: Determine a test container corresponding to the grayscale test, and obtain at least one operating parameter of the test container when running the grayscale test; the operating parameter is data obtained by real-time collection of operating status data of the test container.

[0085] S320 , plotting each operating parameter into an operating parameter trend graph corresponding to each operating parameter; the operating parameter trend graph is used to represent a data change trend of the operating parameter over time.

[0086] S330. In response to the data input operation, the control monitoring model calls the preset information corresponding to the operating parameter trend chart from the preset information library; the data input operation is an operation of inputting the operating parameter trend chart of each operating parameter into the monitoring model; the preset information library stores preset information for judging whether the operating parameter trend chart meets the second alarm condition.

[0087] The monitoring model is associated with a preset information library, which can be located internally or externally within the monitoring model to assist the monitoring model in determining whether the grayscale test meets the second alarm condition. The preset information can be understood as trend information that meets the second alarm condition for different operating parameter trend graphs.

[0088] S340. Based on the monitoring model, the operating parameter trend chart and preset information are identified to determine whether the grayscale test meets the second alarm condition. If the operating parameter trend chart corresponding to the operating parameter meets the second alarm condition, it is determined that the grayscale test is in a preset abnormal state and a second alarm information is issued.

[0089] Specifically, the operating parameter trend chart is identified based on the monitoring model to obtain the third trend data corresponding to the operating parameters. When the third trend data matches the preset information corresponding to the operating parameter trend chart, it is determined that the grayscale test meets the second alarm condition, the monitoring model is controlled to output the grayscale test in a preset abnormal state, and a second alarm message is issued.

[0090] Furthermore, the preset information may include first preset information, second preset information, and third preset information; the first preset information may be parameter change trend information corresponding to when the grayscale test is in a first abnormal state; the second preset information may be parameter change trend information corresponding to when the grayscale test is in a second abnormal state; the third preset information may be parameter change trend information corresponding to when there is a stability problem in the change trend of the operating parameter;

[0091] The second alarm condition may include a stability alarm condition, a first abnormal alarm condition, and a second abnormal alarm condition; the first abnormal alarm condition is used to indicate that the grayscale test is in a first abnormal state; the second abnormal alarm condition is used to indicate that the grayscale test is in a second abnormal state; the severity of the first abnormal state is greater than the severity of the second abnormal state;

[0092] Correspondingly, when the third trend data matches the preset information corresponding to the operating parameter trend graph, it is determined that the grayscale test meets the second alarm condition, and the monitoring model is controlled to output that the grayscale test is in a preset abnormal state, which may include steps B1-B3:

[0093] Step B1: If the third trend data meets the first preset information, determine whether the grayscale test meets the first abnormal alarm condition, and control the monitoring model to output that the grayscale test is in the first abnormal state.

[0094] For example, if the operating parameter is CPU usage, the first preset information may be information indicating that the CPU usage is greater than a preset usage, and the CPU usage is greater than the preset usage for at least a third time period. For example, the CPU usage is greater than 80% for a long period of time.

[0095] If the operating parameter is memory usage, the first preset information may be information that the memory usage continues to increase, and the duration of the continuous increase in the memory usage is greater than the fourth time period, and the absolute value of the memory usage doubles. For example, the memory usage continues to rise, the increase lasts for more than 10 minutes, and the absolute value doubles.

[0096] If the operating parameter is a QPS value, the first preset information is that all QPS values ​​are preset values, and the preset value may be zero.

[0097] Step B2: If the third trend data meets the second preset information, determine whether the grayscale test meets the first abnormal alarm condition, and control the monitoring model to output that the grayscale test is in the first abnormal state.

[0098] For example, if the operating parameter is the CPU occupancy rate, the second preset information is information that the CPU occupancy rate continues to increase, and the time for which the CPU occupancy rate continues to increase is less than the fifth time period.

[0099] If the operating parameter is memory usage, the second preset information is information that the memory usage continues to increase, and the duration of the continuous increase in the memory usage is less than the sixth time period, and the sixth time period is less than the fourth time period. For example, the memory usage continues to increase, but the increase duration is relatively short.

[0100] If the operating parameter is latency, particularly P90 and P99, the second preset information is information that the latency during at least the seventh time period is at least a preset multiple of the latency before the test program corresponding to the grayscale test is restarted. The second preset information is information after the test program corresponding to the grayscale test is restarted. For example, after the test program corresponding to the grayscale test is restarted, the P90 and P99 latency are twice or more than before the program is restarted, and the values ​​persist for a long period of time.

[0101] Step B3: If the third trend data satisfies the third preset information, determine whether the grayscale test meets the stability alarm condition, and control the monitoring model to output that there is a stability problem in the grayscale test.

[0102] The third preset information may include information indicating that the operating parameter has continuously changed at a second preset rate of change for at least an eighth period of time within the monitoring window corresponding to the grayscale test. For example, if the operating parameter has continuously and significantly changed within the monitoring window, the test program may have stability issues.

[0103] The technical solution of an embodiment of the present invention determines a test container corresponding to a grayscale test and obtains at least one operating parameter of the test container during the grayscale test. The operating parameter is data obtained by real-time collection of the operating status data of the test container. Each operating parameter is plotted as an operating parameter trend graph corresponding to each operating parameter. The operating parameter trend graph is used to represent the data change trend of the operating parameter over time. In response to data input, a monitoring model is controlled to retrieve preset information corresponding to the operating parameter trend graph from a preset information library. The preset information is stored in the preset information library to facilitate accurate acquisition of data for comparison with the operating parameter trend graph, thereby facilitating subsequent accurate determination of whether the grayscale test meets a second alarm condition. The monitoring model identifies the operating parameter trend graph and the preset information to determine whether the grayscale test meets the second alarm condition. If the operating parameter trend graph corresponding to the operating parameter meets the second alarm condition, the grayscale test is determined to be in a preset abnormal state and a second alarm is issued. This achieves accurate determination of whether the grayscale test is abnormal and improves the accuracy of monitoring abnormal conditions during the grayscale test.

[0104] Example 4

[0105] Figure 5This is a schematic diagram of the structure of a grayscale test anomaly monitoring device provided by an embodiment of the present invention. This embodiment is applicable to the case of performing anomaly monitoring on grayscale tests. The grayscale test anomaly monitoring device can be implemented in the form of hardware and / or software. The grayscale test anomaly monitoring device can be configured in any electronic device with network communication function. Figure 5 As shown, the abnormality monitoring device for grayscale testing of the present invention includes:

[0106] The parameter acquisition module 410 is configured to determine a test container corresponding to the grayscale test and acquire at least one operating parameter of the test container when the grayscale test is being performed; the operating parameter is data obtained by real-time collection of operating status data of the test container;

[0107] The monitoring module 420 determines whether the grayscale test is in a preset abnormal state based on the monitoring model and the operating parameters.

[0108] On the basis of the above embodiment, optionally, the abnormality monitoring device of the grayscale test includes a data monitoring module, and the data monitoring module is used to issue a first alarm message if the running time of the grayscale test is within a preset monitoring interval and at least one of the operating parameters meets the first alarm condition; the preset monitoring interval is the time lasting for a preset period from the start of the grayscale test; the first alarm message includes at least the operating parameters that meet the first alarm condition and the first alarm condition corresponding to the operating parameters that meet the first alarm condition; wherein, in response to at least one operating parameter meeting the first alarm condition, the grayscale test is interrupted.

[0109] On the basis of the above embodiment, optionally, at least one of the operating parameters satisfies the first alarm condition, including: if there is a preset parameter in at least one of the operating parameters and / or the operating parameter fluctuation amplitude is greater than or equal to the fluctuation abnormality threshold, then it is determined that at least one of the operating parameters satisfies the first alarm condition; wherein, when the operating parameter fluctuation amplitude is greater than or equal to the fluctuation abnormality threshold, it is indicated that the grayscale test is interrupted.

[0110] Based on the above embodiment, optionally, the monitoring module includes a data change trend determination unit and a first monitoring unit; the data change trend determination unit is used to determine the data change trend of each of the operating parameters based on the operating parameters; the monitoring unit is used to identify the data change trend of each of the operating parameters based on the monitoring model, and determine whether the grayscale test is in a preset abnormal state.

[0111] Based on the above embodiment, optionally, a data change trend determination unit is used to: plot each of the operating parameters into an operating parameter trend graph corresponding to each of the operating parameters; the operating parameter trend graph is used to represent the data change trend of the operating parameters changing over time.

[0112] Based on the above embodiment, optionally, the first monitoring unit includes a monitoring subunit and a first judgment subunit; the monitoring subunit is used to determine whether the operating parameter trend graph corresponding to the operating parameter meets the second alarm condition based on the monitoring model; the second alarm condition is used to indicate that the grayscale test is in a preset abnormal state; the first judgment subunit is used to determine that the grayscale test is in a preset abnormal state and issue a second alarm message if the operating parameter trend graph corresponding to the operating parameter meets the second alarm condition.

[0113] Based on the above embodiment, optionally, the second alarm condition includes a stability alarm condition, a first abnormal alarm condition, and a second abnormal alarm condition; the stability alarm condition is used to indicate that the grayscale test has a stability problem; the first abnormal alarm condition is used to indicate that the grayscale test is in a first abnormal state; the second abnormal alarm condition is used to indicate that the grayscale test is in a second abnormal state; the severity of the first abnormal state is greater than the severity of the second abnormal state;

[0114] Among them, the first judgment sub-unit is used to: if the operating parameter trend graph corresponding to the operating parameter meets the stability alarm condition, then control the monitoring model to output that the grayscale test has a stability problem; if the operating parameter trend graph corresponding to the operating parameter meets the first abnormal alarm condition, then control the monitoring model to output that the grayscale test is in a first abnormal state; if the operating parameter trend graph corresponding to the operating parameter meets the second abnormal alarm condition, then control the monitoring model to output that the grayscale test is in a second abnormal state.

[0115] Based on the above embodiment, optionally, a monitoring subunit is used to: in response to a data input operation, control the monitoring model to call the preset information corresponding to the operating parameter trend chart from the preset information library; the data input operation is an operation of inputting the operating parameter trend chart of each operating parameter into the monitoring model; the preset information library stores preset information for judging whether the operating parameter trend chart meets the second alarm condition; based on the monitoring model, the operating parameter trend chart and the preset information are identified to determine whether the grayscale test meets the second alarm condition.

[0116] Based on the above embodiment, optionally, the monitoring module includes a second monitoring unit, which is used to: determine whether the operating parameter trend graph corresponding to the operating parameter meets the false alarm condition based on the monitoring model; the false alarm condition is a condition indicating that the operating parameter matches the preset operating data; if the operating parameter trend graph corresponding to the operating parameter meets the false alarm condition, the grayscale test is not in the preset abnormal state and no alarm information is output.

[0117] Based on the above embodiment, optionally, obtaining at least one operating parameter of the test container includes: each operating parameter corresponds to a different sampling interval and sampling frequency; the sampling interval is a time range for collecting data.

[0118] Based on the above embodiment, optionally, the operating parameters include: at least one of the number of concurrency, memory occupancy, CPU occupancy, number of threads, success rate, and latency.

[0119] The abnormality monitoring device for grayscale testing provided by the embodiment of the present invention can execute the abnormality monitoring method for grayscale testing provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0120] Example 5

[0121] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0122] Figure 6 A schematic diagram of the structure of an electronic device that can be used to implement the abnormality monitoring method of the grayscale test of an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0123] like Figure 6As shown, electronic device 10 includes at least one processor 11 and memory, such as read-only memory (ROM) 12 and random access memory (RAM) 13, communicatively connected to at least one processor 11. The memory stores computer programs executable by the at least one processor. Processor 11 can perform various appropriate actions and processes based on the computer programs stored in ROM 12 or loaded from storage unit 18 into RAM 13. RAM 13 can also store various programs and data required for the operation of electronic device 10. Processor 11, ROM 12, and RAM 13 are interconnected via bus 14. An input / output (I / O) interface 15 is also connected to bus 14.

[0124] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0125] Processor 11 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any other suitable processor, controller, microcontroller, etc. Processor 11 executes the various methods and processes described above, such as the grayscale test anomaly monitoring method.

[0126] In some embodiments, the abnormality monitoring method for grayscale testing can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the abnormality monitoring method for grayscale testing described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the abnormality monitoring method for grayscale testing by any other appropriate means (for example, by means of firmware).

[0127] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on a chip (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0128] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0129] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device, or apparatus. A computer-readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0130] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device that has: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0131] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0132] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0133] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0134] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A grayscale test abnormality monitoring method, characterized in that: The method comprises: Determine a test container corresponding to the grayscale test, and obtain at least one operating parameter of the test container when running the grayscale test; the operating parameter is data obtained by real-time collection of operating status data of the test container; Determining whether the grayscale test is in a preset abnormal state based on a monitoring model and the operating parameters; After obtaining at least one operating parameter of the test container, the method further includes: If the running time of the grayscale test is within a preset monitoring interval and at least one of the operating parameters meets a first alarm condition, a first alarm message is issued; the preset monitoring interval is a time period that lasts for a preset period from the start of the grayscale test; the first alarm message includes at least the operating parameters that meet the first alarm condition and the first alarm condition corresponding to the operating parameters that meet the first alarm condition; wherein, in response to at least one operating parameter meeting the first alarm condition, the grayscale test is interrupted; Wherein, determining whether the grayscale test is in a preset abnormal state based on the monitoring model and the operating parameters includes: Determine a data change trend of each of the operating parameters according to the operating parameters; the data change trend is a change trend of the operating parameter over time determined by fitting the operating parameters using a preset data processing method; Identifying the data change trend of each of the operating parameters based on the monitoring model, and determining whether the grayscale test is in a preset abnormal state; Wherein, determining the data change trend of each operating parameter according to the operating parameters includes: Plotting each of the operating parameters into an operating parameter trend graph corresponding to each of the operating parameters; the operating parameter trend graph is used to represent the data change trend of the operating parameter over time; The step of identifying the data change trend of each of the operating parameters based on the monitoring model and determining whether the grayscale test is in a preset abnormal state includes: Based on the monitoring model, determining whether the operating parameter trend graph corresponding to the operating parameter satisfies a second alarm condition; the second alarm condition is used to indicate that the grayscale test is in a preset abnormal state; If the operating parameter trend graph corresponding to the operating parameter satisfies a second alarm condition, determining that the grayscale test is in a preset abnormal state and issuing a second alarm message; Wherein, determining whether the operating parameter trend graph corresponding to the operating parameter satisfies the second alarm condition based on the monitoring model includes: In response to a data input operation, controlling the monitoring model to call preset information corresponding to the operating parameter trend graph from a preset information library; the data input operation is an operation of inputting the operating parameter trend graph of each operating parameter into the monitoring model; the preset information library stores preset information for determining whether the operating parameter trend graph meets the second alarm condition; The operating parameter trend graph and the preset information are identified based on the monitoring model to determine whether the grayscale test meets a second alarm condition.

2. The method according to claim 1, characterized in that At least one of the operating parameters satisfies a first alarm condition, including: If at least one of the operating parameters contains preset parameters and / or the operating parameter fluctuation amplitude is greater than or equal to the fluctuation abnormality threshold, it is determined that at least one of the operating parameters meets the first alarm condition; wherein, when the operating parameter fluctuation amplitude is greater than or equal to the fluctuation abnormality threshold, it is indicated that the grayscale test is interrupted.

3. The method according to claim 1, characterized in that The second alarm condition includes a stability alarm condition, a first abnormal alarm condition, and a second abnormal alarm condition; the stability alarm condition is used to indicate that the grayscale test has a stability problem; the first abnormal alarm condition is used to indicate that the grayscale test is in a first abnormal state; The second abnormal alarm condition is used to indicate that the grayscale test is in a second abnormal state; the severity of the first abnormal state is greater than the severity of the second abnormal state; If the operating parameter trend graph corresponding to the operating parameter satisfies the second alarm condition, determining that the grayscale test is in a preset abnormal state includes: If the operating parameter trend graph corresponding to the operating parameter meets the stability warning condition, controlling the monitoring model to output that there is a stability problem in the grayscale test; If the operating parameter trend graph corresponding to the operating parameter satisfies a first abnormal alarm condition, controlling the monitoring model to output that the grayscale test is in a first abnormal state; If the operating parameter trend graph corresponding to the operating parameter meets the second abnormal alarm condition, the monitoring model is controlled to output that the grayscale test is in the second abnormal state.

4. The method according to claim 1, wherein Identifying the data change trend of each of the operating parameters based on the monitoring model to determine whether the grayscale test is in a preset abnormal state further includes: Based on the monitoring model, determining whether the operating parameter trend graph corresponding to the operating parameter meets a false alarm condition; the false alarm condition is a condition indicating that the operating parameter matches the preset operating data; If the operating parameter trend graph corresponding to the operating parameter meets the false alarm condition, the grayscale test is not in a preset abnormal state and no alarm information is output.

5. The method according to claim 1, wherein Obtaining at least one operating parameter of the test container when running a grayscale test includes: The sampling interval and sampling frequency corresponding to each of the operating parameters are different; the sampling interval is the time range for collecting data.

6. The method according to any one of claims 1 to 5, characterized in that The operating parameters include: at least one of the number of concurrent connections, memory usage, CPU usage, number of threads, success rate, and latency.

7. An abnormality monitoring device for grayscale testing, characterized in that: The device comprises: a parameter acquisition module, configured to determine a test container corresponding to a grayscale test and acquire at least one operating parameter of the test container when the grayscale test is being performed; the operating parameter being data acquired by real-time collection of operating status data of the test container; A monitoring module, which determines whether the grayscale test is in a preset abnormal state based on a monitoring model and the operating parameters; The abnormality monitoring device for the grayscale test includes a data monitoring module, which is configured to issue a first alarm message if the running time of the grayscale test is within a preset monitoring interval and at least one of the operating parameters meets a first alarm condition; the preset monitoring interval is a time period lasting a preset time from the start of the grayscale test; the first alarm message includes at least the operating parameters meeting the first alarm condition and the first alarm condition corresponding to the operating parameters meeting the first alarm condition; in response to at least one operating parameter meeting the first alarm condition, the grayscale test is interrupted; The monitoring module includes a data change trend determination unit and a first monitoring unit; the data change trend determination unit is used to determine the data change trend of each operating parameter according to the operating parameters; the data change trend is the change trend of the operating parameter over time determined by fitting the operating parameters using a preset data processing method; the monitoring unit is used to identify the data change trend of each operating parameter based on the monitoring model to determine whether the grayscale test is in a preset abnormal state; Wherein, the data change trend determining unit is used to: plot each of the operating parameters into an operating parameter trend graph corresponding to each of the operating parameters; the operating parameter trend graph is used to represent the data change trend of the operating parameter over time; The first monitoring unit includes a monitoring subunit and a first judgment subunit; the monitoring subunit is configured to determine, based on the monitoring model, whether the operating parameter trend graph corresponding to the operating parameter satisfies a second alarm condition; the second alarm condition is configured to indicate that the grayscale test is in a preset abnormal state; the first judgment subunit is configured to determine that the grayscale test is in a preset abnormal state and issue a second alarm message if the operating parameter trend graph corresponding to the operating parameter satisfies the second alarm condition; Among them, the monitoring sub-unit is used to: in response to a data input operation, control the monitoring model to call the preset information corresponding to the operating parameter trend chart from the preset information library; the data input operation is an operation of inputting the operating parameter trend chart of each operating parameter into the monitoring model; the preset information library stores preset information for judging whether the operating parameter trend chart meets the second alarm condition; based on the monitoring model, the operating parameter trend chart and the preset information are identified to determine whether the grayscale test meets the second alarm condition.

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